Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning
BackgroundDiabetic cardiomyopathy (DC) is a serious complication in patients with type 1 diabetes mellitus and has become a growing public health problem worldwide. There is evidence that endoplasmic reticulum stress (ERS) is involved in the pathogenesis of DC, and related diagnostic markers have no...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
|
| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2025.1478139/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849774083083862016 |
|---|---|
| author | Qiao Tang Yanwei Ji Zhongyuan Xia Yuxi Zhang Chong Dong Chong Dong Qian Sun Shaoqing Lei |
| author_facet | Qiao Tang Yanwei Ji Zhongyuan Xia Yuxi Zhang Chong Dong Chong Dong Qian Sun Shaoqing Lei |
| author_sort | Qiao Tang |
| collection | DOAJ |
| description | BackgroundDiabetic cardiomyopathy (DC) is a serious complication in patients with type 1 diabetes mellitus and has become a growing public health problem worldwide. There is evidence that endoplasmic reticulum stress (ERS) is involved in the pathogenesis of DC, and related diagnostic markers have not been well-studied. Therefore, this study aimed to screen ERS-related genes (ERGs) with potential diagnostic value in DC.MethodsGene expression data on DC were downloaded from the GEO database, and ERGs were obtained from The Gene Ontology knowledgebase. Limma package analyzed differentially expressed genes (DEGs) in the DC and control groups, and then integrated with ERGs to identify ERS-related DEGs (ERDEGs). The ERDEGs diagnostic model was developed based on a combination of LASSO and Random Forest approaches, and the diagnostic performance was evaluated by the area under the receiver operating characteristic curve (ROC-AUC) and validated against external datasets. In addition, the association of the signature genes with immune infiltration was analyzed using the CIBERSORT algorithm and the Spearman correlation test.ResultsGene expression data on DC were downloaded from the GEO database and ERGs were obtained from the Gene Ontology Knowledgebase. Limma package analysis identified 3100 DEGs between DC and control groups and then integrated with ERGs to identify 65 ERDEGs. Four diagnostic markers, Npm1, Jkamp, Get4, and Lpcat3, were obtained based on the combination of LASSO and random forest approach, and their ROC-AUCs were 0.9112, 0.9349, 0.8994, and 0.8639, respectively, which proved their diagnostic potential in DC. Meanwhile, Npm1, Jkamp, Get4, and Lpcat3 were validated by external datasets and a mouse model of type 1 DC. In addition, Npm1 was significantly negatively correlated with plasma cells, activated natural killer cells, or quiescent mast cells, whereas Get4 was significantly positively correlated with quiescent natural killer cells and significantly negatively correlated with activated natural killer cells (P < 0.05).ConclusionsThis study provides novel diagnostic biomarkers (Npm1, Jkamp, Get4, and Lpcat3) for DC from the perspective of ERS, which provides new insights into the development of new targets for individualized treatment of type 1 diabetic cardiomyopathy. |
| format | Article |
| id | doaj-art-57ccd63101f44e928ca59bca8e86688d |
| institution | DOAJ |
| issn | 1664-2392 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj-art-57ccd63101f44e928ca59bca8e86688d2025-08-20T03:01:51ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922025-03-011610.3389/fendo.2025.14781391478139Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learningQiao Tang0Yanwei Ji1Zhongyuan Xia2Yuxi Zhang3Chong Dong4Chong Dong5Qian Sun6Shaoqing Lei7Department of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, ChinaOrgan Transplantation Center, Tianjin First Central Hospital, Tianjin, ChinaTianjin Key Laboratory for Organ Transplantation, Tianjin, ChinaDepartment of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, ChinaDepartment of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, ChinaBackgroundDiabetic cardiomyopathy (DC) is a serious complication in patients with type 1 diabetes mellitus and has become a growing public health problem worldwide. There is evidence that endoplasmic reticulum stress (ERS) is involved in the pathogenesis of DC, and related diagnostic markers have not been well-studied. Therefore, this study aimed to screen ERS-related genes (ERGs) with potential diagnostic value in DC.MethodsGene expression data on DC were downloaded from the GEO database, and ERGs were obtained from The Gene Ontology knowledgebase. Limma package analyzed differentially expressed genes (DEGs) in the DC and control groups, and then integrated with ERGs to identify ERS-related DEGs (ERDEGs). The ERDEGs diagnostic model was developed based on a combination of LASSO and Random Forest approaches, and the diagnostic performance was evaluated by the area under the receiver operating characteristic curve (ROC-AUC) and validated against external datasets. In addition, the association of the signature genes with immune infiltration was analyzed using the CIBERSORT algorithm and the Spearman correlation test.ResultsGene expression data on DC were downloaded from the GEO database and ERGs were obtained from the Gene Ontology Knowledgebase. Limma package analysis identified 3100 DEGs between DC and control groups and then integrated with ERGs to identify 65 ERDEGs. Four diagnostic markers, Npm1, Jkamp, Get4, and Lpcat3, were obtained based on the combination of LASSO and random forest approach, and their ROC-AUCs were 0.9112, 0.9349, 0.8994, and 0.8639, respectively, which proved their diagnostic potential in DC. Meanwhile, Npm1, Jkamp, Get4, and Lpcat3 were validated by external datasets and a mouse model of type 1 DC. In addition, Npm1 was significantly negatively correlated with plasma cells, activated natural killer cells, or quiescent mast cells, whereas Get4 was significantly positively correlated with quiescent natural killer cells and significantly negatively correlated with activated natural killer cells (P < 0.05).ConclusionsThis study provides novel diagnostic biomarkers (Npm1, Jkamp, Get4, and Lpcat3) for DC from the perspective of ERS, which provides new insights into the development of new targets for individualized treatment of type 1 diabetic cardiomyopathy.https://www.frontiersin.org/articles/10.3389/fendo.2025.1478139/fulltype 1 diabetesdiabetic cardiomyopathyendoplasmic reticulum stressimmune infiltrationbioinformaticsmarker genes |
| spellingShingle | Qiao Tang Yanwei Ji Zhongyuan Xia Yuxi Zhang Chong Dong Chong Dong Qian Sun Shaoqing Lei Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning Frontiers in Endocrinology type 1 diabetes diabetic cardiomyopathy endoplasmic reticulum stress immune infiltration bioinformatics marker genes |
| title | Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning |
| title_full | Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning |
| title_fullStr | Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning |
| title_full_unstemmed | Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning |
| title_short | Identification and validation of endoplasmic reticulum stress-related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning |
| title_sort | identification and validation of endoplasmic reticulum stress related diagnostic biomarkers for type 1 diabetic cardiomyopathy based on bioinformatics and machine learning |
| topic | type 1 diabetes diabetic cardiomyopathy endoplasmic reticulum stress immune infiltration bioinformatics marker genes |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2025.1478139/full |
| work_keys_str_mv | AT qiaotang identificationandvalidationofendoplasmicreticulumstressrelateddiagnosticbiomarkersfortype1diabeticcardiomyopathybasedonbioinformaticsandmachinelearning AT yanweiji identificationandvalidationofendoplasmicreticulumstressrelateddiagnosticbiomarkersfortype1diabeticcardiomyopathybasedonbioinformaticsandmachinelearning AT zhongyuanxia identificationandvalidationofendoplasmicreticulumstressrelateddiagnosticbiomarkersfortype1diabeticcardiomyopathybasedonbioinformaticsandmachinelearning AT yuxizhang identificationandvalidationofendoplasmicreticulumstressrelateddiagnosticbiomarkersfortype1diabeticcardiomyopathybasedonbioinformaticsandmachinelearning AT chongdong identificationandvalidationofendoplasmicreticulumstressrelateddiagnosticbiomarkersfortype1diabeticcardiomyopathybasedonbioinformaticsandmachinelearning AT chongdong identificationandvalidationofendoplasmicreticulumstressrelateddiagnosticbiomarkersfortype1diabeticcardiomyopathybasedonbioinformaticsandmachinelearning AT qiansun identificationandvalidationofendoplasmicreticulumstressrelateddiagnosticbiomarkersfortype1diabeticcardiomyopathybasedonbioinformaticsandmachinelearning AT shaoqinglei identificationandvalidationofendoplasmicreticulumstressrelateddiagnosticbiomarkersfortype1diabeticcardiomyopathybasedonbioinformaticsandmachinelearning |