Ethnicity affects EGFR and KRAS gene alterations of lung adenocarcinoma
Bibliographic record
Abstract
Mutations or copy number gains (CNGs) of the EGFR and KRAS genes are representative alterations in lung adenocarcinomas that are individually associated with patient characteristics such as ethnicity, smoking status and gender. However, the effects of combinations of these genetic alterations have not been statistically examined. The present study analyzed previously examined lung adenocarcinoma cases in Asian (n=166) and non‑Asian (n=136) individuals in whom all four EGFR and KRAS alterations had been studied. The polynomial logistic regression models were used following adjustment for gender and smoking status, and using patients without any type of EGFR/KRAS alterations as a reference. Between the two ethnic groups, EGFR CNGs (gEGFR) occurred more frequently than EGFR mutations (mEGFR) (46 vs. 38% in Asians; 21 vs. 10% in non‑Asians), whereas KRAS mutations (mKRAS) were more frequent than KRAS CNGs (gKRAS) (13 vs. 7% and 35 vs. 4%, respectively). Additionally, gEGFR and gKRAS occurred significantly more frequently in respective mutant cases, and all EGFR alterations were almost exclusive of all KRAS alterations. The polynomial logistic regression models confirmed that all types of EGFR alterations were significantly more frequent among Asian individuals than among non‑Asian individuals, independent of gender and smoking status (odds ratios, 2.36‑6.67). KRAS alterations occurred less frequently among Asian individuals than among non‑Asian individuals, although a significant difference was not detected. The present study results indicated that the EGFR and KRAS profiles, including mutations and CNGs, differ between Asian and non‑Asian individuals with lung adenocarcinoma, suggesting that ethnicity strongly affects the molecular characteristics of lung adenocarcinoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".