Extracellular Matrix-Associated Biomarkers for Hepatocellular Carcinoma: Insights From Machine Learning and...

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Hepatocellular carcinoma (HCC) has a poor 5-year survival rate (<20%).
• Extracellular matrix (ECM) alterations are critical in HCC development and progression.
• Novel biomarkers are needed for early detection and improved prognosis.

Purpose of the Study:

• To identify and validate ECM-associated genes as diagnostic and prognostic biomarkers for HCC.
• To explore the role of these biomarkers in HCC progression using a systems biology approach.

Main Methods:

• Integrated analysis of The Cancer Genome Atlas (TCGA) omics data using a systems biology framework.
• Machine learning algorithms to identify differentially expressed ECM genes.
• Validation using independent datasets (GSE104310, GSE144269).
• Functional enrichment analysis and single-cell RNA sequencing (scRNA-seq).

Main Results:

• Eight ECM-associated genes (CSPG4, CD34, C1orf35, ESM1, MAPT, PLXDC1, STC2, THBS4) identified as upregulated diagnostic biomarkers.
• MAPT, PLXDC1, and STC2 significantly associated with poor overall survival, defining a prognostic subset.
• scRNA-seq revealed specific cell type enrichment for MAPT, PLXDC1, and STC2 within the tumor microenvironment.
• Functional analysis linked these genes to tissue remodeling and angiogenesis.

Conclusions:

• Identified key ECM-based genes with potential as diagnostic and prognostic biomarkers for HCC.
• These biomarkers may offer new avenues for early detection and therapeutic targeting in HCC.
• Understanding their cellular localization provides insights into HCC pathogenesis.