Taiwanese Journal of Obstetrics & Gynecology (May 2024)
Identification of hub genes in placental dysfunction and recurrent pregnancy loss through transcriptome data mining: A meta-analysis
Abstract
Recurrent pregnancy loss (RPL) is a condition characterized by the loss of two or more pregnancies before 20 weeks of gestation. The causes of RPL are complex and can be due to a variety of factors, including genetic, immunological, hormonal, and environmental factors. This transcriptome data mining study was done to explore the differentially expressed genes (DEGs) and related pathways responsible for pathogenesis of RPL using an Insilco approach. RNAseq datasets from the Gene Expression Omnibus (GEO) database was used to extract RNAseq datasets of RPL. Meta-analysis was done by ExpressAnalyst. The functional and pathway enrichment analysis of DEGs were performed using KEGG and BINGO plugin of Cytoscape software. Protein–protein interaction was done using STRING and hub genes were identified. A total of 91 DEGs were identified, out of which 10 were downregulated and 81 were upregulated. Pathway analysis indicated that majority of DEGs were enriched in immunological pathways (IL-17 signalling pathway, TLR-signalling pathway, autoimmune thyroid disease), angiogenic VEGF-signalling pathway and cell-cycle signalling pathways. Of these, 10 hub genes with high connectivity were selected (CXCL8, CCND1, FOS, PTGS2, CTLA4, THBS1, MMP2, KDR, and CD80). Most of these genes are involved in maintenance of immune response at maternal–fetal interface. Further, in functional enrichment analyses revealed the highest node size in regulation of biological processes followed by cellular processes, their regulation and regulation of multicellular organismal process. This in-silico transcriptomics meta-analysis findings could potentially contribute in identifying novel biomarkers and therapeutic targets for RPL, which could lead to the development of new diagnostic and therapeutic strategies for this condition.
