Associations of genetically determined circulating proteins with breast cancer risk or survival

Associations of genetically determined circulating proteins with breast cancer risk or survival | 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 Associations of genetically determined circulating proteins with breast cancer risk or survival Hanghang Chen, qi liu, Xufeng Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3906265/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background There are few large-scale studies that focus on the associations between circulating proteins and breast cancer (BC) risk or survival. This study aimed to evaluate the potential circulating proteins associated with BC risk or survival using the Mendelian randomization (MR) method. Methods We collected the protein quantitative trait locus (pQTL) data of 4,907 circulating proteins from the DeCODE study (n = 35,559) as exposures. We gathered the genome wide association study (GWAS) data of BC from BCAC (OncoArray, n = 138,508) and BCAC (iCOGS, n = 76,167). The FinnGen study (n = 224,737) as the outcomes. The BC survival data was obtained from BCAC (OncoArray, n = 91,686). We used two sample MR framework to assess the associations between genetically predictive proteins and BC risk. Besides strict quality control, sensitivity tests and false discovery rate (FDR) or bonferroni correction, we further performed meta-analysis to ensure the robustness of the results. Results Four proteins—SIA4B (OR = 0.58, 95% CI (confidence interval): 0.51–0.64), CDH1 (OR = 0.83, 95% CI: 0.77–0.89), ALPI (OR = 0.91, 95% CI: 0.90–0.93) and CCDC134 (OR = 0.84, 95% CI: 0.80–0.88) are associated with reduced BC risk. 57 circulating proteins passed the sensitivity test and causally associated with BC survival. Conclusions Genetically predicted four circulating proteins (SIA4B, CDH1, ALPI and, CCDC134) are associated with reduced BC risk. 57 proteins are associated with BC survival. Our analyses from genetics and MR provide insights into the causes of BC and add evidence for reducing the risk of BC. Breast Cancer (BC) Genome Wide Association Study (GWAS) Mendelian Randomization (MR) Protein Quantitative Trait Locus (pQTL) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Breast cancer (BC) is a prevalent cancer in female, exhibiting a growing incidence rate globally(Bray et al. 2018 ). According to the latest global cancer burden data released in 2020, BC has the highest incidence of cancer in the world(Ferlay et al. 2021 ). Precise screening methods are needed to identify women in increased risk. Epidemiology has shown that a variety of factors are related to BC risk, and a population-based approach based on reducing exposure to modifiable risk factors is required to lower the BC incidence. Circulating proteins are important sources of cancer biomarkers. Changes in circulating protein levels provide information about the patient's physical condition, health status and can be used to track disease progression(Issaq et al. 2007 ). Circulating protein also can reflect early occurrence, invasion and metastasis of cancer(Hanahan and Weinberg 2011 ). Previous researches have reported that circulating proteins are associated with the pathogenesis of several cancer diseases, such as prostate(Nassar and Parat 2020 ), colorectal(Asgharzadeh et al. 2022 ), lung(Parma et al. 2022 ), and esophagus cancer(Hoang et al. 2021 ). However, large-scale researches that focus on causal associations between circulating proteins and BC is still scarce. So, a comprehensive understanding of the associations between circulating proteins and BC risk would be beneficial to improved diagnoses and therapies. Mendelian randomization (MR) is an epidemiological method which uses genetic information as instrumental variables to infer associations between phenotype and disease(Hemani et al. 2018 ). MR holds a significant advantage over traditional research methods in that it minimizes the influence of environmental, behavioral, and other factors on the associations being studied. By doing so, MR effectively reduces the likelihood of confounding factors, thus providing researchers with a more accurate understanding of the actual situation. This approach allows for a clearer examination of the relationships between genetic variants, lifestyle factors, and health outcomes, ultimately contributing to more robust and reliable research findings. In this study, we applied a two-sample MR framework to determine the associations between circulating proteins and BC. Our analyses added evidence from genetics and mendelian randomization that four circulating proteins are associated with BC risk or survival. These findings not only enhance our understanding of the role genetics play in BC but also provide valuable insights into its etiology, diagnosis, and treatment. The study is help to reveal the pathogenesis of BC and provides a theoretical basis for searching for new therapeutic targets. MATERIALS AND METHODS Data source All exposure and outcome cohorts were restricted to European ancestry for large-scale protein quantitative trait loci (pQTL) data access and to reduce bias from population stratification. We obtained large-scale pQTL data of 35,559 healthy individuals from DeCODE (Decoding Cancer Code)(Ferkingstad et al. 2021 ). Summary statistics level genome wide association study (GWAS) data of BC risk were obtained from FinnGen study (n = 224,737)(Kurki et al. 2023 ), BCAC (OncoArray, n = 138,508)(kConFab Investigators et al. 2020 ) and BCAC (iCOGS, n = 76,167)(Kapoor et al. 2020 ). GWAS data of BC survival data was obtained from BCAC (OncoArray, n = 91,686)(Morra et al. 2021 ). More details of these data can be found in the original publication. We used two sample MR to infer the associations between circulating proteins and BC risk or survival. All studies were approved by their corresponding ethics review boards, and all subjects provided informed consent. Two sample MR analyses We employed the "TwoSampleMR" R package(Hemani et al. 2018 ) to perform the two sample MR analyses. The inverse variance weighted (IVW) method is the most potent and widely adopted MR technique(Lin et al. 2021 ), hence we chose IVW as our primary analysis approach. Instrumental variables (IVs) were identified as SNPs showing a strong correlation with the exposure ( p < 5e-8). In cases where there is only one instrumental variable (IV) available, the Wald ratio method becomes the sole option(Pierce and Burgess 2013 ). Firstly, we searched the IVs for the level of each circulating protein. We next performed linkage disequilibrium (LD) clumping and identified nearly independent genetic instrument variables using plink software (V1.9). LD was defined as r 2 < 0.1 within the clumping distance of 100kb, and SNPs with LD were excluded(Cronjé et al. 2023 ). The F value of each instrumental variable was calculated using this formula: F= \(\left(N-2\right)\times {R}^{2}÷\left(1-{R}^{2}\right)\) (R 2 : interpretability of instrumental variables, N: sample size) and R 2 could be calculated using this formula: \({R}^{2}={\beta }^{2}\left(1-EAF\right)\times 2EAF\) (EAF: effect elle frequency, β: beta size)(Palmer et al. 2012 ). Conventionally, IV with F < 10 was defined as weak IV and was removed to avoid weak instrumental variable bias. Secondly, we extracted the identical SNPs from the outcome data. If certain SNPs were not present in the outcome data, we opted not to find proxies. Thirdly, SNPs associated with the outcome and exposure were harmonized to be consistent with the same allele. MR results could be derived by employing the "mr" function on the harmonized data. The odds ratio (OR) could be calculated using the formula: OR = exp(beta). We utilized the "p.adjust" function to perform false discovery rate (FDR) correction to adjust the p value when conducting MR analyses between proteins and BC risk. When conducting MR analyses between proteins and BC survival, we implemented strict bonferroni correction ( p < 0.05/4907) to adjust the p value, given that there was only one exposure and one outcome data available. Sensitivity test We employed Cochran's Q statistics to assess the heterogeneity(Liu et al. 2019 ), and the MR-Egger method to test for pleiotropy(Verbanck et al. 2018 ) in the harmonized data. P value greater than 0.05 was considered to indicate the absence of heterogeneity or pleiotropy. Furthermore, the MR Steiger directionality test was utilized to exclude potential reverse causalities(Hemani et al. 2017 ). Meta analyses for MR results between circulating proteins and BC risk We used all circulating proteins to ensure the credibility the analysis results. Moreover, to ensure the robustness of the results, we performed meta analysis for all associations using the “meta” R package (V6.2-1). Cochran’s Q test and Higgins’s I 2 test were used to test the heterogeneity between studies(Sedgwick 2015 ). If p 50%, heterogeneity was considered to exist between studies, and random effect model was used; otherwise, fixed effect model was selected as the main meta method(Melsen et al. 2014 ). Statistical methods MR analyses were conducted using IVW methods when there were multiple shared SNPs in the exposure and outcome data. If there was only one shared SNP, the wald ratio method was employed. To avoid the weak instrumental variable bias, the F value needed to be greater than 10. Bonferroni correction was applied to adjust the p value, and adjusted p value less than 0.05 was considered indicative of causal associations between the two traits. In meta-analyses, Cochran’s Q test and Higgins’s I 2 test were used to assess the heterogeneity among the studies. RESULTS The overall study design is depicted in Fig. 1 . A two sample MR framework was employed to infer the causal correlations between exposure (circulating proteins) and outcomes (BC). Meta-analyses were performed to assess the stability of all associations. Six circulating proteins are significantly associated with BC risk Through two sample MR analyses for causal assessment between each circulating protein and outcome. The significant associations and the sensitivity analysis results between circulating protein and BC (BCAC OncoArray) were displayed in Table S1. 436 circulating proteins passed the sensitivity test and causally associated with BC after FDR correction (BCAC OncoArray). The effect size and FDR-corrected p value of all circulating proteins associated BC risk (BCAC OncoArray) were displayed in Fig. 2 A. The significant associations and the sensitivity analysis results between circulating protein and BC (BCAC iCOGS) were displayed in Table S2. 1072 circulating proteins passed the sensitivity test and causally associated with BC after FDR correction (BCAC iCOGS). The effect size and FDR-corrected p value of all significant circulating proteins associated BC risk (BCAC iCOGS) were displayed in Fig. 2 B. The significant associations and the sensitivity analysis results between human plasma proteins and BC (FinnGen) were displayed in Table S3. 128 circulating proteins passed the sensitivity test and causally associated with BC after FDR correction (BCAC OncoArray). Results with p > 0.05 in sensitivity analysis was defined without heterogeneity or pleiotropy. 57 plasma proteins were found to pass the sensitivity test and causally associated with BC after FDR correction (FinnGen). The effect size and FDR-corrected p value of all significant circulating proteins associated BC risk (FinnGen) were displayed in Fig. 2 C. All Mendelian randomization results are intersected, and six circulating proteins are significantly associated with BC risk from BCAC (OncoArray), BCAC (iCOGS) and FinnGen. The effect size of MR results between these six circulating proteins and three BC outcomes were showed in Fig. 3 A. Six circulating proteins were identified as the potential causal biomarkers for BC, including GPX7, SIA4B, TBPL1, CDH1, ALPI, and CCDC134. Figure 3 B shows the OR and 95% CI of MR results between six FDR-corrected significant circulating proteins and three BC risk. Meta analysis confirmed four circulating proteins are significantly associated with BC risk All meta results of associations between six significant circulating proteins and BC risk were displayed in Fig. 4 . Cochran’s Q test and Higgins’s I 2 test were used to test the heterogeneity between studies. If p 50%, heterogeneity was considered to exist between studies, and a random effects model was used; otherwise, a fixed effects model was selected as the main meta method. The existence of heterogeneity was acknowledged among studies investigating the three proteins (ALPI, GPX7, and TBPL1) and BC, necessitating the application of random effect model. After meta confirmation, four circulating proteins are associated with reduced BC risk—SIA4B (Fig. 4 A, OR = 0.58, 95% CI (confidence interval): 0.51–0.64, p = 9.61 × 10 − 5 ), CDH1 (Fig. 4 B, OR = 0.83, 95% CI: 0.77–0.89, p = 4.88 × 10 − 5 ), ALPI (Fig. 4 C, OR = 0.91, 95% CI: 0.90–0.93, p = 9.46 × 10 − 5 ), CCDC134 (Fig. 4 D, OR = 0.84, 95% CI: 0.80–0.88, p = 7.00 × 10 − 5 ). Two circulating proteins are not associated with BC risk—GPX7 (Fig. 4 E, OR = 0.99, 95% CI: 0.94–1.04, p = 0.46 × 10 − 5 ), and TBPL1 (Fig. 4 F, OR = 0.96, 95% CI: 0.81–1.14, p = 0.45 × 10 − 5 ). 57 circulating proteins are significantly associated with BC survival The significant associations and the analysis results between circulating proteins and BC survival were displayed in Table S4. Results with p > 0.05 in sensitivity analysis was defined without pleiotropy. 57 circulating proteins were found to pass the sensitivity test and causally associated with BC survival after bonferroni correction. The effect size and bonferroni-corrected p value of all circulating protein associated BC survival were displayed in Fig. 5 . DISCUSSION Circulating proteins are associated with the pathogenesis of several cancer diseases. Some circulating proteins have shown the capability to detect malignancy before clinical diagnosis(Menon et al. 2015 ). In our study, we conducted a proteome-wide MR analyses to explore the causal roles of over 4000 circulating proteins in BC. Besides strict quality control, sensitivity test, and p value correction, we further performed meta analysis to ensure the robustness of the results. We found that SIA4B, CDH1, ALPI, and CCDC134 proteins are associated with reduced BC risk. In addition, we also found that a variety of proteins were associated with BC survival, and LRP4 protein is the most significant for better BC survival. ST3GAL2 (SIA4B) is negatively associated with BC risk and have the characteristics of developing as natural anticancer drugs. Previous reports have demonstrated that high expression of SIA4B leads to a significant decrease in Stage-Specific Embryonic Antigen 4 (SSEA4) expression, thereby reducing the resistance to breast cancer chemotherapy drugs(Aloia et al. 2015 ). CDH1 (Cadherin-1) is a calcium ion-dependent cell adhesion protein(Huang et al. 2015 ). Previous studies have indicated that decreased expression of CDH1 is associated with promoting invasion and metastasis of BC cells(Xie et al. 2022 ). Decreased expression of CDH1 can lead to reduced expression or loss of function of E-cadherin, and promoting cancer cell detachment by reducing cells adhesion forces(Girardi et al. 2022 ). This is consistent with the results of our study. In our study, CDH1 is negatively associated with BC risk in our study, which suggests the protective role of CDH1. Intestinal alkaline phosphatase (ALPI) is a nonspecific enzyme which is a widely used marker of absorptive intestinal epithelial cell differentiation(Mariadason et al. 1997 ). Under the action of histone deacetylase inhibitor (HDACi), increased expression of ALPI through the HDAC-KLF5 pathway promotes intestinal epithelial cell differentiation, thereby inhibiting colon cancer cells(Shin et al. 2014 ). However, a previous study have indicated that increased expression of ALPI may be positively correlated with higher tumor cell activity, growth, invasion, and metastasis of breast cancer(Jiang). On the contrast, we found that ALPI being significantly associated with reduce BC risk. This requires further study. Coiled coil domain containing 134 (CCDC134), as a novel secretory protein, is widely expressed in various adult cancer tissues, normal tissues and cell lines(Huang et al. 2008 ). Previous study has showed that high expression of CCDC134 can inhibit migration and invasion of BC(Huang et al.). Zhong et al. showed that CCDC134 inhibits the malignant transformation of gastric cells by regulating GES-1 and AGS cells(Zhong et al. 2013 ). These studies were in line with our findings that CCDC134 was positively associated with decreased BC risk. CCDC134 may as a potential biomarker and therapeutic target for the early diagnosis, prognosis and treatment of BC patients. Many proteins are associated with BC survival in our study. Previous studies have indicated that high expression of LRP4 can inhibit the proliferation, invasion, and migration of hepatocellular carcinoma cells by affecting the miR-455-5p/LRP4 axis(Zou et al. 2022 ). LRP4 is positively associated with BC survival, which suggests the protective role of LRP4. However, Zhou et al. (Zhou et al. 2018 ) demonstrated that LRP4 is negatively associated with BC survival in vitro. LRP4 promotes papillary thyroid cancer metastasis by inducing PI3K/AKT-mediated EMT. In our study, we found that LRP4 is significantly associated with prolonged survival in BC. Therefore, the role of LRP4 in cancer must be reevaluated. One possible reason for the discrepancy in outcomes between the in vitro cell experiments and the human data could be due to the difference in experimental conditions or biological responses between the two settings. Notably, the impact of LRP4 on cancer progression is dynamic, requiring further investigation. The strengths of this study lie in several aspects. Firstly, one major strength of our study is the sample size, the study explores associations between the largest pQTL dataset and BC risk through MR analyses. Secondly, the study also explores the associations between 4,907 proteins and BC survival. To the best of our knowledge, there are fewer studies that use Mendelian randomization to investigate the relationships between proteins and BC survival. Lastly, we adopted strict criteria to ensure the accuracy of the results and utilized a meta-analysis to further validate our study findings. Some limitations of our analysis should be acknowledged. First, the exposure data does not take gender into consideration when processing the data, but outcome data only includes females, which may limit the accuracy of our results. In addition, all of the participants are European ancestry in our study, which may limit the generalizability of our findings to other populations. Finally, due to the strict criteria to ensure the accuracy of the results, many false negative results were neglected. In summary, our genetic and MR based analyses not only offer insights into the etiology of BC but also provide evidence for reducing the risk. By identifying four genetically predicted circulating proteins (SIA4B, CDH1, ALPI, and CCDC134) associated with a decreased risk of BC, we can focus on developing targeted prevention and intervention strategies. Furthermore, our study identified 57 circulating proteins causally linked to BC survival, which can help in developing novel biomarkers. These novel biomarkers have the potential to improve early diagnosis, disease etiology, and prognostic prediction of BC. CONCLUSION Genetically predicted four circulating proteins (SIA4B, CDH1, ALPI, and CCDC134) are associated with a reduced risk of BC. One protein (LRP4) is associated with improved survival in BC. Our analyses from genetics and mendelian randomization provide insights into the causes of BC and add evidence for reducing the risk of BC. The study is help to reveal the pathogenesis of BC and provides a theoretical basis for searching for new therapeutic targets. Abbreviations BC: Breast cancer BCAC : Breast Cancer Association Consortium CI: Confidence Interval DeCODE: Decoding Cancer Code FDR: false discovery rate GWAS: Genome wide association study IV: Instrumental variable IVW: Inverse variance weighted MR: Mendelian randomization OR: Odds ratio pQTL: Protein quantitative trait loci SNP: Single nucleotide polymorphism Declarations Ethics approval and consent to participate This study used publicly available GWAS summary statistics data without individual information, and thus no ethical approval was required. Consent for publication This study has been approved by all authors for publication. Competing interests The authors declare that they have no competing interests. Funding The study was supported by the Henan science and technology research project (NO. 222102310326); Special Program for Scientific Research of Chinese Medicine from Henan Province, China (2022ZY1048); National Natural Science Foundation of Henan Province of China (232300421183). Authors' contributions Hanghang Chen: Conceptualization, Methodology, Formal analysis, Software, Visualization, Writing original draft. Qi Liu: Validation, Data curation, Formal analysis, Writing original draft, Resources. Xufeng Cheng: Conceptualization, Methodology, Writing-review & Editing, Funding acquisition. Acknowledgments We want to acknowledge the participants and investigators of the DeCODE, FinnGen, and BCAC studies. The BC genome-wide association analyses for BCAC were supported by Cancer Research UK (PPRPGM-Nov20\100002, C1287/A10118, C1287/A16563, C1287/A10710, C12292/A20861, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565) and the Gray Foundation, The National Institutes of Health (CA128978, X01HG007492- the DRIVE consortium), the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant, the Quebec Breast Cancer Foundation, the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), the European Union's Horizon 2020 Research and Innovation Programme (634935 and 633784), the Post-Cancer GWAS initiative (U19 CA148537, CA148065 and CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer (CRN-87521), the Komen Foundation for the Cure, the Breast Cancer Research Foundation and the Ovarian Cancer Research Fund. All studies and funders are listed in Zhang H et al (Nat Genet, 2020). BCAC is funded by Cancer Research UK [C1287/A16563], the European Union's Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST respectively), and by the European Community´s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation or writing of the report. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer Research UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Ministère de l’Économie, Science et Innovation du Québec through Genome Québec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. Funding for the iCOGS infrastructure came from: the European Community's Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The DRIVE Consortium was funded by U19 CA148065. Availability of data and material The GWAS summary statistics data is available at http://practical.icr.ac.uk/blog/?page_id=8164, https://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas-summary-results-survival-2021/ and https://finngen.gitbook.io/documentation/data-download. The pQTL data of DeCODE data is available at https://www.decode.com/summarydata/. References Aloia A, Petrova E, Tomiuk S, et al (2015) The sialyl-glycolipid stage-specific embryonic antigen 4 marks a subpopulation of chemotherapy-resistant breast cancer cells with mesenchymal features. Breast Cancer Res 17:146. https://doi.org/10.1186/s13058-015-0652-6 Asgharzadeh F, Moradi-Marjaneh R, Marjaneh MM (2022) The Role of Heat Shock Protein 40 in Carcinogenesis and Biology of ColorectalCancer. 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Nat Genet 50:693–698. https://doi.org/10.1038/s41588-018-0099-7 Xie D, Chen Y, Wan X, et al (2022) The Potential Role of CDH1 as an Oncogene Combined With Related miRNAs and Their Diagnostic Value in Breast Cancer. Front Endocrinol 13:916469. https://doi.org/10.3389/fendo.2022.916469 Zhong J, Zhao M, Luo Q, et al (2013) CCDC134 is down-regulated in gastric cancer and its silencing promotes cell migration and invasion of GES-1 and AGS cells via the MAPK pathway. Mol Cell Biochem Zhou X, Xia E, Bhandari A, et al (2018) LRP4 promotes proliferation, migration, and invasion in papillary thyroid cancer. Biochemical and Biophysical Research Communications 503:257–263. https://doi.org/10.1016/j.bbrc.2018.06.012 Zou X, Sun P, Xie H, et al (2022) Knockdown of long noncoding RNA HUMT inhibits the proliferation and metastasis by regulating miR-455-5p/LRP4 axis in hepatocellular carcinoma. Bioengineered 13:8051–8063. https://doi.org/10.1080/21655979.2022.2051841 Additional Declarations No competing interests reported. Supplementary Files allpQTLmrresBCACNOheteFDR.csv Table S1 allpQTLmrresBCACiCOGSNOheteFDR.csv Table S2 allpQTLmrresFinnNOheteFDR.csv Table S3 allpQTLmrresBCACsurvival.csv Table S4 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3906265","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269883199,"identity":"d12b4298-8b77-4f25-b3e1-ced70be76c07","order_by":0,"name":"Hanghang Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACxvbGxsc/Kmx4+NkbiNTC3HO42ZjhTJqcZM8BIrWwz3BvE2ZsO2RscCOBSC28MxjbmAvYDiTOnPl44w2GGptoglokZze2PZ7BcyexXzqt2ILhWFpuAyEthnMOthvwSDxLnDk7x0yCseEwYS32NxLbJHgMDiduuHmGSC2MMxLbpHkSDgO9z0Oslp6DzYYzDoACGeiXBGL8wtje/vDBx3+gqDy88caHGhvCWpCBgUQCKcohWkjVMQpGwSgYBSMDAABgdUdKlU/TpAAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Henan University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Hanghang","middleName":"","lastName":"Chen","suffix":""},{"id":269883200,"identity":"2f03e5ff-c03f-4e5f-a25e-7ddc9e0b21c0","order_by":1,"name":"qi liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"qi","middleName":"","lastName":"liu","suffix":""},{"id":269883201,"identity":"2662dc73-e8fa-4efe-b61c-fbc6f600f75f","order_by":2,"name":"Xufeng Cheng","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xufeng","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2024-01-28 16:03:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3906265/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3906265/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50454342,"identity":"9cf09c6a-8301-4398-941e-8c1d0d9ac3b9","added_by":"auto","created_at":"2024-01-31 18:22:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31859,"visible":true,"origin":"","legend":"\u003cp\u003eThe overall study design. We obtained large-scale pQTL data of 35,559 healthy individuals from DeCODE (Decoding Cancer Code) as exposures. We gathered the genome wide association study (GWAS) data of BC from BCAC (OncoArray) and BCAC (iCOGS) (n = 214,675). The FinnGen study (n = 224,737) as the outcomes. The BC survival data was obtained from BCAC (OncoArray, n = 91,686). The two sample mendelian randomization (MR) framework was utilized as the main analytical method. A meta-analysis was conducted to ensure the robustness of the results. pQTL: protein quantitative trait locus; GWAS: genome-wide association study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3906265/v1/c1fa7b83554937526e819921.png"},{"id":50454343,"identity":"a198243a-d419-4993-9bdf-3fbcd2b9c085","added_by":"auto","created_at":"2024-01-31 18:22:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23470,"visible":true,"origin":"","legend":"\u003cp\u003eThe volcano plots show the MR results between circulating proteins and BC risk (three cohorts as outcomes). \u003cstrong\u003eA \u003c/strong\u003eThe volcano plot shows MR results between circulating proteins and BCAC (OncoArray). \u003cstrong\u003eB\u003c/strong\u003e The volcano plot shows the MR results between circulating proteins and BCAC (iCOGS). \u003cstrong\u003eC\u003c/strong\u003e The volcano plot shows the MR results between circulating proteins and BC (FinnGen). The y-axis represents the −log10 of the FDR-corrected \u003cem\u003ep\u003c/em\u003e value; the x-axis shows the effect size.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3906265/v1/7b571719d73278e9e100fd59.png"},{"id":50454627,"identity":"bcf18f86-d16a-4af0-a94d-353e0edd1708","added_by":"auto","created_at":"2024-01-31 18:30:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20679,"visible":true,"origin":"","legend":"\u003cp\u003eSix circulating proteins are significantly associated with BC risk. \u003cstrong\u003eA\u003c/strong\u003e The dot plot shows the effect sizes (beta values) between the MR results of six circulating proteins and three BC outcomes. \u003cstrong\u003eB\u003c/strong\u003e The forest plot\u003cstrong\u003e \u003c/strong\u003eshows the OR and 95% CI of MR results between six FDR-corrected significant circulating proteinsand three BC outcomes. β: beta size; MR: mendelian randomization; OR: odds ratio; CI: confidence interval. FDR: false discovery rate.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3906265/v1/47b54501d6343ce975f22831.png"},{"id":50454341,"identity":"f9cd5eeb-cfe1-401b-84bb-29eeebb3b1d5","added_by":"auto","created_at":"2024-01-31 18:22:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":28726,"visible":true,"origin":"","legend":"\u003cp\u003eMeta analysis confirmed four circulating proteins are significantly associated with BC risk. Four circulating proteins are associated with reduced BC risk—SIA4B (\u003cstrong\u003eA\u003c/strong\u003e, OR = 0.58, 95% CI (confidence interval): 0.51-0.64, \u003cem\u003ep \u003c/em\u003e= 9.61 × 10\u003csup\u003e−5\u003c/sup\u003e), CDH1 (\u003cstrong\u003eB\u003c/strong\u003e, OR = 0.83, 95% CI: 0.77-0.89,\u003cem\u003e p \u003c/em\u003e= 4.88 × 10\u003csup\u003e−5\u003c/sup\u003e), ALPI (\u003cstrong\u003eC\u003c/strong\u003e, OR = 0.91, 95% CI: 0.90-0.93, \u003cem\u003ep \u003c/em\u003e= 9.46 × 10\u003csup\u003e−5\u003c/sup\u003e), CCDC134 (\u003cstrong\u003eD\u003c/strong\u003e, OR = 0.84, 95% CI: 0.80-0.88,\u003cem\u003e p \u003c/em\u003e= 7.00 × 10\u003csup\u003e−5\u003c/sup\u003e). Two circulating proteins are not associated with BC risk—GPX7 (\u003cstrong\u003eE\u003c/strong\u003e, OR = 0.99, 95% CI: 0.94-1.04,\u003cem\u003e p \u003c/em\u003e= 0.46 × 10\u003csup\u003e−5\u003c/sup\u003e), and TBPL1 (\u003cstrong\u003eF\u003c/strong\u003e, OR = 0.96, 95% CI: 0.81-1.14,\u003cem\u003e p \u003c/em\u003e= 0.45 × 10\u003csup\u003e−5\u003c/sup\u003e). Cochran’s Q test and Higgins’s I\u003csup\u003e2\u003c/sup\u003e test were used to test the heterogeneity between studies. If \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 or I\u003csup\u003e2 \u003c/sup\u003e\u0026gt;50%, heterogeneity was considered to exist between studies, and random effect model was used; otherwise, fixed effect model was selected as the main meta method. \u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3906265/v1/4dd8843944cae14137bd173b.png"},{"id":50454348,"identity":"df61de40-f41f-4584-95aa-d0591184205e","added_by":"auto","created_at":"2024-01-31 18:22:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32570,"visible":true,"origin":"","legend":"\u003cp\u003eThe volcano plot shows all circulating protein associated BC survival. The y-axis represents the −log10 of the FDR-corrected \u003cem\u003ep\u003c/em\u003e value; the x-axis shows the effect size.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3906265/v1/32fda1ff69c3f1dd7cb76096.png"},{"id":50497903,"identity":"2a769dae-e027-4be5-997d-f8b0b1f38737","added_by":"auto","created_at":"2024-02-01 12:37:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":904519,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3906265/v1/1ae3eb8f-0209-4915-aa9d-46a2e4e5ca4d.pdf"},{"id":50454345,"identity":"8f74089d-a8db-4360-a5c3-d234d716beec","added_by":"auto","created_at":"2024-01-31 18:22:34","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":140086,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1\u003c/p\u003e","description":"","filename":"allpQTLmrresBCACNOheteFDR.csv","url":"https://assets-eu.researchsquare.com/files/rs-3906265/v1/098529cd0d27ca7127f98a7f.csv"},{"id":50454340,"identity":"154401c8-0aa1-46b6-937d-da31f49a46dc","added_by":"auto","created_at":"2024-01-31 18:22:33","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":359198,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2\u003c/p\u003e","description":"","filename":"allpQTLmrresBCACiCOGSNOheteFDR.csv","url":"https://assets-eu.researchsquare.com/files/rs-3906265/v1/8d011207eac21987b01f9c48.csv"},{"id":50454339,"identity":"1d85039c-2299-4479-8cb3-37aacb798aaf","added_by":"auto","created_at":"2024-01-31 18:22:33","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":41080,"visible":true,"origin":"","legend":"\u003cp\u003eTable S3\u003c/p\u003e","description":"","filename":"allpQTLmrresFinnNOheteFDR.csv","url":"https://assets-eu.researchsquare.com/files/rs-3906265/v1/5dee8476c5233da82c2369a1.csv"},{"id":50454347,"identity":"925e2f74-f70f-4b51-94e5-79d3724f1200","added_by":"auto","created_at":"2024-01-31 18:22:34","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1344513,"visible":true,"origin":"","legend":"\u003cp\u003eTable S4\u003c/p\u003e","description":"","filename":"allpQTLmrresBCACsurvival.csv","url":"https://assets-eu.researchsquare.com/files/rs-3906265/v1/925edbf39665884ac6cd367a.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Associations of genetically determined circulating proteins with breast cancer risk or survival","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBreast cancer (BC) is a prevalent cancer in female, exhibiting a growing incidence rate globally(Bray et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). According to the latest global cancer burden data released in 2020, BC has the highest incidence of cancer in the world(Ferlay et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Precise screening methods are needed to identify women in increased risk. Epidemiology has shown that a variety of factors are related to BC risk, and a population-based approach based on reducing exposure to modifiable risk factors is required to lower the BC incidence. Circulating proteins are important sources of cancer biomarkers. Changes in circulating protein levels provide information about the patient's physical condition, health status and can be used to track disease progression(Issaq et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Circulating protein also can reflect early occurrence, invasion and metastasis of cancer(Hanahan and Weinberg \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious researches have reported that circulating proteins are associated with the pathogenesis of several cancer diseases, such as prostate(Nassar and Parat \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), colorectal(Asgharzadeh et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), lung(Parma et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and esophagus cancer(Hoang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, large-scale researches that focus on causal associations between circulating proteins and BC is still scarce. So, a comprehensive understanding of the associations between circulating proteins and BC risk would be beneficial to improved diagnoses and therapies.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is an epidemiological method which uses genetic information as instrumental variables to infer associations between phenotype and disease(Hemani et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). MR holds a significant advantage over traditional research methods in that it minimizes the influence of environmental, behavioral, and other factors on the associations being studied. By doing so, MR effectively reduces the likelihood of confounding factors, thus providing researchers with a more accurate understanding of the actual situation. This approach allows for a clearer examination of the relationships between genetic variants, lifestyle factors, and health outcomes, ultimately contributing to more robust and reliable research findings.\u003c/p\u003e \u003cp\u003eIn this study, we applied a two-sample MR framework to determine the associations between circulating proteins and BC. Our analyses added evidence from genetics and mendelian randomization that four circulating proteins are associated with BC risk or survival. These findings not only enhance our understanding of the role genetics play in BC but also provide valuable insights into its etiology, diagnosis, and treatment. The study is help to reveal the pathogenesis of BC and provides a theoretical basis for searching for new therapeutic targets.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eAll exposure and outcome cohorts were restricted to European ancestry for large-scale protein quantitative trait loci (pQTL) data access and to reduce bias from population stratification. We obtained large-scale pQTL data of 35,559 healthy individuals from DeCODE (Decoding Cancer Code)(Ferkingstad et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Summary statistics level genome wide association study (GWAS) data of BC risk were obtained from FinnGen study (n\u0026thinsp;=\u0026thinsp;224,737)(Kurki et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), BCAC (OncoArray, n\u0026thinsp;=\u0026thinsp;138,508)(kConFab Investigators et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and BCAC (iCOGS, n\u0026thinsp;=\u0026thinsp;76,167)(Kapoor et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). GWAS data of BC survival data was obtained from BCAC (OncoArray, n\u0026thinsp;=\u0026thinsp;91,686)(Morra et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). More details of these data can be found in the original publication. We used two sample MR to infer the associations between circulating proteins and BC risk or survival. All studies were approved by their corresponding ethics review boards, and all subjects provided informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eTwo sample MR analyses\u003c/h2\u003e \u003cp\u003eWe employed the \"TwoSampleMR\" R package(Hemani et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) to perform the two sample MR analyses. The inverse variance weighted (IVW) method is the most potent and widely adopted MR technique(Lin et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), hence we chose IVW as our primary analysis approach. Instrumental variables (IVs) were identified as SNPs showing a strong correlation with the exposure (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5e-8). In cases where there is only one instrumental variable (IV) available, the Wald ratio method becomes the sole option(Pierce and Burgess \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFirstly, we searched the IVs for the level of each circulating protein. We next performed linkage disequilibrium (LD) clumping and identified nearly independent genetic instrument variables using plink software (V1.9). LD was defined as r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 within the clumping distance of 100kb, and SNPs with LD were excluded(Cronj\u0026eacute; et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The F value of each instrumental variable was calculated using this formula: F= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left(N-2\\right)\\times {R}^{2}\u0026divide;\\left(1-{R}^{2}\\right)\\)\u003c/span\u003e\u003c/span\u003e(R\u003csup\u003e2\u003c/sup\u003e: interpretability of instrumental variables, N: sample size) and R\u003csup\u003e2\u003c/sup\u003e could be calculated using this formula: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}={\\beta }^{2}\\left(1-EAF\\right)\\times 2EAF\\)\u003c/span\u003e\u003c/span\u003e(EAF: effect elle frequency, β: beta size)(Palmer et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Conventionally, IV with F\u0026thinsp;\u0026lt;\u0026thinsp;10 was defined as weak IV and was removed to avoid weak instrumental variable bias.\u003c/p\u003e \u003cp\u003eSecondly, we extracted the identical SNPs from the outcome data. If certain SNPs were not present in the outcome data, we opted not to find proxies.\u003c/p\u003e \u003cp\u003eThirdly, SNPs associated with the outcome and exposure were harmonized to be consistent with the same allele. MR results could be derived by employing the \"mr\" function on the harmonized data. The odds ratio (OR) could be calculated using the formula: OR\u0026thinsp;=\u0026thinsp;exp(beta).\u003c/p\u003e \u003cp\u003eWe utilized the \"p.adjust\" function to perform false discovery rate (FDR) correction to adjust the \u003cem\u003ep\u003c/em\u003e value when conducting MR analyses between proteins and BC risk. When conducting MR analyses between proteins and BC survival, we implemented strict bonferroni correction (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05/4907) to adjust the \u003cem\u003ep\u003c/em\u003e value, given that there was only one exposure and one outcome data available.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity test\u003c/h2\u003e \u003cp\u003eWe employed Cochran's Q statistics to assess the heterogeneity(Liu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the MR-Egger method to test for pleiotropy(Verbanck et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in the harmonized data. \u003cem\u003eP\u003c/em\u003e value greater than 0.05 was considered to indicate the absence of heterogeneity or pleiotropy. Furthermore, the MR Steiger directionality test was utilized to exclude potential reverse causalities(Hemani et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMeta analyses for MR results between circulating proteins and BC risk\u003c/h2\u003e \u003cp\u003eWe used all circulating proteins to ensure the credibility the analysis results. Moreover, to ensure the robustness of the results, we performed meta analysis for all associations using the \u0026ldquo;meta\u0026rdquo; R package (V6.2-1). Cochran\u0026rsquo;s Q test and Higgins\u0026rsquo;s I\u003csup\u003e2\u003c/sup\u003e test were used to test the heterogeneity between studies(Sedgwick \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). If \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50%, heterogeneity was considered to exist between studies, and random effect model was used; otherwise, fixed effect model was selected as the main meta method(Melsen et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eMR analyses were conducted using IVW methods when there were multiple shared SNPs in the exposure and outcome data. If there was only one shared SNP, the wald ratio method was employed. To avoid the weak instrumental variable bias, the F value needed to be greater than 10. Bonferroni correction was applied to adjust the \u003cem\u003ep\u003c/em\u003e value, and adjusted \u003cem\u003ep\u003c/em\u003e value less than 0.05 was considered indicative of causal associations between the two traits. In meta-analyses, Cochran\u0026rsquo;s Q test and Higgins\u0026rsquo;s I\u003csup\u003e2\u003c/sup\u003e test were used to assess the heterogeneity among the studies.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe overall study design is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A two sample MR framework was employed to infer the causal correlations between exposure (circulating proteins) and outcomes (BC). Meta-analyses were performed to assess the stability of all associations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSix circulating proteins are significantly associated with BC risk\u003c/h2\u003e \u003cp\u003eThrough two sample MR analyses for causal assessment between each circulating protein and outcome. The significant associations and the sensitivity analysis results between circulating protein and BC (BCAC OncoArray) were displayed in Table S1. 436 circulating proteins passed the sensitivity test and causally associated with BC after FDR correction (BCAC OncoArray). The effect size and FDR-corrected \u003cem\u003ep\u003c/em\u003e value of all circulating proteins associated BC risk (BCAC OncoArray) were displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe significant associations and the sensitivity analysis results between circulating protein and BC (BCAC iCOGS) were displayed in Table S2. 1072 circulating proteins passed the sensitivity test and causally associated with BC after FDR correction (BCAC iCOGS). The effect size and FDR-corrected p value of all significant circulating proteins associated BC risk (BCAC iCOGS) were displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003eThe significant associations and the sensitivity analysis results between human plasma proteins and BC (FinnGen) were displayed in Table S3. 128 circulating proteins passed the sensitivity test and causally associated with BC after FDR correction (BCAC OncoArray). Results with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 in sensitivity analysis was defined without heterogeneity or pleiotropy. 57 plasma proteins were found to pass the sensitivity test and causally associated with BC after FDR correction (FinnGen). The effect size and FDR-corrected \u003cem\u003ep\u003c/em\u003e value of all significant circulating proteins associated BC risk (FinnGen) were displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC.\u003c/p\u003e \u003cp\u003eAll Mendelian randomization results are intersected, and six circulating proteins are significantly associated with BC risk from BCAC (OncoArray), BCAC (iCOGS) and FinnGen. The effect size of MR results between these six circulating proteins and three BC outcomes were showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. Six circulating proteins were identified as the potential causal biomarkers for BC, including GPX7, SIA4B, TBPL1, CDH1, ALPI, and CCDC134. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB shows the OR and 95% CI of MR results between six FDR-corrected significant circulating proteins and three BC risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeta analysis confirmed four circulating proteins are significantly associated with BC risk\u003c/h3\u003e\n\u003cp\u003eAll meta results of associations between six significant circulating proteins and BC risk were displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Cochran\u0026rsquo;s Q test and Higgins\u0026rsquo;s I\u003csup\u003e2\u003c/sup\u003e test were used to test the heterogeneity between studies. If \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 or I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50%, heterogeneity was considered to exist between studies, and a random effects model was used; otherwise, a fixed effects model was selected as the main meta method. The existence of heterogeneity was acknowledged among studies investigating the three proteins (ALPI, GPX7, and TBPL1) and BC, necessitating the application of random effect model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter meta confirmation, four circulating proteins are associated with reduced BC risk\u0026mdash;SIA4B (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, OR\u0026thinsp;=\u0026thinsp;0.58, 95% CI (confidence interval): 0.51\u0026ndash;0.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.61 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), CDH1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, OR\u0026thinsp;=\u0026thinsp;0.83, 95% CI: 0.77\u0026ndash;0.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.88 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), ALPI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, OR\u0026thinsp;=\u0026thinsp;0.91, 95% CI: 0.90\u0026ndash;0.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.46 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), CCDC134 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, OR\u0026thinsp;=\u0026thinsp;0.84, 95% CI: 0.80\u0026ndash;0.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.00 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). Two circulating proteins are not associated with BC risk\u0026mdash;GPX7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, OR\u0026thinsp;=\u0026thinsp;0.99, 95% CI: 0.94\u0026ndash;1.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.46 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), and TBPL1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, OR\u0026thinsp;=\u0026thinsp;0.96, 95% CI: 0.81\u0026ndash;1.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.45 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e57 circulating proteins are significantly associated with BC survival\u003c/h2\u003e \u003cp\u003eThe significant associations and the analysis results between circulating proteins and BC survival were displayed in Table S4. Results with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 in sensitivity analysis was defined without pleiotropy. 57 circulating proteins were found to pass the sensitivity test and causally associated with BC survival after bonferroni correction. The effect size and bonferroni-corrected \u003cem\u003ep\u003c/em\u003e value of all circulating protein associated BC survival were displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCirculating proteins are associated with the pathogenesis of several cancer diseases. Some circulating proteins have shown the capability to detect malignancy before clinical diagnosis(Menon et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In our study, we conducted a proteome-wide MR analyses to explore the causal roles of over 4000 circulating proteins in BC. Besides strict quality control, sensitivity test, and \u003cem\u003ep\u003c/em\u003e value correction, we further performed meta analysis to ensure the robustness of the results. We found that SIA4B, CDH1, ALPI, and CCDC134 proteins are associated with reduced BC risk. In addition, we also found that a variety of proteins were associated with BC survival, and LRP4 protein is the most significant for better BC survival.\u003c/p\u003e \u003cp\u003eST3GAL2 (SIA4B) is negatively associated with BC risk and have the characteristics of developing as natural anticancer drugs. Previous reports have demonstrated that high expression of SIA4B leads to a significant decrease in Stage-Specific Embryonic Antigen 4 (SSEA4) expression, thereby reducing the resistance to breast cancer chemotherapy drugs(Aloia et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). CDH1 (Cadherin-1) is a calcium ion-dependent cell adhesion protein(Huang et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Previous studies have indicated that decreased expression of CDH1 is associated with promoting invasion and metastasis of BC cells(Xie et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Decreased expression of CDH1 can lead to reduced expression or loss of function of E-cadherin, and promoting cancer cell detachment by reducing cells adhesion forces(Girardi et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is consistent with the results of our study. In our study, CDH1 is negatively associated with BC risk in our study, which suggests the protective role of CDH1. Intestinal alkaline phosphatase (ALPI) is a nonspecific enzyme which is a widely used marker of absorptive intestinal epithelial cell differentiation(Mariadason et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Under the action of histone deacetylase inhibitor (HDACi), increased expression of ALPI through the HDAC-KLF5 pathway promotes intestinal epithelial cell differentiation, thereby inhibiting colon cancer cells(Shin et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, a previous study have indicated that increased expression of ALPI may be positively correlated with higher tumor cell activity, growth, invasion, and metastasis of breast cancer(Jiang). On the contrast, we found that ALPI being significantly associated with reduce BC risk. This requires further study. Coiled coil domain containing 134 (CCDC134), as a novel secretory protein, is widely expressed in various adult cancer tissues, normal tissues and cell lines(Huang et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Previous study has showed that high expression of CCDC134 can inhibit migration and invasion of BC(Huang et al.). Zhong et al. showed that CCDC134 inhibits the malignant transformation of gastric cells by regulating GES-1 and AGS cells(Zhong et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These studies were in line with our findings that CCDC134 was positively associated with decreased BC risk. CCDC134 may as a potential biomarker and therapeutic target for the early diagnosis, prognosis and treatment of BC patients.\u003c/p\u003e \u003cp\u003eMany proteins are associated with BC survival in our study. Previous studies have indicated that high expression of LRP4 can inhibit the proliferation, invasion, and migration of hepatocellular carcinoma cells by affecting the miR-455-5p/LRP4 axis(Zou et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). LRP4 is positively associated with BC survival, which suggests the protective role of LRP4. However, Zhou et al. (Zhou et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) demonstrated that LRP4 is negatively associated with BC survival in vitro. LRP4 promotes papillary thyroid cancer metastasis by inducing PI3K/AKT-mediated EMT. In our study, we found that LRP4 is significantly associated with prolonged survival in BC. Therefore, the role of LRP4 in cancer must be reevaluated. One possible reason for the discrepancy in outcomes between the in vitro cell experiments and the human data could be due to the difference in experimental conditions or biological responses between the two settings. Notably, the impact of LRP4 on cancer progression is dynamic, requiring further investigation.\u003c/p\u003e \u003cp\u003eThe strengths of this study lie in several aspects. Firstly, one major strength of our study is the sample size, the study explores associations between the largest pQTL dataset and BC risk through MR analyses. Secondly, the study also explores the associations between 4,907 proteins and BC survival. To the best of our knowledge, there are fewer studies that use Mendelian randomization to investigate the relationships between proteins and BC survival. Lastly, we adopted strict criteria to ensure the accuracy of the results and utilized a meta-analysis to further validate our study findings.\u003c/p\u003e \u003cp\u003eSome limitations of our analysis should be acknowledged. First, the exposure data does not take gender into consideration when processing the data, but outcome data only includes females, which may limit the accuracy of our results. In addition, all of the participants are European ancestry in our study, which may limit the generalizability of our findings to other populations. Finally, due to the strict criteria to ensure the accuracy of the results, many false negative results were neglected.\u003c/p\u003e \u003cp\u003eIn summary, our genetic and MR based analyses not only offer insights into the etiology of BC but also provide evidence for reducing the risk. By identifying four genetically predicted circulating proteins (SIA4B, CDH1, ALPI, and CCDC134) associated with a decreased risk of BC, we can focus on developing targeted prevention and intervention strategies. Furthermore, our study identified 57 circulating proteins causally linked to BC survival, which can help in developing novel biomarkers. These novel biomarkers have the potential to improve early diagnosis, disease etiology, and prognostic prediction of BC.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eGenetically predicted four circulating proteins (SIA4B, CDH1, ALPI, and CCDC134) are associated with a reduced risk of BC. One protein (LRP4) is associated with improved survival in BC. Our analyses from genetics and mendelian randomization provide insights into the causes of BC and add evidence for reducing the risk of BC. The study is help to reveal the pathogenesis of BC and provides a theoretical basis for searching for new therapeutic targets.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBC: Breast cancer\u003c/p\u003e\n\u003cp\u003eBCAC\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eBreast Cancer Association Consortium\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCI: Confidence Interval\u003c/p\u003e\n\u003cp\u003eDeCODE:\u0026nbsp;Decoding Cancer Code\u003c/p\u003e\n\u003cp\u003eFDR:\u0026nbsp;false discovery rate\u003c/p\u003e\n\u003cp\u003eGWAS: Genome wide association study\u003c/p\u003e\n\u003cp\u003eIV: Instrumental variable\u003c/p\u003e\n\u003cp\u003eIVW: Inverse variance weighted\u003c/p\u003e\n\u003cp\u003eMR: Mendelian randomization\u003c/p\u003e\n\u003cp\u003eOR: Odds ratio\u003c/p\u003e\n\u003cp\u003epQTL: Protein quantitative trait loci\u003c/p\u003e\n\u003cp\u003eSNP: Single nucleotide polymorphism\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available GWAS summary statistics data without individual information, and thus no ethical approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been approved by all authors for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the Henan science and technology research project (NO. 222102310326); Special\u0026ensp;Program\u0026ensp;for Scientific\u0026ensp;Research of Chinese Medicine from Henan Province, China (2022ZY1048); National Natural Science Foundation of Henan Province of China (232300421183).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHanghang Chen: Conceptualization, Methodology, Formal analysis, Software, Visualization, Writing original draft. Qi Liu: Validation, Data curation, Formal analysis, Writing original draft, Resources. Xufeng Cheng: Conceptualization, Methodology, Writing-review \u0026amp; Editing, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to acknowledge the participants and investigators of the DeCODE, FinnGen, and BCAC studies. The BC genome-wide association analyses for BCAC were supported by Cancer Research UK (PPRPGM-Nov20\\100002, C1287/A10118, C1287/A16563, C1287/A10710, C12292/A20861, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565) and the Gray Foundation, The National Institutes of Health (CA128978, X01HG007492- the DRIVE consortium), the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and the Minist\u0026egrave;re de l\u0026rsquo;\u0026Eacute;conomie, Science et Innovation du Qu\u0026eacute;bec through Genome Qu\u0026eacute;bec and the PSRSIIRI-701 grant, the Quebec Breast Cancer Foundation, the European Community\u0026apos;s Seventh Framework Programme under grant agreement n\u0026deg; 223175 (HEALTH-F2-2009-223175) (COGS), the European Union\u0026apos;s Horizon 2020 Research and Innovation Programme (634935 and 633784), the Post-Cancer GWAS initiative (U19 CA148537, CA148065 and CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer (CRN-87521), the Komen Foundation for the Cure, the Breast Cancer Research Foundation and the Ovarian Cancer Research Fund. All studies and funders are listed in Zhang H et al (Nat Genet, 2020). \u003c/p\u003e\n\u003cp\u003eBCAC is funded by Cancer Research UK [C1287/A16563], the European Union\u0026apos;s Horizon 2020 Research and Innovation Programme (grant numbers 634935 and 633784 for BRIDGES and B-CAST respectively), and by the European Community\u0026acute;s Seventh Framework Programme under grant agreement number 223175 (grant number HEALTH-F2-2009-223175) (COGS). The EU Horizon 2020 Research and Innovation Programme funding source had no role in study design, data collection, data analysis, data interpretation or writing of the report. Genotyping of the OncoArray was funded by the NIH Grant U19 CA148065, and Cancer Research UK Grant C1287/A16563 and the PERSPECTIVE project supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (grant GPH-129344) and, the Minist\u0026egrave;re de l\u0026rsquo;\u0026Eacute;conomie, Science et Innovation du Qu\u0026eacute;bec through Genome Qu\u0026eacute;bec and the PSRSIIRI-701 grant, and the Quebec Breast Cancer Foundation. Funding for the iCOGS infrastructure came from: the European Community\u0026apos;s Seventh Framework Programme under grant agreement n\u0026deg; 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer, and Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. The DRIVE Consortium was funded by U19 CA148065.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GWAS summary statistics data is available at http://practical.icr.ac.uk/blog/?page_id=8164, https://bcac.ccge.medschl.cam.ac.uk/bcacdata/oncoarray/oncoarray-and-combined-summary-result/gwas-summary-results-survival-2021/ and https://finngen.gitbook.io/documentation/data-download. The pQTL data of DeCODE data is available at https://www.decode.com/summarydata/.\u003c/p\u003e\n\n\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAloia A, Petrova E, Tomiuk S, et al (2015) The sialyl-glycolipid stage-specific embryonic antigen 4 marks a subpopulation of chemotherapy-resistant breast cancer cells with mesenchymal features. Breast Cancer Res 17:146. https://doi.org/10.1186/s13058-015-0652-6\u003c/li\u003e\n\u003cli\u003eAsgharzadeh F, Moradi-Marjaneh R, Marjaneh MM (2022) The Role of Heat Shock Protein 40 in Carcinogenesis and Biology of ColorectalCancer. CPD 28:1457\u0026ndash;1465. https://doi.org/10.2174/1381612828666220513124603\u003c/li\u003e\n\u003cli\u003eBray F, Ferlay J, Soerjomataram I, et al (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J Clinicians 68:394\u0026ndash;424. https://doi.org/10.3322/caac.21492\u003c/li\u003e\n\u003cli\u003eCronj\u0026eacute; HT, Karhunen V, Hovingh GK, et al (2023) Genetic evidence implicating natriuretic peptide receptor-3 in cardiovascular disease risk: a Mendelian randomization study. 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Bioengineered 13:8051\u0026ndash;8063. https://doi.org/10.1080/21655979.2022.2051841\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 (BC), Genome Wide Association Study (GWAS), Mendelian Randomization (MR), Protein Quantitative Trait Locus (pQTL)","lastPublishedDoi":"10.21203/rs.3.rs-3906265/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3906265/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThere are few large-scale studies that focus on the associations between circulating proteins and breast cancer (BC) risk or survival. This study aimed to evaluate the potential circulating proteins associated with BC risk or survival using the Mendelian randomization (MR) method.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe collected the protein quantitative trait locus (pQTL) data of 4,907 circulating proteins from the DeCODE study (n\u0026thinsp;=\u0026thinsp;35,559) as exposures. We gathered the genome wide association study (GWAS) data of BC from BCAC (OncoArray, n\u0026thinsp;=\u0026thinsp;138,508) and BCAC (iCOGS, n\u0026thinsp;=\u0026thinsp;76,167). The FinnGen study (n\u0026thinsp;=\u0026thinsp;224,737) as the outcomes. The BC survival data was obtained from BCAC (OncoArray, n\u0026thinsp;=\u0026thinsp;91,686). We used two sample MR framework to assess the associations between genetically predictive proteins and BC risk. Besides strict quality control, sensitivity tests and false discovery rate (FDR) or bonferroni correction, we further performed meta-analysis to ensure the robustness of the results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFour proteins\u0026mdash;SIA4B (OR\u0026thinsp;=\u0026thinsp;0.58, 95% CI (confidence interval): 0.51\u0026ndash;0.64), CDH1 (OR\u0026thinsp;=\u0026thinsp;0.83, 95% CI: 0.77\u0026ndash;0.89), ALPI (OR\u0026thinsp;=\u0026thinsp;0.91, 95% CI: 0.90\u0026ndash;0.93) and CCDC134 (OR\u0026thinsp;=\u0026thinsp;0.84, 95% CI: 0.80\u0026ndash;0.88) are associated with reduced BC risk. 57 circulating proteins passed the sensitivity test and causally associated with BC survival.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGenetically predicted four circulating proteins (SIA4B, CDH1, ALPI and, CCDC134) are associated with reduced BC risk. 57 proteins are associated with BC survival. Our analyses from genetics and MR provide insights into the causes of BC and add evidence for reducing the risk of BC.\u003c/p\u003e","manuscriptTitle":"Associations of genetically determined circulating proteins with breast cancer risk or survival","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-31 18:22:18","doi":"10.21203/rs.3.rs-3906265/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":"48305804-9a0f-4cd7-a755-e57026085a7c","owner":[],"postedDate":"January 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-01T12:29:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-31 18:22:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3906265","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3906265","identity":"rs-3906265","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}