{"title":"通过孟德尔随机化从人血浆蛋白中鉴定有希望的肺癌靶点。","authors":"Xiao-Dong Shao, Zhou-Lin Miao, Wei-Jie Yu","doi":"10.1007/s12672-025-03746-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Lung cancer (LC) is the most prevalent form of malignant neoplasm globally, as well as the major cause of cancer-related death. Identifying effective pharmaceutical targets is paramount in advancing the development of treatment modalities for LC.</p><p><strong>Method: </strong>Protein-wide Mendelian randomization (MR) was used in this study. The present study collated data on plasma proteins from a protein quantitative trait loci (pQTL) study with a total of 4907 individuals. Genetic associations with LC were obtained from GWAS, including 3791 cases and 489012 controls. Integration of pQTL and LC genome-wide association study (GWAS) data was employed to identify candidate proteins. MR used single nucleotide polymorphisms (SNPs) as a genetic tool to estimate the causal effect of exposure on the outcome, while reverse Mendelian randomization was performed to assess the presence of false positives. The present study utilized these approaches to evaluate the causal relationship between plasma proteins and LC. Finally, protein-protein interaction (PPI) and functional enrichment analyses were performed to illustrate potential links between proteins and current LC drugs. Finally, drug prediction and molecular docking were performed to predict drugs and explored the expression distribution of key genes by single-cell sequencing.</p><p><strong>Result: </strong>We identified 46 plasma proteins that are strongly associated with LC Fifteen of these proteins have protective effects. Among them, MMP8(OR = 0.87, 95%CI:0.78-0.97, p = 0.013) had the most significant protective effect. In contrast, 31 proteins increased the risk of LC. IL36A༈OR = 1.20, 95%CI:1.041-1.38, p = 0.012) exhibited the most significant MR result. Notably, COL2A1, MMP19 showed reverse causality. This was further verified by enrichment analysis, which confirmed the causal effect of these proteins. Additionally, the researchers utilized the DSigDB database to predict potentially effective intervening drugs, identifying nine possible candidates. Molecular docking showed that the drugs bind very much to the proteins. KDR and ANGPTL4 are abundantly expressed in lung tissue and are differentially expressed between cells.</p><p><strong>Conclusion: </strong>The present study has revealed six potential drug targets for the treatment of LC. Drugs designed to target these proteins will be more likely to attain success in clinical trials and are expected to assist in the development of LC drugs and reduce drug development costs.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1927"},"PeriodicalIF":2.9000,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12540209/pdf/","citationCount":"0","resultStr":"{\"title\":\"Identification of promising lung cancer targets from human plasma proteins via Mendelian randomization.\",\"authors\":\"Xiao-Dong Shao, Zhou-Lin Miao, Wei-Jie Yu\",\"doi\":\"10.1007/s12672-025-03746-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Lung cancer (LC) is the most prevalent form of malignant neoplasm globally, as well as the major cause of cancer-related death. Identifying effective pharmaceutical targets is paramount in advancing the development of treatment modalities for LC.</p><p><strong>Method: </strong>Protein-wide Mendelian randomization (MR) was used in this study. The present study collated data on plasma proteins from a protein quantitative trait loci (pQTL) study with a total of 4907 individuals. Genetic associations with LC were obtained from GWAS, including 3791 cases and 489012 controls. Integration of pQTL and LC genome-wide association study (GWAS) data was employed to identify candidate proteins. MR used single nucleotide polymorphisms (SNPs) as a genetic tool to estimate the causal effect of exposure on the outcome, while reverse Mendelian randomization was performed to assess the presence of false positives. The present study utilized these approaches to evaluate the causal relationship between plasma proteins and LC. Finally, protein-protein interaction (PPI) and functional enrichment analyses were performed to illustrate potential links between proteins and current LC drugs. Finally, drug prediction and molecular docking were performed to predict drugs and explored the expression distribution of key genes by single-cell sequencing.</p><p><strong>Result: </strong>We identified 46 plasma proteins that are strongly associated with LC Fifteen of these proteins have protective effects. Among them, MMP8(OR = 0.87, 95%CI:0.78-0.97, p = 0.013) had the most significant protective effect. In contrast, 31 proteins increased the risk of LC. IL36A༈OR = 1.20, 95%CI:1.041-1.38, p = 0.012) exhibited the most significant MR result. Notably, COL2A1, MMP19 showed reverse causality. This was further verified by enrichment analysis, which confirmed the causal effect of these proteins. Additionally, the researchers utilized the DSigDB database to predict potentially effective intervening drugs, identifying nine possible candidates. Molecular docking showed that the drugs bind very much to the proteins. KDR and ANGPTL4 are abundantly expressed in lung tissue and are differentially expressed between cells.</p><p><strong>Conclusion: </strong>The present study has revealed six potential drug targets for the treatment of LC. Drugs designed to target these proteins will be more likely to attain success in clinical trials and are expected to assist in the development of LC drugs and reduce drug development costs.</p>\",\"PeriodicalId\":11148,\"journal\":{\"name\":\"Discover. Oncology\",\"volume\":\"16 1\",\"pages\":\"1927\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-10-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12540209/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Discover. 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引用次数: 0
摘要
背景:肺癌(LC)是全球最常见的恶性肿瘤,也是癌症相关死亡的主要原因。确定有效的药物靶点对于推进LC治疗方式的发展至关重要。方法:采用全蛋白孟德尔随机化(MR)方法。本研究整理了4907个个体的血浆蛋白数量性状位点(pQTL)研究数据。从GWAS中获得了与LC的遗传关联,包括3791例病例和489012例对照。整合pQTL和LC全基因组关联研究(GWAS)数据鉴定候选蛋白。MR使用单核苷酸多态性(SNPs)作为遗传工具来估计暴露对结果的因果影响,而反向孟德尔随机化则用于评估假阳性的存在。本研究利用这些方法来评估血浆蛋白与LC之间的因果关系。最后,通过蛋白质相互作用(PPI)和功能富集分析来阐明蛋白质与当前LC药物之间的潜在联系。最后进行药物预测和分子对接,通过单细胞测序进行药物预测,探索关键基因的表达分布。结果:鉴定出46种与LC密切相关的血浆蛋白,其中15种具有保护作用。其中,MMP8(OR = 0.87, 95%CI:0.78 ~ 0.97, p = 0.013)的保护作用最为显著。相比之下,31种蛋白质增加了LC的风险。IL36A༈OR = 1.20, 95%CI:1.041 ~ 1.38, p = 0.012)的MR结果最为显著。值得注意的是,COL2A1和MMP19表现出反向因果关系。富集分析进一步证实了这一点,证实了这些蛋白的因果关系。此外,研究人员利用DSigDB数据库预测潜在有效的干预药物,确定了9种可能的候选药物。分子对接表明,药物与蛋白质结合非常紧密。KDR和ANGPTL4在肺组织中大量表达,在细胞间存在差异表达。结论:本研究揭示了6种治疗LC的潜在药物靶点。针对这些蛋白设计的药物将更有可能在临床试验中获得成功,并有望帮助LC药物的开发并降低药物开发成本。
Identification of promising lung cancer targets from human plasma proteins via Mendelian randomization.
Background: Lung cancer (LC) is the most prevalent form of malignant neoplasm globally, as well as the major cause of cancer-related death. Identifying effective pharmaceutical targets is paramount in advancing the development of treatment modalities for LC.
Method: Protein-wide Mendelian randomization (MR) was used in this study. The present study collated data on plasma proteins from a protein quantitative trait loci (pQTL) study with a total of 4907 individuals. Genetic associations with LC were obtained from GWAS, including 3791 cases and 489012 controls. Integration of pQTL and LC genome-wide association study (GWAS) data was employed to identify candidate proteins. MR used single nucleotide polymorphisms (SNPs) as a genetic tool to estimate the causal effect of exposure on the outcome, while reverse Mendelian randomization was performed to assess the presence of false positives. The present study utilized these approaches to evaluate the causal relationship between plasma proteins and LC. Finally, protein-protein interaction (PPI) and functional enrichment analyses were performed to illustrate potential links between proteins and current LC drugs. Finally, drug prediction and molecular docking were performed to predict drugs and explored the expression distribution of key genes by single-cell sequencing.
Result: We identified 46 plasma proteins that are strongly associated with LC Fifteen of these proteins have protective effects. Among them, MMP8(OR = 0.87, 95%CI:0.78-0.97, p = 0.013) had the most significant protective effect. In contrast, 31 proteins increased the risk of LC. IL36A༈OR = 1.20, 95%CI:1.041-1.38, p = 0.012) exhibited the most significant MR result. Notably, COL2A1, MMP19 showed reverse causality. This was further verified by enrichment analysis, which confirmed the causal effect of these proteins. Additionally, the researchers utilized the DSigDB database to predict potentially effective intervening drugs, identifying nine possible candidates. Molecular docking showed that the drugs bind very much to the proteins. KDR and ANGPTL4 are abundantly expressed in lung tissue and are differentially expressed between cells.
Conclusion: The present study has revealed six potential drug targets for the treatment of LC. Drugs designed to target these proteins will be more likely to attain success in clinical trials and are expected to assist in the development of LC drugs and reduce drug development costs.