The Role of Immune Cells in Colorectal Cancer: A Mendelian Randomization Study and Validation in A Single-Center Case-Control Trial.

doi:10.21203/rs.3.rs-4959403/v1

Download PDF

Article

The Role of Immune Cells in Colorectal Cancer: A Mendelian Randomization Study and Validation in A Single-Center Case-Control Trial.

https://doi.org/10.21203/rs.3.rs-4959403/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: Although a growing body of research suggests that alterations in the composition of the immune system play a critical role in the development of colorectal cancer (CRC), the causal and prognostic relationship between specific immune cells and the risk of CRC development remains unclear.

Method: In this study, Mendelian randomization (MR) was employed to investigate the causal relationship between immunophenotypes and colorectal cancer. To explore the potential associations, five MR methods were applied: Inverse Variance Weighting (IVW), MR-Egger, Weighted Median, Weighted Mode, and Simple mode. If the results of the five methods are inconclusive, we favored the IVW as the primary outcome. In addition, heterogeneity and pleiotropy were tested using MR-Egger, MR-PRESSO-Global, and Cochrane's Q. Stability of MR findings was assessed using leave-one-out approach, and the strength of the causal relationship between exposure and outcome was tested using the Bonferroni correction. Additional single-center clinical case-control samples were utilized to validate the results of Mendelian randomization, and prognostic results were visualized by logistic regression models, COX regression models, and Kaplan-Meier survival curves.

Result: Among 731 immunophenotypes were tested, 26 immunophenotypes were associated with CRC risk. The Bonferroni-corrected testing reveals that Lymphocyte %leukocyte and CD3 on CM CD8^br had a significant causal association with CRC. According to Cochrane's Q test, there was no significant heterogeneity across different single-nucleotide polymorphisms. Furthermore, the MR-Egger and MR-PRESSO-Global tests did not show pleiotropy. There was no reverse causality between the CRC risk and immunophenotypes. In the single-center clinical case-control study demonstrated a positive causal relationship between the relative counts of lymphocytes and CD4⁺T cells and the risk of CRC development. Furthermore, our correlation analysis also demonstrated a potential association between lymphocyte relative counts and poor prognosis in CRC cases.

Conclusion: Through MR analysis, we established a causal relationship between lymphocytes and maturation stages of T cell in the risk of CRC development. Additionally, case-control studies focusing on circulating lymphocytes and their subpopulations have further validated that these cells are integral to both the pathogenesis and prognosis of CRC. This finding may provide valuable ideas for early, noninvasive detection and potential immunotherapeutic targets for CRC.

Biological sciences/Cancer

Biological sciences/Cell biology

Biological sciences/Genetics

Biological sciences/Immunology

Biological sciences/Molecular biology

Health sciences/Gastroenterology

immunophenotypes

mendelian randomization

case-control trial

colorectal cancer

immunotherapy

Colorectal cancer interacts with immune cells in the tumor microenvironment and circulation during its development(1). The immune system plays a crucial role in recognizing and destroying cancer cells, but tumors also develop mechanisms to evade immune surveillance, according to the cancer immunoediting doctrine(2). During the growth and proliferation of mutant intestinal epithelial cells, some of the more immunogenic cells are recognized and eliminated by specific immune cells of the body. However, some mutant cells may evade recognition and elimination by the immune system through various mechanisms, resulting in the formation of colorectal cancer cells (3, 4).

To explore the potential link between immune cells and colorectal cancer broadly, we examined genome-wide association analysis (GWAS) data of 731 immunophenotypes and colorectal cancer from UK Biobank(5). Additionally, we established a clinical case-control study at a single center for cohort validation (Fig. 1). The latter comprises clinicopathologic features and prognosis of patients with colorectal cancer. While the relationship between immune cells and cancer in multiple studies have explored, this work advances the field by integrating positive phenotypes obtained from the genetic level and analyzing them for clinical prognosis.

Employing MR methods for dissecting the causal relationship between immunophenotypes and CRC accounting for removing confounders, we illuminated the association between immune cell counting criteria and CRC, explored the pathologic features related to their functions, and associated it with clinical prognostic results. Despite many differences across the 731 immunophenotypes, our analysis revealed 2 statistically significant immunophenotypes that were validated by clinical cohorts.

This indicates multifunctional immune cells play different roles in CRC development. The large sample size of pooled immunophenotypes allowed us to detect a large number of genomic associations of colorectal cancer with each immune cells. Coupling the clinical cohort data with rigorous mathematical statistical analysis tools, we characterized the variability of immune subtypes in different tumor pathologic features, revealing known and potential predictive targets.

Additionally, two methods of prognostic regression analysis were applied to cohort survival data, revealing a correlation between immunophenotype and direct visualization of Disease-free survival. Our work advances the understanding of the diverse immune activation and evasion strategies employed by CRC, casting light on potential immunotherapy strategies.

2.1 MR Study Design

Throughout its duration, the study was conducted in strict adherence to the STROBE-MR guidelines(6) (STROBE-MR checklist of Mendelian randomization studies detailed in Supplementary STROBE-MR-checklist). Two-sample Mendelian Randomization was used to examine the influence of risk between immunophenotypes and CRC. Location of single nucleotide polymorphism (SNP) from the database of GWAS were employed as instrumental variables. A total of 731 immune cell characteristics (7 panels) were defined as exposure factors in relation to the occurrence of CRC, constituting the study outcome. The analysis using MR adheres to three foundational principles. Firstly, the exposure of 731 immunophenotypes maintains a robust association with the chosen SNPs. Secondly, these SNPs are not influenced by external confounding factors. Thirdly, while these polymorphisms do not have a direct relationship with the outcome (colorectal cancer), their causal connection is exclusively through the immunophenotypes(7). The design, hypotheses, and flow chart of the MR study are outlined in Fig. 1.

2.2 Data Sources

GWAS data sources for CRC

The GWAS summary statistics for colorectal cancer were obtained from the UK Biobank (https://gwas.mrcieu.ac.uk/, Dataset: ieu-b-4965)(8, 9). The study conducted a GWAS on 377,673 European individuals (N-case = 5,657, N-control = 372,016), analyzing approximately 11.7 million variants after quality control and imputation. The studies involved in this Mendelian randomization study have all been approved by the local ethics committee. No new data were collected, and therefore no additional ethical approvals were necessary.

Immunophenotypes GWAS data sources

The GWAS Catalog provides publicly available summary statistics for each immune trait (accession numbers from GCST90001391 to GCST90002121)(5).A total of 731 immunophenotypes were included in the study, consisting of relative cell counts (RC) (n = 192), absolute cell counts (AC) (n = 118), median fluorescence intensities (MFI) reflecting surface antigen levels (n = 389), and morphological parameters (MP) (n = 32). The MFI, RC, and AC features include B cells, cDCs, mature stages of T cells, monocytes, myeloid cells, TBNK (T cell, B cell, natural killer cell), and Treg panels, while the MP feature contains cDCs and TBNK panels. The GWAS on immune traits was originally conducted using data from 3,757 European individuals with no overlapping cohorts. Approximately 22 million SNPs were genotyped with high-density arrays and imputed with a Sardinian sequence-based reference panel. Associations were tested after adjusting for covariates such as sex, age, and age²(10).

2.3 Selection of Instrumental Variables (IVs)

The threshold for SNPs related to immune cell phenotypes was set at P < 1×10^− 5. Furthermore, a Linkage Disequilibrium (LD) check was performed on these SNPs (r² = 0.001 and kb = 10,000). The values of r² or kb indicate the degree of linkage disequilibrium between two loci, suggesting that if there is LD between two loci, their allele frequencies are not independent but correlated in some way. For SNPs related to colorectal cancer, we applied a significance threshold of P < 5×10^− 8 and performed LD checks (r² = 0.001 and kb = 10,000). We calculated the F-statistic for each SNP and removed SNPs with low F-values (< 10) as IVs to assess instrumental variable strength and mitigate weak instrument bias.

Finally, we used the National Institutes of Health's LD Trait Tool (https://ldlink.nih.gov/?tab=ldtrait) to eliminate confounders, including BMI, IBD, diabetes(11–13). This resulted in the removal of a total of 222 SNPs (Fig. 2)(14).

2.4 MR analysis

To investigate the causal relationship between immunophenotypes and the risk of CRC, this study utilized the TwoSampleMR and MR-PRESSO packages in R 4.3.2 (http://www.Rproject.org). The main MR analysis methods employed were IVW, MR-Egger, Weighted Median, Simple mode, and Weighted Mode.

These methods of causal reasoning have their own modeling construction. The IVW method is the primary method to analyze causality in MR analysis in the absence of horizontal pleiotropy. The weighted median method can be considered when heterogeneity occurs and there is no horizontal pleiotropy, assuming that less than 50% of the IVs show horizontal multiplicity. The MR-Egger regression method assumes that over 50% of IVs may be affected by horizontal pleiotropy. The simple mode is suitable when data exhibit heterogeneity but lack consistent bias. It is based on the effects of individual genetic variations. There are two modes of estimation: simple and weighted. The weighted mode assigns weights to each effect estimate, and becomes a viable option when most research results carry similar weights. The weighted mode assigns weights to each effect estimate, and becomes a viable option when most research results carry similar weights.

This study utilized a combination of MR-Egger and MR-PRESSO to assess the presence of horizontal pleiotropy (with P < 0.05 indicating horizontal pleiotropy). Additionally, the selected SNPs were assessed for heterogeneity using Cochran's Q test (with P < 0.05 indicating heterogeneity)(15). Compared to MR-Egger, which detects and quantifies the degree of horizontal pleiotropy through intercept testing, MR-PRESSO can identify and address outliers beyond horizontal pleiotropy. Additionally, scatter plots and funnel plots were utilized, which demonstrated that the results were not influenced by outliers. Funnel plots were used to demonstrate the robustness of the correlation and the absence of heterogeneity. A leave-one-out sensitivity analysis was conducted to determine the effect of each SNP on the results of the Mendelian randomization analysis. Furthermore, the article conducted a reverse MR analysis to determine if there is a reverse causality between the immunophenotypes identified in the forward MR analysis and colorectal cancer. Regarding the multiple testing correction, the FDR (Benjamini-Hochberg) method and method were applied(16, 17).

2.5 Clinical Validation

Study Population

We recruited 100 cases with pathologically confirmed colorectal cancer and 100 matched healthy controls. In the cases group, they were not taking corticosteroids, immunosuppressants, or antibiotics prior to sampling and had not undergone any antitumor therapy. All cases were classified using the International Classification of Diseases, 10th edition (ICD-10). All participants were recruited from the China-Japan Union Hospital of Jilin University between May 2020 and December 2021, where patients with colorectal cancer were treated with radical surgery after recruitment. To ensure comparability between groups, we maintained consistency on all factors except for colorectal cancer. This included sex, age, medical history, lifestyle, and dietary habits. Patients with other intestinal or autoimmune diseases were excluded from the study. Healthy controls were recruited from the Physical Examination Center of the China-Japan Union Hospital of Jilin University. The study was approved by the Ethics Committee of the China-Japan Union Hospital of Jilin University in accordance with the Declaration of Helsinki (2024052301), and all experiments were performed in accordance with relevant guidelines and regulations. All participants provided written informed consent to be involved in this study. (Checklist of case-control studies is detailed in the Supplementary STROBE-checklist-Case-Control).

Sample Collection

Peripheral venous blood was collected from study participants on an empty stomach using EDTA-K2 specimen collection tubes on the day after recruitment. The specimens were then sent to the laboratory for testing using a SYSMEX XN-9000 automated hematology analyzer. Results were reviewed and documented through an electronic medical record system. Lymphocyte counts of the collected subjects were measured and indexed.

Furthermore, lymphocyte subpopulations in peripheral blood were detected. Peripheral venous blood (5 mL) was collected from all participants one day after recruitment using EDTA-K2 specimen collection tubes. The specimens were collected in the fasting state and immediately sent to the hospital research center laboratory for testing. If immediate testing was not possible, the specimens were stored at room temperature and tested within 24 hours.

Sample Testing Methods and Related Instruments

All reagents, including Tetrachromatic T-lymphocyte subpopulation antibody (CD3-PC5 / CD4-PE / CD8-ECD / CD45-FITC), Erythrocyte Lysing Solution (Opti-Lyse B No-wash Lysing Solution), and fluorescent control antibody (IgG1-PC5 / IgG2b-FITC / IgG1-ECD / IgG1-PE), were produced by Beckman Coulter, USA.

The sample processing involved collecting peripheral blood on the same day and processing it into two flow cytometry sample tubes named control and experimental tubes using a flow cytometer from Beckman Coulter, USA. Each tube received 100 microliters of anticoagulated whole blood, followed by the addition of 20 microliters of isotype control antibody and tetrachromatic T-lymphocyte subpopulation antibody, respectively. Next, 500 microliters of erythrocyte lysate were added to each tube, which were again mixed thoroughly and incubated for 20 minutes at room temperature, avoiding light. The tubes were then thoroughly mixed and incubated for 20 minutes at room temperature, away from light. After 20 minutes, 500 microliters of phosphate-buffered saline (PBS) were added and mixed thoroughly. The mixture was then centrifuged at 1000 r/min for 5 minutes. The supernatant was discarded, and the washing process was repeated once more. Finally, the supernatant was removed, and 500 microliters of PBS were added again before being placed on a flow cytometer.

To initiate the machine process, first open the flow cytometer and wait until the instrument laser is stable. Then, verify that the instrument is functioning properly by using the fluorescent microsphere provided on the machine. To analyze the lymphocytes, open the four-color program and use CD45-FITC to identify them. Select 10,000 cells for each sample. After collecting the sample, analyze the values of T cells (CD3⁺), CD3⁺CD4⁺T cell, CD3⁺CD8⁺T cell, B cells (CD3⁻CD19⁺), and NK cells (CD3⁻CD56⁺) using CXP Analysis software. The results obtained will be shown in relative counts.

Data Collection

Colorectal cancer cases collect various relevant information from postoperative histopathology reports, including tumor location and tumor differentiation, pathological T stage, clinical N stage, and microsatellite instability. The tumor TNM staging is determined based on the criteria of pathological staging in the American Joint Committee on Cancer's TNM staging system (AJCC, 8th edition). Microsatellite status is defined as the expression of four common mismatch repair proteins, namely MLH1, MSH2, MSH6, and PMS2, which is recorded from immunohistochemical results. Microsatellite instability high (MSI-H) is defined as the absence of expression of one or more of these proteins, while microsatellite instability low (MSI-L) is defined as the positive expression of all four proteins(18).

Follow Up

Colorectal cancer patients will undergo abdominal enhanced CT every 3 months during the first two years after treatment, along with hematological tests such as blood routine, biochemistry, and tumor markers. If additional symptoms or signs are present, corresponding parts will be examined more thoroughly, and PET-CT or definitive pathology will be performed if necessary. Follow-up was conducted through outpatient clinic and telephone. The last follow-up was on February 29, 2024, with a median follow-up time of 24 months. Follow-up records included patients' survival, recurrence status, and metastasis. Disease-free survival (DFS) was calculated from the start of the first treatment until the first tumor recurrence, metastasis, or patient death due to any reason.

Statistical Analysis

Initially, we assessed whether the necessary variables followed a normal distribution and exhibited homogeneity of variance. If these criteria were met, we proceeded with a T-test to evaluate differences between groups. For comparisons involving multiple groups, we used a one-way ANOVA test. In cases where these assumptions were not satisfied, we used a Wilcoxon rank sum test to analyze variables across various groups. This approach ensures a comprehensive and accurate assessment of the data. We examined the relationship between immunophenotypes and the development of colorectal cancer using logistic regression models. The study factors were grouped based on control quartiles. The model calculated odds ratios (OR) and their corresponding 95% confidence intervals (CI). To identify independent predictors of survival events in postoperative patients, COX regression model analysis was conducted. Survival curves were plotted using Kaplan-Meier analysis with Log-rank test, and a significance level of 0.05 was set for all reported p-values, which were two-sided. The statistical tests were performed using SPSS 25.0 software.

3.1 Effect of Genetically Predicted Immunophenotypes On CRC

To investigate the causal effect of immunophenotypes on CRC, we conducted a two-sample MR analysis using five assays. The IVW method served as the primary analytical basis, and the results, presented in Figure 3, indicate that 29 immunophenotypes were linked to colorectal cancer risk. To minimize false-positive results, we employed the FDR method and Bonferroni correction for multiple testing. After correcting the IVW-P values by FDR, we found that 26 immunophenotypes were associated with colorectal cancer risk (P-FDR<0.05). These included six immunophenotypes of the B cell panel, 5 immunophenotypes of the TBNK panel, 7 immunophenotypes of the Treg panel, 4 immunophenotypes of the maturation stages of the T cell panel, 4 immunophenotypes in the Treg panel, 4 immunophenotypes in the maturation stages of the T cell panel, one in the monocyte and cDCs panel, and two in the myeloid cell panel (Supplementary Figure 1, 2, 3, and 4).

Among the Immunophenotypes with a P-FDR<0.05, we found 16 immunophenotypes that suggest similar risk ratios (OR>1). These immunophenotypes show an increased risk of developing colorectal cancer with an increase in RC, AC, or MFI for that specific immunophenotypes. (Such as B cell panel: BAFF-R on naive-mature B cell, BAFF-R on B cell, Treg panel: CD28⁺ CD45RA⁺ CD8^br AC, CD3 on CD39⁺ CD4⁺, CD3 on CD28⁺ CD45RA⁺ CD8^br, CD3 on CD28^-CD8^br, CD25 on secreting Treg , TBNK panel: Lymphocyte %leukocyte, T cell %leukocyte, CD3 on HLA DR+ T cell , CD3 on HLA DR+ CD8^br, Maturation stages of the T cell panel: CD3 on EM CD4⁺, CD3 on CM CD8^br, CD4 on CD45RA⁺ CD4⁺, cDCs panel: CD62L- HLA DR++ monocyte AC, Myeloid cell panel: CD33^br HLA DR+ CD14^- AC).

There were 10 immunophenotypes with OR less than 1, indicating a relative reduction in the risk of colorectal cancer with an increase in RC, AC, or MFI for that immunophenotype. (Such as B cell panel: Naive-mature B cell AC, CD24⁺ CD27⁺ %B cell, Transitional %lymphocyte, CD20 on IgD⁺ CD24^-, Treg panel: CD39⁺ CD4⁺ %T cell, CD25⁺⁺ CD8^br %T cell, TBNK panel: DN (CD4^-CD8^-) %leukocyte, Maturation stages of the T cell panel: HVEM on EM CD8^br, Monocyte panel: CCR2 on monocyte, Myeloid cell panel: Im MDSC %CD33^dim HLA DR- CD66b-). In the TBNK panel, we found that four immunophenotypes were positively causally associated with the risk of colorectal cancer (Supplementary Figure 5).

To further minimize the risk of false-positive P-values, we utilized the most rigorous Bonferroni correction method. After correction, P-Bonferroni was found to be <1.72×10^-3 (0.05/29), only two immunophenotypes were significantly associated with colorectal cancer (Figure 4A): lymphocyte % leukocyte belonging to the TBNK panel, and CD3 on CM CD8^br belonging to the Maturation stages of the T cell panel.

For the Lymphocyte %leukocyte in TBNK panel, the OR was estimated to be 1.0011 (95% CI=1.0005-1.0017, P=4.44×10^-4) using IVW method. Additional analytical methods yielded the following results: MR-Egger (OR=1.0010, 95% CI 1.0002-10017, P=0.028), Weighted Median (OR=1.0010, 95% CI 1.0000-1.0020, P=0.042), Simple mode (OR=1.0008, 95% CI 0.9991-1.0025, P=0.36), Weighted mode (OR=1.0010, 95%CI 1.0002-1.0019, P=0.025). The MR-PRESSO global P-value and Pleiotropy P value were both >0.05 in the test of horizontal pleiotropy and the test of heterogeneity was also >0.05. In the Maturation stages of the T cell panel, CD3 on CM CD8^br held the OR of 1.0013 (95% CI 1.0005-1.0021, P=9.54×10^-4) by IVW method. Results from other methods are listed as follows: MR-Egger (OR=1.0006, 95% CI 0.9991-1.0021, P=0.464), Weighted Median (OR=1.0011, 95% CI 0.9999-1.0023, P=0.065), Simple mode (OR=1.0013, 95% CI 0.9995-1.0032, P=0.178), Weighted mode (OR = 1.0011, 95% CI 0.9999-1.0022, P=0.091). Similarly in the test of horizontal multiplicity, both MR-PRESSO global P-value and Pleiotropy P-value were >0.05 and the test of heterogeneity was also >0.05. Further validation was provided by the stability observed in the scatter plots, funnel plots and leave-one-out analysis, which proved the robustness of the results (Figure 5).

3.2 Effect of Genetically Predicted CRC On Immunophenotypes

Before adjustment, CRC was observed to induce increase in 2 immunophenotypes (Figure 4B). Linear relationships, funnels, and leave-one-out plots for two immune cells in reverse Mendelian randomization analysis, as shown in Figure (Supplementary Figure 6). However, after Bonferroni adjustment, none of these p values remained statistically significant.

3.3 Results of Case-Control Study

3.3.1. Baseline Characteristic

The study included 100 patients diagnosed with CRC and 100 members of the healthy control group (Figure 6). There were no significant differences between the two cohorts in terms of age, sex, and smoking versus alcohol consumption (Supplementary Table 1). However, patients with colorectal cancer had higher absolute counts of lymphocytes, CD3⁺T cells, CD4⁺T cells, CD4⁺ and CD8⁺T cells, and NK cells, as well as higher relative counts of lymphocytes and CD4⁺T cells compared to the healthy control group (P<0.05). However, the counts of CD8⁺T cell and B cells were relatively lower in colorectal cancer patients (Figure 7A and B).

3.3.2 Single-factorial Conditional Logistic Regression Analysis of Immune Cells and Risk of Colorectal Cancer Development

A single-factorial conditional logistic regression model was constructed with the occurrence of colorectal cancer as the dependent variable, stratified by absolute and relative count quartiles of peripheral blood immune cells in colorectal cancer cases and healthy controls (Supplementary Table 2 and 3).

Based on the Supplementary Table 2, the risk of colorectal cancer was 4.317 times higher for study subjects in the Lymphocyte 3rd quartile group compared to the 1st quartile group (OR=4.317, 95% CI: 1.812-10.282), The highest quartile group had a risk 9.25 times higher (OR=9.25, 95% CI: 3.744-22.852).

The study found that subjects in CD4⁺T cell quartile 2 had a 2.043-fold increased risk of colorectal cancer compared to the reference subgroup (OR=3.043, 95% CI: 1.328-6.977). Quartile 3 had a 1.826-fold increased (OR=2.826, 95% CI: 1.225-6.52), while the highest quartile group had a 3.211-fold increased risk (OR=4.211, 95% CI: 1.817-9.758). Study participants in the highest CD8⁺T cell quartile group nevertheless had a 55.6% lower risk of colorectal cancer (OR=0.444, 95% CI: 0.199-0.989). Additionally, the risk of colorectal cancer was 63.2% lower in the 3rd quartile of B-cell subgroup compared to the 1st quartile (OR=0.368, 95% CI 0.162-0.835), and even further reduced by 65.1% in the highest quartile group (OR=0.349, 95% CI: 0.156-0.781).

The Supplementary Table 3 results, stratified by absolute count group quartiles, indicate that only study subjects in CD4⁺T cell quartile 3 had a 1.941-fold increased risk of colorectal cancer compared to quartile 1 (OR=1.941, 95% CI: 1.081-3.485). None of the other absolute count metrics were statistically significant.

3.3.3 Multi-factorial Conditional Logistic Regression of Immune Cells and Risk of Colorectal Cancer Development

Variables that were statistically significant (P<0.05) in the single-factorial analysis were included in the multi-factorial conditional logistic regression model for further analysis (Supplementary Table 4 and Figure 7C). The relative counts of lymphocytes and CD4⁺T cells were positively associated with the risk of colorectal cancer, while the relative counts of B cells were negatively associated with the risk of colorectal cancer.

After adjusting for age, sex, smoking, and alcohol consumption, the risk of colorectal cancer was significantly higher for study participants in quartile 2 of the Lymphocyte relative count (OR=4.113, 95% CI: 1.415-11.95), quartile 3 (OR=13.79, 95% CI: 4.027-47.215), and the highest quartile group (OR=39.48, 95% CI: 9.384-166.097) compared to those in quartile 1. Subjects in the second quartile of CD4⁺T cell relative counts had a 4.949 times greater risk of colorectal cancer than those in the first quartile (OR=4.949, 95% CI: 1.639-14). Similarly, individuals in the 3rd quartile had an increased risk of 8.973 times (OR=8.973, 95% CI: 2.531-31.812), while those in the highest quartile had a 9.367-fold risk (OR=9.367, 95% CI: 2.495-35.159). The risk of colorectal cancer was significantly lower in the 2nd, 3rd, and highest quartile groups of relative B-cell count compared to the 1st quartile group (OR=0.251, 95% CI: 0.085-0.741, OR=0.141, 95% CI: 0.048-0.415, OR=0.183, 95% CI: 0.062-0.541). In the CD4⁺T cell absolute count subgroups, the risk of colorectal cancer was reduced by 71.4% in study subjects in the 2nd quartile group compared to the 1st quartile group (OR=0.286, 95% CI: 0.087-0.935) and by 77.8% in the highest quartile group (OR=0.222, 95% CI: 0.053-0.93). However, the remaining absolute count metrics did not show statistical significance (Figure 7D).

3.3.4 Clinical Baseline Characterization and Prognostic Analysis of Immune Cells in Colorectal Cancer

We analyzed the immune cell counts of CRC cases along with their baseline characteristics and prognosis to investigate the potential association between immune cells and CRC (Table 1). The study found that increased lymphocyte counts were linked to risk factors for metastasis, recurrence, and death in colorectal cancer. Additionally, high values of CD4⁺T cell relative counts were significantly associated with elevated Clinical N stage (Supplementary Table 5, 6, 7, and 8).

To investigate the clinicopathological characteristics of colorectal cancer and the effect of immune cells on DFS, and to analyze the association. In this study, we grouped the cohort of colorectal cancer patients by median, categorized the absolute and relative counts of immune cells into high and low groups, and plotted the KM curves, and the results are shown in Supplementary Table 9 and Supplementary Figure 7. In the Kaplan-Meier curves of 34 colorectal cancer patients who developed recurrence, metastasis, and death, age, Tumor differentiation, and absolute counts of CD3⁺T cells and CD4⁺T cells as well as relative counts of lymphocytes in the immune cells were statistically significantly associated with DFS (P<0.05) (Figure 8). Specifically, lower age, increased CD3⁺T cell, CD4⁺T cell absolute counts, and relative lymphocyte counts resulted in superior DFS.

Subsequently, we conducted Cox regression model analysis on cases of colorectal cancer and found that age, tumor location, absolute CD3⁺T cell count, absolute CD4⁺T cell count, and relative lymphocyte count were associated with DFS after treatment. These indexes were included in the multivariate COX regression model analysis, which showed that only left hemi-colon carcinoma was an independent factor influencing DFS after treatment (HR=0.303, 95% CI: 0.103-0.894) (Supplementary Table 10).

Table 1. Baseline clinicopathologic features of colorectal cancer cases

Characters
Age, year	64.48±10.48
Sex Male: Female, n	58:42
Smoking Yes: No, n	28:72
Alcohol Yes: No, n	30:70
Tumor location Right-side: Left-side: Rectum, n	46:25:29
Tumor differentiation Low: Middle-low: Middle, n	14:31:55
Pathological T stage 1: 2: 3: 4, n	10:6:76:8
Clinical N stage 0: 1: 2: 3, n	48:30:17:5
Microsatellite Instability MSI-L: MSI-H, n	96:4
Recurrence/metastasis/death Yes: No, n	34:66
DFS, month	12.44±4.28

The intricate relationship between the immune system and gut has increasingly been recognized as a critical factor in the pathogenesis of colorectal cancer(19). Current research, including substantial empirical studies, underscores how alterations in the quantity and composition of immune cells within the gut can profoundly impact the mucosal immune landscape, potentially triggering and exacerbating CRC progression(20–23). However, despite these advances, the precise causal roles of specific immune cells in the development of CRC remain poorly defined, representing a significant gap in our understanding. To the best of our knowledge, our study is the first large-scale comprehensive investigation of the causality between specific immune cells and colorectal cancer. Historically, the majority of studies exploring the immune cell dynamics in CRC have employed either cross-sectional designs or animal models(24–26). While these studies have been invaluable in illustrating potential associations and mechanistic insights, they fall short in establishing causality due to their inherent limitations in temporal resolution and species-specific responses that may not accurately reflect human pathophysiology. Our study seeks to bridge this crucial gap through a comprehensive, large-scale investigation into the causal relationships between specific immune cells and CRC, leveraging genetic prediction methodologies. This approach allows for a more definitive exploration of causality by accounting for genetic predispositions that influence both immune cell behavior and CRC risk, thereby circumventing some of the confounding factors commonly encountered in observational studies. This finding may provide valuable evidence for the role of the immune system in the pathogenesis of colorectal cancer and potential therapeutic targets.

In this study, we genetically predicted a causal relationship between 731 immunophenotypes and colorectal cancer. Based on MR analysis, we identified 2 associations between immunophenotypes and colorectal cancer. One of them, Lymphocyte %leukocyte exhibited a strong causal association with the development of colorectal cancer (P = 4.44×10^− 4). Additionally, our findings demonstrated no significant heterogeneity or horizontal pleiotropy, suggesting stability. Notably, after validating the clinical samples in our study, we found a significant difference in both relative (P = 9.57×10^− 8) and absolute counts (P = 0.041) of lymphocytes between colorectal cancer patients and healthy individuals.

Lymphocytes, as pivotal components of the host immune system, play a crucial role in immune surveillance and defense against malignancies, including CRC(27). These cells, capable of differentiating into NK cells, T lymphocytes, and B lymphocytes, are integral in mounting both innate and adaptive immune responses against tumor progression. However, the relationship between lymphocyte levels and CRC remains a subject of ongoing debate within the scientific community. The majority of cross-sectional studies have indicated that patients with CRC tend to exhibit lower lymphocyte counts, suggesting a potential compromise in immune competence associated with tumor presence or progression(28–30). This observation aligns with the broader hypothesis that a robust immune response is protective against the development and escalation of neoplastic diseases. However, these studies, due to their observational nature, often struggle with causality and are susceptible to confounding factors that may obscure the true nature of the relationship between lymphocyte levels and CRC risk.

Contrary to these findings, our research has elucidated a positive causal relationship between relative lymphocyte counts and the risk of developing CRC, as substantiated by Mendelian randomization. Our analysis, which leverages genetic variants as instrumental variables to estimate the causal effect of a trait on an outcome, demonstrates an OR of 1.0011 with 95% CI from 1.0005 to 1.0017. This finding is pivotal as it suggests that even slight increases in lymphocyte counts are associated with a marginally increased risk of CRC, a somewhat counterintuitive result given the traditional view of lymphocytes as protective elements in cancer immunology(31). Further validation through clinical sample analyses employing both single-factorial and multi-factorial conditional logistic regression has reinforced these findings. These methods, which adjust for various confounders and potential interactions, lend robustness to our results, challenging the conventional understanding and suggesting a more nuanced role of lymphocytes in CRC. The implications of this finding may reflect a complex interaction between lymphocytes and CRC tumor cells that is not purely antagonistic but may involve facilitative roles under certain conditions. For instance, certain lymphocyte subsets or a dysfunctional immune response characterized by lymphocytes might inadvertently promote tumor progression through mechanisms such as immunosuppression or the creation of a tumor-promoting microenvironment(32, 33).

Another immunophenotype worth focusing on is the CD3 on CM CD8^br, which represents the median fluorescence intensity of CD3⁺CD8⁺ central memory T cells(34). This immunophenotype was identified as being causally associated with CRC in an analysis of 731 immunophenotypes, with a significant p-value of 9.54×10^− 4, suggesting a noteworthy correlation. From an immunological perspective, this association can be interpreted through the lens of T cell differentiation and function during acute infections. In such contexts, primary CD8⁺T cells are activated by antigen exposure to differentiate into effector CD8⁺T cells (T_eff)(35). Following the clearance of the antigen, a subset of these effector cells transitions into long-lived, self-renewable memory CD8⁺T cells, poised to respond swiftly to re-infections. This dynamic is crucial in understanding how an elevated count of CD3⁺CD8⁺central memory T cells could signify an over-activated state of immune cell infiltration. Such chronic, repeated activation of immune cells can lead to sustained inflammatory responses, which are known to contribute to enterocytes DNA damage, disruption of tissue barriers, and a greatly increased risk of their development into CRC cells(36).

Although the Bonferroni-correction test did not reveal a significant effect on other immunophenotypes, this does not exclude the possibility of an association between them and CRC. Such results highlight the importance of maintaining a cautious interpretation of data, we recognizing that the lack of statistical significance could be due to the stringent nature of the correction rather than an absence of biological relevance. The pathogenesis of CRC is complex and the interactions between the gut and the immune system are diverse. These immune cells have nominal causality for multiple phenotypes and may also be involved in a critical dialogue between the tumor and the immune system. Previous studies have also shown that individual immune cells may not be a key player in the pathogenesis of colorectal cancer. Integrated modulation of the entire immune system may be a novel approach for future intervention in colorectal cancer(37, 38).

Notably, we did not find a statistically significant reverse causality between these specific immune cells and CRC after the Bonferroni-correction test. This predicts that CRC may not be the cause of the altered stoichiometry of these immune cells, but rather a consequence of them. Our study established direct and reverse causality between specific immune cells and CRC, which has not been done in previous studies. This may help identify ways to use specific immune cells as useful biomarkers for early disease detection in CRC, which could significantly enhance clinical outcomes, thus potentially improving patient prognosis(39).

Furthermore, in our single center case-control study, results reveal the higher absolute and relative counts of lymphocytes and individual subpopulations of lymphocytes in colorectal cancer cases compared to normal controls. Not only do we question, what is the reason for this? In order to minimize the impact of regional variability on the results, we consulted data on lymphocyte and subpopulation proportions in northeastern and other regions of China. Our analysis revealed that the average values for these regions were consistent with higher absolute and relative counts of lymphocytes and individual subpopulations in colorectal cancer cases(40–46). Based on the results of Mendelian randomization analyses, an increase in the relative counts of lymphocytes favors colorectal carcinogenesis, and we speculate that the influence of this factor on disease progression in patients with CRC is multifaceted, as immune cells play an important role at all stages of CRC development (albeit a more attenuated role in sporadic colorectal cancer)(47). A moderate immune response resists tumorigenesis, but a chronic inflammatory response characterized by persistent over-activated immune cell infiltration, DNA damage and disruption of the tissue barrier is deleterious, and this is closely associated with colorectal carcinogenesis. This condition not only alters the compositional ratio of lymphocytes and their subpopulations, but can likewise promote colorectal carcinogenesis and metastasis through direct immunosuppressive effects or by regulating tumor cell proliferation and survival, and tumor vascular growth(48, 49). This speculation also applies to the statistically significant relative counts of CD4⁺T cells in the clinically validated results.

According to another result of our case-control study, the relative B-cell count was not only low in colorectal cancer patients but also had a negative causal effect on their risk of developing the disease. This was confirmed by subsequent single-factorial and multi-factorial logistic regression analyses. We conducted the following analysis after collecting the relevant literature, firstly, B cells, as one of the major immune cells in the body, have been shown to suppress tumors by producing anti-tumor antibodies, promoting tertiary lymph-angiogenesis, and thus optimizing local T cell responses(50). And there is also a portion of the literatures that concludes that B cells may attenuate the anti-tumor immune response by producing immunosuppressive cytokines. This is consistent with the results in a study of altered circulating lymphocyte subsets in colorectal cancer patients by Johanna Waid Hauser scholars about our clinical validation(51). The reason for this may be that B cells can be directly involved in anti-tumor immunity through the production of antibodies, which recognize and bind to antigens on the surface of tumor cells, thereby assisting the immune system in clearing the tumor cells. B cells in colorectal cancer patients may be in a state of depletion now, so when the number and role of B cells are activated, their risk of developing colorectal cancer is reduced. Nevertheless, the specific role of B cells in CRC requires further high-quality studies to validate and elucidate the underlying mechanisms.

Nowadays, scholars around the world have conducted a series of clinical cohort studies on whether immune cells affect the prognosis of colorectal cancer patients. Most of the results indicate that low counts of immune cells tend to portend a poorer prognosis, which mirrors the results of our current clinical study(52–54). The results found that subgroups of lymphocyte relative counts, CD3⁺T cell and CD4⁺T cell absolute counts that were at lower values had a poorer prognosis. Although these metrics did not demonstrate statistical significance in subsequent multifactorial COX regression modeling, they can be considered as potential predictors of prognosis in the context of other scholars' studies and predictive modeling.

Lymphocytes and their diverse subpopulations play pivotal roles in the immunological combat against tumor progression, particularly in CRC. The presence of dense lymphocytic infiltration at the margins of tumors is not merely a histopathological observation but serves as a robust independent prognostic factor, often correlating with improved survival outcomes in CRC patients. In the tumor microenvironment of CRC, tumor-infiltrating lymphocytes consist of adaptive immune cells, including CD4⁺ and CD8⁺ subpopulations, and suppressor regulatory Treg cells. CD4⁺T cells make up the majority of lymphocytes and are closely associated with a good prognosis and survival(55). Several studies have shown that a decrease in peripheral blood lymphocyte counts negatively impacts the prognosis of CRC patients(56, 57). Our findings are consistent with this. Although the proportion of Treg cell counts was not detected in our current flow assay, it is important to note that T cells are divided into three broad subpopulations: CD4⁺T cells, CD8⁺T cells, and the Treg subpopulation. The decrease in the proportions of CD4⁺ and CD8⁺T cells, accompanied by a relative increase in the proportions of Treg cells, these cells are known for their immunosuppressive functions, capable of inhibiting the activity of both CD4⁺ and CD8⁺T cells through various mechanisms, including the secretion of inhibitory cytokines such as IL-10 and TGF-β(58). The precision of the experiment's results is further validated. Interestingly, traditional prognostic indicators such as T-stage and N-stage, which are generally considered reflective of tumor size, extent, and nodal involvement, did not reach statistical significance in our study. This finding suggests a possible overshadowing of these traditional markers by the more dominant immune factors, or perhaps a variability in tumor biology that transcends mere anatomical staging. Nevertheless, the observed trend of decreased DFS with increasing T-stage and N-stage aligns with the conventional understanding of cancer progression(59).

It is equally important to acknowledge the limitations of our study. First, the validation of our clinical samples is still based on cross-sectional studies, and we have not been able to use cohort studies to validate the association between specific immune cells and CRC. In addition, we were not able to obtain a larger sample of immune cells from the circulating blood of colorectal cancer patients due to conditions and patient wishes. Although the results were consistent individually, the level of evidence was insufficient. Future cohort studies are needed to provide more conclusive evidence. Secondly, we identified specific immune cell types that may be causally related to CRC. However, the interaction between intestinal tumors and the immune system is a complex process. It is necessary to follow up to understand whether other factors synergize with immune cells to participate in the pathogenesis of CRC, such as the intestinal microbiota. Currently, genome sequencing of immune cells is still being improved. Its complexity far exceeds the 731 immunophenotypes mentioned in this study. Further genetic association studies and clinical validations are necessary as sequencing technology advances.

In conclusion, our research employing MR analysis with P value adjustments has firmly established a causal relationship between lymphocyte counts and T cell maturation stages, confirming these immunophenotypes' significant role in CRC risk. The robustness of this association is further reinforced by validation through single-center case-control studies focused on lymphocyte subpopulations. These findings are critical as they not only position lymphocytes as effective biomarkers for early and non-invasive detection of CRC but also spotlight their potential as targets for innovative immunotherapeutic approaches. Ultimately, this research advances our understanding of immune pathways in CRC, offering dual benefits for both detection and management and guiding the development of advanced immune-based treatments in oncology.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Author contributions

Shuo-Hui Gao: Conceptualization, Funding acquisition, Project administration, Writing-original draft. Luan-Biao Sun: Data curation, Investigation, Methodology, Writing-original draft, Writing-review & editing. Jian-Peng Xing: Data curation, Writing-original draft. Xin-yuan Song: Data curation, Investigation, Methodology. Xuan-Peng Zhou: Data curation, Writing-original draft. All authors contributed to the article and approved the submitted version.

Funding

This study was supported by the Natural Science Foundation of Jilin Province (Grant No. YDZJ202201ZYTS118), the Specialized Health Research Talents in Jilin Province Funding (Grant No. 2023SCZ09) and Jilin Provincial Department of Science and Technology Excellence Talent Program for Young and Middle-aged Science and Technology Innovation and Entrepreneurship (Grant No.20240601013RC).

Acknowledgments

Thanks to GWAS database contributors for sharing the data. We thank Professor and Chair Xin-Yuan Song of the Department of Statistics, The Chinese University of Hong Kong, for his careful guidance on the statistical methods and survival analysis applied in this study.

Informed consent statement

All experiments were performed in accordance with relevant guidelines and regulations. All participants provided written informed consent to be involved in this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ballerini, P. et al. Inflammation and Cancer: From the Development of Personalized Indicators to Novel Therapeutic Strategies. Front. Pharmacol. 13, 838079 (2022).
Hanahan, D. Hallmarks of Cancer: New Dimensions. Cancer Discov. 12 (1), 31–46 (2022).
Picard, E. et al. Relationships Between Immune Landscapes, Genetic Subtypes and Responses to Immunotherapy in Colorectal Cancer. Front. Immunol. 11, 369 (2020).
Kather, J. N. & Halama, N. Harnessing the innate immune system and local immunological microenvironment to treat colorectal cancer. Br. J. Cancer. 120 (9), 871–882 (2019).
Orrù, V. et al. Complex genetic signatures in immune cells underlie autoimmunity and inform therapy. Nat. Genet. 52 (10), 1036–1045 (2020).
Skrivankova, V. W. et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. Jama. 326 (16), 1614–1621 (2021).
Lor, G. C. Y. et al. Reporting and guidelines for mendelian randomization analysis: A systematic review of oncological studies. Cancer Epidemiol. 62, 101577 (2019).
Rusk, N. The UK Biobank. Nat. Methods. 15 (12), 1001 (2018).
Morales Berstein, F. et al. Assessing the causal role of epigenetic clocks in the development of multiple cancers: a Mendelian randomization study. Elife, 11. (2022).
Sidore, C. et al. Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nat. Genet. 47 (11), 1272–1281 (2015).
Lawler, T. et al. Type 2 Diabetes and Colorectal Cancer Risk. JAMA Netw. Open. 6 (11), e2343333 (2023).
Shah, S. C. & Itzkowitz, S. H. Colorectal Cancer in Inflammatory Bowel Disease: Mechanisms and Management. Gastroenterology. 162 (3), 715–730e3 (2022).
Bull, C. J. et al. Adiposity, metabolites, and colorectal cancer risk: Mendelian randomization study. BMC Med. 18 (1), 396 (2020).
Prunier, J. et al. LD-annot: A Bioinformatics Tool to Automatically Provide Candidate SNPs With Annotations for Genetically Linked Genes. Front. Genet. 10, 1192 (2019).
Bowden, J. et al. Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int. J. Epidemiol. 48 (3), 728–742 (2019).
Ghosh, D. Incorporating the empirical null hypothesis into the Benjamini-Hochberg procedure. Stat. Appl. Genet. Mol. Biol., 11(4). (2012).
Curtin, F. & Schulz, P. Multiple correlations and Bonferroni's correction. Biol. Psychiatry. 44 (8), 775–777 (1998).
Taieb, J. et al. Deficient mismatch repair/microsatellite unstable colorectal cancer: Diagnosis, prognosis and treatment. Eur. J. Cancer. 175, 136–157 (2022).
Hanus, M. et al. Immune System, Microbiota, and Microbial Metabolites: The Unresolved Triad in Colorectal Cancer Microenvironment. Front. Immunol. 12, 612826 (2021).
Shuwen, H. et al. Relationship between intestinal microorganisms and T lymphocytes in colorectal cancer. Future Oncol. 15 (14), 1655–1666 (2019).
Toor, S. M. et al. Immune Checkpoints in Circulating and Tumor-Infiltrating CD4(+) T Cell Subsets in Colorectal Cancer Patients. Front. Immunol. 10, 2936 (2019).
Deng, N. et al. Exercise Training Reduces the Inflammatory Response and Promotes Intestinal Mucosa-Associated Immunity in Lynch Syndrome. Clin. Cancer Res. 29 (21), 4361–4372 (2023).
Formica, V. et al. Immune reaction and colorectal cancer: friends or foes? World J. Gastroenterol. 20 (35), 12407–12419 (2014).
Xin, J. et al. Risk assessment for colorectal cancer via polygenic risk score and lifestyle exposure: a large-scale association study of East Asian and European populations. Genome Med. 15 (1), 4 (2023).
Bakshi, A. et al. Aspirin and the Risk of Colorectal Cancer According to Genetic Susceptibility among Older Individuals. Cancer Prev. Res. (Phila). 15 (7), 447–454 (2022).
Lavalette, C. et al. Cancer-Specific and General Nutritional Scores and Cancer Risk: Results from the Prospective NutriNet-Santé Cohort. Cancer Res. 78 (15), 4427–4435 (2018).
Yamamoto, T., Kawada, K. & Obama, K. Inflammation-Related Biomarkers for the Prediction of Prognosis in Colorectal Cancer Patients. Int. J. Mol. Sci., 22(15). (2021).
Fionda, C. et al. NK Cells and Other Cytotoxic Innate Lymphocytes in Colorectal Cancer Progression and Metastasis. Int. J. Mol. Sci., 23(14). (2022).
Idos, G. E. et al. The Prognostic Implications of Tumor Infiltrating Lymphocytes in Colorectal Cancer: A Systematic Review and Meta-Analysis. Sci. Rep. 10 (1), 3360 (2020).
Ménétrier-Caux, C. et al. Lymphopenia in Cancer Patients and its Effects on Response to Immunotherapy: an opportunity for combination with Cytokines? J. Immunother Cancer. 7 (1), 85 (2019).
Mei, Z. et al. Tumour-infiltrating inflammation and prognosis in colorectal cancer: systematic review and meta-analysis. Br. J. Cancer. 110 (6), 1595–1605 (2014).
Kong, J. C. et al. Prognostic Impact of Tumor-Infiltrating Lymphocytes in Primary and Metastatic Colorectal Cancer: A Systematic Review and Meta-analysis. Dis. Colon Rectum. 62 (4), 498–508 (2019).
Liu, H. et al. The prognostic impact of tumor-infiltrating B lymphocytes in patients with solid malignancies: A systematic review and meta-analysis. Crit. Rev. Oncol. Hematol. 181, 103893 (2023).
Xiao, W. et al. CD8 cell counting in whole blood by a paper-based time-resolved fluorescence lateral flow immunoassay. Anal. Chim. Acta. 1179, 338820 (2021).
Giles, J. R. et al. Shared and distinct biological circuits in effector, memory and exhausted CD8(+) T cells revealed by temporal single-cell transcriptomics and epigenetics. Nat. Immunol. 23 (11), 1600–1613 (2022).
Bae, J. et al. Lenalidomide Polarizes Th1-specific Anti-tumor Immune Response and Expands XBP1 Antigen-Specific Central Memory CD3(+)CD8(+) T cells against Various Solid Tumors. J. Leuk. (Los Angel), 3(2). (2015).
Xing, C. et al. Microbiota regulate innate immune signaling and protective immunity against cancer. Cell. Host Microbe. 29 (6), 959–974e7 (2021).
Wang, S. et al. Transdifferentiation of tumor infiltrating innate lymphoid cells during progression of colorectal cancer. Cell. Res. 30 (7), 610–622 (2020).
Bandaru, S. S. et al. Targeting T regulatory cells: Their role in colorectal carcinoma progression and current clinical trials. Pharmacol. Res. 178, 106197 (2022).
Tielong, C. et al. Measurement of the normal value ranges of CD4 and CD8 in adults in Hubei province and the correlation between CD4 and TLC. Chin J AIDS STD, : pp. 252–254 (In Chinese, (2005). (04) https://kns.cnki.net/kcms2/article/abstract?v=m2RMPZxbF1IBicCntDyOTh8ZRLZomV6NUxEFLFYJpBQBgx4bpObfH1U8Y5eALkqJ2Nn-kLNa2DI3xka1oL0nljxcVA0-KI1Qiiu-kSIuxorOMLdgytq04OGahoaJmLGf&uniplatform=NZKPT&flag=copy).
Chengwu, H. et al. Reference ranges of peripheral blood T-lymphocyte subpopulations in healthy adults in the Beijing area. Chinese Journal of Clinical Laboratory Science, 27(05): pp. 390–392 (In Chinese, (2009). https://kns.cnki.net/kcms2/article/abstract?v=m2RMPZxbF1Lon2NtKPYksAEZr1_EkEGq-qzTDkaZy9WYDn4JYhet_PBECvrMpNs5ZXwL8_iKVmQYsIAOp3Fx9OkMj0d9SdLEVRGn3rvNH8skJ23EJHXy71VYOEfmrDNS&uniplatform=NZKPT&flag=copy).
He, Z. et al. The establishment of reference ranges on peripheral blood T-lymphocyte subsets for healthy adults using flow cytometry in Hunan. Modern Preventive Medicine, 41(18): pp. 3395–3397 (In Chinese, (2014). https://kns.cnki.net/kcms2/article/abstract?v=m2RMPZxbF1L8r7IPWDQuUcdPb3OSZWqbyK6_myh0tX899dxkclO_aCHUa8oYY_nUt09d6F-pQ5q5NTIy7MJeoior5ObzMu-UEPiyCz6G_Zz51kRJtep3zu6fQuh2kRiL2jwISrKHgFI=&uniplatform=NZKPT&flag=copy).
Jiang, W. et al. Measurement of the CD4 and CD8 normal value range in adults from Shanghai area. Chinese Journal of Infectious Diseases, : pp. 10–12 (In Chinese, (2002). (04) https://kns.cnki.net/kcms2/article/abstract?v=m2RMPZxbF1I2TiFjbtRa-5YbPNar1JRcPgppz5y_20lYqXp4VrMLqJototYI9_DEe34GlX7JC0U7AXLTipf6qqprA94JiugB0brvF6PhnnUeYOygxh3w2qR-8JSVaaNM&uniplatform=NZKPT&flag=copy)
Ning, T., Zhu, X. & Zheng, M. Normal Reference Values of Peripheral Blood CD4 and CD8 Lymphocytes for the Healthy Population in Tianjin. Chinese Journal of Prevention and Control of Chronic Diseases, 2007(05): pp. 432–434 (In Chinese,https://kns.cnki.net/kcms2/article/abstract?v=m2RMPZxbF1K7hpQztn2E0gN33Ar3uX-1cn7ljl33Asj5b1Dy0f5dLU1C-vsa8wrHzPWulIfDr9SVLNNUyASO26ipvIvs8QXhKDypemFHdYl-Obj6jmtTYZb_C-IM7f83&uniplatform=NZKPT&flag=copy).
Liang, J. et al. Normal reference values of lymphocyte subsets in healthy populations of 6 provinces and ethnic groups in China. Chinese Journal of Viral Diseases, 3(02): pp. 122–127 (In Chinese, (2013). https://kns.cnki.net/kcms2/article/abstract?v=m2RMPZxbF1LWu4yY6FiCcAu16SnArARXixV_xPYmL1cMXRyP3al4HHOgEQtvesIgPgbI0FBGSoGWN_tVoQrGnHTE3-roae_gluLDpK4CI1OkxWyQmyqqNieWLUUVpEjP&uniplatform=NZKPT&flag=copy).
Sun, D. et al. Reference ranges of peripheral blood T lymphocyte subpopulations in 120 healthy adults in Changchun area. Chinese Journal of Laboratory Diagnosis, 21(12): pp. 2160–2161 (In Chinese, (2017). https://kns.cnki.net/kcms2/article/abstract?v=m2RMPZxbF1IrDRmAoOyo5G6wmrZXZtOD7igrsgxV040I6jG_NPJq83yYJJ2RaB6MxvyS52BYAPWMus5nmUp2EABcnMkwEoV9SDGKht_XOGb5YElWJhQGRaYQQVyhri1VN8gxwK9KmbM=&uniplatform=NZKPT&flag=copy).
Brenner, H., Kloor, M. & Pox, C. P. Colorectal cancer Lancet, 383(9927): 1490–1502. (2014).
Galon, J. et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 313 (5795), 1960–1964 (2006).
Maglietta, A. et al. The Immune Landscapes of Polypoid and Nonpolypoid Precancerous Colorectal Lesions. PLoS One. 11 (7), e0159373 (2016).
LeBien, T. W. & Tedder, T. F. B lymphocytes: how they develop and function. Blood. 112 (5), 1570–1580 (2008).
Waidhauser, J. et al. Alterations of circulating lymphocyte subsets in patients with colorectal carcinoma. Cancer Immunol. Immunother. 71 (8), 1937–1947 (2022).
Masuda, K. et al. Multiplexed single-cell analysis reveals prognostic and nonprognostic T cell types in human colorectal cancer. JCI Insight, 7(7). (2022).
Shen, Y. et al. Immunogenomic pathways associated with cytotoxic lymphocyte infiltration and survival in colorectal cancer. BMC Cancer. 20 (1), 124 (2020).
Edin, S. et al. The Prognostic Importance of CD20(+) B lymphocytes in Colorectal Cancer and the Relation to Other Immune Cell subsets. Sci. Rep. 9 (1), 19997 (2019).
Saito, T. et al. Two FOXP3(+)CD4(+) T cell subpopulations distinctly control the prognosis of colorectal cancers. Nat. Med. 22 (6), 679–684 (2016).
Cheng, M. et al. The predictive value of CD4, CD8, and C-reactive protein in the prognosis of schistosomal and non-schistosomal colorectal cancer. BMC Gastroenterol. 23 (1), 194 (2023).
Kuwahara, T. et al. Intratumoural-infiltrating CD4 + and FOXP3 + T cells as strong positive predictive markers for the prognosis of resectable colorectal cancer. Br. J. Cancer. 121 (8), 659–665 (2019).
Aristin Revilla, S., Kranenburg, O. & Coffer, P. J. Colorectal Cancer-Infiltrating Regulatory T Cells: Functional Heterogeneity, Metabolic Adaptation, and Therapeutic Targeting. Front. Immunol. 13, 903564 (2022).
Chen, K. et al. Pathological Features and Prognostication in Colorectal Cancer. Curr. Oncol. 28 (6), 5356–5383 (2021).

No competing interests reported.

Download PDF

Editor invited by journal
05 Sep, 2024
Submission checks completed at journal
04 Sep, 2024
First submitted to journal
22 Aug, 2024

You are reading this latest preprint version

The Role of Immune Cells in Colorectal Cancer: A Mendelian Randomization Study and Validation in A Single-Center Case-Control Trial.

Status:

Version 1

Abstract

Figures

1 Introduction

2 Materials and Methods

2.1 MR Study Design

2.2 Data Sources

2.3 Selection of Instrumental Variables (IVs)

2.4 MR analysis

2.5 Clinical Validation

3 Results

4 Discussion

5 Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1