{"title":"Development of an Inflammation-related Gene-based Diagnostic Risk Model and Immune Infiltration Analysis in Bipolar Disorder.","authors":"Jialin Gu, Kang Qian, Guolin Wu, Houxi Xu","doi":"10.2174/0109298673355842250213103507","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to construct a diagnostic risk model for Bipolar Disorder (BD) using inflammation-related genes (IRGs) and to explore the role of immune cell infiltration in BD pathogenesis.</p><p><strong>Methods: </strong>BD datasets (GSE23848, GSE124326, GSE39653, and GSE46449) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the edgeR package. The intersection of DEGs and IRGs was defined as differentially expressed IRGs. A LASSO regression model was used to identify optimal biomarkers, which were then utilized to construct a diagnostic risk model. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic accuracy of the biomarkers. Internal validation was performed with GSE124326, while external validation utilized GSE23848, GSE39653, and GSE46449. The xCell module in the IOBR package was employed to assess immune cell infiltration proportions. The relationship between IRGs, the diagnostic risk model, and immune cell dynamics was further analyzed.</p><p><strong>Results: </strong>A total of 2345 DEGs were identified in GSE124326. GO and KEGG pathway enrichment analyses indicated that inflammatory pathways are critically involved in BD pathogenesis. A total of 69 BD-related IRGs were identified. Six key IRGs (IL33, DNASE1L3, IL2RA, CD70, CLEC5A, and SLPI) were identified through LASSO regression analysis and used to develop a diagnostic risk model. Internal and external validations confirmed the robust diagnostic performance of the risk model. Immuno-infiltration analysis showed significant differences in immune cell infiltration between BD patients and healthy controls. The diagnostic risk model and four potential biomarkers (DNASE1L3, IL2RA, CD70, and SLPI) showed strong correlations with various immune cell types.</p><p><strong>Conclusion: </strong>A diagnostic risk model for BD was constructed based on IRGs, highlighting the critical role of immune cell infiltration in BD pathogenesis.</p>","PeriodicalId":10984,"journal":{"name":"Current medicinal chemistry","volume":" ","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current medicinal chemistry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/0109298673355842250213103507","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Objective: This study aimed to construct a diagnostic risk model for Bipolar Disorder (BD) using inflammation-related genes (IRGs) and to explore the role of immune cell infiltration in BD pathogenesis.
Methods: BD datasets (GSE23848, GSE124326, GSE39653, and GSE46449) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the edgeR package. The intersection of DEGs and IRGs was defined as differentially expressed IRGs. A LASSO regression model was used to identify optimal biomarkers, which were then utilized to construct a diagnostic risk model. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic accuracy of the biomarkers. Internal validation was performed with GSE124326, while external validation utilized GSE23848, GSE39653, and GSE46449. The xCell module in the IOBR package was employed to assess immune cell infiltration proportions. The relationship between IRGs, the diagnostic risk model, and immune cell dynamics was further analyzed.
Results: A total of 2345 DEGs were identified in GSE124326. GO and KEGG pathway enrichment analyses indicated that inflammatory pathways are critically involved in BD pathogenesis. A total of 69 BD-related IRGs were identified. Six key IRGs (IL33, DNASE1L3, IL2RA, CD70, CLEC5A, and SLPI) were identified through LASSO regression analysis and used to develop a diagnostic risk model. Internal and external validations confirmed the robust diagnostic performance of the risk model. Immuno-infiltration analysis showed significant differences in immune cell infiltration between BD patients and healthy controls. The diagnostic risk model and four potential biomarkers (DNASE1L3, IL2RA, CD70, and SLPI) showed strong correlations with various immune cell types.
Conclusion: A diagnostic risk model for BD was constructed based on IRGs, highlighting the critical role of immune cell infiltration in BD pathogenesis.
目的:利用炎症相关基因(IRGs)构建双相情感障碍(BD)的诊断风险模型,探讨免疫细胞浸润在BD发病中的作用。方法:从Gene Expression Omnibus (GEO)数据库中检索BD数据集(GSE23848、GSE124326、GSE39653和GSE46449)。差异表达基因(DEGs)鉴定使用edgeR包。DEGs和IRGs的交集被定义为差异表达的IRGs。使用LASSO回归模型确定最佳生物标志物,然后利用这些生物标志物构建诊断风险模型。采用受试者工作特征(ROC)曲线评价生物标志物的诊断准确性。内部验证使用GSE124326,外部验证使用GSE23848、GSE39653和GSE46449。采用IOBR包中的xCell模块评估免疫细胞浸润比例。进一步分析IRGs、诊断风险模型和免疫细胞动力学之间的关系。结果:GSE124326共鉴定出2345个deg。GO和KEGG通路富集分析表明炎症通路在BD发病过程中起关键作用。共鉴定出69个与bd相关的irg。通过LASSO回归分析确定6个关键IRGs (IL33、DNASE1L3、IL2RA、CD70、cle5a和SLPI),并用于建立诊断风险模型。内部和外部验证证实了风险模型的稳健诊断性能。免疫浸润分析显示,BD患者免疫细胞浸润与健康对照组有显著差异。诊断风险模型和4个潜在的生物标志物(DNASE1L3、IL2RA、CD70和SLPI)显示出与各种免疫细胞类型的强相关性。结论:基于IRGs构建了BD诊断风险模型,突出了免疫细胞浸润在BD发病机制中的重要作用。
期刊介绍:
Aims & Scope
Current Medicinal Chemistry covers all the latest and outstanding developments in medicinal chemistry and rational drug design. Each issue contains a series of timely in-depth reviews and guest edited thematic issues written by leaders in the field covering a range of the current topics in medicinal chemistry. The journal also publishes reviews on recent patents. Current Medicinal Chemistry is an essential journal for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important developments.