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
ABSTRACT In tumorous conditions, the transcription factor STAT1, traditionally recognized for its anti-tumor role in immunology, exhibits pro-survival characteristics, though the underlying mechanisms remain unclear. Investigating STAT1’s function in isogenic colorectal tumor cells with wild-type or mutant KRAS, we found that STAT1 specifically promotes the survival and proliferation of cells with mutant KRAS. Through gene expression profiling, we discovered a previously unknown role of STAT1 in upregulating sterol and lipid biosynthetic genes specifically in mutant KRAS cells. This effect is driven by STAT1’s phosphorylation at serine 727 and its cooperation with STAT3 and STAT5 for the transcriptional upregulation of sterol regulatory element-binding proteins (SREBP) 1 and 2, which boost de novo sterol and lipid biosynthesis. In mutant KRAS cells, STAT1 amplifies the mevalonate pathway, maintaining its serine 727 phosphorylation through RHO GTPase signaling and establishing a positive feedback loop through the transcription factors YAP1 and TEAD4, further driving lipid biosynthesis and tumor growth. Through xenograft tumor assays in mice, we discovered that the STAT1-YAP1 axis plays a role in mutant KRAS tumor cells’ resistance to mevalonate pathway inhibitors, which can be overcome by pharmacologically targeting the YAP1-TEAD interaction. Additionally, the STAT1-YAP1 arm is essential for the intrinsic resistance to EGFR-targeting therapy in the mutant KRAS colon cancer cells. These findings indicate that the STAT1-YAP1 pathway plays a significant role in therapy resistance and presents a potential 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 | 0.001 |
| 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".