From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma

Abstract

BackgroundLiver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in.MethodsBig data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values.ResultsA list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, ‘pathways in cancer,’ was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982–0.998], P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences.DiscussionThe study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, ‘pathways in cancer.’ The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated given its high evidence level of diagnosis, whereas the prognostic powers of BIRC5 and CCNE1 are equally attractive and worthy of attention.

CYP3A4 Gene Is a Novel Biomarker for Predicting a Poor Prognosis in Hepatocellular Carcinoma

“…Both RT-PCR and IHC correlated with the findings of the microarray analysis, and we identified CYP3A4 as a novel, potential clinically useful biomarker for the prognosis of HCC. Although many reports have performed comprehensive microarray analyses of the GEP for patients with HCC (10,(17)(18)(19)(20)(21)(22)(23)(24), the frequency of down-regulation and the prognostic impact of the CYP3A4 gene have not yet described in any reports of integrated microarray analyses. Most of these reports have instead focused on genes related to cancer pathways, and few have focused on the frequency of aberrant gene expression and their impact on the prognosis.…”

The down-regulation of the CYP2C19 gene is associated with aggressive tumor potential and the poorer recurrence-free survival of hepatocellular carcinoma

“…The mechanism of liver carcinogenesis involves genetic, epigenetic, transcriptomic and metabolic changes that form its unique molecular fingerprint [ 2 ]. To reduce the cancer related death of HCC, elucidating the molecular mechanisms and developing novel biomarkers are essential, and many researchers have reported the results of whole-genome sequencing analyses [ 3 – 6 ] and microarray studies [ 7 – 11 ].…”

Identification and functional analysis of long non‐coding RNAs in the synovial membrane of osteoarthritis patients

“…Recently, novel bioinformatics and computational technologies have been widely applied to human diseases . Weighted coexpression network analysis (WGCNA) is a systematic biological approach to identify the relationship between genes based on microarray or RNAseq data .…”