Abstract

Objective

   To explore the cause-effect relationship between uterine diseases (UDs)
   and breast cancer (BC) and underlying mechanism of the cause-effect
   relationship, enhance understanding of the association between BC and
   UDs.

Methods

   A two-sample bidirectional Mendelian randomization (MR) analysis was
   conducted. We obtained summary statistics data from GWAS for BC,
   endometriosis, endometrial cancer (EC), uterine leiomyoma (UL), uterine
   polyps (UP), and cervical cancer (CC). Independent SNPs were selected
   as instrumental variables (IVs) for each disease. The inverse variance
   weighted (IVW) method was primary used for estimating the causal
   association between UDs and BC. To further evaluate the consistency and
   dependability of the results, we also utilized the weighted median,
   weighted mode, simple mode, and MR-Egger methods, along with
   sensitivity analyses. Furthermore, a supplementary analysis focusing on
   the variants linked to BC and UDs was conducted. This involved
   identifying corresponding genes and subsequently performing KEGG/GO
   analyses to investigate potential molecular mechanisms.

Results

   The results indicated significant associations between genetic
   susceptibility to endometriosis, EC, and UL with BC risk. The odds
   ratios (ORs) were as follows: endometriosis at 0.963 (95 % CI,
   0.942–0.984; p = 7.11e-5), EC at 1.056 (95 % CI, 1.033–1.081;
   p = 2.39e-6), and UL at 1.027 (95 % CI, 1.006–1.048; p = 0.010).
   Conversely, the predisposition to BC inferred from genetic factors was
   markedly correlated with an elevated risk of EC indicated by an OR of
   1.066 (95 % CI, 1.019–1.116; p = 0.006), and was correlated with UP
   risk (OR, 1.001,95 % CI, 1.000–1.002; p = 0.001).

   Sensitivity analyses provided weak evidence for these effects,
   suggesting that the study's outcomes are consistent and trustworthy.
   Further analysis of the genetic variants associated with BC, and these
   related genes are enriched in Cellular senescence, GnRH secretion,
   Phosphatidylinositol signaling system, and so on.

Conclusion

   This study corroborates the existence of a reciprocal causal
   relationship between BC and EC, as well as highlighting the substantial
   correlations between a genetic susceptibility to UL and endometriosis
   with BC. BC may exert their influence on EC and UP through Cellular
   senescence, GnRH secretion, and other pathways. These discoveries offer
   fresh perspectives on the genetic pathogenesis of BC and UDs, and can
   guide future experimental studies. Additionally, they lay down a
   groundwork for the development of tailored preventative and therapeutic
   strategies moving forward.

   Keywords: Breast cancer, Uterine diseases, Mendelian randomization,
   Causal association

1. Introduction

   Breast cancer (BC) ranks as the most common cancer globally and is a
   primary contributor to death among women [[31]1]. Additionally, there
   is a noticeable increase in the incidence of other malignancies in the
   female reproductive system, such as cervical cancer (CC) and
   endometrial cancer (EC) [[32]1,[33]2]. Often diagnosed at advanced
   stages, these female tumors have limited treatment options, leading to
   higher recurrence rates and lower survival rates [[34]3]. Consequently,
   preventing and treating tumors in the female reproductive system pose
   significant challenges. The female malignancies exert a profound
   influence on both the physical health and mental state of women,
   notably diminishing their general life quality [[35]4]. Moreover, these
   malignancies have a unique location in the female reproductive system,
   affecting both the patients' health and lineage transmission [[36]5].
   However, the precise mechanisms underlying their development and
   progression are still unclear, and the risk factors associated with the
   diseases continue to evolve [[37]6]. Besides malignancies,
   endometriosis, uterine leiomyomas (UL) and uterine polyps (UP) are
   common non-cancerous gynecological uterine diseases (UDs). Emerging
   epidemiological evidence suggested that women with these conditions
   tend to exhibit an increased susceptibility to hormone-associated
   cancers, encompassing BC, EC, CC, and ovarian cancer (OC) [[38][7],
   [39][8], [40][9]]. Although the etiology and natural history of these
   conditions differ, accurate differential diagnosis is necessary due to
   potential overlapping symptoms [[41]10]. Therefore, it is crucial to
   understand the disease's etiology, identify high-risk populations,
   promote early detection, implement proactive intervention strategies,
   and provide effective treatment for the prevention and management of
   breast and gynecological malignancies.

   BC can be classified into four major subtypes according to hormonal
   receptor expression and histopathological indicators: luminal A,
   luminal B, HER2-overexpressing, and triple-negative BC (TNBC).
   Approximately 75 % of BC are estrogen receptor (ER)-positive, with the
   luminal subtypes (A and B) accounting for a large proportion of these
   ER-positive cases and generally exhibiting high ER expression. Estrogen
   has been demonstrated to facilitate the advancement of BC via
   ER-mediated signaling cascades [[42]11,[43]12]. The primary source of
   estrogen varies between premenopausal and postmenopausal women. In
   premenopausal women, the ovaries are the major source. Genetic factors
   have been found to influence the occurrence of both breast and ovarian
   cancers. Genome-wide association studies (GWAS) have identified over
   100 BC susceptibility loci and more than 20 OC susceptibility loci,
   with approximately 5 loci being associated with the susceptibility of
   both cancers [[44]13]. The uterus and breast are key organs of the
   female reproductive tract, and there are also potential associations
   between them. A systematic review had underscored the substantial
   impact of sialyltransferases, notably ST6Gal-I, in the progression of
   gynecological tumors, encompassing cancers of the cervix, uterus, and
   breast [[45]14]. Additionally, the presence of GnRH2 and its
   corresponding receptor has been observed in peripheral reproductive
   organs, including the ovaries, the uterus, and the mammary glands.
   Furthermore, GnRH2 and its receptor have been identified across a
   spectrum of human reproductive malignancy cells [[46]15]. Auxiliary
   hormonal treatments, including tamoxifen and fulvestrant that inhibit
   estrogen/ER-mediated signaling, constitute the primary therapeutic
   approach for individuals with ER-positive BC [[47]16]. However, the
   agonistic effects of tamoxifen on the endometrium can contribute to
   endometrial proliferation, hyperplasia, polyp formation, and cancer.
   Consequently, prolonged administration of tamoxifen has been linked to
   an elevated risk of EC or UP [[48]17]. In light of their
   interconnection and potential shared mechanisms, understanding the
   cause-effect relationship between UDs and BC holds great promise for
   advancing the prevention, early detection, and management of UDs and
   BC. Further investigation is required to unravel the complexities of
   this cause-effect relationship and explore its potential clinical
   applications.

   The intricate web of genetic and environmental elements contributes to
   the development of these female malignancies, but the underlying
   genetic basis remains incompletely understood. Previous research on the
   association between BC and UDs has limitations, such as reliance on
   case-control or observational designs, small sample sizes, and the
   potential for reverse causation. Therefore, the causal relationship
   between BC and UDs remains uncertain, emphasizing the necessity for
   additional research. Prospective studies and randomized controlled
   trials provide valuable insights into causality, but conducting such
   studies can be time-consuming, resource-intensive, and ethically
   challenging.

   Mendelian randomization (MR) is a research approach employed to discern
   potential causal links between exposures and outcomes [[49]18]. This
   methodology leverages genetic variants, specifically single nucleotide
   polymorphisms (SNPs), that correlate with the factor of interest,
   employing them as instrumental variables (IVs) to infer causality
   [[50]19]. The random distribution of alleles ensures that the estimated
   causal effects remain theoretically unaffected by confounding factors
   and reverse causation [[51]20]. IVs can be derived from readily
   accessible summary statistics obtained from GWAS, which are extensively
   employed in Two-Sample Mendelian Randomization (2SMR) [[52]21].

   This study aimed to comprehensively evaluate the bidirectional causal
   effects between breast cancer (BC) and a range of uterine diseases
   (UDs), including endometriosis, endometrial cancer (EC), uterine
   leiomyoma (UL), uterine polyps (UP), and cervical cancer (CC). Hence, a
   2SMR analysis was employed. Furthermore, a supplementary analysis was
   conducted to explore the potential molecular mechanisms underlying the
   observed associations. This involved identifying the genetic variants
   associated with both BC and UDs, and subsequently performing KEGG and
   GO analyses on the corresponding genes. Additionally, the study
   investigated the differential relationships between BC subtypes (ER+
   and ER-) and EC. The findings from these comprehensive investigations
   have the potential to guide future research, inform clinical practice,
   and ultimately contribute to improved public health outcomes by
   expanding our comprehension of the intricate dynamics between UDs and
   BC.

2. Materials and methods

2.1. Study design

   A bidirectional 2SMR design was employed to investigate the causal
   relationship between uterine diseases and breast cancer ([53]Fig. 1).
   The MR analysis was based on the following assumptions: 1) the SNPs
   derived from GWAS served as IVs and were associated with the exposures
   of interest; 2) the IVs were not correlated with confounding factors
   that might skew the causal inferences; 3) the effect of IVs on the
   outcome was exclusively transmitted via the exposure being studied. The
   bidirectional MR analysis adhered to the STROBE-MR guidelines
   [[54]22,[55]23].

Fig. 1.

   [56]Fig. 1
   [57]Open in a new tab

   Schematic representation of the MR procedure.

2.2. Data source

   The research data utilized in this investigation were sourced from the
   IEU OPEN GWAS PROJECT [[58]24], an open-access database accessible at
   [59]https://gwas.mrcieu.ac.uk/(accessed on June 13, 2023). Prominent
   SNPs associated with uterine diseases and breast cancer was selected,
   including endometriosis, EC, UL, UP, and CC, as genetic IVs for this
   bidirectional two-sample MR analysis. The necessary ethical clearance
   for the primary research was secured, confirming adherence to ethical
   protocols. To minimize potential biases stemming from population
   heterogeneity, this analysis was restricted to the European population.
   Detailed information regarding the data sources are presented in
   [60]Table 1.

Table 1.

   Details of data sources.
   Exposures/Outcomes GWASID Ethnicity Sample size (cases/controls) SNPs
   Number
   Endometriosis finn-b-N14_ENDOMETRIOSIS European 8,288/68,969 16,377,306
   Endometriosis cancer ebi-a-GCST006464 European 12,906/108,979 9,470,555
   Uterine leiomyomas finn-b-CD2_BENIGN_LEIOMYOMA_UTERI European
   18,060/105,519 16,379,784
   Uterine polyps ukb-b-12944 European 5,419/457,591 9,851,867
   Cervical cancer finn-b-C3_CERVIX_UTERI_EXALLC European 1,648/99,321
   16,378,927
   Breast cancer ieu-a-1126 European 122,977/105,974 10,680,257
   ER- Breast cancer ieu-a-1128 European 21,468/105,974 10,680,257
   ER + Breast cancer ieu-a-1127 European 69,501/105,974 10,680,257
   HPV E7 Type 16 prot-c-2623_54_4 European 997 501,428
   HPV E7 Type 18 prot-c-2624_31_2 European 997 501,428
   [61]Open in a new tab

2.3. Selection of IVs

   To meet the assumptions of MR, a set of criteria were applied for IV
   selection: 1) Inclusion of SNPs was predicated on their robust
   correlation with the exposure factors, with a genome-wide significance
   threshold of p < 5 × 10^−8; 2) LD clumping was performed with criteria
   of R^2 < 0.001 and physical genetic distance >10,000 kb to select
   independent SNPs; 3) SNPs with minor allele frequency (MAF) < 0.03 were
   removed; 4) The PhenoScanner GWAS repository was leveraged to detect
   and eliminate SNPs correlated with possible confounders, including
   alcohol intake and smoking behavior; 5) MR Steiger test was utilized to
   exclude the SNPs exhibiting reverse causation [[62]25].

   It is important to note that including fewer than 10 independent SNPs
   as IVs may reduce the statistical power of MR analysis [[63]19]. To
   address this concern, a relaxed instrument threshold (p < 1 × 10^−5)
   was employed, which has been used in previous studies, to ensure an
   adequate number of SNPs, particularly in cases where traits had limited
   SNP coverage.

2.4. Statistical analysis

2.4.1. Two-sample MR analysis

   The principal methodology employed to assess the causality of the
   exposure's impact on the outcome was the inverse variance weighted
   (IVW) [[64]18]. An OR value > 1 signified that the exposure acted as a
   risk factor, whereas an OR < 1 implied a protective role for the
   exposure. To bolster the reliability of the findings, an outlier
   detection process was implemented via the MR-PRESSO method [[65]26].
   Additionally, weighted median method, MR-Egger regression, weighted
   mode method, and simple mode method were used to validate the
   robustness of the IVW results [[66]27].

2.4.2. Horizontal pleiotropy analysis

   The MR-Egger regression analysis was conducted to detect any horizontal
   pleiotropy, where a p-value below 0.05 suggests its possible
   occurrence. Furthermore, if the intercept term in the MR-Egger
   regression is close to zero, it indicates a lack of evidence for
   horizontal pleiotropy.

2.4.3. Heterogeneity testing

   To investigate potential heterogeneity due to different genetic
   variants, Cochran's Q statistic was applied to evaluate heterogeneity.
   A significant p-value from the Cochran's Q test at less than 0.05 would
   signal heterogeneity among the results, suggesting that the causal
   effects may vary among the IVs. If heterogeneity was detected, the IVW
   method under a multiplicative random effects framework was utilized to
   provide the effect size estimates [[67]28].

2.4.4. Sensitivity analysis

   To further assess the sensitivity, a leave-one-out approach was
   implemented. Specifically, each genetic variant (SNP) was
   systematically removed, and the resultant estimates were recomputed
   without the omitted variant. These recalculated estimates were
   subsequently contrasted with the aggregate estimate. Such an analysis
   is instrumental in discerning the sway of each individual SNP on the
   comprehensive causal inference and in substantiating the stability of
   the outcomes.

2.4.5. Reverse MR analysis

   To assess whether uterine diseases have a causal effect on breast
   cancer, reverse MR analysis was performed via the aforementioned MR
   analysis methods. Each individual uterine-related disease was
   considered as the outcome variable, and breast cancer was analyzed as
   the exposures in separate analyses. The results of this study are
   depicted through ORs and 95 % confidence intervals (CIs), with a
   threshold at α = 0.05. The entire suite of statistical computations was
   carried out via R version 4.1.3, and the software packages TwoSample MR
   and MR-PRESSO were utilized.

2.4.6. GO/KEGG analysis

   Moreover, compiled SNPs for UDs and BC have been, identifying those
   SNPs with a consistent direction of effect on the outcome respectively.
   Then we created a Venn diagram to visualize the data and obtained the
   common SNPs, for which we searched their associated genes in the NCBI
   database ([68]https://www.ncbi.nlm.nih.gov/). Subsequently, GO and KEGG
   enrichment analysis were performed in order to assist us in
   understanding the potential mechanisms of progression and pathogenesis
   between BC and UDs. Venn diagram and GO/KEGG results was portrayed by
   VennDiagram and ggplot2 (version 3.3.6).

3. Results

3.1. Selection of IVs

   A total of 46, 62, 103, 15, and 23 distinct significant SNPs were
   pinpointed for endometriosis, EC, UL, UP, and CC, respectively.
   However, some SNPs were excluded because they contained palindromic
   structures, suggested reverse causality, or were linked to possible
   confounding factors. After excluding the above SNPs, we performed
   causal estimation using 34, 55, 77, 13, and 21 SNPs for endometriosis,
   EC, UL, UP, and CC, respectively ([69]Tables S1–5).

   Notably, during the assessment of UP and BC, the orientation of the OR
   in the MR-Egger analysis was deviated from other methods. To address
   this issue, the p-value threshold was decreased to p < 1e^−8 [[70]29].
   Consequently, only 1 SNPs remained identified as IVs in the UP to BC
   analysis, which could not undergo further MR analysis ([71]Table S6).
   Therefore, an investigation of UP on BC in the forward MR analysis was
   not conducted. Additionally, an additional 142 SNPs were screened from
   the human genotype-phenotype association database that were associated
   with BC based on hypotheses 1, 2, and 3. Furthermore, PhenoScanner was
   utilized to assess the SNP dataset and removed 4 SNPs due to their
   association with confounding variables. As a result, 138 SNPs were
   selected as genetic proxies for BC ([72]Fig. 1, [73]Table S7).

3.2. The causal effect of uterine diseases on breast cancer

   Initially, the genetic correlation between each uterine disease and the
   risk of BC was estimated. The MR analyses results are depicted in
   [74]Fig. 2 and Supplementary Figures. The findings indicated a
   substantial link between genetically anticipated BC and heightened
   susceptibility to endometriosis in the IVW model, with an OR of 0.963
   (95 % CI, 0.942–0.984; p = 7.11^e−5). Consistent results were observed
   in the other three analyses, although statistical significance was not
   reached in the MR-Egger analysis. The MR-PRESSO analysis found no
   evidence of outliers, indicating the robustness and reliability of the
   results ([75]Table S10). Furthermore, the Cochran Q test revealed no
   significant heterogeneity across the individual SNP estimates
   (Q = 29.4; p = 0.598), and the MR-Egger regression analysis showed no
   signs of pleiotropy (intercept p = 0.5; [76]Table 2).

Fig. 2.

   [77]Fig. 2
   [78]Open in a new tab

   Associations of uterine diseases with risk of breast cancer.

Table 2.

   Associations of UDs with risk of BC in sensitivity analyses.
   Uterine diseases Heterogeneity test MR-Egger
     __________________________________________________________________

   Heterogeneity test IVW
     __________________________________________________________________

   Pleiotropy test
     __________________________________________________________________

   Q Q df Q P value Q Q df Q P value Egger intercept SE P value
   Endometriosis 29.4 32 0.598 29.9 33 0.623 −0.002 0.003 0.500
   Endometrial cancer 57.8 53 0.301 59.4 54 0.285 0.004 0.004 0.236
   Uterine leiomyoma 88.5 75 0.136 89.3 76 0.141 0.002 0.002 0.427
   Cervical cancer 27.6 19 0.091 27.7 20 0.115 −0.001 0.004 0.761
   [79]Open in a new tab

   The IVW model results outcomes demonstrated a notable link between BC
   and EC (OR, 1.056 [95 % CI, 1.033–1.081]; p = 2.39e-6), indicating a
   shared genetic predisposition for the two diseases. Besides, a positive
   correlation was observed between BC and UL (OR, 1.027 [95 % CI,
   1.006–1.048]; p = 0.010). The analysis did not uncover any significant
   heterogeneity, pleiotropy, or outliers. ([80]Table 2 and [81]Table S8).

   However, the IVW analysis did not provide evidence of a link between
   genetic propensity for BC and CC. Similar to the other analyses, no
   statistically significant pleiotropy or heterogeneity was observed in
   the CC analysis ([82]Table 2 and [83]Table S8). It should be emphasized
   that additional research is necessary to substantiate the association
   between BC and CC, as more research is required in this area.

3.3. The causal effect of breast cancer on uterine diseases

   In the reverse-direction MR analyses, MR-PRESSO analysis was utilized
   to detect potential outliers that could potentially introduce bias.
   Outliers were detected in the analysis of endometriosis, EC, UL, and
   UP, indicating the presence of IVs that may influence the associations
   ([84]Table 3 and [85]Table S 9,11). The results of the
   reverse-direction MR analyses are shown in [86]Fig. 3 and Supplementary
   Figures.

Table 3.

   Associations of BC with risk of UDs in sensitivity analyses.
   Uterine diseases Heterogeneity test MR-Egger
     __________________________________________________________________

   Heterogeneity test IVW
     __________________________________________________________________

   Pleiotropy test
     __________________________________________________________________

   Q Q df Q P value Q Q df Q P value Egger intercept SE P value
   Endometriosis 121.8 120 0.436 122.3 121 0.450 0.003 0.004 0.493
   Endometrial cancer 110.2 109 0.448 110.6 110 0.466 −0.002 0.003 0.575
   Uterine leiomyoma 104.5 102 0.412 107.9 103 0.352 0.006 0.003 0.074
   Uterine polyps 112.6 106 0.313 112.6 107 0.338 <0.001 <0.001 0.963
   Cervical cancer 138.6 123 0.159 138.7 124 0.173 −0.003 0.009 0.780
   [87]Open in a new tab

Fig. 3.

   [88]Fig. 3
   [89]Open in a new tab

   Associations of breast cancer with risk of uterine diseases.

   After correcting for these outliers, a noteworthy association was
   identified between the genetic predisposition to EC and BC was
   observed. This correlation remained significant in the IVW model,
   boasting an OR of 1.066 (95 % CI, 1.019–1.116; p = 0.006). Regarding
   the association between UP and BC (OR, 1.001,95 % CI, 1.000–1.002;
   p = 0.001), although the OR was close to 1, the p-value was less than
   0.05. This suggests that the observed association may indeed be real.
   Nevertheless, the interpretation of these outcomes requires prudence,
   and they should be in conjunction with other evidence and studies for a
   more comprehensive evaluation.

   On the other hand, no suggestive associations were found between
   genetic susceptibility to BC and other uterine diseases, including
   endometriosis, UL, or CC. These findings suggest that the genetic
   factors influencing BC may not affect on the developing of these
   specific UDs. However, the MR-Egger and Simple mode analyses suggested
   a potential association between BC and UL, with ORs of 0.895 (95 % CI,
   0.817–0.981; p = 0.020) and 0.843 (95 % CI, 0.716–0.993; p = 0.043),
   respectively. The analysis indicated no significant pleiotropy or
   heterogeneity in the UL analysis ([90]Table 3 and [91]Table S9).
   Interestingly, these findings contrast with the results of the forward
   MR analysis, where UL was negatively correlated with BC. It is worth
   noting that additional research is essential for validating the link
   between BC and UL.

   The Cochran Q test exposed no notable heterogeneity among the
   individual SNPs' estimates, signifying uniformity in the findings
   across various SNPs. Additionally, the MR-Egger regression analysis
   found no considerable pleiotropy for the five UDs analyzed, suggesting
   that no sizeable genetic determinants were concurrently influencing
   both BC and the UDs. ([92]Table 3 and [93]Table S9).

3.4. Sensitivity analysis

   Furthermore, extensive sensitivity analyses was executed to
   substantiate the causal link between UDs and BC. In addition,
   leave-one-out analyses was conducted to reinforce the observed
   causality and assess the possible influence of each IV on the outcomes.
   Such analysis revealed that the causality between UDs and BC was not
   predominantly influenced by any single SNP. (Supplementary figures).

3.5. GO/KEGG analyses

   To further investigate these relationships, genetic variant analysis
   was conducted. When BC was as the outcome event, 43 SNPs for
   endometriosis were picked out, 19 SNPs for EC, and 30 SNPs for UL.
   Detailed information on these genetic variants can be found in
   [94]Table S12. However, no common SNPs were found among these three
   exposures. On the other hand, when BC was considered as the exposure,
   60 SNPs for EC and 59 SNPs for UP were selected as outcome events. The
   details of BC-related genetic variants and their effects on the two
   subtypes of UDs can be found in [95]Table S13. Then SNPs that
   consistently showed a positive effect on both EC and UP (beta.outcome
   >0) were identified. These common SNPs were visually represented using
   a Venn diagram in [96]Fig. 4A. Further analysis involved the
   identification of 11 key genes corresponding to the 26 SNPs associated
   with BC. To explore the functional aspects of these disease-gene sets,
   GO and KEGG pathway enrichment analysis were conducted. The results
   revealed that these related genes are enriched in Cellular senescence,
   GnRH secretion, Phosphatidylinositol signaling system, and so on, as
   shown in [97]Fig. 4B.

Fig. 4.

   [98]Fig. 4
   [99]Open in a new tab

   Enrichment analysis of BC-related genes (A)Venn diagram of common genes
   (B) GO/KEGG enrichment analysis.

3.6. The causal effect of ER- and ER + breast cancer on endometrial cancer

   To further investigate the association between different subtypes of BC
   and EC, ER + BC and ER- BC were selected for exposure to further
   analyze their association with EC. After correcting for these outliers,
   a significant association between EC and ER + BC was observed. This
   association remained significant in the IVW model, with an OR of 1.070
   (95 % CI, 1.016–1.127; p = 0.010). And no suggestive associations were
   found between genetic susceptibility to ER- BC and EC ([100]Fig. 5).

Fig. 5.

   [101]Fig. 5
   [102]Open in a new tab

   Associations of ER-breast cancer and ER + breast cancer with risk of
   endometrial cancer.

   The Cochran Q test exposed no notable heterogeneity among the
   individual SNPs' estimates, signaling that the findings were congruent
   across various SNPs. Moreover, the MR-Egger regression analysis found
   no considerable pleiotropy, implying the absence of influential genetic
   factors ([103]Table S14). In addition, a suite of sensitivity analyses
   was executed to affirm the causal relationship (Supplementary figures).

4. Discussion

   Previous epidemiological studies have provided evidence of a
   co-occurrence of UDs and BC [[104]30], supporting the cause-effect
   relationship between UDs and BC. However, the depth mechanisms
   underlying these associations remain poorly understood. In this study,
   publicly available genetic data was utilized to investigate the causal
   relationships between UDs and BC. Using the bidirectional 2SMR
   approach, genetic variants were leveraged as IVs to assess causal
   effects while minimizing biases. This study yielded intriguing findings
   concerning the association between specific UDs and BC ([105]Fig. 6).

Fig. 6.

   [106]Fig. 6
   [107]Open in a new tab

   The association between specific uterine diseases and breast cancer.

   In the forward MR analysis, a noteworthy inverse association between
   genetically anticipated endometriosis and the likelihood of BC was
   observed. This unexpected result suggests that endometriosis may
   potentially have a protective effect against BC development. However,
   prior research has reported inconsistent results and diverse
   interpretations regarding the link between endometriosis and BC. A
   number of studies have suggested that women afflicted with
   endometriosis might be at an elevated risk of BC development, while
   others have suggested a potential protective effect of endometriosis
   against BC. The inconsistencies could be attributed to differences in
   study designs, sample sizes, and the populations under investigation.
   Variations in methodologies and criteria for diagnosing endometriosis
   and BC among different studies may contribute to the divergent
   associations observed. Furthermore, the age has also been a point of
   discrepancy among studies [[108][31], [109][32], [110][33]]. The
   underlying biological mechanisms linking these two conditions remain
   poorly understood and require further investigation.

   Moreover, these findings highlighted a substantial positive association
   between BC and EC, as well as a suggestive positive genetic correlation
   between BC and UL. It is well-known that EC, UL, and BC are
   estrogen-dependent tumors, with estrogen playing a crucial role in
   their development and progression [[111]34]. Interestingly, the
   positive association between EC and BC contradicted these finding of a
   negative association between endometriosis and BC. These findings
   diverge from previous studies that consistently demonstrated an
   increased risk of EC in individuals with endometriosis [[112]32]. It is
   also noteworthy that there is comorbidity between UL and endometriosis,
   which should be considered when managing these conditions [[113]27].
   The unexpected findings from this study pointed to a multifaceted
   interplay between these conditions, underscoring the necessity for
   additional studies to confirm and decipher the potential mechanisms
   driving these connections, as well as to resolve the existing
   discrepancies.

   In contrast, this analysis yield no indication of a causal link between
   BC and UP or CC. CC and BC exhibit distinct etiological mechanisms. CC
   is predominantly attributed to infection with human papillomavirus
   (HPV) [[114]35], whereas BC arises from intricate interactions
   involving genetic and biological factors. Furthermore, an in-depth
   analysis was conducted to explore the potential mutual correlation
   between high-risk HPV types 16 and 18 and BC. These findings
   consistently demonstrated the absence of any causal relationship
   between these specific subtypes and BC (Supplementary figures and
   [115]Table S15). Consequently, this study reveals no significant
   correlation between these two malignancies. Additional research is
   necessary to validate these results and explore potential genetic links
   between BC and CC. Considering UP, it is advisable to conduct further
   investigation into this association once a larger GWAS dataset for UP
   becomes available.

   In the reverse-direction MR analyses, a noteworthy correlation was
   identified between EC and BC was observed. As for the tie between the
   genetic propensity for UP and BC, although the OR was close to 1, the
   p-value was less than 0.05, suggesting a possible association.
   Tamoxifen, functioning as a selective estrogen receptor modulator
   (SERM), is frequently utilized in the treatment of BC as an adjuvant
   therapy. However, tamoxifen's agonistic effects on the endometrium can
   contribute to endometrial proliferation, hyperplasia, polyp formation,
   and cancer. Prolonged administration of tamoxifen has been linked to an
   increased likelihood of developing EC or UP [[116]36]. The KEGG
   enrichment analysis unveiled GnRH secretion as a potential mechanism.
   These findings align with observations suggesting a plausible
   association between tamoxifen use and an elevated risk of EC or UP.
   Interestingly, it has also been noted in clinical practice that BC
   patients who have not received tamoxifen treatment can still present
   with endometrial abnormalities. In order to further investigate the
   potential association between the use of SERMs and EC, the association
   between ER + BC cases treated with SERMs and ER- BC cases without SERM
   treatment was specifically examined. Consistent with previous findings,
   these results showed a significant association between ER + BC and EC,
   indicating it as a risk factor with an OR greater than 1. Conversely,
   no association was found between ER- BC and EC, which aligns with the
   previously reported conclusion that long-term use of tamoxifen is
   linked to a heightened susceptibility of EC. These findings provide
   further support for the notion that the association between SERMs,
   ER + BC, and EC warrants careful consideration.

   In contrast, no significant associations were found between BC and
   other UDs, including endometriosis, UL, or CC. These findings suggested
   that the genetic factors influencing BC may not impact the developing
   of these specific UDs. However, the MR-Egger and Simple mode analyses
   suggested a potential association between BC and UL. It is worth noting
   that previous research has reported a higher prevalence of UL in BC
   patients [[117]37], and the forward MR analysis identified UL as a risk
   factor for BC. However, in the reverse MR analysis, BC showed a
   negative association with UL, resulting in conflicting results.
   Moreover, in previous MR analysis, a genetic correlation between EC and
   UL has been found [[118]38]. These conflicting findings highlight the
   complexity of the relationship between BC and UL, which requires
   further investigation and validation.

   The uterus and breasts play vital roles in the hormonal regulation of
   various physiological processes. Hormonal imbalances can lead to the
   development of UDs and hormone-related cancers, including BC
   [[119]39,[120]40]. Enhancing our understanding of the interplay between
   BC and UDs will lead to improved clinical practices for individuals
   affected by these conditions. Further research in the field of BC and
   estrogen-related diseases holds promise in unraveling the complex
   connections between these conditions, exploring shared risk factors,
   and understanding the underlying biological mechanisms.

5. Limitations and prospects

   However, it is crucial to recognize the constraints inherent in MR
   analysis. Firstly, this analysis exclusively utilized GWAS data from
   individuals of European descent, which may restrict the applicability
   of the results to other ethnic groups. It is advisable that forthcoming
   studies aim for a more comprehensive validation across a broader
   spectrum of populations to ascertain the global relevance of the
   identified associations. Secondly, the present study conducted a 2SMR
   analysis supplemented by sensitivity analyses, it did not incorporate
   multivariable analyses to evaluate the impact of established comorbid
   conditions, such as physical activity or sleep patterns, on the
   observed relationship. This limits the comprehension of potential
   confounding factors. While the PhenoScanner GWAS database was used to
   identify and exclude possible confounders like alcohol consumption and
   smoking, the value of future research implementing multivariable
   analyses would be to thoroughly assess and corroborate the findings,
   ensuring a more meticulous scrutiny of this potential limitation.
   Moreover, due to the limited number of SNPs and their corresponding
   genes found in NCBI, there may be a potential bias in the GO/KEGG
   analysis. Therefore, further research, including large-scale
   epidemiological studies and functional studies at the cellular and
   molecular levels, is warranted to validate the findings and unravel the
   comprehensive interplay between UDs and BC, thus enhancing
   understanding for preventative strategies and refining prognostic
   capabilities. To sum up, while the current study offers important
   evidence suggesting a possible causal relationship between UDs and BC,
   it is imperative that future studies tackle the limitations discussed
   to bolster the robustness and applicability of the results.

6. Conclusion

   In conclusion, this bidirectional MR study revealed intriguing
   associations between UDs and BC. A potential protective effect of
   endometriosis against BC development and a positive association between
   BC and EC were observed. Additionally, there was evidence of a shared
   genetic basis between BC and UL. However, the reverse analysis showed
   conflicting results for the association between BC and UL. BC may exert
   their influence on EC and UP through Cellular senescence, GnRH
   secretion, Phosphatidylinositol signaling system, and other pathways.
   These findings present a fresh viewpoint for exploring the genetic
   origins of the BC and UDs, and they pave the way for subsequent
   experimental research. Moreover, they also contributed valuable new
   insights into the causal links between UDs and BC, enhancing the
   understanding of the knowledge of their etiology and informing
   strategies for prevention, diagnosis, and management.

Ethics statement

   This study did not require ethics approval because the data used had
   already been granted ethical approval by the original studies.

Data availability statement

   The datasets that were created and/or analyzed for this study can be
   obtained from the corresponding author upon request for legitimate
   reasons.

CRediT authorship contribution statement

   Zhipeng Liu: Writing – original draft. Min Jiang: Data curation. Taiyu
   Wang: Resources. Fang Li: Data curation. Yinxing Zhu: Writing – review
   & editing.

Declaration of competing interest

   The authors declare that they have no known competing financial
   interests or personal relationships that could have appeared to
   influence the work reported in this paper.

Acknowledgments