Exploring the Relationship between Gut Microbiota and Breast Cancer Risk in European and East Asian Populations Using Mendelian Randomization

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This study investigated the causal relationship between gut microbiota and breast cancer in European and East Asian populations using a Mendelian Randomization (MR) analysis approach. Methods: We utilized summary statistics from genome-wide association studies (GWAS) of the gut microbiome from the MiBioGen project with summary data from GWAS on breast cancer from the FinnGen consortium and the IEU database. Preliminary statistical analyses were conducted using inverse variance weighting, supplemented by various sensitivity analysis methods, including MR-Egger regression, weighted median, weighted mode, simple median, and simple mode, to ensure the robustness of our findings. Heterogeneity and pleiotropy were assessed to avoid misleading conclusions caused by unconsidered confounders or non-specific effects of genetic variants, ensuring that the results reflect a genuine causal relationship. Results: In European populations, four types of gut microbiota were associated with breast cancer. The genus Erysipelatoclostridium was positively associated with the risk of breast cancer, with an odds ratio (OR) of 1.21 (95% confidence interval [CI] 1.083–1.358), false discovery rate (FDR) = 0.0039. The class Coriobacteriia, order Coriobacteriales, and family Coriobacteriaceae, which belong to the same phylogenetic system, showed a consistent negative association with breast cancer risk, with an OR of 0.757 (95% CI 0.616–0.930), FDR = 0.0281. In East Asian populations, three types of gut microbiota were related to breast cancer. The Eubacterium ruminantium group was positively associated with breast cancer risk, with an OR of 1.259 (95% CI 1.056–1.499), FDR = 0.0497. The families Porphyromonadaceae and Ruminococcaceae were negatively associated with breast cancer risk, with ORs of 0.304 (95% CI 0.155–0.596), FDR = 0.0005, and 0.674 (95% CI 0.508–0.895), FDR = 0.03173, respectively. However, these two taxa had limited instrumental variables, restricting the statistical power and potentially affecting the interpretation of the results. Conclusion: A causal link between specific gut microbiota and breast cancer exists. This finding enhances our understanding of the relationship between the gut microbiome and breast cancer and offers potential directions for developing prevention and treatment methods. gut microbiota breast cancer Mendelian randomization Europeans East Asians Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction According to the International Agency for Research on Cancer of the World Health Organization in 2021, the incidence of breast cancer has surpassed that of lung cancer, becoming the most common cancer worldwide [1] . The pathogenesis of breast cancer is complex, with numerous identified risk factors, including genetic mutations (BRCA1, BRCA2) [2] , lifestyle factors (alcohol consumption, smoking, high-fat diet, lack of exercise, intake of exogenous hormones such as oral contraceptives), reproductive history (early menarche, late menopause, nulliparity or late childbearing) [3] , obesity, and radiation exposure. Given the multifactorial nature of this disease, there are likely unknown factors involved in breast cancer development. The gut microbiota is the complex community of microorganisms residing in the human gastrointestinal tract, which directly or indirectly participate in tumorigenesis and progression through mechanisms such as influencing host inflammatory responses, promoting the formation of the tumor microenvironment, and manipulating tumor cell signaling pathways [4-6] . For example, dysbiosis is characterized by an increase in harmful bacteria and a decrease in beneficial bacteria and may raise the risk of breast cancer. Some carcinogenic substances produced by some gut microbes, including bile acid metabolic products, might affect distant breast tissues via circulation, promoting cell proliferation and apoptosis [7] . Certain bacteria within the gut microbiome, known as the “estrobolome,” possess genes for metabolizing estrogens. The activity of these bacteria may influence the risk of estrogen receptor-positive breast cancer in postmenopausal women [8] . Studies proposed a mechanism known as the “gut-breast axis,” where gut microbiota could affect breast cancer development by transferring immune cells to lymph nodes, which then migrate to the breast via the bloodstream or lymphatic system [5] . Research provides a preliminary understanding of the relationship between gut microbiota and breast cancer; however, it remains primarily confined to observational studies [9-11] . Although observational can reveal potential associations, their conclusions are often affected by potential confounders and cannot establish causality. Mendelian randomization (MR) utilizes genetic variants as instrumental variables and reduces biases inherent in traditional observational studies, making it possible to infer causality where randomized controlled trials are not feasible. Therefore, we employed MR techniques using genome-wide association studies (GWAS) summary statistics to explore the potential causal relationship between the gut microbiome and the risk of developing breast cancer. Methodology Study design The research framework is depicted in Figure 1A. The gut microbiota served as the exposure, and breast cancer was the outcome. Genetic variants significantly associated with the gut microbiome are selected as instrumental variables (IVs) for investigating the potential causal relationship between gut microbiota and breast cancer using the MR method. MR employs summary data from GWAS to isolate confounding factors, offering a more precise evaluation of causal relationships. Within the MR study framework, IVs must meet three core criteria: (1) be significantly associated with the exposure (relevance criterion); (2) not be associated with any known or unknown confounders (independence criterion); and (3) influence the outcome solely through the exposure and not via any other direct causal pathways (exclusion restriction criterion). Source of gut bacteria data Summary data on gut bacteria were obtained from the genome-wide association meta-analysis conducted by the MiBioGen consortium (https://mibiogen.gcc.rug.nl/), which represents the most extensive study to date on the transgenic genetics of the human gut microbiome. This study compiled data from 18,340 samples across 24 populations from Europe, Africa, Asia, the Middle East, and Latin America, analyzing microbial compositions across multiple variable regions of the 16S rRNA gene, including V1-V2, V3-V4, and V4. DNA was extracted from fecal samples using seven methods, yielding genetic information on 211 types of gut microbes. The data from all samples were normalized to 10,000 reads per sample, and direct classification binning was used to categorize the microbes into five different taxonomic levels: phylum, class, order, family, and genus, based on genetic characteristics. To ensure the study's comprehensiveness, all identified gut bacteria taxa were included, comprising 15 unnamed bacterial taxa and one duplicate bacterial taxon, resulting in 211 gut bacteria taxa selected as exposure factors for MR analysis. Breast cancer data sources The GWAS data for the European population on breast cancer were sourced from the FinnGen consortium's R10 release, a large-scale biomedicine project based on the Finnish population, involving 412,181 participants (181,871 males and 230,310 females), with 18,786 breast cancer cases and 182,927 controls. Each GWAS received approval from an ethics review committee, and detailed information on the data can be downloaded from the FinnGen consortium's official website: https://storage.googleapis.com/finngen-public-data-r10/summary_stats/R10_manifest.tsv/. For the East Asian population, breast cancer GWAS data were obtained from the Biobank Japan project, the largest known biobank focusing on the East Asian population. From 2003 to 2018, in collaboration with 12 medical institutions, DNA, serum, and clinical information from over 200,000 patients with 47 diseases were collected [12] . Based on these data, researchers identified various genetic variants associated with disease susceptibility and drug responsiveness. The database includes 5,552 breast cancer patients and 89,732 controls, involving 8,872,152 single nucleotide polymorphisms (SNPs). All related GWAS received approval from the ethics committees of the RIKEN Yokohama Institute and the Institute of Medical Science at the University of Tokyo. The diagnosis of primary breast cancer cases was confirmed based on the International Classification of Diseases (ICD) codes applicable during the study period, which includes from the eighth to the tenth editions of the ICD. Detailed information on the data has been included in the publicly accessible IEU database, available for download via the designated dataset (GWAS ID: bbj-a160; https://gwas.mrcieu.ac.uk/). Selection of instrumental variables SNPs strongly associated with the exposure at the genome-wide significance level (P < 5×10 -6 ) were selected; setting (R 2 < 0.001, distance = 1 MB) to remove SNPs with high linkage disequilibrium, ensuring independence; excluding SNPs with fewer than three occurrences, minor allele frequency ≤ 0.01; eliminating SNPs not found in the outcome GWAS dataset; removing SNPs with inconsistent alleles between exposure and outcome; deleting palindromic SNPs that could lead to bias; The F-statistic was calculated to assess potential IV bias, selecting F > 10 to ensure weak instrumental variables do not influence causality. The calculation formula is F = R 2 × (N - 1 - K) / (1 - R 2 ) × K, where N represents the sample size of exposure data, K represents the number of IVs, and R 2 represents the proportion of variance in the exposure variable explained by the selected IVs, with the calculation formula R 2 = β 2 / (β2 + SE 2 ). Statistical analysis The statistical workflow is depicted in Figure 1B. After harmonizing the data for exposure and outcome, the inverse variance weighted (IVW) MR analysis method was employed to explore the potential causal relationship between gut microbiota and breast cancer. It was used as the primary analysis method because IVW can provide the most accurate causal effect estimate, assuming all instrumental variables are valid. Cochran's Q test was utilized to assess the consistency of the estimated effects of the selected IVs on the exposure variable, with p < 0.05 indicating significant heterogeneity. The MR-Egger intercept was used to detect pleiotropy, with a more significant intercept suggesting more robust evidence of pleiotropy. A zero intercept indicates no pleiotropy, and the p-value was used to test whether it significantly differs from zero, with p > 0.05 considered insufficient evidence of pleiotropy. Leave-one-out analysis was conducted to evaluate the impact of the removal of individual SNPs on the overall statistical results and to reveal the contribution of specific SNPs to the final analysis outcome. The MR-PRESSO method was applied to detect and remove outliers inconsistent with the predicted effect size or direction. After excluding these outliers, five other MR methods are used for sensitivity analysis to ensure the accuracy and reliability of the final analysis results. The effect size of the impact of gut microbiota on breast cancer is expressed as odds ratios (OR) and their 95% confidence intervals (CI). The false discovery rate (FDR) was used to adjust for multiple hypothesis testing. All analyses are performed using the “TwoSampleMR,” “MR-PRESSO,” and “Mendelian Randomization” packages in R software (version 4.3.2). Results The MiBioGen study identified 211 gut microbiota taxa belonging to nine phyla, 15 classes, 20 orders, 32 families, and 119 genera. In the MR analysis of the European population, we identified four categories of gut microbiota significantly associated with the risk of breast cancer, including the class Coriobacteriia, the order Coriobacteriales, the family Coriobacteriaceae, and the genus Erysipelatoclostridium . The first three have a hierarchical taxonomic relationship: Coriobacteriia includes the order Coriobacteriales, which belongs to the family Coriobacteriaceae [ 13 ] . IVs for these three taxonomic levels, selected based on a statistical threshold of p-value less than 5e-6, were completely consistent, reflecting a close genetic link among these microbial taxa. They may be associated with breast cancer risk through common biological pathways. In the East Asian population, we found three categories of gut microbiota associated with breast cancer risk (p < 0.01), including two families (Porphyromonadaceae and Ruminococcaceae) and one genus ( Eubacterium ruminantium group) (see Fig. 2 B). These findings were based on the primary MR analysis method (IVW). We conducted further analyses on the gut microbiota, with detailed results in Table 1 . Table 1 Mendelian randomization analysis statistics Populations Gut microbe No. of IV F-statistics Between-SNP heterogeneity Horizontal pleiotropy P (MR-PRESSO global test) Q-value P-value Egger intercept P-value Europeans Coriobacteriaceae 5 22.01 2.81 0.589 -0.014 0.757 0.656 Erysipelatoclostridium 8 24.27 3.05 0.880 0.007 0.726 0.899 Coriobacteriales 5 22.01 2.81 0.589 -0.014 0.757 0.656 Coriobacteriia 5 22.01 2.81 0.589 -0.014 0.757 0.656 East Asians Porphyromonadaceae 2 23.4 0.998 0.318 - - - Ruminococcaceae 3 23.7 1.64 0.440 -0.112 0.473 - Eubacterium ruminantium group 8 22.2 4.55 0.714 0.033 0.352 0.735 5–8 instrumental variables (IVs) were used for the selected gut microbiota, with an average F-statistic indicating robustness (Table 1 ). No heterogeneity or horizontal pleiotropy was observed among the SNPs of any gut microbiota (Table 1 ).In the European population, Erysipelatoclostridium was observed to have a positive association with breast cancer risk, with an OR of 1.21 (95% CI 1.083–1.358, FDR = 0.0039) (Fig. 3 ). Coriobacteriales, Coriobacteriaceae, and Coriobacteriia were negatively associated with breast cancer risk, with an OR of 0.757 (95% CI 0.616–0.930, FDR = 0.0281) (Fig. 3 ). All findings met the statistical significance threshold of FDR < 0.05 (Fig. 3 ). In the East Asian population, Porphyromonadaceae had a negative association with breast cancer risk, with an OR of 0.304 (95% CI 0.155–0.596, FDR = 0.0005), and Ruminococcaceae also showed a negative association with breast cancer risk, with an OR of 0.674 (95% CI 0.508–0.895, FDR = 0.03173). However, the limited number of instrumental variables for these two microbiota groups restricted the statistical power of the analysis. Despite these results being statistically significant, their interpretation should be cautiously approached. On the other hand, the Eubacterium ruminantium group showed a positive association with breast cancer risk, with an OR of 1.259 (95% CI 1.056–1.499, FDR = 0.0497). The moderate number of IVs for this group made its association with breast cancer risk statistically more reliable. This finding suggests that the Eubacterium ruminantium group plays a significant role in the etiology of breast cancer. Regardless of being in European or East Asian populations and despite the differences in OR values and corresponding FDR values, the six MR methods used all demonstrated consistent causal estimates between gut microbiota and breast cancer risk (Fig. 4 ). Discussion The gut microbiome is a complex ecosystem comprising trillions of microorganisms. Considering its role in human health and disease, in-depth studies hold promise for opening avenues for treating and preventing various diseases [ 14 ] . In recent years, there has been growing interest in the relationship between the gut microbiome and breast cancer. Goedert et al. identified distinctive compositional differences between untreated breast cancer patients and healthy individuals, with characteristic microbiota including Clostridiaceae, Faecalibacterium, and Ruminococcaceae [ 15 ] . Terrisse et al. compared healthy individual samples and found that Bacteroides uniformis , Clostridium bolteae , and Bilophila wadsworthia were associated with poorer breast cancer outcomes [ 16 ] . However, some studies suggest that there is not a significant difference in the gut microbiota between breast cancer and non-breast cancer patients [ 17 ] . This study aimed to use MR to reveal the relationship between gut microbiota and breast cancer in European and East Asian populations. We used GWAS summary data from the MiBioGen consortium, the largest GWAS dataset on microbiomes to date, with breast cancer data from FinnGen and Biobank Japan, providing a solid foundation for our analysis. Despite differences between the two populations, our findings suggest potential associations between specific microbiota and breast cancer. We found potential causal relationships in the European population between the class Coriobacteriia, the order Coriobacteriales, the family Coriobacteriaceae, and the genus Erysipelatoclostridium and breast cancer. Erysipelatoclostridium is an anaerobic bacterium associated with intestinal health and disease. Iadsee et al. showed that levels of Erysipelatoclostridium ramosum were significantly higher in colorectal cancer patients compared to the healthy population [ 18 ] . Cai et al. found that its metabolic product, Ptilosteroid A, is associated with radiation-induced intestinal injury, demonstrating significant value in predicting radiation-induced intestinal damage [ 19 ] . Our study confirms an association between Erysipelatoclostridium and breast cancer, though the specific mechanisms remain unclear. Some bacteria within the Coriobacteriia family can participate in estrogen metabolism, primarily by metabolizing soy isoflavones such as daidzein and genistein, which are converted in the body into compounds with estrogenic activity [ 20 ] . Therefore, they may play an essential role in the development of hormone-dependent cancers, such as breast and prostate cancer, by regulating estrogen levels and affecting the risk and progression of cancer. Our study validates the existence of this potential mechanism. In East Asian populations, we discovered that Porphyromonadaceae and Ruminococcaceae are negatively correlated with the risk of breast cancer, while the Eubacterium ruminantium group is positively correlated. This finding significantly differs from the findings in European populations. This difference reflects the significant disparities in genetic backgrounds, dietary habits, lifestyles, and environmental factors among various populations. These factors can significantly impact the composition of the gut microbiota, influencing the risk of developing breast cancer. These results underscore the importance of considering population-specific factors when studying the relationship between the gut microbiome and breast cancer. Porphyromonadaceae and Ruminococcaceae, known for their relation to gut health and inflammation regulation, are particularly interesting. Some members of the Porphyromonadaceae family have been associated with intestinal inflammation and the maintenance of gut barrier function [ 21 ] , although research in this area remains relatively sparse. The Ruminococcaceae family is noted for producing short-chain fatty acids (SCFAs) [ 22 ] , including butyrate, propionate, and acetate. SCFAs are crucial for promoting the expression of tight junction proteins, reducing intestinal permeability, preventing the transmembrane transport of harmful substances and pathogens, and regulating the intestinal immune response. This phenomenon leads to anti-inflammatory and immune tolerance effects and activates G-protein-coupled receptors (such as GPR41 and GPR43) that regulate the host's energy balance and metabolism. Thus, the Ruminococcaceae family and its metabolic products, SCFAs, have shown substantial potential in preventing and treating various diseases, becoming a focal point of current microbiome research. This study has some limitations. Despite setting a significance threshold that is not very stringent (P < 5×10 − 6 ), including a relatively small number of instrumental variables limited our statistical analysis power and could potentially lead to false positive results. To address this issue, we implemented multiple testing corrections by calculating the FDR to enhance the reliability of our findings. Furthermore, although MR analysis inherently addresses confounding issues, it remains susceptible to pleiotropic effects. Our study applied sensitivity analysis methods such as MR-Egger and MR-PRESSO to mitigate genetic pleiotropy. The consistent results across various MR analysis methods indicate the robustness of our findings. Nevertheless, a significant limitation is the absence of stratification based on specific breast cancer subtypes, such as HER-2, ER, and PR expression statuses. Considering the different interactions between gut microbiota and various breast cancer subtypes, the generality of our findings may be limited. This aspect should be considered when interpreting our results, and future genetic studies should delve deeper into these specific subtypes to provide a more comprehensive understanding. Other limitations include the cross-sectional nature of GWAS data, complicating the determination of a temporal causal relationship between gut microbiota changes and breast cancer onset. Extra caution is required when interpreting the summary data provided by GWAS, as it does not account for the complexity and nuances arising from individual differences. Despite these limitations, an increasing body of evidence suggests that exploring the gut microbiome offers new perspectives in unveiling the pathogenesis of breast cancer and enhancing the predictive accuracy of existing risk assessment methodologies [ 15 , 23 , 24 ] . Research has reported that identifying gut microbiome characteristics can provide critical information for predicting the efficacy and safety of chemotherapy in breast cancer patients, playing a role in developing personalized treatment strategies [ 25 ] . In summary, our study provides new insights into the relationship between the gut microbiota of European and East Asian populations and breast cancer. The identified microbiota may represent potential biomarkers or therapeutic targets for breast cancer. Future research is needed to validate these findings and reveal their specific biological mechanisms. Declarations Ethics approval and consent to participate This study was based on existing publicly available data. Therefore, the requirement for ethical approval and consent is waived. Consent for publication Not applicable as this study utilizes publicly available datasets that do not contain any identifiable personal information. Availability of data and materials The data used in this study were sourced from three publicly available databases: Summary data on gut microbiota were obtained from the whole-genome association meta-analyses conducted by the MiBioGen consortium, accessible at https://mibiogen.gcc.rug.nl/. European population breast cancer GWAS data were derived from the FinnGen consortium's Release R10, with detailed information available for download at https://storage.googleapis.com/finngen-public-data-r10/summary_stats/R10_manifest.tsv/. East Asian population breast cancer GWAS data were acquired from the Japan Biobank, with details listed in the publicly accessible IEU database, available via GWAS ID: bbj-a160 at https://gwas.mrcieu.ac.uk/. Competing interests The authors WL, CG, ZC, SX, and LZ declare that they have no competing interests. Funding This study was funded by the Zhejiang Provincial Program for Medical and Health Science and Technology (Project No. 2024XY152). Authors' contributions WL conceptualized the study and oversaw the project's overall direction and planning. CHG was instrumental in the development of the Mendelian Randomization analysis framework and performed the data analysis. ZC contributed to the literature review and played a key role in interpreting the analysis results in the context of existing research. SX was responsible for data curation, ensuring the accuracy and integrity of the data used in the study. LZ contributed to drafting the manuscript, focusing on the discussion of the findings and their implications for future research. All authors were involved in revising the manuscript critically for important intellectual content and approved the final version to be published. Acknowledgements Not applicable. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. 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Cite Share Download PDF Status: Published Journal Publication published 08 Aug, 2024 Read the published version in BMC Cancer → Version 1 posted Editorial decision: Revision requested 20 Jun, 2024 Reviews received at journal 20 Jun, 2024 Reviews received at journal 19 Jun, 2024 Reviewers agreed at journal 17 Jun, 2024 Reviewers agreed at journal 17 Jun, 2024 Reviewers agreed at journal 16 Jun, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviewers agreed at journal 14 Jun, 2024 Reviewers invited by journal 07 Jun, 2024 Editor invited by journal 18 Mar, 2024 Submission checks completed at journal 18 Mar, 2024 Editor assigned by journal 18 Mar, 2024 First submitted to journal 24 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3986727","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281367141,"identity":"3d2f3724-52eb-4d4b-8bed-4344ed8da3c9","order_by":0,"name":"Wei Lin","email":"","orcid":"","institution":"Xiangshan First People’s Hospital Medical and Health Group","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Lin","suffix":""},{"id":281367142,"identity":"8e550830-9dff-4ccc-8379-7d63facf5185","order_by":1,"name":"Chenghao Gu","email":"","orcid":"","institution":"Xiangshan First People’s Hospital Medical and Health Group","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chenghao","middleName":"","lastName":"Gu","suffix":""},{"id":281367143,"identity":"ff933aeb-cb2d-49d6-ba30-adc41eb3d43d","order_by":2,"name":"Zheyin Chen","email":"","orcid":"","institution":"Xiangshan First People’s Hospital Medical and Health Group","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zheyin","middleName":"","lastName":"Chen","suffix":""},{"id":281367144,"identity":"80012a17-f0a5-490b-9e22-6248df2c1c94","order_by":3,"name":"Shihang Xue","email":"","orcid":"","institution":"Xiangshan First People’s Hospital Medical and Health Group","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shihang","middleName":"","lastName":"Xue","suffix":""},{"id":281367145,"identity":"e15b2275-ccb4-4952-b120-d3fb4307a2d2","order_by":4,"name":"Liuhai Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACNvmDjQ8+VNjI8TMzH3yQUFFDWAufBPNhwxln0owl29mSDR6cOUZYi5wEW5o0b9uhRIPzPGaSD1uYiXCYdI8x0JYDCQyH2dIqEhvYGPjbuxPwa5E5Ywj0y508xmbmYzcSd8gwSJw5uwG/FoYckC3PipmZ2dJuJJ5hYzCQyCWoxQzol8OJbcw8ZgVAkggtEmlpYC09QC0MxGnhOQwJZAlmtmSJhDPHeAj6Rb69ERKV9ucPH/z4o6JGjr+9F78WDMBDmvJRMApGwSgYBVgBAEaRTOqXBbPIAAAAAElFTkSuQmCC","orcid":"","institution":"Xiangshan First People’s Hospital Medical and Health Group","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Liuhai","middleName":"","lastName":"Zeng","suffix":""},{"id":281367146,"identity":"6eae1fd5-692a-45ea-9c23-7d7176c7f04c","order_by":5,"name":"Haiyan Wu","email":"","orcid":"","institution":"Xiangshan First People’s Hospital Medical and Health Group","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haiyan","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-02-25 03:44:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3986727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3986727/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-024-12721-9","type":"published","date":"2024-08-08T15:56:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53154352,"identity":"5dfede67-7931-412e-abbd-2ec13a316b81","added_by":"auto","created_at":"2024-03-21 09:20:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143689,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Design and Flowchart.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) The basic schema of Mendelian Randomization (MR) analysis, where we designated gut microbiota as the exposure and breast cancer as the outcome. Arrow symbols are used to denote the assumptions of Mendelian Randomization.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) Data analysis: We conducted two independent MR analyses using the same exposure data but different outcome data (i.e., breast cancer data from FinnGen and Biobank Japan).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3986727/v1/ebb12dcc96c1a7983c8d1681.png"},{"id":53154353,"identity":"f75b3477-0769-4a5c-81c8-d1ad26250b32","added_by":"auto","created_at":"2024-03-21 09:20:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":155254,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Association Between Gut Microbiota and Breast Cancer. Subfigure (A) and (B) show results in Europeans and East Asians, respectively. The red dashed line denotes the statistical significance threshold (i.e., p \u0026lt; 0.05). The points were jittered to avoid overlap.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3986727/v1/20a94476f6f812e92e6c82f6.png"},{"id":53154351,"identity":"01541fb0-787b-48c0-963d-d391cabf9c0b","added_by":"auto","created_at":"2024-03-21 09:20:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172841,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Link Between Gut Microbiota and Breast Cancer in Europeans. The lines represent the 95% confidence interval (CI) for the odds ratio, with arrows indicating when the CI bounds exceed the x-axis range. \"FDR\" stands for \"False Discovery Rate.\"\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3986727/v1/7a70d8a36006fc8524b1513a.png"},{"id":53154354,"identity":"4eed6fea-474c-4a18-b8cc-572d5c60051f","added_by":"auto","created_at":"2024-03-21 09:20:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":203038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Scatter Plot shows the effect of SNPs on the gut microbiome and breast cancer, with gray error bars representing the 95% confidence interval of the effect.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3986727/v1/207fd4822befb89ddc642c7d.png"},{"id":62298295,"identity":"b1cf8fe1-834e-4719-9443-e0ee908049f4","added_by":"auto","created_at":"2024-08-12 16:11:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1336790,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3986727/v1/cdb80a93-4fbe-4283-a7b0-fa3a0811b588.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Relationship between Gut Microbiota and Breast Cancer Risk in European and East Asian Populations Using Mendelian Randomization","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the International Agency for Research on Cancer of the World Health Organization in 2021, the incidence of breast cancer has surpassed that of lung cancer, becoming the most common cancer worldwide\u003csup\u003e[1]\u003c/sup\u003e. The pathogenesis of breast cancer is complex, with numerous identified risk factors, including genetic mutations (BRCA1, BRCA2)\u003csup\u003e[2]\u003c/sup\u003e, lifestyle factors (alcohol consumption, smoking, high-fat diet, lack of exercise, intake of exogenous hormones such as oral contraceptives), reproductive history (early menarche, late menopause, nulliparity or late childbearing)\u003csup\u003e[3]\u003c/sup\u003e, obesity, and radiation exposure. Given the multifactorial nature of this disease, there are likely unknown factors involved in breast cancer development.\u003c/p\u003e\n\u003cp\u003eThe gut microbiota is the complex community of microorganisms residing in the human gastrointestinal tract, which directly or indirectly participate in tumorigenesis and progression through mechanisms such as influencing host inflammatory responses, promoting the formation of the tumor microenvironment, and manipulating tumor cell signaling pathways\u003csup\u003e[4-6]\u003c/sup\u003e. For example, dysbiosis is characterized by an increase in harmful bacteria and a decrease in beneficial bacteria and may raise the risk of breast cancer. Some carcinogenic substances produced by some gut microbes, including bile acid metabolic products, might affect distant breast tissues via circulation, promoting cell proliferation and apoptosis\u003csup\u003e[7]\u003c/sup\u003e. Certain bacteria within the gut microbiome, known as the \u0026ldquo;estrobolome,\u0026rdquo; possess genes for metabolizing estrogens. The activity of these bacteria may influence the risk of estrogen receptor-positive breast cancer in postmenopausal women\u003csup\u003e[8]\u003c/sup\u003e. Studies proposed a mechanism known as the \u0026ldquo;gut-breast axis,\u0026rdquo; where gut microbiota could affect breast cancer development by transferring immune cells to lymph nodes, which then migrate to the breast via the bloodstream or lymphatic system\u003csup\u003e[5]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eResearch provides a preliminary understanding of the relationship between gut microbiota and breast cancer; however, it remains primarily confined to observational studies\u003csup\u003e[9-11]\u003c/sup\u003e. Although observational can reveal potential associations, their conclusions are often affected by potential confounders and cannot establish causality. Mendelian randomization (MR) utilizes genetic variants as instrumental variables and reduces biases inherent in traditional observational studies, making it possible to infer causality where randomized controlled trials are not feasible. Therefore, we employed MR techniques using genome-wide association studies (GWAS) summary statistics to explore the potential causal relationship between the gut microbiome and the risk of developing breast cancer.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research framework is depicted in Figure 1A. The gut microbiota served as the exposure, and breast cancer was the outcome. Genetic variants significantly associated with the gut microbiome are selected as instrumental variables (IVs) for investigating the potential causal relationship between gut microbiota and breast cancer using the MR method. MR employs summary data from GWAS to isolate confounding factors, offering a more precise evaluation of causal relationships. Within the MR study framework, IVs must meet three core criteria: (1) be significantly associated with the exposure (relevance criterion); (2) not be associated with any known or unknown confounders (independence criterion); and (3) influence the outcome solely through the exposure and not via any other direct causal pathways (exclusion restriction criterion).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource of gut bacteria data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSummary data on gut bacteria were obtained from the genome-wide association meta-analysis conducted by the MiBioGen consortium (https://mibiogen.gcc.rug.nl/), which represents the most extensive study to date on the transgenic genetics of the human gut microbiome. This study compiled data from 18,340 samples across 24 populations from Europe, Africa, Asia, the Middle East, and Latin America, analyzing microbial compositions across multiple variable regions of the 16S rRNA gene, including V1-V2, V3-V4, and V4. DNA was extracted from fecal samples using seven methods, yielding genetic information on 211 types of gut microbes. The data from all samples were normalized to 10,000 reads per sample, and direct classification binning was used to categorize the microbes into five different taxonomic levels: phylum, class, order, family, and genus, based on genetic characteristics. To ensure the study\u0026apos;s comprehensiveness, all identified gut bacteria taxa were included, comprising 15 unnamed bacterial taxa and one duplicate bacterial taxon, resulting in 211 gut bacteria taxa selected as exposure factors for MR analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBreast cancer data sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GWAS data for the European population on breast cancer were sourced from the FinnGen consortium\u0026apos;s R10 release, a large-scale biomedicine project based on the Finnish population, involving 412,181 participants (181,871 males and 230,310 females), with 18,786 breast cancer cases and 182,927 controls. Each GWAS received approval from an ethics review committee, and detailed information on the data can be downloaded from the FinnGen consortium\u0026apos;s official website: https://storage.googleapis.com/finngen-public-data-r10/summary_stats/R10_manifest.tsv/.\u003c/p\u003e\n\u003cp\u003eFor the East Asian population, breast cancer GWAS data were obtained from the Biobank Japan project, the largest known biobank focusing on the East Asian population. From 2003 to 2018, in collaboration with 12 medical institutions, DNA, serum, and clinical information from over 200,000 patients with 47 diseases were collected\u003csup\u003e[12]\u003c/sup\u003e. Based on these data, researchers identified various genetic variants associated with disease susceptibility and drug responsiveness. The database includes 5,552 breast cancer patients and 89,732 controls, involving 8,872,152 single nucleotide polymorphisms (SNPs). All related GWAS received approval from the ethics committees of the RIKEN Yokohama Institute and the Institute of Medical Science at the University of Tokyo. The diagnosis of primary breast cancer cases was confirmed based on the International Classification of Diseases (ICD) codes applicable during the study period, which includes from the eighth to the tenth editions of the ICD. Detailed information on the data has been included in the publicly accessible IEU database, available for download via the designated dataset (GWAS ID: bbj-a160; https://gwas.mrcieu.ac.uk/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of instrumental variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSNPs strongly associated with the exposure at the genome-wide significance level (P \u0026lt; 5\u0026times;10\u003csup\u003e-6\u003c/sup\u003e) were selected; setting (R\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.001, distance = 1 MB) to remove SNPs with high linkage disequilibrium, ensuring independence; excluding SNPs with fewer than three occurrences, minor allele frequency \u0026le; 0.01; eliminating SNPs not found in the outcome GWAS dataset; removing SNPs with inconsistent alleles between exposure and outcome; deleting palindromic SNPs that could lead to bias;\u003c/p\u003e\n\u003cp\u003eThe F-statistic was calculated to assess potential IV bias, selecting F \u0026gt; 10 to ensure weak instrumental variables do not influence causality. The calculation formula is F = R\u003csup\u003e2\u003c/sup\u003e \u0026times; (N - 1 - K) / (1 - R\u003csup\u003e2\u003c/sup\u003e) \u0026times; K, where N represents the sample size of exposure data, K represents the number of IVs, and R\u003csup\u003e2\u003c/sup\u003e represents the proportion of variance in the exposure variable explained by the selected IVs, with the calculation formula R\u003csup\u003e2\u003c/sup\u003e = \u0026beta;\u003csup\u003e2\u003c/sup\u003e / (\u0026beta;2 + SE\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical workflow is depicted in Figure 1B. After harmonizing the data for exposure and outcome, the inverse variance weighted (IVW) MR analysis method was employed to explore the potential causal relationship between gut microbiota and breast cancer. It was used as the primary analysis method because IVW can provide the most accurate causal effect estimate, assuming all instrumental variables are valid. Cochran\u0026apos;s Q test was utilized to assess the consistency of the estimated effects of the selected IVs on the exposure variable, with p \u0026lt; 0.05 indicating significant heterogeneity. The MR-Egger intercept was used to detect pleiotropy, with a more significant intercept suggesting more robust evidence of pleiotropy. A zero intercept indicates no pleiotropy, and the p-value was used to test whether it significantly differs from zero, with p \u0026gt; 0.05 considered insufficient evidence of pleiotropy. Leave-one-out analysis was conducted to evaluate the impact of the removal of individual SNPs on the overall statistical results and to reveal the contribution of specific SNPs to the final analysis outcome. The MR-PRESSO method was applied to detect and remove outliers inconsistent with the predicted effect size or direction. After excluding these outliers, five other MR methods are used for sensitivity analysis to ensure the accuracy and reliability of the final analysis results. The effect size of the impact of gut microbiota on breast cancer is expressed as odds ratios (OR) and their 95% confidence intervals (CI). The false discovery rate (FDR) was used to adjust for multiple hypothesis testing. All analyses are performed using the \u0026ldquo;TwoSampleMR,\u0026rdquo; \u0026ldquo;MR-PRESSO,\u0026rdquo; and \u0026ldquo;Mendelian Randomization\u0026rdquo; packages in R software (version 4.3.2).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe MiBioGen study identified 211 gut microbiota taxa belonging to nine phyla, 15 classes, 20 orders, 32 families, and 119 genera.\u003c/p\u003e \u003cp\u003eIn the MR analysis of the European population, we identified four categories of gut microbiota significantly associated with the risk of breast cancer, including the class Coriobacteriia, the order Coriobacteriales, the family Coriobacteriaceae, and the genus \u003cem\u003eErysipelatoclostridium\u003c/em\u003e. The first three have a hierarchical taxonomic relationship: Coriobacteriia includes the order Coriobacteriales, which belongs to the family Coriobacteriaceae\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. IVs for these three taxonomic levels, selected based on a statistical threshold of p-value less than 5e-6, were completely consistent, reflecting a close genetic link among these microbial taxa. They may be associated with breast cancer risk through common biological pathways.\u003c/p\u003e \u003cp\u003eIn the East Asian population, we found three categories of gut microbiota associated with breast cancer risk (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), including two families (Porphyromonadaceae and Ruminococcaceae) and one genus (\u003cem\u003eEubacterium ruminantium\u003c/em\u003e group) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThese findings were based on the primary MR analysis method (IVW). We conducted further analyses on the gut microbiota, with detailed results in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMendelian randomization analysis statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePopulations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGut microbe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo. of IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eF-statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eBetween-SNP heterogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eHorizontal pleiotropy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP (MR-PRESSO global test)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEgger intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEuropeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoriobacteriaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eErysipelatoclostridium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoriobacteriales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoriobacteriia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEast Asians\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePorphyromonadaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRuminococcaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEubacterium ruminantium group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e5\u0026ndash;8 instrumental variables (IVs) were used for the selected gut microbiota, with an average F-statistic indicating robustness (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No heterogeneity or horizontal pleiotropy was observed among the SNPs of any gut microbiota (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).In the European population, Erysipelatoclostridium was observed to have a positive association with breast cancer risk, with an OR of 1.21 (95% CI 1.083\u0026ndash;1.358, FDR\u0026thinsp;=\u0026thinsp;0.0039) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Coriobacteriales, Coriobacteriaceae, and Coriobacteriia were negatively associated with breast cancer risk, with an OR of 0.757 (95% CI 0.616\u0026ndash;0.930, FDR\u0026thinsp;=\u0026thinsp;0.0281) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). All findings met the statistical significance threshold of FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the East Asian population, Porphyromonadaceae had a negative association with breast cancer risk, with an OR of 0.304 (95% CI 0.155\u0026ndash;0.596, FDR\u0026thinsp;=\u0026thinsp;0.0005), and Ruminococcaceae also showed a negative association with breast cancer risk, with an OR of 0.674 (95% CI 0.508\u0026ndash;0.895, FDR\u0026thinsp;=\u0026thinsp;0.03173). However, the limited number of instrumental variables for these two microbiota groups restricted the statistical power of the analysis. Despite these results being statistically significant, their interpretation should be cautiously approached. On the other hand, the \u003cem\u003eEubacterium ruminantium\u003c/em\u003e group showed a positive association with breast cancer risk, with an OR of 1.259 (95% CI 1.056\u0026ndash;1.499, FDR\u0026thinsp;=\u0026thinsp;0.0497). The moderate number of IVs for this group made its association with breast cancer risk statistically more reliable. This finding suggests that the \u003cem\u003eEubacterium ruminantium\u003c/em\u003e group plays a significant role in the etiology of breast cancer.\u003c/p\u003e \u003cp\u003eRegardless of being in European or East Asian populations and despite the differences in OR values and corresponding FDR values, the six MR methods used all demonstrated consistent causal estimates between gut microbiota and breast cancer risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe gut microbiome is a complex ecosystem comprising trillions of microorganisms. Considering its role in human health and disease, in-depth studies hold promise for opening avenues for treating and preventing various diseases\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. In recent years, there has been growing interest in the relationship between the gut microbiome and breast cancer. Goedert et al. identified distinctive compositional differences between untreated breast cancer patients and healthy individuals, with characteristic microbiota including Clostridiaceae, Faecalibacterium, and Ruminococcaceae\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Terrisse et al. compared healthy individual samples and found that \u003cem\u003eBacteroides uniformis\u003c/em\u003e, \u003cem\u003eClostridium bolteae\u003c/em\u003e, and \u003cem\u003eBilophila wadsworthia\u003c/em\u003e were associated with poorer breast cancer outcomes\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. However, some studies suggest that there is not a significant difference in the gut microbiota between breast cancer and non-breast cancer patients\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study aimed to use MR to reveal the relationship between gut microbiota and breast cancer in European and East Asian populations. We used GWAS summary data from the MiBioGen consortium, the largest GWAS dataset on microbiomes to date, with breast cancer data from FinnGen and Biobank Japan, providing a solid foundation for our analysis. Despite differences between the two populations, our findings suggest potential associations between specific microbiota and breast cancer.\u003c/p\u003e \u003cp\u003eWe found potential causal relationships in the European population between the class Coriobacteriia, the order Coriobacteriales, the family Coriobacteriaceae, and the genus \u003cem\u003eErysipelatoclostridium\u003c/em\u003e and breast cancer. \u003cem\u003eErysipelatoclostridium\u003c/em\u003e is an anaerobic bacterium associated with intestinal health and disease. Iadsee et al. showed that levels of \u003cem\u003eErysipelatoclostridium ramosum\u003c/em\u003e were significantly higher in colorectal cancer patients compared to the healthy population\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Cai et al. found that its metabolic product, Ptilosteroid A, is associated with radiation-induced intestinal injury, demonstrating significant value in predicting radiation-induced intestinal damage\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Our study confirms an association between \u003cem\u003eErysipelatoclostridium\u003c/em\u003e and breast cancer, though the specific mechanisms remain unclear.\u003c/p\u003e \u003cp\u003eSome bacteria within the Coriobacteriia family can participate in estrogen metabolism, primarily by metabolizing soy isoflavones such as daidzein and genistein, which are converted in the body into compounds with estrogenic activity\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Therefore, they may play an essential role in the development of hormone-dependent cancers, such as breast and prostate cancer, by regulating estrogen levels and affecting the risk and progression of cancer. Our study validates the existence of this potential mechanism.\u003c/p\u003e \u003cp\u003eIn East Asian populations, we discovered that Porphyromonadaceae and Ruminococcaceae are negatively correlated with the risk of breast cancer, while the \u003cem\u003eEubacterium ruminantium\u003c/em\u003e group is positively correlated. This finding significantly differs from the findings in European populations. This difference reflects the significant disparities in genetic backgrounds, dietary habits, lifestyles, and environmental factors among various populations. These factors can significantly impact the composition of the gut microbiota, influencing the risk of developing breast cancer. These results underscore the importance of considering population-specific factors when studying the relationship between the gut microbiome and breast cancer. Porphyromonadaceae and Ruminococcaceae, known for their relation to gut health and inflammation regulation, are particularly interesting. Some members of the Porphyromonadaceae family have been associated with intestinal inflammation and the maintenance of gut barrier function\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, although research in this area remains relatively sparse. The Ruminococcaceae family is noted for producing short-chain fatty acids (SCFAs) \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, including butyrate, propionate, and acetate. SCFAs are crucial for promoting the expression of tight junction proteins, reducing intestinal permeability, preventing the transmembrane transport of harmful substances and pathogens, and regulating the intestinal immune response. This phenomenon leads to anti-inflammatory and immune tolerance effects and activates G-protein-coupled receptors (such as GPR41 and GPR43) that regulate the host's energy balance and metabolism. Thus, the Ruminococcaceae family and its metabolic products, SCFAs, have shown substantial potential in preventing and treating various diseases, becoming a focal point of current microbiome research.\u003c/p\u003e \u003cp\u003eThis study has some limitations. Despite setting a significance threshold that is not very stringent (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), including a relatively small number of instrumental variables limited our statistical analysis power and could potentially lead to false positive results. To address this issue, we implemented multiple testing corrections by calculating the FDR to enhance the reliability of our findings. Furthermore, although MR analysis inherently addresses confounding issues, it remains susceptible to pleiotropic effects. Our study applied sensitivity analysis methods such as MR-Egger and MR-PRESSO to mitigate genetic pleiotropy. The consistent results across various MR analysis methods indicate the robustness of our findings.\u003c/p\u003e \u003cp\u003eNevertheless, a significant limitation is the absence of stratification based on specific breast cancer subtypes, such as HER-2, ER, and PR expression statuses. Considering the different interactions between gut microbiota and various breast cancer subtypes, the generality of our findings may be limited. This aspect should be considered when interpreting our results, and future genetic studies should delve deeper into these specific subtypes to provide a more comprehensive understanding. Other limitations include the cross-sectional nature of GWAS data, complicating the determination of a temporal causal relationship between gut microbiota changes and breast cancer onset. Extra caution is required when interpreting the summary data provided by GWAS, as it does not account for the complexity and nuances arising from individual differences.\u003c/p\u003e \u003cp\u003eDespite these limitations, an increasing body of evidence suggests that exploring the gut microbiome offers new perspectives in unveiling the pathogenesis of breast cancer and enhancing the predictive accuracy of existing risk assessment methodologies\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Research has reported that identifying gut microbiome characteristics can provide critical information for predicting the efficacy and safety of chemotherapy in breast cancer patients, playing a role in developing personalized treatment strategies\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, our study provides new insights into the relationship between the gut microbiota of European and East Asian populations and breast cancer. The identified microbiota may represent potential biomarkers or therapeutic targets for breast cancer. Future research is needed to validate these findings and reveal their specific biological mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based on existing publicly available data. Therefore, the requirement for ethical approval and consent is waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable as this study utilizes publicly available datasets that do not contain any identifiable personal information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were sourced from three publicly available databases: Summary data on gut microbiota were obtained from the whole-genome association meta-analyses conducted by the MiBioGen consortium, accessible at https://mibiogen.gcc.rug.nl/. European population breast cancer GWAS data were derived from the FinnGen consortium\u0026apos;s Release R10, with detailed information available for download at https://storage.googleapis.com/finngen-public-data-r10/summary_stats/R10_manifest.tsv/. East Asian population breast cancer GWAS data were acquired from the Japan Biobank, with details listed in the publicly accessible IEU database, available via GWAS ID: bbj-a160 at https://gwas.mrcieu.ac.uk/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors WL, CG, ZC, SX, and LZ declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Zhejiang Provincial Program for Medical and Health Science and Technology (Project No. 2024XY152).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWL conceptualized the study and oversaw the project\u0026apos;s overall direction and planning. CHG was instrumental in the development of the Mendelian Randomization analysis framework and performed the data analysis. ZC contributed to the literature review and played a key role in interpreting the analysis results in the context of existing research. SX was responsible for data curation, ensuring the accuracy and integrity of the data used in the study. LZ contributed to drafting the manuscript, focusing on the discussion of the findings and their implications for future research. All authors were involved in revising the manuscript critically for important intellectual content and approved the final version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBredart A, De Pauw A, Anota A, et al. Information needs on breast cancer genetic and non-genetic risk factors in relatives of women with a BRCA1/2 or PALB2 pathogenic variant. Breast. 2021;60:38\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTamimi RM, Spiegelman D, Smith-Warner SA, et al. Population Attributable Risk of Modifiable and Nonmodifiable Breast Cancer Risk Factors in Postmenopausal Breast Cancer. Am J Epidemiol. 2016;184(12):884\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEsposito MV, Fosso B, Nunziato M, et al. Microbiome composition indicate dysbiosis and lower richness in tumor breast tissues compared to healthy adjacent paired tissue, within the same women. BMC Cancer. 2022;22(1):30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodriguez JM, Fernandez L, Verhasselt V. The Gut\u0026ndash;Breast Axis: Programming Health for Life. Nutrients, 2021, 13(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu LX, Schwabe RF. The gut microbiome and liver cancer: mechanisms and clinical translation. Nat Rev Gastroenterol Hepatol. 2017;14(9):527\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoskinson C, Jiang RY, Stiemsma LT. Elucidating the roles of the mammary and gut microbiomes in breast cancer development. Front Oncol. 2023;13:1198259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwa M, Plottel CS, Blaser MJ et al. The Intestinal Microbiome and Estrogen Receptor-Positive Female Breast Cancer. J Natl Cancer Inst, 2016, 108(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu AH, Tseng C, Vigen C, et al. Gut microbiome associations with breast cancer risk factors and tumor characteristics: a pilot study. Breast Cancer Res Treat. 2020;182(2):451\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTzeng A, Sangwan N, Jia M, et al. Human breast microbiome correlates with prognostic features and immunological signatures in breast cancer. Genome Med. 2021;13(1):60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKovacs T, Miko E, Ujlaki G, et al. The involvement of oncobiosis and bacterial metabolite signaling in metastasis formation in breast cancer. Cancer Metastasis Rev. 2021;40(4):1223\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirata M, Kamatani Y, Nagai A, et al. Cross-sectional analysis of BioBank Japan clinical data: A large cohort of 200,000 patients with 47 common diseases. J Epidemiol. 2017;27(3):S9\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGupta RS, Chen WJ, Adeolu M, et al. Molecular signatures for the class Coriobacteriia and its different clades; proposal for division of the class Coriobacteriia into the emended order Coriobacteriales, containing the emended family Coriobacteriaceae and Atopobiaceae fam. nov., and Eggerthellales ord. nov., containing the family Eggerthellaceae fam. nov. Int J Syst Evol Microbiol. 2013;63(Pt 9):3379\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh RK, Chang HW, Yan D, et al. Influence of diet on the gut microbiome and implications for human health. J Transl Med. 2017;15(1):73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoedert JJ, Jones G, Hua X et al. Investigation of the Association Between the Fecal Microbiota and Breast Cancer in Postmenopausal Women: a Population-Based Case-Control Pilot Study. JNCI: Journal of the National Cancer Institute, 2015, 107(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerrisse S, Derosa L, Iebba V, et al. 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Front Public Health. 2022;10:862598.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoukup ST, Stoll DA, Danylec N et al. Metabolism of Daidzein and Genistein by Gut Bacteria of the Class Coriobacteriia. Foods, 2021, 10(11).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSohn J, Li L, Zhang L, et al. Porphyromonas gingivalis indirectly elicits intestinal inflammation by altering the gut microbiota and disrupting epithelial barrier function through IL9-producing CD4(+) T cells. Mol Oral Microbiol. 2022;37(2):42\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Y, Zhang Y, Zhao X, et al. Microbiota-derived short-chain fatty acids: Implications for cardiovascular and metabolic disease. Front Cardiovasc Med. 2022;9:900381.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez M, Reina-P\u0026eacute;rez I, Astorga J et al. Breast Cancer and Its Relationship with the Microbiota. Int J Environ Res Public Health, 2018, 15(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrbaniak C, Cummins J, Brackstone M, et al. Microbiota of Human Breast Tissue. Appl Environ Microbiol. 2014;80(10):3007\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuan X, Ma F, Sun X et al. Gut Microbiota Profiling in Patients With HER2-Negative Metastatic Breast Cancer Receiving Metronomic Chemotherapy of Capecitabine Compared to Those Under Conventional Dosage. Front Oncol, 2020, 10.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"gut microbiota, breast cancer, Mendelian randomization, Europeans, East Asians","lastPublishedDoi":"10.21203/rs.3.rs-3986727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3986727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eSeveral studies have explored the potential link between gut microbiota and breast cancer; nevertheless, the causal relationship between gut microbiota and breast cancer remains unclear. This study investigated the causal relationship between gut microbiota and breast cancer in European and East Asian populations using a Mendelian Randomization (MR) analysis approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe utilized summary statistics from genome-wide association studies (GWAS) of the gut microbiome from the MiBioGen project with summary data from GWAS on breast cancer from the FinnGen consortium and the IEU database. Preliminary statistical analyses were conducted using inverse variance weighting, supplemented by various sensitivity analysis methods, including MR-Egger regression, weighted median, weighted mode, simple median, and simple mode, to ensure the robustness of our findings. Heterogeneity and pleiotropy were assessed to avoid misleading conclusions caused by unconsidered confounders or non-specific effects of genetic variants, ensuring that the results reflect a genuine causal relationship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In European populations, four types of gut microbiota were associated with breast cancer. The genus \u003cem\u003eErysipelatoclostridium\u003c/em\u003e was positively associated with the risk of breast cancer, with an odds ratio (OR) of 1.21 (95% confidence interval [CI] 1.083–1.358), false discovery rate (FDR) = 0.0039. The class Coriobacteriia, order Coriobacteriales, and family Coriobacteriaceae, which belong to the same phylogenetic system, showed a consistent negative association with breast cancer risk, with an OR of 0.757 (95% CI 0.616–0.930), FDR = 0.0281. In East Asian populations, three types of gut microbiota were related to breast cancer. The \u003cem\u003eEubacterium ruminantium\u003c/em\u003e group was positively associated with breast cancer risk, with an OR of 1.259 (95% CI 1.056–1.499), FDR = 0.0497. The families Porphyromonadaceae and Ruminococcaceae were negatively associated with breast cancer risk, with ORs of 0.304 (95% CI 0.155–0.596), FDR = 0.0005, and 0.674 (95% CI 0.508–0.895), FDR = 0.03173, respectively. However, these two taxa had limited instrumental variables, restricting the statistical power and potentially affecting the interpretation of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eA causal link between specific gut microbiota and breast cancer exists. This finding enhances our understanding of the relationship between the gut microbiome and breast cancer and offers potential directions for developing prevention and treatment methods.\u003c/p\u003e","manuscriptTitle":"Exploring the Relationship between Gut Microbiota and Breast Cancer Risk in European and East Asian Populations Using Mendelian Randomization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 09:20:08","doi":"10.21203/rs.3.rs-3986727/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-21T03:00:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-20T20:55:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-19T12:32:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301600691813415337111325544221081875844","date":"2024-06-17T12:24:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147957320495049995430635576133138575347","date":"2024-06-17T11:01:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3146484164319025750873000221836616159","date":"2024-06-16T23:49:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337831394360192762862547372138468227821","date":"2024-06-15T02:01:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260887092376540987366659721596819772815","date":"2024-06-14T15:07:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-07T18:33:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-19T03:45:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-18T15:40:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-18T15:40:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2024-02-25T03:07:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8245c40c-19d4-4931-a5eb-a00e1df5e8d6","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-12T16:00:45+00:00","versionOfRecord":{"articleIdentity":"rs-3986727","link":"https://doi.org/10.1186/s12885-024-12721-9","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2024-08-08 15:56:55","publishedOnDateReadable":"August 8th, 2024"},"versionCreatedAt":"2024-03-21 09:20:08","video":"","vorDoi":"10.1186/s12885-024-12721-9","vorDoiUrl":"https://doi.org/10.1186/s12885-024-12721-9","workflowStages":[]},"version":"v1","identity":"rs-3986727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3986727","identity":"rs-3986727","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}