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Shaker F, Razi S, Rezaei N. Circulating miRNA and circulating tumor DNA application as liquid biopsy markers in gastric cancer. Clin Biochem 2024; 129:110767. [PMID: 38705444 DOI: 10.1016/j.clinbiochem.2024.110767] [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: 02/05/2024] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 05/07/2024]
Abstract
Liquid biopsy has been investigated as a novel method to overcome the numerous challenges in gastric cancer (GC) management. This non-invasive, feasible, and easy-to-repeat method has been shown to be cost-effective and capable of increasing diagnostic sensitivity and prognostic assessment. Additionally, it is potentially accurate to aid decision-making and personalized treatment planning. MicroRNA (miRNA) and circulating tumor DNA (ctDNA) markers can enhance GC management in various aspects, including diagnosis (mainly earlier diagnosis and the ability to perform population-based screening), prognosis (more precise stratification of prognosis), and treatment (including more accurate prediction of treatment response and earlier detection of resistance to the treatment). Concerning the treatment-related application, miRNAs' mimics and antagonists (by using two main strategies of restoring tumor suppressor miRNAs and inhibiting oncogene miRNAs) have been shown to be effective therapeutic agents. However, these need to be further validated in clinical trials. Furthermore, novel delivery systems, such as lipid-based vectors, polymeric-based vectors, and exosome-based delivery, have been developed to enhance the performance of these agents. Moreover, this paper explores the current detection and measuring methods for these markers. These approaches are categorized into direct methods (e.g., Chem-NAT, HTG EdgeSeq, and Multiplex Circulating Fireplex) and indirect methods (e.g., Reverse transcription-quantitative polymerase chain reaction (RT-qPCR), qPCR, microarray, and NGS) for miRNA detection. For ctDNA measurement, main core technologies like NGS, digital PCR, real-time PCR, and mass spectrometry are suggested.
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Affiliation(s)
- Farhad Shaker
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Cancer Immunology Project (CIP), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Sepideh Razi
- Cancer Immunology Project (CIP), Universal Scientific Education and Research Network (USERN), Tehran, Iran; Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Nima Rezaei
- Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran; Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran; Cancer Immunology Project (CIP), Universal Scientific Education and Research Network (USERN), Stockholm, Sweden.
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2
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Daniel Thomas S, Vijayakumar K, John L, Krishnan D, Rehman N, Revikumar A, Kandel Codi JA, Prasad TSK, S S V, Raju R. Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2024; 28:213-233. [PMID: 38752932 DOI: 10.1089/omi.2024.0047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2024]
Abstract
MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.
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Affiliation(s)
- Sonet Daniel Thomas
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Krithika Vijayakumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Levin John
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Deepak Krishnan
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Niyas Rehman
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | - Amjesh Revikumar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Kerala Genome Data Centre, Kerala Development and Innovation Strategic Council, Thiruvananthapuram, Kerala, India
| | - Jalaluddin Akbar Kandel Codi
- Department of Surgical Oncology, Yenepoya Medical College, Yenepoya (Deemed to Be University), Manglore, Karnataka, India
| | | | - Vinodchandra S S
- Department of Computer Science, University of Kerala, Thiruvananthapuram, Kerala, India
| | - Rajesh Raju
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya (Deemed to Be University), Manglore, Karnataka, India
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3
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Zhou P, Meng X, Nie Z, Wang H, Wang K, Du A, Lei Y. PTEN: an emerging target in rheumatoid arthritis? Cell Commun Signal 2024; 22:246. [PMID: 38671436 PMCID: PMC11046879 DOI: 10.1186/s12964-024-01618-6] [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: 12/28/2023] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) is a critical tumor suppressor protein that regulates various biological processes such as cell proliferation, apoptosis, and inflammatory responses by controlling the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (PI3K/AKT) signaling pathway. PTEN plays a crucial role in the pathogenesis of rheumatoid arthritis (RA). Loss of PTEN may contribute to survival, proliferation, and pro-inflammatory cytokine release of fibroblast-like synoviocytes (FLS). Also, persistent PI3K signaling increases myeloid cells' osteoclastic potential, enhancing localized bone destruction. Recent studies have shown that the expression of PTEN protein in the synovial lining of RA patients with aggressive FLS is minimal. Experimental upregulation of PTEN protein expression could reduce the damage caused by RA. Nonetheless, a complete comprehension of aberrant PTEN drives RA progression and its interactions with other crucial molecules remains elusive. This review is dedicated to promoting a thorough understanding of the signaling mechanisms of aberrant PTEN in RA and aims to furnish pertinent theoretical support for forthcoming endeavors in both basic and clinical research within this domain.
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Affiliation(s)
- Pan Zhou
- Chengdu Rheumatology Hospital, Chengdu, Sichuan Province, China
| | - Xingwen Meng
- Chengdu Rheumatology Hospital, Chengdu, Sichuan Province, China
| | - Zhimin Nie
- Chengdu Rheumatology Hospital, Chengdu, Sichuan Province, China
| | - Hua Wang
- Chengdu Rheumatology Hospital, Chengdu, Sichuan Province, China
| | - Kaijun Wang
- Nanjing Tongshifeng Hospital, Nanjing, Jiangsu Province, China
| | - Aihua Du
- Zhengzhou Gout and Rheumatology Hospital, Zhengzhou, Henan Province, China
| | - Yu Lei
- Chengdu Rheumatology Hospital, Chengdu, Sichuan Province, China.
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4
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Sayed GI, Solyman M, El Gedawy G, Moemen YS, Aboul-Ella H, Hassanien AE. Circulating miRNA's biomarkers for early detection of hepatocellular carcinoma in Egyptian patients based on machine learning algorithms. Sci Rep 2024; 14:4989. [PMID: 38424116 PMCID: PMC10904762 DOI: 10.1038/s41598-024-54795-2] [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: 12/26/2023] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
Liver cancer, which ranks sixth globally and third in cancer-related deaths, is caused by chronic liver disorders and a variety of risk factors. Despite therapeutic improvements, the prognosis for Hepatocellular Carcinoma (HCC) remains poor, with a 5-year survival rate for advanced cases of less than 12%. Although there is a noticeable decrease in the frequency of cases, liver cancer remains a significant worldwide health concern, with estimates surpassing one million cases by 2025. The prevalence of HCC has increased in Egypt, and it includes several neoplasms with distinctive messenger RNA (mRNA) and microRNA (miRNA) expression profiles. In HCC patients, certain miRNAs, such as miRNA-483-5P and miRNA-21, are upregulated, whereas miRNA-155 is elevated in HCV-infected people, encouraging hepatocyte proliferation. Short noncoding RNAs called miRNAs in circulation have the potential as HCC diagnostic and prognostic markers. This paper proposed a model for examining circulating miRNAs as diagnostic and predictive markers for HCC in Egyptian patients and their clinical and pathological characteristics. The proposed HCC detection model consists of three main phases: data preprocessing phase, feature selection based on the proposed Binary African Vulture Optimization Algorithm (BAVO) phase, and finally, classification as well as cross-validation phase. The first phase namely the data preprocessing phase tackle the main problems associated with the adopted datasets. In the feature selection based on the proposed BAVO algorithm phase, a new binary version of the BAVO swarm-based algorithm is introduced to select the relevant markers for HCC. Finally, in the last phase, namely the classification and cross-validation phase, the support vector machine and k-folds cross-validation method are utilized. The proposed model is evaluated on three studies on Egyptians who had HCC. A comparison between the proposed model and traditional statistical studies is reported to demonstrate the superiority of using the machine learning model for evaluating circulating miRNAs as diagnostic markers of HCC. The specificity and sensitivity for differentiation of HCC cases in comparison with the statistical-based method for the first study were 98% against 88% and 99% versus 92%, respectively. The second study revealed the sensitivity and specificity were 97.78% against 90% and 98.89% versus 92.5%, respectively. The third study reported 83.2% against 88.8% and 95.80% versus 92.4%, respectively. Additionally, the results show that circulating miRNA-483-5p, 21, and 155 may be potential new prognostic and early diagnostic biomarkers for HCC.
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Affiliation(s)
- Gehad Ismail Sayed
- School of Computer Science, Canadian International College (CIC), Cairo, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Mona Solyman
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Gamalat El Gedawy
- Clinical Biochemistry and Molecular Diagnostics Department, National Liver Institute, Menofia University, Menofia, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Yasmine S Moemen
- Clinical Pathology Department, National Liver Institute, Menofia University, Menofia, Egypt
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
| | - Hassan Aboul-Ella
- Department of Microbiology, Faculty of Veterinary Medicine, Cairo University, Giza, Egypt.
| | - Aboul Ella Hassanien
- Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt
- College of Business Administration, Kuwait University, Al Shadadiya, Kuwait
- Scientific Research Group in Egypt (SRGE), Cairo, Egypt
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Mohammadi G, Azizmohammad Looha M, Pourhoseingholi MA, Rezaei Tavirani M, Sohrabi S, Zareie Shab Khaneh A, Piri H, Alaei M, Parvani N, Vakilzadeh I, javadi S, Moradian Haft Cheshmeh Z, Razzaghi Z, Mahmoud Robati R, Zamanian Azodi M, Zarean Shahraki S, Talebi R, Charati Yazdani J, Motlagh ME, Khodakarim S, Hadavi M. Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study. Asian Pac J Cancer Prev 2024; 25:333-342. [PMID: 38285801 PMCID: PMC10911721 DOI: 10.31557/apjcp.2024.25.1.333] [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: 10/13/2023] [Accepted: 01/19/2024] [Indexed: 01/31/2024] Open
Abstract
INTRODUCTION Colorectal cancer (CRC) ranks as the second leading cause of cancer-related deaths. This study aimed to predict survival outcomes of CRC patients using machine learning (ML) methods. MATERIAL AND METHODS A retrospective analysis included 1853 CRC patients admitted to three prominent tertiary hospitals in Iran from October 2006 to July 2019. Six ML methods, namely logistic regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (DT), and Light Gradient Boosting Machine (LGBM), were developed with 10-fold cross-validation. Feature selection employed the Random Forest method based on mean decrease GINI criteria. Model performance was assessed using Area Under the Curve (AUC). RESULTS Time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type emerged as crucial predictors of survival based on mean decrease GINI. The NB (AUC = 0.70, 95% Confidence Interval [CI] 0.65-0.75) and LGBM (AUC = 0.70, 95% CI 0.65-0.75) models achieved the highest predictive AUC values for CRC patient survival. CONCLUSIONS This study highlights the significance of variables including time from diagnosis, age, tumor size, metastatic status, lymph node involvement, and treatment type in predicting CRC survival. The NB model exhibited optimal efficacy in mortality prediction, maintaining a balanced sensitivity and specificity. Policy recommendations encompass early diagnosis and treatment initiation for CRC patients, improved data collection through digital health records and standardized protocols, support for predictive analytics integration in clinical decisions, and the inclusion of identified prognostic variables in treatment guidelines to enhance patient outcomes.
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Affiliation(s)
- Gohar Mohammadi
- Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mehdi Azizmohammad Looha
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mohammad Amin Pourhoseingholi
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | | | - Samaneh Sohrabi
- Vice Chancellor in Administration and Resources Development Affairs, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Amirali Zareie Shab Khaneh
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
| | - Hassan Piri
- Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Maryam Alaei
- Cardiovascular Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Naser Parvani
- Vice Chancellor in Administration and Resources Development Affairs, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Iman Vakilzadeh
- Vice Chancellor in Administration and Resources Development Affairs, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Sara javadi
- Vice Chancellor for Research & Technology, Shiraz University of Medical Sciences, Shiraz, Iran.
| | | | - Zahra Razzaghi
- Laser Application in Medical Sciences Research Center. Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Reza Mahmoud Robati
- Department of Dermatology, Director of Skin Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mona Zamanian Azodi
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Saba Zarean Shahraki
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Raheleh Talebi
- Department of Mathematics at Architecture and Computer Engineering, University of Applied Sciences (unit 10), Tehran, Iran.
| | | | - Mohammad Esmaeil Motlagh
- Department of Pediatrics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
| | - Soheila Khodakarim
- Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Melika Hadavi
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
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6
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Chen H. microRNA-Based Cancer Diagnosis and Therapy. Int J Mol Sci 2023; 25:230. [PMID: 38203401 PMCID: PMC10778828 DOI: 10.3390/ijms25010230] [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: 12/06/2023] [Accepted: 12/15/2023] [Indexed: 01/12/2024] Open
Abstract
MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression post-transcriptionally by impeding mRNA translation or stability [...].
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Affiliation(s)
- Hexin Chen
- Department of Biological Sciences, University of South Carolina, Columbia, SC 29208, USA
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7
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Zaidan AM. The leading global health challenges in the artificial intelligence era. Front Public Health 2023; 11:1328918. [PMID: 38089037 PMCID: PMC10711066 DOI: 10.3389/fpubh.2023.1328918] [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: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.
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Affiliation(s)
- Amal Mousa Zaidan
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia
- Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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8
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Khojasteh-Leylakoohi F, Mohit R, Khalili-Tanha N, Asadnia A, Naderi H, Pourali G, Yousefli Z, Khalili-Tanha G, Khazaei M, Maftooh M, Nassiri M, Hassanian SM, Ghayour-Mobarhan M, Ferns GA, Shahidsales S, Lam AKY, Giovannetti E, Nazari E, Batra J, Avan A. Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer. Sci Rep 2023; 13:16678. [PMID: 37794108 PMCID: PMC10551021 DOI: 10.1038/s41598-023-42928-y] [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: 04/20/2023] [Accepted: 09/16/2023] [Indexed: 10/06/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.
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Affiliation(s)
- 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
| | - Reza Mohit
- Department of Anesthesia, Bushehr University of Medical Sciences, Bushehr, Iran
| | - Nima Khalili-Tanha
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Asadnia
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hamid Naderi
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ghazaleh Pourali
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Zahra Yousefli
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ghazaleh Khalili-Tanha
- Metabolic Syndrome Research Center, 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
| | - Mina Maftooh
- 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
| | - Majid Ghayour-Mobarhan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Brighton and Sussex Medical School, Division of Medical Education, Falmer, Brighton, BN1 9PH, Sussex, UK
| | | | - Alfred King-Yin Lam
- Pathology, School of Medicine and Dentistry, Griffith University, Gold Coast Campus, Gold Coast, QLD, 4222, Australia
| | - Elisa Giovannetti
- Department of Medical Oncology, Cancer Center Amsterdam, Amsterdam U.M.C., VU. University Medical Center (VUMC), Amsterdam, The Netherlands
- Cancer Pharmacology Lab, AIRC Start up Unit, Fondazione Pisana Per La Scienza, Pisa, Italy
| | - Elham Nazari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran.
- Department of Health Information, Technology and Management, School of Allied Medical Sciences, Shahid BeheshtiUniversity of Medical Science, Tehran, Iran.
| | - Jyotsna Batra
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, 4000, Australia
- Translational Research Institute, Queensland University of Technology, Brisbane, 4102, Australia
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- College of Medicine, University of Warith Al-Anbiyaa, Karbala, Iraq.
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, 4000, Australia.
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9
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Ruiz-Fresneda MA, Gijón A, Morales-Álvarez P. Bibliometric analysis of the global scientific production on machine learning applied to different cancer types. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:96125-96137. [PMID: 37566331 PMCID: PMC10482761 DOI: 10.1007/s11356-023-28576-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 06/29/2023] [Indexed: 08/12/2023]
Abstract
Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.
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Affiliation(s)
| | - Alfonso Gijón
- Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
- Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain
| | - Pablo Morales-Álvarez
- Research Centre for Information and Communication Technologies (CITIC-UGR), University of Granada, Granada, Spain
- Department of Statistics and Operations Research, University of Granada, Granada, Spain
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10
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Asadnia A, Nazari E, Goshayeshi L, Zafari N, Moetamani-Ahmadi M, Goshayeshi L, Azari H, Pourali G, Khalili-Tanha G, Abbaszadegan MR, Khojasteh-Leylakoohi F, Bazyari M, Kahaei MS, Ghorbani E, Khazaei M, Hassanian SM, Gataa IS, Kiani MA, Peters GJ, Ferns GA, Batra J, Lam AKY, Giovannetti E, Avan A. The Prognostic Value of ASPHD1 and ZBTB12 in Colorectal Cancer: A Machine Learning-Based Integrated Bioinformatics Approach. Cancers (Basel) 2023; 15:4300. [PMID: 37686578 PMCID: PMC10486397 DOI: 10.3390/cancers15174300] [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: 08/01/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
Introduction: Colorectal cancer (CRC) is a common cancer associated with poor outcomes, underscoring a need for the identification of novel prognostic and therapeutic targets to improve outcomes. This study aimed to identify genetic variants and differentially expressed genes (DEGs) using genome-wide DNA and RNA sequencing followed by validation in a large cohort of patients with CRC. Methods: Whole genome and gene expression profiling were used to identify DEGs and genetic alterations in 146 patients with CRC. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the biological process and pathways involved in CRC. Survival analysis on dysregulated genes in patients with CRC was conducted using Cox regression and Kaplan-Meier analysis. The STRING database was used to construct a protein-protein interaction (PPI) network. Moreover, candidate genes were subjected to ML-based analysis and the Receiver operating characteristic (ROC) curve. Subsequently, the expression of the identified genes was evaluated by Real-time PCR (RT-PCR) in another cohort of 64 patients with CRC. Gene variants affecting the regulation of candidate gene expressions were further validated followed by Whole Exome Sequencing (WES) in 15 patients with CRC. Results: A total of 3576 DEGs in the early stages of CRC and 2985 DEGs in the advanced stages of CRC were identified. ASPHD1 and ZBTB12 genes were identified as potential prognostic markers. Moreover, the combination of ASPHD and ZBTB12 genes was sensitive, and the two were considered specific markers, with an area under the curve (AUC) of 0.934, 1.00, and 0.986, respectively. The expression levels of these two genes were higher in patients with CRC. Moreover, our data identified two novel genetic variants-the rs925939730 variant in ASPHD1 and the rs1428982750 variant in ZBTB1-as being potentially involved in the regulation of gene expression. Conclusions: Our findings provide a proof of concept for the prognostic values of two novel genes-ASPHD1 and ZBTB12-and their associated variants (rs925939730 and rs1428982750) in CRC, supporting further functional analyses to evaluate the value of emerging biomarkers in colorectal cancer.
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Affiliation(s)
- Alireza Asadnia
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad 91886-17871, Iran; (M.R.A.); (M.S.K.)
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran;
| | - Elham Nazari
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran 19839-69411, Iran;
| | - Ladan Goshayeshi
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran;
- Surgical Oncology Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48954, Iran;
| | - Nima Zafari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
| | - Mehrdad Moetamani-Ahmadi
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad 91886-17871, Iran; (M.R.A.); (M.S.K.)
| | - Lena Goshayeshi
- Surgical Oncology Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48954, Iran;
| | - Haneih Azari
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
| | - Ghazaleh Pourali
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
| | - Ghazaleh Khalili-Tanha
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
| | - Mohammad Reza Abbaszadegan
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad 91886-17871, Iran; (M.R.A.); (M.S.K.)
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran;
| | - Fatemeh Khojasteh-Leylakoohi
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran;
| | - MohammadJavad Bazyari
- Department of Medical Biotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran;
| | - Mir Salar Kahaei
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad 91886-17871, Iran; (M.R.A.); (M.S.K.)
| | - Elnaz Ghorbani
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
| | - Majid Khazaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran;
| | - Seyed Mahdi Hassanian
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran;
| | | | - Mohammad Ali Kiani
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad 13944-91388, Iran;
| | - Godefridus J. Peters
- Department of Biochemistry, Medical University of Gdansk, 80-211 Gdansk, Poland;
- Cancer Center Amsterdam, Amsterdam U.M.C., VU University Medical Center (VUMC), Department of Medical Oncology, 1081 HV Amsterdam, The Netherlands
| | - Gordon A. Ferns
- Brighton & Sussex Medical School, Department of Medical Education, Falmer, Brighton, Sussex BN1 9PH, UK;
| | - Jyotsna Batra
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
| | - Alfred King-yin Lam
- Pathology, School of Medicine and Dentistry, Gold Coast Campus, Griffith University, Gold Coast, QLD 4222, Australia;
| | - Elisa Giovannetti
- Cancer Center Amsterdam, Amsterdam U.M.C., VU University Medical Center (VUMC), Department of Medical Oncology, 1081 HV Amsterdam, The Netherlands
- Cancer Pharmacology Lab, AIRC Start Up Unit, Fondazione Pisana per La Scienza, 56017 Pisa, Italy
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran; (A.A.); (N.Z.); (M.M.-A.); (H.A.); (G.P.); (G.K.-T.); (F.K.-L.); (E.G.); (M.K.); (S.M.H.)
- Department of Medical Biotechnology, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad 91779-48564, Iran;
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD 4059, Australia;
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