Nomogram for Prediction of Hepatocellular Carcinoma Prognosis

Abstract

Background: Hepatocellular Carcinoma (HCC) is associated with high mortality rates and requires the identification of new therapeutic targets. We sought to develop a nomogram for reliably predicting HCC prognosis. Methods: Gene expression was analyzed in R software, while the hub genes were defined as overlapping candidates across five datasets. A prognostic nomogram was constructed using multivariate Cox analysis and evaluated by receiver operating characteristic curve and concordance index analysis. The fractions of tumor microenvironment cells were determined by using xCell. Hypoxia scores were calculated by single-sample gene set enrichment analysis. Statistically, significance and correlation analyses were processed in R. Results: Tow hub genes were identified, and a prognostic nomogram was established and evaluated in the internal validation dataset (Area Under the Curve [AUC] 0.72, 95% Confidence Interval [CI] 0.63- 0.81) and external cohorts (AUC 0.70, 95% CI 0.55-0.85). The risk scores of the prognostic model were positively and negatively correlated with fractions of the T helper 2 (Th2) cells (R = 0.39, p <0.001) and the hematopoietic stem cells (R = -0.27, p <0.001) and Endothelial Cells (ECs; R = -0.24, p <0.001), respectively. Angiogenesis was more active in the high-risk group, accompanied by increased proliferation of ECs. Furthermore, the significance of Hypoxia-Inducible Factor 1-Alpha (HIF1A) gene-related hypoxia in predicting HCC prognosis was demonstrated. Conclusions: A robust prognostic nomogram for predicting the prognosis of patients with HCC was developed. The results suggested that Th2 cells, VEGF-related angiogenesis and HIF1A-related hypoxia may be promising therapeutic targets for prolonging the overall survival of HCC patients.