DLX1 acts as a crucial target of FOXM1 to promote ovarian cancer aggressiveness by enhancing TGF-β/SMAD4 signaling
Bibliographic record
Abstract
Recent evidence from a comprehensive genome analysis and functional studies have revealed that FOXM1 is a crucial metastatic regulator that drives cancer progression. However, the regulatory mechanism by which FOXM1 exerts its metastatic functions in cancer cells remains obscure. Here, we report that DLX1 acts as a FOXM1 downstream target, exerting pro-metastatic function in ovarian cancers. Both FOXM1 isoforms (FOXM1B or FOXM1C) could transcriptionally upregulate DLX1 through two conserved binding sites, located at +61 to +69bp downstream (TFBS1) and -675 to -667bp upstream (TFBS2) of the DLX1 promoter, respectively. This regulation was further accentuated by the significant correlation between the nuclear expression of FOXM1 and DLX1 in high-grade serous ovarian cancers. Functionally, the ectopic expression of DLX1 promoted ovarian cancer cell growth, cell migration/invasion and intraperitoneal dissemination of ovarian cancer in mice, whereas small interfering RNA-mediated DLX1 knockdown in FOXM1-overexpressing ovarian cancer cells abrogated these oncogenic capacities. In contrast, depletion of FOXM1 by shRNAi only partially attenuated tumor growth and exerted almost no effect on cell migration/invasion and the intraperitoneal dissemination of DLX1-overexpressing ovarian cancer cells. Furthermore, the mechanistic studies showed that DLX1 positively modulates transforming growth factor-β (TGF-β) signaling by upregulating PAI-1 and JUNB through direct interaction with SMAD4 in the nucleus upon TGF-β1 induction. Taken together, these data strongly suggest that DLX1 has a pivotal role in FOXM1 signaling to promote cancer aggressiveness through intensifying TGF-β/SMAD4 signaling in high-grade serous ovarian cancer cells.
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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".