Identification and Validation of Aging- and Endoplasmic Reticulum Stress-Related Genes in Periodontitis Using a Competing Endogenous RNA Network.

Abstract

Periodontitis is a multifactorial chronic inflammatory disease that destroy periodontium. Apart from microbial infection and host immune responses, emerging evidence shows aging and endoplasmic reticulum stress (ER stress) play a key role in periodontitis pathogenesis. The aim of this study is to identify aging-related genes (ARGs) and endoplasmic reticulum stress-related genes (ERGs) in periodontitis. Data were obtained from the Gene Expression Omnibus (GEO), Human Ageing Genomic Resources (HAGR) and GeneCards databases to identify differentially expressed mRNAs/miRNAs/lncRNAs (DEmRNAs/DEmiRNAs/DElncRNAs), ARGs and ERGs, respectively. We used the MultiMiR database for the reverse prediction of miRNAs and predicted miRNA-lncRNA interactions using the STARBase database. Afterwards, we constructed a mRNA-miRNA-lncRNA ceRNA network. A total of 10 hub genes, namely LCK, LYN, CXCL8, IL6, HCK, IL1B, BTK, CXCL12, GNAI1 and FCER1G, and 5 DEmRNAs-ARGs-ERGs were then discovered. Further, weighted gene co-expression network analysis (WGCNA) and single sample gene set enrichment analysis (ssGSEA) were performed to explore co-expression modules and immune infiltration respectively. Finally, we used transmission electron microscope (TEM), inverted fluorescence microscopy, quantitative real-time polymerase chain reaction (qRT-PCR) and Western Blot to verify the bioinformatic results in periodontal ligament stem cells (PDLSCs) infected with Porphyromonas gingivalis (P. gingivalis). The experimental results broadly confirmed the accuracy of bioinformatic analysis. The present study established an aging- and ER stress-related ceRNA network in periodontitis, contributing to a deeper understanding of the pathogenesis of periodontitis.