Mendelian Randomization Identifies Two Immune Cell Traits Associated with Lung Cancer

doi:10.21203/rs.3.rs-3718149/v1

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Mendelian Randomization Identifies Two Immune Cell Traits Associated with Lung Cancer

https://doi.org/10.21203/rs.3.rs-3718149/v1

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Background

Experimental evidence has linked immune cells to lung cancer. It is unclear whether associations for immune cell traits are causal or due to bias. To explore the causal impact of immune cells on lung cancer risk, we conducted a two-sample Mendelian randomization (MR) analysis.

Materials and Methods

We analyzed the relationship between immune cell traits and lung cancer using a two-sample MR approach. Genome-wide association study (GWAS) summary statistics for 731 immune cell traits and lung cancer risk were assessed. The immune cell characteristics included four types: absolute cell (AC) counts, median fluorescence intensities (MFIs) of surface antigens, morphological parameters (MP) and relative cell (RC) counts. GWAS associated with lung cancer was extracted from a large-scale association analysis that included 29,266 cases and 56,450 controls. Inverse Variance Weighted (IVW) was performed as the primary MR analysis method. Multiple sensitivity analyses are used to evaluate the robustness of the results. Finally, a reverse MR analysis was conducted to assess the possibility of reverse causation.

Results

MR analysis showed that CD14- CD16 + monocyte %monocyte(OR = 0.924; 95%CI,0.889–0.960) and CD4 on naive CD4+(0.922, 0.889–0.956) was associated with overall lung cancer after accounting for multiple testing. There was no statistically significant effect of immunophenotypes for lung cancer subtypes.

Conclusions

Our findings suggest that CD14- CD16 + monocyte %monocyte and CD4 on naive CD4 + might protect against lung cancer, and larger-scale genetic data are expected to further validate our findings.

Immunity

Lung cancer

Mendelian randomization

T cell

Monocyte

Lung cancer, primarily consisting of lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD), is the leading cause of cancer deaths in the world(Bray, Ferlay, et al. 2018). In 2020, it is estimated that there were 2206,771 new lung cancer cases, with 1435943 in males and 770828 in females(Zhang, Vaccarella, et al. 2023). Lung cancer not only imposes a heavy burden on the physical and mental health of sufferers around the world but also creates a world economic burden(Choi and Ryu 2018, Liao, Shun, et al. 2014, Bencina, Chami, et al. 2023).

Currently, lung cancer is mostly detected at an advanced stage due to symptoms such as coughing, coughing up blood, difficulty breathing, and chest pains(Florian, Kocher, et al. 2015). Unfortunately, Lung cancer patients are usually asymptomatic in the early stages(Hoffman and Sanchez 2017). Chest X-rays and computed tomography scans are the most commonly used methods for diagnosing lung cancer(Wiener, Gould, et al. 2015, Doroudi, Pinsky, et al. 2018). However, since they can only detect visible, irreversible changes in the lung, more diagnostic methods are needed. To overcome this challenge, new specific biomarkers must be discovered.

In recent years, It has been gradually recognized that the Cellular complexity of the tumor microenvironment (TME) is importantly linked to cancer development, progression, and metastasis (Joyce and Pollard 2009). The TME infiltrates many different cell types, such as carcinoma cells, sarcoma cells, immune cells, endothelial cells, and fibroblasts(Arneth 2019). Immune cells are an important part of the immune system and can recognize and destroy occult cancer cells(Aranda, Chaba, et al. 2018). A study using tissue microarrays and immunohistochemistry to identify immune cells in non-small cell lung cancer (NSCLC) found that NSCLC tumors infiltrated with various types of T cells, B cells, macrophages, NK cells, and DCs(Banat, Tretyn, et al. 2015). In addition, many studies have found immune cells influence survival in NSCLC patients(Donnem, Hald, et al. 2015, Paulsen, Kilvaer, et al. 2015, Feng, Li, et al. 2016). Therefore, exploring immune cells associated with lung cancer development contributes to early screening and prevention of lung cancer. However, there is still a lack of systematic studies to assess the causal relationship between immune cells and lung cancer, and human causal inferences may be biased by confounders because exposure and outcome share a common cause in confounding. The unavoidable design weaknesses of conventional observational studies are because many putative risk factors, including smoking behavior(Aredo, Luo, et al. 2021), alcohol consumption(Freudenheim, Ritz, et al. 2005), and chemical environmental exposures(Zablotska, Lane, et al. 2013), are closely related to lung cancer. This can cause confounds in traditional studies.

MR uses hundreds of genetic variants as instrumental variables and can be used to identify biomarkers for disease-related traits(Burgess, Foley, et al. 2020, Mohammadi-Shemirani, Sjaarda, et al. 2019). It is less confusing because it relies on the random segregation of alleles at meiosis and random assignment at conception, thus breaking the link with almost all interfering elements(Richmond and Davey Smith 2022). Our study performed a framework of MR analysis to investigate the potential causal relationship between 731 immune cell traits and lung cancer.

Study design

With a strict MR design, we evaluated causal associations of 731 immune cell traits concerning lung cancer risk. An outline schematic of the study design is presented in Fig. 1, and a summary of the experimental protocol of our study is presented in Fig. 2. Three critical assumptions must be met for the selected instrumental variables used in MR: (1) the genetic instruments are closely related to immune cell traits(Lawlor, Harbord, et al. 2008); (2) the genetic instruments are not related to potential confounders between immune cell traits and lung cancer(König and Greco 2018); and (3) the genetic instruments only influence lung cancer through immune cell traits(Chen, Yang, et al. 2022). Due to publicly available GWAS summary statistics, there was no need to apply for ethical approval.

GWAS Data on Immune Cell Traits

Effect estimates for single nucleotide polymorphisms (SNPs) associated with immune cell traits are publicly available from the GWAS Catalog, which is genetic data from 3,757 Sardinians. The original GWAS reported the effects of approximately 22 million genetic variants on 731 immunophenotypes. It is based on an increase in sample size, which increases the number of genes associated with immune cell characteristics, thus increasing the information content of the genetic map (Orrù, Steri, et al. 2020).

GWAS Data on Lung Cancer

GWAS associated with lung cancer was from a large-scale association analysis, which involved 29,266 cases and 56,450 control subjects of European individuals. In this study, 10 novel susceptibility loci of genome-wide significance were identified, demonstrating heterogeneity among different subtypes of lung cancer(McKay, Hung, et al. 2017).

Selection of instrumental variables (IVs)

The following quality control steps were performed to select validated genetic tools. First, according to previous studies(Wang, Zhu, et al. 2023), We extracted SNPs with significance levels (P < 1 × 10 − 5) associated with immune cell traits. Second, to ensure the independence of genetic variation, we clustered using calculated estimates of linkage disequilibrium (LD). The threshold of r2 was < 0.001, and the window size was adjusted to 10000 kb. Third, exposed and resulting data were harmonized to eliminate palindromic SNPs and to ensure that the effect alleles belonged to the same allele. Finally, due to multiple tests, we performed these primary MR analyses of 731 immune cell traits using a false discovery rate (FDR) (Storey and John 2003).

In addition, we performed a bidirectional MR to evaluate the potential impact of lung cancer on immune cell traits to ensure the correct orientation. For lung cancer, the significance level of IVs was set to 5×10 − 8. To ensure IV strength, We calculated the F-statistic using the formula (Beta^2/SE^2). F statistic values of > 10 indicate a lack of strong evidence for weak instrument bias(Pierce, Ahsan, et al. 2011). We excluded weak instruments with F statistics values less than 10. Then, 15 IVs were preserved for further analysis. The summary information on instrumental variables related to lung cancer is presented in Supplementary Table 14.

Statistical analysis

We used the IVW approach as the primary method for analyzing the effect of immune cell traits on lung cancer. Complementary analyses were performed using different MR methods. Specifically, the weighted median(WM), a complementary approach to IVW, can derive consistent causality estimates even though up to 50% of the genetic variation is invalid(Bowden, Davey Smith, et al. 2016). The MR-Egger method is widely used as an effective test of multinomial, and it can also be used to estimate causal effects through slope coefficients(Bowden, Davey Smith, et al. 2015). The Cochrane's Q heterogeneity test was used to assess the extent of heterogeneity. When p > 0.05 indicates the absence of heterogeneity(Greco, Minelli, et al. 2015). To rule out the effect of horizontal multiplicity, MR-Egger was used, which implied there was horizontal multiplicity if the intercept term was significant(Bowden, Del Greco, et al. 2016). In addition, the MR Polytropic Residual Sums and Outliers(MR-PRESSO) method is used to exclude outlier variables that are horizontal polytropy(Verbanck, Chen, et al. 2018). To assess the effect of individual SNPs on MR estimates, leave-one-out analyses were also performed(Burgess and Thompson 2017). In addition, scatter plots and funnel plots were used to illustrate the MR results.

All MR analyses were performed in R (version 4.2.3). The R packages included TwoSampleMR(version 0.5.7) and MR-PRESSO (version 1.0) packages. FDR was performed by Microsoft Office Excel 16.0. All results were visualized using R and Adobe Illustrator (version 2023).

No immune cell traits were identified at a significance of 0.05 after multiple test adjustments of LUSC and LUAD. For overall lung cancer, we found a protective effect of two immunophenotypes: CD14-CD16 + monocyte %monocyte and CD4 on naive CD4+ (Maturation stages of T cell). The results of the MR analysis on the association between immune cell traits and different types of lung cancer are presented in Supplementary Table 1–6.

The results of the MR analysis of overall lung cancer are shown in Fig. 3. In the IVW method, CD14-CD16 + monocyte %monocyte was associated with a lower risk of overall lung cancer with an odds ratio (OR) of 0.924 (95% CI = 0.889–0.960, P = 4.43E-05, PFDR = 0.02). Similar results were observed for the other two methods: MR-Egger (OR = 0.910, 95% CI = 0.856–0.968, P = 0.008); and the WM(OR = 0.921, 95% CI = 0.871–0.973, P = 0.003). The OR of CD4 on naive CD4+ (Maturation stages of T cell) on overall lung cancer risk was estimated to be 0.922 (95% CI = 0.889–0.956, P = 9.07E-06, PFDR = 0.007). Similar results were observed for the WM (OR = 0.911, 95% CI = 0.856–0.968, P = 0.008). Although the result of MR-Egger indicated that there was no statistically significant between CD4 on naive CD4+ (Maturation stages of T cell) (OR = 0.940, 95% CI = 0.860–1.027, P = 0.187) and overall lung cancer, the direction was in line with the results of the main analysis, especially IVW. Figure 4 and Fig. 5 showed the results of the scatter plots, which indicated the stability of the results. Additionally, Egger intercepts and the global test of MR-PRESSO ruled out potential horizontal pleiotropy. The leave-one-out method showed that the results were not affected by the removal of each SNP. The results of the sensitivity analysis demonstrated the robustness of the causal relationships in our study. Detailed information on all sensitivity analyses can be found in Supplementary Table 7–10. Supplementary Table Fig. 1 and supplementary Table Fig. 2 showed forest plots, leave-one-out mapping, and funnel plots.

In addition, we investigated the causal effects of lung cancer on CD14- CD16 + monocyte %monocyte and CD4 on naive CD4+ (Maturation stages of T cell) by inverse MR analysis. The analysis indicated that overall lung cancer was not significantly associated with CD14- CD16 + monocyte %monocyte(P = 0.998) or CD4 on naive CD4+ (Maturation stages of T cell)(P = 0.693). All results can be found in the Supplementary table 11–14. There was no evidence of a significant intercept, suggesting that directed pleiotropy was not observed. Each SNP was consistent with the overall effect in the leave-one-out analysis. Figure 6 and Fig. 7 summarized the results of scatter plots, forest plots, leave-one-out mapping, and funnel plots.

We assessed the potential relationship of 731 immune cell traits to lung cancer with large-scale genetic data. In this study, we found no association between any of the immune cell traits and lung cancer subtypes, whereas two immunophenotypes had a significant causal effect on overall lung cancer (FDR < 0.05).

Our study found that the risk of lung cancer was negatively correlated with CD14-CD16 + monocytes % monocytes. Human monocytes are differentiated into subpopulations by different surface markers and functions, thus classifying monocytes into classical (CD14 + CD16−), non-classical (CD14-CD16+), and intermediate (CD14 + CD16+)(Cormican and Griffin 2020, Narasimhan, Marcovecchio, et al. 2019). It is widely recognized that the role of non-classical monocytes is protective, as they play a key role in maintaining vascular endothelial homeostasis(Thomas, Tacke, et al. 2015). They are also the first line of defense to recognize and remove pathogens and even cancer cells(Hanna, Cekic, et al. 2015). A study examining the correlation between monocyte subpopulations and antibody efficacy found that a higher proportion of non-classical monocytes prolonged survival in NSCLC patients(Ohkuma, Fujimoto, et al. 2023). However, there have been few experimental studies on atypical monocytes and lung cancer. Therefore, the protective mechanism of non-classical monocytes for lung cancer needs to be further explored.

The results showed that CD4 on naive CD4+ (Maturation stages of T cell) might decrease lung cancer risk, which is consistent with previous reports(Liu, Xu, et al. 2019, Eberst, Vernerey, et al. 2022). CD4 T cells reduce the risk of lung carcinogenesis through multiple pathways. In immunotherapy for NSCLC, CD4 T cells maintain cytokine-induced killer(CIK) cell infiltration into tumors and increase the ability of CIKs to kill tumor cells(Liu, Meng, et al. 2022). It was reported that CD4 + T cells can help the cytotoxic T cells( CTLs) to be able to invade tumor tissues by down-regulating PD-1 and cooperative inhibition of receptors, optimizing CTL expression of cytotoxicity, and maintaining CTL efficacy(Ahrends, Spanjaard, et al. 2017). Meanwhile, the researchers found that CD4 + T cells exert anti-tumor immunity by recognizing antigens of lung cancer(Veatch, Jesernig, et al. 2019). Therefore, our MR analysis further supports CD4 on naive CD4+ (Maturation stages of T cell) may contribute to decreasing lung cancer risk.

In summary, Our study is the first to explore the link between immune cell traits and lung cancer through the tool of genetic variations. Our study excluded other confounders and made causal inferences through multiple sensitivity analyses. Thus there was no confounding by horizontal pleiotropy and other factors. Some shortcomings of this study should be pointed out. First, two-sample MR requires that both samples come from the same population; our study included only participants of the Western population, so generalizing these results to other populations requires further research. Second, the number of strongly correlated SNPs selected in the MR analysis was relatively small due to sample size limitations. Invalid results may be due to low efficacy and insufficient SNPs, which limits our ability to conclude true causality. However, The results of the sensitivity analysis supported our results. Finally, We used a p-value of less than 1 × 10 − 5, when selecting genetic instrumental variables for immune cell traits, which may lead to an increase in false positive rates. We expect more relevant studies to confirm our findings.

In conclusion, the results of our study suggested two immunophenotypes have potential causal effects on lung cancer. It provided a new way for researchers to explore new biological markers for lung cancer and helped to explore early intervention and therapeutic approaches.

Abbreviations

MR Mendelian randomization

GWAS Genome-wide association study

AC absolute cell

MFIs median fluorescence intensities

MP morphological parameters

RC relative cell

IVW inverse Variance Weighted

LUSC lung squamous cell carcinoma

LUAD lung adenocarcinoma

TME tumor microenvironment

SNPs single nucleotide polymorphisms

IVs instrumental variables

LD linkage disequilibrium

WM weighted median

FDR a false discovery rate

MR-PRESSO MR Polytropic Residual Sums and Outliers

OR odds ratio

Footnotes

Publisher's Note

Springer Nature remains neutral about jurisdictional claims in published maps and institutional affiliations.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Ethics approval

Due to publicly available GWAS summary statistics, there was no need to apply for ethical approval.

Consent to publish

Not applicable.

Funding

This study was supported by the Sanming Project of Medicine in Shenzhen(No.SZZYSM202206014)

Author Contribution

Writing - original draft preparation: LX, JL. Data collection and analysis: LX, JL, CXS. Revising the manuscript: YHL, ZMX. Providing advice: JJX, XNT. All authors read and approved the final manuscript.

Acknowledgments

We thank the developers and staff of IEU Open GWAS for providing us with publicly available GWAS summary data.

Data Availability

All GWAS data used in this study are available in the IEU Open GWAS Project (https://gwas.mrcieu.ac.uk/).

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No competing interests reported.

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Mendelian Randomization Identifies Two Immune Cell Traits Associated with Lung Cancer

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Version 1

Abstract

Background

Materials and Methods

Results

Conclusions

Figures

Introduction

Materials and Methods

GWAS Data on Immune Cell Traits

Statistical analysis

Results

Discussion

Conclusions

Abbreviations

Declarations

Footnotes

Competing Interests

Ethics approval

Funding

Author Contribution

Acknowledgments

Data Availability

References

Additional Declarations

Supplementary Files

Status:

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