Causal role of immune cells in digestive system cancers: A Mendelian randomization study

Causal role of immune cells in digestive system cancers: A Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Causal role of immune cells in digestive system cancers: A Mendelian randomization study Junfeng Zhao, Ying Li, Ruyue Li, Xiujing Yao, Xue Dong, Yintao Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4074806/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 : Immune cell characteristics and digestive system cancers (DSCs) are correlated; however, the causal relationship between immune cell phenotypes and DSCs remains unclear. In this study, a comprehensive two-sample Mendelian randomization (MR) analysis was performed based on publicly available genetic data to investigate the causal relationship between 731 immunophenotypes and the risk of esophageal cancer (EC), gastric cancer (GC), hepatocellular cancer (HCC), gallbladder cancer, small intestine cancer, colorectal cancer (CRC), and pancreatic cancer (PCA) development. Methods: Inverse variance weighting (IVW), MR-Egger regression, and weighted median methods were used for the MR analysis. Results: IVW results confirmed that among the 731 immunophenotypes, three, six, two, two, four, and five immunophenotypes had significant causal effects on the development of GC, HCC, gallbladder cancer, small intestine cancer, CRC, and PCA, respectively. However, immunophenotypes with a significant causal relationship with EC were not found. Moreover, the instrumental variables did not exhibit significant heterogeneity or horizontal pleiotropy. Conclusions: This MR study demonstrated a close association between immune phenotype and DSCs through genetic means and could guide future clinical studies. digestive system cancer immune cells causal inference Mendelian randomization study single nucleotide polymorphism Figures Figure 1 Background Digestive system cancers (DSCs) are a major cause of cancer-related fatalities, accounting for 26.4% of all new cases and 36.3% of all cancer-related fatalities worldwide [ 1 ]. The intricate process of tumor growth, invasion, and metastasis involves genetic irregularities as well as interplay between tumor tissues and immune cells in the tumor microenvironment [ 2 ]. Research findings continue to indicate that the immune microenvironment may play a critical role in the formation of DSCs [ 3 – 6 ]. Immune cells in the tumor microenvironment display both tumor-promoting and tumor-inhibiting functions [ 7 , 8 ]. Tumor-infiltrating immune cells, which constitute a fundamental component of the complex tumor microenvironment, exert a pivotal effect by either impeding or fostering the progression of tumors [ 9 , 10 ]. Various immune cell types from both the adaptive and innate immune systems play a significant role in cancer immunotherapy [ 11 ]. Extensive intratumoral infiltration of natural killer cells has been found to be associated with better prognoses in DSCs [ 12 ]. Different types of immune cells have been found to exert a significant degree of influence on tumorigenesis and progression in various cancers [ 13 – 17 ]. However, the causal relationship between changes in the immune cell phenotype and the development of DSCs remains unclear. Mendelian randomization (MR) is an analytical method based on Mendelian independent distribution laws and is primarily used to determine infer epidemiological and etiological data. Moreover, its rational causal sequence effectively addresses causality problems [ 18 , 19 ]. The MR design mimics a randomized controlled trial because genetic variants are randomly assigned during fertilization and, therefore, are less likely to exert confounding effects [ 20 , 21 ]. In MR, single nucleotide polymorphisms (SNPs) are used as undiscovered instrumental variables (IVs) in place of exposure phenotypes. Based on previous studies that have revealed a correlation between immune cell characteristics and DSCs, this study aimed to determine the causal relationship between immune cell phenotypes and DSCs based on the MR method. Methods Study design MR analysis was applied to two samples to evaluate the causal relationship between 731 immunophenotypes and the following DSCs: esophageal cancer (EC), gastric cancer (GC), hepatocellular cancer (HCC), gallbladder cancer, small intestine cancer, colorectal cancer (CRC), and pancreatic cancer (PCA). The causal relationship between the immune cell phenotypes and these DSCs was explored individually. The study design for this MR analysis is detailed in Supplementary Figure S1 . Immune cell signatures and DSC genome-wide association study (GWAS) data sources Publicly available data from the GWAS catalog (login numbers GCST0001391 to GCST0002121) were utilized as summary statistics of each immunophenotype [ 22 ]. Basic information on the 731 immune cell phenotypes is detailed in Supplementary Table S1 . We selected seven DSCs as outcomes, including EC (N case = 740, N control = 372,016), GC (N case = 6,563, N control = 195,745), HCC (N case = 1,866, N control = 195,745), gallbladder cancer (N case = 41, the N control = 866), small bowel cancer (N case = 252, N control = 218,540), CRC (N case = 5,657, N control = 372,016), and PCA (N case = 1,896, N control = 1,939). The summarized GWAS data for the above DSCs were obtained from the IEU Open GWAS project ( https://gwas.mrcieu.ac.uk/ , accessed on 3 November 2023). Details of the selected GWAS datasets are shown in Supplementary Table S2 . Instrumental variable (IV) selection The significance level of the IVs for each immunophenotype was set to 1 × 10 − 8 based on a recent study [ 23 ]. These SNPs were trimmed (linkage disequilibrium [LD] r 2 threshold < 0.1 within a 500-kb distance) using the clustering program in PLINK software (version v1.90), where LD r 2 was calculated based on the 1000 Genomes Projects as a reference panel [ 22 , 24 , 25 ]. To quantify the strength of the IVs, we calculated the proportion of variation explained and the F statistic for each IV and removed IVs with low F-statistics (< 10). Statistical analysis To assess the causal connection between the 731 immunophenotypes and seven DSCs, we employed inverse variance weighting (IVW), MR-Egger regression, and weighted median (WM) methodologies for MR analysis. A meta-analysis approach combined with Wald estimates for each SNP was previously employed using the IVW method to generate a comprehensive appraisal of the effect of immune cell phenotypes on lung cancer [ 26 , 27 ]. Hence, the IVW method served as the primary research approach in this study [ 21 ]. The MR-Egger regression assumes instrument strength independent of direct effect (InSIDE), allowing the assessment of the presence of multidirectionality using the intercept term. However, the MR-Egger regression estimates may be inaccurate and strongly influenced by peripheral genetic variations [ 28 ]. The WM approach accurately estimates causality when up to 50% of the instrumental variables are invalid [ 29 ]. Moreover, this study excluded possible outliers of horizontal pleiotropy based on the MR pleiotropy residual sum and outlier (MR-PRESSO) method because such outliers may affect the estimation results of MR-PRESSO [ 30 ]. To identify potentially heterogeneous SNPs, a “leave-one-out” analysis was performed by sequentially removing each instrumental SNP [ 31 ]. The strength of the IVs was assessed using the F statistic as follows: F = [R 2 × (N-1-K)]/[(1-R 2 ) × K], where R 2 denotes the variance proportion in the exposure explained by genetic variation, N denotes the sample size, and K denotes the number of instruments. The absence of significantly weak instrumental bias was considered if the corresponding F statistic was > 10 [ 32 ]. Forest, scatter, funnel, and leave-one-out analysis plots were also drawn. Forest plots visually presented the effect of each SNP on the results; leave-one-out analysis plots determined whether the results were robust; scatter plots displayed the fitting results of the different MR analyses; and funnel plots visually displayed IV heterogeneity. All MR analyses were performed using R version 4.3.2 software. Results Exploration of the causal effect of immunophenotypes on GC Two samples were assessed via MR analysis to investigate the causal effect of the immunophenotypes on GC using IVW as the main analysis method. Three immunophenotypes had a significant causal relationship with GC, two of which having a protective effect against GC; these were naïve CD4 + T cell absolute count (AC) and CD45 on B cells (Fig. 1 and Table 1 ). The odds ratio (OR) of naïve CD4 + T cell AC was 0.885 (95% confidence interval [CI] = 0.798–0.981, P = 0.021) according to the IVW method. Although similar results were obtained using the WM method (OR = 0.857, 95% CI = 0.755–0.974, P = 0.018), the MR-Egger regression did not support such a correlation (OR = 0.530, 95% CI = 0.199–1.411, P = 0.425). The OR for CD45 on B cells analyzed using the IVW method was 0.644 (95% CI = 0.519–0.800, P = 6.8 × 10 − 5 ). Although similar results were obtained using the WM method (OR = 0.650, 95% CI = 0.491–0.860, P = 0.003), the MR-Egger regression did not support such a correlation (OR = 0.590, 95% CI = 0.176–2.433, P = 0.590). Table 1 Results of the causal effect of immune cells on gastric cancer, hepatocellular carcinoma, and gallbladder cancer. Traits OR (95% CI) P Gastric cancer Naïve CD4 + T cell absolute count IVW 0.885(0.798,0.981) 0.021 MR-Egger 0.530(0.199,1.411) 0.425 Weighted median 0.857(0.755,0.974) 0.018 CD45 on B cell IVW 0.644(0.519,0.800) 6.80E-05 MR-Egger 0.590(0.176,2.433) 0.590 Weighted median 0.650(0.491,0.860) 0.003 CD8 dim Natural Killer T % lymphocyte IVW 1.312(1.063,1.619) 0.012 MR-Egger 1.807(0.945,3.456) 0.324 Weighted median 1.336(1.034,1.727) 0.027 Hepatocellular carcinoma Terminally Differentiated CD4 − CD8 − T cell%T cell IVW 0.789(0.667,0.934) 0.006 MR-Egger 0.551(0.251,1.210) 0.188 Weighted median 0.799(0.639,0.999) 0.049 CD62L on monocyte IVW 1.167(1.008,1.349) 0.038 MR-Egger 1.125(0.769,1.647) 0.570 Weighted median 1.147(0.963,1.367) 0.125 CD3 on CD4 + T cell IVW 1.176(1.011,1.368) 0.036 MR-Egger 1.679(1.040,2.713) 0.101 Weighted median 1.245(1.025,1.512) 0.027 CD28 on CD4 + T cell IVW 1.177(1.054,1.314) 0.004 MR-Egger 1.679(1.040,2.713) 0.253 Weighted median 1.179(1.033,1.346) 0.015 CCR2 on CD62L + myeloid Dendritic Cell IVW 1.404(1.185,1.664) 9.01E-05 MR-Egger 1.244(0.619,2.495) 0.602 Weighted median 1.244(1.177,1.777) 4.50E-04 Gallbladder cancer CD16 − CD56 on Natural Killer IVW 0.292(0.132,0.644) 0.002 MR-Egger 0.089(0.018,0.459) 0.034 Weighted median 0.305(0.111,0.838) 0.021 SSC − A on HLA DR + Natural Killer IVW 3.363(1.173,21.349) 0.033 MR-Egger 2.624(0.219,29.618) 0.877 Weighted median 4.943(0.943,22.356) 0.059 Abbreviations: OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted; MR, Mendelian randomization. We also discovered one immunophenotype that is a risk factor for GC: CD8 dim Natural Killer T %lymphocyte (Fig. 1 and Table 1 ). The OR for CD8 dim Natural Killer T %lymphocyte calculated using the IVW method was 1.312 (95% CI = 1.063–1.619, P = 0.012). Although the WM method yielded similar results (OR = 1.336, 95% CI = 1.034–1.727, P = 0.027), the MR-Egger regression did not support such a correlation (OR = 1.807, 95% CI = 0.945–3.456, P = 0.324). Scatter plots for the three immunophenotypes illustrated that the MR-Egger intercept excluded the possibility of horizontal pleiotropy (Supplementary Figure S2 ). In addition, the funnel and leave-one-out analysis plots indicated the robustness of the results (Supplementary Figures S3 and S4). Exploration of the causal effect of immunophenotypes on HCC Two samples were assessed via MR analysis to investigate the causal effect of immunophenotypes on HCC using IVW as the main analysis method. In total, six immunophenotypes had a significant causal relationship with HCC, one of which having a protective effect against HCC: terminally differentiated CD4 − CD8 − T cell%T cells (Fig. 1 and Table 1 ). The OR of terminally differentiated CD4 − CD8 − T cell%T cells analyzed using the IVW method was 0.789 (95% CI = 0.667–0.934, P = 0.006). Although similar results were obtained using the WM method (OR = 0.799, 95% CI = 0.639–0.999, P = 0.049), the MR-Egger regression did not support such a correlation (OR = 0.551, 95% CI = 0.251–1.210, P = 0.188). We similarly identified five immunophenotypes that are risk factors for HCC: CD62L on monocytes, CD3 on CD4 + T cells, CD28 on CD4 + T cells, CCR2 on CD62L + myeloid dendritic cells, and CD8 on CD39 + CD8 + T cells (Fig. 1 and Table 1 ). The OR of CD62L on monocytes analyzed using the IVW method was 1.167 (95% CI = 1.008–1.349, P = 0.038); however, such a correlation was not supported by the MR-Egger regression (OR = 1.125, 95% CI = 0.769–1.647, P = 0.570) or WM (OR = 1.147, 95% CI = 0.963–1.367, P = 0.125). The OR of CD3 on CD4 + T cells analyzed using the IVW method was 1.176 (95% CI = 1.011–1.368, P = 0.036). Although similar results were obtained using the WM method (OR = 1.245, 95% CI = 1.025–1.512, P = 0.027), the MR-Egger regression did not support such a correlation (OR = 1.679, 95% CI = 1.040–2.713, P = 0.101). The OR of CD28 on CD4 + T cells analyzed using the IVW method was 1.177 (95% CI = 1.054–1.314, P = 0.004); similar results were obtained using the WM method; however, the MR-Egger regression did not support such a correlation (WM: OR = 1.179, 95% CI = 1.033–1.346, P = 0.015; MR-Egger regression: OR = 1.679, 95% CI = 1.040–2.713, P = 0.101). The OR for CCR2 on CD62L + myeloid dendritic cells analyzed using the IVW method was 1.404 (95% CI = 1.185 ~ 1.664, P = 9.0 × 10 − 5 ). Although the WM method yielded similar results (OR = 1.446, 95% CI = 1.177–1.777, P = 4.5 × 10 − 4 ), the MR-Egger regression did not support such a correlation (OR = 1.244, 95% CI = 0.619–2.495, P = 0.602). The OR of CD8 on CD39 + CD8 + T cells analyzed using the IVW method was 1.163 (95% CI = 1.003–1.349, P = 0.046); however, such a correlation was not supported by the MR-Egger regression (OR = 1.624, 95% CI = 0.729–3.618, P = 0.257) or WM method (OR = 1.143, 95% CI = 0.963–1.356, P = 0.126). Scatter plots for the six immunophenotypes illustrated that the MR-Egger intercept excluded the possibility of horizontal pleiotropy (Supplementary Figure S5 ). In addition, funnel and leave-one-out analysis plots indicated the robustness of the results (Supplementary Figures S6 and S7). Exploration of the causal effect of immunophenotypes on gallbladder cancer Two samples were assessed via MR analysis to investigate the causal effect of the immunophenotypes on gallbladder cancer using IVW as the main analysis method. In total, two immunophenotypes had a significant causal relationship with gallbladder cancer, one of which having a protective effect against gallbladder cancer: CD16 − CD56 on Natural Killer (Fig. 1 and Table 1 ). The OR for CD16 − CD56 on Natural Killer analyzed using the IVW method was 0.292 (95% CI = 0.132–0.644, P = 0.002). Similar results were obtained using the MR-Egger regression (OR = 0.089, 95% CI = 0.018–0.459, P = 0.034) and WM method (OR = 0.305, 95% CI = 0.111–0.838, P = 0.021). We similarly identified one immunophenotype that is a risk factor for gallbladder cancer: SSC − A on HLA DR + Natural Killer (Fig. 1 and Table 1 ). The OR for SSC-A on HLA DR + Natural Killer was 3.363 (95% CI = 1.173–21.349, P = 0.033) when analyzed using the IVW method; however, such a correlation was not supported by the MR-Egger regression (OR = 2.624, 95% CI = 0.219–29.618, P = 0.877) or WM method (OR = 4.943, 95% CI = 0.943–22.356, P = 0.059). Scatter plots for the two immunophenotypes illustrated that the MR-Egger intercept excluded the possibility of horizontal pleiotropy (Supplementary Figure S8 ). In addition, funnel and leave-one-out analysis plots indicated the robustness of the results (Supplementary Figures S9 and S10). Exploration of the causal effect of immunophenotypes on small intestine cancer Two samples were assessed via MR analysis to investigate the causal effect of the immunophenotypes on small intestine cancer using IVW as the main analysis method. In total, two immunophenotypes had a significant causal relationship with small intestine cancer, both of which being risk factors for small intestine cancer: Terminally Differentiated CD4 − CD8 − T cell %CD4 − CD8 − T cell and CD39 + CD4 + T cell AC (Fig. 1 and Table 2 ). The OR of Terminally Differentiated CD4 − CD8 − T cell %CD4-CD8- T cell analyzed using the IVW method was 1.285 (95% CI = 1.000–1.651, P = 0.049). Although similar results were obtained using the WM method (OR = 1.419, 95% CI = 1.029–1.958, P = 0.033), the MR-Egger regression did not support such a correlation (OR = 1.190, 95% CI = 0.841–1.684, P = 0.381). The OR of CD39 + CD4 + T cell AC analyzed using the IVW method was 1.323 (95% CI = 1.162–1.506, P = 2.3 × 10 − 5 ); similar results were obtained for the MR-Egger regression (OR = 1.134, 95% CI = 1.037–1.239, P = 0.006) and WM method (OR = 1.213, 95% CI = 1.016–1.449, P = 0.035). Table 2 Results of the causal effect of immune cells on small intestine cancer, colorectal cancer, and pancreatic cancer. Traits OR (95% CI) P Small intestine cancer Terminally Differentiated CD4 − CD8 − T cell %CD4 − CD8 − T cell IVW 1.285(1.000,1.651) 0.049 MR-Egger 1.190(0.841,1.684) 0.381 Weighted median 1.419(1.028,1.958) 0.033 CD39 + CD4 + T cell absolute count IVW 1.323(1.162,1.506) 2.36E-05 MR-Egger 1.134(1.037,1.239) 0.006 Weighted median 1.213(1.016,1.449) 0.035 Colorectal cancer Plasmacytoid dendritic cell absolute count IVW 0.997(0.995,0.999) 0.002 MR-Egger 0.996(0.983,1.011) 0.714 Weighted median 0.996(0.994,0.998) 0.004 CD64 on CD14 + CD16 + monocyte IVW 0.994(0.991,0.996) 3.03E-06 MR-Egger 0.990(0.982,0.997) 0.227 Weighted median 0.994(0.991,0.997) 0.001 CD127 − CD8 + T cell absolute count IVW 1.004(1.002,1.005) 1.22E-07 MR-Egger 1.006(0.978,1.035) 0.709 Weighted median 1.004(1.002,1.006) 1.17E-05 CD4 on HLA DR + CD4 + T cell IVW 1.003(1.001,1.005) 3.70E-04 MR-Egger 1.006(0.960,1.054) 0.822 Weighted median 1.003(1.000,1.005) 0.013 Pancreatic cancer Basophil %CD33 dim HLA DR − CD66b − IVW 0.827(0.708,0.967) 0.017 MR-Egger 0.800(0.516,1.242) 0.426 Weighted median 0.819(0.692,0.969) 0.021 CD33 on CD33 + HLA DR + CD14 dim IVW 0.908(0.841,0.981) 0.014 MR-Egger 0.933(0.829,1.050) 0.335 Weighted median 0.912(0.842,0.988) 0.024 CD8 on CD28 + CD45RA + CD8 + T cell IVW 0.890(0.802,0.989) 0.030 MR-Egger 0.863(0.679,1.095) 0.253 Weighted median 0.895(0.779,1.029) 0.119 CD39 + resting CD4 regulatory T cell absolute count IVW 1.096(1.011,1.188) 0.027 MR-Egger 1.077(0.923,1.257) 0.360 Weighted median 1.085(0.974,1.209) 0.138 CD27 on IgD − CD38 − B cell IVW 1.295(1.052,1.595) 0.015 MR-Egger 1.390(0.506,3.820) 0.568 Weighted median 1.265(0.965,1.656) 0.088 Abbreviations: OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted; MR, Mendelian randomization. Scatter plots for the two immunophenotypes illustrated that the MR-Egger intercept excluded the possibility of horizontal pleiotropy (Supplementary Figure S11 ). In addition, funnel and leave-one-out analysis plots indicated the robustness of the results (Supplementary Figures S12 and S13). Exploration of the causal effect of immunophenotypes on CRC Two samples were assessed via MR analysis to investigate the causal effect of immunophenotypes on CRC using IVW as the main analysis method. Four immunophenotypes had a significant causal relationship with CRC, two of which having a protective effect against CRC: Plasmacytoid Dendritic Cell AC and CD64 on CD14 + CD16 + monocytes (Fig. 1 and Table 2 ). Plasmacytoid Dendritic Cell AC was analyzed using the IVW method with an OR of 0.997 (95% CI = 0.995–0.999, P = 0.002). Although similar results were obtained using the WM method (OR = 0.996, 95% CI = 0.994–0.998, P = 0.004), the MR-Egger regression did not support such a correlation (OR = 0.996, 95% CI = 0.983–1.011, P = 0.714). The OR for CD64 on CD14 + CD16 + monocytes analyzed using the IVW method was 0.994 (95% CI = 0.991–0.996, P = 3.0 × 10 − 6 ). Although similar results were obtained using the WM method (OR = 0.994, 95% CI = 0.991–0.997, P = 0.001), the MR-Egger regression did not support such a correlation (OR = 0.990, 95% CI = 0.982–0.997, P = 0.227). We similarly identified two immunophenotypes that are risk factors for CRC: CD127 − CD8 + T cell AC and CD4 on HLA DR + CD4 + T cell (Fig. 1 and Table 2 ). The OR for CD127 − CD8 + T cell AC analyzed using the IVW method was 1.004 (95% CI = 1.002–1.005, P = 1.2 × 10 − 7 ). Although similar results were obtained using the WM method (OR = 1.004, 95% CI = 1.002–1.006, P = 1.1 × 10 − 5 ), the MR-Egger regression did not support such a correlation (OR = 1.006, 95% CI = 0.978–1.035, P = 0.709). The OR for CD4 on HLA DR + CD4 + T cells analyzed using the IVW method was 1.003 (95% CI = 1.001–1.005, P = 3.7 × 10 − 4 ). Although similar results were obtained using the WM method (OR = 1.003, 95% CI = 1.000–1.005, P = 0.013), the MR-Egger regression did not support such a correlation (OR = 1.006, 95% CI = 0.960–1.054, P = 0.822). Scatter plots for the four immunophenotypes illustrated that the MR-Egger intercept excluded the possibility of horizontal pleiotropy (Supplementary Figure S14 ). In addition, funnel and leave-one-out analysis plots indicated the robustness of the results (Supplementary Figures S15 and S16). Exploration of the causal effect of immunophenotypes on PCA Two samples were assessed via MR analysis to investigate the causal effect of the immunophenotypes on PCA using IVW as the main analysis method. In total, five immunophenotypes had a significant causal relationship with PCA, three of which having a protective effect against PCA: Basophil %CD33 dim HLA DR − CD66b − , CD33 on CD33 + HLA DR + CD14 dim , and CD8 on CD28 + CD45RA + CD8 + T cells (Fig. 1 and Table 2 ). The OR for Basophil %CD33 dim HLA DR − CD66b − analyzed using the IVW method was 0.827 (95% CI = 0.708–0.967, P = 0.017). Although similar results were obtained using the WM method (OR = 0.819, 95% CI = 0.692–0.969, P = 0.021), the MR-Egger regression did not support such a correlation (OR = 0.800, 95% CI = 0.516–1.242, P = 0.426). The OR of CD33 on CD33 + HLA DR + CD14 dim analyzed using the IVW method was 0.908 (95% CI = 0.841–0.981, P = 0.014). Although similar results were obtained using the WM method (OR = 0.912, 95% CI = 0.842–0.988, P = 0.024), the MR-Egger regression did not support such a correlation (OR = 0.933, 95% CI = 0.829–1.050, P = 0.335). The OR of CD8 on CD28 + CD45RA + CD8 + T cells analyzed using the IVW method was 0.890 (95% CI = 0.802–0.989, P = 0.030); however, such a correlation was not supported by the MR-Egger regression (OR = 0.863, 95% CI = 0.679–1.095, P = 0.253) or WM method (OR = 0.895, 95% CI = 0.779–1.029, P = 0.119). We similarly identified two immunophenotypes that are risk factors for PCA: CD39 + resting CD4 regulatory T cell AC and CD27 on IgD − CD38 − B cells (Fig. 1 and Table 2 ). The OR for CD39 + resting CD4 regulatory T cell AC analyzed using the IVW method was 1.096 (95% CI = 1.011–1.188, P = 0.027); however, such a correlation was not supported by the MR-Egger regression (OR = 1.077, 95% CI = 0.923–1.257, P = 0.360) or WM method (OR = 1.085, 95% CI = 0.974–1.209, P = 0.138). The OR of CD27 on IgD − CD38 − B cells analyzed using the IVW method was 1.295 (95% CI = 1.052–1.595, P = 0.015); however, such a correlation was not supported by the MR-Egger regression (OR = 1.390, 95% CI = 0.506–3.820, P = 0.568) or WM method (OR = 1.265, 95% CI = 0.965–1.656, P = 0.088). Scatter plots for the five immunophenotypes illustrated that the MR-Egger intercept excluded the possibility of horizontal pleiotropy (Supplementary Figure S17 ). In addition, funnel and leave-one-out analysis plots indicated the robustness of the results (Supplementary Figures S18 and S19). Examination of the MR analysis results Heterogeneity or horizontal pleiotropy was not observed for any of the immunophenotypes (Supplementary Table S3 ). Moreover, outliers were not observed in the MR-PRESSO Global Test for all immunophenotypes (P > 0.05; Supplementary Table S4 ). Discussion The causal associations between 731 immune cell phenotypes and seven types of DSCs were analyzed using a vast amount of publicly accessible genetic data. To the best of our knowledge, this study is the first MR analysis delving into the causal connections between numerous immunophenotypes and DSCs. We identified a total of three, six, two, two, four, and five immunophenotypes with a significant causal effect on the development of GC, HCC, gallbladder cancer, small intestine cancer, CRC, and PCA, respectively. However, we did not find any immunophenotypes with a significant causal relationship with EC. Our study showed that naïve CD4 + T cell AC is a protective factor against GC. Being among the most abundant plastic and heterogeneous cell populations in the tumor microenvironment, CD4 + T cells play a crucial role in processes such as angiogenesis, tumor cell migration, and the immune response against tumors [ 33 , 34 ]. As revealed by Kagamu et al., CD4 + T cell immunity plays a role in the clonal proliferation, migration, and cancer cell-killing activity of CD8 + T cells, which are critical in the antitumor immune response [ 35 ]. CD45 is a receptor protein tyrosine phosphatase that is highly evolutionarily conserved and is exclusively expressed on all nucleated cells within the hematopoietic system [ 36 ]. Our findings revealed that CD45 on B cells is a protective factor against GC. Previous studies have demonstrated that CD45 deficiency leads to T and B lymphocyte dysfunction, which manifests as severe combined immunodeficiency. CD45 plays a vital role in regulating the function of B cells, and the expression of this phosphatase is essential for activating B lymphocytes and facilitating their entry into the cell cycle [ 37 ]. NK cells are the main innate immunity effector cells in cancer and exhibit high heterogeneity in the tumor microenvironment [ 38 ]. In this study, CD8 dim Natural Killer T %lymphocyte was found to be a risk factor for GC. CD8, a transmembrane glycoprotein, is the primary molecule expressed on the surface of cytotoxic T lymphocytes. Additionally, it is present in natural killer cells, cortical thymocytes, and dendritic cells [ 39 ]. The present study similarly found that CD8 on CD28 + CD45RA + CD8 + T cells was a protective factor against PCA. CD8 + T cells originate from CD34 + hematopoietic stem cells in the bone marrow, and they exert antitumor immunity by initiating either a direct or indirect killing response against their target cells [ 40 , 41 ]. Research indicates that CD8 + T cell infiltration below 2.2% is associated with a four-fold higher risk of disease progression following cancer surgery (risk ratio = 3.84, p < 0.01) [ 42 , 43 ]. A variety of inflammatory cells express CD62L, which is essential for the relocation of lymphocytes to lymph nodes [ 44 , 45 ]. Our study revealed that CD62L on monocytes is a risk factor for HCC. Previous studies have found that CD62L expression is significantly increased in patients with tumors such as bladder cancer [ 46 , 47 ]. As revealed by Kagamu et al., cells with low CD62L expression promote the production of highly efficient CD4 antitumor effector T lymphocytes [ 48 ]. CC chemokines comprise a subfamily of 27 chemotactic cytokines; they are indispensable for the functioning of the tumor microenvironment and are integral to intercellular communication [ 49 ]. Our findings revealed that CCR2 on CD62L + myeloid dendritic cells is a risk factor for HCC. The chemokine ligand CCL2 and its corresponding receptor CCR2 are implicated in the development and progression of diverse cancers [ 50 ]. CCL2 can activate tumor cell growth and proliferation through multiple mechanisms. Through its interaction with CCR2, CCL2 facilitates cancer cell migration and recruits immunosuppressive cells into the tumor microenvironment, thereby promoting cancer development. The role of local immune responses mediated by CD3 + tumor-infiltrating lymphocytes has been demonstrated [ 51 , 52 ]. Moreover, CD3 cells infiltrate tumors and peritumoral tissues and have been used as a marker of local inflammation [ 53 ]. CD3 has been found to play a role in programmed cell death protein 1-mediated tumor control, and stromal CD3-positive lymphocytes correlate with a favorable prognosis in patients with ovarian cancer [ 54 ]. However, the present study showed that CD3 on CD4 + T cells is a risk factor for HCC; therefore, further studies are needed to determine the role of CD3 on CD4 + T cells in the development of HCC. The CD28 gene plays a crucial role in T cell proliferation and survival, as well as in the production of cytokines and the development of type 2 helper T cells [ 55 ]. Our study revealed that CD28 on CD4 + T cells is a risk factor for HCC, consistent with the findings of Shen et al., who confirmed that the genes CD28 and PTEN are closely associated with survival in patients with cervical cancer [ 56 ]. CD64 is the only functional high-affinity FcγR. It binds to the IgG isotypes IgG1 and IgG3 [ 57 ] and is expressed by bone marrow cells, including monocytes, macrophages, and neutrophils, but not lymphocytes or natural killer cells [ 58 ]. Our study revealed that CD64 on CD14 + CD16 + monocytes was a protective factor in CRC. As revealed by Hintz et al., engineered NK92 cells expressing CD64 are involved in both the tumor and stroma-targeted antibody control of prostate cancer growth [ 59 ]. CD39 is highly expressed in various cell types within the tumor microenvironment, including tumor cells, endothelial cells, and infiltrating immune cells [ 60 ]. Our study revealed that CD39 + CD4 + T cell AC is a risk factor for small intestine cancer. Similarly, we found that CD39 + resting CD4 regulatory T cell AC is a risk factor for PCA. The progression and metastasis of tumors are regulated by the intricate interplay between tumor cells and the tumor microenvironment [ 33 ]. Moreover, the elevated expression of CD39 is strongly correlated with unfavorable outcomes [ 61 ]. Similar to our results, Jacoberger-Foissac et al. found that CD73 inhibits cGAS-STING and synergizes with CD39 to promote PCA development [ 62 ]. Our study has certain limitations. We used relatively loose thresholds, which may have increased the number of false positives. However, they also allowed for a more comprehensive assessment of the association between immunophenotypes and DSCs. Conclusions This MR study comprehensively analyzed the causal relationship between immune phenotypes and seven types of DSCs, highlighting the complex interaction patterns between the immune system and DSCs. Overall, the results of this study provide new insights into the mechanisms underlying immune system-mediated cancer development. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The datasets analyzed during the current study are available in the GWAS repository: https://www.ebi.ac.uk/gwas/. Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding This work was supported by the National Natural Science Foundation of China [grant number 82373044] and the Natural Science Foundation of Shandong Province [grant number ZR2022LSW001]. Author contributions YL and JZ designed the study, analyzed and interpreted the data, and drafted the manuscript. RL, XY, and XD analyzed and interpreted the data. YTL conceptualized and designed the study and revised the manuscript. The authors read and approved the final manuscript. 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Peri-tumoural CD3+ inflammation and neutrophil-to-lymphocyte ratio predict overall survival in patients affected by colorectal liver metastases treated with surgery. J Gastrointest Surg. 2020;24:1061-70. Arman Karakaya Y, Atıgan A, Güler ÖT, Demiray AG, Bir F. The relation of CD3, CD4, CD8 and PD-1 expression with tumor type and prognosis in epithelial ovarian cancers. Ginekol Pol. 2021;92:344-51. Wakamatsu E, Omori H, Ohtsuka S, Ogawa S, Green JM, Abe R. Regulatory T cell subsets are differentially dependent on CD28 for their proliferation. Mol Immunol. 2018;101:92-101. Shen F, Zheng H, Zhou L, Li W, Liu J, Xu X. Identification of CD28 and PTEN as novel prognostic markers for cervical cancer. J Cell Physiol. 2019;234:7004-11. Bruhns P, Iannascoli B, England P, Mancardi DA, Fernandez N, Jorieux S, et al. Specificity and affinity of human Fcγ receptors and their polymorphic variants for human IgG subclasses. Blood. 2009;113:3716-25. Bruhns P. Properties of mouse and human IgG receptors and their contribution to disease models. Blood. 2012;119:5640-9. Hintz HM, Snyder KM, Wu J, Hullsiek R, Dahlvang JD, Hart GT, et al. Simultaneous engagement of tumor and stroma targeting antibodies by engineered NK-92 cells expressing CD64 controls prostate cancer growth. Cancer Immunol Res. 2021;9:1270-82. Li XY, Moesta AK, Xiao C, Nakamura K, Casey M, Zhang H, et al. Targeting CD39 in cancer reveals an extracellular ATP- and inflammasome-driven tumor immunity. Cancer Discov. 2019;9:1754-73. Shuai C, Xia GQ, Yuan F, Wang S, Lv XW. CD39-mediated ATP-adenosine signalling promotes hepatic stellate cell activation and alcoholic liver disease. Eur J Pharmacol. 2021;905:174198. Jacoberger-Foissac C, Cousineau I, Bareche Y, Allard D, Chrobak P, Allard B, et al. CD73 inhibits cGAS-STING and cooperates with CD39 to promote pancreatic cancer. Cancer Immunol Res. 2023;11:56-71. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTableS1.Basicinformationon731immunecellphenotypes.xlsx Supplementary Table 1. Basic information about the 731 immune cell phenotypes. SupplementaryTableS2.TheGWASdatasetsfordigestivesystemcancers..xlsx Supplementary Table 2. GWAS datasets for digestive system cancers. Abbreviation: GWAS, genome-wide association study. SupplementaryTableS3.HeterogeneityandHorizontalPleiotropyTests.xlsx Supplementary Table 3. Heterogeneity and horizontal pleiotropy tests. Abbreviations: MR, Mendelian randomization; IVW, inverse variance weighted. SupplementaryTableS4.ResultsoftheMRPRESSOanalysis.xlsx Suplementary Table 4. Results of the MR-PRESSO analysis. 1 The MR-PRESSO method detected the existence of outlier IVs that may have horizontal pleiotropy through the global test. Abbreviations: MR-PRESSO, Mendelian Randomization Pleiotropy RESidual Sum and Outlier; MR, Mendelian Randomization; Sd, standard deviation. supplementaryinformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4074806","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":283283652,"identity":"ac0c4002-4116-4ab8-8e63-1bd095a828c3","order_by":0,"name":"Junfeng Zhao","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Junfeng","middleName":"","lastName":"Zhao","suffix":""},{"id":283283653,"identity":"c05a7abd-3801-4de1-b931-58807c086e5e","order_by":1,"name":"Ying Li","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Li","suffix":""},{"id":283283654,"identity":"f1551d9c-4c34-4683-aec9-f36501cec3e0","order_by":2,"name":"Ruyue Li","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Affiliated Hospital of Weifang Medical University, Weifang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruyue","middleName":"","lastName":"Li","suffix":""},{"id":283283655,"identity":"2b3c46d0-3957-4f46-a9d5-263aa9381c9b","order_by":3,"name":"Xiujing Yao","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Affiliated Hospital of Weifang Medical University, Weifang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiujing","middleName":"","lastName":"Yao","suffix":""},{"id":283283656,"identity":"789c2ba9-bf0f-45b7-8cdc-bd3057eb50ad","order_by":4,"name":"Xue Dong","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Dong","suffix":""},{"id":283283657,"identity":"bc7af3e5-72a8-40f9-9410-408ca07c7edb","order_by":5,"name":"Yintao Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACZjBpwcPPfIBBAsw+QFgLYwMDgwSPZFsCsVoYIFoYDI4Rq8XgOO/xBz8qJGSMj/EY3vi5g0GO70YC4+cCfFoO8yU29pyR4DE7xmNs2XuGwVjyRgKz9Ay8WngMG3jbgFru95hJ8LYxJG64kcDGzENAS+NfoBbjNh4zyb9tDPVEaWkG2WLAxmMmDbQlwYCQFkmgltkyQL9IHGMrtpZtkzCceeZhszQ+LXznzxh8fFNhY8/fxrzx5ts2G3m+48kHP+PTonAAlQ+KGlBE4QHy+KVHwSgYBaNgFAABAHqbRWqg4SLYAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yintao","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-03-11 13:06:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4074806/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4074806/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53415224,"identity":"48a975d7-495a-458e-b26e-3606c527e775","added_by":"auto","created_at":"2024-03-25 17:33:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99557,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of the causal relationship between immune cell phenotype and digestive system cancers. (a) Causal relationship between immune cell phenotype and gastric cancer. (b) Causal relationship between immune cell phenotype and hepatocellular carcinoma. (c) Causal relationship between immune cell phenotype and gallbladder cancer. (d) Causal relationship between immune cell phenotype and small intestine cancer. (e) Causal relationship between immune cell phenotype and colorectal cancer. (f) Causal relationship between immune cell phenotype and pancreatic cancer.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4074806/v1/127709390b66a0d69075c3a2.jpg"},{"id":55756631,"identity":"c024dd31-ce13-486f-ad0e-212ef1915a75","added_by":"auto","created_at":"2024-05-02 17:20:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":898108,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4074806/v1/0fb4d69b-3709-4a14-ab4c-be6428447c6f.pdf"},{"id":53415756,"identity":"df4fc5ad-2dc0-4466-afce-c740f9d44aad","added_by":"auto","created_at":"2024-03-25 17:41:59","extension":"xlsx","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":37396,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1. \u003c/strong\u003eBasic information about the 731 immune cell phenotypes.\u003c/p\u003e","description":"","filename":"SupplementaryTableS1.Basicinformationon731immunecellphenotypes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4074806/v1/24fdc2a63429f2448b1fce57.xlsx"},{"id":53415225,"identity":"5c8944af-9cd7-442f-9437-192724eec19e","added_by":"auto","created_at":"2024-03-25 17:33:59","extension":"xlsx","order_by":21,"title":"","display":"","copyAsset":false,"role":"supplement","size":11413,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2.\u003c/strong\u003e GWAS datasets for digestive system cancers. Abbreviation: GWAS, genome-wide association study.\u003c/p\u003e","description":"","filename":"SupplementaryTableS2.TheGWASdatasetsfordigestivesystemcancers..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4074806/v1/26d23a1888bcb0d9507d792e.xlsx"},{"id":53415227,"identity":"224891bb-d6e8-412c-b618-82ba1a5fbd9f","added_by":"auto","created_at":"2024-03-25 17:33:59","extension":"xlsx","order_by":22,"title":"","display":"","copyAsset":false,"role":"supplement","size":11805,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 3.\u003c/strong\u003e Heterogeneity and horizontal pleiotropy tests. Abbreviations: MR, Mendelian randomization; IVW, inverse variance weighted.\u003c/p\u003e","description":"","filename":"SupplementaryTableS3.HeterogeneityandHorizontalPleiotropyTests.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4074806/v1/fcb5619d0f4186f3d3767076.xlsx"},{"id":53415228,"identity":"a4f59ca8-9922-4ca9-a232-f71c7c319d7b","added_by":"auto","created_at":"2024-03-25 17:34:00","extension":"xlsx","order_by":23,"title":"","display":"","copyAsset":false,"role":"supplement","size":13132,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSuplementary Table 4. \u003c/strong\u003eResults of the MR-PRESSO analysis. \u003csup\u003e1\u003c/sup\u003eThe MR-PRESSO method detected the existence of outlier IVs that may have horizontal pleiotropy through the global test. Abbreviations: MR-PRESSO, Mendelian Randomization Pleiotropy RESidual Sum and Outlier; MR, Mendelian Randomization; Sd, standard deviation.\u003c/p\u003e","description":"","filename":"SupplementaryTableS4.ResultsoftheMRPRESSOanalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4074806/v1/cb10bdd2fff62e269bbe5ec7.xlsx"},{"id":53415230,"identity":"208a5942-ee5a-4a5a-9985-90cd77a03a6f","added_by":"auto","created_at":"2024-03-25 17:34:02","extension":"docx","order_by":24,"title":"","display":"","copyAsset":false,"role":"supplement","size":35009300,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4074806/v1/85a94847ff974c8ad2361085.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal role of immune cells in digestive system cancers: A Mendelian randomization study","fulltext":[{"header":"Background","content":"\u003cp\u003eDigestive system cancers (DSCs) are a major cause of cancer-related fatalities, accounting for 26.4% of all new cases and 36.3% of all cancer-related fatalities worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The intricate process of tumor growth, invasion, and metastasis involves genetic irregularities as well as interplay between tumor tissues and immune cells in the tumor microenvironment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Research findings continue to indicate that the immune microenvironment may play a critical role in the formation of DSCs [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Immune cells in the tumor microenvironment display both tumor-promoting and tumor-inhibiting functions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Tumor-infiltrating immune cells, which constitute a fundamental component of the complex tumor microenvironment, exert a pivotal effect by either impeding or fostering the progression of tumors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Various immune cell types from both the adaptive and innate immune systems play a significant role in cancer immunotherapy [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Extensive intratumoral infiltration of natural killer cells has been found to be associated with better prognoses in DSCs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Different types of immune cells have been found to exert a significant degree of influence on tumorigenesis and progression in various cancers [\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, the causal relationship between changes in the immune cell phenotype and the development of DSCs remains unclear.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is an analytical method based on Mendelian independent distribution laws and is primarily used to determine infer epidemiological and etiological data. Moreover, its rational causal sequence effectively addresses causality problems [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The MR design mimics a randomized controlled trial because genetic variants are randomly assigned during fertilization and, therefore, are less likely to exert confounding effects [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In MR, single nucleotide polymorphisms (SNPs) are used as undiscovered instrumental variables (IVs) in place of exposure phenotypes. Based on previous studies that have revealed a correlation between immune cell characteristics and DSCs, this study aimed to determine the causal relationship between immune cell phenotypes and DSCs based on the MR method.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eMR analysis was applied to two samples to evaluate the causal relationship between 731 immunophenotypes and the following DSCs: esophageal cancer (EC), gastric cancer (GC), hepatocellular cancer (HCC), gallbladder cancer, small intestine cancer, colorectal cancer (CRC), and pancreatic cancer (PCA). The causal relationship between the immune cell phenotypes and these DSCs was explored individually. The study design for this MR analysis is detailed in Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eImmune cell signatures and DSC genome-wide association study (GWAS) data sources\u003c/h2\u003e \u003cp\u003ePublicly available data from the GWAS catalog (login numbers GCST0001391 to GCST0002121) were utilized as summary statistics of each immunophenotype [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Basic information on the 731 immune cell phenotypes is detailed in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. We selected seven DSCs as outcomes, including EC (N\u003csub\u003ecase\u003c/sub\u003e = 740, N\u003csub\u003econtrol\u003c/sub\u003e = 372,016), GC (N\u003csub\u003ecase\u003c/sub\u003e = 6,563, N\u003csub\u003econtrol\u003c/sub\u003e = 195,745), HCC (N\u003csub\u003ecase\u003c/sub\u003e = 1,866, N\u003csub\u003econtrol\u003c/sub\u003e = 195,745), gallbladder cancer (N\u003csub\u003ecase\u003c/sub\u003e = 41, the N\u003csub\u003econtrol\u003c/sub\u003e = 866), small bowel cancer (N\u003csub\u003ecase\u003c/sub\u003e = 252, N\u003csub\u003econtrol\u003c/sub\u003e = 218,540), CRC (N\u003csub\u003ecase\u003c/sub\u003e = 5,657, N\u003csub\u003econtrol\u003c/sub\u003e = 372,016), and PCA (N\u003csub\u003ecase\u003c/sub\u003e = 1,896, N\u003csub\u003econtrol\u003c/sub\u003e = 1,939). The summarized GWAS data for the above DSCs were obtained from the IEU Open GWAS project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 3 November 2023). Details of the selected GWAS datasets are shown in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental variable (IV) selection\u003c/h2\u003e \u003cp\u003eThe significance level of the IVs for each immunophenotype was set to 1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e based on a recent study [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These SNPs were trimmed (linkage disequilibrium [LD] r\u003csup\u003e2\u003c/sup\u003e threshold\u0026thinsp;\u0026lt;\u0026thinsp;0.1 within a 500-kb distance) using the clustering program in PLINK software (version v1.90), where LD r\u003csup\u003e2\u003c/sup\u003e was calculated based on the 1000 Genomes Projects as a reference panel [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To quantify the strength of the IVs, we calculated the proportion of variation explained and the F statistic for each IV and removed IVs with low F-statistics (\u0026lt;\u0026thinsp;10).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo assess the causal connection between the 731 immunophenotypes and seven DSCs, we employed inverse variance weighting (IVW), MR-Egger regression, and weighted median (WM) methodologies for MR analysis. A meta-analysis approach combined with Wald estimates for each SNP was previously employed using the IVW method to generate a comprehensive appraisal of the effect of immune cell phenotypes on lung cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Hence, the IVW method served as the primary research approach in this study [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The MR-Egger regression assumes instrument strength independent of direct effect (InSIDE), allowing the assessment of the presence of multidirectionality using the intercept term. However, the MR-Egger regression estimates may be inaccurate and strongly influenced by peripheral genetic variations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The WM approach accurately estimates causality when up to 50% of the instrumental variables are invalid [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, this study excluded possible outliers of horizontal pleiotropy based on the MR pleiotropy residual sum and outlier (MR-PRESSO) method because such outliers may affect the estimation results of MR-PRESSO [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo identify potentially heterogeneous SNPs, a \u0026ldquo;leave-one-out\u0026rdquo; analysis was performed by sequentially removing each instrumental SNP [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The strength of the IVs was assessed using the F statistic as follows:\u003c/p\u003e \u003cp\u003eF = [R\u003csup\u003e2\u003c/sup\u003e \u0026times; (N-1-K)]/[(1-R\u003csup\u003e2\u003c/sup\u003e) \u0026times; K],\u003c/p\u003e \u003cp\u003ewhere R\u003csup\u003e2\u003c/sup\u003e denotes the variance proportion in the exposure explained by genetic variation, N denotes the sample size, and K denotes the number of instruments. The absence of significantly weak instrumental bias was considered if the corresponding F statistic was \u0026gt;\u0026thinsp;10 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Forest, scatter, funnel, and leave-one-out analysis plots were also drawn. Forest plots visually presented the effect of each SNP on the results; leave-one-out analysis plots determined whether the results were robust; scatter plots displayed the fitting results of the different MR analyses; and funnel plots visually displayed IV heterogeneity. All MR analyses were performed using R version 4.3.2 software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eExploration of the causal effect of immunophenotypes on GC\u003c/h2\u003e\n \u003cp\u003eTwo samples were assessed via MR analysis to investigate the causal effect of the immunophenotypes on GC using IVW as the main analysis method. Three immunophenotypes had a significant causal relationship with GC, two of which having a protective effect against GC; these were na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cell absolute count (AC) and CD45 on B cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The odds ratio (OR) of na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cell AC was 0.885 (95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;0.798\u0026ndash;0.981, P\u0026thinsp;=\u0026thinsp;0.021) according to the IVW method. Although similar results were obtained using the WM method (OR\u0026thinsp;=\u0026thinsp;0.857, 95% CI\u0026thinsp;=\u0026thinsp;0.755\u0026ndash;0.974, P\u0026thinsp;=\u0026thinsp;0.018), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;0.530, 95% CI\u0026thinsp;=\u0026thinsp;0.199\u0026ndash;1.411, P\u0026thinsp;=\u0026thinsp;0.425). The OR for CD45 on B cells analyzed using the IVW method was 0.644 (95% CI\u0026thinsp;=\u0026thinsp;0.519\u0026ndash;0.800, P\u0026thinsp;=\u0026thinsp;6.8 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). Although similar results were obtained using the WM method (OR\u0026thinsp;=\u0026thinsp;0.650, 95% CI\u0026thinsp;=\u0026thinsp;0.491\u0026ndash;0.860, P\u0026thinsp;=\u0026thinsp;0.003), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;0.590, 95% CI\u0026thinsp;=\u0026thinsp;0.176\u0026ndash;2.433, P\u0026thinsp;=\u0026thinsp;0.590).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of the causal effect of immune cells on gastric cancer, hepatocellular carcinoma, and gallbladder cancer.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"12\"\u003e\n \u003cp\u003e\u003cstrong\u003eGastric cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNa\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cell absolute count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.885(0.798,0.981)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.530(0.199,1.411)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.857(0.755,0.974)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD45 on B cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.644(0.519,0.800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.80E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.590(0.176,2.433)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.650(0.491,0.860)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD8\u003csup\u003edim\u003c/sup\u003e Natural Killer T % lymphocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.312(1.063,1.619)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.807(0.945,3.456)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.336(1.034,1.727)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"20\"\u003e\n \u003cp\u003e\u003cstrong\u003eHepatocellular carcinoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerminally Differentiated CD4\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e\u0026minus;\u003c/sup\u003e T cell%T cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.789(0.667,0.934)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.551(0.251,1.210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.799(0.639,0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD62L on monocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.167(1.008,1.349)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.125(0.769,1.647)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.147(0.963,1.367)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD3 on CD4\u003csup\u003e+\u003c/sup\u003e T cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.176(1.011,1.368)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.679(1.040,2.713)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.245(1.025,1.512)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD28 on CD4\u003csup\u003e+\u003c/sup\u003e T cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.177(1.054,1.314)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.679(1.040,2.713)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.179(1.033,1.346)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCCR2 on CD62L\u003csup\u003e+\u003c/sup\u003e myeloid Dendritic Cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.404(1.185,1.664)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.01E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.244(0.619,2.495)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.244(1.177,1.777)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.50E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eGallbladder cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD16\u0026thinsp;\u0026minus;\u0026thinsp;CD56 on Natural Killer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.292(0.132,0.644)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089(0.018,0.459)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.305(0.111,0.838)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSSC\u0026thinsp;\u0026minus;\u0026thinsp;A on HLA DR\u003csup\u003e+\u003c/sup\u003e Natural Killer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.363(1.173,21.349)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.624(0.219,29.618)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.943(0.943,22.356)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAbbreviations: OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted; MR, Mendelian randomization.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eWe also discovered one immunophenotype that is a risk factor for GC: CD8\u003csup\u003edim\u003c/sup\u003e Natural Killer T %lymphocyte (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The OR for CD8\u003csup\u003edim\u003c/sup\u003e Natural Killer T %lymphocyte calculated using the IVW method was 1.312 (95% CI\u0026thinsp;=\u0026thinsp;1.063\u0026ndash;1.619, P\u0026thinsp;=\u0026thinsp;0.012). Although the WM method yielded similar results (OR\u0026thinsp;=\u0026thinsp;1.336, 95% CI\u0026thinsp;=\u0026thinsp;1.034\u0026ndash;1.727, P\u0026thinsp;=\u0026thinsp;0.027), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;1.807, 95% CI\u0026thinsp;=\u0026thinsp;0.945\u0026ndash;3.456, P\u0026thinsp;=\u0026thinsp;0.324).\u003c/p\u003e\n \u003cp\u003eScatter plots for the three immunophenotypes illustrated that the MR-Egger intercept excluded the possibility of horizontal pleiotropy (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e). In addition, the funnel and leave-one-out analysis plots indicated the robustness of the results (Supplementary Figures \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e and S4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eExploration of the causal effect of immunophenotypes on HCC\u003c/h2\u003e\n \u003cp\u003eTwo samples were assessed via MR analysis to investigate the causal effect of immunophenotypes on HCC using IVW as the main analysis method. In total, six immunophenotypes had a significant causal relationship with HCC, one of which having a protective effect against HCC: terminally differentiated CD4\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e\u0026minus;\u003c/sup\u003e T cell%T cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The OR of terminally differentiated CD4\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e\u0026minus;\u003c/sup\u003e T cell%T cells analyzed using the IVW method was 0.789 (95% CI\u0026thinsp;=\u0026thinsp;0.667\u0026ndash;0.934, P\u0026thinsp;=\u0026thinsp;0.006). Although similar results were obtained using the WM method (OR\u0026thinsp;=\u0026thinsp;0.799, 95% CI\u0026thinsp;=\u0026thinsp;0.639\u0026ndash;0.999, P\u0026thinsp;=\u0026thinsp;0.049), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;0.551, 95% CI\u0026thinsp;=\u0026thinsp;0.251\u0026ndash;1.210, P\u0026thinsp;=\u0026thinsp;0.188).\u003c/p\u003e\n \u003cp\u003eWe similarly identified five immunophenotypes that are risk factors for HCC: CD62L on monocytes, CD3 on CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD28 on CD4\u003csup\u003e+\u003c/sup\u003e T cells, CCR2 on CD62L\u003csup\u003e+\u003c/sup\u003e myeloid dendritic cells, and CD8 on CD39\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The OR of CD62L on monocytes analyzed using the IVW method was 1.167 (95% CI\u0026thinsp;=\u0026thinsp;1.008\u0026ndash;1.349, P\u0026thinsp;=\u0026thinsp;0.038); however, such a correlation was not supported by the MR-Egger regression (OR\u0026thinsp;=\u0026thinsp;1.125, 95% CI\u0026thinsp;=\u0026thinsp;0.769\u0026ndash;1.647, P\u0026thinsp;=\u0026thinsp;0.570) or WM (OR\u0026thinsp;=\u0026thinsp;1.147, 95% CI\u0026thinsp;=\u0026thinsp;0.963\u0026ndash;1.367, P\u0026thinsp;=\u0026thinsp;0.125). The OR of CD3 on CD4\u003csup\u003e+\u003c/sup\u003e T cells analyzed using the IVW method was 1.176 (95% CI\u0026thinsp;=\u0026thinsp;1.011\u0026ndash;1.368, P\u0026thinsp;=\u0026thinsp;0.036). Although similar results were obtained using the WM method (OR\u0026thinsp;=\u0026thinsp;1.245, 95% CI\u0026thinsp;=\u0026thinsp;1.025\u0026ndash;1.512, P\u0026thinsp;=\u0026thinsp;0.027), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;1.679, 95% CI\u0026thinsp;=\u0026thinsp;1.040\u0026ndash;2.713, P\u0026thinsp;=\u0026thinsp;0.101). The OR of CD28 on CD4\u003csup\u003e+\u003c/sup\u003e T cells analyzed using the IVW method was 1.177 (95% CI\u0026thinsp;=\u0026thinsp;1.054\u0026ndash;1.314, P\u0026thinsp;=\u0026thinsp;0.004); similar results were obtained using the WM method; however, the MR-Egger regression did not support such a correlation (WM: OR\u0026thinsp;=\u0026thinsp;1.179, 95% CI\u0026thinsp;=\u0026thinsp;1.033\u0026ndash;1.346, P\u0026thinsp;=\u0026thinsp;0.015; MR-Egger regression: OR\u0026thinsp;=\u0026thinsp;1.679, 95% CI\u0026thinsp;=\u0026thinsp;1.040\u0026ndash;2.713, P\u0026thinsp;=\u0026thinsp;0.101). The OR for CCR2 on CD62L\u003csup\u003e+\u003c/sup\u003e myeloid dendritic cells analyzed using the IVW method was 1.404 (95% CI\u0026thinsp;=\u0026thinsp;1.185\u0026thinsp;~\u0026thinsp;1.664, P\u0026thinsp;=\u0026thinsp;9.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). Although the WM method yielded similar results (OR\u0026thinsp;=\u0026thinsp;1.446, 95% CI\u0026thinsp;=\u0026thinsp;1.177\u0026ndash;1.777, P\u0026thinsp;=\u0026thinsp;4.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;1.244, 95% CI\u0026thinsp;=\u0026thinsp;0.619\u0026ndash;2.495, P\u0026thinsp;=\u0026thinsp;0.602). The OR of CD8 on CD39\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells analyzed using the IVW method was 1.163 (95% CI\u0026thinsp;=\u0026thinsp;1.003\u0026ndash;1.349, P\u0026thinsp;=\u0026thinsp;0.046); however, such a correlation was not supported by the MR-Egger regression (OR\u0026thinsp;=\u0026thinsp;1.624, 95% CI\u0026thinsp;=\u0026thinsp;0.729\u0026ndash;3.618, P\u0026thinsp;=\u0026thinsp;0.257) or WM method (OR\u0026thinsp;=\u0026thinsp;1.143, 95% CI\u0026thinsp;=\u0026thinsp;0.963\u0026ndash;1.356, P\u0026thinsp;=\u0026thinsp;0.126).\u003c/p\u003e\n \u003cp\u003eScatter plots for the six immunophenotypes illustrated that the MR-Egger intercept excluded the possibility of horizontal pleiotropy (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS5\u003c/span\u003e). In addition, funnel and leave-one-out analysis plots indicated the robustness of the results (Supplementary Figures \u003cspan class=\"InternalRef\"\u003eS6\u003c/span\u003e and S7).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eExploration of the causal effect of immunophenotypes on gallbladder cancer\u003c/h2\u003e\n \u003cp\u003eTwo samples were assessed via MR analysis to investigate the causal effect of the immunophenotypes on gallbladder cancer using IVW as the main analysis method. In total, two immunophenotypes had a significant causal relationship with gallbladder cancer, one of which having a protective effect against gallbladder cancer: CD16\u0026thinsp;\u0026minus;\u0026thinsp;CD56 on Natural Killer (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The OR for CD16\u0026thinsp;\u0026minus;\u0026thinsp;CD56 on Natural Killer analyzed using the IVW method was 0.292 (95% CI\u0026thinsp;=\u0026thinsp;0.132\u0026ndash;0.644, P\u0026thinsp;=\u0026thinsp;0.002). Similar results were obtained using the MR-Egger regression (OR\u0026thinsp;=\u0026thinsp;0.089, 95% CI\u0026thinsp;=\u0026thinsp;0.018\u0026ndash;0.459, P\u0026thinsp;=\u0026thinsp;0.034) and WM method (OR\u0026thinsp;=\u0026thinsp;0.305, 95% CI\u0026thinsp;=\u0026thinsp;0.111\u0026ndash;0.838, P\u0026thinsp;=\u0026thinsp;0.021).\u003c/p\u003e\n \u003cp\u003eWe similarly identified one immunophenotype that is a risk factor for gallbladder cancer: SSC\u0026thinsp;\u0026minus;\u0026thinsp;A on HLA DR\u003csup\u003e+\u003c/sup\u003e Natural Killer (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The OR for SSC-A on HLA DR\u003csup\u003e+\u003c/sup\u003e Natural Killer was 3.363 (95% CI\u0026thinsp;=\u0026thinsp;1.173\u0026ndash;21.349, P\u0026thinsp;=\u0026thinsp;0.033) when analyzed using the IVW method; however, such a correlation was not supported by the MR-Egger regression (OR\u0026thinsp;=\u0026thinsp;2.624, 95% CI\u0026thinsp;=\u0026thinsp;0.219\u0026ndash;29.618, P\u0026thinsp;=\u0026thinsp;0.877) or WM method (OR\u0026thinsp;=\u0026thinsp;4.943, 95% CI\u0026thinsp;=\u0026thinsp;0.943\u0026ndash;22.356, P\u0026thinsp;=\u0026thinsp;0.059).\u003c/p\u003e\n \u003cp\u003eScatter plots for the two immunophenotypes illustrated that the MR-Egger intercept excluded the possibility of horizontal pleiotropy (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS8\u003c/span\u003e). In addition, funnel and leave-one-out analysis plots indicated the robustness of the results (Supplementary Figures \u003cspan class=\"InternalRef\"\u003eS9\u003c/span\u003e and S10).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eExploration of the causal effect of immunophenotypes on small intestine cancer\u003c/h2\u003e\n \u003cp\u003eTwo samples were assessed via MR analysis to investigate the causal effect of the immunophenotypes on small intestine cancer using IVW as the main analysis method. In total, two immunophenotypes had a significant causal relationship with small intestine cancer, both of which being risk factors for small intestine cancer: Terminally Differentiated CD4\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e\u0026minus;\u003c/sup\u003e T cell %CD4\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e\u0026minus;\u003c/sup\u003e T cell and CD39\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cell AC (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The OR of Terminally Differentiated CD4\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e\u0026minus;\u003c/sup\u003e T cell %CD4-CD8- T cell analyzed using the IVW method was 1.285 (95% CI\u0026thinsp;=\u0026thinsp;1.000\u0026ndash;1.651, P\u0026thinsp;=\u0026thinsp;0.049). Although similar results were obtained using the WM method (OR\u0026thinsp;=\u0026thinsp;1.419, 95% CI\u0026thinsp;=\u0026thinsp;1.029\u0026ndash;1.958, P\u0026thinsp;=\u0026thinsp;0.033), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;1.190, 95% CI\u0026thinsp;=\u0026thinsp;0.841\u0026ndash;1.684, P\u0026thinsp;=\u0026thinsp;0.381). The OR of CD39\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cell AC analyzed using the IVW method was 1.323 (95% CI\u0026thinsp;=\u0026thinsp;1.162\u0026ndash;1.506, P\u0026thinsp;=\u0026thinsp;2.3 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e); similar results were obtained for the MR-Egger regression (OR\u0026thinsp;=\u0026thinsp;1.134, 95% CI\u0026thinsp;=\u0026thinsp;1.037\u0026ndash;1.239, P\u0026thinsp;=\u0026thinsp;0.006) and WM method (OR\u0026thinsp;=\u0026thinsp;1.213, 95% CI\u0026thinsp;=\u0026thinsp;1.016\u0026ndash;1.449, P\u0026thinsp;=\u0026thinsp;0.035).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of the causal effect of immune cells on small intestine cancer, colorectal cancer, and pancreatic cancer.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"8\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmall intestine cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerminally Differentiated CD4\u003csup\u003e\u0026minus;\u003c/sup\u003eCD8\u003csup\u003e\u0026minus;\u003c/sup\u003e T cell %CD4\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e\u0026minus;\u003c/sup\u003e T cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.285(1.000,1.651)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.190(0.841,1.684)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.419(1.028,1.958)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD39\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cell absolute count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.323(1.162,1.506)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.36E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.134(1.037,1.239)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.213(1.016,1.449)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eColorectal cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlasmacytoid dendritic cell absolute count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.997(0.995,0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.996(0.983,1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.714\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.996(0.994,0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD64 on CD14\u003csup\u003e+\u003c/sup\u003e CD16\u003csup\u003e+\u003c/sup\u003e monocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.994(0.991,0.996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.03E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.990(0.982,0.997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.994(0.991,0.997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD127\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cell absolute count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.004(1.002,1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.006(0.978,1.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.004(1.002,1.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD4 on HLA DR\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.003(1.001,1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.70E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.006(0.960,1.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.003(1.000,1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"20\"\u003e\n \u003cp\u003e\u003cstrong\u003ePancreatic cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasophil %CD33\u003csup\u003edim\u003c/sup\u003e HLA DR\u003csup\u003e\u0026minus;\u003c/sup\u003e CD66b\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.827(0.708,0.967)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.800(0.516,1.242)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.819(0.692,0.969)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD33 on CD33\u003csup\u003e+\u003c/sup\u003e HLA DR\u003csup\u003e+\u003c/sup\u003e CD14\u003csup\u003edim\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.908(0.841,0.981)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.933(0.829,1.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.912(0.842,0.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD8 on CD28\u003csup\u003e+\u003c/sup\u003e CD45RA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.890(0.802,0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.863(0.679,1.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.895(0.779,1.029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD39\u003csup\u003e+\u003c/sup\u003e resting CD4 regulatory T cell absolute count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.096(1.011,1.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.077(0.923,1.257)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.085(0.974,1.209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD27 on IgD\u003csup\u003e\u0026minus;\u003c/sup\u003e CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e B cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.295(1.052,1.595)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.390(0.506,3.820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.265(0.965,1.656)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAbbreviations: OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted; MR, Mendelian randomization.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eScatter plots for the two immunophenotypes illustrated that the MR-Egger intercept excluded the possibility of horizontal pleiotropy (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS11\u003c/span\u003e). In addition, funnel and leave-one-out analysis plots indicated the robustness of the results (Supplementary Figures \u003cspan class=\"InternalRef\"\u003eS12\u003c/span\u003e and S13).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eExploration of the causal effect of immunophenotypes on CRC\u003c/h2\u003e\n \u003cp\u003eTwo samples were assessed via MR analysis to investigate the causal effect of immunophenotypes on CRC using IVW as the main analysis method. Four immunophenotypes had a significant causal relationship with CRC, two of which having a protective effect against CRC: Plasmacytoid Dendritic Cell AC and CD64 on CD14\u003csup\u003e+\u003c/sup\u003e CD16\u003csup\u003e+\u003c/sup\u003e monocytes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Plasmacytoid Dendritic Cell AC was analyzed using the IVW method with an OR of 0.997 (95% CI\u0026thinsp;=\u0026thinsp;0.995\u0026ndash;0.999, P\u0026thinsp;=\u0026thinsp;0.002). Although similar results were obtained using the WM method (OR\u0026thinsp;=\u0026thinsp;0.996, 95% CI\u0026thinsp;=\u0026thinsp;0.994\u0026ndash;0.998, P\u0026thinsp;=\u0026thinsp;0.004), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;0.996, 95% CI\u0026thinsp;=\u0026thinsp;0.983\u0026ndash;1.011, P\u0026thinsp;=\u0026thinsp;0.714). The OR for CD64 on CD14\u003csup\u003e+\u003c/sup\u003e CD16\u003csup\u003e+\u003c/sup\u003e monocytes analyzed using the IVW method was 0.994 (95% CI\u0026thinsp;=\u0026thinsp;0.991\u0026ndash;0.996, P\u0026thinsp;=\u0026thinsp;3.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e). Although similar results were obtained using the WM method (OR\u0026thinsp;=\u0026thinsp;0.994, 95% CI\u0026thinsp;=\u0026thinsp;0.991\u0026ndash;0.997, P\u0026thinsp;=\u0026thinsp;0.001), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;0.990, 95% CI\u0026thinsp;=\u0026thinsp;0.982\u0026ndash;0.997, P\u0026thinsp;=\u0026thinsp;0.227).\u003c/p\u003e\n \u003cp\u003eWe similarly identified two immunophenotypes that are risk factors for CRC: CD127\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cell AC and CD4 on HLA DR\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cell (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The OR for CD127\u003csup\u003e\u0026minus;\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cell AC analyzed using the IVW method was 1.004 (95% CI\u0026thinsp;=\u0026thinsp;1.002\u0026ndash;1.005, P\u0026thinsp;=\u0026thinsp;1.2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e). Although similar results were obtained using the WM method (OR\u0026thinsp;=\u0026thinsp;1.004, 95% CI\u0026thinsp;=\u0026thinsp;1.002\u0026ndash;1.006, P\u0026thinsp;=\u0026thinsp;1.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;1.006, 95% CI\u0026thinsp;=\u0026thinsp;0.978\u0026ndash;1.035, P\u0026thinsp;=\u0026thinsp;0.709). The OR for CD4 on HLA DR\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cells analyzed using the IVW method was 1.003 (95% CI\u0026thinsp;=\u0026thinsp;1.001\u0026ndash;1.005, P\u0026thinsp;=\u0026thinsp;3.7 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). Although similar results were obtained using the WM method (OR\u0026thinsp;=\u0026thinsp;1.003, 95% CI\u0026thinsp;=\u0026thinsp;1.000\u0026ndash;1.005, P\u0026thinsp;=\u0026thinsp;0.013), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;1.006, 95% CI\u0026thinsp;=\u0026thinsp;0.960\u0026ndash;1.054, P\u0026thinsp;=\u0026thinsp;0.822).\u003c/p\u003e\n \u003cp\u003eScatter plots for the four immunophenotypes illustrated that the MR-Egger intercept excluded the possibility of horizontal pleiotropy (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS14\u003c/span\u003e). In addition, funnel and leave-one-out analysis plots indicated the robustness of the results (Supplementary Figures \u003cspan class=\"InternalRef\"\u003eS15\u003c/span\u003e and S16).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eExploration of the causal effect of immunophenotypes on PCA\u003c/h2\u003e\n \u003cp\u003eTwo samples were assessed via MR analysis to investigate the causal effect of the immunophenotypes on PCA using IVW as the main analysis method. In total, five immunophenotypes had a significant causal relationship with PCA, three of which having a protective effect against PCA: Basophil %CD33\u003csup\u003edim\u003c/sup\u003e HLA DR\u003csup\u003e\u0026minus;\u003c/sup\u003e CD66b\u003csup\u003e\u0026minus;\u003c/sup\u003e, CD33 on CD33\u003csup\u003e+\u003c/sup\u003e HLA DR\u003csup\u003e+\u003c/sup\u003e CD14\u003csup\u003edim\u003c/sup\u003e, and CD8 on CD28\u003csup\u003e+\u003c/sup\u003e CD45RA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The OR for Basophil %CD33\u003csup\u003edim\u003c/sup\u003e HLA DR\u003csup\u003e\u0026minus;\u003c/sup\u003e CD66b\u003csup\u003e\u0026minus;\u003c/sup\u003e analyzed using the IVW method was 0.827 (95% CI\u0026thinsp;=\u0026thinsp;0.708\u0026ndash;0.967, P\u0026thinsp;=\u0026thinsp;0.017). Although similar results were obtained using the WM method (OR\u0026thinsp;=\u0026thinsp;0.819, 95% CI\u0026thinsp;=\u0026thinsp;0.692\u0026ndash;0.969, P\u0026thinsp;=\u0026thinsp;0.021), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;0.800, 95% CI\u0026thinsp;=\u0026thinsp;0.516\u0026ndash;1.242, P\u0026thinsp;=\u0026thinsp;0.426). The OR of CD33 on CD33\u003csup\u003e+\u003c/sup\u003e HLA DR\u003csup\u003e+\u003c/sup\u003e CD14\u003csup\u003edim\u003c/sup\u003e analyzed using the IVW method was 0.908 (95% CI\u0026thinsp;=\u0026thinsp;0.841\u0026ndash;0.981, P\u0026thinsp;=\u0026thinsp;0.014). Although similar results were obtained using the WM method (OR\u0026thinsp;=\u0026thinsp;0.912, 95% CI\u0026thinsp;=\u0026thinsp;0.842\u0026ndash;0.988, P\u0026thinsp;=\u0026thinsp;0.024), the MR-Egger regression did not support such a correlation (OR\u0026thinsp;=\u0026thinsp;0.933, 95% CI\u0026thinsp;=\u0026thinsp;0.829\u0026ndash;1.050, P\u0026thinsp;=\u0026thinsp;0.335). The OR of CD8 on CD28\u003csup\u003e+\u003c/sup\u003e CD45RA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells analyzed using the IVW method was 0.890 (95% CI\u0026thinsp;=\u0026thinsp;0.802\u0026ndash;0.989, P\u0026thinsp;=\u0026thinsp;0.030); however, such a correlation was not supported by the MR-Egger regression (OR\u0026thinsp;=\u0026thinsp;0.863, 95% CI\u0026thinsp;=\u0026thinsp;0.679\u0026ndash;1.095, P\u0026thinsp;=\u0026thinsp;0.253) or WM method (OR\u0026thinsp;=\u0026thinsp;0.895, 95% CI\u0026thinsp;=\u0026thinsp;0.779\u0026ndash;1.029, P\u0026thinsp;=\u0026thinsp;0.119).\u003c/p\u003e\n \u003cp\u003eWe similarly identified two immunophenotypes that are risk factors for PCA: CD39\u003csup\u003e+\u003c/sup\u003e resting CD4 regulatory T cell AC and CD27 on IgD\u003csup\u003e\u0026minus;\u003c/sup\u003e CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e B cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The OR for CD39\u003csup\u003e+\u003c/sup\u003e resting CD4 regulatory T cell AC analyzed using the IVW method was 1.096 (95% CI\u0026thinsp;=\u0026thinsp;1.011\u0026ndash;1.188, P\u0026thinsp;=\u0026thinsp;0.027); however, such a correlation was not supported by the MR-Egger regression (OR\u0026thinsp;=\u0026thinsp;1.077, 95% CI\u0026thinsp;=\u0026thinsp;0.923\u0026ndash;1.257, P\u0026thinsp;=\u0026thinsp;0.360) or WM method (OR\u0026thinsp;=\u0026thinsp;1.085, 95% CI\u0026thinsp;=\u0026thinsp;0.974\u0026ndash;1.209, P\u0026thinsp;=\u0026thinsp;0.138). The OR of CD27 on IgD\u003csup\u003e\u0026minus;\u003c/sup\u003e CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e B cells analyzed using the IVW method was 1.295 (95% CI\u0026thinsp;=\u0026thinsp;1.052\u0026ndash;1.595, P\u0026thinsp;=\u0026thinsp;0.015); however, such a correlation was not supported by the MR-Egger regression (OR\u0026thinsp;=\u0026thinsp;1.390, 95% CI\u0026thinsp;=\u0026thinsp;0.506\u0026ndash;3.820, P\u0026thinsp;=\u0026thinsp;0.568) or WM method (OR\u0026thinsp;=\u0026thinsp;1.265, 95% CI\u0026thinsp;=\u0026thinsp;0.965\u0026ndash;1.656, P\u0026thinsp;=\u0026thinsp;0.088).\u003c/p\u003e\n \u003cp\u003eScatter plots for the five immunophenotypes illustrated that the MR-Egger intercept excluded the possibility of horizontal pleiotropy (Supplementary Figure \u003cspan class=\"InternalRef\"\u003eS17\u003c/span\u003e). In addition, funnel and leave-one-out analysis plots indicated the robustness of the results (Supplementary Figures \u003cspan class=\"InternalRef\"\u003eS18\u003c/span\u003e and S19).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eExamination of the MR analysis results\u003c/h2\u003e\n \u003cp\u003eHeterogeneity or horizontal pleiotropy was not observed for any of the immunophenotypes (Supplementary Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e). Moreover, outliers were not observed in the MR-PRESSO Global Test for all immunophenotypes (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Supplementary Table \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe causal associations between 731 immune cell phenotypes and seven types of DSCs were analyzed using a vast amount of publicly accessible genetic data. To the best of our knowledge, this study is the first MR analysis delving into the causal connections between numerous immunophenotypes and DSCs. We identified a total of three, six, two, two, four, and five immunophenotypes with a significant causal effect on the development of GC, HCC, gallbladder cancer, small intestine cancer, CRC, and PCA, respectively. However, we did not find any immunophenotypes with a significant causal relationship with EC.\u003c/p\u003e \u003cp\u003eOur study showed that na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cell AC is a protective factor against GC. Being among the most abundant plastic and heterogeneous cell populations in the tumor microenvironment, CD4\u003csup\u003e+\u003c/sup\u003e T cells play a crucial role in processes such as angiogenesis, tumor cell migration, and the immune response against tumors [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. As revealed by Kagamu et al., CD4\u003csup\u003e+\u003c/sup\u003e T cell immunity plays a role in the clonal proliferation, migration, and cancer cell-killing activity of CD8\u003csup\u003e+\u003c/sup\u003e T cells, which are critical in the antitumor immune response [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCD45 is a receptor protein tyrosine phosphatase that is highly evolutionarily conserved and is exclusively expressed on all nucleated cells within the hematopoietic system [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Our findings revealed that CD45 on B cells is a protective factor against GC. Previous studies have demonstrated that CD45 deficiency leads to T and B lymphocyte dysfunction, which manifests as severe combined immunodeficiency. CD45 plays a vital role in regulating the function of B cells, and the expression of this phosphatase is essential for activating B lymphocytes and facilitating their entry into the cell cycle [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNK cells are the main innate immunity effector cells in cancer and exhibit high heterogeneity in the tumor microenvironment [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In this study, CD8\u003csup\u003edim\u003c/sup\u003e Natural Killer T %lymphocyte was found to be a risk factor for GC. CD8, a transmembrane glycoprotein, is the primary molecule expressed on the surface of cytotoxic T lymphocytes. Additionally, it is present in natural killer cells, cortical thymocytes, and dendritic cells [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The present study similarly found that CD8 on CD28\u003csup\u003e+\u003c/sup\u003e CD45RA\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells was a protective factor against PCA. CD8\u003csup\u003e+\u003c/sup\u003e T cells originate from CD34\u003csup\u003e+\u003c/sup\u003e hematopoietic stem cells in the bone marrow, and they exert antitumor immunity by initiating either a direct or indirect killing response against their target cells [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Research indicates that CD8\u003csup\u003e+\u003c/sup\u003e T cell infiltration below 2.2% is associated with a four-fold higher risk of disease progression following cancer surgery (risk ratio\u0026thinsp;=\u0026thinsp;3.84, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA variety of inflammatory cells express CD62L, which is essential for the relocation of lymphocytes to lymph nodes [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Our study revealed that CD62L on monocytes is a risk factor for HCC. Previous studies have found that CD62L expression is significantly increased in patients with tumors such as bladder cancer [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. As revealed by Kagamu et al., cells with low CD62L expression promote the production of highly efficient CD4 antitumor effector T lymphocytes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCC chemokines comprise a subfamily of 27 chemotactic cytokines; they are indispensable for the functioning of the tumor microenvironment and are integral to intercellular communication [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Our findings revealed that CCR2 on CD62L\u003csup\u003e+\u003c/sup\u003e myeloid dendritic cells is a risk factor for HCC. The chemokine ligand CCL2 and its corresponding receptor CCR2 are implicated in the development and progression of diverse cancers [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. CCL2 can activate tumor cell growth and proliferation through multiple mechanisms. Through its interaction with CCR2, CCL2 facilitates cancer cell migration and recruits immunosuppressive cells into the tumor microenvironment, thereby promoting cancer development.\u003c/p\u003e \u003cp\u003eThe role of local immune responses mediated by CD3\u003csup\u003e+\u003c/sup\u003e tumor-infiltrating lymphocytes has been demonstrated [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Moreover, CD3 cells infiltrate tumors and peritumoral tissues and have been used as a marker of local inflammation [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. CD3 has been found to play a role in programmed cell death protein 1-mediated tumor control, and stromal CD3-positive lymphocytes correlate with a favorable prognosis in patients with ovarian cancer [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. However, the present study showed that CD3 on CD4\u003csup\u003e+\u003c/sup\u003e T cells is a risk factor for HCC; therefore, further studies are needed to determine the role of CD3 on CD4\u003csup\u003e+\u003c/sup\u003e T cells in the development of HCC.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eCD28\u003c/em\u003e gene plays a crucial role in T cell proliferation and survival, as well as in the production of cytokines and the development of type 2 helper T cells [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Our study revealed that CD28 on CD4\u003csup\u003e+\u003c/sup\u003e T cells is a risk factor for HCC, consistent with the findings of Shen et al., who confirmed that the genes \u003cem\u003eCD28\u003c/em\u003e and \u003cem\u003ePTEN\u003c/em\u003e are closely associated with survival in patients with cervical cancer [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCD64 is the only functional high-affinity FcγR. It binds to the IgG isotypes IgG1 and IgG3 [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and is expressed by bone marrow cells, including monocytes, macrophages, and neutrophils, but not lymphocytes or natural killer cells [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Our study revealed that CD64 on CD14\u003csup\u003e+\u003c/sup\u003e CD16\u003csup\u003e+\u003c/sup\u003e monocytes was a protective factor in CRC. As revealed by Hintz et al., engineered NK92 cells expressing CD64 are involved in both the tumor and stroma-targeted antibody control of prostate cancer growth [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCD39 is highly expressed in various cell types within the tumor microenvironment, including tumor cells, endothelial cells, and infiltrating immune cells [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Our study revealed that CD39\u003csup\u003e+\u003c/sup\u003e CD4\u003csup\u003e+\u003c/sup\u003e T cell AC is a risk factor for small intestine cancer. Similarly, we found that CD39\u003csup\u003e+\u003c/sup\u003e resting CD4 regulatory T cell AC is a risk factor for PCA. The progression and metastasis of tumors are regulated by the intricate interplay between tumor cells and the tumor microenvironment [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, the elevated expression of CD39 is strongly correlated with unfavorable outcomes [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Similar to our results, Jacoberger-Foissac et al. found that CD73 inhibits cGAS-STING and synergizes with CD39 to promote PCA development [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study has certain limitations. We used relatively loose thresholds, which may have increased the number of false positives. However, they also allowed for a more comprehensive assessment of the association between immunophenotypes and DSCs.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis MR study comprehensively analyzed the causal relationship between immune phenotypes and seven types of DSCs, highlighting the complex interaction patterns between the immune system and DSCs. Overall, the results of this study provide new insights into the mechanisms underlying immune system-mediated cancer development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available in the GWAS repository: https://www.ebi.ac.uk/gwas/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [grant number 82373044] and the Natural Science Foundation of Shandong Province [grant number ZR2022LSW001].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL and JZ designed the study, analyzed and interpreted the data, and drafted the manuscript. RL, XY, and XD analyzed and interpreted the data. YTL conceptualized and designed the study and revised the manuscript. The authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the participants of all GWAS cohorts included in the present work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, \u003cem\u003eet al.\u003c/em\u003e Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J Clinicians. 2021;71:209-49.\u003c/li\u003e\n\u003cli\u003eRemark R, Becker C, Gomez JE, Damotte D, Dieu-Nosjean MC, Saut\u0026egrave;s-Fridman C, \u003cem\u003eet al.\u003c/em\u003e The non-small cell lung cancer immune contexture. A major determinant of tumor characteristics and patient outcome. 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Cancer Immunol Res. 2023;11:56-71.\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":"digestive system cancer, immune cells, causal inference, Mendelian randomization study, single nucleotide polymorphism","lastPublishedDoi":"10.21203/rs.3.rs-4074806/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4074806/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Immune cell characteristics and digestive system cancers (DSCs) are correlated; however, the causal relationship between immune cell phenotypes and DSCs remains unclear. In this study, a comprehensive two-sample Mendelian randomization (MR) analysis was performed based on publicly available genetic data to investigate the causal relationship between 731 immunophenotypes and the risk of esophageal cancer (EC), gastric cancer (GC), hepatocellular cancer (HCC), gallbladder cancer, small intestine cancer, colorectal cancer (CRC), and pancreatic cancer (PCA) development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Inverse variance weighting (IVW), MR-Egger regression, and weighted median methods were used for the MR analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e IVW results confirmed that among the 731 immunophenotypes, three, six, two, two, four, and five immunophenotypes had significant causal effects on the development of GC, HCC, gallbladder cancer, small intestine cancer, CRC, and PCA, respectively. However, immunophenotypes with a significant causal relationship with EC were not found. Moreover, the instrumental variables did not exhibit significant heterogeneity or horizontal pleiotropy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis MR study demonstrated a close association between immune phenotype and DSCs through genetic means and could guide future clinical studies.\u003c/p\u003e","manuscriptTitle":"Causal role of immune cells in digestive system cancers: A Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-25 17:33:54","doi":"10.21203/rs.3.rs-4074806/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":"204d4cdc-7258-456c-a654-fcb3c0e4e5bd","owner":[],"postedDate":"March 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-02T17:11:58+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-25 17:33:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4074806","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4074806","identity":"rs-4074806","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}