Machine learning-based prognostic model of stemness and angiogenesis-related genes for predicting prognosis and immune infiltration in patients with HCC.

ABSTRACT: Hepatocellular carcinoma (HCC), the predominant form of primary liver cancer, ranks as the fourth leading cause of cancer-associated mortality globally. The heightened mortality associated with HCC is largely attributed to its propensity for recurrence and metastasis, which cannot be achieved without tumor stemness and angiogenesis. Here, we aimed to develop a novel signature of stemness and angiogenesis-related genes (SARGs) for the prediction of clinical prognosis and tumor microenvironment in HCC, with the overarching objective of uncovering novel therapeutic targets capable of concurrently disrupting these intertwined processes, thereby offering potential breakthroughs for more effective anti-HCC strategies. The differentially expressed SARGs were subjected to univariate Cox regression

SUBMITTER: Cheng F 

PROVIDER: S-EPMC12923586 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature