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

Background: Nowadays, clinical treatment outcomes of patients with hepatocellular carcinoma (HCC) has 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. Methods: 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 3 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, mRNA and protein expressions of the 6-gene signature were explored using Oncomine, Human Protein Atlas (HPA) and the International Cancer Genome Consortium (ICGC). Results: A total of 8306 differentially expressed genes (DEG) were obtained between HCC (n = 115) and normal tissues (n = 52). Top 5,000 significant genes were selected and subjected to the Weighted Correlation Network Analysis (WGCNA), which constructed 9 gene co-expression modules that assigns these genes to different modules by cluster dendrogram trees. By analyzing the most significant module (red module), 6 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 6-gene signature and multiple clinicopathological features. The 6-gene signature was then validated as an independent prognostic factor in independent HCC cohort from ICGC. Receiver operating characteristic (ROC) curve analysis confirmed predictive capacity of the 6-gene signature and nomogram. Besides, overexpression of the six genes at the mRNA and protein levels were validated using Oncomine and HPA, respectively. Conclusion: The predictive 6-gene signature and nomograms established in this study can assist clinicians in selecting personalized treatment for patients with HCC.