Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis

Type 2 diabetes mellitus (T2DM) is a multifactorial and multigenetic disease, and its pathogenesis is complex and largely unknown. In the present study, microarray data (GSE201966) of β‑cell enriched tissue obtained by laser capture microdissection were downloaded, including 10 control and 10 type 2 diabetic subjects. A comprehensive bioinformatics analysis of microarray data in the context of protein‑protein interaction (PPI) networks was employed, combined with subcellular location information to mine the potential candidate genes for T2DM and provide further insight on the possible mechanisms involved. First, differential analysis screened 108 differentially expressed genes. Then, 83 candidate genes were identified in the layered network in the context of PPI via network analysis, which were either directly or indirectly linked to T2DM. Of those genes obtained through literature retrieval analysis, 27 of 83 were involved with the development of T2DM; however, the rest of the 56 genes need to be verified by experiments. The functional analysis of candidate genes involved in a number of biological activities, demonstrated that 46 upregulated candidate genes were involved in 'inflammatory response' and 'lipid metabolic process', and 37 downregulated candidate genes were involved in 'positive regulation of cell death' and 'positive regulation of cell proliferation'. These candidate genes were also involved in different signaling pathways associated with 'PI3K/Akt signaling pathway', 'Rap1 signaling pathway', 'Ras signaling pathway' and 'MAPK signaling pathway', which are highly associated with the development of T2DM. Furthermore, a microRNA (miR)‑target gene regulatory network and a transcription factor‑target gene regulatory network were constructed based on miRNet and NetworkAnalyst databases, respectively. Notably, hsa‑miR‑192‑5p, hsa‑miR‑124‑5p and hsa‑miR‑335‑5p appeared to be involved in T2DM by potentially regulating the expression of various candidate genes, including procollagen C‑endopeptidase enhancer 2, connective tissue growth factor and family with sequence similarity 105, member A, protein phosphatase 1 regulatory inhibitor subunit 1 A and C‑C motif chemokine receptor 4. Smad5 and Bcl6, as transcription factors, are regulated by ankyrin repeat domain 23 and transmembrane protein 37, respectively, which might also be used in the molecular diagnosis and targeted therapy of T2DM. Taken together, the results of the present study may offer insight for future genomic‑based individualized treatment of T2DM and help determine the underlying molecular mechanisms that lead to T2DM.