Construction of a bioinformatics-based predictive model for hepatocellular carcinoma prognosis
10.3760/cma.j.cn115396-20250702-00172
- VernacularTitle:基于生物信息学构建预测模型用于预测肝细胞癌患者预后的研究
- Author:
Zhijian CHEN
1
;
Jianda YU
;
Zerun LIN
;
Lizhi LYU
;
Yongbiao CHEN
;
Xinghua HUANG
Author Information
1. 中国人民解放军联勤保障部队第九〇〇医院肝胆胰外科，福州 350025
- Keywords:
Liver neoplasms;
Nomograms;
Prognosis;
Primary liver cancer;
Bioinformatics
- From:
International Journal of Surgery
2025;52(8):517-522
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a prognostic prediction model for hepatocellular carcinoma (HCC) using bioinformatics approaches to guide personalized therapy.Methods:Based on bioinformatics, the differential analysis was carried out on the GSE19665 data set of The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and Gene Expression Omnibus (GEO), and the same differentially expressed genes were obtained by means of Wayne diagram. Functional enrichment analyses using Gene Ontology, Kyoto Encyclopedia of Genes and Genome, and Gene Set Enrichment Analysis were conducted on co-expressed genes. Based on clinicopathological and transcriptomic profiles, TCGA-LIHC patients were stratified into training ( n=246) and internal validation ( n=116) cohorts, with external validation using Japanese liver cancer data ( n=231) from the International Cancer Genome Consortium. A LASSO-Cox regression-derived risk scoring model was established and visualized as a nomogram. The clinical utility of the risk score was evaluated through multiple analytical approaches.A nomogram incorporating the risk score was developed, and its predictive performance was validated using the concordance index (C-index) and calibration curves. The measurement data of normal distribution were expressed as mean±standard deviation( ± s), and the t-test was used for comparison between groups. The measurement data with non-normal distribution were expressed as M( Q1, Q3), and the Wilcoxon test was used for comparison between groups. The Kruskal-Wallis test was applied to evaluate the significance of the differences among multiple groups. The prognostic value of the risk score was estimated using Kaplan-Meier analysis and ROC curve. Multivariate Cox regression clarified the independent prognostic value of the risk score. Results:Differential analysis identified 457 commonly expressed differentially expressed genes (DEGs). Enrichment analysis revealed that these common DEGs were significantly enriched in pathways related to the cell cycle of tumor cells.The LASSO-Cox regression model selected eight candidate genes ( CENPA, NDC80, ANXA10, NEIL3, G6 PD, MCM10, SOCS2, MMP1). The predictive risk score generated using these eight genes demonstrated a strong association with the overall survival of HCC patients.The nomogram combining the predictive risk score with clinicopathological features exhibited high predictive performance in both the training and validation cohorts. Furthermore, the prognostic value of this risk score was successfully validated in the external validation cohort. Conclusion:This study successfully developed a new predictive model that accurately predicts the 1-year, 3-year and 5-year survival rates of patients with liver cancer. This can serve as a potential tool to help guide patients in personalized treatment.
