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微阵列分析与蛋白质-蛋白质相互作用数据库相结合,确定了作为食管鳞状细胞癌转移网络的最小鉴别因子。

Combination of microarray profiling and protein-protein interaction databases delineates the minimal discriminators as a metastasis network for esophageal squamous cell carcinoma.

作者信息

Wong Fen-Hwa, Huang Chi-Ying F, Su Li-Jen, Wu Yu-Chung, Lin Yong-Shiang, Hsia Jiun-Yi, Tsai Hsin-Ting, Lee Sheng-An, Lin Chi-Hung, Tzeng Cheng-Hwai, Chen Po-Min, Chen Yann-Jan, Liang Shu-Ching, Lai Jin-Mei, Yen Chueh-Chuan

机构信息

Faculty of Life Sciences and Institute of Genome Sciences, National Yang-Ming University, No. 155, Sec. 2, Taipei 112, Taiwan, R.O.C.

出版信息

Int J Oncol. 2009 Jan;34(1):117-28.

PMID:19082484
Abstract

Microarray profiling of 15 adjacent normal/tumor-matched esophageal squamous cell carcinoma (ESCC) specimens identified 40 up-regulated and 95 down-regulated genes. Verification of the microarray measurement by quantitative real-time reverse transcription PCR in the same set of samples as well as an additional 15 normal/tumor-matched samples revealed >95% consistency. These signatures can also be used to classify a recently reported ESCC microarray dataset. Moreover, these molecular signatures were used as templates to elucidate their corresponding protein-protein interaction (PPI) networks using the PPI databases, POINT and POINeT. As a result, 18 genes, of which six were not disclosed in the initial expression profile analysis, were found to be able to serve as the minimal discriminators for distinguishing ESCC tumors from normal specimens. Of these discriminators, ten (BGN, COL1A1, COL1A2, MMP9, CD44, FN1, TGFBI, PXN, SPARC and VWF) were associated with tumor metastasis and formed a highly interactive network with the first four molecules as 'hubs'. Our study not only reveals how novel insights can be obtained from gene expression profiling, but also highlights a group of highly interacting genes associated with metastasis in ESCC.

摘要

对15对相邻正常/肿瘤匹配的食管鳞状细胞癌(ESCC)标本进行微阵列分析,鉴定出40个上调基因和95个下调基因。通过定量实时逆转录PCR在同一组样本以及另外15对正常/肿瘤匹配样本中对微阵列测量结果进行验证,结果显示一致性>95%。这些特征还可用于对最近报道的ESCC微阵列数据集进行分类。此外,这些分子特征被用作模板,利用蛋白质-蛋白质相互作用(PPI)数据库POINT和POINeT阐明其相应的PPI网络。结果发现,有18个基因能够作为区分ESCC肿瘤与正常标本的最小判别因子,其中6个基因在最初的表达谱分析中未被披露。在这些判别因子中,有10个(BGN、COL1A1、COL1A2、MMP9、CD44、FN1、TGFBI、PXN、SPARC和VWF)与肿瘤转移相关,并以前四个分子为“枢纽”形成了一个高度交互的网络。我们的研究不仅揭示了如何从基因表达谱分析中获得新的见解,还突出了一组与ESCC转移相关的高度相互作用的基因。

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