A Gene Co-Expression Network-Based Drug Repositioning Approach Identifies Candidates for Treatment of Hepatocellular Carcinoma
Artikel i vetenskaplig tidskrift, 2022

Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning method based on comprehensive integration of molecular signatures in liver cancer tissue and cell lines. First, we identify robust prognostic genes and two gene co-expression modules enriched in unfavorable prognostic genes based on two independent HCC cohorts, which showed great consistency in functional and network topology. Then, we screen 10 genes as potential target genes for HCC on the bias of network topology analysis in these two modules. Further, we perform a drug repositioning method by integrating the shRNA and drug perturbation of liver cancer cell lines and identifying potential drugs for every target gene. Finally, we evaluate the effects of the candidate drugs through an in vitro model and observe that two identified drugs inhibited the protein levels of their corresponding target genes and cell migration, also showing great binding affinity in protein docking analysis. Our study demonstrates the usefulness and efficiency of network-based drug repositioning approach to discover potential drugs for cancer treatment and precision medicine approach.

Hepatocel-lular carcinoma (HCC)

Co-expression network

Systems biology

Survival analysis

Drug repositioning


Meng Yuan

Kungliga Tekniska Högskolan (KTH)

Koeun Shong

Kungliga Tekniska Högskolan (KTH)

Xiangyu Li

Kungliga Tekniska Högskolan (KTH)

Bash Biotech Inc.

Sajda Ashraf

Heka Lab

Mengnan Shi

Kungliga Tekniska Högskolan (KTH)

Woonghee Kim

Kungliga Tekniska Högskolan (KTH)

Jens B Nielsen

Chalmers, Biologi och bioteknik, Systembiologi

BioInnovation Institute

Hasan Turkez

Atatürk Üniversitesi

Saeed Shoaie

Kungliga Tekniska Högskolan (KTH)

King's College London

Mathias Uhlen

Kungliga Tekniska Högskolan (KTH)

C. Zhang

Kungliga Tekniska Högskolan (KTH)

Zhengzhou University

Adil Mardinoglu

Kungliga Tekniska Högskolan (KTH)

King's College London


20726694 (eISSN)

Vol. 14 6 1573


Farmaceutisk vetenskap

Bioinformatik och systembiologi

Cancer och onkologi





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