Genomic profiling and network-level understanding uncover the potential genes and the pathways in hepatocellular carcinoma

Abstract

Data integration with phenotypes such as gene expression, pathways or function, and protein-protein interactions data has proven to be a highly promising technique for improving human complex diseases, particularly cancer patient outcome prediction. Hepatocellular carcinoma is one of the most prevalent cancers, and the most common cause is chronic HBV and HCV infection, which is linked to the majority of cases, and HBV and HCV play a role in multistep carcinogenesis progression. We examined the list of known hepatocellular carcinoma biomarkers with the publicly available expression profile dataset of hepatocellular carcinoma infected with HCV from day 1 to day 10 in this study. The study covers an overexpression pattern for the selected biomarkers in clinical hepatocellular carcinoma patients, a combined investigation of these biomarkers with the gathered temporal dataset, temporal expression profiling changes, and temporal pathway enrichment following HCV infection. Following a temporal analysis, it was discovered that the early stages of HCV infection tend to be more harmful in terms of expression shifting patterns, and that there is no significant change after that, followed by a set of genes that are consistently altered. PI3K, cAMP, TGF, TNF, Rap1, NF-kB, Apoptosis, Longevity regulating pathway, signaling pathways regulating pluripotency of stem cells, Cytokine-cytokine receptor interaction, p53 signaling, Wnt signaling, Toll-like receptor signaling, and Hippo signaling pathways are just a few of the most commonly enriched pathways. The majority of these pathways are well-known for their roles in the immune system, infection and inflammation, and human illnesses like cancer. We also find that ADCY8, MYC, PTK2, CTNNB1, TP53, RB1, PRKCA, TCF7L2, PAK1, ITPR2, CYP3A4, UGT1A6, GCK, and FGFR2/3 appear to be among the prominent genes based on the networks of genes and pathways based on the copy number alterations, mutations, and structural variants study.

Keywords: HCV and HCC; biomarkers; co-expression; gene expression/mutational profiling; network-level understanding.

FIGURE 1

Differential gene expression profiling and pathway enrichment analysis. (A) Co-occurrence network. (B) Temporal evolution of gene expression aberrations and its functional consequences. (C) Venn diagram to represent the shared and specific genes and pathways which are potentially altered as a result of CRC. (D) Enriched pathways followed by their respective p-values. (E) Temporal gene expression profiling of HCC in result to HCV infection. The number of DEGs from day 1 to day 10 and number of common DEGs in different combinations (such day 1 with day 2, day 2 with day 3, day 3 with day 4, day 4 with day 5, and so on). (F) HCC biomarkers profiling for the temporal dataset.

FIGURE 2

Genomic-level alterations in HCC datasets of TCGA database. (A) Histograms to present the mutation count, fraction genome altered, diagnosis age, and MSI mantis score. (B) Percentage of patients with different types of alterations (CNA, Mutations, and SV) in case of HCC. (C) Venn diagrams to display the shared and specific significant genes and pathways.

FIGURE 3

Network-level understanding top-ranked genes. (A) CNA genes network, (B) Mutated genes, and (C) SV genes network followed by their respective analysis (degree distribution and topological coefficients).

FIGURE 4

Network-level understanding top-ranked genes and the associated pathways. (A) CNA genes network, (B) Mutated genes, (C) SV genes network followed by their respective analysis, and (D) mRNA and protein expression in liver and gallbladder tissues (source protein atlas).