Research Paper Volume 13, Issue 9 pp 12865—12895
Integrative analysis identifies key mRNA biomarkers for diagnosis, prognosis, and therapeutic targets of HCV-associated hepatocellular carcinoma
- 1 Molecular Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, P.R. China
- 2 West China School of Medicine, West China Hospital, Sichuan University, Chengdu 610041, P.R. China
- 3 School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, P.R. China
- 4 Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, P.R. China
Received: December 30, 2020 Accepted: March 23, 2021 Published: May 4, 2021https://doi.org/10.18632/aging.202957
How to Cite
Copyright: © 2021 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Hepatitis C virus-associated HCC (HCV-HCC) is a prevalent malignancy worldwide and the molecular mechanisms are still elusive. Here, we screened 240 differentially expressed genes (DEGs) of HCV-HCC from Gene expression omnibus (GEO) and the Cancer Genome Atlas (TCGA), followed by weighted gene coexpression network analysis (WGCNA) to identify the most significant module correlated with the overall survival. 10 hub genes (CCNB1, AURKA, TOP2A, NEK2, CENPF, NUF2, CDKN3, PRC1, ASPM, RACGAP1) were identified by four approaches (Protein-protein interaction networks of the DEGs and of the significant module by WGCNA, and diagnostic and prognostic values), and their abnormal expressions, diagnostic values, and prognostic values were successfully verified. A four hub gene-based prognostic signature was built using the least absolute shrinkage and selection operator (LASSO) algorithm and a multivariate Cox regression model with the ICGC-LIRI-JP cohort (N =112). Kaplan-Meier survival plots (P = 0.0003) and Receiver Operating Characteristic curves (ROC = 0.778) demonstrated the excellent predictive potential for the prognosis of HCV-HCC. Additionally, upstream regulators including transcription factors and miRNAs of hub genes were predicted, and candidate drugs or herbs were identified. These findings provide a firm basis for the exploration of the molecular mechanism and further clinical biomarkers development of HCV-HCC.
HCV-HCC: Hepatitis C virus-associated HCC; DEGs: differently expressed genes; GEO: gene expression omnibus; TCGA: the Cancer Genome Atlas; ICGC: International Cancer Genome Consortium; WGCNA: weighted gene coexpression network analysis; LASSO: the least absolute shrinkage and selection Operator; ROC: Receiver Operating Characteristic; OS: overall survival; TOM: topological overlap matrix; PPI: protein-protein interaction; STRING: the Search Tool for the Retrieval of Interacting Genes; AUROC: area under the receiver operating characteristic curve; UniCox: univariate Cox regression; GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; CCs: cellular components; MFs: molecular functions; DGIdb: the drug-gene interaction database.