Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis

Zebrafish navigating the metabolic maze: insights into human disease - assets, challenges and future implications

Zebrafish (Danio rerio) have become indispensable models for advancing our understanding of multiple metabolic disorders such as obesity, diabetes mellitus, dyslipidemia, and metabolic syndrome. This review provides a comprehensive analysis of zebrafish as a powerful tool for dissecting the genetic and molecular mechanisms of these diseases, focusing on key genes, like pparγ, lepr, ins, and srebp. Zebrafish offer distinct advantages, including genetic tractability, optical transparency in early development, and the conservation of key metabolic pathways with humans. Studies have successfully used zebrafish to uncover conserved metabolic mechanisms, identify novel disease pathways, and facilitate high-throughput screening of potential therapeutic compounds. The review also highlights the novelty of using zebrafish to model multifactorial metabolic disorders, addressing challenges such as interspecies differences in metabolism and the complexity of human metabolic disease etiology. Moving forward, future research will benefit from integrating advanced omics technologies to map disease-specific molecular signatures, applying personalized medicine approaches to optimize treatments, and utilizing computational models to predict therapeutic outcomes. By embracing these innovative strategies, zebrafish research has the potential to revolutionize the diagnosis, treatment, and prevention of metabolic disorders, offering new avenues for translational applications. Continued interdisciplinary collaboration and investment in zebrafish-based studies will be crucial to fully harnessing their potential for advancing therapeutic development.

Exploring the molecular pathways of advanced rectal cancer: A focus on genetic, RNA, and biological technique

Colorectal cancer (CRC) is the third most frequently diagnosed cancer, with rectal cancer (RC) accounting for approximately 35 % of cases, posing a significant health burden. The early phase of R progression is characterized by the accumulation of genetic and epigenetic changes that promote cell growth. These rapidly dividing cells form a benign adenoma, which can eventually transform into malignant tumors and metastasize to other organs. Among the key molecular alterations, a mutation in the Wnt/β-catenin signaling pathway plays a crucial role. Additionally, BRAF mutation contributes to 8-10 % of CRC cases, while mutation in PIK3C pathways is responsible for 20-25 % of cases. The RC involves complex biological mechanisms. This review article highlights the pivotal role of mRNA in diagnosing and predicting the prognosis of RC, explores the various functions of non-coding RNAs (ncRNA,s), and examines the impact of RNA editing and modification on the progression of tumor genesis. Furthermore, we discuss the cellular signaling pathways and microenvironment interaction and pathways like PI3K/Akt/mTOR and Wnt/β-catenin. Advancements in molecular, RNA, and genetic research have evolved the treatment of cancer. Techniques like next-generation sequencing have tremendously opened the biological field of research. Along with this, techniques like CRISPR/Cas9 aid in the developing therapeutic strategies. Proteomics and metabolomics approach further contribute to novel research direction in oncology.

Challenges and opportunities for conceiving genetically diverse sickle cell mice

A milestone in sickle cell disease (SCD) therapeutics was achieved in December 2023 with the FDA-approved gene therapy for patients aged 12 years and older. However, these therapies may only suit a fraction of patients because of cost or health risks. A better understanding of SCD outcome heterogeneity is needed to propose patient-specific pharmacological interventions. To achieve this, humanized and genetically diverse mouse models are essential for associating candidate genotypes with specific hematological traits, organ function, and disease resilience. Here, we discuss the challenges and opportunities in developing genetically diverse sickle cell mice (GDS mice). These models are expected to complement current approaches in SCD research and enhance our understanding of SCD heterogeneity and anemia.

Advancing precision oncology with AI-powered genomic analysis

Multiomics data integration approaches offer a comprehensive functional understanding of biological systems, with significant applications in disease therapeutics. However, the quantitative integration of multiomics data presents a complex challenge, requiring highly specialized computational methods. By providing deep insights into disease-associated molecular mechanisms, multiomics facilitates precision medicine by accounting for individual omics profiles, enabling early disease detection and prevention, aiding biomarker discovery for diagnosis, prognosis, and treatment monitoring, and identifying molecular targets for innovative drug development or the repurposing of existing therapies. AI-driven bioinformatics plays a crucial role in multiomics by computing scores to prioritize available drugs, assisting clinicians in selecting optimal treatments. This review will explain the potential of AI and multiomics data integration for disease understanding and therapeutics. It highlight the challenges in quantitative integration of diverse omics data and clinical workflows involving AI in cancer genomics, addressing the ethical and privacy concerns related to AI-driven applications in oncology. The scope of this text is broad yet focused, providing readers with a comprehensive overview of how AI-powered bioinformatics and integrative multiomics approaches are transforming precision oncology. Understanding bioinformatics in Genomics, it explore the integrative multiomics strategies for drug selection, genome profiling and tumor clonality analysis with clinical application of drug prioritization tools, addressing the technical, ethical, and practical hurdles in deploying AI-driven genomics tools.

Artificial intelligence-driven clinical decision support systems for early detection and precision therapy in oral cancer: a mini review

Oral cancer (OC) is a significant global health burden, with life-saving improvements in survival and outcomes being dependent on early diagnosis and precise treatment planning. However, diagnosis and treatment planning are predicated on the synthesis of complicated information derived from clinical assessment, imaging, histopathology and patient histories. Artificial intelligence-based clinical decision support systems (AI-CDSS) provides a viable solution that can be implemented via advanced methodologies for data analysis, and synthesis for better diagnostic and prognostic evaluation. This review presents AI-CDSS as a promising solution through advanced methodologies for comprehensive data analysis. In addition, it examines current implementations of AI-CDSS that facilitate early OC detection, precise staging, and personalized treatment planning by processing multimodal patient information through machine learning, computer vision, and natural language processing. These systems effectively interpret clinical results, identify critical disease patterns (including clinical stage, site, tumor dimensions, histopathologic grading, and molecular profiles), and construct comprehensive patient profiles. This comprehensive AI-CDSS approach allows for early cancer detection, a reduction in diagnostic delays and improved intervention outcomes. Moreover, the AI-CDSS also optimizes treatment plans on the basis of unique patient parameters, tumor stages and risk factors, providing personalized therapy.

Influence of polyfunctional Tbet+ T cells on specific clinical events in chronic lymphocytic leukaemia

Introduction

T-cell dysfunction is a hallmark of chronic lymphocytic leukemia (CLL), but the extent to which individual CD4+ or CD8+ T-cell subpopulations influence specific clinical events remains unclear. To address this knowledge gap, we utilised high-dimensional mass cytometry to profile circulating CD4+ and CD8+ T-cells in pre-treatment samples from a well-defined cohort of CLL patients undergoing initial therapy as part of a clinical trial.

Methods

Pre-treatment blood samples from 138 CLL patients receiving initial chemoimmunotherapy containing bendamustine or chlorambucil in the NCRI RIAltO trial (NCT01678430; EudraCT 2011-000919-22) were subjected to deep immunophenotyping by mass cytometry using a bespoke panel of 37 antibodies. T-cell clusters were identified through unsupervised clustering and related to treatment outcomes. Additionally, a randomly selected cohort of 30 CLL patients underwent T-cell stimulation with anti-CD3/CD28 microbeads, followed by cytokine analysis using a separate 36-antibody panel, which included seven cytokines.

Results

Seventeen CD4+ and 22 CD8+ T-cell clusters were identified in a discovery cohort of 79 patients. Three of these clusters, measured as a proportion of their parental CD4+ or CD8+ populations, correlated with a reduced risk of grade ≥3 infection, grade ≥3 second primary malignancy (SPM) and death, respectively. Three corresponding T-cell subpopulations prospectively defined by non-redundant markers and Boolean gating (ICOS+HLA-DR+PD1+TIGIT+Tbet+CD4+ T-helper cells; CD27+CD28-PD1+Tbet+Eomes+CD8+ cells; and CD27+CD28-GrymB+Tbet+Eomes+CD8+ terminal effector cells) showed the same clinical correlations as the clusters on which they were based. With the exception of SPM for which there were insufficient events, these correlations were confirmed in a separate validation cohort of 59 patients. In-vitro stimulation of a subset of CLL patients in the discovery cohort showed an enrichment of primed and polyfunctional cells in all three Tbet+ T-cell subpopulations of interest.

Conclusion

Our study provides new insights into the potential for Tbet+ T-cell subpopulations to influence and predict specific clinical events in CLL. This, in turn, raises the possibility that these respective subpopulations could play an important role in controlling infection, solid tumours and CLL itself.

Clinical Trial Registration

https://www.clinicaltrials.gov/, identifier NCT01678430; https://www.isrctn.com/ISRCTN09988575, identifier EudraCT 2011-000919-22.

Omics approaches: Role in acute myeloid leukemia biomarker discovery and therapy

Acute myeloid leukemia (AML) is the most common acute leukemia in adults and has the highest fatality rate. Patients aged 65 and above exhibit the poorest prognosis, with a mere 30 % survival rate within one year. One important issue in optimizing outcomes for AML patients is their limited ability to predict responses to specific therapies, response duration, and likelihood of relapse. Despite rigorous therapeutic interventions, a significant proportion of patients experience relapse. Consequently, there is a pressing need to introduce new targets for therapy. Sequencing and biotechnology have come a long way in the last ten years. This has made it easier for many omics technologies, like genomics, transcriptomics, proteomics, and metabolomics, to study molecular mechanisms of AML. An integrative approach is necessary to understand a complex biological process fully and offers an important opportunity to understand the information underlying diseases. In this review, we studied papers published between 2010 and 2024 employing omics approaches encompassing diagnosis, prognosis, and risk stratification of AML. Finally, we discuss prospects and challenges in applying -omics technologies to the discovery of novel biomarkers and therapy targets. Our review may be helpful for omics researchers who want to study AML from different molecular aspects.

Multimodal data integration in early-stage breast cancer

The use of biomarkers in breast cancer has significantly improved patient outcomes through targeted therapies, such as hormone therapy anti-Her2 therapy and CDK4/6 or PARP inhibitors. However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-negative tumors. The integration of multi-omics and multimodal data has the potential to provide new insights into biological processes, to improve breast cancer patient stratification, enhance prognosis and response prediction, and identify new biomarkers. This review presents a comprehensive overview of the state-of-the-art multimodal (including molecular and image) data integration algorithms developed and with applicability to breast cancer stratification, prognosis, or biomarker identification. We examined the primary challenges and opportunities of these multimodal data integration algorithms, including their advantages, limitations, and critical considerations for future research. We aimed to describe models that are not only academically and preclinically relevant, but also applicable to clinical settings.

A generalized higher-order correlation analysis framework for multi-omics network inference

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.

Neuropilin-1: A Multifaceted Target for Cancer Therapy

Neuropilin-1 (NRP1), initially identified as a neuronal guidance protein, has emerged as a multifaceted regulator in cancer biology. Beyond its role in axonal guidance and angiogenesis, NRP1 is increasingly recognized for its significant impact on tumor progression and therapeutic outcomes. This review explores the diverse functions of NRP1 in cancer, encompassing its influence on tumor cell proliferation, migration, invasion, and metastasis. NRP1 interacts with several key signaling pathways, including vascular endothelial growth factor (VEGF), semaphorins, and transforming growth factor-beta (TGF-β), modulating the tumor microenvironment and promoting angiogenesis. Moreover, NRP1 expression correlates with poor prognosis in various malignancies, underscoring its potential as a prognostic biomarker. Therapeutically, targeting NRP1 holds promise as a novel strategy to inhibit tumor growth and enhance the efficacy of regular treatments such as chemotherapy and radiotherapy. Strategies involving NRP1-targeted therapies, including monoclonal antibodies, small molecule inhibitors, and gene silencing techniques, are being actively investigated in preclinical and clinical settings. Despite challenges in specificity and delivery, advances in understanding NRP1 biology offer new avenues for personalized cancer therapy. Although several types of cancer cells can express NRPs, the role of NRPs in tumor pathogenesis is largely unknown. Future investigations are needed to enhance our understanding of the effects and mechanisms of NRPs on the proliferation, apoptosis, and migration of neuronal, endothelial, and cancer cells. The novel frameworks or multi-omics approaches integrate data from multiple databases to better understand cancer's molecular and clinical features, develop personalized therapies, and help identify biomarkers. This review highlights the pivotal role of NRP1 in cancer pathogenesis and discusses its implications for developing targeted therapeutic approaches to improve patient outcomes, highlighting the role of OMICS in targeting cancer patients for personalized therapy.

Comprehensive molecular characteristics of hepatocellular carcinoma based on multi-omics analysis

BACKGROUND

The high heterogeneity of hepatocellular carcinoma (HCC) poses challenges for precision treatment strategies. This study aims to use multi-omics methodologies to better understand its pathogenesis and discover biomarkers.

METHODS

Quantitative proteomics was used to investigate hepatocellular carcinoma tissues (HCT) and their corresponding adjacent non-tumor tissues (DNT), obtained from six HCC patients. Untargeted metabolomics was applied to analyze the metabolic profiles of HCT and DNT of ten HCC patients. Statistical analyses, such as the Student's t-test, were performed to identify differentially expressed proteins (DEPs) and metabolites (DEMs) between the two groups. The functions and metabolic pathways involving DEPs and DEMs were annotated and enriched using the gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) databases. Bioinformatics methods were then utilized to analyze consistency between proteomics and metabolomics results, leading to identification of potential biomarkers along with key altered pathways associated with HCC.

RESULTS

This study identified 1556 DEPs between HCT and DNT samples. These DEPs were primarily enriched in crucial biological pathways such as amino acid degradation, fatty acid metabolism, and DNA replication. Subsequently, the analysis of metabolomics identified 500 DEMs that mainly participated in glycerophospholipid metabolism, the phospholipase D signaling pathway, and choline metabolism related to cancer. Integrated analysis of proteomics and metabolomics data unveiled significant dysfunctions in bile secretion, multiple amino acid and fatty acid metabolic pathways among HCC patients. Further investigation revealed that five proteins (PTP4A3, B4GALT5, GAB1, ME2, and PKM) along with seven metabolites (PI(6 keto-PGF1alpha/16:0), 13, 16, 19-docosatrienoic acid, PA(18:2(9Z, 12Z)/20:1(11Z)), Citric Acid, PG(20:3(6, 8, 11)-OH(5)/18:2(9Z, 12Z)), Spermidine, and N2-Acetylornithine) exhibited excellent diagnostic efficiency for HCC and could serve as its potential biomarkers.

CONCLUSION

Our integrated proteome and metabolome analysis revealed 10 key HCC-related pathways and proposed 12 potential biomarkers, which may enhance our understanding of HCC pathophysiology and be helpful in facilitating early diagnosis and treatment strategies.

C. S, Devkota HP, Khadka D. Immune Modulation and Immunotherapy in Solid Tumors: Mechanisms of Resistance and Potential Therapeutic Strategies

Understanding the modulation of specific immune cells within the tumor microenvironment (TME) offers new hope in cancer treatments, especially in cancer immunotherapies. In recent years, immune modulation and resistance to immunotherapy have become critical challenges in cancer treatments. However, novel strategies for immune modulation have emerged as promising approaches for oncology due to the vital roles of the immunomodulators in regulating tumor progression and metastasis and modulating immunological responses to standard of care in cancer treatments. With the progress in immuno-oncology, a growing number of novel immunomodulators and mechanisms are being uncovered, offering the potential for enhanced clinical immunotherapy in the near future. Thus, gaining a comprehensive understanding of the broader context is essential. Herein, we particularly summarize the paradoxical role of tumor-related immune cells, focusing on how targeted immune cells and their actions are modulated by immunotherapies to overcome immunotherapeutic resistance in tumor cells. We also highlight the molecular mechanisms employed by tumors to evade the long-term effects of immunotherapeutic agents, rendering them ineffective.

Omics sciences for cervical cancer precision medicine from the perspective of the tumor immune microenvironment

Immunotherapies have demonstrated notable clinical benefits in the treatment of cervical cancer (CC). However, the development of therapeutic resistance and diverse adverse effects in immunotherapy stem from complex interactions among biological processes and factors within the tumor immune microenvironment (TIME). Advanced omic technologies offer novel insights into a more expansive and thorough layer of the TIME. Furthermore, integrating multidimensional omics within the frameworks of systems biology and computational methodologies facilitates the generation of interpretable data outputs to characterize the clinical and biological trajectories of tumor behavior. In this review, we present advanced omics technologies that utilize various clinical samples to address scientific inquiries related to immunotherapies for CC, highlighting their utility in identifying metastasis dissemination, recurrence risk, and therapeutic resistance in patients treated with immunotherapeutic approaches. This review elaborates on the strategy for integrating multi-omics data through artificial intelligence algorithms. Additionally, an analysis of the obstacles encountered in the multi-omics analysis process and potential avenues for future research in this domain are presented.

Tumor microenvironment: recent advances in understanding and its role in modulating cancer therapies

Tumor microenvironment (TME) denotes the non-cancerous cells and components presented in the tumor, including molecules produced and released by them. Interactions between cancer cells, immune cells, stromal cells, and the extracellular matrix within the TME create a dynamic ecosystem that can either promote or hinder tumor growth and spread. The TME plays a pivotal role in either promoting or inhibiting tumor growth and dissemination, making it a critical factor to consider in the development of effective cancer therapies. Understanding the intricate interplay within the TME is crucial for devising effective cancer therapies. Combination therapies involving inhibitors of immune checkpoint blockade (ICB), and/or chemotherapy now offer new approaches for cancer therapy. However, it remains uncertain how to best utilize these strategies in the context of the complex tumor microenvironment. Oncogene-driven changes in tumor cell metabolism can impact the TME to limit immune responses and present barriers to cancer therapy. Cellular and acellular components in tumor microenvironment can reprogram tumor initiation, growth, invasion, metastasis, and response to therapies. Components in the TME can reprogram tumor behavior and influence responses to treatments, facilitating immune evasion, nutrient deprivation, and therapeutic resistance. Moreover, the TME can influence angiogenesis, promoting the formation of blood vessels that sustain tumor growth. Notably, the TME facilitates immune evasion, establishes a nutrient-deprived milieu, and induces therapeutic resistance, hindering treatment efficacy. A paradigm shift from a cancer-centric model to a TME-centric one has revolutionized cancer research and treatment. However, effectively targeting specific cells or pathways within the TME remains a challenge, as the complexity of the TME poses hurdles in designing precise and effective therapies. This review highlights challenges in targeting the tumor microenvironment to achieve therapeutic efficacy; explore new approaches and technologies to better decipher the tumor microenvironment; and discuss strategies to intervene in the tumor microenvironment and maximize therapeutic benefits.

Integrating artificial intelligence in drug discovery and early drug development: a transformative approach

Artificial intelligence (AI) can transform drug discovery and early drug development by addressing inefficiencies in traditional methods, which often face high costs, long timelines, and low success rates. In this review we provide an overview of how to integrate AI to the current drug discovery and development process, as it can enhance activities like target identification, drug discovery, and early clinical development. Through multiomics data analysis and network-based approaches, AI can help to identify novel oncogenic vulnerabilities and key therapeutic targets. AI models, such as AlphaFold, predict protein structures with high accuracy, aiding druggability assessments and structure-based drug design. AI also facilitates virtual screening and de novo drug design, creating optimized molecular structures for specific biological properties. In early clinical development, AI supports patient recruitment by analyzing electronic health records and improves trial design through predictive modeling, protocol optimization, and adaptive strategies. Innovations like synthetic control arms and digital twins can reduce logistical and ethical challenges by simulating outcomes using real-world or virtual patient data. Despite these advancements, limitations remain. AI models may be biased if trained on unrepresentative datasets, and reliance on historical or synthetic data can lead to overfitting or lack generalizability. Ethical and regulatory issues, such as data privacy, also challenge the implementation of AI. In conclusion, in this review we provide a comprehensive overview about how to integrate AI into current processes. These efforts, although they will demand collaboration between professionals, and robust data quality, have a transformative potential to accelerate drug development.

Integrative multi-omics analysis and machine learning refine global histone modification features in prostate cancer

Background

Prostate cancer (PCa) is a major cause of cancer-related mortality in men, characterized by significant heterogeneity in clinical behavior and treatment response. Histone modifications play key roles in tumor progression and treatment resistance, but their regulatory effects in PCa remain poorly understood.

Methods

We utilized integrative multi-omics analysis and machine learning to explore histone modification-driven heterogeneity in PCa. The Comprehensive Machine Learning Histone Modification Score (CMLHMS) was developed to classify PCa into two distinct subtypes based on histone modification patterns. Single-cell RNA sequencing was performed, and drug sensitivity analysis identified potential therapeutic vulnerabilities.

Results

High-CMLHMS tumors exhibited elevated histone modification activity, enriched proliferative and metabolic pathways, and were strongly associated with progression to castration-resistant prostate cancer (CRPC). Low-CMLHMS tumors showed stress-adaptive and immune-regulatory phenotypes. Single-cell RNA sequencing revealed distinct differentiation trajectories related to tumor aggressiveness and histone modification patterns. Drug sensitivity analysis showed that high-CMLHMS tumors were more responsive to growth factor and kinase inhibitors (e.g., PI3K, EGFR inhibitors), while low-CMLHMS tumors demonstrated greater sensitivity to cytoskeletal and DNA damage repair-targeting agents (e.g., Paclitaxel, Gemcitabine).

Conclusion

The CMLHMS model effectively stratifies PCa into distinct subtypes with unique biological and clinical characteristics. This study provides new insights into histone modification-driven heterogeneity in PCa and suggests potential therapeutic targets, contributing to precision oncology strategies for advanced PCa.

Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis

High-throughput sequencing technologies have facilitated a deeper exploration of prognostic biomarkers. While many deep learning (DL) methods primarily focus on feature extraction or employ simplistic fully connected layers within prognostic modules, the interpretability of DL-extracted features can be challenging. To address these challenges, we propose an interpretable cancer prognosis model called Cox-Sage. Specifically, we first propose an algorithm to construct a patient similarity graph from heterogeneous clinical data, and then extract protein-coding genes from the patient's gene expression data to embed them as features into the graph nodes. We utilize multilayer graph convolution to model proportional hazards pattern and introduce a mathematical method to clearly explain the meaning of our model's parameters. Based on this approach, we propose two metrics for measuring gene importance from different perspectives: mean hazard ratio and reciprocal of the mean hazard ratio. These metrics can be used to discover two types of important genes: genes whose low expression levels are associated with high cancer prognosis risk, and genes whose high expression levels are associated with high cancer prognosis risk. We conducted experiments on seven datasets from TCGA, and our model achieved superior prognostic performance compared with some state-of-the-art methods. As a primary research, we performed prognostic biomarker discovery on the LIHC (Liver Hepatocellular Carcinoma) dataset. Our code and dataset can be found at https://github.com/beeeginner/Cox-sage.

A comprehensive review of cancer survival prediction using multi-omics integration and clinical variables

Cancer is an umbrella term that includes a wide spectrum of disease severity, from those that are malignant, metastatic, and aggressive to benign lesions with very low potential for progression or death. The ability to prognosticate patient outcomes would facilitate management of various malignancies: patients whose cancer is likely to advance quickly would receive necessary treatment that is commensurate with the predicted biology of the disease. Former prognostic models based on clinical variables (age, gender, cancer stage, tumor grade, etc.), though helpful, cannot account for genetic differences, molecular etiology, tumor heterogeneity, and important host biological mechanisms. Therefore, recent prognostic models have shifted toward the integration of complementary information available in both molecular data and clinical variables to better predict patient outcomes: vital status (overall survival), metastasis (metastasis-free survival), and recurrence (progression-free survival). In this article, we review 20 survival prediction approaches that integrate multi-omics and clinical data to predict patient outcomes. We discuss their strategies for modeling survival time (continuous and discrete), the incorporation of molecular measurements and clinical variables into risk models (clinical and multi-omics data), how to cope with censored patient records, the effectiveness of data integration techniques, prediction methodologies, model validation, and assessment metrics. The goal is to inform life scientists of available resources, and to provide a complete review of important building blocks in survival prediction. At the same time, we thoroughly describe the pros and cons of each methodology, and discuss in depth the outstanding challenges that need to be addressed in future method development.

Advancements in proteogenomics for preclinical targeted cancer therapy research

Advancements in molecular characterization technologies have accelerated targeted cancer therapy research at unprecedented resolution and dimensionality. Integrating comprehensive multi-omic molecular profiling of a tumor, proteogenomics, marks a transformative milestone for preclinical cancer research. In this paper, we initially provided an overview of proteogenomics in cancer research, spanning genomics, transcriptomics, and proteomics. Subsequently, the applications were introduced and examined from different perspectives, including but not limited to genetic alterations, molecular quantifications, single-cell patterns, different post-translational modification levels, subtype signatures, and immune landscape. We also paid attention to the combined multi-omics data analysis and pan-cancer analysis. This paper highlights the crucial role of proteogenomics in preclinical targeted cancer therapy research, including but not limited to elucidating the mechanisms of tumorigenesis, discovering effective therapeutic targets and promising biomarkers, and developing subtype-specific therapies.

Prognostic factors and outcomes in pediatric acute myeloid leukemia: a comprehensive bibliometric analysis of global research trends

Background

Pediatric AML prognosis research has advanced significantly, yet gaps in understanding genetic and molecular interactions persist. Despite improved outcomes, relapse/refractory cases and personalized treatment integration remain critical clinical challenges.

Objective

To analyze the global research landscape on pediatric AML prognosis, highlight influential components and collaborations, and identify major potential research trends.

Methods

Publications on pediatric AML prognosis research from 1999 to 2023 were retrieved from the Clarivate Analytics Web of Science Core Collection (WoSCC) database. Bibliometric analysis was conducted using CiteSpace and VOSviewer to identify leading countries, prominent institutions, high-impact journals, key research categories, influential authors, and emerging research topics.

Results

The bibliometric analysis encompassed 924 publications, with St. Jude Children's Research Hospital emerging as the most prolific institution. The United States leads globally in terms of countries, institutions, journals, and authors. Todd A. Alonzo ranks highest in publication volume, while U. Creutzig leads in citations. The top research categories were Oncology, Hematology, and Pediatrics. Key research topics included genomics, transcriptomics, epigenomics, targeted therapies, immune therapy, and integrative diagnostic approaches.

Conclusion

This bibliometric analysis highlights significant advancements in pediatric AML prognosis over the past 25 years, driven by the integration of genetic markers, immunological insights, transcriptomics, and epigenomics, which have collectively transformed risk stratification and treatment strategies. Overcoming challenges, such as discovering new therapeutic targets and enhancing treatment combinations, will depend on global collaboration and advanced technologies to propel the field forward.

Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification

Recent studies on integrating multiple omics data highlighted the potential to advance our understanding of the cancer disease process. Computational models based on graph neural networks and attention-based architectures have demonstrated promising results for cancer classification due to their ability to model complex relationships among biological entities. However, challenges related to addressing the high dimensionality and complexity in integrating multi-omics data, as well as in constructing graph structures that effectively capture the interactions between nodes, remain active areas of research. This study evaluates graph neural network architectures for multi-omics (MO) data integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN). Differential gene expression and LASSO (Least Absolute Shrinkage and Selection Operator) regression are employed for reducing the omics data dimensionality and feature selection; hence, the developed models are referred to as LASSO-MOGCN, LASSO-MOGAT, and LASSO-MOGTN. Graph structures constructed using sample correlation matrices and protein-protein interaction networks are investigated. Experimental validation is performed with a dataset of 8,464 samples from 31 cancer types and normal tissue, comprising messenger-RNA, micro-RNA, and DNA methylation data. The results show that the models integrating multi-omics data outperformed the models trained on single omics data, where LASSO-MOGAT achieved the best overall performance, with an accuracy of 95.9%. The findings also suggest that correlation-based graph structures enhance the models' ability to identify shared cancer-specific signatures across patients in comparison to protein-protein interaction networks-based graph structures. The code and data used in this study are available in the link (https://github.com/FadiAlharbi2024/Graph_Based_Architecture.git).

Metabolic crossroads: unravelling immune cell dynamics in gastrointestinal cancer drug resistance

Metabolic reprogramming within the tumor microenvironment (TME) plays a critical role in driving drug resistance in gastrointestinal cancers (GI), particularly through the pathways of fatty acid oxidation and glycolysis. Cancer cells often rewire their metabolism to sustain growth and reshape the TME, creating conditions such as nutrient depletion, hypoxia, and acidity that impair antitumor immune responses. Immune cells within the TME also undergo metabolic alterations, frequently adopting immunosuppressive phenotypes that promote tumor progression and reduce the efficacy of therapies. The competition for essential nutrients, particularly glucose, between cancer and immune cells compromises the antitumor functions of effector immune cells, such as T cells. Additionally, metabolic by-products like lactate and kynurenine further suppress immune activity and promote immunosuppressive populations, including regulatory T cells and M2 macrophages. Targeting metabolic pathways such as fatty acid oxidation and glycolysis presents new opportunities to overcome drug resistance and improve therapeutic outcomes in GI cancers. Modulating these key pathways has the potential to reinvigorate exhausted immune cells, shift immunosuppressive cells toward antitumor phenotypes, and enhance the effectiveness of immunotherapies and other treatments. Future strategies will require continued research into TME metabolism, the development of novel metabolic inhibitors, and clinical trials evaluating combination therapies. Identifying and validating metabolic biomarkers will also be crucial for patient stratification and treatment monitoring. Insights into metabolic reprogramming in GI cancers may have broader implications across multiple cancer types, offering new avenues for improving cancer treatment.

A generative deep neural network for pan-digestive tract cancer survival analysis

BACKGROUND

The accurate identification of molecular subtypes in digestive tract cancer (DTC) is crucial for making informed treatment decisions and selecting potential biomarkers. With the rapid advancement of artificial intelligence, various machine learning algorithms have been successfully applied in this field. However, the complexity and high dimensionality of the data features may lead to overlapping and ambiguous subtypes during clustering.

RESULTS

In this study, we propose GDEC, a multi-task generative deep neural network designed for precise digestive tract cancer subtyping. The network optimization process involves employing an integrated loss function consisting of two modules: the generative-adversarial module facilitates spatial data distribution understanding for extracting high-quality information, while the clustering module aids in identifying disease subtypes. The experiments conducted on digestive tract cancer datasets demonstrate that GDEC exhibits exceptional performance compared to other advanced methodologies and can separate different cancer molecular subtypes that possess both statistical and biological significance. Subsequently, 21 hub genes related to pan-DTC heterogeneity and prognosis were identified based on the subtypes clustered by GDEC. The following drug analysis suggested Dasatinib and YM155 as potential therapeutic agents for improving the prognosis of patients in pan-DTC immunotherapy, thereby contributing to the enhancement of cancer patient survival.

CONCLUSIONS

The experiment indicate that GDEC outperforms better than other deep-learning-based methods, and the interpretable algorithm can select biologically significant genes and potential drugs for DTC treatment.

From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care

Background/Objectives: The dual forces of structured inquiry and serendipitous discovery have long shaped neuropsychiatric research, with groundbreaking treatments such as lithium and ketamine resulting from unexpected discoveries. However, relying on chance is becoming increasingly insufficient to address the rising prevalence of mental health disorders like depression and schizophrenia, which necessitate precise, innovative approaches. Emerging technologies like artificial intelligence, induced pluripotent stem cells, and multi-omics have the potential to transform this field by allowing for predictive, patient-specific interventions. Despite these advancements, traditional methodologies such as animal models and single-variable analyses continue to be used, frequently failing to capture the complexities of human neuropsychiatric conditions. Summary: This review critically evaluates the transition from serendipity to precision-based methodologies in neuropsychiatric research. It focuses on key innovations such as dynamic systems modeling and network-based approaches that use genetic, molecular, and environmental data to identify new therapeutic targets. Furthermore, it emphasizes the importance of interdisciplinary collaboration and human-specific models in overcoming the limitations of traditional approaches. Conclusions: We highlight precision psychiatry's transformative potential for revolutionizing mental health care. This paradigm shift, which combines cutting-edge technologies with systematic frameworks, promises increased diagnostic accuracy, reproducibility, and efficiency, paving the way for tailored treatments and better patient outcomes in neuropsychiatric care.

G2PDeep-v2: a web-based deep-learning framework for phenotype prediction and biomarker discovery for all organisms using multi-omics data

The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/ and can be utilized for all organisms.

Advancing precision cancer immunotherapy drug development, administration, and response prediction with AI-enabled Raman spectroscopy

Molecular characterization of tumors is essential to identify predictive biomarkers that inform treatment decisions and improve precision immunotherapy development and administration. However, challenges such as the heterogeneity of tumors and patient responses, limited efficacy of current biomarkers, and the predominant reliance on single-omics data, have hindered advances in accurately predicting treatment outcomes. Standard therapy generally applies a "one size fits all" approach, which not only provides ineffective or limited responses, but also an increased risk of off-target toxicities and acceleration of resistance mechanisms or adverse effects. As the development of emerging multi- and spatial-omics platforms continues to evolve, an effective tumor assessment platform providing utility in a clinical setting should i) enable high-throughput and robust screening in a variety of biological matrices, ii) provide in-depth information resolved with single to subcellular precision, and iii) improve accessibility in economical point-of-care settings. In this perspective, we explore the application of label-free Raman spectroscopy as a tumor profiling tool for precision immunotherapy. We examine how Raman spectroscopy's non-invasive, label-free approach can deepen our understanding of intricate inter- and intra-cellular interactions within the tumor-immune microenvironment. Furthermore, we discuss the analytical advances in Raman spectroscopy, highlighting its evolution to be utilized as a single "Raman-omics" approach. Lastly, we highlight the translational potential of Raman for its integration in clinical practice for safe and precise patient-centric immunotherapy.

The Human Pathology Atlas for deciphering the prognostic features of human cancers

BACKGROUND

Cancer is one of the leading causes of mortality worldwide, highlighting the urgent need for a deeper molecular understanding and the development of personalized treatments. The present study aims to establish a solid association between gene expression and patient survival outcomes to enhance the utility of the Human Pathology Atlas for cancer research.

METHODS

In this updated analysis, we examined the expression profiles of 6918 patients across 21 cancer types. We integrated data from 10 independent cancer cohorts, creating a cross-validated, reliable collection of prognostic genes. We applied systems biology approach to identify the association between gene expression profiles and patient survival outcomes. We further constructed prognostic regulatory networks for kidney renal clear cell carcinoma (KIRC) and liver hepatocellular carcinoma (LIHC), which elucidate the molecular underpinnings associated with patient survival in these cancers.

FINDINGS

We observed that gene expression during the transition from normal to tumorous tissue exhibited diverse shifting patterns in their original tissue locations. Significant correlations between gene expression and patient survival outcomes were identified in KIRC and LIHC among the major cancer types. Additionally, the prognostic regulatory network established for these two cancers showed the indicative capabilities of the Human Pathology Atlas and provides actionable insights for cancer research.

INTERPRETATION

The updated Human Pathology Atlas provides a significant foundation for precision oncology and the formulation of personalized treatment strategies. These findings deepen our understanding of cancer biology and have the potential to advance targeted therapeutic approaches in clinical practice.

FUNDING

The Knut and Alice Wallenberg Foundation (72110), the China Scholarship Council (Grant No. 202006940003).

Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

Robust feature learning using contractive autoencoders for multi-omics clustering in cancer subtyping

Cancer can manifest in virtually any tissue or organ, necessitating precise subtyping of cancer patients to enhance diagnosis, treatment, and prognosis. With the accumulation of vast amounts of omics data, numerous studies have focused on integrating multi-omics data for cancer subtyping using clustering techniques. However, due to the heterogeneity of different omics data, extracting important features to effectively integrate these data for accurate clustering analysis remains a significant challenge. This study proposes a new multi-omics clustering framework for cancer subtyping, which utilizes contractive autoencoder to extract robust features. By encouraging the learned representation to be less sensitive to small changes, the contractive autoencoder learns robust feature representations from different omics. To incorporate survival information into the clustering analysis, Cox proportional hazards regression is used to further select the key features significantly associated with survival for integration. Finally, we utilize K-means clustering on the integrated feature to obtain the clustering result. The proposed framework is evaluated on ten different cancer datasets across four levels of omics data and compared to other existing methods. The experimental results indicate that the proposed framework effectively integrates the four omics datasets and outperforms other methods, achieving higher C-index scores and showing more significant differences between survival curves. Additionally, differential gene analysis and pathway enrichment analysis are performed to further demonstrate the effectiveness of the proposed method framework.

Multiplexed Biomarker Detection Using DNA Payloads: Design, Assembly, and Analysis

Most biomarker assays are typically designed to detect a single molecule type (DNA, RNA, proteins, etc.) in a single assay. This means that monitoring a diverse biomarker panel could quickly become a complex endeavor, requiring different techniques, labs, and expertise. In this chapter, we describe a method for multiplexed biomarker detection from a single sample using variable-length DNA payload chains as the output signal. Through the course of the assay, payloads are systematically disassembled in the presence of specific biomarkers. The resulting distinctly sized fragments then yield characteristic gel electrophoresis band patterns, which can be detected and quantified using image analysis algorithms. We detail the entire process for constructing DNA payloads and conducting a biomarker detection assay, including the sequence design, laboratory assembly, running the assay, and final image analysis.

Molecular mechanism of geniposide against ANIT-induced intrahepatic cholestasis by integrative analysis of transcriptomics and metabolomics

Geniposide (GE), a bioactive compound extracted from the fruit of Gardenia jasminoides Ellis, has attracted significant attention for its hepatoprotective therapeutic applications. Although GE displays a protective effect on treating intrahepatic cholestasis (IC), the underlying mechanism remains elusive. In this study, we aimed to elucidate the pharmacological mechanisms of GE in treating IC by an integrated analysis of transcriptomics and metabolomics. Firstly, we evaluated the hepatoprotective effect of GE in α-naphthylisothiocyanate (ANIT)-induced IC rats by examining biochemical indices, inflammatory factors, and oxidative stress levels. Secondly, by transcriptomics and serum metabolomics, we identified differentially expressed genes and metabolites, revealing phenotype-related metabolic pathways and gene functions. Lastly, we screened the core targets of GE in the treatment of IC by integrating transcriptomic and metabolomic data and validated these targets using western blotting. The results indicated that GE improved serum indexes and alleviated inflammation reactions and oxidative stress in the liver. The transcriptomics analysis revealed 739 differentially expressed genes after GE treatment, mainly enriched in retinol metabolism, steroid hormone synthesis, PPAR signal transduction, bile secretion metabolism, and other pathways. The metabolomics analysis identified 98 differential metabolites and 10 metabolic pathways. By constructing a "genes-targets-pathways-compounds" network, we identified two pathways: the bile secretion pathway and the glutathione pathway. Within these pathways, we discovered nine crucial targets that were subsequently validated through western blotting. The results revealed that the GE group significantly increased the expression of ABCG5, NCEH1, OAT3, and GST, compared with the ANIT group. We speculate that GE has a therapeutic effect on IC by modulating the bile secretion pathway and the glutathione pathway and regulating the expression of ABCG5, NCEH1, OAT3, and GST.

Integrative analysis of miRNA expression data reveals a minimal signature for tumour cells classification

MicroRNAs (miRNAs) are pivotal biomarkers for cancer screening. Identifying distinctive expression patterns of miRNAs in specific cancer types can serve as an effective strategy for classification and characterization. However, the development of a minimal signature of miRNAs for accurate cancer classification remains challenging, hindered by the lack of integrated approaches that systematically analyse miRNA expression levels of miRNAs alongside their associated biological pathways. In this study, we present a comprehensive integrative approach that utilizes transcriptomic data from lung, breast, and melanoma cancer cell lines to identify specific expression patterns. By combining bioinformatics, dimensionality reduction techniques, machine learning, and experimental validation, we pinpoint miRNAs linked to critical biological pathways. Our results demonstrate a highly significant differentiation of cancer types, achieving 100 % classification accuracy with minimal training time using a streamlined miRNA signature. Validation of the miRNA profile confirms that each of the three identified miRNAs regulates distinct biological pathways with minimal overlap. This specificity highlights their unique roles in tumour biology and set the stage for further exploration of miRNAs interactions and their contributions to tumourigenesis across diverse cancer types. Our work paves the way for multi-cancer classification, emphasizing the transformative potential of miRNA research in oncology. Beyond advancing the understanding of tumour biology, our step-by-step guide offers a robust tool for a wide range of users to investigate precise diagnostics and promising therapeutic strategies.

Multiomics Research: Principles and Challenges in Integrated Analysis

Multiomics research is a transformative approach in the biological sciences that integrates data from genomics, transcriptomics, proteomics, metabolomics, and other omics technologies to provide a comprehensive understanding of biological systems. This review elucidates the fundamental principles of multiomics, emphasizing the necessity of data integration to uncover the complex interactions and regulatory mechanisms underlying various biological processes. We explore the latest advances in computational methodologies, including deep learning, graph neural networks (GNNs), and generative adversarial networks (GANs), which facilitate the effective synthesis and interpretation of multiomics data. Additionally, this review addresses the critical challenges in this field, such as data heterogeneity, scalability, and the need for robust, interpretable models. We highlight the potential of large language models to enhance multiomics analysis through automated feature extraction, natural language generation, and knowledge integration. Despite the important promise of multiomics, the review acknowledges the substantial computational resources required and the complexity of model tuning, underscoring the need for ongoing innovation and collaboration in the field. This comprehensive analysis aims to guide researchers in navigating the principles and challenges of multiomics research to foster advances in integrative biological analysis.

Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis

The prevalence of digestive system tumours (DST) poses a significant challenge in the global crusade against cancer. These neoplasms constitute 20% of all documented cancer diagnoses and contribute to 22.5% of cancer‐related fatalities. The accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal treatments. Addressing this challenge, the authors introduce a novel methodology, denominated as the Multi‐omics Graph Transformer Convolutional Network (MGTCN). This innovative approach aims to discern various DST tumour types and proficiently discern between early‐late stage tumours, ensuring a high degree of accuracy. The MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi‐omics adjacency matrix, thereby illuminating potential associations among diverse samples. A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN model. The outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early‐late stage DST cases. The source code for this groundbreaking study is readily accessible for download at https://github.com/bigone1/MGTCN.

Newer Biomarkers in Gallbladder Carcinoma: A Scoping Review

Biomarkers have the potential to play a crucial role in managing gallbladder cancer post-surgery. They can identify patients more likely to experience a recurrence, allowing oncologists to tailor a more intensive surveillance plan and consider additional therapies. Some biomarkers can even predict how well a patient will respond to specific chemotherapy or targeted treatments. By monitoring these biomarkers, clinicians can track how effective the ongoing treatment is and detect any signs of early recurrence. Various biomarkers, like tumor markers, genetic markers, and genomic and epigenetic markers, are being investigated. The goal is to find the most reliable and accurate biomarkers to enhance patient care and outcomes. Integrating biomarker data into treatment plans can help personalize therapy and make better informed decisions. By identifying which patients are likely to benefit from specific treatments, biomarkers have the potential to improve long-term survival rates significantly. This scoping review discusses newer biomarkers in gallbladder carcinoma; some of them are in clinical use, while most of them are used in research settings. This provides a broad insight to practicing clinicians about the present biomarkers and the futuristic biomarkers.

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial role in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

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 algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores-0.98 ± 0.02 for lung cancer subtype differentiation and 0.83 ± 0.07 for breast cancer PAM50 subtypes, and successfully distinguished 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 nine existing methods. 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.

MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification

BACKGROUND

The integration of multi-omics data through deep learning has greatly improved cancer subtype classification, particularly in feature learning and multi-omics data integration. However, key challenges remain in embedding sample structure information into the feature space and designing flexible integration strategies.

RESULTS

We propose MoAGL-SA, an adaptive multi-omics integration method based on graph learning and self-attention, to address these challenges. First, patient relationship graphs are generated from each omics dataset using graph learning. Next, three-layer graph convolutional networks are employed to extract omic-specific graph embeddings. Self-attention is then used to focus on the most relevant omics, adaptively assigning weights to different graph embeddings for multi-omics integration. Finally, cancer subtypes are classified using a softmax classifier.

CONCLUSIONS

Experimental results show that MoAGL-SA outperforms several popular algorithms on datasets for breast invasive carcinoma, kidney renal papillary cell carcinoma, and kidney renal clear cell carcinoma. Additionally, MoAGL-SA successfully identifies key biomarkers for breast invasive carcinoma.

Liver transcriptomics-metabolomics integration reveals biological pathways associated with fetal programming in beef cattle

We investigated the long-term effects of prenatal nutrition on pre-slaughter Nelore bulls using integrative transcriptome and metabolome analyses of liver tissue. Three prenatal nutritional treatments were administered to 126 cows: NP (control, mineral supplementation only), PP (protein-energy supplementation in the third trimester), and FP (protein-energy supplementation throughout pregnancy). Liver samples from 22.5 ± 1-month-old bulls underwent RNA-Seq and targeted metabolomics. Weighted correlation network analysis (WGCNA) identified treatment-associated gene and metabolite co-expression modules, further analyzed using MetaboAnalyst 6.0 (metabolite over-representation analysis and transcriptome-metabolome integrative analysis) and Enrichr (gene over-representation analysis). We identified several significant gene and metabolite modules, as well as hub components associated with energy, protein and oxidative metabolism, regulatory mechanisms, epigenetics, and immune function. The NP transcriptome-metabolome analysis identified key pathways (aminoacyl t-RNA biosynthesis, gluconeogenesis, and PPAR signaling) and hub components (glutamic acid, SLC6A14). PP highlighted pathways (arginine and proline metabolism, TGF-beta signaling, glyoxylate and dicarboxylate metabolism) with arginine and ODC1 as hub components. This study highlights the significant impact of prenatal nutrition on the liver tissue of Nelore bulls, shedding light on critical metabolic pathways and hub components related to energy and protein metabolism, as well as immune system and epigenetics.

The Impact of Transcriptional Profiling Cadherin Family and Therapeutic Approaches of Gastric Cancer: A Translational Outlook on Multi-omics Data Analysis

The classical cadherin gene has been linked to a variety of human malignancies, including gastric cancer. However, the link between cadherin genes and gastric cancer outcome is still unclear. This study used multi-omics data to examine the cadherin genes that were differentially regulated in gastric cancer. Differential expression of genes, epigenetic, molecular alterations, and protein expression analyses was conducted. Male SD rats were given N-methyl-N-nitrosourea (MNU) to induce stomach carcinoma in order to verify the activation of cadherin genes. CDH5, CDH6, CDH11, and CDH24 levels were found to be considerably higher in gastric cancer and may serve as useful indicators of stomach adenocarcinoma (STAD). Cadherin genes with variable expression had considerably more promoter methylation in cancers than in normal tissues. In individuals with gastric cancer, high expression of these cadherin genes was related to lower total mortality and disease-free survival rates. Furthermore, compared to normal rats, gastric cancer-induced rats had significantly higher expression and distribution of CDH5, CDH6, CDH11, and CDH24. This study sheds new light on the diagnosis and prognosis of gastric cancer by identifying potential prognostic markers such as CDH5, CDH6, CDH11, and CDH24. The multi-omics approach provided a potential tool for target-based therapy by accurately predicting the outcome of stomach cancer. Researchers may gain more knowledge about the role of cadherin genes in the development and dissemination of tumors to the activated rat model of gastric cancer.

Exploring prognostic biomarkers in pathological images of colorectal cancer patients via deep learning

Hematoxylin and eosin (H&E) whole slide images provide valuable information for predicting prognostic outcomes in colorectal cancer (CRC) patients. However, extracting prognostic indicators from pathological images is challenging due to the subtle complexities of phenotypic information. We trained a weakly supervised deep learning model on data from 640 CRC patients in the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial dataset and validated it using data from 522 CRC patients in the cancer genome atlas (TCGA) dataset. We created the colorectal cancer risk score (CRCRS) to assess patient prognosis, visualized the pathological phenotype of the risk score using Grad-CAM, and employed multiomics data from the TCGA CRC cohort to investigate the potential biological mechanisms underlying the risk score. The overall survival analysis revealed that the CRCRS served as an independent prognostic indicator for both the PLCO cohort (p < 0.001) and the TCGA cohort (p < 0.001), with its predictive efficacy remaining unaffected by the clinical staging system. Additionally, satisfactory chemotherapeutic benefits were observed in stage II/III CRC patients with high CRCRS but not in those with low CRCRS. A pathomics nomogram constructed by integrating the CRCRS with the tumor-node-metastasis (TNM) staging system enhanced prognostic prediction accuracy compared with using the TNM staging system alone. Noteworthy features of the risk score were identified, such as immature tumor mesenchyme, disorganized gland structures, small clusters of cancer cells associated with unfavorable prognosis, and infiltrating inflammatory cells associated with favorable prognosis. The TCGA multiomics data revealed potential correlations between the CRCRS and the activation of energy production and metabolic pathways, the tumor immune microenvironment, and genetic mutations in APC, SMAD2, EEF1AKMT4, EPG5, and TANC1. In summary, our deep learning algorithm identified the CRCRS as a prognostic indicator in CRC, providing a significant approach for prognostic risk stratification and tailoring precise treatment strategies for individual patients.

Multiomics in cancer biomarker discovery and cancer subtyping

The advent of multiomics has ushered in a new era of cancer research characterized by integrated genomic, transcriptomic and proteomic analysis to unravel the complexities of cancer biology and facilitate the discovery of novel biomarkers. This chapter provides a comprehensive overview of the concept of multiomics, detailing the significant advances in the underlying technologies and their contributions to our understanding of cancer. It delves into the evolution of genomics and transcriptomics, breakthroughs in proteomics, and overarching progress in multiomic methodologies, highlighting their collective impact on cancer biomarker discovery. Furthermore, this chapter explores the computational methods essential for multiomic studies, including clustering techniques for delineating cancer subtypes, strategies for estimating molecular features and activities, and utility of pathway enrichment analyses for interpreting multiomic datasets. Particular focus has been placed on the application of these methods for identifying distinct cancer subtypes, thereby enabling a more personalized approach to cancer treatment. Through a detailed discussion of the scientific principles, technological advancements, and practical applications of multiomics, this chapter aims to underscore the pivotal role of multiomics in advancing cancer research and paving the way for personalized medicine. The insights provided herein not only illuminate the current landscape of cancer biomarker discovery, but also forecast future directions of multiomics research in oncology, advocating for a more integrated and nuanced approach to understanding and combating cancer.

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

Research and application of omics and artificial intelligence in cancer

Cancer has a high incidence and lethality rate, which is a significant threat to human health. With the development of high-throughput technologies, different types of cancer genomics data have been accumulated, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. A comprehensive analysis of various omics data is needed to understand the underlying mechanisms of tumor development. However, integrating such a massive amount of data is one of the main challenges today. Artificial intelligence (AI) techniques such as machine learning are now becoming practical tools for analyzing and understanding multi-omics data on diseases. Enabling great optimization of existing research paradigms for cancer screening, diagnosis, and treatment. In addition, intelligent healthcare has received widespread attention with the development of healthcare informatization. As an essential part of innovative healthcare, practical, intelligent prognosis analysis and personalized treatment for cancer patients are also necessary. This paper introduces the advanced multi-omics data analysis technology in recent years, presents the cases and advantages of the combination of both omics data and AI applied to cancer diseases, and finally briefly describes the challenges faced by multi-omics analysis and AI at the current stage, aiming to provide new perspectives for oncology research and the possibility of personalized cancer treatment.

Subclassification of lung adenocarcinoma through comprehensive multi-omics data to benefit survival outcomes

OBJECTIVES

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of great clinical significance. This study aims to identify novel molecular markers associated with LUAD subtypes, with the goal of improving the precision of LUAD subtype classification. Additionally, optimization efforts are directed towards enhancing insights from the perspective of patient survival analysis.

MATERIALS AND METHODS

We propose an innovative feature-selection approach that focuses on LUAD classification, which is comprehensive and robust. The proposed method integrates multi-omics data from The Cancer Genome Atlas (TCGA) and leverages a synergistic combination of max-relevance and min-redundancy, least absolute shrinkage and selection operator, and Boruta algorithms. These selected features were deployed in six machine-learning classifiers: logistic regression, random forest, support vector machine, naive Bayes, k-Nearest Neighbor, and XGBoost.

RESULTS

The proposed approach achieved an area under the receiver operating characteristic curve (AUC) of 0.9958 for LR. Notably, the accuracy and AUC of a composite model incorporating copy number, methylation, as well as RNA- sequencing data for expression of exons, genes, and miRNA mature strands surpassed the accuracy and AUC metrics of models with single-omics data or other multi-omics combinations. Survival analyses, revealed the SVM classifier to elicit optimal classification, outperforming that achieved by TCGA. To enhance model interpretability, SHapley Additive exPlanations (SHAP) values were utilized to elucidate the impact of each feature on the predictions. Gene Ontology (GO) enrichment analysis identified significant biological processes, molecular functions, and cellular components associated with LUAD subtypes.

CONCLUSION

In summary, our feature selection process, based on TCGA multi-omics data and combined with multiple machine learning classifiers, proficiently identifies molecular subtypes of lung adenocarcinoma and their corresponding significant genes. Our method could enhance the early detection and diagnosis of LUAD, expedite the development of targeted therapies and, ultimately, lengthen patient survival.

Special Issue "Bioinformatics Study in Human Diseases: Integration of Omics Data for Personalized Medicine"

The field of bioinformatics has made remarkable strides in recent years, revolutionizing our approach to understanding and treating human diseases [...].

Plant Metabolomics: The Future of Anticancer Drug Discovery

Drug development from medicinal plants constitutes an important strategy for finding natural anticancer therapies. While several plant secondary metabolites with potential antitumor activities have been identified, well-defined mechanisms of action remained uncovered. In fact, studies of medicinal plants have often focused on the genome, transcriptome, and proteome, dismissing the relevance of the metabolome for discovering effective plant-based drugs. Metabolomics has gained huge interest in cancer research as it facilitates the identification of potential anticancer metabolites and uncovers the metabolomic alterations that occur in cancer cells in response to treatment. This holds great promise for investigating the mode of action of target metabolites. Although metabolomics has made significant contributions to drug discovery, research in this area is still ongoing. In this review, we emphasize the significance of plant metabolomics in anticancer research, which continues to be a potential technique for the development of anticancer drugs in spite of all the challenges encountered. As well, we provide insights into the essential elements required for performing effective metabolomics analyses.

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology

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.

Gastric cancer-Epidemiology, modifiable and non-modifiable risk factors, challenges and opportunities: An updated review

Gastric cancer represents a significant global health challenge due to its high mortality and incidence rates, particularly in Eastern Asia, Eastern Europe, and South America. This comprehensive review synthesizes the latest epidemiological data and explores both modifiable and non-modifiable risk factors associated with gastric cancer, aiming to delineate the multifactorial etiology of this disease. Modifiable risk factors include Helicobacter pylori infection, obesity, dietary habits, smoking and alcohol consumption, whereas nonmodifiable factors comprise genetic predispositions, age, family history and male gender. The interplay of these factors significantly impacts the risk and progression of gastric cancer, suggesting potential preventive strategies. The challenges in treating gastric cancer are considerable, largely because of the late-stage diagnosis and the heterogeneity of the disease, which complicate effective treatment regimens. Current treatment strategies involve a combination of surgery, chemotherapy, radiotherapy, and targeted therapies. The FLOT regimen (5-FU, Leucovorin, Oxaliplatin and Docetaxel) is now a standard for resectable cases in Europe and the US, showing superior survival and response rates over ECF and ECX regimens. For HER2-positive gastric cancer, trastuzumab combined with chemotherapy improves overall survival, as demonstrated by the ToGA trial. Additionally, immune checkpoint inhibitors like pembrolizumab and nivolumab offer promising results. However, the five-year survival rate remains low, underscoring the urgency for improved therapeutic approaches. Recent advancements in molecular biology and cancer genomics have begun to pave the way for personalized medicine in gastric cancer care, focusing on molecular targeted therapies and immunotherapy. This review also highlights the critical need for better screening methods that could facilitate early detection and treatment, potentially improving the prognosis. By integrating epidemiological insights with new therapeutic strategies, this article aims to thoroughly understand of gastric cancer's dynamics and outline a framework for future research and clinical management, advocating for a multidisciplinary approach to tackle this formidable disease.

A metagene based similarity network fusion approach for multi-omics data integration identified novel subtypes in renal cell carcinoma

Renal cell carcinoma (RCC) ranks among the most prevalent cancers worldwide, with both incidence and mortality rates increasing annually. The heterogeneity among RCC patients presents considerable challenges for developing universally effective treatment strategies, emphasizing the necessity of in-depth research into RCC's molecular mechanisms, understanding the variations among RCC patients and further identifying distinct molecular subtypes for precise treatment. We proposed a metagene-based similarity network fusion (Meta-SNF) method for RCC subtype identification with multi-omics data, using a non-negative matrix factorization technique to capture alternative structures inherent in the dataset as metagenes. These latent metagenes were then integrated to construct a fused network under the Similarity Network Fusion (SNF) framework for more precise subtyping. We conducted simulation studies and analyzed real-world data from two RCC datasets, namely kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) to demonstrate the utility of Meta-SNF. The simulation studies indicated that Meta-SNF achieved higher accuracy in subtype identification compared with the original SNF and other state-of-the-art methods. In analyses of real data, Meta-SNF produced more distinct and well-separated clusters, classifying both KIRC and KIRP into four subtypes with significant differences in survival outcomes. Subsequently, we performed comprehensive bioinformatics analyses focused on subtypes with poor prognoses in KIRC and KIRP and identified several potential biomarkers. Meta-SNF offers a novel strategy for subtype identification using multi-omics data, and its application to RCC datasets has yielded diverse biological insights which are highly valuable for informing clinical decision-making processes in the treatment of RCC.