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Investigating causal associations between pneumonia and lung cancer using a bidirectional mendelian randomization framework

Abstract

Background

Pneumonia and lung cancer are both major respiratory diseases, and observational studies have explored the association between their susceptibility. However, due to the presence of potential confounders and reverse causality, the comprehensive causal relationships between pneumonia and lung cancer require further exploration.

Methods

Genome-wide association study (GWAS) summary-level data were obtained from the hitherto latest FinnGen database, COVID-19 Host Genetics Initiative resource, and International Lung Cancer Consortium. We implemented a bidirectional Mendelian randomization (MR) framework to evaluate the causal relationships between several specific types of pneumonia and lung cancer. The causal estimates were mainly calculated by inverse-variance weighted (IVW) approach. Additionally, sensitivity analyses were also conducted to validate the robustness of the causalty.

Results

In the MR analyses, overall pneumonia demonstrated a suggestive but modest association with overall lung cancer risk (Odds ratio [OR]: 1.21, 95% confidence interval [CI]: 1.01 − 1.44, P = 0.037). The correlations between specific pneumonia types and overall lung cancer were not as significant, including bacterial pneumonia (OR: 1.07, 95% CI: 0.91 − 1.26, P = 0.386), viral pneumonia (OR: 1.00, 95% CI: 0.95 − 1.06, P = 0.891), asthma-related pneumonia (OR: 1.18, 95% CI: 0.92 − 1.52, P = 0.181), and COVID-19 (OR: 1.01, 95% CI: 0.78 − 1.30, P = 0.952). Reversely, with lung cancer as the exposure, we observed that overall lung cancer had statistically crucial associations with bacterial pneumonia (OR: 1.08, 95% CI: 1.03 − 1.13, P = 0.001) and viral pneumonia (OR: 1.09, 95% CI: 1.01 − 1.19, P = 0.037). Sensitivity analysis also confirmed the robustness of these findings.

Conclusion

This study has presented a systematic investigation into the causal relationships between pneumonia and lung cancer subtypes. Further prospective study is warranted to verify these findings.

Peer Review reports

Background

Pneumonia is prevalent and often underestimated regarding the dreadful result [1], posing a significant threat to human health due to its high incidence. Pneumonia can arise from numerous different causes, predominantly bacteria and viruses [2]. Other non-infectious factors, like asthma, have also been highly linked to an increased risk of pneumonia [3]. While most patients recover, pneumonia has been associated with longer-term effects, including cardiovascular disease [4], cognitive decline [5], and impaired immunity [6]. Nevertheless, the relationship between pneumonia and lung cancer risk has not been comprehensively investigated.

Lung cancer accounts for the leading cause of cancer-related deaths worldwide [7]. Among the numerous cases, lung cancer exhibits considerable heterogeneity. Regarding the causal relationship between pneumonia and lung cancer susceptibility, a case–control study has reported that pneumonia elevated the risk of lung cancer with an odds ratio (OR) of up to 2.4 [8]. Similarly, another retrospective analysis suggested that pneumonia was significantly associated with an elevated 1-year incidence of lung cancer, suggesting the demand of follow-up imaging after pneumonia to rule out occult malignancies [9]. In a Danish nationwide study, patients who had experienced pneumonia were found to confront an eight-fold higher risk of developing lung cancer [10]. Conversely, there has also been conflicting point, underscoring a low incidence of lung cancer after pneumonia [11]. Given the contradictory evidence, the detailed relationship between pneumonia and the risk of lung cancer requires further exploration.

Genome-wide association studies (GWAS) have yielded myriad single nucleotide polymorphisms (SNPs) associations with traits. Mendelian randomization (MR) is a reliable method based on GWAS summary-level data to look into the causal relationships between exposure and outcome [12]. In the MR analysis, genetic variations can be leveraged as instrumental variables (IVs) to represent the specific exposures, which can largely avoid potential confounding factors and reverse causal effects [13]. Thus, compared to traditional observational designs, MR may provide more conclusive evidence regarding the causal relationships between pneumonia and lung cancer susceptibility. For example, a previous study has demonstrated that Corona Virus Disease 2019 (COVID-19) would have no significant causal relationship with the risk of lung cancer using MR [14]. Moreover, GWAS data also present opportunities to uncover shared genetic correlations across phenotypes, thus providing novel etiological perspectives [15, 16].

In this manuscript, we sought to assess the causal relationships between pneumonia and lung cancer with large-size GWAS data using MR approach. To avoid reverse causation, a bidirectional design was employed to investigate the relationship of lung cancer on pneumonia risk. In summary, this study has presented a systematic investigation into the causal relationships between pneumonia and lung cancer subtypes.

Methods

Prepositions of MR design

In the bidirectional two-sample MR analyses, we adopted SNPs as IVs based on GWAS summary statistics from publicly available databases and recently published meta-analyses of GWAS data. To reduce the overlap in research population of exposure and outcome, we obtained GWAS datasets from two distinct European ancestry cohorts. To ensure valid causal estimates in MR analysis, three assumptions should be met: (I) strong association between genetic variants and exposure; (II) no independent effect of genetic variants on the outcome; and (III) no association between genetic variants and confounders (Fig. 1). Our study was based upon publicly released data, and all research databases listed here had received an ethics approval.

Fig. 1
figure 1

Schematic illustration illustrated Mendelian randomization assumptions. The assumptions included: (I) strong association between genetic variants and the chosen exposure; (II) no independent effect of genetic variants on the specific outcome; and (III) no association between genetic variants and potential confounders. MR: Mendelian randomization; COVID-19: Corona Virus Disease 2019; ILCCO: International Lung Cancer Consortium; IVW: Inverse-variance weighted; MR-PRESSO: Mendelian randomization-pleiotropy residual sum and outlier. Created with BioRender.com

Data sources for multiple types of pneumonia and lung cancer

Detailed information about the features of every dataset incorporated in this study is provided specifically in Table 1. We retrieved summary-level GWAS data for pneumonia from the hitherto latest FinnGen database (www.finbb.fi). The FinnGen database is a large-scale, population-based biobank study conducted in Finland, aiming to represent a comprehensive genomic dataset and phenotypic information for over 500,000 European participants [17]. Based on different pathogenesis of pneumonia, pneumonia phonotypes comprising all pneumonia (63,377 cases and 348,804 controls), bacterial pneumonia (17,511 cases and 344,010 controls), viral pneumonia (3,777 cases and 344,010 controls), and asthma − related pneumonia (13,185 cases and 365,497 controls) were retrieved. Moreover, summary data of the largest GWAS on COVID-19 (122,616 cases and 2,475,240 controls) as well as severe COVID-19 (13,769 severe cases and 1,072,442 controls) were obtained from the COVID-19 Host Genetics Initiative and the European ancestry resources were selected [18]. Moreover, we also accessed GWAS data of critical pneumonia from 431,365 European individuals, including 2,758 cases and 428,607 controls [19].

Table 1 Summary of data source of different traits

The GWAS data for lung cancer were retrieved from an aggregated analysis conducted by the International Lung Cancer Consortium (ILCCO) [20], a global collaboration of researchers focused on lung cancer. This GWAS data of lung cancer comprised 29,266 cases and 56,450 controls in total. Stratified by histologic subtypes, there were lung adenocarcinoma (LUAD) (11,273 cases and 55,483 controls), lung squamous cell carcinoma (LUSC) (7,426 cases and 55,627 controls), and small-cell lung cancer (SCLC) (2,664 cases and 21,444 controls).

Data sources for potential risk factors

To assess the causal relationship between pneumonia and potential risk factors, we performed further inverse-variance weighted (IVW) analyses to investigate whether pneumonia potentially affected lung cancer risk through these factors. In this section, risk factors associated with risk of cancer commonly considered in the MR analysis were included, namely body mass index (BMI), smoking status (cigarettes smoked per day, ever vs. current smoker, and ever vs. never smoker), and alcohol consumption. Regarding smoking status and alcohol consumption, GWAS data were downloaded from the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN) [21]. The GWAS data for BMI were retrieved from the MRC-IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/). Table 1 depicts the detailed information of GWAS summary data.

Selection of SNPs

To ensure the appropriate selection of SNPs as IVs for our study, several criteria were applied. Firstly, to obtained more genetic instruments, the threshold for single SNP was set as P < 5 × 10− 6. Secondly, regarding the clumping process, a linkage disequilibrium (LD) algorithm was employed to maintain independence among the SNPs (r2 = 0.001 and window size = 10 Mb) [22]. Thirdly, we checked every SNP via Phenoscanner and GWAS catalog [23, 24]. In this way, we could adequately assess whether these SNPs were associated with potential confounders at the genome-wide significance threshold of P < 5 × 10–8. SNPs associated with smoking status, body mass index, alcohol intake, and any malignancy for IVs of pneumonia were removed, and the remaining SNPs were utilized as the IVs in the subsequent MR analyses.

Estimation of causal association

Prior to conducting the analysis, we performed data harmonization to align the effect alleles of the exposure and outcome variables to the forward strand. This alignment was carried out based on specified information or inferred from allele frequencies. Additionally, palindromic genetic variants were excluded from further MR analyses [25]. For causal estimate, we employed the IVW, MR Egger, and weighted median methods. IVW approach could combine SNP-specific ratio estimates, regressing the coefficient of outcome against that of the exposure without the intercept term [26]. If no heterogeneity was detected, the fixed-effect IVW method was performed. In cases where heterogeneity was detected, the multiplicative random effects IVW approach was employed. Different from the IVW method, MR-Egger allows for detecting and correcting for the potential bias caused by the presence of directional pleiotropy [27]. The weighted median method could maintain robustness against the influence of invalid instruments, accommodating up to half of invalid SNPs [28]. To further test the robustness of the causal estimates, sensitivity analyses were thus conducted containing the heterogeneity test measured by Cochran’s Q statistic, pleiotropy by MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO). Additionally, to evaluate the potential impact of each SNP on the IVW estimate, leave-one-out analyses were performed, which removed one SNP at a time. Funnel plots were utilized to illustrate the selection bias of IVs. A prespecified significance threshold of P < 1.25 × 10− 3 (adjusted for multiple testing: P = 0.05/40, considering 5 pneumonia types and 4 lung cancer types in a bidirectional design was applied using the Bonferroni correction in the bidirectional MR analyses. All statistical analyses were performed in R software (version 4.2.2) using the R packages “TwoSampleMR” (version 1.0) and “MRPRESSO” (version 0.5.6) [29, 30].

Statistical power and F‑statistics

Based on an online calculator (https://shiny.cnsgenomics.com/mRnd/) [31], the power in our MR analyses were calculated. The calculation incorporated the type I error of 0.05, proportion of cases (Table 1), explained genetic variation (R2) (Supplementary Table 1), and OR from IVW analyses (Supplementary Table 3). R2 of each SNP was equal to 2×EAF×(1 − EAF)×β2, where EAF represented the effect allele frequency, while β denoted the estimated genetic effect on the exposure [32]. The F statistic in MR analysis measured instrument strength based on R2, sample size (N), as well as the number of instruments (K), which could be calculated by: \( \text{F}=\left(\frac{\text{N}-1-\text{K}}{\text{K}}\right)\left(\frac{{\text{R}}^{2}}{1-{\text{R}}^{2}}\right)\)[33]. Mitigating weak instrument bias is paramount in the design and analysis of MR analysis, and an F statistic exceeding 10 could indicate a sufficient strength [34].

Genetic correlation analysis

To understand the potential shared genetic basis between pneumonia and lung cancer, we conducted a genome-wide genetic correlation analysis. This method involves utilizing large-scale genomic data from GWAS to calculate the genome-wide genetic correlations (rg) between different trait pairs [15]. These correlations quantify the average shared genetic influences between traits, independent of environmental factors. We employed the Linkage Disequilibrium Score Regression (LDSC) method, a robust statistical algorithm that calculates genetic correlations by regressing the product of z-scores for two traits on the LD patterns across SNPs spanning the human genome [16]. In brief, this analysis will suggest the genetic architecture underlying the relationships between pneumonia and lung cancer.

Results

Details of SNPs with pneumonia as the exposure are presented in Supplementary Table 1. The selected SNPs could explain 1.57%, 2.80%, 10.25%, 9.86%, and 1.75% of the variance in overall pneumonia, bacterial pneumonia, viral pneumonia, asthma-related pneumonia, and COVID-19, respectively. And F-statistics were all above 10, which suggested a sufficient strength. The statistical powers of MR results are presented in Supplementary Table 2.

The genetic correlation analysis demonstrated an intrinsic genome-wide sharing between pneumonia and lung cancer. As shown in Fig. 2, the genetic correlations were pronounced between these two major diseases. For example, there was evidence on significant shared genetic basis between all pneumonia with overall lung cancer (rg=0.41), LUAD (rg=0.23), LUSC (rg=0.52) and SCLC (rg=0.29). Inspired by these findings, we systematically analyzed the causal relationships between pneumonia and lung cancer.

Fig. 2
figure 2

Genetic correlations (rg) estimated between pneumonia and lung cancer using genome-wide SNPs via LDSC method. The pairwise estimate was reported in each case, with asterisks *denoting statistical significance at a P-value threshold of 0.05, and asterisks **denoting Bonferroni-corrected significance at a P-value threshold of 0.05/20. The colors of the box indicated the magnitude of correlation. SNP: Single nucleotide polymorphism; LDSC: Linkage disequilibrium score regression; COVID-19: Corona Virus Disease 2019

General effect of pneumonia on lung cancer

Figure 3 has illustrated a comprehensive landscape of IVW estimates when pneumonia served as the exposure. We found a modest yet potentially causal relationship between overall pneumonia and overall lung cancer (Odds ratio [OR]: 1.21, 95% confidence interval [CI]: 1.01 − 1.44, P = 0.037). This association was supported by a high statistical power of 93% (Supplementary Table 2). However, the correlations between specific pneumonia subtypes and overall lung cancer were not as pronounced. Bacterial pneumonia (OR: 1.07, 95% CI: 0.91–1.26, P = 0.386), viral pneumonia (OR: 1.00, 95% CI: 0.95–1.06, P = 0.891), asthma-related pneumonia (OR: 1.18, 95% CI: 0.92–1.52, P = 0.181), and COVID-19 pneumonia (OR: 1.01, 95% CI: 0.78–1.30, P = 0.952) did not exhibit evident associations with lung cancer, as well as other lung cancer subtypes (Fig. 3). Meanwhile, the weighted median along with MR-Egger regression approaches demonstrated similar trends (Supplementary Table 3). To supplement the primary analyses, we also conducted additional MR analyses utilizing IVW approach to evaluate the potential causal effect of pneumonia on the potential confounding risk factors of lung cancer, including BMI, smoking status, and drinking status (Supplementary Table 4). And no causal relationships could be found between most pneumonia types and these risk factors, except for a marginal correlation between COVID-19 and drinking status (OR: 1.03, 95% CI: 1.00–1.06, P = 0.038). Therefore, we believe that our findings could indicate a potential causal relationship of pneumonia with increased lung cancer susceptibility, but considering the significant but modest association, further validations will be needed.

Fig. 3
figure 3

Mendelian randomization using IVW method estimated the causal effects of pneumonia on lung cancer susceptibility. IVW: Inverse-variance weighted; SNP: Single nucleotide polymorphism; OR: Odds ratio; CI: Confidence interval; COVID-19: Corona Virus Disease 2019

To add to the clinical relevance of this study, we also analyzed the association between severe pneumonia types and lung cancer. Based on GWAS data availability, we looked into specific traits including critical pneumonia and very severe respiratory confirmed COVID-19. The result showed that critical pneumonia (OR: 1.00, 95% CI: 0.98–1.02, P = 0.841) and severe COVID-19 (OR: 1.02, 95% CI: 0.96–1.07, P = 0.577) had no direct causal effect on increased lung cancer risks (Supplementary Table 5).

Sensitivity analysis

In the sensitivity analysis, we adopted Cochran’s Q test to detect heterogeneity (Supplementary Table 6). Because we had leveraged the random-effects IVW MR approach in cases we detected heterogeneity, our results still remained applicable. In our study, different methods including IVW, weighted median, and MR-Egger showed consistent estimates, indicating robustness of these findings. Furthermore, the intercepts evaluated via MR-Egger did not exhibit statistically significant P-values (Supplementary Table 7), suggesting our results were not impacted by pleiotropy. Additionally, the leave-one-out analyses barely detect SNPs that might possibly cast a substantial influence on the final estimates, and the funnel plots did not reveal significant evidence of bias in evaluating potential biases in the genetic IVs (Supplementary Figs. 13).

Bidirectional analyses showing the causal effect of lung cancer on pneumonia

On the other hand, we further investigated the causal relationship of lung cancer on pneumonia (Fig. 4). Supplementary Table 8 provides details of the specific SNPs utilized in this section. When lung cancer was considered the exposure, we uncovered a crucial causal link between overall lung cancer and elevated risks of bacterial pneumonia (OR: 1.08, 95% CI: 1.03–1.13, P = 0.001) and viral pneumonia (OR: 1.09, 95% CI: 1.01–1.19, P = 0.037). Moreover, the findings suggested that diverse types of lung cancer exhibited a modest but stable tendency to be possibly associated with higher susceptibility of specific pneumonia types, although these associations were not as statistically evident.

Fig. 4
figure 4

Mendelian randomization using IVW method estimated the causal effects of lung cancer on pneumonia risk. IVW: Inverse-variance weighted; SNP: Single nucleotide polymorphism; OR: Odds ratio; CI: Confidence interval; COVID-19: Corona Virus Disease 2019

Discussion

Motivated by the significant genetic correlations between pneumonia and lung cancer, this study has been the first to systematically investigate the causal relationships between these two major respiratory diseases with two-sample MR approach, and our results were built upon the hitherto most recent genetic data. We included a wide range of pneumonia and lung cancer types, and found a significant but modest causal relationship of overall pneumonia on lung cancer susceptibility. Reversely, with lung cancer as the exposure, the risks of developing bacterial and viral pneumonia were specifically elevated in lung cancer patients.

We found a modest yet suggestive increase of lung cancer risks in pneumonia patients. This effect presented in the overall pneumonia and lung cancer collectively, while the relatively smaller size of the GWAS data on other traits might limit the statistical power, rendering less significant correlation in specific disease types. But supported by a high statistical power of 93%, the potential causal relationship between pneumonia and lung cancer susceptibility generally should be noticed. And this finding has added to the current evidence of the post-pneumonia effects, also consistent with most earlier observational studies which have reported higher lung cancer risk following pneumonia based on conventional regression approaches. For example, a previous study covering 22,034 patients with pneumococcal pneumonia and 88,136 controls found increased lung cancer risk after pneumococcus infection [35]. And another study also stressed a higher lung cancer incidence after pneumonia in smokers [9]. In addition, the time period during which pneumonia might influence lung cancer risk had been discussed. The SYNERGY project, which collected information on a variety of previous respiratory diseases based on case-control studies, domonstrated that within 2 years after pneumonia, the risks for lung cancer were increased, but the impact did not exist after the time period of 2 years [36]. And another nationwide large-scale retrospective study suggested an elevated incidence ratio of lung cancer diagnosis maintaining beyond 5 years months following pneumonia [10]. Alternatively, an existing study has contrarily proposed a very low incidence of lung cancer new cases after pneumonia [11], constituting a minor side of the discussion. Given the intrinsic limitations of observational design in the inference of causality, there has been continuous doubt raised on the confounding factors [37, 38]. Herein, in the current study, we adopted MR design and thus minimized the confounding effects. We found the modest positive correlation between pneumonia and lung cancer susceptibility, which still needs to be further investigated in the larger cohorts or in prospective studies.

Regarding the mechanisms underlying the potential higher risks of lung cancer with pneumonia serving as the exposure, the enduring post-pneumonia effects including permanent lung damage, dysregulated immune function, and extrapulmonary complications have previously been reported [39,40,41]. Maintaining lung homeostasis requires a balance between immune resistance and the resilience of tissue [40]. When pneumonia occur, the immune system functions to combat invading agents, which simultaneously could cause tissue damage [41]. Patients after COVID-19 pneumonia are left with impaired lungs, and the abnormal lung function might even last for long [39, 40]. Therefore, a proper management after pneumonia are still imperative to monitor other potential long-term effects including lung cancer.

In our study, we also suggested elevated risks of bacterial and viral pneumonia among patients with lung cancer. This heightened susceptibility is primarily attributable to compromised immune functions in lung cancer patients. Immune system, pivotal in the defense against pathogenic invasion, is notably disrupted by tumors throughout the body [42]. As elucidated through established analyses especially using single-cell technique, significant alterations in immune cell populations, including but not limited to suppressed T cell activity [43], aberrations in B cell functions [44], compromised dendritic cell functions [45], and accumulation of immunosuppressive neutrophils [46], have been reported in lung cancer patients. Bacterial pneumonia predominantly caused by pathogens such as Streptococcus species, and viral pneumonia caused by a wide variety of viruses like influenza viruses, present the two major pneumonia types as the major cause of incidence and mortality [2, 47], underscoring the critical nature of the risks. Herein, we suggest the pressing need for meticulous pneumonia prophylaxis in the clinical management of lung cancer patients, particularly for infection control measures within hospital settings, thereby mitigating additional health complications and enhancing overall patient outcomes.

These findings have been reliable with the exclusion of effects brought about by major confounding factors. We especially excluded SNPs related with smoking status and BMI. And in our additional MR analyses, evidence for a causal association of the pneumonia subtypes on the potential risk factors was minimized, thus rendering the independence of our study from confounding impacts. Indeed, smoking played a key role in both pneumonia and lung cancer susceptibility. On the one hand, the relationship between smoking and the susceptibility of pneumonia is evident. Inducing physical airway changes like cilia loss and mucus overproduction, smoking has long been found to be a crucial risk factor which could incur a 2- to 4-fold elevated risk of pneumonia [48, 49]. These effects might partially explained by the suppression of the immune system caused by tobacco smoking [50]. On the other hand, smoking accounts for the vast majority among lung cancer cases [51]. Given the enormous impact smoking might have in the long term, lung cancer screening is recommended for heavy smokers aged 55–74 years old with a 30 pack-year history of smoking who are more prone to harbor lung cancer [52]. As is also with BMI, body fatness has been linked to higher risk for multiple cancers including lung cancer [53], and could also impact the pneumonia risk in a dose-related manner [54]. Our MR strategy has eliminated the potential confounding caused by BMI as well.

Our proposed modest yet significant link between pneumonia and lung cancer risk will potentially serve as a reference in clinical practice to recommend screening for lung cancer in post-pneumonia status. As early detection is crucial in lung cancer management, the pursuit for sensitive and reliable features in either clinical assessment or molecular scale is still warranted. From the clinical perspective, respiratory conditions have the potential to cast an impact on lung cancer risks. For example, chronic obstructive pulmonary disease (COPD) has been widely acknowledged as a relative element associated with lung cancer regardless of democratic factors and smoking history [55, 56]. Generally, lung function could predict lung cancer risks, acting as a relevant indicator for lung cancer screening [57]. At the molecular level, high-throughput sequencing of circulating tumor DNA has emerged as effective in differentiating lung cancer from other benign lung nodules, empowering early-stage diagnosis [58, 59]. Blood proteins, DNA methylation features, and RNA airway signatures are all promising candidates for molecular biomarkers in detecting lung cancer at an earlier stage [60,61,62].

The current study had clear strengths. The foremost strength of our study was that the results generated by MR analyses were not influenced by classical types of confounding factors or reverse causation that might bias findings in other observational settings. And our results could potentially inform ongoing or future trials into the traits affecting lung cancer onset. However, there were several limitations to consider for our study. Firstly, our analyses were based on GWAS summary-level data, which limited our ability to investigate potential confounding factors or individual-level characteristics. And although we adopted a relatively large GWAS data, our sample sizes might still be limited to detect more modest causal associations between pneumonia and lung cancer. In addition, we focused on only a few common pneumonia and lung cancer subtypes. In the foreseeable future, continued research in larger cohorts and in-depth investigation into the underlying disease mechanisms are required to further understand the complicated relationships between pneumonia and lung cancer types.

Conclusion

This bidirectional MR study demonstrated a suggestive but modest causal relationship of pneumonia on overall lung cancer, as well as a higher risk of developing bacterial and viral pneumonia in lung cancer patients. Further large-scale, prospective study is warranted to verify these findings.

Data availability

The summary-level data used in this study can be obtained from public datasets GWAS summary statistics for lung cancer are from https://www.ebi.ac.uk/gwas/publications/28604730. GWAS summary statistics for most pneumonia types can be consulted at https://finngen.gitbook.io/documentation/data-download (R10 release). GWAS summary statistics of the COVID-19 Host Genetics Initiative are accessible at https://www.covid19hg.org/. GWAS summary data for critical pneumonia are available from GWAS catalog (ID: GCST90281170). More details of the approaches as well as the codes are available at https://mrcieu.github.io/TwoSampleMR/.

Abbreviations

BMI:

Body mass index

CI:

Confidence interval

COPD:

Chronic obstructive pulmonary disease

COVID-19:

Corona Virus Disease 2019

EAF:

Effect allele frequency

GWAS:

Genome-wide association study

GSCAN:

GWAS & Sequencing Consortium of Alcohol and Nicotine use

ILCCO:

International Lung Cancer Consortium

IV:

Inverse-variance

IVW:

Inverse variance weighted

LC:

Lung cancer

LD:

Linkage disequilibrium

LDSC:

Linkage disequilibrium score regression

LUAD:

Lung adenocarcinoma

LUSC:

Lung squamous cell carcinoma

MR:

Mendelian randomization

MR-PRESSO:

Mendelian randomization-pleiotropy residual sum and outlier

OR:

Odds ratio

SCLC:

Small-cell lung cancer

SNP:

Single nucleotide polymorphism

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Acknowledgements

We sincerely appreciate all the contributors involved in the GWAS databases we utilized. And we thank the experts in our team providing necessary assistance for this study, especially Xiaolong Tang. In addition, we deeply thank Dr. Jianming Zeng as well as all the members of his bioinformatics team, for the computational resources and generous sharing of their experiences. Figure 1 was created with BioRender.com, and many thanks to their team for the innovative contributions.

Funding

This study was supported by Science and Technology Project of Sichuan (Grant Number: 2023NSFSC1889), Science and Technology Project of Chengdu (Grant Number: 2023-YF09-00007-SN), the 1.3.5 Project for Disciplines Excellence of West China Hospital, Sichuan University Grant Number: ZYYC23027 and Postdoctoral Program of West China Hospital, Sichuan University (Grant Number: 2020HXBH084).

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Contributions

C. W. and W.L. designed the study and supervised the project. L.S., D.W., and J.Z. contributed to data curation, formal analysis, methodology, software. L.S., D.W., J.W., and J.Z. participated in writing the original draft. All authors reviewed the manuscript and approved the final submission.

Corresponding authors

Correspondence to Weimin Li or Chengdi Wang.

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Our study was based upon publicly released data and patient confidentiality was protected. And all research databases listed here had received an ethics approval.

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The authors declare no competing interests.

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Song, L., Wu, D., Wu, J. et al. Investigating causal associations between pneumonia and lung cancer using a bidirectional mendelian randomization framework. BMC Cancer 24, 721 (2024). https://0-doi-org.brum.beds.ac.uk/10.1186/s12885-024-12147-3

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