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

Jiaming Su; Jing Peng; Lin Wang; Huidi Xie; Ying Zhou; Haimin Chen; Yang Shi; Yan Guo; Yicheng Zheng; Yuxin Guo; Zhaoxi Dong; Xianhui Zhang; Hongfang Liu

doi:10.3389/fendo.2023.1206154

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

Front Endocrinol (Lausanne). 2023 Sep 1:14:1206154. doi: 10.3389/fendo.2023.1206154. eCollection 2023.

Authors

Jiaming Su^{1

2}, Jing Peng¹, Lin Wang^{1

2}, Huidi Xie^{1

2}, Ying Zhou^{1

2}, Haimin Chen^{1

2}, Yang Shi^{1

2}, Yan Guo^{1

2}, Yicheng Zheng^{1

2}, Yuxin Guo^{1

2}, Zhaoxi Dong^{1

2}, Xianhui Zhang^{1

2}, Hongfang Liu^{1

2}

Affiliations

¹ Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
² 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.

Abstract

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.

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

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biomarkers
Computational Biology
Diabetes Mellitus*
Diabetic Nephropathies* / diagnosis
Diabetic Nephropathies* / genetics
Endoplasmic Reticulum Stress / genetics
Humans
Machine Learning
Receptors, Estrogen

Substances

Receptors, Estrogen
Biomarkers

Grants and funding

This study was supported by the National Natural Science Foundation of China program (No. 81774273 and No. 82004275), and Beijing Municipal Natural Science Foundation (No. 7212180).