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Article

Molecularly Guided Drug Repurposing for Cholangiocarcinoma: An Integrative Bioinformatic Approach

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Graduate Program in Molecular Medicine, Faculty of Science Joint Program Faculty of Medicine Ramathibodi Hospital, Faculty of Medicine Siriraj Hospital, Faculty of Dentistry, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand
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Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
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Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool L69 7ZX, UK
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Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
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Pediatric Translational Research Unit, Department of Pediatrics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
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Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
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Authors to whom correspondence should be addressed.
Academic Editor: Shuangge Ma
Genes 2022, 13(2), 271; https://doi.org/10.3390/genes13020271
Received: 12 January 2022 / Revised: 25 January 2022 / Accepted: 28 January 2022 / Published: 29 January 2022
(This article belongs to the Special Issue Application of Bioinformatics in Human Cancers)
Background: Cholangiocarcinoma (CCA) has a complex immune microenvironment architecture, thus possessing challenges in its characterization and treatment. This study aimed to repurpose FDA-approved drugs for cholangiocarcinoma by transcriptomic-driven bioinformatic approach. Methods: Cox-proportional univariate regression was applied to 3017 immune-related genes known a priori to identify a list of mortality-associated genes, so-called immune-oncogenic gene signature, in CCA tumor-derived RNA-seq profiles of two independent cohorts. Unsupervised clustering stratified CCA tumors into two groups according to the immune-oncogenic gene signature expression, which then confirmed its clinical relevance by Kaplan–Meier curve. Molecularly guided drug repurposing was performed by an integrative connectivity map-prioritized drug-gene network analysis. Results: The immune-oncogenic gene signature consists of 26 mortality-associated immune-related genes. Patients with high-expression signature had a poorer overall survival (log-rank p < 0.001), while gene enrichment analysis revealed cell-cycle checkpoint regulation and inflammatory-immune response signaling pathways affected this high-risk group. The integrative drug-gene network identified eight FDA-approved drugs as promising candidates, including Dasatinib a multi-kinase inhibitor currently investigated for advanced CCA with isocitrate-dehydrogenase mutations. Conclusion: This study proposes the use of the immune-oncogenic gene signature to identify high-risk CCA patients. Future preclinical and clinical studies are required to elucidate the therapeutic efficacy of the molecularly guided drugs as the adjunct therapy, aiming to improve the survival outcome. View Full-Text
Keywords: cholangiocarcinoma; connectivity map; drug–gene network; drug repurposing; immune-oncogenic gene signature; transcriptomics; survival analysis cholangiocarcinoma; connectivity map; drug–gene network; drug repurposing; immune-oncogenic gene signature; transcriptomics; survival analysis
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MDPI and ACS Style

Venkatraman, S.; Balasubramanian, B.; Pongchaikul, P.; Tohtong, R.; Chutipongtanate, S. Molecularly Guided Drug Repurposing for Cholangiocarcinoma: An Integrative Bioinformatic Approach. Genes 2022, 13, 271. https://doi.org/10.3390/genes13020271

AMA Style

Venkatraman S, Balasubramanian B, Pongchaikul P, Tohtong R, Chutipongtanate S. Molecularly Guided Drug Repurposing for Cholangiocarcinoma: An Integrative Bioinformatic Approach. Genes. 2022; 13(2):271. https://doi.org/10.3390/genes13020271

Chicago/Turabian Style

Venkatraman, Simran, Brinda Balasubramanian, Pisut Pongchaikul, Rutaiwan Tohtong, and Somchai Chutipongtanate. 2022. "Molecularly Guided Drug Repurposing for Cholangiocarcinoma: An Integrative Bioinformatic Approach" Genes 13, no. 2: 271. https://doi.org/10.3390/genes13020271

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