Comprehensive Mendelian randomization analysis of low-density lipoprotein cholesterol and multiple cancers

Comprehensive Mendelian randomization analysis of low-density lipoprotein cholesterol and multiple cancers | 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 Comprehensive Mendelian randomization analysis of low-density lipoprotein cholesterol and multiple cancers Hengchang Liang, Chunling Tang, Yue Sun, Mingwei Wang, Tong Tong, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5135086/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose: The aim of this study was to investigate the causal relationship between low-density lipoprotein cholesterol (LDL-C) and five cancers (breast, cervical, thyroid, prostate and colorectal) using the Mendelian Randomization (MR) method, with a view to revealing the potential role of LDL-C in the development of these cancers. Methods: We used gene variant data and disease data from the Genome-Wide Association Study (GWAS) database to assess the causal relationship between LDL-C and each cancer by Mendelian randomisation analysis methods such as inverse variance weighting and MR-Egger. Specifically, we selected Proprotein convertase subtilisin/kexin type 9(PCSK9) and 3-hydroxy-3-methylglutaryl-CoA reductase(HMGCR), genes associated with LDL-C levels, as instrumental variables, extracted the corresponding single nucleotide polymorphism (SNP) data and analysed the associations of these SNPs with five cancers.In addition, sensitivity analyses and heterogeneity tests were performed to ensure the reliability of the results Results: The analyses showed that when using HMGCR gene,LDL-C were significantly and positively associated with breast (OR:1.200, 95% CI:1.082-1.329, p=0.001), prostate (OR:1.198, 95% CI:1.050-1.366, p=0.007), and thyroid cancers (OR:8.291, 95% CI:3.189- 21.555, p=0.00001) were significantly positively correlated, whereas they were significantly negatively correlated with colorectal cancer (OR:0.641, 95% CI:0.442-0.928, p=0.019); the results for cervical cancer were not significant (p=0.050). When using the PCSK9 gene, LDL-C levels were significantly and positively associated with breast (OR:1.107, 95%:CI 1.031-1.187, p=0.005) and prostate (OR:1.219, 95%:CI 1.101-1.349, p=0.0001) cancers, but not with cervical (p=0.294), thyroid cancer (p=0.759) and colorectal cancer ( p=0.572). Conclusion: Analyses using both the HMGCR and PCSK9 genes have shown that LDL-C may be a potential risk factor for breast and prostate cancer, while analyses of the HMGCR gene have also suggested that LDL-C may increase the risk of thyroid cancer and decrease the risk of colorectal cancer. Mendelian randomization LDL cholesterol HMGCR PSCK9 Causality Cancer Figures Figure 1 Figure 2 Figure 3 1 Introduction Low-density lipoprotein cholesterol (LDL-C) is an essential component of blood lipids and has long been considered a critical risk factor for cardiovascular diseases[ 1 ]. Recent studies have started to explore the potential role of LDL-C in cancer development and progression. As the primary carrier of cholesterol, LDL-C may influence cell membrane structure and function, promoting the proliferation and migration of cancer cells[ 2 ].Despite initial investigations into the relationship between LDL-C and cancer, results have been inconsistent, necessitating further research to clarify these causal relationships. We selected breast cancer, cervical cancer, thyroid cancer, prostate cancer, and colorectal cancer for this study based on several considerations. First, these cancers have high incidence or mortality rates globally, posing significant public health challenges[ 3 ]. Second, while previous studies have extensively explored the risk factors and etiologies of these cancers, the causal relationship between LDL-C and these cancers remains controversial, highlighting the need for further investigation[ 4 – 5 ]. Additionally, the availability of large sample data from genome-wide association studies (GWAS) databases for these cancers enhances the reliability and generalizability of our research findings. Previous studies have mainly used epidemiological observational studies and randomised controlled trials (RCTs) to investigate the relationship between LDL cholesterol and cancer. For example, some observational studies have reported that higher LDL cholesterol levels are associated with a higher risk of breast and prostate cancer[ 6 ]. However, there are also articles that suggest the opposite[ 7 ]. However, these studies are susceptible to confounding factors such as dietary habits, lifestyle and other metabolic risk factors. In addition, reverse causality (e.g., changes in LDL cholesterol levels as a result of cancer) may also affect the results of the studies . Mendelian randomisation method uses genetic variants as instrumental variables and simulates randomised controlled trials, which effectively reduces the effect of confounding factors.Since genes are determined at birth, the genetic variants used in the study do not change due to the onset of the disease, thus avoiding reverse causality[ 8 – 10 ]. 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) and Proprotein convertase subtilisin/kexin type 9 (PCSK9) are common targets for lowering low-density lipoprotein cholesterol (LDL-C)[ 11 ]. In addition, studies have shown that HMGCR and PSCK9 are strongly associated with cancer[ 12 – 13 ].Therefore, the present study will evaluate the causal relationship between LDL-C (via HMGCR and PSCK9) and multiple cancers, aiming to provide new insights and scientific evidence for the potential application of LDL-C in cancer prevention and treatment. 2 Methods MR study design As shown in Fig. 1 , we designed an MR study to systematically assess the causal relationship between low-density lipoprotein cholesterol (LDL-C) and five common cancers (breast, cervical, thyroid, prostate, and colorectal).LDL-C was used as exposure, whereas multiple cancers were used as outcomes, and HMGCR and PSCK9 were used as instrumental variables to select SNPs with strong correlations with LDL-C from GWAS statistics according to rigorous screening criteria. Data source The GWAS ID for low-density lipoprotein cholesterol (LDL-C) is ieu-a-300, which is derived from the IEU OpenGWAS project, which encompasses large-scale genetic data related to LDL-C. The GWAS ID for LDL-C is ieu-a-300. The dataset contains 173,082 samples containing 2,437,752 SNPs.The samples are from multi-ethnic populations including both males and females.The data was collected and provided by the GLGC (Global Lipids Genetics Consortium), and the lead author of the study is Willer CJ. Cervical Cancer (ieu-b-4876), Prostate cancer (ieu-b-85) and Breast Cancer (ieu-a-1126) are from the IEU OpenGWAS project, which provides large-scale genome-wide association study (GWAS) datasets.Cervical cancer contains 199,086 samples including 563 cervical cancer cases and 198,523 controls. The samples are predominantly from European women and contain 8,506,261 SNPs.Prostate cancer Contains 79,148 prostate cancer cases and 61,106 controls, mainly from European men, with 20,346,368 SNPs.Breast cancer data were obtained from the GWAS dataset provided by the Breast Cancer Association Consortium (BCAC). This dataset contains 228,951 samples, including 122,977 breast cancer cases and 105,974 controls. There are also 10,680,257 SNPs.The samples are mainly from European women. The data for colorectal cancer (ebi-a-GCST90013866) and thyroid cancer (ebi-a-GCST90013867) were obtained from the European Bioinformatics Institute's (EBI) GWAS catalogue. Colorectal cancer contains 407,746 samples and 11,037,981 SNPs.Thyroid cancer sample size and data quality were assured, containing a total of 407,746 samples and 11,035,981 SNPs. SNP data for LDL cholesterol and five cancers can be found on the IEU OpenGWAS project database [ 14 ] ( https://gwas.mrcieu.ac.uk/ ), which are publicly available summary statistics. The protocol and data collection for the original GWAS were approved by the relevant ethics committees and written informed consent was obtained from each participant prior to data collection. Therefore, no additional ethical approval was required for the use of data in this study. Selection of instrumental variables To substitute for HMGCR, seven SNPs that were LDL-C-related at the genomic significance level (P < 5.0 × 10^-8) and within a ± 100 kb window of the region of the gene encoding HMGCR were obtained.Similarly, 12 LDL-C-related SNPs within ± 100 kb of PCSK9 were used to substitute for PCSK9. To maximise the benefits of the tool, a modest chain imbalance (r^2 = 0.30) was allowed between the relevant SNPs to ensure sufficient independence and diversity between markers while retaining a degree of correlation for more efficient statistical analysis and interpretation of results. data analysis We analysed the data for LDL-C and five cancers (breast, cervical, thyroid, prostate, and colorectal) using five methods (MR-Egger, weighted median, inverse variance weighted [IVW], weighted mode, and simple mode), with IVW being the decisive method of analysis, and the other methods used as references[ 24 ]. We also performed a series of sensitivity analyses because they can defend against violations of certain assumptions in MR studies. First, we performed MR-Egger regression analysis, which detects and corrects for horizontal pleiotropy and provides more robust causal effect estimates. Second, we also performed a leave-one-out sensitivity analysis, systematically removing one SNP at a time to determine whether a single SNP drives the overall causal estimate. The heterogeneity test was performed using the analysis results of the MR Egger and IVW methods. Cochran's Q was used as a measurement indicator.If Q_pval was less than the significance level (usually 0.05), heterogeneity was considered to exist[ 25 , 28 – 30 ]. After MR analysis, we set the significance threshold to pval < 0.05, at which point the results are generally considered statistically significant and the null hypothesis (that there is an association between exposure and outcome) can be rejected. For odds ratios (ORs), when OR > 1, it means that exposure increases the likelihood of the outcome (positive correlation). Conversely, exposure and outcome are negatively correlated. In particular, when OR = 1, it means that there is no association between exposure and outcome. All analyses were conducted with packages named TwoSampleMR in R software (version 4.3.1; R Development Core Team). 3 Results HMGCR analysis results For the HMGCR, the results showed that elevated LDL-C levels were significantly associated with the risk of thyroid, prostate, breast, and colorectal cancer. Specific results are shown in Fig. 2 : For thyroid cancer, in 407,746 samples (including 3,001 thyroid cancer cases and 287,288 controls), the IVW method showed that elevated LDL-C levels were associated with a significantly increased risk of thyroid cancer, with an OR of 8.29 (95% CI: 3.19–21.55), and a p-value of less than 0.001. This suggests that for every unit increase in LDL-C levels, there is an 8.29-fold increase in thyroid cancer risk. Prostate cancer: OR 1.20 (95% CI: 1.05–1.37), p-value 0.007, indicating that elevated LDL-C levels are associated with a significantly increased risk of prostate cancer. Breast Cancer: OR 1.20 (95% CI: 1.08–1.33), p-value 0.00052, indicating that elevated LDL-C levels are associated with a significantly increased risk of breast cancer. Colorectal Cancer: In 407,746 samples (including 58,131 colorectal cancer cases and 349,615 controls), the IVW method showed that elevated LDL-C levels were associated with a significant reduction in colorectal cancer risk with an OR of 0.64 (95% CI: 0.44–0.93) and a p-value of 0.018. This means that for every unit increase in LDL-C levels, the colorectal rectal cancer risk was reduced by 36%. For cervical cancer, HMGCR analysis showed no significant association between LDL-C levels and their risk, with an OR of 1.00 (95% CI: 1.00-1.01) and a P value of 0.050. PSCK9 analysis results For PSCK9, the results showed that elevated LDL-C levels were significantly associated with the risk of breast and prostate cancer. The specific results are shown in Fig. 3 : For breast cancer, in 228,951 samples (including 122,977 breast cancer cases and 105,974 controls), the inverse variance weighted (IVW) method showed that elevated LDL-C levels were associated with a significant increase in the risk of breast cancer, with an OR of 1.20 (95% CI: 1.08–1.33) and a p-value of 0.00052.This suggests that for every one-unit increase in LDL-C level, there is a 20% increase in breast cancer risk. In the analysis of prostate cancer the results showed that out of 140,254 samples (including 79,148 prostate cancer cases and 61,106 controls), the IVW method showed that elevated LDL-C levels were associated with a significantly increased risk of prostate cancer, with an OR of 1.20 (95% CI: 1.05–1.37) and a p-value of 0.007. this implies that for every one unit increase of LDL-C level, there was a 20% increase in prostate cancer risk. In addition, no abnormal results were found in the sensitivity and heterogeneity analyses. Details of the forest plots for the heterogeneity analysis, multiple validity tests, scatterplots, funnel plots, and leave-one-out sensitivity analyses can be found in the additional file. 4 Discussion Interpretation of the findings In this study, we assessed the causal association between low-density lipoprotein cholesterol (LDL-C) and five common cancers (breast, cervical, thyroid, prostate and colorectal) using Mendelian randomisation (MR) analysis. By analysing PSCK9 and HMGCR, we found a significant association between elevated LDL-C levels and the risk of certain cancers[ 15 , 31 ]. For the HMGCR analyses, the results showed that elevated LDL-C levels were significantly associated with the risk of thyroid, prostate, breast and colorectal cancer. Particularly for thyroid cancer, the risk increased 8.29-fold for each unit increase in LDL-C levels. This may be due to the fact that LDL-C affects the metabolism and proliferation of thyroid cells through the HMGCR pathway[ 16 ]. Similarly, LDL-C was significantly associated with an increased risk of prostate and breast cancer, further supporting the potential role of LDL-C in the development and progression of these cancers[ 17 – 18 ]. In addition, elevated LDL-C levels were associated with a significantly lower risk of colorectal cancer, a result that warrants further exploration and may involve different biological mechanisms[ 19 , 32 ]. For the PSCK9 analysis, the results showed that elevated LDL-C levels were significantly associated with the risk of breast and prostate cancer[ 20 ]. Specifically, each unit increase in LDL-C levels was associated with a 20 per cent increase in the risk of breast and prostate cancer, respectively. This is consistent with previous findings suggesting that PSCK9 is not only tightly associated with LDL-C but also highly expressed in various tumour-derived cell lines[ 21 ]. However, PSCK9 analyses showed no significant association between LDL cholesterol levels and the risk of cervical, thyroid, and colorectal cancers, but some studies have illustrated a positive association between PSCK9 inhibitors and cervical cancers[ 22 ]. Moreover, some studies suggest that PSCK9 promotes colorectal cancer and is a therapeutic target[ 23 ].This may indicate that LDL cholesterol has a different mechanism of action in these cancers, and further studies are needed. research significance Therapeutic strategies to lower LDL-C levels, such as the use of statins, have been widely used in the prevention and treatment of cardiovascular disease[ 26 ]. The results of our study suggest that these drugs may also have potential benefits in reducing the risk of certain cancers. Specifically, for breast and prostate cancers, high levels of LDL-C may be an independent risk factor, and therefore, by lowering LDL-C levels, it may help reduce the incidence of these cancers. The findings suggest that the mechanism of action of LDL-C in different cancers may differ. For example, PSCK9 analysis showed no significant association between LDL-C levels and the risk of cervical, thyroid and colorectal cancers, which may indicate that LDL-C affects some cancers but not others through specific pathways. This provides clues for further mechanistic studies, and exploring the specific pathways and molecular mechanisms of LDL-C's action in different cancers will help to unravel the etiological and developmental processes of cancer. In the public health arena, the results of this study suggest the need to reassess strategies for LDL-C management, especially in high-risk populations. For example, in areas with a high prevalence of breast and prostate cancers, active management of LDL-C levels may help to reduce the incidence of these cancers. In addition, for thyroid cancer, the findings suggest that special attention needs to be paid to the management of LDL-C levels, as significantly elevated LDL-C levels are associated with an increased risk of thyroid cancer. Limitations of the study Although this study provides new insights, there are some limitations. Firstly, Mendelian randomisation analysis relies on the validity of the selected genetic variants as instrumental variables. If there is horizontal pleiotropy in these instrumental variables, it may affect the accuracy of causal inference. Second, the GWAS data used in this study were mainly from European populations[ 27 ], and the results may not be applicable to other ethnic populations. Therefore, future studies should consider populations of different ethnicities and regions to verify the generalisability of these findings. Future Research Directions Future studies should further explore the specific mechanisms of LDL-C's role in different cancers, especially for thyroid, prostate and breast cancers. In addition, investigating whether there are racial or geographic differences in the relationship between LDL-C levels and cancer risk, as well as the underlying biological basis, will help to develop personalised prevention and treatment strategies. 5 Conclusion Analyses using both the HMGCR and PCSK9 genes have shown that LDL-C may be a significant potential risk factor for breast and prostate cancer. The elevated levels of LDL-C were consistently associated with an increased risk of these cancers, indicating a possible mechanistic link through lipid metabolism pathways influencing cancer cell proliferation and survival. Furthermore, analyses of the HMGCR gene alone have suggested that LDL-C may also increase the risk of thyroid cancer and decrease the risk of colorectal cancer. Specifically, elevated LDL-C levels were strongly associated with a markedly higher risk of thyroid cancer, potentially due to HMGCR-mediated cholesterol synthesis affecting thyroid cell function and growth. Conversely, the inverse relationship between LDL-C and colorectal cancer risk points to complex and possibly tissue-specific metabolic and inflammatory processes influenced by cholesterol levels. These findings highlight the multifaceted role of LDL-C in cancer development and underscore the importance of considering genetic pathways when assessing cancer risks associated with lipid levels. Declarations Funding This work is supported by Macao Polytechnic University Grant (RP/FCA-15/2022) This work is supported by Macao Polytechnic University Grant (RP/FCA-10/2023) Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Hengchang Liang and hui xie. The first draft of the manuscript was written by Hengchang Liang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The datasets analysed during the current study can be found in the IEU OpenGwas Project [https://gwas.mrcieu.ac.uk/] Ethics approval The protocol and data collection for the original GWAS were approved by the relevant ethics committees and written informed consent was obtained from each participant prior to data collection. Therefore, no additional ethical approval was required for the use of data in this study. References Hedayatnia, M., Asadi, Z., Zare-Feyzabadi, R. et al. Dyslipidemia and cardiovascular disease risk among the MASHAD study population. 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Supplementary Files addition.zip Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 05 Nov, 2024 Reviews received at journal 04 Nov, 2024 Reviewers agreed at journal 04 Nov, 2024 Reviews received at journal 14 Oct, 2024 Reviewers agreed at journal 10 Oct, 2024 Reviewers invited by journal 07 Oct, 2024 Editor assigned by journal 30 Sep, 2024 Submission checks completed at journal 28 Sep, 2024 First submitted to journal 23 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5135086","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374285403,"identity":"f7b686c0-cc92-448e-87a3-135ea20f0eb7","order_by":0,"name":"Hengchang Liang","email":"","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Hengchang","middleName":"","lastName":"Liang","suffix":""},{"id":374285404,"identity":"0cde1eeb-624e-4907-ba5c-d0c190f8dd04","order_by":1,"name":"Chunling Tang","email":"","orcid":"","institution":"King’s College London","correspondingAuthor":false,"prefix":"","firstName":"Chunling","middleName":"","lastName":"Tang","suffix":""},{"id":374285405,"identity":"1028c73b-52c7-4e18-9ed1-6a8b6d72f418","order_by":2,"name":"Yue Sun","email":"","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Sun","suffix":""},{"id":374285406,"identity":"516de0e8-4473-479b-8a7d-d7511a083b10","order_by":3,"name":"Mingwei Wang","email":"","orcid":"","institution":"Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou Institute of Cardiovascular Diseases","correspondingAuthor":false,"prefix":"","firstName":"Mingwei","middleName":"","lastName":"Wang","suffix":""},{"id":374285407,"identity":"879a583c-b2b7-4952-bd56-f99fc734bd03","order_by":4,"name":"Tong Tong","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Tong","suffix":""},{"id":374285408,"identity":"9bd86c7d-a0f6-4e2b-8a6f-f1853a907b8d","order_by":5,"name":"Qinquan Gao","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"prefix":"","firstName":"Qinquan","middleName":"","lastName":"Gao","suffix":""},{"id":374285409,"identity":"48c10038-a899-429f-9df9-076235e0fcd5","order_by":6,"name":"Hui Xie","email":"","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Xie","suffix":""},{"id":374285410,"identity":"4658e78b-3f2a-41df-8bf1-f0f3c262860e","order_by":7,"name":"Tao Tan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYPACGwjFQ4xaoCLGBgaGNNK1HCZBiz374eMPfu44H20ukcD44G0bQzQ/QVt40hIbe8/czt05I4HZcG4bQ+7MBoIOyzFs4G27nbvhRgKbNC9Qy4YDhLTwvzFs/Nt2DqSF/TdxWiRyDJt52w6AbWEmTsuNZ4mzZduSc3f2PGyWnHNOgrBf2PuTD3x822aXu509+eCHN2U2uf0EdCCAATh+GCSI1gDSMgpGwSgYBaMABwAAZstCDr4PUOEAAAAASUVORK5CYII=","orcid":"","institution":"Macao Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Tao","middleName":"","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2024-09-23 04:48:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5135086/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5135086/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69602899,"identity":"ab2537b7-0f71-40cc-9c37-f075fea25705","added_by":"auto","created_at":"2024-11-22 06:34:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":94619,"visible":true,"origin":"","legend":"\u003cp\u003eResearch overview and design of Mendelian stochastic analyses.In order to verify the existence of causal correlations in Mendelian randomisation experiments, the following conditions need to be met (1) the instrumental variable is correlated with the exposure factor (2) the instrumental variable is not correlated with the confounders(3) the instrumental variable is not directly correlated with the outcomes\u003c/p\u003e","description":"","filename":"Figure.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5135086/v1/c0bae0886c809a2b98ceb7c6.jpg"},{"id":69603513,"identity":"636c4799-55d9-4c2d-b54c-e2bcf25f715a","added_by":"auto","created_at":"2024-11-22 06:42:47","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":245278,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomised estimates of the association between HMGCR and five cancers (breast, cervical, prostate, thyroid, colorectal).CI, confidence interval. The observed two-sided P \u0026lt; 0.05 was considered statistically significant.OR = 1: indicates that there is no association between exposure and the outcome.OR \u0026gt; 1: indicates that exposure increases the likelihood of the outcome (positive association).OR \u0026lt; 1: indicates that exposure decreases the likelihood of the outcome (negative association)\u003c/p\u003e","description":"","filename":"Figure.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5135086/v1/273ec9a1684cf93f440dc1a8.jpg"},{"id":69602902,"identity":"ab62f559-812b-4bc2-98ff-6ffe194fa15e","added_by":"auto","created_at":"2024-11-22 06:34:47","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":235940,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomised estimates of the association between PCSK9 and five cancers (breast, cervical, prostate, thyroid, colorectal).CI, confidence interval. The observed two-sided P \u0026lt; 0.05 was considered statistically significant.OR = 1: indicates that there is no association between exposure and the outcome.OR \u0026gt; 1: indicates that exposure increases the likelihood of the outcome (positive association).OR \u0026lt; 1: indicates that exposure decreases the likelihood of the outcome (negative association)\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5135086/v1/5d69b825089aca64de9c51f9.jpg"},{"id":69603514,"identity":"c1c4bfb0-8ee9-4a4d-b5b5-c84dcc07ee7d","added_by":"auto","created_at":"2024-11-22 06:42:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":932380,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5135086/v1/fa00997e-2e1b-47aa-a243-b791b12bd27e.pdf"},{"id":69602901,"identity":"259c4721-0f79-4da4-acd6-de5fef8a0700","added_by":"auto","created_at":"2024-11-22 06:34:47","extension":"zip","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":212868,"visible":true,"origin":"","legend":"","description":"","filename":"addition.zip","url":"https://assets-eu.researchsquare.com/files/rs-5135086/v1/57154071aa1e2c6d630e0219.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive Mendelian randomization analysis of low-density lipoprotein cholesterol and multiple cancers","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLow-density lipoprotein cholesterol (LDL-C) is an essential component of blood lipids and has long been considered a critical risk factor for cardiovascular diseases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recent studies have started to explore the potential role of LDL-C in cancer development and progression. As the primary carrier of cholesterol, LDL-C may influence cell membrane structure and function, promoting the proliferation and migration of cancer cells[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].Despite initial investigations into the relationship between LDL-C and cancer, results have been inconsistent, necessitating further research to clarify these causal relationships.\u003c/p\u003e \u003cp\u003eWe selected breast cancer, cervical cancer, thyroid cancer, prostate cancer, and colorectal cancer for this study based on several considerations. First, these cancers have high incidence or mortality rates globally, posing significant public health challenges[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Second, while previous studies have extensively explored the risk factors and etiologies of these cancers, the causal relationship between LDL-C and these cancers remains controversial, highlighting the need for further investigation[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, the availability of large sample data from genome-wide association studies (GWAS) databases for these cancers enhances the reliability and generalizability of our research findings.\u003c/p\u003e \u003cp\u003ePrevious studies have mainly used epidemiological observational studies and randomised controlled trials (RCTs) to investigate the relationship between LDL cholesterol and cancer. For example, some observational studies have reported that higher LDL cholesterol levels are associated with a higher risk of breast and prostate cancer[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, there are also articles that suggest the opposite[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, these studies are susceptible to confounding factors such as dietary habits, lifestyle and other metabolic risk factors. In addition, reverse causality (e.g., changes in LDL cholesterol levels as a result of cancer) may also affect the results of the studies .\u003c/p\u003e \u003cp\u003eMendelian randomisation method uses genetic variants as instrumental variables and simulates randomised controlled trials, which effectively reduces the effect of confounding factors.Since genes are determined at birth, the genetic variants used in the study do not change due to the onset of the disease, thus avoiding reverse causality[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) and Proprotein convertase subtilisin/kexin type 9 (PCSK9) are common targets for lowering low-density lipoprotein cholesterol (LDL-C)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, studies have shown that HMGCR and PSCK9 are strongly associated with cancer[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].Therefore, the present study will evaluate the causal relationship between LDL-C (via HMGCR and PSCK9) and multiple cancers, aiming to provide new insights and scientific evidence for the potential application of LDL-C in cancer prevention and treatment.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003e \u003cb\u003eMR study design\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, we designed an MR study to systematically assess the causal relationship between low-density lipoprotein cholesterol (LDL-C) and five common cancers (breast, cervical, thyroid, prostate, and colorectal).LDL-C was used as exposure, whereas multiple cancers were used as outcomes, and HMGCR and PSCK9 were used as instrumental variables to select SNPs with strong correlations with LDL-C from GWAS statistics according to rigorous screening criteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eData source\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe GWAS ID for low-density lipoprotein cholesterol (LDL-C) is ieu-a-300, which is derived from the IEU OpenGWAS project, which encompasses large-scale genetic data related to LDL-C. The GWAS ID for LDL-C is ieu-a-300. The dataset contains 173,082 samples containing 2,437,752 SNPs.The samples are from multi-ethnic populations including both males and females.The data was collected and provided by the GLGC (Global Lipids Genetics Consortium), and the lead author of the study is Willer CJ.\u003c/p\u003e \u003cp\u003eCervical Cancer (ieu-b-4876), Prostate cancer (ieu-b-85) and Breast Cancer (ieu-a-1126) are from the IEU OpenGWAS project, which provides large-scale genome-wide association study (GWAS) datasets.Cervical cancer contains 199,086 samples including 563 cervical cancer cases and 198,523 controls. The samples are predominantly from European women and contain 8,506,261 SNPs.Prostate cancer Contains 79,148 prostate cancer cases and 61,106 controls, mainly from European men, with 20,346,368 SNPs.Breast cancer data were obtained from the GWAS dataset provided by the Breast Cancer Association Consortium (BCAC). This dataset contains 228,951 samples, including 122,977 breast cancer cases and 105,974 controls. There are also 10,680,257 SNPs.The samples are mainly from European women.\u003c/p\u003e \u003cp\u003eThe data for colorectal cancer (ebi-a-GCST90013866) and thyroid cancer (ebi-a-GCST90013867) were obtained from the European Bioinformatics Institute's (EBI) GWAS catalogue. Colorectal cancer contains 407,746 samples and 11,037,981 SNPs.Thyroid cancer sample size and data quality were assured, containing a total of 407,746 samples and 11,035,981 SNPs.\u003c/p\u003e \u003cp\u003eSNP data for LDL cholesterol and five cancers can be found on the IEU OpenGWAS project database [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which are publicly available summary statistics. The protocol and data collection for the original GWAS were approved by the relevant ethics committees and written informed consent was obtained from each participant prior to data collection. Therefore, no additional ethical approval was required for the use of data in this study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSelection of instrumental variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo substitute for HMGCR, seven SNPs that were LDL-C-related at the genomic significance level (P\u0026thinsp;\u0026lt;\u0026thinsp;5.0 \u0026times; 10^-8) and within a\u0026thinsp;\u0026plusmn;\u0026thinsp;100 kb window of the region of the gene encoding HMGCR were obtained.Similarly, 12 LDL-C-related SNPs within \u0026plusmn;\u0026thinsp;100 kb of PCSK9 were used to substitute for PCSK9. To maximise the benefits of the tool, a modest chain imbalance (r^2\u0026thinsp;=\u0026thinsp;0.30) was allowed between the relevant SNPs to ensure sufficient independence and diversity between markers while retaining a degree of correlation for more efficient statistical analysis and interpretation of results.\u003c/p\u003e \u003cp\u003e \u003cb\u003edata analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe analysed the data for LDL-C and five cancers (breast, cervical, thyroid, prostate, and colorectal) using five methods (MR-Egger, weighted median, inverse variance weighted [IVW], weighted mode, and simple mode), with IVW being the decisive method of analysis, and the other methods used as references[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We also performed a series of sensitivity analyses because they can defend against violations of certain assumptions in MR studies. First, we performed MR-Egger regression analysis, which detects and corrects for horizontal pleiotropy and provides more robust causal effect estimates. Second, we also performed a leave-one-out sensitivity analysis, systematically removing one SNP at a time to determine whether a single SNP drives the overall causal estimate. The heterogeneity test was performed using the analysis results of the MR Egger and IVW methods. Cochran's Q was used as a measurement indicator.If Q_pval was less than the significance level (usually 0.05), heterogeneity was considered to exist[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter MR analysis, we set the significance threshold to pval\u0026thinsp;\u0026lt;\u0026thinsp;0.05, at which point the results are generally considered statistically significant and the null hypothesis (that there is an association between exposure and outcome) can be rejected. For odds ratios (ORs), when OR\u0026thinsp;\u0026gt;\u0026thinsp;1, it means that exposure increases the likelihood of the outcome (positive correlation). Conversely, exposure and outcome are negatively correlated. In particular, when OR\u0026thinsp;=\u0026thinsp;1, it means that there is no association between exposure and outcome.\u003c/p\u003e \u003cp\u003eAll analyses were conducted with packages named TwoSampleMR in R software (version 4.3.1; R Development Core Team).\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e \u003cb\u003eHMGCR analysis results\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor the HMGCR, the results showed that elevated LDL-C levels were significantly associated with the risk of thyroid, prostate, breast, and colorectal cancer. Specific results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor thyroid cancer, in 407,746 samples (including 3,001 thyroid cancer cases and 287,288 controls), the IVW method showed that elevated LDL-C levels were associated with a significantly increased risk of thyroid cancer, with an OR of 8.29 (95% CI: 3.19\u0026ndash;21.55), and a p-value of less than 0.001. This suggests that for every unit increase in LDL-C levels, there is an 8.29-fold increase in thyroid cancer risk.\u003c/p\u003e \u003cp\u003eProstate cancer: OR 1.20 (95% CI: 1.05\u0026ndash;1.37), p-value 0.007, indicating that elevated LDL-C levels are associated with a significantly increased risk of prostate cancer.\u003c/p\u003e \u003cp\u003eBreast Cancer: OR 1.20 (95% CI: 1.08\u0026ndash;1.33), p-value 0.00052, indicating that elevated LDL-C levels are associated with a significantly increased risk of breast cancer.\u003c/p\u003e \u003cp\u003eColorectal Cancer: In 407,746 samples (including 58,131 colorectal cancer cases and 349,615 controls), the IVW method showed that elevated LDL-C levels were associated with a significant reduction in colorectal cancer risk with an OR of 0.64 (95% CI: 0.44\u0026ndash;0.93) and a p-value of 0.018. This means that for every unit increase in LDL-C levels, the colorectal rectal cancer risk was reduced by 36%.\u003c/p\u003e \u003cp\u003eFor cervical cancer, HMGCR analysis showed no significant association between LDL-C levels and their risk, with an OR of 1.00 (95% CI: 1.00-1.01) and a P value of 0.050.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePSCK9 analysis results\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor PSCK9, the results showed that elevated LDL-C levels were significantly associated with the risk of breast and prostate cancer. The specific results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003c/p\u003e \u003cp\u003eFor breast cancer, in 228,951 samples (including 122,977 breast cancer cases and 105,974 controls), the inverse variance weighted (IVW) method showed that elevated LDL-C levels were associated with a significant increase in the risk of breast cancer, with an OR of 1.20 (95% CI: 1.08\u0026ndash;1.33) and a p-value of 0.00052.This suggests that for every one-unit increase in LDL-C level, there is a 20% increase in breast cancer risk.\u003c/p\u003e \u003cp\u003eIn the analysis of prostate cancer the results showed that out of 140,254 samples (including 79,148 prostate cancer cases and 61,106 controls), the IVW method showed that elevated LDL-C levels were associated with a significantly increased risk of prostate cancer, with an OR of 1.20 (95% CI: 1.05\u0026ndash;1.37) and a p-value of 0.007. this implies that for every one unit increase of LDL-C level, there was a 20% increase in prostate cancer risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, no abnormal results were found in the sensitivity and heterogeneity analyses. Details of the forest plots for the heterogeneity analysis, multiple validity tests, scatterplots, funnel plots, and leave-one-out sensitivity analyses can be found in the additional file.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003e \u003cb\u003eInterpretation of the findings\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study, we assessed the causal association between low-density lipoprotein cholesterol (LDL-C) and five common cancers (breast, cervical, thyroid, prostate and colorectal) using Mendelian randomisation (MR) analysis. By analysing PSCK9 and HMGCR, we found a significant association between elevated LDL-C levels and the risk of certain cancers[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the HMGCR analyses, the results showed that elevated LDL-C levels were significantly associated with the risk of thyroid, prostate, breast and colorectal cancer. Particularly for thyroid cancer, the risk increased 8.29-fold for each unit increase in LDL-C levels. This may be due to the fact that LDL-C affects the metabolism and proliferation of thyroid cells through the HMGCR pathway[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, LDL-C was significantly associated with an increased risk of prostate and breast cancer, further supporting the potential role of LDL-C in the development and progression of these cancers[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In addition, elevated LDL-C levels were associated with a significantly lower risk of colorectal cancer, a result that warrants further exploration and may involve different biological mechanisms[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor the PSCK9 analysis, the results showed that elevated LDL-C levels were significantly associated with the risk of breast and prostate cancer[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Specifically, each unit increase in LDL-C levels was associated with a 20 per cent increase in the risk of breast and prostate cancer, respectively. This is consistent with previous findings suggesting that PSCK9 is not only tightly associated with LDL-C but also highly expressed in various tumour-derived cell lines[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, PSCK9 analyses showed no significant association between LDL cholesterol levels and the risk of cervical, thyroid, and colorectal cancers, but some studies have illustrated a positive association between PSCK9 inhibitors and cervical cancers[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, some studies suggest that PSCK9 promotes colorectal cancer and is a therapeutic target[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].This may indicate that LDL cholesterol has a different mechanism of action in these cancers, and further studies are needed.\u003c/p\u003e \u003cp\u003e \u003cb\u003eresearch significance\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTherapeutic strategies to lower LDL-C levels, such as the use of statins, have been widely used in the prevention and treatment of cardiovascular disease[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The results of our study suggest that these drugs may also have potential benefits in reducing the risk of certain cancers. Specifically, for breast and prostate cancers, high levels of LDL-C may be an independent risk factor, and therefore, by lowering LDL-C levels, it may help reduce the incidence of these cancers.\u003c/p\u003e \u003cp\u003eThe findings suggest that the mechanism of action of LDL-C in different cancers may differ. For example, PSCK9 analysis showed no significant association between LDL-C levels and the risk of cervical, thyroid and colorectal cancers, which may indicate that LDL-C affects some cancers but not others through specific pathways. This provides clues for further mechanistic studies, and exploring the specific pathways and molecular mechanisms of LDL-C's action in different cancers will help to unravel the etiological and developmental processes of cancer.\u003c/p\u003e \u003cp\u003eIn the public health arena, the results of this study suggest the need to reassess strategies for LDL-C management, especially in high-risk populations. For example, in areas with a high prevalence of breast and prostate cancers, active management of LDL-C levels may help to reduce the incidence of these cancers. In addition, for thyroid cancer, the findings suggest that special attention needs to be paid to the management of LDL-C levels, as significantly elevated LDL-C levels are associated with an increased risk of thyroid cancer.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations of the study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough this study provides new insights, there are some limitations. Firstly, Mendelian randomisation analysis relies on the validity of the selected genetic variants as instrumental variables. If there is horizontal pleiotropy in these instrumental variables, it may affect the accuracy of causal inference. Second, the GWAS data used in this study were mainly from European populations[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and the results may not be applicable to other ethnic populations. Therefore, future studies should consider populations of different ethnicities and regions to verify the generalisability of these findings.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFuture Research Directions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFuture studies should further explore the specific mechanisms of LDL-C's role in different cancers, especially for thyroid, prostate and breast cancers. In addition, investigating whether there are racial or geographic differences in the relationship between LDL-C levels and cancer risk, as well as the underlying biological basis, will help to develop personalised prevention and treatment strategies.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eAnalyses using both the HMGCR and PCSK9 genes have shown that LDL-C may be a significant potential risk factor for breast and prostate cancer. The elevated levels of LDL-C were consistently associated with an increased risk of these cancers, indicating a possible mechanistic link through lipid metabolism pathways influencing cancer cell proliferation and survival. Furthermore, analyses of the HMGCR gene alone have suggested that LDL-C may also increase the risk of thyroid cancer and decrease the risk of colorectal cancer. Specifically, elevated LDL-C levels were strongly associated with a markedly higher risk of thyroid cancer, potentially due to HMGCR-mediated cholesterol synthesis affecting thyroid cell function and growth. Conversely, the inverse relationship between LDL-C and colorectal cancer risk points to complex and possibly tissue-specific metabolic and inflammatory processes influenced by cholesterol levels. These findings highlight the multifaceted role of LDL-C in cancer development and underscore the importance of considering genetic pathways when assessing cancer risks associated with lipid levels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by Macao Polytechnic University Grant (RP/FCA-15/2022)\u003c/p\u003e\n\u003cp\u003eThis work is supported by Macao Polytechnic University Grant (RP/FCA-10/2023)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests \u0026nbsp; \u0026nbsp;\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Hengchang Liang and hui xie. The first draft of the manuscript was written by Hengchang Liang, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study can be found in the IEU OpenGwas Project [https://gwas.mrcieu.ac.uk/]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol and data collection for the original GWAS were approved by the relevant ethics committees and written informed consent was obtained from each participant prior to data collection. Therefore, no additional ethical approval was required for the use of data in this study.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHedayatnia, M., Asadi, Z., Zare-Feyzabadi, R. et al. Dyslipidemia and cardiovascular disease risk among the MASHAD study population. Lipids Health Dis 19, 42 (2020). https://doi.org/10.1186/s12944-020-01204-y\u003c/li\u003e\n\u003cli\u003eDeng C-F, Zhu N, Zhao T-J, Li H-F, Gu J, Liao D-F and Qin L (2022) Involvement of LDL and ox-LDL in Cancer Development and Its Therapeutical Potential. Front. Oncol. 12:803473. https://doi.org/10.3389/fonc.2022.803473\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2024). Cancer. Retrieved from https://www.who.int/health-topics/cancer#tab=tab_1\u003c/li\u003e\n\u003cli\u003eMazzuferi, G., Bacchetti, T., Islam, M.O. et al. High density lipoproteins and oxidative stress in breast cancer. 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Kitahara et al., Total Cholesterol and Cancer Risk in a Large Prospective Study in Korea. JCO 29, 1592-1598(2011).DOI:https://doi.org/10.1200/JCO.2010.31.5200\u003c/li\u003e\n\u003cli\u003eYang Z, Tang H, Lu S, et alRelationship between serum lipid level and colorectal cancer: a systemic review and meta-analysisBMJ Open 2022;12:e052373. doi: 10.1136/bmjopen-2021-052373\u003c/li\u003e\n\u003cli\u003eAbdelwahed, K. S., Siddique, A. B., Mohyeldin, M. M., Qusa, M. H., Goda, A. A., Singh, S. S., \u0026hellip; \u0026amp; Sayed, K. A. E. (2020). Pseurotin a as a novel suppressor of hormone dependent breast cancer progression and recurrence by inhibiting pcsk9 secretion and interaction with ldl receptor. Pharmacological Research, 158, 104847. https://doi.org/10.1016/j.phrs.2020.104847\u003c/li\u003e\n\u003cli\u003eSeidah NG. The PCSK9 revolution and the potential of PCSK9-based therapies to reduce LDL-cholesterol. Glob Cardiol Sci Pract. 2017 Mar 31;2017(1):e201702. doi: 10.21542/gcsp.2017.2. PMID: 28971102; PMCID: PMC5621713.\u003c/li\u003e\n\u003cli\u003eWang, W.; Li, W.; Zhang, D.; Mi, Y.; Zhang, J.; He, G. The Causal Relationship between PCSK9 Inhibitors and Malignant Tumors: A Mendelian Randomization Study Based on Drug Targeting. Genes 2024, 15, 132. https://doi.org/10.3390/genes15010132\u003c/li\u003e\n\u003cli\u003eWong CC, Wu JL, Ji F, Kang W, Bian X, Chen H, Chan LS, Luk STY, Tong S, Xu J, Zhou Q, Liu D, Su H, Gou H, Cheung AH, To KF, Cai Z, Shay JW, Yu J. The cholesterol uptake regulator PCSK9 promotes and is a therapeutic target in APC/KRAS-mutant colorectal cancer. Nat Commun. 2022 Jul 8;13(1):3971. doi: 10.1038/s41467-022-31663-z. PMID: 35803966; PMCID: PMC9270407.\u003c/li\u003e\n\u003cli\u003eBurgess, S., Butterworth, A. S., \u0026amp; Thompson, S. G. (2013). Mendelian randomization analysis with multiple genetic variants using summarized data. Genetic Epidemiology, 37(7), 658-665. https://doi.org/10.1002/gepi.21758\u003c/li\u003e\n\u003cli\u003eHiggins, J. P. T. (2003). Measuring inconsistency in meta-analyses. BMJ, 327(7414), 557-560. https://doi.org/10.1136/bmj.327.7414.557\u003c/li\u003e\n\u003cli\u003eRidker, P. M., Danielson, E., Fonseca, F. A., Genest, J., Gotto, A. M., Kastelein, J. J., \u0026hellip; \u0026amp; Glynn, R. J. (2008). Rosuvastatin to prevent vascular events in men and women with elevated c-reactive protein. New England Journal of Medicine, 359(21), 2195-2207. https://doi.org/10.1056/nejmoa0807646\u003c/li\u003e\n\u003cli\u003eLippi, L.; Turco, A.; Moalli, S.; Gallo, M.; Curci, C.; Maconi, A.; de Sire, A.; Invernizzi, M. Role of Prehabilitation and Rehabilitation on Functional Recovery and Quality of Life in Thyroid Cancer Patients: A Comprehensive Review. Cancers 2023, 15, 4502. https://doi.org/10.3390/cancers15184502\u003c/li\u003e\n\u003cli\u003eJack Bowden, George Davey Smith, Stephen Burgess, Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression, International Journal of Epidemiology, Volume 44, Issue 2, April 2015, Pages 512\u0026ndash;525, https://doi.org/10.1093/ije/dyv080\u003c/li\u003e\n\u003cli\u003eBurgess, S., Thompson, S.G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32, 377\u0026ndash;389 (2017). https://doi.org/10.1007/s10654-017-0255-x\u003c/li\u003e\n\u003cli\u003eBowden, J., Smith, G. D., Haycock, P., \u0026amp; Burgess, S. (2016). Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genetic Epidemiology, 40(4), 304-314. https://doi.org/10.1002/gepi.21965\u003c/li\u003e\n\u003cli\u003eDemierre, MF., Higgins, P., Gruber, S. et al. Statins and cancer prevention. Nat Rev Cancer 5, 930\u0026ndash;942 (2005). https://doi.org/10.1038/nrc1751\u003c/li\u003e\n\u003cli\u003eKonrad H Stopsack, Travis A Gerke, Ove Andr\u0026eacute;n, Swen-Olof Andersson, Edward L Giovannucci, Lorelei A Mucci, Jennifer R Rider, Cholesterol uptake and regulation in high-grade and lethal prostate cancers, Carcinogenesis, Volume 38, Issue 8, August 2017, Pages 806\u0026ndash;811, https://doi.org/10.1093/carcin/bgx058 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mendelian randomization, LDL cholesterol, HMGCR, PSCK9, Causality, Cancer","lastPublishedDoi":"10.21203/rs.3.rs-5135086/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5135086/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e The aim of this study was to investigate the causal relationship between low-density lipoprotein cholesterol (LDL-C) and five cancers (breast, cervical, thyroid, prostate and colorectal) using the Mendelian Randomization (MR) method, with a view to revealing the potential role of LDL-C in the development of these cancers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eWe used gene variant data and disease data from the Genome-Wide Association Study (GWAS) database to assess the causal relationship between LDL-C and each cancer by Mendelian randomisation analysis methods such as inverse variance weighting and MR-Egger. Specifically, we selected Proprotein convertase subtilisin/kexin type 9(PCSK9) and 3-hydroxy-3-methylglutaryl-CoA reductase(HMGCR), genes associated with LDL-C levels, as instrumental variables, extracted the corresponding single nucleotide polymorphism (SNP) data and analysed the associations of these SNPs with five cancers.In addition, sensitivity analyses and heterogeneity tests were performed to ensure the reliability of the results\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The analyses showed that when using HMGCR gene,LDL-C were significantly and positively associated with breast (OR:1.200, 95% CI:1.082-1.329, p=0.001), prostate (OR:1.198, 95% CI:1.050-1.366, p=0.007), and thyroid cancers (OR:8.291, 95% CI:3.189- 21.555, p=0.00001) were significantly positively correlated, whereas they were significantly negatively correlated with colorectal cancer (OR:0.641, 95% CI:0.442-0.928, p=0.019); the results for cervical cancer were not significant (p=0.050). When using the PCSK9 gene, LDL-C levels were significantly and positively associated with breast (OR:1.107, 95%:CI 1.031-1.187, p=0.005) and prostate (OR:1.219, 95%:CI 1.101-1.349, p=0.0001) cancers, but not with cervical (p=0.294), thyroid cancer (p=0.759) and colorectal cancer ( p=0.572).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eAnalyses using both the HMGCR and PCSK9 genes have shown that LDL-C may be a potential risk factor for breast and prostate cancer, while analyses of the HMGCR gene have also suggested that LDL-C may increase the risk of thyroid cancer and decrease the risk of colorectal cancer.\u003c/p\u003e","manuscriptTitle":"Comprehensive Mendelian randomization analysis of low-density lipoprotein cholesterol and multiple cancers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-22 06:34:41","doi":"10.21203/rs.3.rs-5135086/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-05T09:43:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-04T14:10:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112517056056776930063078536945459203114","date":"2024-11-04T14:01:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-14T07:48:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304077134075091503545210375732987279736","date":"2024-10-11T01:21:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-07T09:02:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-30T13:16:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-28T06:37:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2024-09-23T04:46:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb99d20a-03d1-4ef9-a258-25b0302dacbf","owner":[],"postedDate":"November 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T05:38:35+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-22 06:34:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5135086","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5135086","identity":"rs-5135086","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}