Identification of Non-Invasive Tumor-specific Biomarkers for Hepatocellular Carcinoma through Multi-transcriptomics Data Integration

Abstract Background Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer and a leading cause of cancer-related mortality worldwide. Early detection is critical for improving patient outcomes; however, current surveillance modalities exhibit insufficient sensitivity for early-stage disease. While tissue biopsy remains the diagnostic gold standard, its invasive nature limits its routine application. This study seeks to identify non-invasive blood-based biomarkers for HCC through transcriptomic analysis. Methods We analyzed transcriptomic datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), focusing on HCC tissues, plasma, and peripheral blood mononuclear cells (PBMCs). Weighted Gene Co-expression Network Analysis (WGCNA) and differential expression profiling were employed to identify candidate biomarkers, with key genes prioritized using STRING network analysis. Additionally, exosomal miRNA profiles were integrated to capture systemic regulatory signatures. Pathway enrichment analysis was conducted via Reactome 2024 to identify consistently dysregulated "tissue fundamental pathways" across liver datasets, which were then compared with blood-derived pathways. Candidate genes within these pathways were analyzed to define potential blood-based biomarker panels. Results We identified 11 plasma-derived genes (e.g., RPS27A, CCND1, HSP90AB1, POLR2F, H2BC13, H2BC14, POLR2C, POLR2L, NPM1, FEN1, XRCC6), 6 PBMC-derived genes (e.g., JUN, POLR2H, CUL1, DICER1, AURKA, CCNA1), and 9 exosomal miRNAs (e.g., hsa-miR-671-5p, hsa-miR-184, hsa-miR-425-5p, hsa-miR-193a-5p, hsa-miR-103a-3p, hsa-miR-371a-5p, hsa-miR-874-3p, hsa-miR-663a, hsa-miR-18a-5p) significantly correlated with HCC. Many identified biomarkers align with established cancer hallmark pathways, underscoring their mechanistic relevance. Notably, several of these genes (e.g., JUN, POLR2C) and miRNAs (e.g., miR-874-3p) have not been previously reported in association with HCC, highlighting their potential as novel biomarker candidates. Conclusion This integrative multi-transcriptomic analysis has unveiled novel, clinically relevant blood-based biomarkers for HCC, providing a promising foundation for sensitive, non-invasive detection strategies. Prospective validation in clinical cohorts is crucial for advancing the translation of these findings into early diagnostic protocols.