Rong Kuan, Kuang Haoming, Ou Liang, Fang Rui, Kuang Jianjun, Yang Hui
Department of Orthopedics, The Affiliated Hospital of Hunan Academy of Traditional Chinese Medicine, Changsha, 410006, China.
Department of Internal Medicine, The Affiliated Hospital of Hunan Academy of Traditional Chinese Medicine, Changsha, 410006, China.
Discov Oncol. 2024 Nov 1;15(1):609. doi: 10.1007/s12672-024-01247-y.
The study focusing on developing an artificial neural network (ANN) model in accordance with genetic characteristics of osteosarcoma (OS) to accurately speculate OS cases. In the present study, we identified 467 DEGs through differentially acting gene investigation and that 345 exist suppressed and 122 exist stimulated. The resultant of GO enrichment analysis displayed the functions mainly included T cell activation, secretory granule lumen, antioxidant property etc. The pathways identified in the differentially acting genes (DAGs) were greatly interacted with Phagosome, Staphylococcus aureus infection, Human T - cell leukemia virus 1 infection, etc. Next, we found out top ten hub DEGs (HDEGs) by PPI network analysis. In addition, through the validation of ANN itself and Test set samples, it was proved that the prediction performance of our constructed ANN model is accurate and reliable. Finally, the penetration of immune cells and its interaction with target CDEGs were examined, and variations in penetration of 22 types of immune cells amongst different classes were found, additionally correlation amongst immune cells and between immune cells and target CDEGs. Furthermore, we analyzed the expression of the top two CDEGs (YES1 and MFNG) in OS tissues and normal tissues, also the interrelationship among the activity of YES1 and MFNG in OS tissues and clinicopathological properties of OS cases. Furthermore, the correlation analysis between the top two CDEGs and immune infiltrating cells was performed in OS tissues. Our research results revealed that CDEGs-based ANN model is effective at predicting OS patients, which facilitates early diagnosis and treatment of OS.
该研究致力于根据骨肉瘤（OS）的基因特征开发一种人工神经网络（ANN）模型，以准确推测骨肉瘤病例。在本研究中，我们通过差异作用基因研究鉴定出467个差异表达基因（DEGs），其中345个被抑制，122个被激活。基因本体（GO）富集分析结果显示，其功能主要包括T细胞活化、分泌颗粒腔、抗氧化特性等。差异作用基因（DAGs）中确定的通路与吞噬体、金黄色葡萄球菌感染、人类T细胞白血病病毒1感染等密切相关。接下来，我们通过蛋白质-蛋白质相互作用（PPI）网络分析找出了前十个核心差异表达基因（HDEGs）。此外，通过对人工神经网络本身和测试集样本的验证，证明了我们构建的人工神经网络模型的预测性能准确可靠。最后，研究了免疫细胞的浸润情况及其与目标关键差异表达基因（CDEGs）的相互作用，发现了不同类别中22种免疫细胞浸润的差异，以及免疫细胞之间和免疫细胞与目标CDEGs之间的相关性。此外，我们分析了骨肉瘤组织和正常组织中前两个关键差异表达基因（YES1和MFNG）的表达情况，以及骨肉瘤组织中YES1和MFNG的活性与骨肉瘤病例临床病理特征之间的相互关系。此外，还在骨肉瘤组织中进行了前两个关键差异表达基因与免疫浸润细胞之间的相关性分析。我们的研究结果表明，基于关键差异表达基因的人工神经网络模型在预测骨肉瘤患者方面是有效的，这有助于骨肉瘤的早期诊断和治疗。
