{"title":"Identification of lysosome-related molecular subtypes and diagnostic biomarkers in diabetic nephropathy.","authors":"Jing Qi, Shanshan Liu, Yu Zhang, Caili Du","doi":"10.1080/10255842.2025.2554262","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>Lysosomes hold a pivotal role in the initiation and advancement of diverse diseases. Nevertheless, the specific biological functions of lysosomes in diabetic nephropathy (DN) remain undisclosed. This study seeks to uncover relevant lysosome-related molecular subtypes and biomarkers for DN through bioinformatics analysis.</p><p><strong>Methods: </strong>Four DN-related mRNA expression profiles (GSE1009, GSE30528, GSE96804, and GSE30122) were downloaded from GEO database, with GSE30122 as validation set. Meanwhile, lysosome-related genes (LRGs) were extracted from hLGDB and MSigDB. Limma and Venn analyses were utilized to screen differential expressed LRGs within DN vs. control, followed by functional enrichment analysis. Lysosomes-associated subtypes were identified by consensus clustering, and differences in immune cells between subtypes were compared. Further, WGCNA and machine learning algorithms were applied to screen key biomarkers. Diagnostic performance and expression levels of these biomarkers were evaluated in validation set. Finally, correlation between diagnostic genes and immune cells were analyzed.</p><p><strong>Result: </strong>A total of 37 LRGs were identified in DN, that were mainly involved in lysosome signaling pathways. Three lysosomes-associated subtypes with significant different immune patterns were obtained. Three machine learning algorithms identified seven overlapping genes as potential biomarkers. Further validation analyses ultimately revealed three genes showing high diagnostic value (AUC > 7), including AP3M2, CTSC, and MAN2B1. Moreover, there was a meaningful correlation between three diagnostic genes and immune cell infiltration.</p><p><strong>Conclusions: </strong>The findings of this study provide new insights for understanding the molecular mechanisms of DN and developing of accurate therapeutic targets.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":" ","pages":"1-13"},"PeriodicalIF":1.6000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Methods in Biomechanics and Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10255842.2025.2554262","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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Abstract
Objective: Lysosomes hold a pivotal role in the initiation and advancement of diverse diseases. Nevertheless, the specific biological functions of lysosomes in diabetic nephropathy (DN) remain undisclosed. This study seeks to uncover relevant lysosome-related molecular subtypes and biomarkers for DN through bioinformatics analysis.
Methods: Four DN-related mRNA expression profiles (GSE1009, GSE30528, GSE96804, and GSE30122) were downloaded from GEO database, with GSE30122 as validation set. Meanwhile, lysosome-related genes (LRGs) were extracted from hLGDB and MSigDB. Limma and Venn analyses were utilized to screen differential expressed LRGs within DN vs. control, followed by functional enrichment analysis. Lysosomes-associated subtypes were identified by consensus clustering, and differences in immune cells between subtypes were compared. Further, WGCNA and machine learning algorithms were applied to screen key biomarkers. Diagnostic performance and expression levels of these biomarkers were evaluated in validation set. Finally, correlation between diagnostic genes and immune cells were analyzed.
Result: A total of 37 LRGs were identified in DN, that were mainly involved in lysosome signaling pathways. Three lysosomes-associated subtypes with significant different immune patterns were obtained. Three machine learning algorithms identified seven overlapping genes as potential biomarkers. Further validation analyses ultimately revealed three genes showing high diagnostic value (AUC > 7), including AP3M2, CTSC, and MAN2B1. Moreover, there was a meaningful correlation between three diagnostic genes and immune cell infiltration.
Conclusions: The findings of this study provide new insights for understanding the molecular mechanisms of DN and developing of accurate therapeutic targets.
期刊介绍：
The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.
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