Abstract / Summary
Abstract Hepatocellular carcinoma (HCC) is the sixth most common cancer type and the third leading cause of cancer-related death worldwide. HBV- and HCV-associated cirrhosis are the main global risk factors for HCC development; about 80% of the HCC cases are associated with hepatitis B or C. The progression from Chronic Hepatitis C (CHC) to HCC involves complex, multi-stage molecular alterations that typically evolve across successive stages of liver fibrosis. Elucidating the underlying molecular mechanisms driving this disease continuum is vital for the development of effective early diagnostic and therapeutic strategies. In this exploratory study, we aimed to map differential gene expression patterns across disease progression, from fibrosis (METAVIR stages F0 to F4) to HCC, in CHC patients. Bulk RNA-Seq was performed on liver tissue specimens derived from CHC patients ( n = 23 across F0-F4), HCC patients ( n = 3), and uninfected liver controls ( n = 3). Differential gene expression and Gene Ontology (GO) enrichment analyses were applied. Our preliminary profiling revealed substantial transcriptional remodeling, particularly in HCC, which exhibited a distinct pattern and a higher number of uniquely dysregulated genes compared to fibrosis stages and controls. Trajectory-oriented clustering identified gene modules displaying monotonic patterns across METAVIR stages, highlighting core pathways associated with the transcriptomic shift observed in HCC. Coordinated programs included modules that progressively increased toward HCC (proliferative/cell-cycle signals) and modules that decreased with fibrosis progression (loss of differentiated hepatocyte functions). Functional enrichment implicated inflammatory/acute-phase response, extracellular matrix dynamics, and lipid/cholesterol metabolism as prominent exploratory themes. While the study design utilizes pooled transcriptomic libraries to optimize group-level representativeness, findings warrant validation in non-pooled independent cohorts. Further independent validation using quantitative PCR, protein-level assays, and external datasets (e.g., TCGA-LIHC, GEO) is required to confirm these candidate markers and mechanistic trajectories in CHC-associated hepatocarcinogenesis.