Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning

Frontiers in Endocrinology

Endocrinology

Jing Peng¹  Yan Guo²  Ying Zhou²  Yang Shi²  Yuxin Guo²  Yicheng Zheng²  Xianhui Zhang²  Huidi Xie²  Hongfang Liu²  Lin Wang²  Zhaoxi Dong²  Jiaming Su²  Haimin Chen² 

[1] Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China;Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China;Key Laboratory of Chinese Internal Medicine of Ministry of Education and Beijing, Renal Research Institution of Beijing University of Chinese Medicine, Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine, Beijing, China;

关键词: diabetic nephropathy;    endoplasmic reticulum stress;    WGCNA (weighted gene co-expression network analysis);    machine learning;    immune cell infiltration;    molecular subtypes;   

DOI  :  10.3389/fendo.2023.1206154

 received in 2023-04-15, accepted in 2023-05-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundsDiabetes 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.MethodsMicroarray 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.ResultsSeveral 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.ConclusionsOur 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.

【 授权许可】

Unknown   
Copyright © 2023 Su, Peng, Wang, Xie, Zhou, Chen, Shi, Guo, Zheng, Guo, Dong, Zhang and Liu