Yadalam Pradeep Kumar, R Ramya, Anegundi Raghavendra Vamsi
Periodontics, Saveetha Dental College, Saveetha Institue of Medical and Technical Sciences (SIMATS) Deemed University, Chennai, IND.
Oral Pathology and Oral Biology, Saveetha Dental College, Saveetha Institue of Medical and Technical Sciences (SIMATS) Deemed University, Chennai, IND.
Cureus. 2024 Aug 19;16(8):e67207. doi: 10.7759/cureus.67207. eCollection 2024 Aug.
Introduction The Wnt (wingless-related integration site) signalling pathway is crucial for bone formation and remodelling, regulating the commitment of mesenchymal stem cells (MSCs) to the osteoblastic lineage. It triggers the transcriptional activation of Wnt target genes and promotes osteoblast proliferation and survival. Weighted co-expression network analysis (WGCNA) and differential gene expression analysis help researchers understand gene roles. Gradient boosting, a machine learning technique, enhances understanding of genetic and molecular mechanisms contributing to overlap genes, improving gene regulation and functional genomics. The aim is to predict overlapping genes in the Wnt signalling pathway. Methods Differential gene expression analysis was performed using the National Center for Biotechnology Information (NCBI) geo dataset-GSE251951, focusing on the effect of Wnt signaling on treatment. The WGCNA module was analyzed using the iDEP tool to identify interconnected gene clusters. Hub genes were identified by calculating module eigengenes, correlated with external traits, and ranked based on module membership values. The study utilized gradient boosting, an ensemble learning method, to predict models, evaluate their performance using metrics like accuracy, precision, recall, and F1 score, and adjust predictions based on gradient and learning rate. Results The dendrogram uses the "Dynamic TreeCut" algorithm to analyze gene clusters, aiding researchers in understanding gene modules and biological processes, identifying co-expressed genes, and discovering new pathways. The confusion matrix displays 88 actual and predicted cases. The gradient boosting model achieves 78.9% accuracy in predicting Wnt pathway overlapping genes, with a respectable area under the curve (AUC) and classification accuracy values. It accurately predicts 73.9% of samples, with a high precision ratio and low recall. Conclusion Future research should enhance differential expression analysis and WGCNA to identify key Wnt pathway genes, improve sensitivity, specificity, hyperparameter tuning, and validation experiments, and use larger datasets.
引言 Wnt（无翅相关整合位点）信号通路对于骨形成和重塑至关重要，它调节间充质干细胞（MSC）向成骨细胞谱系的定向分化。它触发Wnt靶基因的转录激活，并促进成骨细胞增殖和存活。加权共表达网络分析（WGCNA）和差异基因表达分析有助于研究人员了解基因作用。梯度提升作为一种机器学习技术，可增强对导致重叠基因的遗传和分子机制的理解，改善基因调控和功能基因组学。目的是预测Wnt信号通路中的重叠基因。方法 使用美国国立生物技术信息中心（NCBI）的geo数据集-GSE251951进行差异基因表达分析，重点关注Wnt信号对治疗的影响。使用iDEP工具分析WGCNA模块，以识别相互连接的基因簇。通过计算模块特征基因来识别枢纽基因，将其与外部特征相关联，并根据模块成员值进行排名。该研究利用梯度提升这一集成学习方法来预测模型，使用准确率、精确率、召回率和F1分数等指标评估其性能，并根据梯度和学习率调整预测。结果 树形图使用“动态树切割”算法分析基因簇，帮助研究人员理解基因模块和生物学过程，识别共表达基因，并发现新途径。混淆矩阵显示了88个实际和预测案例。梯度提升模型在预测Wnt通路重叠基因方面达到了78.9%的准确率，具有可观的曲线下面积（AUC）和分类准确率值。它准确预测了73.9%的样本，精确率高但召回率低。结论 未来的研究应加强差异表达分析和WGCNA，以识别关键的Wnt通路基因，提高敏感性、特异性、超参数调整和验证实验，并使用更大的数据集。
