A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma.

ABSTRACT:

Background

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.

Methods

Using The Cancer Genome Atlas (TCGA) dataset, we identified genes 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.

Results

We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate

SUBMITTER: Wang W 

PROVIDER: S-EPMC7786458 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature