Identification the Low Oxidative Stress Subtype of Periodontitis

Abstract

ABSTRACT: Objective: The aim of this research was to identify the low oxidative stress–related genes expression (L-OS) subtype in patients with periodontitis. Methods: Microarray data (MA) were retrieved from the Gene Expression Omnibus database. The different oxidative stress (OS) subtypes of periodontitis were identified by K-means clustering analysis and gene set variation analysis (GSVA). Differentially expressed genes (DEGs) (|Log fold change (FC)| >1, q 0.4). Finally, the LASSO-Logistic regression model was adopted to evaluate and predict patients’ OS phenotype in routine clinical practice. Results: The 241 periodontitis samples and 69 HCs were included. Thirty-three DEGs between the L-OS and high oxidative stress–related genes expression (H-OS) subtypes and 96 DEGs, including 8 transcription factors, between L-OS subtype and HCs were identified, respectively. Then, the network of TFs-Genes-Drugs was constructed to determine genomic feature of L-OS subtype. Finally, a 4-gene signature formula and the cutoff value were identified by ML with LASSO model to predict patients’ classification. Conclusions: For the first time, we identified L-OS subtype of periodontitis and evaluated its genomic feature with MA.

Keywords

• Periodontitis
• Low oxidative stress
• Microarray data
• TFs-Genes-Medicines network
• Machine learning