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. 2021 Jun 4;22(11):6083.
doi: 10.3390/ijms22116083.

Genetic Contribution of Endometriosis to the Risk of Developing Hormone-Related Cancers

Affiliations

Genetic Contribution of Endometriosis to the Risk of Developing Hormone-Related Cancers

Aintzane Rueda-Martínez et al. Int J Mol Sci. .

Abstract

Endometriosis is a common gynecological disorder that has been associated with endometrial, breast and epithelial ovarian cancers in epidemiological studies. Since complex diseases are a result of multiple environmental and genetic factors, we hypothesized that the biological mechanism underlying their comorbidity might be explained, at least in part, by shared genetics. To assess their potential genetic relationship, we performed a two-sample mendelian randomization (2SMR) analysis on results from public genome-wide association studies (GWAS). This analysis confirmed previously reported genetic pleiotropy between endometriosis and endometrial cancer. We present robust evidence supporting a causal genetic association between endometriosis and ovarian cancer, particularly with the clear cell and endometrioid subtypes. Our study also identified genetic variants that could explain those associations, opening the door to further functional experiments. Overall, this work demonstrates the value of genomic analyses to support epidemiological data, and to identify targets of relevance in multiple disorders.

Keywords: breast cancer; endometrial cancer; endometriosis; epithelial ovarian cancer; hormone-related cancers; mendelian randomization.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Forest plots showing beta (±standard error) and p-values of the single-SNP 2SMR analysis between endometriosis and endometrial, breast and ovarian cancers.
Figure 2
Figure 2
Scatter plots for 2SMR analyses of the causal effect of endometriosis on overall, clear cell and endometrioid ovarian cancer (o. c.). The slope of the line corresponds to the estimated MR effect (beta value) calculated with the inverse variance weighted method.

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