A Novel Prognostic Model for Identifying the Risk of Hepatocellular Carcinoma Based on Angiogenesis Factors

Abstract

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with poor prognosis. An optimized stratification of HCC patients to discriminate clinical benefit regarding different degrees of malignancy is urgently needed because of no effective and reliable prognostic biomarkers currently. HCC is typically characterized by rich vascular. The dysregulated vascular endothelial growth factor was proved a pivotal regulator of the development of HCC. Therefore, we investigated the capability of angiogenic factors (AFs) in stratifying patients and constructed a prognostic risk model. A total of 6 prognostic correlated AFs (GRM8, SPC25, FSD1L, SLC386A, FAM72A and SLC39A10) were screened via LASSO Cox regression, which provided the basis for developing a novel prognostic risk model. Based on the risk model, HCC patients were subdivided into high-risk and low-risk groups. Kaplan-Meier curve indicated that patients in the high-risk group have a lower survival rate compared with those in the low-risk group. The prognostic model showed good predictive efficacy, with AUCs reaching 0.802 at 1 year, 0.694 at 2 years, and 0.672 at 3 years. Univariate and multivariate cox regression analysis demonstrated that the risk score had significant prognostic value and was an independent prognostic factor for HCC. Moreover, this model also showed a good diagnostic positive rate in the ICGC-LIRI-JP and GSE144269. Finally, we demonstrated the efficacy of the AF-risk model in HCC patients following sorafenib adjuvant chemotherapy. And revealed the underlying molecular features involving tumor stemness, immune regulation, and genomic alterations associated with the risk score. Based on a large population, we established a novel prognostic model based on 6 AFs to help identify HCC patients with a greater risk of death. The model may provide a reference for better clinical management of HCC patients in the era of cancer precision medicine.