A feedforward loop between STAT1 and YAP1 stimulates lipid biosynthesis, accelerates tumor growth, and promotes chemotherapy resistance in mutant KRAS colorectal cancer
Bibliographic record
Abstract
In tumorous conditions, STAT1, traditionally recognized for its anti-tumor role in immunology, exhibits pro-survival characteristics, though unclear mechanisms. Investigating STAT1 function in isogenic colorectal tumor cells with wild-type or mutant KRAS, we found that STAT1 specifically promotes tumor survival and proliferation with mutant KRAS. Gene expression profiling revealed that STAT1 promotes the expression of sterol and lipid biosynthesis genes in these cells. This effect depends on STAT1 phosphorylation at S727, which upregulates SREBP1 and SREBP2 to drive de novo lipid production. In mutant KRAS cells, STAT1 amplifies the mevalonate pathway, maintaining its S727 phosphorylation and establishing a positive feedback loop through the transcription factors YAP1 and TEAD4, further driving lipid biosynthesis and tumor growth. This STAT1-YAP1 axis promotes mutant KRAS tumor cells' resistance to mevalonate pathway inhibitors, which can be overcome by pharmacologically targeting the YAP1-TEAD interaction. Moreover, this axis contributes to the inherent resistance of mutant KRAS colon cancer cells to EGFR-targeted therapy. Together, these findings identify the STAT1-YAP1 pathway as a critical mediator of therapy resistance and a promising therapeutic target in mutant KRAS colorectal cancer.
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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) | 0.002 | 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".