Impacts of NRF2 activation in non–small‐cell lung cancer cell lines on extracellular metabolites
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Bibliographic record
Abstract
Aberrant activation of NRF2 is as a critical prognostic factor that drives the malignant progression of various cancers. Cancer cells with persistent NRF2 activation heavily rely on NRF2 activity for therapeutic resistance and aggressive tumorigenic capacity. To clarify the metabolic features of NRF2-activated lung cancers, we conducted targeted metabolomic (T-Met) and global metabolomic (G-Met) analyses of non-small-cell lung cancer (NSCLC) cell lines in combination with exome and transcriptome analyses. Exome analysis of 88 cell lines (49 adenocarcinoma, 14 large cell carcinoma, 15 squamous cell carcinoma and 10 others) identified non-synonymous mutations in the KEAP1, NRF2 and CUL3 genes. Judging from the elevated expression of NRF2 target genes, these mutations are expected to result in the constitutive stabilization of NRF2. Out of the 88 cell lines, 52 NSCLC cell lines (29 adenocarcinoma, 10 large cell carcinoma, 9 squamous cell carcinoma and 4 others) were subjected to T-Met analysis. Classification of the 52 cell lines into three groups according to the NRF2 target gene expression enabled us to draw typical metabolomic signatures induced by NRF2 activation. From the 52 cell lines, 18 NSCLC cell lines (14 adenocarcinoma, 2 large cell carcinoma, 1 squamous cell carcinoma and 1 others) were further chosen for G-Met and detailed transcriptome analyses. G-Met analysis of their culture supernatants revealed novel metabolites associated with NRF2 activity, which may be potential diagnostic biomarkers of NRF2 activation. This study also provides useful information for the exploration of new metabolic nodes for selective toxicity towards NRF2-activated NSCLC.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it