The effects of coagulation factors on the risk of glioma: a two-sample bi-directional Mendelian randomization study

The effects of coagulation factors on the risk of glioma: a two-sample bi-directional Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effects of coagulation factors on the risk of glioma: a two-sample bi-directional Mendelian randomization study Lin Pan, Laiyu Yang, Yu Gao, Ningxin Wang, Jingning Wang, Ming Gao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4258369/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 Traditional observational studies have shown that the levels of coagulation factors can affect the risk of glioma. It is uncertain, nevertheless, whether coagulation factor levels and various glioma subtypes are causally related. The purpose of this study was to look into any bidirectional correlations between glioma risk and coagulation factor levels. Method Two-sample bi-directional Mendelian randomization (MR) analysis was carried out using openly accessible genome-wide association study (GWAS) data. The data for glioma subtypes were retrieved from an enormous-scale genetic meta-analysis compiled by GWAS data from independent European lineages of glioma, including 12,488 cases and 18,169 controls. The genetic summary data for 10 coagulation factors were retrieved from different GWAS results conducted in participants of European ancestry (up to 21758 participants), involving prothrombin time (PT), activated protein C(APC), von Willebrand factor (VWF), plasmin, a disintegrin-like and metalloproteinase with thrombospondin motifs 13 (ADAMTS13), factor VII (FVII), factor VIII (FVIII), factor X (FVX), plasminogen activator inhibitor-1 (PAI-1), and thrombomodulin (TM). Weighted median estimation (WME), MR-Egger regression, and inverse variance weighting (IVW) were the MR analysis approaches that were applied. IVW was selected as the main research method. Furthermore, the Benjamini-Hochberg false discovery rate (FDR) correction and sensitivity analyses were carried out. Results We discovered a potential relationship between genetically predicted FVII levels and a higher risk of glioblastoma (GBM) (OR = 1.07, 95% CI: 1.01–1.14, P = 0.03). Our results also suggested that genetically predicted plasma PAI-1 level was negatively associated with the incidence of all glioma (OR = 0.85, 95%CI: 0.73–0.98, P = 0.03) and non-GBM (OR = 0.77, 95%CI: 0.63–0.92, P = 0.01). In addition, a suggestively negative correlation between genetically predicted PT level and the risk of GBM (OR = 0.72, 95%CI: 0.53–0.98, P = 0.04) was discovered. Conversely, there was insufficient evidence of a significant causal association of any examined glioma with coagulation factors. Conclusions Our findings suggest that coagulation factors may be important indicators for glioma treatment and may be involved in the pathophysiology of gliomas. Mendelian randomization coagulation factors glioma genetics Figures Figure 1 1 Introduction Glioma is a form of tumor in the brain that originates from glial and neuronal cells 1 . The World Health Organization (WHO) grades gliomas from I to IV according to their morphology and malignant behavior and divides them into subgroups based on the histology of glial cells 2 . Generally speaking, there are two types of gliomas: lower-grade non-GBM tumors and GBM. GBM is a grade IV neoplasm that is mitotically active, necrosis-prone, cytologically malignant, and usually associated with a fast progression of the disease before and following surgery, as well as a deadly result. Current therapies in glioma include surgical resection, radiotherapy, and chemotherapy 3 . Despite advancements in glioma treatment, the survival period for patients with this type of brain tumor remains relatively short. Additionally, even if patients do survive the initial treatment, the recurrence rate of glioma is high 4 . Therefore, Finding the risk factors associated with glioma development is the key to avoiding the disease from the root. To date, few risk factors associated with glioma development have been reliably identified. Angiogenesis and inflammation are key elements in the prevalence and progression of gliomas, according to earlier epidemiological research 5 . Numerous investigations have demonstrated that procoagulant-driven inflammation, in particular, is one of the primary drivers of many types of cancer 6 . The coagulation factors are a series of protein components involved in the blood procoagulant-driven process, including Factors Ⅰ~Ⅻ and so on. Through the activation of coagulation factors, a cascade of reactions is activated, causing blood vessels to constrict and blood to clot 7 . In addition, by activating immune-inflammatory cytokines, platelets can also mediate inflammation 8 . From clinical case data, glioma patients have high expression of tissue factor (TF), significantly increased thrombin activity 9 , and increased levels of fibrin with its degradation products 10 . As a signaling molecule, calcium ions promote tumor growth by inducing epidermal growth factor (EGF) and platelet-derived growth factor 11 . All these studies suggest that there may be a relationship between coagulation factor levels and the risk of glioma. MR is an analytical method used in epidemiology to investigate causal relationships between an exposure factor and a disease outcome. However, previous studies are observational and could be influenced by unidentified confounding factors, insufficient sample size, and reverse causality 12 . These limitations make it impossible to determine the true causal relationship. MR attempts to overcome these limitations by using genetic variants as instrumental variables (IVs). In MR studies, genetic variants linked to the exposure factor are employed as instrumental variables or proxies in MR investigations. MR has become increasingly popular in clinical medicine as it allows researchers to obtain more robust evidence for causal relationships between exposures and diseases. Existing research shows that MR method has been used to investigate the potential causal relationships between glioma and various factors, such as genetic prediction of depression 13 , telomere length 14 , DNA methylation 15 , and vitamin D 16 . It is also noteworthy that MR has been utilized to explore the causal role of obesity 17 , type 2 diabetes (T2D) 18 , smoking 19 , alcohol consumption 20 , and coffee intake 21 in the development of glioma. The findings suggest that these factors may not have a causal impact on glioma 22 . Furthermore, the use of MR in studying coagulation factor levels and their relationship with diseases like endometriosis 23 and multiple myeloma 24 is well-established. Nevertheless, it is still unclear and poorly understood how coagulation factor levels and gliomas are causally related. This highlights the need for further research to better comprehend the potential causal associations between coagulation factor levels and glioma development. In the current study, we used the framework of two-sample bi-directional MR design to systematically explore the relationship between glioma subtypes (all glioma, GBM and non-GBM) and 10 coagulation factor levels (PT, APC, plasmin, ADAMTS13, VWF, FVII, FVIII, FX, PAI-1, and TM). Identifying the direction and strength of the causal link between glioma subtypes and coagulation factor levels was the ultimate aim of this MR research. 2 Method MR design Using two samples, we performed a bi-directional MR study to look at the possible causal relationship between glioma and coagulation factor levels (Fig. 1). We followed the three fundamental MR analysis principles: ( 1 ) There is a strong relationship between genetic variability and exposure variables 25 ; ( 2 ) There is no relationship between genetic variation and confounders 26 ; ( 3 ) Confirming that exposure factors alone—and not any other pathways—are the only ways in which genetic variation affects the results 27 . Because the genetic variation is identified at conception, this approach reduces bias due to confounders and is not subject to reverse causation. We specifically took into account 10 different kinds of coagulation factors, including PT, APC, plasmin, ADAMTS13, VWF, FVII, FVIII, FX, PAI-1, and TM. The IVs for each exposure were built on an independent and publicly available GWAS. At the significance level of P values < 5E-8, there were initially insufficient single nucleotide polymorphisms (SNPs) linked to coagulation factors. In order to find adequate IVs, we therefore used the significance level of P values < 5E-6. To avoid misleading outcomes, we used a window size of 10,000 kb and a threshold of r2 < 0.001 to ensure the absence of linkage disequilibrium (LD) in any of the instrumental SNPs and found independent SNPs 28 . To demonstrate the power of the instruments, we used the F statistic (F = beta 2 /se 2 ) to assess the strength of the selected SNPs and computed the ratios of variation in phenotype calculated by IVs 29 . The F-statistic > 10 was used to identify SNPs with strong instrumentation. Finally, MR analyses were carried out to determine the causative relationships of coagulation factors levels with the risk of several sub-phenotypes of glioma, including all glioma, GBM and non-GBM. Glioma And Coagulation factor GWAS summary statistics Suitable genetic variations were chosen from publicly GWAS databases for this bidirectional MR investigation. Summaries of data on gliomas were obtained from a GWAS meta-analysis of eight studies including individuals of European descent. These included 12,496 cases (6,183 categorized as GBM and 5,820 classified as non-GBM) and 18,190 controls 30 . The following is a summary of statistics on various coagulation factors. Summary statistics of PT contain 34919 European ancestry individuals in the U.S. 31 . Summary statistics of APC cover 3301 European in the U.K. 31 . Summary statistics of plasmin, FVIII, and FX comprise 3301 European ancestry in the U.K. 32 . The GWAS data for VWF and PAI-1 were obtained from 10708 individuals of European ancestry 33 . The ADAMTS13 study includes information on 5359 individuals of European ancestry in Iceland 34 . Summary statistics of FVII can be found from 997 individuals of European ancestry in Germany 35 . Finally, the statistics of TM cover 21758 individuals of European ancestry 36 . All SNPs and the pooled data that accompanied them were limited to populations with European ancestry to avoid population stratification bias skewing the results. Statistical analysis We estimated the causative relationships between 10 coagulation factors levels and glioma using several MR approaches, such as MR-Egger regression, Weight Median Estimator (WME), and Inverse Variance Weighted (IVW) 37 . The IVW approach as the primary statistical method, is recognized for its efficacy and robust statistical power, and does not assume a directional pleiotropic effect for each SNP 38 . The WME method produces consistent causal estimates assuming that at least 50% of SNPs are functional. The MR Egger regression method's intercept indicates whether horizontal pleiotropy is present or absent (a P -value under 0.05 is considered meaningful) 39 . In addition, 95% confidence intervals (CIs) and odds ratios (ORs) are used to represent causal estimations. Sensitivity analyses were necessary to test hypotheses and uncover possible indicators of bias with the aim to evaluate the accuracy and robustness of the MR results. The Cochran's Q statistics were used to quantify the heterogeneities, and a P -value of below 0.05 is thought to be highly heterogeneous. In addition, a Benjamini-Hochberg FDR was employed to address the bias resulting from multiple comparisons. The conclusion of a causative association was reached when the IVW and WME methods' estimations of the causal effects were consistent with direction, and the P -value after FDR correction was less than 0.05. Suggested causative relationships were those significant ( P < 0.05) before, but not after FDR correction. All of the aforementioned investigations were conducted using the R programming language and the "Two Sample MR" package in R 40 . 3 Results Causal association of coagulation factors levels on glioma We evaluated the strength of each instrumental variable (IV) by computing the F-statistic for each instrument-exposure association. The F-statistics in our investigation were significantly higher than 10, indicating that those SNPs were potent IVs (Supplementary Table 1). The study discovered a possible link between a genetically indicated FVII level and a higher risk of GBM (OR = 1.07, 95% CI: 1.01–1.14, P = 0.0336) (Table 1). The genetically predicted levels of PAI-1 were also found to be potentially linked to all glioma (OR = 0.85, 95%CI: 0.73–0.98, P = 0.028) and non-GBM (OR = 0.77, 95%CI: 0.63–0.92, P = 0.0053) using the IVW method (Table 1). Furthermore, there appeared to be a negative correlation of suggestiveness between genetically indicated PT and the risk of GBM (IVW: OR = 0.72, 95%CI: 0.53–0.98, P = 0.036; WME: OR = 0.70, 95%CI:0.50–0.96, P =. 0.0274) (Table 1). Causal association of glioma on coagulation factors levels In reverse Mendelian randomization studies, we investigated the causal relationship between different types of glioma (exposure) and coagulation factors (outcomes). 34and 46 genetic instruments were found to account for 78% and 91% of the variation for particular forms of glioma, such as GBM and non-GBM. More comprehensive details on these variations were displayed in Supplementary Table 4. We found that there is a suggestively positive effect of genetically determined GBM and all glioma on levels of plasmin (WM: OR = 1.08, 95%CI:1.00-1.16, P = 0.0474) (as shown in Table 2). Heterogeneity tests in the sensitivity analysis revealed no discernible heterogeneity among the chosen IVs (Q-value > 0.05). Moreover, the MR-Egger intercept test revealed no evidence of horizontal pleiotropy ( P -value > 0.05) 4 Discussion This research firstly attempted to use bi-directional MR analyses on large-scale European population cohorts to examine the causative links between coagulation factors levels and the risk of glioma. To accomplish this, we leveraged the aggregated 10 coagulation factors statistics derived from the extensive GWAS datasets available. Our results discovered suggestive evidence that the promotion of PT and PAI-1 levels may lower the incidence of glioma. While there was a suggestively positive causal correlation between genetically predicted FVII level and glioma. Glioma also affects the levels of coagulation factors such as plasmin. The development of preventative and therapeutic measures for glioma will be significantly impacted by these findings. One of the most common and invasive primary malignant brain tumors of the central nervous system (CNS) is glioma 41 . In 2020, this illness was responsible for about 308,102 new cases and 251,329 fatalities 42 . Although multimodal treatment regimens including chemotherapy, surgical intervention, and radiation therapy have advanced, the disease's clinical results are still not unsatisfactory 43 . Furthermore, only 10% of glioma patients survive for five years 44 . Numerous earlier investigations on the etiology of glioma have been unsuccessful in determining their cause 45 . Therefore, in order to find new potential targets for the treatment of gliomas, the underlying etiology must be determined. Prior studies have demonstrated a connection between irregularities in the system of coagulation and tumor growth and dissemination, including glioma 46 . A great deal of analytical work has been done on the clinical, hematological, and pathophysiological foundations of the associated procoagulant states, which are commonly referred to as cancer-associated thrombosis (CAT). Crucially, thrombotic complications are associated with both the prognosis and course of the disease 11 . Besides, studies reveal that patients with cancerous tumors have hypercoagulation and hyperfibrinolysis, which indicates an increased risk of clotting and increased fibrin synthesis in the early phases of the illness 47 . This finding, which is consistent with our MR results, implies a connection between blood coagulation dysfunction and the processes of tumor dissemination, invasion, and patient prognosis in genera 47 . The precise mechanism is demonstrated, by which aberrant blood coagulation function in aggressive tumors affects angiogenesis, metastasis, growth, initiation, invasion, dormancy, blood vessel development, and therapeutic response 48 . Therefore, one likely etiologic cause for glioma could be an unbalanced coagulation system. Even though coagulation factors are becoming more and more involved in the pathogenesis of glioma, it is still unclear what causes glioma and how these factors contribute to glioma development. Meanwhile, small sample sizes and intrinsic biases make it challenging to demonstrate causal correlations in general. To clarify such causation, we tried to use MR, which can get over the aforementioned methodological challenges. MR is a method of analytical inquiry in which exposure is proxied by genetic variation. We evaluated GWAS data using a unified MR framework and assessed the causal relationship between 10 coagulation factors and glioma risk using summary statistics. The vascular endothelium, which is necessary for maintaining the proper balance between bleeding and clot formation, circulating platelets, and plasmatic coagulation all play a role in the intricate and dynamic process that governs hemostasis. Research indicates that cancer cells may disrupt this balance by triggering blood clotting. Moreover, many types of cancer are now known to be significantly influenced by tumor-associated procoagulant-driven inflammation particularly 6 . A growing quantity of research suggests that the development of cancer is aided by a procoagulant microenvironment 49 . The tumor microenvironment contains coagulation factors, such as FVII produced by the liver and circulated, can bind the initiator of coagulation tissue factor (TF) 50 . Widely expressed by cancer cells, TF has been linked to increased tumor grade and decreased patient survival 50 . When the blood FVII binds to TF, it activates protease-activated receptors (PARs), particularly PAR2, to initiate downstream signaling 51 . Moreover, according to certain research, cancer progression can be inhibited by blocking FVII/TF/PAR2 signaling independently of the coagulation response 52 . In our study, we discovered that the level of FVII were suggestively related with glioma. FVII level raises the incidence of GBM in genetically predisposed people, which is consistent with findings from other epidemiological studies. Since PAI-1 is the main inhibitor of both tissue-type and urokinase-type plasminogen activator (uPA), it is thought to be a major player in the regulation of extracellular matrix remodeling 53 . A growing body of research has indicated the importance of the plasminogen activator system, particularly uPA, which can activate pro-collagenase and promote tumor growth by breaking down the extracellular matrix 54,55 . Therefore, it was expected that PAI-1, a protease inhibitor belonging to the serine family, would have an anti-tumor effect due to its primary inhibitory activity on uPA 56 . We discovered that higher levels of PAI-1 are linked to a lower risk of all glioma and non-GBM in our study. This discovery implies PAI-1's potential as a preventive factor against the growth of glioma and validates its involvement in their pathophysiology. PT is the time it takes for platelet-deficient plasma to clot after an excess of tissue thrombin and calcium ions are added to the plasma. Thrombin cleaves fibrinogen to cause thrombotic effects, and it also activates human platelets via protease-activated receptors (PARs), which causes a variety of molecules to be secreted 57 . These platelet molecules may be used by tumors to support endothelial cell migration and activation during the development of new blood vessels 58 . Endothelial cells experience a number of cellular changes upon thrombin-mediated PAR activation, including migration and VEGF-A upregulation, both of which are necessary for angiogenesis 59 , whereby it regulates the tumor microenvironment in a number of ways 60 . Different cancerous expressions cause varying degrees of thrombosis susceptibility 61 . There are studies show that glioma exhibits a high VTE risk 62 . Previous research proposed a possible association between thrombin and glioma. In our study, PT was found to have a protective effect associated with glioma. Glioma may be associated with changes in blood plasmin levels, according to our study's bilateral MR analysis. Plasmin is a strong protease that may directly degrade a variety of extracellular matrix constituents, such as proteoglycans, laminin, and fibronectin. Moreover, plasmin promotes angiogenesis and tumor spreading 63 . Research has demonstrated that Glioma ’s ability to invade is made possible by a complex relationship between astrocytes and the uPA-plasmin cascade. In this sense, the plasminogen zymogen is changed into plasmin by the activation of uPA by its binding to uPA-receptor 64 . Our research also shows that glioma cause a higher level of plasmin suggestively. These results confirm the role that modification of the coagulation system plays in the glioma pathogenesis. There are several benefits to our study. This is the first study to use the two-sample bi-directional MR approach to reverse causality, eliminate confounding variables, and draw conclusions about the association between coagulation factors and glioma. Additionally, by using glioma data from the largest GWAS dataset (12,488 cases and 18,169 controls) and exposure data on coagulation factors from trustworthy huge scale GWAS databases (up to 21758 individuals), a sufficiently large sample size of the results could ensure the generalizability of causal associations. Lastly, to confirm the validity of the assumptions made about the instrumental variables, we used some supplemental studies, including pleiotropy analyses and heterogeneity analyses. Nonetheless, it is important to recognize some of this research's shortcomings. Firstly, there is a lack of genetic data and no sex or age segmentation in the GWAS data. We would like to broaden the analysis to encompass all populations when feasible. The results might not apply to other populations because we could only use European origin genome-wide association data. Further research into the causal relationships between coagulation factors and glioma in different populations is also necessary. 5 Conclusions This research examines the associations between coagulation factor levels and glioma risk in European individuals. The results provide strong evidence for the causative relationships between PAI-1, PT and FVII levels and glioma risk. Glioma also affect the levels of coagulation factors such as plasmin. This work advances our knowledge of the role coagulation cascades play in the glioma's growth. The results of our investigation shed more light on the genesis of glioma and call for more investigation to identify the mechanisms underlying the emergence of glioma. Declarations Ethics approval and Consent to Participate This study only used publicly available data and no individual-level data were used. Ethical approval and consent information for the above summary statistics were taken from the original publication. Availability of Data and Materials All data generated or analysed during this study are included in this published article and its supplementary information files. Consent for Publication Consent. Funding Not applicable. Competing Interests All authors declare that they have no conflict of interests. Author contributions Dongmei Sun designed experiments; Lin Pan and Laiyu Yang wrote manuscript; Lin Pan, Yu Gao and Ningxin Wang carried out experiments; Jingning Wang, Ming Gao and Yihan Wang analyzed experiments results. 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Tissue factor expression in breast cancer tissues: its correlation with prognosis and plasma concentration. Br J Cancer. 2000;83:164–70. 10.1054/bjoc.2000.1272 . Camerer E, Huang W, Coughlin SR. Tissue factor- and factor X-dependent activation of protease-activated receptor 2 by factor VIIa. Proc Natl Acad Sci U S A. 2000;97:5255–60. 10.1073/pnas.97.10.5255 . Tang SW, Ducroux A, Jeang KT, Neuveut C. Impact of cellular autophagy on viruses: Insights from hepatitis B virus and human retroviruses. J Biomed Sci. 2012;19:92. 10.1186/1423-0127-19-92 . Stefansson S, McMahon GA, Petitclerc E, Lawrence DA. Plasminogen activator inhibitor-1 in tumor growth, angiogenesis and vascular remodeling. Curr Pharm Des. 2003;9:1545–64. 10.2174/1381612033454621 . Duffy MJ, McGowan PM, Harbeck N, Thomssen C, Schmitt M. uPA and PAI-1 as biomarkers in breast cancer: validated for clinical use in level-of-evidence-1 studies. Breast Cancer Res. 2014;16:428. 10.1186/s13058-014-0428-4 . Lampelj M, et al. Urokinase plasminogen activator (uPA) and plasminogen activator inhibitor type-1 (PAI-1) in breast cancer - correlation with traditional prognostic factors. Radiol Oncol. 2015;49:357–64. 10.2478/raon-2014-0049 . Kubala MH, DeClerck YA. The plasminogen activator inhibitor-1 paradox in cancer: a mechanistic understanding. Cancer Metastasis Rev. 2019;38:483–92. 10.1007/s10555-019-09806-4 . Han N, Jin K, He K, Cao J, Teng L. Protease-activated receptors in cancer: A systematic review. Oncol Lett. 2011;2:599–608. 10.3892/ol.2011.291 . Aleksandrowicz K, et al. The Complex Role of Thrombin in Cancer and Metastasis: Focus on Interactions with the Immune System. Semin Thromb Hemost. 2024;50:462–73. 10.1055/s-0043-1776875 . Dunbar A, et al. Genomic profiling identifies somatic mutations predicting thromboembolic risk in patients with solid tumors. Blood. 2021;137:2103–13. 10.1182/blood.2020007488 . Heidari Z, et al. The Role of Tissue Factor In Signaling Pathways of Pathological Conditions and Angiogenesis. Curr Mol Med. 2023. 10.2174/0115665240258746230919165935 . Falanga A, Schieppati F, Russo L, Pathophysiology. 1. Mechanisms of Thrombosis in Cancer Patients. Cancer Treat Res 179, 11–36, 10.1007/978-3-030-20315-3_2 (2019). Wun T, White RH. Epidemiology of cancer-related venous thromboembolism. Best Pract Res Clin Haematol. 2009;22:9–23. 10.1016/j.beha.2008.12.001 . Danø K, et al. Plasminogen activators, tissue degradation, and cancer. Adv Cancer Res. 1985;44:139–266. 10.1016/s0065-230x(08)60028-7 . Le DM, et al. Exploitation of astrocytes by glioma cells to facilitate invasiveness: a mechanism involving matrix metalloproteinase-2 and the urokinase-type plasminogen activator-plasmin cascade. J Neurosci. 2003;23:4034–43. 10.1523/jneurosci.23-10-04034.2003 . Tables Table 1 to 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx Table 1. Mendelian randomization estimates, heterogeneity test and pleiotropy test of coagulation factors on glioma. Table2.xlsx Table 2. Mendelian randomization estimates, heterogeneity test and pleiotropy test of glioma on coagulation factors. SupplementaryTable.xlsx Supplementary Table. Summary of data source of different phenotype 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-4258369","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292143752,"identity":"8ac50d1a-4fa3-40d3-9954-61b2ed707458","order_by":0,"name":"Lin Pan","email":"","orcid":"","institution":"the First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Pan","suffix":""},{"id":292143753,"identity":"f30ea796-eed5-40ee-a312-2157e51408da","order_by":1,"name":"Laiyu Yang","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Laiyu","middleName":"","lastName":"Yang","suffix":""},{"id":292143755,"identity":"cea556e8-efcc-4e67-a890-1ef3193cd4fb","order_by":2,"name":"Yu Gao","email":"","orcid":"","institution":"Clinical College, the Second Affiliated Hospital of Harbin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Gao","suffix":""},{"id":292143757,"identity":"423bfa41-5018-4919-818a-c52b90d7678c","order_by":3,"name":"Ningxin Wang","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Ningxin","middleName":"","lastName":"Wang","suffix":""},{"id":292143759,"identity":"815eac78-33bb-4856-a13d-83315b2ed1b6","order_by":4,"name":"Jingning Wang","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jingning","middleName":"","lastName":"Wang","suffix":""},{"id":292143761,"identity":"c588ff07-4d82-464f-8de8-327ddf666ce5","order_by":5,"name":"Ming Gao","email":"","orcid":"","institution":"the First Hospital of Jilin University, Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Gao","suffix":""},{"id":292143762,"identity":"dd642d44-95a3-4ac6-be44-2d52b140c53a","order_by":6,"name":"Yihan Wang","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Yihan","middleName":"","lastName":"Wang","suffix":""},{"id":292143763,"identity":"44efb7bc-08ee-4ab2-928b-245230e6e67a","order_by":7,"name":"Dongmei Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACeWaGxAcfeP7J8TMcPkCcFsN2hseGM2QOGEs2Hksg0przjM+EeWwOJG44fMaAOB2MzcxpDDw5dxg3HDvz8cYbBjs53QYCWtiZ2dIeSJx5xix55uxmyzkMycZmBwjawpNuYNjDzMZ34+w2aR6GA4nbCGlhOMz/TSLxHzMPw/03z4jVwpAmcYDnsITAgTNsxGkxbGZINmzgSTOQbDhmbDnHgAi/yPMfSHz8h8emvp/h8MMbbyrs5AhqQQESPERGDbIWUnWMglEwCkbBiAAAsElH4Y0+3t4AAAAASUVORK5CYII=","orcid":"","institution":"Siping city Central People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Dongmei","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-04-12 14:29:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4258369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4258369/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55058775,"identity":"4b9ffdd6-54cc-4bc7-9aab-1ae55a6b4d70","added_by":"auto","created_at":"2024-04-22 02:04:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17106,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design flowchart of the two-sample bi-directional Mendelian randomization study.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4258369/v1/998db9e21f1daffc5d73036b.png"},{"id":55922395,"identity":"28e9324e-2870-4629-bf5b-4a888bb6833d","added_by":"auto","created_at":"2024-05-06 10:30:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":469792,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4258369/v1/a38ba9b4-58fe-4ff9-90bf-c5241a63fb94.pdf"},{"id":55058777,"identity":"cf2c0763-5a87-4d64-832f-cf9568d4ce19","added_by":"auto","created_at":"2024-04-22 02:04:37","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19652,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1. Mendelian randomization estimates, heterogeneity test and pleiotropy test of coagulation factors on glioma.\u003c/p\u003e","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4258369/v1/1aa6cd407744f1974ca3f4e1.xlsx"},{"id":55058778,"identity":"b140166a-7adf-44fc-8ec1-d3be858f2b2d","added_by":"auto","created_at":"2024-04-22 02:04:37","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19459,"visible":true,"origin":"","legend":"\u003cp\u003eTable 2. Mendelian randomization estimates, heterogeneity test and pleiotropy test of glioma on coagulation factors.\u003c/p\u003e","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4258369/v1/15f0c8e63db972c1100a0483.xlsx"},{"id":55058776,"identity":"6c90f5d0-01dc-41ae-a8c8-b77f34ed918a","added_by":"auto","created_at":"2024-04-22 02:04:37","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":70196,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table. Summary of data source of different phenotype\u003c/p\u003e","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4258369/v1/ae2f4523252849a8fffe0f46.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effects of coagulation factors on the risk of glioma: a two-sample bi-directional Mendelian randomization study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGlioma is a form of tumor in the brain that originates from glial and neuronal cells\u003csup\u003e1\u003c/sup\u003e. The World Health Organization (WHO) grades gliomas from I to IV according to their morphology and malignant behavior and divides them into subgroups based on the histology of glial cells\u003csup\u003e2\u003c/sup\u003e. Generally speaking, there are two types of gliomas: lower-grade non-GBM tumors and GBM. GBM is a grade IV neoplasm that is mitotically active, necrosis-prone, cytologically malignant, and usually associated with a fast progression of the disease before and following surgery, as well as a deadly result. Current therapies in glioma include surgical resection, radiotherapy, and chemotherapy\u003csup\u003e3\u003c/sup\u003e. Despite advancements in glioma treatment, the survival period for patients with this type of brain tumor remains relatively short. Additionally, even if patients do survive the initial treatment, the recurrence rate of glioma is high\u003csup\u003e4\u003c/sup\u003e. Therefore, Finding the risk factors associated with glioma development is the key to avoiding the disease from the root. To date, few risk factors associated with glioma development have been reliably identified.\u003c/p\u003e \u003cp\u003eAngiogenesis and inflammation are key elements in the prevalence and progression of gliomas, according to earlier epidemiological research\u003csup\u003e5\u003c/sup\u003e. Numerous investigations have demonstrated that procoagulant-driven inflammation, in particular, is one of the primary drivers of many types of cancer\u003csup\u003e6\u003c/sup\u003e. The coagulation factors are a series of protein components involved in the blood procoagulant-driven process, including Factors Ⅰ~Ⅻ and so on. Through the activation of coagulation factors, a cascade of reactions is activated, causing blood vessels to constrict and blood to clot\u003csup\u003e7\u003c/sup\u003e. In addition, by activating immune-inflammatory cytokines, platelets can also mediate inflammation\u003csup\u003e8\u003c/sup\u003e. From clinical case data, glioma patients have high expression of tissue factor (TF), significantly increased thrombin activity\u003csup\u003e9\u003c/sup\u003e, and increased levels of fibrin with its degradation products\u003csup\u003e10\u003c/sup\u003e. As a signaling molecule, calcium ions promote tumor growth by inducing epidermal growth factor (EGF) and platelet-derived growth factor\u003csup\u003e11\u003c/sup\u003e. All these studies suggest that there may be a relationship between coagulation factor levels and the risk of glioma.\u003c/p\u003e \u003cp\u003eMR is an analytical method used in epidemiology to investigate causal relationships between an exposure factor and a disease outcome. However, previous studies are observational and could be influenced by unidentified confounding factors, insufficient sample size, and reverse causality\u003csup\u003e12\u003c/sup\u003e. These limitations make it impossible to determine the true causal relationship. MR attempts to overcome these limitations by using genetic variants as instrumental variables (IVs). In MR studies, genetic variants linked to the exposure factor are employed as instrumental variables or proxies in MR investigations. MR has become increasingly popular in clinical medicine as it allows researchers to obtain more robust evidence for causal relationships between exposures and diseases. Existing research shows that MR method has been used to investigate the potential causal relationships between glioma and various factors, such as genetic prediction of depression\u003csup\u003e13\u003c/sup\u003e, telomere length\u003csup\u003e14\u003c/sup\u003e, DNA methylation\u003csup\u003e15\u003c/sup\u003e, and vitamin D\u003csup\u003e16\u003c/sup\u003e. It is also noteworthy that MR has been utilized to explore the causal role of obesity\u003csup\u003e17\u003c/sup\u003e, type 2 diabetes (T2D)\u003csup\u003e18\u003c/sup\u003e, smoking\u003csup\u003e19\u003c/sup\u003e, alcohol consumption\u003csup\u003e20\u003c/sup\u003e, and coffee intake\u003csup\u003e21\u003c/sup\u003e in the development of glioma. The findings suggest that these factors may not have a causal impact on glioma\u003csup\u003e22\u003c/sup\u003e. Furthermore, the use of MR in studying coagulation factor levels and their relationship with diseases like endometriosis\u003csup\u003e23\u003c/sup\u003e and multiple myeloma\u003csup\u003e24\u003c/sup\u003e is well-established. Nevertheless, it is still unclear and poorly understood how coagulation factor levels and gliomas are causally related. This highlights the need for further research to better comprehend the potential causal associations between coagulation factor levels and glioma development.\u003c/p\u003e \u003cp\u003eIn the current study, we used the framework of two-sample bi-directional MR design to systematically explore the relationship between glioma subtypes (all glioma, GBM and non-GBM) and 10 coagulation factor levels (PT, APC, plasmin, ADAMTS13, VWF, FVII, FVIII, FX, PAI-1, and TM). Identifying the direction and strength of the causal link between glioma subtypes and coagulation factor levels was the ultimate aim of this MR research.\u003c/p\u003e"},{"header":"2 Method","content":"\u003cp\u003e \u003cb\u003eMR design\u003c/b\u003e \u003c/p\u003e \u003cp\u003eUsing two samples, we performed a bi-directional MR study to look at the possible causal relationship between glioma and coagulation factor levels (Fig.\u0026nbsp;1). We followed the three fundamental MR analysis principles: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) There is a strong relationship between genetic variability and exposure variables\u003csup\u003e25\u003c/sup\u003e; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) There is no relationship between genetic variation and confounders\u003csup\u003e26\u003c/sup\u003e; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Confirming that exposure factors alone\u0026mdash;and not any other pathways\u0026mdash;are the only ways in which genetic variation affects the results\u003csup\u003e27\u003c/sup\u003e. Because the genetic variation is identified at conception, this approach reduces bias due to confounders and is not subject to reverse causation.\u003c/p\u003e \u003cp\u003eWe specifically took into account 10 different kinds of coagulation factors, including PT, APC, plasmin, ADAMTS13, VWF, FVII, FVIII, FX, PAI-1, and TM. The IVs for each exposure were built on an independent and publicly available GWAS. At the significance level of \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;5E-8, there were initially insufficient single nucleotide polymorphisms (SNPs) linked to coagulation factors. In order to find adequate IVs, we therefore used the significance level of \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;5E-6. To avoid misleading outcomes, we used a window size of 10,000 kb and a threshold of r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 to ensure the absence of linkage disequilibrium (LD) in any of the instrumental SNPs and found independent SNPs\u003csup\u003e28\u003c/sup\u003e. To demonstrate the power of the instruments, we used the F statistic (F\u0026thinsp;=\u0026thinsp;beta\u003csup\u003e2\u003c/sup\u003e/se\u003csup\u003e2\u003c/sup\u003e) to assess the strength of the selected SNPs and computed the ratios of variation in phenotype calculated by IVs \u003csup\u003e29\u003c/sup\u003e. The F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 was used to identify SNPs with strong instrumentation. Finally, MR analyses were carried out to determine the causative relationships of coagulation factors levels with the risk of several sub-phenotypes of glioma, including all glioma, GBM and non-GBM.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGlioma And Coagulation factor GWAS summary statistics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSuitable genetic variations were chosen from publicly GWAS databases for this bidirectional MR investigation. Summaries of data on gliomas were obtained from a GWAS meta-analysis of eight studies including individuals of European descent. These included 12,496 cases (6,183 categorized as GBM and 5,820 classified as non-GBM) and 18,190 controls\u003csup\u003e30\u003c/sup\u003e. The following is a summary of statistics on various coagulation factors. Summary statistics of PT contain 34919 European ancestry individuals in the U.S.\u003csup\u003e31\u003c/sup\u003e. Summary statistics of APC cover 3301 European in the U.K.\u003csup\u003e31\u003c/sup\u003e. Summary statistics of plasmin, FVIII, and FX comprise 3301 European ancestry in the U.K.\u003csup\u003e32\u003c/sup\u003e. The GWAS data for VWF and PAI-1 were obtained from 10708 individuals of European ancestry\u003csup\u003e33\u003c/sup\u003e. The ADAMTS13 study includes information on 5359 individuals of European ancestry in Iceland \u003csup\u003e34\u003c/sup\u003e. Summary statistics of FVII can be found from 997 individuals of European ancestry in Germany \u003csup\u003e35\u003c/sup\u003e. Finally, the statistics of TM cover 21758 individuals of European ancestry \u003csup\u003e36\u003c/sup\u003e. All SNPs and the pooled data that accompanied them were limited to populations with European ancestry to avoid population stratification bias skewing the results.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe estimated the causative relationships between 10 coagulation factors levels and glioma using several MR approaches, such as MR-Egger regression, Weight Median Estimator (WME), and Inverse Variance Weighted (IVW)\u003csup\u003e37\u003c/sup\u003e. The IVW approach as the primary statistical method, is recognized for its efficacy and robust statistical power, and does not assume a directional pleiotropic effect for each SNP\u003csup\u003e38\u003c/sup\u003e. The WME method produces consistent causal estimates assuming that at least 50% of SNPs are functional. The MR Egger regression method's intercept indicates whether horizontal pleiotropy is present or absent (a \u003cem\u003eP\u003c/em\u003e-value under 0.05 is considered meaningful)\u003csup\u003e39\u003c/sup\u003e. In addition, 95% confidence intervals (CIs) and odds ratios (ORs) are used to represent causal estimations.\u003c/p\u003e \u003cp\u003eSensitivity analyses were necessary to test hypotheses and uncover possible indicators of bias with the aim to evaluate the accuracy and robustness of the MR results. The Cochran's Q statistics were used to quantify the heterogeneities, and a \u003cem\u003eP\u003c/em\u003e-value of below 0.05 is thought to be highly heterogeneous. In addition, a Benjamini-Hochberg FDR was employed to address the bias resulting from multiple comparisons. The conclusion of a causative association was reached when the IVW and WME methods' estimations of the causal effects were consistent with direction, and the \u003cem\u003eP\u003c/em\u003e-value after FDR correction was less than 0.05. Suggested causative relationships were those significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) before, but not after FDR correction. All of the aforementioned investigations were conducted using the R programming language and the \"Two Sample MR\" package in R \u003csup\u003e40\u003c/sup\u003e.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e \u003cb\u003eCausal association of coagulation factors levels on glioma\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe evaluated the strength of each instrumental variable (IV) by computing the F-statistic for each instrument-exposure association. The F-statistics in our investigation were significantly higher than 10, indicating that those SNPs were potent IVs (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eThe study discovered a possible link between a genetically indicated FVII level and a higher risk of GBM (OR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 1.01\u0026ndash;1.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0336) (Table\u0026nbsp;1). The genetically predicted levels of PAI-1 were also found to be potentially linked to all glioma (OR\u0026thinsp;=\u0026thinsp;0.85, 95%CI: 0.73\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) and non-GBM (OR\u0026thinsp;=\u0026thinsp;0.77, 95%CI: 0.63\u0026ndash;0.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0053) using the IVW method (Table\u0026nbsp;1). Furthermore, there appeared to be a negative correlation of suggestiveness between genetically indicated PT and the risk of GBM (IVW: OR\u0026thinsp;=\u0026thinsp;0.72, 95%CI: 0.53\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036; WME: OR\u0026thinsp;=\u0026thinsp;0.70, 95%CI:0.50\u0026ndash;0.96, \u003cem\u003eP\u003c/em\u003e =. 0.0274) (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCausal association of glioma on coagulation factors levels\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn reverse Mendelian randomization studies, we investigated the causal relationship between different types of glioma (exposure) and coagulation factors (outcomes). 34and 46 genetic instruments were found to account for 78% and 91% of the variation for particular forms of glioma, such as GBM and non-GBM. More comprehensive details on these variations were displayed in Supplementary Table\u0026nbsp;4.\u003c/p\u003e \u003cp\u003eWe found that there is a suggestively positive effect of genetically determined GBM and all glioma on levels of plasmin (WM: OR\u0026thinsp;=\u0026thinsp;1.08, 95%CI:1.00-1.16, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0474) (as shown in Table\u0026nbsp;2). Heterogeneity tests in the sensitivity analysis revealed no discernible heterogeneity among the chosen IVs (Q-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Moreover, the MR-Egger intercept test revealed no evidence of horizontal pleiotropy (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis research firstly attempted to use bi-directional MR analyses on large-scale European population cohorts to examine the causative links between coagulation factors levels and the risk of glioma. To accomplish this, we leveraged the aggregated 10 coagulation factors statistics derived from the extensive GWAS datasets available. Our results discovered suggestive evidence that the promotion of PT and PAI-1 levels may lower the incidence of glioma. While there was a suggestively positive causal correlation between genetically predicted FVII level and glioma. Glioma also affects the levels of coagulation factors such as plasmin. The development of preventative and therapeutic measures for glioma will be significantly impacted by these findings.\u003c/p\u003e \u003cp\u003eOne of the most common and invasive primary malignant brain tumors of the central nervous system (CNS) is glioma\u003csup\u003e41\u003c/sup\u003e. In 2020, this illness was responsible for about 308,102 new cases and 251,329 fatalities\u003csup\u003e42\u003c/sup\u003e. Although multimodal treatment regimens including chemotherapy, surgical intervention, and radiation therapy have advanced, the disease's clinical results are still not unsatisfactory\u003csup\u003e43\u003c/sup\u003e. Furthermore, only 10% of glioma patients survive for five years\u003csup\u003e44\u003c/sup\u003e. Numerous earlier investigations on the etiology of glioma have been unsuccessful in determining their cause\u003csup\u003e45\u003c/sup\u003e. Therefore, in order to find new potential targets for the treatment of gliomas, the underlying etiology must be determined. Prior studies have demonstrated a connection between irregularities in the system of coagulation and tumor growth and dissemination, including glioma\u003csup\u003e46\u003c/sup\u003e. A great deal of analytical work has been done on the clinical, hematological, and pathophysiological foundations of the associated procoagulant states, which are commonly referred to as cancer-associated thrombosis (CAT). Crucially, thrombotic complications are associated with both the prognosis and course of the disease\u003csup\u003e11\u003c/sup\u003e. Besides, studies reveal that patients with cancerous tumors have hypercoagulation and hyperfibrinolysis, which indicates an increased risk of clotting and increased fibrin synthesis in the early phases of the illness\u003csup\u003e47\u003c/sup\u003e. This finding, which is consistent with our MR results, implies a connection between blood coagulation dysfunction and the processes of tumor dissemination, invasion, and patient prognosis in genera\u003csup\u003e47\u003c/sup\u003e. The precise mechanism is demonstrated, by which aberrant blood coagulation function in aggressive tumors affects angiogenesis, metastasis, growth, initiation, invasion, dormancy, blood vessel development, and therapeutic response\u003csup\u003e48\u003c/sup\u003e. Therefore, one likely etiologic cause for glioma could be an unbalanced coagulation system. Even though coagulation factors are becoming more and more involved in the pathogenesis of glioma, it is still unclear what causes glioma and how these factors contribute to glioma development. Meanwhile, small sample sizes and intrinsic biases make it challenging to demonstrate causal correlations in general. To clarify such causation, we tried to use MR, which can get over the aforementioned methodological challenges. MR is a method of analytical inquiry in which exposure is proxied by genetic variation. We evaluated GWAS data using a unified MR framework and assessed the causal relationship between 10 coagulation factors and glioma risk using summary statistics.\u003c/p\u003e \u003cp\u003eThe vascular endothelium, which is necessary for maintaining the proper balance between bleeding and clot formation, circulating platelets, and plasmatic coagulation all play a role in the intricate and dynamic process that governs hemostasis. Research indicates that cancer cells may disrupt this balance by triggering blood clotting. Moreover, many types of cancer are now known to be significantly influenced by tumor-associated procoagulant-driven inflammation particularly\u003csup\u003e6\u003c/sup\u003e. A growing quantity of research suggests that the development of cancer is aided by a procoagulant microenvironment\u003csup\u003e49\u003c/sup\u003e. The tumor microenvironment contains coagulation factors, such as FVII produced by the liver and circulated, can bind the initiator of coagulation tissue factor (TF)\u003csup\u003e50\u003c/sup\u003e. Widely expressed by cancer cells, TF has been linked to increased tumor grade and decreased patient survival\u003csup\u003e50\u003c/sup\u003e. When the blood FVII binds to TF, it activates protease-activated receptors (PARs), particularly PAR2, to initiate downstream signaling\u003csup\u003e51\u003c/sup\u003e. Moreover, according to certain research, cancer progression can be inhibited by blocking FVII/TF/PAR2 signaling independently of the coagulation response\u003csup\u003e52\u003c/sup\u003e. In our study, we discovered that the level of FVII were suggestively related with glioma. FVII level raises the incidence of GBM in genetically predisposed people, which is consistent with findings from other epidemiological studies. Since PAI-1 is the main inhibitor of both tissue-type and urokinase-type plasminogen activator (uPA), it is thought to be a major player in the regulation of extracellular matrix remodeling\u003csup\u003e53\u003c/sup\u003e. A growing body of research has indicated the importance of the plasminogen activator system, particularly uPA, which can activate pro-collagenase and promote tumor growth by breaking down the extracellular matrix\u003csup\u003e54,55\u003c/sup\u003e. Therefore, it was expected that PAI-1, a protease inhibitor belonging to the serine family, would have an anti-tumor effect due to its primary inhibitory activity on uPA \u003csup\u003e56\u003c/sup\u003e. We discovered that higher levels of PAI-1 are linked to a lower risk of all glioma and non-GBM in our study. This discovery implies PAI-1's potential as a preventive factor against the growth of glioma and validates its involvement in their pathophysiology. PT is the time it takes for platelet-deficient plasma to clot after an excess of tissue thrombin and calcium ions are added to the plasma. Thrombin cleaves fibrinogen to cause thrombotic effects, and it also activates human platelets via protease-activated receptors (PARs), which causes a variety of molecules to be secreted\u003csup\u003e57\u003c/sup\u003e. These platelet molecules may be used by tumors to support endothelial cell migration and activation during the development of new blood vessels\u003csup\u003e58\u003c/sup\u003e. Endothelial cells experience a number of cellular changes upon thrombin-mediated PAR activation, including migration and VEGF-A upregulation, both of which are necessary for angiogenesis\u003csup\u003e59\u003c/sup\u003e, whereby it regulates the tumor microenvironment in a number of ways\u003csup\u003e60\u003c/sup\u003e. Different cancerous expressions cause varying degrees of thrombosis susceptibility\u003csup\u003e61\u003c/sup\u003e. There are studies show that glioma exhibits a high VTE risk\u003csup\u003e62\u003c/sup\u003e. Previous research proposed a possible association between thrombin and glioma. In our study, PT was found to have a protective effect associated with glioma.\u003c/p\u003e \u003cp\u003eGlioma may be associated with changes in blood plasmin levels, according to our study's bilateral MR analysis. Plasmin is a strong protease that may directly degrade a variety of extracellular matrix constituents, such as proteoglycans, laminin, and fibronectin. Moreover, plasmin promotes angiogenesis and tumor spreading\u003csup\u003e63\u003c/sup\u003e. Research has demonstrated that Glioma \u0026rsquo;s ability to invade is made possible by a complex relationship between astrocytes and the uPA-plasmin cascade. In this sense, the plasminogen zymogen is changed into plasmin by the activation of uPA by its binding to uPA-receptor\u003csup\u003e64\u003c/sup\u003e. Our research also shows that glioma cause a higher level of plasmin suggestively. These results confirm the role that modification of the coagulation system plays in the glioma pathogenesis.\u003c/p\u003e \u003cp\u003eThere are several benefits to our study. This is the first study to use the two-sample bi-directional MR approach to reverse causality, eliminate confounding variables, and draw conclusions about the association between coagulation factors and glioma. Additionally, by using glioma data from the largest GWAS dataset (12,488 cases and 18,169 controls) and exposure data on coagulation factors from trustworthy huge scale GWAS databases (up to 21758 individuals), a sufficiently large sample size of the results could ensure the generalizability of causal associations. Lastly, to confirm the validity of the assumptions made about the instrumental variables, we used some supplemental studies, including pleiotropy analyses and heterogeneity analyses.\u003c/p\u003e \u003cp\u003eNonetheless, it is important to recognize some of this research's shortcomings. Firstly, there is a lack of genetic data and no sex or age segmentation in the GWAS data. We would like to broaden the analysis to encompass all populations when feasible. The results might not apply to other populations because we could only use European origin genome-wide association data. Further research into the causal relationships between coagulation factors and glioma in different populations is also necessary.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis research examines the associations between coagulation factor levels and glioma risk in European individuals. The results provide strong evidence for the causative relationships between PAI-1, PT and FVII levels and glioma risk. Glioma also affect the levels of coagulation factors such as plasmin. This work advances our knowledge of the role coagulation cascades play in the glioma's growth. The results of our investigation shed more light on the genesis of glioma and call for more investigation to identify the mechanisms underlying the emergence of glioma.\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 only used publicly available data and no individual-level data were used. Ethical approval and consent information for the above summary statistics were taken from the original publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDongmei Sun designed experiments; Lin Pan and Laiyu Yang wrote manuscript; Lin Pan, Yu Gao and Ningxin Wang carried out experiments; Jingning Wang, Ming Gao and Yihan Wang analyzed experiments results. Dongmei Sun and Lin Pan revised the manuscript, figures and tables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData openly available in a public repository.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOmuro A, DeAngelis LM. Glioblastoma and other malignant gliomas: a clinical review. JAMA. 2013;310:1842\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2013.280319\u003c/span\u003e\u003cspan address=\"10.1001/jama.2013.280319\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouis DN, et al. The 2007 WHO classification of tumours of the central nervous system. 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J Neurosci. 2003;23:4034\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1523/jneurosci.23-10-04034.2003\u003c/span\u003e\u003cspan address=\"10.1523/jneurosci.23-10-04034.2003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 2 are available in the Supplementary Files section.\u003c/p\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":"Mendelian randomization, coagulation factors, glioma, genetics","lastPublishedDoi":"10.21203/rs.3.rs-4258369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4258369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTraditional observational studies have shown that the levels of coagulation factors can affect the risk of glioma. It is uncertain, nevertheless, whether coagulation factor levels and various glioma subtypes are causally related. The purpose of this study was to look into any bidirectional correlations between glioma risk and coagulation factor levels.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eTwo-sample bi-directional Mendelian randomization (MR) analysis was carried out using openly accessible genome-wide association study (GWAS) data. The data for glioma subtypes were retrieved from an enormous-scale genetic meta-analysis compiled by GWAS data from independent European lineages of glioma, including 12,488 cases and 18,169 controls. The genetic summary data for 10 coagulation factors were retrieved from different GWAS results conducted in participants of European ancestry (up to 21758 participants), involving prothrombin time (PT), activated protein C(APC), von Willebrand factor (VWF), plasmin, a disintegrin-like and metalloproteinase with thrombospondin motifs 13 (ADAMTS13), factor VII (FVII), factor VIII (FVIII), factor X (FVX), plasminogen activator inhibitor-1 (PAI-1), and thrombomodulin (TM). Weighted median estimation (WME), MR-Egger regression, and inverse variance weighting (IVW) were the MR analysis approaches that were applied. IVW was selected as the main research method. Furthermore, the Benjamini-Hochberg false discovery rate (FDR) correction and sensitivity analyses were carried out.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe discovered a potential relationship between genetically predicted FVII levels and a higher risk of glioblastoma (GBM) (OR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 1.01\u0026ndash;1.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03). Our results also suggested that genetically predicted plasma PAI-1 level was negatively associated with the incidence of all glioma (OR\u0026thinsp;=\u0026thinsp;0.85, 95%CI: 0.73\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) and non-GBM (OR\u0026thinsp;=\u0026thinsp;0.77, 95%CI: 0.63\u0026ndash;0.92, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). In addition, a suggestively negative correlation between genetically predicted PT level and the risk of GBM (OR\u0026thinsp;=\u0026thinsp;0.72, 95%CI: 0.53\u0026ndash;0.98, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) was discovered. Conversely, there was insufficient evidence of a significant causal association of any examined glioma with coagulation factors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings suggest that coagulation factors may be important indicators for glioma treatment and may be involved in the pathophysiology of gliomas.\u003c/p\u003e","manuscriptTitle":"The effects of coagulation factors on the risk of glioma: a two-sample bi-directional Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-22 02:04:33","doi":"10.21203/rs.3.rs-4258369/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":"ab72a343-bb80-4aef-979b-c9ce4e30df00","owner":[],"postedDate":"April 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-06T10:30:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-22 02:04:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4258369","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4258369","identity":"rs-4258369","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}