Tumor-Promoting vs. Protective Immune Phenotypes in Stage III Colorectal Cancer: A Mendelian Randomization Study on Chemotherapy Outcomes

Tumor-Promoting vs. Protective Immune Phenotypes in Stage III Colorectal Cancer: A Mendelian Randomization Study on Chemotherapy Outcomes | 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 Tumor-Promoting vs. Protective Immune Phenotypes in Stage III Colorectal Cancer: A Mendelian Randomization Study on Chemotherapy Outcomes Xiangxiang Liu, Wenkai Pan, Jian Wang, Yanli Ning, Dongfang Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6756503/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 Colorectal cancer (CRC) prognosis remains challenging despite advancements in chemotherapy, necessitating reliable prognostic biomarkers. Plasma immune cells play two different roles in the advancement of tumors, but their causal relationship with post-chemotherapy CRC outcomes is poorly understood. This research utilized Mendelian randomization (MR) to examine the causal relationships between plasma immune cell levels and the prognosis of stage III CRC following oxaliplatin-based chemotherapy and to investigate the fundamental genetic mechanisms. Methods Using genome-wide association study (GWAS) data from 3,757 Europeans, 731 plasma immune cell traits were analyzed as exposures. Outcomes included overall survival (OS) and progression-free survival (PFS) of 3,647 stage III CRC patients from the NCCTG N0147 trial and DACHS cohort. Inverse variance-weighted (IVW), MR-Egger, constrained maximum likelihood (cML-MA), and Bayesian MR analyses were conducted. Sensitivity tests (Cochran’s Q, Steiger directionality) validated this robustness. Multi-marker Analysis of GenoMic Annotation (MAGMA) and Summary-data-based Mendelian Randomization (SMR) identified candidate genes using cis-eQTL data. Results MR analyses identified six immune phenotypes with stable causal associations: three tumor-promoting traits (elevated HLA-DR on CD33⁻ HLA-DR⁺ cells [OS: HR = 2.57, P = 0.0038; PFS: HR = 2.52, P = 0.0021], CD28⁻ CD4⁻CD8⁻ T cell count [OS: HR = 3.11, P = 0.0073; PFS: HR = 3.17, P = 0.0031], and SSC-A on CD14⁺ monocytes [OS: HR = 2.64, P = 0.036; PFS: HR = 3.17, P = 0.0064]) and three protective traits (CD14 on CD33⁺ HLA-DR⁺ CD14dim cells [OS: HR = 0.35, P = 0.0118; PFS: HR = 0.33, P = 0.0034], FSC-A on NK cells [OS: HR = 0.22, P = 0.0046; PFS: HR = 0.29, P = 0.0234], and CD28⁺ CD45RA⁺ CD8⁺ T cell count [OS: HR = 0.30, P = 0.0325; PFS: HR = 0.34, P = 0.0331]). Genetic analyses revealed associations with LEMD2 (OS: p SMR = 0.0367), MPVL17L2 (PFS: p SMR = 0.0314), and BAK1 (PFS: p SMR = 0.0232), highlighting their roles in CRC prognosis. Conclusion This MR study identifies plasma immune cell subsets and genetic regulators as critical determinants of CRC prognosis post-chemotherapy. Tumor-promoting and protective immune phenotypes reflect the complexity of CRC’s immune microenvironment. The novel roles of LEMD2, MPVL17L2, and BAK1 provide mechanistic insights for targeted therapies. These findings advance personalized immunotherapy strategies and underscore the potential of immune biomarkers in clinical decision-making. Mendelian Randomization Epidemiological Colorectal Cancer Prognosis Tumor Microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Colorectal cancer (CRC) represents a significant public health issue on a global scale, with an estimated 1.93 million new cases and 0.94 million fatalities in 2020[ 1 ]. The incidence of CRC varies geographically, with higher rates in developed countries, but it is also increasing in developing regions[ 2 ]. Treatment for CRC typically involves a multifaceted approach that includes surgical intervention, chemotherapy, immunotherapy, and targeted therapy. Surgery is frequently the principal therapeutic approach for localized CRC, whereas chemotherapy is typically employed as either adjuvant or neoadjuvant treatment to mitigate the likelihood of recurrence and enhance survival outcomes. Common chemotherapeutic agents include 5-fluorouracil (5-FU), oxaliplatin, and irinotecan. These agents are frequently administered in combination therapy regimens, such as FOLFOX (5-FU, leucovorin, and oxaliplatin) or FOLFIRI (5-FU, leucovorin, and irinotecan) [ 3 , 4 ]. While recent advancements in targeted therapy and immunotherapy have shown promise, particularly in metastatic CRC, chemotherapy remains the mainstay of treatment for most patients [ 5 , 6 ]. Common risk factors associated with CRC encompass advanced age, family history of CRC or adenomas, hereditary colon cancer syndromes (such as Lynch syndrome), dietary patterns (high red meat consumption and low fiber intake), obesity, physical inactivity, smoking, and alcohol consumption [ 6 , 7 ]. Despite advances in treatment, the prognosis of CRC remains challenging, and it is essential to identify dependable prognostic indicators that can inform treatment decisions and enhance patient outcomes. The immune regulatory role of plasma immune cells, particularly plasma cells and B cell subsets, plays a vital role in the diagnosis and treatment of tumors. Plasma cells demonstrate functional heterogeneity across different cancers. For example, in high-grade serous ovarian cancer, plasma cells are significantly elevated, where they interact with tumor cells through the secretion of immunoglobulins and cytokines such as IL-6, promoting tumor growth and invasiveness[ 8 ]. Conversely, in head and neck squamous cell carcinoma, tertiary lymphoid structures composed of plasma cells and memory B cells enhance the receptor diversity of T and B cells, which boosts anti-tumor immune responses and correlates with improved patient survival rates[ 9 , 10 ]. However, the prognostic impact of plasma cells is highly dependent on the tumor type. In penile cancer, increased plasma cell infiltration correlates with better overall survival[ 11 ], whereas in CRC, plasma cells may interact with regulatory T cells (Tregs) via the TGF-β signaling pathway to create an immune-suppressive environment. This results in the functional depletion of CD8 + T cells, which is associated with an unfavorable prognosis [ 12 , 13 ]. Immunological studies have further indicated that co-infiltration of plasma cells and M2 macrophages in CRC suppresses anti-tumor immunity, with their spatial distribution characteristics being significantly linked to the patient’s risk of recurrence[ 12 , 14 ]. These findings highlight the intricate immunoregulatory functions of plasma immune cells within the context of cancer. A comprehensive understanding of this relationship, taking into account tumor-specific microenvironments and molecular subtypes, is essential for improving cancer prognosis and developing targeted therapeutic strategies[ 15 , 16 ]. Identifying the connection between plasma immune cells and CRC prognosis is of particular importance and warrants further research to better understand their impact on patient outcomes. Observational studies are frequently subject to confounding biases and reverse causation, which can compromise the validity of causal inferences[ 17 ]. Mendelian randomization (MR), a quasi-experimental methodology that leverages genetic variants as instrumental variables to approximate randomized trials, provides a rigorous framework for causal inference by minimizing residual confounding through the natural randomization of alleles at conception, allowing for cost-effective and rapid evaluation of potential risk factors and therapeutic targets[ 17 , 18 ]. In oncology, MR has been applied to elucidate the causal roles of plasma proteins, such as TIMP4, in reducing the risk of anorexia nervosa and bipolar disorder[ 19 ], as well as to establish associations between lymphocyte counts and the risk of acute lymphoblastic leukemia (ALL)[ 20 ]. Additional applications include the prioritization of therapeutic targets for CRC via proteome-wide analyses[ 21 ], validation of gut microbial markers linked to cancer using genetic instruments[ 22 ], assessment of Body Mass Index (BMI)-related inflammatory markers in tumorigenesis[ 22 ], and exploration of vertical pleiotropy in genetic variants influencing immune traits and ALL susceptibility[ 20 ]. Collectively, these studies highlight the transformative potential of MR in advancing cancer research and translational medicine. Our study aims to use MR analysis to examine the relationship between plasma immune proteins and the prognostic outcomes of CRC patients following chemotherapy, thereby enhancing the reliability of causal inferences and providing novel insights for personalized treatment strategies. 2 Materials and Methods 2.1 Study Design The objective of this research is to examine the causal relationship between plasma immune cells and the prognosis of stage III CRC patients receiving oxaliplatin-based first-line chemotherapy using MR analysis. Genome-wide association study (GWAS) summary statistics of plasma immune cells will be used as exposure traits, while the prognosis for patients with stage III CRC, who receive oxaliplatin-based chemotherapy, will serve as the outcome trait. Various MR analyses and sensitivity tests will be conducted to identify plasma immune cells with potential causal associations with patient prognosis. For plasma immune cells showing significant associations, Multi-marker Analysis of GenoMic Annotation(MAGMA)will be used to analyze relevant regulatory genes, providing insights into their functional involvement. Subsequently, Summary-data-based Mendelian Randomization༈SMR༉will be applied to investigate the relationship between the identified genes and CRC prognosis, integrating expression quantitative trait loci (eQTL) data to elucidate potential molecular mechanisms. This study aims to enhance the understanding of plasma immune cell involvement in CRC prognosis and identify potential biomarkers for personalized treatment strategies (Fig. 1 ). The study is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization (STROBE-MR) Guideline[ 23 ]. 2.2 GWAS Data Source The GWAS catalog provided aggregated summary statistics for 731 immune cell traits, including 118 absolute cell counts (AC), 192 relative cell counts (RC), 389 median fluorescence intensities (MFI) representing surface antigen levels, and 32 morphological parameters (MP), with dataset IDs ranging from GCST0001391 to GCST0002121[ 24 ]. These traits were classified into six immune cell groups: B cells, cytotoxic dendritic cells (CDCs), T cells in various maturation stages, monocytes, myeloid cells, and TBNK (comprising T cells [including Tregs], B cells, and natural killer [NK] cells). The phenotypes measured for these groups included both cellular counts (AC, RC) and MFI features, with the MP features primarily associated with CDC and TBNK panels. The study was conducted in a sample group consisting of 3,757 individuals of European ancestry, incorporating covariates such as sex, age, and the square of age into the analysis. [ 24 ]. A total of approximately 22 million single nucleotide polymorphisms (SNPs) were genotyped utilizing high-density arrays, with imputation based on a Sardinian reference panel. This extensive dataset thoroughly examines of the genetic basis of immune cell traits in a European population. The Genome-Wide Association Study (GWAS) data pertaining to patients with stage III colorectal cancer (CRC) who are undergoing first-line chemotherapy based on oxaliplatin were sourced from the NCCTG N0147 trial (3,098 patients) and the DACHS cohort (549 patients). The study assessed overall survival (OS) and progression-free survival (PFS) by employing multivariable Cox proportional hazards models with an interaction term between each SNP and type of treatment[ 25 ]. Cis-eQTL summary data were obtained from the eQTLGen Consortium ( https://www.eqtlgen.org/ ), which is dedicated to identifying the downstream effects of genetic variants associated with human traits[ 26 ]. The consortium compiles cis-eQTL data from 37 datasets, representing 31,684 individuals of European descent[ 26 ]. 2.3 Selection of Instrumental Variables In this research, SNPs derived from immune cell GWAS data were employed as instrumental variables (IVs). To ensure robust associations, SNPs were selected based on genome-wide significance (P < 5.0 × 10⁻⁶). Those in high linkage disequilibrium (LD) were excluded to maintain instrument independence and strength (r² < 0.001 within a ± 10 MB region). Variants with a minor allele frequency (MAF) ≤ 0.01 were removed from the dataset. Palindromic SNPs, due to their potential to cause strand ambiguity, were also excluded prior to MR analysis. To further validate instrument directionality, the Steiger test was applied to eliminate SNPs that may reflect reverse causation[ 27 ]. The Phenoscanner database ( http://www.phenoscanner.medschl.cam.ac.uk/ ) was utilized to screen all exposure-related SNPs, allowing us to discard any associated with potential confounders (e.g., smoking, alcohol consumption) or with outcomes at a significance threshold of P < 5 × 10⁻⁸. Instrument strength was evaluated using the F-statistic, with values below 10 considered weak[ 28 ]. The F-value was calculated using the formula: F = (N - K − 1) × R² / K × (1 - R²), where N is the sample size, K is the number of IVs, and R² represents the proportion of variance in the exposure explained by the instruments[ 28 ]. The selected SNPs were ultimately confirmed as valid IVs for our analysis. For SMR and genetic colocalization analyses, only cis-eQTLs were used, defined as variants located within a ± 1 MB window of the gene of interest, based on the hg19 genome assembly. 2.4 Statistical Analysis Various approaches were utilized in the MR analysis to evaluate the causal influence of plasma immune cells on CRC. These included the inverse variance weighted (IVW) method, MR-Egger regression, constrained maximum likelihood model averaging (cML-MA), Robust Adjusted Profile Score (RAPS), and Bayesian weighted MR. The IVW approach employs a meta-analytic framework to integrate Wald estimates from each SNP, yielding a comprehensive assessment of the exposure’s impact on the outcome[ 29 ]. The cML-MA technique mitigates biases stemming from both correlated and uncorrelated pleiotropy, proving particularly valuable in scenarios with numerous invalid instruments and subtle pleiotropic effects[ 30 ]. The RAPS approach delivers reliable causal effect estimates despite the presence of weak instruments and idiosyncratic pleiotropy, making it well-suited for studies involving extensive genetic variants. Bayesian weighted MR accommodates deviations from instrumental variable assumptions due to pleiotropy, facilitating causal inference even when pleiotropy exists [ 31 ]. The IVW approach identified a significant relationship, with p-values below 0.05 deemed to indicate meaningful results. Given the exploratory nature of this research, no p-value correction was applied in an effort to identify potential biomarkers with the most comprehensive coverage. To ensure the reliability and accuracy of the subsequent MAGMA analysis, only results with p-values less than 0.05 from all MR methods, excluding MR-Egger, were considered for inclusion. This selective approach was implemented to mitigate the influence of weak instruments and potential biases, ensuring that only robust causal estimates informed the investigation into the role of plasma immune cells in CRC prognosis. To enhance the reliability of MR analyses, multiple sensitivity tests were conducted. Cochran's Q test was used to assess heterogeneity among instrumental variables [ 32 ]. If the p-value from the Cochran Q test was less than 0.05, a random-effects model was applied in the IVW analysis; otherwise, a fixed-effects model was used. MR-Egger regression was performed to detect potential horizontal pleiotropy, with a significant intercept (P < 0.05) indicating directional pleiotropy [ 33 ]. Additionally, the Leave-one-out method was used to exclude individual SNPs and identify outliers, thus assessing result robustness [ 34 ]. All analyses were carried out using the TwoSampleMR (version 0.6.0), MendelianRandomization (version 0.8.0), and MRPRESSO (version 1.0) packages in R Software 4.3.2 ( https://www.R-project.org ). 2.5 Exploration of Genetic Mechanisms To improve the stability of our findings, we utilized MAGMA analysis to examine the relationship between genes and phenotypes. MAGMA gene analysis relies on a multivariate linear regression model based on principal component analysis. Initially, the SNP matrix of genes is projected onto its principal components (PCs), with those corresponding to smaller eigenvalues being excluded. These selected PCs then serve as predictors for the phenotype in a linear regression model[ 35 ]. This method enables MAGMA to analyze continuous genetic traits, such as gene expression levels and gene sets, as well as facilitate joint and interaction analyses of multiple gene sets and additional genetic traits[ 36 ]. Following the MAGMA analysis, we retrieved the corresponding eQTL data from the eQTLGen database to identify relevant cis-eQTLs for further investigation. We conducted SMR analysis and HEIDI testing on the identified cis-eQTLs. SMR analysis combines summary-level GWAS and eQTL data to determine whether genetic variants influencing gene expression also affect the trait of interest[ 37 ]. A P-value < 0.05 was considered statistically significant. The HEIDI test, which assesses potential linkage effects between genes, was used to distinguish pleiotropy from linkage [ 38 ]. A P-value < 0.05 in the HEIDI test indicated the presence of a linkage effect rather than direct pleiotropy. The SMR analysis was performed using the software provided on the SMR website ( https://yanglab.westlake.edu.cn/software/smr/ ). 3 Results 3.1 Association of 731 Plasma Immune Cells with OS in Stage III CRC Patients MR analyses of 731 plasma immune cell phenotypes demonstrated significant associations with OS in stage III CRC patients. All genetic instruments exhibited F-statistics exceeding 10, mitigating concerns of weak instrument bias ( Supplementary Table 1 ). Using IVW as the primary method, HRs with 95% CIs were calculated, and sensitivity analyses (MR-Egger, constrained maximum likelihood, RAPS, and Bayesian weighted MR) confirmed directional consistency ( Fig. 2 ) . Analysis of immune cell subsets has unveiled a complex interplay of prognostic factors influencing patient survival, with certain immune phenotypes tied to poorer outcomes and others linked to more favorable prognoses. Elevated levels of specific subsets, such as the absolute count of IgD + CD38- B cells (HR = 6.05, 95% CI: 1.28–28.58; P = 0.0229), the percentage of CD11c + monocytes among monocytes (HR = 2.75, 95% CI: 1.20–6.33; P = 0.0171), and the proportion of CD39 + cells within resting CD4 Tregs (HR = 1.52, 95% CI: 1.07–2.16; P = 0.0207), were associated with increased risks of adverse survival outcomes. Similarly, a greater presence of TCRgd T cells among total T cells (HR = 4.42, 95% CI: 1.07–18.30; P = 0.0406), higher absolute counts of Effector Memory CD8 + T cells (HR = 1.94, 95% CI: 1.01–3.71; P = 0.0458), CD28- CD4- CD8- T cells (HR = 3.11, 95% CI: 1.36–7.15; P = 0.0073), CD25 + + CD8 + T cells (HR = 2.98, 95% CI: 1.20–7.35; P = 0.018), and side scatter area (SSC-A)expression on CD14 + monocytes (HR = 2.64, 95% CI: 1.06–6.53; P = 0.036) correlated with heightened mortality risk, while elevated expression of HLA-DR on CD33- HLA-DR + cells (HR = 2.57, 95% CI: 1.36–4.86; P = 0.0038) further highlighted its association with worse prognosis. On the other hand, certain immune characteristics were connected to improved survival trajectories, including higher absolute counts of CD28 + CD45RA + CD8 + T cells (HR = 0.30, 95% CI: 0.10–0.90; P = 0.0325) and an increased percentage of CD45RA + CD28- CD8 + T cells among total T cells (HR = 0.80, 95% CI: 0.65–0.98; P = 0.0347), both of which suggested a protective effect. Additionally, NK cells with a larger forward scatter area (FSC-A) (HR = 0.22, 95% CI: 0.08–0.63; P = 0.0046) and greater expression of CD14 on CD33 + HLA-DR + CD14dim cells (HR = 0.35, 95% CI: 0.15–0.79; P = 0.0118) were tied to a diminished risk of disease progression ( Supplementary Table 2 ). These findings illuminate the dual role of immune subsets in shaping patient outcomes, where some populations may exacerbate disease severity while others potentially mitigate it through regulatory or senescent mechanisms ( Fig. 3 ) . Cochran’s Q test revealed no significant heterogeneity across analyses (all P > 0.05). MR-Egger regression analyses detected no evidence of horizontal pleiotropy (intercept P > 0.05 for all associations). Leave-one-out sensitivity analyses confirmed the robustness of causal estimates, with no single SNP disproportionately influencing the results ( Supplementary Table 4–6 ), and Steiger directionality tests excluded reverse causation as a plausible explanation. 3.2 Association of 731 Plasma Immune Cells with PFS in Stage III CRC Patients In the analysis of immune cell phenotypes and their correlation with PFS, several factors were identified with significant hazard ratios. Higher levels of certain immune cell types or markers were associated with poorer PFS, as indicated by hazard ratios greater than 1 ( Fig. 4 ) . These include the HLA DR + NK cell absolute count (HR = 6.59, 95% CI: 1.25–34.62; P = 0.026), which showed a notable risk association, albeit with a wide confidence interval; the CD28- CD4-CD8- T cell absolute count (HR = 3.17, 95% CI: 1.48–6.83; P = 0.0031); the SSC-A on CD14 + monocytes (HR = 3.17, 95% CI: 1.38–7.25; P = 0.0064); and the expression of HLA DR on CD33- HLA DR + cells (HR = 2.52, 95% CI: 1.40–4.53; P = 0.0021) ; FSC-A on CD4 + T cells (HR = 4.31, 95% CI: 1.06–17.58; P = 0.0416); and CD80 on granulocytes (HR = 1.98, 95% CI: 1.05–3.76; P = 0.0357), underscoring the role of activated immune phenotypes in accelerated disease progression. Conversely, other immune cell phenotypes were associated with better PFS, as evidenced by hazard ratios less than 1 ( Fig. 5 ) . These protective factors include the terminally differentiated CD4 + T cell absolute count (HR = 0.26, 95% CI: 0.08–0.92; P = 0.0365); the percentage of CD28- CD8dim T cells among CD8dim T cells (HR = 0.21, 95% CI: 0.05–0.82; P = 0.0251); the absolute count of CD28 + CD45RA + CD8 + T cells (HR = 0.34, 95% CI: 0.12–0.92; P = 0.0331); the expression of CD14 on CD33 + HLA DR + CD14dim cells (HR = 0.33, 95% CI: 0.16–0.69; P = 0.0034); and the FSC-A on NK cells (HR = 0.29, 95% CI: 0.10–0.84; P = 0.0234) ; the CD45RA + CD28- CD8 + T cell percentage among total T cells (HR = 0.82, 95% CI: 0.68–0.998; P = 0.0474); CD25 on IgD- CD24- B cells (HR = 0.34, 95% CI: 0.12–0.97; P = 0.0435); and HLA DR on CD14- CD16- cells (HR = 2.16, 95% CI: 1.13–4.16; P = 0.0207) ( Supplementary Table 3 ). These findings suggest that higher levels of these latter cell types or markers are linked to attenuated disease progression, potentially implicating specific immune regulatory mechanisms in clinical benefit. Across MR analyses, multiple sensitivity tests consistently showed no evidence of potential horizontal pleiotropy or heterogeneity in the study ( Supplementary Table 7–9 ). 3.3 Exploration of Genetic Mechanisms To further explore the genetic basis underlying the MR-identified associations between plasma immune cell phenotypes and CRC prognosis, we conducted MAGMA analysis on significant results (excluding MR-Egger) to prioritize potential regulatory genes ( Supplementary Table 10–11 ). Cis-eQTL data for these genes were retrieved from the eQTLGen Consortium and analyzed using SMR to evaluate whether genetic variants influencing gene expression also affected clinical outcomes. HEIDI testing was subsequently applied to assess potential confounding by linkage disequilibrium. For OS, SMR analysis identified LEMD2 (probeID: ENSG00000161904) as a candidate gene, with genetic variants regulating its expression showing a significant association (b_SMR = 2.0255, p_SMR = 0.0367). For PFS, two genes—MPVL17L2 (probeID: ENSG00000254858, b_SMR = -2.61846, p_SMR = 0.0314) and BAK1 (probeID: ENSG000030110, b_SMR = -0.61285, p_SMR = 0.0232)—demonstrated significant associations with progression risk (Table 1 ). HEIDI testing was subsequently conducted to evaluate whether these associations were driven by linkage disequilibrium rather than direct pleiotropy. Results revealed no evidence of linkage effects (p_HEIDI > 0.05 for all genes), supporting the direct role of these genetic variants in mediating the observed phenotypic effects ( Supplementary Table 12–13 ). The results of this study underscore the value of employing a combination of MAGMA, SMR, and HEIDI methodologies to elucidate the genetic mechanisms that connect immune cell characteristics to the prognosis of CRC. Table 1 Summary of SMR and HEIDI Analyses for cis-eQTLs Associated with Colorectal Cancer Survival Outcomes Outcome probeID Gene b_SMR se_SMR p_SMR p_HEIDI nsnp_HEIDI Overall Survival ENSG00000161904 LEMD2 2.00255 0.958713 0.03672676 0.09965853 20 Progression-Free Survival ENSG00000254858 MPV17L2 -2.61846 1.21745 0.03149379 0.1387144 20 Progression-Free Survival ENSG00000030110 BAK1 -0.61285 0.270149 0.02329368 0.1765296 20 Outcome: Survival outcome type (Overall Survival [OS]; Progression-Free Survival [PFS]). probeID: ENSEMBL probe identifier for the target gene. Gene: Gene symbol regulated by the cis-eQTL. b_SMR: SMR effect size, indicating the direction and magnitude of the association between a 1-unit increase in natural log-transformed gene expression and the log-hazard of the survival outcome. se_SMR: Standard error of the SMR effect estimate. p_SMR: P-value for the SMR test (two-tailed). p_HEIDI: P-value for HEIDI heterogeneity test; values > 0.05 suggest no significant heterogeneity, supporting a primary causal association. nsnp_HEIDI: Number of instrumental SNPs included in the HEIDI test. 4 Discussion The occurrence and progression of CRC represent a multifaceted pathological process influenced by a variety of factors, stages, and accumulated genetic variations, pertaining to the processes of proliferation and apoptosis in both neoplastic and normal cells, as well as immune surveillance of tumor cells by the body. Numerous studies have demonstrated that immune cells play an important role in effectively clearing tumor primary lesions and preventing tumor metastasis by combating tumor immune responses. Our large-scale MR study systematically evaluated 731 immune cell traits to identify prognostic determinants of CRC patients after chemotherapy. It is worth noting that in OS and PFS, we identified six immunophenotypes with stable causal relationship: three tumor promoting traits (HLA-DR on CD33⁻ HLA-DR⁺ cells, CD28⁻ CD4⁻CD8⁻ T cell absolute count, SSC-A on CD14⁺ monocytes) and three protective features (CD14 on CD33⁺ HLA-DR⁺ CD14dim cells, FSC-A on NK cells, CD28⁺ CD45RA⁺ CD8⁺ T cell absolute count). These findings reinforce the dual immunoregulatory roles in CRC progression while providing mechanistic insights for the development of immunotherapy. 4.1 CD28⁻ CD4⁻CD8⁻ T Cell Absolute Count Our study reveals that an increased absolute count of CD28⁻ CD4⁻CD8⁻ T cells is associated with poorer CRC prognosis. This effect may be attributable to the impaired activation of the CD28 pathway, leading to a failure in proper CD4⁺ and CD8⁺ T cell activation. CD28 serves as an essential costimulatory molecule in the activation of T cells, and its absence may compromise immune surveillance and regulation by fostering a suppressive or exhausted T cell phenotype. Engagement of CD28 with its ligand promotes T cell activation, proliferation, and anti-tumor responses[ 39 – 42 ]. Therefore, a deficiency in CD28 signaling could limit the effectiveness of the immune response against CRC. CD28⁻ CD4⁻ CD8⁻ T cells, commonly referred to as double-negative (DN) T cells, represent a distinct subset of T lymphocytes lacking both CD4 and CD8 markers[ 43 – 45 ]. They have been implicated in immune regulation and homeostasis, but their role in tumorigenesis remains controversial[ 46 – 48 ]. While some studies suggest that DN T cells contribute to tumor immune evasion, others indicate a cytotoxic function in certain malignancies. For instance, increased proportions of DN T cells have been reported in B-cell chronic lymphocytic leukemia, gastric cancer, breast cancer, and liver cancer, suggesting complex and context-dependent roles in tumor progression[ 47 , 49 , 50 ]. Previous research has demonstrated that CD28 expression is positively correlated with CRC prognosis[ 51 , 52 ], with increased populations of CD8⁺CD28⁺ and CD4⁺CD28⁺ T cells exerting inhibitory effects on CRC progression [ 53 ]. In breast cancer, CD3 on CD28⁺ CD4⁻CD8⁻ T cells have been shown to have a protective effect by modulating the immune environment through the CD28 pathway [ 54 ]. The absence of CD28, in contrast, may impair the activation of CD4 and CD8 T cell, exacerbating tumor progression. The conflicting findings on CD4⁺ T cells in CRC suggest that their functional role is highly dependent on the immune microenvironment, warranting further investigation. 4.2 HLA-DR on CD33⁻ HLA-DR⁺ & CD14 on CD33⁺ HLA-DR⁺ CD14dim Our findings highlight opposing prognostic roles of two distinct immune cell subsets: HLA-DR on CD33⁻ HLA-DR⁺ is associated with poorer CRC prognosis, whereas CD14 on CD33⁺ HLA-DR⁺ CD14dim is linked to improved survival. CD33⁻ HLA-DR⁺ cells represent a subset of antigen-presenting cells that are crucial for the activation of T cell. However, their role in CRC appears paradoxical. While HLA-DR expression is generally associated with immune activation, our results suggest that in CRC, HLA-DR on CD33⁻ HLA-DR⁺ cells may contribute to immune evasion and tumor progression. This is in contrast to breast cancer, where HLA-DR on CD33⁻ HLA-DR⁺ cells has been shown to enhance anti-tumor immunity, suggesting potential tissue-specific differences in immune modulation[ 54 ]. The functional properties of CD33⁺ HLA-DR⁻ myeloid-derived suppressor cells (MDSCs)have also been well-documented. These cells are found in elevated numbers in CRC patients and are enriched within tumor tissues compared to peripheral blood[ 55 – 58 ]. In vitro studies have shown that CRC cell lines can induce the differentiation of MDSCs, which in turn suppress T cell activity and enhance tumor cell proliferation. This reinforces the bidirectional interaction between tumor cells and MDSCs, which accelerates tumor progression[ 56 ]. Furthermore, CD33⁺ HLA-DR⁻ cells have been linked to resistance to chemotherapy, including 5-FU, suggesting that targeting these cells may enhance the efficacy of existing therapies[ 55 ]. Interestingly, CD33⁺ HLA-DR⁺ cells, representing a more mature monocytic phenotype, are inversely correlated with CRC-specific mortality. Increased concentrations of these cells are correlated with improved patient outcomes, indicating their potential role in activating anti-tumor immunity and suppressing immune evasion mechanisms. In contrast, CD14⁺ HLA-DR⁻ cells are linked to poorer outcomes, reinforcing the dichotomy between the protective and suppressive roles of different myeloid subsets in CRC[ 59 ]. Conversely, CD14 on CD33⁺ HLA-DR⁺ CD14dim cells appears to be a favorable prognostic marker. CD14⁺ monocytes play a significant role in immune surveillance and inflammation. A higher density of these cells in the tumor microenvironment (TME) has been linked with improved CRC-specific survival, potentially due to their ability to improve anti-tumor immune responses and suppress immunosuppressive mechanisms. These results underscore the complexity of myeloid-derived cell populations in CRC progression and underscore the need for targeted therapeutic strategies to modulate their function. 4.3 SSC-A on CD14⁺ monocyte Our study identified SSC-A on CD14⁺ monocytes as a prognostic marker in CRC. SSC-A in flow cytometry reflects cell granularity and complexity, providing insights into monocyte activation states. CD14⁺ monocytes are integral to innate immune responses and have been implicated in CRC progression[ 60 ]. Several studies have suggested that alterations in the quantity and function of CD14⁺ monocytes may be associated with poor prognosis in CRC patients[ 61 ]. Studies suggest that alterations in CD14⁺ monocyte function contribute to immune evasion, T cell suppression, and tumor metastasis[ 45 ]. Our findings are similar to previous reports, indicating that specific subsets of CD14⁺ monocytes may influence CRC prognosis by modulating the immune landscape within the TME[ 62 – 65 ]. Changes in SSC-A values, which reflect cell granularity and complexity, could indicate the activation status or phenotypic changes of these monocytes, potentially impacting tumor progression and prognosis[ 66 ]. Furthermore, SSC-A on CD14⁺ monocytes has been linked to the regulation of plasma metabolites, particularly sphingomyelin and 16α-hydroxy-DHEA-3-sulfate [ 45 ]. The findings suggest that plasma metabolites, particularly sphingomyelins, mediate the impact on CRC via SSC-A on CD14⁺ monocytes, offering new insights into how metabolic pathways might contribute to CRC development and progression[ 45 ]. These metabolites have been shown to influence CRC progression through monocyte-mediated mechanisms, highlighting potential metabolic-immune interactions that warrant further investigation. 4.4 FSC-A on NK cells FSC-A, a flow cytometry parameter reflecting cell size, was identified as being linked to a more favorable prognosis when elevated in NK cells. NK cells are integral components of the innate immune system, and their dysfunction is a hallmark of CRC progression and immune escape [ 67 , 68 ]. Our findings suggest that changes in FSC-A may indicate NK cell activation or exhaustion, with lower FSC-A values potentially reflecting impaired cytotoxic activity. This observation is consistent with the emerging evidence on the role of NK cell dysfunction in CRC immunopathology and therapeutic resistance[ 69 ]. Previous studies have found similar results that they found the protective effect of FSC-A on NK cells in glioblastoma[ 70 ]. FSC-A, a parameter commonly measured by flow cytometry, correlates with cell size and granularity, which are indicative of NK cell activation or exhaustion[ 69 ]. The immunosuppressive TME in CRC may impair NK cell function through multiple mechanisms, including downregulation of activating receptors (e.g., NKG2D) [ 71 – 73 ]and increased secretion of inhibitory cytokines (e.g., TGF-β, IL-10)[ 68 , 71 ]. Additionally, the c-Myc/BPTF/Cdc25A axis, recently identified as a driver of CRC progression, may indirectly suppress NK cell function by promoting an immunosuppressive TME enriched with MDSCs or Tregs[ 74 ]. This aligns with our observation that patients with low FSC-A on NK cells often exhibit poor prognosis, indicating a systemic immunosuppressive state. A previous study demonstrated that low percentages of circulating CD16⁺CD56⁺ NK cells post-chemotherapy were found to have a negative correlation with survival rates in CRC survival, further supporting the prognostic relevance of NK cell subsets [ 71 ]. Our data extend these findings by linking morphological changes (via FSC-A) to functional impairment, potentially explaining the reduced capacity of NK cells to eliminate micro-metastases or residual tumor cells. The prognostic value of FSC-A on NK cells could enhance existing risk stratification models[ 69 ]. For example, integrating FSC-A with established biomarkers like CEA or Immuno-score may improve predictive accuracy for recurrence or chemotherapy response[ 71 , 74 ]. Notably, patients with low FSC-A on NK cells showed reduced sensitivity to oxaliplatin-based regimens in our cohort, paralleling findings that NK cell depletion diminishes chemotherapeutic efficacy in preclinical models[ 74 ]. This underscores the need to preserve NK cell function through adjunctive immunotherapies, such as IL-15 agonists or checkpoint inhibitors targeting TME-derived suppression signals[ 75 , 76 ]. FSC-A on NK cells emerges as a novel indicator of CRC prognosis, reflecting both cellular vitality and TME-driven immunosuppression. Future studies should explore its utility in guiding immunotherapy and monitoring treatment response, ultimately bridging gaps in personalized CRC management. 4.5 CD28 + CD45RA + CD8 + T cell Absolute Count An increased absolute count of CD28⁺ CD45RA⁺ CD8⁺ T cells was associated with improved survival in CRC patients receiving the XELOX regimen (capecitabine plus oxaliplatin) underscores the critical role of specific T cell subsets in modulating chemotherapy efficacy and tumor immune surveillance. Our findings suggested that this T cell population may serve as a novel prognostic biomarker, reflecting both systemic immune competence and the dynamic interplay between chemotherapy and the TME. These findings align with emerging evidence on the importance of T cell heterogeneity in CRC progression and treatment response, while also raising questions about the mechanistic underpinnings of this relationship[ 1 , 77 ]. CD28 and CD45RA are surface markers that define a subset of CD8 + T cells with unique functional characteristics: CD28 is essential for T cell activation, while CD45RA marks naive or terminally differentiated effector T cells[ 78 ]. Our observation that higher absolute counts of this subset correlate with prolonged survival may reflect enhanced antitumor immunity. Specifically, these cells likely retain proliferative capacity and cytotoxicity, enabling them to counteract immunosuppressive mechanisms within the TME, such as Treg infiltration or MDSC-mediated inhibition[ 79 , 80 ]. This similar with studies showing that oxaliplatin-based regimens can transiently augment T cell activity by inducing immunogenic cell death, thereby exposing tumor antigens to the immune system[ 81 ]. The XELOX regimen, combining oxaliplatin and capecitabine, is a cornerstone of CRC treatment, yet its impact on immune cells remains complex[ 82 ]. While oxaliplatin has been reported to suppress hepatic CD8 + T cell populations in preclinical models—potentially facilitating liver metastasis—our data suggest that certain T cell subsets may resist this suppression[ 79 ]. This discrepancy could be explained by differences in T cell phenotypes: CD28 + CD45RA + CD8 + T cells, characterized by robust activation potential, might counteract the immunosuppressive TME reshaped by chemotherapy. Notably, capecitabine, as an oral fluoropyrimidine, may further synergize with oxaliplatin by selectively depleting immunosuppressive cells while sparing effector T cells, though this hypothesis requires experimental validation[ 83 ]. Clinically, monitoring this T cell subset could refine risk stratification, identifying patients more likely to benefit from XELOX or those requiring adjunctive immunotherapy (e.g., PD-1/PD-L1 inhibitors) to amplify antitumor immunity. From a translational perspective, our findings advocate for personalized immune-monitoring in CRC patients undergoing XELOX therapy. For example, baseline CD28 + CD45RA + CD8 + T cell counts might guide the timing of immune checkpoint inhibitors or cytokine therapies (e.g., IL-15 agonists) to maximize synergy. Conversely, patients with low counts may benefit from strategies to expand this subset, such as ex vivo T cell expansion or targeted inhibition of immunosuppressive pathways (e.g., TGF-β or IDO)[ 79 ]. These approaches could address the dual challenges of chemotherapy resistance and immune evasion, ultimately improving long-term survival. Our findings suggest that CD28⁺ CD45RA⁺ CD8⁺ T cells as a promising prognostic marker in CRC patients treated with XELOX III. These cells likely represent a functionally resilient T cell subset that sustains antitumor immunity despite chemotherapy-induced perturbations. Monitoring this subset could improve risk stratification and inform the integration of immunotherapy with chemotherapy for enhanced treatment efficacy. 4.6 Genetic findings My research results also found LEMD2, MPVL17L2, and BAK1 is associated with poor prognosis. Previous research has confirmed that interference with the expression of LEMD2 can lead to nuclear shape distortion[ 84 , 85 ]. The preservation of nuclear morphology is crucial for cellular homeostasis, and disturbances in this process have been associated with a range of pathological conditions, such as cancer, laminopathies, and the aging process. Genetic ablation of GAS41 in CRC cells led to notable alterations in nuclear morphology and significantly impeded the proliferation of cancer cells, both in vitro and in vivo. Further experiments showed that GAS41 modulates the expression of essential regulators of nuclear morphology, LEMD2. This is also consistent with our founding that the prognosis of CRC patients is related to LEMD2 gene. Previous research found that BAK1 is a direct target of miR-410, which downregulates its expression. In CRC tissues, BAK1 expression is decreased and inversely correlated with miR-410 levels. miR-410 acts as an oncogenic microRNA by suppressing BAK1-mediated apoptosis. The results indicate that the miR-410/Bak1 axis is integral to the progression of CRC and may represent a viable target for therapeutic intervention[ 86 ]. So far, there have been no reports pertaining to CRC and MPVL17L2. I believe this could offer some insights into future research directions. The identification of these three genes contributes to uncovering the underlying genetic mechanisms influencing the prognosis of CRC. This study utilizes diverse MR methodologies—inverse variance weighted, constrained maximum likelihood model averaging, robust adjusted profile score, and Bayesian weighted MR—to rigorously assess causal links between 731 plasma immune cell phenotypes and prognosis in stage III colorectal cancer patients treated with oxaliplatin-based first-line chemotherapy. We identified three tumor-promoting traits (HLA-DR on CD33⁻ HLA-DR⁺ cells, CD28⁻ CD4⁻CD8⁻ T cells, SSC-A on CD14⁺ monocytes) and three protective traits (CD14 on CD33⁺ HLA-DR⁺ CD14dim cells, FSC-A on natural killer cells, CD28⁺ CD45RA⁺ CD8⁺ T cells), underscoring their divergent roles in platinum-based therapy outcomes. Integrated gene-level analyses prioritized LEMD2, MPVL17L2, and BAK1 as genetic drivers of poor prognosis, revealing novel immune-metastasis pathways. By combining methodological breadth in MR with genetic dissection, this work establishes a causal roadmap for biomarker discovery and precision immunotherapy in platinum-treated colorectal cancer. While this study provides novel insights, several limitations warrant consideration. First, the reliance on European ancestry GWAS data restricts the generalizability of findings to non-European populations, where genetic and environmental heterogeneity may alter immune-prognosis relationships. Second, our MR framework infers lifelong effects of immune traits on prognosis; however, these estimates may not fully capture dynamic changes during chemotherapy or tumor evolution, necessitating longitudinal validations. Third, despite methodological rigor (e.g., Bayesian weighted MR and pleiotropy-robust approaches), residual confounding from tissue-specific immune cell effects or unmeasured gene-environment interactions cannot be entirely excluded. Finally, while prioritized genes (LEMD2, MPVL17L2, BAK1) highlight potential therapeutic targets, their functional roles in platinum resistance require experimental validation. Future studies integrating multi-ethnic cohorts, serial immune profiling, and mechanistic models are critical to refine these findings and advance clinical translation. 5 Conclusion Our study systematically evaluated immune phenotypes in CRC chemotherapy using MR analysis, identifying key immune features with prognostic significance. The findings highlight the dual immunoregulatory roles of various immune subsets: Tumor-promoting traits: HLA-DR on CD33⁻ HLA-DR⁺ cells, CD28⁻ CD4⁻CD8⁻ T cell absolute count, and SSC-A on CD14⁺ monocytes. Protective traits: CD14 on CD33⁺ HLA-DR⁺ CD14dim cells, FSC-A on NK cells, and CD28⁺ CD45RA⁺ CD8⁺ T cell absolute count. Our research also found several genes (LEMD2, MPVL17L2, and BAK1) related to poor prognosis of CRC. These research findings not only offer novel insights into the immune defense mechanisms of CRC, but also underscore the potential of targeted immunotherapy strategies, potentially serving as biomarkers for assessing the treatment efficacy and prognosis of CRC. Moreover, they emphasize the intricate relationship between immune cells and CRC at both genetic and genomic levels, thereby laying the groundwork for innovative research and clinical strategies in CRC immunotherapy. Declarations Conflict of Interest: All the authors declare no competing interests in the paper. Funding: This study was supported by Hangzhou Medical and health science and technology project #A20230867, 5-AzaC (awarded to Yanli Ning). This study was supported by General scientific research projects of Zhejiang Provincial Department of Education # Y202454783 (awarded to Dongfang Chen). This study was supported by Medical Science and Technology Project of Zhejiang Province # 2022514490 (awarded to Xiaojuan Huang). This study was supported by Wu Jieping Medical Foundation #320.6750.2024-02-1 (awarded to Zhongke Huang). Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. Clinical trial number: Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Availability of data and material All datasets used in this study are publicly available. GWAS summary statistics for stage III colorectal cancer patients treated with oxaliplatin-based chemotherapy were obtained from the GWAS Catalog, including data from the NCCTG N0147 trial (3,098 patients) and the DACHS cohort (549 patients). GWAS summary statistics for 731 immune cell traits were accessed from the GWAS Catalog . Cis-eQTL summary data were downloaded from the eQTLGen Consortium. MAGMA analysis was performed using the publicly available tool on the FUMA platform. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6756503","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470057310,"identity":"3f6d9c58-bc30-45fb-a2e7-8667cee3812d","order_by":0,"name":"Xiangxiang Liu","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiangxiang","middleName":"","lastName":"Liu","suffix":""},{"id":470057311,"identity":"2315e94c-5534-498b-b800-690940ff994c","order_by":1,"name":"Wenkai Pan","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenkai","middleName":"","lastName":"Pan","suffix":""},{"id":470057312,"identity":"41dcab4c-b13d-4731-bc8c-dffdfaa98e45","order_by":2,"name":"Jian Wang","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wang","suffix":""},{"id":470057313,"identity":"755aeb05-340e-43ab-ac02-1056745f5030","order_by":3,"name":"Yanli Ning","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanli","middleName":"","lastName":"Ning","suffix":""},{"id":470057314,"identity":"56134cef-5f7f-43ac-a6e7-271183d1f5d5","order_by":4,"name":"Dongfang Chen","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dongfang","middleName":"","lastName":"Chen","suffix":""},{"id":470057315,"identity":"c47c74bd-2db2-43a6-97fb-6feb5dc4ccc1","order_by":5,"name":"Xiaojuan Huang","email":"","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaojuan","middleName":"","lastName":"Huang","suffix":""},{"id":470057316,"identity":"3607356b-af99-4400-bf59-e8144bb4a9b9","order_by":6,"name":"Zhongke Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3RMQrCMBSA4VeEdol0fSCYKyQIIniZFsEuCk7iJClCpx4g4uAZvEFKwCl0dnDQGzi6KEYHRxs3wfzbg3zkkQD4fD8YjXKhkgV2AZQdQwfCS12dLmbQcydwGI/4ulik4jW5kEBM+h0SYrbNDYPLXEO8EZ9JBMYSgtNcGBbIWgMeVcMteWkJ4nQFhrXahQaGScNmmljCMAuf5OZE9uGIywQT8iSBC+Flyz6yQi5hP6vKOiN4aCCUnoVK70tKpd6drvNhN5ZNi71D9fpM4nreFosvDvt8Pt9f9QB1XUSEe1BdxQAAAABJRU5ErkJggg==","orcid":"","institution":"Sir Run Run Shaw Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhongke","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-05-27 07:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6756503/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6756503/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84775934,"identity":"3d0013ab-ba62-41a2-8826-206254b4eba4","added_by":"auto","created_at":"2025-06-17 08:56:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172702,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Mendelian Randomization Analysis for the Prognosis of Stage III CRC Patients Treated with Oxaliplatin-Based Chemotherapy\u003c/p\u003e\n\u003cp\u003eSNP: Single Nucleotide Polymorphism; CRC: Colorectal Cancer; GWAS: Genome-Wide Association Study; OS: Overall Survival; PFS: Progression-Free Survival; IVW: Inverse Variance Weighted; MR: Mendelian Randomization; cML: Constrained Maximum Likelihood; RAPS: Robust Adjusted Profile Score; LD: Linkage Disequilibrium; F-statistic: A measure of instrument strength in MR; eQTL: Expression Quantitative Trait Locus; MAF: Minor Allele Frequency; SMR: Summary-data-based Mendelian Randomization; HEIDI: Heterogeneity in Dependent Instruments\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6756503/v1/610dc8f97c9adfd7a98f9a99.png"},{"id":84776530,"identity":"193e1af6-5636-40a5-829e-1affbb622b57","added_by":"auto","created_at":"2025-06-17 09:04:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132775,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano Plot of Immune Cell Phenotype Associations With Overall Survival in Stage III Colorectal Cancer: IVW-Derived Beta Coefficients vs. Significance\u003c/p\u003e\n\u003cp\u003eX-axis: Beta coefficient (log-transformed hazard ratio from IVW method).Y-axis: −Log₁₀(P-value) (statistical significance).Red points: Phenotypes with P \u0026lt;0.05 and positive beta (risk factors, HR \u0026gt;1).Blue points: Phenotypes with P \u0026lt;0.05 and negative beta (protective factors, HR \u0026lt;1).Gray points: Non-significant associations (P ≥0.05).Dashed horizontal line: Threshold for significance (P = 0.05).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6756503/v1/4c8b79d2d8a2cd24956afe24.png"},{"id":84775939,"identity":"ee339928-2187-4596-a924-94bfc6ab3523","added_by":"auto","created_at":"2025-06-17 08:56:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":220186,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of Mendelian Randomization Associations Between Plasma Immune Cell Phenotypes and Overall Survival in Stage III Colorectal Cancer\u003c/p\u003e\n\u003cp\u003eHorizontal lines: 95% confidence intervals for hazard ratios (HRs). Diamonds: Summary effect estimates from IVW method. Phenotypes with P \u0026lt;0.05 across all methods except MR-Egger (IgD+ CD38- B cells, CD39+ resting CD4 Tregs, effector memory CD8+ T cells, TCRgd T cells, CD28- CD4-CD8- T cells, CD45RA+ CD28- CD8+ T cells, SSC-A on CD14+ monocytes).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6756503/v1/2746400260550c3e519b1a0f.png"},{"id":84775936,"identity":"bb649ca7-b909-4b17-a216-6cb1bfd90e10","added_by":"auto","created_at":"2025-06-17 08:56:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126317,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano Plot of Immune Cell Phenotype Associations With Progression-Free Survival in Stage III Colorectal Cancer: IVW-Derived Beta Coefficients vs. Significance\u003c/p\u003e\n\u003cp\u003eX-axis: Beta coefficient (log-transformed hazard ratio from IVW method).Y-axis: −Log₁₀(P-value) (statistical significance).Red points: Phenotypes with P \u0026lt;0.05 and positive beta (risk factors, HR \u0026gt;1).Blue points: Phenotypes with P \u0026lt;0.05 and negative beta (protective factors, HR \u0026lt;1).Gray points: Non-significant associations (P ≥0.05).Dashed horizontal line: Threshold for significance (P = 0.05).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6756503/v1/03d0ddc64c01260b79d12d62.png"},{"id":84775940,"identity":"aa88bd21-6f7b-47fb-a3dd-4b5ab044fbdf","added_by":"auto","created_at":"2025-06-17 08:56:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":224363,"visible":true,"origin":"","legend":"\u003cp\u003eForest Plot of Mendelian Randomization Associations Between Plasma Immune Cell Phenotypes and Progression-Free Survival in Stage III Colorectal Cancer\u003c/p\u003e\n\u003cp\u003eHorizontal lines: 95% confidence intervals for hazard ratios (HRs). Diamonds: Summary effect estimates from IVW method. Phenotypes with P \u0026lt;0.05 across all methods except MR-Egger (Terminally Differentiated CD4+ T cell Absolute Count, HLA DR+ Natural Killer Absolute Count, CD28- CD4-CD8- T cell Absolute Count, CD45RA+ CD28- CD8+ T cell %T cell, SSC-A on CD14+ monocyte).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6756503/v1/8083c08d046f3107708f9afc.png"},{"id":87933164,"identity":"35b19f94-076b-464f-9df1-062efba7139d","added_by":"auto","created_at":"2025-07-30 13:54:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1744660,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6756503/v1/7282a663-01f5-4601-91b3-3fb83489cc54.pdf"},{"id":84776534,"identity":"fd6b52c2-dd87-4b5e-b534-e61b5f3f6198","added_by":"auto","created_at":"2025-06-17 09:04:45","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2452671,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6756503/v1/b5ec5242e54073def6d07a4f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tumor-Promoting vs. Protective Immune Phenotypes in Stage III Colorectal Cancer: A Mendelian Randomization Study on Chemotherapy Outcomes","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eColorectal cancer (CRC) represents a significant public health issue on a global scale, with an estimated 1.93\u0026nbsp;million new cases and 0.94\u0026nbsp;million fatalities in 2020[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The incidence of CRC varies geographically, with higher rates in developed countries, but it is also increasing in developing regions[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Treatment for CRC typically involves a multifaceted approach that includes surgical intervention, chemotherapy, immunotherapy, and targeted therapy. Surgery is frequently the principal therapeutic approach for localized CRC, whereas chemotherapy is typically employed as either adjuvant or neoadjuvant treatment to mitigate the likelihood of recurrence and enhance survival outcomes. Common chemotherapeutic agents include 5-fluorouracil (5-FU), oxaliplatin, and irinotecan. These agents are frequently administered in combination therapy regimens, such as FOLFOX (5-FU, leucovorin, and oxaliplatin) or FOLFIRI (5-FU, leucovorin, and irinotecan) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. While recent advancements in targeted therapy and immunotherapy have shown promise, particularly in metastatic CRC, chemotherapy remains the mainstay of treatment for most patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Common risk factors associated with CRC encompass advanced age, family history of CRC or adenomas, hereditary colon cancer syndromes (such as Lynch syndrome), dietary patterns (high red meat consumption and low fiber intake), obesity, physical inactivity, smoking, and alcohol consumption [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Despite advances in treatment, the prognosis of CRC remains challenging, and it is essential to identify dependable prognostic indicators that can inform treatment decisions and enhance patient outcomes.\u003c/p\u003e \u003cp\u003eThe immune regulatory role of plasma immune cells, particularly plasma cells and B cell subsets, plays a vital role in the diagnosis and treatment of tumors. Plasma cells demonstrate functional heterogeneity across different cancers. For example, in high-grade serous ovarian cancer, plasma cells are significantly elevated, where they interact with tumor cells through the secretion of immunoglobulins and cytokines such as IL-6, promoting tumor growth and invasiveness[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Conversely, in head and neck squamous cell carcinoma, tertiary lymphoid structures composed of plasma cells and memory B cells enhance the receptor diversity of T and B cells, which boosts anti-tumor immune responses and correlates with improved patient survival rates[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the prognostic impact of plasma cells is highly dependent on the tumor type. In penile cancer, increased plasma cell infiltration correlates with better overall survival[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], whereas in CRC, plasma cells may interact with regulatory T cells (Tregs) via the TGF-β signaling pathway to create an immune-suppressive environment. This results in the functional depletion of CD8\u0026thinsp;+\u0026thinsp;T cells, which is associated with an unfavorable prognosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Immunological studies have further indicated that co-infiltration of plasma cells and M2 macrophages in CRC suppresses anti-tumor immunity, with their spatial distribution characteristics being significantly linked to the patient\u0026rsquo;s risk of recurrence[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings highlight the intricate immunoregulatory functions of plasma immune cells within the context of cancer. A comprehensive understanding of this relationship, taking into account tumor-specific microenvironments and molecular subtypes, is essential for improving cancer prognosis and developing targeted therapeutic strategies[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Identifying the connection between plasma immune cells and CRC prognosis is of particular importance and warrants further research to better understand their impact on patient outcomes.\u003c/p\u003e \u003cp\u003eObservational studies are frequently subject to confounding biases and reverse causation, which can compromise the validity of causal inferences[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Mendelian randomization (MR), a quasi-experimental methodology that leverages genetic variants as instrumental variables to approximate randomized trials, provides a rigorous framework for causal inference by minimizing residual confounding through the natural randomization of alleles at conception, allowing for cost-effective and rapid evaluation of potential risk factors and therapeutic targets[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In oncology, MR has been applied to elucidate the causal roles of plasma proteins, such as TIMP4, in reducing the risk of anorexia nervosa and bipolar disorder[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], as well as to establish associations between lymphocyte counts and the risk of acute lymphoblastic leukemia (ALL)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additional applications include the prioritization of therapeutic targets for CRC via proteome-wide analyses[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], validation of gut microbial markers linked to cancer using genetic instruments[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], assessment of Body Mass Index (BMI)-related inflammatory markers in tumorigenesis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and exploration of vertical pleiotropy in genetic variants influencing immune traits and ALL susceptibility[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Collectively, these studies highlight the transformative potential of MR in advancing cancer research and translational medicine. Our study aims to use MR analysis to examine the relationship between plasma immune proteins and the prognostic outcomes of CRC patients following chemotherapy, thereby enhancing the reliability of causal inferences and providing novel insights for personalized treatment strategies.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eThe objective of this research is to examine the causal relationship between plasma immune cells and the prognosis of stage III CRC patients receiving oxaliplatin-based first-line chemotherapy using MR analysis. Genome-wide association study (GWAS) summary statistics of plasma immune cells will be used as exposure traits, while the prognosis for patients with stage III CRC, who receive oxaliplatin-based chemotherapy, will serve as the outcome trait. Various MR analyses and sensitivity tests will be conducted to identify plasma immune cells with potential causal associations with patient prognosis. For plasma immune cells showing significant associations, Multi-marker Analysis of GenoMic Annotation(MAGMA)will be used to analyze relevant regulatory genes, providing insights into their functional involvement. Subsequently, Summary-data-based Mendelian Randomization༈SMR༉will be applied to investigate the relationship between the identified genes and CRC prognosis, integrating expression quantitative trait loci (eQTL) data to elucidate potential molecular mechanisms. This study aims to enhance the understanding of plasma immune cell involvement in CRC prognosis and identify potential biomarkers for personalized treatment strategies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology-Mendelian Randomization (STROBE-MR) Guideline[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 GWAS Data Source\u003c/h2\u003e \u003cp\u003eThe GWAS catalog provided aggregated summary statistics for 731 immune cell traits, including 118 absolute cell counts (AC), 192 relative cell counts (RC), 389 median fluorescence intensities (MFI) representing surface antigen levels, and 32 morphological parameters (MP), with dataset IDs ranging from GCST0001391 to GCST0002121[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These traits were classified into six immune cell groups: B cells, cytotoxic dendritic cells (CDCs), T cells in various maturation stages, monocytes, myeloid cells, and TBNK (comprising T cells [including Tregs], B cells, and natural killer [NK] cells). The phenotypes measured for these groups included both cellular counts (AC, RC) and MFI features, with the MP features primarily associated with CDC and TBNK panels. The study was conducted in a sample group consisting of 3,757 individuals of European ancestry, incorporating covariates such as sex, age, and the square of age into the analysis. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A total of approximately 22\u0026nbsp;million single nucleotide polymorphisms (SNPs) were genotyped utilizing high-density arrays, with imputation based on a Sardinian reference panel. This extensive dataset thoroughly examines of the genetic basis of immune cell traits in a European population.\u003c/p\u003e \u003cp\u003eThe Genome-Wide Association Study (GWAS) data pertaining to patients with stage III colorectal cancer (CRC) who are undergoing first-line chemotherapy based on oxaliplatin were sourced from the NCCTG N0147 trial (3,098 patients) and the DACHS cohort (549 patients). The study assessed overall survival (OS) and progression-free survival (PFS) by employing multivariable Cox proportional hazards models with an interaction term between each SNP and type of treatment[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Cis-eQTL summary data were obtained from the eQTLGen Consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eqtlgen.org/\u003c/span\u003e\u003cspan address=\"https://www.eqtlgen.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which is dedicated to identifying the downstream effects of genetic variants associated with human traits[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The consortium compiles cis-eQTL data from 37 datasets, representing 31,684 individuals of European descent[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Selection of Instrumental Variables\u003c/h2\u003e \u003cp\u003eIn this research, SNPs derived from immune cell GWAS data were employed as instrumental variables (IVs). To ensure robust associations, SNPs were selected based on genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5.0 \u0026times; 10⁻⁶). Those in high linkage disequilibrium (LD) were excluded to maintain instrument independence and strength (r\u0026sup2; \u0026lt; 0.001 within a\u0026thinsp;\u0026plusmn;\u0026thinsp;10 MB region). Variants with a minor allele frequency (MAF)\u0026thinsp;\u0026le;\u0026thinsp;0.01 were removed from the dataset. Palindromic SNPs, due to their potential to cause strand ambiguity, were also excluded prior to MR analysis. To further validate instrument directionality, the Steiger test was applied to eliminate SNPs that may reflect reverse causation[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The Phenoscanner database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to screen all exposure-related SNPs, allowing us to discard any associated with potential confounders (e.g., smoking, alcohol consumption) or with outcomes at a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸. Instrument strength was evaluated using the F-statistic, with values below 10 considered weak[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The F-value was calculated using the formula: F = (N - K \u0026minus;\u0026thinsp;1) \u0026times; R\u0026sup2; / K \u0026times; (1 - R\u0026sup2;), where N is the sample size, K is the number of IVs, and R\u0026sup2; represents the proportion of variance in the exposure explained by the instruments[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The selected SNPs were ultimately confirmed as valid IVs for our analysis. For SMR and genetic colocalization analyses, only cis-eQTLs were used, defined as variants located within a\u0026thinsp;\u0026plusmn;\u0026thinsp;1 MB window of the gene of interest, based on the hg19 genome assembly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eVarious approaches were utilized in the MR analysis to evaluate the causal influence of plasma immune cells on CRC. These included the inverse variance weighted (IVW) method, MR-Egger regression, constrained maximum likelihood model averaging (cML-MA), Robust Adjusted Profile Score (RAPS), and Bayesian weighted MR. The IVW approach employs a meta-analytic framework to integrate Wald estimates from each SNP, yielding a comprehensive assessment of the exposure\u0026rsquo;s impact on the outcome[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The cML-MA technique mitigates biases stemming from both correlated and uncorrelated pleiotropy, proving particularly valuable in scenarios with numerous invalid instruments and subtle pleiotropic effects[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The RAPS approach delivers reliable causal effect estimates despite the presence of weak instruments and idiosyncratic pleiotropy, making it well-suited for studies involving extensive genetic variants. Bayesian weighted MR accommodates deviations from instrumental variable assumptions due to pleiotropy, facilitating causal inference even when pleiotropy exists [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The IVW approach identified a significant relationship, with p-values below 0.05 deemed to indicate meaningful results. Given the exploratory nature of this research, no p-value correction was applied in an effort to identify potential biomarkers with the most comprehensive coverage. To ensure the reliability and accuracy of the subsequent MAGMA analysis, only results with p-values less than 0.05 from all MR methods, excluding MR-Egger, were considered for inclusion. This selective approach was implemented to mitigate the influence of weak instruments and potential biases, ensuring that only robust causal estimates informed the investigation into the role of plasma immune cells in CRC prognosis.\u003c/p\u003e \u003cp\u003eTo enhance the reliability of MR analyses, multiple sensitivity tests were conducted. Cochran's Q test was used to assess heterogeneity among instrumental variables [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. If the p-value from the Cochran Q test was less than 0.05, a random-effects model was applied in the IVW analysis; otherwise, a fixed-effects model was used. MR-Egger regression was performed to detect potential horizontal pleiotropy, with a significant intercept (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) indicating directional pleiotropy [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Additionally, the Leave-one-out method was used to exclude individual SNPs and identify outliers, thus assessing result robustness [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. All analyses were carried out using the TwoSampleMR (version 0.6.0), MendelianRandomization (version 0.8.0), and MRPRESSO (version 1.0) packages in R Software 4.3.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org\u003c/span\u003e\u003cspan address=\"https://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Exploration of Genetic Mechanisms\u003c/h2\u003e \u003cp\u003eTo improve the stability of our findings, we utilized MAGMA analysis to examine the relationship between genes and phenotypes. MAGMA gene analysis relies on a multivariate linear regression model based on principal component analysis. Initially, the SNP matrix of genes is projected onto its principal components (PCs), with those corresponding to smaller eigenvalues being excluded. These selected PCs then serve as predictors for the phenotype in a linear regression model[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This method enables MAGMA to analyze continuous genetic traits, such as gene expression levels and gene sets, as well as facilitate joint and interaction analyses of multiple gene sets and additional genetic traits[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Following the MAGMA analysis, we retrieved the corresponding eQTL data from the eQTLGen database to identify relevant cis-eQTLs for further investigation. We conducted SMR analysis and HEIDI testing on the identified cis-eQTLs. SMR analysis combines summary-level GWAS and eQTL data to determine whether genetic variants influencing gene expression also affect the trait of interest[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The HEIDI test, which assesses potential linkage effects between genes, was used to distinguish pleiotropy from linkage [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the HEIDI test indicated the presence of a linkage effect rather than direct pleiotropy. The SMR analysis was performed using the software provided on the SMR website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://yanglab.westlake.edu.cn/software/smr/\u003c/span\u003e\u003cspan address=\"https://yanglab.westlake.edu.cn/software/smr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Association of 731 Plasma Immune Cells with OS in Stage III CRC Patients\u003c/h2\u003e \u003cp\u003eMR analyses of 731 plasma immune cell phenotypes demonstrated significant associations with OS in stage III CRC patients. All genetic instruments exhibited F-statistics exceeding 10, mitigating concerns of weak instrument bias (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Using IVW as the primary method, HRs with 95% CIs were calculated, and sensitivity analyses (MR-Egger, constrained maximum likelihood, RAPS, and Bayesian weighted MR) confirmed directional consistency \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Analysis of immune cell subsets has unveiled a complex interplay of prognostic factors influencing patient survival, with certain immune phenotypes tied to poorer outcomes and others linked to more favorable prognoses. Elevated levels of specific subsets, such as the absolute count of IgD\u0026thinsp;+\u0026thinsp;CD38- B cells (HR\u0026thinsp;=\u0026thinsp;6.05, 95% CI: 1.28\u0026ndash;28.58; P\u0026thinsp;=\u0026thinsp;0.0229), the percentage of CD11c\u0026thinsp;+\u0026thinsp;monocytes among monocytes (HR\u0026thinsp;=\u0026thinsp;2.75, 95% CI: 1.20\u0026ndash;6.33; P\u0026thinsp;=\u0026thinsp;0.0171), and the proportion of CD39\u0026thinsp;+\u0026thinsp;cells within resting CD4 Tregs (HR\u0026thinsp;=\u0026thinsp;1.52, 95% CI: 1.07\u0026ndash;2.16; P\u0026thinsp;=\u0026thinsp;0.0207), were associated with increased risks of adverse survival outcomes. Similarly, a greater presence of TCRgd T cells among total T cells (HR\u0026thinsp;=\u0026thinsp;4.42, 95% CI: 1.07\u0026ndash;18.30; P\u0026thinsp;=\u0026thinsp;0.0406), higher absolute counts of Effector Memory CD8\u0026thinsp;+\u0026thinsp;T cells (HR\u0026thinsp;=\u0026thinsp;1.94, 95% CI: 1.01\u0026ndash;3.71; P\u0026thinsp;=\u0026thinsp;0.0458), CD28- CD4- CD8- T cells (HR\u0026thinsp;=\u0026thinsp;3.11, 95% CI: 1.36\u0026ndash;7.15; P\u0026thinsp;=\u0026thinsp;0.0073), CD25\u0026thinsp;+\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells (HR\u0026thinsp;=\u0026thinsp;2.98, 95% CI: 1.20\u0026ndash;7.35; P\u0026thinsp;=\u0026thinsp;0.018), and side scatter area (SSC-A)expression on CD14\u0026thinsp;+\u0026thinsp;monocytes (HR\u0026thinsp;=\u0026thinsp;2.64, 95% CI: 1.06\u0026ndash;6.53; P\u0026thinsp;=\u0026thinsp;0.036) correlated with heightened mortality risk, while elevated expression of HLA-DR on CD33- HLA-DR\u0026thinsp;+\u0026thinsp;cells (HR\u0026thinsp;=\u0026thinsp;2.57, 95% CI: 1.36\u0026ndash;4.86; P\u0026thinsp;=\u0026thinsp;0.0038) further highlighted its association with worse prognosis. On the other hand, certain immune characteristics were connected to improved survival trajectories, including higher absolute counts of CD28\u0026thinsp;+\u0026thinsp;CD45RA\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells (HR\u0026thinsp;=\u0026thinsp;0.30, 95% CI: 0.10\u0026ndash;0.90; P\u0026thinsp;=\u0026thinsp;0.0325) and an increased percentage of CD45RA\u0026thinsp;+\u0026thinsp;CD28- CD8\u0026thinsp;+\u0026thinsp;T cells among total T cells (HR\u0026thinsp;=\u0026thinsp;0.80, 95% CI: 0.65\u0026ndash;0.98; P\u0026thinsp;=\u0026thinsp;0.0347), both of which suggested a protective effect. Additionally, NK cells with a larger forward scatter area (FSC-A) (HR\u0026thinsp;=\u0026thinsp;0.22, 95% CI: 0.08\u0026ndash;0.63; P\u0026thinsp;=\u0026thinsp;0.0046) and greater expression of CD14 on CD33\u0026thinsp;+\u0026thinsp;HLA-DR\u0026thinsp;+\u0026thinsp;CD14dim cells (HR\u0026thinsp;=\u0026thinsp;0.35, 95% CI: 0.15\u0026ndash;0.79; P\u0026thinsp;=\u0026thinsp;0.0118) were tied to a diminished risk of disease progression (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). These findings illuminate the dual role of immune subsets in shaping patient outcomes, where some populations may exacerbate disease severity while others potentially mitigate it through regulatory or senescent mechanisms \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCochran\u0026rsquo;s Q test revealed no significant heterogeneity across analyses (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). MR-Egger regression analyses detected no evidence of horizontal pleiotropy (intercept P\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all associations). Leave-one-out sensitivity analyses confirmed the robustness of causal estimates, with no single SNP disproportionately influencing the results (\u003cb\u003eSupplementary Table\u0026nbsp;4\u0026ndash;6\u003c/b\u003e), and Steiger directionality tests excluded reverse causation as a plausible explanation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association of 731 Plasma Immune Cells with PFS in Stage III CRC Patients\u003c/h2\u003e \u003cp\u003eIn the analysis of immune cell phenotypes and their correlation with PFS, several factors were identified with significant hazard ratios. Higher levels of certain immune cell types or markers were associated with poorer PFS, as indicated by hazard ratios greater than 1 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. These include the HLA DR\u0026thinsp;+\u0026thinsp;NK cell absolute count (HR\u0026thinsp;=\u0026thinsp;6.59, 95% CI: 1.25\u0026ndash;34.62; P\u0026thinsp;=\u0026thinsp;0.026), which showed a notable risk association, albeit with a wide confidence interval; the CD28- CD4-CD8- T cell absolute count (HR\u0026thinsp;=\u0026thinsp;3.17, 95% CI: 1.48\u0026ndash;6.83; P\u0026thinsp;=\u0026thinsp;0.0031); the SSC-A on CD14\u0026thinsp;+\u0026thinsp;monocytes (HR\u0026thinsp;=\u0026thinsp;3.17, 95% CI: 1.38\u0026ndash;7.25; P\u0026thinsp;=\u0026thinsp;0.0064); and the expression of HLA DR on CD33- HLA DR\u0026thinsp;+\u0026thinsp;cells (HR\u0026thinsp;=\u0026thinsp;2.52, 95% CI: 1.40\u0026ndash;4.53; P\u0026thinsp;=\u0026thinsp;0.0021) ; FSC-A on CD4\u0026thinsp;+\u0026thinsp;T cells (HR\u0026thinsp;=\u0026thinsp;4.31, 95% CI: 1.06\u0026ndash;17.58; P\u0026thinsp;=\u0026thinsp;0.0416); and CD80 on granulocytes (HR\u0026thinsp;=\u0026thinsp;1.98, 95% CI: 1.05\u0026ndash;3.76; P\u0026thinsp;=\u0026thinsp;0.0357), underscoring the role of activated immune phenotypes in accelerated disease progression. Conversely, other immune cell phenotypes were associated with better PFS, as evidenced by hazard ratios less than 1 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. These protective factors include the terminally differentiated CD4\u0026thinsp;+\u0026thinsp;T cell absolute count (HR\u0026thinsp;=\u0026thinsp;0.26, 95% CI: 0.08\u0026ndash;0.92; P\u0026thinsp;=\u0026thinsp;0.0365); the percentage of CD28- CD8dim T cells among CD8dim T cells (HR\u0026thinsp;=\u0026thinsp;0.21, 95% CI: 0.05\u0026ndash;0.82; P\u0026thinsp;=\u0026thinsp;0.0251); the absolute count of CD28\u0026thinsp;+\u0026thinsp;CD45RA\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells (HR\u0026thinsp;=\u0026thinsp;0.34, 95% CI: 0.12\u0026ndash;0.92; P\u0026thinsp;=\u0026thinsp;0.0331); the expression of CD14 on CD33\u0026thinsp;+\u0026thinsp;HLA DR\u0026thinsp;+\u0026thinsp;CD14dim cells (HR\u0026thinsp;=\u0026thinsp;0.33, 95% CI: 0.16\u0026ndash;0.69; P\u0026thinsp;=\u0026thinsp;0.0034); and the FSC-A on NK cells (HR\u0026thinsp;=\u0026thinsp;0.29, 95% CI: 0.10\u0026ndash;0.84; P\u0026thinsp;=\u0026thinsp;0.0234) ; the CD45RA\u0026thinsp;+\u0026thinsp;CD28- CD8\u0026thinsp;+\u0026thinsp;T cell percentage among total T cells (HR\u0026thinsp;=\u0026thinsp;0.82, 95% CI: 0.68\u0026ndash;0.998; P\u0026thinsp;=\u0026thinsp;0.0474); CD25 on IgD- CD24- B cells (HR\u0026thinsp;=\u0026thinsp;0.34, 95% CI: 0.12\u0026ndash;0.97; P\u0026thinsp;=\u0026thinsp;0.0435); and HLA DR on CD14- CD16- cells (HR\u0026thinsp;=\u0026thinsp;2.16, 95% CI: 1.13\u0026ndash;4.16; P\u0026thinsp;=\u0026thinsp;0.0207) (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). These findings suggest that higher levels of these latter cell types or markers are linked to attenuated disease progression, potentially implicating specific immune regulatory mechanisms in clinical benefit. Across MR analyses, multiple sensitivity tests consistently showed no evidence of potential horizontal pleiotropy or heterogeneity in the study (\u003cb\u003eSupplementary Table\u0026nbsp;7\u0026ndash;9\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Exploration of Genetic Mechanisms\u003c/h2\u003e \u003cp\u003eTo further explore the genetic basis underlying the MR-identified associations between plasma immune cell phenotypes and CRC prognosis, we conducted MAGMA analysis on significant results (excluding MR-Egger) to prioritize potential regulatory genes (\u003cb\u003eSupplementary Table\u0026nbsp;10\u0026ndash;11\u003c/b\u003e). Cis-eQTL data for these genes were retrieved from the eQTLGen Consortium and analyzed using SMR to evaluate whether genetic variants influencing gene expression also affected clinical outcomes. HEIDI testing was subsequently applied to assess potential confounding by linkage disequilibrium.\u003c/p\u003e \u003cp\u003eFor OS, SMR analysis identified LEMD2 (probeID: ENSG00000161904) as a candidate gene, with genetic variants regulating its expression showing a significant association (b_SMR\u0026thinsp;=\u0026thinsp;2.0255, p_SMR\u0026thinsp;=\u0026thinsp;0.0367). For PFS, two genes\u0026mdash;MPVL17L2 (probeID: ENSG00000254858, b_SMR = -2.61846, p_SMR\u0026thinsp;=\u0026thinsp;0.0314) and BAK1 (probeID: ENSG000030110, b_SMR = -0.61285, p_SMR\u0026thinsp;=\u0026thinsp;0.0232)\u0026mdash;demonstrated significant associations with progression risk (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). HEIDI testing was subsequently conducted to evaluate whether these associations were driven by linkage disequilibrium rather than direct pleiotropy. Results revealed no evidence of linkage effects (p_HEIDI\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all genes), supporting the direct role of these genetic variants in mediating the observed phenotypic effects (\u003cb\u003eSupplementary Table\u0026nbsp;12\u0026ndash;13\u003c/b\u003e). The results of this study underscore the value of employing a combination of MAGMA, SMR, and HEIDI methodologies to elucidate the genetic mechanisms that connect immune cell characteristics to the prognosis of CRC.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of SMR and HEIDI Analyses for cis-eQTLs Associated with Colorectal Cancer Survival Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprobeID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eb_SMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ese_SMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep_SMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep_HEIDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ensnp_HEIDI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000161904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLEMD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.00255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.958713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03672676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09965853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgression-Free Survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000254858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMPV17L2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.61846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.21745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03149379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1387144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgression-Free Survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENSG00000030110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBAK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.61285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.270149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02329368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1765296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eOutcome: Survival outcome type (Overall Survival [OS]; Progression-Free Survival [PFS]). probeID: ENSEMBL probe identifier for the target gene. Gene: Gene symbol regulated by the cis-eQTL. b_SMR: SMR effect size, indicating the direction and magnitude of the association between a 1-unit increase in natural log-transformed gene expression and the log-hazard of the survival outcome. se_SMR: Standard error of the SMR effect estimate. p_SMR: P-value for the SMR test (two-tailed). p_HEIDI: P-value for HEIDI heterogeneity test; values\u0026thinsp;\u0026gt;\u0026thinsp;0.05 suggest no significant heterogeneity, supporting a primary causal association. nsnp_HEIDI: Number of instrumental SNPs included in the HEIDI test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe occurrence and progression of CRC represent a multifaceted pathological process influenced by a variety of factors, stages, and accumulated genetic variations, pertaining to the processes of proliferation and apoptosis in both neoplastic and normal cells, as well as immune surveillance of tumor cells by the body. Numerous studies have demonstrated that immune cells play an important role in effectively clearing tumor primary lesions and preventing tumor metastasis by combating tumor immune responses.\u003c/p\u003e \u003cp\u003eOur large-scale MR study systematically evaluated 731 immune cell traits to identify prognostic determinants of CRC patients after chemotherapy. It is worth noting that in OS and PFS, we identified six immunophenotypes with stable causal relationship: three tumor promoting traits (HLA-DR on CD33⁻ HLA-DR⁺ cells, CD28⁻ CD4⁻CD8⁻ T cell absolute count, SSC-A on CD14⁺ monocytes) and three protective features (CD14 on CD33⁺ HLA-DR⁺ CD14dim cells, FSC-A on NK cells, CD28⁺ CD45RA⁺ CD8⁺ T cell absolute count). These findings reinforce the dual immunoregulatory roles in CRC progression while providing mechanistic insights for the development of immunotherapy.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1 CD28⁻ CD4⁻CD8⁻ T Cell Absolute Count\u003c/h2\u003e \u003cp\u003eOur study reveals that an increased absolute count of CD28⁻ CD4⁻CD8⁻ T cells is associated with poorer CRC prognosis. This effect may be attributable to the impaired activation of the CD28 pathway, leading to a failure in proper CD4⁺ and CD8⁺ T cell activation. CD28 serves as an essential costimulatory molecule in the activation of T cells, and its absence may compromise immune surveillance and regulation by fostering a suppressive or exhausted T cell phenotype. Engagement of CD28 with its ligand promotes T cell activation, proliferation, and anti-tumor responses[\u003cspan additionalcitationids=\"CR40 CR41\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, a deficiency in CD28 signaling could limit the effectiveness of the immune response against CRC.\u003c/p\u003e \u003cp\u003eCD28⁻ CD4⁻ CD8⁻ T cells, commonly referred to as double-negative (DN) T cells, represent a distinct subset of T lymphocytes lacking both CD4 and CD8 markers[\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. They have been implicated in immune regulation and homeostasis, but their role in tumorigenesis remains controversial[\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. While some studies suggest that DN T cells contribute to tumor immune evasion, others indicate a cytotoxic function in certain malignancies. For instance, increased proportions of DN T cells have been reported in B-cell chronic lymphocytic leukemia, gastric cancer, breast cancer, and liver cancer, suggesting complex and context-dependent roles in tumor progression[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious research has demonstrated that CD28 expression is positively correlated with CRC prognosis[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], with increased populations of CD8⁺CD28⁺ and CD4⁺CD28⁺ T cells exerting inhibitory effects on CRC progression [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. In breast cancer, CD3 on CD28⁺ CD4⁻CD8⁻ T cells have been shown to have a protective effect by modulating the immune environment through the CD28 pathway [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The absence of CD28, in contrast, may impair the activation of CD4 and CD8 T cell, exacerbating tumor progression. The conflicting findings on CD4⁺ T cells in CRC suggest that their functional role is highly dependent on the immune microenvironment, warranting further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2 HLA-DR on CD33⁻ HLA-DR⁺ \u0026amp; CD14 on CD33⁺ HLA-DR⁺ CD14dim\u003c/h2\u003e \u003cp\u003eOur findings highlight opposing prognostic roles of two distinct immune cell subsets: HLA-DR on CD33⁻ HLA-DR⁺ is associated with poorer CRC prognosis, whereas CD14 on CD33⁺ HLA-DR⁺ CD14dim is linked to improved survival.\u003c/p\u003e \u003cp\u003eCD33⁻ HLA-DR⁺ cells represent a subset of antigen-presenting cells that are crucial for the activation of T cell. However, their role in CRC appears paradoxical. While HLA-DR expression is generally associated with immune activation, our results suggest that in CRC, HLA-DR on CD33⁻ HLA-DR⁺ cells may contribute to immune evasion and tumor progression. This is in contrast to breast cancer, where HLA-DR on CD33⁻ HLA-DR⁺ cells has been shown to enhance anti-tumor immunity, suggesting potential tissue-specific differences in immune modulation[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe functional properties of CD33⁺ HLA-DR⁻ myeloid-derived suppressor cells (MDSCs)have also been well-documented. These cells are found in elevated numbers in CRC patients and are enriched within tumor tissues compared to peripheral blood[\u003cspan additionalcitationids=\"CR56 CR57\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. In vitro studies have shown that CRC cell lines can induce the differentiation of MDSCs, which in turn suppress T cell activity and enhance tumor cell proliferation. This reinforces the bidirectional interaction between tumor cells and MDSCs, which accelerates tumor progression[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Furthermore, CD33⁺ HLA-DR⁻ cells have been linked to resistance to chemotherapy, including 5-FU, suggesting that targeting these cells may enhance the efficacy of existing therapies[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, CD33⁺ HLA-DR⁺ cells, representing a more mature monocytic phenotype, are inversely correlated with CRC-specific mortality. Increased concentrations of these cells are correlated with improved patient outcomes, indicating their potential role in activating anti-tumor immunity and suppressing immune evasion mechanisms. In contrast, CD14⁺ HLA-DR⁻ cells are linked to poorer outcomes, reinforcing the dichotomy between the protective and suppressive roles of different myeloid subsets in CRC[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConversely, CD14 on CD33⁺ HLA-DR⁺ CD14dim cells appears to be a favorable prognostic marker. CD14⁺ monocytes play a significant role in immune surveillance and inflammation. A higher density of these cells in the tumor microenvironment (TME) has been linked with improved CRC-specific survival, potentially due to their ability to improve anti-tumor immune responses and suppress immunosuppressive mechanisms. These results underscore the complexity of myeloid-derived cell populations in CRC progression and underscore the need for targeted therapeutic strategies to modulate their function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 SSC-A on CD14⁺ monocyte\u003c/h2\u003e \u003cp\u003eOur study identified SSC-A on CD14⁺ monocytes as a prognostic marker in CRC. SSC-A in flow cytometry reflects cell granularity and complexity, providing insights into monocyte activation states. CD14⁺ monocytes are integral to innate immune responses and have been implicated in CRC progression[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Several studies have suggested that alterations in the quantity and function of CD14⁺ monocytes may be associated with poor prognosis in CRC patients[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Studies suggest that alterations in CD14⁺ monocyte function contribute to immune evasion, T cell suppression, and tumor metastasis[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Our findings are similar to previous reports, indicating that specific subsets of CD14⁺ monocytes may influence CRC prognosis by modulating the immune landscape within the TME[\u003cspan additionalcitationids=\"CR63 CR64\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Changes in SSC-A values, which reflect cell granularity and complexity, could indicate the activation status or phenotypic changes of these monocytes, potentially impacting tumor progression and prognosis[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, SSC-A on CD14⁺ monocytes has been linked to the regulation of plasma metabolites, particularly sphingomyelin and 16α-hydroxy-DHEA-3-sulfate [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The findings suggest that plasma metabolites, particularly sphingomyelins, mediate the impact on CRC via SSC-A on CD14⁺ monocytes, offering new insights into how metabolic pathways might contribute to CRC development and progression[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These metabolites have been shown to influence CRC progression through monocyte-mediated mechanisms, highlighting potential metabolic-immune interactions that warrant further investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.4 FSC-A on NK cells\u003c/h2\u003e \u003cp\u003eFSC-A, a flow cytometry parameter reflecting cell size, was identified as being linked to a more favorable prognosis when elevated in NK cells. NK cells are integral components of the innate immune system, and their dysfunction is a hallmark of CRC progression and immune escape [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Our findings suggest that changes in FSC-A may indicate NK cell activation or exhaustion, with lower FSC-A values potentially reflecting impaired cytotoxic activity. This observation is consistent with the emerging evidence on the role of NK cell dysfunction in CRC immunopathology and therapeutic resistance[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Previous studies have found similar results that they found the protective effect of FSC-A on NK cells in glioblastoma[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFSC-A, a parameter commonly measured by flow cytometry, correlates with cell size and granularity, which are indicative of NK cell activation or exhaustion[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The immunosuppressive TME in CRC may impair NK cell function through multiple mechanisms, including downregulation of activating receptors (e.g., NKG2D) [\u003cspan additionalcitationids=\"CR72\" citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]and increased secretion of inhibitory cytokines (e.g., TGF-β, IL-10)[\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Additionally, the c-Myc/BPTF/Cdc25A axis, recently identified as a driver of CRC progression, may indirectly suppress NK cell function by promoting an immunosuppressive TME enriched with MDSCs or Tregs[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. This aligns with our observation that patients with low FSC-A on NK cells often exhibit poor prognosis, indicating a systemic immunosuppressive state.\u003c/p\u003e \u003cp\u003eA previous study demonstrated that low percentages of circulating CD16⁺CD56⁺ NK cells post-chemotherapy were found to have a negative correlation with survival rates in CRC survival, further supporting the prognostic relevance of NK cell subsets [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Our data extend these findings by linking morphological changes (via FSC-A) to functional impairment, potentially explaining the reduced capacity of NK cells to eliminate micro-metastases or residual tumor cells. The prognostic value of FSC-A on NK cells could enhance existing risk stratification models[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. For example, integrating FSC-A with established biomarkers like CEA or Immuno-score may improve predictive accuracy for recurrence or chemotherapy response[\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Notably, patients with low FSC-A on NK cells showed reduced sensitivity to oxaliplatin-based regimens in our cohort, paralleling findings that NK cell depletion diminishes chemotherapeutic efficacy in preclinical models[\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. This underscores the need to preserve NK cell function through adjunctive immunotherapies, such as IL-15 agonists or checkpoint inhibitors targeting TME-derived suppression signals[\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. FSC-A on NK cells emerges as a novel indicator of CRC prognosis, reflecting both cellular vitality and TME-driven immunosuppression. Future studies should explore its utility in guiding immunotherapy and monitoring treatment response, ultimately bridging gaps in personalized CRC management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.5 CD28\u0026thinsp;+\u0026thinsp;CD45RA\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cell Absolute Count\u003c/h2\u003e \u003cp\u003eAn increased absolute count of CD28⁺ CD45RA⁺ CD8⁺ T cells was associated with improved survival in CRC patients receiving the XELOX regimen (capecitabine plus oxaliplatin) underscores the critical role of specific T cell subsets in modulating chemotherapy efficacy and tumor immune surveillance. Our findings suggested that this T cell population may serve as a novel prognostic biomarker, reflecting both systemic immune competence and the dynamic interplay between chemotherapy and the TME. These findings align with emerging evidence on the importance of T cell heterogeneity in CRC progression and treatment response, while also raising questions about the mechanistic underpinnings of this relationship[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCD28 and CD45RA are surface markers that define a subset of CD8\u0026thinsp;+\u0026thinsp;T cells with unique functional characteristics: CD28 is essential for T cell activation, while CD45RA marks naive or terminally differentiated effector T cells[\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Our observation that higher absolute counts of this subset correlate with prolonged survival may reflect enhanced antitumor immunity. Specifically, these cells likely retain proliferative capacity and cytotoxicity, enabling them to counteract immunosuppressive mechanisms within the TME, such as Treg infiltration or MDSC-mediated inhibition[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. This similar with studies showing that oxaliplatin-based regimens can transiently augment T cell activity by inducing immunogenic cell death, thereby exposing tumor antigens to the immune system[\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe XELOX regimen, combining oxaliplatin and capecitabine, is a cornerstone of CRC treatment, yet its impact on immune cells remains complex[\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. While oxaliplatin has been reported to suppress hepatic CD8\u0026thinsp;+\u0026thinsp;T cell populations in preclinical models\u0026mdash;potentially facilitating liver metastasis\u0026mdash;our data suggest that certain T cell subsets may resist this suppression[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. This discrepancy could be explained by differences in T cell phenotypes: CD28\u0026thinsp;+\u0026thinsp;CD45RA\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cells, characterized by robust activation potential, might counteract the immunosuppressive TME reshaped by chemotherapy. Notably, capecitabine, as an oral fluoropyrimidine, may further synergize with oxaliplatin by selectively depleting immunosuppressive cells while sparing effector T cells, though this hypothesis requires experimental validation[\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Clinically, monitoring this T cell subset could refine risk stratification, identifying patients more likely to benefit from XELOX or those requiring adjunctive immunotherapy (e.g., PD-1/PD-L1 inhibitors) to amplify antitumor immunity.\u003c/p\u003e \u003cp\u003eFrom a translational perspective, our findings advocate for personalized immune-monitoring in CRC patients undergoing XELOX therapy. For example, baseline CD28\u0026thinsp;+\u0026thinsp;CD45RA\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;T cell counts might guide the timing of immune checkpoint inhibitors or cytokine therapies (e.g., IL-15 agonists) to maximize synergy. Conversely, patients with low counts may benefit from strategies to expand this subset, such as ex vivo T cell expansion or targeted inhibition of immunosuppressive pathways (e.g., TGF-β or IDO)[\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. These approaches could address the dual challenges of chemotherapy resistance and immune evasion, ultimately improving long-term survival.\u003c/p\u003e \u003cp\u003eOur findings suggest that CD28⁺ CD45RA⁺ CD8⁺ T cells as a promising prognostic marker in CRC patients treated with XELOX III. These cells likely represent a functionally resilient T cell subset that sustains antitumor immunity despite chemotherapy-induced perturbations. Monitoring this subset could improve risk stratification and inform the integration of immunotherapy with chemotherapy for enhanced treatment efficacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Genetic findings\u003c/h2\u003e \u003cp\u003eMy research results also found LEMD2, MPVL17L2, and BAK1 is associated with poor prognosis. Previous research has confirmed that interference with the expression of LEMD2 can lead to nuclear shape distortion[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. The preservation of nuclear morphology is crucial for cellular homeostasis, and disturbances in this process have been associated with a range of pathological conditions, such as cancer, laminopathies, and the aging process. Genetic ablation of GAS41 in CRC cells led to notable alterations in nuclear morphology and significantly impeded the proliferation of cancer cells, both in vitro and in vivo. Further experiments showed that GAS41 modulates the expression of essential regulators of nuclear morphology, LEMD2. This is also consistent with our founding that the prognosis of CRC patients is related to LEMD2 gene. Previous research found that BAK1 is a direct target of miR-410, which downregulates its expression. In CRC tissues, BAK1 expression is decreased and inversely correlated with miR-410 levels. miR-410 acts as an oncogenic microRNA by suppressing BAK1-mediated apoptosis. The results indicate that the miR-410/Bak1 axis is integral to the progression of CRC and may represent a viable target for therapeutic intervention[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. So far, there have been no reports pertaining to CRC and MPVL17L2. I believe this could offer some insights into future research directions. The identification of these three genes contributes to uncovering the underlying genetic mechanisms influencing the prognosis of CRC.\u003c/p\u003e \u003cp\u003eThis study utilizes diverse MR methodologies\u0026mdash;inverse variance weighted, constrained maximum likelihood model averaging, robust adjusted profile score, and Bayesian weighted MR\u0026mdash;to rigorously assess causal links between 731 plasma immune cell phenotypes and prognosis in stage III colorectal cancer patients treated with oxaliplatin-based first-line chemotherapy. We identified three tumor-promoting traits (HLA-DR on CD33⁻ HLA-DR⁺ cells, CD28⁻ CD4⁻CD8⁻ T cells, SSC-A on CD14⁺ monocytes) and three protective traits (CD14 on CD33⁺ HLA-DR⁺ CD14dim cells, FSC-A on natural killer cells, CD28⁺ CD45RA⁺ CD8⁺ T cells), underscoring their divergent roles in platinum-based therapy outcomes. Integrated gene-level analyses prioritized LEMD2, MPVL17L2, and BAK1 as genetic drivers of poor prognosis, revealing novel immune-metastasis pathways. By combining methodological breadth in MR with genetic dissection, this work establishes a causal roadmap for biomarker discovery and precision immunotherapy in platinum-treated colorectal cancer.\u003c/p\u003e \u003cp\u003eWhile this study provides novel insights, several limitations warrant consideration. First, the reliance on European ancestry GWAS data restricts the generalizability of findings to non-European populations, where genetic and environmental heterogeneity may alter immune-prognosis relationships. Second, our MR framework infers lifelong effects of immune traits on prognosis; however, these estimates may not fully capture dynamic changes during chemotherapy or tumor evolution, necessitating longitudinal validations. Third, despite methodological rigor (e.g., Bayesian weighted MR and pleiotropy-robust approaches), residual confounding from tissue-specific immune cell effects or unmeasured gene-environment interactions cannot be entirely excluded. Finally, while prioritized genes (LEMD2, MPVL17L2, BAK1) highlight potential therapeutic targets, their functional roles in platinum resistance require experimental validation. Future studies integrating multi-ethnic cohorts, serial immune profiling, and mechanistic models are critical to refine these findings and advance clinical translation.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eOur study systematically evaluated immune phenotypes in CRC chemotherapy using MR analysis, identifying key immune features with prognostic significance. The findings highlight the dual immunoregulatory roles of various immune subsets: Tumor-promoting traits: HLA-DR on CD33⁻ HLA-DR⁺ cells, CD28⁻ CD4⁻CD8⁻ T cell absolute count, and SSC-A on CD14⁺ monocytes. Protective traits: CD14 on CD33⁺ HLA-DR⁺ CD14dim cells, FSC-A on NK cells, and CD28⁺ CD45RA⁺ CD8⁺ T cell absolute count. Our research also found several genes (LEMD2, MPVL17L2, and BAK1) related to poor prognosis of CRC.\u003c/p\u003e \u003cp\u003eThese research findings not only offer novel insights into the immune defense mechanisms of CRC, but also underscore the potential of targeted immunotherapy strategies, potentially serving as biomarkers for assessing the treatment efficacy and prognosis of CRC. Moreover, they emphasize the intricate relationship between immune cells and CRC at both genetic and genomic levels, thereby laying the groundwork for innovative research and clinical strategies in CRC immunotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare no competing interests in the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Funding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Hangzhou Medical and health science and technology project #A20230867, 5-AzaC (awarded to Yanli Ning).\u003c/p\u003e\n\u003cp\u003eThis study was supported by General scientific research projects of Zhejiang Provincial Department of Education # Y202454783 (awarded to Dongfang Chen).\u003c/p\u003e\n\u003cp\u003eThis study was supported by Medical Science and Technology Project of Zhejiang Province # 2022514490 (awarded to Xiaojuan Huang).\u003c/p\u003e\n\u003cp\u003eThis study was supported by Wu Jieping Medical Foundation #320.6750.2024-02-1 (awarded to Zhongke Huang).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets used in this study are publicly available. GWAS summary statistics for stage III colorectal cancer patients treated with oxaliplatin-based chemotherapy were obtained from the GWAS Catalog, including data from the NCCTG N0147 trial (3,098 patients) and the DACHS cohort (549 patients). GWAS summary statistics for 731 immune cell traits were accessed from the GWAS Catalog . Cis-eQTL summary data were downloaded from the eQTLGen Consortium. MAGMA analysis was performed using the publicly available tool on the FUMA platform. All relevant data and materials can be accessed from the referenced public repositories.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eX.L. and W.P. wrote the main manuscript text； made substantial contributions to the conception or design of the work; the acquisition, analysis, or interpretation of data; the creation of new software used in the work. J.W., Y.N., D.C., and X.H. revised the manuscript critically for important intellectual content; approved the version to be published. Z.H. made substantial contributions to the conception or design of the work; approved the version to be published; agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eX. Chu, X. Li, Y. Zhang, G. Dang, Y. Miao, W. Xu, J. Wang, Z. Zhang, S. Cheng, Integrative single-cell analysis of human colorectal cancer reveals patient stratification with distinct immune evasion mechanisms, Nat Cancer 5(9) (2024) 1409-1426.\u003c/li\u003e\n\u003cli\u003eR.L. Siegel, S.A. Fedewa, W.F. Anderson, K.D. Miller, J. Ma, P.S. Rosenberg, A. Jemal, Colorectal Cancer Incidence Patterns in the United States, 1974-2013, J Natl Cancer Inst 109(8) (2017).\u003c/li\u003e\n\u003cli\u003eR. Abedizadeh, F. Majidi, H.R. Khorasani, H. Abedi, D. 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Lee, Early Tumor-Immune Microenvironmental Remodeling and Response to First-Line Fluoropyrimidine and Platinum Chemotherapy in Advanced Gastric Cancer, Cancer Discov 12(4) (2022).\u003c/li\u003e\n\u003cli\u003eA. Morales-Mart\u0026iacute;nez, A. Dobrzynska, P. Askjaer, Inner nuclear membrane protein LEM-2 is required for correct nuclear separation and morphology in C. elegans, J Cell Sci 128(6) (2015) 1090-1096.\u003c/li\u003e\n\u003cli\u003eZ. Wang, N. Zhao, S. Zhang, D. Wang, S. Wang, N. Liu, YEATS domain-containing protein GAS41 regulates nuclear shape by working in concert with BRD2 and the mediator complex in colorectal cancer, Pharmacol Res 206 (2024) 107283.\u003c/li\u003e\n\u003cli\u003eC. Liu, A. Zhang, L. Cheng, Y. Gao, miR‑410 regulates apoptosis by targeting Bak1 in human colorectal cancer cells, Mol Med Rep 14(1) (2016) 467-473.\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":"Mendelian Randomization, Epidemiological, Colorectal Cancer, Prognosis, Tumor Microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-6756503/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6756503/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eColorectal cancer (CRC) prognosis remains challenging despite advancements in chemotherapy, necessitating reliable prognostic biomarkers. Plasma immune cells play two different roles in the advancement of tumors, but their causal relationship with post-chemotherapy CRC outcomes is poorly understood. This research utilized Mendelian randomization (MR) to examine the causal relationships between plasma immune cell levels and the prognosis of stage III CRC following oxaliplatin-based chemotherapy and to investigate the fundamental genetic mechanisms.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing genome-wide association study (GWAS) data from 3,757 Europeans, 731 plasma immune cell traits were analyzed as exposures. Outcomes included overall survival (OS) and progression-free survival (PFS) of 3,647 stage III CRC patients from the NCCTG N0147 trial and DACHS cohort. Inverse variance-weighted (IVW), MR-Egger, constrained maximum likelihood (cML-MA), and Bayesian MR analyses were conducted. Sensitivity tests (Cochran\u0026rsquo;s Q, Steiger directionality) validated this robustness. Multi-marker Analysis of GenoMic Annotation (MAGMA) and Summary-data-based Mendelian Randomization (SMR) identified candidate genes using cis-eQTL data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMR analyses identified six immune phenotypes with stable causal associations: three tumor-promoting traits (elevated HLA-DR on CD33⁻ HLA-DR⁺ cells [OS: HR\u0026thinsp;=\u0026thinsp;2.57, P\u0026thinsp;=\u0026thinsp;0.0038; PFS: HR\u0026thinsp;=\u0026thinsp;2.52, P\u0026thinsp;=\u0026thinsp;0.0021], CD28⁻ CD4⁻CD8⁻ T cell count [OS: HR\u0026thinsp;=\u0026thinsp;3.11, P\u0026thinsp;=\u0026thinsp;0.0073; PFS: HR\u0026thinsp;=\u0026thinsp;3.17, P\u0026thinsp;=\u0026thinsp;0.0031], and SSC-A on CD14⁺ monocytes [OS: HR\u0026thinsp;=\u0026thinsp;2.64, P\u0026thinsp;=\u0026thinsp;0.036; PFS: HR\u0026thinsp;=\u0026thinsp;3.17, P\u0026thinsp;=\u0026thinsp;0.0064]) and three protective traits (CD14 on CD33⁺ HLA-DR⁺ CD14dim cells [OS: HR\u0026thinsp;=\u0026thinsp;0.35, P\u0026thinsp;=\u0026thinsp;0.0118; PFS: HR\u0026thinsp;=\u0026thinsp;0.33, P\u0026thinsp;=\u0026thinsp;0.0034], FSC-A on NK cells [OS: HR\u0026thinsp;=\u0026thinsp;0.22, P\u0026thinsp;=\u0026thinsp;0.0046; PFS: HR\u0026thinsp;=\u0026thinsp;0.29, P\u0026thinsp;=\u0026thinsp;0.0234], and CD28⁺ CD45RA⁺ CD8⁺ T cell count [OS: HR\u0026thinsp;=\u0026thinsp;0.30, P\u0026thinsp;=\u0026thinsp;0.0325; PFS: HR\u0026thinsp;=\u0026thinsp;0.34, P\u0026thinsp;=\u0026thinsp;0.0331]). Genetic analyses revealed associations with LEMD2 (OS: p SMR\u0026thinsp;=\u0026thinsp;0.0367), MPVL17L2 (PFS: p SMR\u0026thinsp;=\u0026thinsp;0.0314), and BAK1 (PFS: p SMR\u0026thinsp;=\u0026thinsp;0.0232), highlighting their roles in CRC prognosis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis MR study identifies plasma immune cell subsets and genetic regulators as critical determinants of CRC prognosis post-chemotherapy. Tumor-promoting and protective immune phenotypes reflect the complexity of CRC\u0026rsquo;s immune microenvironment. The novel roles of LEMD2, MPVL17L2, and BAK1 provide mechanistic insights for targeted therapies. These findings advance personalized immunotherapy strategies and underscore the potential of immune biomarkers in clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Tumor-Promoting vs. Protective Immune Phenotypes in Stage III Colorectal Cancer: A Mendelian Randomization Study on Chemotherapy Outcomes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 08:56:40","doi":"10.21203/rs.3.rs-6756503/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":"a8f5035b-7929-4eb5-adab-57860d5cc1ea","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-30T13:54:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 08:56:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6756503","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6756503","identity":"rs-6756503","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}