Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning DOI Creative Commons
Qiao Tang, Yanwei Ji,

Zhongyuan Xia

et al.

Frontiers in Endocrinology, Journal Year: 2025, Volume and Issue: 16

Published: March 18, 2025

Diabetic cardiomyopathy (DC) is a serious complication in patients with type 1 diabetes mellitus and has become growing public health problem worldwide. There evidence that endoplasmic reticulum stress (ERS) involved the pathogenesis of DC, related diagnostic markers have not been well-studied. Therefore, this study aimed to screen ERS-related genes (ERGs) potential value DC. Gene expression data on DC were downloaded from GEO database, ERGs obtained The Ontology knowledgebase. Limma package analyzed differentially expressed (DEGs) control groups, then integrated identify DEGs (ERDEGs). ERDEGs model was developed based combination LASSO Random Forest approaches, performance evaluated by area under receiver operating characteristic curve (ROC-AUC) validated against external datasets. In addition, association signature immune infiltration using CIBERSORT algorithm Spearman correlation test. database Knowledgebase. analysis identified 3100 between groups 65 ERDEGs. Four markers, Npm1, Jkamp, Get4, Lpcat3, random forest approach, their ROC-AUCs 0.9112, 0.9349, 0.8994, 0.8639, respectively, which proved Meanwhile, Lpcat3 datasets mouse Npm1 significantly negatively correlated plasma cells, activated natural killer or quiescent mast whereas Get4 positively cells (P < 0.05). This provides novel biomarkers (Npm1, Lpcat3) for perspective ERS, new insights into development targets individualized treatment diabetic cardiomyopathy.

Language: Английский

Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning DOI Creative Commons
Qiao Tang, Yanwei Ji,

Zhongyuan Xia

et al.

Frontiers in Endocrinology, Journal Year: 2025, Volume and Issue: 16

Published: March 18, 2025

Diabetic cardiomyopathy (DC) is a serious complication in patients with type 1 diabetes mellitus and has become growing public health problem worldwide. There evidence that endoplasmic reticulum stress (ERS) involved the pathogenesis of DC, related diagnostic markers have not been well-studied. Therefore, this study aimed to screen ERS-related genes (ERGs) potential value DC. Gene expression data on DC were downloaded from GEO database, ERGs obtained The Ontology knowledgebase. Limma package analyzed differentially expressed (DEGs) control groups, then integrated identify DEGs (ERDEGs). ERDEGs model was developed based combination LASSO Random Forest approaches, performance evaluated by area under receiver operating characteristic curve (ROC-AUC) validated against external datasets. In addition, association signature immune infiltration using CIBERSORT algorithm Spearman correlation test. database Knowledgebase. analysis identified 3100 between groups 65 ERDEGs. Four markers, Npm1, Jkamp, Get4, Lpcat3, random forest approach, their ROC-AUCs 0.9112, 0.9349, 0.8994, 0.8639, respectively, which proved Meanwhile, Lpcat3 datasets mouse Npm1 significantly negatively correlated plasma cells, activated natural killer or quiescent mast whereas Get4 positively cells (P < 0.05). This provides novel biomarkers (Npm1, Lpcat3) for perspective ERS, new insights into development targets individualized treatment diabetic cardiomyopathy.

Language: Английский

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