Fibroblast growth factor receptor 2 promotes the proliferation, migration, and invasion of ectopic stromal cells via activation of extracellular-signal-regulated kinase signaling pathway in endometriosis
Bibliographic record
Abstract
Endometriosis is defined as the presence of endometrial tissues with cancer-like features in extrauterine locations. Fibroblast growth factor receptor 2 (FGFR2) is a tyrosine kinase that is involved in cancer pathogenesis. This study aimed to determine the role of FGFR2 in endometriosis. A total of 29 pairs of ectopic and eutopic endometrial tissues were collected from women with endometriosis. Endometrial tissues from women with hysteromyomas were considered as normal controls. Primary ectopic stromal cells (ESCs) were isolated from the ectopic endometrium. The role of FGFR2 in ESCs was assessed using immunohistochemistry, polymerase chain reaction, cell counting kit-8 assay, EdU staining, flow cytometry, transwell assay, and western blotting. The following signaling pathways were detected using bioinformatic analysis and confirmed in vitro. By searching the GSE171154, GSE86543, and GSE77182 datasets, FGFR2 was identified as an upregulated overlapping gene in endometriosis. Compared to eutopic and normal endometria, FGFR2 was highly expressed in ectopic tissues. Transfection of primary ESCs with FGFR2 small interfering RNA (siRNA) repressed the viability and proliferation of cells and induced apoptosis. FGFR2 siRNA inhibited the migration, invasion, and transforming growth factor-β1-triggered epithelial-mesenchymal transition (EMT). Extracellular signal-regulated kinase (ERK) signaling was found to be a downstream signaling pathway for FGFR2. The ERK1/2 inhibitor PD98059 was found to reverse the promoting effects of FGFR2 on ESC proliferation and invasion. FGFR2 silencing effectively inhibited the growth, migration, invasion, and EMT of ESCs. The effects of FGFR2 on endometriosis might be mediated via the activation of ERK signaling.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".