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 (<i>CSPG4</i>, <i>CD34</i>, <i>C1orf35</i>, <i>ESM1</i>, <i>MAPT</i>, <i>PLXDC1</i>, <i>STC2</i>, and <i>THBS4</i>) were identified as upregulated diagnostic biomarkers with strong discriminatory power. Among them, <i>MAPT</i>, <i>PLXDC1</i>, and <i>STC2</i> 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 <i>MAPT</i> upregulation in T cells, <i>PLXDC1</i> enrichment in cancer-associated fibroblasts, and mild <i>STC2</i> 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.