Relationship between three reproductive factors and lung cancer: a two-sample Mendelian randomization study

doi:10.21203/rs.3.rs-3012323/v1

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Relationship between three reproductive factors and lung cancer: a two-sample Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-3012323/v1

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Background

There is increasing evidence that sex hormones are involved in the development of lung cancer, but the correlation between reproductive behavior that changes sex hormone levels and lung cancer is not clear. Therefore, we conducted a two-sample Mendelian randomization (MR) analysis to explore the potential relationship between age at first sexual intercourse (AFS), menarche, menopause and lung cancer.

Methods

We performed MR analysis of the data from genome-wide association study (GWAS) of European ancestry to evaluate the independent effects of three reproductive behaviors on lung cancer over all (LUCA), lung adenocarcinoma (LUAD), squamous cell lung cancer (LUSC) and small cell lung cancer (SCLC). We mainly used inverse variance weighting (IVW) method for MR analysis. Sensitivity was determined by MR-pleiotropy residual sum and outlier (MR-PRESSO) analysis, weighted median analysis, MR-Egger analysis, and Leave-one-out analysis.

Results

We found that older AFS had a causal relationship with LUCA [odds ratio (OR) = 0.6283, 95% confidence interval (CI) = 0.4959 ~ 0.7961, P = 0.0001], LUAD (OR = 0.7042, 95% CI = 0.4967 ~ 0.9984, P = 0.049) and LUSC (OR = 0.6231, 95% CI = 0.4386 ~ 0.8853, P = 0.0083) by MR analysis.

Conclusion

Our results observed a causal relationship between older AFS and lower lung cancer risk. It emphasizes the importance of providing sex education since early sexual intercourse may have undesirable effects. In addition, early psychological treatment is also essential.

Reproductive behaviors

Lung cancer

Mendelian randomization analysis

Age at first sexual intercourse

Lung cancer prevention

Globally, although the incidence of lung cancer is declining (1), it remains the second most common cancer and the most common cause of cancer death (2). With the development of surgery, targeted therapy, immunotherapy and other means, the five-year survival rate of lung cancer has improved. However, the prognosis of most patients with lung cancer is still poor (3). Smoking is a recognized risk factor for lung cancer (4–6), but we are seeing an increasing number of patients with lung cancer who have no history of smoking. Consequently, identifying potential modifiable risk factors and making adjustments accordingly are essential for lung cancer prevention. Previous studies have reported that hormones, especially sex hormones, may be related to the occurrence, development and prognosis of lung cancer (7–9). These findings suggest that sex hormones may play a role in the development of lung cancer. Therefore, we believe that some reproductive behaviors which can alter sex hormone levels, such as age at first sexual intercourse (AFS), menarche and menopause, may affect the development of lung cancer.

However, due to the inherent limitations of observational studies, such as confounding factors, information bias and reverse causation, the observed association between sex hormones and lung cancer may only be a chance finding and requires further verification. Taking these factors into account, we used a new genetic epidemiological tool, Mendelian randomization (MR) analysis.

2.1 Data source

In this study, we used pooled data from genome-wide association study (GWAS) to explore the relationship between three reproductive factors and lung cancer through two-sample MR analysis. We extracted the single nucleotide polymorphisms (SNPs) that were reliably associated with AFS (ebi-a-GCST90000047), menarche (ukb-a-315) and age at menopause (ukb-b-17422) from the datasets archived in the GWAS database. For the same situation, we obtained summary-level data for the outcome events “LUCA, LUAD, LUSC, SCLC” from the “ieu-a-966, ieu-a-965, ieu-a-967, finn-b-C3_SCLC” datasets. All SNPs were selected from European populations to meet the Hardy–Weinberg law and eliminate demographic distribution bias(10). These GWAS data were listed in Table 1.

2.2 Statistical approach

MR analysis is a novel approach for solving problems in human biology and epidemiology by using genetic variation as instrumental variables (IVs)(11). Genetic information refers to the information that organisms transmit from their parents to their offspring, or from cells to cells during each cell division, in order to replicate the same things as themselves (12), and is difficult to be influenced by confounding factors and reverse causality. MR analysis identified genetic variations related to target exposure through large-scale genome-wide association study (GWAS) and then applied them to independent datasets to generate unbiased estimates of exposure and outcome (13).

2.3 Selection of instrumental variables

A reliable instrumental variable needs to meet the following conditions:(1) instrument variable is reliably associated with the exposure(p < 5*10^− 8); (2) instrument variable is not associated with confounding factors; and (3) instrument variable only affects outcomes through exposure pathways(14,15). Figure 1 provides an overview of the basic principles, design, and process of our MR analysis.

First, we extracted SNPs which strongly associated with three reproductive factors (P < 5*10^− 8) from the GWAS datasets as their respective IVs. Second, we selected SNPs with a distance of 10000 kb from each other to exclude SNPs with linkage imbalance (LD) (r2 < 0.001). As smoking is a common risk factor for lung cancer, SNPs associated with “smoking” and “cigarette using” were also removed. Third, to avoid the bias caused by weak instrumental variables, we calculated the F-statistic for each SNP by using the formula(16):

to estimate the strength of genetic instruments, where N is the sample size, k is the total number of SNPs selected for MR analysis. The following formula was used to calculate the R² for each significant SNP(16):

, where BETA is the estimated effect on exposures, EAF is the effect allele frequency, N is the sample size, and SE is the standard error for each SNP. F-statistic > 10 suggests that SNPs are a sufficiently strong instrument to explain phenotypic variation, while F-statistic < 10 implies a weak instrument(17). And the results of F-statistic about three reproductive factors were shown in Supplementary Table 1. Fourth, we extracted the SNPs that were strongly associated with four outcomes from the three reproductive factors associated SNPs at a correlation criterion of r² < 0.01, respectively. Furthermore, fuzzy SNPs with alleles of different origin and palindromic SNPs with fuzzy chains were directly excluded in MR analysis. Exclude SNPs with LD distance > 10000KB and minor allele frequency (MAF) < 0.01, as well as palindrome and multidirectional outlier SNPs. Eventually, the numbers of SNPs of three reproductive factors on lung cancer were shown in Supplementary Table 1.

2.4 Statistical analysis

Our study applied MR analysis to explore genetic associations of three reproductive factors [Age at first sexual intercourse (AFS), Age at menarche, Age at menopause] and four outcomes [Lung cancer over all (LUCA), Lung adenocarcinoma (LUAD), Squamous cell lung cancer (LUSC), Small cell lung cancer (SCLC)].

In order to evaluate the causal impact of each reproductive factors on lung cancer risk and test the sensitivity of the results to different patterns that violate instrumental variable assumptions, we mainly used the following three statistical methods: Inverse variance weighted (IVW), MR-Egger and Weighted Median (WM) (18). The IVW method requires each genetic variation to satisfy the assumption of instrumental variables, when met this condition, its statistical efficiency is significantly higher than the other two methods(19), so it was used as the major statistical method. The MR-Egger method can provides a sensitivity analysis for the robustness of MR analysis results(20). The WM method can generate consistent estimates even if more than 50% of the information comes from invalid instrumental variables (21). The estimated effects of three reproductive factors are reported as odds ratios (OR) with corresponding 95% confidence interval (CI).

The MR-pleiotropy residual sum and outlier (MR-PRESSO) method was used to check whether there are outlier in SNPs and remove them (22). After this, we mainly used three methods to test the sensitivity of the results of our MR analysis. We tested the heterogeneity of SNPs using IVW and MR-Egger methods, and used funnel plots as validation (23,24). We apply the MR-Egger method to evaluate whether there is horizontal pleiotropy in SNPs(25). In addition, we employed the leave-one-out method to detect whether there is individual single SNP that has a significant impact on outcomes (26). All MR analysis were conducted using R software (version 4.2.3) with R packages including Two-Sample MR, Mendelian Randomization, and MR-PRESSO.

We found that older AFS is a protective factor for LUCA (OR = 0.6283, 95%CI = 0.4959 ~ 0.7961, P = 0.0001), LUSC (OR = 0.6231, 95% CI, 0.4386 ~ 0.8853, P = 0.0083), LUAD (OR = 0.7042, 95%CIs = 0.4967 ~ 0.9984, P = 0.049), and is not related to SCLC (OR = 0.3916, 95%CIs = 0.0982 ~ 1.5608, P = 0.1839). Age at menopause and age at menarche are not associated with lung cancer. The effects of genetic causality between three reproductive factors and lung cancer were analyzed by the three MR methods (IVW, MR-Egger, WM) as shown in Fig. 2. In addition, the effects of three reproductive factors on the risk of lung cancer were shown in the scatter plots (Supplementary Fig. 1). The results of our MR analysis for the effect size of three reproductive factors on four outcomes were shown in forest maps (Supplementary Fig. 2). As a method of assisting in verifying heterogeneity, the results are all in the funnel plots (Supplementary Fig. 3). It is considered to have no heterogeneity if the funnel plot is symmetrical on both sides (27,28).

Although three reproductive factors showed significant heterogeneity in some outcomes, the most results remained consistent after MR-PRESSO analysis (Supplementary Table 2). No significant difference in pleiotropy was observed in the effect estimates of three exposures (Supplementary Table 2). This indicates that after data correction, the results show that older AFS may reduce the risk of lung cancer, especially squamous cell carcinoma and adenocarcinoma. When any SNP is removed, the sensitivity analysis of leave-one-out method did not detect any significant changes that affect the estimation (Supplementary Fig. 4), indicating that our MR analysis results are robust.

In summary, out of three reproductive factors only AFS was genetically associated with lung cancer on IVW method, indicating that older AFS had significantly lower risk of lung cancer, especially squamous cell carcinoma and adenocarcinoma.

In this two-sample MR analysis, we found that older AFS was a protective factor for lung cancer. More specifically, an increase in the number of units of AFS (1 year) predicted a nearly one-third reduction in lung cancer risk. After further subgroup analysis, we found that older AFS can reduce the incidence rate of LUSC and LUAD, but has nothing to do with the incidence rate of SCLC. In addition, the other two reproductive factors are not associated with the risk of lung cancer.

To the best of our knowledge, our study is the first MR study to use large-scale GWAS data to focus on genetic causality between AFS and lung cancer risks. Possibly due to the relatively long incubation period between AFS and lung cancer, randomized controlled trials considered the gold standard for investigating causal relationships are not applicable. We could find no published studies that explored a causal relationship between AFS and lung cancer. Based on the results of this study, we suggest that the association between AFS and lung cancer risk lies in physical and psychological changes after sexual intercourse.

It is well known that sex hormones play a crucial role in sexual activity (29–32). Changes in hormone levels in adolescents after first sexual intercourse may be a physiological factor in the increased incidence of lung cancer. Laura P Stabile et al. suggest that estrogen signaling plays a biological role in both the epithelium and the mesenchyme in the lung and that estrogens could potentially promote lung cancer, either through direct actions on preneoplastic or neoplastic cells or through indirect actions on lung fibroblasts (33). Apart from Stabile et al., numerous studies have shown that estrogen plays an important role in the development and progression of lung cancer (34–38). For example, elevated estrogen in women had been linked to a reduced ability to repair DNA, making women more susceptible to tobacco's carcinogenic effects (36). Estrogen can act as a direct or indirect carcinogen by altering cell proliferation or regulating cell growth factors (39,40). In addition to estrogen, the role of androgens also could not be ignored (41–45). Mireya et al. suggest that androgens promote the polarization of M2 macrophages, thereby enhancing tumor growth and inhibiting anti-tumor immune responses (43). All above these, we suspect that younger AFS may increase the risk of lung cancer by exposing adolescents to hormone-related risk factors earlier.

At the same time, younger AFS may also play a role in the development of lung cancer through psychological factors. Numerous studies have reported that younger AFS are associated with depression in adolescents (46–48). And a large prospective cohort over 24 years suggests that depressive symptoms may contribute to lung cancer incidence(49). All these studies have verified the reliability of the results of our MR analysis.

As far as we know, our study described the association between AFS and risk of lung cancer by MR analysis for the first time. And we found that older AFS was a protective factor for lung cancer. This finding may have important contributions to public health. We recommend that adolescents refrain from sexual intercourse at a young age, especially if there is a family history of lung cancer. For adolescents who already have younger AFS, early psychotherapy and earlier screening for chest CT should be recommended.

MR mendelian randomization, AFS age at first sexual intercourse

GWAS genome-wide association study, LUCA lung cancer, LUSC lung squamous cell carcinoma

LUAD lung adenocarcinoma, SCLC small cell lung cancer, IVW inverse variance weighting

SNP single nucleotide polymorphism, IV instrumental variable

MAF minor allele frequency, WM Weighted Median

Availability of data and materials

The GWAS data were retrieved from IEU-Open GWAS project (https://gwas.mrcieu.ac.uk/).

The GWAS-ID were shown in Table 1.

Author Contributions

LYF and HZF proposed and designed the study. LYF and JWW performed the experiments and analyzed the data. LYF wrote the original manuscript. ZXY and WXH proofread the manuscript and data. LYF, JWW and WXH made figures and tables. All authors read and approved the manuscript.

Correspondence:
Correspondence to Zhengfu He ，[email protected]

Ethics approval and consent to participate

The data were retrieved from previous studies in open database. Therefore, ethical approval was not required.

Consent for publication

Not applicable

Funding

The Major Research Project of Science Technology Department of Zhejiang Province (2021C03124)

Acknowledgments

We acknowledge all GWAS participants for their contributions to the summary statistics data.

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No competing interests reported.

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Relationship between three reproductive factors and lung cancer: a two-sample Mendelian randomization study

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Abstract

Figures

1 Introduction

2 Materials and methods

2.1 Data source

2.2 Statistical approach

2.3 Selection of instrumental variables

2.4 Statistical analysis

3 Results

4 Discussion

5 Conclusion

Abbreviations

Declarations

References

Table 1

Additional Declarations

Supplementary Files

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