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

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 analysis to identify SARGs with prognostic significance. A nine-SARGs risk score model was constructed using Least Absolute Shrinkage Selection Operator (LASSO) Cox regression with 10-fold cross-validation. Furthermore, a nomogram incorporating the SARGs score and other clinicopathological features was developed for accurate prediction of survival rate in patients with HCC. Knockdown of ELOVL3 expression was performed, and its effects on tumor stemness and angiogenic potential were verified through in vitro and in vivo experiments. Patients with HCC were categorized into high- and low-risk groups based on the median risk score values, with higher risk scores indicating worse overall survival (log-rank P < 0.001). The nine-SARGs risk score, comprising DRD1, CDX2, ELOVL3, TKTL1, IGLON5, SHISA9, WNT1, CNTN6, and MMP3, demonstrated robust predictive performance (C-index: 0.72) for clinical prognosis, tumor microenvironment characteristics, and immunotherapy response in HCC. ELOVL3 knockdown reduced tumor stemness and angiogenic potential, leading to the inhibition of tumor growth and metastasis. This study established a direct molecular correlation between tumor stemness and angiogenesis, encompassing clinical features, tumor microenvironment, and immune response, thereby offering valuable insights for predicting clinical outcomes and immunotherapy responses in HCC. Our findings demonstrate that ELOVL3 correlates with cancer cell stemness and angiogenic potential, thereby identifying it as a promising therapeutic target. Further investigations are warranted to elucidate the downstream molecular pathways that mediate these functional effects.