Machine learning-based metabolism-related genes signature and immune infiltration landscape in diabetic nephropathy.

ABSTRACT:

Background

To identify the diagnostic biomarkers of metabolism-related genes (MRGs), and investigate the association of the MRGs and immune infiltration landscape in diabetic nephropathy (DN).

Methods

The transcriptome matrix was downloaded from the GEO database. R package "limma" was utilized to identify the differential expressed MRGs (DE-MRGs) of HC and DN samples. Genetic Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DE-MRGs were performed using "clusterProfiler" R package. WGCNA, LASSO, SVM-RFE, and RFE algorithms were employed to select the diagnostic feature biomarkers for DN. The ROC curve was used to evaluate discriminatory ability for diagnostic feature biomarkers. CIBERSORT algorithm was performed to investigate the fraction of the 2

SUBMITTER: Zhang H 

PROVIDER: S-EPMC9722730 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature