Circulating immune cells and multiple myeloma: A mendelian randomization study

Circulating immune cells and multiple myeloma: A mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Circulating immune cells and multiple myeloma: A mendelian randomization study Zexin Zhang, Gulizeba Muhetaer, Zhikai Xiahou, Jun Han, Yafeng Song This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4013936/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Multiple myeloma (MM) is a malignant proliferative disease of plasma cells. Although plasma cells play an important role in this process, their relationship with other circulating immune cells has not been systematically investigated. Methods The single nucleotide polymorphism (SNP) data of 721 circulating immune cells and MM were obtained from GWAS summary data. After meeting the three assumptions of mendelian randomization (MR), we used inverse-variance weighted (IVW) as the main method to evaluate the causal association between the two. For positive results, we used multivariable mendelian randomization (MVMR) for adjustion and performed reverse MR analysis to assess the stability of the results. Results A total of 3 circulating immune cells are causally related to MM. Among them, Naive CD8 + T cell %T cell (IVW OR: 1.00123, 95%CI: 1.00015–1.00231, P value: 0.02518), Natural Killer T Absolute Count (IVW OR: 1.00062, 95%CI: 1.00006-1.00118, P value :0.03075) was a risk factor for MM, and CD28 + CD45RA + CD8 + T cell %T cell (IVW OR: 0.99993, 95%CI: 0.99987-1.00000, P value: 0.03549) was a protective factor for MM. This result remained stable in the MVMR analysis. Among them, Naive CD8 + T cell %T cell (IVW OR: 1.00200, 95%CI: 1.00058–1.00343, P value: 0.00586), Natural Killer T Absolute Count (IVW OR: 1.00051, 95%CI: 1.00002-1.00101, P value : 0.04225) was a risk factor for MM, and CD28 + CD45RA + CD8 + T cell %T cell (IVW OR: 0.99913, 95%CI: 0.99860–0.99967, P value: 0.00158) was a protective factor for MM. No reverse causal relationship was found between MM and these 3 circulating immune cells. Conclusions There was a causal association between 3 circulating immune cells and MM, which may provide a new strategy for the prevention and treatment of MM. Further randomized controlled studies are still needed to further elucidate their relationship. circulating immune cells multiple myeloma mendelian randomization study casual association Figures Figure 1 Figure 2 Background Multiple myeloma (MM) is considered one of the primary neoplasms affecting the hematologic system, accounting for approximately 10% of hematologic malignancies and 1% of all malignancies. Moreover, MM has emerged as the second most prevalent tumor of the hematologic system in numerous countries, surpassing leukemia ( 1 ). The incidence of MM continues to escalate as the aging process accelerates. Notably, in Europe and the U.S., the incidence rate of MM has exceeded that of leukemia, positioning it as the second most common hematologic tumor. Conversely, in China, MM ranks third in terms of incidence rate, with lymphoma being the most prevalent, followed by leukemia ( 2 , 3 ). Based on statistical data, the incidence rate of MM in China is approximately 2 per 100,000 individuals. It is projected that the number of newly diagnosed MM cases in China will reach 114,000 in 2020, and is expected to increase to 167,000 in 2024 and 266,000 in 2030. These estimates indicate a compound annual growth rate (CAGR) of 10.4% during the period of 2020–2024, and 8.1% during the period of 2024–2030 ( 4 ) . MM is classified as a malignant tumor originating from plasma cells, with the abnormal proliferation of these malignant cells primarily occurring in the bone marrow. However, it can also impact the peripheral blood and other extramedullary sites ( 5 ). The primary characteristic of MM is the secretion of monoclonal immunoglobulins or their fragments (M proteins) by abnormal plasma cells, resulting in the development of hypercalcemia, renal dysfunction, anemia, and bone deterioration. These manifestations are commonly known as the CRAB features ( 6 ). Additional clinical manifestations of MM encompass infection, hemorrhage, hyperviscosity syndrome, extramedullary infiltration, amyloidosis, and neurological impairment ( 7 ). Presently, the primary therapeutic approach for MM entails the administration of chemotherapy in conjunction with hematopoietic stem cell transplantation, utilizing proteasome inhibitors (PIs), immunomodulatory drugs (IMiD), daratumumab, and other relevant medications ( 8 ). PIs such as Isazomib, Carfilzomib, and Bortezomib, are currently the primary therapeutic agents for MM ( 9 ). MM cells exhibit high sensitivity to the inhibition of 26S proteasome, which subsequently triggers various downstream effects. These effects include the inhibition of NF-κB signaling, accumulation of misfolded and unfolded proteins, induction of endoplasmic reticulum stress, activation of the unfolded protein response, downregulation of growth factor receptors, suppression of adhesion molecule expression, and inhibition of angiogenesis ( 10 ). IMiDs, such as thalidomide, lenalidomide, and pomalidomide, exhibit diverse activities encompassing anti-angiogenic, cytotoxic, and immunomodulatory effects, while also regulating the ubiquitination of crucial transcription factors, namely IKZF1 and IKZF3 ( 10 , 11 ). Despite their widespread application in MM therapy, the efficacy of these drugs remains somewhat constrained, accompanied by significant adverse effects, thereby significantly impeding the extension of overall survival in MM patients. Therefore, there is an urgent need to find new treatments. Research has demonstrated a significant correlation between the progression of MM and the intricate composition of the bone marrow microenvironment (BMME), encompassing both cellular and non-cellular constituents ( 12 ). By means of soluble factor release, intercellular communication, and exosome production, these components exert dual roles in either enhancing or suppressing MM immunity, thereby exerting regulatory influence over the initiation and progression of MM ( 13 ). Therefore, studying the role of different immune cells in MM is beneficial to the development of new treatments. T lymphocytes comprise the predominant subset of lymphocytes and play a crucial role in mediating targeted cellular immunity. Wang et.al. evealed that the CD28 CD4 FoxP3 Treg-like cell subset represents a senescent regulatory T cell subset with limited suppressive capabilities, potentially susceptible to impairment during the development of myelomagenesis ( 14 ). Lad et.al. aimed to characterize the T-cell subsets, including Treg function, in the blood and marrow compartments of individuals with monoclonal gammopathy of undetermined significance (MGUS) and MM. However, their findings revealed no significant clinical correlation between any of the T-cell subsets and the time to progression in MGUS or the progression-free survival in MM ( 15 ). Alrasheed et.al. conducted a study with the objective of characterizing the presence of marrow-infiltrating T cells in newly diagnosed patients and investigating their correlation with the outcomes of initial therapy. The study revealed that patients diagnosed with MM exhibited a higher prevalence of BM regulatory T cells (Tregs) compared to healthy donors. Furthermore, these Tregs demonstrated elevated expression levels of the activation marker CD25, and this finding suggests a potential role of Tregs in the pathogenesis and progression of MM ( 16 ). Furthermore, NK cells are large granular lymphocytes that express CD16 and CD56 antigens and participate in nonspecific immunity by directly acting on target cells or secreting antibodies to kill pathogenic microorganisms ( 17 ). Braquet et.al. suggested that increased peripheral blood NK and CD3 + CD56 + T cells may be an auto-protective immune mechanism ( 18 ). Mendelian randomization (MR) is a methodological approach that employs genetic variants as instrumental variables to ascertain the causal impact of a particular risk factor on an outcome ( 19 ). Went et.al. introduce a two-sample multivariable MR technique, utilizing Bayesian model averaging (MR-BMA), which is capable of accommodating high-throughput experiments ( 20 ). By leveraging summary data obtained from genome-wide association studies (GWASs) encompassing various phenotypes, a MR phenome-wide association study (PheWAS) can be conducted to identify factors that potentially influence the risk of MM. Therefore, in this study, we used two sample MR (TSMR) analysis to explore the causal association between 721 immune cells and MM. For immune cells that are causally associated with MM, we used reverse MR and multivariate MR (MVMR) analysis to strengthen the reliability of this result. Materials and Methods Data sources The SNP data of 721 circulating immune cells and MM were obtained from GWAS summary data ( https://gwas.mrcieu.ac.uk/ ). The GWAS summary data of 721 circulating immune cells was derived from the data published by Orrù V et al. in 2020( 21 ). In this study, the authors reported the impact of approximately 22 million variants on 731 immune cell signatures in a cohort of 3,757 Italian Sardinians. There were many types of these immune cells, including Lymphoid cells, Tregs, Monocytes, etc. More importantly, these cell surface molecular markers have also been systematically investigated, which was very beneficial to our understanding of the biological regulatory mechanisms of immune diseases. The SNP data of MM is from UK Biobank, and these samples were also from Europe. In this study, 372,617 samples were included, including 372,016 control samples and 601 disease samples. The samples included both male and female by gender. Finally, a total of 8,615,746 SNPs were obtained from these samples. Screening of instrumental variables (IVs) For the screening of IVs, MR analysis usually has strict requirements, which can ensure that the screened IVs are reliable for analysis instead of exposures and outcomes( 22 ). For SNP screening of 721 circulating immune cells, we set the threshold p1 = 5e-08 to ensure a high correlation between SNP and exposures. Linkage Disequilibrium sets r2 = 0.001 and kb = 10000, which ensured that the genes can be inherited independently. When r2 = 0 indicates no linkage disequilibrium, and r2 = 1, this indicates complete linkage disequilibrium( 23 ). At the same time, this part of SNPs was extracted from the MM data as the IVs of the outcome. It is worth noting that in the IVs screening of outcomes, we also set p2 = 5e-05 to filter out SNPs that are highly correlated with outcome. When combining data for exposures and outcome, we also excluded SNPs with palindromes. Steiger test was next used to exclude IVs with reverse causality. The F test was used to screen for strong IVs, which are defined as F > 10. The F test formula is as follow: (24) Among them, the Beta value represents the effect size of the exposure instrument, SE represents the standard error of the exposure instrument, and the F-value is obtained by squaring the result of Beta divided by SE. Statistic analysis Two sample MR (TSMR) analysis was performed on the merged SNP data using the TwoSampleMR package in R language. The results evaluated by the IVW method are used as the main evaluation index of causal association. IVW is an important method for MR causal association assessment. Its characteristic is that the existence of the intercept term is not considered during regression and the inverse of the outcome variance is used as the weight for fitting. In addition, MR Egger, Weighted model and Weighted median model were used as supplements to the results of the IVW method. Similar to the IVW method, MR Egger also uses the inverse of the outcome variance as a weight for fitting. The biggest difference is that the intercept term is considered during regression, which allows MR Egger's aspect to be used to evaluate the presence of pleiotropy at the same time. If the beta directions of these 4 methods are consistent in the MR analysis results and the p value is < 0.05, it proves that the results are stable. To ensure the robustness of MR analysis, we systematically evaluate the final tools used and the results. The results were tested for heterogeneity using the mr_heterogeneity function of the TwosampleMR package, for pleiotropy testing using the mr_pleiotropy_test function and MR presso test, and for sensitivity analysis using mr_leaveoneout. For results where there is no heterogeneity, we use a fixed-effects model for evaluation, and conversely, a random-effects model is used. Pleiotropy was not allowed in this study, and it failed to meet the three core assumptions of MR. Sensitivity analysis evaluates the change in MR results by eliminating one of the IVs. The impact of the IV on the results needs to be controlled within an acceptable range. A reversal MR analysis was performed to evaluate the causal association between MM and circulating immune cells with positive results. In order to further clarify the role of different types of circulating immune cells in MM and consolidate the reliability of TSMR analysis results, MVMR was used to analyze circulating immune cells with positive results. First, we extracted SNPs shared between multiple circulating immune cells. Then further extract this part of SNPs from the results. The data were used for MVMR analysis after removing linkage disequilibrium. Similar to TSMR, the IVW method serves as an assessment of the primary outcome of MVMR. MR Egger, Lasso and Weighted median are three supplementary methods to further consolidate the reliability of the IVW method. Heterogeneity testing was performed using the IVW method, and pleiotropic effects testing was evaluated using Egger intercept. Results Characteristics of SNPs Based on the above selection criteria, we extracted SNPs from the exposures and outcome for the current analysis. Finally, 3 circulating immune cells showed causally associated with MM. 2 SNPs came from Naive CD8 + T cell %T cell, 5 SNPs came from CD28 + CD45RA + CD8 + T cell %T cell, and 5 SNPs came from Natural Killer T Absolute Count and were finally included in the MR analysis. The F values of these instrumental variables were all > 10, indicating that there were no weak instrumental variables. The detailed information of IVs was showed in Supplement Table 1. TSMR analysis The IVW results indicated that a total of 3 circulating immune cells are causally associated with MM. Specially, we only found that CD28 + CD45RA + CD8 + T cell %T cell (IVW OR: 0.99993, 95%CI: 0.99987-1.00000, P value: 0.03549) is a protective factor for MM. While Naive CD8 + T cell %T cell (IVW OR: 1.00123, 95%CI: 1.00015–1.00231, P value: 0.02518), Natural Killer T Absolute Count (IVW OR: 1.00062, 95%CI: 1.00006-1.00118, P value : 0.03075) is a risk factor for MM. Figure 1 . Supplement Table 2. This result can also be reflected in the scatter plot of MR analysis. The scatter plot provided the effect of each SNP on 3 circulating immune cells and MM, which can visually see the impact of exposure on the outcome. Supplement Fig. 1 . The Forest plot showed the MR effect size of 3 circulating immune cells for MM for each IVs. MR Egger and IVW calculated the MR effect sizes of all IVs and displayed them in red intervals. Supplement Fig. 2 . Heterogeneity testing demonstrated that the included IVs were homogeneous, although the funnel plot did not visually show their symmetry, which may be due to the small number of IVs. Supplement Fig. 3. Supplement Table 3. Leave-one-out sensitivity analysis showed that the results remained robust when each IVs was eliminated stepwise. Supplement Fig. 4. No reverse causal relationship was found between MM and these 3 circulating immune cells. MVMR analysis In order to further clarify the role of different types of circulating immune cells in MM and consolidate the reliability of TSMR analysis results, MVMR was used to analyze circulating immune cells with positive results. 11 shared IVs were extracted among the 3 circulating immune cells. This results of TSMR remained stable in the MVMR analysis. Among them, Naive CD8 + T cell %T cell (IVW OR: 1.00200, 95%CI: 1.00058–1.00343, P value: 0.00586), Natural Killer T Absolute Count (IVW OR: 1.00051, 95%CI: 1.00002-1.00101, P value : 0.04225) was a risk factor for MM, and CD28 + CD45RA + CD8 + T cell %T cell (IVW OR: 0.99913, 95%CI: 0.99860–0.99967, P value: 0.00158) was a protective factor for MM. Figure 2 . MR Egger and IVW confirmed the absence of heterogeneity in IVs. The Egger intercept was very close to 0, and the P value is > 0.05, indicating that there was no horizontal pleiotropy. Discussion Based on a large amount of publicly available data, we explored the causal relationship between 721 circulating immune cells and MM. To the best of our knowledge, this is the first MR analysis to explore the causal relationship between multiple immune cells and MM. In this study, we found that among the three circulating immune cells causally associated with MM, the risk of MM decreased with increasing CD28 + CD45RA + CD8 + T cell %T cells (IVW OR: 0.99993, 95% CI: 0.99987-1.00000, p-value: 0. 03549) proportionally decreased, and furthermore, it is noteworthy that but with the increase in naïve CD8 + T-cell %T cells (IVW OR: 1.00200, 95% CI: 1.00058–1.00343, p-value: 0.00586) and natural killer T-cells ( IVW OR: 1.00051, 95% CI: 1.00002-1. 00101, p-value: 0.04225) ratios increased and the risk of MM was elevated. Finally, we used MR Egger and IVW to confirm the absence of heterogeneity in IVs. the Egger intercept was very close to 0, with a p-value greater than 0.05, indicating the absence of horizontal multidimensionality. This data conducted a TSMR analysis based on the results of a large published GWAS database with a large sample size of approximately 370,000 individuals, making it statistically efficient. As an emerging treatment method, immunotherapy has been used to improve the prognosis of MM. Tumor microenvironment (TME) plays a critical role in disease progression in MM. This requires us to elucidate as much as possible the role of each immune cell in MM. In most cases though, immune cells act as a double-edged sword. As our results showed, CD28 + CD45RA + CD8 + T cells %T cells played a protective role in MM, while naive CD8 + T-cell %T cells played a dangerous role in MM. Therefore, identifying CD8 + T cells with specific phenotypes was of great significance in the prevention and treatment of MM. Luoma S et al. ( 25 )used time-of-flight cytometry to study the immune profile of longitudinal bone marrow samples from newly diagnosed MM patients and found that the phenotype of the treatment-responsive group shifted toward CD8 + T cells expressing cytotoxic markers (CD45RA and CD57) and CD8 + naïve T cell abundance is low. Wang J et al. ( 14 )found that bone marrow-activated CD8 + T and NK cells were significantly increased in MM and found abnormalities in immune cell composition. Moreover, A number of activated PD-1 + CD8 T cells lacking CD28 were distinguished in MM patients. Zelle-Rieser C et al. ( 26 )found that T cell senescence was significantly enhanced and these T cells lacked the CD28 molecule on their surface. This phenotype was associated with lower proliferation capacity and impaired function. NK cells possess both cytotoxic and immunomodulatory capabilities, originating from the bone marrow and undergoing training to identify autologous MHC I molecules, thereby acquiring the capacity to differentiate between self and non-self. The presence of NK cells has been extensively documented in MM, rendering them a promising therapeutic avenue due to their aptitude for recognizing and eliminating surplus plasma cells generated in MM. Seymour F et al. found that in patients with newly diagnosed multiple myeloma (NDMM), NK cells undergo a phenotypic shift towards a CD56bright subtype that is dysfunctional in terms of cytotoxicity, displaying a specific reduction in cytokine production, which illustrated that in MM patients, highly expressed dysfunctional NK cell phenotype is one of the risk factors for MM( 27 ). In contrast, CD56dim, considered a more mature NK cell capable of releasing cytotoxic granules, was significantly reduced in MM( 28 ). Monoclonal gammopathy of unknown significance (MGUS) has the potential to transform into MM. Zavidij O et al. ( 29 )found through single-cell sequencing technology that an increase in the number of NK cells can often be observed in patients with MGUS, which suggested that the increase a specific phenotype of NK cells in the early stage may promote the transformation of MGUS into MM. The influence on the function of NK cells may be closely related to the expression of NKG2D and DNAM-1 ligands on myeloma cells( 30 ). Zhou L et al. ( 31 )found that CD73 is highly expressed in NK lymphocytes in bone marrowhe and peripheral blood isolated from MM patients, and CD73Dysregulates immune cells to participate in the formation of an immunosuppressive microenvironment. Conclusions In summary, our study employed TSMR, MVMR and reverse MR analysis to establish the causal association between three distinct types of circulating immune cells and MM, highlighting the intricate interplay between the immune system and MM. Additionally, our research effectively mitigated the influence of inevitable confounding variables, reverse causality, and other pertinent factors. Our discoveries offer a novel pathway for researchers to investigate the underlying biological mechanisms of MM and facilitate the exploration of early intervention and treatment strategies. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The data of this study were acquired from IEU Open GWAS project database. Competing interests The authors declared that they have no competing interests. Funding his study was supported by the National Natural Science Foundation of China (Grant no. 82071413). Authors' contributions ZZ: Conceptualization, Methodology, Writing original draft. Gulizeba: Writing original draft and Investigation. Zhikai and HJ: Investigation. SY: Writing-review & editing. All authors read and approved the final manuscript. Acknowledgements Not applicable. References de Jong MME, Kellermayer Z, Papazian N, Tahri S, Hofste Op Bruinink D, Hoogenboezem R, et al. The multiple myeloma microenvironment is defined by an inflammatory stromal cell landscape. Nat Immunol. 2021;22(6):769–80. 10.1038/s41590-021-00931-3 . Schutt J, Nagler T, Schenk T, Brioli A. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4013936","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276640084,"identity":"7ee2df68-4912-44a4-a47e-ce7875e2893a","order_by":0,"name":"Zexin Zhang","email":"","orcid":"","institution":"The second clinical school of Guanzhou university of Chinese medicine","correspondingAuthor":false,"prefix":"","firstName":"Zexin","middleName":"","lastName":"Zhang","suffix":""},{"id":276640085,"identity":"209995d7-b053-4fb5-ab5b-fd10f87c0c5f","order_by":1,"name":"Gulizeba Muhetaer","email":"","orcid":"","institution":"The second clinical 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Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYPACOSBmPgZhHyBOizEQs6WRrIXHjDgt8u29h1/z1BjImfOv+fbwZxuDHN+NBMbPBXi0GJw5l2bNc8zA2HLG2+3GvG0MxpI3EpilZ+DTIpFjZszD9idxw42z26QZ2xiAjAQ2Zh58DpsB0vLPoH7DjTPPJIEOqyeoheFGjvFj3jaDBIPzPWwSQIclGBDSYnDmjBnj3D4Dww032MyNec5JGM4887BZGq/D2nuMP7z5ZiBvcP7ws4c/ymzk+Y4nH/yM12HAKJQCK5BIAJNAzNiAXwMwoXz8AaL4DxBSOApGwSgYBSMVAABdeU02fvh5OAAAAABJRU5ErkJggg==","orcid":"","institution":"China Institute of Sport and Health Science, Beijing Sport University","correspondingAuthor":true,"prefix":"","firstName":"Yafeng","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2024-03-04 18:33:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4013936/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4013936/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52312005,"identity":"367bc66e-c232-4c1d-bd02-b79962fb99ba","added_by":"auto","created_at":"2024-03-08 21:51:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":102749,"visible":true,"origin":"","legend":"\u003cp\u003eThe IVW results of 3 circulating immune cells against MM.\u003c/p\u003e","description":"","filename":"figure100.png","url":"https://assets-eu.researchsquare.com/files/rs-4013936/v1/19ee05e9c73bdf1c1afcf35e.png"},{"id":52311626,"identity":"e804f8ea-2925-48b5-a36c-52278385bf43","added_by":"auto","created_at":"2024-03-08 21:43:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":199469,"visible":true,"origin":"","legend":"\u003cp\u003eThe MVMR results of 3 circulating immune cells against MM.\u003c/p\u003e","description":"","filename":"figure200.png","url":"https://assets-eu.researchsquare.com/files/rs-4013936/v1/495546f91f242261cd8e6f51.png"},{"id":53523733,"identity":"14d3b912-d514-404e-a787-f285f530edda","added_by":"auto","created_at":"2024-03-27 04:25:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":466250,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4013936/v1/4e3237ef-a1f0-43ce-822a-2a3880bd6e99.pdf"},{"id":52311629,"identity":"92e73213-0303-41a8-a70c-02fd70fa6473","added_by":"auto","created_at":"2024-03-08 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21:43:31","extension":"xls","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":29696,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable1.xls","url":"https://assets-eu.researchsquare.com/files/rs-4013936/v1/be2452a7d01102e6958936d9.xls"},{"id":52311632,"identity":"9a58ab94-8dd3-426d-9d29-8f2a9dc88bf2","added_by":"auto","created_at":"2024-03-08 21:43:31","extension":"xls","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":21504,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable2.xls","url":"https://assets-eu.researchsquare.com/files/rs-4013936/v1/e89d4d22c1e802ad7daed989.xls"},{"id":52311635,"identity":"97d55208-fc11-4842-a9ff-bb7e5bdc8907","added_by":"auto","created_at":"2024-03-08 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Moreover, MM has emerged as the second most prevalent tumor of the hematologic system in numerous countries, surpassing leukemia (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The incidence of MM continues to escalate as the aging process accelerates. Notably, in Europe and the U.S., the incidence rate of MM has exceeded that of leukemia, positioning it as the second most common hematologic tumor. Conversely, in China, MM ranks third in terms of incidence rate, with lymphoma being the most prevalent, followed by leukemia (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Based on statistical data, the incidence rate of MM in China is approximately 2 per 100,000 individuals. It is projected that the number of newly diagnosed MM cases in China will reach 114,000 in 2020, and is expected to increase to 167,000 in 2024 and 266,000 in 2030. These estimates indicate a compound annual growth rate (CAGR) of 10.4% during the period of 2020\u0026ndash;2024, and 8.1% during the period of 2024\u0026ndash;2030 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eMM is classified as a malignant tumor originating from plasma cells, with the abnormal proliferation of these malignant cells primarily occurring in the bone marrow. However, it can also impact the peripheral blood and other extramedullary sites (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The primary characteristic of MM is the secretion of monoclonal immunoglobulins or their fragments (M proteins) by abnormal plasma cells, resulting in the development of hypercalcemia, renal dysfunction, anemia, and bone deterioration. These manifestations are commonly known as the CRAB features (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Additional clinical manifestations of MM encompass infection, hemorrhage, hyperviscosity syndrome, extramedullary infiltration, amyloidosis, and neurological impairment (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Presently, the primary therapeutic approach for MM entails the administration of chemotherapy in conjunction with hematopoietic stem cell transplantation, utilizing proteasome inhibitors (PIs), immunomodulatory drugs (IMiD), daratumumab, and other relevant medications (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). PIs such as Isazomib, Carfilzomib, and Bortezomib, are currently the primary therapeutic agents for MM (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). MM cells exhibit high sensitivity to the inhibition of 26S proteasome, which subsequently triggers various downstream effects. These effects include the inhibition of NF-κB signaling, accumulation of misfolded and unfolded proteins, induction of endoplasmic reticulum stress, activation of the unfolded protein response, downregulation of growth factor receptors, suppression of adhesion molecule expression, and inhibition of angiogenesis (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). IMiDs, such as thalidomide, lenalidomide, and pomalidomide, exhibit diverse activities encompassing anti-angiogenic, cytotoxic, and immunomodulatory effects, while also regulating the ubiquitination of crucial transcription factors, namely IKZF1 and IKZF3 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Despite their widespread application in MM therapy, the efficacy of these drugs remains somewhat constrained, accompanied by significant adverse effects, thereby significantly impeding the extension of overall survival in MM patients. Therefore, there is an urgent need to find new treatments.\u003c/p\u003e \u003cp\u003eResearch has demonstrated a significant correlation between the progression of MM and the intricate composition of the bone marrow microenvironment (BMME), encompassing both cellular and non-cellular constituents (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). By means of soluble factor release, intercellular communication, and exosome production, these components exert dual roles in either enhancing or suppressing MM immunity, thereby exerting regulatory influence over the initiation and progression of MM (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Therefore, studying the role of different immune cells in MM is beneficial to the development of new treatments.\u003c/p\u003e \u003cp\u003eT lymphocytes comprise the predominant subset of lymphocytes and play a crucial role in mediating targeted cellular immunity. Wang et.al. evealed that the CD28 CD4 FoxP3 Treg-like cell subset represents a senescent regulatory T cell subset with limited suppressive capabilities, potentially susceptible to impairment during the development of myelomagenesis (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Lad et.al. aimed to characterize the T-cell subsets, including Treg function, in the blood and marrow compartments of individuals with monoclonal gammopathy of undetermined significance (MGUS) and MM. However, their findings revealed no significant clinical correlation between any of the T-cell subsets and the time to progression in MGUS or the progression-free survival in MM (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Alrasheed et.al. conducted a study with the objective of characterizing the presence of marrow-infiltrating T cells in newly diagnosed patients and investigating their correlation with the outcomes of initial therapy. The study revealed that patients diagnosed with MM exhibited a higher prevalence of BM regulatory T cells (Tregs) compared to healthy donors. Furthermore, these Tregs demonstrated elevated expression levels of the activation marker CD25, and this finding suggests a potential role of Tregs in the pathogenesis and progression of MM (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, NK cells are large granular lymphocytes that express CD16 and CD56 antigens and participate in nonspecific immunity by directly acting on target cells or secreting antibodies to kill pathogenic microorganisms (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Braquet et.al. suggested that increased peripheral blood NK and CD3\u0026thinsp;+\u0026thinsp;CD56\u0026thinsp;+\u0026thinsp;T cells may be an auto-protective immune mechanism (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a methodological approach that employs genetic variants as instrumental variables to ascertain the causal impact of a particular risk factor on an outcome (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Went et.al. introduce a two-sample multivariable MR technique, utilizing Bayesian model averaging (MR-BMA), which is capable of accommodating high-throughput experiments (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). By leveraging summary data obtained from genome-wide association studies (GWASs) encompassing various phenotypes, a MR phenome-wide association study (PheWAS) can be conducted to identify factors that potentially influence the risk of MM.\u003c/p\u003e \u003cp\u003eTherefore, in this study, we used two sample MR (TSMR) analysis to explore the causal association between 721 immune cells and MM. For immune cells that are causally associated with MM, we used reverse MR and multivariate MR (MVMR) analysis to strengthen the reliability of this result.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cb\u003eData sources\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe SNP data of 721 circulating immune cells and MM were obtained from GWAS summary data (\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 GWAS summary data of 721 circulating immune cells was derived from the data published by Orr\u0026ugrave; V et al. in 2020(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In this study, the authors reported the impact of approximately 22\u0026nbsp;million variants on 731 immune cell signatures in a cohort of 3,757 Italian Sardinians. There were many types of these immune cells, including Lymphoid cells, Tregs, Monocytes, etc. More importantly, these cell surface molecular markers have also been systematically investigated, which was very beneficial to our understanding of the biological regulatory mechanisms of immune diseases.\u003c/p\u003e \u003cp\u003eThe SNP data of MM is from UK Biobank, and these samples were also from Europe. In this study, 372,617 samples were included, including 372,016 control samples and 601 disease samples. The samples included both male and female by gender. Finally, a total of 8,615,746 SNPs were obtained from these samples.\u003c/p\u003e\n\u003ch3\u003eScreening of instrumental variables (IVs)\u003c/h3\u003e\n\u003cp\u003eFor the screening of IVs, MR analysis usually has strict requirements, which can ensure that the screened IVs are reliable for analysis instead of exposures and outcomes(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor SNP screening of 721 circulating immune cells, we set the threshold p1\u0026thinsp;=\u0026thinsp;5e-08 to ensure a high correlation between SNP and exposures. Linkage Disequilibrium sets r2\u0026thinsp;=\u0026thinsp;0.001 and kb\u0026thinsp;=\u0026thinsp;10000, which ensured that the genes can be inherited independently. When r2\u0026thinsp;=\u0026thinsp;0 indicates no linkage disequilibrium, and r2\u0026thinsp;=\u0026thinsp;1, this indicates complete linkage disequilibrium(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). At the same time, this part of SNPs was extracted from the MM data as the IVs of the outcome. It is worth noting that in the IVs screening of outcomes, we also set p2\u0026thinsp;=\u0026thinsp;5e-05 to filter out SNPs that are highly correlated with outcome. When combining data for exposures and outcome, we also excluded SNPs with palindromes. Steiger test was next used to exclude IVs with reverse causality. The F test was used to screen for strong IVs, which are defined as F\u0026thinsp;\u0026gt;\u0026thinsp;10. The F test formula is as follow:\u003c/p\u003e \u003cp\u003e\u003cimg width=\"109\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e (24)\u003c/p\u003e \u003cp\u003eAmong them, the Beta value represents the effect size of the exposure instrument, SE represents the standard error of the exposure instrument, and the F-value is obtained by squaring the result of Beta divided by SE.\u003c/p\u003e\n\u003ch3\u003eStatistic analysis\u003c/h3\u003e\n\u003cp\u003eTwo sample MR (TSMR) analysis was performed on the merged SNP data using the TwoSampleMR package in R language. The results evaluated by the IVW method are used as the main evaluation index of causal association. IVW is an important method for MR causal association assessment. Its characteristic is that the existence of the intercept term is not considered during regression and the inverse of the outcome variance is used as the weight for fitting. In addition, MR Egger, Weighted model and Weighted median model were used as supplements to the results of the IVW method. Similar to the IVW method, MR Egger also uses the inverse of the outcome variance as a weight for fitting. The biggest difference is that the intercept term is considered during regression, which allows MR Egger's aspect to be used to evaluate the presence of pleiotropy at the same time. If the beta directions of these 4 methods are consistent in the MR analysis results and the p value is \u0026lt;\u0026thinsp;0.05, it proves that the results are stable.\u003c/p\u003e \u003cp\u003eTo ensure the robustness of MR analysis, we systematically evaluate the final tools used and the results. The results were tested for heterogeneity using the mr_heterogeneity function of the TwosampleMR package, for pleiotropy testing using the mr_pleiotropy_test function and MR presso test, and for sensitivity analysis using mr_leaveoneout. For results where there is no heterogeneity, we use a fixed-effects model for evaluation, and conversely, a random-effects model is used. Pleiotropy was not allowed in this study, and it failed to meet the three core assumptions of MR. Sensitivity analysis evaluates the change in MR results by eliminating one of the IVs. The impact of the IV on the results needs to be controlled within an acceptable range. A reversal MR analysis was performed to evaluate the causal association between MM and circulating immune cells with positive results.\u003c/p\u003e \u003cp\u003eIn order to further clarify the role of different types of circulating immune cells in MM and consolidate the reliability of TSMR analysis results, MVMR was used to analyze circulating immune cells with positive results. First, we extracted SNPs shared between multiple circulating immune cells. Then further extract this part of SNPs from the results. The data were used for MVMR analysis after removing linkage disequilibrium. Similar to TSMR, the IVW method serves as an assessment of the primary outcome of MVMR. MR Egger, Lasso and Weighted median are three supplementary methods to further consolidate the reliability of the IVW method. Heterogeneity testing was performed using the IVW method, and pleiotropic effects testing was evaluated using Egger intercept.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eCharacteristics of SNPs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the above selection criteria, we extracted SNPs from the exposures and outcome for the current analysis. Finally, 3 circulating immune cells showed causally associated with MM. 2 SNPs came from Naive CD8\u0026thinsp;+\u0026thinsp;T cell %T cell, 5 SNPs came from CD28\u0026thinsp;+\u0026thinsp;CD45RA\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cell %T cell, and 5 SNPs came from Natural Killer T Absolute Count and were finally included in the MR analysis. The F values of these instrumental variables were all \u0026gt;\u0026thinsp;10, indicating that there were no weak instrumental variables. The detailed information of IVs was showed in \u003cb\u003eSupplement Table\u0026nbsp;1.\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eTSMR analysis\u003c/h3\u003e\n\u003cp\u003eThe IVW results indicated that a total of 3 circulating immune cells are causally associated with MM. Specially, we only found that CD28\u0026thinsp;+\u0026thinsp;CD45RA\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cell %T cell (IVW OR: 0.99993, 95%CI: 0.99987-1.00000, P value: 0.03549) is a protective factor for MM. While Naive CD8\u0026thinsp;+\u0026thinsp;T cell %T cell (IVW OR: 1.00123, 95%CI: 1.00015\u0026ndash;1.00231, P value: 0.02518), Natural Killer T Absolute Count (IVW OR: 1.00062, 95%CI: 1.00006-1.00118, P value : 0.03075) is a risk factor for MM. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cb\u003eSupplement Table\u0026nbsp;2.\u003c/b\u003e This result can also be reflected in the scatter plot of MR analysis. The scatter plot provided the effect of each SNP on 3 circulating immune cells and MM, which can visually see the impact of exposure on the outcome. \u003cb\u003eSupplement\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The Forest plot showed the MR effect size of 3 circulating immune cells for MM for each IVs. MR Egger and IVW calculated the MR effect sizes of all IVs and displayed them in red intervals. \u003cb\u003eSupplement\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Heterogeneity testing demonstrated that the included IVs were homogeneous, although the funnel plot did not visually show their symmetry, which may be due to the small number of IVs. \u003cb\u003eSupplement Fig.\u0026nbsp;3. Supplement Table\u0026nbsp;3.\u003c/b\u003e Leave-one-out sensitivity analysis showed that the results remained robust when each IVs was eliminated stepwise. \u003cb\u003eSupplement Fig.\u0026nbsp;4.\u003c/b\u003e No reverse causal relationship was found between MM and these 3 circulating immune cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMVMR analysis\u003c/h3\u003e\n\u003cp\u003eIn order to further clarify the role of different types of circulating immune cells in MM and consolidate the reliability of TSMR analysis results, MVMR was used to analyze circulating immune cells with positive results. 11 shared IVs were extracted among the 3 circulating immune cells. This results of TSMR remained stable in the MVMR analysis. Among them, Naive CD8\u0026thinsp;+\u0026thinsp;T cell %T cell (IVW OR: 1.00200, 95%CI: 1.00058\u0026ndash;1.00343, P value: 0.00586), Natural Killer T Absolute Count (IVW OR: 1.00051, 95%CI: 1.00002-1.00101, P value : 0.04225) was a risk factor for MM, and CD28\u0026thinsp;+\u0026thinsp;CD45RA\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cell %T cell (IVW OR: 0.99913, 95%CI: 0.99860\u0026ndash;0.99967, P value: 0.00158) was a protective factor for MM. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. MR Egger and IVW confirmed the absence of heterogeneity in IVs. The Egger intercept was very close to 0, and the P value is \u0026gt;\u0026thinsp;0.05, indicating that there was no horizontal pleiotropy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on a large amount of publicly available data, we explored the causal relationship between 721 circulating immune cells and MM. To the best of our knowledge, this is the first MR analysis to explore the causal relationship between multiple immune cells and MM.\u003c/p\u003e \u003cp\u003eIn this study, we found that among the three circulating immune cells causally associated with MM, the risk of MM decreased with increasing CD28\u0026thinsp;+\u0026thinsp;CD45RA\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cell %T cells (IVW OR: 0.99993, 95% CI: 0.99987-1.00000, p-value: 0. 03549) proportionally decreased, and furthermore, it is noteworthy that but with the increase in na\u0026iuml;ve CD8\u0026thinsp;+\u0026thinsp;T-cell %T cells (IVW OR: 1.00200, 95% CI: 1.00058\u0026ndash;1.00343, p-value: 0.00586) and natural killer T-cells ( IVW OR: 1.00051, 95% CI: 1.00002-1. 00101, p-value: 0.04225) ratios increased and the risk of MM was elevated. Finally, we used MR Egger and IVW to confirm the absence of heterogeneity in IVs. the Egger intercept was very close to 0, with a p-value greater than 0.05, indicating the absence of horizontal multidimensionality. This data conducted a TSMR analysis based on the results of a large published GWAS database with a large sample size of approximately 370,000 individuals, making it statistically efficient.\u003c/p\u003e \u003cp\u003eAs an emerging treatment method, immunotherapy has been used to improve the prognosis of MM. Tumor microenvironment (TME) plays a critical role in disease progression in MM. This requires us to elucidate as much as possible the role of each immune cell in MM. In most cases though, immune cells act as a double-edged sword. As our results showed, CD28\u0026thinsp;+\u0026thinsp;CD45RA\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells %T cells played a protective role in MM, while naive CD8\u0026thinsp;+\u0026thinsp;T-cell %T cells played a dangerous role in MM. Therefore, identifying CD8\u0026thinsp;+\u0026thinsp;T cells with specific phenotypes was of great significance in the prevention and treatment of MM.\u003c/p\u003e \u003cp\u003eLuoma S et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)used time-of-flight cytometry to study the immune profile of longitudinal bone marrow samples from newly diagnosed MM patients and found that the phenotype of the treatment-responsive group shifted toward CD8\u0026thinsp;+\u0026thinsp;T cells expressing cytotoxic markers (CD45RA and CD57) and CD8\u0026thinsp;+\u0026thinsp;na\u0026iuml;ve T cell abundance is low. Wang J et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)found that bone marrow-activated CD8\u0026thinsp;+\u0026thinsp;T and NK cells were significantly increased in MM and found abnormalities in immune cell composition. Moreover, A number of activated PD-1\u0026thinsp;+\u0026thinsp;CD8 T cells lacking CD28 were distinguished in MM patients. Zelle-Rieser C et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)found that T cell senescence was significantly enhanced and these T cells lacked the CD28 molecule on their surface. This phenotype was associated with lower proliferation capacity and impaired function.\u003c/p\u003e \u003cp\u003eNK cells possess both cytotoxic and immunomodulatory capabilities, originating from the bone marrow and undergoing training to identify autologous MHC I molecules, thereby acquiring the capacity to differentiate between self and non-self. The presence of NK cells has been extensively documented in MM, rendering them a promising therapeutic avenue due to their aptitude for recognizing and eliminating surplus plasma cells generated in MM. Seymour F et al. found that in patients with newly diagnosed multiple myeloma (NDMM), NK cells undergo a phenotypic shift towards a CD56bright subtype that is dysfunctional in terms of cytotoxicity, displaying a specific reduction in cytokine production, which illustrated that in MM patients, highly expressed dysfunctional NK cell phenotype is one of the risk factors for MM(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In contrast, CD56dim, considered a more mature NK cell capable of releasing cytotoxic granules, was significantly reduced in MM(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Monoclonal gammopathy of unknown significance (MGUS) has the potential to transform into MM. Zavidij O et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)found through single-cell sequencing technology that an increase in the number of NK cells can often be observed in patients with MGUS, which suggested that the increase a specific phenotype of NK cells in the early stage may promote the transformation of MGUS into MM. The influence on the function of NK cells may be closely related to the expression of NKG2D and DNAM-1 ligands on myeloma cells(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Zhou L et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)found that CD73 is highly expressed in NK lymphocytes in bone marrowhe and peripheral blood isolated from MM patients, and CD73Dysregulates immune cells to participate in the formation of an immunosuppressive microenvironment.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study employed TSMR, MVMR and reverse MR analysis to establish the causal association between three distinct types of circulating immune cells and MM, highlighting the intricate interplay between the immune system and MM. Additionally, our research effectively mitigated the influence of inevitable confounding variables, reverse causality, and other pertinent factors. Our discoveries offer a novel pathway for researchers to investigate the underlying biological mechanisms of MM and facilitate the exploration of early intervention and treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of this study were acquired from IEU Open GWAS project database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ehis study was supported by the National Natural Science Foundation of China (Grant no. 82071413).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZZ: Conceptualization, Methodology, Writing original draft. Gulizeba: Writing original draft and Investigation. Zhikai and HJ: Investigation. SY: Writing-review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ede Jong MME, Kellermayer Z, Papazian N, Tahri S, Hofste Op Bruinink D, Hoogenboezem R, et al. The multiple myeloma microenvironment is defined by an inflammatory stromal cell landscape. Nat Immunol. 2021;22(6):769\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41590-021-00931-3\u003c/span\u003e\u003cspan address=\"10.1038/s41590-021-00931-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchutt J, Nagler T, Schenk T, Brioli A. 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Cancer Manag Res. 2023;15(Print):1179\u0026ndash;322. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/CMAR.S411547\u003c/span\u003e\u003cspan address=\"10.2147/CMAR.S411547\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"circulating immune cells, multiple myeloma, mendelian randomization study, casual association","lastPublishedDoi":"10.21203/rs.3.rs-4013936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4013936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eMultiple myeloma (MM) is a malignant proliferative disease of plasma cells. Although plasma cells play an important role in this process, their relationship with other circulating immune cells has not been systematically investigated.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe single nucleotide polymorphism (SNP) data of 721 circulating immune cells and MM were obtained from GWAS summary data. After meeting the three assumptions of mendelian randomization (MR), we used inverse-variance weighted (IVW) as the main method to evaluate the causal association between the two. For positive results, we used multivariable mendelian randomization (MVMR) for adjustion and performed reverse MR analysis to assess the stability of the results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 3 circulating immune cells are causally related to MM. Among them, Naive CD8\u0026thinsp;+\u0026thinsp;T cell %T cell (IVW OR: 1.00123, 95%CI: 1.00015\u0026ndash;1.00231, P value: 0.02518), Natural Killer T Absolute Count (IVW OR: 1.00062, 95%CI: 1.00006-1.00118, P value :0.03075) was a risk factor for MM, and CD28\u0026thinsp;+\u0026thinsp;CD45RA\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cell %T cell (IVW OR: 0.99993, 95%CI: 0.99987-1.00000, P value: 0.03549) was a protective factor for MM. This result remained stable in the MVMR analysis. Among them, Naive CD8\u0026thinsp;+\u0026thinsp;T cell %T cell (IVW OR: 1.00200, 95%CI: 1.00058\u0026ndash;1.00343, P value: 0.00586), Natural Killer T Absolute Count (IVW OR: 1.00051, 95%CI: 1.00002-1.00101, P value : 0.04225) was a risk factor for MM, and CD28\u0026thinsp;+\u0026thinsp;CD45RA\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cell %T cell (IVW OR: 0.99913, 95%CI: 0.99860\u0026ndash;0.99967, P value: 0.00158) was a protective factor for MM. No reverse causal relationship was found between MM and these 3 circulating immune cells.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThere was a causal association between 3 circulating immune cells and MM, which may provide a new strategy for the prevention and treatment of MM. Further randomized controlled studies are still needed to further elucidate their relationship.\u003c/p\u003e","manuscriptTitle":"Circulating immune cells and multiple myeloma: A mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-08 21:43:26","doi":"10.21203/rs.3.rs-4013936/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":"ae024045-eb6a-4a66-b63b-715874bb130b","owner":[],"postedDate":"March 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-27T04:17:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-08 21:43:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4013936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4013936","identity":"rs-4013936","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}