Multi-omics and machine learning refine HCC molecular subtypes and prognosis based on liquid–liquid phase separation related genes
Bibliographic record
Abstract
Accumulating evidence has demonstrated that biological processes associated with liquid-liquid phase separation (LLPS) play a critical role in cancer development. However, the effect of LLPS on hepatocellular carcinoma (HCC) remains largely unknown. In this study, we integrated consensus clustering with an ensemble machine learning framework to establish robust LLPS-related molecular subtypes and a consensus machine learning-driven LLPS-related signature (CMLLS) for HCC. The consensus clustering robustly identified three fundamental LLPS-driven subtypes (LS1-LS3), and the subsequent machine learning integration, which encompassed 101 algorithm combinations, objectively identified the most generalizable prognostic signature from multiple candidate genes. Our analysis revealed that LS3 exhibits the worst prognosis, significant upregulation of cell cycle and epithelial-mesenchymal transition (EMT)-related pathways, and enhanced immune resistance. Conversely, LS2 displays the best prognosis, enrichment in metabolism-related pathways, and increased sensitivity to immunotherapy. The CMLLS demonstrated robust predictive performance in prognostic stratification and effectively distinguished patients who would benefit from immunotherapy. This study provides novel insights into HCC heterogeneity at the LLPS level and offers a powerful tool for individualized treatment decision-making.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".