REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease
Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Clinical features are traditionally used to predict DKD, yet with low diagnostic efficacy. Most of the recent biomarkers used to predict DKD are based on transcriptomics and metabolomics; however, they also should be used...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2022.935796/full |
| _version_ | 1818476066761605120 |
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| author | Xinyu Wang Han Wu Guangyan Yang Jiaqing Xiang Lijiao Xiong Li Zhao Tingfeng Liao Xinyue Zhao Lin Kang Lin Kang Shu Yang Shu Yang Zhen Liang Zhen Liang |
| author_facet | Xinyu Wang Han Wu Guangyan Yang Jiaqing Xiang Lijiao Xiong Li Zhao Tingfeng Liao Xinyue Zhao Lin Kang Lin Kang Shu Yang Shu Yang Zhen Liang Zhen Liang |
| author_sort | Xinyu Wang |
| collection | DOAJ |
| description | Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Clinical features are traditionally used to predict DKD, yet with low diagnostic efficacy. Most of the recent biomarkers used to predict DKD are based on transcriptomics and metabolomics; however, they also should be used in combination with many other predictive indicators. The purpose of this study was thus to identify a simplified class of blood biomarkers capable of predicting the risk of developing DKD. The Gene Expression Omnibus database was screened for DKD biomarkers, and differentially expressed genes (DEGs) in human blood and kidney were identified via gene expression analysis and the Least Absolute Shrinkage and Selection Operator regression. A comparison of the area under the curve (AUC) profiles on multiple receiver operating characteristic curves of the DEGs in DKD and other renal diseases revealed that REG1A and RUNX3 had the highest specificity for DKD diagnosis. The AUCs of the combined expression of REG1A and RUNX3 in kidney (AUC = 0.929) and blood samples (AUC = 0.917) of DKD patients were similar to each other. The AUC of blood samples from DKD patients and healthy individuals obtained for external validation further demonstrated that REG1A combined with RUNX3 had significant diagnostic efficacy (AUC=0.948). REG1A and RUNX3 expression levels were found to be positively and negatively correlated with urinary albumin creatinine ratio and estimated glomerular filtration rate, respectively. Kaplan-Meier curves also revealed the potential of REG1A and RUNX3 for predicting the risk of DKD. In conclusion, REG1A and RUNX3 may serve as biomarkers for predicting the risk of developing DKD. |
| first_indexed | 2024-12-10T09:20:46Z |
| format | Article |
| id | doaj.art-f4e97fd333e54d7eb862761afde417f0 |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-12-10T09:20:46Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-f4e97fd333e54d7eb862761afde417f02022-12-22T01:54:43ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922022-07-011310.3389/fendo.2022.935796935796REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney DiseaseXinyu Wang0Han Wu1Guangyan Yang2Jiaqing Xiang3Lijiao Xiong4Li Zhao5Tingfeng Liao6Xinyue Zhao7Lin Kang8Lin Kang9Shu Yang10Shu Yang11Zhen Liang12Zhen Liang13Department of Geriatrics, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, ChinaDepartment of Endocrinology, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, ChinaDepartment of Geriatrics, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, ChinaDepartment of Geriatrics, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, ChinaDepartment of Geriatrics, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, ChinaDepartment of Health Management, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, ChinaDepartment of Geriatrics, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, ChinaDepartment of Nephrology, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, ChinaDepartment of Geriatrics, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, ChinaThe Biobank of National Innovation Center for Advanced Medical Devices, Shenzhen People’s Hospital, Shenzhen, ChinaDepartment of Geriatrics, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, ChinaShenzhen Clinical Research Center for Aging, Shenzhen, ChinaDepartment of Geriatrics, The Second Clinical Medical College of Jinan University, Shenzhen People’s Hospital, Shenzhen, ChinaShenzhen Clinical Research Center for Aging, Shenzhen, ChinaDiabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Clinical features are traditionally used to predict DKD, yet with low diagnostic efficacy. Most of the recent biomarkers used to predict DKD are based on transcriptomics and metabolomics; however, they also should be used in combination with many other predictive indicators. The purpose of this study was thus to identify a simplified class of blood biomarkers capable of predicting the risk of developing DKD. The Gene Expression Omnibus database was screened for DKD biomarkers, and differentially expressed genes (DEGs) in human blood and kidney were identified via gene expression analysis and the Least Absolute Shrinkage and Selection Operator regression. A comparison of the area under the curve (AUC) profiles on multiple receiver operating characteristic curves of the DEGs in DKD and other renal diseases revealed that REG1A and RUNX3 had the highest specificity for DKD diagnosis. The AUCs of the combined expression of REG1A and RUNX3 in kidney (AUC = 0.929) and blood samples (AUC = 0.917) of DKD patients were similar to each other. The AUC of blood samples from DKD patients and healthy individuals obtained for external validation further demonstrated that REG1A combined with RUNX3 had significant diagnostic efficacy (AUC=0.948). REG1A and RUNX3 expression levels were found to be positively and negatively correlated with urinary albumin creatinine ratio and estimated glomerular filtration rate, respectively. Kaplan-Meier curves also revealed the potential of REG1A and RUNX3 for predicting the risk of DKD. In conclusion, REG1A and RUNX3 may serve as biomarkers for predicting the risk of developing DKD.https://www.frontiersin.org/articles/10.3389/fendo.2022.935796/fulldiabetic kidney diseasebiomarkersdiagnosisgene expression omnibusdisease risk prediction |
| spellingShingle | Xinyu Wang Han Wu Guangyan Yang Jiaqing Xiang Lijiao Xiong Li Zhao Tingfeng Liao Xinyue Zhao Lin Kang Lin Kang Shu Yang Shu Yang Zhen Liang Zhen Liang REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease Frontiers in Endocrinology diabetic kidney disease biomarkers diagnosis gene expression omnibus disease risk prediction |
| title | REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease |
| title_full | REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease |
| title_fullStr | REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease |
| title_full_unstemmed | REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease |
| title_short | REG1A and RUNX3 Are Potential Biomarkers for Predicting the Risk of Diabetic Kidney Disease |
| title_sort | reg1a and runx3 are potential biomarkers for predicting the risk of diabetic kidney disease |
| topic | diabetic kidney disease biomarkers diagnosis gene expression omnibus disease risk prediction |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2022.935796/full |
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