Informatics in Medicine Unlocked (Jan 2022)
Deciphering potential biomarkers for celiac disease by using an integrated bioinformatics approach
Abstract
Background: Celiac disease (CD) is an autoimmune condition caused by gluten in genetically predisposed people. This study aims to identify potential biomarkers of CD by using bioinformatics approaches. Materials and methods: The microarray dataset (GSE113469) was selected from the GEO database and reanalyzed in this study. Differentially expressed genes (DEGs) of CD patients and healthy individuals were obtained through the LIMMA package of the R software. By using the STRING online database, the protein-protein interaction (PPI) network were established and visualized via Cytoscape. Hub genes were identified by the Cytoscape's cytoHubba plugin using different methods. Receiver operating characteristic (ROC) analysis was employed to assess the diagnostic precision of hub genes. The association of hub genes with related transcription factors (TFs) and microRNAs (miRNAs) was evaluated, and candidate drugs that interacted with the hub genes were subsequently identified using the DGIdb database. Results: Ninety-nine DEGs were identified through the LIMMA package of R software from the GSE113469 dataset. Then, by using the STRING online database, the PPI network of DEGs was established and visualized via Cytoscape. The five hub genes (PTPRC, HIF1A, CXCL8, CCL2, and CXCR4) were identified by cytoHubba using different methods. The ROC analysis revealed that all of the hub genes had a good diagnostic value. Moreover, the miRNA; miR-155–5p, and the TFs; SMAD4, MYC, and BACH1 were screened to have the most correlation with the hub genes. Ultimately, 87 candidate drugs that interacted with the hub genes were found by using the DGIdb database. Conclusion: Eventually, it can be concluded that identifying genes and molecular pathways in CD can contribute to a better understanding of disease pathogenesis and designing therapeutic methods.
