Unveiling immune and signalling proteins in recurrent pregnancy loss: GEO2R analysis sheds light.

BACKGROUND

Recurrent pregnancy loss (RPL) is defined as the spontaneous loss of two or more pregnancies on or before 24 weeks of gestation. It has multifactorial aetiology, including genetic abnormalities, immune dysfunction, hormonal imbalances, and environmental factors. The Gene Expression Omnibus (GEO) database and its analytical tool GEO2R enable differential gene expression analysis to identify potential biomarkers in RPL.

OBJECTIVE

This study aims to identify differentially expressed genes (DEGs) and pathways contributing to RPL pathogenesis using transcriptome data mining and in silico approaches.

METHODOLOGY

High-throughput gene expression data from four datasets (GSE141716, GSE204721, GSE161969, and GSE139180) were analysed. Enrichment analysis of DEGs conducted using the KEGG database and the BINGO plugin in Cytoscape. Protein-protein interaction (PPI) networks constructed using STRING, and molecular docking of key hub genes performed using HDOCK and Discovery Studio. ELISA validation of TNF-α, CD44, and MMP2 analysed on serum samples.

RESULTS

Pathway analysis revealed immune-related pathways, including TNF-α, CD44, MMP2, VEGFR, and IL-17 signalling. Ten hub genes identified TNF, CD44, MMP2, CCL2, FN1, IL1A, THBS1, STAT1, ICAM1, and PXDN. Docking analysis confirmed TNFα-CD44 interactions, emphasizing their role in immune tolerance. ELISA results showed significantly elevated TNF-α (p = 0.001) and MMP2 (p = 0.0001) levels in RPL cases, while CD44 was not found significant (p = 0.632).

CONCLUSION

This transcriptomic study highlights immune modulation as a key factor in RPL, identifying potential biomarkers and therapeutic targets for improved diagnosis and management.