Gestational diabetes mellitus (GDM) is a common complication during pregnancy, but the role of the basement membrane (BM) in GDM is not well understood. This study aims to investigate BM-related genes in GDM to provide new insights for diagnosis and treatment. Differentially expressed genes were identified from the GSE203346 dataset in the Gene Expression Omnibus and intersected with BM-related genes to identify BM-related differential genes. Machine learning and gene expression validation were used to identify key genes, which were further validated using an artificial neural network. Additional analyses included gene set enrichment analysis, immunoprecipitation, drug prediction, gene localization, and the construction of lncRNA-miRNA-mRNA and transcription factor-mRNA regulatory networks to explore underlying mechanisms. Among 801 differentially expressed genes, 24 BM-related differential genes were identified. COL5A1, TGFBI, AGRN, TNC, and ITGB6 were identified as candidate genes, with COL5A1 and TGFBI showing consistent low expression across datasets and being designated as key genes. The artificial neural network demonstrated that these key genes effectively distinguished GDM from control samples. Gene set enrichment analysis revealed the involvement of these genes in pathways such as systemic lupus erythematosus and cytokine-cytokine receptor interaction. TGFBI showed a significant positive correlation with CD4+ memory T cells, common lymphoid progenitors, hematopoietic stem cells, and smooth muscle, while COL5A1 was positively correlated with common lymphoid progenitors and smooth muscle. Six drugs were identified as interacting with both key genes. Our study suggests that COL5A1 and TGFBI offer the possibility of personalized treatment strategies for GDM in the future.
Weng et al. (Fri,) studied this question.