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

• Published in: Computational biology and chemistry Vol. 119; p. 108546
• Main Authors: Palanisamy, Tamil Barathi, Arumugam, Mohanapriya
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.12.2025
• Subjects: Drug-gene interaction; Lasso regression; Preeclampsia; Receiver operating characteristics analysis; Weighted gene co-expression network analysis; Receiver operating characteristics analysis; Drug-gene interaction; Lasso regression; Preeclampsia; Weighted gene co-expression network analysis
• ISSN: 1476-9271; 1476-928X; 1476-928X
• DOI: 10.1016/j.compbiolchem.2025.108546

Pre-eclampsia (PE) remains one of the leading causes of maternal and fetal morbidity, affecting 2-8 % of pregnancies worldwide. Despite great efforts in research, the precise molecular mechanisms underlying this complex disorder have not been identified. In this study, we used RNA sequence data (RNA-Seq) and applied advanced bioinformatics approaches to study the pathophysiology of PE. The weighted gene co-expression network analysis (WGCNA) method was used to construct a co-expression network of 239 differentially expressed genes (DEGs) between healthy and PE, which led to the identification of seven specific modules. Two modules, turquoise and yellow, showed strong co-relationships with PE. Further, functional enrichment pointed toward various important biological pathways, such as NAD metabolism, HIF-1 signaling, glycolysis/gluconeogenesis, PI3K AKT-signaling pathway, and JAK-STAT pathway. Further candidate genes were identified through clustering and analysis of protein interaction networks, and least absolute shrinkage and selection operator (LASSO) regression analyses identified five crucial predictor genes, such as GAPDH, LEP, PKM, TRIM24, and NDRG1, which are highly essential in PE. The prognostic potential of the identified biomarkers was confirmed by a receiver operating characteristic (ROC) curve analysis that achieved an area under the curve (AUC) of 0.987 and demonstrated high discriminatory power between the groups of healthy subjects and PE. To validate these findings, external validation was performed using microarray dataset. In addition, drug-gene interaction analyses were performed using the drug gene interaction database (DGIdb) database and revealed interactions for only three biomarkers: GAPDH, PKM, and LEP. These integrated systems biology approaches have identified key biomarkers and potential therapeutic targets for PE, providing a strong basis for future research into its molecular mechanisms and clinical management. [Display omitted] •GAPDH, PKM and LEP were identified as predictive key predictive biomarkers for preeclampsia.•LEP had the highest predictive value, followed by GAPDH and PKM.•Estradiol Valerate interacted with both GAPDH and LEP, highlighting therapeutic potential.