Bioinformatics analysis for identifying micro-RNAs, long noncoding RNAs, transcription factors, and immune genes regulatory networks in diabetic cardiomyopathy using an integrated bioinformatics analysis (April, 10.1007/s00011-022-01571-5, 2022)

Objectives: We identified functional genes and studied the underlying molecular mechanisms of diabetic cardiomyopathy (DCM) using bioinformatics tools. Methods: Original gene expression profiles were obtained from the GSE21610 and GSE112556 data sets. We used GEO2R to screen the differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on DEGs. Protein–protein interaction (PPI) networks of DEGs were constructed using STRING and hub genes of signaling pathways were identified using Cytoscape. Aberrant hub gene expression was verified using The Cancer Genome Atlas data set. Results: The DEGs in DCM were mainly enriched in the nuclei and cytoplasm and involved in DCM and chemokine-related signaling pathways. In the PPI network, 32 nodes were chosen as hub nodes and an RNA interaction network was constructed with 517 interactions. The expression of key genes (JPIK3R1, CCR9, XIST, WDFY3.AS2, hsa-miR-144-5p, and hsa-miR-146b-5p) was significantly different between DCM and normal tissues. Conclusions: The identified hub genes could be associated with DCM pathogenesis and could be used for treating DCM. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.