Identification and verification of diagnostic biomarkers in recurrent pregnancy loss via machine learning algorithm and WGCNA.

ABSTRACT:

Background

Recurrent pregnancy loss defined as the occurrence of two or more pregnancy losses before 20-24 weeks of gestation, is a prevalent and significant pathological condition that impacts human reproductive health. However, the underlying mechanism of RPL remains unclear. This study aimed to investigate the biomarkers and molecular mechanisms associated with RPL and explore novel treatment strategies for clinical applications.

Methods

The GEO database was utilized to retrieve the RPL gene expression profile GSE165004. This profile underwent differential expression analysis, WGCNA, functional enrichment, and subsequent analysis of RPL gene expression using LASSO regression, SVM-RFE, and RandomForest algorithms for hub gene screening. ANN model were constructed to assess

SUBMITTER: Wei C 

PROVIDER: S-EPMC10485775 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature