Causal Effects of Genetically Determined Lipidome on Lung Cancer and Its Subtypes: A Mendelian Randomization Study  

Causal Effects of Genetically Determined Lipidome on Lung Cancer and Its Subtypes: A 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 Causal Effects of Genetically Determined Lipidome on Lung Cancer and Its Subtypes: A Mendelian Randomization Study Cong Luo, Jie Mi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4437234/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 Previous observational studies have identified abnormalities in lipid metabolism among lung cancer patients, but the causal relationship between lipidomes and lung cancer risk remains unclear. Herein, we investigate the causal effect of lipidomes on the incidence of lung cancer and its subtypes through two-sample Mendelian randomization (MR) analysis. Methods A genome-wide association study of 179 lipid metabolites was used as the exposure, while lung cancer and its subtypes were the outcomes. All the datasets were obtained from an open database. The inverse variance weighted method was used as the primary analysis, and MR-Egger regression, the weighted median method, and the weighted mode method were employed to test the robustness of the results. MR-Egger intercept and Cochran's Q statistical analysis were used to assess potential pleiotropy and heterogeneity. Leave-one-out sensitivity analysis was also used to test the stability of the findings. Results Forty-two significant lipids were identified as causative exposures for lung cancer. Seventeen lipids affected lung cancer risk in smokers, while only four affected non-smokers. There were two overlapping lipids among the three pathological types of lung cancer. Phosphatidylcholine (O-16:1_18:0) had protective effects on small cell lung cancer (odds ratio (OR) = 0.828, P = 0.038) and lung squamous cell carcinoma (LUSC) (OR = 0.859, P = 0.005). Phosphatidylethanolamine (18:0_18:2) also exhibited protective effects on lung adenocarcinoma (OR = 0.943, P = 0.038) and LUSC (OR = 0.912, P = 0.003). Our results were robust even without a single SNP due to a "leave-one-out" analysis. The MR Egger intercept test indicated that genetic pleiotropy had no effect on the results. No heterogeneity was detected by Cochran's Q test. Conclusion This study unveiled the causal effect of specific lipid species on lung cancer and its subtypes. Smoking patients are more susceptible to abnormal lipid metabolism and are at a higher risk of developing lung cancer. Different lipid species are closely associated with various pathological types of lung cancer. Our study suggests that lipids may be utilized in the early screening, prevention, and treatment of lung cancer. Lung cancer Lipidome MR study Figures Figure 1 Figure 2 Introduction Lung cancer is the leading cause of cancer-related death worldwide. GLOBOCAN estimated that there were 2.2 million new cases of lung cancer and 1.8 million deaths related to lung cancer in 2020 [ 1 ]. Globally, the age-standardized incidence rate ranged from 36.8 per 100,000 to 5.9 per 100,000, and the age-standardized mortality rate varied from 32.8 per 100,000 to 4.9 per 100,000 [ 2 ]. Early detection of lung cancer is effective in reducing the lung cancer-related mortality, while prevention is even more crucial for reducing the incidence of lung cancer. Currently, smoking is the most well-established risk factor for lung cancer, but over 25% of lung cancer patients are non-smokers [ 3 ]. Some environmental agents like ionizing radiation, certain diseases such as pulmonary fibrosis, and abnormal biological metabolism have also been identified as potential risk factors for lung cancer [ 4 ]. Recent observational studies suggest a close relationship between abnormal lipid metabolism and the incidence of lung cancer. Wang et al . found that lipid metabolism was broadly dysregulated in early-stage lung cancer patients as compared with healthy individuals or patients with benign tumors. They validated that nine of these lipids were able to detect early-stage cancer across multiple independent cohorts [ 5 ]. Several signatures of lipid metabolism-related genes have been identified as valuable prognostic biomarkers for lung cancer [ 6 ]. However, the observational results were inconsistent. For example, Yang et al. found that a higher plasma cholesterol level was associated with an increased incidence of lung cancer [ 7 ], but Chang et al. demonstrated a significant inverse association between low cholesterol and lung cancer [ 8 ]. Lee et al. discovered that sphingomyelin (d18:1/22:0) level were elevated in lung cancer tumor tissue compared to normal tissue [ 9 ]. On the other hand, Takanashi et al. observed a decrease in SM (t34:1) levels in lung squamous cell carcinoma tissue compared to normal tissue in patients from the recurrent group [ 10 ]. In addition, although these conventional epidemiological studies indicate a strong connection between the lipidome and lung cancer risk, they are unable to establish causal relationships due to the potential influences from unmeasured confounding factors and reverse causality. Mendelian randomization (MR) study is an increasingly popular statistical method that can provide credible evidence of a causal effect of one trait on another [ 11 ]. MR utilizes genetic variants that are strongly associated with the exposure but are not influenced by the outcome or confounding variables as instrumental variables (IVs) to investigate the causal effect of the exposure on the outcome using summary statistics from genome-wide association studies (GWAS). As IVs are less susceptible to confounders and reverse causality, MR has been recently used as a powerful tool to estimate the causal effects of exposure in post-GWAS analysis[ 11 ]. Thus, in this study, we utilized MR analysis and GWAS data to investigate the causal effect of 179 lipids on lung cancer. Materials and methods Study design The overall design of our two-sample MR study is illustrated in Fig. 1 . For MR analyses, the chosen IVs must meet the three crucial assumptions: first, IVs should exhibit a strong correlation with the exposure; second, IVs should not be associated with any confounders; third, IVs should not have a direct relationship with outcomes but may influence the outcome solely through their impact on the exposure. As the GWAS data was downloaded from the publicly available databases, ethical approval was not required as no individual data was used in this study. GWAS data for lipidome The plasma lipid species data were derived from the GWAS data published by Linda et al. in 2023 [ 12 ]. Linda et al. conducted a comprehensive genome-wide analysis of 179 lipid species in 7174 Finnish individuals, uncovering the genetic connections between coronary artery disease risk and lipid species. These 179 species consist of four categories: triglycerides, glycerophospholipids, sphingolipids, and sterols. After quality control procedures, SNPs for 179 lipid species were downloaded and used in this study ( Supplementary Material ). The relevant GWAS results are accessible via the GWAS Catalog ( http://www.ebi.ac.uk/gwas/ , from GCST90277238-GCST90277416). GWAS data for lung cancer The GWAS data for lung cancer was derived from the International Lung Cancer Consortium [ 13 ]. The study involved 85,716 individuals, with 29,266 cases and 56,450 controls. Meanwhile, the study was stratified by histological subtype (small cell carcinoma: SCLC, and adenocarcinoma: LUAD, squamous cell carcinoma: LUSC) and smoking status (ever and never smokers). Detailed information on GWAS data for lung cancer is presented in Table 1 . Table 1 GWAS data on lung cancer GWAS-ID Name Sample size ncase ncontrol Population GCST004748 lung carcinoma 85716 29,266 56,450 European GCST004749 Lung cancer in ever smokers 40187 23,223 16,964 European GCST004747 Lung cancer in never smokers 9859 2,355 7,504 European GCST004746 SCLC 24108 2,664 21,444 European GCST004744 LUAD 66756 11,273 55,483 European GCST004750 LUSC 63053 7,426 55,627 European ncase: number of case; ncontrol: number of control; SCLC: small-cell lung cancer; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma. Instrumental Variables Selection Various criteria were utilized to ensure the selection of optimal IVs[ 14 ]. Initially, SNPs were mandated to demonstrate a significant correlation with lipidomes ( P < 5 × 10 − 6 ). Subsequently, SNPs were clumped using a strict linkage disequilibrium (LD) threshold of r 2 = 0.001 and window size = 10,000 kb to guarantee their independence. Additionally, palindromic SNPs were particularly scrutinized in the original dataset to prevent unforeseen reverse effects. Furthermore, to ascertain the efficacy of the IVs, F-statistic parameters were computed to evaluate the robustness of the chosen IVs, and those with an F-statistic < 10 were eliminated [ 15 ]. MR analysis The primary analysis utilized the inverse-variance weighted (IVW) method to evaluate the causal impact of 179 lipids on lung cancer. To ensure the reliability of the primary findings, additional methods such as MR Egger, weighted median and weighted mode were employed to estimate the potential causal effects when IVs deviated from standard assumptions [ 16 , 17 ]. Subsequently, MR-Egger regression was employed to detect potential horizontal pleiotropy, with statistical significance set at P -values for intercept < 0.05 [ 17 ]. Cochran’s Q test was utilized to examine IV heterogeneity and its potential influence on the causal estimate, with significance indicated by P < 0.05 [ 18 ]. Lastly, “leave-one-out” sensitivity analysis was conducted to assess the impact of individual SNP on the MR results [ 19 ]. Statistical Analysis Statistical analyses were conducted using the R software (version 4.1.2) with the utilization of“TwoSampleMR,” “Mendelian-Randomization,” and “MRPRESSO” packages. Results were presented as odds ratios (ORs) along with a 95% confidence interval (CI). The ORs were interpreted as the probability of one trait occurring with each incremental increase in the inverse normalized value of another trait. An OR greater than 1 indicates an increased likelihood of lung cancer incidence, while a value less than 1 suggests a decreased risk of cancer. Statistical significance was defined as P < 0.05. Additionally, to address the issue of multiple testing in the study, P values obtained from IVW method underwent False Discovery Rate (FER) adjustment to improve the robustness of our findings [ 20 ]. Results Strength of IVs In this study, a two-sample MR analysis was conducted to investigated the potential causal relationship between lipidome and the risk of lung cancer and its subtypes. The IVs utilized in the analysis consisted of 179 metabolites, each comprising a varying number of SNPs 12 to 44 ( Supplementary material ). Notably, all IVs exhibited F statistic exceeding 10, indicating the reliability and strength of the IVs. Causality of genetically determined lipids on lung cancer The MR analysis using the IVW method revealed that 42 lipids were significantly linked to lung cancer or its subtypes (Fig. 2 , with P ivw < 0.05). These lipids were categorized into sterol ester, diacylglycerol, phosphatidylcholine, phosphatidylinositol, phosphatidylethanolamine, sphingomyelin, triacylglycerol and cholesterol. Further details on the SNPs and their genetic associations with lung cancer and its subtypes can be found in the Additional file . Among the identified lipids, sphingomyelin (d38:2) exhibited the most pronounced protective effect against lung cancer in non-smoking individuals (OR = 0.785, 95%CI: 0.634–0.972, P = 0.026), while sphingomyelin (d36:2) demonstrated the most significant promoting effect on the incidence of SCLC (OR = 1.241, 95%CI: 1.055–1.460, P = 0.009). Notably, the impact of lipids on lung cancer varied between smoking and non-smoking patients, with 4 lipids influencing lung cancer risk in non-smokers and 17 lipids affecting lung cancer outcomes in smokers ( Supplementary material ), suggesting a higher susceptibility of smoking patients to alterations in lipid metabolism leading to lung cancer development. SCLC: small-cell lung cancer; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma. Table 2 illustrates the correlation between lipid profiles and various pathological subtypes of lung cancer. Diverse compositions of phosphatidylcholine exhibited varying impacts on the development of specific lung cancer types. Only two lipids were found to be common among SCLC, LUAD and LUSC. For instance, phosphatidylcholine (O-16:1_18:0) demonstrated protective properties against SCLC (OR = 0.828, 95%CI: 0.693–0.990, P = 0.038) and LUSC (OR = 0.859, 95%CI: 0.774–0.955, P = 0.005). Similarly, phosphatidylethanolamine (18:0_18:2) exhibited protective effects on LUAD (OR = 0.943, 95%CI: 0.894–0.995, P = 0.038) and LUSC (OR = 0.912, 95%CI: 0.858–0.970, P = 0.003). Table 2 The causal effect of lipids on different pathological lung cancer. SCLC LUAD LUSC Phosphatidylcholine (16:0_16:1) 0.790(0.629–0.991) 0.042 Phosphatidylcholine (16:0_18:0) 1.188(1.042–1.353) 0.010 Phosphatidylcholine (16:0_20:2) 0.918(0.858–0.983) 0.014 Phosphatidylcholine (16:0_20:4) 1.079(1.020–1.141) 0.008 Phosphatidylcholine (16:0_20:5) 1.075(1.007–1.147) 0.030 Phosphatidylcholine (16:1_18:2) 0.938(0.8881–0.998) 0.042 Phosphatidylcholine (17:0_18:1) 0.869(0.777–0.972) 0.014 Phosphatidylcholine (18:0_20:2) 0.870(0.799–0.948) 0.002 Phosphatidylcholine (18:0_20:3) 1.122(1.036–1.214) 0.005 Phosphatidylcholine (18:0_20:4) 1.053(1.002–1.108) 0.043 Phosphatidylcholine (18:0_22:5) 1.077(1.003–1.156) 0.041 Phosphatidylcholine (18:1_18:1) 0.880(0.787–0.984) 0.025 Phosphatidylcholine (20:4_0:0) 0.812(0.697–0.945) 0.007 Phosphatidylcholine (O-16:0_18:1) 0.888(0.798–0.989) 0.031 Phosphatidylcholine (O-16:0_20:3) 1.212(1.046–1.405) 0.011 Phosphatidylcholine (O-16:1_18:0) 0.828(0.693–0.990) 0.038 0.859(0.774–0.955) 0.005 Phosphatidylcholine (O-16:1_20:3) 1.088(1.002–1.182) 0.045 Phosphatidylcholine (O-16:1_20:4) 1.084(1.013–1.161) 0.020 Phosphatidylcholine (O-18:0_14:0) 1.189(1.013–1.397) 0.034 Phosphatidylcholine (O-18:0_16:1) 0.905(0.822–0.996) 0.041 Phosphatidylcholine (O-18:0_20:4) 1.091(1.010–1.179) 0.026 Phosphatidylcholine (O-18:2_20:4) 1.118(1.017–1.229) 0.021 Phosphatidylinositol (18:1_20:4) 0.815(0.698–0.951) 0.009 Phosphatidylethanolamine (18:0_18:2) 0.943(0.894–0.995) 0.033 0.912(0.858–0.970) 0.003 Phosphatidylethanolamine (O-18:1_20:4) 1.158(1.062–1.263) 0.001 Phosphatidylethanolamine (O-18:2_20:4) 1.134(1.018–1.264) 0.022 Sphingomyelin (d34:0) 1.139(1.022–1.269) 0.019 Sphingomyelin (d34:2) 1.118(1.040–1.202) 0.002 Sphingomyelin (d36:2) 1.241(1.055–1.460) 0.009 Sphingomyelin (d38:2) 1.154(1.039–1.282) 0.008 Triacylglycerol (46:1) 0.849(0.728–0.990) 0.037 Triacylglycerol (49:2) 0.871(0.760–0.999) 0.048 Cholesterol 0.909(0.829–0.997) 0.043 SCLC: small-cell lung cancer; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma. Sensitivity and pleiotropy analysis In order to mitigate the potential issue of horizontal pleiotropy in MR studies, sensitivity and pleiotropy analyses were conducted to assess the reliability of the estimates. The findings of these analyses are presented in the supplementary materials. The MR-Egger intercept analysis indicated the absence of horizontal pleiotropy in all four MR analyses ( P > 0.05). Following the removal of SNPs exhibiting heterogeneity through MR-PRESSO, no heterogeneity was observed in the MR analysis ( P > 0.05). Furthermore, leave-one-out analysis revealed that no individual SNP significantly influenced the MR estimates ( Supplemental material ). Discussion This study represents the initial MR investigation into the influence of diverse lipidomes with varying structures on the occurrence of lung cancer and its subtypes. The research findings revealed that 42 lipids exhibited significant impacts on the risk of developing lung cancer, irrespective of the specific pathological subtype or smoking status. Moreover, the study highlighted that 17 lipids influenced lung cancer risk in individuals who smoke, while only 4 lipids had a similar effect in non-smokers, suggesting that smokers may be more susceptible to alterations in lipid metabolism leading to lung cancer development. Additionally, the analysis of lipid impact on different pathological types of lung cancer indicated that two lipids, namely phosphatidylcholine (O-16:1_18:0) and phosphatidylethanolamine (18:0_18:2), were common across the three distinct pathological subtypes, namely SCLC), LUAD, and LUSC. In addition to observational research, our study provides further evidence supporting the close connection between lipid metabolism and the progression of lung cancer [ 21 ]. Our findings suggest that total cholesterol level acted as a protective effector for LUSC (OR = 0.909, 95%CI: 0.829–0.997), aligning with a meta-analysis conducted by Lin et al. , which a significant inverse relationship between total cholesterol level and lung cancer risk (relative risk = 0.89, 95%CI: 0.83–0.94) [ 22 ]. Furthermore, a prospective study on a Korean population by Kitahara et al. revealed that men with elevated serum total cholesterol had a reduced risk of lung cancer compared to those with normal levels [ 23 ]. However, conflicting studies exist that propose no correlation between serum total cholesterol and the lung cancer risk [ 24 ]. Our study also identified a negative association between triacylglycerol level and SCLC (OR = 0.849, 95%CI: 0.728–0.990) and LUSC (OR = 0.871, 95%CI༚0.760–0.999). Conversely, a cohort study investigating the link between triacylglycerol level and cancer incidence indicated that higher serum triacylglycerol level was associated with an increased risk of lung cancer [ 25 ]. Everatt et al. reported no significant association between triacylglycerol level and lung cancer incidence, although the risk of lung cancer was negatively correlated with the body mass index of Lithuanian men[ 24 ]. Our study also found a positive association between diacylglycerol (16:1_18:1) level and the occurrence of lung carcinoma and lung cancer in smoking patients, although similar observational studies are lacking. Sterol ester, phosphatidylcholine, phosphatidylethanolamine and sphingomyelin exhibit inconsistent causal relationships across different pathological types of lung cancer and smoking statuses, thus warranting further investigation beyond the scope of this study. One of the key findings of our study is the potential synergistic effect of smoking and lipids on the development of lung cancer. Recent studies have highlighted differences in lipid metabolism between smokers and non-smokers, particularly in patients with primary lung adenocarcinoma. For instance, Ortega- Gómez and colleagues identified differentially expressed genes related to lipid metabolism in smoking patients with this type of cancer [ 26 ]. Additionally, Titz et al. observed that tobacco exposure influenced various categories of lung lipids and lipid-related proteins, such as surfactant lipids and ceramide [ 27 ]. Furthermore, individuals with emphysema who smoke were found to have elevated levels of ceramide, increasing their susceptibility to lung cancer, possibly due to the impact of tobacco smoking on lipid metabolism [ 28 , 29 ]. Furthermore, a recent study by Éric Jubinville et al. demonstrated a functional interplay between smoking and reverse lipid transport, which is critical for maintaining lipid homeostasis [ 30 ]. Most lipids appear to influence the incidence of only one pathological type of lung cancer, with only two lipids showing a similar effect across different types. In a drug-target MR analysis, Li and colleagues discovered that inhibiting APOC3 reduced the risk of LUAD but increased the risk of SCLC [ 31 ]. Moreover, even within the same type of lung cancer, there is significant heterogeneity in lipid metabolism [ 32 ]. The shared lipids across different pathological types of lung cancer include phosphatidylcholine (O-16:1_18:0) and phosphatidylethanolamine (18:0_18:2). Marien et al. reported an increase in several phosphatidylethanolamine and phosphatidylcholine species in non-small cell lung cancer patients, particularly those with fatty acyl chains containing 40 or 42 carbon atoms [ 21 ]. The crucial role of phosphatidylcholine and phosphatidylethanolamine in lung cancer risk was further supported by alterations in biosynthesis genes ETNK2, EPT1, CHPT1 and CDS2 in lung cancer[ 21 , 33 ]. The impact of lipids on the incidence of lung cancer be attributed to several mechanisms. Firstly, disturbed lipid metabolism can result in abnormal lipid accumulation in lung tissue and a chronic inflammatory response, which can facilitate cancer development [ 34 – 36 ]. Secondly, certain sphingomyelin and cholesterol molecules play roles in cellular physiological processes and signal transduction, with overactive signaling pathways and pathophysiological processes are critical for tumor progression. Thirdly, enzymes involved in lipid metabolism have been implicated in tumor development [ 37 ]. For instance, monoacylglycerol lipase facilitates the hydrolysis of triacylglycerol to fatty acids and glycerol[ 38 ], and the absence of monoacylglycerol lipase can trigger EGFR and ERK activation, leading to lung adenocarcinoma in older mice[ 39 ]. This study has some limitations. Firstly, the study participants were of European descent, thus further investigation and validation are needed to ascertain the generalizability of our findings to other populations. Secondly, with FDR exceeding 0.05 in all analysis, the presence of false positive results in this study is a possibility. Thirdly, while this study offers valuable insights into etiology, it is essential to emphasize the necessity of conducting rigorous randomized controlled trials and basic research to corroborate our findings. Conclusion In conclusion, there appears to be a notable causal relationship between plasma lipid species and lung cancer risk. Individuals who smoke may be more prone to abnormal lipid metabolism and subsequent lung cancer. Specific lipid species are closely associated with different pathological types of lung cancer, with phosphatidylcholine (O-16:1_18:0) and phosphatidylethanolamine (18:0_18:2) being two lipids that overlap across various lung cancer pathological types. Overall, our study suggests that lipids could potentially be utilized in early screening, prevention, and even the treatment of lung cancer. Declarations Ethics declarations The data involved in this study are from public summary data and ethical review and approval were not required for this study. Competing interests The authors declare no competing interests. Funding This work was supported by the Shanghai Science and Technology Innovation Action Plan - Rising Star Cultivation (Yangfan Special Project) (23YF1435500). Author Contribution C. L. performed data analysis, wrote the first draft, and prepared figures and tables. J. M. designed the project and revised the manuscript. All authors read and approved the final manuscript. Acknowledgements We thank GWAS Catalog database and all the researchers who share research data. Availability of data and materials The datasets generated and/or analyzed during the current study are available in GWAS Catalog ( http://www.ebi.ac.uk/gwas/ ). References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–63. Li C, Lei S, Ding L, Xu Y, Wu X, Wang H, Zhang Z, Gao T, Zhang Y, Li L. Global burden and trends of lung cancer incidence and mortality. Chin Med J (Engl). 2023;136:1583–90. Bade BC, Dela Cruz CS. Lung Cancer 2020: Epidemiology, Etiology, and Prevention. Clin Chest Med. 2020;41:1–24. Nooreldeen R, Bach H. Current and Future Development in Lung Cancer Diagnosis. Int J Mol Sci 2021, 22. Wang G, Qiu M, Xing X, Zhou J, Yao H, Li M, Yin R, Hou Y, Li Y, Pan S, et al. Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis. Sci Transl Med. 2022;14:eabk2756. Zhu M, Zeng Q, Fan T, Lei Y, Wang F, Zheng S, Wang X, Zeng H, Tan F, Sun N, et al. Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis. Front Immunol. 2022;13:783495. Yang JJ, Yu D, Takata Y, Smith-Warner SA, Blot W, White E, Robien K, Park Y, Xiang YB, Sinha R, et al. Dietary Fat Intake and Lung Cancer Risk: A Pooled Analysis. J Clin Oncol. 2017;35:3055–64. Chang AK, Barrett-Connor E, Edelstein S. Low plasma cholesterol predicts an increased risk of lung cancer in elderly women. Prev Med. 1995;24:557–62. Lee GB, Lee JC, Moon MH. Plasma lipid profile comparison of five different cancers by nanoflow ultrahigh performance liquid chromatography-tandem mass spectrometry. Anal Chim Acta. 2019;1063:117–26. Takanashi Y, Funai K, Eto F, Mizuno K, Kawase A, Tao H, Kitamoto T, Takahashi Y, Sugimura H, Setou M, et al. Decreased sphingomyelin (t34:1) is a candidate predictor for lung squamous cell carcinoma recurrence after radical surgery: a case-control study. BMC Cancer. 2021;21:1232. Sekula P, Del Greco MF, Pattaro C, Köttgen A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol. 2016;27:3253–65. Ottensmann L, Tabassum R, Ruotsalainen SE, Gerl MJ, Klose C, Widén E, Simons K, Ripatti S, Pirinen M. Genome-wide association analysis of plasma lipidome identifies 495 genetic associations. Nat Commun. 2023;14:6934. McKay JD, Hung RJ, Han Y, Zong X, Carreras-Torres R, Christiani DC, Caporaso NE, Johansson M, Xiao X, Li Y, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat Genet. 2017;49:1126–32. Qian L, Fan Y, Gao F, Zhao B, Yan B, Wang W, Yang J, Ma X. Genetically Determined Levels of Serum Metabolites and Risk of Neuroticism: A Mendelian Randomization Study. Int J Neuropsychopharmacol. 2021;24:32–9. Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40:755–64. Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015;30:543–52. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512–25. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65. Cortez Cardoso Penha R, Smith-Byrne K, Atkins JR, Haycock PC, Kar S, Codd V, Samani NJ, Nelson C, Milojevic M, Gabriel AAG et al. Common genetic variations in telomere length genes and lung cancer: a Mendelian randomisation study and its novel application in lung tumour transcriptome. Elife 2023, 12. Glickman ME, Rao SR, Schultz MR. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol. 2014;67:850–7. Marien E, Meister M, Muley T, Fieuws S, Bordel S, Derua R, Spraggins J, Van de Plas R, Dehairs J, Wouters J, et al. Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles. Int J Cancer. 2015;137:1539–48. Lin X, Lu L, Liu L, Wei S, He Y, Chang J, Lian X. Blood lipids profile and lung cancer risk in a meta-analysis of prospective cohort studies. J Clin Lipidol. 2017;11:1073–81. Kitahara CM, Berrington de González A, Freedman ND, Huxley R, Mok Y, Jee SH, Samet JM. Total cholesterol and cancer risk in a large prospective study in Korea. J Clin Oncol. 2011;29:1592–8. Everatt R, Virvičiūtė D, Kuzmickienė I, Tamošiūnas A. Body mass index, cholesterol level and risk of lung cancer in Lithuanian men. Lung Cancer. 2014;85:361–5. Ulmer H, Borena W, Rapp K, Klenk J, Strasak A, Diem G, Concin H, Nagel G. Serum triglyceride concentrations and cancer risk in a large cohort study in Austria. Br J Cancer. 2009;101:1202–6. Ortega-Gómez A, Rangel-Escareño C, Molina-Romero C, Macedo-Pérez EO, Avilés-Salas A, Lara-García A, Alanis-Funes G, Rodríguez-Bautista R, Hidalgo-Miranda A, Arrieta O. Gene-expression profiles in lung adenocarcinomas related to chronic wood smoke or tobacco exposure. Respir Res. 2016;17:42. Titz B, Boué S, Phillips B, Talikka M, Vihervaara T, Schneider T, Nury C, Elamin A, Guedj E, Peck MJ, et al. Effects of Cigarette Smoke, Cessation, and Switching to Two Heat-Not-Burn Tobacco Products on Lung Lipid Metabolism in C57BL/6 and Apoe-/- Mice-An Integrative Systems Toxicology Analysis. Toxicol Sci. 2016;149:441–57. Gastaldelli A, Folli F, Maffei S. Impact of tobacco smoking on lipid metabolism, body weight and cardiometabolic risk. Curr Pharm Des. 2010;16:2526–30. Goldkorn T, Chung S, Filosto S. Lung cancer and lung injury: the dual role of ceramide. Handb Exp Pharmacol 2013:93–113. Jubinville É, Talbot M, Bérubé JC, Hamel-Auger M, Maranda-Robitaille M, Beaulieu MJ, Aubin S, Paré M, Kallend DG, Arsenault B et al. Interplay between cigarette smoking and pulmonary reverse lipid transport. Eur Respir J 2017, 50. Li H, Zhang L, Yang F, Feng X, Fu R, Zhao R, Li X, Li H. Lipid-lowering drugs affect lung cancer risk via sphingolipid metabolism: a drug-target Mendelian randomization study. Front Genet. 2023;14:1269291. Zhang X, Li W, Liu T, Guo H, Sun Q, Li B. Heterogeneity of Lipid Metabolism and its Clinical and Immune Correlation in Lung Adenocarcinoma. Curr Med Chem. 2024;31:1561–77. Lesko J, Triebl A, Stacher-Priehse E, Fink-Neuböck N, Lindenmann J, Smolle-Jüttner FM, Köfeler HC, Hrzenjak A, Olschewski H, Leithner K. Phospholipid dynamics in ex vivo lung cancer and normal lung explants. Exp Mol Med. 2021;53:81–90. Mollinedo F, Gajate C. Lipid rafts as major platforms for signaling regulation in cancer. Adv Biol Regul. 2015;57:130–46. Simons K, Toomre D. Lipid rafts and signal transduction. Nat Rev Mol Cell Biol. 2000;1:31–9. O'Hagan R, Berg AR, Hong CG, Parel PM, Mehta NN, Teague HL. Systemic consequences of abnormal cholesterol handling: Interdependent pathways of inflammation and dyslipidemia. Front Immunol. 2022;13:972140. Merino Salvador M, Gómez de Cedrón M, Moreno Rubio J, Falagán Martínez S, Sánchez Martínez R, Casado E, de Ramírez A, Sereno M. Lipid metabolism and lung cancer. Crit Rev Oncol Hematol. 2017;112:31–40. Zechner R, Zimmermann R, Eichmann TO, Kohlwein SD, Haemmerle G, Lass A, Madeo F. FAT SIGNALS–lipases and lipolysis in lipid metabolism and signaling. Cell Metab. 2012;15:279–91. Liu R, Wang X, Curtiss C, Landas S, Rong R, Sheikh MS, Huang Y. Monoglyceride lipase gene knockout in mice leads to increased incidence of lung adenocarcinoma. Cell Death Dis. 2018;9:36. Additional Declarations No competing interests reported. Supplementary Files Supplementary179lipids.csv SupplementarysummaryresultsofMRstudy.xlsx 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. 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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-4437234","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308493831,"identity":"2982b961-5877-42f5-99e3-5872b706e02c","order_by":0,"name":"Cong Luo","email":"","orcid":"","institution":"Minhang District Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Luo","suffix":""},{"id":308493832,"identity":"023da7d7-182a-4e29-9757-3d73878dff1d","order_by":1,"name":"Jie Mi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYDCCAxCKh7GZgfFBQkUNUVoYG6BamA0enDlGvBYQYJN82MJMWAff8ebnDz7u2SbD3M58rCKxgY2Bv707Aa8WyTPHDBtnPLsNdBhb2o3EHTIMEmfObsCrxeBGDmMzzwGQFh6zG4ln2BgMJHKJ1sL/rSCxjZkkLTxsDERpAfll5gywFjZjiYQzx3gI+gUYYg8+fDhw296w//DDjz8qauT423vxa4EDwwYIzUOcchCQJ17pKBgFo2AUjDQAAJlyTQExapxuAAAAAElFTkSuQmCC","orcid":"","institution":"Shanghai Pulmonary Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Mi","suffix":""}],"badges":[],"createdAt":"2024-05-17 14:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4437234/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4437234/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57866721,"identity":"030ebfc5-0725-4aae-b090-ec18f38d449b","added_by":"auto","created_at":"2024-06-06 15:54:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59494,"visible":true,"origin":"","legend":"\u003cp\u003eOverall design.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4437234/v1/135d587e031616d31127e165.png"},{"id":57866723,"identity":"23b7e2c7-a6fe-4766-8bd2-7df282ebbb56","added_by":"auto","created_at":"2024-06-06 15:54:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105235,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization associations of lipids on the risk of lung cancer (derived from the inverse-variance weighted analysis.\u003c/p\u003e\n\u003cp\u003eSCLC: small-cell lung cancer; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4437234/v1/be13c95554d367c4045587cf.png"},{"id":87483530,"identity":"f285871a-2a9f-4a4a-9132-28a3b84f59fc","added_by":"auto","created_at":"2025-07-24 10:32:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1067706,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4437234/v1/17e498ee-c3e8-4f1c-afbf-131bbefebf41.pdf"},{"id":57866724,"identity":"1a1700b7-b37e-4c75-8e20-bb9b38ef3b52","added_by":"auto","created_at":"2024-06-06 15:54:58","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":545498,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary179lipids.csv","url":"https://assets-eu.researchsquare.com/files/rs-4437234/v1/8bcc653146116d32b834685b.csv"},{"id":57866722,"identity":"eb32f874-ef40-4c09-b38d-2e1985bff5ec","added_by":"auto","created_at":"2024-06-06 15:54:58","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":69757,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarysummaryresultsofMRstudy.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4437234/v1/1a3e94f56a6bccbff3fbe0bd.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Effects of Genetically Determined Lipidome on Lung Cancer and Its Subtypes: A Mendelian Randomization Study ","fulltext":[{"header":"Introduction","content":" \u003cp\u003eLung cancer is the leading cause of cancer-related death worldwide. GLOBOCAN estimated that there were 2.2\u0026nbsp;million new cases of lung cancer and 1.8\u0026nbsp;million deaths related to lung cancer in 2020 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Globally, the age-standardized incidence rate ranged from 36.8 per 100,000 to 5.9 per 100,000, and the age-standardized mortality rate varied from 32.8 per 100,000 to 4.9 per 100,000 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Early detection of lung cancer is effective in reducing the lung cancer-related mortality, while prevention is even more crucial for reducing the incidence of lung cancer. Currently, smoking is the most well-established risk factor for lung cancer, but over 25% of lung cancer patients are non-smokers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Some environmental agents like ionizing radiation, certain diseases such as pulmonary fibrosis, and abnormal biological metabolism have also been identified as potential risk factors for lung cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent observational studies suggest a close relationship between abnormal lipid metabolism and the incidence of lung cancer. Wang \u003cem\u003eet al\u003c/em\u003e. found that lipid metabolism was broadly dysregulated in early-stage lung cancer patients as compared with healthy individuals or patients with benign tumors. They validated that nine of these lipids were able to detect early-stage cancer across multiple independent cohorts [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Several signatures of lipid metabolism-related genes have been identified as valuable prognostic biomarkers for lung cancer [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the observational results were inconsistent. For example, Yang \u003cem\u003eet al.\u003c/em\u003e found that a higher plasma cholesterol level was associated with an increased incidence of lung cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], but Chang \u003cem\u003eet al.\u003c/em\u003e demonstrated a significant inverse association between low cholesterol and lung cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Lee \u003cem\u003eet al.\u003c/em\u003e discovered that sphingomyelin (d18:1/22:0) level were elevated in lung cancer tumor tissue compared to normal tissue [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. On the other hand, Takanashi \u003cem\u003eet al.\u003c/em\u003e observed a decrease in SM (t34:1) levels in lung squamous cell carcinoma tissue compared to normal tissue in patients from the recurrent group [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In addition, although these conventional epidemiological studies indicate a strong connection between the lipidome and lung cancer risk, they are unable to establish causal relationships due to the potential influences from unmeasured confounding factors and reverse causality.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) study is an increasingly popular statistical method that can provide credible evidence of a causal effect of one trait on another [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. MR utilizes genetic variants that are strongly associated with the exposure but are not influenced by the outcome or confounding variables as instrumental variables (IVs) to investigate the causal effect of the exposure on the outcome using summary statistics from genome-wide association studies (GWAS). As IVs are less susceptible to confounders and reverse causality, MR has been recently used as a powerful tool to estimate the causal effects of exposure in post-GWAS analysis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Thus, in this study, we utilized MR analysis and GWAS data to investigate the causal effect of 179 lipids on lung cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThe overall design of our two-sample MR study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For MR analyses, the chosen IVs must meet the three crucial assumptions: first, IVs should exhibit a strong correlation with the exposure; second, IVs should not be associated with any confounders; third, IVs should not have a direct relationship with outcomes but may influence the outcome solely through their impact on the exposure. As the GWAS data was downloaded from the publicly available databases, ethical approval was not required as no individual data was used in this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGWAS data for lipidome\u003c/h2\u003e \u003cp\u003eThe plasma lipid species data were derived from the GWAS data published by Linda \u003cem\u003eet al.\u003c/em\u003e in 2023 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Linda \u003cem\u003eet al.\u003c/em\u003e conducted a comprehensive genome-wide analysis of 179 lipid species in 7174 Finnish individuals, uncovering the genetic connections between coronary artery disease risk and lipid species. These 179 species consist of four categories: triglycerides, glycerophospholipids, sphingolipids, and sterols. After quality control procedures, SNPs for 179 lipid species were downloaded and used in this study (\u003cb\u003eSupplementary Material\u003c/b\u003e). The relevant GWAS results are accessible via the GWAS Catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, from GCST90277238-GCST90277416).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGWAS data for lung cancer\u003c/h2\u003e \u003cp\u003eThe GWAS data for lung cancer was derived from the International Lung Cancer Consortium [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The study involved 85,716 individuals, with 29,266 cases and 56,450 controls. Meanwhile, the study was stratified by histological subtype (small cell carcinoma: SCLC, and adenocarcinoma: LUAD, squamous cell carcinoma: LUSC) and smoking status (ever and never smokers). Detailed information on GWAS data for lung cancer is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGWAS data on lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWAS-ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003encase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003encontrol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCST004748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elung carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29,266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56,450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCST004749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung cancer in ever smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23,223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCST004747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung cancer in never smokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCST004746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21,444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCST004744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLUAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55,483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCST004750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLUSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55,627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003encase: number of case; ncontrol: number of control; SCLC: small-cell lung cancer; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental Variables Selection\u003c/h2\u003e \u003cp\u003eVarious criteria were utilized to ensure the selection of optimal IVs[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Initially, SNPs were mandated to demonstrate a significant correlation with lipidomes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e). Subsequently, SNPs were clumped using a strict linkage disequilibrium (LD) threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001 and window size\u0026thinsp;=\u0026thinsp;10,000 kb to guarantee their independence. Additionally, palindromic SNPs were particularly scrutinized in the original dataset to prevent unforeseen reverse effects. Furthermore, to ascertain the efficacy of the IVs, F-statistic parameters were computed to evaluate the robustness of the chosen IVs, and those with an F-statistic\u0026thinsp;\u0026lt;\u0026thinsp;10 were eliminated [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMR analysis\u003c/h2\u003e \u003cp\u003eThe primary analysis utilized the inverse-variance weighted (IVW) method to evaluate the causal impact of 179 lipids on lung cancer. To ensure the reliability of the primary findings, additional methods such as MR Egger, weighted median and weighted mode were employed to estimate the potential causal effects when IVs deviated from standard assumptions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Subsequently, MR-Egger regression was employed to detect potential horizontal pleiotropy, with statistical significance set at \u003cem\u003eP\u003c/em\u003e-values for intercept\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Cochran\u0026rsquo;s Q test was utilized to examine IV heterogeneity and its potential influence on the causal estimate, with significance indicated by \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Lastly, \u0026ldquo;leave-one-out\u0026rdquo; sensitivity analysis was conducted to assess the impact of individual SNP on the MR results [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using the R software (version 4.1.2) with the utilization of\u0026ldquo;TwoSampleMR,\u0026rdquo; \u0026ldquo;Mendelian-Randomization,\u0026rdquo; and \u0026ldquo;MRPRESSO\u0026rdquo; packages. Results were presented as odds ratios (ORs) along with a 95% confidence interval (CI). The ORs were interpreted as the probability of one trait occurring with each incremental increase in the inverse normalized value of another trait. An OR greater than 1 indicates an increased likelihood of lung cancer incidence, while a value less than 1 suggests a decreased risk of cancer. Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Additionally, to address the issue of multiple testing in the study, P values obtained from IVW method underwent False Discovery Rate (FER) adjustment to improve the robustness of our findings [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStrength of IVs\u003c/h2\u003e \u003cp\u003eIn this study, a two-sample MR analysis was conducted to investigated the potential causal relationship between lipidome and the risk of lung cancer and its subtypes. The IVs utilized in the analysis consisted of 179 metabolites, each comprising a varying number of SNPs 12 to 44 (\u003cb\u003eSupplementary material\u003c/b\u003e). Notably, all IVs exhibited F statistic exceeding 10, indicating the reliability and strength of the IVs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCausality of genetically determined lipids on lung cancer\u003c/h2\u003e \u003cp\u003eThe MR analysis using the IVW method revealed that 42 lipids were significantly linked to lung cancer or its subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, with \u003cem\u003eP\u003c/em\u003e\u003csub\u003eivw\u003c/sub\u003e \u0026lt; 0.05). These lipids were categorized into sterol ester, diacylglycerol, phosphatidylcholine, phosphatidylinositol, phosphatidylethanolamine, sphingomyelin, triacylglycerol and cholesterol. Further details on the SNPs and their genetic associations with lung cancer and its subtypes can be found in the \u003cb\u003eAdditional file\u003c/b\u003e. Among the identified lipids, sphingomyelin (d38:2) exhibited the most pronounced protective effect against lung cancer in non-smoking individuals (OR\u0026thinsp;=\u0026thinsp;0.785, 95%CI: 0.634\u0026ndash;0.972, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), while sphingomyelin (d36:2) demonstrated the most significant promoting effect on the incidence of SCLC (OR\u0026thinsp;=\u0026thinsp;1.241, 95%CI: 1.055\u0026ndash;1.460, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). Notably, the impact of lipids on lung cancer varied between smoking and non-smoking patients, with 4 lipids influencing lung cancer risk in non-smokers and 17 lipids affecting lung cancer outcomes in smokers (\u003cb\u003eSupplementary material\u003c/b\u003e), suggesting a higher susceptibility of smoking patients to alterations in lipid metabolism leading to lung cancer development.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSCLC: small-cell lung cancer; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the correlation between lipid profiles and various pathological subtypes of lung cancer. Diverse compositions of phosphatidylcholine exhibited varying impacts on the development of specific lung cancer types. Only two lipids were found to be common among SCLC, LUAD and LUSC. For instance, phosphatidylcholine (O-16:1_18:0) demonstrated protective properties against SCLC (OR\u0026thinsp;=\u0026thinsp;0.828, 95%CI: 0.693\u0026ndash;0.990, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038) and LUSC (OR\u0026thinsp;=\u0026thinsp;0.859, 95%CI: 0.774\u0026ndash;0.955, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). Similarly, phosphatidylethanolamine (18:0_18:2) exhibited protective effects on LUAD (OR\u0026thinsp;=\u0026thinsp;0.943, 95%CI: 0.894\u0026ndash;0.995, P\u0026thinsp;=\u0026thinsp;0.038) and LUSC (OR\u0026thinsp;=\u0026thinsp;0.912, 95%CI: 0.858\u0026ndash;0.970, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe causal effect of lipids on different pathological lung cancer.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLUAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLUSC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (16:0_16:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.790(0.629\u0026ndash;0.991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (16:0_18:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.188(1.042\u0026ndash;1.353)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (16:0_20:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.918(0.858\u0026ndash;0.983)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (16:0_20:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.079(1.020\u0026ndash;1.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (16:0_20:5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.075(1.007\u0026ndash;1.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (16:1_18:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.938(0.8881\u0026ndash;0.998)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (17:0_18:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.869(0.777\u0026ndash;0.972)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (18:0_20:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.870(0.799\u0026ndash;0.948)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (18:0_20:3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.122(1.036\u0026ndash;1.214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (18:0_20:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.053(1.002\u0026ndash;1.108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (18:0_22:5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.077(1.003\u0026ndash;1.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (18:1_18:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.880(0.787\u0026ndash;0.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (20:4_0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.812(0.697\u0026ndash;0.945)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (O-16:0_18:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.888(0.798\u0026ndash;0.989)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (O-16:0_20:3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.212(1.046\u0026ndash;1.405)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (O-16:1_18:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.828(0.693\u0026ndash;0.990)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.859(0.774\u0026ndash;0.955)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (O-16:1_20:3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.088(1.002\u0026ndash;1.182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (O-16:1_20:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.084(1.013\u0026ndash;1.161)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (O-18:0_14:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.189(1.013\u0026ndash;1.397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (O-18:0_16:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.905(0.822\u0026ndash;0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (O-18:0_20:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.091(1.010\u0026ndash;1.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylcholine (O-18:2_20:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.118(1.017\u0026ndash;1.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylinositol (18:1_20:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.815(0.698\u0026ndash;0.951)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylethanolamine (18:0_18:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.943(0.894\u0026ndash;0.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.912(0.858\u0026ndash;0.970)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylethanolamine (O-18:1_20:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.158(1.062\u0026ndash;1.263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphatidylethanolamine (O-18:2_20:4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.134(1.018\u0026ndash;1.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingomyelin (d34:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.139(1.022\u0026ndash;1.269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingomyelin (d34:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.118(1.040\u0026ndash;1.202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingomyelin (d36:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.241(1.055\u0026ndash;1.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSphingomyelin (d38:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.154(1.039\u0026ndash;1.282)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriacylglycerol (46:1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.849(0.728\u0026ndash;0.990)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriacylglycerol (49:2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.871(0.760\u0026ndash;0.999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.909(0.829\u0026ndash;0.997)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSCLC: small-cell lung cancer; LUAD: lung adenocarcinoma; LUSC: lung squamous cell carcinoma.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity and pleiotropy analysis\u003c/h2\u003e \u003cp\u003eIn order to mitigate the potential issue of horizontal pleiotropy in MR studies, sensitivity and pleiotropy analyses were conducted to assess the reliability of the estimates. The findings of these analyses are presented in the \u003cb\u003esupplementary materials.\u003c/b\u003e The MR-Egger intercept analysis indicated the absence of horizontal pleiotropy in all four MR analyses (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Following the removal of SNPs exhibiting heterogeneity through MR-PRESSO, no heterogeneity was observed in the MR analysis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Furthermore, leave-one-out analysis revealed that no individual SNP significantly influenced the MR estimates (\u003cb\u003eSupplemental material\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents the initial MR investigation into the influence of diverse lipidomes with varying structures on the occurrence of lung cancer and its subtypes. The research findings revealed that 42 lipids exhibited significant impacts on the risk of developing lung cancer, irrespective of the specific pathological subtype or smoking status. Moreover, the study highlighted that 17 lipids influenced lung cancer risk in individuals who smoke, while only 4 lipids had a similar effect in non-smokers, suggesting that smokers may be more susceptible to alterations in lipid metabolism leading to lung cancer development. Additionally, the analysis of lipid impact on different pathological types of lung cancer indicated that two lipids, namely phosphatidylcholine (O-16:1_18:0) and phosphatidylethanolamine (18:0_18:2), were common across the three distinct pathological subtypes, namely SCLC), LUAD, and LUSC.\u003c/p\u003e \u003cp\u003eIn addition to observational research, our study provides further evidence supporting the close connection between lipid metabolism and the progression of lung cancer [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our findings suggest that total cholesterol level acted as a protective effector for LUSC (OR\u0026thinsp;=\u0026thinsp;0.909, 95%CI: 0.829\u0026ndash;0.997), aligning with a meta-analysis conducted by Lin \u003cem\u003eet al.\u003c/em\u003e, which a significant inverse relationship between total cholesterol level and lung cancer risk (relative risk\u0026thinsp;=\u0026thinsp;0.89, 95%CI: 0.83\u0026ndash;0.94) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, a prospective study on a Korean population by Kitahara \u003cem\u003eet al.\u003c/em\u003e revealed that men with elevated serum total cholesterol had a reduced risk of lung cancer compared to those with normal levels [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, conflicting studies exist that propose no correlation between serum total cholesterol and the lung cancer risk [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our study also identified a negative association between triacylglycerol level and SCLC (OR\u0026thinsp;=\u0026thinsp;0.849, 95%CI: 0.728\u0026ndash;0.990) and LUSC (OR\u0026thinsp;=\u0026thinsp;0.871, 95%CI༚0.760\u0026ndash;0.999). Conversely, a cohort study investigating the link between triacylglycerol level and cancer incidence indicated that higher serum triacylglycerol level was associated with an increased risk of lung cancer [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Everatt \u003cem\u003eet al.\u003c/em\u003e reported no significant association between triacylglycerol level and lung cancer incidence, although the risk of lung cancer was negatively correlated with the body mass index of Lithuanian men[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our study also found a positive association between diacylglycerol (16:1_18:1) level and the occurrence of lung carcinoma and lung cancer in smoking patients, although similar observational studies are lacking. Sterol ester, phosphatidylcholine, phosphatidylethanolamine and sphingomyelin exhibit inconsistent causal relationships across different pathological types of lung cancer and smoking statuses, thus warranting further investigation beyond the scope of this study.\u003c/p\u003e \u003cp\u003eOne of the key findings of our study is the potential synergistic effect of smoking and lipids on the development of lung cancer. Recent studies have highlighted differences in lipid metabolism between smokers and non-smokers, particularly in patients with primary lung adenocarcinoma. For instance, Ortega- G\u0026oacute;mez and colleagues identified differentially expressed genes related to lipid metabolism in smoking patients with this type of cancer [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, Titz \u003cem\u003eet al.\u003c/em\u003e observed that tobacco exposure influenced various categories of lung lipids and lipid-related proteins, such as surfactant lipids and ceramide [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, individuals with emphysema who smoke were found to have elevated levels of ceramide, increasing their susceptibility to lung cancer, possibly due to the impact of tobacco smoking on lipid metabolism [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, a recent study by \u0026Eacute;ric Jubinville \u003cem\u003eet al.\u003c/em\u003e demonstrated a functional interplay between smoking and reverse lipid transport, which is critical for maintaining lipid homeostasis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost lipids appear to influence the incidence of only one pathological type of lung cancer, with only two lipids showing a similar effect across different types. In a drug-target MR analysis, Li and colleagues discovered that inhibiting APOC3 reduced the risk of LUAD but increased the risk of SCLC [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Moreover, even within the same type of lung cancer, there is significant heterogeneity in lipid metabolism [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The shared lipids across different pathological types of lung cancer include phosphatidylcholine (O-16:1_18:0) and phosphatidylethanolamine (18:0_18:2). Marien \u003cem\u003eet al.\u003c/em\u003e reported an increase in several phosphatidylethanolamine and phosphatidylcholine species in non-small cell lung cancer patients, particularly those with fatty acyl chains containing 40 or 42 carbon atoms [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The crucial role of phosphatidylcholine and phosphatidylethanolamine in lung cancer risk was further supported by alterations in biosynthesis genes ETNK2, EPT1, CHPT1 and CDS2 in lung cancer[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe impact of lipids on the incidence of lung cancer be attributed to several mechanisms. Firstly, disturbed lipid metabolism can result in abnormal lipid accumulation in lung tissue and a chronic inflammatory response, which can facilitate cancer development [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Secondly, certain sphingomyelin and cholesterol molecules play roles in cellular physiological processes and signal transduction, with overactive signaling pathways and pathophysiological processes are critical for tumor progression. Thirdly, enzymes involved in lipid metabolism have been implicated in tumor development [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. For instance, monoacylglycerol lipase facilitates the hydrolysis of triacylglycerol to fatty acids and glycerol[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and the absence of monoacylglycerol lipase can trigger EGFR and ERK activation, leading to lung adenocarcinoma in older mice[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has some limitations. Firstly, the study participants were of European descent, thus further investigation and validation are needed to ascertain the generalizability of our findings to other populations. Secondly, with FDR exceeding 0.05 in all analysis, the presence of false positive results in this study is a possibility. Thirdly, while this study offers valuable insights into etiology, it is essential to emphasize the necessity of conducting rigorous randomized controlled trials and basic research to corroborate our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, there appears to be a notable causal relationship between plasma lipid species and lung cancer risk. Individuals who smoke may be more prone to abnormal lipid metabolism and subsequent lung cancer. Specific lipid species are closely associated with different pathological types of lung cancer, with phosphatidylcholine (O-16:1_18:0) and phosphatidylethanolamine (18:0_18:2) being two lipids that overlap across various lung cancer pathological types. Overall, our study suggests that lipids could potentially be utilized in early screening, prevention, and even the treatment of lung cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics declarations\u003c/h2\u003e \u003cp\u003eThe data involved in this study are from public summary data and ethical review and approval were not required for this study.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Shanghai Science and Technology Innovation Action Plan - Rising Star Cultivation (Yangfan Special Project) (23YF1435500).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC. L. performed data analysis, wrote the first draft, and prepared figures and tables. J. M. designed the project and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe thank GWAS Catalog database and all the researchers who share research data.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e \u003cp\u003eThe datasets generated and/or analyzed during the current study are available in GWAS Catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi C, Lei S, Ding L, Xu Y, Wu X, Wang H, Zhang Z, Gao T, Zhang Y, Li L. Global burden and trends of lung cancer incidence and mortality. Chin Med J (Engl). 2023;136:1583\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBade BC, Dela Cruz CS. Lung Cancer 2020: Epidemiology, Etiology, and Prevention. Clin Chest Med. 2020;41:1\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNooreldeen R, Bach H. Current and Future Development in Lung Cancer Diagnosis. Int J Mol Sci 2021, 22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang G, Qiu M, Xing X, Zhou J, Yao H, Li M, Yin R, Hou Y, Li Y, Pan S, et al. Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis. Sci Transl Med. 2022;14:eabk2756.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu M, Zeng Q, Fan T, Lei Y, Wang F, Zheng S, Wang X, Zeng H, Tan F, Sun N, et al. Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis. Front Immunol. 2022;13:783495.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang JJ, Yu D, Takata Y, Smith-Warner SA, Blot W, White E, Robien K, Park Y, Xiang YB, Sinha R, et al. Dietary Fat Intake and Lung Cancer Risk: A Pooled Analysis. J Clin Oncol. 2017;35:3055\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang AK, Barrett-Connor E, Edelstein S. Low plasma cholesterol predicts an increased risk of lung cancer in elderly women. Prev Med. 1995;24:557\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee GB, Lee JC, Moon MH. Plasma lipid profile comparison of five different cancers by nanoflow ultrahigh performance liquid chromatography-tandem mass spectrometry. Anal Chim Acta. 2019;1063:117\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakanashi Y, Funai K, Eto F, Mizuno K, Kawase A, Tao H, Kitamoto T, Takahashi Y, Sugimura H, Setou M, et al. Decreased sphingomyelin (t34:1) is a candidate predictor for lung squamous cell carcinoma recurrence after radical surgery: a case-control study. BMC Cancer. 2021;21:1232.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSekula P, Del Greco MF, Pattaro C, K\u0026ouml;ttgen A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol. 2016;27:3253\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOttensmann L, Tabassum R, Ruotsalainen SE, Gerl MJ, Klose C, Wid\u0026eacute;n E, Simons K, Ripatti S, Pirinen M. Genome-wide association analysis of plasma lipidome identifies 495 genetic associations. Nat Commun. 2023;14:6934.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKay JD, Hung RJ, Han Y, Zong X, Carreras-Torres R, Christiani DC, Caporaso NE, Johansson M, Xiao X, Li Y, et al. Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes. Nat Genet. 2017;49:1126\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQian L, Fan Y, Gao F, Zhao B, Yan B, Wang W, Yang J, Ma X. Genetically Determined Levels of Serum Metabolites and Risk of Neuroticism: A Mendelian Randomization Study. Int J Neuropsychopharmacol. 2021;24:32\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40:755\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015;30:543\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44:512\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortez Cardoso Penha R, Smith-Byrne K, Atkins JR, Haycock PC, Kar S, Codd V, Samani NJ, Nelson C, Milojevic M, Gabriel AAG et al. Common genetic variations in telomere length genes and lung cancer: a Mendelian randomisation study and its novel application in lung tumour transcriptome. Elife 2023, 12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlickman ME, Rao SR, Schultz MR. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies. J Clin Epidemiol. 2014;67:850\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarien E, Meister M, Muley T, Fieuws S, Bordel S, Derua R, Spraggins J, Van de Plas R, Dehairs J, Wouters J, et al. Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles. Int J Cancer. 2015;137:1539\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin X, Lu L, Liu L, Wei S, He Y, Chang J, Lian X. Blood lipids profile and lung cancer risk in a meta-analysis of prospective cohort studies. J Clin Lipidol. 2017;11:1073\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitahara CM, Berrington de Gonz\u0026aacute;lez A, Freedman ND, Huxley R, Mok Y, Jee SH, Samet JM. Total cholesterol and cancer risk in a large prospective study in Korea. J Clin Oncol. 2011;29:1592\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEveratt R, Virvičiūtė D, Kuzmickienė I, Tamošiūnas A. Body mass index, cholesterol level and risk of lung cancer in Lithuanian men. Lung Cancer. 2014;85:361\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUlmer H, Borena W, Rapp K, Klenk J, Strasak A, Diem G, Concin H, Nagel G. Serum triglyceride concentrations and cancer risk in a large cohort study in Austria. Br J Cancer. 2009;101:1202\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtega-G\u0026oacute;mez A, Rangel-Escare\u0026ntilde;o C, Molina-Romero C, Macedo-P\u0026eacute;rez EO, Avil\u0026eacute;s-Salas A, Lara-Garc\u0026iacute;a A, Alanis-Funes G, Rodr\u0026iacute;guez-Bautista R, Hidalgo-Miranda A, Arrieta O. Gene-expression profiles in lung adenocarcinomas related to chronic wood smoke or tobacco exposure. Respir Res. 2016;17:42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTitz B, Bou\u0026eacute; S, Phillips B, Talikka M, Vihervaara T, Schneider T, Nury C, Elamin A, Guedj E, Peck MJ, et al. Effects of Cigarette Smoke, Cessation, and Switching to Two Heat-Not-Burn Tobacco Products on Lung Lipid Metabolism in C57BL/6 and Apoe-/- Mice-An Integrative Systems Toxicology Analysis. Toxicol Sci. 2016;149:441\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGastaldelli A, Folli F, Maffei S. Impact of tobacco smoking on lipid metabolism, body weight and cardiometabolic risk. Curr Pharm Des. 2010;16:2526\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldkorn T, Chung S, Filosto S. Lung cancer and lung injury: the dual role of ceramide. Handb Exp Pharmacol 2013:93\u0026ndash;113.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJubinville \u0026Eacute;, Talbot M, B\u0026eacute;rub\u0026eacute; JC, Hamel-Auger M, Maranda-Robitaille M, Beaulieu MJ, Aubin S, Par\u0026eacute; M, Kallend DG, Arsenault B et al. Interplay between cigarette smoking and pulmonary reverse lipid transport. Eur Respir J 2017, 50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Zhang L, Yang F, Feng X, Fu R, Zhao R, Li X, Li H. Lipid-lowering drugs affect lung cancer risk via sphingolipid metabolism: a drug-target Mendelian randomization study. Front Genet. 2023;14:1269291.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Li W, Liu T, Guo H, Sun Q, Li B. Heterogeneity of Lipid Metabolism and its Clinical and Immune Correlation in Lung Adenocarcinoma. Curr Med Chem. 2024;31:1561\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLesko J, Triebl A, Stacher-Priehse E, Fink-Neub\u0026ouml;ck N, Lindenmann J, Smolle-J\u0026uuml;ttner FM, K\u0026ouml;feler HC, Hrzenjak A, Olschewski H, Leithner K. Phospholipid dynamics in ex vivo lung cancer and normal lung explants. Exp Mol Med. 2021;53:81\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMollinedo F, Gajate C. Lipid rafts as major platforms for signaling regulation in cancer. Adv Biol Regul. 2015;57:130\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimons K, Toomre D. Lipid rafts and signal transduction. Nat Rev Mol Cell Biol. 2000;1:31\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Hagan R, Berg AR, Hong CG, Parel PM, Mehta NN, Teague HL. Systemic consequences of abnormal cholesterol handling: Interdependent pathways of inflammation and dyslipidemia. Front Immunol. 2022;13:972140.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerino Salvador M, G\u0026oacute;mez de Cedr\u0026oacute;n M, Moreno Rubio J, Falag\u0026aacute;n Mart\u0026iacute;nez S, S\u0026aacute;nchez Mart\u0026iacute;nez R, Casado E, de Ram\u0026iacute;rez A, Sereno M. Lipid metabolism and lung cancer. Crit Rev Oncol Hematol. 2017;112:31\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZechner R, Zimmermann R, Eichmann TO, Kohlwein SD, Haemmerle G, Lass A, Madeo F. FAT SIGNALS\u0026ndash;lipases and lipolysis in lipid metabolism and signaling. Cell Metab. 2012;15:279\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu R, Wang X, Curtiss C, Landas S, Rong R, Sheikh MS, Huang Y. Monoglyceride lipase gene knockout in mice leads to increased incidence of lung adenocarcinoma. Cell Death Dis. 2018;9:36.\u003c/span\u003e\u003c/li\u003e\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, Lipidome, MR study","lastPublishedDoi":"10.21203/rs.3.rs-4437234/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4437234/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrevious observational studies have identified abnormalities in lipid metabolism among lung cancer patients, but the causal relationship between lipidomes and lung cancer risk remains unclear. Herein, we investigate the causal effect of lipidomes on the incidence of lung cancer and its subtypes through two-sample Mendelian randomization (MR) analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA genome-wide association study of 179 lipid metabolites was used as the exposure, while lung cancer and its subtypes were the outcomes. All the datasets were obtained from an open database. The inverse variance weighted method was used as the primary analysis, and MR-Egger regression, the weighted median method, and the weighted mode method were employed to test the robustness of the results. MR-Egger intercept and Cochran's Q statistical analysis were used to assess potential pleiotropy and heterogeneity. Leave-one-out sensitivity analysis was also used to test the stability of the findings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eForty-two significant lipids were identified as causative exposures for lung cancer. Seventeen lipids affected lung cancer risk in smokers, while only four affected non-smokers. There were two overlapping lipids among the three pathological types of lung cancer. Phosphatidylcholine (O-16:1_18:0) had protective effects on small cell lung cancer (odds ratio (OR)\u0026thinsp;=\u0026thinsp;0.828, P\u0026thinsp;=\u0026thinsp;0.038) and lung squamous cell carcinoma (LUSC) (OR\u0026thinsp;=\u0026thinsp;0.859, P\u0026thinsp;=\u0026thinsp;0.005). Phosphatidylethanolamine (18:0_18:2) also exhibited protective effects on lung adenocarcinoma (OR\u0026thinsp;=\u0026thinsp;0.943, P\u0026thinsp;=\u0026thinsp;0.038) and LUSC (OR\u0026thinsp;=\u0026thinsp;0.912, P\u0026thinsp;=\u0026thinsp;0.003). Our results were robust even without a single SNP due to a \"leave-one-out\" analysis. The MR Egger intercept test indicated that genetic pleiotropy had no effect on the results. No heterogeneity was detected by Cochran's Q test.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study unveiled the causal effect of specific lipid species on lung cancer and its subtypes. Smoking patients are more susceptible to abnormal lipid metabolism and are at a higher risk of developing lung cancer. Different lipid species are closely associated with various pathological types of lung cancer. Our study suggests that lipids may be utilized in the early screening, prevention, and treatment of lung cancer.\u003c/p\u003e","manuscriptTitle":"Causal Effects of Genetically Determined Lipidome on Lung Cancer and Its Subtypes: A Mendelian Randomization Study ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-06 15:54:53","doi":"10.21203/rs.3.rs-4437234/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":"b18f9d6b-2ae9-490d-8341-049131485a35","owner":[],"postedDate":"June 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-24T10:23:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-06 15:54:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4437234","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4437234","identity":"rs-4437234","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}