Exploring the hub gene CERS6 as a therapeutic target in type 1 diabetes through a bioinformatics and network analyst approach

Abstract

Insulin-producing β-cells are destroyed in type 1 diabetes mellitus (T1DM), a chronic autoimmune disease that results in complete insulin insufficiency and metabolic dysfunction. According to a survival study that used p values, some hub genes are important for predicting and diagnosing illness. Scientists have inferred medicines to identify possible therapies that interact with the identified hub genes. The GSE10586 gene expression dataset from the Gene Expression Omnibus (GEO) was used for this investigation, which included 27 samples from 15 healthy controls and 12 diabetic patients. Normalization methods such as variance stabilization normalization (VSN) were used as part of the data pretreatment. A protein‒protein interaction (PPI) network was constructed, principal component analysis (PCA) was performed, heatmaps were created, and the Limma algorithm was used to analyze differential gene expression. Using DAVID v6.8 and KEGG pathway annotations, the functional enrichment of differentially expressed genes (DEGs) was evaluated. Furthermore, a computational study revealed CERS6 to be one of the potential hub genes. Four drugs, methotrexate, eliglustat, myriocin and statin, were the focus of further studies on the basis of predictions made via ChemSpider and PubChem database analysis. To determine the optimal binding positions of these drugs with CERS6, we used molecular docking techniques. The binding affinity of methotrexate was 8.48 kcal/mol, that of myriocin was 7.85 kcal/mol, that of eliglustat was − 6.62 kcal/mol, and that of serine was − 4.90 kcal/mol against the binding pocket’s active residues. To determine how consistently each drug interacted with the CERS6 protein over time, molecular dynamics (MD) simulations were run. Throughout the simulation intervals, both medications were confirmed to be stable, with minor alterations in the CERS6 protein loop region. Therefore, the investigation of structure-based drug design has potential for identifying specific therapeutic targets. Ten hub genes were identified via network analysis of differentially expressed genes. These hub genes could serve as novel targets for T1DM detection, prognosis, and targeting. CERS6 exhibited the highest degree of interaction. Methotrexate, eliglustat, myriocin and statins were identified as potential drugs for CERS6. Overall, these findings provide valuable insights that could pave the way for new experimental strategies in T1DM therapy.