Ma Yihui, Guo Jiaping, Li Da, Cai Xianhua
Department of Stomatology, General Hospital of Central Theater Command of the People's Liberation Army, Wuhan, Hubei 430070, P.R. China.
Department of Orthopedics, General Hospital of Central Theater Command of the People's Liberation Army, Wuhan, Hubei 430070, P.R. China.
Exp Ther Med. 2022 Jan;23(1):80. doi: 10.3892/etm.2021.11003. Epub 2021 Nov 25.
Osteosarcoma, which arises from bone tissue, is considered to be one of the most common types of cancer in children and teenagers. As the etiology of osteosarcoma has not been fully elucidated, the overall prognosis for patients is generally poor. In recent years, the development of bioinformatical technology has allowed researchers to identify numerous molecular biological characteristics associated with the prognosis of osteosarcoma using online databases. In the present study, Gene Expression Omnibus (GEO) database was used and three microarray datasets were obtained. The GEO2R web tool was utilized and differentially expressed genes (DEGs) in osteosarcoma tissue were identified. Venn analysis was performed to determine the intersection of the DEG profiles. DEGs were analyzed by Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Protein-protein interactions (PPIs) between these DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes database, and the PPI network was then visualized using Cytoscape software. The top ten genes were identified based on measurement of degree, density of maximum neighborhood component, maximal clique centrality and mononuclear cell counts in the PPI network, and five overlapping genes [origin recognition complex subunit 6 (ORC6), IGF-binding protein 5 (IGFBP5), minichromosome maintenance 10 replication initiation factor (MCM10), MET proto-oncogene, receptor tyrosine kinase (MET) and centromere protein F (CENPF)] were identified. Additionally, three module networks were analyzed by Molecular Complex Detection (MCODE), and six key genes [ORC6, MCM10, DEP domain containing 1 (DEPDC1), CENPF, TIMELESS interacting protein (TIPIN) and shugoshin 1 (SGOL1)] were screened. Combined with the results from Cytoscape and MCODE, eight hub genes (ORC6, MCM10, DEPDC1, CENPF, TIPIN, SGOL1, MET and IGFBP5) were obtained. Furthermore, Kaplan-Meier plotter survival analysis was used to evaluate the prognostic value of these eight hub genes in patients with osteosarcoma. Oncomine and GEPIA databases were applied to further confirm the expression levels of hub genes in tissue. Finally, the functional roles of the core gene CENPF were investigated using Cell Counting Kit-8, wound healing and Transwell assays, which indicated that CENPF knockdown inhibited the proliferation, migration and invasion of osteosarcoma cells. These results provided potential prognostic markers, as well as a basis for further investigation of the mechanism underlying osteosarcoma.
骨肉瘤起源于骨组织，被认为是儿童和青少年中最常见的癌症类型之一。由于骨肉瘤的病因尚未完全阐明，患者的总体预后通常较差。近年来，生物信息学技术的发展使研究人员能够利用在线数据库识别与骨肉瘤预后相关的众多分子生物学特征。在本研究中，使用了基因表达综合数据库（GEO），并获得了三个微阵列数据集。利用GEO2R网络工具，识别骨肉瘤组织中的差异表达基因（DEG）。进行Venn分析以确定DEG图谱的交集。通过基因本体功能和京都基因与基因组百科全书通路富集分析对DEG进行分析。使用检索相互作用基因数据库的搜索工具分析这些DEG之间的蛋白质-蛋白质相互作用（PPI），然后使用Cytoscape软件可视化PPI网络。根据PPI网络中的度、最大邻域成分密度、最大团中心性和单核细胞计数，确定了前十个基因，并确定了五个重叠基因[复制起始因子6（ORC6）、胰岛素样生长因子结合蛋白5（IGFBP5）、微小染色体维持蛋白10复制起始因子（MCM10）、原癌基因MET、受体酪氨酸激酶（MET）和着丝粒蛋白F（CENPF）]。此外，通过分子复合物检测（MCODE）分析了三个模块网络，筛选出六个关键基因[ORC6、MCM10、含DEP结构域蛋白1（DEPDC）、CENPF、与TIMELESS相互作用蛋白（TIPIN）和减数分裂重组蛋白1（SGOL1）]。结合Cytoscape和MCODE的结果，获得了八个核心基因（ORC6、MCM10、DEPDC1、CENPF、TIPIN、SGOL1、MET和IGFBP5）。此外，使用Kaplan-Meier绘图仪生存分析评估这八个核心基因在骨肉瘤患者中的预后价值。应用Oncomine和GEPIA数据库进一步确认核心基因在组织中的表达水平。最后，使用细胞计数试剂盒-8、伤口愈合和Transwell实验研究核心基因CENPF的功能作用，结果表明敲低CENPF可抑制骨肉瘤细胞的增殖、迁移和侵袭。这些结果提供了潜在的预后标志物，并为进一步研究骨肉瘤的潜在机制奠定了基础。
