1
|
Saravanan KS, Satish KS, Saraswathy GR, Kuri U, Vastrad SJ, Giri R, Dsouza PL, Kumar AP, Nair G. Innovative target mining stratagems to navigate drug repurposing endeavours. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:303-355. [PMID: 38789185 DOI: 10.1016/bs.pmbts.2024.03.025] [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: 05/26/2024]
Abstract
The conventional theory linking a single gene with a particular disease and a specific drug contributes to the dwindling success rates of traditional drug discovery. This requires a substantial shift focussing on contemporary drug design or drug repurposing, which entails linking multiple genes to diverse physiological or pathological pathways and drugs. Lately, drug repurposing, the art of discovering new/unlabelled indications for existing drugs or candidates in clinical trials, is gaining attention owing to its success rates. The rate-limiting phase of this strategy lies in target identification, which is generally driven through disease-centric and/or drug-centric approaches. The disease-centric approach is based on exploration of crucial biomolecules such as genes or proteins underlying pathological cascades of the disease of interest. Investigating these pathological interplays aids in the identification of potential drug targets that can be leveraged for novel therapeutic interventions. The drug-centric approach involves various strategies such as exploring the mechanism of adverse drug reactions that can unearth potential targets, as these untoward reactions might be considered desirable therapeutic actions in other disease conditions. Currently, artificial intelligence is an emerging robust tool that can be used to translate the aforementioned intricate biological networks to render interpretable data for extracting precise molecular targets. Integration of multiple approaches, big data analytics, and clinical corroboration are essential for successful target mining. This chapter highlights the contemporary strategies steering target identification and diverse frameworks for drug repurposing. These strategies are illustrated through case studies curated from recent drug repurposing research inclined towards neurodegenerative diseases, cancer, infections, immunological, and cardiovascular disorders.
Collapse
Affiliation(s)
- Kamatchi Sundara Saravanan
- Department of Pharmacognosy, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Kshreeraja S Satish
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Ganesan Rajalekshmi Saraswathy
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India.
| | - Ushnaa Kuri
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Soujanya J Vastrad
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Ritesh Giri
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Prizvan Lawrence Dsouza
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Adusumilli Pramod Kumar
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Gouri Nair
- Department of Pharmacology, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| |
Collapse
|
2
|
Zhu Y, Zhang H, Yang Y, Zhang C, Ou-Yang L, Bai L, Deng M, Yi M, Liu S, Wang C. Discovery of pan-cancer related genes via integrative network analysis. Brief Funct Genomics 2022; 21:325-338. [PMID: 35760070 DOI: 10.1093/bfgp/elac012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 05/14/2022] [Accepted: 05/25/2022] [Indexed: 01/02/2023] Open
Abstract
Identification of cancer-related genes is helpful for understanding the pathogenesis of cancer, developing targeted drugs and creating new diagnostic and therapeutic methods. Considering the complexity of the biological laboratory methods, many network-based methods have been proposed to identify cancer-related genes at the global perspective with the increasing availability of high-throughput data. Some studies have focused on the tissue-specific cancer networks. However, cancers from different tissues may share common features, and those methods may ignore the differences and similarities across cancers during the establishment of modeling. In this work, in order to make full use of global information of the network, we first establish the pan-cancer network via differential network algorithm, which not only contains heterogeneous data across multiple cancer types but also contains heterogeneous data between tumor samples and normal samples. Second, the node representation vectors are learned by network embedding. In contrast to ranking analysis-based methods, with the help of integrative network analysis, we transform the cancer-related gene identification problem into a binary classification problem. The final results are obtained via ensemble classification. We further applied these methods to the most commonly used gene expression data involving six tissue-specific cancer types. As a result, an integrative pan-cancer network and several biologically meaningful results were obtained. As examples, nine genes were ultimately identified as potential pan-cancer-related genes. Most of these genes have been reported in published studies, thus showing our method's potential for application in identifying driver gene candidates for further biological experimental verification.
Collapse
Affiliation(s)
- Yuan Zhu
- School of Automation, China University of Geosciences, Lumo Road, 430074, Wuhan, China.,Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Lumo Road, 430074, Wuhan, China.,Engineering Research Center of Intelligent Technology for Geo-Exploration, Lumo Road, 430074, Wuhan, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University), Ministry of Education, Handan Road, 200433, Shanghai, China
| | - Houwang Zhang
- Electrical Engineering, City University of HongKong, Kowloon, 999077, HongKong, China
| | - Yuanhang Yang
- School of Mathematics and Physics, China University of Geosciences, Lumo Road, 430074, Wuhan, China
| | - Chaoyang Zhang
- School of Computing Sciences and Computer Engineering, The University of Southern Mississippi, Hattiesburg, USA
| | - Le Ou-Yang
- Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, Shenzhen University, Nanhai Avenue, 518060, Shenzhen, China
| | - Litai Bai
- School of Automation, China University of Geosciences, Lumo Road, 430074, Wuhan, China.,Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Lumo Road, 430074, Wuhan, China.,Engineering Research Center of Intelligent Technology for Geo-Exploration, Lumo Road, 430074, Wuhan, China
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, No.5 Yiheyuan Road, 100871, Beijing, China
| | - Ming Yi
- School of Mathematics and Physics, China University of Geosciences, Lumo Road, 430074, Wuhan, China
| | - Song Liu
- School of Automation, China University of Geosciences, Lumo Road, 430074, Wuhan, China.,Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Lumo Road, 430074, Wuhan, China.,Engineering Research Center of Intelligent Technology for Geo-Exploration, Lumo Road, 430074, Wuhan, China
| | - Chao Wang
- Hepatic Surgery Center, Institute of Hepato-Pancreato-Biliary Surgery, Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, 430030, Wuhan, China
| |
Collapse
|
3
|
Ye Z, Ke H, Chen S, Cruz-Cano R, He X, Zhang J, Dorgan J, Milton DK, Ma T. Biomarker Categorization in Transcriptomic Meta-Analysis by Concordant Patterns With Application to Pan-Cancer Studies. Front Genet 2021; 12:651546. [PMID: 34276766 PMCID: PMC8283696 DOI: 10.3389/fgene.2021.651546] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Accepted: 05/28/2021] [Indexed: 01/21/2023] Open
Abstract
With the increasing availability and dropping cost of high-throughput technology in recent years, many-omics datasets have accumulated in the public domain. Combining multiple transcriptomic studies on related hypothesis via meta-analysis can improve statistical power and reproducibility over single studies. For differential expression (DE) analysis, biomarker categorization by DE pattern across studies is a natural but critical task following biomarker detection to help explain between study heterogeneity and classify biomarkers into categories with potentially related functionality. In this paper, we propose a novel meta-analysis method to categorize biomarkers by simultaneously considering the concordant pattern and the biological and statistical significance across studies. Biomarkers with the same DE pattern can be analyzed together in downstream pathway enrichment analysis. In the presence of different types of transcripts (e.g., mRNA, miRNA, and lncRNA, etc.), integrative analysis including miRNA/lncRNA target enrichment analysis and miRNA-mRNA and lncRNA-mRNA causal regulatory network analysis can be conducted jointly on all the transcripts of the same category. We applied our method to two Pan-cancer transcriptomic study examples with single or multiple types of transcripts available. Targeted downstream analysis identified categories of biomarkers with unique functionality and regulatory relationships that motivate new hypothesis in Pan-cancer analysis.
Collapse
Affiliation(s)
- Zhenyao Ye
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| | - Hongjie Ke
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| | - Shuo Chen
- Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Raul Cruz-Cano
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| | - Xin He
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| | - Jing Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| | - Joanne Dorgan
- Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Baltimore, MD, United States
| | - Donald K Milton
- Maryland Institute for Applied Environmental Health, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, College Park, MD, United States
| |
Collapse
|