Comprehensive transcriptomic analysis of hepatocellular Carcinoma: Uncovering shared and unique molecular signatures across diverse etiologies
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality, often diagnosed at advanced stages where treatment options are limited. This study undertakes a comprehensive meta-analysis of gene expression profiles from 19 independent datasets sourced from the Gene Expression Omnibus (GEO), encompassing a diverse range of HCC etiologies, including HBV and HCV infections, cirrhosis, and normal liver comparisons. Our analysis identified 125 genes consistently altered across all datasets (e.g., CYP2C9 , SLC22A1 , RDH5 ) that represent a pan-etiology HCC signature, implicating retinol metabolism and solute transport as key pathways in HCC pathogenesis. Notably, 14 HBV-specific differentially expressed genes (DEGs) (e.g., ABCA8 , GADD45B ) and 221 HCV-specific DEGs (e.g., CDK1 , CCNB1 ) were identified, highlighting etiology-specific molecular signatures. Protein-protein interaction (PPI) networks revealed central hubs (e.g., CDK1, CCNE1, TYMS) involved in cell cycle dysregulation and metabolic reprogramming (Warburg effect). These findings provide a robust molecular framework for HCC subtyping and prioritize novel biomarkers and therapeutic targets for further validation. This resource advances the potential for personalized HCC diagnostics and therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".