Comprehensive transcriptomic analysis of hepatocellular Carcinoma: Uncovering shared and unique molecular signatures across diverse etiologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it