Background Lung squamous cell carcinoma(LUSC)is a major subtype of non-small cell lung cancer with a high mortality rate.Identifying causal plasma proteins associated with LUSC could provide new insights into the path...Background Lung squamous cell carcinoma(LUSC)is a major subtype of non-small cell lung cancer with a high mortality rate.Identifying causal plasma proteins associated with LUSC could provide new insights into the pathophysiology of the disease and potential therapeutic targets.This study aimed to identify plasma proteins causally linked to LUSC risk using proteome-wide Mendelian randomization(MR)and colocalization analyses.Methods Proteome-wide MR analysis was conducted using data from the UK Biobank Pharma Proteomics Project and deCODE genetics.Summary-level data for LUSC were obtained from the ILCCO Consortium,the FinnGen study,and a separate GWAS study.A total of 1,046 shared protein quantitative trait loci(pQTLs)were analyzed.Sensitivity analyses included the HEIDI test for horizontal pleiotropy and colocalization analysis to validate the causal associations.Results MR analysis identified six plasma proteins associated with LUSC risk:HSPA1L,PCSK7,POLI,SPINK2,TCL1A,and VARS.HSPA1L(OR=0.47;95%CI:0.34–0.65;P=4.89×10^(–6)),SPINK2(OR=0.68;95%CI:0.58–0.80;P=3.17×10^(–6)),and VARS(OR=0.44;95%CI:0.31–0.63;P=5.94×10^(–6))were associated with a decreased risk of LUSC.Conversely,PCSK7(OR=1.37;95%CI:1.21–1.56;P=1.40×10^(–6)),POLI(OR=4.50;95%CI:2.25–9.00;P=2.13×10–5),and TCL1A(OR=1.72;95%CI:1.34–2.21;P=1.89×10–5)were associated with an increased risk.The SMR analysis and HEIDI test confirmed the robustness of these associations.HSPA1L,SPINK2,and VARS showed significant inverse associations,with strong colocalization evidence for TCL1A(PPH4=0.817).Conclusions This study identified six plasma proteins potentially causal for LUSC risk.HSPA1L,SPINK2,and VARS are associated with decreased risk,while PCSK7,POLI,and TCL1A are linked to increased risk.These findings provide new insights into LUSC pathogenesis and highlight potential targets for therapeutic intervention.展开更多
Background:Diabetic retinopathy(DR)urgently needs novel and effective therapeutic targets.Integrated analyses of plasma proteomic and genetic markers can clarify the causal relevance of proteins and discover novel tar...Background:Diabetic retinopathy(DR)urgently needs novel and effective therapeutic targets.Integrated analyses of plasma proteomic and genetic markers can clarify the causal relevance of proteins and discover novel targets for diseases,but no systematic screening for DR has been performed.Methods:Summary statistics of plasma protein quantitative trait loci(pQTL)were derived from two extensive genome-wide analysis study(GWAS)datasets and one systematic review,with over 100 thousand participants covering thousands of plasma proteins.DR data were sourced from the largest FinnGen study,comprising 10,413 DR cases and 308,633 European controls.Genetic instrumental variables were identified using multiple filters.In the two-sample MR analysis,Wald ratio and inverse variance-weighted(IVW)MR were utilized to investigate the causality of plasma proteins with DR.Bidirectional MR,Bayesian Co-localization,and phenotype scanning were employed to test for potential reverse causality and confounding factors in the main MR analyses.By systemically searching druggable gene lists,the ChEMBL database,DrugBank,and Gene Ontology database,the druggability and relevant functional pathways of the identified proteins were systematically evaluated.Results:Genetically predicted levels of 24 proteins were significantly associated with DR risk at a false discovery rate<0.05 including 11 with positive associations and 13 with negative associations.For each standard deviation increase in plasm protein levels,the odds ratios(ORs)for DR varied from 0.51(95%CI:0.36-0.73;P=2.22×10-5)for tubulin polymerization-promoting protein family member 3(TPPP3)to 2.02(95%CI:1.44-2.83;P=5.01×10-5)for olfactomedin like 3(OLFML3).Bidirectional MR indicated there was no reverse causality that interfered with the results of the main MR analyses.Four proteins exhibited strong co-localization evidence(PH4≥0.8):cytoplasmic tRNA synthetase(WARS),acrosin binding protein(ACRBP),and intercellular adhesion molecule 1(ICAM1)were negatively associated with DR risk,while neurogenic locus notch homolog protein 2(NOTCH2)showed a positive association.No confounding factors were detected between pQTLs and DR according to the phenotypic scan.Drugability assessments highlighted 6 proteins already in drug development endeavor and 18 novel drug targets,with metalloproteinase inhibitor 3(TIMP)currently in phase I clinical trials for DR.GO analysis identified 18 of 24 plasma proteins enriching 22 pathways related to cell differentiation and proliferation regulation.Conclusions:Twenty-four promising drug targets for DR were identified,including four plasma proteins with particular co-localization evidence.These findings offer new insights into DR's etiology and therapeutic targeting,exemplifying the value of genomic and proteomic data in drug target discovery.展开更多
Background:Cardiovascular diseases(CVD)are a major global health issue strongly associated with altered lipid metabolism.However,lipid metabolism-related pharmacological targets remain limited,leaving the therapeutic ...Background:Cardiovascular diseases(CVD)are a major global health issue strongly associated with altered lipid metabolism.However,lipid metabolism-related pharmacological targets remain limited,leaving the therapeutic challenge of residual lipid-associated cardiovascular risk.The purpose of this study is to identify potentially novel lipid metabolism-related genes by systematic genomic and phenomics analysis,with an aim to discovering potentially new therapeutic targets and diagnosis biomarkers for CVD.Methods:In this study,we conducted a comprehensive and multidimensional evaluation of 881 lipid metabolism-related genes.Using genome-wide association study(GWAS)-based mendelian randomization(MR)causal inference methods,we screened for genes causally linked to the occurrence and development of CVD.Further validation was performed through colocalization analysis in 2 independent cohorts.Then,we employed reverse screening using phenonome-wide association studies(PheWAS)and a drug target-drug association analysis.Finally,we integrated serum proteomic data to develop a machine learning model comprising 5 proteins for disease prediction.Results:Our initial screening yielded 54 genes causally linked to CVD.Colocalization analysis in validation cohorts prioritized this to 29 genes marked correlated with CVD.Comparison and interaction analysis identified 13 therapeutic targets with potential for treating CVD and its complications.A machine learning model incorporating 5 proteins for CVD prediction achieved a high accuracy of 96.1%,suggesting its potential as a diagnostic tool in clinical practice.Conclusion:This study comprehensively reveals the complex relationship between lipid metabolism regulatory targets and CVD.Our findings provide new insights into the pathogenesis of CVD and identify potential therapeutic targets and drugs for its treatment.Additionally,the machine learning model developed in this study offers a promising tool for the diagnosis and prediction of CVD,paving the way for future research and clinical applications.展开更多
基金supported by The Medical Engineering Cross Research Funding of Shanghai Jiaotong University"Star of Jiaotong University"Program(24X010301595).
文摘Background Lung squamous cell carcinoma(LUSC)is a major subtype of non-small cell lung cancer with a high mortality rate.Identifying causal plasma proteins associated with LUSC could provide new insights into the pathophysiology of the disease and potential therapeutic targets.This study aimed to identify plasma proteins causally linked to LUSC risk using proteome-wide Mendelian randomization(MR)and colocalization analyses.Methods Proteome-wide MR analysis was conducted using data from the UK Biobank Pharma Proteomics Project and deCODE genetics.Summary-level data for LUSC were obtained from the ILCCO Consortium,the FinnGen study,and a separate GWAS study.A total of 1,046 shared protein quantitative trait loci(pQTLs)were analyzed.Sensitivity analyses included the HEIDI test for horizontal pleiotropy and colocalization analysis to validate the causal associations.Results MR analysis identified six plasma proteins associated with LUSC risk:HSPA1L,PCSK7,POLI,SPINK2,TCL1A,and VARS.HSPA1L(OR=0.47;95%CI:0.34–0.65;P=4.89×10^(–6)),SPINK2(OR=0.68;95%CI:0.58–0.80;P=3.17×10^(–6)),and VARS(OR=0.44;95%CI:0.31–0.63;P=5.94×10^(–6))were associated with a decreased risk of LUSC.Conversely,PCSK7(OR=1.37;95%CI:1.21–1.56;P=1.40×10^(–6)),POLI(OR=4.50;95%CI:2.25–9.00;P=2.13×10–5),and TCL1A(OR=1.72;95%CI:1.34–2.21;P=1.89×10–5)were associated with an increased risk.The SMR analysis and HEIDI test confirmed the robustness of these associations.HSPA1L,SPINK2,and VARS showed significant inverse associations,with strong colocalization evidence for TCL1A(PPH4=0.817).Conclusions This study identified six plasma proteins potentially causal for LUSC risk.HSPA1L,SPINK2,and VARS are associated with decreased risk,while PCSK7,POLI,and TCL1A are linked to increased risk.These findings provide new insights into LUSC pathogenesis and highlight potential targets for therapeutic intervention.
基金funded by the Hainan Province Clinical Medical Center(82171084)the National Natural Science Foundation of China(82371086).
文摘Background:Diabetic retinopathy(DR)urgently needs novel and effective therapeutic targets.Integrated analyses of plasma proteomic and genetic markers can clarify the causal relevance of proteins and discover novel targets for diseases,but no systematic screening for DR has been performed.Methods:Summary statistics of plasma protein quantitative trait loci(pQTL)were derived from two extensive genome-wide analysis study(GWAS)datasets and one systematic review,with over 100 thousand participants covering thousands of plasma proteins.DR data were sourced from the largest FinnGen study,comprising 10,413 DR cases and 308,633 European controls.Genetic instrumental variables were identified using multiple filters.In the two-sample MR analysis,Wald ratio and inverse variance-weighted(IVW)MR were utilized to investigate the causality of plasma proteins with DR.Bidirectional MR,Bayesian Co-localization,and phenotype scanning were employed to test for potential reverse causality and confounding factors in the main MR analyses.By systemically searching druggable gene lists,the ChEMBL database,DrugBank,and Gene Ontology database,the druggability and relevant functional pathways of the identified proteins were systematically evaluated.Results:Genetically predicted levels of 24 proteins were significantly associated with DR risk at a false discovery rate<0.05 including 11 with positive associations and 13 with negative associations.For each standard deviation increase in plasm protein levels,the odds ratios(ORs)for DR varied from 0.51(95%CI:0.36-0.73;P=2.22×10-5)for tubulin polymerization-promoting protein family member 3(TPPP3)to 2.02(95%CI:1.44-2.83;P=5.01×10-5)for olfactomedin like 3(OLFML3).Bidirectional MR indicated there was no reverse causality that interfered with the results of the main MR analyses.Four proteins exhibited strong co-localization evidence(PH4≥0.8):cytoplasmic tRNA synthetase(WARS),acrosin binding protein(ACRBP),and intercellular adhesion molecule 1(ICAM1)were negatively associated with DR risk,while neurogenic locus notch homolog protein 2(NOTCH2)showed a positive association.No confounding factors were detected between pQTLs and DR according to the phenotypic scan.Drugability assessments highlighted 6 proteins already in drug development endeavor and 18 novel drug targets,with metalloproteinase inhibitor 3(TIMP)currently in phase I clinical trials for DR.GO analysis identified 18 of 24 plasma proteins enriching 22 pathways related to cell differentiation and proliferation regulation.Conclusions:Twenty-four promising drug targets for DR were identified,including four plasma proteins with particular co-localization evidence.These findings offer new insights into DR's etiology and therapeutic targeting,exemplifying the value of genomic and proteomic data in drug target discovery.
基金funded by grants from China’s National Key R&D Program(no.2021YFC2500500)National Natural Science Foundation of China(nos.82102804,82370444,82070464,and 82003741)+4 种基金Additional support was provided by the Innovative Research Team Program of the First Affiliated Hospital of USTC(no.CXGG02)the Anhui Provincial Natural Science Foundation(no.2208085J08)supported by USTC Research Funds of the Double First-Class Initiative(no.YD9110002089)the Research Funds of Centre for Leading Medicine and Advanced Technologies of IHMholds a Humboldt Senior Fellowship awarded by the Alexander von Humboldt Foundation in Germany.
文摘Background:Cardiovascular diseases(CVD)are a major global health issue strongly associated with altered lipid metabolism.However,lipid metabolism-related pharmacological targets remain limited,leaving the therapeutic challenge of residual lipid-associated cardiovascular risk.The purpose of this study is to identify potentially novel lipid metabolism-related genes by systematic genomic and phenomics analysis,with an aim to discovering potentially new therapeutic targets and diagnosis biomarkers for CVD.Methods:In this study,we conducted a comprehensive and multidimensional evaluation of 881 lipid metabolism-related genes.Using genome-wide association study(GWAS)-based mendelian randomization(MR)causal inference methods,we screened for genes causally linked to the occurrence and development of CVD.Further validation was performed through colocalization analysis in 2 independent cohorts.Then,we employed reverse screening using phenonome-wide association studies(PheWAS)and a drug target-drug association analysis.Finally,we integrated serum proteomic data to develop a machine learning model comprising 5 proteins for disease prediction.Results:Our initial screening yielded 54 genes causally linked to CVD.Colocalization analysis in validation cohorts prioritized this to 29 genes marked correlated with CVD.Comparison and interaction analysis identified 13 therapeutic targets with potential for treating CVD and its complications.A machine learning model incorporating 5 proteins for CVD prediction achieved a high accuracy of 96.1%,suggesting its potential as a diagnostic tool in clinical practice.Conclusion:This study comprehensively reveals the complex relationship between lipid metabolism regulatory targets and CVD.Our findings provide new insights into the pathogenesis of CVD and identify potential therapeutic targets and drugs for its treatment.Additionally,the machine learning model developed in this study offers a promising tool for the diagnosis and prediction of CVD,paving the way for future research and clinical applications.
