Prediction of Differentially Expressed Genes and a Diagnostic Signature of Preeclampsia via Integrated Bioinformatics Analysis.

ABSTRACT:

Background

Preeclampsia (PE), which has a high incidence rate worldwide, is a potentially dangerous syndrome to pregnant women and newborns. However, the exact mechanism of its pathogenesis is still unclear. In this study, we used bioinformatics analysis to identify hub genes, establish a logistic model, and study immune cell infiltration to clarify the physiopathogenesis of PE.

Methods

We downloaded the GSE75010 and GSE10588 datasets from the GEO database and performed weighted gene coexpression network analysis (WGCNA) as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The online search tool for the retrieval of interacting genes and Cytoscape software were used to identify hub genes, which were then used to establish a logistic mode

SUBMITTER: Huang S 

PROVIDER: S-EPMC9197614 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature