GATA4 loss of function in liver cancer impedes precursor to hepatocyte transition
Bibliographic record
Abstract
The most frequent chromosomal structural loss in hepatocellular carcinoma (HCC) is of the short arm of chromosome 8 (8p). Genes on the remaining homologous chromosome, however, are not recurrently mutated, and the identity of key 8p tumor-suppressor genes (TSG) is unknown. In this work, analysis of minimal commonly deleted 8p segments to identify candidate TSG implicated GATA4, a master transcription factor driver of hepatocyte epithelial lineage fate. In a murine model, liver-conditional deletion of 1 Gata4 allele to model the haploinsufficiency seen in HCC produced enlarged livers with a gene expression profile of persistent precursor proliferation and failed hepatocyte epithelial differentiation. HCC mimicked this gene expression profile, even in cases that were morphologically classified as well differentiated. HCC with intact chromosome 8p also featured GATA4 loss of function via GATA4 germline mutations that abrogated GATA4 interactions with a coactivator, MED12, or by inactivating mutations directly in GATA4 coactivators, including ARID1A. GATA4 reintroduction into GATA4-haploinsufficient HCC cells or ARID1A reintroduction into ARID1A-mutant/GATA4-intact HCC cells activated hundreds of hepatocyte genes and quenched the proliferative precursor program. Thus, disruption of GATA4-mediated transactivation in HCC suppresses hepatocyte epithelial differentiation to sustain replicative precursor phenotype.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".