B-S.B.61: Synergising Explainable AI and Multi-Omics to Unveil Heterogeneity-Defining Biomarkers in Hepatocellular Carcinoma
Veena Puri
Panjab University, Chandigarh
Prabha Garg
National Institute of Pharmaceutical Education and Research, S.A.S. Nagar
Keywords
Biomarkers, Multi-omics, Predictive Modelling, Explainable AI, Hepatocellular Carcinoma
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Background: Hepatocellular carcinoma (HCC) remains a major clinical challenge due to late-stage diagnosis, pronounced tumour heterogeneity, and the absence of reliable pathognomonic biomarkers. To overcome these limitations, we developed an integrated framework to identify and prioritise clinically relevant biomarkers systematically.
Methods: Publicly available RNA-seq datasets were analysed to identify differentially expressed genes associated with HCC, followed by pathway enrichment and protein-protein interaction assessment to determine hub genes. An explainable AI-driven predictive modelling approach was employed to classify tumour samples and prioritise biomarkers with significant patient survival outcomes. Their biomarker potential was validated across cell line models, immunohistochemical profiles, single-cell transcriptomic landscapes, immune infiltration patterns, and genomic alteration profiles. Finally, their expression patterns were contextualised across distinct HCC etiologies, and therapeutic potential was assessed via drug-gene interaction networks.
Results: The XGBoost-based classifier demonstrated outstanding diagnostic performance in independent tissue cohorts (AUROC 0.998) and serum-derived exosomal datasets (AUROC 0.990). Five biomarkers were prioritised, including established mitotic regulators, CDK1, CDC20, and E2F1, which exhibited sustained overexpression in malignant hepatocytes and novel HCC markers, ADRA1D and GLP2R, which showed prominent enrichment within stromal and endothelial compartments and demonstrated tumour-suppressive characteristics through consistent downregulation, respectively. Their expression was pronounced across HCC comorbidities, varying between conditions, thereby reflecting disease heterogeneity and highlighting their potential to target diverse pathological contexts.
Conclusion: Collectively, this framework defines context-specific molecular signatures and establishes a mechanistically grounded foundation for precision diagnostics and individualised HCC management.
Co-authors: Veena Puri, Prabha Garg
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