Metformin inhibits growth and enhances radiation response of non-small cell lung cancer (NSCLC) through ATM and AMPK
Bibliographic record
Abstract
BACKGROUND: We examined the potential of metformin (MET) to enhance non-small cell lung cancer (NSCLC) responses to ionising radiation (IR). METHODS: Human NSCLC cells, mouse embryonic fibroblasts from wild-type and AMP-activated kinase (AMPK) α1/2-subunit(-/-) embryos (AMPKα1/2(-/-)-MEFs) and NSCLC tumours grafted into Balb/c-nude mice were treated with IR and MET and subjected to proliferation, clonogenic, immunoblotting, cell cycle and apoptosis assays and immunohistochemistry (IHC). RESULTS: Metformin (2.5 μM-5 mM) inhibited proliferation and radio-sensitised NSCLC cells. Metformin (i) activated the ataxia telengiectasia-mutated (ATM)-AMPK-p53/p21(cip1) and inhibited the Akt-mammalian target of rapamycin (mTOR)-eIF4E-binding protein 1 (4EBP1) pathways, (ii) induced G1 cycle arrest and (iii) enhanced apoptosis. ATM inhibition blocked MET and IR activation of AMPK. Non-small cell lung cancer cells with inhibited AMPK and AMPKα1/2(-/-)-MEFs were resistant to the antiproliferative effects of MET and IR. Metformin or IR inhibited xenograft growth and combined treatment enhanced it further than each treatment alone. Ionising radiation and MET induced (i) sustained activation of ATM-AMPK-p53/p21(cip1) and inhibition of Akt-mTOR-4EBP1 pathways in tumours, (ii) reduced expression of angiogenesis and (iii) enhanced expression of apoptosis markers. CONCLUSION: Clinically achievable MET doses inhibit NSCLC cell and tumour growth and sensitise them to IR. Metformin and IR mediate their action through an ATM-AMPK-dependent pathway. Our results suggest that MET can be a clinically useful adjunct to radiotherapy in NSCLC.
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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) | 0.002 | 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".