{"title":"基于WGCNA和机器学习方法的异氟醚诱导麻醉的药物靶基因及分子机制研究","authors":"Honglei Yuan, Shengqiang Yang, Peng Han, Mingya Sun, Chao Zhou","doi":"10.1080/15376516.2023.2286619","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>This study sought to identify drug target genes and their associated molecular mechanisms during isoflurane-induced anesthesia in clinical applications.</p><p><strong>Methods: </strong>Microarray data (ID: GSE64617; isoflurane-treated vs. normal samples) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened and hub genes were investigated using weighted correlation network analysis (WGCNA). Protein-protein interactions (PPIs) were constructed among the co-DEGs (common genes between DEGs and hub genes), followed by functional enrichment analyses. Then, three machine learning methods were used to reveal drug targets, followed by validation, nomogram analysis, and gene set enrichment analysis. Finally, an miRNA-target network was constructed.</p><p><strong>Results: </strong>A total of 686 DEGs were identified between the two groups-of which, 183 DEGs integrated with genes revealed by WCGNA were identified as co-genes. These genes, including contactin-associated protein 1 (CNTNAP1), are mainly involved in functions such as action potentials. PPI network analysis revealed three models, with the machine learning analysis exploring four drug target genes: A2H, FAM155B, SCARF2, and SDR16C5. ROC and nomogram analyses demonstrated the ideal diagnostic value of these target genes. Finally, miRNA-mRNA pairs were constructed based on the four mRNAs and associated 174 miRNAs.</p><p><strong>Conclusion: </strong>FA2H, FAM155B, SCARF2, and SDR16C5 may be novel drug target genes for isoflurane-induced anesthesia. CNTNAP1 may participate in the progression of isoflurane-induced anesthesia <i>via</i> its action potential function.</p>","PeriodicalId":23177,"journal":{"name":"Toxicology Mechanisms and Methods","volume":" ","pages":"319-333"},"PeriodicalIF":3.2000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Drug target genes and molecular mechanism investigation in isoflurane-induced anesthesia based on WGCNA and machine learning methods.\",\"authors\":\"Honglei Yuan, Shengqiang Yang, Peng Han, Mingya Sun, Chao Zhou\",\"doi\":\"10.1080/15376516.2023.2286619\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>This study sought to identify drug target genes and their associated molecular mechanisms during isoflurane-induced anesthesia in clinical applications.</p><p><strong>Methods: </strong>Microarray data (ID: GSE64617; isoflurane-treated vs. normal samples) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened and hub genes were investigated using weighted correlation network analysis (WGCNA). Protein-protein interactions (PPIs) were constructed among the co-DEGs (common genes between DEGs and hub genes), followed by functional enrichment analyses. Then, three machine learning methods were used to reveal drug targets, followed by validation, nomogram analysis, and gene set enrichment analysis. Finally, an miRNA-target network was constructed.</p><p><strong>Results: </strong>A total of 686 DEGs were identified between the two groups-of which, 183 DEGs integrated with genes revealed by WCGNA were identified as co-genes. These genes, including contactin-associated protein 1 (CNTNAP1), are mainly involved in functions such as action potentials. PPI network analysis revealed three models, with the machine learning analysis exploring four drug target genes: A2H, FAM155B, SCARF2, and SDR16C5. ROC and nomogram analyses demonstrated the ideal diagnostic value of these target genes. Finally, miRNA-mRNA pairs were constructed based on the four mRNAs and associated 174 miRNAs.</p><p><strong>Conclusion: </strong>FA2H, FAM155B, SCARF2, and SDR16C5 may be novel drug target genes for isoflurane-induced anesthesia. CNTNAP1 may participate in the progression of isoflurane-induced anesthesia <i>via</i> its action potential function.</p>\",\"PeriodicalId\":23177,\"journal\":{\"name\":\"Toxicology Mechanisms and Methods\",\"volume\":\" \",\"pages\":\"319-333\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Toxicology Mechanisms and Methods\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/15376516.2023.2286619\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/12/6 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"Pharmacology, Toxicology and Pharmaceutics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Toxicology Mechanisms and Methods","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/15376516.2023.2286619","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/12/6 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Pharmacology, Toxicology and Pharmaceutics","Score":null,"Total":0}
Drug target genes and molecular mechanism investigation in isoflurane-induced anesthesia based on WGCNA and machine learning methods.
Purpose: This study sought to identify drug target genes and their associated molecular mechanisms during isoflurane-induced anesthesia in clinical applications.
Methods: Microarray data (ID: GSE64617; isoflurane-treated vs. normal samples) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened and hub genes were investigated using weighted correlation network analysis (WGCNA). Protein-protein interactions (PPIs) were constructed among the co-DEGs (common genes between DEGs and hub genes), followed by functional enrichment analyses. Then, three machine learning methods were used to reveal drug targets, followed by validation, nomogram analysis, and gene set enrichment analysis. Finally, an miRNA-target network was constructed.
Results: A total of 686 DEGs were identified between the two groups-of which, 183 DEGs integrated with genes revealed by WCGNA were identified as co-genes. These genes, including contactin-associated protein 1 (CNTNAP1), are mainly involved in functions such as action potentials. PPI network analysis revealed three models, with the machine learning analysis exploring four drug target genes: A2H, FAM155B, SCARF2, and SDR16C5. ROC and nomogram analyses demonstrated the ideal diagnostic value of these target genes. Finally, miRNA-mRNA pairs were constructed based on the four mRNAs and associated 174 miRNAs.
Conclusion: FA2H, FAM155B, SCARF2, and SDR16C5 may be novel drug target genes for isoflurane-induced anesthesia. CNTNAP1 may participate in the progression of isoflurane-induced anesthesia via its action potential function.
期刊介绍:
Toxicology Mechanisms and Methods is a peer-reviewed journal whose aim is twofold. Firstly, the journal contains original research on subjects dealing with the mechanisms by which foreign chemicals cause toxic tissue injury. Chemical substances of interest include industrial compounds, environmental pollutants, hazardous wastes, drugs, pesticides, and chemical warfare agents. The scope of the journal spans from molecular and cellular mechanisms of action to the consideration of mechanistic evidence in establishing regulatory policy.
Secondly, the journal addresses aspects of the development, validation, and application of new and existing laboratory methods, techniques, and equipment. A variety of research methods are discussed, including:
In vivo studies with standard and alternative species
In vitro studies and alternative methodologies
Molecular, biochemical, and cellular techniques
Pharmacokinetics and pharmacodynamics
Mathematical modeling and computer programs
Forensic analyses
Risk assessment
Data collection and analysis.