FOXC1, the new player in the cancer sandbox
Bibliographic record
Abstract
// Fahed A. Elian 1 , Elizabeth Yan 1 and Michael A. Walter 1 1 Department of Medical Genetics, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada Correspondence to: Michael A. Walter, email: mwalter@ualberta.ca Keywords: transcription factor; breast cancer; basal-like Received: September 21, 2017 Accepted: October 28, 2017 Published: November 28, 2017 ABSTRACT In recent years, rapidly accumulating evidence implicates forkhead box C1 ( FOXC1 ) in cancer, especially in studies of basal-like breast cancer (BLBC). Other studies have followed suit, demonstrating that FOXC1 is not only a major player in this breast cancer subtype, but also in hepatocellular carcinoma (HCC), endometrial cancer, Hodgkin’s lymphoma (HL), and non-Hodgkin’s lymphoma (NHL). The FOXC1 gene encodes a transcription factor that is crucial to mesodermal, neural crest, and ocular development, and mutations found in FOXC1 have been found to cause dominantly inherited Axenfeld-Rieger Syndrome (ARS). Interestingly, while FOXC1 missense mutations that are associated with ARS usually reduce gene activity, increased FOXC1 function now appears to be often linked to more aggressive cancer phenotypes in BLBC, HCC, HL, and NHL. This review discusses not only the role of FOXC1 in cancer cell progression, proliferation, differentiation, and metastasis, but also the underlying mechanisms of how FOXC1 can contribute to aggressive cancer phenotypes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".