A Tumor Microenvironment-Derived CAF-VEGF Model and Its Application in Biomarker Screening for HCC

Area of Science:

• Oncology
• Molecular Biology
• Bioinformatics

Background:

• Hepatocellular carcinoma (HCC) is a rapidly progressing cancer with a poor prognosis.
• Fibroblast infiltration and angiogenesis are critical factors in HCC tumor microenvironment and progression.

Purpose of the Study:

• To develop and validate a prognostic scoring model for HCC using cancer-associated fibroblasts (CAFs) and vascular endothelial growth factor (VEGF) related factors.
• To identify key molecular markers associated with HCC prognosis and angiogenesis.

Main Methods:

• Single-cell sequencing data analysis from GEO database.
• Utilized CellChat and Monocle3 for fibroblast trajectory and angiogenesis pathway analysis.
• Constructed and validated the CAF-VEGF prognostic model using COX and LASSO algorithms, differential gene expression, WGCNA, and PPI network analysis.

Main Results:

• Fibroblast infiltration is significantly higher in HCC tissues.
• The developed CAF-VEGF model effectively predicts HCC prognosis.
• Identified ESCO2 and WDHD1 as key genes upregulated in HCC, promoting angiogenesis, proliferation, and invasion.

Conclusions:

• The CAF-VEGF prognostic model is effective for HCC patient stratification.
• ESCO2 and WDHD1 promote HCC progression by regulating angiogenesis, offering potential therapeutic targets.