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
Författare
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
Cancers
20726694 (eISSN)
Vol. 14 6 1573Ämneskategorier
Farmaceutisk vetenskap
Bioinformatik och systembiologi
Cancer och onkologi
DOI
10.3390/cancers14061573
PubMed
35326724