In Silico Research in BiomedicineBioinformaticsPeer-reviewed journal article
Cross-etiology transcriptomic conservation in hepatocellular carcinoma reveals opposing proliferation and hepatocyte-loss programs validated across cohorts
Abstract
BackgroundHepatocellular carcinoma (HCC) arises from diverse etiologies, but the extent to which viral etiologies converge on reproducible transcriptomic state axes remains incompletely resolved. MethodsWe analyzed HBV- and HCV-associated HCC discovery cohorts using Hallmark GSVA, limma-based differential modeling, and cross-cohort meta-analysis. Conserved tumor-upregulated and tumor-downregulated genes were distilled into ProlifHub and HepLoss modules, combined as HCCStateScore = ProlifHubScore - HepLossScore. Module performance was evaluated across multiple independent GEO cohorts, module-size robustness was tested across alternative top-N definitions, and TCGA-LIHC was used for continuous Cox survival modeling. An HBV-derived injury axis was constructed from an ordinal ALT/AST/HBV-DNA injury index in GSE83148 and tested in GSE121248 with adjustment for E2F/G2M activity and CIBERSORTx-inferred immune composition. ResultsHBV- and HCV-associated HCC showed conserved activation of proliferation/repair programs and suppression of hepatocyte functional programs. The HCCStateScore validated across independent HCC cohorts with consistently positive tumor-non-tumor deltas and high discrimination, and module-size sensitivity analysis showed that performance was not dependent on the top-20 cutoff. In TCGA-LIHC, higher ProlifHubScore and HCCStateScore were associated with poorer overall survival in continuous Cox models, including after age/sex/stage adjustment. A compact HBV injury program remained tumor-associated after simultaneous adjustment for E2F/G2M activity and CIBERSORTx-derived immune-composition covariates, with concordant results using an extended FDR-defined injury set. ConclusionsHCC exhibits a robust cross-etiology transcriptomic state characterized by opposing proliferation and hepatocyte-loss programs. The module framework provides a portable bulk transcriptomic state score and supports a residual tumor-associated HBV injury component that is not fully explained by proliferation or inferred immune composition.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: benchmarking
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 46th percentile
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