Sun Baoyong, Wang Fangxin, Li Min, Yang Mingshan
Department of Bone and Soft Tissue Tumor, Shandong Cancer Hospital and Institute, Ji Yan Road No. 440, Jinan, 250117, China,
Med Oncol. 2015 May;32(5):153. doi: 10.1007/s12032-015-0604-0. Epub 2015 Apr 2.
To investigate the differences in gene expression level between metastatic and non-metastatic osteosarcoma (OS) samples and the potential mechanism. Gene expression profile data GSE9508 were downloaded from Gene Expression Omnibus database to identify the differentially expressed genes (DEGs) between metastatic, non-metastatic OS samples, and normal control samples via SAM method. Function expression matrix of the DEGs was constructed by calculating the functional node scores based on the genes sets collected from the pathways recorded in MsigDB database. Next, t test was applied to screen the differentially expressed functional nodes between each two kinds of samples. Finally, we compared the significant genes between selected DEGs and genes in differentially expressed functional nodes. There were 79 up-regulated DEGs between non-metastatic OS and normal samples, 380 up-regulated and 134 down-regulated DEGs between the metastatic OS and normal samples, and 761 up-regulated plus 186 down-regulated DEGs between metastatic and non-metastatic OS samples. A total of 3846 functional gene sets were collected to form the function expression profile matrix. The numbers of differentially expressed functional nodes between non-metastatic OS and normal samples, metastatic OS and normal samples, and metastatic and non-metastatic OS samples were 8, 39, and 5, respectively. The gene level difference between metastatic and non-metastatic OS samples can be distinguished using bioinformatics analysis. TGFB1, LFT3, KDM1A, and KRAS genes have the potential to be used as biomarkers for OS; however, further analysis is needed to verify the current results.
研究转移性与非转移性骨肉瘤（OS）样本之间基因表达水平的差异及其潜在机制。从基因表达综合数据库下载基因表达谱数据GSE9508，通过SAM方法鉴定转移性、非转移性OS样本与正常对照样本之间的差异表达基因（DEGs）。基于从MsigDB数据库记录的通路中收集的基因集计算功能节点得分，构建DEGs的功能表达矩阵。接下来，应用t检验筛选每两种样本之间差异表达的功能节点。最后，比较所选DEGs与差异表达功能节点中的基因之间的显著基因。非转移性OS与正常样本之间有79个上调的DEGs，转移性OS与正常样本之间有380个上调和134个下调的DEGs，转移性与非转移性OS样本之间有761个上调加186个下调的DEGs。共收集3846个功能基因集以形成功能表达谱矩阵。非转移性OS与正常样本、转移性OS与正常样本以及转移性与非转移性OS样本之间差异表达的功能节点数量分别为8、39和5。使用生物信息学分析可以区分转移性与非转移性OS样本之间的基因水平差异。TGFB1、LFT3、KDM1A和KRAS基因有潜力用作OS的生物标志物；然而，需要进一步分析来验证当前结果。
