KREMEN2 promotes the proliferation and the metastasis through activating PI3K/AKT/mTOR signaling pathway in non-small cell lung cancer
Bibliographic record
Abstract
Our purpose was to explore the role and regulatory mechanisms of kringle containing transmembrane protein 2 (KREMEN2) in the development and progression of non-small cell lung cancer (NSCLC). KREMEN2 expression levels were higher in NSCLC tissues and cells than in normal tissues and cells. Down-regulation of KREMEN2 by siRNAs suppressed proliferation, migration, invasion, and epithelial mesenchymal transition (EMT), and accelerated apoptosis in NSCLC cells. Furthermore, KREMEN2 knockdown repressed PI3K/AKT/mTOR signaling, and KREMEN2 overexpression activated PI3K/AKT/mTOR signaling. Additionally, PI3K activator (740Y-P) treatment or PI3K overexpression reversed the inhibitory function of KREMEN2 knockdown on proliferation and metastasis, as well as the strengthened function of KREMEN2 knockdown on the apoptosis of NSCLC cells. Moreover, KREMEN2 suppressed tumor growth by inhibiting PI3K/AKT/mTOR signaling in mice. The pharmacologic inhibitor of KREMEN2 (genistein) was also demonstrated to suppress tumor growth in mice. In conclusion, our study suggested that KREMEN2 knockdown could repress the proliferative, migratory, and invasive capacity, as well as EMT, while accelerating the apoptotic capacity of NSCLC cells by inhibiting PI3K/AKT/mTOR 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) | 0.000 | 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".