Yadalam Pradeep Kumar, Ramadoss Ramya, Arumuganainar Deepavalli
Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND.
Department of Oral Pathology and Oral Biology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND.
Cureus. 2024 Jul 2;16(7):e63639. doi: 10.7759/cureus.63639. eCollection 2024 Jul.
Introduction The Wnt signaling pathway is crucial for tooth development, odontoblast differentiation, and dentin formation. It interacts with epithelial cadherin (E-cadherin) and beta-catenin in tooth development and periodontal ligament (PDL) formation. Dysregulation of Wnt signaling is linked to periodontal diseases, requiring an understanding of therapeutic interventions. Weighted gene co-expression network analysis (WGCNA) can identify co-expressed gene modules. Our study aims to identify hub genes in WGCNA analysis of Wnt signaling-based PDL formation. Methods The study used a microarray dataset GSE201313 from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus to analyze the impact of DMP1 expression on XLH dental pulp cell differentiation and PDL formation. The standardized dataset was used for WGCNA analysis, which generated a co-expression network by calculating pairwise correlations between genes and constructing an adjacency matrix. The topological overlap matrix (TOM) was transformed into a hierarchical clustering tree and then cut into modules or clusters of highly interconnected genes. The module eigengene (ME) was calculated for each module, and the genes within this module were identified as hub genes. Gene ontology (GO) and KEGG pathway enrichment analysis were performed to gain insights into the biological functions of the hub genes. The integrated Differential Expression and Pathway analysis (iDEP) tool (http://bioinformatics.sdstate.edu/idep/; South Dakota State University, Brookings, USA) was used for WGCNA analysis. Results The study used the WGCNA package to analyze 1,000 differentially expressed genes, constructing a gene co-expression network and generating a hierarchical clustering tree and TOM. The analysis reveals a scale-free topology fitting index R2 and mean connectivity for various soft threshold powers, with an R2 value of 5. COL6A1, MMP3, BGN, COL1A2, and FBN2 are hub genes implicated in PDL development. Conclusion The study identified key hub genes, including COL6A1, MMP3, BGN, and FBN2, crucial for PDL formation, tissue remodeling, and cell-matrix interactions, guiding future therapeutic strategies.
Wnt信号通路对于牙齿发育、成牙本质细胞分化和牙本质形成至关重要。它在牙齿发育和牙周韧带(PDL)形成过程中与上皮钙黏蛋白(E-钙黏蛋白)和β-连环蛋白相互作用。Wnt信号通路的失调与牙周疾病有关,这需要我们了解治疗干预措施。加权基因共表达网络分析(WGCNA)可以识别共表达的基因模块。我们的研究旨在通过基于Wnt信号的PDL形成的WGCNA分析来识别枢纽基因。
本研究使用了来自美国国立生物技术信息中心(NCBI)基因表达综合数据库的微阵列数据集GSE201313,以分析牙本质基质蛋白1(DMP1)表达对XLH牙髓细胞分化和PDL形成的影响。标准化后的数据集用于WGCNA分析,通过计算基因之间的成对相关性并构建邻接矩阵来生成共表达网络。拓扑重叠矩阵(TOM)被转换为层次聚类树,然后切割成高度相互连接的基因模块或簇。计算每个模块的模块特征基因(ME),并将该模块内的基因鉴定为枢纽基因。进行基因本体(GO)和KEGG通路富集分析,以深入了解枢纽基因的生物学功能。使用综合差异表达和通路分析(iDEP)工具(http://bioinformatics.sdstate.edu/idep/;美国南达科他州立大学,布鲁金斯)进行WGCNA分析。
本研究使用WGCNA软件包分析了1000个差异表达基因,构建了基因共表达网络,并生成了层次聚类树和TOM。分析揭示了各种软阈值功率下的无标度拓扑拟合指数R2和平均连通性,R2值为5。胶原蛋白VIα1(COL6A1)、基质金属蛋白酶3(MMP3)、基底膜聚糖(BGN)、胶原蛋白Iα2(COL1A2)和纤连蛋白2(FBN2)是与PDL发育相关的枢纽基因。
本研究确定了关键枢纽基因,包括COL6A1、MMP3、BGN和FBN2,它们对PDL形成、组织重塑和细胞-基质相互作用至关重要,为未来的治疗策略提供了指导。