Identification and Evaluation of New Biomarkers for the Diagnosis of Hepatocellular Carcinoma using Weighted Gene Co-expression Network Analysis

Samira Nomiri, Adib Miraki feriz, Mohammad Fereydoni, Hossein Safarpour



In this study, we investigated the expression profile of this disease to identify new hub genes to help diagnose hepatocellular carcinoma (HCC).

Materials and Methods:

Weighted gene co-expression network (WGCNA) analysis was used in this study to identify key modules and hub genes associated with HCC in the GSE176271 dataset. We also looked at the clinical significance of key genes and the biological pathways linked to them in external databases. We validated the identified hub genes using data from the GEPIA and XenaBrowser databases.


The Midnight blue module was found to be significantly related to the pathological stage (r=0.94, P=1e-11). Five hub genes (CLEC4M, CLEC4G, FCN2, OIT3, and ASPG) were associated with prognosis using DEG identification and WGCNA analysis. The three biological pathways associated with the Midnight blue module were copper ion detoxification, cell ion homeostasis, and complement activation, as well as the lectin pathway.


The current study's findings provide new and effective molecular targets for the detection of HCC, which can improve patients’ prognosis.


Carcinoma, Hepatocellular, Biomarker, Diagnosis, Weighted gene co-expression network analysis (WGCNA)

Full Text:


Copyright (c) 2023 GOVARESH

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.