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. 2024 Sep 26;13(10):765.
doi: 10.3390/biology13100765.

Comprehensive RNA-Seq Gene Co-Expression Analysis Reveals Consistent Molecular Pathways in Hepatocellular Carcinoma across Diverse Risk Factors

Nicholas Dale D Talubo  1   2 , Po-Wei Tsai  3 , Lemmuel L Tayo  4
Affiliations

Comprehensive RNA-Seq Gene Co-Expression Analysis Reveals Consistent Molecular Pathways in Hepatocellular Carcinoma across Diverse Risk Factors

Nicholas Dale D Talubo et al. Biology (Basel). .

Abstract

Hepatocellular carcinoma (HCC) has the highest mortality rate and is the most frequent of liver cancers. The heterogeneity of HCC in its etiology and molecular expression increases the difficulty in identifying possible treatments. To elucidate the molecular mechanisms of HCC across grades, data from The Cancer Genome Atlas (TCGA) were used for gene co-expression analysis, categorizing each sample into its pre-existing risk factors. The R library BioNERO was used for preprocessing and gene co-expression network construction. For those modules most correlated with a grade, functional enrichments from different databases were then tested, which appeared to have relatively consistent patterns when grouped by G1/G2 and G3/G4. G1/G2 exhibited the involvement of pathways related to metabolism and the PI3K/Akt pathway, which regulates cell proliferation and related pathways, whereas G3/G4 showed the activation of cell adhesion genes and the p53 signaling pathway, which regulates apoptosis, cell cycle arrest, and similar processes. Module preservation analysis was then used with the no history dataset as the reference network, which found cell adhesion molecules and cell cycle genes to be preserved across all risk factors, suggesting they are imperative in the development of HCC regardless of potential etiology. Through hierarchical clustering, modules related to the cell cycle, cell adhesion, the immune system, and the ribosome were found to be consistently present across all risk factors, with distinct clusters linked to oxidative phosphorylation in viral HCC and pentose and glucuronate interconversions in non-viral HCC, underscoring their potential roles in cancer progression.

Keywords: cancer etiology; hepatocellular carcinoma; histological grades; module enrichment; module preservation; pathway analysis.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Number of modules per dataset with (A) alcohol consumption (B) hepatitis B, (C) hepatitis C, (D) NAFLD, (E) no history.
Figure 2
Figure 2
Module–trait correlation results per dataset with (A) alcohol consumption (B) hepatitis B, (C) hepatitis C, (D) NAFLD, (E) no history. Asterisk depicts significance starting at less than 0.05 at one asterisk to less than 0.001 at three.
Figure 3
Figure 3
Faceted dotplot of the Gene Ontology enrichment results from clusterProfiler. The results for Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) significantly enriched in selected gene clusters are visible per datasets.
Figure 4
Figure 4
Faceted dotplot of the KEGG enrichment results from clusterProfiler. The results for KEGG significantly enriched in selected gene clusters are visible per datasets.
Figure 5
Figure 5
Alluvial diagram depicting the relationship between approved and in-trial drugs for HCC and the membership of their known gene interactions across different histological grades.
Figure 6
Figure 6
Hierarchical clustering of co-expression modules of all the risk factors considered.
Figure 7
Figure 7
Gene co-expression network of the black and cyan module, placed in (A) and (C), along with corresponding enrichment barplot (B) and (D).
Figure 8
Figure 8
Gene–concept network based on module–trait correlation results, grouped by HCC grades. Each KEGG term is connected to a histological grade and to other pathways through a hub gene shared by both.
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