Uncovering the potential role of oxidative stress in the development of periodontitis and establishing a stable diagnostic model via combining single-cell and machine learning analysis

Frontiers in Immunology

Immunology

Siqi Gou¹  Gaoge Peng¹  Hao Chi¹  Gang Tian²  Binyu Song³  Guanhu Yang⁴  Jing Zhang⁵  Jinyan Yang⁶  Jinhao Zhang⁶  Xixi Xie⁶  Guobin Song⁶ 

[1] Clinical Medical College, Southwest Medical University, Luzhou, China;Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China;Department of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi’an, China;Department of Specialty Medicine, Ohio University, Athens, OH, United States;Division of Basic Biomedical Sciences, The University of South Dakota Sanford School of Medicine, Vermillion, SD, United States;School of Stomatology, Southwest Medical University, Luzhou, China;

关键词: oxidative stress;    periodontitis;    inflammation;    machine learning;    diagnostic signature;    WGCNA;    single-cell RNA-seq;   

DOI  :  10.3389/fimmu.2023.1181467

 received in 2023-03-07, accepted in 2023-06-20,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundThe primary pathogenic cause of tooth loss in adults is periodontitis, although few reliable diagnostic methods are available in the early stages. One pathological factor that defines periodontitis pathology has previously been believed to be the equilibrium between inflammatory defense mechanisms and oxidative stress. Therefore, it is necessary to construct a model of oxidative stress-related periodontitis diagnostic markers through machine learning and bioinformatic analysis.MethodsWe used LASSO, SVM-RFE, and Random Forest techniques to screen for periodontitis-related oxidative stress variables and construct a diagnostic model by logistic regression, followed by a biological approach to build a Protein-Protein interaction network (PPI) based on modelled genes while using modelled genes. Unsupervised clustering analysis was performed to screen for oxidative stress subtypes of periodontitis. we used WGCNA to explore the pathways correlated with oxidative stress in periodontitis patients. Networks. Finally, we used single-cell data to screen the cellular subpopulations with the highest correlation by scoring oxidative stress genes and performed a proposed temporal analysis of the subpopulations.ResultsWe discovered 3 periodontitis-associated genes (CASP3, IL-1β, and TXN). A characteristic line graph based on these genes can be helpful for patients. The primary hub gene screened by the PPI was constructed, where immune-related and cellular metabolism-related pathways were significantly enriched. Consistent clustering analysis found two oxidative stress categories, with the C2 subtype showing higher immune cell infiltration and immune function ratings. Therefore, we hypothesized that the high expression of oxidative stress genes was correlated with the formation of the immune environment in patients with periodontitis. Using the WGCNA approach, we examined the co-expressed gene modules related to the various subtypes of oxidative stress. Finally, we selected monocytes for mimetic time series analysis and analyzed the expression changes of oxidative stress genes with the mimetic time series axis, in which the expression of JUN, TXN, and IL-1β differed with the change of cell status.ConclusionThis study identifies a diagnostic model of 3-OSRGs from which patients can benefit and explores the importance of oxidative stress genes in building an immune environment in patients with periodontitis.

【 授权许可】

Unknown   
Copyright © 2023 Song, Peng, Zhang, Song, Yang, Xie, Gou, Zhang, Yang, Chi and Tian