Association of CYP1A1, GSTM1 and GSTT1 gene polymorphisms with risk of non-small cell lung cancer in Andhra Pradesh region of South India
Bibliographic record
Abstract
BACKGROUND: Lung cancer is one of the most preventable causes of death globally both in developed and developing countries. Although it is well established that smokers develop lung cancer, there are some smokers who are free from the disease risk. The predisposition to lung cancer is attributed to genetic polymorphisms in xenobiotic metabolizing genes. Reports on assessment of xenobiotic metabolizing genes like Cytochrome P 450 1A1 (CYP1A1), Glutathione -S -transferase M1 (GSTM1) and T1 (GSTT1) polymorphisms from India are meagre, and reports from Andhra Pradesh are lacking. METHODS AND RESULTS: Assessment of polymorphisms in CYP1A1, GSTM1 and GSTT1 in NSCLC patients and healthy individuals specific to population of Andhra Pradesh, a South Indian state was attempted by multiplex PCR and RFLP, and this is the first study which tried to correlate oxidative stress with the polymorphisms in xenobiotic metabolizing genes. Results showed that CYP1A1 m1 'CC' genotype was significantly associated with lung cancer susceptibility with a 2.3-fold risk, CYP1A1 m2 'AG' gene polymorphisms with 8.8-fold risk and GSTT1 (-/-) genotype demonstrated a twofold risk of disease susceptibility. CONCLUSIONS: A combined role of genetic polymorphisms and smoking status can be attributed for the cause of lung cancer. Further, the association between oxidative stress and genetic polymorphisms showed a correlation between GSTT1 and super oxide dismutase activity; CYP1A1 m1, m2 and GSTT1 with glutathione peroxidase activity; CYP1A1 m1 and GSTM1 with melondialdehyde levels; and CYP1A1 m1 and GSTT1 with 8-oxo-7,8-dihydro-2'-deoxyguanosine. A higher risk of lung cancer seems to be associated with combined gene polymorphisms of phase I and phase II enzymes than that ascribed to single gene polymorphism.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".