Abstract
PURPOSE: Pulpitis is one of the most common oral inflammatory diseases. There are many limitations in the traditional methods of diagnosing pulpitis. By replacing new diagnostic ways based on biomarkers, it is possible to quickly and accurately identify this disease. Biological indicators have greatly helped not only in the screening of infectious diseases but also in early and appropriate treatment. In this research, differentially expressed genes (DEGs) related to pulpitis were analyzed, and prognostic biomarkers were introduced. MATERIALS AND METHODS: In this in silico study, we applied the GSE77459 dataset as the gene expression profile of pulpitis. Web tool, GEO2R was used to separate up-regulated and down-regulated DEGs. |logFC|>2 and adjusted p-value < 0.05 was set as the cut-off criterion. For the pathway enrichment study of obtained genes, EnrichR was implemented. After constructing a protein‑protein interaction (PPI) network, hub genes that are involved in pulpitis were selected. Finally, functionally grouped networks by ClueGO software (v2.5.10) were generated. RESULTS: GEO2R analysis of the GSE77459 dataset showed 672 up-regulated genes and 239 down-regulated genes with GB_ACC code. Based on Cytoscape results, the 15 top hubba nodes were ranked including PTPRC, ITGAM, CCL2, ICAM1, MMP9, CXCL8, TLR2, CD86, CXCR4, IL1A, CD44, CCL3, ITGAX, CXCL10, and CCR7. Functionally grouped networks determined that these genes were mainly enriched in chemokine-mediated signaling pathway, morphogenesis of endothelium, and neuroinflammatory response. CONCLUSION: In our research, 15 genes were introduced as diagnostic biomarkers in pulpitis and their functionally grouped networks were constructed. However, the obtained results need to be validated using in vitro and in vivo methods.