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Cited by 42Chronic Kidney Disease and DiabetesRenal Diseases and GlomerulopathiesGene expression and cancer classification

Identification and Verification of Diagnostic Biomarkers for Glomerular Injury in Diabetic Nephropathy Based on Machine Learning Algorithms

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Han, Hongdong, Chen, Yanrong, Yang, Hao, Cheng, Wei, Zhang, Sijing, Liu, Yunting, Liu, Qiuhong, Liu, Dongfang +2 more·2022·Frontiers in Endocrinology
DOI 10.3389/fendo.2022.876960PMC PMC9162431
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Abstract

Diabetic nephropathy (DN) is regarded as the leading cause of end-stage renal disease worldwide and lacks novel therapeutic targets. To screen and verify special biomarkers for glomerular injury in patients with DN, fifteen datasets were retrieved from the Gene Expression Omnibus (GEO) database, correspondingly divided into training and testing cohorts and then merged. Using the limma package, 140 differentially expressed genes (DEGs) were screened out between 81 glomerular DN samples and 41 normal ones from the training cohort. With the help of the ConsensusClusterPlus and WGCNA packages, the 81 glomerular DN samples were distinctly divided into two subclusters, and two highly associated modules were identified. By using machine learning algorithms (LASSO, RF, and SVM-RFE) and the Venn diagram, two overlapping genes (PRKAR2B and TGFBI) were finally determined as potential biomarkers, which were further validated in external testing datasets and the HFD/STZ-induced mouse models. Based on the biomarkers, the diagnostic model was developed with reliable predictive ability for diabetic glomerular injury. Enrichment analyses indicated the apparent abnormal immune status in patients with DN, and the two biomarkers played an important role in the immune microenvironment. The identified biomarkers demonstrated a meaningful correlation between the immune cells' infiltration and renal function. In conclusion, two robust genes were identified as diagnostic biomarkers and may serve as potential targets for therapeutics of DN, which were closely associated with multiple immune cells.

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References 33 · Cited by 42

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Authors
Han, H., Chen, Y., Yang, H., Cheng, W., Zhang, S., Liu, Y., Liu, Q., Liu, D., Yang, G., & Li, K.
Affiliations
  • Second Affiliated Hospital of Chongqing Medical University (CN)
  • Chongqing Jiulongpo People's Hospital (CN)
Corresponding author
Li, Ke
ORCID
Venue
Frontiers in Endocrinology
Publisher
Frontiers Media SA
Published
2022-05-19
Volume
13
Pages
876960-876960
ISSN
1664-2392 (ISSN-L)
DOI
10.3389/fendo.2022.876960
PMC
PMC9162431
OpenAlex
W4280640234
Cited by
42
References
33
Citations by year
2026: 2 · 2025: 13 · 2024: 15 · 2023: 11
FWCI
6.19
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Endocrinology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China
  • Natural Science Foundation of Chongqing
Topics
  • Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
  • Health Sciences › Medicine › Nephrology › Renal Diseases and Glomerulopathies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
diabetic nephropathy, glomerular injury, diagnostic biomarkers, PRKAR2B, TGFBI, differentially expressed genes, gene co-expression modules, immune cell infiltration, diagnostic model
MeSH terms
Machine Learning, Algorithms, Animals, Diabetes Mellitus, Diabetic Nephropathies, Humans, Biomarkers, Mice

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