Identification of biomarkers related to prognosis and diagnosis of periodontitis by bioinformatics based on public database.

Abstract

Periodontitis is a multifactorial disease that has a negative impact on people's life. However, studies on potential key genes with excellent diagnostic value for periodontitis disease have not been systematically explored. GSE10334 data set was downloaded from the Gene Expression Omnibus database. Following the gene expression profiles were normalized by the Robust multi-array average (RMA) algorithm, the differentially expressed genes were screened and incorporated into Weight gene correlation network analysis to obtain hub genes. Receiver-operating characteristic curve analysis was used to verify the validity and agility of the hub genes-based least absolute shrinkage and selection operator model. Furthermore, we validated the expression of these hub genes by real-time polymerase chain reaction and western blotting. Eight hub genes were identified and had good diagnostic values. Besides, the upregulations of eight hub genes were verified both in protein and mRNA levels in clinical periodontitis gum tissue. We discovered potential biomarkers in periodontitis based on the public database and these biomarkers focused on several immune responses and inflammatory pathways. Thus, this study may provide potential therapeutic targets for early diagnosis and treatment of periodontitis.