Assessing the causal relationship between P2RY12 and breast cancer: a Mendelian Randomization study

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Studies have shown that the P2RY12 gene can be a potential target for BC chemotherapy drugs, but the causal relationship between them remains unclear. Methods This study obtained the P2RY12 dataset (ENSG00000169313) and BC dataset (ukb-b-13584) from the IEU OpenGWAS database for two-way Mendelian Randomization (MR) analysis. Univariate analysis was performed using five methods: Mr Egger, weighted median, inverse variance weighting (IVW), simple mode and weighted mode. To evaluate the reliability of MR results, a heterogeneity test, a horizontal pleiotropic test, and a leave-one-out (LOO) method were performed. Gene Ontology (GO) was used to perform enrichment analysis of genes corresponding to instrumental variables (IVs). We explored the potential interactions of P2RY12 by constructing a protein-protein interaction (PPI) network. Results After screening IVs, 10 and 13 single-nucleotide polymorphisms (SNPs) were used for forward and reverse MR analyses, respectively. The results of forward analysis showed that P2RY12 increased the risk of BC, P = 0.0306, odds ratio (OR) = 1.004, 95% CI: 1-1.008, In contrast, in reverse MR analysis, the incidence of BC was not a direct factor leading to changes in P2RY12 (P = 0.262, OR = 0.361, 95% CI: 0.061–2.143). The reliability of the forward MR analysis results was demonstrated through a sensitivity analysis. Through GO enrichment analysis, genes related to SNPs are mainly enriched in muscle cell growth and expansion, G protein-coupled receptors, etc. Based on the PPI network, the biological processes primarily involved in P2RY12 include purine nucleotide receptor activity, G protein-coupled receptor activation, etc. Conclusion There is a causal relationship between P2RY12 and BC, and P2RY12 is a risk factor for BC. In contrast, there is no direct causal relationship between BC and P2RY12. This study provides a theoretical basis for finding therapeutic targets for BC through P2RY12. Breast cancer P2RY12 Two-way Mendelian Randomization analysis Single nucleotide polymorphism Causal relationship Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Breast cancer (BC) is a malignant tumor originating in the epithelial cells of the breast tissue. Based on differences in the expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), and Ki-67, BC is classified into four molecular subtypes: Luminal-A, Luminal-B, HER2(3+), and triple-negative breast cancer (TNBC) 1 . Globally, the incidence of breast cancer is showing an increasing trend, and according to the World Health Organization (WHO),approximately 2 million people are diagnosed with breast cancer each year worldwide 2 .With the development of modern medicine, the treatment modalities for breast cancer are constantly being updated. In addition, the widespread implementation of high-quality prevention, early detection, and timely treatment interventions has led to a decreasing trend in breast cancer mortality 3 . However, breast cancer is a highly heterogeneous tumor that includes multiple subtypes and molecular classifications, exhibiting complex diversity in diagnosis and treatment 4 , 5 , which makes treatment and prognosis prediction more challenging. Therefore, breast cancer still faces multiple challenges in diagnosis, treatment, and prevention. Especially in terms of treatment, there is a need to expand new research and therapeutic directions, and thus, new therapeutic targets for breast cancer need to be identified. P2RY12 is an ADP receptor that has only gained attention in recent years. As a member of the G protein-coupled receptor family and the receptor for P2Y12, it is involved in platelet aggregation. It is a potential target for treating thromboembolism and other coagulation disorders 6 . Its molecular functions primarily involve G protein-coupled receptor activity and G protein-coupled adenosine receptor activity 7 , 8 . Cellular experiments have shown that the P2RY12 gene is upregulated in cisplatin-treated breast cancer cells, and the combination of P2Y12 inhibitors with cisplatin can significantly enhance the cytotoxic response in 4T1 cancer cells, indicating that the P2RY12 gene may serve as a potential therapeutic target for chemotherapeutic drugs 9 . P2RY12 is expressed in various tumor cell lines, including C6 glioma, renal cancer, pancreatic tumors, and colon cancer 10 – 12 . In vitro studies by Sarangi et al. demonstrated that P2RY12 is expressed in both normal breast epithelial cells and malignant breast cells, with elevated expression observed in the 4T1 breast cancer cell line induced by cisplatin. Furthermore, inhibiting P2Y12 synergistically increased the cytotoxicity of cisplatin 9 . This finding suggests a new research direction, indicating that P2RY12 could be a novel therapeutic target for breast cancer. However, there are currently few studies on the relationship between P2RY12 and breast cancer, and most are observational or interventional studies. The final results may be affected by various biases, such as confounding, selection bias, and measurement bias. Therefore, we have noted an alternative approach to traditional epidemiology: Mendelian randomization (MR). Mendelian randomization is a type of instrumental variable (IV) analysis that uses genetic variation as an IV to detect and quantify causal relationships 13 . Due to its unique advantages and the rapid development of genomics, it has gained favor in medical research in recent years. Potential confounding and reverse causation can affect causal inference capabilities in traditional observational studies. When estimating the causal impact of an exposure on an outcome, genetic variation related to the modifiable exposure is used as an instrumental variable. Three assumptions for the instrumental variable must be met to ensure its validity 14 . First, the instrumental variable must be closely related to the exposure. Second, the instrumental variable must not be associated with any confounders. Third, the exposure should be the only pathway through which the genetic variation affects the outcome. Since genetic variations are typically randomly distributed in human populations and remain relatively stable despite environmental influences, they are more likely to represent the effect of long-term exposure to a factor, thereby identifying a potential causal relationship between the exposure and the outcome. Thus, Mendelian randomization studies can minimize the interference of confounding factors by using genetic variation. Based on the advantages of MR, this study uses data from genome-wide association study (GWAS) related public databases and a bidirectional two-sample Mendelian randomization study method to explore the causal relationship between P2RY12 and breast cancer, aiming to elucidate the pathogenesis of breast cancer further. 2 Materials and Methods 2.1 Data sources We conducted a bidirectional two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between P2RY12 and breast cancer (BC) based on data obtained from the IEU OpenGWAS database ( https://gwas.mrcieu.ac.uk/ ). In this study, we analyzed the P2RY12 dataset (N = 31,430, including 18,299 single-nucleotide polymorphisms (SNPs)) and the BC dataset (N = 423,458, including 9,851,867 SNPs) 15 . The overall analysis method is shown in Fig. 1 . 2.2 Data pre-processing As shown in Fig. 2 , the TwoSampleMR package was used for exposure data extraction and instrumental variable (IV) selection 15 . SNPs were chosen as IVs for the MR analysis. The selection criteria included genome-wide significance (P < 5 × 10^-8), independent genetic variants with no linkage disequilibrium (LD) (R^2 10,000) 16 . 2.3 MR analysis The causal relationship between P2RY12 and BC was explored by five methods: MR Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode, with IVW being the predominant analytical method. The IVW method was used to calculate the ratio of each SNP to estimate the relationship between the exposure factor and the outcome. When all selected SNPs were valid IVs, it would provide the most accurate results 17 . All analyses were performed using the "TwoSampleMR" R software package. 2.4 Sensitivity analysis To assess the reliability of the MR analysis results, we performed sensitivity analyses, which mainly included a heterogeneity test, a horizontal multidimensionality test, and leave-one-out (LOO) analysis. This study assessed heterogeneity using the mr_heterogeneity function, which quantifies heterogeneity using the Cochrane Q test 18 . If the Q value is less than 0.05, the IVW test for random effects is required. The intercept term in MR-Egger regression is a valid indicator of whether the results of MR analyses are affected by directional horizontal multidimensionality 19 . To assess the presence of confounders in the current study, we conducted a horizontal multidirectionality test by using the mr_pleiotropy_test function in R. A p-value greater than 0.05 indicates that no significant horizontal multidirectionality was detected, which suggests that the IVW methodology enables unbiased causal estimation and that the results of the IVW analyses are reliable. To confirm the stability of the MR analyses, we performed LOO analyses via the mr_leaveoneout function. The aim was to remove each SNP step by step, calculate the meta-effects of the remaining SNPs, and then observe whether the results changed after removing each SNP 16 . 2.5 Gene ontology (GO) enrichment analysis and protein-protein interaction (PPI) network construction To explore whether SNP corresponding genes and P2RY12 are jointly involved in relevant functional pathways, firstly, we performed gene annotation of SNPs by gProfiler ( https://biit.cs.ut.ee/gprofiler ) 20 . Then, the joint functional enrichment analysis of SNPs-related genes and P2RY12 was performed using Clusterprofiler, an R package based on the Gene Ontology database (version 4.2.2) 21 . Finally, PPI network construction was performed by GeneMania ( http://genemania.org ) to observe the potential mechanism of action of the exposure factor (P2RY12) 22 . 3 Results 3.1 Two-way MR analysis of P2RY12 and BC Among the five methods to assess the causal relationship of P2RY12 on BC, the results of two-way MR analysis are shown in Table 1 . First, we screened 9 SNPs as IVs for forward MR analysis. A causal relationship between P2RY12 and BC was found by IVW analysis (P = 0.0306), and P2RY12 served as a risk factor for BC (OR = 1.004, 95% CI: 1-1.008), indicating that P2RY12 may increase the risk of BC (Fig. 3 A). In contrast, 5 SNPs were used in the reverse MR analysis, and the results showed that none of the five methods were statistically significant (P > 0.05), indicating that the incidence of BC is not a direct factor causing changes in P2RY12 (Fig. 3 A). For forward MR analysis, the scatter plot illustrates an overall positive relationship between P2RY12 and BC, and the intercept of the IVW method in the plot is close to 0 (Fig. 3 B). The forest plot was used to determine the diagnostic efficacy of the predicted exposure factors at each SNP site on the outcome. The IVW analysis in the figure showed that the overall effect values of the exposure factors on the outcome variables were all greater than 0, indicating that elevated P2RY12 expression increases the risk of BC development. (Fig. 3 C). The funnel plot in Fig. 3 D illustrates that IVW-based forward MR analysis complies with Mendel’s second law of randomization. Table 1 Statistics of MR analysis results outcome exposure method nsnp b pval OR Breast cancer P2RY12 MR Egger 9 6.21E-03 2.01E-01 1.006E + 00 Weighted median 9 6.03E-03 1.49E-03 1.006E + 00 Inverse variance weighted 9 4.18E-03 3.06E-02 1.004E + 00 Simple mode 9 6.38E-03 1.25E-01 1.006E + 00 Weighted mode 9 6.21E-03 1.24E-02 1.006E + 00 P2RY12 Breast cancer MR Egger 5 2.35E + 00 5.76E-01 1.053E + 01 Weighted median 5 -9.53E-01 3.74E-01 3.855E-01 Inverse variance weighted 5 -1.02E + 00 2.62E-01 3.613E-01 Simple mode 5 -1.04E + 00 5.17E-01 3.545E-01 Weighted mode 5 -8.67E-01 5.27E-01 4.201E-01 3.2 Sensitivity analysis shows that forward MR results are reliable In the sensitivity analysis, we first performed a heterogeneity test on the forward MR analysis, and the result of Cochrane Q was more significant than 0.05, indicating no heterogeneity between the SNPs of P2RY12 and BC (Table 2 ). The results of the horizontal pleiotropic test are shown in Table 3 . The P value is more significant than 0.05, indicating no horizontal pleiotropic effect. Each SNP was gradually eliminated through the LOO method. The remaining SNPs had little effect on the outcome variable. The overall effect value was more significant than 0 (Fig. 4 ). These results demonstrate the reliability and stability of forward MR analysis results. Table 2 Heterogeneity test analysis results outcome exposure method Q Q_df Q_pval Breast cancer P2RY12 MR Egger 13.662 7 0.058 Inverse variance weighted 14.188 8 0.077 Q_pval > 0.05 indicates no heterogeneity. Table 3 Horizontal pleiotropy test outcome exposure egger_intercept se pval Breast cancer P2RY12 -0.00035 0.00067 0.6198 P_val > 0.05 indicates no pleiotropy. 3.3 SNP-corresponding genes that play an important role in biological functions and signaling pathways The nine SNPs in the forward MR analysis corresponded to 14 genes, including P2RY12 (Table 4 ). Next, we performed functional enrichment analysis on 9 SNP-related genes and P2RY12. As shown in Fig. 5 , these genes are mainly involved in muscle cell growth and expansion in biological processes, as well as G protein-coupled receptors in molecular functions. In addition, the protein-protein interaction network shows that the biological processes involved in genes mainly include purine nucleotide receptor activity, G protein-coupled receptor activation, etc. ( Fig. 6 ). Table 4 Annotations for SNPs mutation types id chr start end gene_names rs10876550 12 54318524 54318524 COPZ1,ENSG00000258344 rs114694170 5 88884379 88884379 MEF2C,MEF2C-AS1 rs1434282 1 199041592 199041592 LINC01221,ENSG00000286541 rs2172249 3 151342976 151342976 MED12L,P2RY12 rs342296 7 106732457 106732457 ENSG00000243797 rs6136489 20 1943088 1943088 rs6993770 8 105569300 105569300 ZFPM2,ZFPM2-AS1 rs7612010 3 151369860 151369860 MED12L,P2RY12 rs8073060 17 35548243 35548243 SLFN14 4 Discussion We utilized bidirectional Mendelian randomization studies to analyze and explore the causal relationship between P2RY12 and breast cancer (BC) at the genetic variation level. A total of 10 and 13 single-nucleotide polymorphisms (SNPs) were selected for the forward and reverse MR analyses, respectively. The forward analysis results indicated that P2RY12 increased the risk of BC (IVW: P = 0.0306, odds ratio (OR) = 1.004), suggesting a direct causal relationship between P2RY12 and the occurrence of breast cancer. In the reverse MR analysis, the incidence of BC was not a direct factor causing changes in P2RY12 (IVW: P = 0.262, OR = 0.361). We found that these SNPs effectively predict the increased risk of breast cancer (BC) associated with P2RY12. We conducted sensitivity analyses on the forward MR results, including heterogeneity tests, horizontal pleiotropy tests, and leave-one-out tests. The results showed: (1) there was no heterogeneity between the selected two-sample datasets; (2) there were no other confounding factors affecting the outcome variable through the instrumental variables for the exposure factor; (3) the overall effect value of the instrumental variables on the outcome variable was greater than 0. These three aspects demonstrate the reliability of the forward MR analysis results. We performed functional enrichment analysis on the genes related to the 9 SNPs used in the forward MR analysis and P2RY12. Gene annotation based on instrumental variable SNPs and functional pathway enrichment showed that the annotated genes and P2RY12 were mainly involved in biological processes such as muscle cell growth and expansion and G-protein-coupled receptor (GPCR) activity. GPCRs are receptor proteins on the cell surface, whose primary function is to transduce external signals into the cell. When a ligand (e.g., an extracellular chemical signaling substance) binds to the receptor, the receptor activates intracellular G proteins, initiating a series of signal transduction pathways that ultimately affect physiological functions such as cell proliferation and growth, cell migration and invasion, and the tumor microenvironment 23 , 24 . Therefore, P2RY12 is likely involved in regulating muscle cell growth and expansion by modulating GPCR signaling pathways. We also conducted protein-protein interaction (PPI) and functional enrichment analyses for the P2RY12 gene, which showed its involvement in GPCR-related biological processes, further validating the importance of P2RY12 in regulating cell signaling and biological processes. This supports previous hypotheses about the potential role of P2RY12 in breast cancer pathogenesis, as GPCRs usually play crucial roles in tumor growth, metastasis, and treatment response. Therefore, this finding provides a more in-depth theoretical basis for further investigating the specific mechanisms of P2RY12 in breast cancer. Current research indicates that the physiological function of P2RY12 is primarily involved in activating platelet function. It plays a significant role in regulating physiological processes such as platelet function, thrombosis, vascular tone, and inflammatory responses. Studies have shown that activated platelets can directly bind to cancer cells and promote the invasion and proliferation of tumor cells by releasing soluble factors 25 – 28 . P2RY12 may play an important role in the binding of platelets to cancer cells, suggesting that P2RY12 could be a new potential therapeutic target for intervening in cancer proliferation and metastasis. Regarding the potential mechanism of action between P2RY12 and breast cancer, Gebremeskel et al. 25 were the first to demonstrate a positive effect by establishing a mouse model of breast cancer. After treatment with the P2RY12 inhibitor ticagrelor, the number of breast cancer lung and bone metastases in mice decreased compared to the control group. The potential mechanism may involve ticagrelor inhibiting the binding of platelets to tumor cells and endothelial cells. Gebremeskel et al. 29 further investigated the underlying mechanism and found that ticagrelor could inhibit platelet activation by antagonizing P2RY12, reducing the binding of human breast cancer cells to platelets, thereby decreasing the adhesion and metastasis of breast cancer cells. Additionally, this study observed that ticagrelor reduced the expression of P-selectin on the platelet surface. Current research has found that when platelets are activated, the expression of P-selectin, an adhesion molecule, increases on the platelet surface, promoting interactions between platelets and breast cancer cells. This interaction facilitates the metastasis of breast cancer cells 30 , 31 . Therefore, P2RY12 inhibitors can inhibit breast cancer cell metastasis by antagonizing P2RY12 and reducing the expression of P-selectin. Our research findings align with the above studies, indicating that P2RY12 is a risk factor for breast cancer (BC) and partially validating our results. Unlike previous studies, we are the first to demonstrate a causal relationship between P2RY12 and BC. At the level of genetic prediction, we are confident in asserting that P2RY12 is a risk factor for the development of breast cancer. However, to further elucidate the potential mechanisms of action between P2RY12 and breast cancer, current research still has limitations. More comprehensive studies are needed, including those with larger sample sizes, better-designed cell and animal models, clinical research, and molecular mechanism studies. These efforts are necessary to discover and validate findings and to obtain more real-world data to ensure the reliability of the research results. In the forward magnetic resonance analysis of this study, functional enrichment and protein-protein interaction (PPI) network analysis of 14 genes corresponding to 9 single nucleotide polymorphisms (SNPs) implicated several G protein-coupled receptors (GPCRs). These GPCRs include protease-activated receptors (PAR1 & PAR2), lysophosphatidic acid receptors (LPAR), angiotensin II receptor type 1 (AT1R), and gastrin-releasing peptide receptor (GRPR). Published studies indicate that these GPCRs can promote cancer cell proliferation by activating relevant signaling pathways 32 – 35 . For instance, PAR1 and PAR2 exhibit high expression in breast cancer cells, where they not only enhance cellular proliferation but also drive cell migration. Overexpression of AT1R in breast cancer cells activates associated signaling pathways, thereby promoting tumor growth. Concurrently, angiogenic factors released by tumor cells act on GPCRs expressed on endothelial cells, facilitating neovascularization. This process provides essential nutritional support for tumors and further promotes metastasis 36 . Furthermore, GPCRs play pivotal roles in intercellular communication within the tumor microenvironment, influencing cancer cell proliferation, survival, and metastasis. Chemokine receptors, for example, assist cancer cells in proliferation and apoptosis resistance. Upon binding to relevant GPCRs, prostaglandin E (PGE) promotes tumor cell growth and survival while modulating immune cell function to facilitate immune evasion 37 . Given the significant involvement of GPCRs in breast cancer pathogenesis, they have emerged as promising therapeutic targets. Current drug development targeting GPCRs is actively progressing, with certain LPAR antagonists demonstrating potential to inhibit tumor growth and metastasis in preclinical and clinical trials 38 , 39 . Additionally, modulation of GPCR signaling pathways—such as inhibition of YAP/TAZ activity—is being explored as a novel therapeutic strategy for breast cancer 40 . Based on these findings, P2RY12, identified as a risk factor for breast cancer (BC), likely promotes cancer cell proliferation through analogous activation of relevant signaling pathways. It may couple with specific G proteins to activate classical pro-proliferative pathways such as PI3K-Akt and MAPK, thereby enhancing the proliferative capacity of cancer cells and accelerating tumor growth. 5 Conclusion In conclusion, this study represents the first attempt to elucidate and substantiate the causal link between P2RY12 and breast cancer from a genetic standpoint, offering an important insights and avenues for future research and treatment of breast cancer. Meanwhile, the limited sample size and mutation loci in the dataset utilized may impose constraints on the robustness of the analyses and conclusions. Therefore, validation of the Mendelian randomization analyses using large-scale GWAS pooled data and the incorporation of additional genetic tools are warranted. Therefore, further investigations, particularly molecular experiments, are imperative. We will continue to monitoring research developments concerning P2RY12 and breast cancer while actively exploring other potential exposure factors associated with breast cancer. Declarations Ethics approval and consent to participate. Ethical approval was not required for this specific analysis, as the entirety of the data was sourced from the summary statistics of a published GWAS and no individual-level data were used. Data availability Data from IEU OpenGWAS (https://gwas.mrcieu.ac.uk/ ; data set 1: eqtl-a-ENSG00000169313; data set 2: ukb-b-13584). Acknowledgements The authors thank IEU OpenGWAS for their publicly available genetic data. Author contributions Managed the project: Yinhua Pan. Drafted the manuscript: Shuting Qin, Weifeng Fan. Acquired and analyzed the data: Yinhua Pan, Shuting Qin, Weifeng Fan. Participated in the discussion: Shuting Qin, Junyang Mo, Qian Tang, Linjie Lu. All the authors made substantial contributions and revisions to the drafts and approved the final manuscript. Funding This study was supported by the self funded scientific research project of the Health Commission of Guangxi Zhuang Autonomous Region (No. Z-B20231341) and the Natural Science Foundation of Guangxi (No. 2023JJA140714). Competing interests The authors declare no competing interests. References Sohel, M. et al. Exploring the anti-cancer potential of dietary phytochemicals for the patients with breast cancer: A comprehensive review. Cancer Med 12 , 14556-14583, doi:10.1002/cam4.5984 (2023). Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71 , 209-249, doi:10.3322/caac.21660 (2021). DeSantis, C. E. et al. Breast cancer statistics, 2019. CA Cancer J Clin 69 , 438-451, doi:10.3322/caac.21583 (2019). Brenner, D. R. et al. Projected estimates of cancer in Canada in 2020. 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Discovery of novel non-carboxylic acid 5-amino-4-cyanopyrazole derivatives as potent and highly selective LPA1R antagonists. Bioorg Med Chem Lett 24 , 4450-4454, doi:10.1016/j.bmcl.2014.08.001 (2014). Cunningham, R. & Hansen, C. G. The Hippo pathway in cancer: YAP/TAZ and TEAD as therapeutic targets in cancer. Clin Sci (Lond) 136 , 197-222, doi:10.1042/CS20201474 (2022). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7613007","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":559997061,"identity":"ac638ca2-260f-4b09-8f0a-bcfbfe18c790","order_by":0,"name":"Shuting Qin","email":"","orcid":"","institution":"Liuzhou People's Hospital Affiliated to Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuting","middleName":"","lastName":"Qin","suffix":""},{"id":559997062,"identity":"b6f4979f-4e43-4c5f-ad3f-f4651c186c15","order_by":1,"name":"Weifeng Fan","email":"","orcid":"","institution":"Liuzhou People's Hospital 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13:35:01","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":105094,"visible":true,"origin":"","legend":"","description":"","filename":"482356cc81074811b1f759f50bbe637e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7613007/v1/33def31bd3a61988ffeb832b.xml"},{"id":98409710,"identity":"b2010f0e-ae30-43ff-8d9f-43a259eb1c3d","added_by":"auto","created_at":"2025-12-17 13:35:01","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":114983,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7613007/v1/a172bcd8808b870dc5421ff9.html"},{"id":98441734,"identity":"93ad2166-81c1-48a0-b722-7ccd06837889","added_by":"auto","created_at":"2025-12-17 17:05:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104011,"visible":true,"origin":"","legend":"\u003cp\u003eBidirectional Mendelian Randomization (MR) Study Design. \"×\" indicates that SNPs are unrelated to confounding factors and can only influence the outcome through the exposure pathway. \"√\" indicates a strong correlation between genetic variants and exposure. Solid lines are crucial; dashed lines should not exist in MR studies. SNP: Single Nucleotide Polymorphism.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7613007/v1/3aa4d8ab2359d5ec78ce7635.png"},{"id":98409682,"identity":"11512780-546b-42d5-a62a-646b99e9ec0e","added_by":"auto","created_at":"2025-12-17 13:35:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":195748,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis methodology and process\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7613007/v1/b45b34b0ee4a7aee53a41924.png"},{"id":98441655,"identity":"8aaac085-54da-46aa-96e1-cdcb7aa34538","added_by":"auto","created_at":"2025-12-17 17:05:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":257608,"visible":true,"origin":"","legend":"\u003cp\u003eA: The Mendelian analysis relationship between P2RY12 and breast cancer risk. The left panel shows forward MR, while the right panel shows reverse MR. OR_95CI indicates the odds ratio and 95% confidence interval, and nSNP represents the number of instrumental variables.\u003c/p\u003e\n\u003cp\u003eB: The scatter plot shows the effects of SNPs on P2RY12 and breast cancer, with MR representing Mendelian randomization. The slopes of IVW, MR-Egger, weighted median, simple mode, and weighted mode indicate the results of these regression analyses, with IVW representing inverse variance weighted.\u003c/p\u003e\n\u003cp\u003eC: For the forward MR, the IVW method analysis shows that the effect values of the exposure factor on the outcome variable are generally greater than 0, indicating that increased P2RY12 expression raises the risk of breast cancer. IVW stands for inverse variance weighted.\u003c/p\u003e\n\u003cp\u003eD: The funnel plot for the forward MR, with IVW representing inverse variance weighted.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7613007/v1/817807f8ad5aa2c3993ee49c.png"},{"id":98409687,"identity":"f7959530-b86f-4457-bc40-2207910939db","added_by":"auto","created_at":"2025-12-17 13:35:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41852,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out analysis of estimated effects of P2RY12 on breast cancer.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7613007/v1/eda969572af38be36521816c.png"},{"id":98441713,"identity":"fdf43d57-8927-41de-938d-7bb30f231086","added_by":"auto","created_at":"2025-12-17 17:05:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":105851,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plot of gene functional enrichment (GO)\u003c/strong\u003e. Functional enrichment analysis was performed on the combinatorial genes associated with the 9 SNPs and P2RY12 using the R package ClusterProfiler. The biological processes involved in these genes mainly include muscle cell growth and expansion, and the molecular functions they participate in involve G protein-coupled receptors.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7613007/v1/ad4989085b349a5c66174eaf.png"},{"id":98409689,"identity":"a1277f46-8959-480d-9411-b65e667e4520","added_by":"auto","created_at":"2025-12-17 13:35:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":224917,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProtein-protein interaction network diagram\u003c/strong\u003e. We used GeneMania for PPI network construction and generated a protein-protein interaction network. Its biological processes mainly include purine nucleotide receptor activity, activation of G protein-coupled receptors, etc.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7613007/v1/1a6cef7ce615335ba21d789d.png"},{"id":104780034,"identity":"085127c6-82c3-47a5-b1d1-cc590c98c2f8","added_by":"auto","created_at":"2026-03-17 07:49:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1706542,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7613007/v1/5e66e3a6-54cf-4671-87b9-807851c1623f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the causal relationship between P2RY12 and breast cancer: a Mendelian Randomization study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eBreast cancer (BC) is a malignant tumor originating in the epithelial cells of the breast tissue. Based on differences in the expression of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), and Ki-67, BC is classified into four molecular subtypes: Luminal-A, Luminal-B, HER2(3+), and triple-negative breast cancer (TNBC)\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Globally, the incidence of breast cancer is showing an increasing trend, and according to the World Health Organization (WHO),approximately 2\u0026nbsp;million people are diagnosed with breast cancer each year worldwide\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.With the development of modern medicine, the treatment modalities for breast cancer are constantly being updated. In addition, the widespread implementation of high-quality prevention, early detection, and timely treatment interventions has led to a decreasing trend in breast cancer mortality\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, breast cancer is a highly heterogeneous tumor that includes multiple subtypes and molecular classifications, exhibiting complex diversity in diagnosis and treatment\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, which makes treatment and prognosis prediction more challenging. Therefore, breast cancer still faces multiple challenges in diagnosis, treatment, and prevention. Especially in terms of treatment, there is a need to expand new research and therapeutic directions, and thus, new therapeutic targets for breast cancer need to be identified.\u003c/p\u003e \u003cp\u003eP2RY12 is an ADP receptor that has only gained attention in recent years. As a member of the G protein-coupled receptor family and the receptor for P2Y12, it is involved in platelet aggregation. It is a potential target for treating thromboembolism and other coagulation disorders\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Its molecular functions primarily involve G protein-coupled receptor activity and G protein-coupled adenosine receptor activity\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Cellular experiments have shown that the P2RY12 gene is upregulated in cisplatin-treated breast cancer cells, and the combination of P2Y12 inhibitors with cisplatin can significantly enhance the cytotoxic response in 4T1 cancer cells, indicating that the P2RY12 gene may serve as a potential therapeutic target for chemotherapeutic drugs\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. P2RY12 is expressed in various tumor cell lines, including C6 glioma, renal cancer, pancreatic tumors, and colon cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In vitro studies by Sarangi et al. demonstrated that P2RY12 is expressed in both normal breast epithelial cells and malignant breast cells, with elevated expression observed in the 4T1 breast cancer cell line induced by cisplatin. Furthermore, inhibiting P2Y12 synergistically increased the cytotoxicity of cisplatin\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This finding suggests a new research direction, indicating that P2RY12 could be a novel therapeutic target for breast cancer. However, there are currently few studies on the relationship between P2RY12 and breast cancer, and most are observational or interventional studies. The final results may be affected by various biases, such as confounding, selection bias, and measurement bias. Therefore, we have noted an alternative approach to traditional epidemiology: Mendelian randomization (MR).\u003c/p\u003e \u003cp\u003eMendelian randomization is a type of instrumental variable (IV) analysis that uses genetic variation as an IV to detect and quantify causal relationships\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Due to its unique advantages and the rapid development of genomics, it has gained favor in medical research in recent years. Potential confounding and reverse causation can affect causal inference capabilities in traditional observational studies. When estimating the causal impact of an exposure on an outcome, genetic variation related to the modifiable exposure is used as an instrumental variable. Three assumptions for the instrumental variable must be met to ensure its validity\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. First, the instrumental variable must be closely related to the exposure. Second, the instrumental variable must not be associated with any confounders. Third, the exposure should be the only pathway through which the genetic variation affects the outcome. Since genetic variations are typically randomly distributed in human populations and remain relatively stable despite environmental influences, they are more likely to represent the effect of long-term exposure to a factor, thereby identifying a potential causal relationship between the exposure and the outcome. Thus, Mendelian randomization studies can minimize the interference of confounding factors by using genetic variation. Based on the advantages of MR, this study uses data from genome-wide association study (GWAS) related public databases and a bidirectional two-sample Mendelian randomization study method to explore the causal relationship between P2RY12 and breast cancer, aiming to elucidate the pathogenesis of breast cancer further.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data sources\u003c/h2\u003e \u003cp\u003eWe conducted a bidirectional two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between P2RY12 and breast cancer (BC) based on data obtained from the IEU OpenGWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In this study, we analyzed the P2RY12 dataset (N\u0026thinsp;=\u0026thinsp;31,430, including 18,299 single-nucleotide polymorphisms (SNPs)) and the BC dataset (N\u0026thinsp;=\u0026thinsp;423,458, including 9,851,867 SNPs)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The overall analysis method is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data pre-processing\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the TwoSampleMR package was used for exposure data extraction and instrumental variable (IV) selection\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. SNPs were chosen as IVs for the MR analysis. The selection criteria included genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10^-8), independent genetic variants with no linkage disequilibrium (LD) (R^2\u0026thinsp;\u0026lt;\u0026thinsp;0.001, kb\u0026thinsp;\u0026gt;\u0026thinsp;10,000)\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.3 MR analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe causal relationship between P2RY12 and BC was explored by five methods: MR Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode, with IVW being the predominant analytical method. The IVW method was used to calculate the ratio of each SNP to estimate the relationship between the exposure factor and the outcome. When all selected SNPs were valid IVs, it would provide the most accurate results\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. All analyses were performed using the \"TwoSampleMR\" R software package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eTo assess the reliability of the MR analysis results, we performed sensitivity analyses, which mainly included a heterogeneity test, a horizontal multidimensionality test, and leave-one-out (LOO) analysis. This study assessed heterogeneity using the mr_heterogeneity function, which quantifies heterogeneity using the Cochrane Q test\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. If the Q value is less than 0.05, the IVW test for random effects is required. The intercept term in MR-Egger regression is a valid indicator of whether the results of MR analyses are affected by directional horizontal multidimensionality\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. To assess the presence of confounders in the current study, we conducted a horizontal multidirectionality test by using the mr_pleiotropy_test function in R. A p-value greater than 0.05 indicates that no significant horizontal multidirectionality was detected, which suggests that the IVW methodology enables unbiased causal estimation and that the results of the IVW analyses are reliable. To confirm the stability of the MR analyses, we performed LOO analyses via the mr_leaveoneout function. The aim was to remove each SNP step by step, calculate the meta-effects of the remaining SNPs, and then observe whether the results changed after removing each SNP\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Gene ontology (GO) enrichment analysis and protein-protein interaction (PPI) network construction\u003c/h2\u003e \u003cp\u003eTo explore whether SNP corresponding genes and P2RY12 are jointly involved in relevant functional pathways, firstly, we performed gene annotation of SNPs by gProfiler (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biit.cs.ut.ee/gprofiler\u003c/span\u003e\u003cspan address=\"https://biit.cs.ut.ee/gprofiler\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e )\u003csup\u003e20\u003c/sup\u003e. Then, the joint functional enrichment analysis of SNPs-related genes and P2RY12 was performed using Clusterprofiler, an R package based on the Gene Ontology database (version 4.2.2)\u003csup\u003e21\u003c/sup\u003e. Finally, PPI network construction was performed by GeneMania (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genemania.org\u003c/span\u003e\u003cspan address=\"http://genemania.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to observe the potential mechanism of action of the exposure factor (P2RY12)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Two-way MR analysis of P2RY12 and BC\u003c/h2\u003e\n \u003cp\u003eAmong the five methods to assess the causal relationship of P2RY12 on BC, the results of two-way MR analysis are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. First, we screened 9 SNPs as IVs for forward MR analysis. A causal relationship between P2RY12 and BC was found by IVW analysis (P\u0026thinsp;=\u0026thinsp;0.0306), and P2RY12 served as a risk factor for BC (OR\u0026thinsp;=\u0026thinsp;1.004, 95% CI: 1-1.008), indicating that P2RY12 may increase the risk of BC (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). In contrast, 5 SNPs were used in the reverse MR analysis, and the results showed that none of the five methods were statistically significant (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the incidence of BC is not a direct factor causing changes in P2RY12 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). For forward MR analysis, the scatter plot illustrates an overall positive relationship between P2RY12 and BC, and the intercept of the IVW method in the plot is close to 0 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). The forest plot was used to determine the diagnostic efficacy of the predicted exposure factors at each SNP site on the outcome. The IVW analysis in the figure showed that the overall effect values of the exposure factors on the outcome variables were all greater than 0, indicating that elevated P2RY12 expression increases the risk of BC development. (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). The funnel plot in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD illustrates that IVW-based forward MR analysis complies with Mendel\u0026rsquo;s second law of randomization.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistics of MR analysis results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eoutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eexposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emethod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ensnp\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epval\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP2RY12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.21E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.006E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.03E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.49E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.006E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.18E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.004E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.38E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.006E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.21E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.006E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP2RY12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.76E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.053E\u0026thinsp;+\u0026thinsp;01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.53E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.855E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.02E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.613E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.04E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.17E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.545E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.67E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.27E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.201E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Sensitivity analysis shows that forward MR results are reliable\u003c/h2\u003e\n \u003cp\u003eIn the sensitivity analysis, we first performed a heterogeneity test on the forward MR analysis, and the result of Cochrane Q was more significant than 0.05, indicating no heterogeneity between the SNPs of P2RY12 and BC (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The results of the horizontal pleiotropic test are shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The P value is more significant than 0.05, indicating no horizontal pleiotropic effect. Each SNP was gradually eliminated through the LOO method. The remaining SNPs had little effect on the outcome variable. The overall effect value was more significant than 0 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). These results demonstrate the reliability and stability of forward MR analysis results.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHeterogeneity test analysis results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eoutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eexposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003emethod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ_df\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ_pval\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP2RY12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eQ_pval\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicates no heterogeneity.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHorizontal pleiotropy test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eoutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eexposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eegger_intercept\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ese\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epval\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBreast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP2RY12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eP_val\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicates no pleiotropy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 SNP-corresponding genes that play an important role in biological functions and signaling pathways\u003c/h2\u003e\n \u003cp\u003eThe nine SNPs in the forward MR analysis corresponded to 14 genes, including P2RY12 (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Next, we performed functional enrichment analysis on 9 SNP-related genes and P2RY12. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, these genes are mainly involved in muscle cell growth and expansion in biological processes, as well as G protein-coupled receptors in molecular functions. In addition, the protein-protein interaction network shows that the biological processes involved in genes mainly include purine nucleotide receptor activity, G protein-coupled receptor activation, etc. \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnnotations for SNPs mutation types\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003echr\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003estart\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eend\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003egene_names\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10876550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54318524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54318524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOPZ1,ENSG00000258344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers114694170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88884379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88884379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMEF2C,MEF2C-AS1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1434282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e199041592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e199041592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLINC01221,ENSG00000286541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2172249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151342976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151342976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMED12L,P2RY12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers342296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106732457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106732457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eENSG00000243797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers6136489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1943088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1943088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers6993770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105569300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105569300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZFPM2,ZFPM2-AS1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7612010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151369860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151369860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMED12L,P2RY12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers8073060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35548243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35548243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSLFN14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eWe utilized bidirectional Mendelian randomization studies to analyze and explore the causal relationship between P2RY12 and breast cancer (BC) at the genetic variation level. A total of 10 and 13 single-nucleotide polymorphisms (SNPs) were selected for the forward and reverse MR analyses, respectively. The forward analysis results indicated that P2RY12 increased the risk of BC (IVW: P\u0026thinsp;=\u0026thinsp;0.0306, odds ratio (OR)\u0026thinsp;=\u0026thinsp;1.004), suggesting a direct causal relationship between P2RY12 and the occurrence of breast cancer. In the reverse MR analysis, the incidence of BC was not a direct factor causing changes in P2RY12 (IVW: P\u0026thinsp;=\u0026thinsp;0.262, OR\u0026thinsp;=\u0026thinsp;0.361). We found that these SNPs effectively predict the increased risk of breast cancer (BC) associated with P2RY12. We conducted sensitivity analyses on the forward MR results, including heterogeneity tests, horizontal pleiotropy tests, and leave-one-out tests. The results showed: (1) there was no heterogeneity between the selected two-sample datasets; (2) there were no other confounding factors affecting the outcome variable through the instrumental variables for the exposure factor; (3) the overall effect value of the instrumental variables on the outcome variable was greater than 0. These three aspects demonstrate the reliability of the forward MR analysis results. We performed functional enrichment analysis on the genes related to the 9 SNPs used in the forward MR analysis and P2RY12. Gene annotation based on instrumental variable SNPs and functional pathway enrichment showed that the annotated genes and P2RY12 were mainly involved in biological processes such as muscle cell growth and expansion and G-protein-coupled receptor (GPCR) activity. GPCRs are receptor proteins on the cell surface, whose primary function is to transduce external signals into the cell. When a ligand (e.g., an extracellular chemical signaling substance) binds to the receptor, the receptor activates intracellular G proteins, initiating a series of signal transduction pathways that ultimately affect physiological functions such as cell proliferation and growth, cell migration and invasion, and the tumor microenvironment\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Therefore, P2RY12 is likely involved in regulating muscle cell growth and expansion by modulating GPCR signaling pathways. We also conducted protein-protein interaction (PPI) and functional enrichment analyses for the P2RY12 gene, which showed its involvement in GPCR-related biological processes, further validating the importance of P2RY12 in regulating cell signaling and biological processes. This supports previous hypotheses about the potential role of P2RY12 in breast cancer pathogenesis, as GPCRs usually play crucial roles in tumor growth, metastasis, and treatment response. Therefore, this finding provides a more in-depth theoretical basis for further investigating the specific mechanisms of P2RY12 in breast cancer.\u003c/p\u003e \u003cp\u003eCurrent research indicates that the physiological function of P2RY12 is primarily involved in activating platelet function. It plays a significant role in regulating physiological processes such as platelet function, thrombosis, vascular tone, and inflammatory responses. Studies have shown that activated platelets can directly bind to cancer cells and promote the invasion and proliferation of tumor cells by releasing soluble factors\u003csup\u003e\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. P2RY12 may play an important role in the binding of platelets to cancer cells, suggesting that P2RY12 could be a new potential therapeutic target for intervening in cancer proliferation and metastasis. Regarding the potential mechanism of action between P2RY12 and breast cancer, Gebremeskel et al.\u003csup\u003e25\u003c/sup\u003e were the first to demonstrate a positive effect by establishing a mouse model of breast cancer. After treatment with the P2RY12 inhibitor ticagrelor, the number of breast cancer lung and bone metastases in mice decreased compared to the control group. The potential mechanism may involve ticagrelor inhibiting the binding of platelets to tumor cells and endothelial cells. Gebremeskel et al.\u003csup\u003e29\u003c/sup\u003e further investigated the underlying mechanism and found that ticagrelor could inhibit platelet activation by antagonizing P2RY12, reducing the binding of human breast cancer cells to platelets, thereby decreasing the adhesion and metastasis of breast cancer cells. Additionally, this study observed that ticagrelor reduced the expression of P-selectin on the platelet surface. Current research has found that when platelets are activated, the expression of P-selectin, an adhesion molecule, increases on the platelet surface, promoting interactions between platelets and breast cancer cells. This interaction facilitates the metastasis of breast cancer cells\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Therefore, P2RY12 inhibitors can inhibit breast cancer cell metastasis by antagonizing P2RY12 and reducing the expression of P-selectin. Our research findings align with the above studies, indicating that P2RY12 is a risk factor for breast cancer (BC) and partially validating our results. Unlike previous studies, we are the first to demonstrate a causal relationship between P2RY12 and BC. At the level of genetic prediction, we are confident in asserting that P2RY12 is a risk factor for the development of breast cancer. However, to further elucidate the potential mechanisms of action between P2RY12 and breast cancer, current research still has limitations. More comprehensive studies are needed, including those with larger sample sizes, better-designed cell and animal models, clinical research, and molecular mechanism studies. These efforts are necessary to discover and validate findings and to obtain more real-world data to ensure the reliability of the research results.\u003c/p\u003e \u003cp\u003eIn the forward magnetic resonance analysis of this study, functional enrichment and protein-protein interaction (PPI) network analysis of 14 genes corresponding to 9 single nucleotide polymorphisms (SNPs) implicated several G protein-coupled receptors (GPCRs). These GPCRs include protease-activated receptors (PAR1 \u0026amp; PAR2), lysophosphatidic acid receptors (LPAR), angiotensin II receptor type 1 (AT1R), and gastrin-releasing peptide receptor (GRPR). Published studies indicate that these GPCRs can promote cancer cell proliferation by activating relevant signaling pathways\u003csup\u003e\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor instance, PAR1 and PAR2 exhibit high expression in breast cancer cells, where they not only enhance cellular proliferation but also drive cell migration. Overexpression of AT1R in breast cancer cells activates associated signaling pathways, thereby promoting tumor growth. Concurrently, angiogenic factors released by tumor cells act on GPCRs expressed on endothelial cells, facilitating neovascularization. This process provides essential nutritional support for tumors and further promotes metastasis\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, GPCRs play pivotal roles in intercellular communication within the tumor microenvironment, influencing cancer cell proliferation, survival, and metastasis. Chemokine receptors, for example, assist cancer cells in proliferation and apoptosis resistance. Upon binding to relevant GPCRs, prostaglandin E (PGE) promotes tumor cell growth and survival while modulating immune cell function to facilitate immune evasion\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven the significant involvement of GPCRs in breast cancer pathogenesis, they have emerged as promising therapeutic targets. Current drug development targeting GPCRs is actively progressing, with certain LPAR antagonists demonstrating potential to inhibit tumor growth and metastasis in preclinical and clinical trials\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Additionally, modulation of GPCR signaling pathways\u0026mdash;such as inhibition of YAP/TAZ activity\u0026mdash;is being explored as a novel therapeutic strategy for breast cancer\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBased on these findings, P2RY12, identified as a risk factor for breast cancer (BC), likely promotes cancer cell proliferation through analogous activation of relevant signaling pathways. It may couple with specific G proteins to activate classical pro-proliferative pathways such as PI3K-Akt and MAPK, thereby enhancing the proliferative capacity of cancer cells and accelerating tumor growth.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, this study represents the first attempt to elucidate and substantiate the causal link between P2RY12 and breast cancer from a genetic standpoint, offering an important insights and avenues for future research and treatment of breast cancer. Meanwhile, the limited sample size and mutation loci in the dataset utilized may impose constraints on the robustness of the analyses and conclusions. Therefore, validation of the Mendelian randomization analyses using large-scale GWAS pooled data and the incorporation of additional genetic tools are warranted. Therefore, further investigations, particularly molecular experiments, are imperative. We will continue to monitoring research developments concerning P2RY12 and breast cancer while actively exploring other potential exposure factors associated with breast cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required for this specific analysis, as the entirety of the data was sourced from the summary statistics of a published GWAS and no individual-level data were used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData from IEU OpenGWAS (https://gwas.mrcieu.ac.uk/ ; data set 1: eqtl-a-ENSG00000169313; data set 2: ukb-b-13584).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank IEU OpenGWAS for their publicly available genetic data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eManaged the project: Yinhua Pan.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDrafted the manuscript: Shuting Qin, Weifeng Fan.\u003c/p\u003e\n\u003cp\u003eAcquired and analyzed the data: Yinhua Pan, Shuting Qin, Weifeng Fan.\u003c/p\u003e\n\u003cp\u003eParticipated in the discussion: Shuting Qin, Junyang Mo, Qian Tang, Linjie Lu.\u003c/p\u003e\n\u003cp\u003eAll the authors made substantial contributions and revisions to the drafts and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the self funded scientific research project of the Health Commission of Guangxi Zhuang Autonomous Region (No. Z-B20231341) and the Natural Science Foundation of Guangxi (No. 2023JJA140714).\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"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSohel, M.\u003cem\u003e et al.\u003c/em\u003e Exploring the anti-cancer potential of dietary phytochemicals for the patients with breast cancer: A comprehensive review. \u003cem\u003eCancer Med\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 14556-14583, doi:10.1002/cam4.5984 (2023).\u003c/li\u003e\n\u003cli\u003eSung, H.\u003cem\u003e et al.\u003c/em\u003e Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 209-249, doi:10.3322/caac.21660 (2021).\u003c/li\u003e\n\u003cli\u003eDeSantis, C. 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The Hippo pathway in cancer: YAP/TAZ and TEAD as therapeutic targets in cancer. \u003cem\u003eClin Sci (Lond)\u003c/em\u003e \u003cstrong\u003e136\u003c/strong\u003e, 197-222, doi:10.1042/CS20201474 (2022).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, P2RY12, Two-way Mendelian Randomization analysis, Single nucleotide polymorphism, Causal relationship","lastPublishedDoi":"10.21203/rs.3.rs-7613007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7613007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreast cancer (BC) is one of the most common malignant tumors in women, and its incidence ranks among the highest among female malignancies. Studies have shown that the P2RY12 gene can be a potential target for BC chemotherapy drugs, but the causal relationship between them remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study obtained the P2RY12 dataset (ENSG00000169313) and BC dataset (ukb-b-13584) from the IEU OpenGWAS database for two-way Mendelian Randomization (MR) analysis. Univariate analysis was performed using five methods: Mr Egger, weighted median, inverse variance weighting (IVW), simple mode and weighted mode. To evaluate the reliability of MR results, a heterogeneity test, a horizontal pleiotropic test, and a leave-one-out (LOO) method were performed. Gene Ontology (GO) was used to perform enrichment analysis of genes corresponding to instrumental variables (IVs). We explored the potential interactions of P2RY12 by constructing a protein-protein interaction (PPI) network.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAfter screening IVs, 10 and 13 single-nucleotide polymorphisms (SNPs) were used for forward and reverse MR analyses, respectively. The results of forward analysis showed that P2RY12 increased the risk of BC, P\u0026thinsp;=\u0026thinsp;0.0306, odds ratio (OR)\u0026thinsp;=\u0026thinsp;1.004, 95% CI: 1-1.008, In contrast, in reverse MR analysis, the incidence of BC was not a direct factor leading to changes in P2RY12 (P\u0026thinsp;=\u0026thinsp;0.262, OR\u0026thinsp;=\u0026thinsp;0.361, 95% CI: 0.061\u0026ndash;2.143). The reliability of the forward MR analysis results was demonstrated through a sensitivity analysis. Through GO enrichment analysis, genes related to SNPs are mainly enriched in muscle cell growth and expansion, G protein-coupled receptors, etc. Based on the PPI network, the biological processes primarily involved in P2RY12 include purine nucleotide receptor activity, G protein-coupled receptor activation, etc.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere is a causal relationship between P2RY12 and BC, and P2RY12 is a risk factor for BC. In contrast, there is no direct causal relationship between BC and P2RY12. This study provides a theoretical basis for finding therapeutic targets for BC through P2RY12.\u003c/p\u003e","manuscriptTitle":"Assessing the causal relationship between P2RY12 and breast cancer: a Mendelian Randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 13:34:55","doi":"10.21203/rs.3.rs-7613007/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"679a2e75-02f7-4610-9d3a-f3f3368f9659","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T13:14:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 13:34:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7613007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7613007","identity":"rs-7613007","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}