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基于DNA微阵列数据的转移性骨肉瘤生物标志物鉴定。

Identification of biomarkers for metastatic osteosarcoma based on DNA microarray data.

作者信息

Wang Q

出版信息

Neoplasma. 2015;62(3):365-71. doi: 10.4149/neo_2015_044.

DOI:10.4149/neo_2015_044
PMID:25866217
Abstract

Osteosarcoma (OS) is a malignant bone tumor very often with pulmonary metastasis and is the main cause of OS mortality. The objective of this study was to screen for possible biomarkers of metastatic OS to explore the mechanisms of pulmonary metastasis of OS through network construction. GSE14359 was downloaded from the Gene Expression Omnibus database, which included 5 samples from conventional OS group with 2 replicates and 4 samples from OS pulmonary metastasis group in duplicate. Differentially expressed genes (DEGs) between two groups were identified by limma packages in R and classical t-test with the threshold of the false discovery rate (FDR) <0.05. The Database for Annotation, Visualization and Integrated Discovery (DAVID) were then used to perform functional annotation (FDR < 0.01). Differential coexpression network was constructed with subspace differential coexpression analysis (SDC), and genes with high degrees in the differential coexpression network were identified. A total of 1344 genes were screened as DEGs, including 677 up- and 667 down-regulated DEGs in the pulmonary metastasis of OS. Thirty-one significantly enriched functions were obtained, such as blood vessel morphogenesis, defense response, cell death and so on. DEGs with high degrees (brain-specific angiogenesis inhibitor 2 (BAI2), formin-like 1 (FMNL1), dual-specificity phosphatase 7 (DUSP7), transient receptor potential melastatin 2 (TRPM2), CBP80/20-dependent translation initiation factor (KIAA0427) and C120rf35) in the differential coexpression network were found. BAI2, FMNL1, DUSP7 and TRPM2 may be useful markers for predicting tumor metastasis and therapeutic targets for the treatment of OS patients with metastasis.

摘要

骨肉瘤(OS)是一种常伴有肺转移的恶性骨肿瘤,是骨肉瘤患者死亡的主要原因。本研究的目的是筛选转移性骨肉瘤可能的生物标志物,通过网络构建探索骨肉瘤肺转移的机制。从基因表达综合数据库下载GSE14359,其中包括常规骨肉瘤组的5个样本(每个样本重复2次)和骨肉瘤肺转移组的4个样本(每个样本重复2次)。使用R语言中的limma软件包和经典t检验,以错误发现率(FDR)<0.05为阈值,鉴定两组之间的差异表达基因(DEG)。然后使用注释、可视化和综合发现数据库(DAVID)进行功能注释(FDR<0.01)。采用子空间差异共表达分析(SDC)构建差异共表达网络,并鉴定差异共表达网络中度数较高的基因。共筛选出1344个差异表达基因,其中骨肉瘤肺转移中上调的差异表达基因有677个,下调的有667个。获得了31个显著富集的功能,如血管形态发生、防御反应、细胞死亡等。在差异共表达网络中发现了度数较高的差异表达基因(脑特异性血管生成抑制因子2(BAI2)、formin样蛋白1(FMNL1)、双特异性磷酸酶7(DUSP7)、瞬时受体电位 melastatin 2(TRPM2)、CBP80/20依赖性翻译起始因子(KIAA0427)和C120rf35)。BAI2、FMNL1、DUSP7和TRPM2可能是预测肿瘤转移的有用标志物,也是治疗骨肉瘤转移患者的治疗靶点。

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