A Multimodal Diagnostic Model for Hepatocellular Carcinoma Integrating Biomarkers Related to Programmed Cell Death...

Area of Science:

• Oncology
• Genetics
• Bioinformatics

Background:

• Hepatocellular carcinoma (HCC) presents with insidious onset and rapid progression.
• Targeting programmed cell death (PCD) pathways offers a promising avenue for HCC diagnosis and treatment.

Purpose of the Study:

• To identify novel diagnostic biomarkers for HCC by analyzing PCD-related genes.
• To develop a predictive model for HCC diagnosis and risk stratification using integrated multi-omics data.

Main Methods:

• Programmed cell death (PCD) pathway activation was assessed using single-sample gene-set enrichment analysis (ssGSEA).
• Differentially expressed genes (DEGs) were identified from bulk and single-cell RNA sequencing data.
• LASSO regression, ROC analysis, and molecular docking were employed for gene selection, diagnostic validation, and drug prediction.
• A radiomics-driven LASSO model was constructed to develop a diagnostic nomogram.

Main Results:

• Three PCD-related genes (CAPG, MS4A6A, and TREM2) were identified as potential diagnostic markers for HCC.
• Receiver operating characteristic (ROC) analysis confirmed the diagnostic reliability of these genes, with AUC values exceeding 0.7.
• A CAPG-based radiomics nomogram achieved an AUC above 0.8, demonstrating strong diagnostic performance.

Conclusions:

• Systemic dysregulation of PCD pathways in HCC was revealed through integrated multi-omics data analysis.
• The identified genes (CAPG, MS4A6A, and TREM2) hold diagnostic value across datasets.
• A multimodal diagnostic framework incorporating biomarkers and a radiomics tool was developed for early HCC detection and risk stratification.