Identification of a Novel Four-Gene Signature Correlated With the Prognosis of Patients With Hepatocellular Carcinoma: A Comprehensive Analysis

Abstract

PurposeHepatocellular carcinoma (HCC) is a common solid-tumor malignancy with high heterogeneity, and accurate prognostic prediction in HCC remains difficult. This analysis was performed to find a novel prognostic multigene signature.MethodsThe TCGA-LIHC dataset was analyzed for differentially coexpressed genes through weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. A protein-protein interaction (PPI) network and univariate Cox regression analysis of overall survival (OS) were utilized to identify their prognostic value. Next, we used least absolute shrinkage and selection operator (LASSO) Cox regression to establish a prognostic module. Subsequently, the ICGC-LIRI-JP dataset was applied for further validation. Based on this module, HCC cases were stratified into high-risk and low-risk groups, and differentially expressed genes (DEGs) were identified. Functional enrichment analyses of these DEGs were conducted. Finally, single-sample gene set enrichment analysis (ssGSEA) was performed to explore the correlation between the prognostic signature and immune status.ResultsA total of 393 differentially coexpressed genes were obtained. Forty differentially coexpressed hub genes were identified using the CytoHubba plugin, and 38 of them were closely correlated with OS. Afterward, we established the four-gene prognostic signature with an acceptable accuracy (area under the curve [AUC] of 1-year survival: 0.739). The ICGC-LIRI-JP dataset also supported the acceptable accuracy (AUC of 1-year survival:0.752). Compared with low-risk cohort, HCC cases in the high-risk cohort had shorter OS, higher tumor grades, and higher T stages. The risk scores of this signature still act as independent predictors of OS (P<0.001). Functional enrichment analyses suggest that it was mainly organelle fission and nuclear division that were enriched. Finally, ssGSEA revealed that this signature is strongly associated with the immune status of HCC patients.ConclusionsThe proposed prognostic signature of four differentially coexpressed hub genes has satisfactory prognostic ability, providing important insight into the prediction of HCC prognosis.

A CD8+ T cell‐related genes prognostic model for hepatocellular carcinoma patients

“…However, the identification of tumour subtype needs whole‐transcriptome sequencing data, which may be challenging in real‐world practice 30,31 . We established a new CD8T‐RGPI from CD8 + T cell infiltration‐associated genes by calculating the risk score, which had same prediction abilities for prognostic outcomes of HCC patients compared with other prognostic models 32,33 . Based on the characteristics of HCC patients, CD8T‐RGPI exhibited a differential expression pattern.…”

“…30,31 We established a new CD8T-RGPI from CD8 + T cell infiltration-associated genes by calculating the risk score, which had same prediction abilities for prognostic outcomes of HCC patients compared with other prognostic models. 32,33 Based on the characteristics of HCC patients, CD8T-RGPI exhibited a differential expression pattern. CD8T-RGPI was markedly increased in patients with higher T, M and TNM stages.…”

Evaluate the immune-related eRNA models and signature score to predict the response to immunotherapy in thyroid carcinoma

“…WGCNA was performed to identify THCA-specific immune genes related to the co-expression modules using “WGCNA” R package based on the DE-IRGs [ 19 ]. The Aij = |Sij| β (Aij: adjacency matrix between gene i and gene j, Sij: similarity matrix made by Pearson’s correlation coefficient of all pairs of genes, and β: soft thresholding value) was used to show the weighted adjacency matrix with a scale-free co-expression network and then transformed into a topological overlap matrix (TOM), and gene modules were identified [ 20 ]. We calculated the module eigengene (ME) of each module to identify the most significant module.…”

A novel immune-related eRNAs prognostic signature to predict the immunotherapy response for thyroid carcinoma

“…WGCNA was performed to identify THCA-speci c immune genes related to the co-expression modules using "WGCNA" R package based on the DE-IRGs 19 . The Aij = |Sij| β (Aij: adjacency matrix between gene i and gene j, Sij: similarity matrix made by Pearson's correlation coe cient of all pairs of genes, and β: soft thresholding value) was used to show the weighted adjacency matrix with a scale-free co-expression network and then transformed into a topological overlap matrix (TOM), and gene modules were identi ed 20 . We calculated the module eigengene (ME) of each module to identify the most signi cant module.…”