Long noncoding RNA XIST regulates the EGF receptor to promote TGF-β1-induced epithelial–mesenchymal transition in pancreatic cancer
Bibliographic record
Abstract
BACKGROUND: This study focuses on the lncRNA XIST (X inactive-specific transcript), an lncRNA involved in multiple human cancers, and investigates the functional significance of XIST and the molecular mechanisms underlying the epithelial-mesenchymal transition (EMT) in pancreatic cancer (PC). METHODS: Clinical specimens from 25 patients as well as 5 human PC cell lines were analyzed for XIST, YAP, and microRNA(miR)-34a by quantitative real-time PCR (qRT-PCR) and immunohistochemistry. To investigate how XIST influences cell proliferation, invasiveness, and apoptosis in PC, we performed the CCK-8 assays, Transwell assays, and flow cytometry. Luciferase reporter assays, qRT-PCR, and Western blot were applied to prove that miR-34a directly binds to XIST. RESULTS: Up-regulation of XIST and Yes associated protein (YAP) and down-regulation of miR-34a were consistently observed in the clinical specimens and PC cell lines. Silencing XIST reduced the expression of YAP and suppressed transforming growth factor (TGF)-β1-induced EMT, while over-expression of XIST increased the expression of YAP and promoted EMT. In addition, inhibition of epidermal growth factor receptor (EGFR) hampered the XIST-promoted EMT. The results from the luciferase reporter assays confirmed that miR-34a directly targets XIST and suggested that XIST regulates cell proliferation, invasiveness, and apoptosis in PC by sponging miR-34a. CONCLUSIONS: XIST promotes TGF-β1-induced EMT by regulating the miR-34a-YAP-EGFR axis in PC.
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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".