Identification of key immune-related genes and immune infiltration in diabetic nephropathy based on machine learning algorithms

IF 1.9 4区生物学 Q4 CELL BIOLOGY

IET Systems Biology Pub Date : 2023-03-14 DOI:10.1049/syb2.12061

Yue Sun, Weiran Dai, Wenwen He

{"title":"Identification of key immune-related genes and immune infiltration in diabetic nephropathy based on machine learning algorithms","authors":"Yue Sun, Weiran Dai, Wenwen He","doi":"10.1049/syb2.12061","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Diabetic nephropathy (DN) is a complication of diabetes. This study aimed to identify potential diagnostic markers of DN and explore the significance of immune cell infiltration in this pathology.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The GSE30528, GSE96804, and GSE1009 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by merging the GSE30528 and GSE96804 datasets. Enrichment analyses of the DEGs were performed. A LASSO regression model, support vector machine recursive feature elimination analysis and random forest analysis methods were performed to identify candidate biomarkers. The CIBERSORT algorithm was utilised to compare immune infiltration between DN and normal controls.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>In total, 115 DEGs were obtained. The enrichment analysis showed that the DEGs were prominent in immune and inflammatory responses. The DEGs were closely related to kidney disease, urinary system disease, kidney cancer etc. CXCR2, DUSP1, and LPL were recognised as diagnostic markers of DN. The immune cell infiltration analysis indicated that DN patients contained a higher ratio of memory B cells, gamma delta T cells, M1 macrophages, M2 macrophages etc. cells than normal people.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Immune cell infiltration is important for the occurrence of DN. CXCR2, DUSP1, and LPL may become novel diagnostic markers of DN.</p>\n </section>\n </div>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"17 3","pages":"95-106"},"PeriodicalIF":1.9000,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ba/25/SYB2-17-95.PMC10280611.pdf","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Systems Biology","FirstCategoryId":"99","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/syb2.12061","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CELL BIOLOGY","Score":null,"Total":0}

引用次数: 1

Abstract

Background

Diabetic nephropathy (DN) is a complication of diabetes. This study aimed to identify potential diagnostic markers of DN and explore the significance of immune cell infiltration in this pathology.

Methods

The GSE30528, GSE96804, and GSE1009 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by merging the GSE30528 and GSE96804 datasets. Enrichment analyses of the DEGs were performed. A LASSO regression model, support vector machine recursive feature elimination analysis and random forest analysis methods were performed to identify candidate biomarkers. The CIBERSORT algorithm was utilised to compare immune infiltration between DN and normal controls.

Results

In total, 115 DEGs were obtained. The enrichment analysis showed that the DEGs were prominent in immune and inflammatory responses. The DEGs were closely related to kidney disease, urinary system disease, kidney cancer etc. CXCR2, DUSP1, and LPL were recognised as diagnostic markers of DN. The immune cell infiltration analysis indicated that DN patients contained a higher ratio of memory B cells, gamma delta T cells, M1 macrophages, M2 macrophages etc. cells than normal people.

Conclusion

Immune cell infiltration is important for the occurrence of DN. CXCR2, DUSP1, and LPL may become novel diagnostic markers of DN.

Abstract Image

查看原文本刊更多论文

基于机器学习算法的糖尿病肾病关键免疫相关基因鉴定和免疫浸润

背景:糖尿病肾病是糖尿病的一种并发症。本研究旨在寻找DN的潜在诊断标志物，并探讨免疫细胞浸润在该病理中的意义。方法从Gene Expression Omnibus数据库下载GSE30528、GSE96804和GSE1009数据集。通过合并GSE30528和GSE96804数据集鉴定差异表达基因(deg)。对deg进行富集分析。采用LASSO回归模型、支持向量机递归特征消除分析和随机森林分析方法对候选生物标志物进行识别。采用CIBERSORT算法比较DN与正常对照的免疫浸润情况。结果共获得115个deg。富集分析表明，deg在免疫和炎症反应中表现突出。deg与肾脏疾病、泌尿系统疾病、肾癌等密切相关。CXCR2、DUSP1和LPL被认为是DN的诊断标志物。免疫细胞浸润分析显示，DN患者的记忆性B细胞、γ δ T细胞、M1巨噬细胞、M2巨噬细胞等细胞比例高于正常人。结论免疫细胞浸润在DN的发生中起重要作用。CXCR2、DUSP1和LPL可能成为新的DN诊断标志物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

IET Systems Biology 生物-数学与计算生物学

CiteScore

4.20

自引率

4.30%

发文量

审稿时长

>12 weeks

期刊介绍： IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.