Hepatocyte–Stellate Cell Cross-Talk in the Liver Engenders a Permissive Inflammatory Microenvironment That Drives Progression in Hepatocellular Carcinoma
Bibliographic record
Abstract
Many solid malignant tumors arise on a background of inflamed and/or fibrotic tissues, features that are found in more than 80% hepatocellular carcinomas (HCC). Activated hepatic stellate cells (HSC) play a critical role in fibrogenesis associated with HCC onset and progression, yet their functional impact on hepatocyte fate remains largely unexplored. Here, we used a coculture model to investigate the cross-talk between hepatocytes (human hepatoma cells) and activated human HSCs. Unsupervised genome-wide expression profiling showed that hepatocyte-HSC cross-talk is bidirectional and results in the deregulation of functionally relevant gene networks. Notably, coculturing increased the expression of proinflammatory cytokines and modified the phenotype of hepatocytes toward motile cells. Hepatocyte-HSC cross-talk also generated a permissive proangiogenic microenvironment, particularly by inducing VEGFA and matrix metalloproteinase (MMP)9 expression in HSCs. An integrative genomic analysis revealed that the expression of genes associated with hepatocyte-HSC cross-talk correlated with HCC progression in mice and was predictive of a poor prognosis and metastasis propensity in human HCCs. Interestingly, the effects of cross-talk on migration and angiogenesis were reversed by the histone deacetylase inhibitor trichostatin A. Our findings, therefore, indicate that the cross-talk between hepatoma cells and activated HSCs is an important feature of HCC progression, which may be targeted by epigenetic modulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".