Immune cells mediate the causal relationship between the gut microbiota and different types of endometrial cancer: A bidirectional two-sample, two-step Mendelian randomization study

Immune cells mediate the causal relationship between the gut microbiota and different types of endometrial cancer: A bidirectional two-sample, two-step 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 Immune cells mediate the causal relationship between the gut microbiota and different types of endometrial cancer: A bidirectional two-sample, two-step Mendelian randomization study Shuyang Yu, Wan Shu, Jiarui Zhang, Shuangshuang Cheng, Xiaoyu Shen, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6189084/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 Endometrial cancer (EC), one of the most common gynecological cancers classified as either type I or II, is an immunogenic cancer whose tumor microenvironment and immune cell infiltration significantly regulate its prognosis. The gut microbiome affects the occurrence and development of endometrial cancer, and its changes can influence immune conditions. However, whether the gut microbiome regulates different types of endometrial cancer progression through immune cells remains unclear. Method The data used for genome-wide association studies (GWAS) included gut microbiome data from the Dutch Microbiome Project (DMP) (N = 7,738), endometrial cancer data from the IEU Open GWAS project: endometrial cancer (N = 240,027), endometrial cancer with endometrioid histology (N = 54,884), non-endometrioid histology (N = 36,677), and immune cell trait data from European populations (N = 3,757). Using two-sample Mendelian randomization, we investigated the causal relationship between gut microbiota and the three endometrial cancer types. Subsequently, two-step Mendelian randomization and mediation analyses were performed to explore the mediating role of immune traits in the relationship between the gut microbiome and three types of endometrial cancer. Result According to the traditional definition of endometrial cancer and gut microbiome analysis, there are four positive causal effects, three adverse causal effects, and three established mediating effects combined with immune cell traits. Five negative and two positive relationships of the gut microbiome on endometrial cancer with endometrioid histology, together with three immune trait-mediated effects. Analysis of endometrial cancer with non-endometrioid histology showed two positive and one negative causalities, with identified one intermediate causality. Conclusion Our findings emphasize the elusive relationship between gut microbiota, immune cell traits, and various types of endometrial cancer. The distinct connections and mediating effects provide novel perspectives for the distinct therapies targeting the gut microbiota and the immune microenvironment of endometrial cancer with different subtypes. Endometrial cancer Gut microbiota Immune cell Mendelian randomization study Immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Endometrial cancer (EC) is the most common gynecological cancer, the incidence of which has progressively increased in high-income regions[1]. According to their histological features, grade, and hormone receptor (ER and PR) expression, EC can generally be classified into types I and II[1–3]. Type I EC is the most frequent subtype, presenting as low-grade, endometrioid, and hormone receptor-positive and has a good prognosis (85% 5-year OS rate)[1, 2]. Conversely, Type II EC is non-endometrioid, high-grade, and hormone receptor-negative, with a high risk of metastasis and poor prognosis (5-year OS rate of ~ 55%)[2, 4]. Genetic and environmental factors, including obesity and metabolic and reproductive factors, are the primary risk factors for the occurrence and development of EC[1, 5]. Notably, these risks are strongly linked to the gut and vaginal microbiome; for instance, the gut microbiome may affect endometrial carcinogenesis by altering the systemic and uterine cavity microenvironment, providing a novel therapy for EC[2, 6, 7]. In addition, EC, especially type II EC, is regarded as an immunogenic disease, and its infiltrating immune cells affect anti-cancer therapy to facilitate tumor progression, ultimately leading to the development of immune tolerance[8–10]. Malignant and non-malignant immune cells and signaling molecules constitute the tumor microenvironment surrounded by blood vessels, disrupting therapeutic efficacy[11–13]. Immunotherapy is a prospective therapeutic intervention for carcinostasis by activating the immune system[14, 15]. Recently, immunotherapy has demonstrated benefits in treating recurrent and advanced EC[14–17]. Microbiota refers to the collection of microorganisms in a particular community, whereas the genome of microorganisms is defined as the microbiome[18–20]. Evidence suggests that crosstalk between microbiota and the gut immune system is crucial[21]. With further exploration of the gut microbiota, the disrupted balance of the gut microbiota (GM) may influence various disease states such as obesity, mental disorders, and autoimmune diseases[22–24]. GM can impact human health by regulating host immunity and metabolism[25–28]. Therefore, regulating host immunity by the gut microbiome can prominently affect a variety of treatment efficacies and toxicities in cancer[18]. Mendelian randomization (MR), a data analysis tool used in epidemiological studies to evaluate causal inferences, uses genetic variants strongly correlated with exposure factors as instrumental variables to assess the causal relationship between exposure factors and outcomes[29–31]. MR generally presents single nucleotide polymorphisms (SNPs) that serve as genetic variants to estimate the causal effect of exposure on outcomes with less susceptibility to environmental confounders and reverse causality[29]. Single-sample MR data are derived from the same individual, whereas two-sample MR uses large-scale GWAS data from independent study populations[32, 33]. Large-scale summary statistics can be used to analyze the relationship between the gut microbiota, immune cells, and EC, enhancing the statistical power of the two-sample MR Analysis. In this study, a comprehensive MR Analysis is represented to explore the causal effects among gut microbiome, immune cells and various EC types including EC, EC with endometrioid and non-endometrioid histology. Afterwards, we discussed whether immune cells serve as mediators from the gut microbiota to the EC. Method Study design Bidirectional two-sample univariable Mendelian randomization (UVMR) was applied to explore the causal association between the gut microbiota and three types of EC, including endometrial cancer, endometrial cancer with endometrioid histology, and non-endometrioid histology. Subsequently, a two-step MR analysis was used to determine whether immune cell traits mediate these causal associations (Fig. 1 ). First, the causal relationship between the GM and EC was assessed with UVMR using three filters: SNPs should (1) be strongly associated with EC, (2) affect EC only through the causal effect of the GM, and (3) remove other potential confounders[34]. Subsequently, reverse MR analysis was conducted to screen for GMs with reverse functions in the EC. In the second step, the mediating effects of various immune cells in the corresponding GM and EC were evaluated and quantified using UVMR and Mendelian randomization. The above analysis followed the guidelines for Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STRBOE-MR) (Supplement Table 1)[35]. Data sources Gut microbiome data were summarized from the Dutch Microbiome Project (DMP), a GWAS of 7,738 European individuals[36]. The project applied shotgun metagenomic sequencing of faecal samples, which identified 207 microbial taxa (5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species). A total of 731 immune-related genome-wide features with ~ 22 million variants in 3,757 European individuals were identified using GWAS Summary Statistics[37]. Integrated GWAS summary statistics, with classified numbers from GCST0001391 to GCST0002121, were published in the GWAS directory. Genetic summary data of the two-sample and mediated MR analyses were primarily obtained from the IEU Open GWAS project, including endometrial cancer (ebi-a-GCST90018838), endometrial cancer with endometrioid histology (ebi-a-GCST006465), and non-endometrioid histology (ebi-a-GCST006466). All data are publicly available as GWAS summary data and received ethical approval (Table 1). Tabel 1. Summary of data sources. DMP, Dutch Microbiome Project; EC, Endometrial Cancer. Phenotypes Cases/controls or sample sizes Data source Phenotypic code Ancestry Exposure Gut microbiota 7,738 DMP GCST90027446 to GCST90027857 European Mediator Immune cells 3,757 Orru et al., 2020 GCST0001391 to GCST0002121 European Outcome EC 2,188/237,839 IEU Open GWAS project ebi-a-GCST90018838 European EC with endometrioid histology 46,126/54,884 IEU Open GWAS project ebi-a-GCST006465 European EC with non-endometrioid histology 35,447/36,677 IEU Open GWAS project ebi-a-GCST006466 European Genetic instrumental variable (IVs) selection Effective MR analysis relies on three foundations: IVs should (1) be strongly associated with EC, (2) affect EC only through the causal effect of GM, and (3) remove other potential confounders[34]. The first step was to select SNPs associated with exposure with a significance threshold of 5e-08, which were further screened via linkage disequilibrium analysis (r 2 10[39]. The study assumptions were presented in Fig. 2 , and selected SNPs would view in Supplement Table 2. Statistical analysis All statistical analyses were performed using the R 4.4.0 ( https://www.r-project.org ). The “VariantAnnotation” package, “ieugwasr” package, and “TwoSampleMR” package were used to perform UVMR. Among the five methods (“MR Egger,” “Weighted median,” “Inverse variance weighted (IVW),” “Simple mode,” and “Weighted mode”), IVW is the primary method of causal estimation; P < 0.05 was identified as a significant causal association. Moreover, the heterogeneity and horizontal pleiotropy were based on the Cochran Q statistic and the MR-Egger intercept of the IVW and MR-Egger methods. Heterogeneity and pleiotropy were identified by a P-value of < 0.05, which indicated unsustainable causality. The “leave-one-out” test was used to investigate the effects of possible uncorrelated SNPs. Mediation analysis After two-sample UVMR, GM and immune cells with significant causal relationships with various EC were selected for further mediation analysis. We determined whether the GM had causal effects on immune cells; if so, multiple MR analyses were conducted to explore the mediating effect of immune cells from GM to EC. Results Instrumental variable selection Initially, 4,031 gut microbiota-associated SNPs were identified by P < 1×10 − 5 with closely linked SNPs removed (single nucleotide polymorphisms), which served as IV of 412 gut microbiota. The R² and F-values were calculated for screened SNPs (r 2 10), which were unlikely to be affected by weak instrument bias (Supplement Table 2). Similarly, the same process was used to selected SNPs of immune cell traits (Supplement Table 2). Causal effects of gut microbiota on different types of EC The IVW method was used to evaluate the potential causal relationship between the gut microbiota and the invasion of different types of EC (Fig. 3 D-F). Although other methods, including MRE, WMed, SMod, and WMod, did not show statistical significance, the estimated causal effect demonstrated a similar tendency to that obtained using the IVW method. There was no heterogeneity or horizontal pleiotropy in this MR analysis, as shown by the Cochran’s Q statistic, MR-Egger intercept test, and MR-PRESSO test (Supplement Table 3). Furthermore, none of the SNP severely interfered with the overall effect of GM on EC. Traditional definition of EC. Genetic predictions of genus Erysipelotrichaceae_noname (odds ratio (OR) = 1.157, 95% Confidence Interval (CI) [1.021, 1.316], P = 0.021), genus Dialister (OR = 1.192, 95% CI [1.017, 1.398], P = 0.030), species Dialister_invisus (OR = 1.236, 95% CI [1.055, 1.450], P = 0.009) and species Ruminococcus_torques (OR = 1.238, 95% CI [1.034, 1.482], P = 0.020) are associated with increased risk in traditionally defined EC. Species Bacteroides faeces (OR = 0.931, 95% CI [0.868, 0.997], P = 0.042), species Bacteroides massiliensis (OR = 0.769, 95% CI [0.642, 0.922], P = 0.004), and species Ruminococcus obeum (OR = 0.830, 95% CI [0.702, 0.981], P = 0.029) were associated with a reduced risk of EC (Fig. 3 A, 3 D). EC with endometrioid histology (type I EC). For endometrial EC, class Bacilli (OR = 0.880, 95% CI [0.801, 0.968], P = 0.008), family Bacteroidaceae (OR = 0.863, 95% CI [0.757, 0.985], P = 0.028), order Lactobacillales (OR = 0.905, 95% CI [0.820, 0.998], P = 0.046), species Eggerthella_ unclassified (OR = 0.855, 95% CI [0.734, 0.996], P = 0.044), species Alistipes_sp_AP11 (OR = 0.900, 95% CI [0.814, 0.996], P = 0.042) is a protective factor for endometrial EC. Species Aspergillus senegalensis (OR = 1.154, 95% CI [1.015, 1.311], P = 0.029) and species Holdemania_ unclassified (OR = 1.109, 95% CI [1.003, 1.225], P = 0.044) were risk factors for endometrial EC (Fig. 3 B, 3 E). EC with non-endometrioid histology (type II EC). Focusing on non-endometrial EC, species Bacteroides stercoris (OR = 1.450, 95% CI [1.010, 2.082], P = 0.044) and species Lachnospiraceae_bacterium_5_1_63FAA (OR = 1.251, 95% CI [1.027, 1.525], P = 0.026) were associated with an increased risk of developing this type of EC, whereas species Ruminococcus_bromii (OR = 0.633, 95% CI [0.446, 0.900], P = 0.011) was associated with decreased risk of EC (Fig. 3 C, 3 F). Reverse MR analysis of gut microbiota on different types of EC Next, reverse MR analysis was conducted on the three EC types and their causal gut microbiota. The results showed no obvious causal effects (p > 0.05, Table 2) expect for species Lachnospiraceae_bacterium_5_1_63FAA , between three types of EC and the relevant GM through using the IVW method of reverse analysis (Table 2). Therefore, species Lachnospiraceae_bacterium_5_1_63FAA would not be further analyzed in EC with non-endometrioid histology. Tabel 2. Reverse MR_IVW analysis of gut microbiota on 3 types of EC. SNPs, single nucleotide polymorphisms; OR, odds ratio; 95% CI, 95% confidence interval. Exposure SNPs OR (95% CI) pvalue revserse_Pvaleue Traditional definition of EC Genus_Erysipelotrichaceae_noname 12 1.157 (1.021–1.312) 0.022 0.863 Genus_Dialister 6 1.192 (1.017–1.398) 0.030 0.253 Species_Dialister_invisus 6 1.236 (1.055–1.449) 0.009 0.163 Species_Bacteroides_faecis 14 0.931 (0.868–0.997) 0.042 0.323 Species_Bacteroides_massiliensis 6 0.769 (0.642–0.922) 0.004 0.064 Species_Ruminococcus_obeum 13 0.830 (0.702–0.981) 0.029 0.421 Species_Ruminococcus_torques 7 1.238 (1.034–1.482) 0.020 0.117 EC with endometrioid histology (type I EC) Class_Bacilli 14 0.88 (0.801–0.968) 0.008 0.923 Family_Bacteroidaceae 14 0.863 (0.757–0.985) 0.028 0.183 Order_Lactobacillales 12 0.905 (0.82–0.998) 0.046 0.925 Species_Eggerthella_unclassified 3 0.855 (0.734–0.996) 0.044 0.306 Species_Alistipes_senegalensis 12 1.154 (1.015–1.311) 0.029 0.621 Species_Alistipes_sp_AP11 10 0.9 (0.814–0.996) 0.042 0.802 Species_Holdemania_unclassified 10 1.109 (1.003–1.225) 0.044 0.078 EC with non-endometrioid histology (type II EC) Species_Ruminococcus_bromii 7 0.633 (0.446-0.900) 0.011 0.926 Species_Bacteroides_stercoris 7 1.450 (0.010–2.082) 0.044 0.911 Effect of immune cell traits on different types of EC The IVW method, as the primary assessment method, was used to explore the effect of immune cells on different types of EC with similar causal effects of MRE, WMed, SMod, and WMod. In addition, the MR-Egger intercept test, MR-PRESSO test, and leave-one-out sensitivity analysis showed no heterogeneity or horizontal pleiotropy, and the removal of any single SNP did not significantly affect the overall influence of immune cells on EC (Supplement Table 3). The analysis also revealed that protecting 6 immune cell traits and 14 genetically predicted immune cell traits enhanced the risk of traditionally defined EC (Fig. 4 A). Thirteen immune cell traits were associated with an increased risk, whereas 14 were associated with a reduced risk of endometrial EC (Fig. 4 B). In addition, MR analysis showed that non-endometrial EC was amplified by 19 immune cell traits and suppressed by 19 other immune cell traits (Fig. 4 C). Effect of gut microbiota on immune cell traits We have testified the role of gut microbiota and immune cell traits for various EC types, and following the causation of these gut microbiota to immune cells in various EC types were explored through MR Analysis (Supplement Table 4). Traditional definition of EC. In traditionally defined EC, 7 gut microbiota and 20 immune cell traits were analysed, of which species Dialister_invisus was a risk factor for CD25hi CD45RA-CD4 but not for Treg AC (OR = 1.222, 95% CI [1.026, 1.456], P = 0.089). Species Bacteroides_massiliensis protected against CD62L-HLA Dr + monocyte AC (OR = 0.761, 95% CI [0.617, 0.939], P = 0.011). Besides, species Ruminococcus_obeum is a protective factor for CD86 + myeloid DC AC (OR = 0.817, 95% CI [0.687, 0.971], P = 0.222). However, it is also dangerous for CD27 on IgD + CD38-unsw mem (OR = 1.356, 95% CI [1.081, 1.701], P = 0.008) and CD27 on IgD-CD38BR (OR = 1.222, 95% CI [1.036, 1.443], P = 0.018). EC with endometrioid histology (type I EC). MR analysis revealed seven gut microbiota and 27 immune cell traits. The results show that class Bacilli are harmful to IgD + CD38br % lymphocytes (OR = 1.172, 95% CI [1.034, 1.327], P = 0.013) and Transitional AC (OR = 1.175, 95% CI [1.038, 1.329], P = 0.011), whereas species Holdemania_unclassified is also dangerous relative to Transitional AC (OR = 1.201, 95% CI [1.055, 1.369], P = 0.006), and order Lactobacillales was a risk factor for CD39 + activated Treg %CD4 Treg (OR = 1.121, 95% CI [1.007, 1.247], P = 0.036). EC with non-endometrioid histology (type II EC) . For non-endometrial EC with 3 gut microbiota and 38 immune cell traits, species Ruminococcus_bromii for IgD-CD38dim %lymphocyte (OR = 1.217, 95% CI [1.008, 1.468], P = 0.041), CD8dim %leukocyte (OR = 1.243, 95% CI [1.025, 1.508], P = 0.027) and CD8br NKT %lymphocyte (OR = 1.223, 95% CI [1.010, P = 0.027) 1.482], P = 0.039) was detrimental; however, it was protective to CD25hi AC (OR = 0.772, 95%CI [0.632, 0.943], P = 0.011). Mediation analysis The proportion of mediating effects was quantified by determining the ratio of indirect to direct effects after confirming the cause-effect relationship between the gut microbiota, immune cell traits, and various EC types (Table 3). Tabel 3. Mediation effect of various GM on 3 types of EC via immune cell traits. EC, endometrial cancer; AC, absolute cell counts; Treg, regulatory T cell; NKT, natural killer T cells; 95% CI, 95% confidence interval. β0 (total effect): The causal function of GM on EC; β1 (direct effect A): The causal function of GM on immune cell traits; β2 (direct effect B): The causal function of immune cell traits on EC; β (mediating effect) = β1(Direct effect A) × β2(Direct effect B); mediated proportion = β (mediating effect) / β0(total effect). The symbol "c_/o_/f_/g_/s_" demonstrated the class, order, family, genus, and species. Gut microbiome Immune cell beta0 beta1 beta2 Mediated effect (95% CI) Mediated proportion Traditional definition of EC s__Bacteroides_massiliensis CD62L- HLA DR + + monocyte AC -0.262 -0.273 0.121 -0.0331 (-0.0959, 0.0296) 12.60% s__Dialister_invisus CD25hi CD45RA- CD4 not Treg AC 0.212 0.201 0.125 0.025 (-0.0163, 0.0662) 11.80% s__Ruminococcus_obeum CD86 + myeloid DC AC -0.187 -0.202 0.085 -0.0171 (-0.0551, 0.0209) 9.17% s__Ruminococcus_obeum CD27 on IgD + CD38- unsw mem -0.106 0.305 0.056 0.017 (-0.0531, 0.0871) -16.10% s__Ruminococcus_obeum CD27 on IgD- CD38br -0.187 0.201 0.156 0.0313 (-0.0108, 0.0735) -16.80% EC with endometrioid histology (type I EC) c__Bacilli IgD + CD38br %lymphocyte -0.128 0.159 -0.06 -0.00957 (-0.0307, 0.0116) 7.49% c__Bacilli Transitional AC -0.128 0.161 -0.067 -0.0108 (-0.0324, 0.0107) 8.48% o__Lactobacillales CD39 + activated Treg %CD4 Treg -0.1 0.183 -0.055 -0.0102 (-0.0351, 0.0148) 10.20% s__Holdemania_unclassified Transitional AC 0.103 0.114 -0.067 -0.00765 (-0.0217, 0.00643) -7.43% EC with non-endometrioid histology (type II EC) s__Ruminococcus_bromii CD8br NKT %lymphocyte -0.457 0.202 -0.178 -0.036 (-0.0876, 0.0157) 7.87% s__Ruminococcus_bromii IgD- CD38dim %lymphocyte -0.457 0.196 0.216 0.0423 (-0.0125, 0.097) -9.25% s__Ruminococcus_bromii CD25hi AC -0.457 -0.259 -0.284 0.0736 (-0.0034, 0.151) -16.10% s__Ruminococcus_bromii CD8dim %leukocyte -0.457 0.217 0.254 0.0552 (-0.00935, 0.12) -12.10% Traditional definition of EC. Species Ruminococcus_obeum impacted EC through 3 different mediators: CD86 + myeloid DC AC (mediated effect β = -0.017, mediated proportion = 9.17%), CD27 on IgD + CD38- unsw mem (β = 0.017, mediated proportion = -16.10%), and CD27 on IgD- CD38br (β = 0.031, mediated proportion = -16.80%) (Fig. 5 C). Notably, the mediated proportion of mediator CD27 on IgD + CD38- unsw mem and CD27 on IgD- CD38br was negative, indicating that exposure yielded an opposite effect on the outcome, as expected, through the mediator. This may be owing to 1) the reverse effect of the mediating variable. The mediating variable may have a reverse effect, and its regulation leads to a reverse change in the outcome owing to various factors, such as biological mechanisms, environmental factors, or individual differences. 2) Other unconsidered variables: There may be other unidentified variables that could influence the mediating effect. In addition, statistical errors during calculations might have contributed to this result. Besides, the result presented species Bacteroides_massiliensis affect EC via CD62L- HLA DR + + monocyte AC (β = -0.033, mediated proportion = 12.60%) with a negative mediating role (Fig. 5 B). CD25hi CD45RA- CD4, not Treg AC, positively mediated species Dialister_invisus acting on EC (β = 0.025, Mediated proportion = 11.80%) (Fig. 5 A). EC with endometrioid histology (type I EC). Class Bacilli could protect endometrial EC through IgD + CD38br %lymphocyte (β = -0.010, mediated proportion = 7.49%) and transitional AC (β = -0.011, mediated proportion = 8.48%) with negative mediations (Fig. 6 A, 6 B). Transitional AC also mediated species Holdemania_ unclassified influence on endometrial EC (β = -0.008, mediated proportion = -7.43%) that was illogical. In addition, order Lactobacillales is protective to endometrial EC by CD39 + activated Treg %CD4 Treg (β = -0.010, mediated proportion = 10.20%) (Fig. 6 C). EC with non-endometrioid histology (type II EC). Species Ruminococcus_bromii could reduce the risk of non-endometrial EC by IgD- CD38dim %lymphocyte (β = 0.042, mediated proportion = -9.25%), CD25hi AC (β = 0.074, mediated proportion = -16.10%), CD8dim %leukocyte (β = 0.055, mediated proportion = -12.10%), and CD8br NKT %lymphocyte (β = -0.036, mediated proportion = 7.87%), although only CD8br NKT %lymphocyte was logical (Fig. 7 ). Discussion Intestinal microbiota and its metabolites affect several pathophysiological processes covering host metabolism and immune response, known as the “second endocrine organ”[40]. Previous studies have verified that the regulation of host immunity is closely interrelated with the microbiota, and a disrupted balance would motivate the occurrence and deterioration of diseases[41, 42] ; hence, it is crucial to equilibrate the complex communions between them. In this study, we conducted a comprehensive large-scale two-sample MR analysis based on the LifeLine Biobank and GWAS databases, followed by reverse MR analysis for each of the three types of EC. There were 17 causal relationships, including 7 gut microbiota species and conventionally defined EC, 7 species and endometrial EC, and 3 species and non-endometrial EC, which were evaluated by predicting 158 SNP loci. Therefore, the three types of EC were primarily causally related to the phyla Firmicutes and Bacteroidetes ; type I EC presented a causal correlation with the Phylum Actinobacteria . Next, a two-sample MR analysis was conducted on immune cell traits and different types of EC, and causal relationships were discussed based on various SNPs. Twenty immune traits were associated with typical EC, 27 were causally linked to endometrial EC, and non-endometrial EC showed a causal correlation with 38 immune cells. Finally, we performed a two-step MR analysis and mediation analysis that focused on causal associations among the gut microbiota, immune cell traits, and each of the three types of EC and whether immune cell traits exert their mediating functions. Traditional definition of EC Bacteroides massiliensis could reduce the risk of EC by repressing the immune cell trait “CD62L HLA DR + + monocyte AC”. Bacteroides regulate the immune system to maintain homeostasis, and their metabolites maintain immune system stability [43]. Bacteroides are the major producers of short-chain fatty acids in the gut[44], which are important for maintaining microecological equilibrium, mainly in the form of acetic and propionic acids. Acetate and propionate are potent anti-inflammatory mediators that inhibit the release of pro-inflammatory cytokines from neutrophils and macrophages[45]. The anticancer effects of propionic acid on apoptosis have been described in human colon cancer cells[46]. The anti-tumor effects of cytotoxic T lymphocyte antigen 4 (CTLA-4) blockers depend on different Bacteroides species, with specific T cell responses to B. thetaiotaomicron or B. fragilis affecting the efficacy of CTLA-4 blockers in mice and patients[47]. In addition, the HLA-DR phenotype is closely related to anti-tumor immunotherapy, with CD62L as a marker of monocyte activation[48, 49], and CD62L HLA DR + + monocyte was analyzed to facilitate EC progress. Nevertheless, the immunosuppressive cytokines interleukin (IL)-10 and pro-inflammatory factors IL-6 and TNFα negatively correlate with HLA-DR within B-cell non-Hodgkin lymphoma[50, 51]. Mengos et al. found that monocytes with reduced or no HLA-DR expression are crucial mediators of tumor-induced immunosuppression and negatively affect programed death-1 (PD-1) and CTLA-4 checkpoint inhibition [52–58], chimeric antigen receptor T-cell (CAR-T) immunotherapy[59–61], cancer vaccines[62–66], and hematopoietic stem cell transplantation[67–70]. Existing studies have presented conflicting views to our analysis; therefore, further exploration is necessary to investigate the effect of CD62L-HLA DR + + monocytes on EC. Ruminococcus obeum , which belongs to the genus Blautia , protects EC via the trait “CD86 + myeloid DC AC”. Gut microbiome analysis of immune checkpoint inhibitor (ICI)-treated patients showed that the high diversity and presence of immunogenic bacteria, such as Ruminococcus , resulted in more significant CD8 + T cell and CD4 + Th1-dependent anti-tumor responses, leading to better outcomes[71–74]. However, Xu et al. found that Blautia obeum was enriched in patients who were nonreactive to anti-PD-1 and ICI treatments and may have antibiotic properties along with antibiotic resistance[75, 76]. Therefore, the distinct mechanism of action of Ruminococcus obeum on EC warrants further investigation. In addition, CD86 expression is considered a poor prognostic indicator and is down-regulated by transendocytosis of CTLA-4[76, 77]. Blocking CD28: CD80/CD86 in vivo re-sensitizes multiple myeloma cells to chemotherapy and significantly reduces the tumor load[78]. Combined with MR analysis, Ruminococcus obeum may inhibit CD86 + on myeloid DC to enhance immunotherapy, thereby restraining EC. Dialister invisus was a risk factor for EC functioned through the immune cell trait “CD25hi CD45RA- CD4, not Treg AC.” Enriching Dialister invisus , isolated from the human oral cavity, indicates a high risk and rapid progression of tumors in colorectal cancer[79, 80] and increases the risk of HPV-infected cervical cancer in female reproductive system tumors[81–83]. However, Byrd et al. found that dialister status was associated with higher survival in MSI-H colorectal tumours[84]. CD4 + CD25hi T cells maintain immune tolerance to autoantigens in various cancers, including ovarian and cervical[85–87]. Typically, T cells can be divided into CD45RA + initial and CD45RA- memory subtypes. The activated CD45RA- T cells inhibit the anti-tumor function of CD8 + T cells with IL-10 secretion and intercellular contacts. This facilitates immunosuppression and gastric cancer progression[88–90]. Tassi et al. confirmed that CD45RA-T lymphocytes were increased in the epithelial ovarian tumor microenvironment[91]. Therefore, together with our analysis, Dialister invisus down-regulates the anti-tumor function of CD8 + T cells by stimulating the immunosuppressive trait “CD25hi CD45RA-CD4 not Treg,” following accelerated tumor progression of EC. EC with endometrioid histology (type I EC) Class Bacilli mainly play a protective role in endometrial EC, colonizing the gastrointestinal tract[92]. The Secretome regulates tumor pathophysiological processes, and its secretions exhibit bacteriological and anti-tumor properties. Typically, short-chain fatty acids (SCFAs) from Bacilli are beneficial for gut peristalsis and secretion and inhibit tumor proliferation by inducing apoptosis and controlling epigenetic modification[92–96]. Bacteriocins and other secretions derived from bacilli can suppress phospholipase A2, down-regulating pro-inflammatory cytokines and up-regulating anti-inflammatory cytokines[97, 98]. Extracellular vesicles (EVs) of bacilli induce apoptosis in HepG2 cells by increasing the expression ratio of bax/bcl-2[99]. Bacilli stimulate the production of insulin-like growth factor 1 (IGF-1), participating in regulating blood glucose and lipids and are closely associated with the pathogenesis of type I EC[95]. In a clinical study on colorectal cancer, the abundance of bacilli in the metastatic group was significantly decreased compared with that in tumor patients[100], indicating that it may restrain tumor invasion. CD38, a type II transmembrane glycoprotein, is a crucial metabolic enzyme located on the cell surface; its products are vital during immune regulation[101, 102]. CD38 is an anti-tumor therapeutic target involved in adenosine formation and exerts remarkable immunosuppressive effects on the solid tumor microenvironment[101, 103]. Thavaneswaran et al. reported that EC patients with high CD38 + expression in peripheral tertiary lymphoid structures (TLSs) had favorable survival outcomes during chemotherapy[104, 105]. Our MR analysis showed that lymphocytes with lgD + and bright CD38 were protective against type I EC. Bacilli play a protective role by enhancing lymphocyte function; however, their specific mechanisms warrant further investigations. In particular, the order Lactobacillales belongs to the Class Bacilli ; hence, their metabolites and secretions, such as SCFAs, present similar antitumor mechanisms. Bactericin mainly disrupts the membrane intimal potential, leading to uncontrolled ion leakage and cell death[106, 107]. Moreover, these bacteria can reduce the concentration of soluble bile salts in faeces, thereby neutralizing the cancer-promoting effects of bile acids, including DNA damage and apoptosis[107–110]. Importantly, the Lactobacillales microbiome is the main component of a healthy vaginal microecosystem, and its disturbance promotes inflammation and cancer progression[111–113]. In contrast, CD39+-activated Treg %CD4 Treg mainly play an anti-inflammatory role. CD39, an extranuclear triphosphate diphosphate hydrolase-1, can hydrolyze ATP into AMP, obstructing inflammatory signal transduction of extracellular ATP[114–116]. Maddaloni et al. showed that lactic acid bacteria can deliver IL-35 in collagen-induced arthritis in mice, thus stimulating CD39 + CD4 + Tregs to release increased IL-10, exerting anti-inflammatory and immune effects[116]. Furthermore, lactic acid bacteria increase the proliferation of CD39 + Tregs in mouse allergic asthma models, following the regulation of the inflammatory response induced by immune disorders[117]. Therefore, we speculated that Lactobacillales activate and promote the proliferation of CD39+-activated Treg %CD4 Tregs by delivering special inflammatory mediators, such as IL-35, in endometrial EC, manifesting its protective role. EC with non-endometrioid histology (type II EC) Ruminococcus bromii is beneficial for patients with non-endometrial EC. A colon cancer study proposed the microbiome characteristics of R. bromii as a reliable indicator, combined with the immune rejection constant, to create a score for evaluating survival prognosis, which contributed to the discovery of personalized therapy[118]. Notably, colon cancer has a genetic phenotype similar to that of non-endometrial EC: DNA mismatch repair (MMR) defects or microsatellite instability (MSI)[119]. The increased abundance of R. bromii following symbiotic treatment (MS-20) combined with anti-PD-1 treatment positively correlated with reduced tumor load and CD8 + T cell infiltration in xenograft mouse models[120]. Additionally, rumen cocci can bind to castalagin and promote anti-cancer responses; castalagin rich in R. bromii influences the efficacy of anti-PD-L1 immunotherapy and increases the ratio of CD8 + / FOXP3 + CD4 + in the tumor microenvironment[121]. NKT cells specifically assemble T-cell receptor (TCR) and NK cell receptor on the surface, which produce numerous cytokines and play a similar cytotoxic role as NK cells[122–127]. This enhances the immune response and mediates the inhibition of tumor growth in various cancers, including liver, ovarian, and colon cancers[128–132]. In mismatch repair-deficient EC, a large infiltration of CD8 + and NKT cells is a marker for better survival prognosis, indicating a favorable immune microenvironment[132, 133]. Therefore, combined with the MR analysis, R. bromii may improve the tumor immune response and the efficacy of immunotherapy by increasing CD8 + NKT cells, thus playing a protective role. Recently, universal CAR-engineered NKT (UCAR-NKT) cells were developed and demonstrated powerful anti-tumor efficacy against blood and solid tumors in vitro and in vivo with multiple tumor-targeting mechanisms[134, 135]. These cells regulate the tumor microenvironment by selectively depleting immunosuppressive macrophages. Therefore, based on this analysis and previous research, targeting R. bromii and the immune microenvironment helps improve the poor prognosis of type II EC. Strengths of this study This study has several strengths. First, a large sample of a comprehensive GWAS dataset was used for independent and comparative analysis, including the traditional definition of EC and different types of EC, producing comprehensive results and improving the reliability and clinical significance of the findings based on subtypes. Rigorous two-sample and two-step MR analyses were used to explore the causal effects of the gut microbiota on different types of EC, which identified seven mediating immune cell phenotypes through mediation analysis. Finally, multiple sensitivity tests eliminated confounding variables, confirming the study's robustness. This strengthens the results for future mechanistic studies and offers new insights for enhancing survival in EC patients with various subtypes. Limitations of this study This study has some limitations. First, the subjects were mainly concentrated in the European population, and a single data source limited the universality of the results. Second, the occurrence and development of EC resulted from multiple factors. UVMR and two-step Mendelian analyses were conducted from a genetic perspective, ignoring the potential influences of other factors, such as the environment. Subsequent studies are warranted to explore and verify these distinct mechanisms. Conclusion Our study investigated the causal relationships and mediating effects among the gut microbiome, immune cell traits, and different types of EC, providing novel insights into the gut microbiome and immune conditions in EC. These findings suggest potential immune mechanisms of the intestinal microbiome and EC progression, and present new perspectives on the distinctive immunotherapy of EC based on the gut microbiome and diverse subtypes. Abbreviations EC Endometrial cancer GWAS Genome-wide association studies DMP Dutch Microbiome Project ER Estrogen receptor, PR:Progesterone receptor GM Gut microbiota MR Mendelian randomization SNPs Single nucleotide polymorphisms UVMR Univariable Mendelian randomization STRBOE-MR Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization IVs, instrumental variables IVW Inverse variance weighted MRE MR Egger WMed Weighted median SMod Simple mode WMod Weighted mode OR Odds ratio CI Confidence Interval c_/o_/f_/g_/s_ class, order, family, genus, and species AC Absolute cell counts Treg Regulatory T cell NKT Natural killer T cells CTLA-4 Cytotoxic T lymphocyte antigen 4 IL-10 Interleukin 10 PD-1 Programmed death-1 ICI Immune checkpoint inhibitors CAR-T chimeric antigen receptor T-cell SCFAs Short-chain fatty acids EVs Extracellular vesicles IGF-1 Insulin-like growth factor 1 TLSs Tertiary lymphoid structures MMR Mismatch repair MSI Microsatellite instability TCR T-cell receptor. Declarations Acknowledgements The authors appreciate the Dutch Microbiome Project for releasing GM-related GWAS summary data. The authors also want to acknowledge the participants and investigators of the IEU Open GWAS project and the immune cells study of Orru et al. Authors’ contributions SY: Conceptualization, Data Management, formal analysis, funding acquisition, survey, methodology, project management, software, visualization, writing-manuscript, writing-review and editing. WS, JZ, SC, XS, GC, TZ, KD, JZ, Laudański, Gutowska: Data Management, Regulation, Writing - Review and editing. HW: Project management, supervision, visualization, writing - review and editing. The authors read and approved the final manuscript. Funding The authors declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the National key research and development plan (No. 2023YFC2705400), Wuhan Knowledge Innovation Special project (202202080101045). Availability of data and materials The datasets for this study can be found in the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/). The name and number of datasets can be found in the article/Table 1. Clinical trial number: not applicable. Ethics approval and consent to participate This research has been conducted relied on publicly available de-identification data from participant studies authorized by the Ethical Standards Committee. All original studies have been approved by the corresponding ethical review board, and the participants have provided informed consent. In addition, no individual-level data was used in this study. Therefore, no new ethical review board approval was required. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China. 2 Clinical Research Center of Cancer Immunotherapy, Hubei, 430022, Wuhan, China. 3 Department of Obstetrics, Gynecology and Gynecological Oncology, Medical University of Warsaw, Poland 4 Women's Health Research Institute, Calisia University, Kalisz, Poland 5 OVIklinika Infertility Center, Warsaw, Poland References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries . CA Cancer J Clin 2021, 71 (3):209-249. Makker V, MacKay H, Ray-Coquard I, Levine DA, Westin SN, Aoki D, Oaknin A: Endometrial cancer . Nat Rev Dis Primers 2021, 7 (1):88. Bokhman JV: Two pathogenetic types of endometrial carcinoma . Gynecol Oncol 1983, 15 (1):10-17. 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Wang K, Hou H, Zhang Y, Ao M, Luo H, Li B: Ovarian cancer-associated immune exhaustion involves SPP1+ T cell and NKT cell, symbolizing more malignant progression . Front Endocrinol (Lausanne) 2023, 14 :1168245. Li B-L, Wan X-P: Prognostic significance of immune landscape in tumour microenvironment of endometrial cancer . J Cell Mol Med 2020, 24 (14):7767-7777. Ma J, Lin J, Lin X, Ren Y, Liu D, Tang S, Huang L, Xu S, Mao X, Sun P: Assessment of Immune Status in Patients with Mismatch Repair Deficiency Endometrial Cancer . J Inflamm Res 2024, 17 :2039-2050. Li Y-R, Zhou Y, Yu J, Zhu Y, Lee D, Zhu E, Li Z, Kim YJ, Zhou K, Fang Y et al : Engineering allorejection-resistant CAR-NKT cells from hematopoietic stem cells for off-the-shelf cancer immunotherapy . Mol Ther 2024, 32 (6):1849-1874. Heczey A, Courtney AN, Montalbano A, Robinson S, Liu K, Li M, Ghatwai N, Dakhova O, Liu B, Raveh-Sadka T et al : Anti-GD2 CAR-NKT cells in patients with relapsed or refractory neuroblastoma: an interim analysis . Nat Med 2020, 26 (11):1686-1690. Additional Declarations No competing interests reported. Supplementary Files SupplementTable.xlsx Additional file 1: Table S1:STROBE-MR checklist of the study. Table S2: The instruments of Gut microbiota/Immune cells related to Endometrial cancer. Table S3:Sensitivity analysis of MR study on 3 types of EC. Table S4: MR and Sensitivity analysis between gut microbiota and immune cells. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6189084","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431437010,"identity":"4c0822c1-073d-4918-a51d-c5a926cf85e4","order_by":0,"name":"Shuyang Yu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shuyang","middleName":"","lastName":"Yu","suffix":""},{"id":431437012,"identity":"43d2e9d4-0095-44a6-8e6d-c2756090d9a8","order_by":1,"name":"Wan Shu","email":"","orcid":"","institution":"Huazhong University of Science and 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14:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6189084/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6189084/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79436811,"identity":"20cff9ca-cdb5-4f70-93cd-edff8dd47bf0","added_by":"auto","created_at":"2025-03-28 12:11:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":296704,"visible":true,"origin":"","legend":"\u003cp\u003eOverview flow of the two-sample univariable Mendelian randomization (UVMR) and mediation analysis design. GM, gut microbiota; EC, endometrial cancer; SNPs, single nucleotide polymorphisms; IVs, instrumental variables. β0 (total effect): The causal function of GM on EC; β1 (direct effect A): The causal function of GM on immune cell traits; β2 (direct effect B): The causal function of immune cell traits on EC; β (mediating effect) = β1 (Direct effect A) × β2 (Direct effect B); mediated proportion = β (mediating effect) / β0 (total effect).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6189084/v1/e2caf66b64c98dba8e1f0db8.jpg"},{"id":79437307,"identity":"23c549f8-fedd-40a2-b4bf-b6a471315ce6","added_by":"auto","created_at":"2025-03-28 12:19:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":292509,"visible":true,"origin":"","legend":"\u003cp\u003eAssumptions of the two-sample UVMR and mediation analysis. SNPs, single nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6189084/v1/fe1df43e15b5ab50b7902fee.jpg"},{"id":79436813,"identity":"5d1dba1c-36b1-462f-a27c-2fddde3c1fec","added_by":"auto","created_at":"2025-03-28 12:11:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":402464,"visible":true,"origin":"","legend":"\u003cp\u003eVarious microorganisms of 412 gut microbes presented causal relationships with 3 different types of endometrial cancer. (A, D) Results of MR and IVW method on causation between gut microbiota and endometrial cancer with traditional defined. (B, E) Results of MR and IVW method on causation between gut microbiota and endometrial cancer with endometrioid histology. (C, F) Results of MR and IVW method on causation between gut microbiota and endometrial cancer with non-endometrioid histology. MR, mendelian randomization; GM, gut microbiota; EC, endometrial cancer; IVW, inverse variance weighted method; OR, odds ratio; 95% CI, 95% confidence interval. The symbol \"c_/o_/f_/g_/s_\" demonstrated the class, order, family, genus, and species.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6189084/v1/b3cf73d6c1bab770d0d4ace3.jpg"},{"id":79436819,"identity":"d1992175-2cdd-4094-9176-717c60595a08","added_by":"auto","created_at":"2025-03-28 12:11:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":841831,"visible":true,"origin":"","legend":"\u003cp\u003eVarious immune cell traits of 713 traits presented causal relationships with 3 different types of endometrial cancer. (A) Results of MR with IVW method on causality between immune cells and endometrial cancer with traditional defined. (B) Results of MR with IVW method on causality between immune cells and endometrial cancer with endometrioid histology. (C) Results of MR with IVW method on causality between immune cells and endometrial cancer with non-endometrioid histology. MR, mendelian randomization; EC, endometrial cancer; IVW, inverse variance weighted method; AC, absolute cell counts; Treg, regulatory T cell; NKT, natural killer T cells; OR, odds ratio; 95% CI, 95% confidence interval.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6189084/v1/72ceff7acf9e43bcbb1a0f0d.jpg"},{"id":79436817,"identity":"f2e683b0-e0a1-44f4-b29b-cb7d41646b23","added_by":"auto","created_at":"2025-03-28 12:11:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":349338,"visible":true,"origin":"","legend":"\u003cp\u003eMediation effect of various GM on EC with traditional definition via immune cell traits. (A) Mediation analysis of Dialister_invisus on EC via CD25hi CD45RA- CD4 not Treg AC. (B) Mediation analysis of Bacteroides_massiliensis on EC via CD62L- HLA DR++ monocyte AC. (C) Mediation analysis of Ruminococcus_obeum on EC via CD86+ myeloid DC AC. EC, endometrial cancer; AC, absolute cell counts; Treg, regulatory T cell; OR, odds ratio; 95% CI, 95% confidence interval. The symbol \"c_/o_/f_/g_/s_\" demonstrated the class, order, family, genus, and species.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6189084/v1/57b7d33d1106bcaf24cb7156.jpg"},{"id":79436826,"identity":"5c71ce8f-0df8-4762-8003-45b9893d15e0","added_by":"auto","created_at":"2025-03-28 12:11:59","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":371125,"visible":true,"origin":"","legend":"\u003cp\u003eMediation effect of various GM on EC with endometrioid histology via immune cell traits. (A) Mediation analysis of Bacilli on EC via IgD+ CD38br %lymphocyte. (B) Mediation analysis of Bacilli on EC via Transitional AC. (C) Mediation analysis of Lactobacillales on EC via CD39+ activated Treg %CD4 Treg. EC, endometrial cancer; AC, absolute cell counts; Treg, regulatory T cell; OR, odds ratio; 95% CI, 95% confidence interval. The symbol \"c_/o_/f_/g_/s_\" demonstrated the class, order, family, genus, and species.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6189084/v1/e011eabe455453f2f802b802.jpg"},{"id":79437309,"identity":"01b4cda9-5700-4465-8099-58164b082848","added_by":"auto","created_at":"2025-03-28 12:19:59","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":151336,"visible":true,"origin":"","legend":"\u003cp\u003eMediation effect of Ruminococcus_bromii on EC with non-endometrioid histology via CD8br NKT %lymphocyte. EC, endometrial cancer; AC, absolute cell counts; NKT, natural killer T cells; OR, odds ratio; 95% CI, 95% confidence interval. The symbol \"c_/o_/f_/g_/s_\" demonstrated the class, order, family, genus, and species.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6189084/v1/c1bd9e94e087df36e7804abc.jpg"},{"id":86831533,"identity":"a9608bee-2cd1-41a6-833b-506b4cf9fd5c","added_by":"auto","created_at":"2025-07-16 06:16:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9682297,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6189084/v1/eb282dc9-8cda-45a9-bee5-50e068230211.pdf"},{"id":79436824,"identity":"15b60435-8d5c-40a0-a0fa-8eb406f9399f","added_by":"auto","created_at":"2025-03-28 12:11:59","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3567110,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1: Table S1:\u003c/strong\u003eSTROBE-MR checklist of the study. \u003cstrong\u003eTable S2:\u003c/strong\u003e The instruments of Gut microbiota/Immune cells related to Endometrial cancer. \u003cstrong\u003eTable S3:\u003c/strong\u003eSensitivity analysis of MR study on 3 types of EC. \u003cstrong\u003eTable S4:\u003c/strong\u003e MR and Sensitivity analysis between gut microbiota and immune cells.\u003c/p\u003e","description":"","filename":"SupplementTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6189084/v1/84153702f7fcec4fd77c089c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Immune cells mediate the causal relationship between the gut microbiota and different types of endometrial cancer: A bidirectional two-sample, two-step Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer (EC) is the most common gynecological cancer, the incidence of which has progressively increased in high-income regions[1]. According to their histological features, grade, and hormone receptor (ER and PR) expression, EC can generally be classified into types I and II[1\u0026ndash;3].\u003c/p\u003e \u003cp\u003eType I EC is the most frequent subtype, presenting as low-grade, endometrioid, and hormone receptor-positive and has a good prognosis (85% 5-year OS rate)[1, 2]. Conversely, Type II EC is non-endometrioid, high-grade, and hormone receptor-negative, with a high risk of metastasis and poor prognosis (5-year OS rate of ~\u0026thinsp;55%)[2, 4]. Genetic and environmental factors, including obesity and metabolic and reproductive factors, are the primary risk factors for the occurrence and development of EC[1, 5]. Notably, these risks are strongly linked to the gut and vaginal microbiome; for instance, the gut microbiome may affect endometrial carcinogenesis by altering the systemic and uterine cavity microenvironment, providing a novel therapy for EC[2, 6, 7]. In addition, EC, especially type II EC, is regarded as an immunogenic disease, and its infiltrating immune cells affect anti-cancer therapy to facilitate tumor progression, ultimately leading to the development of immune tolerance[8\u0026ndash;10]. Malignant and non-malignant immune cells and signaling molecules constitute the tumor microenvironment surrounded by blood vessels, disrupting therapeutic efficacy[11\u0026ndash;13]. Immunotherapy is a prospective therapeutic intervention for carcinostasis by activating the immune system[14, 15]. Recently, immunotherapy has demonstrated benefits in treating recurrent and advanced EC[14\u0026ndash;17].\u003c/p\u003e \u003cp\u003eMicrobiota refers to the collection of microorganisms in a particular community, whereas the genome of microorganisms is defined as the microbiome[18\u0026ndash;20]. Evidence suggests that crosstalk between microbiota and the gut immune system is crucial[21]. With further exploration of the gut microbiota, the disrupted balance of the gut microbiota (GM) may influence various disease states such as obesity, mental disorders, and autoimmune diseases[22\u0026ndash;24]. GM can impact human health by regulating host immunity and metabolism[25\u0026ndash;28]. Therefore, regulating host immunity by the gut microbiome can prominently affect a variety of treatment efficacies and toxicities in cancer[18].\u003c/p\u003e \u003cp\u003eMendelian randomization (MR), a data analysis tool used in epidemiological studies to evaluate causal inferences, uses genetic variants strongly correlated with exposure factors as instrumental variables to assess the causal relationship between exposure factors and outcomes[29\u0026ndash;31]. MR generally presents single nucleotide polymorphisms (SNPs) that serve as genetic variants to estimate the causal effect of exposure on outcomes with less susceptibility to environmental confounders and reverse causality[29]. Single-sample MR data are derived from the same individual, whereas two-sample MR uses large-scale GWAS data from independent study populations[32, 33]. Large-scale summary statistics can be used to analyze the relationship between the gut microbiota, immune cells, and EC, enhancing the statistical power of the two-sample MR Analysis.\u003c/p\u003e \u003cp\u003eIn this study, a comprehensive MR Analysis is represented to explore the causal effects among gut microbiome, immune cells and various EC types including EC, EC with endometrioid and non-endometrioid histology. Afterwards, we discussed whether immune cells serve as mediators from the gut microbiota to the EC.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eBidirectional two-sample univariable Mendelian randomization (UVMR) was applied to explore the causal association between the gut microbiota and three types of EC, including endometrial cancer, endometrial cancer with endometrioid histology, and non-endometrioid histology. Subsequently, a two-step MR analysis was used to determine whether immune cell traits mediate these causal associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFirst, the causal relationship between the GM and EC was assessed with UVMR using three filters: SNPs should (1) be strongly associated with EC, (2) affect EC only through the causal effect of the GM, and (3) remove other potential confounders[34]. Subsequently, reverse MR analysis was conducted to screen for GMs with reverse functions in the EC. In the second step, the mediating effects of various immune cells in the corresponding GM and EC were evaluated and quantified using UVMR and Mendelian randomization. The above analysis followed the guidelines for Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STRBOE-MR) (Supplement Table\u0026nbsp;1)[35].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData sources\u003c/h3\u003e\n\u003cp\u003eGut microbiome data were summarized from the Dutch Microbiome Project (DMP), a GWAS of 7,738 European individuals[36]. The project applied shotgun metagenomic sequencing of faecal samples, which identified 207 microbial taxa (5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species).\u003c/p\u003e \u003cp\u003eA total of 731 immune-related genome-wide features with ~\u0026thinsp;22\u0026nbsp;million variants in 3,757 European individuals were identified using GWAS Summary Statistics[37]. Integrated GWAS summary statistics, with classified numbers from GCST0001391 to GCST0002121, were published in the GWAS directory.\u003c/p\u003e \u003cp\u003eGenetic summary data of the two-sample and mediated MR analyses were primarily obtained from the IEU Open GWAS project, including endometrial cancer (ebi-a-GCST90018838), endometrial cancer with endometrioid histology (ebi-a-GCST006465), and non-endometrioid histology (ebi-a-GCST006466). All data are publicly available as GWAS summary data and received ethical approval (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eTabel 1. Summary of data sources. DMP, Dutch Microbiome Project; EC, Endometrial Cancer.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases/controls or sample sizes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhenotypic code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAncestry\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGut microbiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDMP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGCST90027446 to GCST90027857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMediator\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmune cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOrru et al., 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGCST0001391 to GCST0002121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOutcome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,188/237,839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIEU Open GWAS project\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eebi-a-GCST90018838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC with endometrioid histology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46,126/54,884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIEU Open GWAS project\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eebi-a-GCST006465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC with non-endometrioid histology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35,447/36,677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIEU Open GWAS project\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eebi-a-GCST006466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003ch3\u003eGenetic instrumental variable (IVs) selection\u003c/h3\u003e\n\u003cp\u003eEffective MR analysis relies on three foundations: IVs should (1) be strongly associated with EC, (2) affect EC only through the causal effect of GM, and (3) remove other potential confounders[34]. The first step was to select SNPs associated with exposure with a significance threshold of 5e-08, which were further screened via linkage disequilibrium analysis (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; distance within 10,000 kb)[38]. Finally, F values [F\u0026thinsp;=\u0026thinsp;β/r\u003csup\u003e2\u003c/sup\u003e (square of the standard error)] were calculated to identify the strength of IV, retaining SNPs with an F value of \u0026gt;\u0026thinsp;10[39]. The study assumptions were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and selected SNPs would view in Supplement Table\u0026nbsp;2.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using the R 4.4.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The \u0026ldquo;VariantAnnotation\u0026rdquo; package, \u0026ldquo;ieugwasr\u0026rdquo; package, and \u0026ldquo;TwoSampleMR\u0026rdquo; package were used to perform UVMR. Among the five methods (\u0026ldquo;MR Egger,\u0026rdquo; \u0026ldquo;Weighted median,\u0026rdquo; \u0026ldquo;Inverse variance weighted (IVW),\u0026rdquo; \u0026ldquo;Simple mode,\u0026rdquo; and \u0026ldquo;Weighted mode\u0026rdquo;), IVW is the primary method of causal estimation; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was identified as a significant causal association. Moreover, the heterogeneity and horizontal pleiotropy were based on the Cochran Q statistic and the MR-Egger intercept of the IVW and MR-Egger methods. Heterogeneity and pleiotropy were identified by a P-value of \u0026lt;\u0026thinsp;0.05, which indicated unsustainable causality. The \u0026ldquo;leave-one-out\u0026rdquo; test was used to investigate the effects of possible uncorrelated SNPs.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMediation analysis\u003c/h3\u003e\n\u003cp\u003eAfter two-sample UVMR, GM and immune cells with significant causal relationships with various EC were selected for further mediation analysis. We determined whether the GM had causal effects on immune cells; if so, multiple MR analyses were conducted to explore the mediating effect of immune cells from GM to EC.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental variable selection\u003c/h2\u003e \u003cp\u003eInitially, 4,031 gut microbiota-associated SNPs were identified by P\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e with closely linked SNPs removed (single nucleotide polymorphisms), which served as IV of 412 gut microbiota. The R\u0026sup2; and F-values were calculated for screened SNPs (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; F-values\u0026thinsp;\u0026gt;\u0026thinsp;10), which were unlikely to be affected by weak instrument bias (Supplement Table\u0026nbsp;2). Similarly, the same process was used to selected SNPs of immune cell traits (Supplement Table\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCausal effects of gut microbiota on different types of EC\u003c/h3\u003e\n\u003cp\u003eThe IVW method was used to evaluate the potential causal relationship between the gut microbiota and the invasion of different types of EC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F). Although other methods, including MRE, WMed, SMod, and WMod, did not show statistical significance, the estimated causal effect demonstrated a similar tendency to that obtained using the IVW method. There was no heterogeneity or horizontal pleiotropy in this MR analysis, as shown by the Cochran\u0026rsquo;s Q statistic, MR-Egger intercept test, and MR-PRESSO test (Supplement Table\u0026nbsp;3). Furthermore, none of the SNP severely interfered with the overall effect of GM on EC.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTraditional definition of EC.\u003c/b\u003e Genetic predictions of genus \u003cem\u003eErysipelotrichaceae_noname\u003c/em\u003e (odds ratio (OR)\u0026thinsp;=\u0026thinsp;1.157, 95% Confidence Interval (CI) [1.021, 1.316], P\u0026thinsp;=\u0026thinsp;0.021), genus Dialister (OR\u0026thinsp;=\u0026thinsp;1.192, 95% CI [1.017, 1.398], P\u0026thinsp;=\u0026thinsp;0.030), species \u003cem\u003eDialister_invisus\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.236, 95% CI [1.055, 1.450], P\u0026thinsp;=\u0026thinsp;0.009) and species \u003cem\u003eRuminococcus_torques\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.238, 95% CI [1.034, 1.482], P\u0026thinsp;=\u0026thinsp;0.020) are associated with increased risk in traditionally defined EC. Species \u003cem\u003eBacteroides faeces\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.931, 95% CI [0.868, 0.997], P\u0026thinsp;=\u0026thinsp;0.042), species \u003cem\u003eBacteroides massiliensis\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.769, 95% CI [0.642, 0.922], P\u0026thinsp;=\u0026thinsp;0.004), and species \u003cem\u003eRuminococcus obeum\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.830, 95% CI [0.702, 0.981], P\u0026thinsp;=\u0026thinsp;0.029) were associated with a reduced risk of EC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEC with endometrioid histology (type I EC).\u003c/b\u003e For endometrial EC, class \u003cem\u003eBacilli\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.880, 95% CI [0.801, 0.968], P\u0026thinsp;=\u0026thinsp;0.008), family \u003cem\u003eBacteroidaceae\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.863, 95% CI [0.757, 0.985], P\u0026thinsp;=\u0026thinsp;0.028), order \u003cem\u003eLactobacillales\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.905, 95% CI [0.820, 0.998], P\u0026thinsp;=\u0026thinsp;0.046), species \u003cem\u003eEggerthella_\u003c/em\u003eunclassified (OR\u0026thinsp;=\u0026thinsp;0.855, 95% CI [0.734, 0.996], P\u0026thinsp;=\u0026thinsp;0.044), species \u003cem\u003eAlistipes_sp_AP11\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.900, 95% CI [0.814, 0.996], P\u0026thinsp;=\u0026thinsp;0.042) is a protective factor for endometrial EC. Species \u003cem\u003eAspergillus senegalensis\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.154, 95% CI [1.015, 1.311], P\u0026thinsp;=\u0026thinsp;0.029) and species \u003cem\u003eHoldemania_\u003c/em\u003eunclassified (OR\u0026thinsp;=\u0026thinsp;1.109, 95% CI [1.003, 1.225], P\u0026thinsp;=\u0026thinsp;0.044) were risk factors for endometrial EC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEC with non-endometrioid histology (type II EC).\u003c/b\u003e Focusing on non-endometrial EC, species \u003cem\u003eBacteroides stercoris\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.450, 95% CI [1.010, 2.082], P\u0026thinsp;=\u0026thinsp;0.044) and species \u003cem\u003eLachnospiraceae_bacterium_5_1_63FAA\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;1.251, 95% CI [1.027, 1.525], P\u0026thinsp;=\u0026thinsp;0.026) were associated with an increased risk of developing this type of EC, whereas species \u003cem\u003eRuminococcus_bromii\u003c/em\u003e (OR\u0026thinsp;=\u0026thinsp;0.633, 95% CI [0.446, 0.900], P\u0026thinsp;=\u0026thinsp;0.011) was associated with decreased risk of EC (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eReverse MR analysis of gut microbiota on different types of EC\u003c/h2\u003e \u003cp\u003eNext, reverse MR analysis was conducted on the three EC types and their causal gut microbiota. The results showed no obvious causal effects (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table\u0026nbsp;2) expect for species \u003cem\u003eLachnospiraceae_bacterium_5_1_63FAA\u003c/em\u003e, between three types of EC and the relevant GM through using the IVW method of reverse analysis (Table\u0026nbsp;2). Therefore, species \u003cem\u003eLachnospiraceae_bacterium_5_1_63FAA\u003c/em\u003e would not be further analyzed in EC with non-endometrioid histology.\u003c/p\u003e \u003cp\u003eTabel 2. Reverse MR_IVW analysis of gut microbiota on 3 types of EC. SNPs, single nucleotide polymorphisms; OR, odds ratio; 95% CI, 95% confidence interval.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003erevserse_Pvaleue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eTraditional definition of EC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenus_Erysipelotrichaceae_noname\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.157 (1.021\u0026ndash;1.312)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.863\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenus_Dialister\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.192 (1.017\u0026ndash;1.398)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.253\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies_Dialister_invisus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.236 (1.055\u0026ndash;1.449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.163\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies_Bacteroides_faecis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.931 (0.868\u0026ndash;0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.323\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies_Bacteroides_massiliensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.769 (0.642\u0026ndash;0.922)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.064\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies_Ruminococcus_obeum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.830 (0.702\u0026ndash;0.981)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.421\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies_Ruminococcus_torques\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.238 (1.034\u0026ndash;1.482)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.117\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEC with endometrioid histology (type I EC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass_Bacilli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.801\u0026ndash;0.968)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.923\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily_Bacteroidaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.863 (0.757\u0026ndash;0.985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.183\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrder_Lactobacillales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905 (0.82\u0026ndash;0.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.925\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies_Eggerthella_unclassified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.855 (0.734\u0026ndash;0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.306\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies_Alistipes_senegalensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.154 (1.015\u0026ndash;1.311)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.621\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies_Alistipes_sp_AP11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9 (0.814\u0026ndash;0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.802\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies_Holdemania_unclassified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.109 (1.003\u0026ndash;1.225)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.078\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEC with non-endometrioid histology (type II EC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies_Ruminococcus_bromii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.633 (0.446-0.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.926\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies_Bacteroides_stercoris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.450 (0.010\u0026ndash;2.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.911\u003c/b\u003e\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEffect of immune cell traits on different types of EC\u003c/h2\u003e \u003cp\u003eThe IVW method, as the primary assessment method, was used to explore the effect of immune cells on different types of EC with similar causal effects of MRE, WMed, SMod, and WMod. In addition, the MR-Egger intercept test, MR-PRESSO test, and leave-one-out sensitivity analysis showed no heterogeneity or horizontal pleiotropy, and the removal of any single SNP did not significantly affect the overall influence of immune cells on EC (Supplement Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eThe analysis also revealed that protecting 6 immune cell traits and 14 genetically predicted immune cell traits enhanced the risk of traditionally defined EC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Thirteen immune cell traits were associated with an increased risk, whereas 14 were associated with a reduced risk of endometrial EC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In addition, MR analysis showed that non-endometrial EC was amplified by 19 immune cell traits and suppressed by 19 other immune cell traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEffect of gut microbiota on immune cell traits\u003c/h2\u003e \u003cp\u003eWe have testified the role of gut microbiota and immune cell traits for various EC types, and following the causation of these gut microbiota to immune cells in various EC types were explored through MR Analysis (Supplement Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003cb\u003eTraditional definition of EC.\u003c/b\u003e In traditionally defined EC, 7 gut microbiota and 20 immune cell traits were analysed, of which species \u003cem\u003eDialister_invisus\u003c/em\u003e was a risk factor for CD25hi CD45RA-CD4 but not for Treg AC (OR\u0026thinsp;=\u0026thinsp;1.222, 95% CI [1.026, 1.456], P\u0026thinsp;=\u0026thinsp;0.089). Species \u003cem\u003eBacteroides_massiliensis\u003c/em\u003e protected against CD62L-HLA Dr\u0026thinsp;+\u0026thinsp;monocyte AC (OR\u0026thinsp;=\u0026thinsp;0.761, 95% CI [0.617, 0.939], P\u0026thinsp;=\u0026thinsp;0.011). Besides, species \u003cem\u003eRuminococcus_obeum\u003c/em\u003e is a protective factor for CD86\u0026thinsp;+\u0026thinsp;myeloid DC AC (OR\u0026thinsp;=\u0026thinsp;0.817, 95% CI [0.687, 0.971], P\u0026thinsp;=\u0026thinsp;0.222). However, it is also dangerous for CD27 on IgD\u0026thinsp;+\u0026thinsp;CD38-unsw mem (OR\u0026thinsp;=\u0026thinsp;1.356, 95% CI [1.081, 1.701], P\u0026thinsp;=\u0026thinsp;0.008) and CD27 on IgD-CD38BR (OR\u0026thinsp;=\u0026thinsp;1.222, 95% CI [1.036, 1.443], P\u0026thinsp;=\u0026thinsp;0.018).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEC with endometrioid histology (type I EC).\u003c/b\u003e MR analysis revealed seven gut microbiota and 27 immune cell traits. The results show that class \u003cem\u003eBacilli\u003c/em\u003e are harmful to IgD\u0026thinsp;+\u0026thinsp;CD38br % lymphocytes (OR\u0026thinsp;=\u0026thinsp;1.172, 95% CI [1.034, 1.327], P\u0026thinsp;=\u0026thinsp;0.013) and Transitional AC (OR\u0026thinsp;=\u0026thinsp;1.175, 95% CI [1.038, 1.329], P\u0026thinsp;=\u0026thinsp;0.011), whereas species \u003cem\u003eHoldemania_unclassified\u003c/em\u003e is also dangerous relative to Transitional AC (OR\u0026thinsp;=\u0026thinsp;1.201, 95% CI [1.055, 1.369], P\u0026thinsp;=\u0026thinsp;0.006), and order \u003cem\u003eLactobacillales\u003c/em\u003e was a risk factor for CD39\u0026thinsp;+\u0026thinsp;activated Treg %CD4 Treg (OR\u0026thinsp;=\u0026thinsp;1.121, 95% CI [1.007, 1.247], P\u0026thinsp;=\u0026thinsp;0.036).\u003c/p\u003e \u003cp\u003e \u003cb\u003eEC with non-endometrioid histology (type II EC) .\u003c/b\u003e For non-endometrial EC with 3 gut microbiota and 38 immune cell traits, species \u003cem\u003eRuminococcus_bromii\u003c/em\u003e for IgD-CD38dim %lymphocyte (OR\u0026thinsp;=\u0026thinsp;1.217, 95% CI [1.008, 1.468], P\u0026thinsp;=\u0026thinsp;0.041), CD8dim %leukocyte (OR\u0026thinsp;=\u0026thinsp;1.243, 95% CI [1.025, 1.508], P\u0026thinsp;=\u0026thinsp;0.027) and CD8br NKT %lymphocyte (OR\u0026thinsp;=\u0026thinsp;1.223, 95% CI [1.010, P\u0026thinsp;=\u0026thinsp;0.027) 1.482], P\u0026thinsp;=\u0026thinsp;0.039) was detrimental; however, it was protective to CD25hi AC (OR\u0026thinsp;=\u0026thinsp;0.772, 95%CI [0.632, 0.943], P\u0026thinsp;=\u0026thinsp;0.011).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMediation analysis\u003c/h2\u003e \u003cp\u003eThe proportion of mediating effects was quantified by determining the ratio of indirect to direct effects after confirming the cause-effect relationship between the gut microbiota, immune cell traits, and various EC types (Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eTabel 3. Mediation effect of various GM on 3 types of EC via immune cell traits. EC, endometrial cancer; AC, absolute cell counts; Treg, regulatory T cell; NKT, natural killer T cells; 95% CI, 95% confidence interval. β0 (total effect): The causal function of GM on EC; β1 (direct effect A): The causal function of GM on immune cell traits; β2 (direct effect B): The causal function of immune cell traits on EC; β (mediating effect) = β1(Direct effect A) \u0026times; β2(Direct effect B); mediated proportion\u0026thinsp;=\u0026thinsp;β (mediating effect) / β0(total effect). The symbol \"c_/o_/f_/g_/s_\" demonstrated the class, order, family, genus, and species.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGut microbiome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmune cell\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ebeta0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ebeta1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ebeta2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMediated effect\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMediated proportion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTraditional definition of EC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003es__Bacteroides_massiliensis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD62L- HLA DR\u0026thinsp;+\u0026thinsp;+\u0026thinsp;monocyte AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0331\u003c/p\u003e \u003cp\u003e(-0.0959, 0.0296)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.60%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003es__Dialister_invisus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD25hi CD45RA- CD4 not Treg AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003cp\u003e(-0.0163, 0.0662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003es__Ruminococcus_obeum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD86\u0026thinsp;+\u0026thinsp;myeloid DC AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0171\u003c/p\u003e \u003cp\u003e(-0.0551, 0.0209)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003es__Ruminococcus_obeum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD27 on IgD\u0026thinsp;+\u0026thinsp;CD38- unsw mem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003cp\u003e(-0.0531, 0.0871)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-16.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003es__Ruminococcus_obeum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD27 on IgD- CD38br\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0313\u003c/p\u003e \u003cp\u003e(-0.0108, 0.0735)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-16.80%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEC with endometrioid histology (type I EC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ec__Bacilli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD\u0026thinsp;+\u0026thinsp;CD38br %lymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00957\u003c/p\u003e \u003cp\u003e(-0.0307, 0.0116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.49%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ec__Bacilli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransitional AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0108\u003c/p\u003e \u003cp\u003e(-0.0324, 0.0107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eo__Lactobacillales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD39\u0026thinsp;+\u0026thinsp;activated Treg %CD4 Treg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0102\u003c/p\u003e \u003cp\u003e(-0.0351, 0.0148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003es__Holdemania_unclassified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransitional AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.00765\u003c/p\u003e \u003cp\u003e(-0.0217, 0.00643)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-7.43%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEC with non-endometrioid histology (type II EC)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003es__Ruminococcus_bromii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD8br NKT %lymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003cp\u003e(-0.0876, 0.0157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.87%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003es__Ruminococcus_bromii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIgD- CD38dim %lymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0423\u003c/p\u003e \u003cp\u003e(-0.0125, 0.097)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-9.25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003es__Ruminococcus_bromii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD25hi AC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0736\u003c/p\u003e \u003cp\u003e(-0.0034, 0.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-16.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003es__Ruminococcus_bromii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCD8dim %leukocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0552\u003c/p\u003e \u003cp\u003e(-0.00935, 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-12.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTraditional definition of EC.\u003c/b\u003e Species \u003cem\u003eRuminococcus_obeum\u003c/em\u003e impacted EC through 3 different mediators: CD86\u0026thinsp;+\u0026thinsp;myeloid DC AC (mediated effect β = -0.017, mediated proportion\u0026thinsp;=\u0026thinsp;9.17%), CD27 on IgD\u0026thinsp;+\u0026thinsp;CD38- unsw mem (β\u0026thinsp;=\u0026thinsp;0.017, mediated proportion = -16.10%), and CD27 on IgD- CD38br (β\u0026thinsp;=\u0026thinsp;0.031, mediated proportion = -16.80%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Notably, the mediated proportion of mediator CD27 on IgD\u0026thinsp;+\u0026thinsp;CD38- unsw mem and CD27 on IgD- CD38br was negative, indicating that exposure yielded an opposite effect on the outcome, as expected, through the mediator. This may be owing to 1) the reverse effect of the mediating variable. The mediating variable may have a reverse effect, and its regulation leads to a reverse change in the outcome owing to various factors, such as biological mechanisms, environmental factors, or individual differences. 2) Other unconsidered variables: There may be other unidentified variables that could influence the mediating effect. In addition, statistical errors during calculations might have contributed to this result.\u003c/p\u003e \u003cp\u003eBesides, the result presented species \u003cem\u003eBacteroides_massiliensis\u003c/em\u003e affect EC via CD62L- HLA DR\u0026thinsp;+\u0026thinsp;+\u0026thinsp;monocyte AC (β = -0.033, mediated proportion\u0026thinsp;=\u0026thinsp;12.60%) with a negative mediating role (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). CD25hi CD45RA- CD4, not Treg AC, positively mediated species \u003cem\u003eDialister_invisus\u003c/em\u003e acting on EC (β\u0026thinsp;=\u0026thinsp;0.025, Mediated proportion\u0026thinsp;=\u0026thinsp;11.80%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEC with endometrioid histology (type I EC).\u003c/b\u003e Class \u003cem\u003eBacilli\u003c/em\u003e could protect endometrial EC through IgD\u0026thinsp;+\u0026thinsp;CD38br %lymphocyte (β = -0.010, mediated proportion\u0026thinsp;=\u0026thinsp;7.49%) and transitional AC (β = -0.011, mediated proportion\u0026thinsp;=\u0026thinsp;8.48%) with negative mediations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Transitional AC also mediated species \u003cem\u003eHoldemania_\u003c/em\u003eunclassified influence on endometrial EC (β = -0.008, mediated proportion = -7.43%) that was illogical. In addition, order \u003cem\u003eLactobacillales\u003c/em\u003e is protective to endometrial EC by CD39\u0026thinsp;+\u0026thinsp;activated Treg %CD4 Treg (β = -0.010, mediated proportion\u0026thinsp;=\u0026thinsp;10.20%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEC with non-endometrioid histology (type II EC).\u003c/b\u003e Species \u003cem\u003eRuminococcus_bromii\u003c/em\u003e could reduce the risk of non-endometrial EC by IgD- CD38dim %lymphocyte (β\u0026thinsp;=\u0026thinsp;0.042, mediated proportion = -9.25%), CD25hi AC (β\u0026thinsp;=\u0026thinsp;0.074, mediated proportion = -16.10%), CD8dim %leukocyte (β\u0026thinsp;=\u0026thinsp;0.055, mediated proportion = -12.10%), and CD8br NKT %lymphocyte (β = -0.036, mediated proportion\u0026thinsp;=\u0026thinsp;7.87%), although only CD8br NKT %lymphocyte was logical (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIntestinal microbiota and its metabolites affect several pathophysiological processes covering host metabolism and immune response, known as the \u0026ldquo;second endocrine organ\u0026rdquo;[40]. Previous studies have verified that the regulation of host immunity is closely interrelated with the microbiota, and a disrupted balance would motivate the occurrence and deterioration of diseases[41, 42] ; hence, it is crucial to equilibrate the complex communions between them. In this study, we conducted a comprehensive large-scale two-sample MR analysis based on the LifeLine Biobank and GWAS databases, followed by reverse MR analysis for each of the three types of EC. There were 17 causal relationships, including 7 gut microbiota species and conventionally defined EC, 7 species and endometrial EC, and 3 species and non-endometrial EC, which were evaluated by predicting 158 SNP loci. Therefore, the three types of EC were primarily causally related to the \u003cem\u003ephyla Firmicutes\u003c/em\u003e and \u003cem\u003eBacteroidetes\u003c/em\u003e; type I EC presented a causal correlation with the Phylum \u003cem\u003eActinobacteria\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eNext, a two-sample MR analysis was conducted on immune cell traits and different types of EC, and causal relationships were discussed based on various SNPs. Twenty immune traits were associated with typical EC, 27 were causally linked to endometrial EC, and non-endometrial EC showed a causal correlation with 38 immune cells.\u003c/p\u003e \u003cp\u003eFinally, we performed a two-step MR analysis and mediation analysis that focused on causal associations among the gut microbiota, immune cell traits, and each of the three types of EC and whether immune cell traits exert their mediating functions.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTraditional definition of EC\u003c/h2\u003e \u003cp\u003e \u003cem\u003eBacteroides massiliensis\u003c/em\u003e could reduce the risk of EC by repressing the immune cell trait \u0026ldquo;CD62L HLA DR\u0026thinsp;+\u0026thinsp;+\u0026thinsp;monocyte AC\u0026rdquo;. \u003cem\u003eBacteroides\u003c/em\u003e regulate the immune system to maintain homeostasis, and their metabolites maintain immune system stability [43]. \u003cem\u003eBacteroides\u003c/em\u003e are the major producers of short-chain fatty acids in the gut[44], which are important for maintaining microecological equilibrium, mainly in the form of acetic and propionic acids. Acetate and propionate are potent anti-inflammatory mediators that inhibit the release of pro-inflammatory cytokines from neutrophils and macrophages[45]. The anticancer effects of propionic acid on apoptosis have been described in human colon cancer cells[46]. The anti-tumor effects of cytotoxic T lymphocyte antigen 4 (CTLA-4) blockers depend on different \u003cem\u003eBacteroides\u003c/em\u003e species, with specific T cell responses to \u003cem\u003eB. thetaiotaomicron\u003c/em\u003e or \u003cem\u003eB. fragilis\u003c/em\u003e affecting the efficacy of CTLA-4 blockers in mice and patients[47]. In addition, the HLA-DR phenotype is closely related to anti-tumor immunotherapy, with CD62L as a marker of monocyte activation[48, 49], and CD62L HLA DR\u0026thinsp;+\u0026thinsp;+\u0026thinsp;monocyte was analyzed to facilitate EC progress. Nevertheless, the immunosuppressive cytokines interleukin (IL)-10 and pro-inflammatory factors IL-6 and TNFα negatively correlate with HLA-DR within B-cell non-Hodgkin lymphoma[50, 51]. Mengos et al. found that monocytes with reduced or no HLA-DR expression are crucial mediators of tumor-induced immunosuppression and negatively affect programed death-1 (PD-1) and CTLA-4 checkpoint inhibition [52\u0026ndash;58], chimeric antigen receptor T-cell (CAR-T) immunotherapy[59\u0026ndash;61], cancer vaccines[62\u0026ndash;66], and hematopoietic stem cell transplantation[67\u0026ndash;70]. Existing studies have presented conflicting views to our analysis; therefore, further exploration is necessary to investigate the effect of CD62L-HLA DR\u0026thinsp;+\u0026thinsp;+\u0026thinsp;monocytes on EC.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRuminococcus obeum\u003c/em\u003e, which belongs to the genus \u003cem\u003eBlautia\u003c/em\u003e, protects EC via the trait \u0026ldquo;CD86\u0026thinsp;+\u0026thinsp;myeloid DC AC\u0026rdquo;. Gut microbiome analysis of immune checkpoint inhibitor (ICI)-treated patients showed that the high diversity and presence of immunogenic bacteria, such as \u003cem\u003eRuminococcus\u003c/em\u003e, resulted in more significant CD8\u0026thinsp;+\u0026thinsp;T cell and CD4\u0026thinsp;+\u0026thinsp;Th1-dependent anti-tumor responses, leading to better outcomes[71\u0026ndash;74]. However, Xu et al. found that \u003cem\u003eBlautia obeum\u003c/em\u003e was enriched in patients who were nonreactive to anti-PD-1 and ICI treatments and may have antibiotic properties along with antibiotic resistance[75, 76]. Therefore, the distinct mechanism of action of \u003cem\u003eRuminococcus obeum\u003c/em\u003e on EC warrants further investigation. In addition, CD86 expression is considered a poor prognostic indicator and is down-regulated by transendocytosis of CTLA-4[76, 77]. Blocking CD28: CD80/CD86 \u003cem\u003ein vivo\u003c/em\u003e re-sensitizes multiple myeloma cells to chemotherapy and significantly reduces the tumor load[78]. Combined with MR analysis, \u003cem\u003eRuminococcus obeum\u003c/em\u003e may inhibit CD86\u0026thinsp;+\u0026thinsp;on myeloid DC to enhance immunotherapy, thereby restraining EC.\u003c/p\u003e \u003cp\u003e \u003cem\u003eDialister invisus\u003c/em\u003e was a risk factor for EC functioned through the immune cell trait \u0026ldquo;CD25hi CD45RA- CD4, not Treg AC.\u0026rdquo; Enriching \u003cem\u003eDialister invisus\u003c/em\u003e, isolated from the human oral cavity, indicates a high risk and rapid progression of tumors in colorectal cancer[79, 80] and increases the risk of HPV-infected cervical cancer in female reproductive system tumors[81\u0026ndash;83]. However, Byrd et al. found that \u003cem\u003edialister\u003c/em\u003e status was associated with higher survival in MSI-H colorectal tumours[84]. CD4\u0026thinsp;+\u0026thinsp;CD25hi T cells maintain immune tolerance to autoantigens in various cancers, including ovarian and cervical[85\u0026ndash;87]. Typically, T cells can be divided into CD45RA\u0026thinsp;+\u0026thinsp;initial and CD45RA- memory subtypes. The activated CD45RA- T cells inhibit the anti-tumor function of CD8\u0026thinsp;+\u0026thinsp;T cells with IL-10 secretion and intercellular contacts. This facilitates immunosuppression and gastric cancer progression[88\u0026ndash;90]. Tassi et al. confirmed that CD45RA-T lymphocytes were increased in the epithelial ovarian tumor microenvironment[91]. Therefore, together with our analysis, \u003cem\u003eDialister invisus\u003c/em\u003e down-regulates the anti-tumor function of CD8\u0026thinsp;+\u0026thinsp;T cells by stimulating the immunosuppressive trait \u0026ldquo;CD25hi CD45RA-CD4 not Treg,\u0026rdquo; following accelerated tumor progression of EC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEC with endometrioid histology (type I EC)\u003c/h2\u003e \u003cp\u003eClass \u003cem\u003eBacilli\u003c/em\u003e mainly play a protective role in endometrial EC, colonizing the gastrointestinal tract[92]. The Secretome regulates tumor pathophysiological processes, and its secretions exhibit bacteriological and anti-tumor properties. Typically, short-chain fatty acids (SCFAs) from \u003cem\u003eBacilli\u003c/em\u003e are beneficial for gut peristalsis and secretion and inhibit tumor proliferation by inducing apoptosis and controlling epigenetic modification[92\u0026ndash;96]. Bacteriocins and other secretions derived from bacilli can suppress phospholipase A2, down-regulating pro-inflammatory cytokines and up-regulating anti-inflammatory cytokines[97, 98]. Extracellular vesicles (EVs) of bacilli induce apoptosis in HepG2 cells by increasing the expression ratio of bax/bcl-2[99]. \u003cem\u003eBacilli\u003c/em\u003e stimulate the production of insulin-like growth factor 1 (IGF-1), participating in regulating blood glucose and lipids and are closely associated with the pathogenesis of type I EC[95]. In a clinical study on colorectal cancer, the abundance of bacilli in the metastatic group was significantly decreased compared with that in tumor patients[100], indicating that it may restrain tumor invasion. CD38, a type II transmembrane glycoprotein, is a crucial metabolic enzyme located on the cell surface; its products are vital during immune regulation[101, 102]. CD38 is an anti-tumor therapeutic target involved in adenosine formation and exerts remarkable immunosuppressive effects on the solid tumor microenvironment[101, 103]. Thavaneswaran et al. reported that EC patients with high CD38\u0026thinsp;+\u0026thinsp;expression in peripheral tertiary lymphoid structures (TLSs) had favorable survival outcomes during chemotherapy[104, 105]. Our MR analysis showed that lymphocytes with lgD\u0026thinsp;+\u0026thinsp;and bright CD38 were protective against type I EC. \u003cem\u003eBacilli\u003c/em\u003e play a protective role by enhancing lymphocyte function; however, their specific mechanisms warrant further investigations.\u003c/p\u003e \u003cp\u003eIn particular, the order \u003cem\u003eLactobacillales\u003c/em\u003e belongs to the Class \u003cem\u003eBacilli\u003c/em\u003e; hence, their metabolites and secretions, such as SCFAs, present similar antitumor mechanisms. Bactericin mainly disrupts the membrane intimal potential, leading to uncontrolled ion leakage and cell death[106, 107]. Moreover, these bacteria can reduce the concentration of soluble bile salts in faeces, thereby neutralizing the cancer-promoting effects of bile acids, including DNA damage and apoptosis[107\u0026ndash;110]. Importantly, the \u003cem\u003eLactobacillales\u003c/em\u003e microbiome is the main component of a healthy vaginal microecosystem, and its disturbance promotes inflammation and cancer progression[111\u0026ndash;113]. In contrast, CD39+-activated Treg %CD4 Treg mainly play an anti-inflammatory role. CD39, an extranuclear triphosphate diphosphate hydrolase-1, can hydrolyze ATP into AMP, obstructing inflammatory signal transduction of extracellular ATP[114\u0026ndash;116]. Maddaloni et al. showed that lactic acid bacteria can deliver IL-35 in collagen-induced arthritis in mice, thus stimulating CD39\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;Tregs to release increased IL-10, exerting anti-inflammatory and immune effects[116]. Furthermore, lactic acid bacteria increase the proliferation of CD39\u0026thinsp;+\u0026thinsp;Tregs in mouse allergic asthma models, following the regulation of the inflammatory response induced by immune disorders[117]. Therefore, we speculated that \u003cem\u003eLactobacillales\u003c/em\u003e activate and promote the proliferation of CD39+-activated Treg %CD4 Tregs by delivering special inflammatory mediators, such as IL-35, in endometrial EC, manifesting its protective role.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEC with non-endometrioid histology (type II EC)\u003c/h2\u003e \u003cp\u003e \u003cem\u003eRuminococcus bromii\u003c/em\u003e is beneficial for patients with non-endometrial EC. A colon cancer study proposed the microbiome characteristics of \u003cem\u003eR. bromii\u003c/em\u003e as a reliable indicator, combined with the immune rejection constant, to create a score for evaluating survival prognosis, which contributed to the discovery of personalized therapy[118]. Notably, colon cancer has a genetic phenotype similar to that of non-endometrial EC: DNA mismatch repair (MMR) defects or microsatellite instability (MSI)[119]. The increased abundance of \u003cem\u003eR. bromii\u003c/em\u003e following symbiotic treatment (MS-20) combined with anti-PD-1 treatment positively correlated with reduced tumor load and CD8\u0026thinsp;+\u0026thinsp;T cell infiltration in xenograft mouse models[120]. Additionally, rumen cocci can bind to castalagin and promote anti-cancer responses; castalagin rich in \u003cem\u003eR. bromii\u003c/em\u003e influences the efficacy of anti-PD-L1 immunotherapy and increases the ratio of CD8 + / FOXP3\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;in the tumor microenvironment[121]. NKT cells specifically assemble T-cell receptor (TCR) and NK cell receptor on the surface, which produce numerous cytokines and play a similar cytotoxic role as NK cells[122\u0026ndash;127]. This enhances the immune response and mediates the inhibition of tumor growth in various cancers, including liver, ovarian, and colon cancers[128\u0026ndash;132]. In mismatch repair-deficient EC, a large infiltration of CD8\u0026thinsp;+\u0026thinsp;and NKT cells is a marker for better survival prognosis, indicating a favorable immune microenvironment[132, 133]. Therefore, combined with the MR analysis, \u003cem\u003eR. bromii\u003c/em\u003e may improve the tumor immune response and the efficacy of immunotherapy by increasing CD8\u0026thinsp;+\u0026thinsp;NKT cells, thus playing a protective role. Recently, universal CAR-engineered NKT (UCAR-NKT) cells were developed and demonstrated powerful anti-tumor efficacy against blood and solid tumors in vitro and in vivo with multiple tumor-targeting mechanisms[134, 135]. These cells regulate the tumor microenvironment by selectively depleting immunosuppressive macrophages. Therefore, based on this analysis and previous research, targeting \u003cem\u003eR. bromii\u003c/em\u003e and the immune microenvironment helps improve the poor prognosis of type II EC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStrengths of this study\u003c/h2\u003e \u003cp\u003eThis study has several strengths. First, a large sample of a comprehensive GWAS dataset was used for independent and comparative analysis, including the traditional definition of EC and different types of EC, producing comprehensive results and improving the reliability and clinical significance of the findings based on subtypes. Rigorous two-sample and two-step MR analyses were used to explore the causal effects of the gut microbiota on different types of EC, which identified seven mediating immune cell phenotypes through mediation analysis. Finally, multiple sensitivity tests eliminated confounding variables, confirming the study's robustness. This strengthens the results for future mechanistic studies and offers new insights for enhancing survival in EC patients with various subtypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations of this study\u003c/h2\u003e \u003cp\u003eThis study has some limitations. First, the subjects were mainly concentrated in the European population, and a single data source limited the universality of the results. Second, the occurrence and development of EC resulted from multiple factors. UVMR and two-step Mendelian analyses were conducted from a genetic perspective, ignoring the potential influences of other factors, such as the environment. Subsequent studies are warranted to explore and verify these distinct mechanisms.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study investigated the causal relationships and mediating effects among the gut microbiome, immune cell traits, and different types of EC, providing novel insights into the gut microbiome and immune conditions in EC. These findings suggest potential immune mechanisms of the intestinal microbiome and EC progression, and present new perspectives on the distinctive immunotherapy of EC based on the gut microbiome and diverse subtypes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEndometrial cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-wide association studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDutch Microbiome Project\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eER\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEstrogen receptor, PR:Progesterone receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGut microbiota\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNPs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle nucleotide polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUVMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnivariable Mendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTRBOE-MR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVs, instrumental variables\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWMed\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMod\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWMod\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ec_/o_/f_/g_/s_\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eclass, order, family, genus, and species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAbsolute cell counts\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTreg\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegulatory T cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNKT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNatural killer T cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTLA-4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCytotoxic T lymphocyte antigen 4\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin 10\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePD-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgrammed death-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmune checkpoint inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCAR-T\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echimeric antigen receptor T-cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCFAs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShort-chain fatty acids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExtracellular vesicles\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIGF-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsulin-like growth factor 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTLSs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTertiary lymphoid structures\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMismatch repair\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicrosatellite instability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT-cell receptor.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors appreciate the Dutch Microbiome Project for releasing GM-related GWAS summary data. The authors also want to acknowledge the participants and investigators of the IEU Open GWAS project and the immune cells study of Orru et al.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSY: Conceptualization, Data Management, formal analysis, funding acquisition, survey, methodology, project management, software, visualization, writing-manuscript, writing-review and editing. WS, JZ, SC, XS, GC, TZ, KD, JZ,\u0026nbsp;Laudański, Gutowska: Data Management, Regulation, Writing - Review and editing. HW: Project management, supervision, visualization, writing - review and editing. The authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by the National key research and development plan (No. 2023YFC2705400), Wuhan Knowledge Innovation Special project (202202080101045).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets for this study can be found in the NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/). The name and number of datasets can be found in the article/Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has been conducted relied on publicly available de-identification data from participant studies authorized by the Ethical Standards Committee. All original studies have been approved by the corresponding ethical review board, and the participants have provided informed consent. In addition, no individual-level data was used in this study. Therefore, no new ethical review board approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Clinical Research Center of Cancer Immunotherapy, Hubei, 430022, Wuhan, China.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003e Department of Obstetrics, Gynecology and Gynecological Oncology, Medical University of Warsaw, Poland\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003e Women\u0026apos;s Health Research Institute, Calisia University, Kalisz, Poland\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003e OVIklinika Infertility Center, Warsaw, Poland\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: \u003cstrong\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2021, \u003cstrong\u003e71\u003c/strong\u003e(3):209-249.\u003c/li\u003e\n\u003cli\u003eMakker V, MacKay H, Ray-Coquard I, Levine DA, Westin SN, Aoki D, Oaknin A: \u003cstrong\u003eEndometrial cancer\u003c/strong\u003e. \u003cem\u003eNat Rev Dis Primers \u003c/em\u003e2021, \u003cstrong\u003e7\u003c/strong\u003e(1):88.\u003c/li\u003e\n\u003cli\u003eBokhman JV: \u003cstrong\u003eTwo 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\u003cstrong\u003e26\u003c/strong\u003e(11):1686-1690.\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":"Endometrial cancer, Gut microbiota, Immune cell, Mendelian randomization study, Immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-6189084/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6189084/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEndometrial cancer (EC), one of the most common gynecological cancers classified as either type I or II, is an immunogenic cancer whose tumor microenvironment and immune cell infiltration significantly regulate its prognosis. The gut microbiome affects the occurrence and development of endometrial cancer, and its changes can influence immune conditions. However, whether the gut microbiome regulates different types of endometrial cancer progression through immune cells remains unclear.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThe data used for genome-wide association studies (GWAS) included gut microbiome data from the Dutch Microbiome Project (DMP) (N\u0026thinsp;=\u0026thinsp;7,738), endometrial cancer data from the IEU Open GWAS project: endometrial cancer (N\u0026thinsp;=\u0026thinsp;240,027), endometrial cancer with endometrioid histology (N\u0026thinsp;=\u0026thinsp;54,884), non-endometrioid histology (N\u0026thinsp;=\u0026thinsp;36,677), and immune cell trait data from European populations (N\u0026thinsp;=\u0026thinsp;3,757). Using two-sample Mendelian randomization, we investigated the causal relationship between gut microbiota and the three endometrial cancer types. Subsequently, two-step Mendelian randomization and mediation analyses were performed to explore the mediating role of immune traits in the relationship between the gut microbiome and three types of endometrial cancer.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e \u003cp\u003eAccording to the traditional definition of endometrial cancer and gut microbiome analysis, there are four positive causal effects, three adverse causal effects, and three established mediating effects combined with immune cell traits. Five negative and two positive relationships of the gut microbiome on endometrial cancer with endometrioid histology, together with three immune trait-mediated effects. Analysis of endometrial cancer with non-endometrioid histology showed two positive and one negative causalities, with identified one intermediate causality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings emphasize the elusive relationship between gut microbiota, immune cell traits, and various types of endometrial cancer. The distinct connections and mediating effects provide novel perspectives for the distinct therapies targeting the gut microbiota and the immune microenvironment of endometrial cancer with different subtypes.\u003c/p\u003e","manuscriptTitle":"Immune cells mediate the causal relationship between the gut microbiota and different types of endometrial cancer: A bidirectional two-sample, two-step Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-28 12:11:54","doi":"10.21203/rs.3.rs-6189084/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":"ac0ce204-0f40-40eb-b3b2-8f583c4548a9","owner":[],"postedDate":"March 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-16T06:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-28 12:11:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6189084","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6189084","identity":"rs-6189084","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}