Abstract
This study aimed to identify core genes of Gestational diabetes mellitus (GDM) and explore its immune microenvironment. Using the limma package, we were able to identify differentially expressed genes (DEGs) between GDM and normal placental tissue. Weighted gene co-expression network analysis (WGCNA) and various machine-learning algorithms were subsequently employed to identify core genes that may influence the occurrence of GDM. Analysis was used to evaluate the diagnostic usefulness of the core genes by using the receiver operating characteristic (ROC) analysis method. In gene enrichment analysis, we utilized the CIBERSORT algorithm to assess the immune cell composition in various samples, followed by the application of the Wilcoxon test to evaluate the immune cell content in diabetes samples during pregnancy. Conversely, analysis was done on the relationship between immune cells and core genes. Finally, we used RUN PCA to integrate different data sets and cluster cells with different functions. 527 up-regulated genes were found, and 690 down-regulated were found. Combining the results of the algorithms and ROC analysis, we identified CCL3/FAM3B/IL1RL1 as potential diagnostic biomarkers for GDM, and validated their diagnosibility using an external dataset. The results of the functional enrichment analysis indicated that core genes are associated with immune cells. When compared to pregnant women who were having diabetes, there was a considerable rise in the percentage of macrophages in immunological cells. The expression of three core genes in different cells of different samples showed that the expression of CCL3 was increased in macrophages of GDM. Cell communication analysis showed that macrophage communication was significantly active in GDM, and CCL signal was significantly increased, which mainly played a significant role through CCL3-CCR1 pathway. The findings suggest that CCL3 closely related to GDM occurrence and progression, represent new GDM marker, and that the modification of immune microenvironment plays a significant role in the occurrence of GDM.