Predicting diagnostic gene expression profiles associated with immune infiltration in patients with lupus nephritis.

ABSTRACT:

Objective

To identify potential diagnostic markers of lupus nephritis (LN) based on bioinformatics and machine learning and to explore the significance of immune cell infiltration in this pathology.

Methods

Seven LN gene expression datasets were downloaded from the GEO database, and the larger sample size was used as the training group to obtain differential genes (DEGs) between LN and healthy controls, and to perform gene function, disease ontology (DO), and gene set enrichment analyses (GSEA). Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify candidate biomarkers. The diagnostic value of LN diagnostic gene biomarkers was further evaluated in t

SUBMITTER: Wang L 

PROVIDER: S-EPMC9755505 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature