A prognostic model for predicting recurrence-free survival in hepatocellular carcinoma patients

Abstract

Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice. Using the The Cancer Genome Atlas （TCGA） dataset, we identified genes that are associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness. We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. Validation in both the TCGA cohort and the GEO cohort demonstrated that the 7-gene prognostic model has the capability of predicting the RFS of HCC patients. Meanwhile, the result of multivariate Cox regression showed that the 7-gene prognostic model could work as an independent prognostic factor. In addition, according to the time dependent ROC curve, the 7-gene prognostic model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. What’s more, these seven genes were found to be related to the occurrence and development of liver cancer by exploring other three databases. Our study identified a seven-gene signature for HCC RFS prediction that is a novel and convenient prognostic tool. The seven genes might provide potential target genes for metabolic therapy and the treatment of HCC.