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.

CD81 represses NF-κB in HCV-expressing hepatoma cells

“…We confirm this mechanistic interplay and further demonstrate that CD81 efficiently suppresses this activation (Figure 3a). Given the importance of NF-κB for HCV replication and persistence, as well as its role in tumor development and hepatocellular carcinoma (39)(40)(41)(42)(43)(44), it is tempting to speculate that HCV-mediated downregulation of CD81 is a thus far unprecedented mediator of viral tumorigenesis (14,(68)(69)(70)(71). Indeed, low levels of CD81 correlate with HCC metastasis and tumor proliferation (72,73) and expression of CD81 suppresses hepatocellular carcinoma development (74).…”

“…One candidate described in the literature is cellular stress, which has been shown in some studies to be increased in patients with chronic HCV infection (10)(11)(12). Other studies found a general dysregulation of pathways that are associated with cancer development such as the cell cycle, DNA repair, pro-survival signaling and apoptosis (13)(14)(15).…”

An Approach for Systems-Level Understanding of Prostate Cancer from High-Throughput Data Integration to Pathway Modeling and Simulation

“…In the first step we have processed the raw data for normalization applying RMA [ 7 , 54 ]. A standard statistical t-test ( mattest MATLAB function) has been applied for identifying significant changes between two groups of data (normal or control and cancer or target) for differential gene expression analysis [ 8 , 15 , 16 , 17 , 18 , 19 , 22 , 23 , 24 , 53 , 55 , 56 , 57 ]. To assess the differences in gene expression between two experimental conditions or phenotypes, mattest ( , (accessed on 1 November 2022)) uses a two-sample t-test.…”

“…In cancer, mutations and copy number alterations coupled with epigenetic aberrations and altered gene expression are commonly focused points of study towards understanding the cause, heterogeneity, and drug resistance or defining other phenomena related to the aggressiveness and progression of cancers [ 10 , 11 ]. Accordingly, compared to normal cells, drastic changes occur in cancer cells at multiple levels, including molecular functions and cellular processes [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. In different cancer types, the pattern of aberrations behind the disrupted tissue and cell functions varies substantially [ 25 , 26 ].…”

Role of ZNF143 and Its Association with Gene Expression Patterns, Noncoding Mutations, and the Immune System in Human Breast Cancer

“…Once the connectivities are fetched, we then import the file with genes and connectivities into Cytoscape, where the nodes’ and edges’ color and styles are selected as per our interest. As metioned above, MATLAB was used for coding purpose, FunCoup to predict PPI networks, and Cytoscape for visualization and for more details these references could also be seen [ 20 , 24 , 25 , 40 , 41 , 42 , 43 , 44 ].…”