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Ahmed F, Samantasinghar A, Manzoor Soomro A, Kim S, Hyun Choi K. A systematic review of computational approaches to understand cancer biology for informed drug repurposing. J Biomed Inform 2023; 142:104373. [PMID: 37120047 DOI: 10.1016/j.jbi.2023.104373] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/25/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023]
Abstract
Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea
| | | | | | - Sejong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
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2
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Sharma M, Jha IP, Chawla S, Pandey N, Chandra O, Mishra S, Kumar V. Associating pathways with diseases using single-cell expression profiles and making inferences about potential drugs. Brief Bioinform 2022; 23:6623725. [PMID: 35772850 DOI: 10.1093/bib/bbac241] [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: 03/03/2022] [Revised: 05/22/2022] [Accepted: 05/23/2022] [Indexed: 11/14/2022] Open
Abstract
Finding direct dependencies between genetic pathways and diseases has been the target of multiple studies as it has many applications. However, due to cellular heterogeneity and limitations of the number of samples for bulk expression profiles, such studies have faced hurdles in the past. Here, we propose a method to perform single-cell expression-based inference of association between pathway, disease and cell-type (sci-PDC), which can help to understand their cause and effect and guide precision therapy. Our approach highlighted reliable relationships between a few diseases and pathways. Using the example of diabetes, we have demonstrated how sci-PDC helps in tracking variation of association between pathways and diseases with changes in age and species. The variation in pathways-disease associations in mice and humans revealed critical facts about the suitability of the mouse model for a few pathways in the context of diabetes. The coherence between results from our method and previous reports, including information about the drug target pathways, highlights its reliability for multidimensional utility.
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Affiliation(s)
- Madhu Sharma
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
| | - Indra Prakash Jha
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
| | - Smriti Chawla
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
| | - Neetesh Pandey
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
| | - Omkar Chandra
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
| | - Shreya Mishra
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
| | - Vibhor Kumar
- Department of computational biology, Indraprastha Institute of Information Technology, Okhla Ph-III, New Delhi
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Yu L, Wang M, Yang Y, Xu F, Zhang X, Xie F, Gao L, Li X. Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways. PLoS Comput Biol 2021; 17:e1008696. [PMID: 33561121 PMCID: PMC7920387 DOI: 10.1371/journal.pcbi.1008696] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 03/01/2021] [Accepted: 01/12/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a significant health problem worldwide with poor prognosis. Drug repositioning represents a profitable strategy to accelerate drug discovery in the treatment of HCC. In this study, we developed a new approach for predicting therapeutic drugs for HCC based on tissue-specific pathways and identified three newly predicted drugs that are likely to be therapeutic drugs for the treatment of HCC. We validated these predicted drugs by analyzing their overlapping drug indications reported in PubMed literature. By using the cancer cell line data in the database, we constructed a Connectivity Map (CMap) profile similarity analysis and KEGG enrichment analysis on their related genes. By experimental validation, we found securinine and ajmaline significantly inhibited cell viability of HCC cells and induced apoptosis. Among them, securinine has lower toxicity to normal liver cell line, which is worthy of further research. Our results suggested that the proposed approach was effective and accurate for discovering novel therapeutic options for HCC. This method also could be used to indicate unmarked drug-disease associations in the Comparative Toxicogenomics Database. Meanwhile, our method could also be applied to predict the potential drugs for other types of tumors by changing the database.
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Affiliation(s)
- Liang Yu
- School of Computer Science and Technology, Xidian University, Shaanxi, China
| | - Meng Wang
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Advanced Medical Research Institute, Shandong University, 72, Jimo District, Qingdao, Shandong, China
| | - Yang Yang
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Advanced Medical Research Institute, Shandong University, 72, Jimo District, Qingdao, Shandong, China
| | - Fengdan Xu
- School of Computer Science and Technology, Xidian University, Shaanxi, China
| | - Xu Zhang
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Advanced Medical Research Institute, Shandong University, 72, Jimo District, Qingdao, Shandong, China
| | - Fei Xie
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Advanced Medical Research Institute, Shandong University, 72, Jimo District, Qingdao, Shandong, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Shaanxi, China
| | - Xiangzhi Li
- Shandong Provincial Key Laboratory of Animal Cell and Developmental Biology, School of Life Sciences, Advanced Medical Research Institute, Shandong University, 72, Jimo District, Qingdao, Shandong, China
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Kaspi A, Ziemann M. mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data. BMC Genomics 2020; 21:447. [PMID: 32600408 PMCID: PMC7325150 DOI: 10.1186/s12864-020-06856-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 06/19/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Inference of biological pathway activity via gene set enrichment analysis is frequently used in the interpretation of clinical and other omics data. With the proliferation of new omics profiling approaches and ever-growing size of data sets generated, there is a lack of tools available to perform and visualise gene set enrichments in analyses involving multiple contrasts. RESULTS To address this, we developed mitch, an R package for multi-contrast gene set enrichment analysis. It uses a rank-MANOVA statistical approach to identify sets of genes that exhibit joint enrichment across multiple contrasts. Its unique visualisation features enable the exploration of enrichments in up to 20 contrasts. We demonstrate the utility of mitch with case studies spanning multi-contrast RNA expression profiling, integrative multi-omics, tool benchmarking and single-cell RNA sequencing. Using simulated data we show that mitch has similar accuracy to state of the art tools for single-contrast enrichment analysis, and superior accuracy in identifying multi-contrast enrichments. CONCLUSION mitch is a versatile tool for rapidly and accurately identifying and visualising gene set enrichments in multi-contrast omics data. Mitch is available from Bioconductor ( https://bioconductor.org/packages/mitch ).
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Affiliation(s)
- Antony Kaspi
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC, 3052, Australia
- Department of Medical Biology, University of Melbourne, 1G Royal Parade, Parkville, VIC, 3052, Australia
| | - Mark Ziemann
- School of Life and Environmental Sciences, Deakin University, Geelong, Australia.
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Sevim Bayrak C, Zhang P, Tristani-Firouzi M, Gelb BD, Itan Y. De novo variants in exomes of congenital heart disease patients identify risk genes and pathways. Genome Med 2020; 12:9. [PMID: 31941532 PMCID: PMC6961332 DOI: 10.1186/s13073-019-0709-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Accepted: 12/26/2019] [Indexed: 12/14/2022] Open
Abstract
Background Congenital heart disease (CHD) affects ~ 1% of live births and is the most common birth defect. Although the genetic contribution to the CHD has been long suspected, it has only been well established recently. De novo variants are estimated to contribute to approximately 8% of sporadic CHD. Methods CHD is genetically heterogeneous, making pathway enrichment analysis an effective approach to explore and statistically validate CHD-associated genes. In this study, we performed novel gene and pathway enrichment analyses of high-impact de novo variants in the recently published whole-exome sequencing (WES) data generated from a cohort of CHD 2645 parent-offspring trios to identify new CHD-causing candidate genes and mutations. We performed rigorous variant- and gene-level filtrations to identify potentially damaging variants, followed by enrichment analyses and gene prioritization. Results Our analyses revealed 23 novel genes that are likely to cause CHD, including HSP90AA1, ROCK2, IQGAP1, and CHD4, and sharing biological functions, pathways, molecular interactions, and properties with known CHD-causing genes. Conclusions Ultimately, these findings suggest novel genes that are likely to be contributing to CHD pathogenesis.
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Affiliation(s)
- Cigdem Sevim Bayrak
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Peng Zhang
- St. Giles Laboratory of Human Genetics of Infectious Diseases, The Rockefeller University, New York, NY, USA
| | - Martin Tristani-Firouzi
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Bruce D Gelb
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yuval Itan
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Sunil Kumar PV, Gopakumar G. Inferring disease and pathway associations of long non-coding RNAs using heterogeneous information network model. J Bioinform Comput Biol 2019; 17:1950020. [PMID: 31617466 DOI: 10.1142/s0219720019500203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recent findings from biological experiments demonstrate that long non-coding RNAs (lncRNAs) are actively involved in critical cellular processes and are associated with innumerable diseases. Computational prediction of lncRNA-disease association draws tremendous research attention nowadays. This paper proposes a machine learning model that predicts lncRNA-disease associations using Heterogeneous Information Network (HIN) of lncRNAs and diseases. A Support Vector Machine classifier is developed using the feature set extracted from a meta-path-based parameter, Association Index derived from the HIN. Performance of the model is validated using standard statistical metrics and it generated an AUC value of 0.87, which is better than the existing methods in the literature. Results are further validated using the recent literature and many of the predicted lncRNA-disease associations are identified as actually existing. This paper also proposes an HIN-based methodology to associate lncRNAs with pathways in which they may have biological influence. A case study on the pathway associations of four well-known lncRNAs (HOTAIR, TUG1, NEAT1, and MALAT1) has been conducted. It has been observed that many times the same lncRNA is associated with more than one biologically related pathways. Further exploration is needed to substantiate whether such lncRNAs have any role in determining the pathway interplay. The script and sample data for the model construction is freely available at http://bdbl.nitc.ac.in/LncDisPath/index.html.
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Affiliation(s)
- P V Sunil Kumar
- Department of Computer Science and Engineering, National Institute of Technology Calicut, Kozhikkode, Kerala 673601, India
| | - G Gopakumar
- Department of Computer Science and Engineering, National Institute of Technology Calicut, Kozhikkode, Kerala 673601, India
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Khan V, Putluri N, Sreekumar A, Mindikoglu AL. Current Applications of Metabolomics in Cirrhosis. Metabolites 2018; 8:metabo8040067. [PMID: 30360420 PMCID: PMC6316274 DOI: 10.3390/metabo8040067] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Revised: 09/30/2018] [Accepted: 10/08/2018] [Indexed: 02/06/2023] Open
Abstract
Metabolomics is the identification and quantification of all or specified metabolites in a living system under a specific condition or disease. Metabolomics in cirrhosis can be used in diagnosing complications, determining prognosis and assessment of response to therapy. In this review, we summarized representative applications of metabolomics in cirrhosis and significant metabolites associated with cirrhosis and its complications.
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Affiliation(s)
- Vinshi Khan
- Margaret M. and Albert B. Alkek Department of Medicine, Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Nagireddy Putluri
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Arun Sreekumar
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Ayse L Mindikoglu
- Margaret M. and Albert B. Alkek Department of Medicine, Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX 77030, USA.
- Michael E. DeBakey Department of Surgery, Division of Abdominal Transplantation, Baylor College of Medicine, Houston, TX 77030, USA.
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Pathway-based association analysis of two genome-wide screening data identifies rheumatoid arthritis-related pathways. Genes Immun 2014; 15:487-94. [DOI: 10.1038/gene.2014.48] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Revised: 05/06/2014] [Accepted: 06/23/2014] [Indexed: 12/26/2022]
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