Integrating transcriptomic and epigenomic data to identify potential biomarkers in gestational diabetes mellitus patients

Gestational diabetes mellitus (GDM), one of the prevalent pregnancy-related metabolic disorders, have shown immediate or long-term adverse health outcomes for maternal and fetal health. Therefore, it is crucial to understand the ongoing cellular and molecular changes in GDM patients for characterizing novel biomarkers for diagnosis and therapeutic purposes. In the current study, we analyzed 3 transcriptomic datasets, characterized 449 unique upregulated and 785 downregulated DEGs, and performed several analyses. Gene ontology shows enrichment of migration, development, and immune-related processes in GDM patients. KEGG pathway shows enrichment of pathways like "type 1 diabetes mellitus" and "graft versus host disease". Disease ontology shows enrichment of "female reproductive system disease," "anemia," etc. Integration of methylation and transcriptomic data identified 11 genes (RASSF2, WSCD1, TNFAIP3, TPST1, UBASH3B, ZFP36, CRISPLD2, IGFBP7, TNS3, TPM2, and VTRNA1-2), as potential novel diagnostic biomarkers and therapeutic targets. Furthermore, immune cell-type infiltration analysis shows higher memory B-cells and lower M1 macrophages and CD8 T-cells. Protein-protein interaction analysis followed by ROC analysis in an independent dataset identified 7 hub genes (POLR2G, VWF, COL5A1, COL6A1, CD44, COL3A1, and COL1A1) with high diagnostic potential. Overall, we obtained 18 genes that could serve as novel diagnostic biomarkers and therapeutic targets in GDM patients.