RETRACTED ARTICLE: Shp2 positively regulates cigarette smoke-induced epithelial mesenchymal transition by mediating MMP-9 production
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Cigarette smoke (CS) is a major risk factor for the development of lung cancer and chronic obstructive pulmonary disease (COPD). Epithelial-mesenchymal transition (EMT) commonly coexists in lung cancer and COPD. CS triggers many factors including matrix metalloproteinases (MMPs) production, contributing to EMT progression in the lungs. Here, how Shp2 signaling regulates the CS-induced MMP-9 production and EMT progression were investigated in mouse lungs and in pulmonary epithelial cell cultures (NCI-H292) found CS induced MMP-9 production, EMT progression (increased vimentin and α-SMA; decreased E-cadherin) and collagen deposition in lung tissues; cigarette smoke extract (CSE) induced MMP-9 production and EMT-related phenotypes in NCI-H292 cells, which were partially prevented by Shp2 KO/KD or Shp2 inhibition. The CSE exposure induced EMT phenotypes were suppressed by MMP-9 inhibition. Recombinant MMP-9 induced EMT, which was prevented by MMP-9 inhibition or Shp2 KD/inhibition. Mechanistically, CS and CSE exposure resulted in ERK1/2, JNK and Smad2/3 phosphorylation, which were suppressed by Shp2 KO/KD/inhibition. Consequentially, the CSE exposure-induced MMP-9 production and EMT progression were suppressed by ERK1/2, JNK and Smad2/3 inhibitors. Thus, CS induced MMP-9 production and EMT resulted from activation of Shp2/ERK1/2/JNK/Smad2/3 signaling pathways. Our study contributes to the underlying mechanisms of pulmonary epithelial structural changes in response to CS, which may provide novel therapeutic solutions for treating associated diseases, such as COPD and lung cancer.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.071 | 0.027 |
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".