Causal association of immune cell phenotypes with osteosarcoma and the mediation role of blood metabolites: A two-steps, two-samples Mendelian randomization study

Causal association of immune cell phenotypes with osteosarcoma and the mediation role of blood metabolites: A two-steps, two-samples 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 Causal association of immune cell phenotypes with osteosarcoma and the mediation role of blood metabolites: A two-steps, two-samples Mendelian randomization study Chicheng Niu, Qingyuan Xu, Weiwei Wang, Hao Li, Qiang Ding, Liang Guo, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4454204/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Immunogenic nature of osteosarcoma is well-established, but the precise roles of immune cells and the potential influence of blood metabolites on its advancement remain unclear. Methods : Two-step, two-sample Mendelian randomization (MR) strategy was employed to investigate causal relation between osteosarcoma risk and immune cell distribution, we sought to uncover and measure the potential mediating role of blood metabolites. Our analysis incorporated a diverse range of MR estimation techniques, encompassing inverse variance weighting (IVW), MR-Egger regression, weighted median, weighted mode, and simple mode. Additionally, we conducted sensitivity analyses to assess the reliability of our results. Results : MR analysis revealed that three immune cell phenotypes exhibited positive relation with osteosarcoma risk (CX3CR1 on CD14 - CD16 - , CD25 on CD45RA - CD4 not Treg, and CD45 on HLA DR + CD8 br ), while four immune cell phenotypes illustrated negative relation to osteosarcoma risk (BAFF - R on IgD + CD38 - unsw mem, CD20 on IgD - CD38 - , Naive CD4 + %T cell, and CD28 + CD45RA + CD8br %CD8 br ). Moreover, mediation MR analysis demonstrated causal effect of CX3CR1 on CD14 - CD16 - within monocyte panel on osteosarcoma (Total effect IVW: OR = 0.3330) was predominantly mediated by dimethyl sulfone (0.0288, constituting 8.70% of Total effect) and unidentified metabolite X-12680 (0.0524, constituting 15.74% of Total effect). Conclusions : This investigation unveiled a causal link between immune cells and osteosarcoma, potentially mediated by blood metabolites. Osteosarcoma Immune cells Blood metabolite Mendelian randomization Mediation Genome-wide association analysis Causal relationship Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Osteosarcoma (OS) stands as the prevailing primary bone-originated malignancy, marked by elevated mortality and disability rates. Initial manifestations of OS frequently manifest subtly, typically as localized pain and swelling. OS advances rapidly and is highly predisposed to metastasis, with lung metastasis emerging as leading cause of mortality among OS patients[ 1 ]. The pathogenesis of osteosarcoma is notably intricate, despite strides in medical technology, there has been little substantive progress in improving the 5-year survival rate[ 2 ]. Evidence underscores osteosarcoma's immunogenic nature[ 3 ], highlighting the critical importance of comprehending the interplay between OS and the immune system is crucial for delineating novel treatment strategies, especially for patients grappling with recurrent and metastatic OS[ 4 ]. Recent studies[ 4 ] propose that tumor cells engage in a perpetual struggle with the immune system. However, this equilibrium could be disrupted at certain junctures, rendering complete tumor eradication challenging once they develop. Immunotherapy holds promise for eliminating tumor cells within the body at the cellular level. The immune cell constituents within the tumor immune microenvironment encompass tumor-associated macrophages (TAM), B cells, NK cells, T cells, among others[ 5 ]. As per related investigations, maintaining balance between M1 (anti-tumor) and M2 (pro-tumor) macrophages holds pivotal significance in tumor progression[ 6 ]. M1-type TAMs contribute to inflammatory responses and exhibit inhibitory effects on osteosarcoma metastasis, whereas M2-type TAMs engage in wound healing and immune regulation, thereby fostering metastasis[ 6 ]. T cell infiltration assumes critical role in anti-tumor immunity within osteosarcoma. For instance, helper and cytotoxic T cells, affected by external factors (inhibitory cytokines, membrane surface regulatory molecules), can attenuate immune responses against cancer[ 7 ]. Metabolites represent stable end products from various metabolic pathways, making them valuable biomarkers for cancer diagnosis, prognosis, and treatment evaluation. Tumor cells display distinct metabolic profiles compared to their normal counterparts[ 8 ]. In a primary tumor model experiment, researchers identified 52 metabolites exhibiting significant differential expression compared to normal cells[ 9 ]. Research indicates that during tumor metastasis, there is a decrease in carbohydrate and amino acid metabolism decrease, while lipid metabolism intensifies. Moreover, a multitude of observational studies have unveiled numerous relations among immune cells, blood metabolites, and osteosarcoma, underscoring corresponding interconnection[ 10 ]. Studies indicate that modifications in cancer cell metabolism are in part attributable to various inflammatory and immune cells recruitment[ 11 ]. Additionally, aberrant metabolites or intermediates within cancer metabolism play pivotally in modulating immune cell proliferation, differentiation, activation, and function[ 12 ]. In essence, both metabolites and immune cells exert modulatory influences on osteosarcoma. Immune cells possess the capacity to perceive diverse signals within the tumor immune microenvironment, prompting specific immune responses. Growing evidence suggests that immune reactions correlate with significant alterations in tissue metabolism, encompassing heightened nutrient consumption, elevated oxygen utilization, and reactive nitrogen, oxygen intermediates generation[ 13 ]. Likewise, numerous metabolites present within the tumor microenvironment impact the differentiation and effector functions of immune cells[ 14 ]. However, most of these results stem from observational studies, potentially encumbered by limitations stemming from confounding variables and reverse causality[ 13 ]. Furthermore, the degree to which blood metabolites genetically influence osteosarcoma regulation through immune cell modulation remains uncertain. Mendelian randomization (MR) analysis stands as genetic epidemiology methodology leveraging data from extensive genome-wide association studies (GWAS) to pinpoint genetic variants related with particular traits or diseases[ 15 ]. Single nucleotide polymorphisms (SNPs) were employed as instrumental variables (IVs)[ 16 ], adhering to Mendel's law, where alleles undergo random allocation and fixation during embryonic formation. This characteristic diminishes the impact of confounding factors on the outcomes, bolstering the evidence for causal inference[ 17 ]. Following Mendelian genetic principles, MR analysis circumvents issues of reverse causality, simulating the design of randomized controlled trial[ 18 ]. Therefore, the research marks the first integrated assessment of reciprocal relationship between 731 immune cell phenotypes and osteosarcoma risk through MR analysis. Furthermore, a two-step approach was adopted to scrutinize the mediating impacts of 1400 blood metabolites on the linkage between immune cell phenotypes and osteosarcoma. Reverse MR analysis not only assesses the reverse causality between the two factors but also validates the positive causal relationships, thereby mitigating false positives risk caused by reverse causality in the positive results. Materials and methods No additional ethics approval was required as the pre-existing and published data were used in this reanalysis. Study design We embarked on a two-step MR analysis to probe relation between immune cell and genetically predicted OS risk, also to investigate if blood metabolites might mediate this relationship. The initial step involved evaluating the causal impact of immune cell phenotypes and blood metabolites on OS through two-sample MR, alongside the filtration of immune cell phenotypes and blood metabolites highly correlated with OS risk. Subsequently, we proceeded to gauge the causal impact of filtered immune cell phenotypes on filtered blood metabolites, alongside computing the proportion of mediators for each mediator concerning the effect of immune cell phenotypes on OS. To minimize the bias of population stratification, this study exclusively analyzed data from individuals of European ancestry. This study fulfills the three fundamental assumptions of MR analysis, as illustrated in Fig. 1 : Firstly, there exists a robust and strong correlation between the IVs and the exposure factor (relevance assumption). Secondly, confounding factors and IVs impacting "exposure-outcome" relation are mutually independent (independence assumption). Thirdly, genetic variation solely influence outcome through exposure factor instead of others (exclusion restriction assumption)[ 19 ]. Research design was depicted in Fig. 2 . Data sources Data for OS (finngen_R10_C3_OSTEOSARCOMA_EXALLC) were sourced from FinnGen comprising 64 OS cases and 314193 control cases. Diagnostic criteria for OS adhered to ICD-10 standard[ 20 ]. Data on 731 immune cell phenotypes (GCST90001391 to GCST90002121) and 1400 blood metabolites (GCST90199621 to GCST90201020) were from the GWAS Catalog [ 21 , 22 ]. All study subjects were from Europe descent. For data specifications, see Table 1 .. Table 1 Summary of studies and datasets utilized in the research Exposure/outcome Data source GWAS ID Population SNPs size Sample size Year osteosarcoma https://www.finngen.fi/en/access_results finngen_R10_C3_OSTEOSARCOMA_EXALLC European 21.3 million 314257 2023 731 immune cell phenotypes https://www.ebi.ac.uk/gwas/ GCST90001391 ~GCST90002121 European 22 million 3757 2020 1400 metabolites https://www.ebi.ac.uk/gwas/ GCST90199621 ~GCST90201020 European 15.40 million 8299 2023 Selection of IVs To fulfill the requirement of the association hypothesis, necessitating a robust correlation between the chosen IVs and the exposure factor, this study employed stringent criteria. SNPs exhibiting genome-wide significant differences, with a condition P < 1 × 10 − 5, were screened. plink_win64_20231018 (code getdata2.R) was utilized to eliminate SNPs in linkage disequilibrium ( r 2 < 0.001, 10000kb). Subsequently, to adhere to independence assumption, selected IVs were required to exclude other confounding factors associated with both outcome variable and exposure factor. For this purpose, the study manually scrutinized and excluded SNPs linked to confounders and outcomes utilizing Phenoscanner database[ 23 ]. Furthermore, harmonize_data function from TwoSampleMR R package to conduct consistency analysis of exposure and outcome-related SNPs effect alleles to ensure alignment and eliminated all SNPs with palindromic structures, thus mitigating the risk of errors stemming from chain problems[ 24 ]. To fulfill the exclusion restriction assumption and mitigate weak instrument bias, this study employed a filtering process using F = [R 2 × (N − 1 - K)] / [K × (1 - R 2 )] for weak instruments with F < 10 [ 25 , 26 ], where N is the sample size in the exposure factor GWAS study, K denotes SNPs number selected as IVs, R 2 represents variance proportion explained by SNPs in the exposure database R 2 = 2 × EAF × (1 - EAF) × β 2 , where EAF represents mutated gene frequency[ 27 ], and β denotes allele effect size [ 28 , 29 ]. This meticulous filtering process yielded IVs final set for MR analysis. MR analysis Statistical assessments were accomplished with R 4.3.2. The prepared IVs were analyzed utilizing “TwoSampleMR” package. Odds ratios (OR) and confidence intervals (CI) were derived from various regression models, comprising weighted median[ 30 ], weighted mode[ 31 ], IVW, MR-Egger regression, and simple mode. We opted for the IVW method as primary approach because it employs meta-analysis strategy, pooling causal effects Wald estimates derived from all IVs meeting three assumptions. This process yields an overarching unbiased estimate[ 32 , 33 ]. Immune cell phenotypes indirect effect on OS risk were assessed via potential mediator using “product of coefficients” approach. Standard errors for these indirect effects were computed using delta method[ 34 ]. Sensitivity analysis This study employed Cochran's Q statistic for evaluating heterogeneity[ 35 ]. A statistically notable Cochran's Q statistic ( P < 0.05) suggests substantial heterogeneity in the analysis results. Additionally, the study employed MR-Egger intercept method and MR-PRESSO to detect and address pleiotropy. MR-Egger intercept method is utilized to assess pleiotropic relationship between IVs and other potential confounding factors, as indicated by the intercept. If MR-Egger intercept analysis yields P < 0.05, it suggests horizontal pleiotropy presence[ 36 ]. Followingly, pleiotropy residual sum and outlier (PRESSO) are utilized to detect and adjust outliers for horizontal pleiotropy by eliminating any outliers among IVs. Leave-one-out analysis is employed to assess data stability by systematically excluding individual IVs to ascertain if any single SNP disproportionately influences causal association. A substantial alteration in the MR analysis upon removing a specific SNP suggests that the analysis is unduly influenced by that particular IV. Results Immune cell phenotypes overall effect on osteosarcoma This study employed IVW as predominant analysis tool to assess causal relation between 731 immune cell phenotypes and OS. The findings unveiled that 7 immune cell phenotypes exhibited a significant causal relation with OS ( P < 0.01), as illustrated in Fig. 3 . Among these, CX3CR1 on CD14 − CD16 − (OR = 1.3952, 95% CI: 1.1011–1.7679, P < 0.01), CD25 on CD45RA − CD4 not Treg (OR = 1.4099, 95% CI: 1.1027–1.8027, P < 0.01), and CD45 on HLA DR + CD8 br (OR = 1.6075, 95% CI: 1.1311–2.2845, P < 0.01) were positively associated with OS risk. Oppositely, BAFF-R on IgD + CD38 − unsw mem (OR = 0.6385, 95%CI: 0.4693–0.8686, P < 0.01), CD20 on IgD − CD38 − (OR = 0.4324, 95% CI: 0.2425–0.7707, P < 0.01), Naive CD4 + %T cell (OR = 0.6189, 95% CI: 0.4360–0.8786, P < 0.01), and CD28 + CD45RA + CD8 br %CD8 br (OR = 0.8540, 95% CI: 0.7588–0.9611, P < 0.01) were negatively related to OS risk. Moreover, sensitivity analyses indicated that Cochran’s Q test, MR-Egger intercept method, and MR-PRESSO yielded P -values exceeding 0.05, signifying significant heterogeneity or pleiotropy absence in causal effect analysis between immune cell phenotypes and OS, as depicted in Table S1 . Utilizing leave-one-out approach for SNP exclusion didn’t identify any individual SNP significantly disrupting the overall impact of immune cell phenotypes on OS, thereby bolstering robustness to these findings to a certain extent (Figure S1 ). Effect of blood metabolites on OS We identified 33 protective blood metabolites on OS using IVW approach (Fig. 4 ). Conversely, genetically predicted 27 blood metabolites would increase the risk of OS. P -values for Cochran's Q statistic derived from both IVW and MR Egger methods exceeding 0.05, indicating lack of significant heterogeneity (Table S2). MR-Egger intercepts test and MR-PRESSO yielded non-statistically notable results, suggesting the absence of horizontal pleiotropy (Table S2). Leave-one-out analysis demonstrated removal of particular SNP wouldn’t alter causal estimates (Figure S2). Effect of immune cell phenotypes on blood metabolites Earlier, we identified seven immune cell phenotypes and sixty blood metabolites crucial for OS. Subsequently, we delved into causal relation between these 7 immune cell phenotypes and 60 blood metabolites. Our MR analysis highlighted a strong association between only CX3CR1 on CD14 − CD16 − in monocyte panel and dimethyl sulfone (OR = 1.0300, 95% CI [1.0030, 1.0578], P = 0.0293) and X-12680 (OR = 1.0330, 95% CI [1.0063, 1.0605], P = 0.0152) (Fig. 5 ). Meanwhile, other methodologies failed to reveal any significant causal effect. There were no signs of heterogeneity or horizontal pleiotropy, and no single SNP appeared to disproportionately influence the causal estimates. (Table S3 and Figure S3) A reverse MR analysis Casual role of CX3CR1 was observed on CD14- CD16- in monocyte panel, dimethyl sulfone and X-12680 on OS, as well as the casual role of CX3CR1 on CD14 − CD16 − in monocyte panel on dimethyl sulfone and X-12680. Followingly, reverse MR analysis was performed. Figure 6 illustrates that no obvious OS causal effect on CX3CR1 on CD14 − CD16 − in monocyte panel (OR = 1.0150, 95% CI [0.9845, 1.0465], P = 0.3397), dimethyl sulfone (OR = 0.9981, 95% CI [0.9739, 1.0230], P = 0.8810) and X-12680 (OR = 1.0009, 95% CI [0.9780, 1.0244], P = 0.9366) was observed. Furthermore, no causal role of CX3CR1 on CD14 − CD16 − in monocyte panel on dimethyl sulfone (OR = 0.9462, 95% CI [0.8295, 1.0794], P = 0.4106) and X-12680 (OR = 1.0413, 95% CI [0.8654, 1.2529], P = 0.6684) were witnessed. Mediation effect of immune cell phenotypes on OS We investigated CX3CR1 causal effect on CD14 − CD16 − in monocyte panel on OS, dimethyl sulfone and X-12680. Mediation analysis was conducted to illustrate the mediating effect of dimethyl sulfone and X-12680 between CX3CR1 on CD14 − CD16 − in monocyte panel on OS. The mediation effect of dimethyl sulfone and X-12680 in the causal pathway from CX3CR1 on CD14 − CD16 − in monocyte panel to OS were 0.0288 (accounting for 8.7% of the total effect) and 0.0524 (accounting for 15.74% of the total effect), respectively. (Table 2 ) Table 2 Mediating effects of blood metabolites dimethyl sulfone and X-12680 mediated monocyte CX3CR1 on CD14 − CD16 − on causality in osteosarcoma Total effect Direct effect A Direct effect B Mediate effect Mediate ratio(%) β (95% CI) β (95% CI) β (95% CI) β (95% CI) 0.3330(0.097–0.571) Dimethyl sulfone 0.0296 (0.0030 ~ 0.0562) 0.9738 (0.0655 ~ 1.8820) 0.0288 (-0.856 ~ 0.913) 8.7 X-12680 0.0325 (0.0063 ~ 0.0587) 1.6136 (0.4725 ~ 2.7546) 0.0524 (-1.789 ~ 1.894) 15.7 Note: Total effect: Causal effect of monocyte CX3CR1 on CD14 − CD16 − on osteosarcoma; Direct effect A: Causal effect of immune cell phenotype on blood metabolites; Direct effect B: Causal effect of blood metabolites on osteosarcoma; β mediated effect = β direct effect A*β direct effect B; Intermediate ratio = β indirect effect / β total effect. Discussion This research represents the first comprehensive two-sample, two-way MR analysis exploring the causal relation between OS and 731 immune cell types. Leveraging a vast repository of publicly available genetic data, this study benefits from a larger sample size and a more targeted analytical approach compared to traditional double-blind controlled experiments focusing on single or a few immune cells in relation to OS. Furthermore, employing a two-step methodology, it identifies and quantifies the mediating roles of 1,400 blood metabolites as potential mediators. Through this approach, the study aims to mitigate the effects of confounding, reverse causation, and other factors on the observed results, thus enhancing the robustness of its findings. The study's findings unveil significant causal relationships ( P IVW < 0.01) between 7 immune cell phenotypes and OS when considering immune cell phenotypes as exposure factors and OS as outcome data. Specifically, OS risk escalates with heightened levels of CX3CR1 on CD14 − CD16 − phenotype in monocytes panel, CD25 on CD45RA − CD4 not Treg in Treg panel, CD45 on HLA DR + CD8 br in TBNK panel. Oppositely, elevation in BAFF-R on IgD + CD38 − unsw mem and CD20 on IgD − CD38 − phenotype in B cell panel, Naive CD4 + %T cell in T cell panel maturation stages, and CD28 + CD45RA + CD8 br %CD8 br phenotype in Treg panel is related to decreased OS risk. Monocytes expressing CX3CR1 (chemokine receptor 3, class I) on their surface, while lacking CD14 or CD16, are termed CX3CR1 on CD14 − CD16 − monocytes. Originating from bone marrow, monocytes circulate in the vasculature and transform into macrophages upon exiting the vasculature. Monocytes are recognized for their pivotal role in fostering lung metastasis in OS. Upon reaching the metastatic site, undergo differentiation into metastasis-associated macrophages, crucial for facilitating metastatic colonization by aiding tumor cell extravasation, growth, and angiogenesis[ 37 , 38 ]. CX3CR1, a G-protein-coupled 7-transmembrane-domain receptor, is expressed on specific cells surface. Notably, CX3C chemokine family comprises only one member, CX3CL1. Liu et al.[ 39 ] demonstrated that CX3CL1 promotes OS cell migration and facilitates lung metastasis by upregulating ICAM-1 expression. Conversely, CX3CL1 knockdown inhibits OS lung metastasis. CD14, a surface antigen, plays central role in macrophage M2 polarization[ 40 ]. CD14 + macrophage M2 is related to OS metastasis reduction and improved survival [ 41 ]. CD16 is member of immunoglobulin superfamily (IgSF) and, upon binding to antibodies, triggers immune cells to initiate responses such as degranulation, antibody-dependent cell-mediated cytotoxicity, respiratory bursts, phagocytosis, and targeting of cancerous or virus-infected cells[ 42 ]. Cillo et al.[ 43 ] observed an elevated trend in the CD14 − CD16 − monocyte population among OS patients compared to control. This finding hints at potential correlation between CD14 − CD16 − monocytes and pulmonary metastasis in OS. The studies mentioned above collectively indicate that OS progression and lung metastasis are linked to heightened expression of monocyte subtypes exhibiting CX3CR1-positive expression, along with the CD14 − CD16 − monocyte cluster. These findings align with analysis result conducted in the present study. Regulatory T cells (Treg) represent a crucial subset of T cells involved in maintaining immune homeostasis [ 44 ]. Among Treg cell clusters, CD25 on CD45RA − CD4 not Treg phenotype denotes a subset of activated non-Treg CD4 + T cells expressing CD25 (IL-2 receptor α-chain) and CD4, while lacking CD45RA expression. YANG et al.[ 45 ] observed a significant decrease in Treg cells within the osteosarcoma (OS) tumor microenvironment. CD25 serves as the heterologous alpha chain of the trimeric IL-2 receptor, with its expression varying across different hematologic malignancies and solid tumors[ 46 ]. RISSETTO et al.[ 46 ] noted a higher prevalence of CD25 + Treg cells in the blood of dogs with OS. While no direct association between the CD25 on CD45RA − CD4 not Treg phenotype and OS has been explicitly demonstrated, the analysis conducted in this study, combined with the integration of the aforementioned research findings, hints at a potential causal relationship between them. CD45 on HLA DR + CD8br represents a cytotoxic T cell expressing both CD45 and HLA-DR. CD45, also known as Protein Tyrosine Phosphatase Receptor Type C, plays a pivotal role in immunology by regulating the differentiation of T cells through the modulation of Src family kinases - Lck and Fyn. The absence of CD45 leads to functional deficiencies in both T and B cells, resulting in severe combined immunodeficiency and increasing susceptibility to autoimmune diseases and cancer[ 47 ]. HLA-DR, a gene within the major histocompatibility complex class II, is part of HLA system, encoded by the HLA complex located on chromosome 6p21. This antigen is capable of presenting antigen fragments to T cells, thereby triggering an immune response[ 48 ]. Currently, there is limited research regarding the cytotoxic T lymphocyte CD45 on HLA DR + CD8 br phenotype in OS. However, studies conducted by Lim et al.[ 49 ] suggest that HLA DR gene family demonstrates elevated expression in OS. Elevated expression of HLA-E and HLA-F may potentially enable tumor cells to evade immune surveillance by NK cells and T cells, thus facilitating immune escape of tumor cells. BAFF-R on IgD + CD38 − unsw mem represents a subset of memory B cells that have not undergone somatic hypermutation or class switching, expressing both BAFF-R and IgD. This B cells subset plays a role in suppressing anti-tumor T cells through the secretion of IL-10 and contributes to tumorigenesis by releasing antibodies that exacerbate chronic inflammation[ 50 ]. BAFF-R (B cell activating factor receptor), a member of TNFR family, is notably expressed at high levels in OS[ 51 ]. Researchers have observed elevated expression of BAFF-R and BAFF in samples from two types of OS (conventional and bone membrane subtype), indicating a close association between heightened BAFF/BAFF-R expression and OS occurrence and progression[ 51 ]. Meanwhile, IgD is crucial for transitioning from highly primary self-reactive to secondary antigen-specific antibody responses[ 52 ]. However, there is currently no evidence indicating a close association between IgD and OS occurrence and development. The CD20 on IgD − CD38 − in B cell panel demonstrates a noteworthy causal relationship with a decreased risk of osteosarcoma. CD20 is a protein expressed on the surface of B cells, pivotal in their maturation and differentiation processes. Within the tumor microenvironment, the presence of CD20 + tumor-infiltrating immune cells is significantly correlated with favorable prognosis in various cancers. According to research by SATO et al.[ 53 ], CD20 + cells locally activate CD8 + T cells within the tumor and participate in antigen presentation, shedding light on functional role of CD20 + cells within the tumor microenvironment. Naive CD4+ %T cells represent undifferentiated T cells, expressing CD4 on their surface. When being antigen exposed, naive CD4 + %T cells differentiated into effector T cells. CD4 + Tregs serve as the principal immunosuppressive cells, pivotal in facilitating tumor immune evasion through co-inhibitory molecules or immunosuppressive cytokines[ 54 ]. Interplays between osteoclasts and CD4 + Tregs modulate the tumor immune microenvironment. Osteoclasts attract surrounding CD4 + T cells by releasing chemokines[ 55 ], secrete T cell stimulants, express MHC, process soluble antigens, thus inducing proliferation and activation of CD4 + Tregs[ 56 ]. CD4 + Tregs are pivotal immunosuppressive cells within tumor immune microenvironment, instrumental in facilitating tumor immune evasion through co-inhibitory molecules or immunosuppressive cytokines[ 57 ]. CD28 + CD45RA + CD8 br %CD8 br within Treg panel expresses CD28, CD45RA, and CD8 br . CD28 receptor family encompasses receptors present on immune cells surface, functioning as positive activation receptors on T cells. These receptors play pivotally in T cell development and proliferation, amplifying signals from T cell receptor to trigger immune responses, and regulating anti-inflammatory actions of Treg cells[ 58 ]. Research by Li et al. [ 59 ] suggests that elevated CD28 expression in tumor is favorable for OS prognosis, and blocking CD86/CTLA4 signal transduction while enhancing CD86/CD28 signal transduction represents a promising strategy for OS immunotherapy. CD8 br , also known as CD8 bright , is a CD8 + T cells subset. Elevated levels of CD8 + Tregs have been linked to a poorer prognosis, as they possess the ability to suppress anti-tumor immune responses[ 60 ]. A noteworthy causal relationship was observed between the blood metabolite DMSO and an increased OS risk. DMSO, also referred to as methyl sulfoxide, is an organic sulfur compound. Studies suggest that DMSO exhibits antioxidant and anti-inflammatory properties, which may potentially synergize to inhibit tumors by suppressing the JAK2/STAT5b pathway[ 61 ]. Research by S et al.[ 62 ] revealed that DMSO impede STAT5b and STAT3 pathways, leading to significant reduction in human cancer cells viability. DMSO demonstrates anticancer properties in metastatic cancer cells, as reported in literature[ 63 ]. Studies have highlighted its ability to induce endoplasmic reticulum stress-mediated apoptosis in HCT116 cells[ 64 ]. Moreover, research has indicated the anti-proliferative effects of DMSO, suggesting its potential to inhibit the invasion and prostate cancer cells migration capabilities[ 65 ]. DMSO triggers hepatic cells apoptosis, comprising HepG2 and Huh7, through exogenous apoptosis pathway[ 66 ]. Moreover, a study conducted on A549 lung and YD-38 gingival cancer cells corroborated the anticancer properties of DMSO. Research underscored alterations in cell viability, cell cycle progression, and apoptosis in tumor cells upon exposure to DMSO[ 67 ]. While these investigations support the anticancer efficacy of DMSO, there is no direct literature addressing its impact on OS. Surprisingly, the current study unveils a significant causal association between DMSO and an increased OS risk, contrary to previous understandings. This implies that unlike its effects on other cancers, dimethyl sulfoxide might promote OS onset and progression, although the precise underlying mechanism warrants further elucidation. Additionally, reverse MR analysis was undertaken to verify positive findings. The results suggest that when OS was utilized as exposure data, no notable causal relation observed with CX3CR1 on CD14 − CD16 − , DMSO, and X-12680 ( P IVW < 0.05). Similarly, when DMSO and X-12680 were employed as exposure data, no notable causal relation with CX3CR1 on CD14 − CD16 − ( P IVW < 0.05). MR analysis leverages SNPs as IVs to infer causal relation between the exposure factor under investigation and outcome factor. Since genetic variation occurs before disease onset, the sequence of events is predetermined and cannot be reversed[ 68 ], thus MR analysis impede interference from reverse causality as well. Given this characteristic of MR analysis, reverse MR analysis serves as a validation of the positive results from another perspective. Negative outcomes observed in reverse MR analysis precisely indicate that the significant causal relation identified in positive analysis remained unaffected by reverse causality, thus mitigating false positives risk. In summary, we delved into causal relation between 731 immune cell phenotypes, 1400 blood metabolites and OS using a two-step mediation MR analysis. Our findings unveiled seven immune cell phenotypes with significant causal links to OS. Moreover, we identified dimethyl sulfoxide and an unidentified metabolite, X-12680, as mediators in the pathway from monocyte CX3CR1 on CD14 − CD16 − to OS, with respective mediation proportions of 8.7% and 15.7%. However, several limitations should be acknowledged: 1) The study couldn't fully evaluate potential heterogeneity and horizontal pleiotropy. 2) The generalizability of the conclusions is constrained since all data were sourced from European populations, necessitating validation in other demographics. 3) While DMSO and X-12680 partially mediated the causal pathway from monocyte CX3CR1 on CD14 − CD16 − to OS within blood metabolites, accounting for 8.7% and 15.7% of the total mediation effect, respectively, there could exist additional mediating factors warranting further exploration. This study offers novel perspectives on the genetic mechanisms underlying OS, potentially guiding precision treatment strategies. It sets the stage for deeper investigations into the interplay among immune cells, blood metabolites, and OS. This holistic understanding promises valuable insights into the intricate connections between the human immune and metabolic systems and OS. Furthermore, it paves the way for identifying potential biomarkers and therapeutic targets in OS immunity and metabolism, fostering advancements in diagnosis and intervention strategies. Conclusion Research of ours elucidated causal interplays among immune cells, blood metabolites and OS. Spcially, CX3CR1 on CD14 − CD16 − was taken as underlying OS risk factor, with a substantial portion of its effect mediated by DMSO and X-12680 Declarations Acknowledgements We acknowledge all the genetics consortiums for making the GWAS summary data publicly available. Authors’ contributions Ping Zeng and Jinfu Liu designed the project. Chicheng Niu and Qingyuan Xu conducted the data analysis. Chicheng Niu wrote the manuscript. Weiwei Wang and Hao Li carried out data collection. Qiang Ding and Liang Guo critically reviewed the manuscript. Chicheng Niu and Qingyuan Xu contributed equally to this work. All authors have read and approved the submitted manuscript. Funding This work was supported by the National Natural Science Foundation of China (grant number 82160913) Data availability The datasets mentioned in this study can be found in online repositories. The OS GWAS data source: https://www.finngen.fi/en/access_results. The acquired 731 immune cell phenotypes GWAS data source: https://www.ebi.ac.uk/gwas/. The acquired 1400 blood metabolites GWAS data source: https://www.ebi.ac.uk/gwas/. Ethics approval and consent to participate This study is based on data from open-access public databases. Ethics and consent statements are not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Beird HC, Bielack SS, Flanagan AM, Gill J, Heymann D, Janeway KA, Livingston JA, Roberts RD, Strauss SJ, Gorlick R: Osteosarcoma . Nature reviews Disease primers 2022, 8 (1):77. Panez-Toro I, Muñoz-García J, Vargas-Franco JW, Renodon-Cornière A, Heymann MF, Lézot F, Heymann D: Advances in Osteosarcoma . Current osteoporosis reports 2023, 21 (4):330-343. Wycislo KL, Fan TM: The immunotherapy of canine osteosarcoma: a historical and systematic review . Journal of veterinary internal medicine 2015, 29 (3):759-769. Chen C, Xie L, Ren T, Huang Y, Xu J, Guo W: Immunotherapy for osteosarcoma: Fundamental mechanism, rationale, and recent breakthroughs . Cancer letters 2021, 500 :1-10. Zheng Y, Wang G, Chen R, Hua Y, Cai Z: Mesenchymal stem cells in the osteosarcoma microenvironment: their biological properties, influence on tumor growth, and therapeutic implications . Stem cell research & therapy 2018, 9 (1):22. Zhou Q, Xian M, Xiang S, Xiang D, Shao X, Wang J, Cao J, Yang X, Yang B, Ying M et al : All-Trans Retinoic Acid Prevents Osteosarcoma Metastasis by Inhibiting M2 Polarization of Tumor-Associated Macrophages . Cancer immunology research 2017, 5 (7):547-559. Sarvaria A, Madrigal JA, Saudemont A: B cell regulation in cancer and anti-tumor immunity . Cellular & molecular immunology 2017, 14 (8):662-674. Cantor JR, Sabatini DM: Cancer cell metabolism: one hallmark, many faces . Cancer discovery 2012, 2 (10):881-898. Hua Y, Qiu Y, Zhao A, Wang X, Chen T, Zhang Z, Chi Y, Li Q, Sun W, Li G et al : Dynamic metabolic transformation in tumor invasion and metastasis in mice with LM-8 osteosarcoma cell transplantation . Journal of proteome research 2011, 10 (8):3513-3521. Xia L, Oyang L, Lin J, Tan S, Han Y, Wu N, Yi P, Tang L, Pan Q, Rao S et al : The cancer metabolic reprogramming and immune response . Molecular cancer 2021, 20 (1):28. Ramapriyan R, Caetano MS, Barsoumian HB, Mafra ACP, Zambalde EP, Menon H, Tsouko E, Welsh JW, Cortez MA: Altered cancer metabolism in mechanisms of immunotherapy resistance . Pharmacology & therapeutics 2019, 195 :162-171. Cronin SJF, Seehus C, Weidinger A, Talbot S, Reissig S, Seifert M, Pierson Y, McNeill E, Longhi MS, Turnes BL et al : The metabolite BH4 controls T cell proliferation in autoimmunity and cancer . Nature 2018, 563 (7732):564-568. Huang B, Song BL, Xu C: Cholesterol metabolism in cancer: mechanisms and therapeutic opportunities . Nature metabolism 2020, 2 (2):132-141. Karayama M, Masuda J, Mori K, Yasui H, Hozumi H, Suzuki Y, Furuhashi K, Fujisawa T, Enomoto N, Nakamura Y et al : Comprehensive assessment of multiple tryptophan metabolites as potential biomarkers for immune checkpoint inhibitors in patients with non-small cell lung cancer . Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico 2021, 23 (2):418-423. Uffelmann E, Huang QQ, Munung NS, De Vries J, Okada Y, Martin AR, Martin HC, Lappalainen T, Posthuma D: Genome-wide association studies . Nature Reviews Methods Primers 2021, 1 (1):59. Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C et al : Guidelines for performing Mendelian randomization investigations: update for summer 2023 . Wellcome open research 2019, 4 :186. Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G: Mendelian randomization: using genes as instruments for making causal inferences in epidemiology . Statistics in medicine 2008, 27 (8):1133-1163. Clarke SLN, Mitchell RE, Sharp GC, Ramanan AV, Relton CL: Vitamin D Levels and Risk of Juvenile Idiopathic Arthritis: A Mendelian Randomization Study . Arthritis care & research 2023, 75 (3):674-681. Birney E: Mendelian Randomization . Cold Spring Harbor perspectives in medicine 2022, 12 (4). Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA et al : FinnGen provides genetic insights from a well-phenotyped isolated population . Nature 2023, 613 (7944):508-518. Chen Y, Lu T, Pettersson-Kymmer U, Stewart ID, Butler-Laporte G, Nakanishi T, Cerani A, Liang KYH, Yoshiji S, Willett JDS et al : Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases . Nature genetics 2023, 55 (1):44-53. Orrù V, Steri M, Sidore C, Marongiu M, Serra V, Olla S, Sole G, Lai S, Dei M, Mulas A et al : Complex genetic signatures in immune cells underlie autoimmunity and inform therapy . Nature genetics 2020, 52 (10):1036-1045. Zhou T, Sun D, Li X, Ma H, Heianza Y, Qi L: Educational attainment and drinking behaviors: Mendelian randomization study in UK Biobank . Molecular psychiatry 2021, 26 (8):4355-4366. Hartwig FP, Davies NM, Hemani G, Davey Smith G: Two-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique . International journal of epidemiology 2016, 45 (6):1717-1726. Codd V, Nelson CP, Albrecht E, Mangino M, Deelen J, Buxton JL, Hottenga JJ, Fischer K, Esko T, Surakka I et al : Identification of seven loci affecting mean telomere length and their association with disease . Nature genetics 2013, 45 (4):422-427, 427e421-422. Li W, Lu Q, Qian J, Feng Y, Luo J, Luo C, He W, Dong B, Liu H, Liu Z et al : Assessing the causal relationship between genetically determined inflammatory biomarkers and low back pain risk: a bidirectional two-sample Mendelian randomization study . Frontiers in immunology 2023, 14 :1174656. Levin MG, Judy R, Gill D, Vujkovic M, Verma SS, Bradford Y, Ritchie MD, Hyman MC, Nazarian S, Rader DJ et al : Genetics of height and risk of atrial fibrillation: A Mendelian randomization study . PLoS medicine 2020, 17 (10):e1003288. Rosoff DB, Clarke TK, Adams MJ, McIntosh AM, Davey Smith G, Jung J, Lohoff FW: Educational attainment impacts drinking behaviors and risk for alcohol dependence: results from a two-sample Mendelian randomization study with ~780,000 participants . Molecular psychiatry 2021, 26 (4):1119-1132. Vermeulen JM, Wootton RE, Treur JL, Sallis HM, Jones HJ, Zammit S, van den Brink W, Goodwin GM, de Haan L, Munafò MR: Smoking and the risk for bipolar disorder: evidence from a bidirectional Mendelian randomisation study . The British journal of psychiatry : the journal of mental science 2021, 218 (2):88-94. Bowden J, Davey Smith G, Haycock PC, Burgess S: Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator . Genetic epidemiology 2016, 40 (4):304-314. Burgess S, Dudbridge F, Thompson SG: Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods . Statistics in medicine 2016, 35 (11):1880-1906. Han Y, Zhang Y, Zeng X: Assessment of causal associations between uric acid and 25-hydroxyvitamin D levels . Frontiers in endocrinology 2022, 13 :1024675. Zhao J, Wang J, Xu H, Hu W, Shi F, Fan Z, Zhou C, Mu H: Intervertebral Disk Degeneration and Bone Mineral Density: A Bidirectional Mendelian Randomization Study . Calcified tissue international 2024, 114 (3):228-236. Chen J, Yu X, Wu X, Chai K, Wang S: Causal relationships between gut microbiota, immune cell, and Non-small cell lung cancer: a two-step, two-sample Mendelian randomization study . Journal of Cancer 2024, 15 (7):1890-1897. Hu M, Sun Z, Tang X, Zeng W, Yan H, Jiang Z, Huang F: Systemic lupus erythematosus Association between Osteomyelitis: A two-sample Mendelian randomization study in European population . Heliyon 2023, 9 (12):e22999. Gala H, Tomlinson I: The use of Mendelian randomisation to identify causal cancer risk factors: promise and limitations . The Journal of pathology 2020, 250 (5):541-554. Mazzieri R, Pucci F, Moi D, Zonari E, Ranghetti A, Berti A, Politi LS, Gentner B, Brown JL, Naldini L et al : Targeting the ANG2/TIE2 axis inhibits tumor growth and metastasis by impairing angiogenesis and disabling rebounds of proangiogenic myeloid cells . Cancer cell 2011, 19 (4):512-526. Qian BZ, Li J, Zhang H, Kitamura T, Zhang J, Campion LR, Kaiser EA, Snyder LA, Pollard JW: CCL2 recruits inflammatory monocytes to facilitate breast-tumour metastasis . Nature 2011, 475 (7355):222-225. Liu JF, Tsao YT, Hou CH: Fractalkine/CX3CL1 induced intercellular adhesion molecule-1-dependent tumor metastasis through the CX3CR1/PI3K/Akt/NF-κB pathway in human osteosarcoma . Oncotarget 2017, 8 (33):54136-54148. Dou C, Ding N, Zhao C, Hou T, Kang F, Cao Z, Liu C, Bai Y, Dai Q, Ma Q et al : Estrogen Deficiency-Mediated M2 Macrophage Osteoclastogenesis Contributes to M1/M2 Ratio Alteration in Ovariectomized Osteoporotic Mice . Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research 2018, 33 (5):899-908. Buddingh EP, Kuijjer ML, Duim RA, Bürger H, Agelopoulos K, Myklebost O, Serra M, Mertens F, Hogendoorn PC, Lankester AC et al : Tumor-infiltrating macrophages are associated with metastasis suppression in high-grade osteosarcoma: a rationale for treatment with macrophage activating agents . Clinical cancer research : an official journal of the American Association for Cancer Research 2011, 17 (8):2110-2119. Hu J, Zhang L, Xia H, Yan Y, Zhu X, Sun F, Sun L, Li S, Li D, Wang J et al : Tumor microenvironment remodeling after neoadjuvant immunotherapy in non-small cell lung cancer revealed by single-cell RNA sequencing . Genome medicine 2023, 15 (1):14. Cillo AR, Mukherjee E, Bailey NG, Onkar S, Daley J, Salgado C, Li X, Liu D, Ranganathan S, Burgess M et al : Ewing Sarcoma and Osteosarcoma Have Distinct Immune Signatures and Intercellular Communication Networks . Clinical cancer research : an official journal of the American Association for Cancer Research 2022, 28 (22):4968-4982. Kang JH, Zappasodi R: Modulating Treg stability to improve cancer immunotherapy . Trends in cancer 2023, 9 (11):911-927. Yang M, Zheng H, Xu K, Yuan Q, Aihaiti Y, Cai Y, Xu P: A novel signature to guide osteosarcoma prognosis and immune microenvironment: Cuproptosis-related lncRNA . Frontiers in immunology 2022, 13 :919231. Rissetto KC, Rindt H, Selting KA, Villamil JA, Henry CJ, Reinero CR: Cloning and expression of canine CD25 for validation of an anti-human CD25 antibody to compare T regulatory lymphocytes in healthy dogs and dogs with osteosarcoma . Veterinary immunology and immunopathology 2010, 135 (1-2):137-145. Rheinländer A, Schraven B, Bommhardt U: CD45 in human physiology and clinical medicine . Immunology letters 2018, 196 :22-32. Jiang CY, Zhao L, Green MD, Ravishankar S, Towlerton AMH, Scott AJ, Raghavan M, Cusick MF, Warren EH, Ramnath N: Class II HLA-DRB4 is a predictive biomarker for survival following immunotherapy in metastatic non-small cell lung cancer . Scientific reports 2024, 14 (1):345. Lim WC, Marques Da Costa ME, Godefroy K, Jacquet E, Gragert L, Rondof W, Marchais A, Nhiri N, Dalfovo D, Viard M et al : Divergent HLA variations and heterogeneous expression but recurrent HLA loss-of- heterozygosity and common HLA-B and TAP transcriptional silencing across advanced pediatric solid cancers . Frontiers in immunology 2023, 14 :1265469. Fridman WH, Petitprez F, Meylan M, Chen TW, Sun CM, Roumenina LT, Sautès-Fridman C: B cells and cancer: To B or not to B? The Journal of experimental medicine 2021, 218 (1). Kohno T, Daa T, Otani H, Shimokawa I, Yokoyama S, Matsuyama T: Aberrant expression of BAFF receptor, a member of the tumor necrosis factor receptor family, in malignant cells of nonhematopoietic origins . Genes to cells : devoted to molecular & cellular mechanisms 2008, 13 (10):1061-1073. Amendt T, Ayoubi OE, Linder AT, Allies G, Young M, Setz CS, Jumaa H: Primary Immune Responses and Affinity Maturation Are Controlled by IgD . Frontiers in immunology 2021, 12 :709240. Sato J, Kitano S, Motoi N, Ino Y, Yamamoto N, Watanabe S, Ohe Y, Hiraoka N: CD20(+) tumor-infiltrating immune cells and CD204(+) M2 macrophages are associated with prognosis in thymic carcinoma . Cancer science 2020, 111 (6):1921-1932. Granito A, Muratori L, Lalanne C, Quarneti C, Ferri S, Guidi M, Lenzi M, Muratori P: Hepatocellular carcinoma in viral and autoimmune liver diseases: Role of CD4+ CD25+ Foxp3+ regulatory T cells in the immune microenvironment . World journal of gastroenterology 2021, 27 (22):2994-3009. Ibáñez L, Abou-Ezzi G, Ciucci T, Amiot V, Belaïd N, Obino D, Mansour A, Rouleau M, Wakkach A, Blin-Wakkach C: Inflammatory Osteoclasts Prime TNFα-Producing CD4(+) T Cells and Express CX(3) CR1 . Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research 2016, 31 (10):1899-1908. Li H, Hong S, Qian J, Zheng Y, Yang J, Yi Q: Cross talk between the bone and immune systems: osteoclasts function as antigen-presenting cells and activate CD4+ and CD8+ T cells . Blood 2010, 116 (2):210-217. Li F, Tang H, Luo X, Li X, Luo K, Liu S, Liang J, Liao S, Zhong C, Zhan X et al : Interaction gene set between osteoclasts and regulatory CD4(+) T cells can accurately predict the prognosis of patients with osteosarcoma . Cancer science 2023, 114 (7):3014-3026. Duraiswamy J, Turrini R, Minasyan A, Barras D, Crespo I, Grimm AJ, Casado J, Genolet R, Benedetti F, Wicky A et al : Myeloid antigen-presenting cell niches sustain antitumor T cells and license PD-1 blockade via CD28 costimulation . Cancer cell 2021, 39 (12):1623-1642.e1620. Li J, Su L, Xiao X, Wu F, Du G, Guo X, Kong F, Yao J, Zhu H: Development and Validation of Novel Prognostic Models for Immune-Related Genes in Osteosarcoma . Frontiers in molecular biosciences 2022, 9 :828886. Yang QK, Su YN, Wang W, Wang N, Yao ZX, Zhang XJ: CONUT Score or/and Peripheral Blood CD4+/CD8+ Ratio-Based Web Dynamic Nomograms to Predict the Individualized Survival of Patients with Advanced Osteosarcoma . Cancer management and research 2020, 12 :4193-4208. Magnuson BA, Appleton J, Ames GB: Pharmacokinetics and distribution of [35S]methylsulfonylmethane following oral administration to rats . Journal of agricultural and food chemistry 2007, 55 (3):1033-1038. S PN, Darvin P, Yoo YB, Joung YH, Kang DY, Kim DN, Hwang TS, Kim SY, Kim WS, Lee HK et al : The combination of methylsulfonylmethane and tamoxifen inhibits the Jak2/STAT5b pathway and synergistically inhibits tumor growth and metastasis in ER-positive breast cancer xenografts . BMC cancer 2015, 15 :474. Caron JM, Monteagudo L, Sanders M, Bannon M, Deckers PJ: Methyl sulfone manifests anticancer activity in a metastatic murine breast cancer cell line and in human breast cancer tissue--part 2: human breast cancer tissue . Chemotherapy 2013, 59 (1):24-34. Karabay AZ, Koc A, Ozkan T, Hekmatshoar Y, Sunguroglu A, Aktan F, Buyukbingol Z: Methylsulfonylmethane Induces p53 Independent Apoptosis in HCT-116 Colon Cancer Cells . International journal of molecular sciences 2016, 17 (7). Kowalska K, Habrowska-Górczyńska DE, Domińska K, Urbanek KA, Piastowska-Ciesielska AW: Methylsulfonylmethane (organic sulfur) induces apoptosis and decreases invasiveness of prostate cancer cells . Environmental toxicology and pharmacology 2018, 64 :101-111. Kim JH, Shin HJ, Ha HL, Park YH, Kwon TH, Jung MR, Moon HB, Cho ES, Son HY, Yu DY: Methylsulfonylmethane suppresses hepatic tumor development through activation of apoptosis . World journal of hepatology 2014, 6 (2):98-106. Kim DH, Sp N, Kang DY, Jo ES, Rugamba A, Jang KJ, Yang YM: Effect of Methylsulfonylmethane on Proliferation and Apoptosis of A549 Lung Cancer Cells Through G(2)/M Cell-cycle Arrest and Intrinsic Cell Death Pathway . Anticancer research 2020, 40 (4):1905-1913. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N et al : Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement . Jama 2021, 326 (16):1614-1621. Additional Declarations No competing interests reported. Supplementary Files supplementmaterials.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-4454204","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308630808,"identity":"e2be9bd8-df7b-4713-8c9b-bdf4edd1e076","order_by":0,"name":"Chicheng Niu","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chicheng","middleName":"","lastName":"Niu","suffix":""},{"id":308630809,"identity":"d7db62d9-7eaf-4c3f-a8b2-dbfe6c2a5ee1","order_by":1,"name":"Qingyuan Xu","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qingyuan","middleName":"","lastName":"Xu","suffix":""},{"id":308630810,"identity":"e08e939e-ee4b-4d5c-a169-d4b978887434","order_by":2,"name":"Weiwei Wang","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Wang","suffix":""},{"id":308630811,"identity":"3f5c208c-b55b-4cef-8c6d-b5409a7625ff","order_by":3,"name":"Hao Li","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Li","suffix":""},{"id":308630812,"identity":"e9776981-c588-4917-9ebd-75302030ecbe","order_by":4,"name":"Qiang Ding","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Ding","suffix":""},{"id":308630813,"identity":"ea21d71c-2840-479a-937b-1289d18e81d9","order_by":5,"name":"Liang Guo","email":"","orcid":"","institution":"Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Guo","suffix":""},{"id":308630814,"identity":"b7c779a5-0df9-4cf5-a4dd-27791104b81b","order_by":6,"name":"Ping Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYNCCCjiLmVgtZxgYeEjTwthGihb59t7Drwvn1SbuZz+dJsFQYZ3YwH72AH4Les6lWc/cdtyYhyd3mwTDmfTEBp68BLxamCVyzIx5tx2T45Hg3SbB2HY4sUGCxwCvFjb5N0Atc47xQLT8I0ILjwSP8WPehhqoLQ1EaJHgyTFjnnHsgDHPmdzNFgnH0o3beHLwa5FvP2P8uaCmLrG9/ezGGx9qrGX72c/g1wLyjjQDw2EIMwHEJaQeCJg/MzDUEaFuFIyCUTAKRiwAAIcdPXDBMvElAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Guangxi University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ping","middleName":"","lastName":"Zeng","suffix":""},{"id":308630815,"identity":"c0d9ca14-3f01-43e9-b223-637729c98b34","order_by":7,"name":"Jinfu Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jinfu","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-05-21 10:45:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4454204/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4454204/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58111473,"identity":"b1d7314f-626f-4d28-97bc-02ae93004309","added_by":"auto","created_at":"2024-06-11 09:34:43","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34527,"visible":true,"origin":"","legend":"\u003cp\u003eThe three fundamental principle assumptions of MR study.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4454204/v1/a40752e38017e8501ec4eb98.jpg"},{"id":58111474,"identity":"d7bed531-6347-490f-9bdf-4f96841c9bc3","added_by":"auto","created_at":"2024-06-11 09:34:43","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23482,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design - A two-step MR analysis of Immune cell influence on Osteosarcoma induced by Blood metabolites.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4454204/v1/34999c1659489873df0d7360.jpg"},{"id":58111478,"identity":"c58d70c1-2591-4c94-91c8-463270f22c6a","added_by":"auto","created_at":"2024-06-11 09:34:44","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":158119,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis showed the causality of 7 immune cell phenotypes on osteosarcoma were significant\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4454204/v1/eed62a8c003ea70fa0a1ec1f.jpg"},{"id":58111476,"identity":"76e16dff-4cf6-4850-886f-640e34fa28c2","added_by":"auto","created_at":"2024-06-11 09:34:43","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":227784,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis showed the causality of 60 blood metabolites on osteosarcoma were significant\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4454204/v1/d27cd857b5728e5244b6616d.jpg"},{"id":58111475,"identity":"d82e6344-95f4-4470-8734-209b7a1b5bf3","added_by":"auto","created_at":"2024-06-11 09:34:43","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":64116,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR analysis showed the causality of CX3CR1 on CD14\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e CD16\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e in monocyte panel on dimethyl sulfone and X-12680 were significant\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4454204/v1/29e9c4683fd65686928a3a72.jpg"},{"id":58111826,"identity":"e3ec638a-f7ec-45e6-b5fd-690790467409","added_by":"auto","created_at":"2024-06-11 09:42:43","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":166906,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInverse Mendelian Randomization Causal Forest Plot\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4454204/v1/f4be61acbef501e78cdace5a.jpg"},{"id":58112949,"identity":"b208558c-b3e3-40b8-aa03-424dfd7b0485","added_by":"auto","created_at":"2024-06-11 09:58:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3128747,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4454204/v1/bda9b452-62ff-4497-aa8e-1fc56540d238.pdf"},{"id":58111479,"identity":"46d569fa-71d9-4c66-b8e5-8e929cb06a0b","added_by":"auto","created_at":"2024-06-11 09:34:44","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":712730,"visible":true,"origin":"","legend":"","description":"","filename":"supplementmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4454204/v1/6a6201b8e6742329d6fb6ec9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal association of immune cell phenotypes with osteosarcoma and the mediation role of blood metabolites: A two-steps, two-samples Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteosarcoma (OS) stands as the prevailing primary bone-originated malignancy, marked by elevated mortality and disability rates. Initial manifestations of OS frequently manifest subtly, typically as localized pain and swelling. OS advances rapidly and is highly predisposed to metastasis, with lung metastasis emerging as leading cause of mortality among OS patients[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The pathogenesis of osteosarcoma is notably intricate, despite strides in medical technology, there has been little substantive progress in improving the 5-year survival rate[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Evidence underscores osteosarcoma's immunogenic nature[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], highlighting the critical importance of comprehending the interplay between OS and the immune system is crucial for delineating novel treatment strategies, especially for patients grappling with recurrent and metastatic OS[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] propose that tumor cells engage in a perpetual struggle with the immune system. However, this equilibrium could be disrupted at certain junctures, rendering complete tumor eradication challenging once they develop. Immunotherapy holds promise for eliminating tumor cells within the body at the cellular level. The immune cell constituents within the tumor immune microenvironment encompass tumor-associated macrophages (TAM), B cells, NK cells, T cells, among others[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As per related investigations, maintaining balance between M1 (anti-tumor) and M2 (pro-tumor) macrophages holds pivotal significance in tumor progression[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. M1-type TAMs contribute to inflammatory responses and exhibit inhibitory effects on osteosarcoma metastasis, whereas M2-type TAMs engage in wound healing and immune regulation, thereby fostering metastasis[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. T cell infiltration assumes critical role in anti-tumor immunity within osteosarcoma. For instance, helper and cytotoxic T cells, affected by external factors (inhibitory cytokines, membrane surface regulatory molecules), can attenuate immune responses against cancer[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMetabolites represent stable end products from various metabolic pathways, making them valuable biomarkers for cancer diagnosis, prognosis, and treatment evaluation. Tumor cells display distinct metabolic profiles compared to their normal counterparts[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In a primary tumor model experiment, researchers identified 52 metabolites exhibiting significant differential expression compared to normal cells[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Research indicates that during tumor metastasis, there is a decrease in carbohydrate and amino acid metabolism decrease, while lipid metabolism intensifies. Moreover, a multitude of observational studies have unveiled numerous relations among immune cells, blood metabolites, and osteosarcoma, underscoring corresponding interconnection[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Studies indicate that modifications in cancer cell metabolism are in part attributable to various inflammatory and immune cells recruitment[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, aberrant metabolites or intermediates within cancer metabolism play pivotally in modulating immune cell proliferation, differentiation, activation, and function[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In essence, both metabolites and immune cells exert modulatory influences on osteosarcoma. Immune cells possess the capacity to perceive diverse signals within the tumor immune microenvironment, prompting specific immune responses. Growing evidence suggests that immune reactions correlate with significant alterations in tissue metabolism, encompassing heightened nutrient consumption, elevated oxygen utilization, and reactive nitrogen, oxygen intermediates generation[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Likewise, numerous metabolites present within the tumor microenvironment impact the differentiation and effector functions of immune cells[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, most of these results stem from observational studies, potentially encumbered by limitations stemming from confounding variables and reverse causality[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, the degree to which blood metabolites genetically influence osteosarcoma regulation through immune cell modulation remains uncertain.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) analysis stands as genetic epidemiology methodology leveraging data from extensive genome-wide association studies (GWAS) to pinpoint genetic variants related with particular traits or diseases[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Single nucleotide polymorphisms (SNPs) were employed as instrumental variables (IVs)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], adhering to Mendel's law, where alleles undergo random allocation and fixation during embryonic formation. This characteristic diminishes the impact of confounding factors on the outcomes, bolstering the evidence for causal inference[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Following Mendelian genetic principles, MR analysis circumvents issues of reverse causality, simulating the design of randomized controlled trial[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, the research marks the first integrated assessment of reciprocal relationship between 731 immune cell phenotypes and osteosarcoma risk through MR analysis. Furthermore, a two-step approach was adopted to scrutinize the mediating impacts of 1400 blood metabolites on the linkage between immune cell phenotypes and osteosarcoma. Reverse MR analysis not only assesses the reverse causality between the two factors but also validates the positive causal relationships, thereby mitigating false positives risk caused by reverse causality in the positive results.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eNo additional ethics approval was required as the pre-existing and published data were used in this reanalysis.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe embarked on a two-step MR analysis to probe relation between immune cell and genetically predicted OS risk, also to investigate if blood metabolites might mediate this relationship. The initial step involved evaluating the causal impact of immune cell phenotypes and blood metabolites on OS through two-sample MR, alongside the filtration of immune cell phenotypes and blood metabolites highly correlated with OS risk. Subsequently, we proceeded to gauge the causal impact of filtered immune cell phenotypes on filtered blood metabolites, alongside computing the proportion of mediators for each mediator concerning the effect of immune cell phenotypes on OS. To minimize the bias of population stratification, this study exclusively analyzed data from individuals of European ancestry. This study fulfills the three fundamental assumptions of MR analysis, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: Firstly, there exists a robust and strong correlation between the IVs and the exposure factor (relevance assumption). Secondly, confounding factors and IVs impacting \"exposure-outcome\" relation are mutually independent (independence assumption). Thirdly, genetic variation solely influence outcome through exposure factor instead of others (exclusion restriction assumption)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Research design was depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eData for OS (finngen_R10_C3_OSTEOSARCOMA_EXALLC) were sourced from FinnGen comprising 64 OS cases and 314193 control cases. Diagnostic criteria for OS adhered to ICD-10 standard[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Data on 731 immune cell phenotypes (GCST90001391 to GCST90002121) and 1400 blood metabolites (GCST90199621 to GCST90201020) were from the GWAS Catalog [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. All study subjects were from Europe descent. For data specifications, see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e..\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\u003eSummary of studies and datasets utilized in the research\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure/outcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNPs size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eosteosarcoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en/access_results\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en/access_results\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efinngen_R10_C3_OSTEOSARCOMA_EXALLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.3 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e314257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e731 immune cell\u003c/p\u003e \u003cp\u003ephenotypes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCST90001391\u003c/p\u003e \u003cp\u003e~GCST90002121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1400 metabolites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCST90199621\u003c/p\u003e \u003cp\u003e~GCST90201020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.40 million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSelection of IVs\u003c/h2\u003e \u003cp\u003eTo fulfill the requirement of the association hypothesis, necessitating a robust correlation between the chosen IVs and the exposure factor, this study employed stringent criteria. SNPs exhibiting genome-wide significant differences, with a condition \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026thinsp;5, were screened. plink_win64_20231018 (code getdata2.R) was utilized to eliminate SNPs in linkage disequilibrium (\u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 10000kb). Subsequently, to adhere to independence assumption, selected IVs were required to exclude other confounding factors associated with both outcome variable and exposure factor. For this purpose, the study manually scrutinized and excluded SNPs linked to confounders and outcomes utilizing Phenoscanner database[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, harmonize_data function from TwoSampleMR R package to conduct consistency analysis of exposure and outcome-related SNPs effect alleles to ensure alignment and eliminated all SNPs with palindromic structures, thus mitigating the risk of errors stemming from chain problems[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To fulfill the exclusion restriction assumption and mitigate weak instrument bias, this study employed a filtering process using \u003cem\u003eF\u003c/em\u003e = [R\u003csup\u003e2\u003c/sup\u003e \u0026times; (N \u0026minus;\u0026thinsp;1 - K)] / [K \u0026times; (1 - R\u003csup\u003e2\u003c/sup\u003e)] for weak instruments with \u003cem\u003eF\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;10 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], where N is the sample size in the exposure factor GWAS study, K denotes SNPs number selected as IVs, R\u003csup\u003e2\u003c/sup\u003e represents variance proportion explained by SNPs in the exposure database R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2 \u0026times; EAF \u0026times; (1 - EAF) \u0026times; β\u003csup\u003e2\u003c/sup\u003e, where EAF represents mutated gene frequency[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and β denotes allele effect size [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This meticulous filtering process yielded IVs final set for MR analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMR analysis\u003c/h2\u003e \u003cp\u003eStatistical assessments were accomplished with R 4.3.2. The prepared IVs were analyzed utilizing \u0026ldquo;TwoSampleMR\u0026rdquo; package. Odds ratios (OR) and confidence intervals (CI) were derived from various regression models, comprising weighted median[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], weighted mode[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], IVW, MR-Egger regression, and simple mode. We opted for the IVW method as primary approach because it employs meta-analysis strategy, pooling causal effects Wald estimates derived from all IVs meeting three assumptions. This process yields an overarching unbiased estimate[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Immune cell phenotypes indirect effect on OS risk were assessed via potential mediator using \u0026ldquo;product of coefficients\u0026rdquo; approach. Standard errors for these indirect effects were computed using delta method[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eThis study employed Cochran's Q statistic for evaluating heterogeneity[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. A statistically notable Cochran's Q statistic (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) suggests substantial heterogeneity in the analysis results. Additionally, the study employed MR-Egger intercept method and MR-PRESSO to detect and address pleiotropy. MR-Egger intercept method is utilized to assess pleiotropic relationship between IVs and other potential confounding factors, as indicated by the intercept. If MR-Egger intercept analysis yields \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, it suggests horizontal pleiotropy presence[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Followingly, pleiotropy residual sum and outlier (PRESSO) are utilized to detect and adjust outliers for horizontal pleiotropy by eliminating any outliers among IVs. Leave-one-out analysis is employed to assess data stability by systematically excluding individual IVs to ascertain if any single SNP disproportionately influences causal association. A substantial alteration in the MR analysis upon removing a specific SNP suggests that the analysis is unduly influenced by that particular IV.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eImmune cell phenotypes overall effect on osteosarcoma\u003c/h2\u003e \u003cp\u003eThis study employed IVW as predominant analysis tool to assess causal relation between 731 immune cell phenotypes and OS. The findings unveiled that 7 immune cell phenotypes exhibited a significant causal relation with OS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among these, CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e (OR\u0026thinsp;=\u0026thinsp;1.3952, 95% CI: 1.1011\u0026ndash;1.7679, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), CD25 on CD45RA\u003csup\u003e\u0026minus;\u003c/sup\u003e CD4 not Treg (OR\u0026thinsp;=\u0026thinsp;1.4099, 95% CI: 1.1027\u0026ndash;1.8027, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and CD45 on HLA DR\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003ebr\u003c/sup\u003e (OR\u0026thinsp;=\u0026thinsp;1.6075, 95% CI: 1.1311\u0026ndash;2.2845, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were positively associated with OS risk. Oppositely, BAFF-R on IgD\u003csup\u003e+\u003c/sup\u003e CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e unsw mem (OR\u0026thinsp;=\u0026thinsp;0.6385, 95%CI: 0.4693\u0026ndash;0.8686, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), CD20 on IgD\u003csup\u003e\u0026minus;\u003c/sup\u003e CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e (OR\u0026thinsp;=\u0026thinsp;0.4324, 95% CI: 0.2425\u0026ndash;0.7707, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Naive CD4\u003csup\u003e+\u003c/sup\u003e %T cell (OR\u0026thinsp;=\u0026thinsp;0.6189, 95% CI: 0.4360\u0026ndash;0.8786, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and CD28\u003csup\u003e+\u003c/sup\u003e CD45RA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003ebr\u003c/sup\u003e %CD8\u003csup\u003ebr\u003c/sup\u003e (OR\u0026thinsp;=\u0026thinsp;0.8540, 95% CI: 0.7588\u0026ndash;0.9611, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were negatively related to OS risk. Moreover, sensitivity analyses indicated that Cochran\u0026rsquo;s Q test, MR-Egger intercept method, and MR-PRESSO yielded \u003cem\u003eP\u003c/em\u003e-values exceeding 0.05, signifying significant heterogeneity or pleiotropy absence in causal effect analysis between immune cell phenotypes and OS, as depicted in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Utilizing leave-one-out approach for SNP exclusion didn\u0026rsquo;t identify any individual SNP significantly disrupting the overall impact of immune cell phenotypes on OS, thereby bolstering robustness to these findings to a certain extent (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEffect of blood metabolites on OS\u003c/h2\u003e \u003cp\u003eWe identified 33 protective blood metabolites on OS using IVW approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Conversely, genetically predicted 27 blood metabolites would increase the risk of OS. \u003cem\u003eP\u003c/em\u003e-values for Cochran's Q statistic derived from both IVW and MR Egger methods exceeding 0.05, indicating lack of significant heterogeneity (Table S2). MR-Egger intercepts test and MR-PRESSO yielded non-statistically notable results, suggesting the absence of horizontal pleiotropy (Table S2). Leave-one-out analysis demonstrated removal of particular SNP wouldn\u0026rsquo;t alter causal estimates (Figure S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEffect of immune cell phenotypes on blood metabolites\u003c/h2\u003e \u003cp\u003eEarlier, we identified seven immune cell phenotypes and sixty blood metabolites crucial for OS. Subsequently, we delved into causal relation between these 7 immune cell phenotypes and 60 blood metabolites. Our MR analysis highlighted a strong association between only CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e in monocyte panel and dimethyl sulfone (OR\u0026thinsp;=\u0026thinsp;1.0300, 95% CI [1.0030, 1.0578], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0293) and X-12680 (OR\u0026thinsp;=\u0026thinsp;1.0330, 95% CI [1.0063, 1.0605], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0152) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Meanwhile, other methodologies failed to reveal any significant causal effect. There were no signs of heterogeneity or horizontal pleiotropy, and no single SNP appeared to disproportionately influence the causal estimates. (Table S3 and Figure S3)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eA reverse MR analysis\u003c/h2\u003e \u003cp\u003eCasual role of CX3CR1 was observed on CD14- CD16- in monocyte panel, dimethyl sulfone and X-12680 on OS, as well as the casual role of CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e in monocyte panel on dimethyl sulfone and X-12680. Followingly, reverse MR analysis was performed. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates that no obvious OS causal effect on CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e in monocyte panel (OR\u0026thinsp;=\u0026thinsp;1.0150, 95% CI [0.9845, 1.0465], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3397), dimethyl sulfone (OR\u0026thinsp;=\u0026thinsp;0.9981, 95% CI [0.9739, 1.0230], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8810) and X-12680 (OR\u0026thinsp;=\u0026thinsp;1.0009, 95% CI [0.9780, 1.0244], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9366) was observed. Furthermore, no causal role of CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e in monocyte panel on dimethyl sulfone (OR\u0026thinsp;=\u0026thinsp;0.9462, 95% CI [0.8295, 1.0794], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4106) and X-12680 (OR\u0026thinsp;=\u0026thinsp;1.0413, 95% CI [0.8654, 1.2529], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.6684) were witnessed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMediation effect of immune cell phenotypes on OS\u003c/h2\u003e \u003cp\u003eWe investigated CX3CR1 causal effect on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e in monocyte panel on OS, dimethyl sulfone and X-12680. Mediation analysis was conducted to illustrate the mediating effect of dimethyl sulfone and X-12680 between CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e in monocyte panel on OS. The mediation effect of dimethyl sulfone and X-12680 in the causal pathway from CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e in monocyte panel to OS were 0.0288 (accounting for 8.7% of the total effect) and 0.0524 (accounting for 15.74% of the total effect), respectively. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediating effects of blood metabolites dimethyl sulfone and X-12680 mediated monocyte CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003eCD16\u003csup\u003e\u0026minus;\u003c/sup\u003e on causality in osteosarcoma\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect effect A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDirect effect B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMediate effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMediate ratio(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.3330(0.097\u0026ndash;0.571)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDimethyl sulfone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0296\u003c/p\u003e \u003cp\u003e(0.0030\u0026thinsp;~\u0026thinsp;0.0562)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9738\u003c/p\u003e \u003cp\u003e(0.0655\u0026thinsp;~\u0026thinsp;1.8820)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0288\u003c/p\u003e \u003cp\u003e(-0.856\u0026thinsp;~\u0026thinsp;0.913)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX-12680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0325\u003c/p\u003e \u003cp\u003e(0.0063\u0026thinsp;~\u0026thinsp;0.0587)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6136\u003c/p\u003e \u003cp\u003e(0.4725\u0026thinsp;~\u0026thinsp;2.7546)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0524\u003c/p\u003e \u003cp\u003e(-1.789\u0026thinsp;~\u0026thinsp;1.894)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Total effect: Causal effect of monocyte CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e on osteosarcoma; Direct effect A: Causal effect of immune cell phenotype on blood metabolites; Direct effect B: Causal effect of blood metabolites on osteosarcoma; β mediated effect\u0026thinsp;=\u0026thinsp;β direct effect A*β direct effect B; Intermediate ratio\u0026thinsp;=\u0026thinsp;β indirect effect / β total effect.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research represents the first comprehensive two-sample, two-way MR analysis exploring the causal relation between OS and 731 immune cell types. Leveraging a vast repository of publicly available genetic data, this study benefits from a larger sample size and a more targeted analytical approach compared to traditional double-blind controlled experiments focusing on single or a few immune cells in relation to OS. Furthermore, employing a two-step methodology, it identifies and quantifies the mediating roles of 1,400 blood metabolites as potential mediators. Through this approach, the study aims to mitigate the effects of confounding, reverse causation, and other factors on the observed results, thus enhancing the robustness of its findings.\u003c/p\u003e \u003cp\u003eThe study's findings unveil significant causal relationships (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eIVW\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.01) between 7 immune cell phenotypes and OS when considering immune cell phenotypes as exposure factors and OS as outcome data. Specifically, OS risk escalates with heightened levels of CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e phenotype in monocytes panel, CD25 on CD45RA\u003csup\u003e\u0026minus;\u003c/sup\u003e CD4 not Treg in Treg panel, CD45 on HLA DR\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003ebr\u003c/sup\u003e in TBNK panel. Oppositely, elevation in BAFF-R on IgD\u003csup\u003e+\u003c/sup\u003e CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e unsw mem and CD20 on IgD\u003csup\u003e\u0026minus;\u003c/sup\u003e CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e phenotype in B cell panel, Naive CD4\u003csup\u003e+\u003c/sup\u003e %T cell in T cell panel maturation stages, and CD28\u003csup\u003e+\u003c/sup\u003e CD45RA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003ebr\u003c/sup\u003e %CD8\u003csup\u003ebr\u003c/sup\u003e phenotype in Treg panel is related to decreased OS risk.\u003c/p\u003e \u003cp\u003eMonocytes expressing CX3CR1 (chemokine receptor 3, class I) on their surface, while lacking CD14 or CD16, are termed CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e monocytes. Originating from bone marrow, monocytes circulate in the vasculature and transform into macrophages upon exiting the vasculature. Monocytes are recognized for their pivotal role in fostering lung metastasis in OS. Upon reaching the metastatic site, undergo differentiation into metastasis-associated macrophages, crucial for facilitating metastatic colonization by aiding tumor cell extravasation, growth, and angiogenesis[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. CX3CR1, a G-protein-coupled 7-transmembrane-domain receptor, is expressed on specific cells surface. Notably, CX3C chemokine family comprises only one member, CX3CL1. Liu et al.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] demonstrated that CX3CL1 promotes OS cell migration and facilitates lung metastasis by upregulating ICAM-1 expression. Conversely, CX3CL1 knockdown inhibits OS lung metastasis. CD14, a surface antigen, plays central role in macrophage M2 polarization[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. CD14\u003csup\u003e+\u003c/sup\u003e macrophage M2 is related to OS metastasis reduction and improved survival [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. CD16 is member of immunoglobulin superfamily (IgSF) and, upon binding to antibodies, triggers immune cells to initiate responses such as degranulation, antibody-dependent cell-mediated cytotoxicity, respiratory bursts, phagocytosis, and targeting of cancerous or virus-infected cells[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Cillo et al.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] observed an elevated trend in the CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e monocyte population among OS patients compared to control. This finding hints at potential correlation between CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e monocytes and pulmonary metastasis in OS. The studies mentioned above collectively indicate that OS progression and lung metastasis are linked to heightened expression of monocyte subtypes exhibiting CX3CR1-positive expression, along with the CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e monocyte cluster. These findings align with analysis result conducted in the present study.\u003c/p\u003e \u003cp\u003eRegulatory T cells (Treg) represent a crucial subset of T cells involved in maintaining immune homeostasis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Among Treg cell clusters, CD25 on CD45RA\u003csup\u003e\u0026minus;\u003c/sup\u003e CD4 not Treg phenotype denotes a subset of activated non-Treg CD4\u003csup\u003e+\u003c/sup\u003e T cells expressing CD25 (IL-2 receptor α-chain) and CD4, while lacking CD45RA expression. YANG et al.[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] observed a significant decrease in Treg cells within the osteosarcoma (OS) tumor microenvironment. CD25 serves as the heterologous alpha chain of the trimeric IL-2 receptor, with its expression varying across different hematologic malignancies and solid tumors[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. RISSETTO et al.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] noted a higher prevalence of CD25\u003csup\u003e+\u003c/sup\u003e Treg cells in the blood of dogs with OS. While no direct association between the CD25 on CD45RA\u003csup\u003e\u0026minus;\u003c/sup\u003e CD4 not Treg phenotype and OS has been explicitly demonstrated, the analysis conducted in this study, combined with the integration of the aforementioned research findings, hints at a potential causal relationship between them.\u003c/p\u003e \u003cp\u003eCD45 on HLA DR\u0026thinsp;+\u0026thinsp;CD8br represents a cytotoxic T cell expressing both CD45 and HLA-DR. CD45, also known as Protein Tyrosine Phosphatase Receptor Type C, plays a pivotal role in immunology by regulating the differentiation of T cells through the modulation of Src family kinases - Lck and Fyn. The absence of CD45 leads to functional deficiencies in both T and B cells, resulting in severe combined immunodeficiency and increasing susceptibility to autoimmune diseases and cancer[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. HLA-DR, a gene within the major histocompatibility complex class II, is part of HLA system, encoded by the HLA complex located on chromosome 6p21. This antigen is capable of presenting antigen fragments to T cells, thereby triggering an immune response[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Currently, there is limited research regarding the cytotoxic T lymphocyte CD45 on HLA DR\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003ebr\u003c/sup\u003e phenotype in OS. However, studies conducted by Lim et al.[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] suggest that HLA DR gene family demonstrates elevated expression in OS. Elevated expression of HLA-E and HLA-F may potentially enable tumor cells to evade immune surveillance by NK cells and T cells, thus facilitating immune escape of tumor cells.\u003c/p\u003e \u003cp\u003eBAFF-R on IgD\u003csup\u003e+\u003c/sup\u003e CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e unsw mem represents a subset of memory B cells that have not undergone somatic hypermutation or class switching, expressing both BAFF-R and IgD. This B cells subset plays a role in suppressing anti-tumor T cells through the secretion of IL-10 and contributes to tumorigenesis by releasing antibodies that exacerbate chronic inflammation[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. BAFF-R (B cell activating factor receptor), a member of TNFR family, is notably expressed at high levels in OS[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Researchers have observed elevated expression of BAFF-R and BAFF in samples from two types of OS (conventional and bone membrane subtype), indicating a close association between heightened BAFF/BAFF-R expression and OS occurrence and progression[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Meanwhile, IgD is crucial for transitioning from highly primary self-reactive to secondary antigen-specific antibody responses[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. However, there is currently no evidence indicating a close association between IgD and OS occurrence and development.\u003c/p\u003e \u003cp\u003eThe CD20 on IgD\u003csup\u003e\u0026minus;\u003c/sup\u003e CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e in B cell panel demonstrates a noteworthy causal relationship with a decreased risk of osteosarcoma. CD20 is a protein expressed on the surface of B cells, pivotal in their maturation and differentiation processes. Within the tumor microenvironment, the presence of CD20\u003csup\u003e+\u003c/sup\u003e tumor-infiltrating immune cells is significantly correlated with favorable prognosis in various cancers. According to research by SATO et al.[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], CD20\u003csup\u003e+\u003c/sup\u003e cells locally activate CD8\u003csup\u003e+\u003c/sup\u003e T cells within the tumor and participate in antigen presentation, shedding light on functional role of CD20\u0026thinsp;+\u0026thinsp;cells within the tumor microenvironment.\u003c/p\u003e \u003cp\u003eNaive CD4+ %T cells represent undifferentiated T cells, expressing CD4 on their surface. When being antigen exposed, naive CD4\u003csup\u003e+\u003c/sup\u003e %T cells differentiated into effector T cells. CD4\u003csup\u003e+\u003c/sup\u003e Tregs serve as the principal immunosuppressive cells, pivotal in facilitating tumor immune evasion through co-inhibitory molecules or immunosuppressive cytokines[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Interplays between osteoclasts and CD4\u003csup\u003e+\u003c/sup\u003e Tregs modulate the tumor immune microenvironment. Osteoclasts attract surrounding CD4\u003csup\u003e+\u003c/sup\u003e T cells by releasing chemokines[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], secrete T cell stimulants, express MHC, process soluble antigens, thus inducing proliferation and activation of CD4\u003csup\u003e+\u003c/sup\u003e Tregs[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. CD4\u003csup\u003e+\u003c/sup\u003e Tregs are pivotal immunosuppressive cells within tumor immune microenvironment, instrumental in facilitating tumor immune evasion through co-inhibitory molecules or immunosuppressive cytokines[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCD28\u003csup\u003e+\u003c/sup\u003e CD45RA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003ebr\u003c/sup\u003e %CD8\u003csup\u003ebr\u003c/sup\u003e within Treg panel expresses CD28, CD45RA, and CD8\u003csup\u003ebr\u003c/sup\u003e. CD28 receptor family encompasses receptors present on immune cells surface, functioning as positive activation receptors on T cells. These receptors play pivotally in T cell development and proliferation, amplifying signals from T cell receptor to trigger immune responses, and regulating anti-inflammatory actions of Treg cells[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Research by Li et al. [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] suggests that elevated CD28 expression in tumor is favorable for OS prognosis, and blocking CD86/CTLA4 signal transduction while enhancing CD86/CD28 signal transduction represents a promising strategy for OS immunotherapy. CD8\u003csup\u003ebr\u003c/sup\u003e, also known as CD8\u003csup\u003ebright\u003c/sup\u003e, is a CD8\u003csup\u003e+\u003c/sup\u003e T cells subset. Elevated levels of CD8\u003csup\u003e+\u003c/sup\u003e Tregs have been linked to a poorer prognosis, as they possess the ability to suppress anti-tumor immune responses[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA noteworthy causal relationship was observed between the blood metabolite DMSO and an increased OS risk. DMSO, also referred to as methyl sulfoxide, is an organic sulfur compound. Studies suggest that DMSO exhibits antioxidant and anti-inflammatory properties, which may potentially synergize to inhibit tumors by suppressing the JAK2/STAT5b pathway[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Research by S et al.[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] revealed that DMSO impede STAT5b and STAT3 pathways, leading to significant reduction in human cancer cells viability. DMSO demonstrates anticancer properties in metastatic cancer cells, as reported in literature[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Studies have highlighted its ability to induce endoplasmic reticulum stress-mediated apoptosis in HCT116 cells[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Moreover, research has indicated the anti-proliferative effects of DMSO, suggesting its potential to inhibit the invasion and prostate cancer cells migration capabilities[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. DMSO triggers hepatic cells apoptosis, comprising HepG2 and Huh7, through exogenous apoptosis pathway[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Moreover, a study conducted on A549 lung and YD-38 gingival cancer cells corroborated the anticancer properties of DMSO. Research underscored alterations in cell viability, cell cycle progression, and apoptosis in tumor cells upon exposure to DMSO[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. While these investigations support the anticancer efficacy of DMSO, there is no direct literature addressing its impact on OS. Surprisingly, the current study unveils a significant causal association between DMSO and an increased OS risk, contrary to previous understandings. This implies that unlike its effects on other cancers, dimethyl sulfoxide might promote OS onset and progression, although the precise underlying mechanism warrants further elucidation.\u003c/p\u003e \u003cp\u003eAdditionally, reverse MR analysis was undertaken to verify positive findings. The results suggest that when OS was utilized as exposure data, no notable causal relation observed with CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e, DMSO, and X-12680 (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eIVW\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.05). Similarly, when DMSO and X-12680 were employed as exposure data, no notable causal relation with CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003eIVW\u003c/em\u003e\u003c/sub\u003e \u0026lt; 0.05). MR analysis leverages SNPs as IVs to infer causal relation between the exposure factor under investigation and outcome factor. Since genetic variation occurs before disease onset, the sequence of events is predetermined and cannot be reversed[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], thus MR analysis impede interference from reverse causality as well. Given this characteristic of MR analysis, reverse MR analysis serves as a validation of the positive results from another perspective. Negative outcomes observed in reverse MR analysis precisely indicate that the significant causal relation identified in positive analysis remained unaffected by reverse causality, thus mitigating false positives risk.\u003c/p\u003e \u003cp\u003eIn summary, we delved into causal relation between 731 immune cell phenotypes, 1400 blood metabolites and OS using a two-step mediation MR analysis. Our findings unveiled seven immune cell phenotypes with significant causal links to OS. Moreover, we identified dimethyl sulfoxide and an unidentified metabolite, X-12680, as mediators in the pathway from monocyte CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e to OS, with respective mediation proportions of 8.7% and 15.7%. However, several limitations should be acknowledged: 1) The study couldn't fully evaluate potential heterogeneity and horizontal pleiotropy. 2) The generalizability of the conclusions is constrained since all data were sourced from European populations, necessitating validation in other demographics. 3) While DMSO and X-12680 partially mediated the causal pathway from monocyte CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e to OS within blood metabolites, accounting for 8.7% and 15.7% of the total mediation effect, respectively, there could exist additional mediating factors warranting further exploration. This study offers novel perspectives on the genetic mechanisms underlying OS, potentially guiding precision treatment strategies. It sets the stage for deeper investigations into the interplay among immune cells, blood metabolites, and OS. This holistic understanding promises valuable insights into the intricate connections between the human immune and metabolic systems and OS. Furthermore, it paves the way for identifying potential biomarkers and therapeutic targets in OS immunity and metabolism, fostering advancements in diagnosis and intervention strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eResearch of ours elucidated causal interplays among immune cells, blood metabolites and OS. Spcially, CX3CR1 on CD14\u003csup\u003e\u0026minus;\u003c/sup\u003e CD16\u003csup\u003e\u0026minus;\u003c/sup\u003e was taken as underlying OS risk factor, with a substantial portion of its effect mediated by DMSO and X-12680\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge all the genetics consortiums for making the GWAS summary data publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePing Zeng and Jinfu Liu designed the project. Chicheng Niu and Qingyuan Xu conducted the data analysis. Chicheng Niu wrote the manuscript. Weiwei Wang and Hao Li carried out data collection. Qiang Ding and Liang Guo critically reviewed the manuscript. Chicheng Niu and Qingyuan Xu contributed equally to this work. All authors have read and approved the submitted manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grant number 82160913)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets mentioned in this study can be found in online repositories.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe OS GWAS data source: https://www.finngen.fi/en/access_results.\u003c/p\u003e\n\u003cp\u003eThe acquired 731 immune cell phenotypes GWAS data source: https://www.ebi.ac.uk/gwas/.\u003c/p\u003e\n\u003cp\u003eThe acquired 1400 blood metabolites GWAS data source: https://www.ebi.ac.uk/gwas/.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study is based on data from open-access public databases. Ethics and consent statements are not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBeird HC, Bielack SS, Flanagan AM, Gill J, Heymann D, Janeway KA, Livingston JA, Roberts RD, Strauss SJ, Gorlick R: \u003cstrong\u003eOsteosarcoma\u003c/strong\u003e. \u003cem\u003eNature reviews Disease primers \u003c/em\u003e2022, \u003cstrong\u003e8\u003c/strong\u003e(1):77.\u003c/li\u003e\n\u003cli\u003ePanez-Toro I, Mu\u0026ntilde;oz-Garc\u0026iacute;a J, Vargas-Franco JW, Renodon-Corni\u0026egrave;re A, Heymann MF, L\u0026eacute;zot F, Heymann D: \u003cstrong\u003eAdvances in Osteosarcoma\u003c/strong\u003e. \u003cem\u003eCurrent osteoporosis reports \u003c/em\u003e2023, \u003cstrong\u003e21\u003c/strong\u003e(4):330-343.\u003c/li\u003e\n\u003cli\u003eWycislo KL, Fan TM: \u003cstrong\u003eThe immunotherapy of canine osteosarcoma: a historical and systematic review\u003c/strong\u003e. \u003cem\u003eJournal of veterinary internal medicine \u003c/em\u003e2015, \u003cstrong\u003e29\u003c/strong\u003e(3):759-769.\u003c/li\u003e\n\u003cli\u003eChen C, Xie L, Ren T, Huang Y, Xu J, Guo W: \u003cstrong\u003eImmunotherapy for osteosarcoma: Fundamental mechanism, rationale, and recent breakthroughs\u003c/strong\u003e. \u003cem\u003eCancer letters \u003c/em\u003e2021, \u003cstrong\u003e500\u003c/strong\u003e:1-10.\u003c/li\u003e\n\u003cli\u003eZheng Y, Wang G, Chen R, Hua Y, Cai Z: \u003cstrong\u003eMesenchymal stem cells in the osteosarcoma microenvironment: their biological properties, influence on tumor growth, and therapeutic implications\u003c/strong\u003e. \u003cem\u003eStem cell research \u0026amp; therapy \u003c/em\u003e2018, \u003cstrong\u003e9\u003c/strong\u003e(1):22.\u003c/li\u003e\n\u003cli\u003eZhou Q, Xian M, Xiang S, Xiang D, Shao X, Wang J, Cao J, Yang X, Yang B, Ying M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAll-Trans Retinoic Acid Prevents Osteosarcoma Metastasis by Inhibiting M2 Polarization of Tumor-Associated Macrophages\u003c/strong\u003e. \u003cem\u003eCancer immunology research \u003c/em\u003e2017, \u003cstrong\u003e5\u003c/strong\u003e(7):547-559.\u003c/li\u003e\n\u003cli\u003eSarvaria A, Madrigal JA, Saudemont A: \u003cstrong\u003eB cell regulation in cancer and anti-tumor immunity\u003c/strong\u003e. \u003cem\u003eCellular \u0026amp; molecular immunology \u003c/em\u003e2017, \u003cstrong\u003e14\u003c/strong\u003e(8):662-674.\u003c/li\u003e\n\u003cli\u003eCantor JR, Sabatini DM: \u003cstrong\u003eCancer cell metabolism: one hallmark, many faces\u003c/strong\u003e. \u003cem\u003eCancer discovery \u003c/em\u003e2012, \u003cstrong\u003e2\u003c/strong\u003e(10):881-898.\u003c/li\u003e\n\u003cli\u003eHua Y, Qiu Y, Zhao A, Wang X, Chen T, Zhang Z, Chi Y, Li Q, Sun W, Li G\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eDynamic metabolic transformation in tumor invasion and metastasis in mice with LM-8 osteosarcoma cell transplantation\u003c/strong\u003e. \u003cem\u003eJournal of proteome research \u003c/em\u003e2011, \u003cstrong\u003e10\u003c/strong\u003e(8):3513-3521.\u003c/li\u003e\n\u003cli\u003eXia L, Oyang L, Lin J, Tan S, Han Y, Wu N, Yi P, Tang L, Pan Q, Rao S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe cancer metabolic reprogramming and immune response\u003c/strong\u003e. \u003cem\u003eMolecular cancer \u003c/em\u003e2021, \u003cstrong\u003e20\u003c/strong\u003e(1):28.\u003c/li\u003e\n\u003cli\u003eRamapriyan R, Caetano MS, Barsoumian HB, Mafra ACP, Zambalde EP, Menon H, Tsouko E, Welsh JW, Cortez MA: \u003cstrong\u003eAltered cancer metabolism in mechanisms of immunotherapy resistance\u003c/strong\u003e. \u003cem\u003ePharmacology \u0026amp; therapeutics \u003c/em\u003e2019, \u003cstrong\u003e195\u003c/strong\u003e:162-171.\u003c/li\u003e\n\u003cli\u003eCronin SJF, Seehus C, Weidinger A, Talbot S, Reissig S, Seifert M, Pierson Y, McNeill E, Longhi MS, Turnes BL\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe metabolite BH4 controls T cell proliferation in autoimmunity and cancer\u003c/strong\u003e. \u003cem\u003eNature \u003c/em\u003e2018, \u003cstrong\u003e563\u003c/strong\u003e(7732):564-568.\u003c/li\u003e\n\u003cli\u003eHuang B, Song BL, Xu C: \u003cstrong\u003eCholesterol metabolism in cancer: mechanisms and therapeutic opportunities\u003c/strong\u003e. \u003cem\u003eNature metabolism \u003c/em\u003e2020, \u003cstrong\u003e2\u003c/strong\u003e(2):132-141.\u003c/li\u003e\n\u003cli\u003eKarayama M, Masuda J, Mori K, Yasui H, Hozumi H, Suzuki Y, Furuhashi K, Fujisawa T, Enomoto N, Nakamura Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eComprehensive assessment of multiple tryptophan metabolites as potential biomarkers for immune checkpoint inhibitors in patients with non-small cell lung cancer\u003c/strong\u003e. \u003cem\u003eClinical \u0026amp; translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico \u003c/em\u003e2021, \u003cstrong\u003e23\u003c/strong\u003e(2):418-423.\u003c/li\u003e\n\u003cli\u003eUffelmann E, Huang QQ, Munung NS, De Vries J, Okada Y, Martin AR, Martin HC, Lappalainen T, Posthuma D: \u003cstrong\u003eGenome-wide association studies\u003c/strong\u003e. \u003cem\u003eNature Reviews Methods Primers \u003c/em\u003e2021, \u003cstrong\u003e1\u003c/strong\u003e(1):59.\u003c/li\u003e\n\u003cli\u003eBurgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGuidelines for performing Mendelian randomization investigations: update for summer 2023\u003c/strong\u003e. \u003cem\u003eWellcome open research \u003c/em\u003e2019, \u003cstrong\u003e4\u003c/strong\u003e:186.\u003c/li\u003e\n\u003cli\u003eLawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G: \u003cstrong\u003eMendelian randomization: using genes as instruments for making causal inferences in epidemiology\u003c/strong\u003e. \u003cem\u003eStatistics in medicine \u003c/em\u003e2008, \u003cstrong\u003e27\u003c/strong\u003e(8):1133-1163.\u003c/li\u003e\n\u003cli\u003eClarke SLN, Mitchell RE, Sharp GC, Ramanan AV, Relton CL: \u003cstrong\u003eVitamin D Levels and Risk of Juvenile Idiopathic Arthritis: A Mendelian Randomization Study\u003c/strong\u003e. \u003cem\u003eArthritis care \u0026amp; research \u003c/em\u003e2023, \u003cstrong\u003e75\u003c/strong\u003e(3):674-681.\u003c/li\u003e\n\u003cli\u003eBirney E: \u003cstrong\u003eMendelian Randomization\u003c/strong\u003e. \u003cem\u003eCold Spring Harbor perspectives in medicine \u003c/em\u003e2022, \u003cstrong\u003e12\u003c/strong\u003e(4).\u003c/li\u003e\n\u003cli\u003eKurki MI, Karjalainen J, Palta P, Sipil\u0026auml; TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eFinnGen provides genetic insights from a well-phenotyped isolated population\u003c/strong\u003e. \u003cem\u003eNature \u003c/em\u003e2023, \u003cstrong\u003e613\u003c/strong\u003e(7944):508-518.\u003c/li\u003e\n\u003cli\u003eChen Y, Lu T, Pettersson-Kymmer U, Stewart ID, Butler-Laporte G, Nakanishi T, Cerani A, Liang KYH, Yoshiji S, Willett JDS\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGenomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases\u003c/strong\u003e. \u003cem\u003eNature genetics \u003c/em\u003e2023, \u003cstrong\u003e55\u003c/strong\u003e(1):44-53.\u003c/li\u003e\n\u003cli\u003eOrr\u0026ugrave; V, Steri M, Sidore C, Marongiu M, Serra V, Olla S, Sole G, Lai S, Dei M, Mulas A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eComplex genetic signatures in immune cells underlie autoimmunity and inform therapy\u003c/strong\u003e. \u003cem\u003eNature genetics \u003c/em\u003e2020, \u003cstrong\u003e52\u003c/strong\u003e(10):1036-1045.\u003c/li\u003e\n\u003cli\u003eZhou T, Sun D, Li X, Ma H, Heianza Y, Qi L: \u003cstrong\u003eEducational attainment and drinking behaviors: Mendelian randomization study in UK Biobank\u003c/strong\u003e. \u003cem\u003eMolecular psychiatry \u003c/em\u003e2021, \u003cstrong\u003e26\u003c/strong\u003e(8):4355-4366.\u003c/li\u003e\n\u003cli\u003eHartwig FP, Davies NM, Hemani G, Davey Smith G: \u003cstrong\u003eTwo-sample Mendelian randomization: avoiding the downsides of a powerful, widely applicable but potentially fallible technique\u003c/strong\u003e. \u003cem\u003eInternational journal of epidemiology \u003c/em\u003e2016, \u003cstrong\u003e45\u003c/strong\u003e(6):1717-1726.\u003c/li\u003e\n\u003cli\u003eCodd V, Nelson CP, Albrecht E, Mangino M, Deelen J, Buxton JL, Hottenga JJ, Fischer K, Esko T, Surakka I\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eIdentification of seven loci affecting mean telomere length and their association with disease\u003c/strong\u003e. \u003cem\u003eNature genetics \u003c/em\u003e2013, \u003cstrong\u003e45\u003c/strong\u003e(4):422-427, 427e421-422.\u003c/li\u003e\n\u003cli\u003eLi W, Lu Q, Qian J, Feng Y, Luo J, Luo C, He W, Dong B, Liu H, Liu Z\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAssessing the causal relationship between genetically determined inflammatory biomarkers and low back pain risk: a bidirectional two-sample Mendelian randomization study\u003c/strong\u003e. \u003cem\u003eFrontiers in immunology \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e:1174656.\u003c/li\u003e\n\u003cli\u003eLevin MG, Judy R, Gill D, Vujkovic M, Verma SS, Bradford Y, Ritchie MD, Hyman MC, Nazarian S, Rader DJ\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGenetics of height and risk of atrial fibrillation: A Mendelian randomization study\u003c/strong\u003e. \u003cem\u003ePLoS medicine \u003c/em\u003e2020, \u003cstrong\u003e17\u003c/strong\u003e(10):e1003288.\u003c/li\u003e\n\u003cli\u003eRosoff DB, Clarke TK, Adams MJ, McIntosh AM, Davey Smith G, Jung J, Lohoff FW: \u003cstrong\u003eEducational attainment impacts drinking behaviors and risk for alcohol dependence: results from a two-sample Mendelian randomization study with ~780,000 participants\u003c/strong\u003e. \u003cem\u003eMolecular psychiatry \u003c/em\u003e2021, \u003cstrong\u003e26\u003c/strong\u003e(4):1119-1132.\u003c/li\u003e\n\u003cli\u003eVermeulen JM, Wootton RE, Treur JL, Sallis HM, Jones HJ, Zammit S, van den Brink W, Goodwin GM, de Haan L, Munaf\u0026ograve; MR: \u003cstrong\u003eSmoking and the risk for bipolar disorder: evidence from a bidirectional Mendelian randomisation study\u003c/strong\u003e. \u003cem\u003eThe British journal of psychiatry : the journal of mental science \u003c/em\u003e2021, \u003cstrong\u003e218\u003c/strong\u003e(2):88-94.\u003c/li\u003e\n\u003cli\u003eBowden J, Davey Smith G, Haycock PC, Burgess S: \u003cstrong\u003eConsistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator\u003c/strong\u003e. \u003cem\u003eGenetic epidemiology \u003c/em\u003e2016, \u003cstrong\u003e40\u003c/strong\u003e(4):304-314.\u003c/li\u003e\n\u003cli\u003eBurgess S, Dudbridge F, Thompson SG: \u003cstrong\u003eCombining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods\u003c/strong\u003e. \u003cem\u003eStatistics in medicine \u003c/em\u003e2016, \u003cstrong\u003e35\u003c/strong\u003e(11):1880-1906.\u003c/li\u003e\n\u003cli\u003eHan Y, Zhang Y, Zeng X: \u003cstrong\u003eAssessment of causal associations between uric acid and 25-hydroxyvitamin D levels\u003c/strong\u003e. \u003cem\u003eFrontiers in endocrinology \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:1024675.\u003c/li\u003e\n\u003cli\u003eZhao J, Wang J, Xu H, Hu W, Shi F, Fan Z, Zhou C, Mu H: \u003cstrong\u003eIntervertebral Disk Degeneration and Bone Mineral Density: A Bidirectional Mendelian Randomization Study\u003c/strong\u003e. \u003cem\u003eCalcified tissue international \u003c/em\u003e2024, \u003cstrong\u003e114\u003c/strong\u003e(3):228-236.\u003c/li\u003e\n\u003cli\u003eChen J, Yu X, Wu X, Chai K, Wang S: \u003cstrong\u003eCausal relationships between gut microbiota, immune cell, and Non-small cell lung cancer: a two-step, two-sample Mendelian randomization study\u003c/strong\u003e. \u003cem\u003eJournal of Cancer \u003c/em\u003e2024, \u003cstrong\u003e15\u003c/strong\u003e(7):1890-1897.\u003c/li\u003e\n\u003cli\u003eHu M, Sun Z, Tang X, Zeng W, Yan H, Jiang Z, Huang F: \u003cstrong\u003eSystemic lupus erythematosus Association between Osteomyelitis: A two-sample Mendelian randomization study in European population\u003c/strong\u003e. \u003cem\u003eHeliyon \u003c/em\u003e2023, \u003cstrong\u003e9\u003c/strong\u003e(12):e22999.\u003c/li\u003e\n\u003cli\u003eGala H, Tomlinson I: \u003cstrong\u003eThe use of Mendelian randomisation to identify causal cancer risk factors: promise and limitations\u003c/strong\u003e. \u003cem\u003eThe Journal of pathology \u003c/em\u003e2020, \u003cstrong\u003e250\u003c/strong\u003e(5):541-554.\u003c/li\u003e\n\u003cli\u003eMazzieri R, Pucci F, Moi D, Zonari E, Ranghetti A, Berti A, Politi LS, Gentner B, Brown JL, Naldini L\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eTargeting the ANG2/TIE2 axis inhibits tumor growth and metastasis by impairing angiogenesis and disabling rebounds of proangiogenic myeloid cells\u003c/strong\u003e. \u003cem\u003eCancer cell \u003c/em\u003e2011, \u003cstrong\u003e19\u003c/strong\u003e(4):512-526.\u003c/li\u003e\n\u003cli\u003eQian BZ, Li J, Zhang H, Kitamura T, Zhang J, Campion LR, Kaiser EA, Snyder LA, Pollard JW: \u003cstrong\u003eCCL2 recruits inflammatory monocytes to facilitate breast-tumour metastasis\u003c/strong\u003e. \u003cem\u003eNature \u003c/em\u003e2011, \u003cstrong\u003e475\u003c/strong\u003e(7355):222-225.\u003c/li\u003e\n\u003cli\u003eLiu JF, Tsao YT, Hou CH: \u003cstrong\u003eFractalkine/CX3CL1 induced intercellular adhesion molecule-1-dependent tumor metastasis through the CX3CR1/PI3K/Akt/NF-\u0026kappa;B pathway in human osteosarcoma\u003c/strong\u003e. \u003cem\u003eOncotarget \u003c/em\u003e2017, \u003cstrong\u003e8\u003c/strong\u003e(33):54136-54148.\u003c/li\u003e\n\u003cli\u003eDou C, Ding N, Zhao C, Hou T, Kang F, Cao Z, Liu C, Bai Y, Dai Q, Ma Q\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eEstrogen Deficiency-Mediated M2 Macrophage Osteoclastogenesis Contributes to M1/M2 Ratio Alteration in Ovariectomized Osteoporotic Mice\u003c/strong\u003e. \u003cem\u003eJournal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research \u003c/em\u003e2018, \u003cstrong\u003e33\u003c/strong\u003e(5):899-908.\u003c/li\u003e\n\u003cli\u003eBuddingh EP, Kuijjer ML, Duim RA, B\u0026uuml;rger H, Agelopoulos K, Myklebost O, Serra M, Mertens F, Hogendoorn PC, Lankester AC\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eTumor-infiltrating macrophages are associated with metastasis suppression in high-grade osteosarcoma: a rationale for treatment with macrophage activating agents\u003c/strong\u003e. \u003cem\u003eClinical cancer research : an official journal of the American Association for Cancer Research \u003c/em\u003e2011, \u003cstrong\u003e17\u003c/strong\u003e(8):2110-2119.\u003c/li\u003e\n\u003cli\u003eHu J, Zhang L, Xia H, Yan Y, Zhu X, Sun F, Sun L, Li S, Li D, Wang J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eTumor microenvironment remodeling after neoadjuvant immunotherapy in non-small cell lung cancer revealed by single-cell RNA sequencing\u003c/strong\u003e. \u003cem\u003eGenome medicine \u003c/em\u003e2023, \u003cstrong\u003e15\u003c/strong\u003e(1):14.\u003c/li\u003e\n\u003cli\u003eCillo AR, Mukherjee E, Bailey NG, Onkar S, Daley J, Salgado C, Li X, Liu D, Ranganathan S, Burgess M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eEwing Sarcoma and Osteosarcoma Have Distinct Immune Signatures and Intercellular Communication Networks\u003c/strong\u003e. \u003cem\u003eClinical cancer research : an official journal of the American Association for Cancer Research \u003c/em\u003e2022, \u003cstrong\u003e28\u003c/strong\u003e(22):4968-4982.\u003c/li\u003e\n\u003cli\u003eKang JH, Zappasodi R: \u003cstrong\u003eModulating Treg stability to improve cancer immunotherapy\u003c/strong\u003e. \u003cem\u003eTrends in cancer \u003c/em\u003e2023, \u003cstrong\u003e9\u003c/strong\u003e(11):911-927.\u003c/li\u003e\n\u003cli\u003eYang M, Zheng H, Xu K, Yuan Q, Aihaiti Y, Cai Y, Xu P: \u003cstrong\u003eA novel signature to guide osteosarcoma prognosis and immune microenvironment: Cuproptosis-related lncRNA\u003c/strong\u003e. \u003cem\u003eFrontiers in immunology \u003c/em\u003e2022, \u003cstrong\u003e13\u003c/strong\u003e:919231.\u003c/li\u003e\n\u003cli\u003eRissetto KC, Rindt H, Selting KA, Villamil JA, Henry CJ, Reinero CR: \u003cstrong\u003eCloning and expression of canine CD25 for validation of an anti-human CD25 antibody to compare T regulatory lymphocytes in healthy dogs and dogs with osteosarcoma\u003c/strong\u003e. \u003cem\u003eVeterinary immunology and immunopathology \u003c/em\u003e2010, \u003cstrong\u003e135\u003c/strong\u003e(1-2):137-145.\u003c/li\u003e\n\u003cli\u003eRheinl\u0026auml;nder A, Schraven B, Bommhardt U: \u003cstrong\u003eCD45 in human physiology and clinical medicine\u003c/strong\u003e. \u003cem\u003eImmunology letters \u003c/em\u003e2018, \u003cstrong\u003e196\u003c/strong\u003e:22-32.\u003c/li\u003e\n\u003cli\u003eJiang CY, Zhao L, Green MD, Ravishankar S, Towlerton AMH, Scott AJ, Raghavan M, Cusick MF, Warren EH, Ramnath N: \u003cstrong\u003eClass II HLA-DRB4 is a predictive biomarker for survival following immunotherapy in metastatic non-small cell lung cancer\u003c/strong\u003e. \u003cem\u003eScientific reports \u003c/em\u003e2024, \u003cstrong\u003e14\u003c/strong\u003e(1):345.\u003c/li\u003e\n\u003cli\u003eLim WC, Marques Da Costa ME, Godefroy K, Jacquet E, Gragert L, Rondof W, Marchais A, Nhiri N, Dalfovo D, Viard M\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eDivergent HLA variations and heterogeneous expression but recurrent HLA loss-of- heterozygosity and common HLA-B and TAP transcriptional silencing across advanced pediatric solid cancers\u003c/strong\u003e. \u003cem\u003eFrontiers in immunology \u003c/em\u003e2023, \u003cstrong\u003e14\u003c/strong\u003e:1265469.\u003c/li\u003e\n\u003cli\u003eFridman WH, Petitprez F, Meylan M, Chen TW, Sun CM, Roumenina LT, Saut\u0026egrave;s-Fridman C: \u003cstrong\u003eB cells and cancer: To B or not to B?\u003c/strong\u003e \u003cem\u003eThe Journal of experimental medicine \u003c/em\u003e2021, \u003cstrong\u003e218\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eKohno T, Daa T, Otani H, Shimokawa I, Yokoyama S, Matsuyama T: \u003cstrong\u003eAberrant expression of BAFF receptor, a member of the tumor necrosis factor receptor family, in malignant cells of nonhematopoietic origins\u003c/strong\u003e. \u003cem\u003eGenes to cells : devoted to molecular \u0026amp; cellular mechanisms \u003c/em\u003e2008, \u003cstrong\u003e13\u003c/strong\u003e(10):1061-1073.\u003c/li\u003e\n\u003cli\u003eAmendt T, Ayoubi OE, Linder AT, Allies G, Young M, Setz CS, Jumaa H: \u003cstrong\u003ePrimary Immune Responses and Affinity Maturation Are Controlled by IgD\u003c/strong\u003e. \u003cem\u003eFrontiers in immunology \u003c/em\u003e2021, \u003cstrong\u003e12\u003c/strong\u003e:709240.\u003c/li\u003e\n\u003cli\u003eSato J, Kitano S, Motoi N, Ino Y, Yamamoto N, Watanabe S, Ohe Y, Hiraoka N: \u003cstrong\u003eCD20(+) tumor-infiltrating immune cells and CD204(+) M2 macrophages are associated with prognosis in thymic carcinoma\u003c/strong\u003e. \u003cem\u003eCancer science \u003c/em\u003e2020, \u003cstrong\u003e111\u003c/strong\u003e(6):1921-1932.\u003c/li\u003e\n\u003cli\u003eGranito A, Muratori L, Lalanne C, Quarneti C, Ferri S, Guidi M, Lenzi M, Muratori P: \u003cstrong\u003eHepatocellular carcinoma in viral and autoimmune liver diseases: Role of CD4+ CD25+ Foxp3+ regulatory T cells in the immune microenvironment\u003c/strong\u003e. \u003cem\u003eWorld journal of gastroenterology \u003c/em\u003e2021, \u003cstrong\u003e27\u003c/strong\u003e(22):2994-3009.\u003c/li\u003e\n\u003cli\u003eIb\u0026aacute;\u0026ntilde;ez L, Abou-Ezzi G, Ciucci T, Amiot V, Bela\u0026iuml;d N, Obino D, Mansour A, Rouleau M, Wakkach A, Blin-Wakkach C: \u003cstrong\u003eInflammatory Osteoclasts Prime TNF\u0026alpha;-Producing CD4(+) T Cells and Express CX(3) CR1\u003c/strong\u003e. \u003cem\u003eJournal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research \u003c/em\u003e2016, \u003cstrong\u003e31\u003c/strong\u003e(10):1899-1908.\u003c/li\u003e\n\u003cli\u003eLi H, Hong S, Qian J, Zheng Y, Yang J, Yi Q: \u003cstrong\u003eCross talk between the bone and immune systems: osteoclasts function as antigen-presenting cells and activate CD4+ and CD8+ T cells\u003c/strong\u003e. \u003cem\u003eBlood \u003c/em\u003e2010, \u003cstrong\u003e116\u003c/strong\u003e(2):210-217.\u003c/li\u003e\n\u003cli\u003eLi F, Tang H, Luo X, Li X, Luo K, Liu S, Liang J, Liao S, Zhong C, Zhan X\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eInteraction gene set between osteoclasts and regulatory CD4(+) T cells can accurately predict the prognosis of patients with osteosarcoma\u003c/strong\u003e. \u003cem\u003eCancer science \u003c/em\u003e2023, \u003cstrong\u003e114\u003c/strong\u003e(7):3014-3026.\u003c/li\u003e\n\u003cli\u003eDuraiswamy J, Turrini R, Minasyan A, Barras D, Crespo I, Grimm AJ, Casado J, Genolet R, Benedetti F, Wicky A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eMyeloid antigen-presenting cell niches sustain antitumor T cells and license PD-1 blockade via CD28 costimulation\u003c/strong\u003e. \u003cem\u003eCancer cell \u003c/em\u003e2021, \u003cstrong\u003e39\u003c/strong\u003e(12):1623-1642.e1620.\u003c/li\u003e\n\u003cli\u003eLi J, Su L, Xiao X, Wu F, Du G, Guo X, Kong F, Yao J, Zhu H: \u003cstrong\u003eDevelopment and Validation of Novel Prognostic Models for Immune-Related Genes in Osteosarcoma\u003c/strong\u003e. \u003cem\u003eFrontiers in molecular biosciences \u003c/em\u003e2022, \u003cstrong\u003e9\u003c/strong\u003e:828886.\u003c/li\u003e\n\u003cli\u003eYang QK, Su YN, Wang W, Wang N, Yao ZX, Zhang XJ: \u003cstrong\u003eCONUT Score or/and Peripheral Blood CD4+/CD8+ Ratio-Based Web Dynamic Nomograms to Predict the Individualized Survival of Patients with Advanced Osteosarcoma\u003c/strong\u003e. \u003cem\u003eCancer management and research \u003c/em\u003e2020, \u003cstrong\u003e12\u003c/strong\u003e:4193-4208.\u003c/li\u003e\n\u003cli\u003eMagnuson BA, Appleton J, Ames GB: \u003cstrong\u003ePharmacokinetics and distribution of [35S]methylsulfonylmethane following oral administration to rats\u003c/strong\u003e. \u003cem\u003eJournal of agricultural and food chemistry \u003c/em\u003e2007, \u003cstrong\u003e55\u003c/strong\u003e(3):1033-1038.\u003c/li\u003e\n\u003cli\u003eS PN, Darvin P, Yoo YB, Joung YH, Kang DY, Kim DN, Hwang TS, Kim SY, Kim WS, Lee HK\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eThe combination of methylsulfonylmethane and tamoxifen inhibits the Jak2/STAT5b pathway and synergistically inhibits tumor growth and metastasis in ER-positive breast cancer xenografts\u003c/strong\u003e. \u003cem\u003eBMC cancer \u003c/em\u003e2015, \u003cstrong\u003e15\u003c/strong\u003e:474.\u003c/li\u003e\n\u003cli\u003eCaron JM, Monteagudo L, Sanders M, Bannon M, Deckers PJ: \u003cstrong\u003eMethyl sulfone manifests anticancer activity in a metastatic murine breast cancer cell line and in human breast cancer tissue--part 2: human breast cancer tissue\u003c/strong\u003e. \u003cem\u003eChemotherapy \u003c/em\u003e2013, \u003cstrong\u003e59\u003c/strong\u003e(1):24-34.\u003c/li\u003e\n\u003cli\u003eKarabay AZ, Koc A, Ozkan T, Hekmatshoar Y, Sunguroglu A, Aktan F, Buyukbingol Z: \u003cstrong\u003eMethylsulfonylmethane Induces p53 Independent Apoptosis in HCT-116 Colon Cancer Cells\u003c/strong\u003e. \u003cem\u003eInternational journal of molecular sciences \u003c/em\u003e2016, \u003cstrong\u003e17\u003c/strong\u003e(7).\u003c/li\u003e\n\u003cli\u003eKowalska K, Habrowska-G\u0026oacute;rczyńska DE, Domińska K, Urbanek KA, Piastowska-Ciesielska AW: \u003cstrong\u003eMethylsulfonylmethane (organic sulfur) induces apoptosis and decreases invasiveness of prostate cancer cells\u003c/strong\u003e. \u003cem\u003eEnvironmental toxicology and pharmacology \u003c/em\u003e2018, \u003cstrong\u003e64\u003c/strong\u003e:101-111.\u003c/li\u003e\n\u003cli\u003eKim JH, Shin HJ, Ha HL, Park YH, Kwon TH, Jung MR, Moon HB, Cho ES, Son HY, Yu DY: \u003cstrong\u003eMethylsulfonylmethane suppresses hepatic tumor development through activation of apoptosis\u003c/strong\u003e. \u003cem\u003eWorld journal of hepatology \u003c/em\u003e2014, \u003cstrong\u003e6\u003c/strong\u003e(2):98-106.\u003c/li\u003e\n\u003cli\u003eKim DH, Sp N, Kang DY, Jo ES, Rugamba A, Jang KJ, Yang YM: \u003cstrong\u003eEffect of Methylsulfonylmethane on Proliferation and Apoptosis of A549 Lung Cancer Cells Through G(2)/M Cell-cycle Arrest and Intrinsic Cell Death Pathway\u003c/strong\u003e. \u003cem\u003eAnticancer research \u003c/em\u003e2020, \u003cstrong\u003e40\u003c/strong\u003e(4):1905-1913.\u003c/li\u003e\n\u003cli\u003eSkrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eStrengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement\u003c/strong\u003e. \u003cem\u003eJama \u003c/em\u003e2021, \u003cstrong\u003e326\u003c/strong\u003e(16):1614-1621.\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":"Osteosarcoma, Immune cells, Blood metabolite, Mendelian randomization, Mediation, Genome-wide association analysis, Causal relationship","lastPublishedDoi":"10.21203/rs.3.rs-4454204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4454204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: \u003c/strong\u003eImmunogenic nature of osteosarcoma is well-established, but the precise roles of immune cells and the potential influence of blood metabolites on its advancement remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: \u003c/strong\u003eTwo-step, two-sample Mendelian randomization (MR) strategy was employed to investigate causal relation between osteosarcoma risk and immune cell distribution, we sought to uncover and measure the potential mediating role of blood metabolites. Our analysis incorporated a diverse range of MR estimation techniques, encompassing inverse variance weighting (IVW), MR-Egger regression, weighted median, weighted mode, and simple mode. Additionally, we conducted sensitivity analyses to assess the reliability of our results.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e MR analysis revealed that three immune cell phenotypes exhibited positive relation with osteosarcoma risk (CX3CR1 on CD14\u003csup\u003e-\u003c/sup\u003e CD16\u003csup\u003e-\u003c/sup\u003e,\u003csup\u003e \u003c/sup\u003eCD25 on CD45RA\u003csup\u003e-\u003c/sup\u003e CD4 not Treg, and CD45 on HLA DR\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003ebr\u003c/sup\u003e), while four immune cell phenotypes illustrated \u0026nbsp;negative relation to osteosarcoma risk (BAFF\u003csup\u003e-\u003c/sup\u003eR on IgD\u003csup\u003e+\u003c/sup\u003e CD38\u003csup\u003e-\u003c/sup\u003e unsw mem, CD20 on IgD\u003csup\u003e-\u003c/sup\u003e CD38\u003csup\u003e-\u003c/sup\u003e, Naive CD4\u003csup\u003e+\u003c/sup\u003e %T cell, and CD28\u003csup\u003e+\u003c/sup\u003e CD45RA\u003csup\u003e+\u003c/sup\u003e CD8br %CD8\u003csup\u003ebr\u003c/sup\u003e). Moreover, mediation MR analysis demonstrated causal effect of CX3CR1 on CD14\u003csup\u003e-\u003c/sup\u003e CD16\u003csup\u003e-\u003c/sup\u003e within monocyte panel on osteosarcoma (Total effect IVW: OR = 0.3330) was predominantly mediated by dimethyl sulfone (0.0288, constituting 8.70% of Total effect) and unidentified metabolite X-12680 (0.0524, constituting 15.74% of Total effect).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e This investigation unveiled a causal link between immune cells and osteosarcoma, potentially mediated by blood metabolites.\u003c/p\u003e","manuscriptTitle":"Causal association of immune cell phenotypes with osteosarcoma and the mediation role of blood metabolites: A two-steps, two-samples Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 09:34:39","doi":"10.21203/rs.3.rs-4454204/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":"a7918052-ee33-4b3b-a01d-b27387ff8ee9","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-11T09:34:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-11 09:34:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4454204","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4454204","identity":"rs-4454204","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}