Causal role of ischemic heart disease in ovarian cancer subtypes.

The IVW results indicated no statistically significant association between IHD and overall ovarian cancer risk (OR = 0.97, 95% CI: 0.92–1.03, P  = 0.378), and this finding was also supported by the other three supplementary MR methods (Fig.  2 ), including weighted median, weighted mode, and simple mode, all of which yielded consistent results with no statistically significant association observed. Fig. 2 Forest plot for the causal association between IHD and overall ovarian cancer. nSNP, numbers of single nucleotide polymorphism; IVW, inverse variance-weighted; OR, odds ratio; CI, confidence interval Forest plot for the causal association between IHD and overall ovarian cancer. nSNP, numbers of single nucleotide polymorphism; IVW, inverse variance-weighted; OR, odds ratio; CI, confidence interval The IVW results indicated no genetic causal association between IHD and overall serous ovarian cancer (OR = 0.99, 95% CI 0.92–1.06, P  = 0.778), and this finding was also supported by the subgroup analysis (high grade serous ovarian cancer: OR = 1.00, 95% CI 0.94–1.08, P  = 0.900; low grade serous ovarian cancer: OR = 1.00, 95% CI 0.80–1.24, P  = 0.972) (Fig.  3 ). Fig. 3 Forest plot for the causal association between IHD and serous ovarian cancer. nSNP, numbers of single nucleotide polymorphism; IVW, inverse variance-weighted; OR, odds ratio; CI, confidence interval Forest plot for the causal association between IHD and serous ovarian cancer. nSNP, numbers of single nucleotide polymorphism; IVW, inverse variance-weighted; OR, odds ratio; CI, confidence interval The IVW results suggested that IHD does not have a causal effect on overall mucinous ovarian cancer (OR = 0.88, 95% CI 0.77–1.01, P  = 0.077). However, the subgroup analysis indicates that IHD may be a protective factor for invasive mucinous ovarian cancer (invasive mucinous ovarian cancer: OR = 0.77, 95% CI 0.64–0.93, P  = 0.005; low malignant potential mucinous ovarian cancer: OR = 0.98, 95% CI 0.80–1.21, P  = 0.875). Nevertheless, MR-Egger analysis did not support this finding, and given the inconsistency in the direction of the OR between the two MR methods, this result is not robust (Fig.  4 , Fig.  7 A). Fig. 4 Forest plots for the causal association between IHD and mucinous ovarian cancer. nSNP, numbers of single nucleotide polymorphism; IVW, inverse variance-weighted; OR, odds ratio; CI, confidence interval Forest plots for the causal association between IHD and mucinous ovarian cancer. nSNP, numbers of single nucleotide polymorphism; IVW, inverse variance-weighted; OR, odds ratio; CI, confidence interval MR analysis suggested no significant association between IHD and clear cell ovarian cancer (OR = 0.98, 95% CI 0.82–1.18, P  = 0.848) (Fig.  5 ). Fig. 5 Forest plots for the causal association between IHD and clear cell ovarian cancer. nSNP, numbers of single nucleotide polymorphism; IVW, inverse variance-weighted; OR, odds ratio; CI, confidence interval Forest plots for the causal association between IHD and clear cell ovarian cancer. nSNP, numbers of single nucleotide polymorphism; IVW, inverse variance-weighted; OR, odds ratio; CI, confidence interval The IVW results suggested that IHD may be a potential protective factor for endometrioid ovarian cancer (OR = 0.86, 95% CI 0.76–0.98, P  = 0.027), and the direction of the OR from the other three MR methods is consistent with that of the IVW (Fig.  6 ). Fig. 6 Forest plots for the causal association between IHD and endometrioid ovarian cancer. nSNP, numbers of single nucleotide polymorphism; IVW, inverse variance-weighted; OR, odds ratio; CI, confidence interval Forest plots for the causal association between IHD and endometrioid ovarian cancer. nSNP, numbers of single nucleotide polymorphism; IVW, inverse variance-weighted; OR, odds ratio; CI, confidence interval Cochran's Q test suggested no significant heterogeneity in any of the MR analyses, including the subgroup analysis (all P  > 0.05) (Table  1 ). MR-Egger did not find any evidence suggestive of horizontal pleiotropy (all P  > 0.05) (Table  2 ). Additionally, the “leave-one-out” test of the MR analysis for IHD on endometrioid ovarian cancer revealed that the causal effect is nearly unaffected by any single SNP (Fig.  7 B). Finally, the funnel plot for the MR analysis of IHD on endometrioid ovarian cancer is also largely symmetric, indicating that the possibility of heterogeneity among the SNPs acting as genetic proxies for IHD is minimal (Fig.  7 C). Table 1 results of heterogeneity analysis Exposure Outcome Cochran's Q test (P-value) MR-Egger IVW IHD Ovarian cancer 0.436 0.467 IHD High grade and low grade serous ovarian cancer 0.794 0.814 IHD High grade serous ovarian cancer 0.578 0.610 IHD Low grade serous ovarian cancer 0.894 0.892 IHD Mucinous ovarian cancer 0.869 0.886 IHD Invasive mucinous ovarian cancer 0.977 0.960 IHD Low malignant potential mucinous ovarian cancer 0.765 0.780 IHD Clear cell ovarian cancer 0.892 0.906 IHD Endometrioid ovarian cancer 0.952 0.960 IHD, ischemic heart disease; IVW, inverse-variance weighted Table 2 results of pleiotropy analysis Exposure Outcome MR-Egger intercept test intercept P-value IHD Ovarian cancer 0.000 0.922 IHD High grade and low grade serous ovarian cancer − 0.002 0.725 IHD High grade serous ovarian cancer 0.000 0.985 IHD Low grade serous ovarian cancer − 0.015 0.320 IHD Mucinous ovarian cancer 0.001 0.894 IHD Invasive mucinous ovarian cancer − 0.022 0.082 IHD Low malignant potential mucinous ovarian cancer 0.009 0.583 IHD Clear cell ovarian cancer 0.004 0.759 IHD Endometrioid ovarian cancer − 0.002 0.831 IHD, ischemic heart disease Fig. 7 Additional complementary analyses for the causal effect of IHD on endometrioid ovarian cancer. A Scatter plot of the MR analysis to explore the causal effect of IHD on endometrioid ovarian cancer. B Leave-one-out plot of the MR analysis to explore the causal effect of IHD on endometrioid ovarian cancer. C Funnel plot of the MR analysis to explore the causal effect of IHD on endometrioid ovarian cancer. IHD, ischemic heart disease results of heterogeneity analysis IHD, ischemic heart disease; IVW, inverse-variance weighted results of pleiotropy analysis IHD, ischemic heart disease Additional complementary analyses for the causal effect of IHD on endometrioid ovarian cancer. A Scatter plot of the MR analysis to explore the causal effect of IHD on endometrioid ovarian cancer. B Leave-one-out plot of the MR analysis to explore the causal effect of IHD on endometrioid ovarian cancer. C Funnel plot of the MR analysis to explore the causal effect of IHD on endometrioid ovarian cancer. IHD, ischemic heart disease

Based on the core assumptions of MR [ 26 ], a TSMR study was conducted to explore the causal effect of IHD on ovarian cancer and its subtypes (Fig.  1 ). The genome-wide association study (GWAS) data on IHD were obtained from the latest R12 release of FinnGen [ 27 ], with a sample size of up to 450,000. The GWAS data on ovarian cancer and its subtypes were obtained from the Ovarian Cancer Association Consortium (OCAC) [ 28 ], a global collaborative network aimed at advancing the understanding of ovarian cancer through large-scale genetic studies. This organization brings together researchers and research data from multiple countries and regions to explore the genetic basis, risk factors, clinical characteristics, and relationships between different subtypes of ovarian cancer. OCAC's research is typically based on large case–control studies, focusing on the genetic susceptibility and environmental interactions in ovarian cancer. It provides important genomic resources and support for global ovarian cancer research. All the GWAS summary data employed in this study are publicly available (Supplementary Table 1). Fig. 1 Schematic diagram of this MR study to explore the causal effect of IHD on ovarian cancer and its subtypes. IVW, inverse variance-weighted Schematic diagram of this MR study to explore the causal effect of IHD on ovarian cancer and its subtypes. IVW, inverse variance-weighted Single nucleotide polymorphisms (SNPs) are typically used as IVs in MR study. To ensure the robustness and reliability of the MR analysis, a series of quality control measures were taken in the selection of IVs. First, SNPs significantly associated with IHD were selected, with a threshold of P  < 5 × 10⁻⁸ [ 29 ], which is widely accepted as the genome-wide significance threshold in GWAS. Furthermore, for all SNPs with linkage disequilibrium (LD), an r 2 threshold of 0.001 and the genetic distance of 10,000 kb were applied [ 30 ], and SNPs with palindromic sequences were excluded to ensure that the selected SNPs largely maintain their independence. Additionally, MR-PRESSO was used to identify outlier SNPs. This approach improves the reliability of MR analyses by detecting and correcting for horizontal pleiotropy, which occurs when genetic variants influence the outcome through pathways other than the exposure of interest. Finally, SNPs potentially causing pleiotropy were excluded, and the F-statistic was calculated, with the formula as follows [ 31 ]: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F=\frac{{\beta }^{2}}{{SE}^{2}}$$\end{document} F = β 2 SE 2 . An F-statistic > 10 is typically considered to indicate minimal influence from weak instruments. The remaining SNPs were utilized as genetic proxies for IHD in subsequent MR analyses. In the MR analysis, IVW [ 32 ], MR-Egger [ 33 ], weighted median [ 34 ], weighted mode [ 35 ], and simple mode methods were used to assess the causal relationship between IHD and ovarian cancer. Given the potential heterogeneity in the estimates from different IVs, IVW was chosen as the primary method for the analysis. to enhance the robustness of our results by detecting outliers and evaluating the consistency of the causal estimates across different MR methods, multiple sensitivity analyzes were employed. Cochran's Q test was used to evaluate heterogeneity, and MR pleiotropy residual sum and outlier (MR-PRESSO) was applied to check for outliers [ 36 ]. The MR-Egger intercept test was implemented as an effective method to detect horizontal pleiotropy. This method allows for the identification of potential bias introduced by pleiotropic genetic variants, which influence the outcome through pathways other than the exposure of interest. Additionally, the “leave-one-out” analysis was conducted by sequentially excluding each SNP to assess the effects of the remaining SNPs, thereby identifying whether any individual SNP exerted a disproportionate influence on the results. Finally, a funnel plot was used to visually assess the heterogeneity among the SNPs by the degree of symmetry. All statistical analyses were performed using R software, with the "TwoSampleMR" package, "MendelianRandomization" package, and "MR-PRESSO" package for data analysis. All tests were two-tailed, and the statistical threshold for evidence of effects was set at P < 0.05.

Our MR analysis suggests that IHD may be a potential protective factor for endometrioid ovarian cancer, while no causal relationship was found between IHD and other ovarian cancer subtypes. However, the mechanisms underlying this association remain unclear and warrant further investigation.

This study revealed no significant association between IHD and overall ovarian cancer risk. However, a notable finding was the observed protective association between IHD and endometrioid ovarian cancer, with a reduced risk indicated by the MR analysis. This result highlights potential subtype-specific effects of systemic cardiovascular conditions on ovarian cancer. The observed association may be explained by shared biological mechanisms, including chronic inflammation, oxidative stress, and endothelial dysfunction, which are common in both IHD and cancer. Moreover, hormonal regulation and immune responses, known to differ between ovarian cancer subtypes, could play a crucial role in mediating this protective effect. These findings emphasize the need for further research to better understand the interplay between cardiovascular diseases and ovarian cancer subtypes. Our results suggest that IHD may be a potential protective factor for endometrioid ovarian cancer, while no causal relationship was found between IHD and other ovarian cancer subtypes, such as serous, mucinous, and clear cell ovarian cancers. IHD and its potential association with the risk of endometrioid ovarian cancer may involve several mechanisms, including hormonal levels and pharmacological effects. First, hormonal levels may play a crucial role in this relationship. Particularly in postmenopausal women, where estrogen levels are relatively low, the incidence of IHD is often higher [ 37 ]. Lower estrogen levels may reduce the promotive effects of estrogen on endometrioid ovarian cancer cells, thus lowering the risk of cancer development. Estrogen has been shown to play a significant role in the initiation and progression of endometrioid ovarian cancer by promoting the proliferation and metastasis of cancer cells. Therefore, lower estrogen levels may reduce susceptibility to this cancer. Additionally, statins, commonly used in the treatment of IHD, may also exert anti-cancer effects. Statins lower cholesterol levels, improving cardiovascular health, but they may also influence the occurrence and progression of cancer, including ovarian cancer [ 38 ]. Studies suggest that statins, by inhibiting HMG-CoA reductase, reduce cholesterol synthesis and modulate the tumor microenvironment, which could help reduce the proliferation of endometrioid ovarian cancer cells. In addition to regulating cholesterol levels, statins may inhibit cancer cell proliferation, metastasis, and induce apoptosis, thereby influencing the development of endometrioid ovarian cancer. Research indicates that statins could provide a potential protective effect through these mechanisms, particularly in relation to IHD. Moreover, statins may also influence the risk of endometrioid ovarian cancer through immune modulation. Statins have been shown to enhance T-cell immune function, which may help suppress tumor development and progression. By reducing inflammation and regulating immune cell function, statins could alter the tumor microenvironment, suppress immune evasion by cancer cells, and lower the risk of endometrioid ovarian cancer as well as other cancers. The immune system plays a critical role in tumor immune surveillance, and modulation of immune responses may represent another pathway through which IHD influences cancer risk. In conclusion, IHD may affect the risk of endometrioid ovarian cancer through changes in hormonal levels, the direct anti-tumor effects of statins, and immune modulation mechanisms. Although these mechanisms provide theoretical support for understanding the relationship between IHD and endometrioid ovarian cancer, further clinical studies and experimental validation are needed to explore these pathways and their potential implications in greater detail. This study addresses a critical gap in the literature by investigating the causal relationship between IHD and ovarian cancer subtypes using a robust MR approach. Prior research has largely focused on the overall link between cardiovascular diseases and cancer, often overlooking the heterogeneity among cancer subtypes. By stratifying analyses according to ovarian cancer subtypes, this study provides novel insights, including a potential protective effect of IHD on endometrioid ovarian cancer, which has not been previously reported. Understanding such subtype-specific associations is crucial for advancing both clinical practice and research, as it enables the development of more tailored prevention strategies and therapeutic interventions. Furthermore, these findings underscore the importance of considering tumor heterogeneity in future research to unravel the complex interplay between systemic diseases and cancer biology. MR offers distinct advantages over traditional observational studies, especially in assessing causal relationships. By utilizing genetic variants as IVs, MR minimizes the risks of confounding factors and reverse causality, providing a more reliable inference of causal effects. The use of randomly allocated genetic variants ensures that findings are less influenced by environmental or lifestyle factors, enhancing the robustness of the conclusions. Furthermore, rigorous quality control measures in MR, such as ensuring the independence of SNPs and testing for pleiotropy, strengthen the validity of the results. These methodological strengths make MR a powerful tool for studying complex diseases like cancer, offering a more accurate estimate of causal relationships between exposures and outcomes. This study highlights the potential of MR to uncover meaningful biological insights that may inform future research and clinical decision-making. One possible explanation for the lack of association between IHD and other ovarian cancer subtypes, such as serous, mucinous, and clear cell ovarian cancers, lies in the differing pathophysiology and hormonal dependencies of these subtypes. For instance, serous ovarian cancer is primarily driven by genetic mutations such as BRCA1/2 and TP53, which may have limited overlap with the pathways influenced by cardiovascular conditions like IHD [ 39 ]. Similarly, mucinous and clear cell ovarian cancers are characterized by distinct molecular and environmental risk factors, such as chronic inflammation and endometriosis, which may not be directly linked to IHD-related mechanisms [ 40 ]. These subtype-specific differences highlight the complexity of ovarian cancer biology and underscore the importance of stratifying analyses by histological subtypes in future research. However, there are certain limitations inherent in MR studies [ 41 ]. One of the major concerns is the potential for horizontal pleiotropy, where genetic variants influence multiple traits, which could bias the results. While we performed rigorous tests to detect and correct for pleiotropy, it remains a limitation that requires careful consideration. Another limitation is the selection of appropriate instrumental variables. In this study, we ensured that the SNPs used were strongly associated with IHD and independent of each other, but the choice of genetic proxies is always crucial in MR analysis. Additionally, while MR can suggest causal relationships, it does not fully capture the biological mechanisms behind these associations. Further research, including functional studies, is needed to investigate how IHD could influence endometrioid ovarian cancer at the molecular and cellular levels. Finally, despite the use of MR to reduce confounding, there remains the possibility of residual confounding. While MR is less susceptible to confounding and reverse causation compared to traditional observational studies, it could still be influenced by unmeasured or inadequately controlled confounders. For example, although we have adjusted for multiple potential confounders by selecting appropriate genetic variants, factors such as lifestyle, environmental exposures, or other genetic influences not captured in the study could still affect the results. Therefore, while our findings suggest a potential causal relationship between IHD and ovarian cancer, further research, including experimental and cohort-based studies, is needed to confirm these results and explore other potential confounding factors.

Ischemic heart disease (IHD) remains one of the leading causes of mortality and morbidity globally, accounting for approximately 16% of total deaths worldwide in 2020, as reported by the World Health Organization (WHO) [ 1 – 3 ]. This condition is primarily driven by the accumulation of risk factors such as hypertension [ 3 ], diabetes, dyslipidemia [ 4 ], and smoking [ 5 ]. IHD is characterized by a reduced blood supply to the heart muscle, leading to ischemia and subsequent myocardial injury [ 6 ]. Although the cardiovascular implications of IHD are well-documented, its potential impact on other diseases, including cancer [ 7 – 9 ], is less understood. Ovarian cancer, the most lethal gynecological cancer, presents a significant global health burden, with an estimated 313,000 new cases and 207,000 deaths worldwide in 2020, according to the WHO [ 10 – 12 ]. Its development is influenced by a variety of factors, including genetic mutations [ 13 ], hormonal imbalances [ 14 ], and environmental exposures [ 15 , 16 ]. Previous studies have highlighted potential shared risk factors and biological pathways between cardiovascular diseases and various cancers, including chronic inflammation, oxidative stress, and hormonal dysregulation [ 17 ]. However, specific investigations into the relationship between IHD and ovarian cancer remain limited. Few studies have explored whether IHD might contribute to the development or progression of ovarian cancer, particularly across its histological subtypes. Ovarian cancer encompasses several histological subtypes [ 18 ], including endometrioid, serous, mucinous, and clear cell ovarian cancers, each with distinct molecular and clinical characteristics. These subtypes differ significantly in prognosis, treatment response, and underlying biological mechanisms, highlighting the importance of stratifying analyses to better understand their unique etiologies and potential links to IHD. Understanding whether IHD may influence the risk of these subtypes is therefore critical to refining prevention and treatment strategies. Previous studies have suggested that cardiovascular diseases, including IHD, may share common risk factors with cancer, such as inflammation [ 19 ], obesity [ 20 ], and insulin resistance [ 21 , 22 ], which could influence the development and progression of tumors. Additionally, the chronic inflammatory state often associated with IHD could alter the immune microenvironment, potentially affecting tumor growth and metastasis. Despite this biological plausibility, there is currently a lack of clinical studies on the relationship between IHD and ovarian cancer, and existing research has not reached a consensus on whether IHD is a risk factor for ovarian cancer or how it affects ovarian cancer. Mendelian randomization (MR) offers a robust analytical approach to investigating the causal relationship between risk factors and disease outcomes [ 23 – 25 ]. By utilizing genetic variants as instrumental variables (IVs), MR could help overcome the confounding biases that often limit observational studies. MR provides a unique opportunity to explore whether IHD is causally linked to ovarian cancer, as well as to determine if the association differs across ovarian cancer subtypes. This approach allows us to assess the potential protective or risk-enhancing effects of IHD on ovarian cancer, free from reverse causality and confounding. In this study, we utilized a two-sample MR (TSMR) framework to investigate the causal association between IHD and overall ovarian cancer risk, with particular emphasis on its subtypes. Our hypothesis is that IHD may influence the risk of developing ovarian cancer, and that this relationship may differ across ovarian cancer subtypes. By using genetic instruments for IHD, we aimed to provide novel insights into how IHD may influence the development of ovarian cancer and whether its effects vary by histological subtype. We also performed sensitivity analyses to ensure the robustness and reliability of our findings, considering potential issues such as pleiotropy and heterogeneity.

Supplementary material 1 Supplementary material 1