Identification of Potential Prognostic Markers and Key Therapeutic Targets in Hepatocellular Carcinoma Using Weighted Gene Co-Expression Network Analysis: A Systems Biology Approach.

Abstract

Background: As the most prevalent form of liver cancer, hepatocellular carcinoma (HCC) ranks the fifth highest cause of cancer-related death worldwide. Despite recent advancements in diagnostic and therapeutic techniques, the prognosis for HCC is still unknown.

Objectives: This study aimed to identify potential genes contributing to HCC pathogenicity.

Materials and methods: To this end, we examined the GSE39791 microarray dataset, which included 72 HCC samples and 72 normal samples. An investigation of co-expression networks using WGCNA found a highly conserved blue module with 665 genes that were strongly linked to HCC.

Results: APOF, NAT2, LCAT, TTC36, IGFALS, ASPDH, and VIPR1 were the blue module's top 7 hub genes. According to the results of hub gene enrichment, the most related issues in the biological process and KEGG were peroxisome organization and metabolic pathways, respectively. In addition, using the drug-target network, we discovered 19 FDA-approved medication candidates for different reasons that might potentially be employed to treat HCC patients through the modulation of 3 hub genes of the co-expression network (LCAT, NAT2, and VIPR1). Our findings also demonstrated that the 3 scientifically validated miRNAs regulated the co-expression network by the VIPR1 hub gene.

Conclusion: We found co-expressed gene modules and hub genes linked with HCC advancement, offering insights into the mechanisms underlying HCC progression as well as some potential HCC treatments.