Association between breast and endometrial cancer: a two-way Mendelian randomization study
Bibliographic record
Abstract
Abstract Background Breast cancer is the most prevalent cancer worldwide, and endometrial cancer is one of the most common gynecological cancers. Observational studies have shown an association between breast and endometrial cancers, but it may be influenced by potential confounding factors. Meanwhile, Mendelian randomization (MR) studies can overcome these confounding factors to assess causality. Methods We obtained breast cancer data (122,977 breast cancer cases and 105,974 controls) and endometrial cancer data (12,906 endometrial cancer cases and 108,979 controls) indirectly from the Breast Cancer Association Coalition (BCAC) and the Endometrial Cancer Association Consortium (ECAC) through the IEU Open GWAS program(https://gwas.mrcieu.ac.uk/). Then, Inverse variance weighting (IVW) was used as the primary analysis method. Sensitivity analyses were performed by multiple MR methods to ensure the accuracy of the results. Results Based on the IVW approach, our study found that patients with endometrial cancer have an increased risk of developing breast cancer (OR:1.072; 95% CI: 1.027–1.119; p = 0.002), especially the ER + subtype of breast cancer (OR:1.072; 95% CI: 1.029–1.129; p = 0.001). Similarly, reverse MR analyses showed an increased risk of endometrial cancer in breast cancer patients (OR:1.078; 95% CI: 1.018–1.141; p = 0.009), particularly in those who were ER+ (OR:1.075; 95% CI: 1.015–1.137; p = 0.013). However, the bidirectional MR analyses did not reveal any proof of a connection between endometrial cancer and ER- breast cancer. Conclusions We found a bidirectional causal effect between breast and endometrial cancer, especially ER + breast cancer. Therefore, our study supports timely screening and prevention of endometrial cancer in breast cancer patients and vice versa. At the same time, we suggest further exploration of the potential pathogenic mechanisms between breast cancer and endometrial cancer.
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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 | 0.044 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".