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Advances in Brief |
Brady Urological Institute, Johns Hopkins Medical Institutions, Baltimore, Maryland 21287-2101 [J. L., J. S., C. M. E., W. B. I.], and Cancer Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland 20892-4470 [D. J. D., Y. C., M. L. B., J. M. T.]
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ABSTRACT |
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Introduction |
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The emerging technology of cDNA microarrays provides the ability to comparatively analyze mRNA expression of thousands of genes in parallel (7 , 8) . Previous studies (9, 10, 11) have revealed novel features of human cancers by classifying tumors based on gene expression profiles. Human gene expression patterns derived from cDNA microarray measurements have been increasingly used to identify genes associated with human malignancies in a number of organ sites (12, 13, 14, 15, 16) . On the basis of these studies, it seems apparent that cDNA microarray-based gene expression analysis of human prostate tissues, especially those from well-documented clinical sources, would reveal molecular characteristics associated with prostate tumorigenesis. In this study, we obtained gene expression profiles of 16 primary prostate cancers and nine BPH specimens. A complete pairwise comparison of the 25 samples revealed consistently distinctive patterns of gene expression between these two groups of prostate tissues. Statistical tools were used to identify genes with sufficient discriminative power to differentiate these two groups of samples, generating a list of genes with significantly different expression levels between malignant growth and benign growth of the prostate gland.
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Materials and Methods |
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RNA Preparation.
Trimmed prostate blocks were cut into 10-µm sections in a cryostat. A total of 200 frozen sections/specimen were cut and maintained on dry ice for RNA extraction. Sectioning of the samples facilitates subsequent tissue homogenization, ensuring the maximum quality and yield of RNA preparations. In addition, the first and last sections from each specimen were preserved for pathological confirmation and calculation of percentages of tumor and epithelium. Efforts were made to enrich the epithelial composition in the samples. After trimming, the 16 tumor specimens contained at least 60% (range, 60–85%) adenocarcinoma cells in cellular composition. Six of the seven BPH samples from open prostatectomy contain at least 50% (range, 50–70%) epithelial cells, whereas the two BPH samples obtained by transurethral resection were 40% and 45% in epithelial content. Detailed tissue data are provided in supplemental information.5
Total RNA was isolated as described (9)
. Briefly, the aqueous portion from the Trizol/chloroform (Life Technologies, Inc., Rockville, MD) extraction step was mixed with equal volume of 70% ethanol and loaded on a Qiagen Rneasy (Qiagen, Valencia, CA) column. The columns were then processed according to manufacturer’s recommendations. RNA samples were subsequently concentrated using Microcon 100 concentrators (Millipore, Bedford, MA) to the desired concentration and stored at -80°C until use.
Array Fabrication.
The 6500 sequence-verified human cDNAs, representing 6112 unique genes (4573 known genes) on the basis of Unigene build 128, were obtained under a Cooperative Research and Development Agreement with Research Genetics. A complete annotated list of these cDNAs is available from the supplemental information.5
Printing of the cDNA clones was carried out as described previously (9)
. Briefly, amplified fragments from the clones were printed onto poly-L-lysine-coated glass slides. One week after printing, the arrayed slides were UV radiated to cross-link the DNA targets, treated with succinic anhydride to block poly-L-lysine, and boiled to denature DNA targets.
Fluorescent Labeling and Hybridization.
Labeling of total RNA was achieved by direct incorporation of Cy5-dUTP or Cy3-dUTP (Amersham Pharmacia, Piscataway, NJ) in a reverse transcription reaction using anchored oligodeoxythymidylate primer (Genosys, The Woodlands, TX) and Superscript II reverse transcriptase (Life Technologies, Inc.). Fluor-tagged cDNAs were then concentrated to the desired volume using Microcon concentrators (Millipore). Detailed labeling procedures are available from the website.6
For each of the 25 surgical samples, Cy3-dUTP-tagged cDNAs were mixed with Cy5-dUTP-tagged common reference (Fig. 1)
and subsequently cohybridized to a microarray. A single reference sample composed of a pool of RNA from two BPH specimens was used throughout all of the hybridizations to ensure normalized measures for each gene in each individual sample.
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Data Analysis.
The similarity between gene expression patterns is measured by computing the Euclidean distances for each pair of samples based on log-transformed ratios across all of the genes (18)
. Calculation of the Euclidean distance between sample x and y, dxy, was modified by introducing the quality score into the equation to yield
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; where dB is the between group Euclidean distance, dw1 is the average Euclidean distance among all of the prostate samples, dw2 is the average Euclidean distance among all of the BPH samples, k1 = 16/(16 + 9), k2 = 9/(16 + 9), and 
is a small constant to ensure the denominator is never equal to zero. Genes are ranked according to the w value. Genes with high w values create greater separation between groups and denser compaction within the groups; i.e., they have more discriminative power to differentiate the two groups. To test the statistical significance of the discriminative weights, sample labels were randomly permuted (9
, 20)
among the two groups, and the w value for each gene was again computed. This random permutation of sample labels was repeated 1000 times to generate a w distribution that would be expected under the assumption of random gene expression; i.e., no difference between the groups. The w values generated from the actual data were then assigned Ps based on the w distribution of randomized data. An agglomerative hierarchical clustering algorithm (9)
based on Euclidean distance measure was used to cluster the genes with statistically significant (P < 0.001) w values; i.e., genes statistically different in expression between prostate cancer and BPH samples.
RT-PCR.
Expression of the hepsin gene was verified using RT-PCR in six prostate cancer samples and six BPH samples randomly chosen from the 25 prostate tissue specimens. The cDNA synthesis was performed following the manufacturer’s instructions (Roche Molecular Biochemical, Indianapolis, IN) using a primer set for hepsin (forward, gatgtctgcaatggcgctgac; reverse, ccacacagccgccaacgtg). Prostate-specific antigen (forward, ccacacccgctctacga; reverse, ttgatccacttccggtaatgc) was used as a control for equal amount of prostate epithelial cells represented in each loading.
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Results |
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TopABSTRACTIntroductionMaterials and MethodsResultsDiscussionREFERENCES |
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. Normalized fluorescent intensity ratios from each hybridization experiment represent the relative mRNA abundance for each gene in each sample compared with the common reference. Analysis of the extent of similarity of the gene expression ratios between samples then provided a measure of the overall similarity in gene expression patterns between samples. A complete pairwise comparison of all of the samples was performed by computing the Euclidean distance for each pair of samples based on all of the log-transformed ratios. The quality score associated with each ratio measurement was incorporated into the calculation to ensure that the Euclidean distance measurements were not sensitive to unreliable data points with low quality score, which is typically a result of low signal intensity value and small target size. A matrix of Euclidean distances from a complete pairwise comparison was generated. To create a visual representation of relationships among all of the samples in terms of their similarities in gene expression profiles, a three-dimensional mapping of the samples, where the Euclidean distance between samples was closely approximated by the inter-sample map distances, was created using a multidimensional scaling method (9
, 19)
. Samples that have gene expression profiles that are more similar to each other will lie closer and form aggregation (cluster) in three-dimensional space. As seen in this plot (Fig. 2A)
, a strong distinction in the pattern of overall gene expression is evident between prostate tumor samples (blue) and BPH (golden brown) samples (see supplemental information5
for a three-dimensional animation of the MDS plot). Samples within each group showed similar gene expression patterns by forming a localized grouping of BPH samples that is readily separable from the cancer sample grouping. This result indicates that it is possible to draw a distinction between benign growth and malignant growth of prostatic cells solely based on the overall similarity of gene expression patterns.
To determine which gene expression patterns exhibited the greatest difference between BPH and prostate cancer samples, weighted gene analysis (9
, 20)
was performed. This analysis generates an ordered list of genes with statistically significant differences in expression between BPH and prostate cancer. Filtering out unreliable ratio measurements results in a set of 3215 genes for weighted gene analysis. First, the w value for each gene was computed to analyze the discriminative power of that gene to separate prostate cancer and BPH; i.e., the difference in expression of that gene between prostate cancer and BPH. Genes were then ranked according to w values, with the largest w value indicating the most discriminative power to separate prostate cancer from BPH. A fitted line representing the w distribution from the actual data is displayed in Fig. 2B
(red line). Next, a w distribution was created from the randomly permuted gene expression data sets (Fig. 2B
, blue line), representing the w distribution that would be expected under the null hypothesis that no true difference exists between the two groups. Therefore, each w value from the actual data can be assigned a P to determine the statistical significance of the associated gene to differentiate prostate cancer from BPH, by corresponding the w value (from the actual data) to the w distribution from the randomized data. Genes with w value above a critical value 1.7 were determined to be statistically significant (P < 0.001) in expression between prostate cancer and BPH (see supplemental information5
for details). As shown in Fig. 2B
, it is apparent that the observed gene expression difference between prostate cancer and BPH is not the result of random events. There are 210 genes with w values >1.7 (and thus P < 0.001) from the actual dataset (red line), whereas no gene in the random datasets has a w value >1.7 (blue line). An MDS plot was created to visualize the relationships among the 25 samples based on these 210 genes (Fig. 2C)
. As expected, a greater degree of separation was observed because this list of genes represents the subset of genes with the most expression differences between BPH and prostate cancer samples.
The 210 genes are clustered and displayed in Fig. 3
along with their relative expression in each sample compared with a common reference. Samples are ordered as groups of prostate cancer and BPH to facilitate visual comparison of the expression levels. The measured expression ratios for each gene are presented graphically as colored images, with the green squares (rectangular in compressed image) representing higher expression in sample compared with the reference, the red squares meaning lower expression in sample than reference, and the black squares indicating a ratio of approximately 1. Color intensities are scaled according to the ratio (reference:sample), with the brightest color having a ratio of greater than 5 (red) or smaller than 0.2 (green). For clarity of data presentation, we only list three clusters of genes with their associated names and IMAGE clone ID numbers (Fig. 3)
. A complete list of the 210 genes with associated clustering tree and other details can be accessed from supplemental information.5
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; first gene). RT-PCR analysis was used to determine the expression level of the hepsin gene in six prostate cancer samples and six BPH samples. Prostate-specific antigen, a prostate luminal epithelial marker and also a serine protease, was used as a loading control as well as an indicator of the epithelial content in the samples. We confirmed the high expression of hepsin in prostate tumor samples, whereas minimal or no signal was detected in BPH samples (Fig. 4)
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returned 17 genes with available data on relative expression in prostate cancer versus that in normal prostate. Strikingly, 12 of the 17 genes were confirmed to be differentially expressed with reasonable confidence (at least 2-fold change in serial analysis of gene expression data derived from PR317 prostate libraries).7
This observation also suggests that gene expression changes between prostate cancer and BPH reflect in large degree the differences between normal and cancerous prostate epithelium. Additional efforts will be needed to fully characterize the expression levels of these 210 genes in benign and malignant prostate tissues. |
Discussion |
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and at the individual gene level (Fig. 3)
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Interpretation of the observed differences is bound both by the complex nature of cellular heterogeneity and by our knowledge of the tissue origin for BPH and prostate cancer. Any comparison is limited by the homogeneity of the samples being compared. A typical surgical prostate tissue specimen usually presents a mixture of different cell types, each with a potentially unique gene expression profile. The prostate samples used in this study were processed to maximize the percentage of the target epithelia from which RNA was extracted to reduce the contributions of the contaminating tissues to the final profiles. The likelihood of the observed differences in expression representing differences in BPH and prostate cancer biology is further heightened by using multiple samples. The contaminating tissues will be more randomly represented in the samples analyzed, and their contribution to the analysis will thus be further diluted. The other source of expression differences between BPH and prostate tumor samples that may be tangential to the cancer-specific differences is the tissue of origin of the two sample types. BPH and prostate cancer are pathological entities arising in two different areas of the prostate gland (22)
. The majority (
80%) of prostate cancers are found in the peripheral zone, and almost all of the BPH occurs in a periurethral region, termed the transition zone. Clarification of the expression differences that arise from the differences in normal peripheral and transition zone tissue will require studies of the relative expression of genes of interest in these tissues. Nevertheless, many genes that are consistently up-regulated and down-regulated in the majority of prostate cancer samples when compared with BPH are most likely representative of molecular features associated with prostate malignancy. On the other hand, future studies focusing on the identification of genes that have expression that is zone-specific should shed light on the mechanisms underlying the regional difference in the incidence of benign and malignant growth of the prostatic cells.
Genomic instability of prostate tumors could lead to an extensive variation in gene expression even within a single tumor (2) . Therefore, identification of tumor-specific gene expression changes common to all of the tumors is of particular interest; e.g., mRNA expression of the hepsin gene is strikingly high in all of the prostate cancer samples compared with minimal expression in all of the BPH samples examined. Although it is not clear at this point what implications this gene as well as the other highly discriminating genes might have on prostate malignancies, the cellular function of the gene products and the potential use of those malignancy-associated genes as molecular markers warrants further study.
Although important features of prostate tumor biology remain to be investigated by including additional prostate tissue samples differing in pathological characteristics, the current study reports both a clear overall and gene-by-gene difference between gene expression profiles associated with malignant growth and benign growth of the prostatic cells. This study is currently being expanded by using microarrays containing more genes known to be important in prostate biology and by reanalysis of the profiles as new sample sets are added. Analysis of the roles of the genes already suggested as possibly important in prostate cancer and the further development of profiles of the various types of normal and cancerous prostate epithelia offer a reasonable approach to developing an understanding of the biology of prostate malignancy.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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1 Supported by Public Health Service SPORE CA 58236 and DK 52675. 
2 These authors participated equally.
3 To whom requests for reprints should be addressed, at 115 Marburg, 600 N. Wolfe Street, Johns Hopkins Hospital, Baltimore, MD 21287. Phone: (410) 955-2518; Fax: (410) 955-0833; E-mail: wisaacs{at}jhmi.edu
4 The abbreviations used are: BPH, benign prostatic hyperplasia; MDS, multidimensional scaling; RT-PCR, reverse transcription-PCR.
5 http://www.nhgri.nih.gov/DIR/Microarray/Prostate_Supplement.
6 http://www.nhgri.nih.gov/DIR/microarray.
7 http://www.ncbi.nlm.nih.gov/SAGE/SAGEcid.cgi?.
Received 2/28/01. Accepted 5/ 1/01.
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J. Lapointe, C. Li, J. P. Higgins, M. van de Rijn, E. Bair, K. Montgomery, M. Ferrari, L. Egevad, W. Rayford, U. Bergerheim, et al. Gene expression profiling identifies clinically relevant subtypes of prostate cancer PNAS, January 20, 2004; 101(3): 811 - 816. [Abstract] [Full Text] [PDF]
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S. Zha, S. Ferdinandusse, S. Denis, R. J. Wanders, C. M. Ewing, J. Luo, A. M. De Marzo, and W. B. Isaacs {alpha}-Methylacyl-CoA Racemase as an Androgen-Independent Growth Modifier in Prostate Cancer Cancer Res., November 1, 2003; 63(21): 7365 - 7376. [Abstract] [Full Text] [PDF]
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N. Schulke, O. A. Varlamova, G. P. Donovan, D. Ma, J. P Gardner, D. M. Morrissey, R. R. Arrigale, C. Zhan, A. J. Chodera, K. G. Surowitz, et al. The homodimer of prostate-specific membrane antigen is a functional target for cancer therapy PNAS, October 28, 2003; 100(22): 12590 - 12595. [Abstract] [Full Text] [PDF]
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G. Weston, A. C. Trajstman, C. E. Gargett, U. Manuelpillai, B. J. Vollenhoven, and P. A.W. Rogers Fibroids display an anti-angiogenic gene expression profile when compared with adjacent myometrium Mol. Hum. Reprod., September 1, 2003; 9(9): 541 - 549. [Abstract] [Full Text] [PDF]
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W.A. Schulz, M. Burchardt, and M.V. Cronauer Molecular biology of prostate cancer Mol. Hum. Reprod., August 1, 2003; 9(8): 437 - 448. [Abstract] [Full Text] [PDF]
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D. K. Vanaja, J. C. Cheville, S. J. Iturria, and C. Y. F. Young Transcriptional Silencing of Zinc Finger Protein 185 Identified by Expression Profiling Is Associated with Prostate Cancer Progression Cancer Res., July 15, 2003; 63(14): 3877 - 3882. [Abstract] [Full Text] [PDF]
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D. R. Rhodes, M. G. Sanda, A. P. Otte, A. M. Chinnaiyan, and M. A. Rubin Multiplex Biomarker Approach for Determining Risk of Prostate-Specific Antigen-Defined Recurrence of Prostate Cancer J Natl Cancer Inst, May 7, 2003; 95(9): 661 - 668. [Abstract] [Full Text] [PDF]
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 A. Khanolkar, Z. Fu, L. J. Underwood, K. L. Bondurant, R. Rochford, and M. J. Cannon CD4+ T Cell-Induced Differentiation of EBV-Transformed Lymphoblastoid Cells Is Associated with Diminished Recognition by EBV-Specific CD8+ Cytotoxic T Cells J. Immunol., March 15, 2003; 170(6): 3187 - 3194. [Abstract] [Full Text] [PDF]
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W. Xin, D. R. Rhodes, C. Ingold, A. M. Chinnaiyan, and M. A. Rubin Dysregulation of the Annexin Family Protein Family Is Associated with Prostate Cancer Progression Am. J. Pathol., January 1, 2003; 162(1): 255 - 261. [Abstract] [Full Text] [PDF]
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A. Bar-Shira, J. H. Pinthus, U. Rozovsky, M. Goldstein, W. R. Sellers, Y. Yaron, Z. Eshhar, and A. Orr-Urtreger Multiple Genes in Human 20q13 Chromosomal Region Are Involved in an Advanced Prostate Cancer Xenograft Cancer Res., December 1, 2002; 62(23): 6803 - 6807. [Abstract] [Full Text] [PDF]
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V. Srikantan, M. Valladares, J. S. Rhim, J. W. Moul, and S. Srivastava HEPSIN Inhibits Cell Growth/Invasion in Prostate Cancer Cells Cancer Res., December 1, 2002; 62(23): 6812 - 6816. [Abstract] [Full Text] [PDF]
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S. L. Zheng, B.-l. Chang, D. A. Faith, J. R. Johnson, S. D. Isaacs, G. A. Hawkins, A. Turner, K. E. Wiley, E. R. Bleecker, P. C. Walsh, et al. Sequence Variants of {alpha}-Methylacyl-CoA Racemase Are Associated with Prostate Cancer Risk Cancer Res., November 15, 2002; 62(22): 6485 - 6488. [Abstract] [Full Text] [PDF]
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G. Untergasser, H. B. Koch, A. Menssen, and H. Hermeking Characterization of Epithelial Senescence by Serial Analysis of Gene Expression: Identification of Genes Potentially Involved in Prostate Cancer Cancer Res., November 1, 2002; 62(21): 6255 - 6262. [Abstract] [Full Text] [PDF]
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A. Dash, I. P. Maine, S. Varambally, R. Shen, A. M. Chinnaiyan, and M. A. Rubin Changes in Differential Gene Expression because of Warm Ischemia Time of Radical Prostatectomy Specimens Am. J. Pathol., November 1, 2002; 161(5): 1743 - 1748. [Abstract] [Full Text] [PDF]
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G. Velasco, S. Cal, V. Quesada, L. M. Sanchez, and C. Lopez-Otin Matriptase-2, a Membrane-bound Mosaic Serine Proteinase Predominantly Expressed in Human Liver and Showing Degrading Activity against Extracellular Matrix Proteins J. Biol. Chem., September 27, 2002; 277(40): 37637 - 37646. [Abstract] [Full Text] [PDF]
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R. Kuefer, S. Varambally, M. Zhou, P. C. Lucas, M. Loeffler, H. Wolter, T. Mattfeldt, R. E. Hautmann, J. E. Gschwend, T. R. Barrette, et al. {alpha}-Methylacyl-CoA Racemase: Expression Levels of this Novel Cancer Biomarker Depend on Tumor Differentiation Am. J. Pathol., September 1, 2002; 161(3): 841 - 848. [Abstract] [Full Text] [PDF]
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D. R. Rhodes, T. R. Barrette, M. A. Rubin, D. Ghosh, and A. M. Chinnaiyan Meta-Analysis of Microarrays: Interstudy Validation of Gene Expression Profiles Reveals Pathway Dysregulation in Prostate Cancer Cancer Res., August 1, 2002; 62(15): 4427 - 4433. [Abstract] [Full Text] [PDF]
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E. LaTulippe, J. Satagopan, A. Smith, H. Scher, P. Scardino, V. Reuter, and W. L. Gerald Comprehensive Gene Expression Analysis of Prostate Cancer Reveals Distinct Transcriptional Programs Associated with Metastatic Disease Cancer Res., August 1, 2002; 62(15): 4499 - 4506. [Abstract] [Full Text] [PDF]
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T.-K. Jenssen, M. Langaas, W. P. Kuo, B. Smith-Sorensen, O. Myklebost, and E. Hovig Analysis of repeatability in spotted cDNA microarrays Nucleic Acids Res., July 15, 2002; 30(14): 3235 - 3244. [Abstract] [Full Text] [PDF]
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N. Iizuka, M. Oka, H. Yamada-Okabe, N. Mori, T. Tamesa, T. Okada, N. Takemoto, A. Tangoku, K. Hamada, H. Nakayama, et al. Comparison of Gene Expression Profiles between Hepatitis B Virus- and Hepatitis C Virus-infected Hepatocellular Carcinoma by Oligonucleotide Microarray Data on the Basis of a Supervised Learning Method Cancer Res., July 15, 2002; 62(14): 3939 - 3944. [Abstract] [Full Text] [PDF]
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D. Karan, D. L. Kelly, A. Rizzino, M.-F. Lin, and S. K. Batra Expression profile of differentially-regulated genes during progression of androgen-independent growth in human prostate cancer cells Carcinogenesis, June 1, 2002; 23(6): 967 - 976. [Abstract] [Full Text] [PDF]
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T. Ernst, M. Hergenhahn, M. Kenzelmann, C. D. Cohen, M. Bonrouhi, A. Weninger, R. Klaren, E. F. Grone, M. Wiesel, C. Gudemann, et al. Decrease and Gain of Gene Expression Are Equally Discriminatory Markers for Prostate Carcinoma : A Gene Expression Analysis on Total and Microdissected Prostate Tissue Am. J. Pathol., June 1, 2002; 160(6): 2169 - 2180. [Abstract] [Full Text] [PDF]
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F. Francioso, F. Carinci, L. Tosi, L. Scapoli, F. Pezzetti, E. Passerella, R. Evangelisti, A. Pastore, S. Pelucchi, A. Piattelli, et al. Identification of Differentially Expressed Genes in Human Salivary Gland Tumors by DNA Microarrays Mol. Cancer Ther., May 1, 2002; 1(7): 533 - 538. [Abstract] [Full Text] [PDF]
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 M. A. Rubin, M. Zhou, S. M. Dhanasekaran, S. Varambally, T. R. Barrette, M. G. Sanda, K. J. Pienta, D. Ghosh, and A. M. Chinnaiyan {alpha}-Methylacyl Coenzyme A Racemase as a Tissue Biomarker for Prostate Cancer JAMA, April 3, 2002; 287(13): 1662 - 1670. [Abstract] [Full Text] [PDF]
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J. Luo, S. Zha, W. R. Gage, T. A. Dunn, J. L. Hicks, C. J. Bennett, C. M. Ewing, E. A. Platz, S. Ferdinandusse, R. J. Wanders, et al. {alpha}-Methylacyl-CoA Racemase: A New Molecular Marker for Prostate Cancer Cancer Res., April 1, 2002; 62(8): 2220 - 2226. [Abstract] [Full Text] [PDF]
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J. Shou, R. Soriano, S. W. Hayward, G. R. Cunha, P. M. Williams, and W.-Q. Gao Expression profiling of a human cell line model of prostatic cancer reveals a direct involvement of interferon signaling in prostate tumor progression PNAS, March 5, 2002; 99(5): 2830 - 2835. [Abstract] [Full Text] [PDF]
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 J. A. Macoska The Progressing Clinical Utility of DNA Microarrays CA Cancer J Clin, January 1, 2002; 52(1): 50 - 59. [Full Text] [PDF]
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R. L. Strausberg and G. J. Riggins Navigating the human transcriptome PNAS, October 9, 2001; 98(21): 11837 - 11838. [Full Text] [PDF]
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J. B. Welsh, L. M. Sapinoso, A. I. Su, S. G. Kern, J. Wang-Rodriguez, C. A. Moskaluk, H. F. Frierson Jr., and G. M. Hampton Analysis of Gene Expression Identifies Candidate Markers and Pharmacological Targets in Prostate Cancer Cancer Res., August 1, 2001; 61(16): 5974 - 5978. [Abstract] [Full Text] [PDF]
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