Mitochondrial DNA mutations in respiratory complex‐I in never‐smoker lung cancer patients contribute to lung cancer progression and associated with <i>EGFR</i> gene mutation
Bibliographic record
Abstract
Mitochondrial DNA (mtDNA) mutations were reported in different cancers. However, the nature and role of mtDNA mutation in never-smoker lung cancer patients including patients with epidermal growth factor receptor (EGFR) and KRAS gene mutation are unknown. In the present study, we sequenced entire mitochondrial genome (16.5 kb) in matched normal and tumors obtained from 30 never-smoker and 30 current-smoker lung cancer patients, and determined the mtDNA content. All the patients' samples were sequenced for KRAS (exon 2) and EGFR (exon 19 and 21) gene mutation. The impact of forced overexpression of a respiratory complex-I gene mutation was evaluated in a lung cancer cell line. We observed significantly higher (P = 0.006) mtDNA mutation in the never-smokers compared to the current-smoker lung cancer patients. MtDNA mutation was significantly higher (P = 0.026) in the never-smoker Asian compared to the current-smoker Caucasian patients' population. MtDNA mutation was significantly (P = 0.007) associated with EGFR gene mutation in the never-smoker patients. We also observed a significant increase (P = 0.037) in mtDNA content among the never-smoker lung cancer patients. The majority of the coding mtDNA mutations targeted respiratory complex-I and forced overexpression of one of these mutations resulted in increased in vitro proliferation, invasion, and superoxide production in lung cancer cells. We observed a higher prevalence and new relationship between mtDNA alterations among never-smoker lung cancer patients and EGFR gene mutation. Moreover, a representative mutation produced strong growth effects after forced overexpression in lung cancer cells. Signature mtDNA mutations provide a basis to develop novel biomarkers and therapeutic strategies for never-smoker lung cancer patients.
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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".