PDF(Pubmed)
Abstract：
UNASSIGNED: Periodontitis, a common chronic inflammatory disease, significantly impacted oral health. To provide novel biological indicators for the diagnosis and treatment of periodontitis, we analyzed public microarray datasets to identify biomarkers associated with periodontitis.
UNASSIGNED: The Gene Expression Omnibus (GEO) datasets GSE16134 and GSE106090 were downloaded. We performed differential analysis and robust rank aggregation (RRA) to obtain a list of differential genes. To obtain the core modules and core genes related to periodontitis, we evaluated differential genes through enrichment analysis, correlation analysis, protein-protein interaction (PPI) network and competing endogenous RNA (ceRNA) network analysis. Potential biomarkers for periodontitis were identified through comparative analysis of dual networks (PPI network and ceRNA network). PPI network analysis was performed in STRING. The ceRNA network consisted of RRA differentially expressed messenger RNAs (RRA_DEmRNAs) and RRA differentially expressed long non-coding RNAs (RRA_DElncRNAs), which regulated each other\'s expression by sharing microRNA (miRNA) target sites.
UNASSIGNED: RRA_DEmRNAs were significantly enriched in inflammation-related biological processes, osteoblast differentiation, inflammatory response pathways and immunomodulatory pathways. Comparing the core ceRNA module and the core PPI module, C1QA, CENPK, CENPU and BST2 were found to be the common genes of the two core modules, and C1QA was highly correlated with inflammatory functionality. C1QA and BST2 were significantly enriched in immune-regulatory pathways. Meanwhile, LINC01133 played a significant role in regulating the expression of the core genes during the pathogenesis of periodontitis.
UNASSIGNED: The identified biomarkers C1QA, CENPK, CENPU, BST2 and LINC01133 provided valuable insight into periodontitis pathology.
UNASSIGNED: The Gene Expression Omnibus (GEO) datasets GSE16134 and GSE106090 were downloaded. We performed differential analysis and robust rank aggregation (RRA) to obtain a list of differential genes. To obtain the core modules and core genes related to periodontitis, we evaluated differential genes through enrichment analysis, correlation analysis, protein-protein interaction (PPI) network and competing endogenous RNA (ceRNA) network analysis. Potential biomarkers for periodontitis were identified through comparative analysis of dual networks (PPI network and ceRNA network). PPI network analysis was performed in STRING. The ceRNA network consisted of RRA differentially expressed messenger RNAs (RRA_DEmRNAs) and RRA differentially expressed long non-coding RNAs (RRA_DElncRNAs), which regulated each other\'s expression by sharing microRNA (miRNA) target sites.
UNASSIGNED: RRA_DEmRNAs were significantly enriched in inflammation-related biological processes, osteoblast differentiation, inflammatory response pathways and immunomodulatory pathways. Comparing the core ceRNA module and the core PPI module, C1QA, CENPK, CENPU and BST2 were found to be the common genes of the two core modules, and C1QA was highly correlated with inflammatory functionality. C1QA and BST2 were significantly enriched in immune-regulatory pathways. Meanwhile, LINC01133 played a significant role in regulating the expression of the core genes during the pathogenesis of periodontitis.
UNASSIGNED: The identified biomarkers C1QA, CENPK, CENPU, BST2 and LINC01133 provided valuable insight into periodontitis pathology.
摘要：
■牙周炎，一种常见的慢性炎症性疾病,严重影响口腔健康。为牙周炎的诊断和治疗提供新的生物学指标,我们分析了公共微阵列数据集,以鉴定与牙周炎相关的生物标志物.
■下载基因表达综合(GEO)数据集GSE16134和GSE106090。我们进行了差异分析和稳健秩聚集（RRA）以获得差异基因列表。获得牙周炎相关的核心模块和核心基因,我们通过富集分析评估差异基因，相关分析，蛋白质-蛋白质相互作用(PPI)网络和竞争内源性RNA(ceRNA)网络分析。通过双网络（PPI网络和ceRNA网络）的比较分析，确定了牙周炎的潜在生物标志物。PPI网络分析在STRING中进行。ceRNA网络由RRA差异表达的信使RNA（RRA_DEmRNA）和RRA差异表达的长链非编码RNA（RRA_DElncRNA）组成，它们通过共享microRNA(miRNA)靶位点来调节彼此的表达。
■RRA_DMRNAs在炎症相关的生物过程中显著富集，成骨细胞分化,炎症反应途径和免疫调节途径。比较核心ceRNA模块和核心PPI模块，C1QA,CENPK,CENPU和BST2被发现是两个核心模块的共同基因，C1QA与炎症功能高度相关。C1QA和BST2在免疫调节途径中显著富集。同时,LINC01133在牙周炎的发病过程中对核心基因的表达起着重要的调节作用。
■确定的生物标志物C1QA，CENPK,CENPU,BST2和LINC01133为牙周炎病理学提供了有价值的见解。
■下载基因表达综合(GEO)数据集GSE16134和GSE106090。我们进行了差异分析和稳健秩聚集（RRA）以获得差异基因列表。获得牙周炎相关的核心模块和核心基因,我们通过富集分析评估差异基因，相关分析，蛋白质-蛋白质相互作用(PPI)网络和竞争内源性RNA(ceRNA)网络分析。通过双网络（PPI网络和ceRNA网络）的比较分析，确定了牙周炎的潜在生物标志物。PPI网络分析在STRING中进行。ceRNA网络由RRA差异表达的信使RNA（RRA_DEmRNA）和RRA差异表达的长链非编码RNA（RRA_DElncRNA）组成，它们通过共享microRNA(miRNA)靶位点来调节彼此的表达。
■RRA_DMRNAs在炎症相关的生物过程中显著富集，成骨细胞分化,炎症反应途径和免疫调节途径。比较核心ceRNA模块和核心PPI模块，C1QA,CENPK,CENPU和BST2被发现是两个核心模块的共同基因，C1QA与炎症功能高度相关。C1QA和BST2在免疫调节途径中显著富集。同时,LINC01133在牙周炎的发病过程中对核心基因的表达起着重要的调节作用。
■确定的生物标志物C1QA，CENPK,CENPU,BST2和LINC01133为牙周炎病理学提供了有价值的见解。
