Development Of A Three-Gene Prognostic Signature For Hepatitis B Virus Associated Hepatocellular Carcinoma Based On Integrated Transcriptomic Analysis

Abstract

Integration of public genome-wide gene expression data together with Cox regression analysis is a powerful weapon to identify new prognostic gene signatures for cancer diagnosis and prognosis. Hepatitis B virus (HBV) is a major cause of hepatocellular carcinoma (HCC), however, it remains largely unknown about the specific gene prognostic signature of HBV-associated HCC. Using Robust Rank Aggreg (RRA) method to integrate seven whole genome expression datasets, we identified 82 up-regulated genes and 577 down-regulated genes in HBV-associated HCC patients. Combination of several enrichment analysis, univariate and multivariate Cox proportional hazards regression analysis, we revealed that a three-gene (SPP2, CDC37L1, and ECHDC2) prognostic signature could act as an independent prognostic indicator for HBV-associated HCC in both the discovery cohort and the internal testing cohort. Gene set enrichment analysis showed that the high-risk group with lower expression levels of the three genes was enriched in bladder cancer and cell cycle pathway, whereas the low-risk group with higher expression levels of the three genes was enriched in drug metabolism-cytochrome P450, PPAR signaling pathway, fatty acid and histidine metabolisms. This indicates that patients of HBV-associated HCC with higher expression of these three genes may preserve relatively good hepatic cellular metabolism and function, which may also protect HCC patients from persistent drug toxicity in response to various medication. Our findings suggest a three-gene prognostic model that serves as a specific prognostic signature for HBV-associated HCC.

IRF1 association with tumor immune microenvironment and use as a diagnostic biomarker for colorectal cancer recurrence

“…P<0.05 was considered to indicate a statistically significant difference. Hazard ratios (HRs) were used to identify protective (HR<1) and risk genes (HR>1) (17).…”

“…The WGCNA package (version 1.63), in R, was used to assess correlation patterns among genes, and identify modules of highly correlated genes (18). WGCNA was used in the present study to construct the co-expression network for the recurrence-associated genes that were identified using univariate Cox analysis, as described previously (17). β was a soft-thresholding parameter that was able to emphasize strong correlations between genes and penalize weak correlations (18,19).…”

A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

“…75, 0.77 and 0.77 for the 0.5-, 1-, 3-and 4-year survival times ( Figure 4D). Meanwhile, we attempted to compare the hypoxia-related signature with other prognostic models published previously [26,27]. For the hypoxia-related signature, methylation-driven prognostic model and three-gene prognostic model, the AUCs was 0.78, 0.67 and 0.67 in TCGA cohort and 0.75, 0.64 and 0.64 in ICGC cohort, respectively ( Figure S2).…”

NCF2, MYO1F, S1PR4, and FCN1 as potential noninvasive diagnostic biomarkers in patients with obstructive coronary artery: A weighted gene co‐expression network analysis

“…The heatmap was visualized by using the heatmap package for "R" statistical software (version 3.3.3), as described previously. [13][14][15] 2.3 | Enrichment analysis KEGG pathway and GO, including cellular component, molecular function, and biological process, were analyzed using the package clusterProfiler (version 3.2.14) of R (version 3.3.3), as described previously. 13,14 2.4 | Weighted gene co-expression network analysis WGCNA package (version 1.60) in R was used to identify key modules based on the expression levels of DEGs in the data set GSE90074.…”