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

Publisher: BioMed Central Country of Publication: England NLM ID: 100967800 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2407 (Electronic) Linking ISSN: 14712407 NLM ISO Abbreviation: BMC Cancer Subsets: MEDLINE

Original Publication: London : BioMed Central, [2001-

Gene Expression Profiling*; Biomarkers, Tumor/*genetics ; Carcinoma, Hepatocellular/*mortality ; Liver Neoplasms/*mortality ; Neoplasm Recurrence, Local/*mortality; Carcinoma, Hepatocellular/genetics ; Carcinoma, Hepatocellular/pathology ; Female ; Follow-Up Studies ; Gene Expression Regulation, Neoplastic ; Humans ; Liver Neoplasms/genetics ; Liver Neoplasms/pathology ; Male ; Middle Aged ; Neoplasm Recurrence, Local/genetics ; Neoplasm Recurrence, Local/pathology ; Prognosis ; ROC Curve ; Risk Factors ; Survival Rate

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 the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases.
Conclusion: Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

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91180525 the National Natural Science Foundation of China

Keywords: Hepatocellular carcinoma; Prognostic model; Recurrence-free survival; Risk score; TCGA

0 (Biomarkers, Tumor)

Date Created: 20210106 Date Completed: 20210514 Latest Revision: 20210514

20221213

PMC7786458

10.1186/s12885-020-07692-6

33402113