Extracellular Matrix–Associated Biomarkers for Hepatocellular Carcinoma: Insights From Machine Learning and Single‐Cell Analysis

The 5‐year overall survival rate for hepatocellular carcinoma (HCC) patients remains below 20%. Alterations in the extracellular matrix (ECM) are increasingly recognized as central drivers of HCC initiation and progression. This study applied a system biology framework integrating omics data and machine learning to analyze gene expression and regulatory networks in HCC using The Cancer Genome Atlas. Eight ECM‐associated genes ( CSPG4 , CD34 , C1orf35 , ESM1 , MAPT , PLXDC1 , STC2 , and THBS4 ) were identified as upregulated diagnostic biomarkers with strong discriminatory power. Among them, MAPT , PLXDC1 , and STC2 showed significant associations with poor overall survival, defining a prognostic subset. Validation in the GSE104310 and GSE144269 datasets confirmed consistent expression patterns across cohorts. Functional enrichment linked these genes to tissue remodeling and angiogenesis. Single‐cell RNA sequencing revealed MAPT upregulation in T cells, PLXDC1 enrichment in cancer‐associated fibroblasts, and mild STC2 elevation in tumor‐associated macrophages and endothelial cells. These findings identify key ECM‐based biomarkers with potential for early detection, prognosis, and therapeutic targeting in HCC.