Association between lung cancer and bladder cancer risk: a bidirectional Mendelian randomization study

Association between lung cancer and bladder cancer risk: a bidirectional Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between lung cancer and bladder cancer risk: a bidirectional Mendelian randomization study Jiaqi Zhou, Chunyuan Fei, Yingqing Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4664769/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The second primary cancer (SPC) poses a significant threat to lung cancer survivors, with bladder cancer being one of the most common SPCs. The relationship between lung cancer and bladder cancer has remained unclear. In this study, we aimed to evaluate the causal effect between these two cancers through bidirectional two-sample Mendelian randomization (MR) analysis. Methods Genetic instruments associated with lung cancer and its subgroups were derived from the International Lung Cancer Consortium (ILCCO), while the data of bladder cancer was obtained from the FinnGen biobank. To estimate the causal relationship, we employed inverse-variance weighted (IVW) method, MR-Egger, and weighted-median method. Additionally, we conducted Cochran's Q test, MR-Egger regression, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and leave-one-out analysis to assess potential pleiotropy effects. Results Our analysis revealed that genetically overall lung cancer increased the risk of bladder cancer based on the IVW and weighted median method. However, subgroup analysis showed no causal relationship between LUSC or LUAD and bladder cancer. In the reverse MR analysis, we found no evidence of any causal relationship between bladder cancer and overall lung cancer. Subgroup analysis suggested that bladder cancer increased the risk of LUSC. The assessment of heterogeneity and pleiotropy provided further support for the robustness and validity of these MR results. Conclusions Our study provided evidence in support of causality between lung cancer and bladder cancer in individuals of European ancestry. We should focus on SPC-bladder cancer or SPC-LUSC to intervene in time. Lung cancer bladder cancer Mendelian randomization causal effect Figures Figure 1 Figure 2 Figure 3 Introduction Lung cancer is a highly prevalent cancer with an estimated 2.2 million (11.4%) new cases in 2020[1]. It is the leading cause of cancer-related deaths in men and the second leading cause of cancer deaths in women, only behind breast cancer globally, with an estimated 1.8 million (18.0%) deaths[1]. Lung cancer is classified as small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC accounting for >85 % of all cases[2]. Globally, the most common subtype of NSCLC is adenocarcinoma (40 %), followed by squamous carcinoma (25 %)[2, 3]. Despite high lethality of lung cancer, the mortality rate has declined in recent years because of the early screening of CT and the use of targeted therapies and immunotherapy[4-6]. The population of lung cancer survivors has gradually increased as mortality rates have declined[1]. Studies have indicated that cancer survivors faced a high risk of developing second primary cancers (SPC)[7-9]. It was reported that SPC significantly increased mortality in lung cancer survivors[10]. The incidence of second primary lung cancer is also on the rise in other cancers, affecting the survival of cancer survivors[11]. Therefore, it is important to explore the causal relationship between lung cancer and other cancer. Matthew et al. indicated that bladder cancer was the second most common smoking-related SPC in primary lung cancer[12]. Another study using the Surveillance, Epidemiology and End Results (SEER) database showed that bladder cancer as the fourth most common SPC besides lung cancer in primary lung cancer[10]. However, Aishah et al. did not establish any causal relationship between primary lung cancer and second primary bladder cancer[13]. Another study using SEER database and NHANES database indicated that patients with bladder cancer were prone to second primary lung cancer[14]. It was consistent with another conclusion by Aishah et al.[13]. Due to the inherent limitations in observational and retrospective studies, such as confounding factors, the conclusions drawn from these studies lack consistency and reliability. Therefore, we aimed to identify the causal relationship between lung and bladder cancer using Mendelian randomization (MR). MR is a method that uses genetic variation as an indicator of exposure, independent of traditional study design biases[15]. By using genetic variants significantly associated with the exposure in Genome-wide Association Studies (GWAS) summary statistics as instrumental variables (IVs), MR estimates the causal effect of the exposure on the outcome[15, 16]. The MR design is advantageous as it can overcome confounding factors caused by environmental or behavioral influences, making it comparable to randomized controlled trials (RCTs) in terms of its ability to establish causality[17]. In this study, we applied a bidirectional two-sample MR analysis to elucidate the causal relationship between lung cancer and bladder cancer. Methods Study design An overview of our study design is display in Fig.1. Our research must fulfil the three MR assumptions: 1) The IVs are strongly associated with exposure factors. 2.) The IVs are independent of confounding factors. 3) The IVs can only affect outcomes through exposure factors. Data source The GWAS data for lung cancer were acquired from the International Lung Cancer Consortium (ILCCO), which involved 29,266 cases of European ancestry and 56,450 controls of European ancestry[18]. To further investigate the relationship between the subtypes of lung cancer and bladder cancer, we obtained GWAS data for lung adenocarcinoma (LUAD) and lung squamous cancer (LUSC)[18]. To avoid the overlap of participants, the summary statistics of bladder cancer were obtained from the FinnGen biobank (Finn) consortium, including 1,115 cases and 217,677 controls from European ancestry. The summary statistics of bladder cancer were acquired from the following website: https://gwas.mrcieu.ac.uk/datasets/finn-b-C3_BLADDER/. Selection of IVs To identify single nucleotide polymorphisms (SNPs) significantly associated with lung cancer as IVs, we set a P-value of 5*10 -8 as the threshold for lung cancer, LUAD and LUSC[16]. In addition, we performed the clumping process (r 2 < 0.001, window size = 10,000 kb) and estimated the linkage disequilibrium (LD) between SNPs in order to exclude the bias caused by LD. We set a more relaxed threshold (P< 5*10 -6 ) for bladder cancer to include more IVs associated with bladder cancer. We also performed the clumping process (r 2 < 0.001, window size = 10,000 kb) and estimated the linkage disequilibrium (LD). Subsequently, we screened SNPs in the PhenoScanncer database to remove those potentially associated with confounders or outcome (P10, it indicated that there was no weak instrumental bias[20]. Statistical Analyses After harmonizing the effects of SNPs selected from exposure and the GWAS of outcome, we applied various MR methods to evaluate the causal relationship between lung cancer and bladder cancer, including inverse-variance weighted (IVW), MR-Egger and weighted median methods. IVW served as the primary method, determining the MR estimate of exposure for outcome meta-analysis of the Wald ratio for each SNP[21]. MR-Egger, weighted median methods were also employed to evaluate the causal effect, generally as a complement to IVW. Sensitivity Analyses It is very important for sensitivity analyses in MR studies to assess heterogeneity and pleiotropy. We calculated Cochran's Q-Statistics using IVW to evaluate heterogeneity (P <0.05 was considered as the presence of heterogeneity)[22]. The horizontal pleiotropy of IVs was assessed based on the intercept obtained from MR-Egger regression[23]. Additionally, we utilized the MR pleiotropy residual sum and outlier test (MR-PRESSO) to detect pleiotropy, exclude outlying SNPs and reassess the effect estimates[24]. Furthermore, leave-one-out analysis was used to identify outlying values. All analyses were conducted in R 4.2.2 by using packages of “TwoSampleMR” and “MR-PRESSO”[24, 25]. A two-tailed P < 0.05 was considered statistically significant. Results Selection of instrumental variables of lung cancer We at first selected 15 IVs for lung cancer, 14 IVs for LUAD and 7 IVs for LUSC, respectively. Then, 5 IVs for lung cancer, 5 IVs for LUAD and 3 IVs for LUSC were removed after searching in the PhenoScanncer database due to confounding variables. Ultimately, we identified 10 IVs for lung cancer, 9 IVs for LUAD and 4 IVs for LUSC. All the F-statistics of these IVs ranged from 34.63 to 91.77, showing the absence of weak IVs (Additional file 1: Table S1). MR analysis of lung cancer to bladder cancer In overall lung cancer to bladder cancer MR analysis, we discovered that lung cancer was an elevated risk of bladder cancer (IVW: OR=1.88, 95%CI 1.29 - 2.75, P= 0.001; weighted median: OR= 1.68, 95%CI 1.08 - 2.59, P= 0.020). The similar causal estimates were obtained from MR-Egger (OR= 2.32, 95%CI 0.91 - 5.89, P= 0.114), although the association was not statistically significant. In LUAD to bladder cancer MR analysis, no association was identified between LUAD and bladder cancer (IVW: OR=0.92, 95%CI 0.68 - 1.26, P= 0.612; MR-Egger: OR= 0.66, 95%CI 0.13 - 3.37, P= 0.629; weighted median: OR= 1.13, 95%CI 0.79 - 1.63, P= 0.495). Similarly, no association was identified between LUSC and bladder cancer in LUSC to bladder MR analysis (IVW: OR=1.34, 95%CI 0.78 – 2.30, P= 0.281; MR-Egger: OR= 0.99, 95%CI 0.23 – 4.31, P= 0.994; weighted median: OR= 1.11, 95%CI 0.75 - 1.66, P= 0.596) (Fig.2). Selection of instrumental variables of bladder cancer In bladder cancer to lung cancer MR analysis, 13 SNPs were selected as IVs. One IV associated with confounding variable was removed using PhenoScanncer database. Another IV was removed because of palindromic structure. Ultimately, we identified 11 IVs for bladder cancer. After matching with the GWAS of lung cancer and its subgroups, 8 IVs remained. All the F-statistics of these IVs ranged from 20.93 to 30.18 (Additional file 1: Table S2). MR analysis of bladder cancer to lung cancer In bladder cancer to overall lung cancer MR analysis, we didn’t discover any association with lung cancer (IVW: OR=1.03, 95%CI 0.99 - 1.07, P=0.146; MR-Egger: OR=1.05, 95%CI 0.97 - 1.14, P=0.230; weight median: OR=1.04, 95%CI 0.99 - 1.09, P=0.133). In bladder cancer to LUAD MR analysis, we also didn’t identify causal association between bladder cancer and LUAD (IVW: OR=1.03, 95%CI 0.98 - 1.09, P=0.231; MR-Egger: OR=1.11, 95%CI 1.00 - 1.23, P=0.101; weight median: OR=1.07, 95%CI 0.98 - 1.13, P=0.126). In bladder cancer to LUSC MR analysis, we found that bladder cancer increased the risk of LUSC (IVW: OR=1.09, 95%CI 1.02 - 1.16, P=0.008; MR-Egger: OR=1.18, 95%CI 1.04 - 1.33, P=0.042). Although the association was not statistically significant in the weight median method, the similar estimate was obtained (OR=1.08, 95%CI 0.99 - 1.23, P=1.18) (Fig.3). Sensitivity Analyses We performed sensitivity analysis to reliable to our results. The result of Cochran's Q-Statistics indicated that there was heterogeneity between LUSC and bladder cancer in lung cancer to bladder cancer MR analysis (P=0.025). Despite of the heterogeneity detected, the random effect of the IVW remained valid. There was no heterogeneity in any other MR analysis (Table 1). The results of MR-Egger regression revealed no horizontal pleiotropy in any MR analysis. Additionally, the MR-PRESSO also didn’t identify any outlier and pleiotropy (Table 1). The results of leave-one-out indicated the estimates were not biased by a single SNP (Additional file 2: Fig. S1). Furthermore, scatter plots, forest plots and funnel plots are shown in Supplementary Materials (Additional file 2: Figs. S2-S4). Table 1 Evaluation of heterogeneity and directional pleiotropy Exposure-Outcome Heterogeneity Horizontal pleiotropy MR-PRESSO Cochran’s Q P-value Egger intercept P-value P-value LC-BC 15.016 0.090 -0.027 0.639 0.110 LUAD-BC 10.717 0.218 0.045 0.689 0.231 LUSC-BC 9.336 0.025 0.063 0.698 0.076 BC-LC 4.691 0.698 -0.008 0.522 0.769 BC-LUAD 4.744 0.691 -0.025 0.175 0.768 BC-LUSC 7.108 0.418 -0.027 0.213 0.346 MR-PRESSO: Mendelian randomization pleiotropy residual sum and outlier; LC: lung cancer; BC: bladder cancer; LUAD: lung adenocarcinoma; LUSC: lung squamous cancer Discussion With early CT screening and advance treatments, the life expectancy of lung cancer has prolonged. As a result, the incidence of SPC has increased significantly. After 20 years or more of follow-up, more than 19% of patients are likely to develop SPCs[26]. Approximately 13.4-22% of lung cancer patients will develop SPCs[27]. Bladder cancer is one of the common SPCs of lung cancer. It is beneficial to explore the causal relationship between lung cancer and bladder cancer to identify high-risk patient for early screening and timely treatment to improve patient survival. A study by Hong et al., based on the SEER database, found that lung cancer increased the risk of second primary bladder cancer[28]. However, Aishah et al. did not find a causal relationship between lung cancer and second primary bladder cancer[13]. Another study based on Korean population showed that bladder cancer increased the risk of second primary lung cancer, including LUAD and LUSC[29]. Therefore, it is unclear whether there is any causal relationship between lung and bladder cancer as SPCs. Observational studies often struggle to avoid the interference of various confounding factors. In our study, we applied a bidirectional two-sample MR analysis to assess the causal relationship between lung cancer and bladder cancer by using three approaches. In our study, we identified overall lung cancer as a risk factor for second primary bladder cancer. Many studies have found that the development of lung cancer and bladder cancer was associated with activation of the PI3K/Akt/mTOR and MAPK signaling pathway[30-33]. Chen et al. discovered that RNA Polymerase III Subunit G (POLR3G) plays a contributory role in the development of lung cancer and bladder cancer[34]. Lung cancer and bladder cancer may share similar biological mechanisms, which could explain the causal relationship between these two cancers. On the other hand, we showed that bladder cancer increased risk of second primary LUSC rather than LUAD. A study by James et al. showed that while LUAD and LUSC were subtypes of NSCLC, the mechanisms by which they developed may be different[35]. One potential mechanism through which bladder cancer increases the risk of second primary LUSC is that the development of LUSC is associated with the bladder cancer pathway[36]. Furthermore, a study using rank-based Bayesian clustering to analyze pan-cancer identified three pan-squamous clusters consist of LUSC, head and neck squamous cancer, and bladder cancer[37]. Vitelli et al. found that these three cancers were not clustered based on tissue of origin, but on cell morphology[37]. They suggested that some tumors should be classified according to tissue type not the same tissue of origin[37]. This may explain bladder cancer increased risk of LUSC rather than LUAD. Wu et al. revealed that approximately half of lung cancer patients develop SPCs in the first year after diagnosis[38]. The risk of developing a new tumor in bladder cancer patients was 60% higher than in the general population[39]. Our study confirmed the genetical causality between LUSC and bladder cancer. As a result, we should pay attention to the occurrence of bladder cancer in LUSC or LUSC in bladder cancer for early screening and timely treatment. Our study has several strengths. As far as we know, this is the first bidirectional two- sample MR study to evaluate the causal effect between lung cancer, LUAD, and LUSC and bladder cancer based on GWAS database. The MR design can avoid various confounding bias and reverse effect, which is the closest to RCT. Furthermore, we performed multiple sensitivity analyses to detect whether there was horizontal pleiotropy in our results. The intercepts obtained from our MR-Egger analysis indicated that all identified causal relationships remained robust against the influence of horizontal pleiotropy, reinforcing the validity and reliability of our findings. However, our study also has some limitations. First, all the GWAS data were from individuals of European ancestry. As a result, our findings may not be consistent for other ancestries, which need further studies to evaluate the causal relationship between lung cancer and bladder cancer in other ancestries. Second, despite our efforts to exclude SNPs associated with potential confounders, the possibility of residual unknown confounders influencing the causal estimates cannot be entirely ruled out. Third, the limited number of SNPs significantly associated with LUSC included in our study may affect the statistical robustness of our findings. Conclusion Our study provided evidence in support of causality between lung and bladder cancer. We should focus on SPC-bladder cancer or SPC-LUSC to intervene in time. Declarations Acknowledgments We are grateful to all participants and investigators who contributed to the GWAS data. Author contributions Yingqing Zhang and Jiaqi Zhou conceived this study; Jiaqi Zhou analyzed the data; Jiaqi Zhou and Chunyuan Fei prepared the first draft of this manuscript; and Yingqing Zhang revised this manuscript critically. All authors have read and approved the final manuscript. Data availability All of the datasets analyzed during the current study are available at (https://gwas.mrcieu.ac.uk/). Funding Zhejiang Province and Jiaxing City Jointly Develop Medical Key Discipline—Respirology (2023-SSGJ-002). The Key Discipline of Jiaxing Respiratory Medicine Construction Project (No.2019-zc-04). Jiaxing Key Laboratory of Precision Treatment for Lung Cancer (No.2019-fazdsys). Wu Jieping Medical Foundation Clinical Research Special Fund (No. 320.6750.2022-09-53). Ethics approval and consent to participate The data used in this paper are publicly available, ethically approved. Consent for publication Not applicable. Competing Interest The authors declare no conflict of interest. References Sung H, Ferlay J, Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA: A Cancer Journal for Clinicians, 2021, 71(3): 209-249. doi:10.3322/caac.21660 Leiter A, Veluswamy R R, Wisnivesky J P. The global burden of lung cancer: current status and future trends[J]. Nat Rev Clin Oncol, 2023, 20(9): 624-639. doi:10.1038/s41571-023-00798-3 Schabath M B, Cote M L. Cancer Progress and Priorities: Lung Cancer[J]. Cancer Epidemiology, Biomarkers & Prevention, 2019, 28(10): 1563-1579. doi:10.1158/1055-9965.Epi-19-0221 Bray F, Ren J S, Masuyer E, et al. Global estimates of cancer prevalence for 27 sites in the adult population in 2008[J]. Int J Cancer, 2013, 132(5): 1133-1145. doi:10.1002/ijc.27711 Howlader N, Forjaz G, Mooradian M J, et al. The Effect of Advances in Lung-Cancer Treatment on Population Mortality[J]. New England Journal of Medicine, 2020, 383(7): 640-649. doi:10.1056/NEJMoa1916623 Mouritzen M T, Carus A, Ladekarl M, et al. Nationwide Survival Benefit after Implementation of First-Line Immunotherapy for Patients with Advanced NSCLC-Real World Efficacy[J]. Cancers (Basel), 2021, 13(19). doi:10.3390/cancers13194846 Wood M E, Vogel V, Ng A, et al. Second malignant neoplasms: assessment and strategies for risk reduction[J]. J Clin Oncol, 2012, 30(30): 3734-3745. doi:10.1200/jco.2012.41.8681 Duchateau C S, Stokkel M P. Second primary tumors involving non-small cell lung cancer: prevalence and its influence on survival[J]. Chest, 2005, 127(4): 1152-1158. doi:10.1378/chest.127.4.1152 Travis L B, Rabkin C S, Brown L M, et al. Cancer survivorship--genetic susceptibility and second primary cancers: research strategies and recommendations[J]. J Natl Cancer Inst, 2006, 98(1): 15-25. doi:10.1093/jnci/djj001 Wu B, Cui Y, Tian J, et al. Effect of second primary cancer on the prognosis of patients with non-small cell lung cancer[J]. Journal of Thoracic Disease, 2019, 11(2): 573-582. doi:10.21037/jtd.2018.11.96 Jang Y J, Kim S Y, Jung H K, et al. Association of EGFR mutations in second primary lung cancer and HER2 expression in breast cancer survivors[J]. Translational Cancer Research, 2021, 10(12): 5204-5211. doi:10.21037/tcr-21-1235 Barclay M E, Lyratzopoulos G, Walter F M, et al. Incidence of second and higher order smoking-related primary cancers following lung cancer: a population-based cohort study[J]. Thorax, 2019, 74(5): 466-472. doi:10.1136/thoraxjnl-2018-212456 Coyte A, Morrison D S, McLoone P. Second primary cancer risk - the impact of applying different definitions of multiple primaries: results from a retrospective population-based cancer registry study[J]. BMC Cancer, 2014, 14: 272. doi:10.1186/1471-2407-14-272 Wang X, Zeng M, Ju X, et al. Correlation between second and first primary cancer: systematic review and meta-analysis of 9 million cancer patients[J]. British Journal of Surgery, 2024, 111(1). doi:10.1093/bjs/znad377 Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies[J]. Hum Mol Genet, 2014, 23(R1): R89-98. doi:10.1093/hmg/ddu328 Davies N M, Holmes M V, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians[J]. Bmj, 2018, 362: k601. doi:10.1136/bmj.k601 Davey Smith G, Ebrahim S. What can mendelian randomisation tell us about modifiable behavioural and environmental exposures?[J]. Bmj, 2005, 330(7499): 1076-1079. doi:10.1136/bmj.330.7499.1076 McKay J D, Hung R J, Han Y, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes[J]. Nature Genetics, 2017, 49(7): 1126-1132. doi:10.1038/ng.3892 Kamat M A, Blackshaw J A, Young R, et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations[J]. Bioinformatics, 2019, 35(22): 4851-4853. doi:10.1093/bioinformatics/btz469 Burgess S, Thompson S G. Avoiding bias from weak instruments in Mendelian randomization studies[J]. Int J Epidemiol, 2011, 40(3): 755-764. doi:10.1093/ije/dyr036 Burgess S, Davey Smith G, Davies N M, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023[J]. Wellcome Open Res, 2019, 4: 186. doi:10.12688/wellcomeopenres.15555.3 Higgins J P, Thompson S G, Deeks J J, et al. Measuring inconsistency in meta-analyses[J]. Bmj, 2003, 327(7414): 557-560. doi:10.1136/bmj.327.7414.557 Burgess S, Thompson S G. Interpreting findings from Mendelian randomization using the MR-Egger method[J]. Eur J Epidemiol, 2017, 32(5): 377-389. doi:10.1007/s10654-017-0255-x Verbanck M, Chen C Y, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases[J]. Nat Genet, 2018, 50(5): 693-698. doi:10.1038/s41588-018-0099-7 Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome[J]. Elife, 2018, 7. doi:10.7554/eLife.34408 Zhong Y J, Wen Y F, Wong H M, et al. Trends and Patterns of Disparities in Burden of Lung Cancer in the United States, 1974-2015[J]. Front Oncol, 2019, 9: 404. doi:10.3389/fonc.2019.00404 Baum P, Winter H, Eichhorn M E, et al. Trends in age- and sex-specific lung cancer mortality in Europe and Northern America: Analysis of vital registration data from the WHO Mortality Database between 2000 and 2017[J]. Eur J Cancer, 2022, 171: 269-279. doi:10.1016/j.ejca.2022.05.011 Hong J, Wei R, Nie C, et al. The risk and prognosis of secondary primary malignancy in lung cancer: a population-based study[J]. Future Oncol, 2021, 17(33): 4497-4509. doi:10.2217/fon-2021-0045 Kwon W-A, Joung J Y, Lim J, et al. Risk of second primary Cancer among bladder Cancer patients: a population-based cohort study in Korea[J]. BMC Cancer, 2018, 18(1). doi:10.1186/s12885-018-4530-3 Wang X, Li R, Feng L, et al. Hsa_circ_0001666 promotes non-small cell lung cancer migration and invasion through miR-1184/miR-548I/AGO1 axis[J]. Mol Ther Oncolytics, 2022, 24: 597-611. doi:10.1016/j.omto.2022.02.011 Yang C, Mou Z, Wu S, et al. High-throughput sequencing identified circular RNA circUBE2K mediating RhoA associated bladder cancer phenotype via regulation of miR-516b-5p/ARHGAP5 axis[J]. Cell Death Dis, 2021, 12(8): 719. doi:10.1038/s41419-021-03977-1 Yu J, Zhang L, Peng J, et al. Dictamnine, a novel c-Met inhibitor, suppresses the proliferation of lung cancer cells by downregulating the PI3K/AKT/mTOR and MAPK signaling pathways[J]. Biochem Pharmacol, 2022, 195: 114864. doi:10.1016/j.bcp.2021.114864 Biswas P K, Kwak Y, Kim A, et al. TTYH3 Modulates Bladder Cancer Proliferation and Metastasis via FGFR1/H-Ras/A-Raf/MEK/ERK Pathway[J]. Int J Mol Sci, 2022, 23(18). doi:10.3390/ijms231810496 Chen H, Ma L, Yang W, et al. POLR3G promotes EMT via PI3K/AKT signaling pathway in bladder cancer[J]. Faseb j, 2023, 37(12): e23260. doi:10.1096/fj.202301095R McKay J D, Hung R J, Han Y, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes[J]. Nat Genet, 2017, 49(7): 1126-1132. doi:10.1038/ng.3892 Cai B, Jiang X. Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis[J]. Cancer Inform, 2014, 13(Suppl 1): 113-121. doi:10.4137/cin.S13882 Vitelli V, Fleischer T, Ankill J, et al. Transcriptomic pan-cancer analysis using rank-based Bayesian inference[J]. Mol Oncol, 2023, 17(4): 548-563. doi:10.1002/1878-0261.13354 Wu B, Cui Y, Tian J, et al. Effect of second primary cancer on the prognosis of patients with non-small cell lung cancer[J]. J Thorac Dis, 2019, 11(2): 573-582. doi:10.21037/jtd.2018.11.96 Muller J, Grosclaude P, Lapôtre-Ledoux B, et al. Trends in the risk of second primary cancer among bladder cancer survivors: a population-based cohort of 10 047 patients[J]. BJU Int, 2016, 118(1): 53-59. doi:10.1111/bju.13351 Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.xlsx Additionalfile2.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4664769","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":324790827,"identity":"20ef9728-2dd0-45c0-9276-7b68ba3cd1cd","order_by":0,"name":"Jiaqi Zhou","email":"","orcid":"","institution":"The First Hospital of Jiaxing (Affiliated Hospital of Jiaxing University)","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Zhou","suffix":""},{"id":324790829,"identity":"dc5ddced-8821-4fe0-9cc8-b833913c4f8d","order_by":1,"name":"Chunyuan Fei","email":"","orcid":"","institution":"The First Hospital of Jiaxing (Affiliated Hospital of Jiaxing University)","correspondingAuthor":false,"prefix":"","firstName":"Chunyuan","middleName":"","lastName":"Fei","suffix":""},{"id":324790831,"identity":"0e77d8f4-1d14-4e82-961d-84206ac559d7","order_by":2,"name":"Yingqing Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYJACZhDBz8x8wOCDgY0c8Vok29sSCmcUpBkTr8XgzBmDzzwfDicSVG5wPIH5c0HNHbuGGwmGm20MmBMY2A8f3YBXy5kHDMYzjj1LbpyRkGycY8CWx8CTlnYDr5YbCQzJPGyHk5klEo4BtfAUM0jwmBHUcpjn3+FkNonE9t8WBhKJDURoYWzmbTtsx8NzmMGYwcCAsBbJMw+YmXn7DidIsLcxGPYYJBizEfILHyjEeL4dtrc/zP/B4Mef/3L87IeP4dWicCD/A4hObICJsOFTDgLyDQlg2p6QwlEwCkbBKBjBAADO0U0SNt69KQAAAABJRU5ErkJggg==","orcid":"","institution":"The First Hospital of Jiaxing (Affiliated Hospital of Jiaxing University)","correspondingAuthor":true,"prefix":"","firstName":"Yingqing","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-07-01 02:12:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4664769/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4664769/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62135728,"identity":"3c49ec3c-9b67-408e-8e03-3370e06940c4","added_by":"auto","created_at":"2024-08-09 16:20:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":159917,"visible":true,"origin":"","legend":"\u003cp\u003eThe study design in the Mendelian randomization analysis\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4664769/v1/45d79dee2c4c3805e7c21dd9.jpg"},{"id":62135727,"identity":"827481de-e29b-4fed-a376-73bf6643a971","added_by":"auto","created_at":"2024-08-09 16:20:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154208,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of Mendelian randomization estimation of the associations of lung cancer and its subgroups with bladder cancer\u003c/p\u003e\n\u003cp\u003eAbbreviations: OR: odds ratio; CI: confidence interval; IVW: inverse variance weighting; LUAD: lung adenocarcinoma; LUSC: lung squamous cancer\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4664769/v1/8922b6b373200b42fe0492b3.jpg"},{"id":62136777,"identity":"90950e75-8b3f-4aef-a345-8b057a5fa91a","added_by":"auto","created_at":"2024-08-09 16:28:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153973,"visible":true,"origin":"","legend":"\u003cp\u003eForest plots of Mendelian randomization estimation of the associations of bladder cancer with lung cancer and its subgroups\u003c/p\u003e\n\u003cp\u003eAbbreviations: OR: odds ratio; CI: confidence interval; IVW: inverse variance weighting; LUAD: lung adenocarcinoma; LUSC: lung squamous cancer\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4664769/v1/5212cf0a3f6c79bf2a013b74.jpg"},{"id":67854910,"identity":"d49e6636-7233-4a10-8fd7-74c39b6a105e","added_by":"auto","created_at":"2024-10-30 11:16:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":740713,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4664769/v1/c2b9311d-e462-46be-92b2-8b884b373cbe.pdf"},{"id":62135726,"identity":"81f9a637-7e8f-48f7-9a6f-7a8b3b3b59a2","added_by":"auto","created_at":"2024-08-09 16:20:26","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16173,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4664769/v1/9cf53b6b048d34a290246b3f.xlsx"},{"id":62135730,"identity":"0e2e54ac-e968-49fe-9d52-746aaafd62b0","added_by":"auto","created_at":"2024-08-09 16:20:26","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":628130,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4664769/v1/f25f8817eb94a401ecedf607.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between lung cancer and bladder cancer risk: a bidirectional Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is a highly prevalent cancer with an estimated 2.2 million (11.4%) new cases in 2020[1]. It is the leading cause of cancer-related deaths in men and the second leading cause of cancer deaths in women, only behind breast cancer globally, with an estimated 1.8 million (18.0%) deaths[1]. Lung cancer is classified as small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC accounting for \u0026gt;85 % of all cases[2]. Globally, the most common subtype of NSCLC is adenocarcinoma (40 %), followed by squamous carcinoma (25 %)[2, 3]. Despite high lethality of lung cancer, the mortality rate has declined in recent years because of the early screening of CT and the use of targeted therapies and immunotherapy[4-6]. The population of lung cancer survivors has gradually increased as mortality rates have declined[1]. Studies have indicated that cancer survivors faced a high risk of developing second primary cancers (SPC)[7-9]. It was reported that SPC significantly increased mortality in lung cancer survivors[10]. The incidence of second primary lung cancer is also on the rise in other cancers, affecting the survival of cancer survivors[11]. Therefore, it is important to explore the causal relationship between lung cancer and other cancer.\u003c/p\u003e\n\u003cp\u003eMatthew et al. indicated that bladder cancer was the second most common smoking-related SPC in primary lung cancer[12]. Another study using the Surveillance, Epidemiology and End Results (SEER) database showed that bladder cancer as the fourth most common SPC besides lung cancer in primary lung cancer[10]. However, Aishah et al. did not establish any causal relationship between primary lung cancer and second primary bladder cancer[13]. Another study using SEER database and NHANES database indicated that patients with bladder cancer were prone to second primary lung cancer[14]. It was consistent with another conclusion by Aishah et al.[13]. Due to the inherent limitations in observational and retrospective studies, such as confounding factors, the conclusions drawn from these studies lack consistency and reliability. Therefore, we aimed to identify the causal relationship between lung and bladder cancer using Mendelian randomization (MR).\u003c/p\u003e\n\u003cp\u003eMR is a method that uses genetic variation as an indicator of exposure, independent of traditional study design biases[15]. By using genetic variants significantly associated with the exposure in Genome-wide Association Studies (GWAS) summary statistics as instrumental variables (IVs), MR estimates the causal effect of the exposure on the outcome[15, 16]. The MR design is advantageous as it can overcome confounding factors caused by environmental or behavioral influences, making it comparable to randomized controlled trials (RCTs) in terms of its ability to establish causality[17]. In this study, we applied a bidirectional two-sample MR analysis to elucidate the causal relationship between lung cancer and bladder cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design\u003c/p\u003e\n\u003cp\u003eAn overview of our study design is display in Fig.1. Our research must fulfil the three MR assumptions: 1) The IVs are strongly associated with exposure factors. 2.) The IVs are independent of confounding factors. 3) The IVs can only affect outcomes through exposure factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData source\u003c/p\u003e\n\u003cp\u003eThe GWAS data for lung cancer were acquired from the International Lung Cancer Consortium (ILCCO), which involved 29,266 cases of European ancestry and 56,450 controls of European ancestry[18]. To further investigate the relationship between the subtypes of lung cancer and bladder cancer, we obtained GWAS data for lung adenocarcinoma (LUAD) and lung squamous cancer (LUSC)[18]. To avoid the overlap of participants, the summary statistics of bladder cancer were obtained from the FinnGen biobank (Finn) consortium, including 1,115 cases and 217,677 controls from European ancestry. The summary statistics of bladder cancer were acquired from the following website: https://gwas.mrcieu.ac.uk/datasets/finn-b-C3_BLADDER/.\u003c/p\u003e\n\u003cp\u003eSelection of IVs\u003c/p\u003e\n\u003cp\u003eTo identify single nucleotide polymorphisms (SNPs) significantly associated with lung cancer as IVs, we set a P-value of 5*10\u003csup\u003e-8\u003c/sup\u003e as the threshold for lung cancer, LUAD and LUSC[16]. In addition, we performed the clumping process (r\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.001, window size = 10,000 kb) and estimated the linkage disequilibrium (LD) between SNPs in order to exclude the bias caused by LD. We set a more relaxed threshold (P\u0026lt; 5*10\u003csup\u003e-6\u003c/sup\u003e) for bladder cancer to include more IVs associated with bladder cancer. We also performed the clumping process (r\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.001, window size = 10,000 kb) and estimated the linkage disequilibrium (LD). Subsequently, we screened SNPs in the PhenoScanncer database to remove those potentially associated with confounders or outcome (P\u0026lt;1*10\u003csup\u003e-5\u003c/sup\u003e)\u0026nbsp;[19]. Finally, we calculated the F-statistic to quantify the strength of the IVs. If the F-statistic was \u0026gt;10, it indicated that there was no weak instrumental bias[20].\u003c/p\u003e\n\u003cp\u003eStatistical Analyses\u003c/p\u003e\n\u003cp\u003eAfter harmonizing the effects of SNPs selected from exposure and the GWAS of outcome, we applied various MR methods to evaluate the causal relationship between lung cancer and bladder cancer, including inverse-variance weighted (IVW), MR-Egger and weighted median methods. IVW served as the primary method, determining the MR estimate of exposure for outcome meta-analysis of the Wald ratio for each SNP[21]. MR-Egger, weighted median methods were also employed to evaluate the causal effect, generally as a complement to IVW.\u003c/p\u003e\n\u003cp\u003eSensitivity Analyses\u003c/p\u003e\n\u003cp\u003eIt is very important for sensitivity analyses in MR studies to assess heterogeneity and pleiotropy. We calculated Cochran\u0026apos;s Q-Statistics using IVW to evaluate heterogeneity (P \u0026lt;0.05 was considered as the presence of heterogeneity)[22]. The horizontal pleiotropy of IVs was assessed based on the intercept obtained from MR-Egger regression[23]. Additionally, we utilized the MR pleiotropy residual sum and outlier test\u0026nbsp;(MR-PRESSO) to detect pleiotropy, exclude outlying SNPs and reassess the effect estimates[24]. Furthermore, leave-one-out analysis was used to identify outlying values.\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted in R 4.2.2 by using packages of \u0026ldquo;TwoSampleMR\u0026rdquo; and \u0026ldquo;MR-PRESSO\u0026rdquo;[24, 25]. A two-tailed\u003cem\u003e\u0026nbsp;\u003c/em\u003eP \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSelection of instrumental variables of lung cancer\u003c/p\u003e\n\u003cp\u003eWe at first selected 15 IVs for lung cancer, 14 IVs for LUAD and 7 IVs for LUSC, respectively. Then, 5 IVs for lung cancer, 5 IVs for LUAD and 3 IVs for LUSC were removed after searching in the PhenoScanncer database due to confounding variables. Ultimately, we identified 10 IVs for lung cancer, 9 IVs for LUAD and 4 IVs for LUSC. All the F-statistics of these IVs ranged from 34.63 to 91.77, showing the absence of weak IVs (Additional file 1: Table S1).\u003c/p\u003e\n\u003cp\u003eMR analysis of lung cancer to bladder cancer\u003c/p\u003e\n\u003cp\u003eIn overall lung cancer to bladder cancer MR analysis, we discovered that lung cancer was an elevated risk of bladder cancer (IVW: OR=1.88, 95%CI 1.29 - 2.75, P= 0.001; weighted median: OR= 1.68, 95%CI 1.08 - 2.59, P= 0.020). The similar causal estimates were obtained from MR-Egger (OR= 2.32, 95%CI 0.91 - 5.89, P= 0.114), although the association was not statistically significant. In LUAD to bladder cancer MR analysis, no association was identified between LUAD and bladder cancer (IVW: OR=0.92, 95%CI 0.68 - 1.26, P= 0.612; MR-Egger: OR= 0.66, 95%CI 0.13 - 3.37, P= 0.629; weighted median: OR= 1.13, 95%CI 0.79 - 1.63, P= 0.495). Similarly, no association was identified between LUSC and bladder cancer in LUSC to bladder MR analysis (IVW: OR=1.34, 95%CI 0.78 \u0026ndash; 2.30, P= 0.281; MR-Egger: OR= 0.99, 95%CI 0.23 \u0026ndash; 4.31, P= 0.994; weighted median: OR= 1.11, 95%CI 0.75 - 1.66, P= 0.596) (Fig.2).\u003c/p\u003e\n\u003cp\u003eSelection of instrumental variables of bladder cancer\u003c/p\u003e\n\u003cp\u003eIn bladder cancer to lung cancer MR analysis, 13 SNPs were selected as IVs. One IV associated with confounding variable was removed using PhenoScanncer database. Another IV was removed because of palindromic structure. Ultimately, we identified 11 IVs for bladder cancer. After matching with the GWAS of lung cancer and its subgroups, 8 IVs remained. All the F-statistics of these IVs ranged from 20.93 to 30.18 (Additional file 1: Table S2).\u003c/p\u003e\n\u003cp\u003eMR analysis of bladder cancer to lung cancer\u003c/p\u003e\n\u003cp\u003eIn bladder cancer to overall lung cancer MR analysis, we didn\u0026rsquo;t discover any association with lung cancer (IVW: OR=1.03, 95%CI 0.99 - 1.07, P=0.146; MR-Egger: OR=1.05, 95%CI 0.97 - 1.14, P=0.230; weight median: OR=1.04, 95%CI 0.99 - 1.09, P=0.133). In bladder cancer to LUAD MR analysis, we also didn\u0026rsquo;t identify causal association between bladder cancer and LUAD (IVW: OR=1.03, 95%CI 0.98 - 1.09, P=0.231; MR-Egger: OR=1.11, 95%CI 1.00 - 1.23, P=0.101; weight median: OR=1.07, 95%CI 0.98 - 1.13, P=0.126). In bladder cancer to LUSC MR analysis, we found that bladder cancer increased the risk of LUSC (IVW: OR=1.09, 95%CI 1.02 - 1.16, P=0.008; MR-Egger: OR=1.18, 95%CI 1.04 - 1.33, P=0.042). Although the association was not statistically significant in the weight median method, the similar estimate was obtained (OR=1.08, 95%CI 0.99 - 1.23, P=1.18) (Fig.3).\u003c/p\u003e\n\u003cp\u003eSensitivity Analyses\u003c/p\u003e\n\u003cp\u003eWe performed sensitivity analysis to reliable to our results. The result of Cochran\u0026apos;s Q-Statistics indicated that there was heterogeneity between LUSC and bladder cancer in lung cancer to bladder cancer MR analysis (P=0.025). Despite of the heterogeneity detected, the random effect of the IVW remained valid. There was no heterogeneity in any other MR analysis (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of MR-Egger regression revealed no horizontal pleiotropy in any MR analysis. Additionally, the MR-PRESSO also didn\u0026rsquo;t identify any outlier and pleiotropy (Table 1). The results of leave-one-out indicated the estimates were not biased by a single SNP (Additional file 2: Fig. S1). Furthermore, scatter plots, forest plots and funnel plots are shown in Supplementary Materials (Additional file 2: Figs. S2-S4).\u003c/p\u003e\n\u003cp\u003eTable 1 Evaluation of heterogeneity and directional pleiotropy\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\" rowspan=\"2\"\u003e\n \u003cp\u003eExposure-Outcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" colspan=\"2\"\u003e\n \u003cp\u003eHeterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" colspan=\"2\"\u003e\n \u003cp\u003eHorizontal pleiotropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003eMR-PRESSO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.794871794871796%\"\u003e\n \u003cp\u003eCochran\u0026rsquo;s Q\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.94871794871795%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.794871794871796%\"\u003e\n \u003cp\u003eEgger intercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eLC-BC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e15.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eLUAD-BC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e10.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eLUSC-BC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e9.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eBC-LC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e4.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eBC-LUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e4.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003eBC-LUSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e7.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.583333333333334%\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.708333333333332%\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMR-PRESSO: Mendelian randomization pleiotropy residual sum and outlier; LC: lung cancer; BC: bladder cancer; LUAD: lung adenocarcinoma; LUSC: lung squamous cancer\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith early CT screening and advance treatments, the life expectancy of lung cancer has prolonged. As a result, the incidence of SPC has increased significantly. After 20 years or more of follow-up, more than 19% of patients are likely to develop SPCs[26]. Approximately 13.4-22% of lung cancer patients will develop SPCs[27]. Bladder cancer is one of the common SPCs of lung cancer. It is beneficial to explore the causal relationship between lung cancer and bladder cancer to identify high-risk patient for early screening and timely treatment to improve patient survival.\u003c/p\u003e\n\u003cp\u003eA study by Hong et al., based on the SEER database, found that lung cancer increased the risk of second primary bladder cancer[28]. However, Aishah et al. did not find a causal relationship between lung cancer and second primary bladder cancer[13]. Another study based on Korean population showed that bladder cancer increased the risk of second primary lung cancer, including LUAD and LUSC[29]. Therefore, it is unclear whether there is any causal relationship between lung and bladder cancer as SPCs. Observational studies often struggle to avoid the interference of various confounding factors. In our study, we applied a bidirectional two-sample MR analysis to assess the causal relationship between lung cancer and bladder cancer by using three approaches.\u003c/p\u003e\n\u003cp\u003eIn our study, we identified overall lung cancer as a risk factor for second primary bladder cancer. Many studies have found that the development of lung cancer and bladder cancer was associated with activation of the PI3K/Akt/mTOR and MAPK signaling pathway[30-33]. Chen et al. discovered that RNA Polymerase III Subunit G (POLR3G) plays a contributory role in the development of lung cancer and bladder cancer[34]. Lung cancer and bladder cancer may share similar biological mechanisms, which could explain the causal relationship between these two cancers. On the other hand, we showed that bladder cancer increased risk of second primary LUSC rather than LUAD. A study by James et al. showed that while LUAD and LUSC were subtypes of NSCLC, the mechanisms by which they developed may be different[35]. One potential mechanism through which bladder cancer increases the risk of second primary LUSC is that the development of LUSC is associated with the bladder cancer pathway[36]. Furthermore, a study using rank-based Bayesian clustering to analyze pan-cancer identified three pan-squamous clusters consist of LUSC, head and neck squamous cancer, and bladder cancer[37]. Vitelli et al. found that these three cancers were not clustered based on tissue of origin, but on cell morphology[37]. They suggested that some tumors should be classified according to tissue type not the same tissue of origin[37]. This may explain bladder cancer increased risk of LUSC rather than LUAD.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWu et al. revealed that approximately half of lung cancer patients develop SPCs in the first year after diagnosis[38]. The risk of developing a new tumor in bladder cancer patients was 60% higher than in the general population[39]. Our study confirmed the genetical causality between LUSC and bladder cancer. As a result, we should pay attention to the occurrence of bladder cancer in LUSC or LUSC in bladder cancer for early screening and timely treatment.\u003c/p\u003e\n\u003cp\u003eOur study has several strengths. As far as we know, this is the first bidirectional two- sample MR study to evaluate the causal effect between lung cancer, LUAD, and LUSC and bladder cancer based on GWAS database. The MR design can avoid various confounding bias and reverse effect, which is the closest to RCT. Furthermore, we performed multiple sensitivity analyses to detect whether there was horizontal pleiotropy in our results. The intercepts obtained from our MR-Egger analysis indicated that all identified causal relationships remained robust against the influence of horizontal pleiotropy, reinforcing the validity and reliability of our findings.\u003c/p\u003e\n\u003cp\u003eHowever, our study also has some limitations. First, all the GWAS data were from individuals of European ancestry. As a result, our findings may not be consistent for other ancestries, which need further studies to evaluate the causal relationship between lung cancer and bladder cancer in other ancestries. Second, despite our efforts to exclude SNPs associated with potential confounders, the possibility of residual unknown confounders influencing the causal estimates cannot be entirely ruled out. Third, the limited number of SNPs significantly associated with LUSC included in our study may affect the statistical robustness of our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study provided evidence in support of causality between lung and bladder cancer. We should focus on SPC-bladder cancer or SPC-LUSC to intervene in time.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe are grateful to all participants and investigators who contributed to the GWAS data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYingqing Zhang and Jiaqi Zhou conceived this study; Jiaqi Zhou analyzed the data; Jiaqi Zhou and Chunyuan Fei prepared the first draft of this manuscript; and Yingqing Zhang\u003csup\u003e\u0026nbsp;\u003c/sup\u003erevised this manuscript critically. All authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll of the datasets analyzed during the current study are available at (https://gwas.mrcieu.ac.uk/).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eZhejiang Province and Jiaxing City Jointly Develop Medical Key Discipline\u0026mdash;Respirology (2023-SSGJ-002). The Key Discipline of Jiaxing Respiratory Medicine Construction Project (No.2019-zc-04). Jiaxing Key Laboratory of Precision Treatment for Lung Cancer (No.2019-fazdsys). \u0026nbsp;Wu Jieping Medical Foundation Clinical Research Special Fund (No. 320.6750.2022-09-53).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe data used in this paper are publicly available, ethically approved.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting Interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA: A Cancer Journal for Clinicians, 2021, 71(3): 209-249. doi:10.3322/caac.21660\u003c/li\u003e\n\u003cli\u003eLeiter A, Veluswamy R R, Wisnivesky J P. The global burden of lung cancer: current status and future trends[J]. Nat Rev Clin Oncol, 2023, 20(9): 624-639. doi:10.1038/s41571-023-00798-3\u003c/li\u003e\n\u003cli\u003eSchabath M B, Cote M L. Cancer Progress and Priorities: Lung Cancer[J]. Cancer Epidemiology, Biomarkers \u0026amp; Prevention, 2019, 28(10): 1563-1579. doi:10.1158/1055-9965.Epi-19-0221\u003c/li\u003e\n\u003cli\u003eBray F, Ren J S, Masuyer E, et al. Global estimates of cancer prevalence for 27 sites in the adult population in 2008[J]. Int J Cancer, 2013, 132(5): 1133-1145. doi:10.1002/ijc.27711\u003c/li\u003e\n\u003cli\u003eHowlader N, Forjaz G, Mooradian M J, et al. The Effect of Advances in Lung-Cancer Treatment on Population Mortality[J]. New England Journal of Medicine, 2020, 383(7): 640-649. doi:10.1056/NEJMoa1916623\u003c/li\u003e\n\u003cli\u003eMouritzen M T, Carus A, Ladekarl M, et al. Nationwide Survival Benefit after Implementation of First-Line Immunotherapy for Patients with Advanced NSCLC-Real World Efficacy[J]. Cancers (Basel), 2021, 13(19). doi:10.3390/cancers13194846\u003c/li\u003e\n\u003cli\u003eWood M E, Vogel V, Ng A, et al. Second malignant neoplasms: assessment and strategies for risk reduction[J]. J Clin Oncol, 2012, 30(30): 3734-3745. doi:10.1200/jco.2012.41.8681\u003c/li\u003e\n\u003cli\u003eDuchateau C S, Stokkel M P. Second primary tumors involving non-small cell lung cancer: prevalence and its influence on survival[J]. Chest, 2005, 127(4): 1152-1158. doi:10.1378/chest.127.4.1152\u003c/li\u003e\n\u003cli\u003eTravis L B, Rabkin C S, Brown L M, et al. Cancer survivorship--genetic susceptibility and second primary cancers: research strategies and recommendations[J]. J Natl Cancer Inst, 2006, 98(1): 15-25. doi:10.1093/jnci/djj001\u003c/li\u003e\n\u003cli\u003eWu B, Cui Y, Tian J, et al. Effect of second primary cancer on the prognosis of patients with non-small cell lung cancer[J]. Journal of Thoracic Disease, 2019, 11(2): 573-582. doi:10.21037/jtd.2018.11.96\u003c/li\u003e\n\u003cli\u003eJang Y J, Kim S Y, Jung H K, et al. Association of EGFR mutations in second primary lung cancer and HER2 expression in breast cancer survivors[J]. Translational Cancer Research, 2021, 10(12): 5204-5211. doi:10.21037/tcr-21-1235\u003c/li\u003e\n\u003cli\u003eBarclay M E, Lyratzopoulos G, Walter F M, et al. Incidence of second and higher order smoking-related primary cancers following lung cancer: a population-based cohort study[J]. Thorax, 2019, 74(5): 466-472. doi:10.1136/thoraxjnl-2018-212456\u003c/li\u003e\n\u003cli\u003eCoyte A, Morrison D S, McLoone P. Second primary cancer risk - the impact of applying different definitions of multiple primaries: results from a retrospective population-based cancer registry study[J]. BMC Cancer, 2014, 14: 272. doi:10.1186/1471-2407-14-272\u003c/li\u003e\n\u003cli\u003eWang X, Zeng M, Ju X, et al. Correlation between second and first primary cancer: systematic review and meta-analysis of 9 million cancer patients[J]. British Journal of Surgery, 2024, 111(1). doi:10.1093/bjs/znad377\u003c/li\u003e\n\u003cli\u003eDavey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies[J]. Hum Mol Genet, 2014, 23(R1): R89-98. doi:10.1093/hmg/ddu328\u003c/li\u003e\n\u003cli\u003eDavies N M, Holmes M V, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians[J]. Bmj, 2018, 362: k601. doi:10.1136/bmj.k601\u003c/li\u003e\n\u003cli\u003eDavey Smith G, Ebrahim S. What can mendelian randomisation tell us about modifiable behavioural and environmental exposures?[J]. Bmj, 2005, 330(7499): 1076-1079. doi:10.1136/bmj.330.7499.1076\u003c/li\u003e\n\u003cli\u003eMcKay J D, Hung R J, Han Y, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes[J]. Nature Genetics, 2017, 49(7): 1126-1132. doi:10.1038/ng.3892\u003c/li\u003e\n\u003cli\u003eKamat M A, Blackshaw J A, Young R, et al. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations[J]. Bioinformatics, 2019, 35(22): 4851-4853. doi:10.1093/bioinformatics/btz469\u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson S G. Avoiding bias from weak instruments in Mendelian randomization studies[J]. Int J Epidemiol, 2011, 40(3): 755-764. doi:10.1093/ije/dyr036\u003c/li\u003e\n\u003cli\u003eBurgess S, Davey Smith G, Davies N M, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023[J]. Wellcome Open Res, 2019, 4: 186. doi:10.12688/wellcomeopenres.15555.3\u003c/li\u003e\n\u003cli\u003eHiggins J P, Thompson S G, Deeks J J, et al. Measuring inconsistency in meta-analyses[J]. Bmj, 2003, 327(7414): 557-560. doi:10.1136/bmj.327.7414.557\u003c/li\u003e\n\u003cli\u003eBurgess S, Thompson S G. Interpreting findings from Mendelian randomization using the MR-Egger method[J]. Eur J Epidemiol, 2017, 32(5): 377-389. doi:10.1007/s10654-017-0255-x\u003c/li\u003e\n\u003cli\u003eVerbanck M, Chen C Y, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases[J]. Nat Genet, 2018, 50(5): 693-698. doi:10.1038/s41588-018-0099-7\u003c/li\u003e\n\u003cli\u003eHemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome[J]. Elife, 2018, 7. doi:10.7554/eLife.34408\u003c/li\u003e\n\u003cli\u003eZhong Y J, Wen Y F, Wong H M, et al. Trends and Patterns of Disparities in Burden of Lung Cancer in the United States, 1974-2015[J]. Front Oncol, 2019, 9: 404. doi:10.3389/fonc.2019.00404\u003c/li\u003e\n\u003cli\u003eBaum P, Winter H, Eichhorn M E, et al. Trends in age- and sex-specific lung cancer mortality in Europe and Northern America: Analysis of vital registration data from the WHO Mortality Database between 2000 and 2017[J]. Eur J Cancer, 2022, 171: 269-279. doi:10.1016/j.ejca.2022.05.011\u003c/li\u003e\n\u003cli\u003eHong J, Wei R, Nie C, et al. The risk and prognosis of secondary primary malignancy in lung cancer: a population-based study[J]. Future Oncol, 2021, 17(33): 4497-4509. doi:10.2217/fon-2021-0045\u003c/li\u003e\n\u003cli\u003eKwon W-A, Joung J Y, Lim J, et al. Risk of second primary Cancer among bladder Cancer patients: a population-based cohort study in Korea[J]. BMC Cancer, 2018, 18(1). doi:10.1186/s12885-018-4530-3\u003c/li\u003e\n\u003cli\u003eWang X, Li R, Feng L, et al. Hsa_circ_0001666 promotes non-small cell lung cancer migration and invasion through miR-1184/miR-548I/AGO1 axis[J]. Mol Ther Oncolytics, 2022, 24: 597-611. doi:10.1016/j.omto.2022.02.011\u003c/li\u003e\n\u003cli\u003eYang C, Mou Z, Wu S, et al. High-throughput sequencing identified circular RNA circUBE2K mediating RhoA associated bladder cancer phenotype via regulation of miR-516b-5p/ARHGAP5 axis[J]. Cell Death Dis, 2021, 12(8): 719. doi:10.1038/s41419-021-03977-1\u003c/li\u003e\n\u003cli\u003eYu J, Zhang L, Peng J, et al. Dictamnine, a novel c-Met inhibitor, suppresses the proliferation of lung cancer cells by downregulating the PI3K/AKT/mTOR and MAPK signaling pathways[J]. Biochem Pharmacol, 2022, 195: 114864. doi:10.1016/j.bcp.2021.114864\u003c/li\u003e\n\u003cli\u003eBiswas P K, Kwak Y, Kim A, et al. TTYH3 Modulates Bladder Cancer Proliferation and Metastasis via FGFR1/H-Ras/A-Raf/MEK/ERK Pathway[J]. Int J Mol Sci, 2022, 23(18). doi:10.3390/ijms231810496\u003c/li\u003e\n\u003cli\u003eChen H, Ma L, Yang W, et al. POLR3G promotes EMT via PI3K/AKT signaling pathway in bladder cancer[J]. Faseb j, 2023, 37(12): e23260. doi:10.1096/fj.202301095R\u003c/li\u003e\n\u003cli\u003eMcKay J D, Hung R J, Han Y, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes[J]. Nat Genet, 2017, 49(7): 1126-1132. doi:10.1038/ng.3892\u003c/li\u003e\n\u003cli\u003eCai B, Jiang X. Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis[J]. Cancer Inform, 2014, 13(Suppl 1): 113-121. doi:10.4137/cin.S13882\u003c/li\u003e\n\u003cli\u003eVitelli V, Fleischer T, Ankill J, et al. Transcriptomic pan-cancer analysis using rank-based Bayesian inference[J]. Mol Oncol, 2023, 17(4): 548-563. doi:10.1002/1878-0261.13354\u003c/li\u003e\n\u003cli\u003eWu B, Cui Y, Tian J, et al. Effect of second primary cancer on the prognosis of patients with non-small cell lung cancer[J]. J Thorac Dis, 2019, 11(2): 573-582. doi:10.21037/jtd.2018.11.96\u003c/li\u003e\n\u003cli\u003eMuller J, Grosclaude P, Lap\u0026ocirc;tre-Ledoux B, et al. Trends in the risk of second primary cancer among bladder cancer survivors: a population-based cohort of 10 047 patients[J]. BJU Int, 2016, 118(1): 53-59. doi:10.1111/bju.13351\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung cancer, bladder cancer, Mendelian randomization, causal effect","lastPublishedDoi":"10.21203/rs.3.rs-4664769/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4664769/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe second primary cancer (SPC) poses a significant threat to lung cancer survivors, with bladder cancer being one of the most common SPCs. The relationship between lung cancer and bladder cancer has remained unclear. In this study, we aimed to evaluate the causal effect between these two cancers through bidirectional two-sample Mendelian randomization (MR) analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGenetic instruments associated with lung cancer and its subgroups were derived from the International Lung Cancer Consortium (ILCCO), while the data of bladder cancer was obtained from the FinnGen biobank. To estimate the causal relationship, we employed inverse-variance weighted (IVW) method, MR-Egger, and weighted-median method. Additionally, we conducted Cochran's Q test, MR-Egger regression, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) and leave-one-out analysis to assess potential pleiotropy effects.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur analysis revealed that genetically overall lung cancer increased the risk of bladder cancer based on the IVW and weighted median method. However, subgroup analysis showed no causal relationship between LUSC or LUAD and bladder cancer. In the reverse MR analysis, we found no evidence of any causal relationship between bladder cancer and overall lung cancer. Subgroup analysis suggested that bladder cancer increased the risk of LUSC. The assessment of heterogeneity and pleiotropy provided further support for the robustness and validity of these MR results.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study provided evidence in support of causality between lung cancer and bladder cancer in individuals of European ancestry. We should focus on SPC-bladder cancer or SPC-LUSC to intervene in time.\u003c/p\u003e","manuscriptTitle":"Association between lung cancer and bladder cancer risk: a bidirectional Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 16:20:21","doi":"10.21203/rs.3.rs-4664769/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4204f7ea-8a92-460a-828e-4b4f35ab063f","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-30T11:08:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 16:20:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4664769","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4664769","identity":"rs-4664769","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}