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Wang M, Yan X, Dong Y, Li X, Gao B. Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment. PLoS Comput Biol 2024; 20:e1012113. [PMID: 38728362 DOI: 10.1371/journal.pcbi.1012113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 04/24/2024] [Indexed: 05/12/2024] Open
Abstract
The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies.
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Affiliation(s)
- Meng Wang
- Faculty of Environment and Life of Beijing University of Technology, Beijing, China
| | - Xinyue Yan
- Faculty of Environment and Life of Beijing University of Technology, Beijing, China
| | - Yanan Dong
- Faculty of Environment and Life of Beijing University of Technology, Beijing, China
| | - Xiaoqin Li
- Faculty of Environment and Life of Beijing University of Technology, Beijing, China
| | - Bin Gao
- Faculty of Environment and Life of Beijing University of Technology, Beijing, China
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2
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Zeng IS. Integrating omics atlas in health informatics system design-an opinion article. Front Digit Health 2024; 6:1374359. [PMID: 38784702 PMCID: PMC11111845 DOI: 10.3389/fdgth.2024.1374359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024] Open
Affiliation(s)
- Irene Suilan Zeng
- Department of Biostatistics and Epidemiology, Auckland University of Technology, Auckland, New Zealand
- School of Clinical Science, Faculty of Health and Environmental Sciences, Auckland University of Technology, Auckland, New Zealand
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3
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Yoon JH, Lee D, Lee C, Cho E, Lee S, Cazenave-Gassiot A, Kim K, Chae S, Dennis EA, Suh PG. Paradigm shift required for translational research on the brain. Exp Mol Med 2024; 56:1043-1054. [PMID: 38689090 PMCID: PMC11148129 DOI: 10.1038/s12276-024-01218-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/07/2024] [Accepted: 02/20/2024] [Indexed: 05/02/2024] Open
Abstract
Biomedical research on the brain has led to many discoveries and developments, such as understanding human consciousness and the mind and overcoming brain diseases. However, historical biomedical research on the brain has unique characteristics that differ from those of conventional biomedical research. For example, there are different scientific interpretations due to the high complexity of the brain and insufficient intercommunication between researchers of different disciplines owing to the limited conceptual and technical overlap of distinct backgrounds. Therefore, the development of biomedical research on the brain has been slower than that in other areas. Brain biomedical research has recently undergone a paradigm shift, and conducting patient-centered, large-scale brain biomedical research has become possible using emerging high-throughput analysis tools. Neuroimaging, multiomics, and artificial intelligence technology are the main drivers of this new approach, foreshadowing dramatic advances in translational research. In addition, emerging interdisciplinary cooperative studies provide insights into how unresolved questions in biomedicine can be addressed. This review presents the in-depth aspects of conventional biomedical research and discusses the future of biomedical research on the brain.
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Affiliation(s)
- Jong Hyuk Yoon
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea.
| | - Dongha Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Chany Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Eunji Cho
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Seulah Lee
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Amaury Cazenave-Gassiot
- Department of Biochemistry and Precision Medicine Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119077, Singapore
- Singapore Lipidomics Incubator (SLING), Life Sciences Institute, National University of Singapore, Singapore, 117456, Singapore
| | - Kipom Kim
- Research Strategy Office, Korea Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Sehyun Chae
- Neurovascular Unit Research Group, Korean Brain Research Institute, Daegu, 41062, Republic of Korea
| | - Edward A Dennis
- Department of Pharmacology and Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, 92093-0601, USA
| | - Pann-Ghill Suh
- Korea Brain Research Institute, Daegu, 41062, Republic of Korea
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Tang X, Prodduturi N, Thompson KJ, Weinshilboum RM, O'Sullivan CC, Boughey JC, Tizhoosh H, Klee EW, Wang L, Goetz MP, Suman V, Kalari KR. OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.21.586001. [PMID: 38585820 PMCID: PMC10996492 DOI: 10.1101/2024.03.21.586001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing Deep Neural Networks and incorporating the SHapley Additive exPlanations (SHAP) algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas (TCGA) data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high Area Under Curve (AUC) scores - 0.98±0.02 for lung cancer subtype differentiation, 0.83±0.07 for breast cancer PAM50 subtypes, and successfully distinguishe between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing existing algorithms. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient, and interpretable approach that contributes to a deeper understanding of disease mechanisms.
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Niraula D, Cuneo KC, Dinov ID, Gonzalez BD, Jamaluddin JB, Jin JJ, Luo Y, Matuszak MM, Ten Haken RK, Bryant AK, Dilling TJ, Dykstra MP, Frakes JM, Liveringhouse CL, Miller SR, Mills MN, Palm RF, Regan SN, Rishi A, Torres-Roca JF, Yu HHM, El Naqa I. Intricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in Response-Adaptive Radiotherapy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.27.24306434. [PMID: 38746238 PMCID: PMC11092730 DOI: 10.1101/2024.04.27.24306434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Background Adaptive treatment strategies that can dynamically react to individual cancer progression can provide effective personalized care. Longitudinal multi-omics information, paired with an artificially intelligent clinical decision support system (AI-CDSS) can assist clinicians in determining optimal therapeutic options and treatment adaptations. However, AI-CDSS is not perfectly accurate, as such, clinicians' over/under reliance on AI may lead to unintended consequences, ultimately failing to develop optimal strategies. To investigate such collaborative decision-making process, we conducted a Human-AI interaction case study on response-adaptive radiotherapy (RT). Methods We designed and conducted a two-phase study for two disease sites and two treatment modalities-adaptive RT for non-small cell lung cancer (NSCLC) and adaptive stereotactic body RT for hepatocellular carcinoma (HCC)-in which clinicians were asked to consider mid-treatment modification of the dose per fraction for a number of retrospective cancer patients without AI-support (Unassisted Phase) and with AI-assistance (AI-assisted Phase). The AI-CDSS graphically presented trade-offs in tumor control and the likelihood of toxicity to organs at risk, provided an optimal recommendation, and associated model uncertainties. In addition, we asked for clinicians' decision confidence level and trust level in individual AI recommendations and encouraged them to provide written remarks. We enrolled 13 evaluators (radiation oncology physicians and residents) from two medical institutions located in two different states, out of which, 4 evaluators volunteered in both NSCLC and HCC studies, resulting in a total of 17 completed evaluations (9 NSCLC, and 8 HCC). To limit the evaluation time to under an hour, we selected 8 treated patients for NSCLC and 9 for HCC, resulting in a total of 144 sets of evaluations (72 from NSCLC and 72 from HCC). Evaluation for each patient consisted of 8 required inputs and 2 optional remarks, resulting in up to a total of 1440 data points. Results AI-assistance did not homogeneously influence all experts and clinical decisions. From NSCLC cohort, 41 (57%) decisions and from HCC cohort, 34 (47%) decisions were adjusted after AI assistance. Two evaluations (12%) from the NSCLC cohort had zero decision adjustments, while the remaining 15 (88%) evaluations resulted in at least two decision adjustments. Decision adjustment level positively correlated with dissimilarity in decision-making with AI [NSCLC: ρ = 0.53 ( p < 0.001); HCC: ρ = 0.60 ( p < 0.001)] indicating that evaluators adjusted their decision closer towards AI recommendation. Agreement with AI-recommendation positively correlated with AI Trust Level [NSCLC: ρ = 0.59 ( p < 0.001); HCC: ρ = 0.7 ( p < 0.001)] indicating that evaluators followed AI's recommendation if they agreed with that recommendation. The correlation between decision confidence changes and decision adjustment level showed an opposite trend [NSCLC: ρ = -0.24 ( p = 0.045), HCC: ρ = 0.28 ( p = 0.017)] reflecting the difference in behavior due to underlying differences in disease type and treatment modality. Decision confidence positively correlated with the closeness of decisions to the standard of care (NSCLC: 2 Gy/fx; HCC: 10 Gy/fx) indicating that evaluators were generally more confident in prescribing dose fractionations more similar to those used in standard clinical practice. Inter-evaluator agreement increased with AI-assistance indicating that AI-assistance can decrease inter-physician variability. The majority of decisions were adjusted to achieve higher tumor control in NSCLC and lower normal tissue complications in HCC. Analysis of evaluators' remarks indicated concerns for organs at risk and RT outcome estimates as important decision-making factors. Conclusions Human-AI interaction depends on the complex interrelationship between expert's prior knowledge and preferences, patient's state, disease site, treatment modality, model transparency, and AI's learned behavior and biases. The collaborative decision-making process can be summarized as follows: (i) some clinicians may not believe in an AI system, completely disregarding its recommendation, (ii) some clinicians may believe in the AI system but will critically analyze its recommendations on a case-by-case basis; (iii) when a clinician finds that the AI recommendation indicates the possibility for better outcomes they will adjust their decisions accordingly; and (iv) When a clinician finds that the AI recommendation indicate a worse possible outcome they will disregard it and seek their own alternative approach.
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6
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Guan H, Wang Y, Niu P, Zhang Y, Zhang Y, Miao R, Fang X, Yin R, Zhao S, Liu J, Tian J. The role of machine learning in advancing diabetic foot: a review. Front Endocrinol (Lausanne) 2024; 15:1325434. [PMID: 38742201 PMCID: PMC11089132 DOI: 10.3389/fendo.2024.1325434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 04/09/2024] [Indexed: 05/16/2024] Open
Abstract
Background Diabetic foot complications impose a significant strain on healthcare systems worldwide, acting as a principal cause of morbidity and mortality in individuals with diabetes mellitus. While traditional methods in diagnosing and treating these conditions have faced limitations, the emergence of Machine Learning (ML) technologies heralds a new era, offering the promise of revolutionizing diabetic foot care through enhanced precision and tailored treatment strategies. Objective This review aims to explore the transformative impact of ML on managing diabetic foot complications, highlighting its potential to advance diagnostic accuracy and therapeutic approaches by leveraging developments in medical imaging, biomarker detection, and clinical biomechanics. Methods A meticulous literature search was executed across PubMed, Scopus, and Google Scholar databases to identify pertinent articles published up to March 2024. The search strategy was carefully crafted, employing a combination of keywords such as "Machine Learning," "Diabetic Foot," "Diabetic Foot Ulcers," "Diabetic Foot Care," "Artificial Intelligence," and "Predictive Modeling." This review offers an in-depth analysis of the foundational principles and algorithms that constitute ML, placing a special emphasis on their relevance to the medical sciences, particularly within the specialized domain of diabetic foot pathology. Through the incorporation of illustrative case studies and schematic diagrams, the review endeavors to elucidate the intricate computational methodologies involved. Results ML has proven to be invaluable in deriving critical insights from complex datasets, enhancing both the diagnostic precision and therapeutic planning for diabetic foot management. This review highlights the efficacy of ML in clinical decision-making, underscored by comparative analyses of ML algorithms in prognostic assessments and diagnostic applications within diabetic foot care. Conclusion The review culminates in a prospective assessment of the trajectory of ML applications in the realm of diabetic foot care. We believe that despite challenges such as computational limitations and ethical considerations, ML remains at the forefront of revolutionizing treatment paradigms for the management of diabetic foot complications that are globally applicable and precision-oriented. This technological evolution heralds unprecedented possibilities for treatment and opportunities for enhancing patient care.
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Affiliation(s)
- Huifang Guan
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Ying Wang
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Ping Niu
- Department of Encephalopathy, The Affiliated Hospital of Changchun University of Chinese Medicine, Changchun, Jilin, China
| | - Yuxin Zhang
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanjiao Zhang
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Runyu Miao
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xinyi Fang
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ruiyang Yin
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Shuang Zhao
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Jun Liu
- Department of Hand Surgery, Second Hospital of Jilin University, Changchun, China
| | - Jiaxing Tian
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
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7
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Acharya D, Mukhopadhyay A. A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology. Brief Funct Genomics 2024:elae013. [PMID: 38600757 DOI: 10.1093/bfgp/elae013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 03/12/2024] [Accepted: 03/22/2024] [Indexed: 04/12/2024] Open
Abstract
Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact: anirban@klyuniv.ac.in.
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Affiliation(s)
- Debabrata Acharya
- Department of Computer Science & Engineering, University of Kalyani, Kalyani-741235, West Bengal, India
| | - Anirban Mukhopadhyay
- Department of Computer Science & Engineering, University of Kalyani, Kalyani-741235, West Bengal, India
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Zhang W, Mou M, Hu W, Lu M, Zhang H, Zhang H, Luo Y, Xu H, Tao L, Dai H, Gao J, Zhu F. MOINER: A Novel Multiomics Early Integration Framework for Biomedical Classification and Biomarker Discovery. J Chem Inf Model 2024; 64:2720-2732. [PMID: 38373720 DOI: 10.1021/acs.jcim.4c00013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
In the context of precision medicine, multiomics data integration provides a comprehensive understanding of underlying biological processes and is critical for disease diagnosis and biomarker discovery. One commonly used integration method is early integration through concatenation of multiple dimensionally reduced omics matrices due to its simplicity and ease of implementation. However, this approach is seriously limited by information loss and lack of latent feature interaction. Herein, a novel multiomics early integration framework (MOINER) based on information enhancement and image representation learning is thus presented to address the challenges. MOINER employs the self-attention mechanism to capture the intrinsic correlations of omics-features, which make it significantly outperform the existing state-of-the-art methods for multiomics data integration. Moreover, visualizing the attention embedding and identifying potential biomarkers offer interpretable insights into the prediction results. All source codes and model for MOINER are freely available https://github.com/idrblab/MOINER.
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Affiliation(s)
- Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Wei Hu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Hongquan Xu
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Haibin Dai
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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Wang X, Zhang K, He W, Zhang L, Gao B, Tian R, Xu R. Plasma proteomic characterization of colorectal cancer patients with FOLFOX chemotherapy by integrated proteomics technology. Clin Proteomics 2024; 21:27. [PMID: 38580967 PMCID: PMC10998366 DOI: 10.1186/s12014-024-09454-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 01/24/2024] [Indexed: 04/07/2024] Open
Abstract
BACKGROUND Colorectal Cancer (CRC) is a prevalent form of cancer, and the effectiveness of the main postoperative chemotherapy treatment, FOLFOX, varies among patients. In this study, we aimed to identify potential biomarkers for predicting the prognosis of CRC patients treated with FOLFOX through plasma proteomic characterization. METHODS Using a fully integrated sample preparation technology SISPROT-based proteomics workflow, we achieved deep proteome coverage and trained a machine learning model from a discovery cohort of 90 CRC patients to differentiate FOLFOX-sensitive and FOLFOX-resistant patients. The model was then validated by targeted proteomics on an independent test cohort of 26 patients. RESULTS We achieved deep proteome coverage of 831 protein groups in total and 536 protein groups in average for non-depleted plasma from CRC patients by using a Orbitrap Exploris 240 with moderate sensitivity. Our results revealed distinct molecular changes in FOLFOX-sensitive and FOLFOX-resistant patients. We confidently identified known prognostic biomarkers for colorectal cancer, such as S100A4, LGALS1, and FABP5. The classifier based on the biomarker panel demonstrated a promised AUC value of 0.908 with 93% accuracy. Additionally, we established a protein panel to predict FOLFOX effectiveness, and several proteins within the panel were validated using targeted proteomic methods. CONCLUSIONS Our study sheds light on the pathways affected in CRC patients treated with FOLFOX chemotherapy and identifies potential biomarkers that could be valuable for prognosis prediction. Our findings showed the potential of mass spectrometry-based proteomics and machine learning as an unbiased and systematic approach for discovering biomarkers in CRC.
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Affiliation(s)
- Xi Wang
- The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, 518020, China
- The First Affiliated Hospital, Jinan University, Guangzhou, 510632, China
- Department of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Keren Zhang
- Department of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Wan He
- The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, 518020, China
- The First Affiliated Hospital, Jinan University, Guangzhou, 510632, China
| | - Luobin Zhang
- Department of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Biwei Gao
- The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, 518020, China
| | - Ruijun Tian
- Department of Chemistry and Research Center for Chemical Biology and Omics Analysis, School of Science, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Ruilian Xu
- The Second Clinical Medical College of Jinan University, the First Affiliated Hospital of Southern University of Science and Technology, Shenzhen People's Hospital, Shenzhen, 518020, China.
- The First Affiliated Hospital, Jinan University, Guangzhou, 510632, China.
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Lac L, Leung CK, Hu P. Computational frameworks integrating deep learning and statistical models in mining multimodal omics data. J Biomed Inform 2024; 152:104629. [PMID: 38552994 DOI: 10.1016/j.jbi.2024.104629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 02/26/2024] [Accepted: 03/25/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND In health research, multimodal omics data analysis is widely used to address important clinical and biological questions. Traditional statistical methods rely on the strong assumptions of distribution. Statistical methods such as testing and differential expression are commonly used in omics analysis. Deep learning, on the other hand, is an advanced computer science technique that is powerful in mining high-dimensional omics data for prediction tasks. Recently, integrative frameworks or methods have been developed for omics studies that combine statistical models and deep learning algorithms. METHODS AND RESULTS The aim of these integrative frameworks is to combine the strengths of both statistical methods and deep learning algorithms to improve prediction accuracy while also providing interpretability and explainability. This review report discusses the current state-of-the-art integrative frameworks, their limitations, and potential future directions in survival and time-to-event longitudinal analysis, dimension reduction and clustering, regression and classification, feature selection, and causal and transfer learning.
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Affiliation(s)
- Leann Lac
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada; Department of Statistics, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Carson K Leung
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Pingzhao Hu
- Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada; Department of Biochemistry, Western University, London, Ontario, Canada; Department of Computer Science, Western University, London, Ontario, Canada; Department of Oncology, Western University, London, Ontario, Canada; Department of Epidemiology and Biostatistics, Western University, London, Ontario, Canada; The Children's Health Research Institute, Lawson Health Research Institute, London, Ontario, Canada.
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11
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Li X, Ding G, Li S, Liu C, Zheng X, Luo J, He S, Zeng F, Huang X, Zeng F. Proteomic characteristics of the treatment trajectory of patients with COVID-19. Arch Virol 2024; 169:84. [PMID: 38532129 DOI: 10.1007/s00705-024-05991-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 12/31/2023] [Indexed: 03/28/2024]
Abstract
The ongoing COVID-19 pandemic caused by SARS-CoV-2 has prompted global concern due to its profound impact on public health and the economy. Effective treatment of COVID-19 patients in the acute phase or of those with long COVID is a major challenge. Using data-independent acquisition (DIA) technology, we performed proteomic profiling on plasma samples from 22 COVID-19 patients and six healthy controls at Dazhou Central Hospital. Random forest and least absolute shrinkage and selection operator algorithms were used for analysis at various COVID-19 treatment stages. We identified 79 proteins that were differentially expressed between COVID-19 patients and healthy controls, mainly involving pathways associated with cell processes and binding. Across different treatment stages of COVID-19, five proteins-PI16, GPLD1, IGFBP3, KRT19, and VCAM1-were identified as potential molecular markers for dynamic disease monitoring. Furthermore, the proteins BTD, APOM, IGKV2-28, VWF, C4BPA, and C7 were identified as candidate biomarkers for distinguishing between SARS-CoV-2 positivity and negativity. Analysis of protein change profiles between the follow-up and healthy control groups highlighted cardiovascular changes as a concern for patients recovering from COVID-19. Our study revealed the infection profiles of SARS-CoV-2 at the protein expression level comparing different phases of COVID-19. DIA mass spectrometry analysis of plasma samples from COVID-19 patients undergoing treatment identified key proteins involved in signaling pathways that might be used as markers of the recovery phase. These findings provide insight for the development of therapy options and suggest potential blood biomarkers for COVID-19.
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Affiliation(s)
- Xue Li
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Guilan Ding
- Department of Intensive Care Medicine, Dazhou Central Hospital, Dazhou, 635000, Sichuan, China
| | - Shilin Li
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Chun Liu
- Department of Intensive Care Medicine, Dazhou Central Hospital, Dazhou, 635000, Sichuan, China
| | - Xiangde Zheng
- Department of Intensive Care Medicine, Dazhou Central Hospital, Dazhou, 635000, Sichuan, China
| | - Jinliang Luo
- Department of General Practice, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Sichun He
- Department of Laboratory Medicine, Dazhou Central Hospital, Dazhou, Sichuan, China
| | - Fanwei Zeng
- Department of Orthopedics, Sichuan Province Orthopedic Hospital, Chengdu, Sichuan, China.
| | - Xuan Huang
- Medical Research Center, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
| | - Fanxin Zeng
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, Sichuan, China.
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12
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Ban D, Housley SN, Matyunina LV, McDonald LD, Bae-Jump VL, Benigno BB, Skolnick J, McDonald JF. A personalized probabilistic approach to ovarian cancer diagnostics. Gynecol Oncol 2024; 182:168-175. [PMID: 38266403 PMCID: PMC10960662 DOI: 10.1016/j.ygyno.2023.12.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 12/18/2023] [Accepted: 12/29/2023] [Indexed: 01/26/2024]
Abstract
OBJECTIVE The identification/development of a machine learning-based classifier that utilizes metabolic profiles of serum samples to accurately identify individuals with ovarian cancer. METHODS Serum samples collected from 431 ovarian cancer patients and 133 normal women at four geographic locations were analyzed by mass spectrometry. Reliable metabolites were identified using recursive feature elimination coupled with repeated cross-validation and used to develop a consensus classifier able to distinguish cancer from non-cancer. The probabilities assigned to individuals by the model were used to create a clinical tool that assigns a likelihood that an individual patient sample is cancer or normal. RESULTS Our consensus classification model is able to distinguish cancer from control samples with 93% accuracy. The frequency distribution of individual patient scores was used to develop a clinical tool that assigns a likelihood that an individual patient does or does not have cancer. CONCLUSIONS An integrative approach using metabolomic profiles and machine learning-based classifiers has been employed to develop a clinical tool that assigns a probability that an individual patient does or does not have ovarian cancer. This personalized/probabilistic approach to cancer diagnostics is more clinically informative and accurate than traditional binary (yes/no) tests and represents a promising new direction in the early detection of ovarian cancer.
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Affiliation(s)
- Dongjo Ban
- Integrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA
| | - Stephen N Housley
- Integrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA
| | - Lilya V Matyunina
- Integrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA
| | - L DeEtte McDonald
- Integrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA
| | - Victoria L Bae-Jump
- Department of Obstetrics and Gynecology, University of North Carolina, 3009 Old Clinic Building, Chapel Hill, NC 27599, USA
| | - Benedict B Benigno
- Ovarian Cancer Institute, 1266 W. Paces Ferry Rd NW #339, Atlanta, GA 30327, USA
| | - Jeffrey Skolnick
- Integrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA; Ovarian Cancer Institute, 1266 W. Paces Ferry Rd NW #339, Atlanta, GA 30327, USA; Center for the Study of Systems Biology, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA
| | - John F McDonald
- Integrated Cancer Research Center, School of Biological Sciences, Georgia Institute of Technology, 315 Ferst Drive, Atlanta, GA 30332, USA.
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13
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Mallick H, Porwal A, Saha S, Basak P, Svetnik V, Paul E. An integrated Bayesian framework for multi-omics prediction and classification. Stat Med 2024; 43:983-1002. [PMID: 38146838 DOI: 10.1002/sim.9953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 10/06/2023] [Accepted: 10/24/2023] [Indexed: 12/27/2023]
Abstract
With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner.
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Affiliation(s)
- Himel Mallick
- Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, 10065, New York, USA
- Department of Statistics and Data Science, Cornell University, Ithaca, New York, USA
| | - Anupreet Porwal
- Department of Statistics, University of Washington, Seattle, Washington, USA
| | - Satabdi Saha
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Piyali Basak
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Vladimir Svetnik
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
| | - Erina Paul
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA
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14
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Antonisamy AJ, Rajendran K, Dhanaraj P. Network pharmacology integrated molecular docking of fucoidan against oral cancer and in vitro evaluation- A study using GEO datasets. J Biomol Struct Dyn 2024:1-24. [PMID: 38385359 DOI: 10.1080/07391102.2024.2316771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 02/02/2024] [Indexed: 02/23/2024]
Abstract
Oral cancer is a widespread health concern in rural India due to a lack of awareness, delayed diagnosis and limited access to affordable treatment options. The current chemotherapy has notable side effects, underscoring the need for new drug candidates with improved bioavailability and specificity. In this current research, fucoidan, a sulphated polysaccharide, was extracted from the brown algae Spatoglossum asperum, and shown to be cytotoxic in vitro against oral cancer cells (KB cell line) at an IC50 of 107.76 µg/ml, suggesting its potential as a drug candidate. This study further aimed to explore the potential therapeutic implications of fucoidan in managing oral cancer using network pharmacology. PharmMapper, Comparative Toxicogenomics Database and SuperPred were initially used to identify fucoidan protein targets. The identified targets were further screened against Gene Expression Omnibus (GSE23558, GSE25099 and GSE146483), OMIM, TCGA and GeneCards datasets to identify oral cancer-specific protein targets. The interactions between the selected proteins were visualised using STRING and Cytoscape. Subsequently, Database for Annotation, Visualization and Integrated Discovery was used for gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of candidate targets. The cancer-related network was assessed using CancerGeneNet, while life expectancy based on the expression of the top 10 CytoHubba ranked hub genes was evaluated using Kaplan-Meier plots. Finally, EGFR, AKT1, HSP90AA1 and SRC were selected for docking and molecular dynamics simulation with fucoidan, using Maestro and GROMACS, respectively.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Arul Jayanthi Antonisamy
- Department of Biotechnology, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamil Nadu, India
| | - Karthikeyan Rajendran
- Department of Biotechnology, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamil Nadu, India
| | - Premnath Dhanaraj
- Department of Biotechnology, School of Agriculture and Bio sciences, Karunya Institute of Technology and Science, Coimbatore, Tamil Nadu, India
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15
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Wang L, Sun T, Liu X, Wang Y, Qiao X, Chen N, Liu F, Zhou X, Wang H, Shen H. Myocarditis: A multi-omics approach. Clin Chim Acta 2024; 554:117752. [PMID: 38184138 DOI: 10.1016/j.cca.2023.117752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 12/29/2023] [Accepted: 12/29/2023] [Indexed: 01/08/2024]
Abstract
Myocarditis, an inflammatory condition of weakened heart muscles often triggered by a variety of causes, that can result in heart failure and sudden death. Novel ways to enhance our understanding of myocarditis pathogenesis is available through newer modalities (omics). In this review, we examine the roles of various biomolecules and associated functional pathways across genomics, transcriptomics, proteomics, and metabolomics in the pathogenesis of myocarditis. Our analysis further explores the reproducibility and variability intrinsic to omics studies, underscoring the necessity and significance of employing a multi-omics approach to gain profound insights into myocarditis pathogenesis. This integrated strategy not only enhances our understanding of the disease, but also confirms the critical importance of a holistic multi-omics approach in disease analysis.
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Affiliation(s)
- Lulu Wang
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Tao Sun
- Department of Cardiology, Affiliated Hospital of Jiangsu University, Zhenjiang 212000, Jiangsu, China
| | - Xiaolan Liu
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Yan Wang
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Xiaorong Qiao
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Nuo Chen
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Fangqian Liu
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Xiaoxiang Zhou
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Hua Wang
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China
| | - Hongxing Shen
- School of Medicine, Jiangsu University, Zhenjiang 212013, Jiangsu, China.
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16
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Liu W, Pratte KA, Castaldi PJ, Hersh C, Bowler RP, Banaei-Kashani F, Kechris KJ. A Generalized Higher-order Correlation Analysis Framework for Multi-Omics Network Inference. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.22.576667. [PMID: 38328226 PMCID: PMC10849540 DOI: 10.1101/2024.01.22.576667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Multiple -omics (genomics, proteomics, etc.) profiles are commonly generated to gain insight into a disease or physiological system. Constructing multi-omics networks with respect to the trait(s) of interest provides an opportunity to understand relationships between molecular features but integration is challenging due to multiple data sets with high dimensionality. One approach is to use canonical correlation to integrate one or two omics types and a single trait of interest. However, these types of methods may be limited due to (1) not accounting for higher-order correlations existing among features, (2) computational inefficiency when extending to more than two omics data when using a penalty term-based sparsity method, and (3) lack of flexibility for focusing on specific correlations (e.g., omics-to-phenotype correlation versus omics-to-omics correlations). In this work, we have developed a novel multi-omics network analysis pipeline called Sparse Generalized Tensor Canonical Correlation Analysis Network Inference (SGTCCA-Net) that can effectively overcome these limitations. We also introduce an implementation to improve the summarization of networks for downstream analyses. Simulation and real-data experiments demonstrate the effectiveness of our novel method for inferring omics networks and features of interest.
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Affiliation(s)
- Weixuan Liu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Peter J. Castaldi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, United States
| | - Craig Hersh
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Boston, United States
| | - Russell P. Bowler
- Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, CO, USA
| | - Farnoush Banaei-Kashani
- Department of Computer Science and Engineering, College of Engineering, Design and Computing, University of Colorado Denver, Denver, CO, USA
| | - Katerina J. Kechris
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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17
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Brouard C, Mourad R, Vialaneix N. Should we really use graph neural networks for transcriptomic prediction? Brief Bioinform 2024; 25:bbae027. [PMID: 38349060 PMCID: PMC10939369 DOI: 10.1093/bib/bbae027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/20/2023] [Accepted: 01/17/2024] [Indexed: 02/15/2024] Open
Abstract
The recent development of deep learning methods have undoubtedly led to great improvement in various machine learning tasks, especially in prediction tasks. This type of methods have also been adapted to answer various problems in bioinformatics, including automatic genome annotation, artificial genome generation or phenotype prediction. In particular, a specific type of deep learning method, called graph neural network (GNN) has repeatedly been reported as a good candidate to predict phenotypes from gene expression because its ability to embed information on gene regulation or co-expression through the use of a gene network. However, up to date, no complete and reproducible benchmark has ever been performed to analyze the trade-off between cost and benefit of this approach compared to more standard (and simpler) machine learning methods. In this article, we provide such a benchmark, based on clear and comparable policies to evaluate the different methods on several datasets. Our conclusion is that GNN rarely provides a real improvement in prediction performance, especially when compared to the computation effort required by the methods. Our findings on a limited but controlled simulated dataset shows that this could be explained by the limited quality or predictive power of the input biological gene network itself.
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Affiliation(s)
- Céline Brouard
- Université Fédérale de Toulouse, INRAE, MIAT, 31326 Castanet-Tolosan, France
| | - Raphaël Mourad
- Université Fédérale de Toulouse, INRAE, MIAT, 31326 Castanet-Tolosan, France
- Université Paul Sabatier, 31062 Toulouse, France
| | - Nathalie Vialaneix
- Université Fédérale de Toulouse, INRAE, MIAT, 31326 Castanet-Tolosan, France
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18
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Li B, Nabavi S. A multimodal graph neural network framework for cancer molecular subtype classification. BMC Bioinformatics 2024; 25:27. [PMID: 38225583 PMCID: PMC10789042 DOI: 10.1186/s12859-023-05622-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 12/15/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND The recent development of high-throughput sequencing has created a large collection of multi-omics data, which enables researchers to better investigate cancer molecular profiles and cancer taxonomy based on molecular subtypes. Integrating multi-omics data has been proven to be effective for building more precise classification models. Most current multi-omics integrative models use either an early fusion in the form of concatenation or late fusion with a separate feature extractor for each omic, which are mainly based on deep neural networks. Due to the nature of biological systems, graphs are a better structural representation of bio-medical data. Although few graph neural network (GNN) based multi-omics integrative methods have been proposed, they suffer from three common disadvantages. One is most of them use only one type of connection, either inter-omics or intra-omic connection; second, they only consider one kind of GNN layer, either graph convolution network (GCN) or graph attention network (GAT); and third, most of these methods have not been tested on a more complex classification task, such as cancer molecular subtypes. RESULTS In this study, we propose a novel end-to-end multi-omics GNN framework for accurate and robust cancer subtype classification. The proposed model utilizes multi-omics data in the form of heterogeneous multi-layer graphs, which combine both inter-omics and intra-omic connections from established biological knowledge. The proposed model incorporates learned graph features and global genome features for accurate classification. We tested the proposed model on the Cancer Genome Atlas (TCGA) Pan-cancer dataset and TCGA breast invasive carcinoma (BRCA) dataset for molecular subtype and cancer subtype classification, respectively. The proposed model shows superior performance compared to four current state-of-the-art baseline models in terms of accuracy, F1 score, precision, and recall. The comparative analysis of GAT-based models and GCN-based models reveals that GAT-based models are preferred for smaller graphs with less information and GCN-based models are preferred for larger graphs with extra information.
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Affiliation(s)
- Bingjun Li
- Department of Computer Science and Engineering, University of Connecticut, Storrs, USA
| | - Sheida Nabavi
- Department of Computer Science and Engineering, University of Connecticut, Storrs, USA.
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19
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Rahban M, Joushi S, Bashiri H, Saso L, Sheibani V. Characterization of prevalent tyrosine kinase inhibitors and their challenges in glioblastoma treatment. Front Chem 2024; 11:1325214. [PMID: 38264122 PMCID: PMC10804459 DOI: 10.3389/fchem.2023.1325214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 12/21/2023] [Indexed: 01/25/2024] Open
Abstract
Glioblastoma multiforme (GBM) is a highly aggressive malignant primary tumor in the central nervous system. Despite extensive efforts in radiotherapy, chemotherapy, and neurosurgery, there remains an inadequate level of improvement in treatment outcomes. The development of large-scale genomic and proteomic analysis suggests that GBMs are characterized by transcriptional heterogeneity, which is responsible for therapy resistance. Hence, knowledge about the genetic and epigenetic heterogeneity of GBM is crucial for developing effective treatments for this aggressive form of brain cancer. Tyrosine kinases (TKs) can act as signal transducers, regulate important cellular processes like differentiation, proliferation, apoptosis and metabolism. Therefore, TK inhibitors (TKIs) have been developed to specifically target these kinases. TKIs are categorized into allosteric and non-allosteric inhibitors. Irreversible inhibitors form covalent bonds, which can lead to longer-lasting effects. However, this can also increase the risk of off-target effects and toxicity. The development of TKIs as therapeutics through computer-aided drug design (CADD) and bioinformatic techniques enhance the potential to improve patients' survival rates. Therefore, the continued exploration of TKIs as drug targets is expected to lead to even more effective and specific therapeutics in the future.
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Affiliation(s)
- Mahdie Rahban
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| | - Sara Joushi
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| | - Hamideh Bashiri
- Physiology Research Center, Institute of Neuropharmacology, Department of Physiology and Pharmacology, Medical School, Kerman University of Medical Sciences, Kerman, Iran
| | - Luciano Saso
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University, Rome, Italy
| | - Vahid Sheibani
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
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20
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Jurenaite N, León-Periñán D, Donath V, Torge S, Jäkel R. SetQuence & SetOmic: Deep set transformers for whole genome and exome tumour analysis. Biosystems 2024; 235:105095. [PMID: 38065399 DOI: 10.1016/j.biosystems.2023.105095] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 10/17/2023] [Accepted: 11/28/2023] [Indexed: 12/21/2023]
Abstract
In oncology, Deep Learning has shown great potential to personalise tasks such as tumour type classification, based on per-patient omics data-sets. Being high dimensional, incorporation of such data in one model is a challenge, often leading to one-dimensional studies and, therefore, information loss. Instead, we first propose relying on non-fixed sets of whole genome or whole exome variant-associated sequences, which can be used for supervised learning of oncology-relevant tasks by our Set Transformer based Deep Neural Network, SetQuence. We optimise this architecture to improve its efficiency. This allows for exploration of not just coding but also non-coding variants, from large datasets. Second, we extend the model to incorporate these representations together with multiple other sources of omics data in a flexible way with SetOmic. Evaluation, using these representations, shows improved robustness and reduced information loss compared to previous approaches, while still being computationally tractable. By means of Explainable Artificial Intelligence methods, our models are able to recapitulate the biological contribution of highly attributed features in the tumours studied. This validation opens the door to novel directions in multi-faceted genome and exome wide biomarker discovery and personalised treatment among other presently clinically relevant tasks.
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Affiliation(s)
- Neringa Jurenaite
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany.
| | - Daniel León-Periñán
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany; Max-Delbrück-Centrum für Molekulare Medizin, Hannoversche Str. 28, Berlin, 10115, Germany.
| | - Veronika Donath
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany.
| | - Sunna Torge
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany.
| | - René Jäkel
- Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), TU Dresden, Chemnitzer Str 46b, Dresden, 01187, Saxony, Germany.
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21
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Casella C, Kiles F, Urquhart C, Michaud DS, Kirwa K, Corlin L. Methylomic, Proteomic, and Metabolomic Correlates of Traffic-Related Air Pollution in the Context of Cardiorespiratory Health: A Systematic Review, Pathway Analysis, and Network Analysis. TOXICS 2023; 11:1014. [PMID: 38133415 PMCID: PMC10748071 DOI: 10.3390/toxics11121014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/18/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023]
Abstract
A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead to cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease and highlight contemporary challenges and opportunities associated with such efforts.
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Affiliation(s)
- Cameron Casella
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
| | - Frances Kiles
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
| | - Catherine Urquhart
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
| | - Dominique S. Michaud
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
| | - Kipruto Kirwa
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
- Department of Environmental Health, Boston University School of Public Health, Boston, MA 02118, USA
| | - Laura Corlin
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA; (C.C.); (F.K.); (C.U.); (D.S.M.); (K.K.)
- Department of Civil and Environmental Engineering, Tufts University School of Engineering, Medford, MA 02155, USA
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22
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Guo H, Lv X, Li Y, Li M. Attention-based GCN integrates multi-omics data for breast cancer subtype classification and patient-specific gene marker identification. Brief Funct Genomics 2023; 22:463-474. [PMID: 37114942 DOI: 10.1093/bfgp/elad013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 02/16/2023] [Accepted: 03/17/2023] [Indexed: 04/29/2023] Open
Abstract
Breast cancer is a heterogeneous disease and can be divided into several subtypes with unique prognostic and molecular characteristics. The classification of breast cancer subtypes plays an important role in the precision treatment and prognosis of breast cancer. Benefitting from the relation-aware ability of a graph convolution network (GCN), we present a multi-omics integrative method, the attention-based GCN (AGCN), for breast cancer molecular subtype classification using messenger RNA expression, copy number variation and deoxyribonucleic acid methylation multi-omics data. In the extensive comparative studies, our AGCN models outperform state-of-the-art methods under different experimental conditions and both attention mechanisms and the graph convolution subnetwork play an important role in accurate cancer subtype classification. The layer-wise relevance propagation (LRP) algorithm is used for the interpretation of model decision, which can identify patient-specific important biomarkers that are reported to be related to the occurrence and development of breast cancer. Our results highlighted the effectiveness of the GCN and attention mechanisms in multi-omics integrative analysis and the implement of the LRP algorithm can provide biologically reasonable insights into model decision.
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Affiliation(s)
- Hui Guo
- College of Chemistry at Sichuan University
| | - Xiang Lv
- College of Chemistry at Sichuan University
| | - Yizhou Li
- College of Cyber Science and Engineering at Sichuan University
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23
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Ranjbari S, Arslanturk S. Integration of incomplete multi-omics data using Knowledge Distillation and Supervised Variational Autoencoders for disease progression prediction. J Biomed Inform 2023; 147:104512. [PMID: 37813325 DOI: 10.1016/j.jbi.2023.104512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 08/31/2023] [Accepted: 10/03/2023] [Indexed: 10/11/2023]
Abstract
OBJECTIVE The rapid advancement of high-throughput technologies in the biomedical field has resulted in the accumulation of diverse omics data types, such as mRNA expression, DNA methylation, and microRNA expression, for studying various diseases. Integrating these multi-omics datasets enables a comprehensive understanding of the molecular basis of cancer and facilitates accurate prediction of disease progression. METHODS However, conventional approaches face challenges due to the dimensionality curse problem. This paper introduces a novel framework called Knowledge Distillation and Supervised Variational AutoEncoders utilizing View Correlation Discovery Network (KD-SVAE-VCDN) to address the integration of high-dimensional multi-omics data with limited common samples. Through our experimental evaluation, we demonstrate that the proposed KD-SVAE-VCDN architecture accurately predicts the progression of breast and kidney carcinoma by effectively classifying patients as long- or short-term survivors. Furthermore, our approach outperforms other state-of-the-art multi-omics integration models. RESULTS Our findings highlight the efficacy of the KD-SVAE-VCDN architecture in predicting the disease progression of breast and kidney carcinoma. By enabling the classification of patients based on survival outcomes, our model contributes to personalized and targeted treatments. The favorable performance of our approach in comparison to several existing models suggests its potential to contribute to the advancement of cancer understanding and management. CONCLUSION The development of a robust predictive model capable of accurately forecasting disease progression at the time of diagnosis holds immense promise for advancing personalized medicine. By leveraging multi-omics data integration, our proposed KD-SVAE-VCDN framework offers an effective solution to this challenge, paving the way for more precise and tailored treatment strategies for patients with different types of cancer.
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Affiliation(s)
- Sima Ranjbari
- Department of Computer Science, Wayne State University, Detroit, 48202, MI, USA.
| | - Suzan Arslanturk
- Department of Computer Science, Wayne State University, Detroit, 48202, MI, USA.
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24
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Casella C, Kiles F, Urquhart C, Michaud DS, Kirwa K, Corlin L. Methylomic, proteomic, and metabolomic correlates of traffic-related air pollution: A systematic review, pathway analysis, and network analysis relating traffic-related air pollution to subclinical and clinical cardiorespiratory outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.30.23296386. [PMID: 37873294 PMCID: PMC10592990 DOI: 10.1101/2023.09.30.23296386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
A growing body of literature has attempted to characterize how traffic-related air pollution (TRAP) affects molecular and subclinical biological processes in ways that could lead to cardiorespiratory disease. To provide a streamlined synthesis of what is known about the multiple mechanisms through which TRAP could lead cardiorespiratory pathology, we conducted a systematic review of the epidemiological literature relating TRAP exposure to methylomic, proteomic, and metabolomic biomarkers in adult populations. Using the 139 papers that met our inclusion criteria, we identified the omic biomarkers significantly associated with short- or long-term TRAP and used these biomarkers to conduct pathway and network analyses. We considered the evidence for TRAP-related associations with biological pathways involving lipid metabolism, cellular energy production, amino acid metabolism, inflammation and immunity, coagulation, endothelial function, and oxidative stress. Our analysis suggests that an integrated multi-omics approach may provide critical new insights into the ways TRAP could lead to adverse clinical outcomes. We advocate for efforts to build a more unified approach for characterizing the dynamic and complex biological processes linking TRAP exposure and subclinical and clinical disease, and highlight contemporary challenges and opportunities associated with such efforts.
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Affiliation(s)
- Cameron Casella
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Frances Kiles
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Catherine Urquhart
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Dominique S. Michaud
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
| | - Kipruto Kirwa
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Laura Corlin
- Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, MA 02111, USA
- Department of Civil and Environmental Engineering, Tufts University School of Engineering, Medford, MA 02155, USA
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25
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Holterhus PM, Kulle A, Busch H, Spielmann M. Classic genetic and hormonal switches during fetal sex development and beyond. MED GENET-BERLIN 2023; 35:163-171. [PMID: 38840820 PMCID: PMC10842585 DOI: 10.1515/medgen-2023-2036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
Critical genetic and hormonal switches characterize fetal sex development in humans. They are decisive for gonadal sex determination and subsequent differentiation of the genital and somatic sex phenotype. Only at the first glace these switches seem to behave like the dual 0 and 1 system in computer sciences and lead invariably to either typically male or female phenotypes. More recent data indicate that this model is insufficient. In addition, in case of distinct mutations, many of these switches may act variably, causing a functional continuum of alterations of gene functions and -dosages, enzymatic activities, sex hormone levels, and sex hormone sensitivity, giving rise to a broad clinical spectrum of biological differences of sex development (DSD) and potentially diversity of genital and somatic sex phenotypes. The gonadal anlage is initially a bipotential organ that can develop either into a testis or an ovary. Sex-determining region Y (SRY) is the most important upstream switch of gonadal sex determination inducing SOX9 further downstream, leading to testicular Sertoli cell differentiation and the repression of ovarian pathways. If SRY is absent (virtually "switched off"), e. g., in 46,XX females, RSPO1, WNT4, FOXL2, and other factors repress the male pathway and promote ovarian development. Testosterone and its more potent derivative, dihydrotestosterone (DHT) as well as AMH, are the most important upstream hormonal switches in phenotypic sex differentiation. Masculinization of the genitalia, i. e., external genital midline fusion forming the scrotum, growth of the genital tubercle, and Wolffian duct development, occurs in response to testosterone synthesized by steroidogenic cells in the testis. Müllerian ducts will not develop into a uterus and fallopian tubes in males due to Anti-Müllerian-Hormone (AMH) produced by the Sertoli cells. The functionality of these two hormone-dependent switches is ensured by their corresponding receptors, the intracellular androgen receptor (AR) and the transmembrane AMH type II receptor. The absence of high testosterone and high AMH is crucial for anatomically female genital development during fetal life. Recent technological advances, including single-cell and spatial transcriptomics, will likely shed more light on the nature of these molecular switches.
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Affiliation(s)
- Paul-Martin Holterhus
- Christian-Albrechts University of Kiel (CAU)Pediatric Endocrinology and Diabetes, Department of Pediatrics IKielGermany
| | - Alexandra Kulle
- Christian-Albrechts University of Kiel (CAU)Pediatric Endocrinology and Diabetes, Department of Pediatrics IKielGermany
| | - Hauke Busch
- University of LübeckMedical Systems Biology Group, Lübeck Institute of Experimental Dermatology (LIED)Ratzeburger Allee 16023562LübeckGermany
| | - Malte Spielmann
- University of LübeckInstitute of Human GeneticsLübeckGermany
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26
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Garg AD. The dynamic interface of genetics and immunity: toward future horizons in health & disease. Genes Immun 2023; 24:155-158. [PMID: 37464025 DOI: 10.1038/s41435-023-00213-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Affiliation(s)
- Abhishek D Garg
- Cell Stress & Immunity (CSI) Lab, Department for Cellular & Molecular Medicine (CMM), KU Leuven, Leuven, Belgium.
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27
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Robinson JP, Ostafe R, Iyengar SN, Rajwa B, Fischer R. Flow Cytometry: The Next Revolution. Cells 2023; 12:1875. [PMID: 37508539 PMCID: PMC10378642 DOI: 10.3390/cells12141875] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/06/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Unmasking the subtleties of the immune system requires both a comprehensive knowledge base and the ability to interrogate that system with intimate sensitivity. That task, to a considerable extent, has been handled by an iterative expansion in flow cytometry methods, both in technological capability and also in accompanying advances in informatics. As the field of fluorescence-based cytomics matured, it reached a technological barrier at around 30 parameter analyses, which stalled the field until spectral flow cytometry created a fundamental transformation that will likely lead to the potential of 100 simultaneous parameter analyses within a few years. The simultaneous advance in informatics has now become a watershed moment for the field as it competes with mature systematic approaches such as genomics and proteomics, allowing cytomics to take a seat at the multi-omics table. In addition, recent technological advances try to combine the speed of flow systems with other detection methods, in addition to fluorescence alone, which will make flow-based instruments even more indispensable in any biological laboratory. This paper outlines current approaches in cell analysis and detection methods, discusses traditional and microfluidic sorting approaches as well as next-generation instruments, and provides an early look at future opportunities that are likely to arise.
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Affiliation(s)
- J Paul Robinson
- Department of Basic Medical Sciences, Purdue University, West Lafayette, IN 47907, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907, USA
| | - Raluca Ostafe
- Molecular Evolution, Protein Engineering and Production Facility (PI4D), Purdue University, West Lafayette, IN 47907, USA
| | | | - Bartek Rajwa
- Bindley Bioscience Center, Purdue University, West Lafayette, IN 47907, USA
| | - Rainer Fischer
- Department of Comparative Pathobiology, College of Veterinary Medicine, Purdue University, West Lafayette, IN 47907, USA
- Purdue Institute of Inflammation, Immunology and Infectious Diseases, Purdue University, West Lafayette, IN 47907, USA
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28
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Bhat BA, Saifi I, Khamjan NA, Hamdani SS, Algaissi A, Rashid S, Alshehri MM, Ganie SA, Lohani M, Abdelwahab SI, Dar SA. Exploring the tumor immune microenvironment in ovarian cancer: a way-out to the therapeutic roadmap. Expert Opin Ther Targets 2023; 27:841-860. [PMID: 37712621 DOI: 10.1080/14728222.2023.2259096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 07/21/2023] [Accepted: 09/11/2023] [Indexed: 09/16/2023]
Abstract
INTRODUCTION Despite cancer treatment strides, mortality due to ovarian cancer remains high globally. While immunotherapy has proven effective in treating cancers with low cure rates, it has limitations. Growing evidence suggests that both tumoral and non-tumoral components of the tumor immune microenvironment (TIME) play a significant role in cancer growth. Therefore, developing novel and focused therapy for ovarian cancer is critical. Studies indicate that TIME is involved in developing ovarian cancer, particularly genome-, transcriptome-, and proteome-wide studies. As a result, TIME may present a prospective therapeutic target for ovarian cancer patients. AREAS COVERED We examined several TIME-targeting medicines and the connection between TIME and ovarian cancer. The key protagonists and events in the TIME and therapeutic strategies that explicitly target these events in ovarian cancer are discussed. EXPERT OPINION We highlighted various targeted therapies against TIME in ovarian cancer, including anti-angiogenesis therapies and immune checkpoint inhibitors. While these therapies are in their infancy, they have shown promise in controlling ovarian cancer progression. The use of 'omics' technology is helping in better understanding of TIME in ovarian cancer and potentially identifying new therapeutic targets. TIME-targeted strategies could account for an additional treatment strategy when treating ovarian cancer.
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Affiliation(s)
- Basharat Ahmad Bhat
- Department of Bioresources, Amar Singh College Campus, Cluster University, Srinagar, India
| | - Ifra Saifi
- Department of Botany, Chaudhary Charan Singh University, Meerut India
| | - Nizar A Khamjan
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Syed Suhail Hamdani
- Department of Bioresources, Amar Singh College Campus, Cluster University, Srinagar, India
| | - Abdullah Algaissi
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
- Medical Research Centre, Jazan University, Jazan, Saudi Arabia
| | - Safeena Rashid
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | | | - Showkat Ahmad Ganie
- Department of Clinical Biochemistry, School of Biological Sciences, University of Kashmir, Srinagar, India
| | - Mohtashim Lohani
- Department of Emergency Medical Services, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | | | - Sajad Ahmad Dar
- Research and Scientific Studies Unit, College of Nursing, Jazan University, Jazan, Saudi Arabia
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29
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Demir Karaman E, Işık Z. Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers. Med Sci (Basel) 2023; 11:44. [PMID: 37489460 PMCID: PMC10366886 DOI: 10.3390/medsci11030044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/18/2023] [Accepted: 06/20/2023] [Indexed: 07/26/2023] Open
Abstract
Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common modules were evaluated by several biological metrics adapted to cancer. Then, a new prognostic scoring method was developed by weighting mRNA expression, methylation, and mutation status of genes. The survival analysis pointed out statistically significant results for GNG11, CBX2, CDKN3, ARHGEF10, CLN8, SEC61G and PTDSS1 genes. The literature search reveals that the identified biomarkers are associated with the same or different types of cancers. Our method does not only identify known cancer-specific biomarker genes, but also proposes new potential biomarkers. Thus, this study provides a rationale for identifying new gene targets and expanding treatment options across cancer types.
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Affiliation(s)
- Ezgi Demir Karaman
- Department of Computer Engineering, Institute of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, Turkey
| | - Zerrin Işık
- Department of Computer Engineering, Faculty of Engineering, Dokuz Eylul University, Izmir 35390, Turkey
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30
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Salemme V, Centonze G, Avalle L, Natalini D, Piccolantonio A, Arina P, Morellato A, Ala U, Taverna D, Turco E, Defilippi P. The role of tumor microenvironment in drug resistance: emerging technologies to unravel breast cancer heterogeneity. Front Oncol 2023; 13:1170264. [PMID: 37265795 PMCID: PMC10229846 DOI: 10.3389/fonc.2023.1170264] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 04/28/2023] [Indexed: 06/03/2023] Open
Abstract
Breast cancer is a highly heterogeneous disease, at both inter- and intra-tumor levels, and this heterogeneity is a crucial determinant of malignant progression and response to treatments. In addition to genetic diversity and plasticity of cancer cells, the tumor microenvironment contributes to tumor heterogeneity shaping the physical and biological surroundings of the tumor. The activity of certain types of immune, endothelial or mesenchymal cells in the microenvironment can change the effectiveness of cancer therapies via a plethora of different mechanisms. Therefore, deciphering the interactions between the distinct cell types, their spatial organization and their specific contribution to tumor growth and drug sensitivity is still a major challenge. Dissecting intra-tumor heterogeneity is currently an urgent need to better define breast cancer biology and to develop therapeutic strategies targeting the microenvironment as helpful tools for combined and personalized treatment. In this review, we analyze the mechanisms by which the tumor microenvironment affects the characteristics of tumor heterogeneity that ultimately result in drug resistance, and we outline state of the art preclinical models and emerging technologies that will be instrumental in unraveling the impact of the tumor microenvironment on resistance to therapies.
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Affiliation(s)
- Vincenzo Salemme
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Giorgia Centonze
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Lidia Avalle
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Dora Natalini
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Alessio Piccolantonio
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Pietro Arina
- UCL, Bloomsbury Institute of Intensive Care Medicine, Division of Medicine, University College London, London, United Kingdom
| | - Alessandro Morellato
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Ugo Ala
- Department of Veterinary Sciences, University of Turin, Grugliasco, TO, Italy
| | - Daniela Taverna
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
| | - Emilia Turco
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Paola Defilippi
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
- Molecular Biotechnology Center (MBC) “Guido Tarone”, Turin, Italy
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31
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Thafar MA, Albaradei S, Uludag M, Alshahrani M, Gojobori T, Essack M, Gao X. OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features. Front Genet 2023; 14:1139626. [PMID: 37091791 PMCID: PMC10117673 DOI: 10.3389/fgene.2023.1139626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 03/24/2023] [Indexed: 04/08/2023] Open
Abstract
Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein’s amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the “OncologyTT” datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins’ amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.
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Affiliation(s)
- Maha A. Thafar
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- College of Computers and Information Technology, Computer Science Department, Taif University, Taif, Saudi Arabia
| | - Somayah Albaradei
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mahmut Uludag
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Mona Alshahrani
- National Center for Artificial Intelligence (NCAI), Saudi Data and Artificial Intelligence Authority (SDAIA), Riyadh, Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- *Correspondence: Xin Gao, ; Magbubah Essack,
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center, Computer (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- *Correspondence: Xin Gao, ; Magbubah Essack,
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32
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Bostanci E, Kocak E, Unal M, Guzel MS, Acici K, Asuroglu T. Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer. SENSORS (BASEL, SWITZERLAND) 2023; 23:3080. [PMID: 36991790 PMCID: PMC10052105 DOI: 10.3390/s23063080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/09/2023] [Accepted: 03/11/2023] [Indexed: 06/19/2023]
Abstract
Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.
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Affiliation(s)
- Erkan Bostanci
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Engin Kocak
- Department of Analytical Chemistry, Faculty of Gülhane Pharmacy, University of Health Sciences, 06018 Ankara, Turkey
| | - Metehan Unal
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Mehmet Serdar Guzel
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Koray Acici
- Department of Artificial Intelligence and Data Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
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33
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Allesøe RL, Lundgaard AT, Hernández Medina R, Aguayo-Orozco A, Johansen J, Nissen JN, Brorsson C, Mazzoni G, Niu L, Biel JH, Brasas V, Webel H, Benros ME, Pedersen AG, Chmura PJ, Jacobsen UP, Mari A, Koivula R, Mahajan A, Vinuela A, Tajes JF, Sharma S, Haid M, Hong MG, Musholt PB, De Masi F, Vogt J, Pedersen HK, Gudmundsdottir V, Jones A, Kennedy G, Bell J, Thomas EL, Frost G, Thomsen H, Hansen E, Hansen TH, Vestergaard H, Muilwijk M, Blom MT, 't Hart LM, Pattou F, Raverdy V, Brage S, Kokkola T, Heggie A, McEvoy D, Mourby M, Kaye J, Hattersley A, McDonald T, Ridderstråle M, Walker M, Forgie I, Giordano GN, Pavo I, Ruetten H, Pedersen O, Hansen T, Dermitzakis E, Franks PW, Schwenk JM, Adamski J, McCarthy MI, Pearson E, Banasik K, Rasmussen S, Brunak S, Thomas CE, Haussler R, Beulens J, Rutters F, Nijpels G, van Oort S, Groeneveld L, Elders P, Giorgino T, Rodriquez M, Nice R, Perry M, Bianzano S, Graefe-Mody U, Hennige A, Grempler R, Baum P, Stærfeldt HH, Shah N, Teare H, Ehrhardt B, Tillner J, Dings C, Lehr T, Scherer N, Sihinevich I, Cabrelli L, Loftus H, Bizzotto R, Tura A, Dekkers K, van Leeuwen N, Groop L, Slieker R, Ramisch A, Jennison C, McVittie I, Frau F, Steckel-Hamann B, Adragni K, Thomas M, Pasdar NA, Fitipaldi H, Kurbasic A, Mutie P, Pomares-Millan H, Bonnefond A, Canouil M, Caiazzo R, Verkindt H, Holl R, Kuulasmaa T, Deshmukh H, Cederberg H, Laakso M, Vangipurapu J, Dale M, Thorand B, Nicolay C, Fritsche A, Hill A, Hudson M, Thorne C, Allin K, Arumugam M, Jonsson A, Engelbrechtsen L, Forman A, Dutta A, Sondertoft N, Fan Y, Gough S, Robertson N, McRobert N, Wesolowska-Andersen A, Brown A, Davtian D, Dawed A, Donnelly L, Palmer C, White M, Ferrer J, Whitcher B, Artati A, Prehn C, Adam J, Grallert H, Gupta R, Sackett PW, Nilsson B, Tsirigos K, Eriksen R, Jablonka B, Uhlen M, Gassenhuber J, Baltauss T, de Preville N, Klintenberg M, Abdalla M. Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models. Nat Biotechnol 2023; 41:399-408. [PMID: 36593394 PMCID: PMC10017515 DOI: 10.1038/s41587-022-01520-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 09/20/2022] [Indexed: 01/03/2023]
Abstract
The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.
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Affiliation(s)
- Rosa Lundbye Allesøe
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.,Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Agnete Troen Lundgaard
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ricardo Hernández Medina
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Alejandro Aguayo-Orozco
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Joachim Johansen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Jakob Nybo Nissen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Caroline Brorsson
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Gianluca Mazzoni
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lili Niu
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jorge Hernansanz Biel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Valentas Brasas
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henry Webel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Michael Eriksen Benros
- Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark.,Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anders Gorm Pedersen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Piotr Jaroslaw Chmura
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ulrik Plesner Jacobsen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Andrea Mari
- C.N.R. Institute of Neuroscience, Padova, Italy
| | - Robert Koivula
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Ana Vinuela
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland.,Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | | | - Sapna Sharma
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany.,Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Bavaria, Germany.,Chair of Food Chemistry and Molecular and Sensory Science, Technical University of Munich, Freising, Germany
| | - Mark Haid
- Metabolomics and Proteomics Core, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany
| | - Mun-Gwan Hong
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Petra B Musholt
- Research and Development Global Development, Translational Medicine and Clinical Pharmacology, Sanofi-Aventis Deutschland, Frankfurt, Germany
| | - Federico De Masi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Josef Vogt
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Helle Krogh Pedersen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.,Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Valborg Gudmundsdottir
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Angus Jones
- University of Exeter Medical School, Exeter, UK
| | - Gwen Kennedy
- The Immunoassay Biomarker Core Laboratory, School of Medicine, University of Dundee, Dundee, UK
| | - Jimmy Bell
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, Department of Life Sciences, University of Westminster, London, UK
| | - Gary Frost
- Section for Nutrition Research, Faculty of Medicine, Imperial College London, London, UK
| | - Henrik Thomsen
- Department of Radiology, Copenhagen University Hospital Herlev-Gentofte, Herlev, Denmark
| | - Elizaveta Hansen
- Department of Radiology, Copenhagen University Hospital Herlev-Gentofte, Herlev, Denmark
| | - Tue Haldor Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Henrik Vestergaard
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mirthe Muilwijk
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Marieke T Blom
- Department of General Practice, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Leen M 't Hart
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.,Department of Biomedical Data Science, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.,Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Francois Pattou
- Inserm, Univ Lille, CHU Lille, Lille Pasteur Institute, EGID, Lille, France
| | - Violeta Raverdy
- Inserm, Univ Lille, CHU Lille, Lille Pasteur Institute, EGID, Lille, France
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Tarja Kokkola
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Alison Heggie
- Institute of Cellular Medicine, Newcastle University, Newcastle, UK
| | - Donna McEvoy
- Diabetes Research Network, Royal Victoria Infirmary, Newcastle, UK
| | - Miranda Mourby
- Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, UK
| | - Jane Kaye
- Centre for Health, Law and Emerging Technologies (HeLEX), Faculty of Law, University of Oxford, Oxford, UK
| | | | | | - Martin Ridderstråle
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Mark Walker
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - Ian Forgie
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Giuseppe N Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, CRC, Lund University, SUS, Malmö, Sweden
| | - Imre Pavo
- Eli Lilly Regional Operations, Vienna, Austria
| | - Hartmut Ruetten
- Research and Development Global Development, Translational Medicine and Clinical Pharmacology, Sanofi-Aventis Deutschland, Frankfurt, Germany
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Emmanouil Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Paul W Franks
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden.,Harvard T.H. Chan School of Public Health, Boston, MA, USA.,OCDEM, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Solna, Sweden
| | - Jerzy Adamski
- Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.,Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Institute of Biochemistry, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK.,Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.,Genentech, South San Francisco, CA, USA
| | - Ewan Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. .,Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
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Zafari N, Bathaei P, Velayati M, Khojasteh-Leylakoohi F, Khazaei M, Fiuji H, Nassiri M, Hassanian SM, Ferns GA, Nazari E, Avan A. Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer. Comput Biol Med 2023; 155:106639. [PMID: 36805214 DOI: 10.1016/j.compbiomed.2023.106639] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/14/2023] [Accepted: 02/05/2023] [Indexed: 02/12/2023]
Abstract
The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.
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Affiliation(s)
- Nima Zafari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Parsa Bathaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahla Velayati
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fatemeh Khojasteh-Leylakoohi
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Khazaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamid Fiuji
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammadreza Nassiri
- Recombinant Proteins Research Group, The Research Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Seyed Mahdi Hassanian
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex, BN1 9PH, UK
| | - Elham Nazari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
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Mohammed MA, Abdulkareem KH, Dinar AM, Zapirain BG. Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13040664. [PMID: 36832152 PMCID: PMC9955380 DOI: 10.3390/diagnostics13040664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 02/05/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.
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Affiliation(s)
- Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq
- eVIDA Lab, University of Deusto, 48007 Bilbao, Spain
- Correspondence: (M.A.M.); (B.G.Z.)
| | - Karrar Hameed Abdulkareem
- College of Agriculture, Al-Muthanna University, Samawah 66001, Iraq
- College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq
| | - Ahmed M. Dinar
- Computer Engineering Department, University of Technology- Iraq, Baghdad 19006, Iraq
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Flores JE, Claborne DM, Weller ZD, Webb-Robertson BJM, Waters KM, Bramer LM. Missing data in multi-omics integration: Recent advances through artificial intelligence. Front Artif Intell 2023; 6:1098308. [PMID: 36844425 PMCID: PMC9949722 DOI: 10.3389/frai.2023.1098308] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/23/2023] [Indexed: 02/11/2023] Open
Abstract
Biological systems function through complex interactions between various 'omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across 'omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more 'omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.
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Affiliation(s)
- Javier E. Flores
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Daniel M. Claborne
- Pacific Northwest National Laboratory, Artificial Intelligence and Data Analytics Division, National Security Directorate, Richland, WA, United States
| | - Zachary D. Weller
- Pacific Northwest National Laboratory, Artificial Intelligence and Data Analytics Division, National Security Directorate, Richland, WA, United States
| | - Bobbie-Jo M. Webb-Robertson
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Katrina M. Waters
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States
| | - Lisa M. Bramer
- Pacific Northwest National Laboratory, Biological Sciences Division, Earth and Biological Sciences Directorate, Richland, WA, United States,*Correspondence: Lisa M. Bramer ✉
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Non-Association of Driver Alterations in PTEN with Differential Gene Expression and Gene Methylation in IDH1 Wildtype Glioblastomas. Brain Sci 2023; 13:brainsci13020186. [PMID: 36831729 PMCID: PMC9953940 DOI: 10.3390/brainsci13020186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/18/2023] [Accepted: 01/20/2023] [Indexed: 01/24/2023] Open
Abstract
During oncogenesis, alterations in driver genes called driver alterations (DAs) modulate the transcriptome, methylome and proteome through oncogenic signaling pathways. These modulatory effects of any DA may be analyzed by examining differentially expressed mRNAs (DEMs), differentially methylated genes (DMGs) and differentially expressed proteins (DEPs) between tumor samples with and without that DA. We aimed to analyze these modulations with 12 common driver genes in Isocitrate Dehydrogenase 1 wildtype glioblastomas (IDH1-W-GBs). Using Cbioportal, groups of tumor samples with and without DAs in these 12 genes were generated from the IDH1-W-GBs available from "The Cancer Genomics Atlas Firehose Legacy Study Group" (TCGA-FL-SG) on Glioblastomas (GBs). For all 12 genes, samples with and without DAs were compared for DEMs, DMGs and DEPs. We found that DAs in PTEN were unassociated with any DEM or DMG in contrast to DAs in all other drivers, which were associated with several DEMs and DMGs. This contrasting PTEN-related property of being unassociated with differential gene expression or methylation in IDH1-W-GBs was unaffected by concurrent DAs in other common drivers or by the types of DAs affecting PTEN. From the lists of DEMs and DMGs associated with some common drivers other than PTEN, enriched gene ontology terms and insights into the co-regulatory effects of these drivers on the transcriptome were obtained. The findings from this study can improve our understanding of the molecular mechanisms underlying gliomagenesis with potential therapeutic benefits.
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Zeković M, Bumbaširević U, Živković M, Pejčić T. Alteration of Lipid Metabolism in Prostate Cancer: Multifaceted Oncologic Implications. Int J Mol Sci 2023; 24:ijms24021391. [PMID: 36674910 PMCID: PMC9863986 DOI: 10.3390/ijms24021391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/12/2023] Open
Abstract
Cancer is increasingly recognized as an extraordinarily heterogeneous disease featuring an intricate mutational landscape and vast intra- and intertumor variability on both genetic and phenotypic levels. Prostate cancer (PCa) is the second most prevalent malignant disease among men worldwide. A single metabolic program cannot epitomize the perplexing reprogramming of tumor metabolism needed to sustain the stemness of neoplastic cells and their prominent energy-consuming functional properties, such as intensive proliferation, uncontrolled growth, migration, and invasion. In cancerous tissue, lipids provide the structural integrity of biological membranes, supply energy, influence the regulation of redox homeostasis, contribute to plasticity, angiogenesis and microenvironment reshaping, mediate the modulation of the inflammatory response, and operate as signaling messengers, i.e., lipid mediators affecting myriad processes relevant for the development of the neoplasia. Comprehensive elucidation of the lipid metabolism alterations in PCa, the underlying regulatory mechanisms, and their implications in tumorigenesis and the progression of the disease are gaining growing research interest in the contemporary urologic oncology. Delineation of the unique metabolic signature of the PCa featuring major aberrant pathways including de novo lipogenesis, lipid uptake, storage and compositional reprogramming may provide novel, exciting, and promising avenues for improving diagnosis, risk stratification, and clinical management of such a complex and heterogeneous pathology.
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Affiliation(s)
- Milica Zeković
- Centre of Research Excellence in Nutrition and Metabolism, Institute for Medical Research, National Institute of Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | - Uros Bumbaširević
- Clinic of Urology, University Clinical Center of Serbia, 11000 Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Marko Živković
- Clinic of Urology, University Clinical Center of Serbia, 11000 Belgrade, Serbia
| | - Tomislav Pejčić
- Clinic of Urology, University Clinical Center of Serbia, 11000 Belgrade, Serbia
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
- Correspondence:
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Wang S, Wang S, Wang Z. A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer. Front Med (Lausanne) 2023; 9:1109365. [PMID: 36703893 PMCID: PMC9871466 DOI: 10.3389/fmed.2022.1109365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
Gastrointestinal cancer is becoming increasingly common, which leads to over 3 million deaths every year. No typical symptoms appear in the early stage of gastrointestinal cancer, posing a significant challenge in the diagnosis and treatment of patients with gastrointestinal cancer. Many patients are in the middle and late stages of gastrointestinal cancer when they feel uncomfortable, unfortunately, most of them will die of gastrointestinal cancer. Recently, various artificial intelligence techniques like machine learning based on multi-omics have been presented for cancer diagnosis and treatment in the era of precision medicine. This paper provides a survey on multi-omics-based cancer diagnosis using machine learning with potential application in gastrointestinal cancer. Particularly, we make a comprehensive summary and analysis from the perspective of multi-omics datasets, task types, and multi-omics-based integration methods. Furthermore, this paper points out the remaining challenges of multi-omics-based cancer diagnosis using machine learning and discusses future topics.
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Affiliation(s)
- Suixue Wang
- School of Information and Communication Engineering, Hainan University, Haikou, China
| | - Shuling Wang
- Department of Neurology, Affiliated Haikou Hospital of Xiangya School of Medicine, Central South University, Haikou, China
| | - Zhengxia Wang
- School of Computer Science and Technology, Hainan University, Haikou, China
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40
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Liao J, Li X, Gan Y, Han S, Rong P, Wang W, Li W, Zhou L. Artificial intelligence assists precision medicine in cancer treatment. Front Oncol 2023; 12:998222. [PMID: 36686757 PMCID: PMC9846804 DOI: 10.3389/fonc.2022.998222] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/22/2022] [Indexed: 01/06/2023] Open
Abstract
Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.
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Affiliation(s)
- Jinzhuang Liao
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xiaoying Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Yu Gan
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Shuangze Han
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Pengfei Rong
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Wang
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Wei Li
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
| | - Li Zhou
- Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China,Cell Transplantation and Gene Therapy Institute, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China,Department of Pathology, The Xiangya Hospital of Central South University, Changsha, Hunan, China,*Correspondence: Pengfei Rong, ; Wei Wang, ; Wei Li, ; Li Zhou,
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41
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Niranjan V, Uttarkar A, Kaul A, Varghese M. A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways. Methods Mol Biol 2023; 2553:441-452. [PMID: 36227554 DOI: 10.1007/978-1-0716-2617-7_19] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorithms in order to provide results with high accuracy. This protocol chapter defines the steps required for the ML-multi-omics integration methods that are applied on biological datasets for its analysis and the visual interpretation of the results thus obtained.
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Affiliation(s)
- Vidya Niranjan
- Department of Biotechnology, R V College of Engineering, Mysuru Road, Kengeri, Bengaluru, India.
| | - Akshay Uttarkar
- Department of Biotechnology, R V College of Engineering, Mysuru Road, Kengeri, Bengaluru, India
| | - Aakaanksha Kaul
- Department of Biotechnology, R V College of Engineering, Mysuru Road, Kengeri, Bengaluru, India
| | - Maryanne Varghese
- Department of Biotechnology, R V College of Engineering, Mysuru Road, Kengeri, Bengaluru, India
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Zhang Z, Zhang S, Huang J, Cao X, Hou C, Luo Z, Wang X, Liu X, Li Q, Zhang X, Guo Y, Xiao H, Xie T, Zhou X. Association between abnormal plasma metabolism and brain atrophy in alcohol-dependent patients. Front Mol Neurosci 2022; 15:999938. [PMID: 36583081 PMCID: PMC9792671 DOI: 10.3389/fnmol.2022.999938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 11/16/2022] [Indexed: 12/15/2022] Open
Abstract
Objective In this study, we aimed to characterize the plasma metabolic profiles of brain atrophy and alcohol dependence (s) and to identify the underlying pathogenesis of brain atrophy related to alcohol dependence. Methods We acquired the plasma samples of alcohol-dependent patients and performed non-targeted metabolomic profiling analysis to identify alterations of key metabolites in the plasma of BA-ADPs. Machine learning algorithms and bioinformatic analysis were also used to identify predictive biomarkers and investigate their possible roles in brain atrophy related to alcohol dependence. Results A total of 26 plasma metabolites were significantly altered in the BA-ADPs group when compared with a group featuring alcohol-dependent patients without brain atrophy (NBA-ADPs). Nine of these differential metabolites were further identified as potential biomarkers for BA-ADPs. Receiver operating characteristic curves demonstrated that these potential biomarkers exhibited good sensitivity and specificity for distinguishing BA-ADPs from NBA-ADPs. Moreover, metabolic pathway analysis suggested that glycerophospholipid metabolism may be highly involved in the pathogenesis of alcohol-induced brain atrophy. Conclusion This plasma metabolomic study provides a valuable resource for enhancing our understanding of alcohol-induced brain atrophy and offers potential targets for therapeutic intervention.
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Affiliation(s)
- Zheyu Zhang
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,Department of Integrated Traditional Chinese & Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Sifang Zhang
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,Department of Integrated Traditional Chinese & Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jianhua Huang
- Hunan Academy of Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Xiaoyun Cao
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,The School of Clinical Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Chao Hou
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,The School of Clinical Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Zhihong Luo
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,The School of Clinical Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Xiaoyan Wang
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,The School of Clinical Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Xuejun Liu
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,The School of Clinical Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Qiang Li
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,The School of Clinical Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Xi Zhang
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,The School of Clinical Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Yujun Guo
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,The School of Clinical Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Huiqiong Xiao
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,The School of Clinical Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Ting Xie
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,The School of Clinical Medicine, Hunan University of Chinese Medicine, Changsha, China
| | - Xuhui Zhou
- Department of Addiction Medicine, Hunan Institute of Mental Health, Brain Hospital of Hunan Province (The Second People’s Hospital of Hunan Province), Changsha, China,The School of Clinical Medicine, Hunan University of Chinese Medicine, Changsha, China,*Correspondence: Xuhui Zhou,
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Su Q, Chen Q, Li Z, Zhao J, Li L, Xu L, Yang B, Liu C. Multi-omics analysis reveals GABAergic dysfunction after traumatic brainstem injury in rats. Front Neurosci 2022; 16:1003300. [PMID: 36507346 PMCID: PMC9726735 DOI: 10.3389/fnins.2022.1003300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 10/31/2022] [Indexed: 11/24/2022] Open
Abstract
Background Traumatic brainstem injury (TBSI) is one of the forms of brain injury and has a very high mortality rate. Understanding the molecular mechanism of injury can provide additional information for clinical treatment. Materials and methods In this study, we detected transcriptome, proteomics, and metabolome expression changes in the brainstem of TBSI rats, and comprehensively analyzed the underlying mechanisms of TBSI. Results After TBSI, there was significant diffuse axonal injury (DAI) in the brainstem of rats. A total of 579 genes, 70 proteins, and 183 metabolites showed significant changes in brainstem tissue. Through molecular function and pathway analysis, the differentially expressed genes, proteins, and metabolites of TBSI were mainly attributed to neural signal regulation, inflammation, neuroprotection, and immune system. In addition, a comprehensive analysis of transcripts, proteins, and metabolites showed that the genes, proteins, and metabolic pathways regulated in the brainstem after TBSI were involved in neuroactive ligand-receptor interaction. A variety of GCPR-regulated pathways were affected, especially GAGA's corresponding receptors GABAA, GABAB, GABAC, and transporter GAT that were inhibited to varying degrees. Conclusion This study provides insights into the development of a rapid diagnostic kit and making treatment strategies for TBSI.
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Affiliation(s)
- Qin Su
- Guangzhou Forensic Science Institute, Guangzhou, China,Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Qianling Chen
- School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Zhigang Li
- Guangzhou Forensic Science Institute, Guangzhou, China
| | - Jian Zhao
- Guangzhou Forensic Science Institute, Guangzhou, China
| | - Lingyue Li
- School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Luyao Xu
- School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Bin Yang
- School of Forensic Medicine, Southern Medical University, Guangzhou, China
| | - Chao Liu
- Guangzhou Forensic Science Institute, Guangzhou, China,*Correspondence: Chao Liu,
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Jalalifar SA, Soliman H, Sahgal A, Sadeghi-Naini A. A Self-Attention-Guided 3D Deep Residual Network With Big Transfer to Predict Local Failure in Brain Metastasis After Radiotherapy Using Multi-Channel MRI. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 11:13-22. [PMID: 36478770 PMCID: PMC9721353 DOI: 10.1109/jtehm.2022.3219625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 10/15/2022] [Accepted: 11/02/2022] [Indexed: 11/06/2022]
Abstract
A noticeable proportion of larger brain metastases (BMs) are not locally controlled after stereotactic radiotherapy, and it may take months before local progression is apparent on standard follow-up imaging. This work proposes and investigates new explainable deep-learning models to predict the radiotherapy outcome for BM. A novel self-attention-guided 3D residual network is introduced for predicting the outcome of local failure (LF) after radiotherapy using the baseline treatment-planning MRI. The 3D self-attention modules facilitate capturing long-range intra/inter slice dependencies which are often overlooked by convolution layers. The proposed model was compared to a vanilla 3D residual network and 3D residual network with CBAM attention in terms of performance in outcome prediction. A training recipe was adapted for the outcome prediction models during pretraining and training the down-stream task based on the recently proposed big transfer principles. A novel 3D visualization module was coupled with the model to demonstrate the impact of various intra/peri-lesion regions on volumetric multi-channel MRI upon the network's prediction. The proposed self-attention-guided 3D residual network outperforms the vanilla residual network and the residual network with CBAM attention in accuracy, F1-score, and AUC. The visualization results show the importance of peri-lesional characteristics on treatment-planning MRI in predicting local outcome after radiotherapy. This study demonstrates the potential of self-attention-guided deep-learning features derived from volumetric MRI in radiotherapy outcome prediction for BM. The insights obtained via the developed visualization module for individual lesions can possibly be applied during radiotherapy planning to decrease the chance of LF.
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Affiliation(s)
- Seyed Ali Jalalifar
- Department of Electrical Engineering and Computer ScienceLassonde School of EngineeringYork University Toronto ON M3J 1P3 Canada
| | - Hany Soliman
- Physical Sciences PlatformSunnybrook Research Institute, Sunnybrook Health Sciences Centre Toronto ON M4N 3M5 Canada
- Department of Radiation OncologyOdette Cancer CentreSunnybrook Health Sciences Centre Toronto ON M4N 3M5 Canada
- Department of Radiation OncologyUniversity of Toronto Toronto ON M5T 1P5 Canada
| | - Arjun Sahgal
- Physical Sciences PlatformSunnybrook Research Institute, Sunnybrook Health Sciences Centre Toronto ON M4N 3M5 Canada
- Department of Radiation OncologyOdette Cancer CentreSunnybrook Health Sciences Centre Toronto ON M4N 3M5 Canada
- Department of Radiation OncologyUniversity of Toronto Toronto ON M5T 1P5 Canada
| | - Ali Sadeghi-Naini
- Department of Electrical Engineering and Computer ScienceLassonde School of EngineeringYork University Toronto ON M3J 1P3 Canada
- Physical Sciences PlatformSunnybrook Research Institute, Sunnybrook Health Sciences Centre Toronto ON M4N 3M5 Canada
- Department of Radiation OncologyOdette Cancer CentreSunnybrook Health Sciences Centre Toronto ON M4N 3M5 Canada
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45
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Zhanpeng H, Jiekang W. A Multiview Clustering Method With Low-Rank and Sparsity Constraints for Cancer Subtyping. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:3213-3223. [PMID: 34705654 DOI: 10.1109/tcbb.2021.3122917] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Multiomics data clustering is one of the major challenges in the field of precision medicine. Integration of multiomics data for cancer subtyping can improve the understanding on cancer and reveal systems-level insights. How to integrate multiomics data for accurate cancer subtyping is an interesting and challenging research problem. To capture the global and the local structure of omics data, a novel framework for integrating multiomics data is proposed for cancer subtyping. Multiview clustering with low-rank and sparsity constraints (MVCLRS) can measure the local similarities of samples in each omics data and obtain global consensus structures by integrating the multiomics data. The main insight provided by MVCLRS is that low-rank sparse subspace clustering for the construction of an affinity matrix can best capture the local similarities in omics data. Extensive testing is conducted on 10 real world cancer datasets with multiomics from The Cancer Genome Atlas. Compared with 10 state-of-the-art multiomics clustering algorithms, the MVCLRS performs better in the 10 cancer datasets by providing its clustering results with at least one enriched clinical label in nine of ten cancer subtypes, the most of any method.
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46
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Maghsoudi Z, Nguyen H, Tavakkoli A, Nguyen T. A comprehensive survey of the approaches for pathway analysis using multi-omics data integration. Brief Bioinform 2022; 23:6761962. [PMID: 36252928 PMCID: PMC9677478 DOI: 10.1093/bib/bbac435] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/26/2022] [Accepted: 09/08/2022] [Indexed: 02/07/2023] Open
Abstract
Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.
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Affiliation(s)
- Zeynab Maghsoudi
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA
| | - Ha Nguyen
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA
| | - Alireza Tavakkoli
- Department of Computer Science and Engineering, University of Nevada, Reno, 89557, Nevada, USA
| | - Tin Nguyen
- Corresponding author: Tin Nguyen, Department of Computer Science and Engineering, University of Nevada, Reno, NV, USA. Tel.: +1-775-784-6619;
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47
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Raufaste-Cazavieille V, Santiago R, Droit A. Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology. Front Mol Biosci 2022; 9:962743. [PMID: 36304921 PMCID: PMC9595279 DOI: 10.3389/fmolb.2022.962743] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases. In cancerology, the omics approach, initiated by genomics and transcriptomics studies, has revealed an incredible complexity with unsuspected molecular diversity within a same tumor type as well as spatial and temporal heterogeneity of tumors. The integration of multiple biological layers of omics studies brought oncology to a new paradigm, from tumor site classification to pan-cancer molecular classification, offering new therapeutic opportunities for precision medicine. In this review, we will provide a comprehensive overview of the latest innovations for multi-omics integration in oncology and summarize the largest multi-omics dataset available for adult and pediatric cancers. We will present multi-omics techniques for characterizing cancer biology and show how multi-omics data can be combined with clinical data for the identification of prognostic and treatment-specific biomarkers, opening the way to personalized therapy. To conclude, we will detail the newest strategies for dissecting the tumor immune environment and host–tumor interaction. We will explore the advances in immunomics and microbiomics for biomarker identification to guide therapeutic decision in immuno-oncology.
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Affiliation(s)
| | - Raoul Santiago
- CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- Division of Pediatric Hematology-Oncology, Centre Hospitalier Universitaire de L’Université Laval, Charles Bruneau Cancer Center, Québec, QC, Canada
- *Correspondence: Raoul Santiago, ; Arnaud Droit,
| | - Arnaud Droit
- CHU de Québec Research Center, Université Laval, Québec, QC, Canada
- *Correspondence: Raoul Santiago, ; Arnaud Droit,
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48
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Nie H, Liu H, Shi Y, Lai W, Liu X, Xi Z, Lin B. Combined multi-omics analysis reveals oil mist particulate matter-induced lung injury in rats: Pathological damage, proteomics, metabolic disturbances, and lung dysbiosis. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 241:113759. [PMID: 35714485 DOI: 10.1016/j.ecoenv.2022.113759] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/18/2022] [Accepted: 06/06/2022] [Indexed: 06/15/2023]
Abstract
Oil mist particulate matter (OMPM) causes acute and chronic diseases and exacerbations. Owing to the characteristics of poor ventilation, high oil mist concentration, and a relatively closed working environment, the existence of OMPM in the cabin is inevitable, and its impact on the health of occupations on ships cannot be ignored. However, compared with several studies that summarized the health effects of OMPM from traditional sources, few studies have focused on the occupational exposure risk of OMPM from oil pollution sources in ships. In this study, we collected OMPM from oil pollution in cabins and assessed the exposure to OMPM from oil pollution and the corresponding health risks through acute exposure experiments in rats. OMPM exposure induces protein regulation in the extracellular matrix and immune responses, leading to severe inflammatory responses. The abundance and composition of the lung microbial community changed significantly. It interferes with the lung metabolite levels. However, more research is needed to fully understand the extent of health risks associated with OMPM exposure. Further research on vulnerable groups exposed to OMPM from ships is needed to inform public health interventions.
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Affiliation(s)
- Huipeng Nie
- Tianjin Institute of Environment and Operational Medicine, Tianjin 300050, China
| | - Huanliang Liu
- Tianjin Institute of Environment and Operational Medicine, Tianjin 300050, China
| | - Yue Shi
- Tianjin Institute of Environment and Operational Medicine, Tianjin 300050, China
| | - Wenqing Lai
- Tianjin Institute of Environment and Operational Medicine, Tianjin 300050, China
| | - Xuan Liu
- Tianjin Institute of Environment and Operational Medicine, Tianjin 300050, China
| | - Zhuge Xi
- Tianjin Institute of Environment and Operational Medicine, Tianjin 300050, China.
| | - Bencheng Lin
- Tianjin Institute of Environment and Operational Medicine, Tianjin 300050, China.
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Gupta S, Vundavilli H, Osorio RSA, Itoh MN, Mohsen A, Datta A, Mizuguchi K, Tripathi LP. Integrative Network Modeling Highlights the Crucial Roles of Rho-GDI Signaling Pathway in the Progression of Non-Small Cell Lung Cancer. IEEE J Biomed Health Inform 2022; 26:4785-4793. [PMID: 35820010 DOI: 10.1109/jbhi.2022.3190038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer and a leading cause of cancer-related deaths worldwide. Using an integrative approach, we analyzed a publicly available merged NSCLC transcriptome dataset using machine learning, protein-protein interaction (PPI) networks and bayesian modeling to pinpoint key cellular factors and pathways likely to be involved with the onset and progression of NSCLC. First, we generated multiple prediction models using various machine learning classifiers to classify NSCLC and healthy cohorts. Our models achieved prediction accuracies ranging from 0.83 to 1.0, with XGBoost emerging as the best performer. Next, using functional enrichment analysis (and gene co-expression network analysis with WGCNA) of the machine learning feature-selected genes, we determined that genes involved in Rho GTPase signaling that modulate actin stability and cytoskeleton were likely to be crucial in NSCLC. We further assembled a PPI network for the feature-selected genes that was partitioned using Markov clustering to detect protein complexes functionally relevant to NSCLC. Finally, we modeled the perturbations in RhoGDI signaling using a bayesian network; our simulations suggest that aberrations in ARHGEF19 and/or RAC2 gene activities contributed to impaired MAPK signaling and disrupted actin and cytoskeleton organization and were arguably key contributors to the onset of tumorigenesis in NSCLC. We hypothesize that targeted measures to restore aberrant ARHGEF19 and/or RAC2 functions could conceivably rescue the cancerous phenotype in NSCLC. Our findings offer promising avenues for early predictive biomarker discovery, targeted therapeutic intervention and improved clinical outcomes in NSCLC.
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50
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Hamamoto R, Takasawa K, Machino H, Kobayashi K, Takahashi S, Bolatkan A, Shinkai N, Sakai A, Aoyama R, Yamada M, Asada K, Komatsu M, Okamoto K, Kameoka H, Kaneko S. Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine. Brief Bioinform 2022; 23:6628783. [PMID: 35788277 PMCID: PMC9294421 DOI: 10.1093/bib/bbac246] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/06/2022] [Accepted: 05/25/2022] [Indexed: 12/19/2022] Open
Abstract
The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Rina Aoyama
- Showa University Graduate School of Medicine School of Medicine
| | | | - Ken Asada
- RIKEN Center for Advanced Intelligence Project
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