| AJTR Copyright © 2009-All rights reserved. Published by e-Century Publishing Corporation, Madison, WI 53711 |
Original Article
Microarray gene expression profiling using core biopsies of renal
neoplasia
Craig G. Rogers, Jonathon A. Ditlev, Min-Han Tan, Jun Sugimura, Chao-Nan Qian, Jeff Cooper, Brian Lane,
Michael A. Jewett, Richard J. Kahnoski, Eric J. Kort, Bin T. Teh
Rapids, MI 49503, USA; Department of Medical Oncology, National Cancer Centre Singapore, 11 Hospital Drive, of
Cancer Genetics, Laboratory of Molecular Epidemiology, Van Andel Research Institute, 333 Bostwick NE, Grand
Rapids, MI 49503, USA; Department of Medical Oncology, National Cancer Centre Singapore, 11 Singapore
169610; Department of Urology, School of Medicine, Iwate Medical University, Morioka 020-8505, Japan; State Key
Laboratory of Oncology in Southern China, Sun Yat-sen University Cancer Center, 651 Dongfeng East Rd,
Guangzhou 510060, China; Division of Urology, Metropolitan Hospital, 1919 Boston St. SE, Grand Rapids, MI
49506, USA; Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio
44195, USA; Division of Urology, University of Toronto, Princess Margaret Hospital, Toronto, Ontario, Canada M5G
2M9; Department of Urology, Spectrum Health Hospital, Michigan St NE, Grand Rapids, MI 49503, USA
USA
Received November 18, accepted November and available online November, 2008
Abstract: We investigate the feasibility of using microarray gene expression profiling technology to analyze core
biopsies of renal tumors for classification of tumor histology. Core biopsies were obtained ex-vivo from 7 renal
tumors—comprised of four histological subtypes—following radical nephrectomy using 18-gauge biopsy needles.
RNA was isolated from these samples and, in the case of biopsy samples, amplified by in vitro transcription.
Microarray analysis was then used to quantify the mRNA expression patterns in these samples relative to non-
diseased renal tissue mRNA. Genes with significant variation across all non-biopsy tumor samples were
identified, and the relationship between tumor and biopsy samples in terms of expression levels of these genes
was then quantified in terms of Euclidean distance, and visualized by complete linkage clustering. Final
pathologic assessment of kidney tumors demonstrated clear cell renal cell carcinoma (4), oncocytoma (1),
angiomyolipoma (1) and adrenalcortical carcinoma (1). Five of the seven biopsy samples were most similar in
terms of gene expression to the resected tumors from which they were derived in terms of Euclidean distance. All
seven biopsies were assigned to the correct histological class by hierarchical clustering. We demonstrate the
feasibility of gene expression profiling of core biopsies of renal tumors to classify tumor histology. (AJTR811003).
Key Words: cDNA microarray, gene expression profiling, biopsy, amplification, kidney tumor
Full Text PDF
Address all correspondence to:
Bin T. The, PhD
Laboratory of Cancer Genetics
9 Laboratory of Molecular Epidemiology
Van Andel Research Institute
333 Bostwick NE
Grand Rapids, MI 49503, USA
E-mail: bin.teh@vai.org
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