Transcriptional Cooperation between the Transforming Growth Factor-β and Wnt Pathways in Mammary and Intestinal Tumorigenesis
Bibliographic record
Abstract
Transforming growth factor-beta (TGF-beta) and Wnt ligands function in numerous developmental processes, and alterations of both signaling pathways are associated with common pathologic conditions, including cancer. To obtain insight into the extent of interdependence of the two signaling cascades in regulating biological responses, we used an oligonucleotide microarray approach to identify Wnt and TGF-beta target genes using normal murine mammary gland epithelial cells as a model. Combination treatment of TGF-beta and Wnt revealed a novel transcriptional program that could not have been predicted from single ligand treatments and included a cohort of genes that were cooperatively induced by both pathways. These included both novel and known components or modulators of TGF-beta and Wnt pathways, suggesting that mutual feedback is a feature of the coordinated activities of the ligands. The majority of the cooperative targets display increased expression in tumors derived from either Min (many intestinal neoplasia) or mouse mammary tumor virus (MMTV)-Wnt1 mice, two models of Wnt-induced tumors, with nine of these genes (Ankrd1, Ccnd1, Ctgf, Gpc1, Hs6st2, IL11, Inhba, Mmp14, and Robo1) showing increases in both. Reduction of TGF-beta signaling by expression of a dominant-negative TGF-beta type II receptor in bigenic MMTV-Wnt1/DNIIR mice increased mammary tumor latency and was correlated with a decrease in expression of Gpc1, Inhba, and Robo1, three of the TGF-beta/Wnt cooperative targets. Our results indicate that the TGF-beta and Wnt/beta-catenin pathways are firmly intertwined and generate a unique gene expression pattern that can contribute to tumor progression.
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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".