Cell Markers Present in the TGF-β-Activated Transdifferentiation of Normal Fibroblasts to Cancer-Associated Fibroblast in Endometrial Cancer
Bibliographic record
Abstract
Introduction: Cancer-associated fibroblasts (CAFs) are an essential component of carcinogenesis. The biological origins of CAFs in humans depend on the histotype of the tumour and the region where it first originated, and thus CAFs could be derived from many different cell types. Normal fibroblasts (NFs) are abundant in the endometrium and are highly susceptible to transdifferentiation to CAFs through TGF-β activation. This study aims to identify the cell markers present in the TGF-β signalling pathways for the transition of NFs to CAFs in endometrial cancer (EC). Methods: EC will be chemically induced in ICR (Institute of Cancer Research) mice with N-methyl-N-nitrosourea (MNU) and a 17β-estradiol (E2) diet. Cancer progression will be monitored using magnetic resonance imaging (MRI) at a field of 4.7 T. CAFs will then be isolated from the TME using PDGFRα as the cell marker. Immunohistochemistry (IHC) staining will be used on EC tumour cells to identity the presence the location of cell markers phosphorylated Smad2/3 (pSmad2/3), ERK1/2, and PI3K. Anticipated Results: CAF cells are expected to test positive for markers expressed in PDGFRα mediated signalling pathways. Presence of pSmad2/3 is expected to increase over time as usage of the canonical pathway increases in CAF establishment and cancer progression. Non-canonical pathway activation would show levels of ERK2/3 and PI3K. Discussion: pSmad2/3 levels will be examined to determine the usage of the canonical pathway in CAF expansion. Detection of pSmad 2/3 or PI3K/ERK2/3 allows for targeted therapy on the appropriate TGF-ß pathway to block CAF production, thus stopping tumour progression. Suppression of the pathways by targeting specific biomarkers such as PTEN to inhibit mTOR or CAV-1 inhibitors could normalize an upregulated or downregulated TGF-ß pathway. Conclusion: Identifying the key cell markers in the transdifferentiation of NFs allows for the targeting of specific proteins that play a role in the signalling pathways. Standardizing identification of significant cell markers in CAF establishment improves individualized treatment to the cancer patient. Treatment(s) would target the cell markers involved to prevent further CAF proliferation and tumour development
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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 | 0.001 | 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 | 0.001 |
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".