Identification and validation of a classifier based on hub aging-related genes and aging subtypes correlation with immune microenvironment for periodontitis

作者全名："Peng, Limin; Chen, Hang; Wang, Zhenxiang; He, Yujuan; Zhang, Xiaonan"

作者地址："[Peng, Limin; Chen, Hang; Wang, Zhenxiang; Zhang, Xiaonan] Chongqing Med Univ, Coll Stomatol, Chongqing, Peoples R China; [Peng, Limin; Chen, Hang; Wang, Zhenxiang; Zhang, Xiaonan] Chongqing Municipal Key Lab Oral Biomed Engn Highe, Chongqing, Peoples R China; [Peng, Limin; Chen, Hang; Wang, Zhenxiang; Zhang, Xiaonan] Chongqing Key Lab Oral Dis & Biomed Sci, Chongqing, Peoples R China; [He, Yujuan] Chongqing Med Univ, Dept Lab Med, Key Lab Diagnost Med, Minist Educ, Chongqing, Peoples R China"

通信作者："Zhang, XA (通讯作者)，Chongqing Med Univ, Coll Stomatol, Chongqing, Peoples R China.; Zhang, XA (通讯作者)，Chongqing Municipal Key Lab Oral Biomed Engn Highe, Chongqing, Peoples R China.; Zhang, XA (通讯作者)，Chongqing Key Lab Oral Dis & Biomed Sci, Chongqing, Peoples R China."

来源：FRONTIERS IN IMMUNOLOGY

ESI学科分类：IMMUNOLOGY

WOS号：WOS:000885239500001

JCR分区：Q1

影响因子：7.3

年份：2022

卷号：13

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：aging-related genes; diagnosis; immune microenvironment; periodontitis; bioinformatics

摘要："BackgroundPeriodontitis (PD), an age-related disease, is characterized by inflammatory periodontal tissue loss, and with the general aging of the global population, the burden of PD is becoming a major health concern. Nevertheless, the mechanism underlying this phenomenon remains indistinct. We aimed to develop a classification model for PD and explore the relationship between aging subtypes and the immune microenvironment for PD based on bioinformatics analysis. Materials and MethodsThe PD-related datasets were acquired from the Gene Expression Omnibus (GEO) database, and aging-related genes (ARGs) were obtained from the Human Aging Genomic Resources (HAGR). Four machine learning algorithms were applied to screen out the hub ARGs. Then, an artificial neural network (ANN) model was constructed and the accuracy of the model was validated by receiver operating characteristic (ROC) curve analysis. The clinical effect of the model was evaluated by decision curve analysis (DCA). Consensus clustering was employed to determine the aging expression subtypes. A series of bioinformatics analyses were performed to explore the PD immune microenvironment and its subtypes. The hub aging-related modules were defined using weighted correlation network analysis (WGCNA). ResultsTwenty-seven differentially expressed ARGs were dysregulated and a classifier based on four hub ARGs (BLM, FOS, IGFBP3, and PDGFRB) was constructed to diagnose PD with excellent accuracy. Subsequently, the mRNA levels of the hub ARGs were validated by quantitative real-time PCR (qRT-PCR). Based on differentially expressed ARGs, two aging-related subtypes were identified. Distinct biological functions and immune characteristics including infiltrating immunocytes, immunological reaction gene sets, the human leukocyte antigen (HLA) gene, and immune checkpoints were revealed between the subtypes. Additionally, the black module correlated with subtype-1 was manifested as the hub aging-related module and its latent functions were identified. ConclusionOur findings highlight the critical implications of aging-related genes in modulating the immune microenvironment. Four hub ARGs (BLM, FOS, IGFBP3, and PDGFRB) formed a classification model, and accompanied findings revealed the essential role of aging in the immune microenvironment for PD, providing fresh inspiration for PD etiopathogenesis and potential immunotherapy."

基金机构：National Natural Science Foundation of China [81700982]; Chongqing Medical Reserve Talent Studio for Young People [ZQNYXGDRCGZS2019004]

基金资助正文：This work was financially supported by the National Natural Science Foundation of China (81700982) and the Chongqing Medical Reserve Talent Studio for Young People (ZQNYXGDRCGZS2019004).