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Abstract：
Introduction Oral cancer is a significant global health issue that is mainly caused by factors, such as smoking, alcohol consumption, poor oral hygiene, age, and the human papillomavirus. Unfortunately, delayed diagnosis contributes to high rates of illness and mortality. However, saliva shows promise as a potential source for early detection, prognosis, and treatment. By analyzing the proteins and their interactions in saliva, we can gain insights that can assist in early detection and prediction. In this study, we aim to identify and predict the key genes, known as hub genes, in the salivary transcriptomics data of oral cancer patients and healthy individuals. Methods The data used for the analysis were obtained from salivaryproteome.org (https://salivaryproteome.org/) . The retrieved data consisted of individuals with oral cancer who were assigned unique identification numbers (IDs) 1025, 1030, 1027, and 1029, while the healthy individuals were assigned IDs 4256, 4257, 4255, and 4258, respectively. Differential gene expression analysis was used to identify genes that showed significant differences between the two groups. Uniformity and clustering were assessed through heatmaps and principal component analysis. Protein-protein interactions were investigated using the STRING database and Cytoscape. In addition, machine learning algorithms were employed to identify key genes involved in the interatomic interactions by analyzing transcriptomics data generated from the differential gene expression analysis. Results The accuracy and class accuracy of the extra tree classifier showed 98% and 97% in predicting interactomic hub genes, and HSPB1 was identified as a hub gene using Cytohubba from Cytoscape. Conclusion The predictive extra tree classifier, with its high accuracy in analysing interactomic hub genes in oral cancer, can potentially improve diagnosis and treatment strategies.
摘要：
简介口腔癌是一个重要的全球健康问题，主要是由因素引起的。比如吸烟，酒精消费,口腔卫生差，年龄,和人乳头瘤病毒。不幸的是,延迟诊断会导致高发病率和死亡率.然而,唾液有望成为早期检测的潜在来源，预后,和治疗。通过分析唾液中的蛋白质及其相互作用，我们可以获得有助于早期发现和预测的见解。在这项研究中,我们的目标是识别和预测关键基因，被称为枢纽基因，在口腔癌患者和健康个体的唾液转录组学数据中。方法用于分析的数据来自salivaryproteome.org(https://salivaryproteome.org/)。检索到的数据包括被分配唯一识别号(ID)1025、1030、1027和1029的患有口腔癌的个体,而健康个体分别被分配ID4256、4257、4255和4258。使用差异基因表达分析来鉴定在两组之间显示显着差异的基因。通过热图和主成分分析评估均匀性和聚类。使用STRING数据库和Cytoscape研究了蛋白质-蛋白质相互作用。此外,通过分析差异基因表达分析产生的转录组学数据，采用机器学习算法来识别参与原子间相互作用的关键基因。结果额外的树分类器在预测相互作用的hub基因方面的准确性和类别准确性分别为98％和97％，使用Cytoscape的Cytohubba将HSPB1鉴定为hub基因。结论预测性额外树分类器，在分析口腔癌中相互作用的中枢基因时具有很高的准确性，可以改善诊断和治疗策略。
