Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning.

ABSTRACT:

Purpose

This study aimed to identify novel biomarkers for preeclampsia (PE) diagnosis by integrating Weighted Gene Co-expression Network Analysis (WGCNA) with machine learning techniques.

Patients and methods

We obtained the PE dataset GSE25906 from the gene expression omnibus (GEO) database. Analysis of differentially expressed genes (DEGs) and module genes with Limma and Weighted Gene Co-expression Network analysis (WGCNA). Candidate hub genes for PE were identified using machine learning. Subsequently, we used western-blotting (WB) and real-time fluorescence quantitative (qPCR) to verify the expression of F13A1 and SCCPDH in preeclampsia patients. Finally, we estimated the extent of immune cell infiltration in PE samples by employing the CIBERSORT algorithms.

Results</

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC11662826 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature