Development of a classification model and an immune-related network based on ferroptosis in periodontitis

作者全名："Xu, Zhihong; Tan, Ruolan; Li, Xiaodong; Pan, Lanlan; Ji, Ping; Tang, Han"

作者地址："[Xu, Zhihong; Tan, Ruolan; Li, Xiaodong; Pan, Lanlan; Ji, Ping; Tang, Han] Chongqing Med Univ, Stomatol Hosp, Chongqing Key Lab Oral Dis & Biomed Sci, Chongqing Municipal Key Lab Oral Biomed Engn Highe, Chongqing, Peoples R China; [Xu, Zhihong] Peoples Hosp Dadukou Dist, Chongqing, Peoples R China; [Ji, Ping; Tang, Han] Chongqing Med Univ, Stomatol Hosp, Chongqing Key Lab Oral Dis & Biomed Sci, Chongqing Municipal Key Lab Oral Biomed Engn Highe, Chongqing 401147, Peoples R China"

通信作者："Ji, P; Tang, H (通讯作者)，Chongqing Med Univ, Stomatol Hosp, Chongqing Key Lab Oral Dis & Biomed Sci, Chongqing Municipal Key Lab Oral Biomed Engn Highe, Chongqing 401147, Peoples R China."

来源：JOURNAL OF PERIODONTAL RESEARCH

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000913933000001

JCR分区：Q1

影响因子：3.4

年份：2023

卷号：58

期号：2

开始页：403

结束页：413

文献类型：Article

关键词：ferroptosis; immune infiltration; periodontitis; weighted correlation network analysis

摘要："Background and ObjectivesPeriodontitis is an immunoinflammatory disease characterized by irreversible periodontal attachment loss and bone destruction. Ferroptosis is a kind of immunogenic cell death that depends on the participation of iron ions and is involved in various inflammatory and immune processes. However, information regarding the relationship between ferroptosis and immunomodulation processes in periodontitis is extremely limited. The purpose of this study was to investigate the correlation between ferroptosis and immune responses in periodontitis. MethodsGene expression profiles of gingivae were collected from the Gene Expression Omnibus data portal. After detecting differentially expressed ferroptosis-related genes (FRGs), we used univariate logistic regression analysis followed by logistic least absolute shrinkage and selection operator (LASSO) regression to establish a ferroptosis-related classification model in an attempt to accurately distinguish periodontitis gingival tissues from healthy samples. The infiltration level of immunocytes in periodontitis was then assessed through single-sample gene-set enrichment analysis. Subsequently, we screened out immune-related genes by weighted correlation network analysis and protein-protein interaction (PPI) analysis and constructed an immune-related network based on FRGs and immune-related genes. ResultsA total of 24 differentially expressed FRGs were detected, and an 8-FRG combined signature constituted the classification model. The established model showed outstanding discriminating ability according to the results of receiver operating characteristic (ROC) curve analysis. In addition, the periodontitis samples had a higher degree of immunocyte infiltration. Activated B cells had the strongest positive correlation while macrophages had a strong negative correlation with certain FRGs, and we found that XBP1, ALOX5 and their interacting genes might be crucial genes in the immune-related network. ConclusionsThe FRG-based classification model had a satisfactory determination ability, which could bring new insights into the pathogenesis of periodontitis. Those genes in the immune-related network, especially hub genes along with XBP1 and ALOX5, would have the potential to serve as promising targets of immunomodulatory treatments for periodontitis."

基金机构："Chongqing Special Postdoctoral Science Foundation [2010010005784771]; Natural Science Foundation of Chongqing, China [cstc2018jcyjAX0200, cstc2021jcyj-bshX0140]"

基金资助正文："ACKNOWLEDGEMENTS This work was supported by the Chongqing Special Postdoctoral Science Foundation (No. 2010010005784771) and the Natural Science Foundation of Chongqing, China (No. cstc2018jcyjAX0200 and cstc2021jcyj-bshX0140). We would like to thank all the study team members for their support and contribution in any form."