Association of B cells and the risk of glioblastoma: a bidirectional two sample mendelian randomization study

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To date, the exploration of B-cell involvement in GBM remains relatively underexplored. Methods The two-sample Mendelian Randomization (MR) analysis was used to assess the causal relationship between the 190 B cell phenotypes and GBM. Bayesian Weighted Mendelian Randomization (BWMR) was also employed to complement MR analysis, and sensitivity analyses were conducted to assess the robustness of the results. Result Our results demonstrate a causal association between two B-cell phenotypes and the risk of GBM. Specifically, IgD + CD24 + B cell %B cell is significantly associated with a reduced risk of GBM (IVW OR = 0.676, 95% CI = 0.507–0.901, P ivw = 0.008); and CD38 on Plasma Blast-Plasma Cell is also significantly associated with a lower risk of GBM (IVW OR = 0.789, 95% CI = 0.626–0.995, P ivw = 0.045). Conclusion Our study suggests a potential connection between B cell phenotypes and GBM through bidirectional two-sample MR combined with BWMR analysis, providing a preliminary basis for future research. Glioblastoma (GBM) Mendelian randomization (MR) Bayesian weighted Mendelian randomization (BWMR) B cell Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction GBM represents a highly aggressive, infiltrative, and heterogeneous form of brain tumor, characterized by intricate genetic alterations[ 1 ]. Despite significant strides in elucidating the pathogenesis of GBM, the prognosis for patients remains poor, with a median overall survival rate of merely 15–20 months post-initial diagnosis[ 2 , 3 ]. The efficacy of current clinical treatments for GBM is substantially constrained by several challenges, including the presence of the blood-brain barrier (BBB), the tumor's propensity for infiltrative growth, and the heterogeneity of the tumor microenvironment (TME). These factors underscore an urgent requirement for novel therapeutic strategies capable of effectively combating GBM[ 4 ]. Leveraging the immune system to modulate tumor progression and invasion at distant sites is an enticing strategy in the treatment of GBM[ 5 ]. While the majority of immunotherapeutic research has historically concentrated on T cells, the role of B cells, particularly in the context of GBM, has been less explored[ 6 , 7 ]. Recent studies have revealed that B cells and their secreted antibodies can exert a significant influence on tumor growth, metastasis, and responsiveness to therapeutic interventions[ 8 , 9 ]. Moreover, the work by Lee-Chang et al. suggests that B cell-based vaccines constitute a promising, albeit under-investigated, immunotherapeutic avenue that merits deeper exploration within the GBM landscape[ 10 , 11 ]. B cells are a second population of adaptive immune cells found in the TME, typically classified according to their expression of specific surface proteins,which may not adequately reflect the heterogeneity of these cells in the TME[ 12 ]. Additionally, the functional impact of B cells on human cancer has been a subject of debate, with conflicting conclusions drawn from various studies. These discrepancies may be attributed to the significant heterogeneity in surface immunophenotypes and functions among B cell populations[ 13 , 14 ]. Therefore, a comprehensive study of the intricate relationship between B cells and GBM holds significant importance for enhancing our understanding of the disease and for the development of immunotherapeutic strategies. Due to ethical constraints and high costs, it is challenging to conduct clinical or epidemiological studies to investigate whether B cell phenotypes influence GBM. Furthermore, given the complex interplay between the immune system and tumors, the selection of appropriate research methods is crucial. MR is an epidemiological strategy that uses measurable genetic variants to investigate the causal effect of exposure on outcomes, which could solve shortcomings of traditional clinical investigations[ 15 ]. MR employs genetic variants as instrumental variables (IVs), which are fixed at conception, to perform causal inferences regarding the effects of modifiable risk factors[ 16 ]. Hence, reverse causation and confounding are less likely to occur with this method than with conventional observational research[ 17 ]. Furthermore, we utilized the BWMR method for final confirmation. This approach enables the handling of all IVs issues with a single methodology, without requiring any particular extension to deal with binary outcomes, weak instruments, missing data, and within-individual changes[ 18 ]. Therefore, our study is the first to employ the MR and BWMR methods in exploring the interaction between B cell phenotypes and GBM. This research provides new directions for the treatment and management of GBM. Materials and methods Study design Our study employs the two-way, double-sampling MR method to investigate causal associations between 190 B-cell traits and GBM, with the BWMR method serving as a supplementary approach. To ensure the robustness of causal inference, IVs must adhere to three basic assumptions: (1) Each IV must establish a direct link with the exposure factor; (2) Each IV should not be associated with potential confounders between exposure and outcome; (3) Each IV should not influence the outcome through pathways unrelated to exposure[ 19 ]. Strict quality control measures, including tests for multicollinearity and heterogeneity, were conducted to enhance the reliability of causal results. B cell trait data synopsis In our research endeavor, the dataset of B-cell characteristics was procured from the IEU OpenGWAS database ( https://gwas.mrcieu.ac.uk/ ). The initial GWAS pertaining to immunological traits were conducted by utilizing genetic data obtained from 3,757 European subjects. This dataset encompasses a diverse array of 190 distinct B-cell phenotypes, each associated with the unique identifiers GCST90001391 to GCST90001830[ 20 ]. GBM data synopsis FinnGen Research Initiative, which commenced in the autumn of 2017, encompasses the participation of approximately 500,000 Finnish individuals. The primary objective of this endeavor is to augment the comprehension of the etiology of diseases and to facilitate the development of diagnostic, preventive, and therapeutic modalities. This research initiative is predicated on genetic information, specifically genomic data, and is synergized with information obtained from the national healthcare registries. Given the scarcity of studies of similar magnitude globally, Finland possesses a unique advantage in conducting comprehensive genomic research that encompasses its entire population. The outcome variable currently under scrutiny is GBM, with the feature data originating from the FinnGen R12 database, encompassing 406 cases and 378,749 controls. Interested parties can access this publicly available dataset through the official website of the database. Instrumental variable selection Single-nucleotide polymorphisms (SNPs) are commonly used genetic variants in MR analysis, reflecting the sequence diversity at the genomic level caused by single nucleotide changes. Under the assumption of three basic IVs, we used a less stringent threshold of P < 1e-5 to ensure that there were enough SNPs for MR analysis. The study shows that this threshold still maintains the stability and accuracy of the data[ 21 ]. In addition, when selecting IVs, we also referred to the research by Fuchun Si et al. to verify the effectiveness of IVs[ 22 ]. Then, the linkage disequilibrium (LD) standard was set at r2 < 0.001 and a genetic distance of 10,000 kb, to exclude highly correlated SNPs and ensure independence among the included SNPs[ 23 ]. In addition, to alleviate bias caused by weak IVs, the F-value for each SNP was calculated, and those with an F-value less than 10 were identified as weak instruments[ 24 ]. The formula for calculating the F-value is F = R^2 × (N − 2) / (1 - R^2), where R^2 represents the proportion of variance explained by the selected SNP and N denotes the sample size from the GWAS for that SNP. More importantly, we meticulously identified and excluded any SNPs that showed significant association with potential confounding factors using the IEU OpenGWAS database. We retained SNPs with unambiguous alignment. No imputation was performed for missing SNPs. MR analysis and BWMR analysis We conducted bidirectional MR analysis to investigate the causative relationship between 190 B-cell phenotypes and GBM. Given that the Instrumental Variable Weighted (IVW) method offers better test power compared to other MR methods, we selected it as the primary approach. Additionally, we employed four other methods: simple model, weighted median, weighted mode, and MR-Egger to complement our analysis and assess the reliability and consistency of the results. The bidirectional MR analysis was used to examine the impact of GBM on 190 B-cell phenotypes, which is in line with the research by Tsou and Lee-Chang et al., which confirms that B cells can rapidly switch between anti-tumor and pro-tumor phenotypes within their peripheral microenvironment[ 25 , 26 ]. BWMR analysis complements bidirectional MR by addressing issues of polygenic architecture and pleiotropy, which are often overlooked in standard MR analyses[ 27 ]. Statistical analysis Unmeasured confounders may lead to bias in the estimation of genetic pleiotropy and effect size. We employed Cochran's Q test, the MR-Egger intercept test, and MR-PRESSO to evaluate the heterogeneity and pleiotropy of the results[ 28 , 29 ]. Due to algorithmic limitations, MR-PRESSO could not be computed when fewer than 3 SNPs matched with both exposure and outcome. In such cases where screening for pleiotropy, we referred to the MR-Egger intercept test. When there is only one SNP matching both exposure and outcome, none of these three methods can be calculated. The MR-PRESSO analysis was conducted using the "MRPRESSO" R package. All analyses were performed using R software (version 4.4.1). Furthermore, sensitivity analyses were conducted using the "leave-one-out" method to assess the impact of individual IVs on the overall MR results, providing robust evidence for the stability of the study findings. Results In this study, we finally selected 4,510 SNPs associated with 190 B cell traits and 53 SNPs associated with GBM as IVs. Detailed features of the SNPs are shown in Supplemental Material 1. The results of the heterogeneity test and pleiotropy assessment for these significant findings are presented in Table. 1. Table 1 The results of the heterogeneity test and pleiotropy assessment exposure outcome method Q Q_df Q_pval egger intercept se pval IgD + CD24+ %B cell GBM MR Egger 16.759 18.000 0.540 0.126 0.051 0.025 IgD + CD24+ %B cell GBM Inverse variance weighted 22.774 19.000 0.247 IgD + CD24+ %B cell GBM MR-PRESSO 0.228 CD38 on PB/PC GBM MR Egger 9.378 13.000 0.744 0.015 0.041 0.714 CD38 on PB/PC GBM Inverse variance weighted 9.518 14.000 0.796 CD38 on PB/PC GBM MR-PRESSO 0.844 GBM Switched memory B cell Absolute Count MR Egger 38.457 49.000 0.861 -0.005 0.010 0.642 GBM Switched memory B cell Absolute Count Inverse variance weighted 38.675 50.000 0.878 GBM Switched memory B cell Absolute Count MR-PRESSO 0.883 GBM IgD- CD38dim B cell %lymphocyte MR Egger 49.282 49.000 0.462 -0.004 0.010 0.690 GBM IgD- CD38dim B cell %lymphocyte Inverse variance weighted 49.443 50.000 0.496 GBM IgD- CD38dim B cell %lymphocyte MR-PRESSO 0.520 GBM IgD- CD38dim B cell %B cell MR Egger 43.323 49.000 0.702 -0.008 0.010 0.438 GBM IgD- CD38dim B cell %B cell Inverse variance weighted 43.935 50.000 0.714 GBM IgD- CD38dim B cell %B cell MR-PRESSO 0.709 GBM IgD- CD38dim B cell Absolute Count MR Egger 41.039 49.000 0.783 -0.001 0.009 0.948 GBM IgD- CD38dim B cell Absolute Count Inverse variance weighted 41.044 50.000 0.813 GBM IgD- CD38dim B cell Absolute Count MR-PRESSO 0.818 GBM BAFF-R on IgD- CD38 + B cell MR Egger 53.389 49.000 0.309 -0.008 0.010 0.451 GBM BAFF-R on IgD- CD38 + B cell Inverse variance weighted 54.019 50.000 0.324 GBM BAFF-R on IgD- CD38 + B cell MR-PRESSO 0.368 GBM CD19 on IgD- CD24- B cell MR Egger 47.924 49.000 0.517 -0.006 0.010 0.516 GBM CD19 on IgD- CD24- B cell Inverse variance weighted 48.353 50.000 0.540 GBM CD19 on IgD- CD24- B cell MR-PRESSO 0.523 GBM CD19 on IgD- CD27- B cell MR Egger 53.169 49.000 0.317 -0.010 0.010 0.305 GBM CD19 on IgD- CD27- B cell Inverse variance weighted 54.333 50.000 0.313 GBM CD19 on IgD- CD27- B cell MR-PRESSO 0.312 GBM CD24 on IgD + CD24 + B cell MR Egger 51.808 49.000 0.365 -0.012 0.010 0.226 GBM CD24 on IgD + CD24 + B cell Inverse variance weighted 53.399 50.000 0.345 GBM CD24 on IgD + CD24 + B cell MR-PRESSO 0.308 GBM CD24 on IgD- CD38dim B cell MR Egger 51.762 49.000 0.367 -0.017 0.010 0.103 GBM CD24 on IgD- CD38dim B cell Inverse variance weighted 54.678 50.000 0.301 GBM CD24 on IgD- CD38dim B cell MR-PRESSO 0.300 Exploration of the causal effect of B cell phenotypes on GBM The sensitivity analysis suggests that the B-cell phenotype IgD + CD24+ %B cell demonstrates pleiotropy at the MR Egger intercept (egger_intercept = 0.126, P = 0.025), indicating a potential for confounding factors in the MR analysis. However, the Cochrane Q test did not identify any significant heterogeneity or horizontal pleiotropy across different SNPs (P = 0.228), thus supporting the absence of substantial heterogeneity or level effect modification in the MR analysis. Therefore, the results from the IVW analysis were used as the primary outcomes for the MR analysis and presented using a fixed-effects model to depict the final findings. The combined results of the MR analysis with BWMR analysis indicate that two B-cell phenotypes are associated with GBM. Specifically, these phenotypes include: (1) IgD + CD24+ %B cell (IVW OR = 0.676, 95% CI = 0.507–0.901, P = 0.008); (2) CD38 on PB/PC (IVW OR = 0.789, 95% CI = 0.626–0.995, P = 0.045). (Fig. 1). Scatter plots for two identified B-cell phenotypes across various tests are displayed in Fig. 2. The leave-one-out analysis confirmed that excluding any single SNP did not introduce bias into the MR estimation in Fig. 3. The funnel plots are presented in Fig. 4. Exploration of the causal effect of GBM onset on B cell phenotypes The reverse two-sample MR analysis revealed a weak negative regulatory correlation between GBM and nine B-cell phenotypes. This conclusion was based on an assessment of heterogeneity and horizontal pleiotropy under normal conditions, and it has not been challenged by the BWMR analysis. Specifically, these phenotypes include: Switched memory B cell Absolute Count (IVW OR = 0.979, 95% CI = 0.958 ~ 0.999, P = 0.041); IgD- CD38dim B cell %lymphocyte (IVW OR = 0.978, 95% CI = 0.959 ~ 0.999, P = 0.036); IgD- CD38dim B cell %B cell (IVW OR = 0.978, 95% CI = 0.958 ~ 0.999, P = 0.041); IgD- CD38dim B cell Absolute Count (IVW OR = 0.975, 95% CI = 0.955 ~ 0.995, P = 0.013); BAFF-R on IgD- CD38 + B cell (IVW OR = 0.973, 95% CI = 0.952 ~ 0.994, P = 0.013); CD19 on IgD- CD24- B cell (IVW OR = 0.975, 95% CI = 0.954 ~ 0.995, P = 0.015); CD19 on IgD- CD27- B cell (IVW OR = 0.975, 95% CI = 0.954 ~ 0.997, P = 0.023); CD24 on IgD + CD24 + B cell (IVW OR = 0.979, 95% CI = 0.958 ~ 1.000, P = 0.047); CD24 on IgD- CD38dim B cell (IVW OR = 0.977, 95% CI = 0.956 ~ 0.998, P = 0.036) (Fig. 5). The results from sensitivity analyses indicate that our conclusions are reliable; detailed information can be found in the Supplemental Material 2. Our study demonstrates that there is an interaction between B-cell phenotypes and the progression of GBM. Specifically, two B-cell phenotypes have a causal influence on GBM. Concurrently, GBM exhibits causal associations with nine different B-cell phenotypes. However, bidirectional MR analysis also confirms that there are no overlapping B-cell phenotypes between these two factors, thus eliminating the interference of reverse causality. The conclusions supported by sensitivity analyses are robust. Therefore, this study is the first to confirm an interaction between B-cell phenotypes and GBM using MR and BWMR methods. Discussion Our study represents a groundbreaking approach by leveraging GWAS data associated with 190 B-cell phenotypes to explore their complex associations with GBM at the level of summary statistics. To our knowledge, this is the first study to employ bidirectional two-sample MR combined with BWMR to investigate the potential causal relationships between multiple B-cell phenotypes and GBM. Following robustness validation, our study confirms significant causal associations between two specific B-cell phenotypes and the risk of developing GBM. These phenotypes include IgD + CD24 + B cell %B cell, CD38 on Plasma Blast-Plasma Cell (CD38 on PB/PC). Understanding the dynamic interactions between B-cell phenotypes and GBM not only improves our diagnostic abilities but also has the potential to facilitate the development of immunotherapeutic intervention strategies aimed at disrupting key pathways related to GBM tumor initiation and progression. For most patients, the causes of GBM remain largely unknown. Approximately 5% of individuals carry germline mutations that make them predisposed to various types of cancers, including GBM[ 30 , 31 ]. Other potential risk factors, such as exposure to mobile phones, viral triggers (such as cytomegalovirus), and other environmental exposures, are under investigation, but the extent of their influence remains unclear[ 32 , 33 ]. Although GBM was among the first solid tumors to undergo comprehensive molecular analysis, targeted therapies have provided limited benefits in clinical trials, prompting the development of other therapeutic approaches, including immunotherapies[ 34 ]. The latest advancements in immunotherapy, such as immune checkpoint inhibitors and chimeric antigen receptor (CAR) T cells, have significantly improved the prognosis and overall survival of patients with many types of cancer. However, these benefits have not yet been observed in GBM[ 35 ]. With the significant contributions by Lee-Chang et al. in utilizing activated B cells as a cell-based vaccine against GBM, it highlights the potential of B cells as a new horizon for immunotherapy. Our study reveals associations between two B-cell phenotypes and GBM using MR and BWMR analyses. The first B-cell phenotype that is associated with GBM risk, IgD + CD24 + B cell %B cell, represents a lower risk. It represents the proportion of IgD + CD24 + cells in B cells. This type of B cells is characterized by the expression of immunoglobulin D (IgD) and CD24 on their surface. These B cells are typically naive or memory B cells that have not yet been activated to produce antibodies. The association between this particular B-cell phenotype and GBM suggests that a higher proportion of IgD + CD24 + B cells in the blood might be associated with a reduced risk of developing GBM. This finding could be significant for understanding the immunological landscape of individuals at risk for GBM and may have implications for potential therapeutic strategies or biomarker development. Furthermore, Yuan Cheng et al., through an analysis of 731 immunocyte phenotypes related to GBM risk, have confirmed that an increased absolute count of IgD + CD24 + B cells is associated with a heightened risk of GBM[ 36 ]. Thus, their findings align with ours, indicating without doubt that IgD + CD24 + B cells are significantly involved in the pathogenesis of GBM, making this area a key focus for future research endeavors. Furthermore, our study results suggest that an increased proportion of CD38-positive plasma blasts within plasma cells is causally associated with a reduced risk of GBM. CD38 is a type II transmembrane glycoprotein with cyclase and hydrolase activities, representing a bifunctional extracellular enzyme involved in nucleotide metabolism[ 37 ]. Recent studies have elucidated a strong correlation between CD38 expression levels and the elevation of adenosine, an essential immunosuppressive factor within the TME of solid tumors[ 38 ]. In certain instances, elevated CD38 expression in these tumors can lead to the production of substantial amounts of adenosine within the TME. This adenosine then attracts immune suppressive cells such as regulatory T cells (Treg), myeloid-derived suppressor cells (MDSCs), and cancer-associated fibroblasts (CAF) into the TME, thereby dampening immune system activity[ 39 ]. However, research on melanoma, gliomas, esophageal cancer, cervical cancer, and lung cancer indicates that CD38 functions as a tumor-promoting factor in these diseases. Therefore, further investigation is warranted to elucidate its role and potential therapeutic implications[ 40 ]. Our study presents some encouraging findings, but also some limitations. Firstly, the IgD + CD24+ %B cell pleiotropy test result was less than 0.05, indicating the presence of pleiotropy. However, further SNP screening using MR-PRESSO did not reveal any need to remove SNPs. There are many possible reasons for this result, such as sample size, limited statistical power, the complexity of pleiotropy, and methodological limitations. Nevertheless, we recommend that readers approach the results with caution. Secondly, our study is based on aggregated-level analysis and cannot perform subgroup analyses or discussions at an individual level, thus unable to explain individual phenomena. Additionally, our study only explores GBM risk factors from the perspective of B-cell phenotypes and does not represent the actual situation because of the complex pathophysiological mechanisms of GBM. Finally, conclusions based on data analysis should be treated with caution and are still worthy of experimental verification through pathology and large-scale clinical studies. Conclusion In summary, our research utilized bidirectional two-sample MR combined with BWMR to identify potential links between B-cell phenotypes and GBM. Through MR analysis, our findings emphasize the significance of dynamic interactions between B-cell phenotypes represented by IgD + CD24+ %B cell and GBM. These findings not only enhance our understanding of the complex physiological and pathological conditions associated with GBM, but also provide new theoretical support for the development of targeted immunotherapy intervention strategies. Future experimental validation may lead to improvements in GBM treatment approaches and clinical management protocols. Abbreviations GBM Glioblastoma MR Mendelian randomization BWMR Bayesian weighted Mendelian randomization GWAS Genome-wide association study BBB Blood-brain barrier TME Tumor microenvironment IVs Instrumental variables SNPs Single-nucleotide polymorphisms LD Linkage disequilibrium IVW Inverse variance weighted CD38 on PB/PC CD38 on Plasma Blast-Plasma Cell CAR Chimeric antigen receptor IgD Immunoglobulin D Treg Regulatory T cells MDSCs Myeloid-derived suppressor cells CAF Cancer-associated fibroblasts Declarations Acknowledgements The authors thank all the participants and researchers who contributed and collected data. Author contributions statement Hao Yuan: Responsible for conceptualization, data curation, formal analysis, investigation, methodology, and project administration; also contributed to validation, and drafted the original manuscript. Shiyan Weng: Engaged in formal analysis, investigation, and contributed to drafting the original manuscript. Xin Feng: Involved in conceptualization, data curation, investigation, and provided critical review and editing of the manuscript. Chuanzhi Duan: Played a role in conceptualization, data curation, formal analysis, secured funding, conducted investigation, developed methodology, and managed project administration; also responsible for validation, and critically reviewed and edited the manuscript. All authors have reviewed and provided feedback on the manuscript, and have approved the final version. Funding statement This research was supported by Research Foundation for the Natural Science Foundation of China No.82201427. Data availability The original contributions presented in the study are included in the article/ supplementary material, further inquiries can be directed to the corresponding author. Ethics statement All data analyzed in this study were sourced from publicly available databases. 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Cancer Genet. 2012 Dec;205(12):613-21. doi: 10.1016/j.cancergen.2012.10.009. Epub 2012 Dec 11. PMID: 23238284. Joseph GP, McDermott R, Baryshnikova MA, Cobbs CS, Ulasov IV. Cytomegalovirus as an oncomodulatory agent in the progression of glioma. Cancer Lett. 2017 Jan 1;384:79-85. doi: 10.1016/j.canlet.2016.10.022. Epub 2016 Oct 21. PMID: 27777041. Little MP, Azizova TV, Bazyka D, Bouffler SD, Cardis E, Chekin S, Chumak VV, Cucinotta FA, de Vathaire F, Hall P, Harrison JD, Hildebrandt G, Ivanov V, Kashcheev VV, Klymenko SV, Kreuzer M, Laurent O, Ozasa K, Schneider T, Tapio S, Taylor AM, Tzoulaki I, Vandoolaeghe WL, Wakeford R, Zablotska LB, Zhang W, Lipshultz SE. Systematic review and meta-analysis of circulatory disease from exposure to low-level ionizing radiation and estimates of potential population mortality risks. Environ Health Perspect. 2012 Nov;120(11):1503-11. doi: 10.1289/ehp.1204982. Epub 2012 Jun 22. PMID: 22728254; PMCID: PMC3556625. Wang S, Huang T, Wu Q, Yuan H, Wu X, Yuan F, Duan T, Taori S, Zhao Y, Snyder NW, Placantonakis DG, Rich JN. Lactate reprograms glioblastoma immunity through CBX3-regulated histone lactylation. J Clin Invest. 2024 Nov 15;134(22):e176851. doi: 10.1172/JCI176851. PMID: 39545414; PMCID: PMC11563687. Wu CY, Chen Y, Lin YJ, Wei KC, Chang KY, Feng LY, Chen KT, Li G, Ren AL, Nitta RT, Wu JY, Cho KB, Pant A, Choi J, Mackall CL, Kim LH, Wu AC, Chuang JY, Huang CY, Jackson CM, Chen PY, Lim M. Tumor-Associated Microglia Secrete Extracellular ATP to Support Glioblastoma Progression. Cancer Res. 2024 Dec 2;84(23):4017-4030. doi: 10.1158/0008-5472.CAN-24-0018. PMID: 39618248. Huang M, Liu Y, Peng J, Cheng Y. Causal effects of immune cells in glioblastoma: a Bayesian Mendelian Randomization study. Front Neurol. 2024 Apr 29;15:1375723. doi: 10.3389/fneur.2024.1375723IF: 2.7 Q2 B3. PMID: 38742049; PMCID: PMC11089213. Durnin L, Mutafova-Yambolieva VN. Cyclic ADP-ribose requires CD38 to regulate the release of ATP in visceral smooth muscle. FEBS J. 2011 Sep;278(17):3095-108. doi: 10.1111/j.1742-4658.2011.08233.x. Epub 2011 Aug 8. PMID: 21740519; PMCID: PMC4838287. Fang C, Li T, Li Y, Xu GJ, Deng QW, Chen YJ, Hou YN, Lee HC, Zhao YJ. CD38 produces nicotinic acid adenosine dinucleotide phosphate in the lysosome. J Biol Chem. 2018 May 25;293(21):8151-8160. doi: 10.1074/jbc.RA118.002113. Epub 2018 Apr 9. Erratum in: J Biol Chem. 2019 Dec 13;294(50):19447. doi: 10.1074/jbc.AAC119.011919. PMID: 29632067; PMCID: PMC5971459. Raychaudhuri B, Rayman P, Ireland J, Ko J, Rini B, Borden EC, Garcia J, Vogelbaum MA, Finke J. Myeloid-derived suppressor cell accumulation and function in patients with newly diagnosed glioblastoma. Neuro Oncol. 2011 Jun;13(6):591-9. doi: 10.1093/neuonc/nor042IF: 16.4 Q1 B1. PMID: 21636707; PMCID: PMC3107102. Konen JM, Fradette JJ, Gibbons DL. The Good, the Bad and the Unknown of CD38 in the Metabolic Microenvironment and Immune Cell Functionality of Solid Tumors. Cells. 2019 Dec 24;9(1):52. doi: 10.3390/cells9010052. PMID: 31878283; PMCID: PMC7016859. Additional Declarations No competing interests reported. Supplementary Files SupplementalMaterial1.xlsx SupplementalMaterial2.docx 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-6212888","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430799737,"identity":"2ac3d047-9cb2-45ef-97ef-926fecdfa797","order_by":0,"name":"Hao Yuan","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Yuan","suffix":""},{"id":430799738,"identity":"b8db1e50-1663-49bc-8c11-e28c0bda8856","order_by":1,"name":"Shiyan Weng","email":"","orcid":"","institution":"Dongguan Kanghua Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shiyan","middleName":"","lastName":"Weng","suffix":""},{"id":430799742,"identity":"979fe003-6187-4dec-a0da-449c5f33c6c0","order_by":2,"name":"Xin Feng","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Feng","suffix":""},{"id":430799743,"identity":"5ae2e47e-2060-4506-8372-75c1d9d634e9","order_by":3,"name":"Chuanzhi Duan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDCCAxCKsQFESlRIyMkToQWsGqrljIWxYQNJWhjbKhJh9uIEfMebnz/4uIdBdsPxs4c/WM6TSGBsYH746AYeLZJnjhk2znjGYLzhTF6CgeQ2iTx2BjZj4xw8Wgxu5DA28xxgSNxwIMcgAailmLGBh02aOC3n3xgckJwjkdhwgGgtN3IMGyQbiNAC8svMGQcYjGfeeGPMIHFMwtiwmYBfgCH24MOHAwyyfedzjD9L1NTJybM3P3yMTwsU/AeTzBJgkrByBGD8QIrqUTAKRsEoGDEAAIgjUVbuf+snAAAAAElFTkSuQmCC","orcid":"","institution":"Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chuanzhi","middleName":"","lastName":"Duan","suffix":""}],"badges":[],"createdAt":"2025-03-12 14:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6212888/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6212888/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78949974,"identity":"a22898b6-dd09-45b7-b77c-975c1dc28147","added_by":"auto","created_at":"2025-03-21 08:43:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":122873,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots showing significantly causal relationships between two kinds of B cell traits and GBM. BWMR, Bayesian Weighted Mendelian Randomization; nsnp, number of single-nucleotide polymorphism; OR, odds ratio; CI, confidence interval\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6212888/v1/1660d0a4ae399e4b36c81c2b.png"},{"id":78949980,"identity":"7580317b-429d-448b-8005-3ea39e16629b","added_by":"auto","created_at":"2025-03-21 08:43:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112634,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots between two kinds of B cell traits and GBM\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6212888/v1/98184acfa89fb75c2c82f81a.png"},{"id":78950439,"identity":"6b0e785f-2ebf-4cf7-9e18-d0a98db0ffcf","added_by":"auto","created_at":"2025-03-21 08:51:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96353,"visible":true,"origin":"","legend":"\u003cp\u003eleave-one-out sensitivity analysis between two kinds of B cell traits and GBM\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6212888/v1/75da2a471dda1df19e89ca6a.png"},{"id":78949976,"identity":"86c08991-1c65-4bb5-9a71-153265cee85e","added_by":"auto","created_at":"2025-03-21 08:43:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52172,"visible":true,"origin":"","legend":"\u003cp\u003eThe funnel plots between two kinds of B cell traits and GBM: (A) IgD+ CD24+ %B cell on GBM; (B) CD38 on PB/PC on GBM\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6212888/v1/9797b31ca98f9e5e2a532085.png"},{"id":78950440,"identity":"0cc446bc-c28a-4420-b591-7f22d7822e25","added_by":"auto","created_at":"2025-03-21 08:51:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":434867,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots showing significantly causal relationships between GBM and nine kinds of B cell traits. BWMR, Bayesian Weighted Mendelian Randomization; nsnp, number of single-nucleotide polymorphism; OR, odds ratio; CI, confidence interval\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6212888/v1/1cafd58541eb817d89da103e.png"},{"id":84474926,"identity":"0c73c845-af1c-4858-965c-b24c0b00055b","added_by":"auto","created_at":"2025-06-12 11:08:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1577402,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6212888/v1/514cdbf8-cf58-4e87-a4cc-68801f8e0451.pdf"},{"id":78949975,"identity":"f9a95567-aff0-4260-961c-b02ddaf0a015","added_by":"auto","created_at":"2025-03-21 08:43:30","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":458426,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6212888/v1/f74f5da98764bf175372ea77.xlsx"},{"id":78950442,"identity":"00e02754-e30b-495a-afb2-d6c146e0747d","added_by":"auto","created_at":"2025-03-21 08:51:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":408322,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalMaterial2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6212888/v1/4d96441c7fc653ea9b6296eb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of B cells and the risk of glioblastoma: a bidirectional two sample mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGBM represents a highly aggressive, infiltrative, and heterogeneous form of brain tumor, characterized by intricate genetic alterations[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite significant strides in elucidating the pathogenesis of GBM, the prognosis for patients remains poor, with a median overall survival rate of merely 15\u0026ndash;20 months post-initial diagnosis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The efficacy of current clinical treatments for GBM is substantially constrained by several challenges, including the presence of the blood-brain barrier (BBB), the tumor's propensity for infiltrative growth, and the heterogeneity of the tumor microenvironment (TME). These factors underscore an urgent requirement for novel therapeutic strategies capable of effectively combating GBM[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Leveraging the immune system to modulate tumor progression and invasion at distant sites is an enticing strategy in the treatment of GBM[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While the majority of immunotherapeutic research has historically concentrated on T cells, the role of B cells, particularly in the context of GBM, has been less explored[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Recent studies have revealed that B cells and their secreted antibodies can exert a significant influence on tumor growth, metastasis, and responsiveness to therapeutic interventions[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, the work by Lee-Chang et al. suggests that B cell-based vaccines constitute a promising, albeit under-investigated, immunotherapeutic avenue that merits deeper exploration within the GBM landscape[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eB cells are a second population of adaptive immune cells found in the TME, typically classified according to their expression of specific surface proteins,which may not adequately reflect the heterogeneity of these cells in the TME[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, the functional impact of B cells on human cancer has been a subject of debate, with conflicting conclusions drawn from various studies. These discrepancies may be attributed to the significant heterogeneity in surface immunophenotypes and functions among B cell populations[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, a comprehensive study of the intricate relationship between B cells and GBM holds significant importance for enhancing our understanding of the disease and for the development of immunotherapeutic strategies.\u003c/p\u003e \u003cp\u003eDue to ethical constraints and high costs, it is challenging to conduct clinical or epidemiological studies to investigate whether B cell phenotypes influence GBM. Furthermore, given the complex interplay between the immune system and tumors, the selection of appropriate research methods is crucial. MR is an epidemiological strategy that uses measurable genetic variants to investigate the causal effect of exposure on outcomes, which could solve shortcomings of traditional clinical investigations[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. MR employs genetic variants as instrumental variables (IVs), which are fixed at conception, to perform causal inferences regarding the effects of modifiable risk factors[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Hence, reverse causation and confounding are less likely to occur with this method than with conventional observational research[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, we utilized the BWMR method for final confirmation. This approach enables the handling of all IVs issues with a single methodology, without requiring any particular extension to deal with binary outcomes, weak instruments, missing data, and within-individual changes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, our study is the first to employ the MR and BWMR methods in exploring the interaction between B cell phenotypes and GBM. This research provides new directions for the treatment and management of GBM.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eOur study employs the two-way, double-sampling MR method to investigate causal associations between 190 B-cell traits and GBM, with the BWMR method serving as a supplementary approach. To ensure the robustness of causal inference, IVs must adhere to three basic assumptions: (1) Each IV must establish a direct link with the exposure factor; (2) Each IV should not be associated with potential confounders between exposure and outcome; (3) Each IV should not influence the outcome through pathways unrelated to exposure[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Strict quality control measures, including tests for multicollinearity and heterogeneity, were conducted to enhance the reliability of causal results.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eB cell trait data synopsis\u003c/h3\u003e\n\u003cp\u003eIn our research endeavor, the dataset of B-cell characteristics was procured from the IEU OpenGWAS database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The initial GWAS pertaining to immunological traits were conducted by utilizing genetic data obtained from 3,757 European subjects. This dataset encompasses a diverse array of 190 distinct B-cell phenotypes, each associated with the unique identifiers GCST90001391 to GCST90001830[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eGBM data synopsis\u003c/h3\u003e\n\u003cp\u003eFinnGen Research Initiative, which commenced in the autumn of 2017, encompasses the participation of approximately 500,000 Finnish individuals. The primary objective of this endeavor is to augment the comprehension of the etiology of diseases and to facilitate the development of diagnostic, preventive, and therapeutic modalities. This research initiative is predicated on genetic information, specifically genomic data, and is synergized with information obtained from the national healthcare registries. Given the scarcity of studies of similar magnitude globally, Finland possesses a unique advantage in conducting comprehensive genomic research that encompasses its entire population. The outcome variable currently under scrutiny is GBM, with the feature data originating from the FinnGen R12 database, encompassing 406 cases and 378,749 controls. Interested parties can access this publicly available dataset through the official website of the database.\u003c/p\u003e\n\u003ch3\u003eInstrumental variable selection\u003c/h3\u003e\n\u003cp\u003eSingle-nucleotide polymorphisms (SNPs) are commonly used genetic variants in MR analysis, reflecting the sequence diversity at the genomic level caused by single nucleotide changes. Under the assumption of three basic IVs, we used a less stringent threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;1e-5 to ensure that there were enough SNPs for MR analysis. The study shows that this threshold still maintains the stability and accuracy of the data[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In addition, when selecting IVs, we also referred to the research by Fuchun Si et al. to verify the effectiveness of IVs[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Then, the linkage disequilibrium (LD) standard was set at r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and a genetic distance of 10,000 kb, to exclude highly correlated SNPs and ensure independence among the included SNPs[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, to alleviate bias caused by weak IVs, the F-value for each SNP was calculated, and those with an F-value less than 10 were identified as weak instruments[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The formula for calculating the F-value is F\u0026thinsp;=\u0026thinsp;R^2 \u0026times; (N \u0026minus;\u0026thinsp;2) / (1 - R^2), where R^2 represents the proportion of variance explained by the selected SNP and N denotes the sample size from the GWAS for that SNP. More importantly, we meticulously identified and excluded any SNPs that showed significant association with potential confounding factors using the IEU OpenGWAS database. We retained SNPs with unambiguous alignment. No imputation was performed for missing SNPs.\u003c/p\u003e\n\u003ch3\u003eMR analysis and BWMR analysis\u003c/h3\u003e\n\u003cp\u003eWe conducted bidirectional MR analysis to investigate the causative relationship between 190 B-cell phenotypes and GBM. Given that the Instrumental Variable Weighted (IVW) method offers better test power compared to other MR methods, we selected it as the primary approach. Additionally, we employed four other methods: simple model, weighted median, weighted mode, and MR-Egger to complement our analysis and assess the reliability and consistency of the results. The bidirectional MR analysis was used to examine the impact of GBM on 190 B-cell phenotypes, which is in line with the research by Tsou and Lee-Chang et al., which confirms that B cells can rapidly switch between anti-tumor and pro-tumor phenotypes within their peripheral microenvironment[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. BWMR analysis complements bidirectional MR by addressing issues of polygenic architecture and pleiotropy, which are often overlooked in standard MR analyses[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eUnmeasured confounders may lead to bias in the estimation of genetic pleiotropy and effect size. We employed Cochran's Q test, the MR-Egger intercept test, and MR-PRESSO to evaluate the heterogeneity and pleiotropy of the results[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Due to algorithmic limitations, MR-PRESSO could not be computed when fewer than 3 SNPs matched with both exposure and outcome. In such cases where screening for pleiotropy, we referred to the MR-Egger intercept test. When there is only one SNP matching both exposure and outcome, none of these three methods can be calculated. The MR-PRESSO analysis was conducted using the \"MRPRESSO\" R package. All analyses were performed using R software (version 4.4.1). Furthermore, sensitivity analyses were conducted using the \"leave-one-out\" method to assess the impact of individual IVs on the overall MR results, providing robust evidence for the stability of the study findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eIn this study, we finally selected 4,510 SNPs associated with 190 B cell traits and 53 SNPs associated with GBM as IVs. Detailed features of the SNPs are shown in Supplemental Material 1. The results of the heterogeneity test and pleiotropy assessment for these significant findings are presented in Table. 1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of the heterogeneity test and pleiotropy assessment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eexposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ_df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ_pval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eegger\u003c/p\u003e \u003cp\u003eintercept\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgD\u0026thinsp;+\u0026thinsp;CD24+ %B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgD\u0026thinsp;+\u0026thinsp;CD24+ %B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgD\u0026thinsp;+\u0026thinsp;CD24+ %B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD38 on PB/PC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD38 on PB/PC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD38 on PB/PC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwitched memory B cell Absolute Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwitched memory B cell Absolute Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSwitched memory B cell Absolute Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD- CD38dim B cell %lymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD- CD38dim B cell %lymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD- CD38dim B cell %lymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD- CD38dim B cell %B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD- CD38dim B cell %B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD- CD38dim B cell %B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD- CD38dim B cell Absolute Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD- CD38dim B cell Absolute Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD- CD38dim B cell Absolute Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBAFF-R on IgD- CD38\u0026thinsp;+\u0026thinsp;B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBAFF-R on IgD- CD38\u0026thinsp;+\u0026thinsp;B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBAFF-R on IgD- CD38\u0026thinsp;+\u0026thinsp;B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD19 on IgD- CD24- B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD19 on IgD- CD24- B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD19 on IgD- CD24- B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD19 on IgD- CD27- B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD19 on IgD- CD27- B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD19 on IgD- CD27- B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD24 on IgD\u0026thinsp;+\u0026thinsp;CD24\u0026thinsp;+\u0026thinsp;B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD24 on IgD\u0026thinsp;+\u0026thinsp;CD24\u0026thinsp;+\u0026thinsp;B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD24 on IgD\u0026thinsp;+\u0026thinsp;CD24\u0026thinsp;+\u0026thinsp;B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD24 on IgD- CD38dim B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD24 on IgD- CD38dim B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD24 on IgD- CD38dim B cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eExploration of the causal effect of B cell phenotypes on GBM\u003c/h3\u003e\n\u003cp\u003eThe sensitivity analysis suggests that the B-cell phenotype IgD\u0026thinsp;+\u0026thinsp;CD24+ %B cell demonstrates pleiotropy at the MR Egger intercept (egger_intercept\u0026thinsp;=\u0026thinsp;0.126, P\u0026thinsp;=\u0026thinsp;0.025), indicating a potential for confounding factors in the MR analysis. However, the Cochrane Q test did not identify any significant heterogeneity or horizontal pleiotropy across different SNPs (P\u0026thinsp;=\u0026thinsp;0.228), thus supporting the absence of substantial heterogeneity or level effect modification in the MR analysis. Therefore, the results from the IVW analysis were used as the primary outcomes for the MR analysis and presented using a fixed-effects model to depict the final findings. The combined results of the MR analysis with BWMR analysis indicate that two B-cell phenotypes are associated with GBM. Specifically, these phenotypes include: (1) IgD\u0026thinsp;+\u0026thinsp;CD24+ %B cell (IVW OR\u0026thinsp;=\u0026thinsp;0.676, 95% CI\u0026thinsp;=\u0026thinsp;0.507\u0026ndash;0.901, P\u0026thinsp;=\u0026thinsp;0.008); (2) CD38 on PB/PC (IVW OR\u0026thinsp;=\u0026thinsp;0.789, 95% CI\u0026thinsp;=\u0026thinsp;0.626\u0026ndash;0.995, P\u0026thinsp;=\u0026thinsp;0.045). (Fig.\u0026nbsp;1). Scatter plots for two identified B-cell phenotypes across various tests are displayed in Fig.\u0026nbsp;2. The leave-one-out analysis confirmed that excluding any single SNP did not introduce bias into the MR estimation in Fig.\u0026nbsp;3. The funnel plots are presented in Fig.\u0026nbsp;4.\u003c/p\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003eExploration of the causal effect of GBM onset on B cell phenotypes\u003c/h2\u003e\n \u003cp\u003eThe reverse two-sample MR analysis revealed a weak negative regulatory correlation between GBM and nine B-cell phenotypes. This conclusion was based on an assessment of heterogeneity and horizontal pleiotropy under normal conditions, and it has not been challenged by the BWMR analysis. Specifically, these phenotypes include: Switched memory B cell Absolute Count (IVW OR\u0026thinsp;=\u0026thinsp;0.979, 95% CI\u0026thinsp;=\u0026thinsp;0.958\u0026thinsp;~\u0026thinsp;0.999, P\u0026thinsp;=\u0026thinsp;0.041); IgD- CD38dim B cell %lymphocyte (IVW OR\u0026thinsp;=\u0026thinsp;0.978, 95% CI\u0026thinsp;=\u0026thinsp;0.959\u0026thinsp;~\u0026thinsp;0.999, P\u0026thinsp;=\u0026thinsp;0.036); IgD- CD38dim B cell %B cell (IVW OR\u0026thinsp;=\u0026thinsp;0.978, 95% CI\u0026thinsp;=\u0026thinsp;0.958\u0026thinsp;~\u0026thinsp;0.999, P\u0026thinsp;=\u0026thinsp;0.041); IgD- CD38dim B cell Absolute Count (IVW OR\u0026thinsp;=\u0026thinsp;0.975, 95% CI\u0026thinsp;=\u0026thinsp;0.955\u0026thinsp;~\u0026thinsp;0.995, P\u0026thinsp;=\u0026thinsp;0.013); BAFF-R on IgD- CD38\u0026thinsp;+\u0026thinsp;B cell (IVW OR\u0026thinsp;=\u0026thinsp;0.973, 95% CI\u0026thinsp;=\u0026thinsp;0.952\u0026thinsp;~\u0026thinsp;0.994, P\u0026thinsp;=\u0026thinsp;0.013); CD19 on IgD- CD24- B cell (IVW OR\u0026thinsp;=\u0026thinsp;0.975, 95% CI\u0026thinsp;=\u0026thinsp;0.954\u0026thinsp;~\u0026thinsp;0.995, P\u0026thinsp;=\u0026thinsp;0.015); CD19 on IgD- CD27- B cell (IVW OR\u0026thinsp;=\u0026thinsp;0.975, 95% CI\u0026thinsp;=\u0026thinsp;0.954\u0026thinsp;~\u0026thinsp;0.997, P\u0026thinsp;=\u0026thinsp;0.023); CD24 on IgD\u0026thinsp;+\u0026thinsp;CD24\u0026thinsp;+\u0026thinsp;B cell (IVW OR\u0026thinsp;=\u0026thinsp;0.979, 95% CI\u0026thinsp;=\u0026thinsp;0.958\u0026thinsp;~\u0026thinsp;1.000, P\u0026thinsp;=\u0026thinsp;0.047); CD24 on IgD- CD38dim B cell (IVW OR\u0026thinsp;=\u0026thinsp;0.977, 95% CI\u0026thinsp;=\u0026thinsp;0.956\u0026thinsp;~\u0026thinsp;0.998, P\u0026thinsp;=\u0026thinsp;0.036) (Fig. 5). The results from sensitivity analyses indicate that our conclusions are reliable; detailed information can be found in the Supplemental Material 2.\u003c/p\u003e\n \u003cp\u003eOur study demonstrates that there is an interaction between B-cell phenotypes and the progression of GBM. Specifically, two B-cell phenotypes have a causal influence on GBM. Concurrently, GBM exhibits causal associations with nine different B-cell phenotypes. However, bidirectional MR analysis also confirms that there are no overlapping B-cell phenotypes between these two factors, thus eliminating the interference of reverse causality. The conclusions supported by sensitivity analyses are robust. Therefore, this study is the first to confirm an interaction between B-cell phenotypes and GBM using MR and BWMR methods.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study represents a groundbreaking approach by leveraging GWAS data associated with 190 B-cell phenotypes to explore their complex associations with GBM at the level of summary statistics. To our knowledge, this is the first study to employ bidirectional two-sample MR combined with BWMR to investigate the potential causal relationships between multiple B-cell phenotypes and GBM. Following robustness validation, our study confirms significant causal associations between two specific B-cell phenotypes and the risk of developing GBM. These phenotypes include IgD\u0026thinsp;+\u0026thinsp;CD24\u0026thinsp;+\u0026thinsp;B cell %B cell, CD38 on Plasma Blast-Plasma Cell (CD38 on PB/PC). Understanding the dynamic interactions between B-cell phenotypes and GBM not only improves our diagnostic abilities but also has the potential to facilitate the development of immunotherapeutic intervention strategies aimed at disrupting key pathways related to GBM tumor initiation and progression.\u003c/p\u003e \u003cp\u003eFor most patients, the causes of GBM remain largely unknown. Approximately 5% of individuals carry germline mutations that make them predisposed to various types of cancers, including GBM[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Other potential risk factors, such as exposure to mobile phones, viral triggers (such as cytomegalovirus), and other environmental exposures, are under investigation, but the extent of their influence remains unclear[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Although GBM was among the first solid tumors to undergo comprehensive molecular analysis, targeted therapies have provided limited benefits in clinical trials, prompting the development of other therapeutic approaches, including immunotherapies[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The latest advancements in immunotherapy, such as immune checkpoint inhibitors and chimeric antigen receptor (CAR) T cells, have significantly improved the prognosis and overall survival of patients with many types of cancer. However, these benefits have not yet been observed in GBM[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. With the significant contributions by Lee-Chang et al. in utilizing activated B cells as a cell-based vaccine against GBM, it highlights the potential of B cells as a new horizon for immunotherapy.\u003c/p\u003e \u003cp\u003eOur study reveals associations between two B-cell phenotypes and GBM using MR and BWMR analyses. The first B-cell phenotype that is associated with GBM risk, IgD\u0026thinsp;+\u0026thinsp;CD24\u0026thinsp;+\u0026thinsp;B cell %B cell, represents a lower risk. It represents the proportion of IgD\u0026thinsp;+\u0026thinsp;CD24\u0026thinsp;+\u0026thinsp;cells in B cells. This type of B cells is characterized by the expression of immunoglobulin D (IgD) and CD24 on their surface. These B cells are typically naive or memory B cells that have not yet been activated to produce antibodies. The association between this particular B-cell phenotype and GBM suggests that a higher proportion of IgD\u0026thinsp;+\u0026thinsp;CD24\u0026thinsp;+\u0026thinsp;B cells in the blood might be associated with a reduced risk of developing GBM. This finding could be significant for understanding the immunological landscape of individuals at risk for GBM and may have implications for potential therapeutic strategies or biomarker development. Furthermore, Yuan Cheng et al., through an analysis of 731 immunocyte phenotypes related to GBM risk, have confirmed that an increased absolute count of IgD\u0026thinsp;+\u0026thinsp;CD24\u0026thinsp;+\u0026thinsp;B cells is associated with a heightened risk of GBM[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Thus, their findings align with ours, indicating without doubt that IgD\u0026thinsp;+\u0026thinsp;CD24\u0026thinsp;+\u0026thinsp;B cells are significantly involved in the pathogenesis of GBM, making this area a key focus for future research endeavors.\u003c/p\u003e \u003cp\u003eFurthermore, our study results suggest that an increased proportion of CD38-positive plasma blasts within plasma cells is causally associated with a reduced risk of GBM. CD38 is a type II transmembrane glycoprotein with cyclase and hydrolase activities, representing a bifunctional extracellular enzyme involved in nucleotide metabolism[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Recent studies have elucidated a strong correlation between CD38 expression levels and the elevation of adenosine, an essential immunosuppressive factor within the TME of solid tumors[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In certain instances, elevated CD38 expression in these tumors can lead to the production of substantial amounts of adenosine within the TME. This adenosine then attracts immune suppressive cells such as regulatory T cells (Treg), myeloid-derived suppressor cells (MDSCs), and cancer-associated fibroblasts (CAF) into the TME, thereby dampening immune system activity[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. However, research on melanoma, gliomas, esophageal cancer, cervical cancer, and lung cancer indicates that CD38 functions as a tumor-promoting factor in these diseases. Therefore, further investigation is warranted to elucidate its role and potential therapeutic implications[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study presents some encouraging findings, but also some limitations. Firstly, the IgD\u0026thinsp;+\u0026thinsp;CD24+ %B cell pleiotropy test result was less than 0.05, indicating the presence of pleiotropy. However, further SNP screening using MR-PRESSO did not reveal any need to remove SNPs. There are many possible reasons for this result, such as sample size, limited statistical power, the complexity of pleiotropy, and methodological limitations. Nevertheless, we recommend that readers approach the results with caution. Secondly, our study is based on aggregated-level analysis and cannot perform subgroup analyses or discussions at an individual level, thus unable to explain individual phenomena. Additionally, our study only explores GBM risk factors from the perspective of B-cell phenotypes and does not represent the actual situation because of the complex pathophysiological mechanisms of GBM. Finally, conclusions based on data analysis should be treated with caution and are still worthy of experimental verification through pathology and large-scale clinical studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our research utilized bidirectional two-sample MR combined with BWMR to identify potential links between B-cell phenotypes and GBM. Through MR analysis, our findings emphasize the significance of dynamic interactions between B-cell phenotypes represented by IgD\u0026thinsp;+\u0026thinsp;CD24+ %B cell and GBM. These findings not only enhance our understanding of the complex physiological and pathological conditions associated with GBM, but also provide new theoretical support for the development of targeted immunotherapy intervention strategies. Future experimental validation may lead to improvements in GBM treatment approaches and clinical management protocols.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGBM \u0026nbsp; \u0026nbsp;Glioblastoma\u003c/p\u003e\n\u003cp\u003eMR \u0026nbsp; \u0026nbsp;Mendelian randomization\u003c/p\u003e\n\u003cp\u003eBWMR \u0026nbsp; \u0026nbsp;Bayesian weighted Mendelian randomization\u003c/p\u003e\n\u003cp\u003eGWAS \u0026nbsp; \u0026nbsp;Genome-wide association study\u003c/p\u003e\n\u003cp\u003eBBB \u0026nbsp; \u0026nbsp;Blood-brain barrier\u003c/p\u003e\n\u003cp\u003eTME \u0026nbsp; \u0026nbsp;Tumor microenvironment\u003c/p\u003e\n\u003cp\u003eIVs \u0026nbsp; \u0026nbsp;Instrumental variables\u003c/p\u003e\n\u003cp\u003eSNPs \u0026nbsp; \u0026nbsp;Single-nucleotide polymorphisms\u003c/p\u003e\n\u003cp\u003eLD \u0026nbsp; \u0026nbsp;Linkage disequilibrium\u003c/p\u003e\n\u003cp\u003eIVW \u0026nbsp; \u0026nbsp; Inverse variance weighted\u003c/p\u003e\n\u003cp\u003eCD38 on PB/PC \u0026nbsp; \u0026nbsp;CD38 on Plasma Blast-Plasma Cell\u003c/p\u003e\n\u003cp\u003eCAR \u0026nbsp; \u0026nbsp; Chimeric antigen receptor\u003c/p\u003e\n\u003cp\u003eIgD \u0026nbsp; \u0026nbsp;Immunoglobulin D\u003c/p\u003e\n\u003cp\u003eTreg \u0026nbsp; \u0026nbsp; Regulatory T cells\u003c/p\u003e\n\u003cp\u003eMDSCs \u0026nbsp; \u0026nbsp; Myeloid-derived suppressor cells\u003c/p\u003e\n\u003cp\u003eCAF \u0026nbsp; \u0026nbsp; Cancer-associated fibroblasts\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the participants and researchers who contributed and collected data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHao Yuan: Responsible for conceptualization, data curation, formal analysis, investigation, methodology, and project administration; also contributed to validation, and drafted the original manuscript. Shiyan Weng: Engaged in formal analysis, investigation, and contributed to drafting the original manuscript. Xin Feng: Involved in conceptualization, data curation, investigation, and provided critical review and editing of the manuscript. Chuanzhi Duan: Played a role in conceptualization, data curation, formal analysis, secured funding, conducted investigation, developed methodology, and managed project administration; also responsible for validation, and critically reviewed and edited the manuscript. All authors have reviewed and provided feedback on the manuscript, and have approved the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Research Foundation for the Natural Science Foundation of China No.82201427.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/ supplementary material, further inquiries can be directed to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analyzed in this study were sourced from publicly available databases. Ethical approval was secured for each cohort, and informed consent was obtained from all participants prior to their involvement. Therefore, there is no need to provide additional ethical statements for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMyers BL, Brayer KJ, Paez-Beltran LE, Villicana E, Keith MS, Suzuki H, Newville J, Anderson RH, Lo Y, Mertz CM, Kollipara RK, Borromeo MD, Lu QR, Bachoo RM, Johnson JE, Vue TY. Transcription factors ASCL1 and OLIG2 drive glioblastoma initiation and co-regulate tumor cell types and migration. 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Cells. 2019 Dec 24;9(1):52. doi: 10.3390/cells9010052. PMID: 31878283; PMCID: PMC7016859. \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":"Glioblastoma (GBM), Mendelian randomization (MR), Bayesian weighted Mendelian randomization (BWMR), B cell","lastPublishedDoi":"10.21203/rs.3.rs-6212888/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6212888/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGlioblastoma (GBM) represents an exceptionally aggressive form of primary malignant brain neoplasm, distinguished by its rapid growth kinetics, unfavorable prognostic indicators, and associated high mortality rates. To date, the exploration of B-cell involvement in GBM remains relatively underexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe two-sample Mendelian Randomization (MR) analysis was used to assess the causal relationship between the 190 B cell phenotypes and GBM. Bayesian Weighted Mendelian Randomization (BWMR) was also employed to complement MR analysis, and sensitivity analyses were conducted to assess the robustness of the results.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eOur results demonstrate a causal association between two B-cell phenotypes and the risk of GBM. Specifically, IgD\u0026thinsp;+\u0026thinsp;CD24\u0026thinsp;+\u0026thinsp;B cell %B cell is significantly associated with a reduced risk of GBM (IVW OR\u0026thinsp;=\u0026thinsp;0.676, 95% CI\u0026thinsp;=\u0026thinsp;0.507\u0026ndash;0.901, P\u003csub\u003eivw\u003c/sub\u003e = 0.008); and CD38 on Plasma Blast-Plasma Cell is also significantly associated with a lower risk of GBM (IVW OR\u0026thinsp;=\u0026thinsp;0.789, 95% CI\u0026thinsp;=\u0026thinsp;0.626\u0026ndash;0.995, P\u003csub\u003eivw\u003c/sub\u003e = 0.045).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study suggests a potential connection between B cell phenotypes and GBM through bidirectional two-sample MR combined with BWMR analysis, providing a preliminary basis for future research.\u003c/p\u003e","manuscriptTitle":"Association of B cells and the risk of glioblastoma: a bidirectional two sample mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-21 08:43:25","doi":"10.21203/rs.3.rs-6212888/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":"f2e530da-cbaf-43d3-af17-f08cd9398378","owner":[],"postedDate":"March 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-12T11:08:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-21 08:43:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6212888","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6212888","identity":"rs-6212888","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}