Transcriptomic profiling and biomarker discovery in pre-eclampsia: An integrated approach leveraging WGCNA and LASSO...

Area of Science:

• Genomics
• Bioinformatics
• Reproductive Medicine

Background:

• Pre-eclampsia (PE) affects 2-8% of pregnancies globally, causing significant maternal and fetal harm.
• The underlying molecular mechanisms of PE remain largely unidentified despite extensive research efforts.

Purpose of the Study:

• To investigate the pathophysiology of pre-eclampsia using RNA sequencing (RNA-Seq) and advanced bioinformatics.
• To identify key molecular biomarkers and potential therapeutic targets for PE.

Main Methods:

• Weighted Gene Co-expression Network Analysis (WGCNA) to identify gene modules associated with PE.
• Functional enrichment analysis of differentially expressed genes (DEGs).
• Protein-protein interaction network analysis, LASSO regression, and ROC curve analysis for biomarker identification and validation.

Main Results:

• Two gene modules (turquoise and yellow) strongly correlated with PE.
• Key biological pathways implicated include NAD metabolism, HIF-1 signaling, and glycolysis.
• Five crucial predictor genes (GAPDH, LEP, PKM, TRIM24, NDRG1) were identified with high diagnostic accuracy (AUC=0.987).
• External validation confirmed findings; drug-gene interactions identified for GAPDH, PKM, and LEP.

Conclusions:

• Integrated systems biology approaches successfully identified key biomarkers for PE.
• The identified genes and pathways represent potential targets for future PE research and clinical management.