Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
BMC Oral Health. 2023 Jan 2;23(1):2. doi: 10.1186/s12903-022-02638-9.
Pulpitis is a common disease mainly caused by bacteria. Conventional approaches of diagnosing the state of dental pulp are mainly based on clinical symptoms, thereby harbor deficiencies. The accurate and rapid diagnosis of pulpitis is important for choosing the suitable therapy. The study aimed to identify pulpits related key genes by integrating micro-array data analysis and systems biology network-based methods such as weighted gene co-expression network analysis (WGCNA).
The micro-array data of 13 inflamed pulp and 11 normal pulp were acquired from Gene Expression Omnibus (GEO). WGCNA was utilized to establish a genetic network and categorize genes into diverse modules. Hub genes in the most associated module to pulpitis were screened out using high module group members (MM) methods. Pulpitis model in rat was constructed and iRoot BP plus was applied to cap pulp. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used for validation of hub genes.
WGCNA was established and genes were categorized into 22 modules. The darkgrey module had the highest correlation with pulpitis among them. A total of 5 hub genes (HMOX1, LOX, ACTG1, STAT3, GNB5) were identified. RT-qPCR proved the differences in expression levels of HMOX1, LOX, ACTG1, STAT3, GNB5 in inflamed dental pulp. Pulp capping reversed the expression level of HMOX1, LOX, ACTG1.
The study was the first to produce a holistic view of pulpitis, screen out and validate hub genes involved in pulpitis using WGCNA method. Pulp capping using iRoot BP plus could reverse partial hub genes.
牙髓病是一种常见的疾病，主要由细菌引起。传统的牙髓状态诊断方法主要基于临床症状，因此存在缺陷。准确快速地诊断牙髓病对于选择合适的治疗方法非常重要。本研究旨在通过整合微阵列数据分析和系统生物学网络方法，如加权基因共表达网络分析（WGCNA），鉴定牙髓病相关的关键基因。
从基因表达综合数据库（GEO）中获取 13 例炎症牙髓和 11 例正常牙髓的微阵列数据。利用 WGCNA 构建遗传网络，并将基因分为不同的模块。采用高模块组成员（MM）方法筛选与牙髓病最相关模块中的枢纽基因。构建大鼠牙髓炎模型，应用 iRoot BP plus 盖髓。采用反转录定量聚合酶链反应（RT-qPCR）验证枢纽基因。
建立了 WGCNA，并将基因分为 22 个模块。其中，深灰色模块与牙髓病相关性最高。共鉴定出 5 个枢纽基因（HMOX1、LOX、ACTG1、STAT3、GNB5）。RT-qPCR 证明了 HMOX1、LOX、ACTG1、STAT3、GNB5 在炎症牙髓中的表达水平存在差异。牙髓盖髓术逆转了 HMOX1、LOX、ACTG1 的表达水平。
本研究首次采用 WGCNA 方法全面观察牙髓病，筛选和验证与牙髓病相关的枢纽基因。iRoot BP plus 盖髓术可逆转部分枢纽基因。
