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Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning
BACKGROUNDS: Diabetes nephropathy (DN) is a growing public health concern worldwide. Renal dysfunction impairment in DN is intimately linked to ER stress and its related signaling pathways. Nonetheless, the underlying mechanism and biomarkers for this function of ER stress in the DN remain unknown....
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513048/ https://www.ncbi.nlm.nih.gov/pubmed/37745718 http://dx.doi.org/10.3389/fendo.2023.1206154 |
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author | Su, Jiaming Peng, Jing Wang, Lin Xie, Huidi Zhou, Ying Chen, Haimin Shi, Yang Guo, Yan Zheng, Yicheng Guo, Yuxin Dong, Zhaoxi Zhang, Xianhui Liu, Hongfang |
author_facet | Su, Jiaming Peng, Jing Wang, Lin Xie, Huidi Zhou, Ying Chen, Haimin Shi, Yang Guo, Yan Zheng, Yicheng Guo, Yuxin Dong, Zhaoxi Zhang, Xianhui Liu, Hongfang |
author_sort | Su, Jiaming |
collection | PubMed |
description | BACKGROUNDS: Diabetes nephropathy (DN) is a growing public health concern worldwide. Renal dysfunction impairment in DN is intimately linked to ER stress and its related signaling pathways. Nonetheless, the underlying mechanism and biomarkers for this function of ER stress in the DN remain unknown. METHODS: Microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database, and ER stress-related genes (ERSRGs) were downloaded from the MSigDB and GeneCards database. We identified hub ERSRGs for DN progression by intersecting ERSRGs with differentially expressed genes and significant genes in WGCNA, followed by a functional analysis. After analyzing hub ERSRGs with three machine learning techniques and taking the intersection, we did external validation as well as developed a DN diagnostic model based on the characteristic genes. Immune infiltration was performed using CIBERSORT. Moreover, patients with DN were then categorized using a consensus clustering approach. Eventually, the candidate ERSRGs-specific small-molecule compounds were defined by CMap. RESULTS: Several biological pathways driving pathological injury of DN and disordered levels of immune infiltration were revealed in the DN microarray datasets and strongly related to deregulated ERSRGs by bioinformatics multi-chip integration. Moreover, CDKN1B, EGR1, FKBP5, GDF15, and MARCKS were identified as ER stress signature genes associated with DN by machine learning algorithms, demonstrating their potential as DN biomarkers. CONCLUSIONS: Our research sheds fresh light on the function of ER stress in DN pathophysiology and the development of early diagnostic and ER stress-related treatment targets in patients with DN. |
format | Online Article Text |
id | pubmed-10513048 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105130482023-09-22 Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning Su, Jiaming Peng, Jing Wang, Lin Xie, Huidi Zhou, Ying Chen, Haimin Shi, Yang Guo, Yan Zheng, Yicheng Guo, Yuxin Dong, Zhaoxi Zhang, Xianhui Liu, Hongfang Front Endocrinol (Lausanne) Endocrinology BACKGROUNDS: Diabetes nephropathy (DN) is a growing public health concern worldwide. Renal dysfunction impairment in DN is intimately linked to ER stress and its related signaling pathways. Nonetheless, the underlying mechanism and biomarkers for this function of ER stress in the DN remain unknown. METHODS: Microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database, and ER stress-related genes (ERSRGs) were downloaded from the MSigDB and GeneCards database. We identified hub ERSRGs for DN progression by intersecting ERSRGs with differentially expressed genes and significant genes in WGCNA, followed by a functional analysis. After analyzing hub ERSRGs with three machine learning techniques and taking the intersection, we did external validation as well as developed a DN diagnostic model based on the characteristic genes. Immune infiltration was performed using CIBERSORT. Moreover, patients with DN were then categorized using a consensus clustering approach. Eventually, the candidate ERSRGs-specific small-molecule compounds were defined by CMap. RESULTS: Several biological pathways driving pathological injury of DN and disordered levels of immune infiltration were revealed in the DN microarray datasets and strongly related to deregulated ERSRGs by bioinformatics multi-chip integration. Moreover, CDKN1B, EGR1, FKBP5, GDF15, and MARCKS were identified as ER stress signature genes associated with DN by machine learning algorithms, demonstrating their potential as DN biomarkers. CONCLUSIONS: Our research sheds fresh light on the function of ER stress in DN pathophysiology and the development of early diagnostic and ER stress-related treatment targets in patients with DN. Frontiers Media S.A. 2023-09-01 /pmc/articles/PMC10513048/ /pubmed/37745718 http://dx.doi.org/10.3389/fendo.2023.1206154 Text en Copyright © 2023 Su, Peng, Wang, Xie, Zhou, Chen, Shi, Guo, Zheng, Guo, Dong, Zhang and Liu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Endocrinology Su, Jiaming Peng, Jing Wang, Lin Xie, Huidi Zhou, Ying Chen, Haimin Shi, Yang Guo, Yan Zheng, Yicheng Guo, Yuxin Dong, Zhaoxi Zhang, Xianhui Liu, Hongfang Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning |
title | Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning |
title_full | Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning |
title_fullStr | Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning |
title_full_unstemmed | Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning |
title_short | Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning |
title_sort | identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning |
topic | Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513048/ https://www.ncbi.nlm.nih.gov/pubmed/37745718 http://dx.doi.org/10.3389/fendo.2023.1206154 |
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