Identifying functional subtypes of IgA nephropathy based on three machine learning algorithms and WGCNA.

ABSTRACT:

Background

IgA nephropathy (IgAN) is one of the most common primary glomerulonephritis, which is a significant cause of renal failure. At present, the classification of IgAN is often limited to pathology, and its molecular mechanism has not been established. Therefore we aim to identify subtypes of IgAN at the molecular level and explore the heterogeneity of subtypes in terms of immune cell infiltration, functional level.

Methods

Two microarray datasets (GSE116626 and GSE115857) were downloaded from GEO. Differential expression genes (DEGs) for IgAN were screened with limma. Three unsupervised clustering algorithms (hclust, PAM, and ConsensusClusterPlus) were combined to develop a single-sample subtype random forest classifier (SSRC). Functional subtypes of IgAN were defined

SUBMITTER: Ren H 

PROVIDER: S-EPMC10893719 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature