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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

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.

Original languageEnglish
Pages (from-to)117-128
Number of pages12
JournalInternational journal of oncology
Volume34
Issue number1
DOIs
StatePublished - 2009

Keywords

  • AURKA
  • Esophageal neoplasm
  • Metastasis
  • Microarray
  • Protein-protein interaction network
  • Quantitative RT-PCR

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