The Polymorphism of EME1 Gene is Associated with an Increased Risk of Lung Cancer: A Case-Control Study from Chinese Population
Bibliographic record
Abstract
DNA double-strand breaks (DSBs) can lead to genomic instability and cancer susceptibility if unrepaired. EME1 is one of the key proteins that participate in the recognition and repair of DSBs in humans. We hypothesized that the exonic variants of EME1 are associated with lung cancer risk. In a two-stage case-control study of 1559 lung cancer patients and 1679 cancer-free controls, we genotyped two exonic variants of EME1(Glu69Asp: rs3760413T>G and Ile350Thr: rs12450550T>C) and analyzed their associations with risk of lung cancer. We found that the Asp variant genotypes conferred 1.35-folds risk of lung cancer compared to the Glu/Glu genotype (OR = 1.35, 95%CI = 1.18-1.56, P = 2.18 ƒ 10-5) in both stages. However, the SNP Ile350Thr was not confirmed to be associated with cancer risk in both stages. Moreover, by querying the gene expression database, we further found that the 69Asp variant genotypes confer a significantly lower mRNA expression of EME1 than the Glu/Glu genotype in 260 cases of lymphoblastoid cells (P=0.013). Our findings suggested that the SNP Glu69Asp of EME1 is associated with an increased risk of lung cancer, and may be a functional biomarker to predict lung cancer risk in Chinese. Validations in other ethnics are warranted.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".