Roles of plasma proteins in mediating the causal effect of the lipid species on gastric cancer and exploring potential drug targets for gastric cancer：insights from proteomic and two-step mendelian randomization and macromolecular docking

Roles of plasma proteins in mediating the causal effect of the lipid species on gastric cancer and exploring potential drug targets for gastric cancer：insights from proteomic and two-step mendelian randomization and macromolecular docking | 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 Article Roles of plasma proteins in mediating the causal effect of the lipid species on gastric cancer and exploring potential drug targets for gastric cancer：insights from proteomic and two-step mendelian randomization and macromolecular docking Zhenhua Dong, Zhiqing Chen, Dingling Zhao, Jianling Jia, Hongliang Cao, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4574875/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The change of plasma lipid species has close contacts with gastric cancer (GC). However, the specific mechanism still needs to explore further. Objectives We aim to utilize plasma proteins to decipher the association between lipid species and GC, and seek possible drug targets for GC. Methods A two-step mendelian randomization (MR) is conducted to identify the causal relationship among 179 lipid species, 4907 plasma proteins and GC. We employ the summary mendelian randomization (SMR) and colocalization to explore relationship between plasma proteins and GC. We use one protein data including 35559 individuals as discovery group, meanwhile the other one from 54219 subjects as validation group. MR is performed to identify the association between lipid species and GC or plasma proteins. Based on chosen proteins, we use macromolecular docking to find potential components as ligands. Results MR identifies the causality between 12 lipid species and GC, 3 proteins and GC, 2 lipid species and 2 proteins. After the test of propagation of error method, we conclude that CCDC80 protein mediates (30.8%; 95% confidents interval (Cl), 6.4%-64.0%) of the association between Diacylglycerol (16:1_18:1) and GC. For CCDC80, we choose 4 components including 2,3,7,8-Tetrachlorodibenzo-P-dioxin, Benzo[a]pyrene, Bisphenol A, Valproic Acid as potential drugs. Conclusion Our study suggests that CCDC80, a drug target, is a mediator between Diacylglycerol (16:1_18:1) and GC, which may guide a novel direction for GC treatment. Health sciences/Gastroenterology/Gastrointestinal diseases/Gastrointestinal cancer Health sciences/Diseases/Gastrointestinal diseases plasma lipid species plasma proteins gastric cancer drug target MR SMR colocalization analysis macromolecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction There are approximately 100,0000 people are newly diagnosed as GC in the world every year, and GC is the third most common death cause related with cancer, which indicates that GC is a global health problem and brings huge financial burden to health care system[ 1 , 2 ]. First, blood lipidome is related with GC. A prospective study including 33733 adults concludes that the low total cholesterol is associated with increasing of cancer risk, such as GC[ 3 ], and this conclusion is consistent with a mate analysis referring to the association between blood lipids level and GC[ 4 ]. What’s more, a lipidomic study identities 11 plasma lipids inversely related with GC occurrence and these lipids are also utilized to divide risks of gastric lesion progression so that we could identify people with high risk for GC as early as possible[ 5 ]. Second, plasma protein is also relevant to GC. A study suggests that plasma Hsp90α level in GC is higher than healthy people, and it may be a potential biomarker for GC[ 6 ]. Based on liquid chromatography mass spectrophotometry, the other study finds sex hormone-binding globulin expressing highly in GC[ 7 ]. Third, it is revealed that blood proteins have complex association with lipids species in many diseases. However, the causal relationship is still uncertain. We hypothesize that plasma proteins mediate the causality between lipid species and GC. The deep exploration for these pathways not only helps understand the pathogenesis of GC, but also provides support evidence for GC treatment. Currently, the standard treatment for GC consists of definitive surgical resection and chemoradiotherapy[ 8 ] which causes many adverse events including disgust, sickness, low Leukocyte and weak immune, but targeted therapy could decrease these events greatly and possesses huge development prospect[ 9 ]. Unluckily, the variants of targeted therapy are few, which limits their application in clinic. Thus, we aimed to find more drug targets by performing systematic investigation between blood proteins and GC. MR, a novel method, is used for accessing causality between exposure and outcome based on genome wide association studies (GWAS)[ 10 ]. Genetic variants are chosen as instruments variants (IVs) which are hardly influenced by confounders because genes are located randomly during conception and environment can’t change genetic structure.[ 11 ] Considering the strength of MR and availability of GWAS, we perform this study, with the final goal to illustrate the protein pathway linking plasma lipidome to GC occurrence. In addition, our study will find and dock potential drug for selected blood protein to explore novel treatment for GC. 2. Methods 2.1 Study design Figure 1 shows the profile of study design. In this study, we first perform proteomic SMR and colocalization analysis to find GC-associated plasma proteins. In this step, we use two independent protein data to calculate, decode 2021 as discovery data and the UK Biobank Pharma Proteomics Project (UKBppp) 2023 as validation one. Then, we conduct unidirectional MR to identify GC-related lipid species. Next, the bidirectional MR is utilized to explore the causal relationship between GC-associated plasma proteins and GC-related lipid species. Finally, we leverage propagation of error method to evaluate the mediated effect. Based on selected plasma protein, we perform two steps including identifying approved drugs for new application to GC treatment through DrugBank database and exploring possible components used for drug development which could accomplish docking with plasma protein. 2.2 GWAS information For protein, the decode2021 is composed of 4907 proteins and obtained from 35559 Icelanders whose mean age is 50 and gender ratio is close to 1:1. The measurement of protein is based on SomaScan version 4 assay (SomaLogic). They further use linear mixed model to assess the relationship between gene and protein levels[ 12 ]. Concerning identified proteins related with GC in discovery data, we use UKBppp2023 to validate. The UKBppp2023 consists of 2923 proteins and its subjects include 54219 people. The protein levels are measured through Olink Explore 3072 platform[ 13 , 14 ]. For lipid species, the GWAS includes 179 lipid species from 13 lipid classes and is obtained from 7174 Finnish participants belonging to GeneRISK cohort. The participants consist of 4642 women and 2624 men and their age range varies from 45 to 66. Finally, 56 lipid-associated loci are identified, including 8 new loci through the method of univariate and multivariate GWAS[ 15 ]. For GC, the data is gotten from IEU catalog whose accessed number is ebi-a-GCST90018629. Its’ subjects include 7921 cases and 159201 controls. It is obtained through mate analysis among UK Biobank, FinnGen and Biobank Japan, and main population is European[ 16 ]. 2.3 Mendelian Randomization The MR results will be believable, if it satisfies three assumptions: (1) the IVs is strongly associated with exposure; (2) IVs should be not related with any confounders; (3) IVs influence outcomes only through exposure not other pathways. We select IVs at the threshold at P < 1e-5 for lipid species and 5e-8 for proteins. For avoiding linkage disequilibrium (LD), we choose IVs at R2 < 0.001 within the window of 10000kb. We utilize F statistic to calculate the strength of chosen IVs. If F < 10, these IVs will be removed from MR analysis. We view inverse variance weighted (IVW) as a main method, which is supplemented by 4 sensitive analyses, including weighted median, maximum likelihood, MR egger and Cochran’s Q statistic, to strengthen MR outcome and estimate pleiotropy or heterogeneity. 2.4 Summary-data-based MR analysis We leverage SMR to explore the association between protein levels and the risk of GC. SMR depending on top cis-QTLs is more accurate than traditional MR when exposure and outcome from two GWAS with enough sample sizes. The top cis-QTLs are selected from genes at P 0.2 or its prop > 0.05 between paired datasets, including referring data, protein data and GC data[ 17 ]. HEIDI is applied to test pleiotropy and we will abandon the SMR results when HEIDI < 0.01 which indicates the existence of pleiotropy. we perform Benjamini-Hochberg method to calculate P value and the proteins P < 0.05 will be retained for colocalization. 2.5 Colocalization analysis To investigate whether the causality between identified proteins and GC is driven by same gene fragments, we use coloc R package to perform the colocalization analysis. The prior probabilities that SNPs within colocalization region are related with only protein, only GC or both, are set at 1e-4, 1e-4 and 1e-5 respectively. This analysis possesses five hypotheses for SNPs within colocalization region, H0: nothing to do with either trait, H1: related with trait 1 only, H2: related with trait 2 only, H3: related with both traits, influenced by different gene locus, H4: related with both traits, influenced by same gene locus. The five hypotheses correspond to five posterior probabilities (PP), and PPH4 > 0.6 is viewed as evidence supporting colocalization. 2.6 Mediated analysis Mediated effect is calculated by the causal effect of lipid species on protein multiplied by the casual effect of protein on GC, and mediated prop is estimated by mediated effect divided by the total effect of lipid species on GC. we utilize the propagation of error method to calculate standard error relevant to mediated effect and 95%Cl of mediated prop. This method is based on one rule that error of calculation could propagate so that it could influence the accuracy of subsequent calculation. 2.7 Potential therapeutic drugs prediction and Molecular docking First, we inquire genes expressing identified proteins through National Center of Biotechnology Information (NCBI). Second, we search these genes on DrugBank database to explore possible drugs for GC treatment so that we may find the new application of approved drug. Third, we will identify small molecule ligands related with genes in GC. Forth, we use PubChem database to obtain the two-dimensional structures of small molecular ligands which will be further inputted into Chem3D software to get their three-dimensional structure. Fifth, through Protein Data Bank (PDB) database, we could get the structure of plasma protein which is a receptor actually. Sixth, AutoDock Tool is employed to prepare the PDBQT formats of receptor and ligand and create the space for molecular docking. Seventh, we utilize AutoDock Vina to conduct molecular docking. Less the binding energy, more effective the binding of receptor-ligand. It is reported that the binding energy<-5kcal/mol suggests the binding is stable. 3. Results 3.1 plasma proteins and GC In discovery data, there are 69 proteins associated with GC at SMR p 0.01. (Supplementary sheet1, Fig. 2 for manhattan plot) Among 69 proteins, CCDC80 and IGFALS have high colocalization support evidence with PPH4 close to 0.8, and PDCD1LG2 has medium colocalization support evidence with PPH4 > 0.6. (Supplementary sheet2) Genetically predicted per SD increase in expression of CCDC80 (OR 0.513, 95% CI 0.139–0.889), PDCD1LG2 (OR 0.818, 95% CI 0.698–0.938), IGFALS (OR 0.774, 95% CI 0.618–0.929) are negatively related with GC risk. Figure 3 shows the colocalization locus compare and SMR scatter plots of three protein-GC associations. In validation data, owing to limited protein in UKBppp, SMR just identify CCDC80 whose SMR p 0.01(Supplementary sheet3), but CCDC80-GC association isn’t supported by colocalization results. (Supplementary sheet4). 3.2 197 lipid species and GC At P < 1e-5, we select enough IVs varying from 4 to 26 for lipid species, the results of IVW indicate that 8 lipid species are negatively related with GC, including Sterol ester (27:1/14:0) (OR = 0.877, 95% CI 0.772–0.995, P = 0.042); Sterol ester (27:1/18:0) (OR = 0.889, 95% CI 0.804–0.983, P = 0.022); Diacylglycerol (16:1_18:1) (OR = 0.913, 95% CI 0.846–0.984, P = 0.018); Phosphatidylcholine (O-16:0_16:1) (OR = 0.888, 95% CI 0.797–0.989, P = 0.030); Phosphatidylcholine (O-16:1_18:2) (OR = 0.898, 95% CI 0.807–0.997, P = 0.047); Phosphatidylethanolamine (16:0_18:2) (OR = 0.953, 95% CI 0.910–0.998, P = 0.041); Phosphatidylethanolamine (18:1_18:1) (OR = 0.927, 95% CI 0.866–0.992, P = 0.029); Triacylglycerol (56:7) (OR = 0.920, 95% CI 0.851–0.995, P = 0.036), meanwhile 4 lipid species are positively related with GC, including Sterol ester (27:1/17:0) (OR = 1.131, 95% CI 1.031–1.242, P = 0.009); Ceramide (d42:1) (OR = 1.102, 95% CI 1.015–1.196, P = 0.02); Phosphatidylcholine (18:0_22:6) (OR = 1.095, 95% CI 1.004–1.194, P = 0.040) ; Triacylglycerol (48:0) (OR = 1.078, 95% CI 1.007–1.153, P = 0.030). (Fig. 4 for circle heatmap) There aren’t evidence of pleiotropy (the intercept of MR egger P > 0.05) and heterogeneity (Cochran Q test p > 0.05) between 12 lipid species and GC. All F > 20 suggests IV strongly associated with exposure. (Supplementary sheet5) 3.3 12 lipid species and 3 plasma proteins Although we just prove CDCC80 in validation data, our further study still involves three proteins including CDCC80, PDCD1LG2 and IGFALS to make study more comprehensive. At P < 1e-5, we choose enough IVs varying from 14 to 24 to proxy lipid species. (Supplementary sheet6) The results of IVW suggests that there are negative association between Sterol ester (27:1/14:0) (OR = 0.937, 95% CI 0.898–0.979, P = 0.003) and PDCD1LG2; Diacylglycerol (16:1_18:1) (OR = 0.956, 95% CI 0.925–0.988, P = 0.007) and CCDC80. (Supplementary sheet7, Fig. 5 for forest plot) There is no pleiotropy and heterogeneity. All F > 10 suggests IVs have enough strength with exposure. (supplementary sheet6) 3.4 3 plasma proteins and 12 lipid species At P < 5e-8, we choose enough IVs varying from 6 to 10 to proxy proteins. (Supplementary sheet8) The results of IVW suggests that there are negative association between CDCC80 (OR = 0.790, 95% CI 0.635–0.982, P = 0.033) and Sterol ester (27:1/14:0). (supplementary sheet9, Fig. 6 for forest plot) what’s more, all F > 10. (supplementary sheet8) 3.5 Mediated analysis After calculation, we find three potential pathways including Sterol ester (27:1/14:0)-CCDC80-GC, Sterol ester (27:1/14:0)-PDCD1LG2-GC, and Diacylglycerol (16:1_18:1) - CDCC80-GC, but the former two can’t pass the test of propagation of error method. For last one, the mediated effect is 0.030 (95% Cl 0.006–0.062) and the mediated prop is 30.80% (95% Cl, 6.39%-63.98%). 3.6 Molecular docking In DrugBank, we cannot find any approved drug for GC treatment. We just focus on CDCC80 protein and find that gene CCDC80 expresses protein CCDC80 and four components are associated with CCDC80 reported by pudlished papers. The PDB id of CCDC80 is 2GGU, and four components include 2,3,7,8-Tetrachlorodibenzo-P-dioxin (PubChem id, 15625), Bisphenol A (PubChem id, 6623), Benzo[a]pyrene (PubChem id, 2336) and Valproic Acid (PubChem id, 3121). We find that 2,3,7,8-Tetrachlorodibenzo-P-dioxin binds with CCDC80 by LYS(Amino Acid) 230(the location of Amino Acid), LEU233, LEU260, ALA264 and ALA286, whose binding energy is -5.5151kcal/mol; Bisphenol A binds with CCDC80 by LEU223, GLN238, ALA264, whose binding energy is -5.1477kcal/mol; Benzo[a]pyrene binds with CCDC80 by LYS230, LEU233, PHE 234, ALA264, whose binding energy is -5.0077 kcal/mol; Valproic Acid binds with CDCC80 by GLU232, PRO235, whose binding energy is -4.9364lcal/mol. The bind energy close to 5 kcal/mol indicate the binding of ligands and CDCC80 is stable, and these components may become drugs to treat GC. (Fig. 7 ) 4. Discussion In this study, we first conduct proteomic SMR and colocalization analysis, which find 3 plasma proteins relevant to GC risk at the genetic level. Next, we identify 12 plasma lipid species associated with GC risk by performing MR between 179 lipid species and GC. What’s more, the bidirectional MR between lipid species and plasma protein is beneficial to ascertain their complex relationship. Base on two-step MR and mediated analysis, we discover the role of CCDC80 protein in mediating the association of Diacylglycerol (16:1_18:1) with GC. CCDC80 protein would be viewed to be druggable, and we find 4 components binding with CCDC80 which may be potential drugs to treat GC. In summary, our study deepens the understanding of protein and lipid pathogenesis of GC and may guide a novel direction to explore drug for GC treatment. Concerning the causal relationship between 3 proteins and GC, our study conclusion is supported by many several papers. CDCC80 containing P-DUDES domain region is relevant to peroxide turnover and signaling and involves in tumor suppression.[ 18 ] Based on targeted plasma proteomic analysis, researchers find that plasma CCDC80 expresses higher in GC patients than healthy people, and could be GC diagnosis biomarker with high sensitivity.[ 19 ] CDCC80 involving in cell adhesion and matrix assembly, also inhibits other cancer occurrence, such as colon cancer[ 20 ], pancreatic cancer, thyroid cancer[ 21 , 22 ], ovarian cancer and so on. These studies support our conclusion about CCDC80. Currently, there is no paper about relationship between IGFALS and GC. ICGALS, insulin like growth factor binding protein acid labile subunit, is associated with cell growth and metabolism[ 23 ]. In hepatocellular carcinoma, ICGALS plays the role of tumor inhibitor and its function in other tumors is still unclear[ 24 ]. Our study discovers ICFALS being the protective factor for GC, which needs more study to prove. PDCD1LG2, programmed death-ligand 2, often participates in cellar signaling transduction to prompt tumor growth and invasion and drug resistance. A study discovers that PDCD1LG2 expresses highly in GC tumor, which induces immunosuppression and accelerates the progression of tumor.[ 25 ] However, Liu X et al. finds that PDCD1LG2 could increase CD8T cell quantity and enhance its lethality in murine tumor cells.[ 26 ] The other animal study also discovers that murine tumor cells can express PDCD1LG2 to enhance immunity by inducing T cell to produce lymphokine at a specific mode.[ 27 ] Thus, the biological function of PDCD1LG2 is controversial, and our study support the view that PDCD1LG2 could decrease GC risk. Next the complex association between 12 lipid species and GC will be discussed. Diacylglycerol is a lipid second messenger linking external stimulation to intracellular signals translation, Diacylglycerol dysregulation of activity or abundance influences occurrence, invasion and metastasis of tumor. What’s more, diacylglycerol is essential for maintaining of T cell function which is important for body immunity surveillance[ 28 ]. Bae CS delivers diacylglycerol to kill tumor through Cationic Nanoparticles, which is associated with triggering oxidative stress.[ 29 ] These evidences supports our conclusion that Diacylglycerol (16:1_18:1) could reduce GC risk. Next, Phosphatidylcholine is related with GC. some scholars compare the lipid composition of GC tissue and adjacent normal one based on imaging mass spectrometry, and discovers that the over expression lysophosphatidylcholine acyltransferase 1 in GC tissue, which prompts the producing of Phosphatidylcholine.[ 30 ] The other study finds the content of Phosphatidylcholine in GC blood is different from healthy people through the technology of nanoflow ultrahigh performance liquid chromatography-electrospray ionization-tandem mass spectrometry[ 31 ]. Then, we illustrate the relation between Ceramide and GC. Ceramides, a signal molecule, belongs to sphingolipids participating in cellar growth and proliferation, and its metabolic disorders will prompt GC [ 32 ]. Ceramides could regulate cellar apoptosis and autophagy in tumor and play the function suppressing tumor, so it possesses big potential to become tumor biomarker and drug target.[ 33 ] There are some strengths in this study. (1) it has rigorous design scheme. (2) we use big cross-species GWAS data on GC to ensure the MR result more comprehensive. (3) many sensitivity analyses make MR outcome more stable. (4) thousands of proteins and hundreds of lipid species are put into consideration. (5) many statistic methods like MR, SMR and colocalization, are conducted. But several limitations must be mentioned. (1) the GWAS data on protein and lipid species focus on European, which limits our results generalization. (2) we may miss or overlook the effect of the protein and lipid species on GC, which is related with the quality of GWAS. (3) we don’t consider the age- or sex-specific effects because the limitation of GWAS. Although MR is a tool to explore causality, our conclusion still needs more researches to prove. 5. Conclusion Our study suggests that CCDC80, a drug target, is a mediator between Diacylglycerol (16:1_18:1) and GC. For CCDC80, we find 4 components binding with it. This study helps us understand the etiology of GC in some extent and guides us a novel direction for developing drug for GC. Abbreviations GC gastric cancer SMR summary mendelian randomization GWAS genome wide association studies SNP single nucleotide polymorphism IVs instruments variants UKBppp UK Biobank Pharma Proteomics Project LD linkage disequilibrium NCBI National Center of Biotechnology Information MR mendelian randomization PDB Protein Data Bank Declarations Ethics approval and consent to participate All GWAS studies have obtained an approval from corresponding ethical review committees and all people have signed informed consents. This study doesn’t need extra ethical review. Consent for publication All authors have read and agreed to the published version of the manuscript. Data availability statement The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by the Major Research Program of the National Natural Science Foundation of China (52072142). Author contributions ZHD and ZQC: Study design, literature search and manuscript writing. DLZ and JLJ: Study selection and data analysis. KY, HLC, XLG and PYW: Data collection. DGW: Article Guidance. All authors revised the manuscript and approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Acknowledgements Thank for all the patients in this research, thank for all the scholars in this article. Thank for all the teammates for supporting this research. We are also particularly grateful to our colleagues in The First Affiliated Hospital of Jilin University for their contributions. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. References Thrift AP, El-Serag HB: Burden of Gastric Cancer. Clin Gastroenterol Hepatol 2020, 18:534–542. Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F: Gastric cancer. Lancet 2020, 396:635–648. Xie Y, Jiang Y, Wu Y, Su X, Zhu D, Gao P, Yuan H, Xiang Y, Wang J, Zhao Q, et al: Association of serum lipids and abnormal lipid score with cancer risk: a population-based prospective study. J Endocrinol Invest 2024, 47:367–376. Xu S, Fan Y, Tan Y, Zhang L, Li X: Association between blood lipid levels and risk of gastric cancer: A systematic review and meta-analysis. PLoS One 2023, 18:e0288111. 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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, 1063:117–126. Wajapeyee N, Beamon TC, Gupta R: Roles and therapeutic targeting of ceramide metabolism in cancer. Mol Metab 2024, 83:101936. Alizadeh J, da Silva Rosa SC, Weng X, Jacobs J, Lorzadeh S, Ravandi A, Vitorino R, Pecic S, Zivkovic A, Stark H, et al: Ceramides and ceramide synthases in cancer: Focus on apoptosis and autophagy. Eur J Cell Biol 2023, 102:151337. Additional Declarations No competing interests reported. Supplementary Files supplementarysheet.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. 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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-4574875","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":323474088,"identity":"9c630507-ffb8-49ee-ab7c-d9a6b3ebd48e","order_by":0,"name":"Zhenhua Dong","email":"","orcid":"","institution":"The First Hospital of Jilin University Changchun","correspondingAuthor":false,"prefix":"","firstName":"Zhenhua","middleName":"","lastName":"Dong","suffix":""},{"id":323474089,"identity":"2b8730fa-55b8-434b-a376-21f3f53053bd","order_by":1,"name":"Zhiqing Chen","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Zhiqing","middleName":"","lastName":"Chen","suffix":""},{"id":323474090,"identity":"91f0fcf0-3171-4db0-8f0b-e3c32ffc7181","order_by":2,"name":"Dingling Zhao","email":"","orcid":"","institution":"The First Hospital of Jilin University Changchun","correspondingAuthor":false,"prefix":"","firstName":"Dingling","middleName":"","lastName":"Zhao","suffix":""},{"id":323474091,"identity":"da215caf-8a2b-4f4c-8a12-8b77340328e5","order_by":3,"name":"Jianling Jia","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jianling","middleName":"","lastName":"Jia","suffix":""},{"id":323474092,"identity":"1b6271d5-d146-450d-b182-d41aff95f1ac","order_by":4,"name":"Hongliang Cao","email":"","orcid":"","institution":"The First Hospital of Jilin University Changchun","correspondingAuthor":false,"prefix":"","firstName":"Hongliang","middleName":"","lastName":"Cao","suffix":""},{"id":323474093,"identity":"f15bebba-51bd-4cf8-aca2-1cd83725deee","order_by":5,"name":"Pengyu Wang","email":"","orcid":"","institution":"The First Hospital of Jilin University Changchun","correspondingAuthor":false,"prefix":"","firstName":"Pengyu","middleName":"","lastName":"Wang","suffix":""},{"id":323474094,"identity":"5ffe533b-9429-4155-a3a3-97362e1c388a","order_by":6,"name":"Kai Yu","email":"","orcid":"","institution":"The First Hospital of Jilin University Changchun","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Yu","suffix":""},{"id":323474095,"identity":"3f813990-665c-4b6e-9580-17dcbd2b356a","order_by":7,"name":"Xulei Gao","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Xulei","middleName":"","lastName":"Gao","suffix":""},{"id":323474096,"identity":"db79d503-67ef-4544-ac3a-f6bf959aaec8","order_by":8,"name":"Daguang Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBACNmbmgw8+VNQw87M3HyBOCx97W7LhjDPH2CV7jiUQp0WO54yaNGcbM7/BDR8DIh0mkcMgzcDGJs1wg+fjjTcMdnK6DQS15B4wLuCRMWac3bvZcg5DsrHZAYJa8hKSZ0iwJTPLnN0mzcNwIHEbYS05Bod5DJjr2yRynhGpheeMYTNPAjMzj0QOG5FagIHMOOPAMWYJnmPGlnMMiPCLfDPz8R8f/9Uw2x9vfnjjTYWdHEEtKECCh8ioQdZCqo5RMApGwSgYEQAAV8g+enwTty8AAAAASUVORK5CYII=","orcid":"","institution":"The First Hospital of Jilin University Changchun","correspondingAuthor":true,"prefix":"","firstName":"Daguang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-06-13 09:00:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4574875/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4574875/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60448561,"identity":"8cd38ff3-dc39-46e6-ba77-b2659d246cfd","added_by":"auto","created_at":"2024-07-16 22:15:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":234871,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of study.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4574875/v1/f4a35d5e310bcd6af20974f9.png"},{"id":60448566,"identity":"fcd6ee25-664c-4c1d-943e-1bb27f6f4d75","added_by":"auto","created_at":"2024-07-16 22:15:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1914237,"visible":true,"origin":"","legend":"\u003cp\u003eThe manhattan plot between decode proteins and GC.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4574875/v1/4054d0066efbec713476f8e7.png"},{"id":60448560,"identity":"b9a92944-194b-4a58-b0df-75e5d15075d4","added_by":"auto","created_at":"2024-07-16 22:15:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4144172,"visible":true,"origin":"","legend":"\u003cp\u003eSMR scatter and colocalization locuscompare plots between proteins and GC. (a) SMR scatter plot between CCDC80 and GC; (b) colocalization locuscompare plots between CCDC80 and GC; (c) SMR scatter plot between IGFALS and GC; (d) colocalization locuscompare plots between IGFALS and GC; (e) SMR scatter plot between PDCD1LG2 and GC; (f) colocalization locuscompare plots between PDCD1LG2 and GC.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4574875/v1/c46619bca7063958da6bdb1b.png"},{"id":60448562,"identity":"90d32b25-f117-46b6-a687-2da472319aff","added_by":"auto","created_at":"2024-07-16 22:15:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1025301,"visible":true,"origin":"","legend":"\u003cp\u003eThe heatmap of MR resluts between lipid species and GC. The legend represents the P value of five MR methods. The green indicating P\u0026gt;0.05, the white indicating P=0.05, the red indicating P\u0026lt;0.05. The deeper color, the less or more the P value.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4574875/v1/6b07d41fff7dbaca4a944b9f.png"},{"id":60448565,"identity":"6a5b4b86-18c1-47ba-a6f4-b2b01926f207","added_by":"auto","created_at":"2024-07-16 22:15:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4284172,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for causal association between lipid speises and proteins.\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4574875/v1/b51d563dda8592eefdbc189b.png"},{"id":60448564,"identity":"9d85fac4-1e90-44bb-ab7a-4485813aec1e","added_by":"auto","created_at":"2024-07-16 22:15:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4254802,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for causal association between proteins and lipid speises.\u003c/p\u003e","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4574875/v1/8f0b5300684e33724ae924ff.png"},{"id":60449042,"identity":"9973f481-2e5f-4945-967e-8be0a17a86f5","added_by":"auto","created_at":"2024-07-16 22:23:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":5652841,"visible":true,"origin":"","legend":"\u003cp\u003eMacromolecular docking plots between componentents and CCDC80. (a) Macromolecular docking plots between 2,3,7,8-Tetrachlorodibenzo-P-dioxin and CCDC80 ; (b) Macromolecular docking plots between Benzo[a]pyrene and CCDC80 ;(c) Macromolecular docking plots between Bisphenol A and CCDC80.(d) Macromolecular docking plots between Valproic Acid and CCDC80.\u003c/p\u003e","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4574875/v1/684aa7f20dc49c5a6ddcaf96.png"},{"id":61775130,"identity":"f1e3e859-90a8-43da-807e-755a5f146fcb","added_by":"auto","created_at":"2024-08-05 12:25:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3823938,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4574875/v1/60ad4139-5265-4afe-95c1-cbce4c0b5cbf.pdf"},{"id":60448563,"identity":"5533cc6f-4b2f-4388-8835-ca2444521edb","added_by":"auto","created_at":"2024-07-16 22:15:55","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":431804,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarysheet.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4574875/v1/0436e96f2a5a4617dc57119f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Roles of plasma proteins in mediating the causal effect of the lipid species on gastric cancer and exploring potential drug targets for gastric cancer：insights from proteomic and two-step mendelian randomization and macromolecular docking","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThere are approximately 100,0000 people are newly diagnosed as GC in the world every year, and GC is the third most common death cause related with cancer, which indicates that GC is a global health problem and brings huge financial burden to health care system[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. First, blood lipidome is related with GC. A prospective study including 33733 adults concludes that the low total cholesterol is associated with increasing of cancer risk, such as GC[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and this conclusion is consistent with a mate analysis referring to the association between blood lipids level and GC[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. What\u0026rsquo;s more, a lipidomic study identities 11 plasma lipids inversely related with GC occurrence and these lipids are also utilized to divide risks of gastric lesion progression so that we could identify people with high risk for GC as early as possible[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Second, plasma protein is also relevant to GC. A study suggests that plasma Hsp90α level in GC is higher than healthy people, and it may be a potential biomarker for GC[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Based on liquid chromatography mass spectrophotometry, the other study finds sex hormone-binding globulin expressing highly in GC[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Third, it is revealed that blood proteins have complex association with lipids species in many diseases. However, the causal relationship is still uncertain. We hypothesize that plasma proteins mediate the causality between lipid species and GC. The deep exploration for these pathways not only helps understand the pathogenesis of GC, but also provides support evidence for GC treatment.\u003c/p\u003e \u003cp\u003eCurrently, the standard treatment for GC consists of definitive surgical resection and chemoradiotherapy[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] which causes many adverse events including disgust, sickness, low Leukocyte and weak immune, but targeted therapy could decrease these events greatly and possesses huge development prospect[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Unluckily, the variants of targeted therapy are few, which limits their application in clinic. Thus, we aimed to find more drug targets by performing systematic investigation between blood proteins and GC.\u003c/p\u003e \u003cp\u003eMR, a novel method, is used for accessing causality between exposure and outcome based on genome wide association studies (GWAS)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Genetic variants are chosen as instruments variants (IVs) which are hardly influenced by confounders because genes are located randomly during conception and environment can\u0026rsquo;t change genetic structure.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Considering the strength of MR and availability of GWAS, we perform this study, with the final goal to illustrate the protein pathway linking plasma lipidome to GC occurrence. In addition, our study will find and dock potential drug for selected blood protein to explore novel treatment for GC.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eFigure\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the profile of study design. In this study, we first perform proteomic SMR and colocalization analysis to find GC-associated plasma proteins. In this step, we use two independent protein data to calculate, decode 2021 as discovery data and the UK Biobank Pharma Proteomics Project (UKBppp) 2023 as validation one. Then, we conduct unidirectional MR to identify GC-related lipid species. Next, the bidirectional MR is utilized to explore the causal relationship between GC-associated plasma proteins and GC-related lipid species. Finally, we leverage propagation of error method to evaluate the mediated effect. Based on selected plasma protein, we perform two steps including identifying approved drugs for new application to GC treatment through DrugBank database and exploring possible components used for drug development which could accomplish docking with plasma protein.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 GWAS information\u003c/h2\u003e \u003cp\u003eFor protein, the decode2021 is composed of 4907 proteins and obtained from 35559 Icelanders whose mean age is 50 and gender ratio is close to 1:1. The measurement of protein is based on SomaScan version 4 assay (SomaLogic). They further use linear mixed model to assess the relationship between gene and protein levels[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Concerning identified proteins related with GC in discovery data, we use UKBppp2023 to validate. The UKBppp2023 consists of 2923 proteins and its subjects include 54219 people. The protein levels are measured through Olink Explore 3072 platform[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor lipid species, the GWAS includes 179 lipid species from 13 lipid classes and is obtained from 7174 Finnish participants belonging to GeneRISK cohort. The participants consist of 4642 women and 2624 men and their age range varies from 45 to 66. Finally, 56 lipid-associated loci are identified, including 8 new loci through the method of univariate and multivariate GWAS[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor GC, the data is gotten from IEU catalog whose accessed number is ebi-a-GCST90018629. Its\u0026rsquo; subjects include 7921 cases and 159201 controls. It is obtained through mate analysis among UK Biobank, FinnGen and Biobank Japan, and main population is European[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Mendelian Randomization\u003c/h2\u003e \u003cp\u003eThe MR results will be believable, if it satisfies three assumptions: (1) the IVs is strongly associated with exposure; (2) IVs should be not related with any confounders; (3) IVs influence outcomes only through exposure not other pathways. We select IVs at the threshold at P\u0026thinsp;\u0026lt;\u0026thinsp;1e-5 for lipid species and 5e-8 for proteins. For avoiding linkage disequilibrium (LD), we choose IVs at R2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 within the window of 10000kb. We utilize F statistic to calculate the strength of chosen IVs. If F\u0026thinsp;\u0026lt;\u0026thinsp;10, these IVs will be removed from MR analysis. We view inverse variance weighted (IVW) as a main method, which is supplemented by 4 sensitive analyses, including weighted median, maximum likelihood, MR egger and Cochran\u0026rsquo;s Q statistic, to strengthen MR outcome and estimate pleiotropy or heterogeneity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Summary-data-based MR analysis\u003c/h2\u003e \u003cp\u003eWe leverage SMR to explore the association between protein levels and the risk of GC. SMR depending on top cis-QTLs is more accurate than traditional MR when exposure and outcome from two GWAS with enough sample sizes. The top cis-QTLs are selected from genes at P\u0026thinsp;\u0026lt;\u0026thinsp;5e-8 within the window of \u0026plusmn;\u0026thinsp;1000 kb. we will exclude the single nucleotide polymorphism (SNP) whose differential allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.2 or its prop\u0026thinsp;\u0026gt;\u0026thinsp;0.05 between paired datasets, including referring data, protein data and GC data[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. HEIDI is applied to test pleiotropy and we will abandon the SMR results when HEIDI\u0026thinsp;\u0026lt;\u0026thinsp;0.01 which indicates the existence of pleiotropy. we perform Benjamini-Hochberg method to calculate P value and the proteins P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 will be retained for colocalization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Colocalization analysis\u003c/h2\u003e \u003cp\u003eTo investigate whether the causality between identified proteins and GC is driven by same gene fragments, we use coloc R package to perform the colocalization analysis.\u003c/p\u003e \u003cp\u003eThe prior probabilities that SNPs within colocalization region are related with only protein, only GC or both, are set at 1e-4, 1e-4 and 1e-5 respectively. This analysis possesses five hypotheses for SNPs within colocalization region, H0: nothing to do with either trait, H1: related with trait 1 only, H2: related with trait 2 only, H3: related with both traits, influenced by different gene locus, H4: related with both traits, influenced by same gene locus. The five hypotheses correspond to five posterior probabilities (PP), and PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.6 is viewed as evidence supporting colocalization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Mediated analysis\u003c/h2\u003e \u003cp\u003eMediated effect is calculated by the causal effect of lipid species on protein multiplied by the casual effect of protein on GC, and mediated prop is estimated by mediated effect divided by the total effect of lipid species on GC. we utilize the propagation of error method to calculate standard error relevant to mediated effect and 95%Cl of mediated prop. This method is based on one rule that error of calculation could propagate so that it could influence the accuracy of subsequent calculation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Potential therapeutic drugs prediction and Molecular docking\u003c/h2\u003e \u003cp\u003eFirst, we inquire genes expressing identified proteins through National Center of Biotechnology Information (NCBI). Second, we search these genes on DrugBank database to explore possible drugs for GC treatment so that we may find the new application of approved drug. Third, we will identify small molecule ligands related with genes in GC. Forth, we use PubChem database to obtain the two-dimensional structures of small molecular ligands which will be further inputted into Chem3D software to get their three-dimensional structure. Fifth, through Protein Data Bank (PDB) database, we could get the structure of plasma protein which is a receptor actually. Sixth, AutoDock Tool is employed to prepare the PDBQT formats of receptor and ligand and create the space for molecular docking. Seventh, we utilize AutoDock Vina to conduct molecular docking. Less the binding energy, more effective the binding of receptor-ligand. It is reported that the binding energy\u0026lt;-5kcal/mol suggests the binding is stable.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 plasma proteins and GC\u003c/h2\u003e \u003cp\u003eIn discovery data, there are 69 proteins associated with GC at SMR p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and HEIDI p\u0026thinsp;\u0026gt;\u0026thinsp;0.01. (Supplementary sheet1, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for manhattan plot) Among 69 proteins, CCDC80 and IGFALS have high colocalization support evidence with PPH4 close to 0.8, and PDCD1LG2 has medium colocalization support evidence with PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.6. (Supplementary sheet2) Genetically predicted per SD increase in expression of CCDC80 (OR 0.513, 95% CI 0.139\u0026ndash;0.889), PDCD1LG2 (OR 0.818, 95% CI 0.698\u0026ndash;0.938), IGFALS (OR 0.774, 95% CI 0.618\u0026ndash;0.929) are negatively related with GC risk. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the colocalization locus compare and SMR scatter plots of three protein-GC associations. In validation data, owing to limited protein in UKBppp, SMR just identify CCDC80 whose SMR p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and HEIDI p\u0026thinsp;\u0026gt;\u0026thinsp;0.01(Supplementary sheet3), but CCDC80-GC association isn\u0026rsquo;t supported by colocalization results. (Supplementary sheet4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 197 lipid species and GC\u003c/h2\u003e \u003cp\u003eAt P\u0026thinsp;\u0026lt;\u0026thinsp;1e-5, we select enough IVs varying from 4 to 26 for lipid species, the results of IVW indicate that 8 lipid species are negatively related with GC, including Sterol ester (27:1/14:0) (OR\u0026thinsp;=\u0026thinsp;0.877, 95% CI 0.772\u0026ndash;0.995, P\u0026thinsp;=\u0026thinsp;0.042); Sterol ester (27:1/18:0) (OR\u0026thinsp;=\u0026thinsp;0.889, 95% CI 0.804\u0026ndash;0.983, P\u0026thinsp;=\u0026thinsp;0.022); Diacylglycerol (16:1_18:1) (OR\u0026thinsp;=\u0026thinsp;0.913, 95% CI 0.846\u0026ndash;0.984, P\u0026thinsp;=\u0026thinsp;0.018); Phosphatidylcholine (O-16:0_16:1) (OR\u0026thinsp;=\u0026thinsp;0.888, 95% CI 0.797\u0026ndash;0.989, P\u0026thinsp;=\u0026thinsp;0.030); Phosphatidylcholine (O-16:1_18:2) (OR\u0026thinsp;=\u0026thinsp;0.898, 95% CI 0.807\u0026ndash;0.997, P\u0026thinsp;=\u0026thinsp;0.047); Phosphatidylethanolamine (16:0_18:2) (OR\u0026thinsp;=\u0026thinsp;0.953, 95% CI 0.910\u0026ndash;0.998, P\u0026thinsp;=\u0026thinsp;0.041); Phosphatidylethanolamine (18:1_18:1) (OR\u0026thinsp;=\u0026thinsp;0.927, 95% CI 0.866\u0026ndash;0.992, P\u0026thinsp;=\u0026thinsp;0.029); Triacylglycerol (56:7) (OR\u0026thinsp;=\u0026thinsp;0.920, 95% CI 0.851\u0026ndash;0.995, P\u0026thinsp;=\u0026thinsp;0.036), meanwhile 4 lipid species are positively related with GC, including Sterol ester (27:1/17:0) (OR\u0026thinsp;=\u0026thinsp;1.131, 95% CI 1.031\u0026ndash;1.242, P\u0026thinsp;=\u0026thinsp;0.009); Ceramide (d42:1) (OR\u0026thinsp;=\u0026thinsp;1.102, 95% CI 1.015\u0026ndash;1.196, P\u0026thinsp;=\u0026thinsp;0.02); Phosphatidylcholine (18:0_22:6) (OR\u0026thinsp;=\u0026thinsp;1.095, 95% CI 1.004\u0026ndash;1.194, P\u0026thinsp;=\u0026thinsp;0.040) ; Triacylglycerol (48:0) (OR\u0026thinsp;=\u0026thinsp;1.078, 95% CI 1.007\u0026ndash;1.153, P\u0026thinsp;=\u0026thinsp;0.030). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for circle heatmap) There aren\u0026rsquo;t evidence of pleiotropy (the intercept of MR egger P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and heterogeneity (Cochran Q test p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between 12 lipid species and GC. All F\u0026thinsp;\u0026gt;\u0026thinsp;20 suggests IV strongly associated with exposure. (Supplementary sheet5)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 12 lipid species and 3 plasma proteins\u003c/h2\u003e \u003cp\u003eAlthough we just prove CDCC80 in validation data, our further study still involves three proteins including CDCC80, PDCD1LG2 and IGFALS to make study more comprehensive. At P\u0026thinsp;\u0026lt;\u0026thinsp;1e-5, we choose enough IVs varying from 14 to 24 to proxy lipid species. (Supplementary sheet6) The results of IVW suggests that there are negative association between Sterol ester (27:1/14:0) (OR\u0026thinsp;=\u0026thinsp;0.937, 95% CI 0.898\u0026ndash;0.979, P\u0026thinsp;=\u0026thinsp;0.003) and PDCD1LG2; Diacylglycerol (16:1_18:1) (OR\u0026thinsp;=\u0026thinsp;0.956, 95% CI 0.925\u0026ndash;0.988, P\u0026thinsp;=\u0026thinsp;0.007) and CCDC80. (Supplementary sheet7, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e for forest plot) There is no pleiotropy and heterogeneity. All F\u0026thinsp;\u0026gt;\u0026thinsp;10 suggests IVs have enough strength with exposure. (supplementary sheet6)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 3 plasma proteins and 12 lipid species\u003c/h2\u003e \u003cp\u003eAt P\u0026thinsp;\u0026lt;\u0026thinsp;5e-8, we choose enough IVs varying from 6 to 10 to proxy proteins. (Supplementary sheet8) The results of IVW suggests that there are negative association between CDCC80 (OR\u0026thinsp;=\u0026thinsp;0.790, 95% CI 0.635\u0026ndash;0.982, P\u0026thinsp;=\u0026thinsp;0.033) and Sterol ester (27:1/14:0). (supplementary sheet9, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e for forest plot) what\u0026rsquo;s more, all F\u0026thinsp;\u0026gt;\u0026thinsp;10. (supplementary sheet8)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Mediated analysis\u003c/h2\u003e \u003cp\u003eAfter calculation, we find three potential pathways including Sterol ester (27:1/14:0)-CCDC80-GC, Sterol ester (27:1/14:0)-PDCD1LG2-GC, and Diacylglycerol (16:1_18:1) - CDCC80-GC, but the former two can\u0026rsquo;t pass the test of propagation of error method. For last one, the mediated effect is 0.030 (95% Cl 0.006\u0026ndash;0.062) and the mediated prop is 30.80% (95% Cl, 6.39%-63.98%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Molecular docking\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn DrugBank, we cannot find any approved drug for GC treatment. We just focus on CDCC80 protein and find that gene CCDC80 expresses protein CCDC80 and four components are associated with CCDC80 reported by pudlished papers. The PDB id of CCDC80 is 2GGU, and four components include 2,3,7,8-Tetrachlorodibenzo-P-dioxin (PubChem id, 15625), Bisphenol A (PubChem id, 6623), Benzo[a]pyrene (PubChem id, 2336) and Valproic Acid (PubChem id, 3121). We find that 2,3,7,8-Tetrachlorodibenzo-P-dioxin binds with CCDC80 by LYS(Amino Acid) 230(the location of Amino Acid), LEU233, LEU260, ALA264 and ALA286, whose binding energy is -5.5151kcal/mol; Bisphenol A binds with CCDC80 by LEU223, GLN238, ALA264, whose binding energy is -5.1477kcal/mol; Benzo[a]pyrene binds with CCDC80 by LYS230, LEU233, PHE 234, ALA264, whose binding energy is -5.0077 kcal/mol; Valproic Acid binds with CDCC80 by GLU232, PRO235, whose binding energy is -4.9364lcal/mol. The bind energy close to 5 kcal/mol indicate the binding of ligands and CDCC80 is stable, and these components may become drugs to treat GC. (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we first conduct proteomic SMR and colocalization analysis, which find 3 plasma proteins relevant to GC risk at the genetic level. Next, we identify 12 plasma lipid species associated with GC risk by performing MR between 179 lipid species and GC. What\u0026rsquo;s more, the bidirectional MR between lipid species and plasma protein is beneficial to ascertain their complex relationship. Base on two-step MR and mediated analysis, we discover the role of CCDC80 protein in mediating the association of Diacylglycerol (16:1_18:1) with GC. CCDC80 protein would be viewed to be druggable, and we find 4 components binding with CCDC80 which may be potential drugs to treat GC. In summary, our study deepens the understanding of protein and lipid pathogenesis of GC and may guide a novel direction to explore drug for GC treatment.\u003c/p\u003e \u003cp\u003eConcerning the causal relationship between 3 proteins and GC, our study conclusion is supported by many several papers. CDCC80 containing P-DUDES domain region is relevant to peroxide turnover and signaling and involves in tumor suppression.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Based on targeted plasma proteomic analysis, researchers find that plasma CCDC80 expresses higher in GC patients than healthy people, and could be GC diagnosis biomarker with high sensitivity.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] CDCC80 involving in cell adhesion and matrix assembly, also inhibits other cancer occurrence, such as colon cancer[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], pancreatic cancer, thyroid cancer[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], ovarian cancer and so on. These studies support our conclusion about CCDC80. Currently, there is no paper about relationship between IGFALS and GC. ICGALS, insulin like growth factor binding protein acid labile subunit, is associated with cell growth and metabolism[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In hepatocellular carcinoma, ICGALS plays the role of tumor inhibitor and its function in other tumors is still unclear[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Our study discovers ICFALS being the protective factor for GC, which needs more study to prove. PDCD1LG2, programmed death-ligand 2, often participates in cellar signaling transduction to prompt tumor growth and invasion and drug resistance. A study discovers that PDCD1LG2 expresses highly in GC tumor, which induces immunosuppression and accelerates the progression of tumor.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] However, Liu X et al. finds that PDCD1LG2 could increase CD8T cell quantity and enhance its lethality in murine tumor cells.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] The other animal study also discovers that murine tumor cells can express PDCD1LG2 to enhance immunity by inducing T cell to produce lymphokine at a specific mode.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] Thus, the biological function of PDCD1LG2 is controversial, and our study support the view that PDCD1LG2 could decrease GC risk.\u003c/p\u003e \u003cp\u003eNext the complex association between 12 lipid species and GC will be discussed. Diacylglycerol is a lipid second messenger linking external stimulation to intracellular signals translation, Diacylglycerol dysregulation of activity or abundance influences occurrence, invasion and metastasis of tumor. What\u0026rsquo;s more, diacylglycerol is essential for maintaining of T cell function which is important for body immunity surveillance[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Bae CS delivers diacylglycerol to kill tumor through Cationic Nanoparticles, which is associated with triggering oxidative stress.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] These evidences supports our conclusion that Diacylglycerol (16:1_18:1) could reduce GC risk. Next, Phosphatidylcholine is related with GC. some scholars compare the lipid composition of GC tissue and adjacent normal one based on imaging mass spectrometry, and discovers that the over expression lysophosphatidylcholine acyltransferase 1 in GC tissue, which prompts the producing of Phosphatidylcholine.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] The other study finds the content of Phosphatidylcholine in GC blood is different from healthy people through the technology of nanoflow ultrahigh performance liquid chromatography-electrospray ionization-tandem mass spectrometry[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Then, we illustrate the relation between Ceramide and GC. Ceramides, a signal molecule, belongs to sphingolipids participating in cellar growth and proliferation, and its metabolic disorders will prompt GC [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Ceramides could regulate cellar apoptosis and autophagy in tumor and play the function suppressing tumor, so it possesses big potential to become tumor biomarker and drug target.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThere are some strengths in this study. (1) it has rigorous design scheme. (2) we use big cross-species GWAS data on GC to ensure the MR result more comprehensive. (3) many sensitivity analyses make MR outcome more stable. (4) thousands of proteins and hundreds of lipid species are put into consideration. (5) many statistic methods like MR, SMR and colocalization, are conducted. But several limitations must be mentioned. (1) the GWAS data on protein and lipid species focus on European, which limits our results generalization. (2) we may miss or overlook the effect of the protein and lipid species on GC, which is related with the quality of GWAS. (3) we don\u0026rsquo;t consider the age- or sex-specific effects because the limitation of GWAS. Although MR is a tool to explore causality, our conclusion still needs more researches to prove.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur study suggests that CCDC80, a drug target, is a mediator between Diacylglycerol (16:1_18:1) and GC. For CCDC80, we find 4 components binding with it. This study helps us understand the etiology of GC in some extent and guides us a novel direction for developing drug for GC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egastric cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esummary mendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egenome wide association studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esingle nucleotide polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einstruments variants\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUKBppp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUK Biobank Pharma Proteomics Project\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elinkage disequilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Center of Biotechnology Information\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePDB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProtein Data Bank\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll GWAS studies have obtained an approval from corresponding ethical review committees and all people have signed informed consents. This study doesn\u0026rsquo;t need extra ethical review. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Major Research Program of the National Natural Science Foundation of China (52072142).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZHD and ZQC: Study design, literature search and manuscript writing. DLZ and JLJ: Study selection and data analysis. KY, HLC, XLG and PYW: Data collection. DGW: Article Guidance. All authors revised the manuscript and approved the final manuscript as submitted and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThank for all the patients in this research, thank for all the scholars in this article. Thank for all the teammates for supporting this research. We are also particularly grateful to our colleagues in The First Affiliated Hospital of Jilin University for their contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThrift AP, El-Serag HB: Burden of Gastric Cancer. Clin Gastroenterol Hepatol 2020, 18:534\u0026ndash;542.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F: Gastric cancer. Lancet 2020, 396:635\u0026ndash;648.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie Y, Jiang Y, Wu Y, Su X, Zhu D, Gao P, Yuan H, Xiang Y, Wang J, Zhao Q, et al: Association of serum lipids and abnormal lipid score with cancer risk: a population-based prospective study. 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Eur J Cell Biol 2023, 102:151337.\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":"plasma lipid species, plasma proteins, gastric cancer, drug target, MR, SMR, colocalization analysis, macromolecular docking","lastPublishedDoi":"10.21203/rs.3.rs-4574875/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4574875/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe change of plasma lipid species has close contacts with gastric cancer (GC). However, the specific mechanism still needs to explore further.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe aim to utilize plasma proteins to decipher the association between lipid species and GC, and seek possible drug targets for GC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA two-step mendelian randomization (MR) is conducted to identify the causal relationship among 179 lipid species, 4907 plasma proteins and GC. We employ the summary mendelian randomization (SMR) and colocalization to explore relationship between plasma proteins and GC. We use one protein data including 35559 individuals as discovery group, meanwhile the other one from 54219 subjects as validation group.\u003c/p\u003e\n\u003cp\u003eMR is performed to identify the association between lipid species and GC or plasma proteins. Based on chosen proteins, we use macromolecular docking to find potential components as ligands.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMR identifies the causality between 12 lipid species and GC, 3 proteins and GC, 2 lipid species and 2 proteins. After the test of propagation of error method, we conclude that CCDC80 protein mediates (30.8%; 95% confidents interval (Cl), 6.4%-64.0%) of the association between Diacylglycerol (16:1_18:1) and GC. For CCDC80, we choose 4 components including 2,3,7,8-Tetrachlorodibenzo-P-dioxin, Benzo[a]pyrene, Bisphenol A, Valproic Acid as potential drugs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study suggests that CCDC80, a drug target, is a mediator between Diacylglycerol (16:1_18:1) and GC, which may guide a novel direction for GC treatment.\u003c/p\u003e","manuscriptTitle":"Roles of plasma proteins in mediating the causal effect of the lipid species on gastric cancer and exploring potential drug targets for gastric cancer：insights from proteomic and two-step mendelian randomization and macromolecular docking","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-16 22:15:49","doi":"10.21203/rs.3.rs-4574875/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":"ba3d33c2-9ed4-4005-bb19-a48b1663a461","owner":[],"postedDate":"July 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34211296,"name":"Health sciences/Gastroenterology/Gastrointestinal diseases/Gastrointestinal cancer"},{"id":34211297,"name":"Health sciences/Diseases/Gastrointestinal diseases"}],"tags":[],"updatedAt":"2024-08-05T12:17:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-16 22:15:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4574875","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4574875","identity":"rs-4574875","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}