Abstract 5535: C-EDRN and US-EDRN collaboration on systematic review and meta-analysis of the association between <i>RAD51</i> 135G/C genetic biomarker and cancer risk
Bibliographic record
Abstract
Abstract Introduction: The Early Detection Research Network (EDRN) is an initiative of the National Cancer Institute (NCI) to support the translation of biomarker research into clinical applications. The US-EDRN and China-EDRN (C-EDRN), led by CICAMS, China, have strong collaborations on discovering and validating molecular biomarkers for early detection, diagnosis, and prevention. Our collaboration projects promote scientific exchanges, visiting scientists, biomarker discoveries and validations, as well as system review. Here, we report the system review of a genetic marker, RAD51 135G/C polymorphism and cancer risk. Background: The RAD51 gene is essential for the repair of damaged DNA related to tumor development. Although some studies have investigated the association between RAD51135G/C polymorphism and the risk of different cancers, the results are conflicting rather than conclusive. Therefore, it is necessary to further perform the system review of the polymorphism related to cancer risk. Method: The PubMed, Embase, and Cochrane library databases were searched to identify eligible studies that were published in English up to July 2017. Two investigators independently screened, extracted the data, and evaluated the quality of all eligible studies using Newcastle-Ottawa Scale (NOS). Crude odds ratios (ORs) together with their corresponding 95% confidence interval (CI) were calculated to assess the strength of association under dominant and recessive models. Subgroup analyses were performed based on ethnicity and cancers. Begg's test was used to measure publication bias. Result: A total of 75 studies from 71 articles were finally qualified and enrolled in this meta-analysis. The publication year of selected studies ranged from 2002 to 2017. The pooled results involving 24,284 cases and 27,649 controls showed that RAD51 polymorphism was associated with increased cancer risk under both dominant and recessive models. In subgroup analysis, the association varied among different ethnicities and cancers. Significantly elevated cancer risk was observed in Caucasians under both models, and in Asians under dominant model. Breast cancer, hematologic malignances, ovarian cancer, endometrial cancer, prostate cancer also showed significant associations with RAD51 polymorphism. Begg's test results showed there were no publication bias in the study. Conclusion: This meta-analysis indicated that the RAD51135G/C polymorphism was significantly associated with the susceptibility of cancer and it may be utilized as a valuable biomarker in early diagnostics and risk assessment. Further efforts are needed to identify and validate this finding in prospective studies and to explore the potential function of the variants in different cancers for clinical applications. Citation Format: Run Chen, Wendy Wang, Wenqiang Wei, Sudhir Srivastava, Jie He. C-EDRN and US-EDRN collaboration on systematic review and meta-analysis of the association between RAD51 135G/C genetic biomarker and cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5535.
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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 | 0.045 | 0.145 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.017 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".