Identification of autophagy-related genes as potential biomarkers for type 1 diabetes mellitus

BACKGROUND: Type 1 diabetes mellitus (T1DM) is a metabolic disease in which the autoimmune destruction of pancreatic islet β-cells occurs. This study sought to investigate the role of autophagy-related genes and immune cells in the development of T1DM. METHODS: We acquired the raw gene expression profiles of 302 T1DM and 422 normal control peripheral blood samples from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the Limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The Search Tool for the Retrieval of Interacting Genes/Proteins (https://string-db.org/) and Cytoscape autophagy genes were intersected with the DEGs for the immune cell analysis and the correlation analysis. RESULTS: A total of 568 DEGs were identified in the T1DM and normal samples, of which 301 were upregulated and 267 were downregulated. The results of the functional and pathway enrichment analyses showed that the DEGs were closely associated with autophagy and immunity. Member RAS oncogene family (RAB11A), protein tyrosine phosphatase non-receptor type 11, lamin A/C, heat shock protein70, heat shock protein family A member 4, cluster of differentiation 8A, caspase 3 (CASP3), exportin 1, proto-oncogene, non-receptor tyrosine kinase, SMAD family member 4, and sirtuin 1 (SIRT1) were located at the center of the protein-protein interaction network as the core genes. The peripheral blood T cells were more elevated in the T1DM subjects than the normal subjects. RAB11A, CASP3, and SIRT1 are autophagy-associated genes. RAB11A and CASP3 were positively correlated with most immune cells, while SIRT1 was negatively correlated with most immune cells. CONCLUSIONS: Autophagy-related genes (i.e., RAB11A, CASP3, and SIRT1) and immune cells (i.e., T and B cells) may play important regulatory roles in the development of T1DM. Our findings provide novel insights into and potential targets for T1DM prediction and treatment.