Han Tang1, Shuai Yuan1, Tao Chen1, Ping Ji1. 1. Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Stomatological Hospital of Chongqing Medical University, Chongqing, China.
Abstract
AIM: To explore the relationship between long non-coding RNAs (lncRNAs) and immune response and to construct an immune-related competing endogenous RNA (ceRNA) network in periodontitis. MATERIALS AND METHODS: Gene expression profiles in gingival tissues were acquired from the Gene Expression Omnibus database. Bioinformatic analysis was performed to establish an immune-related ceRNA network. Subsequently, functional enrichment analysis was performed to detect the biological processes in which the ceRNA network might be involved. RESULTS: A combined classification model involving seven lncRNAs was constructed. Receiver operating characteristic curve analysis showed satisfactory classification ability of the established model. Further analysis revealed that the screened lncRNAs were significantly correlated with patient immunity. Finally, an immune-related ceRNA network was constructed based on the lncRNA MIAT, miR-1246, miR-1260b, miR-3652, miR-4286, and 27 mRNAs. Accordingly, functional enrichment analysis demonstrated that this network is closely related to the proliferation, differentiation, and activation of B cells. CONCLUSIONS: The lncRNA MIAT and the MIAT-based ceRNA network may be instrumental in regulating the immune response, especially of B cells, during the progression of periodontitis.
AIM: To explore the relationship between long non-coding RNAs (lncRNAs) and immune response and to construct an immune-related competing endogenous RNA (ceRNA) network in periodontitis. MATERIALS AND METHODS: Gene expression profiles in gingival tissues were acquired from the Gene Expression Omnibus database. Bioinformatic analysis was performed to establish an immune-related ceRNA network. Subsequently, functional enrichment analysis was performed to detect the biological processes in which the ceRNA network might be involved. RESULTS: A combined classification model involving seven lncRNAs was constructed. Receiver operating characteristic curve analysis showed satisfactory classification ability of the established model. Further analysis revealed that the screened lncRNAs were significantly correlated with patient immunity. Finally, an immune-related ceRNA network was constructed based on the lncRNA MIAT, miR-1246, miR-1260b, miR-3652, miR-4286, and 27 mRNAs. Accordingly, functional enrichment analysis demonstrated that this network is closely related to the proliferation, differentiation, and activation of B cells. CONCLUSIONS: The lncRNA MIAT and the MIAT-based ceRNA network may be instrumental in regulating the immune response, especially of B cells, during the progression of periodontitis.