Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein–protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.

Construction of the PPI network and module analysis. The PPI network of the DEGs was constructed with 5111 nodes and 9392 edges by using the IntAct database (Fig. 3). A node with a higher node degree, betweenness centrality, stress centrality and closeness centrality consider as a hub genes and are listed in Table 3. The hub genes included MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9.
MiRNA-hub gene regulatory network construction. The network of miRNAs and predicted targets (hub genes) is presented in Table 4. Based on the miRNAs, a miRNA -hub gene regulatory network was constructed with 2568 nodes (miRNA: 2259; hub gene: 309) and 16,618 interaction pairs (Fig. 5) Validation of hub genes by receiver operating characteristic curve (ROC) analysis. As these 10 hub genes are prominently expressed in T1DM, we performed a ROC curve analysis to evaluate their sensitivity and specificity for the diagnosis of T1DM. As shown in Fig. 7

Discussion
T1DM is the common forms of chronic autoimmune diabetes that affect an individual's quality of childhood life 42 . However, the potential causes of T1DM remain uncertain. Understanding the underlying molecular pathogenesis of T1DM is of key importance for diagnosis, prognosis and identifying drug targets. As NGS data can provide information regarding the expression levels of thousands of genes in the human genome simultaneously, this methodology has been widely used to predict the potential diagnostic and therapeutic targets for T1DM. In the present investigation, we analyzed the NGS dataset GSE162689, which includes 27 T1DM samples and 32 normal control samples. We identified 477 up regulated and 475 down regulated genes between T1DM samples and normal control samples using DESeq2 package in R language software. FGA (fibrinogen alpha chain) 43 and FGB (fibrinogen beta chain) 44 levels are correlated with disease severity in patients with cardiovascular disease, but these genes might provide new targets for the development of drugs to treat T1DM. IGF2 45 , IAPP (islet amyloid      47 and MAFA (MAF bZIP transcription factor A) 48 are proved to be involved in T1DM. Altered expression of ADCYAP1 was observed to be associated with the progression of type 2 diabetes mellitus 49 . Gold et al. 50 reported that CSNK1G1 might be essential for cognitive impairment. Therefore, these genes are might be essential in the advancement of T1DM and its complications. Furthermore, we investigated the biological functions of these DEGs by using online website, and GO and pathway enrichment analysis. Husemoen et al. 51 246 and SAMHD1 247 have been reported to be associated with cardiovascular disease. Previous studies had shown that the altered expression of genes include MAOB (monoamine oxidase B) 248 , VEGFC (vascular endothelial growth factor C) 249 , DBP (D-box binding PAR bZIP transcription factor) 250 , MYADM (myeloid associated differentiation marker) 251 , NES (nestin) 252 266 and Wang et al. 267 revealed that genes include SYVN1, BTG1 and CFB (complement factor B) might be the potential targets for diabetic retinopathy diagnosis and treatment. Study indicating that these enriched genes might play important roles in the progression of T1DM. Construction of PPI network of DEGs may be favorable for understanding the relationship of advancing T1DM. The results of the present investigation might provide potential biomarkers for the diagnosis of T1DM. SMAD9 plays an important role in the development of hypertension 268 . Our results indicate the importance of this hub gene might be involved in occurrence and development of T1DM. MYC (MYC proto-oncogene, bHLH transcription factor), LNX1, YBX1, FN1, TK1 and ANLN (anillin actin binding protein) are likely to provide new potential biomarkers for clinical practice or treatment of T1DM with further research.
In conclusion, the study used a comprehensive bioinformatics analysis methods to identify DEGs, as well as unique biological functions and pathways of T1DM, thereby enhancing the current understanding of the molecular pathogenesis of T1DM. Moreover, these results might provide potential biomarkers for the initial and proper diagnosis of T1DM, as well as potential therapeutic targets for the advancementof novel T1DM treatments.

Data availability
The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https:// www. ncbi. nlm. nih. gov/ geo/) repository. [