Yadalam Pradeep Kumar, Ramadoss Ramya, Suresh Ramya
Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND.
Oral Pathology and Oral Biology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND.
Cureus. 2024 Jun 30;16(6):e63510. doi: 10.7759/cureus.63510. eCollection 2024 Jun.
Background and aim Osteocytes regulate bone metabolism and balance through various mechanisms, including the Wnt (Wingless-related integration site signal transduction) signaling pathway. Weighted gene co-expression network analysis (WGCNA) is a computational method to identify functionally related genes based on expression patterns, especially in the Wnt-beta-catenin and osteo-regenerative pathways. This study aims to analyze gene modules of the Wnt signaling pathway from WGCNA analysis. Methods The study used a microarray dataset from the GEO (GSE228306) to analyze differential gene expression in human primary monocytes. The study standardized datasets using Robust Multi-Array Average (RMA) expression measure and Integrated Differential Expression and Pathway (IDEP) analysis tool, building a co-expression network for group-specific component (GC) genes. Results The study uses WGCNA to identify co-expression modules with dysregulated mRNAs, revealing enrichment in Wnt-associated pathways and top hub-enriched genes like colony-stimulating factor 3 (CSF3), interleukin-6 (IL-6), IL-23 subunit alpha (IL23A), suppressor of cytokine signaling 1 (SOCS1), and C-C motif chemokine ligand 19 (CCL19). Conclusion WGCNA analysis of the Wnt signaling pathway will involve functional annotation, network visualization, validation, integration with other omics data, and addressing method limitations for better understanding.
背景与目的 骨细胞通过多种机制调节骨代谢和平衡，包括Wnt（无翅相关整合位点信号转导）信号通路。加权基因共表达网络分析（WGCNA）是一种基于表达模式识别功能相关基因的计算方法，特别是在Wnt-β-连环蛋白和骨再生途径中。本研究旨在通过WGCNA分析来分析Wnt信号通路的基因模块。方法 本研究使用来自GEO（GSE228306）的微阵列数据集来分析人原代单核细胞中的差异基因表达。该研究使用稳健多阵列平均（RMA）表达量度和综合差异表达与通路（IDEP）分析工具对数据集进行标准化，为组特异性成分（GC）基因构建共表达网络。结果 本研究使用WGCNA来识别mRNA失调的共表达模块，揭示Wnt相关通路以及集落刺激因子3（CSF3）、白细胞介素-6（IL-6）、IL-23亚基α（IL23A）、细胞因子信号传导抑制因子1（SOCS1）和C-C基序趋化因子配体19（CCL19）等顶级枢纽富集基因的富集情况。结论 对Wnt信号通路进行WGCNA分析将涉及功能注释、网络可视化、验证、与其他组学数据整合以及解决方法局限性，以实现更好的理解。
