An Integrated Model Based on a Six-Gene Signature Predicts Overall Survival in Patients With Hepatocellular Carcinoma

• Published in: Frontiers in genetics Vol. 10; p. 1323
• Main Authors: Li, Wenli, Lu, Jianjun, Ma, Zhanzhong, Zhao, Jiafeng, Liu, Jun
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 14-01-2020
• Subjects: Genetics; hepatocellular carcinoma; mRNA signature; overall survival; risk score; weighted gene co-expression network analysis; overall survival; weighted gene co-expression network analysis; mRNA signature; hepatocellular carcinoma; risk score
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2019.01323

Nowadays, clinical treatment outcomes of patients with hepatocellular carcinoma (HCC) have been improved. However, due to the complexity of the molecular mechanisms, the recurrence rate and mortality in HCC inpatients are still at a high level. Therefore, there is an urgent need in screening biomarkers of HCC to show therapeutic effects and improve the prognosis.
In this study, we aim to establish a gene signature that can predict the prognosis of HCC patients by downloading and analyzing RNA sequencing data and clinical information from three independent public databases. Firstly, we applied the limma R package to analyze biomarkers by the genetic data and clinical information downloaded from the Gene Expression Omnibus database (GEO), and then used the least absolute shrinkage and selection operator (LASSO) Cox regression and survival analysis to establish a gene signature and a prediction model by data from the Cancer Genome Atlas (TCGA). Besides, messenger RNA (mRNA) and protein expressions of the six-gene signature were explored using Oncomine, Human Protein Atlas (HPA) and the International Cancer Genome Consortium (ICGC).
A total of 8,306 differentially expressed genes (DEGs) were obtained between HCC (
= 115) and normal tissues (
= 52). Top 5,000 significant genes were selected and subjected to the weighted correlation network analysis (WGCNA), which constructed nine gene co-expression modules that assign these genes to different modules by cluster dendrogram trees. By analyzing the most significant module (red module), six genes (SQSTM1, AHSA1, VNN2, SMG5, SRXN1, and GLS) were screened by univariate, LASSO, and multivariate Cox regression analysis. By a survival analysis with the HCC data in TCGA, we established a nomogram based on the six-gene signature and multiple clinicopathological features. The six-gene signature was then validated as an independent prognostic factor in independent HCC cohort from ICGC. Receiver operating characteristic (ROC) curve analysis confirmed the predictive capacity of the six-gene signature and nomogram. Besides, overexpression of the six genes at the mRNA and protein levels was validated using Oncomine and HPA, respectively.
The predictive six-gene signature and nomograms established in this study can assist clinicians in selecting personalized treatment for patients with HCC.