Bioinformatics gene analysis of potential biomarkers associated with chronic kidney disease related ischemic stroke

IF 2.5 4区综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES

Journal of Radiation Research and Applied Sciences Pub Date : 2025-08-06 DOI:10.1016/j.jrras.2025.101841

Mingshan Xie , Ziyi Shen , Guohui Jiang

{"title":"Bioinformatics gene analysis of potential biomarkers associated with chronic kidney disease related ischemic stroke","authors":"Mingshan Xie , Ziyi Shen , Guohui Jiang","doi":"10.1016/j.jrras.2025.101841","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><div>Chronic kidney disease (CKD) is a key risk factor for ischemic stroke (IS), but the underlying key molecules and mechanisms linking CKD and IS remain unclear. This study aims to identify novel potential diagnostic biomarkers for chronic kidney disease-related ischemic stroke (CKD-IS) using bioinformatics and machine learning.</div></div><div><h3>Methods</h3><div>Relevant gene expression datasets (CKD: GSE37171, GSE66494; IS: GSE16561, GSE58294) were downloaded from the GEO database. Differentially expressed genes (DEGs) in peripheral blood samples from patients and healthy controls were identified, and common DEGs between CKD and IS were screened. Functional enrichment analysis (GO/KEGG) was performed to explore biological functions. Three machine learning algorithms (LASSO, Random Forest, SVM-RFE) were used to select candidate biomarkers, followed by construction of an artificial neural network model and validation via ROC curves. Immune infiltration analysis (CIBERSORT) was conducted to investigate associations between candidate genes and immune cells.</div></div><div><h3>Results</h3><div>A total of 2648 DEGs were identified in CKD datasets and 337 in IS datasets, with 29 common DEGs. GO enrichment analysis showed these common DEGs were significantly enriched in immune system processes and immune responses. Seven candidate genes (CYTM1, EVL, IFI27, PCED1B, S100A12, S100P, TMEM158) were screened by machine learning. The neural network model based on these genes showed good diagnostic performance (AUC: 0.966 in the training set), and ROC curves confirmed their diagnostic value (AUC: 0.50–0.96 in validation sets). Immune infiltration analysis revealed correlations between these genes and immune cells (e.g., neutrophils, T cells).</div></div><div><h3>Conclusion</h3><div>The seven identified candidate genes (CYTM1, EVL, IFI27, PCED1B, S100A12, S100P, TMEM158) are potential diagnostic biomarkers for CKD-IS, providing insights into the immune-related mechanisms underlying CKD-IS and supporting future precise diagnosis and treatment.</div></div>","PeriodicalId":16920,"journal":{"name":"Journal of Radiation Research and Applied Sciences","volume":"18 4","pages":"Article 101841"},"PeriodicalIF":2.5000,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Radiation Research and Applied Sciences","FirstCategoryId":"103","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1687850725005539","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

Objectives

Chronic kidney disease (CKD) is a key risk factor for ischemic stroke (IS), but the underlying key molecules and mechanisms linking CKD and IS remain unclear. This study aims to identify novel potential diagnostic biomarkers for chronic kidney disease-related ischemic stroke (CKD-IS) using bioinformatics and machine learning.

Methods

Relevant gene expression datasets (CKD: GSE37171, GSE66494; IS: GSE16561, GSE58294) were downloaded from the GEO database. Differentially expressed genes (DEGs) in peripheral blood samples from patients and healthy controls were identified, and common DEGs between CKD and IS were screened. Functional enrichment analysis (GO/KEGG) was performed to explore biological functions. Three machine learning algorithms (LASSO, Random Forest, SVM-RFE) were used to select candidate biomarkers, followed by construction of an artificial neural network model and validation via ROC curves. Immune infiltration analysis (CIBERSORT) was conducted to investigate associations between candidate genes and immune cells.

Results

A total of 2648 DEGs were identified in CKD datasets and 337 in IS datasets, with 29 common DEGs. GO enrichment analysis showed these common DEGs were significantly enriched in immune system processes and immune responses. Seven candidate genes (CYTM1, EVL, IFI27, PCED1B, S100A12, S100P, TMEM158) were screened by machine learning. The neural network model based on these genes showed good diagnostic performance (AUC: 0.966 in the training set), and ROC curves confirmed their diagnostic value (AUC: 0.50–0.96 in validation sets). Immune infiltration analysis revealed correlations between these genes and immune cells (e.g., neutrophils, T cells).

Conclusion

The seven identified candidate genes (CYTM1, EVL, IFI27, PCED1B, S100A12, S100P, TMEM158) are potential diagnostic biomarkers for CKD-IS, providing insights into the immune-related mechanisms underlying CKD-IS and supporting future precise diagnosis and treatment.

查看原文本刊更多论文

慢性肾脏疾病相关缺血性脑卒中潜在生物标志物的生物信息学基因分析

慢性肾脏疾病（CKD）是缺血性脑卒中（is）的关键危险因素，但CKD和is之间的潜在关键分子和机制尚不清楚。本研究旨在利用生物信息学和机器学习技术鉴定慢性肾脏疾病相关缺血性中风（CKD-IS）的新型潜在诊断生物标志物。方法相关基因表达数据集(CKD: GSE37171, GSE66494；IS: GSE16561, GSE58294)从GEO数据库下载。鉴定患者和健康对照外周血样本中的差异表达基因（deg），筛选CKD和IS之间的共同deg。功能富集分析（GO/KEGG）探究其生物学功能。采用LASSO、Random Forest、SVM-RFE三种机器学习算法筛选候选生物标志物，构建人工神经网络模型，并通过ROC曲线进行验证。免疫浸润分析（CIBERSORT）研究候选基因与免疫细胞之间的关系。结果CKD数据集中共鉴定出2648个deg， IS数据集中鉴定出337个deg，其中共有29个deg。氧化石墨烯富集分析表明，这些常见的氧化石墨烯蛋白在免疫系统过程和免疫反应中显著富集。通过机器学习筛选出7个候选基因（CYTM1、EVL、IFI27、PCED1B、S100A12、S100P、TMEM158）。基于这些基因的神经网络模型具有良好的诊断性能（训练集中AUC为0.966），ROC曲线证实了其诊断价值（验证集中AUC为0.50 ~ 0.96）。免疫浸润分析揭示了这些基因与免疫细胞（如中性粒细胞、T细胞）之间的相关性。结论7个候选基因（CYTM1， EVL, IFI27, PCED1B, S100A12, S100P, TMEM158）是CKD-IS的潜在诊断生物标志物，为CKD-IS的免疫相关机制提供了深入了解，为未来的精确诊断和治疗提供了支持。

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

Journal of Radiation Research and Applied Sciences MULTIDISCIPLINARY SCIENCES-

自引率

5.90%

发文量

130

审稿时长

16 weeks

期刊介绍： Journal of Radiation Research and Applied Sciences provides a high quality medium for the publication of substantial, original and scientific and technological papers on the development and applications of nuclear, radiation and isotopes in biology, medicine, drugs, biochemistry, microbiology, agriculture, entomology, food technology, chemistry, physics, solid states, engineering, environmental and applied sciences.