Circulating white blood cell traits, chemokines and small cell lung cancer risk: a Mendelian randomization study

Introduction

Small cell lung cancer (SCLC) makes up 13% to 15% lung cancer cases, with around 250,000 annual diagnoses reported globally (1). Despite years of intensive research, the prognosis for individuals with SCLC remains bleak. This is attributed to the tumor’s remarkably rapid growth, a pronounced tendency for early and widespread metastasis, and the emergence of resistance to chemotherapy (2). Given the substantial threat to human health and the subsequent economic burdens associated with cancer, the significance of early cancer screening and prevention cannot be emphasized enough (3).

Numerous inflammatory factors contribute significantly to tumor growth, progression, angiogenesis, and metastases (4). Particularly, white blood cell (WBC) counts are widely recognized as biomarkers for systemic inflammation (5). There are several types of WBCs, each with its own distinct traits and functions. The main types of WBCs are neutrophils, lymphocytes, monocytes, eosinophils, and basophils. The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in peripheral blood have emerged as dependable indicators of the host’s inflammatory status. These ratios have been recognized as both prognostic and predictive biomarkers in various cancer types, including SCLC (6-9). Conventional epidemiological analyses of SCLC, including cross-sectional, case-control, or cohort studies, constitute the predominant research approach. However, these methods are susceptible to specific constraints, such as confounding and reverse causation, which may introduce biases into the estimates of effects (10). Given that the causal relationships between WBC subtypes and SCLC risk have not been fully explored, there is an urgent need to identify host factors that predispose individuals to SCLC. This will aid in enhancing primary prevention and developing treatment strategies.

C-C motif chemokine ligand 3 (CCL3) levels refer to the concentration or quantity of the chemokine CCL3 in a biological sample, such as blood or tissue. CCL3, also known as macrophage inflammatory protein-1 alpha (MIP-1α), is a specific chemokine involved in the recruitment and activation of immune cells. Abnormal CCL3 levels may be observed in various conditions, including infections, autoimmune disorders, and certain cancers (11). CXCL16, also known as chemokine (C-X-C motif) ligand 16, is a protein that plays a role in immune response and inflammation. It functions as a chemokine, which is a type of signaling molecule involved in the movement of immune cells to sites of infection or injury (12). The positive rate of CXCL16 in lung cancer tissue is significantly higher than that in adjacent tissue. The positive rate of CXCL16 was significantly positively correlated with poor prognosis of lung cancer (13). P-selectin glycoprotein ligand 1 (PSGL-1) is a glycoprotein that plays a crucial role in the interaction between WBCs, particularly leukocytes, and endothelial cells during inflammation. This interaction is an essential step in the process of immune cell recruitment to sites of infection or tissue damage. PSGL-1 may affect the metastasis of SCLC. Although there has been extensive discussion regarding the use of chemokine level reduction to inhibit cancer progression in cancer therapy, there is a scarcity of observational studies connecting particular circulating inflammatory cytokine to cancer risk. These studies often have relatively small sample sizes, and their results may be influenced by unmeasured confounding, reverse causation, and other biases (14,15). So the relationship between PSGL-1, CCL3, CXCL16 and SCLC remains to be determined.

Mendelian randomization (MR) is a causal inference method based on genetic variations. Its fundamental principle involves utilizing the impact of randomly allocated genotypes in the natural world on phenotypes to infer the influence of biological factors on diseases. In this study, univariate and multivariate MR methods were used to investigate the causal relationship between circulating WBCs and chemokines in SCLC. We used the most comprehensive genome-wide association study (GWAS) data available, including circulating WBCs, chemokines and SCLC. Sensitivity analysis was used to assess the impact of the hypothesis on the results and confirm the robustness of the results. We present this article in accordance with the STROBE-MR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1211/rc).

Methods

Study design

This study is based on three core assumptions: (I) genetic variations should be associated with exposure; (II) genetic variations should be independent of confounding factors; (III) genetic variations should only affect the outcome through exposure. We used the SCLC GWAS dataset (ebi-a-GCST004746) as the outcome factor (Figure 1). This data comes from GWAS summary statistics from the original GWAS study. The population selection, gene genotyping, and relevant baseline data involving GWAS (https://gwas.mrcieu.ac.uk/) data have been previously reported in other studies (16). Data collection was approved by the original GWAS ethics committee. The study included 8 GWAS datasets: SCLC, ebi-a-GCST004746; CD8⁺ T cell, ebi-a-GCST90001561; CD14⁻ CD16⁺ monocyte, ebi-a-GCST90001984; CD4⁺ T cell, ebi-a-GCST90002061; dendritic cell, ebi-a-GCST90002104; PSGL-1 levels, ebi-a-GCST90010165; C-C motif chemokine 3 levels, ebi-a-GCST90012055; C-X-C motif chemokine 16 levels, ebi-a-GCST90012059.

Figure 1 Overview of the design and methods used in this MR study. The figure was created with BioRender.com. SNP, single nucleotide polymorphisms; WBC, white blood cell; IVW, inverse variance weighted; MR, Mendelian randomization; MVMR, multivariable Mendelian randomization; SCLC, small cell lung cancer.

Data availability statement

The genetic-association data of the selected risk factors are available in https://cdn.amegroups.cn/static/public/tcr-24-1211-1.xlsx.

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). We used publicly available summary data so no ethical approval is required (17-19).

Statistical analysis

Candidate instrumental variables (IVs) for the two-sample MR study were selected from single nucleotide polymorphisms (SNPs) associated with SCLC (P<5e–08). Statistical significance for causal associations was considered at P value <0.05. An SNP is a DNA sequence polymorphism that is caused by a single nucleotide variation at the genomic level. Subsequently, a clumping procedure was applied with r²=0.01 and a clumping window of 10 Mb to remove linkage disequilibrium variants from the IVs. The selection and quality control of IVs were computed using TwoSampleMR. Finally, causal relationships between WBCs, chemokines and SCLC occurrence were analyzed using inverse variance weighted (IVW), MR-Egger, and weighted median (WM) methods. IVW estimates the causal effect of genes on the disease through weighted averaging; MR-Egger estimates the causal effect of genes on the disease by fitting a linear regression model and detects and corrects for genetic bias through Egger regression; WM provides robust estimates in the presence of genetic bias. Additionally, Cochrane’s Q statistic was used to assess heterogeneity, and outliers were removed if detected, followed by re-evaluation of MR causal relationships. MR-PRESSO tested for horizontal pleiotropy and provided corrected estimates. Statistical analysis and data visualization were performed in R software version 4.3.2 with the R package ‘TwosampleMR’, ‘MendelianRandomization’, and ‘MR-PRESSO’.

We then performed a multivariable MR (MVMR) analysis (20,21). MVMR considers multiple exposure factors simultaneously. By regaining aggregate genetic associations with exposure and risk factors in a weighted regression model, the corresponding effect of SNP-exposure on other assumed risk factor characteristics along the indirect pathway was limited. MVMR was performed to evaluate the direct causal effects of WBCs and chemokines on the risk of SCLC. The statistical codes are listed in Appendix 1.

Results

F-statistics

In the study of MR, a very important problem is weak IV bias. A weak IV is a genetic variant with a low power to explain exposure, which is associated with exposure, but the strength of the association is not very strong, so it is substantially different from an invalid IV. Some scholars have proposed to use F statistics to evaluate the effects of weak IVs. When the F statistic is less than 10, we will generally consider the genetic variation used to be a weak IV, which may produce a certain bias in the results (22). Prior to conducting the MR analysis, we calculated the average F-statistic for each WBC trait to identify the potential existence of weak instrument bias, typically denoted by an average F-statistic below 10 (23). For SCLC, these were 55.48 (CD8⁺ T cell, 5 SNPs); 93.64 (CD14⁻ CD16⁺ monocyte, 5 SNPs); 90.77 (CD4⁺ T cell, 8 SNPs); 109.45 (dendritic cell, 6 SNPs); 85.90 (PSGL-1 levels, 9 SNPs); 277.53 (C-C motif chemokine 3 levels, 6 SNPs); 80.15 (C-X-C motif chemokine 16 levels, 10 SNPs), indicating strong MR instruments (available online: https://cdn.amegroups.cn/static/public/tcr-24-1211-1.xlsx).

The detection of heterogeneity, directional pleiotropy and outliers

IVs from different analysis platforms, experiments, populations, etc. may be heterogeneous, thus affecting the results of MR analysis. IVW and MR-Egger tests were used to assess heterogeneity, and P value <0.05 indicated that there was heterogeneity in the study. If the IV affects the outcome by other factors than the exposure factor, it indicates that the IV has pleiotropy (23). Pleiotropy makes the assumption of independence and exclusivity untenable. Through the MR-Egger intercept test, the multi-effect of the data can be detected and the robustness of the results can be evaluated. If P value <0.05, the data has pleiotropy (24). (If pleiotropy is present, you need to re-select IVs or re-select exposure and outcome). In MR, identifying and dealing with outliers is an important step because outliers may affect the accuracy and reliability of the analysis results. Outliers generally refer to observations that deviate significantly from other data points, possibly due to measurement errors, reporting errors, or biological extremes. MR-PRESSO is a method specifically designed to detect outliers in MR studies. MR-PRESSO is able to detect abnormal SNPs that may affect the results of an MR analysis and identify these outliers through specific tests, such as outlier tests (25).

Odds ratios (ORs) and confidence intervals (CIs) of SCLC for exposures were scaled to the unit listed in https://cdn.amegroups.cn/static/public/tcr-24-1211-1.xlsx. Phenotypic descriptive statistics of studies included in the GWAS of exposure and outcome were displayed in https://cdn.amegroups.cn/static/public/tcr-24-1211-1.xlsx. There were no heterogeneity and the MR-Egger intercept analyses did not indicate directional pleiotropy in all analyses (Figure S1; available online: https://cdn.amegroups.cn/static/public/tcr-24-1211-1.xlsx). No outliers were detected by MR-PRESSO analyses (all P for Global test >0.05) (available online: https://cdn.amegroups.cn/static/public/tcr-24-1211-1.xlsx; Figures S2,S3).

Univariate MR analysis

Effect of WBC count on SCLC

The causal effect estimates of WBC count on SCLC susceptibility are summarized in https://cdn.amegroups.cn/static/public/tcr-24-1211-1.xlsx. Notably, CD45RA⁺ CD8⁺ T cell count might have a positive causal relationship with the risk of SCLC using either IVW (OR: 1.09, 95% CI: 1.01–1.18, P=0.03) or WM (OR: 1.09, 95% CI: 1.00–1.18, P=0.045) methods. The IVW (OR: 0.85, 95% CI: 0.77–0.94, P=0.002) and MW (OR: 0.82, 95% CI: 0.73–0.93, P=0.002) estimate of dendritic cell count showed its suggestive protective effect against SCLC. CD14⁻ CD16⁺ monocyte counts might all have protective relationship with the risk of SCLC both using IVW (OR: 0.84, 95% CI: 0.73–0.96, P=0.01) method and MW (OR: 0.86, 95% CI: 0.73–1.00, P=0.046) method. Our results demonstrated a causal association between a higher liability of SCLC and an increased level of CD39⁺ CD4⁺ T cell (OR: 1.12, 95% CI: 1.02–1.23, P=0.02) using IVW analysis. However, WM method (OR: 1.10, 95% CI: 0.99–1.22, P=0.09) showed no correlation (Figure 2; available online: https://cdn.amegroups.cn/static/public/tcr-24-1211-1.xlsx).

Figure 2 Mendelian randomization results for the potential relationships of circulating WBCs and chemokines with SCLC. CCL3, C-C motif chemokine ligand 3; CXCL16, chemokine (C-X-C motif) ligand 16; PSGL-1, P-selectin glycoprotein ligand 1; MR, Mendelian randomization; OR, odds ratio; U, upper; CI, confidence interval; WBC, white blood cell; SCLC, small cell lung cancer.

Effect of chemokines on SCLC

The IVW estimates of PSGL-1 (IVW OR: 0.89, 95% CI: 0.81–0.98, P=0.02), CCL3 (IVW OR: 0.85, 95% CI: 0.73–0.99, P=0.043), and CXCL16 levels (IVW OR: 1.28, 95% CI: 1.03–1.61, P=0.03) showed a suggestive association with SCLC. However, with WM method, these associations were no longer significant (P>0.05) (Figure 2; available online: https://cdn.amegroups.cn/static/public/tcr-24-1211-1.xlsx).

MVMR

When CD45RA⁺ CD8⁺ T cell (OR: 1.03, 95% CI: 0.94–1.14, P=0.46), dendritic cell (OR: 0.92, 95% CI: 0.83–1.03, P=0.17), CD14⁻ CD16⁺ monocyte, and CD39⁺ CD4⁺ T cell (OR: 1.10, 95% CI: 1.00–1.21, P=0.06) were assessed together in MVMR, only CD14⁻ CD16⁺ monocyte (OR: 0.86, 95% CI: 0.76–0.97, P=0.01) retained a potentially causal relationship with SCLC (Figure S4; available online: https://cdn.amegroups.cn/static/public/tcr-24-1211-1.xlsx).

When CXCL16, CCL3 and PSGL-1 were examined together in MVMR, CXCL16 maintained a direct impact on SCLC (OR: 0.90, 95% CI: 0.83–0.98, P=0.01) and PSGL-1 (OR: 1.27, 95% CI: 1.01–1.60, P=0.04) still a protective factor, while its associations with CCL3 (OR: 0.88, 95% CI: 0.75–1.02, P=0.09) were diminished with no sense (Figure S4; available online: https://cdn.amegroups.cn/static/public/tcr-24-1211-1.xlsx).

Discussion

Epidemiologic and genetic studies have suggested a connection between peripheral immune responses and cancer risk (6,8,9). Given that the majority of identified GWAS variants are noncoding and situated in the regulatory regions of genes, unraveling their functional implications poses considerable challenges. Additionally, the presence of reverse causation and confounding factors makes drawing causal inferences difficult and susceptible to bias. In contrast, the MR approach, employing genetic variants strongly linked to the exposure as IVs, allows for causal inference while accounting for various confounding factors (26-28). Using two-sample MR, we found that genetic predisposition to CD45RA⁺ CD8⁺ T cell, CD39⁺ CD4⁺ T cell, CXCL16 was associated with an increased risk of SCLC. There were suggestive inverse associations of genetically predicted dendritic cell, CD14⁻ CD16⁺ monocyte, PSGL-1 and CCL3 with SCLC risk. MVMR further confirmed that CXCL16 exerted a direct effect on SCLC, while CD14⁻ CD16⁺ monocyte and PSGL-1 indicated that they are protective in SCLC.

CD45RA⁺ CD8⁺ T cells refer to a specific subset of T lymphocytes characterized by the presence of the surface markers CD45RA and CD8. These cells play a role in the immune system and are identified based on the expression of these particular cell surface proteins. CD45RA is a marker associated with naive T cells, which have not yet encountered an antigen. CD8, on the other hand, is a co-receptor found on the surface of cytotoxic T cells, which are involved in recognizing and destroying infected or abnormal cells (29). Some studies have suggested that different T cell subpopulations may have different immunotherapy responses in lung cancer patients. The presence of CD45RA⁺ CD8⁺ T cells may be related to the immune status and therapeutic effect of patients. The number and functional status of CD45RA⁺ CD8⁺ T cells may be related to the prognosis of lung cancer patients (30). Several studies have attempted to monitor this subpopulation of T cells to assess patient response to treatment and survival (31-34).

The increased expression levels of CD39⁺ CD4⁺ T cells in peripheral blood and tumor tissues may be one of the mechanisms related to immune escape of tumor cells, acceleration of checkpoint immunotherapy resistance, poor overall survival disease progression and poor prognosis in many (35-37).

Currently, circulating human monocytes can be divided into subpopulations based on the expression of surface CD14 [cell co-receptor of lipopolysaccharide (LPS)] and CD16 (low affinity IgG receptor), which are subdivided into three main subpopulations, of which about 90% of monocytes are CD14^hiCD16⁻ (38-40). CD14⁺ CD16⁻ monocytes include pro-tumor-associated macrophage differentiation, metastatic cell colonization, T cell function inhibition, regulatory T cell recruitment, angiogenesis, and extracellular matrix remodeling, and their anti-tumor functions are tumor cytotoxicity and antigen presentation. According to the results, while protective factors were quantitatively dominant, the overall effect of risk factors was stronger. This suggests that in practical applications, it is necessary to pay attention to both types of factors. Enhancing the role of protective factors and reducing the impact of risk factors are both important strategies.

These chemokines can influence the survival, proliferation, invasion, and metastasis of lung cancer cells. The causal relationship between circulating WBCs and chemokines with SCLC has not been studied. This is the first MR research to evaluate the causal relationship between circulating WBCs and chemokines with SCLC.

Both SCLC and chronic obstructive pulmonary disease (COPD) are characterized by chronic inflammation. Chronic inflammation can lead to continuous damage and repair processes in lung tissue, and this repeated damage and repair may promote the formation and development of cancer cells. Patients with COPD have a significantly increased risk of lung cancer, of which SCLC is a type of lung cancer. Studies have shown that about 10 to 20 percent of people with COPD will eventually develop lung cancer, and SCLC accounts for a percentage of these lung cancers. Chronic inflammation and oxidative stress may play a key role in the comorbidity of COPD and SCLC. Long-term inflammatory response can lead to DNA damage, cell proliferation and apoptosis imbalance, thus promoting the formation of cancer cells (41). At present, studies have observed an increase in the number of CD45RA⁺ CD8⁺ T cells, CD4⁺ T cells, CD14⁺ CD16⁻ monocytes and dendritic cells, and increased expression of CCL3 and CXCL16 in COPD patients (39,42-44). These results suggest that COPD and SCLC, while different in many ways, share some important links, particularly common risk factors and chronic inflammatory states.

However, there are several limitations that must be considered. First, the data for our study originated from GWAS, and subgroup analyses were not feasible due to the absence of detailed demographic information and clinical records for the study participants. Second, potential ethnic bias might exist as the individuals in the study were of European descent. Therefore, caution should be exercised when extrapolating the conclusions to others. Third, fewer SNPs were screened. It may be because the incidence of SCLC is less than that of non-SCLC or other cancers, coupled with factors related to tumor heterogeneity. More research should be conducted to confirm our results and to try to apply them to clinical diagnosis procedures and therapy options (5).

Conclusions

This MR study identified additional inflammatory factors in patients with SCLC relative to previous studies, and raised a possibility of SCLC-caused immune abnormalities. These identified inflammatory factors may be potential biomarkers of immunologic dysfunction in SCLC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE-MR reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1211/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1211/prf

Funding: This study was supported by the Beijing Tongzhou District Science and Technology Plan Project (No. KJ2023CX037 to T.M.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-1211/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are 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. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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