Relationship Between Breast Cancer Risk and Polymorphisms in CLOCK Gene: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Previous studies found that the circadian clock gene participated in the genesis and development of breast cancer. However, research findings on the relationship between polymorphisms in the CLOCK gene and breast cancer risk were inconsistent. This study performed a meta-analysis of the association between CLOCK gene polymorphisms and breast cancer risk. PubMed, Cochrane Library, and Embase databases were electronically searched to collect studies on the association between CLOCK gene polymorphisms and breast cancer risk from inception to February 14, 2022. The quality of the included literature was assessed using the Newcastle-Ottawa Scale. For statistical analysis, odds ratio (OR) and 95% confidence intervals (CIs) were calculated using STATA 14.0. In addition, publication bias was performed by the funnel diagram and the Harbord's regression test. And sensitivity analysis was assessed by the trim and fill method. A total of 6 eligible studies, including 10,164 subjects (5488 breast cancer cases and 4676 controls), were screened in this meta-analysis. Though we did not find a significant association between the polymorphisms in the overall CLOCK gene with breast cancer risk [OR (95%CI) = 0.98 (0.96, 1.01), P = 0.148], we found that compared with T/T types of rs3749474 in CLOCK, T/C and C/C types of rs3749474 were associated with lower risk of breast cancer [OR (95%CI) = 0.93 (0.88, 0.98), P = 0.003]. The sensitivity analysis confirmed the robustness of the results. The funnel plot showed no significant publication bias. Polymorphisms in the CLOCK gene might be associated with breast cancer risk. More studies are needed to confirm the conclusion.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".