Angiogenesis–metabolism transcriptional relationship in HCC and iCCA liver cancer throughout tumor progression from single-cell RNA sequencing

Liver cancer, specifically Hepatocellular Carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (iCCA), remains one of the major causes of cancer-related mortality due to rapid progression, late diagnosis, and therapy resistance. While many options for different types of therapy are available, a deeper understanding of the evolving tumor microenvironment (TME) is essential to select the appropriate therapy for the tumor’s state. Malignant cells undergo metabolic reprogramming, and angiogenesis contributes to tumor growth, yet their interplay across disease stages and therapies has been minimally explored. Here, we investigated the metabolic state of malignant cells and tumor progression using publicly available single-cell RNA sequencing (scRNA-seq) data from tumor biopsies. We used GSEA for pathway analysis and receptor-ligand interaction analysis for cell communication. Our research used machine learning to establish a correlation between angiogenesis and metabolism, identifying key angiogenic ligand gene signatures that predict specific metabolic states of the same malignant cells. This analysis revealed angiogenic ligands that could help to better classify stages of HCC and iCCA liver cancers. To apply these insights, we developed an AI interface that identifies unique patient-specific levels of these angiogenic biomarkers to determine metabolic ‘hotspots’ from a patient’s gene expression matrix. This tool aims to guide personalized treatment strategies by better understanding the transcriptional state of malignant cells in HCC/iCCA biopsies.