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. 2022 Jul 1;20(3):e2968.
doi: 10.30498/ijb.2022.269817.2968. eCollection 2022 Jul.

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

Hengameh Sharifi  1 , Hossein Safarpour  2 , Maryam Moossavi  1 , Mohsen Khorashadizadeh  1   2   3
Affiliations

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

Hengameh Sharifi et al. Iran J Biotechnol. .

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.

Keywords: Drug Repositioning; Hepatocellular Carcinoma; MicroRNAs; Systems Biology; WGCNA.

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Conflict of interest statement

The authors declare that there is no conflict of interest.

Figures

Figure 1
Figure 1
The flowchart of the study.
Figure 2
Figure 2
DEG analysis of GSE39791 using two different approach (limma package of R and GEO2R online software). The logFC of the gene expression is presented under the Venn diagram. A total of 50 genes were considered as DEG and were selected for hub-g.
Figure 3
Figure 3
Processes and pathways identified within the DEGs. Gene ontology and pathway analysis were performed using significant genes across all datasets. Node size corresponds to the number of associated genes, and node color reflects the statistical.
Figure 4
Figure 4
Module-trait relationship and enrichment analysis of blue module. A) Module-trait relationship of GSE39791. Each row corresponds to a module eigengene and the column corresponds to HCC status. Numbers in each cell represent the corresponding.
Figure 5
Figure 5
Similarity assessment between DEGs and hub genes of the blue module using a Venn diagram. A total of 7 hub genes which were similar in both list were chosen and then imported to GeneMANIA to construct a co-expression network.
Figure 6
Figure 6
Co-expression network of selected hub-genes with related miRNAs and drugs. Experimentally validated miRNAs were downloaded from the miRWalk database for each gene. FDA approved drugs were acquired from DGIDB database for each gene.
See this image and copyright information in PMC

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