Causal effects of genetically predicted endometriosis on breast cancer: a two-sample Mendelian randomization study

Yan, Shuixin; Li, Jiadi; Chen, Jiafeng; Chen, Yan; Qiu, Yu; Zhou, Yuxin; Wu, Weizhu

doi:10.1038/s41598-023-43999-7

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…However, result of Bonferroni correction method indicated that the link between endometriosis and the risk of bladder cancer should be regarded as a false positive finding. Notebaly, a recent study showed a causal relationship between endometriosis and a decreased risk of breast cancer (odds ratio [OR] 0.95; 95% CI 0.90–0.99, p = 0.02) 42 . Given the application of the Bonferroni correction method, we advocate caution in interpreting the association between endometriosis and a reduced risk of overall breast cancer.…”

Section: Discussionmentioning

confidence: 99%

Genetically identification of endometriosis and cancers risk in women through a two-sample Mendelian randomization study

Zhang,

Yan

2024

Sci Rep

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Endometriosis is a prevalent and chronic inflammatory gynecologic disorder affecting approximately 6–10% of women globally, and has been associated with an increased risk of cancer. Nevertheless, previous studies have been hindered by methodological limitations that compromise the validity and robustness of their findings. In this study we conducted a comprehensive two-sample Mendelian randomization analysis to explore the genetically driven causal relationship between endometriosis and the risk of cancer. We conducted the analysis via the inverse variance weighted method, MR Egger method, and weighted median method utilizing publicly available genome-wide association study summary statistics. Furthermore, we implemented additional sensitivity analyses to assess the robustness and validity of the causal associations identified. We found strong evidence of a significant causal effect of endometriosis on a higher risk of ovarian cancer via inverse-variance weighted method (OR = 1.19, 95% CI 1.11–1.29, p < 0.0001), MR-Egger regression, and weighted median methodologies. Remarkably, our findings revealed a significant association between endometriosis and an increased risk of clear cell ovarian cancer (OR = 2.04, 95% CI 1.66–2.51, p < 0.0001) and endometrioid ovarian cancer (OR = 1.45, 95% CI 1.27–1.65, p < 0.0001). No association between endometriosis and other types of cancer was observed. We uncovered a causal relationship between endometriosis and an elevated risk of ovarian cancer, particularly clear cell ovarian cancer and endometrioid ovarian cancer. No significant associations between endometriosis and other types of cancer could be identified.

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Section: Discussionmentioning

confidence: 99%

Genetically identification of endometriosis and cancers risk in women through a two-sample Mendelian randomization study

Zhang,

Yan

2024

Sci Rep

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Reconstructing Molecular Networks by Causal Diffusion Do‐Calculus Analysis with Deep Learning

Wang,

Zhang,

Chen

et al. 2024

Advanced Science

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Quantifying molecular regulations between genes/molecules causally from observed data is crucial for elucidating the molecular mechanisms underlying biological processes at the network level. Presently, most methods for inferring gene regulatory and biological networks rely on association studies or observational causal‐analysis approaches. This study introduces a novel approach that combines intervention operations and diffusion models within a do‐calculus framework by deep learning, i.e., Causal Diffusion Do‐calculus (CDD) analysis, to infer causal networks between molecules. CDD can extract causal relations from observed data owing to its intervention operations, thereby significantly enhancing the accuracy and generalizability of causal network inference. Computationally, CDD has been applied to both simulated data and real omics data, which demonstrates that CDD outperforms existing methods in accurately inferring gene regulatory networks and identifying causal links from genes to disease phenotypes. Especially, compared with the Mendelian randomization algorithm and other existing methods, the CDD can reliably identify the disease genes or molecules for complex diseases with better performances. In addition, the causal analysis between various diseases and the potential factors in different populations from the UK Biobank database is also conducted, which further validated the effectiveness of CDD.

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Causal effects of genetically predicted endometriosis on breast cancer: a two-sample Mendelian randomization study

Cited by 2 publications

References 31 publications

Genetically identification of endometriosis and cancers risk in women through a two-sample Mendelian randomization study

Genetically identification of endometriosis and cancers risk in women through a two-sample Mendelian randomization study

Reconstructing Molecular Networks by Causal Diffusion Do‐Calculus Analysis with Deep Learning

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