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Proteome analysis and tissue microarray for profiling protein markers associated with lymph node metastasis in colorectal cancer.

Pei H, Zhu H, Zeng S, Li Y, Yang H, Shen L, Chen J, Zeng L, Fan J, Li X, Gong Y, Shen H

Department of General Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, China.

BACKGROUND: Understanding the proteins associated with lymph node metastasis (LNM) in colorectal cancer (CRC) will benefit us in the prediction of CRC prognosis and provide us new potential targets in the intervention of CRC. The aim of this study is to investigate the LNM-associated proteins and to evaluate the clinicopathological characteristics of these target proteins' expression in CRC. METHODS: Fresh tumor and paired normal mucosa from five cases for each group of non-LNM CRC and LNM CRC were analyzed by two-dimensional electrophoresis coupled with MALDI-TOF-MS, followed by Western blotting confirmation. In 40 paraffin-embedded CRC samples, each for non-LNM CRC and LNM CRC, four differentially expressed proteins identified by proteomics analysis were detected by tissue microarray with immunohistochemistry staining to access the clinicopathological characteristics of these proteins in LNM of CRC. RESULTS: Twenty-five proteins were found to be differentially expressed between normal mucosa and CRC tissue. Increased expression levels of heat shock protein-27 (HSP-27), glutathione S-transferase (GST), and Annexin II, but a decreased expression level of liver-fatty acid binding protein (L-FABP), existed in LNM CRC as compared with non-LNM CRC (p<0.01 or p<0.05, respectively). CONCLUSION: The techniques of proteomic analysis combined with tissue microarray provide us a dramatic tool for screening of LNM-associated proteins in cancer research. The increased expression of HSP-27, GST, and Annexin II, but decreased expression of L-FABP, suggests a significantly elevated incidence of LNM in CRC.

Published 6 July 2007 in J Proteome Res, 6(7): 2495-501.
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Microarrays Books

Methods of Microarray Data Analysis V

Methods of Microarray Data Analysis V