Influence of <i>Lysyl oxidase</i> Polymorphisms in Cancer Risk: An Updated Meta-analysis
Bibliographic record
Abstract
Background: The aim of this study was to investigate associations between polymorphisms in the Lysyl oxidase (LOX) gene with susceptibility to cancer. The role of LOX in carcinogenesis has prompted several association studies in various cancer types; however the outcomes of these studies have been inconsistent. Thus, we performed a meta-analysis to obtain more precise estimates. Materials and Methods: A literature search yielded 14 articles from which we examined five cancer groups: breast, bone, lung, gastrointestinal, and gynecological cancers. For each cancer group, pooled odds ratios (ORs) and confidence intervals (95% CIs) were calculated using standard genetic models. High significance (p-value for association [pa] < 0.00001), homogeneity (I2 = 0%), and high precision of effects (CI difference [CID] <1.0 [upper CI − lower CI]) comprised the three criteria for strength of evidence. We used sensitivity analysis to assess robustness of the outcomes. Results: We generated 28 comparisons from which 13 were significant (pa < 0.05), indicating increased risk, (OR >1.00) found in all cancer groups except breast (pa = 0.10–0.91). Of the 13, three met all criteria (core) for strength of evidence (pa < 0.00001, CIDs 0.49–0.56 and I2 = 0%), found in dominant/codominant models of gynecological cancers (ORs 1.52–1.62, 95% CIs 1.26–1.88) and codominant model of lung cancer (OR 1.44, 95% CI 1.19–1.74). These three were deemed robust. Conclusion: Based on the three core outcomes, associations of LOX 473G/A with lung, ovarian, and cervical cancers indicate 1.4–1.6-fold increased risks, underpinned by robustness and high statistical power at the aggregate level.
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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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.058 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".