Causal Links and Immune Mediators Between Blood Cells and Breast Cancer Risk: A Mendelian Randomization Study

Causal Links and Immune Mediators Between Blood Cells and Breast Cancer Risk: A Mendelian Randomization Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal Links and Immune Mediators Between Blood Cells and Breast Cancer Risk: A Mendelian Randomization Study Meizi Song, Yu Liu, Jiaxu Dong, Jiafang Xu, Minghao Yang, Qingjie Hu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6122403/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background: Previous studies have indicated a potential association between blood cells and breast cancer risk, but the causal relationships involving specific blood cell metrics and the role of immune cell mediators remain unclear. This study employs Mendelian randomization to explore the causal relationships between diverse blood cell profiles and breast cancer risk, while also seeking to identify potential mediating factors within immune cell metrics. Methods: We utilized Mendelian randomization to explore the causal effects of 91 different blood cell types on breast cancer risk, using genetic variants as instrumental variables. The analysis employed the inverse-variance weighted (GWAS) method, with a significance threshold of 0.05, to evaluate the causal relationships. Multivariate analysis was conducted to determine the mediating effects of immune cells in the association between blood cells and breast cancer. Heterogeneity test and multi-small size test are performed to complete the sensitivity analysis, ensuring the stability and reliability of the results. Results: We identified significant causal relationships between blood cells and breast cancer risk. Specifically, the Neutrophil perturbation response (the ratio of neutrophil 2 to neutrophil 4 in response to KCl perturbation measured by WDF dye) was found to be causally associated with breast cancer risk. Furthermore, CD45RA-CD4+ T cell Absolute Count was identified as a mediator in this relationship. The mediating effect of CD45RA-CD4+ T cell Absolute Count was -0.055 (P=0.022), indicating that the impact of Neutrophil perturbation response on breast cancer risk is mediated through CD45RA-CD4+ T cell Absolute Count. Conclusion: Our study highlights a causal relationship between Neutrophil perturbation response and breast cancer risk, with CD45RA-CD4+ T cell Absolute Count acting as a significant mediator. These findings provide new insights into the role of immune cells in the relationship between blood cell metrics and breast cancer risk, suggesting potential targets for further research and intervention. Breast cancer immune cells Mendelian randomization Figures Figure 1 Figure 2 Introduction Breast cancer is a leading cause of morbidity, disability, and mortality among women worldwide. According to recent data, it is projected that in 2023, there will be approximately 2.3 million new breast cancer diagnoses globally, ranking it as the second most common cancer after lung cancer. Out of these cases, around 670,000 individuals are expected to succumb to the disease, positioning breast cancer as the fourth leading cause of cancer-related fatalities following lung, colorectal, and stomach cancers. Its death rate significantly surpasses that of gynecological cancers like cervical and ovarian cancers. 1 . With over 7.7 million women surviving at least five years post-diagnosis, breast cancer is also the most prevalent malignancy globally 2 . Early detection is a crucial factor in reducing mortality 3–5 , emphasizing the need for increased public health efforts aimed at prevention. Identifying causal risk factors is essential to improve preventive strategies for breast cancer. Circulating blood cells, including neutrophils, lymphocyte subsets, and platelets, are easily measurable through routine laboratory tests 6 . Abnormalities in these components can provide key diagnostic insights into various diseases 6 . Several studies have investigated the associations between blood cell components and breast cancer. For instance, observational studies have explored the relationships between neutrophils 7–10 , neutrophil-to-lymphocyte ratio 11–14 , lymphocyte subsets 15–17 , and platelet parameters 18–20 and breast cancer. Despite extensive observational research, animal studies, and clinical trials, identifying blood cell components-related risk factors for breast cancer remains challenging. Traditional research approaches are prone to confounding biases and reverse causality. Additionally, randomized controlled trials (RCTs) are expensive, time-consuming, and often impractical, especially for diseases like breast cancer, where there can be significant delays between exposure to risk factors and disease onset. Mendelian randomization (MR) is a genetic epidemiological approach that evaluates the causal relationships between exposures—often risk factors—and outcomes, such as diseases, by using genetic variants as instrumental variables (IVs) 21 . By mimicking the structure of a naturally occurring randomized clinical trial, MR offers a robust tool for identifying causal associations with fewer confounding factors than traditional observational studies 22 . Furthermore, MR studies eliminate reverse causality, as the genetic variants used as IVs are established prior to the onset of most outcomes 23 . Recent MR studies have revealed potential causal associations between breast cancer and several factors, including immune cells 24 , alcohol intake 25 , genetic predisposition to systemic lupus erythematosus (SLE) 26 , bone mineral density 27 , blood metabolites such as high-density lipoprotein cholesterol and acetate 28 , micronutrient consumption and circulating concentrations 29 , and dietary factors 30 .Recently, an MR Article on the causal relationship between blood cells and breast cancer was published, but it only carried out a bidirectional Mendelian randomization study. I hope our study will go further and make this study more specific by introducing immune mediation.And this study may offer new strategies and insights for treating breast cancer, particularly for patients with limited responses to immunotherapy. Materials and methods Study design This study employed two-sample and multivariate MR analyses to investigate the mediating causal relationship between 91 blood cell traits and breast cancer risk. We utilized data from 731 immune cells to evaluate these relationships. MR leverages genetic variation as IVs to infer causal relationships, requiring the fulfillment of three essential assumptions: (1) The genetic variation must be directly associated with the exposure; (2) The genetic variation must be independent of potential confounding factors that could affect the relationship between exposure and outcome; (3) The genetic variation must influence the outcome solely through its effect on the exposure, without affecting the outcome through other pathways. Breast cancer data were sourced from the IEU openGWAS database, comprising 17,389 cases and 240,341 controls. GWAS data for all blood cells and immune cells The data on blood cells were obtained from the GWAS Catalog, which identified potential genetically determined variations in human blood cells through perturbative phenotype analysis and linked these variations to various common diseases. This approach involved utilizing genome-wide association studies (GWAS) to examine gene variations associated with changes in blood cell responses under different perturbation conditions 31 . The immune cell data were sourced from IEU openGWAS and included a total of 731 immune phenotypes. These phenotypes encompass absolute cell counts (AC) (n=118), median fluorescence intensity (MFI) reflecting surface antigen levels (n=389), morphological parameters (MP) (n=32), and relative cell counts (RC) (n=192). Specifically, MFI, AC, and RC features cover a range of cell types including B cells, common dendritic cells (CDCs), mature T cells, monocytes, bone marrow cells, TBNK cells (T cells, B cells, natural killer cells), and regulatory T cells (Treg). The MP features include CDC and TBNK cells. The initial GWAS for immune cells utilized data from 3,757 individuals of European descent, ensuring no overlap in the dataset. Approximately 22 million high-density array SNP genotypes were analyzed using a reference panel based on Sardinian population sequences. Correlations were examined after adjusting for covariates such as gender and age 32 . Selection of Instrumental Variables (IVs) To select IVs for each blood cell and immune cell, we set the significance level at 1×10 -5 , ensuring a strong correlation between genetic variation and exposure. To obtain independent IVs, we applied a linkage disequilibrium (LD) threshold of R 2 <0.001 using the "TwoSampleMR" package, with an aggregation distance of 10,000 kb. For breast cancer, we adjusted the significance level to 5×10 -8 , a standard threshold for genome-wide significance in GWAS studies. We used the same LD threshold of R 2 <0.001 and an aggregation distance of 10,000 kb. We calculated the F-value for each SNP and excluded single nucleotide polymorphisms (SNPs) with an F-value < 10 to ensure the robustness of our IVs. For blood cells, there were 987 SNPs in 91 kinds of blood cells after screening, 18728 SNPs in 731 immune cells after screening, and 46 SNPs in breast cancer after screening for reverse MR analysis. Statistical Analysis All analyses were performed using R software version 4.3.3, a widely used environment for statistical computing and graphical analysis (http://www.Rproject.org). The "TwoSampleMR" package (version 0.6.6) was employed within R for conducting MR analyses. This package is specifically tailored for MR analysis, providing tools for estimating, testing, and conducting sensitivity analyses of causal effects. We utilized the inverse variance weighted (IVW) method, a standard approach in MR that aggregates Wald estimates from multiple genetic variations. This method combines the ratio of SNP associations with exposure to SNP associations with outcomes, weighted by the inverse variance of each SNP result. Additionally, we employed weighted median and pattern-based methods as supplementary techniques, which offer robust causal estimates even if some IVs are invalid, provided certain assumptions are satisfied. To ensure the reliability and accuracy of the results, we conducted rigorous sensitivity analyses, including Cochran's Q-test, to assess heterogeneity among IVs. This comprehensive statistical evaluation helps validate the robustness of our findings based on the available data. Mendelian Mediation Analysis Firstly, we analyzed the causal relationship between blood cells and breast cancer, ensuring that reverse causation was not influencing the results. Following this, we examined the causal relationship between immune cells and breast cancer. Positive associations identified in both blood cells and immune cells were selected for further analysis to determine the mediating pathways. In our analysis, the overall impact of blood cells on breast cancer was decomposed into direct and indirect effects. The indirect effect was calculated by multiplying the β value for the association between blood cells and immune cells by the β value for the association between immune cells and breast cancer. The direct effect was then determined by subtracting the indirect effect from the total effect. Results The Causal Relationship between Blood Cells and Breast Cancer We utilized the IVW method with a significance level of 0.05 to investigate the causal relationships between blood cells and breast cancer. Our analysis identified nine significant causal relationships (Supplementary Material 1). To ensure robustness, we excluded results where MR Egger, Weighted median, and IVW methods yielded conflicting directions. Ultimately, consistent results across all three methods confirmed nine positive associations. Among these, five associations were linked to an increased risk of breast cancer, while four were associated with a decreased risk (Figure 1). Additionally, we observed a reverse causal relationship between Neutrophil perturbation response (median of neutrophil 1 at baseline measured by WDF dye) and breast cancer (Supplementary Material 2). We also found pleiotropy between Neutrophil perturbation response (coefficient of variation of neutrophil 1 in response to Pam3CSK4 perturbation measured by WDF dye) and breast cancer outcomes (Supplementary Material 3). After using MR egger, we found that all positive results did not have level pleiotropy (supplementary material 4), and cochran's Q-test showed that all positive results had no heterogeneity (supplementary material 5). The Causal Relationship between Immune Cells and Breast Cancer Using the IVW method, we identified 27 immune cell types with a significant association with breast cancer at a p-value threshold of 0.05 (Supplementary Material 6). All positive findings showed consistency across MR Egger, weighted regression, and IVW methodologies. Among these, 9 immune cell types were associated with an increased risk of breast cancer, while 18 were associated with a decreased risk (Figure 2). Additionally, we observed a reverse causal relationship involving CD45 on CD33+ HLA DR+ CD14dim cells (Supplementary Material 7). Pleiotropy were noted between HLA DR++ monocyte %monocyte, CD4+/CD8+ T cells, and CD8 on Terminally Differentiated CD8+ T cells in relation to the breast cancer outcome (Supplementary Material 8), which were therefore excluded from further analysis. After using MR egger, we found that all positive results did not have level pleiotropy (supplementary material 9), and cochran's Q-test showed that all positive results had no heterogeneity (supplementary material 10). Immune Cells act as Mediators between Blood Cells and Breast Cancer Using the IVW method as the primary analytical approach, we conducted MR analysis on positive blood cells and immune cells, applying a significance threshold of 0.05. After performing multivariate consistency screening for the overall direction of action, we identified that the Neutrophil perturbation response (the ratio of neutrophil 2 to neutrophil 4 in response to KCl perturbation measured by WDF dye) serves as the pathway characteristic of exposure. The CD45RA-CD4+ T cell Absolute Count was determined to be the mediator influencing breast cancer risk. The mediating effect of CD45RA-CD4+ T cell Absolute Count was found to be -0.055 (P=0.022). Discussion Through MR analysis, we found that among 91 types of human blood cells, an increased neutrophil perturbation response (measured by the neutrophil 2/neutrophil 4 ratio in response to KCl perturbation using WDF dye) raises the risk of breast cancer. Additionally, the absolute count of CD45RA-CD4 + T cells plays a mediating role, amplifying this effect. Our MR analysis of the relationships between neutrophil perturbation response, CD45RA-CD4 + T cell absolute count, and breast cancer provides evidence supporting a causal link between neutrophil perturbation response and breast cancer, with CD45RA-CD4 + T cell absolute count acting as a mediator. MR is considered a form of naturally occurring RCT. Compared to traditional RCTs, the advantage of MR lies in using (SNPs) significantly associated with the exposure variable as IVs. This approach minimizes the impact of confounding factors on the results. Neutrophil perturbation response refers to the series of biological changes neutrophils undergo upon stimulation, including the activation, migration, phagocytosis, and cytotoxicity of different neutrophil subsets 33 . Neutrophils are a critical component of the innate immune system, primarily responsible for engulfing and digesting bacteria, fungi, and clearing damaged or dead cells. They play a key role in combating infections and mediating inflammatory responses by rapidly responding to infections or tissue damage and releasing various inflammatory mediators to recruit and activate other immune cells 34 . However, in certain conditions, such as chronic inflammation or immunosuppressive states, excessive activation or abnormal responses of neutrophils may contribute to the onset and progression of diseases, including breast cancer 35,36 . Chronic inflammation can promote tumor cell proliferation, invasion, and metastasis, and increased neutrophil numbers or dysfunctional neutrophil activity may be associated with an elevated risk of breast cancer 37 . The tumor microenvironment in breast cancer contains large numbers of immune cells, including neutrophils, which can influence tumor growth and metastasis by releasing cytokines and chemokines 38–40 . Some studies suggest that tumor-associated neutrophils (TANs) may promote tumor growth and metastasis in breast cancer 41 . High neutrophil counts in peripheral blood or tumor tissues of breast cancer patients have been linked to poor prognosis, possibly due to the pro-tumor effects of neutrophils within the tumor microenvironment 41 . However, neutrophils are not a homogeneous cell population. Neutrophil perturbation response leads to abnormal changes in the numbers or functions of different neutrophil subsets, which may increase breast cancer risk by promoting chronic inflammation or altering the tumor microenvironment. Currently, there is no direct clinical evidence elucidating the precise mechanisms by which neutrophil perturbation response increases breast cancer risk. Therefore, further research on the relationship between neutrophil perturbation response and breast cancer risk is of significant importance. CD45RA-CD4 + T cells are a subset of CD4 + memory T cells with specific immune functions and phenotypic characteristics. These cells are typically considered part of the memory T cell population, formed after an initial immune response. They are capable of long-term survival and can rapidly respond to antigen stimulation, swiftly initiating an immune reaction upon re-exposure to the same antigen 24,42–44 . Within the immune system, CD4 + T cells play diverse roles, including assisting in the activation and differentiation of other immune cells, such as CD8 + T cells and B cells, as well as directly participating in immune responses 45 . Current research primarily focuses on the role of CD4 + T cells in breast cancer development, progression, and immunotherapy. Studies have shown that CD4 + T cells can assist other immune cells, like CD8 + T cells, in killing tumor cells 46,47 . Additionally, they can directly suppress tumor cell growth and division by secreting cytokines such as IFN-γ and TNF-α, which inhibit the cell cycle of tumor cells and ultimately limit tumor growth 48–50 . In the tumor microenvironment (TME) of breast cancer, the number and functional state of CD4 + T cells may influence tumor progression and patient prognosis through various mechanisms 51 . Although the role of CD4 + T cells in tumor immune surveillance and anti-tumor immunity is widely recognized, there is limited literature specifically addressing the association between CD45RA-CD4 + T cell Absolute Count and breast cancer 44 . CD45RA-CD4 + T cells may be involved in the immune response of breast cancer patients 44 , but the precise mechanisms remain unclear. These cells may influence breast cancer occurrence and progression by either enhancing or suppressing the activity of other immune cells. Future research should further explore the relationship between the quantity and functional state of CD45RA-CD4 + T cells in breast cancer patients and disease prognosis. Additionally, investigating their potential role in immunotherapy could provide insights into whether these cells can serve as therapeutic targets or predictive markers, offering more precise and effective treatment strategies for patients. To date, no research has specifically explored the association between Neutrophil perturbation response and CD45RA-CD4 + T cell Absolute Count. This study is the first comprehensive investigation based on publicly available GWAS data to examine this relationship. Metagenomic sequencing studies have established a link between Neutrophil perturbation response and CD45RA-CD4 + T cell Absolute Count. Although there is no direct evidence yet to confirm the mediating role of CD45RA-CD4 + T cells between Neutrophil perturbation response and breast cancer risk, the development and progression of breast cancer are closely associated with immune regulation and inflammatory responses within the tumor microenvironment 52,53 . Based on the function and mechanisms of CD45RA-CD4 + T cells as immune mediators, it is plausible to hypothesize that they may interact with Neutrophil perturbation response in breast cancer development. Neutrophils and CD45RA-CD4 + T cells typically act through different pathways in immune responses—neutrophils primarily participate in innate immunity, while CD45RA-CD4 + T cells are more involved in adaptive immunity. The Neutrophil perturbation response may enhance inflammatory responses within the tumor microenvironment, thereby promoting breast cancer progression. At the same time, CD45RA-CD4 + T cells, through their immune-regulatory functions, may influence the activation state of neutrophils. For example, CD45RA-CD4 + T cells can secrete cytokines such as IFN-γ and IL-2, which regulate neutrophil migration, phagocytosis, and cytotoxic abilities, as well as modulate the inflammatory response within the tumor microenvironment 54–56 . Through these actions, CD45RA-CD4 + T cells may serve as mediators between Neutrophil perturbation response and breast cancer risk. At the same time, other studies have shown the opposite results, namely, a negative association between CD4 + T cells and breast cancer risk (Discovery: OR, 0.996; P = 0.030. Validation: OR, 0.843; P = 4.09E-07), this relationship was mainly mediated by Caspase 8 57 .,There is also evidence that the immune cell phenotypes CD3 on CD28 + CD4-CD8- T cells and HLA DR on CD33- HLA DR + protect against BC. This protective effect may be achieved through various mechanisms, including enhancing immune surveillance to recognize and eliminate tumor cells; secreting cytokines to inhibit tumor cell proliferation and growth directly; triggering apoptotic pathways in tumor cells to reduce their number; modulating the tumor microenvironment to make it unfavorable for tumor growth and spread; activating other immune cells to boost the overall immune response; and inhibiting angiogenesis to reduce the tumor's nutrient supply 58 . This is a complex biological process involving multiple cellular and molecular interactions, and its precise mechanisms require further investigation. At the same time, through the continuous mining of the database and the full use of Mendelian randomization, other blood cells also have unexpected effects, such as CD24 + CD27 + B cells are associated with a reduced risk of breast cancer (OR = 0.9978,95% CI: 0.996–0.999, p = 0.001), while IgD-CD38 B cells were associated with an increased risk of breast cancer (OR = 1.002,95% CI: 1.001–1.004, p = 0.005). CD14 + CD16 + monocytes were associated with an increased risk of breast cancer (OR = 1.000,95% CI: 1.000-1.001, p = 0.005) 59 .Future research in this area will help us better understand the immunopathology of breast cancer and provide a theoretical foundation for developing new strategies for the prevention and treatment of breast cancer. Limitations While our study provides valuable insights, several limitations must be acknowledged. Firstly, MR analysis offers robust methods for evaluating causal relationships, but it reflects lifetime genetic exposure rather than short-term effects. This limitation means that MR might not fully capture the benefits of short-term interventions on Neutrophil perturbation response. Secondly, our research relies on genetic data from European populations due to the limited availability of GWAS data for Asian populations. This reliance introduces potential limitations in generalizability, as genetic distributions can vary significantly between ethnic groups. Therefore, our findings may not be fully representative and could be influenced by racial and regional genetic differences. Future studies should include diverse ethnic groups to validate and extend these results. Finally, the lack of specific clinical data, such as age and underlying health conditions, for the study populations restricts further analysis and understanding. Addressing these gaps in future GWAS studies and incorporating molecular experimental validations will be crucial for refining our conclusions and enhancing the applicability of our findings. Conclusion Our study reveals that Neutrophil perturbation response (the ratio of neutrophil 2 to neutrophil 4 in response to KCl perturbation measured by WDF dye) increases the risk of breast cancer, with CD45RA-CD4 + T cell Absolute Count acting as a mediator that amplifies this effect. Specifically, Neutrophil perturbation response elevates breast cancer risk through its interaction with CD45RA-CD4 + T cell Absolute Count. This research provides a novel perspective on the mechanisms underlying breast cancer development. Future investigations should further explore the immunomodulatory mechanisms involved in breast cancer pathogenesis and identify potential intervention targets, thereby guiding future therapeutic strategies. Abbreviations MR IVs GWAS IVW SNPs Mendelian randomization Instrumental variables Genome-wide association studies Inverse variance weighted Single nucleotide polymorphisms Declarations Availability of data and materials Datasets collected and analyzed during this study are available from the corresponding authors upon reasonable request. Competing interests The authors declare no competing interests. Consent for publication All authors have read and agreed to the published version of the manuscript. Author contributions Meizi Song contributed to study conception and design, data collection and drafting of the manuscript. Qingjie Hu and Jiaxu Dong contributed to data collection. Jiafang Xu and Siqi Yin contributed to data analysis and interpretation. Yu Liu and Xun Bi contributed to study conception and design, analysis and interpretation of the data, and revision of the final manuscript. All authors gave final approval of the version to be published. 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Genetically predicted Caspase 8 levels mediates the causal association between CD4+ T cell and breast cancer. Front Immunol. 2024;15:1410994. Xu W, Zhang T, Zhu Z, Yang Y. The association between immune cells and breast cancer: insights from Mendelian randomization and meta-analysis. Int J Surg. Jan 1 2025;111(1):230-241. Ming R, Wu H, Liu H, Zhan F, Qiu X, Ji M. Causal effects and metabolites mediators between immune cell and risk of breast cancer: a Mendelian randomization study. Front Genet. 2024;15:1380249. Additional Declarations No competing interests reported. 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15:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6122403/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6122403/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81549749,"identity":"e34788f1-bc59-46f3-98e4-ee57be60f5c8","added_by":"auto","created_at":"2025-04-28 12:36:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":721647,"visible":true,"origin":"","legend":"\u003cp\u003eResults of MR positive analysis of blood cells and breast cancer.\u003c/p\u003e\n\u003cp\u003e*Abbreviations: IVW, inverse variance weighting; CI, confidence interval.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6122403/v1/14b6f18bfc3fd13f649c2e13.png"},{"id":81549472,"identity":"196f460b-a78a-442c-86e1-3518a8d5d008","added_by":"auto","created_at":"2025-04-28 12:28:02","extension":"png","order_by":2,"title":"Figure 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12:28:02","extension":"csv","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":7632,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial9.csv","url":"https://assets-eu.researchsquare.com/files/rs-6122403/v1/f069394a96a08bd81cf56720.csv"},{"id":81549762,"identity":"d0b0d715-96bb-4f44-9e3d-063f958d6943","added_by":"auto","created_at":"2025-04-28 12:36:03","extension":"csv","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":256817,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial10.csv","url":"https://assets-eu.researchsquare.com/files/rs-6122403/v1/35b37898e693b40ea6ab708f.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Links and Immune Mediators Between Blood Cells and Breast Cancer Risk: A Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is a leading cause of morbidity, disability, and mortality among women worldwide. According to recent data, it is projected that in 2023, there will be approximately 2.3\u0026nbsp;million new breast cancer diagnoses globally, ranking it as the second most common cancer after lung cancer. Out of these cases, around 670,000 individuals are expected to succumb to the disease, positioning breast cancer as the fourth leading cause of cancer-related fatalities following lung, colorectal, and stomach cancers. Its death rate significantly surpasses that of gynecological cancers like cervical and ovarian cancers.\u003csup\u003e1\u003c/sup\u003e. With over 7.7\u0026nbsp;million women surviving at least five years post-diagnosis, breast cancer is also the most prevalent malignancy globally\u003csup\u003e2\u003c/sup\u003e. Early detection is a crucial factor in reducing mortality\u003csup\u003e3\u0026ndash;5\u003c/sup\u003e, emphasizing the need for increased public health efforts aimed at prevention. Identifying causal risk factors is essential to improve preventive strategies for breast cancer.\u003c/p\u003e \u003cp\u003eCirculating blood cells, including neutrophils, lymphocyte subsets, and platelets, are easily measurable through routine laboratory tests\u003csup\u003e6\u003c/sup\u003e. Abnormalities in these components can provide key diagnostic insights into various diseases\u003csup\u003e6\u003c/sup\u003e. Several studies have investigated the associations between blood cell components and breast cancer. For instance, observational studies have explored the relationships between neutrophils\u003csup\u003e7\u0026ndash;10\u003c/sup\u003e, neutrophil-to-lymphocyte ratio\u003csup\u003e11\u0026ndash;14\u003c/sup\u003e, lymphocyte subsets\u003csup\u003e15\u0026ndash;17\u003c/sup\u003e, and platelet parameters\u003csup\u003e18\u0026ndash;20\u003c/sup\u003eand breast cancer. Despite extensive observational research, animal studies, and clinical trials, identifying blood cell components-related risk factors for breast cancer remains challenging. Traditional research approaches are prone to confounding biases and reverse causality. Additionally, randomized controlled trials (RCTs) are expensive, time-consuming, and often impractical, especially for diseases like breast cancer, where there can be significant delays between exposure to risk factors and disease onset.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a genetic epidemiological approach that evaluates the causal relationships between exposures\u0026mdash;often risk factors\u0026mdash;and outcomes, such as diseases, by using genetic variants as instrumental variables (IVs)\u003csup\u003e21\u003c/sup\u003e. By mimicking the structure of a naturally occurring randomized clinical trial, MR offers a robust tool for identifying causal associations with fewer confounding factors than traditional observational studies\u003csup\u003e22\u003c/sup\u003e. Furthermore, MR studies eliminate reverse causality, as the genetic variants used as IVs are established prior to the onset of most outcomes\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent MR studies have revealed potential causal associations between breast cancer and several factors, including immune cells\u003csup\u003e24\u003c/sup\u003e, alcohol intake\u003csup\u003e25\u003c/sup\u003e, genetic predisposition to systemic lupus erythematosus (SLE)\u003csup\u003e26\u003c/sup\u003e, bone mineral density\u003csup\u003e27\u003c/sup\u003e, blood metabolites such as high-density lipoprotein cholesterol and acetate\u003csup\u003e28\u003c/sup\u003e, micronutrient consumption and circulating concentrations\u003csup\u003e29\u003c/sup\u003e, and dietary factors\u003csup\u003e30\u003c/sup\u003e.Recently, an MR Article on the causal relationship between blood cells and breast cancer was published, but it only carried out a bidirectional Mendelian randomization study. I hope our study will go further and make this study more specific by introducing immune mediation.And this study may offer new strategies and insights for treating breast cancer, particularly for patients with limited responses to immunotherapy.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed two-sample and multivariate MR analyses to investigate the mediating causal relationship between 91 blood cell traits and breast cancer risk. We utilized data from 731 immune cells to evaluate these relationships. MR leverages genetic variation as IVs to infer causal relationships, requiring the fulfillment of three essential assumptions: (1) The genetic variation must be directly associated with the exposure; (2) The genetic variation must be independent of potential confounding factors that could affect the relationship between exposure and outcome; (3) The genetic variation must influence the outcome solely through its effect on the exposure, without affecting the outcome through other pathways. Breast cancer data were sourced from the IEU openGWAS database, comprising 17,389 cases and 240,341 controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGWAS data for all blood cells and immune cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data on blood cells were obtained from the GWAS Catalog, which identified potential genetically determined variations in human blood cells through perturbative phenotype analysis and linked these variations to various common diseases. This approach involved utilizing genome-wide association studies (GWAS) to examine gene variations associated with changes in blood cell responses under different perturbation conditions\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe immune cell data were sourced from IEU openGWAS and included a total of 731 immune phenotypes. These phenotypes encompass absolute cell counts (AC) (n=118), median fluorescence intensity (MFI) reflecting surface antigen levels (n=389), morphological parameters (MP) (n=32), and relative cell counts (RC) (n=192). Specifically, MFI, AC, and RC features cover a range of cell types including B cells, common dendritic cells (CDCs), mature T cells, monocytes, bone marrow cells, TBNK cells (T cells, B cells, natural killer cells), and regulatory T cells (Treg). The MP features include CDC and TBNK cells. The initial GWAS for immune cells utilized data from 3,757 individuals of European descent, ensuring no overlap in the dataset. Approximately 22 million high-density array SNP genotypes were analyzed using a reference panel based on Sardinian population sequences. Correlations were examined after adjusting for covariates such as gender and age\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection of Instrumental Variables (IVs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo select IVs for each blood cell and immune cell, we set the significance level at 1\u0026times;10\u003csup\u003e-5\u003c/sup\u003e, ensuring a strong correlation between genetic variation and exposure. To obtain independent IVs, we applied a linkage disequilibrium (LD) threshold of R\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.001 using the \u0026quot;TwoSampleMR\u0026quot; package, with an aggregation distance of 10,000 kb. For breast cancer, we adjusted the significance level to 5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e , a standard threshold for genome-wide significance in GWAS studies. We used the same LD threshold of R\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.001 and an aggregation distance of 10,000 kb. We calculated the F-value for each SNP and excluded single nucleotide polymorphisms (SNPs) with an F-value \u0026lt; 10 to ensure the robustness of our IVs. For blood cells, there were 987 SNPs in 91 kinds of blood cells after screening, 18728 SNPs in 731 immune cells after screening, and 46 SNPs in breast cancer after screening for reverse MR analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using R software version 4.3.3, a widely used environment for statistical computing and graphical analysis (http://www.Rproject.org). The \u0026quot;TwoSampleMR\u0026quot; package (version 0.6.6) was employed within R for conducting MR analyses. This package is specifically tailored for MR analysis, providing tools for estimating, testing, and conducting sensitivity analyses of causal effects. We utilized the inverse variance weighted (IVW) method, a standard approach in MR that aggregates Wald estimates from multiple genetic variations. This method combines the ratio of SNP associations with exposure to SNP associations with outcomes, weighted by the inverse variance of each SNP result. Additionally, we employed weighted median and pattern-based methods as supplementary techniques, which offer robust causal estimates even if some IVs are invalid, provided certain assumptions are satisfied. To ensure the reliability and accuracy of the results, we conducted rigorous sensitivity analyses, including Cochran\u0026apos;s Q-test, to assess heterogeneity among IVs. This comprehensive statistical evaluation helps validate the robustness of our findings based on the available data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMendelian Mediation Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, we analyzed the causal relationship between blood cells and breast cancer, ensuring that reverse causation was not influencing the results. Following this, we examined the causal relationship between immune cells and breast cancer. Positive associations identified in both blood cells and immune cells were selected for further analysis to determine the mediating pathways.\u003c/p\u003e\n\u003cp\u003eIn our analysis, the overall impact of blood cells on breast cancer was decomposed into direct and indirect effects. The indirect effect was calculated by multiplying the \u0026beta; value for the association between blood cells and immune cells by the \u0026beta; value for the association between immune cells and breast cancer. The direct effect was then determined by subtracting the indirect effect from the total effect.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eThe Causal Relationship between Blood Cells and Breast Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized the IVW method with a significance level of 0.05 to investigate the causal relationships between blood cells and breast cancer. Our analysis identified nine significant causal relationships (Supplementary Material 1). To ensure robustness, we excluded results where MR Egger, Weighted median, and IVW methods yielded conflicting directions. Ultimately, consistent results across all three methods confirmed nine positive associations. Among these, five associations were linked to an increased risk of breast cancer, while four were associated with a decreased risk (Figure 1). Additionally, we observed a reverse causal relationship between Neutrophil perturbation response (median of neutrophil 1 at baseline measured by WDF dye) and breast cancer (Supplementary Material 2). We also found pleiotropy between Neutrophil perturbation response (coefficient of variation of neutrophil 1 in response to Pam3CSK4 perturbation measured by WDF dye) and breast cancer outcomes (Supplementary Material 3).\u0026nbsp;After using MR egger, we found that all positive results did not have level pleiotropy (supplementary material 4), and cochran\u0026apos;s Q-test showed that all positive results had no heterogeneity (supplementary material 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Causal Relationship between Immune Cells and Breast Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the IVW method, we identified 27 immune cell types with a significant association with breast cancer at a p-value threshold of 0.05 (Supplementary Material 6). All positive findings showed consistency across MR Egger, weighted regression, and IVW methodologies. Among these, 9 immune cell types were associated with an increased risk of breast cancer, while 18 were associated with a decreased risk (Figure 2). Additionally, we observed a reverse causal relationship involving CD45 on CD33+ HLA DR+ CD14dim cells (Supplementary Material 7). Pleiotropy were noted between HLA DR++ monocyte %monocyte, CD4+/CD8+ T cells, and CD8 on Terminally Differentiated CD8+ T cells in relation to the breast cancer outcome (Supplementary Material 8), which were therefore excluded from further analysis. After using MR egger, we found that all positive results did not have level pleiotropy (supplementary material 9), and cochran\u0026apos;s Q-test showed that all positive results had no heterogeneity (supplementary material 10).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Cells act as Mediators between Blood Cells and Breast Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the IVW method as the primary analytical approach, we conducted MR analysis on positive blood cells and immune cells, applying a significance threshold of 0.05. After performing multivariate consistency screening for the overall direction of action, we identified that the Neutrophil perturbation response (the ratio of neutrophil 2 to neutrophil 4 in response to KCl perturbation measured by WDF dye) serves as the pathway characteristic of exposure. The CD45RA-CD4+ T cell Absolute Count was determined to be the mediator influencing breast cancer risk. The mediating effect of CD45RA-CD4+ T cell Absolute Count was found to be -0.055 (P=0.022).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough MR analysis, we found that among 91 types of human blood cells, an increased neutrophil perturbation response (measured by the neutrophil 2/neutrophil 4 ratio in response to KCl perturbation using WDF dye) raises the risk of breast cancer. Additionally, the absolute count of CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cells plays a mediating role, amplifying this effect. Our MR analysis of the relationships between neutrophil perturbation response, CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cell absolute count, and breast cancer provides evidence supporting a causal link between neutrophil perturbation response and breast cancer, with CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cell absolute count acting as a mediator. MR is considered a form of naturally occurring RCT. Compared to traditional RCTs, the advantage of MR lies in using (SNPs) significantly associated with the exposure variable as IVs. This approach minimizes the impact of confounding factors on the results.\u003c/p\u003e \u003cp\u003eNeutrophil perturbation response refers to the series of biological changes neutrophils undergo upon stimulation, including the activation, migration, phagocytosis, and cytotoxicity of different neutrophil subsets\u003csup\u003e33\u003c/sup\u003e. Neutrophils are a critical component of the innate immune system, primarily responsible for engulfing and digesting bacteria, fungi, and clearing damaged or dead cells. They play a key role in combating infections and mediating inflammatory responses by rapidly responding to infections or tissue damage and releasing various inflammatory mediators to recruit and activate other immune cells\u003csup\u003e34\u003c/sup\u003e. However, in certain conditions, such as chronic inflammation or immunosuppressive states, excessive activation or abnormal responses of neutrophils may contribute to the onset and progression of diseases, including breast cancer\u003csup\u003e35,36\u003c/sup\u003e. Chronic inflammation can promote tumor cell proliferation, invasion, and metastasis, and increased neutrophil numbers or dysfunctional neutrophil activity may be associated with an elevated risk of breast cancer\u003csup\u003e37\u003c/sup\u003e. The tumor microenvironment in breast cancer contains large numbers of immune cells, including neutrophils, which can influence tumor growth and metastasis by releasing cytokines and chemokines\u003csup\u003e38\u0026ndash;40\u003c/sup\u003e. Some studies suggest that tumor-associated neutrophils (TANs) may promote tumor growth and metastasis in breast cancer\u003csup\u003e41\u003c/sup\u003e. High neutrophil counts in peripheral blood or tumor tissues of breast cancer patients have been linked to poor prognosis, possibly due to the pro-tumor effects of neutrophils within the tumor microenvironment\u003csup\u003e41\u003c/sup\u003e. However, neutrophils are not a homogeneous cell population. Neutrophil perturbation response leads to abnormal changes in the numbers or functions of different neutrophil subsets, which may increase breast cancer risk by promoting chronic inflammation or altering the tumor microenvironment. Currently, there is no direct clinical evidence elucidating the precise mechanisms by which neutrophil perturbation response increases breast cancer risk. Therefore, further research on the relationship between neutrophil perturbation response and breast cancer risk is of significant importance.\u003c/p\u003e \u003cp\u003eCD45RA-CD4\u0026thinsp;+\u0026thinsp;T cells are a subset of CD4\u0026thinsp;+\u0026thinsp;memory T cells with specific immune functions and phenotypic characteristics. These cells are typically considered part of the memory T cell population, formed after an initial immune response. They are capable of long-term survival and can rapidly respond to antigen stimulation, swiftly initiating an immune reaction upon re-exposure to the same antigen\u003csup\u003e24,42\u0026ndash;44\u003c/sup\u003e. Within the immune system, CD4\u0026thinsp;+\u0026thinsp;T cells play diverse roles, including assisting in the activation and differentiation of other immune cells, such as CD8\u0026thinsp;+\u0026thinsp;T cells and B cells, as well as directly participating in immune responses\u003csup\u003e45\u003c/sup\u003e. Current research primarily focuses on the role of CD4\u0026thinsp;+\u0026thinsp;T cells in breast cancer development, progression, and immunotherapy. Studies have shown that CD4\u0026thinsp;+\u0026thinsp;T cells can assist other immune cells, like CD8\u0026thinsp;+\u0026thinsp;T cells, in killing tumor cells\u003csup\u003e46,47\u003c/sup\u003e. Additionally, they can directly suppress tumor cell growth and division by secreting cytokines such as IFN-γ and TNF-α, which inhibit the cell cycle of tumor cells and ultimately limit tumor growth\u003csup\u003e48\u0026ndash;50\u003c/sup\u003e. In the tumor microenvironment (TME) of breast cancer, the number and functional state of CD4\u0026thinsp;+\u0026thinsp;T cells may influence tumor progression and patient prognosis through various mechanisms\u003csup\u003e51\u003c/sup\u003e. Although the role of CD4\u0026thinsp;+\u0026thinsp;T cells in tumor immune surveillance and anti-tumor immunity is widely recognized, there is limited literature specifically addressing the association between CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cell Absolute Count and breast cancer\u003csup\u003e44\u003c/sup\u003e. CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cells may be involved in the immune response of breast cancer patients\u003csup\u003e44\u003c/sup\u003e, but the precise mechanisms remain unclear. These cells may influence breast cancer occurrence and progression by either enhancing or suppressing the activity of other immune cells. Future research should further explore the relationship between the quantity and functional state of CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cells in breast cancer patients and disease prognosis. Additionally, investigating their potential role in immunotherapy could provide insights into whether these cells can serve as therapeutic targets or predictive markers, offering more precise and effective treatment strategies for patients.\u003c/p\u003e \u003cp\u003eTo date, no research has specifically explored the association between Neutrophil perturbation response and CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cell Absolute Count. This study is the first comprehensive investigation based on publicly available GWAS data to examine this relationship. Metagenomic sequencing studies have established a link between Neutrophil perturbation response and CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cell Absolute Count. Although there is no direct evidence yet to confirm the mediating role of CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cells between Neutrophil perturbation response and breast cancer risk, the development and progression of breast cancer are closely associated with immune regulation and inflammatory responses within the tumor microenvironment\u003csup\u003e52,53\u003c/sup\u003e. Based on the function and mechanisms of CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cells as immune mediators, it is plausible to hypothesize that they may interact with Neutrophil perturbation response in breast cancer development. Neutrophils and CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cells typically act through different pathways in immune responses\u0026mdash;neutrophils primarily participate in innate immunity, while CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cells are more involved in adaptive immunity. The Neutrophil perturbation response may enhance inflammatory responses within the tumor microenvironment, thereby promoting breast cancer progression. At the same time, CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cells, through their immune-regulatory functions, may influence the activation state of neutrophils. For example, CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cells can secrete cytokines such as IFN-γ and IL-2, which regulate neutrophil migration, phagocytosis, and cytotoxic abilities, as well as modulate the inflammatory response within the tumor microenvironment\u003csup\u003e54\u0026ndash;56\u003c/sup\u003e. Through these actions, CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cells may serve as mediators between Neutrophil perturbation response and breast cancer risk. At the same time, other studies have shown the opposite results, namely, a negative association between CD4\u0026thinsp;+\u0026thinsp;T cells and breast cancer risk (Discovery: OR, 0.996; P\u0026thinsp;=\u0026thinsp;0.030. Validation: OR, 0.843; P\u0026thinsp;=\u0026thinsp;4.09E-07), this relationship was mainly mediated by Caspase 8\u003csup\u003e57\u003c/sup\u003e.,There is also evidence that the immune cell phenotypes CD3 on CD28\u0026thinsp;+\u0026thinsp;CD4-CD8- T cells and HLA DR on CD33- HLA DR\u0026thinsp;+\u0026thinsp;protect against BC. This protective effect may be achieved through various mechanisms, including enhancing immune surveillance to recognize and eliminate tumor cells; secreting cytokines to inhibit tumor cell proliferation and growth directly; triggering apoptotic pathways in tumor cells to reduce their number; modulating the tumor microenvironment to make it unfavorable for tumor growth and spread; activating other immune cells to boost the overall immune response; and inhibiting angiogenesis to reduce the tumor's nutrient supply\u003csup\u003e58\u003c/sup\u003e. This is a complex biological process involving multiple cellular and molecular interactions, and its precise mechanisms require further investigation. At the same time, through the continuous mining of the database and the full use of Mendelian randomization, other blood cells also have unexpected effects, such as CD24\u0026thinsp;+\u0026thinsp;CD27\u0026thinsp;+\u0026thinsp;B cells are associated with a reduced risk of breast cancer (OR\u0026thinsp;=\u0026thinsp;0.9978,95% CI: 0.996\u0026ndash;0.999, p\u0026thinsp;=\u0026thinsp;0.001), while IgD-CD38 B cells were associated with an increased risk of breast cancer (OR\u0026thinsp;=\u0026thinsp;1.002,95% CI: 1.001\u0026ndash;1.004, p\u0026thinsp;=\u0026thinsp;0.005). CD14\u0026thinsp;+\u0026thinsp;CD16\u0026thinsp;+\u0026thinsp;monocytes were associated with an increased risk of breast cancer (OR\u0026thinsp;=\u0026thinsp;1.000,95% CI: 1.000-1.001, p\u0026thinsp;=\u0026thinsp;0.005)\u003csup\u003e59\u003c/sup\u003e.Future research in this area will help us better understand the immunopathology of breast cancer and provide a theoretical foundation for developing new strategies for the prevention and treatment of breast cancer.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eWhile our study provides valuable insights, several limitations must be acknowledged. Firstly, MR analysis offers robust methods for evaluating causal relationships, but it reflects lifetime genetic exposure rather than short-term effects. This limitation means that MR might not fully capture the benefits of short-term interventions on Neutrophil perturbation response. Secondly, our research relies on genetic data from European populations due to the limited availability of GWAS data for Asian populations. This reliance introduces potential limitations in generalizability, as genetic distributions can vary significantly between ethnic groups. Therefore, our findings may not be fully representative and could be influenced by racial and regional genetic differences. Future studies should include diverse ethnic groups to validate and extend these results. Finally, the lack of specific clinical data, such as age and underlying health conditions, for the study populations restricts further analysis and understanding. Addressing these gaps in future GWAS studies and incorporating molecular experimental validations will be crucial for refining our conclusions and enhancing the applicability of our findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study reveals that Neutrophil perturbation response (the ratio of neutrophil 2 to neutrophil 4 in response to KCl perturbation measured by WDF dye) increases the risk of breast cancer, with CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cell Absolute Count acting as a mediator that amplifies this effect. Specifically, Neutrophil perturbation response elevates breast cancer risk through its interaction with CD45RA-CD4\u0026thinsp;+\u0026thinsp;T cell Absolute Count. This research provides a novel perspective on the mechanisms underlying breast cancer development. Future investigations should further explore the immunomodulatory mechanisms involved in breast cancer pathogenesis and identify potential intervention targets, thereby guiding future therapeutic strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 22.3404%;\"\u003e\n \u003cp\u003eMR\u003c/p\u003e\n \u003cp\u003eIVs\u003c/p\u003e\n \u003cp\u003eGWAS\u003c/p\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003cp\u003eSNPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77.6596%;\"\u003e\n \u003cp\u003eMendelian randomization\u003c/p\u003e\n \u003cp\u003eInstrumental variables\u003c/p\u003e\n \u003cp\u003eGenome-wide association studies\u003c/p\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003cp\u003eSingle nucleotide polymorphisms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatasets collected and analyzed during this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeizi Song contributed to study conception and design, data collection and drafting of the manuscript. Qingjie Hu and Jiaxu Dong contributed to data collection. Jiafang Xu and Siqi Yin contributed to data analysis and interpretation. Yu Liu and Xun Bi contributed to study conception and design, analysis and interpretation of the data, and revision of the final manuscript. All authors gave final approval of the version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Health Science and Technology Innovation Joint Project of Hainan Province (WSJK2024MS197) and the Key Research and Development Program of Hainan Province (ZDYF2020139).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003e\u003c/strong\u003e Freddie B, Mathieu L, Hyuna S, et al. 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This study employs Mendelian randomization to explore the causal relationships between diverse blood cell profiles and breast cancer risk, while also seeking to identify potential mediating factors within immune cell metrics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We utilized Mendelian randomization to explore the causal effects of 91 different blood cell types on breast cancer risk, using genetic variants as instrumental variables. The analysis employed the inverse-variance weighted (GWAS) method, with a significance threshold of 0.05, to evaluate the causal relationships. Multivariate analysis was conducted to determine the mediating effects of immune cells in the association between blood cells and breast cancer. Heterogeneity test and multi-small size test are performed to complete the sensitivity analysis, ensuring the stability and reliability of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We identified significant causal relationships between blood cells and breast cancer risk. Specifically, the Neutrophil perturbation response (the ratio of neutrophil 2 to neutrophil 4 in response to KCl perturbation measured by WDF dye) was found to be causally associated with breast cancer risk. Furthermore, CD45RA-CD4+ T cell Absolute Count was identified as a mediator in this relationship. The mediating effect of CD45RA-CD4+ T cell Absolute Count was -0.055 (P=0.022), indicating that the impact of Neutrophil perturbation response on breast cancer risk is mediated through CD45RA-CD4+ T cell Absolute Count.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our study highlights a causal relationship between Neutrophil perturbation response and breast cancer risk, with CD45RA-CD4+ T cell Absolute Count acting as a significant mediator. These findings provide \u0026nbsp;new insights into the role of immune cells in the relationship between blood cell metrics and breast cancer risk, suggesting potential targets for further research and intervention.\u003c/p\u003e","manuscriptTitle":"Causal Links and Immune Mediators Between Blood Cells and Breast Cancer Risk: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-28 12:27:57","doi":"10.21203/rs.3.rs-6122403/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-09T09:59:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-08T12:19:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-02T19:45:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153023277053873247781125333846204575219","date":"2025-04-30T02:05:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285113027423788413839971984253821809126","date":"2025-04-27T07:42:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-25T07:29:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-23T08:52:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-04-03T09:25:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1381efe6-0e8e-47e4-b40a-2746a8cd6682","owner":[],"postedDate":"April 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-30T13:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-28 12:27:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6122403","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6122403","identity":"rs-6122403","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}