Abstract
BackgroundHepatocellular carcinoma (HCC) is a highly lethal disease. Effective prognostic tools to guide clinical decision-making for HCC patients are lacking.ObjectiveWe aimed to establish a robust prognostic model based on differentially expressed genes (DEGs) in HCC.MethodsUsing datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Genome Consortium (ICGC), DEGs between HCC tissues and adjacent normal tissues were identified. Using TCGA dataset as the training cohort, we applied the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analyses to identify a multi-gene expression signature. Proportional hazard assumptions and multicollinearity among covariates were evaluated while building the model. The ICGC cohort was used for validation. The Pearson test was used to evaluate the correlation between tumor mutational burden and risk score. Through single-sample gene set enrichment analysis, we investigated the role of signature genes in the HCC microenvironment.ResultsA total of 274 DEGs were identified, and a six-DEG prognostic model was developed. Patients were stratified into low- or high-risk groups based on risk scoring by the model. Kaplan–Meier analysis revealed significant differences in overall survival and progression-free interval. Through univariate and multivariate Cox analyses, the model proved to be an independent prognostic factor compared to other clinic-pathological parameters. Time-dependent receiver operating characteristic curve analysis revealed satisfactory prediction of overall survival, but not progression-free interval. Functional enrichment analysis showed that cancer-related pathways were enriched, while immune infiltration analyses differed between the two risk groups. The risk score did not correlate with levels of PD-1, PD-L1, CTLA4, or tumor mutational burden.ConclusionsWe propose a six-gene expression signature that could help to determine HCC patient prognosis. These genes may serve as biomarkers in HCC and support personalized disease management.
Highlights
Liver cancer is one of the most common types of malignancies and is associated with a high mortality rate
The 339 cases of hepatocellular carcinoma (HCC) in The Cancer Genome Atlas (TCGA)-Liver hepatocellular carcinoma (LIHC) were used as the training cohort
The International Cancer Genome Consortium (ICGC) (LIRI-JP) data of 231 HCC patients were used as the Correlation of the Risk Score With the Proportion of 28 Types of TumorInfiltrating Immune Cells
Summary
Liver cancer is one of the most common types of malignancies and is associated with a high mortality rate. Chronic hepatitis B virus infection is the leading cause of hepatocellular carcinoma (HCC) in Asia, while chronic hepatitis C virus, alcoholic cirrhosis, and nonalcoholic steatohepatitis are the primary causes in Western countries [2]. Since symptoms of HCC often present when the disease has reached an advanced stage and therapeutic strategies are limited, the five-year survival rate of HCC patients remains low. The poor prognosis of HCC is due mainly to metastasis, poor liver function, and deteriorating overall physical condition [4,5,6]. There is an urgent need to develop new prognostic markers for HCC to predict patient outcomes. Hepatocellular carcinoma (HCC) is a highly lethal disease. Effective prognostic tools to guide clinical decision-making for HCC patients are lacking
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