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[Cancer Research 61, 5974-5978, August 15, 2001]
© 2001 American Association for Cancer Research


Advances in Brief

Analysis of Gene Expression Identifies Candidate Markers and Pharmacological Targets in Prostate Cancer

John B. Welsh, Lisa M. Sapinoso, Andrew I. Su, Suzanne G. Kern, Jessica Wang-Rodriguez, Christopher A. Moskaluk, Henry F. Frierson, Jr. and Garret M. Hampton1

Genomics Institute of the Novartis Research Foundation, San Diego, California 92121 [J. B. W., L. M. S., S. G. K., G. M. H.]; The Scripps Research Institute, La Jolla, California 92037 [A. I. S.]; Department of Pathology, University of California, San Diego, La Jolla, California 92093 [J. W-R.]; and Department of Pathology, University of Virginia, Charlottesville, Virginia 22908 [C. A. M., H. F. F.]


    ABSTRACT
 Top
 ABSTRACT
 Introduction
 Materials and Methods
 Results and Discussion
 REFERENCES
 
Detection, treatment, and prediction of outcome for men with prostate cancer increasingly depend on a molecular understanding of tumor development and behavior. We characterized primary prostate cancer by monitoring expression levels of more than 8900 genes in normal and malignant tissues. Patterns of gene expression across tissues revealed a precise distinction between normal and tumor samples, and revealed a striking group of about 400 genes that were overexpressed in tumor tissues. We ranked these genes according to their differential expression in normal and cancer tissues by selecting for highly and specifically overexpressed genes in the majority of cancers with correspondingly low or absent expression in normal tissues. Several such genes were identified that act within a variety of biochemical pathways and encode secreted molecules with diagnostic potential, such as the secreted macrophage inhibitory cytokine, MIC-1. Other genes, such as fatty acid synthase, encode enzymes known as drug targets in other contexts, which suggests new therapeutic approaches.


    Introduction
 Top
 ABSTRACT
 Introduction
 Materials and Methods
 Results and Discussion
 REFERENCES
 
Prostate cancer is the most common malignancy in men and the second most frequent cause of cancer death in the United States (1) . Its development proceeds through a series of defined states, including preinvasive disease [prostatic intraepithelial neoplasia (PIN)], invasive cancer, and androgen-dependent or androgen-independent metastases (2) . The early stages of organ-confined prostate cancer are generally curable by surgery or radiation therapy, and detection efforts based on PSA2 screening (3) have led to the identification of thousands of men with localized disease. Although serum PSA is widely recognized as the best prostate tumor marker currently available, levels between 4 and 10 ng/ml are seen in men with obstructive or inflammatory uropathies, lowering the specificity of PSA as a cancer marker (4) . Other biomarkers, such as glandular kallikrein-2 (hK2), have been proposed as adjuncts to PSA to increase diagnostic specificity (5) and to reduce the number of men subjected to unnecessary biopsy.

A molecular understanding of prostate cancer development and progression is an important step toward the identification of additional biomarkers with increased specificity for invasive prostate cancer and of new tumor-specific therapeutic targets. Previous systematic interrogations of gene expression levels in human cancers by microarray hybridization (6) suggest that this is a broad and efficient approach toward identifying candidate markers and drug targets. Here, we analyzed the expression levels of more than 8900 different human genes in a set of normal and malignant prostate tissues. Quantitative and qualitative differences in gene expression seen in tissue samples led to a molecular definition of prostate cancer. We identified several genes as overexpressed in most or all of the tumors. Some of these may form the basis for new diagnostic or therapeutic approaches as well as provide insight into prostate tumor development or behavior.


    Materials and Methods
 Top
 ABSTRACT
 Introduction
 Materials and Methods
 Results and Discussion
 REFERENCES
 
Cell Culture.
PC3, LNCaP, and Du145 cells (American Type Culture Collection) were grown in DMEM supplemented with 10% FCS and 100 µg/ml streptomycin (Life Technologies, Inc.). LNCaP cells were also grown in androgen-depleted medium in which dialyzed charcoal-stripped FCS was substituted for FCS. Normal prostate epithelial cells (PrEC; Clonetics) and a human papillomavirus (HPV) E6-transfected derivative (hPr1) were grown in serum-free keratinocyte growth medium (Life Technologies, Inc.). Fibroblastic cell strains CAF1598, CAF1303, CAF1852, and CAF2585 were expanded from fragments of prostates removed for adenocarcinoma. Frozen sections were stained with H&E at the time of surgery and were determined to contain malignant epithelium; adjacent fragments were minced and expanded in supplemented DMEM. BPHF1598 fibroblastic cells were propagated from fragments of prostate tissue that contained only benign hypertrophic glands. Prostate stromal cells (PrSC) A and B (Clonetics) from cancer-free men were grown according to the manufacturer’s recommendations. Adherent cells were expanded and harvested at 60–70% confluence after 4–8 passages. LNCaP grown in the absence of androgens and two cultures of HUVECs were each sampled twice.

Tissue Procurement, cRNA Synthesis, and Hybridization to Oligonucleotide Arrays.
The use of human tissue samples at the University of Virginia (UVA) was approved by the UVA Human Investigation Committee. Fresh samples of adenocarcinoma were obtained from men with elevated PSA levels. H&E-stained sections from prostatectomy specimens or (in one case) from a lymph node metastasis were examined to assess the relative amounts of tumor, benign epithelium, stroma, and lymphocytes. Tissues containing cancer were trimmed to enrich for neoplastic cells. All of the samples were stored at -80°C before processing for microarray analysis. The set of prostate tissues included 23 primary cancer tissues, 1 lymph node metastasis, and 9 nonneoplastic tissues. Eight of the cancers were paired with normal tissue obtained from the same patient. One cancer tissue (case 13) was divided and processed as two independent samples. In each case, several milligrams of tissue were sharply dissected and homogenized with a rotary homogenizer (Omni International) in RNeasy lysis buffer (Qiagen). RNA was prepared from tissues and cells using the RNeasy Mini kit (Qiagen). mRNAs from T-lymphoblastic MOLT4 and myeloleukemic HL60 cells were purchased (Clontech), and RNA from endothelial cells and from three individual isolates of activated B-cells were gifts from Drs. Akira Kawamura (The Scripps Research Institute, La Jolla, CA) and Michael Cooke (Genomics Institute of The Novartis Research Foundation, San Diego, CA) respectively. Labeled cRNA was prepared and hybridized to oligonucleotide arrays ("U95a"; Affymetrix) as described previously (7) .

Data Analysis.
Scanned image files were visually inspected for artifacts and analyzed with GENECHIP 3.1 (Affymetrix). Each image was then scaled to an average hybridization intensity of 200, which corresponds to ~3–5 transcripts per cell (7) . The hybridization intensity for each gene was ranked according to intersample variability (SD), and 3530 genes with the most variable expression across all of the samples were median-centered and normalized with respect to other genes in the sample and corresponding genes in other samples. Genes and samples were subjected to hierarchical clustering essentially as described previously (8) . Differential expression of genes in benign and malignant prostate tissues was estimated using an algorithm (9) based on equally weighted contributions from the difference of hybridization intensities Tumor–µNormal), the quotient of hybridization intensities (µTumorNormal), and the result of an unpaired t test between expression levels in tumor and normal tissues. The genes were scored with respect to each of the three metrics, then ranked according to the sum of the three scores.

Reverse Transcription-PCR Analysis of hepsin and MIC-1 Expression.
cDNA was prepared using 1 µg of total RNA isolated from prostate tissues. Primers used to amplify specific gene products were: hepsin sense, 5'-CGGGACCCCAACAGCGAGGAGAAC-3'; hepsin antisense, 5'-TCGGGGTAGCCAGCACAGAACATC-3'; MIC-1 sense, 5'-CGCGCAACGGGGACGACT-3'; and MIC-1 antisense, 5'-TGAGCACCATGGGATTGTAGC-3'. PCR conditions for hepsin and MIC-1 comprised 95°C for 10 min, 30 cycles of 95°C for 30 s, 55°C for 30 s (annealing), and 72°C for 30 s, and a final elongation step of 72°C for 7 min. All of the PCR reactions used a volume of 20 µl, with 1 unit of AmpliTaq Gold (Perkin-Elmer). Amplification products (10 µl) were separated by 2% agarose gel electrophoresis.

Immunohistochemistry.
The avidin-biotin immunoperoxidase method was performed on deparaffinized zinc formalin-fixed, paraffin-embedded sections. Slides placed in citrate buffer were heated with a microwave for 20 min prior to the application of the anti-FASN antibody for 1 h at room temperature.


    Results and Discussion
 Top
 ABSTRACT
 Introduction
 Materials and Methods
 Results and Discussion
 REFERENCES
 
Gene Expression Profiles in Prostate Tissue Samples and Cell Lines.
We monitored the expression levels of genes in tissues and cells by hybridization of RNA samples to oligonucleotide microarrays representing ~8920 different genes. In total, we hybridized 55 RNA samples derived from 25 prostate cancer tissues (24 unique samples), 9 nonmalignant prostate tissues, and 21 cell line samples (18 unique lines).3 Clinical and pathological information on the tumors is detailed in Table 1Citation .


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Table 1 Clinicopathological information and selected transcript levels in normal and malignant prostate tissues

 
To reveal distinctions between individual tissue samples and cells, we selected a subset of 3530 genes that varied most across the samples and grouped both the genes and the samples according to their overall similarities in levels of expression (8) . These samples were separated on a dendrogram in which their overall similarity was proportional to the length of the vertical branches between them (Fig. 1a)Citation . As expected, independently prepared samples of tumor 13 (13A and 13B) and replicate samples of LNCaP cells and HUVECs were highly correlated, demonstrating the overall reproducibility of the technique. This analysis revealed a major division between cells grown in vitro and the human tissue specimens, with highly divergent gene expression patterns. Within the "tissue" branch of the dendrogram, samples were further subdivided into two distinct groups of normal and malignant samples.



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Fig. 1. Expression of 3530 genes in 55 experimental samples. Rows, individual genes; columns, individual samples. Each cell in the matrix represents the expression level of a single transcript in a single sample; red and green, transcript levels above and below, respectively, the median for that gene across all of the samples. Color saturation is proportional to the magnitude of the difference from the mean. a, dendrogram of samples showing overall similarity in gene expression profiles across the samples. b, demonstration of overall groupings of genes and samples. Black bars to the right, the extent of cell type-specific or functionally relevant gene clusters (identified next to the bars). Colored bars, gene clusters of special interest. c, enlarged view of selected clusters of genes. Three groups of columns are designated by the predominant sample type; nl prost, normal prostate.

 
Subdivisions in the "cell line" branch of the dendrogram (Fig. 1a)Citation largely reflected the embryological origin of the cells. For example, profiles of activated B cells from three different donors were highly correlated with one another and with cells derived from two hematological cancers (MOLT4 and HL60). Six mesenchymal lines derived from normal or malignant prostates of different individuals (designated as BPHF, PrSC, or CAF) were highly related. Similarity was also seen among LNCaP cells grown in the presence or absence of dihydrotestosterone (DHT).

Expression of Genes in Malignant and Cancer-associated Cell Types.
We identified a striking group of ~400 genes as being highly and specifically expressed in malignant samples (Fig. 1bCitation , malignant luminal epithelial; expanded in MALIGNANT LUMINAL, Fig. 1cCitation ). Among this group, we identified keratins 8 and 18, which strongly suggested that this cluster of genes signified malignant luminal epithelium within the cancer tissue (10) . We identified several other clusters of genes for which expression levels were similar in some of the cells and tissues. These clusters suggested the presence of varying amounts of B cells, normal basal epithelial cells, and stromal fibroblasts within normal and tumor tissues (Fig. 1cCitation , B-CELL, BA, and STROMAL, respectively). The varying proportion of cancer-associated cells implied by expression profiling was consistent with our histological estimates of these cell types in each of the tissue samples. High expression of B-cell genes in normal and malignant samples from cases 9 and 10 (Fig. 1cCitation , B-CELL) was consistent with histologically diagnosed prostatitis in these patients. A fourth group of genes was highly expressed in LNCaP cells and in most of the tumor samples (Fig. 1bCitation and METAB, Fig. 1cCitation ) and included multiple genes the products of which are involved in intermediary metabolism, such as ATP synthase and cytochrome C oxidase. Expression of genes within this cluster likely reflects accelerated metabolism of the malignant cells.

Genes with Potential Diagnostic and Therapeutic Utility in Prostate Cancer.
The strong molecular distinction between normal and tumor tissues and the relative homogeneity of gene expression in malignant epithelial cells when compared with normal prostate samples suggested that we could identify genes that were overexpressed in the majority of tumors. The tumor samples examined here contained, on average, about twice as many epithelial cells as the normal tissues. Thus, we focused on methods that could rank highest those genes with the greatest and most uniform differences in gene expression. We used a metric that would take into account the equal importance of average hybridization intensities and fold differences, as well as uniformity of expression within a tissue type (penalizing genes with broad and overlapping ranges of expression; Ref. 9 ). This metric returned genes the expression of which was generally low in normal tissues, elevated in tumors (average, >5-fold for the top 20 genes; Fig. 2Citation ) with nonoverlapping expression ranges. Among the 20 highest scoring genes were ones encoding known tumor markers such as the carcinoma-associated antigen GA733–2 (TACSTD1) and FASN. We also identified MIC-1, which encodes a secreted cytokine (11) , and hepsin, which encodes a membrane-bound extracellular serine protease involved in cell growth control (12) . Expression levels of PSA, MIC-1, and hepsin transcripts are given in Table 1Citation .



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Fig. 2. Expression levels of selected genes in normal and malignant prostate tissues. Each gene is represented by two mean values derived from its expression level in 24 malignant ({diamondsuit}) and 9 normal ({blacksquare}) samples. Error bars, 99% confidence intervals. Shown are 20 genes ranked most highly by a hybrid metric (9) .

 
Differential expression of genes that were ranked high by our method was validated in several ways. First, tumor-specific overexpression of MIC-1 and hepsin transcripts in selected RNAs was confirmed by reverse transcription-PCR amplification (Fig. 3)Citation . Second, the public "Gene-to-Tag" database4 was queried for differential expression of the genes in Fig. 2Citation in normal and prostate cancer samples. Reliable estimates of the expression levels in a single microdissected pair of normal and malignant prostate samples were available for 12 of the 20 genes. Of these 12, 10 showed >5-fold overexpression in cancer tissue. Third, we stained tissue sections with a monoclonal antibody against FASN (13) and found strong and specific immunopositivity in malignant epithelium in all 10 cases of prostate cancer that we examined (Fig. 4)Citation .



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Fig. 3. Amplification of hepsin and MIC-1 transcripts from selected prostate tissues. Transcripts from normal (N8, N10) and tumor (T16, T22) tissues were coamplified with 18s rRNA. Number beneath the PCR product generated from each case, hybridization intensities of hepsin and MIC-1 on corresponding microarrays.

 


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Fig. 4. Immunohistochemical staining of prostate tissue showing differential reactivity of anti-FASN antibody to normal (arrows) and malignant glands. x200.

 
Differences in Gene Expression among Prostate Tumors.
The apparent molecular similarity among prostate cancers (typified by the malignant luminal epithelial cluster, Fig. 1, b and cCitation ) resulted from the initial analysis of 3530 genes the expression of which varied most across normal tissues, malignant tissues, and cell lines (Fig. 1b)Citation . To better estimate the extent of the similarity among prostate cancers, we selected 788 of these genes the expression of which was inferred to be specific to the malignant cells. Clustering of the data showed a dichotomy among the tumors, which was largely attributable to differential expression of a group of ribosomal genes (Fig. 1aCitation , Ribo). This division roughly corresponded to the degree of differentiation among the tumors, with significantly higher Gleason scores (P < 0.01) in the tumor samples with lower ribosomal gene expression. We next sought genes the expression of which might better differentiate low- and high-grade tumors. ANOVA was performed on tumors divided into subgroups with different Gleason scores (group 1: Gleason score, 5 or 6; group 2: Gleason score, 7; group 3: Gleason score, 8 or 9). Among the 20 most significantly different genes identified by this method, we found high expression of IGFBP-2 and IGFBP-5 in group 3 (Gleason score, 8 or 9) compared with groups 1 and 2 (Gleason scores, 5, 6, or 7; data not shown). Both of these genes have been reported as elevated in high-grade tumors (14) .

In this study, we exploited the observed similarities among prostate tumors to identify genes with potential clinical use. The list of genes highly ranked by our metric (Fig. 2)Citation is functionally diverse and includes several genes involved in fatty acid, sugar, nucleotide, and steroid intermediary metabolism. FASN up-regulation is consistent with a previous report (15) and is of particular interest because pharmacological inhibition of FASN results in dose-dependent growth inhibition in a prostate cancer xenograft model (13) . The serine protease, hepsin, which we and others (16) find up-regulated in all of the primary tumors examined, is a potential drug target (17) . The potential benefit of hepsin inhibition is shown by growth arrest induced in hepatoma cells using anti-hepsin antibodies and antisense oligonucleotides (12) . We also found overexpression of the gene encoding the secreted cytokine MIC-1, a member of the transforming growth factor-ß superfamily (18) in 21 of the 24 cancers examined (Table 1)Citation . High MIC-1 levels are found in amniotic (19) and cerebrospinal (20) fluids, and recent studies suggest its overexpression in the serum of patients with metastatic breast, colon, and prostate carcinomas.5

We profiled androgen-dependent and -independent prostate cancer-derived cells grown in vitro to evaluate the fidelity with which these cells replicate features of malignant prostate cells in vivo. Overall, we found only a small number of genes with concordant expression in cell lines and malignant tissues, which suggests that these cell lines have lost many features that characterize prostate cancer in vivo. Thus, it is not surprising that many of the genes that we identified as overexpressed in this study have not been reported as overexpressed in prostate cancer cell types. The fact that we, and others (16) , now find genes such as hepsin to be overexpressed in all of the primary tumors examined underscores the usefulness of profiling primary tumors and the likelihood that these data will provide new insight into prostate cancer.


    ACKNOWLEDGMENTS
 
We thank Drs. Peter Schultz for discussions and encouragement, David Lockhart (Affymetrix and The Salk Institute for Biological Studies), Elizabeth Kaback and Steve Kay for comments, Akira Kawamura (The Scripps Research Institute) and Michael Cooke for expression data, and Ellen Pizer (The Johns Hopkins University, Baltimore, MD) for the anti-FASN antibody, and C. K. Choo (University of Hong Kong, Hong Kong, China) for the hPr-1 cell line.


    FOOTNOTES
 
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1 To whom requests for reprints should be addressed, at Genomics Institute of the Novartis Research Foundation, 3115 Merryfield Row, San Diego, CA 92121. Phone: (858) 812-1522; Fax: (858) 812-1746; E-mail: hampton{at}gnf.org Back

2 The abbreviations used are: FASN, fatty acid synthase; HUVEC, human umbilical vein endothelial cell; PSA, prostate-specific antigen. Back

3 The complete dataset is available on our web site, address: http://www.gnf.org/cancer/prostate. Back

4 Internet address: http://www.ncbi.nlm.nih.gov/SAGE/. Back

5 S. N. Breit, personal communication. Back

Received 4/23/01. Accepted 7/ 5/01.


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 Introduction
 Materials and Methods
 Results and Discussion
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C. D. McCabe, D. D. Spyropoulos, D. Martin, and C. S. Moreno
Genome-Wide Analysis of the Homeobox C6 Transcriptional Network in Prostate Cancer
Cancer Res., March 15, 2008; 68(6): 1988 - 1996.
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T. A. Wallace, R. L. Prueitt, M. Yi, T. M. Howe, J. W. Gillespie, H. G. Yfantis, R. M. Stephens, N. E. Caporaso, C. A. Loffredo, and S. Ambs
Tumor Immunobiological Differences in Prostate Cancer between African-American and European-American Men
Cancer Res., February 1, 2008; 68(3): 927 - 936.
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A. Fritah, C. Saucier, O. De Wever, M. Bracke, I. Bieche, R. Lidereau, C. Gespach, S. Drouot, G. Redeuilh, and M. Sabbah
Role of WISP-2/CCN5 in the Maintenance of a Differentiated and Noninvasive Phenotype in Human Breast Cancer Cells
Mol. Cell. Biol., February 1, 2008; 28(3): 1114 - 1123.
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JNMHome page
A. L. Vavere, S. J. Kridel, F. B. Wheeler, and J. S. Lewis
1-11C-Acetate as a PET Radiopharmaceutical for Imaging Fatty Acid Synthase Expression in Prostate Cancer
J. Nucl. Med., February 1, 2008; 49(2): 327 - 334.
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BloodHome page
M. Sauvageau, M. Miller, S. Lemieux, J. Lessard, J. Hebert, and G. Sauvageau
Quantitative expression profiling guided by common retroviral insertion sites reveals novel and cell type specific cancer genes in leukemia
Blood, January 15, 2008; 111(2): 790 - 799.
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Proc. Natl. Acad. Sci. USAHome page
A. Oldberg, S. Kalamajski, A. V. Salnikov, L. Stuhr, M. Morgelin, R. K. Reed, N.-E. Heldin, and K. Rubin
Collagen-binding proteoglycan fibromodulin can determine stroma matrix structure and fluid balance in experimental carcinoma
PNAS, August 28, 2007; 104(35): 13966 - 13971.
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Proc. Natl. Acad. Sci. USAHome page
S. Y. Wong, H. Haack, J. L. Kissil, M. Barry, R. T. Bronson, S. S. Shen, C. A. Whittaker, D. Crowley, and R. O. Hynes
Protein 4.1B suppresses prostate cancer progression and metastasis
PNAS, July 31, 2007; 104(31): 12784 - 12789.
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A. S. Bjartell, H. Al-Ahmadie, A. M. Serio, J. A. Eastham, S. E. Eggener, S. W. Fine, L. Udby, W. L. Gerald, A. J. Vickers, H. Lilja, et al.
Association of Cysteine-Rich Secretory Protein 3 and {beta}-Microseminoprotein with Outcome after Radical Prostatectomy
Clin. Cancer Res., July 15, 2007; 13(14): 4130 - 4138.
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BioinformaticsHome page
F. Tai and W. Pan
Incorporating prior knowledge of predictors into penalized classifiers with multiple penalty terms
Bioinformatics, July 15, 2007; 23(14): 1775 - 1782.
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BioinformaticsHome page
L. Song, J. Bedo, K. M. Borgwardt, A. Gretton, and A. Smola
Gene selection via the BAHSIC family of algorithms
Bioinformatics, July 1, 2007; 23(13): i490 - i498.
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L. R. Rodrigues, J. A. Teixeira, F. L. Schmitt, M. Paulsson, and H. Lindmark-Mansson
The Role of Osteopontin in Tumor Progression and Metastasis in Breast Cancer
Cancer Epidemiol. Biomarkers Prev., June 1, 2007; 16(6): 1087 - 1097.
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K. S. Selander, D. A. Brown, G. B. Sequeiros, M. Hunter, R. Desmond, T. Parpala, J. Risteli, S. N. Breit, and A. Jukkola-Vuorinen
Serum Macrophage Inhibitory Cytokine-1 Concentrations Correlate with the Presence of Prostate Cancer Bone Metastases
Cancer Epidemiol. Biomarkers Prev., March 1, 2007; 16(3): 532 - 537.
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R. S. Turley, E. C. Finger, N. Hempel, T. How, T. A. Fields, and G. C. Blobe
The Type III Transforming Growth Factor-{beta} Receptor as a Novel Tumor Suppressor Gene in Prostate Cancer
Cancer Res., February 1, 2007; 67(3): 1090 - 1098.
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I. Meinhold-Heerlein, D. Bauerschlag, Y. Zhou, L. M. Sapinoso, K. Ching, H. Frierson Jr., K. Brautigam, J. Sehouli, E. Stickeler, D. Konsgen, et al.
An Integrated Clinical-Genomics Approach Identifies a Candidate Multi-Analyte Blood Test for Serous Ovarian Carcinoma
Clin. Cancer Res., January 15, 2007; 13(2): 458 - 466.
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S Tozlu, I Girault, S Vacher, J Vendrell, C Andrieu, F Spyratos, P Cohen, R Lidereau, and I Bieche
Identification of novel genes that co-cluster with estrogen receptor alpha in breast tumor biopsy specimens, using a large-scale real-time reverse transcription-PCR approach
Endocr. Relat. Cancer, December 1, 2006; 13(4): 1109 - 1120.
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B. Knudsen
Migrating with Myosin VI
Am. J. Pathol., November 1, 2006; 169(5): 1523 - 1526.
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BioinformaticsHome page
T. Manoli, N. Gretz, H.-J. Grone, M. Kenzelmann, R. Eils, and B. Brors
Group testing for pathway analysis improves comparability of different microarray datasets
Bioinformatics, October 15, 2006; 22(20): 2500 - 2506.
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P. Moran, W. Li, B. Fan, R. Vij, C. Eigenbrot, and D. Kirchhofer
Pro-urokinase-type Plasminogen Activator Is a Substrate for Hepsin
J. Biol. Chem., October 13, 2006; 281(41): 30439 - 30446.
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BioinformaticsHome page
A. E. Teschendorff, A. Naderi, N. L. Barbosa-Morais, and C. Caldas
PACK: Profile Analysis using Clustering and Kurtosis to find molecular classifiers in cancer
Bioinformatics, September 15, 2006; 22(18): 2269 - 2275.
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Mol. Cell. ProteomicsHome page
J. Madoz-Gurpide, P. Lopez-Serra, J. L. Martinez-Torrecuadrada, L. Sanchez, L. Lombardia, and J. I. Casal
Proteomics-based Validation of Genomic Data: Applications in Colorectal Cancer Diagnosis
Mol. Cell. Proteomics, August 1, 2006; 5(8): 1471 - 1483.
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P. J.M. Hendriksen, N. F.J. Dits, K. Kokame, A. Veldhoven, W. M. van Weerden, C. H. Bangma, J. Trapman, and G. Jenster
Evolution of the Androgen Receptor Pathway during Progression of Prostate Cancer.
Cancer Res., May 15, 2006; 66(10): 5012 - 5020.
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CarcinogenesisHome page
Z. Li, M. Szabolcs, J. D. Terwilliger, and A. Efstratiadis
Prostatic intraepithelial neoplasia and adenocarcinoma in mice expressing a probasin-Neu oncogenic transgene
Carcinogenesis, May 1, 2006; 27(5): 1054 - 1067.
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P. Liu, S. Ramachandran, M. Ali Seyed, C. D. Scharer, N. Laycock, W. B. Dalton, H. Williams, S. Karanam, M. W. Datta, D. L. Jaye, et al.
Sex-determining region y box 4 is a transforming oncogene in human prostate cancer cells.
Cancer Res., April 15, 2006; 66(8): 4011 - 4019.
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H.-R. Li, J. Wang-Rodriguez, T. M. Nair, J. M. Yeakley, Y.-S. Kwon, M. Bibikova, C. Zheng, L. Zhou, K. Zhang, T. Downs, et al.
Two-dimensional transcriptome profiling: identification of messenger RNA isoform signatures in prostate cancer from archived paraffin-embedded cancer specimens.
Cancer Res., April 15, 2006; 66(8): 4079 - 4088.
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J.-A. Xuan, D. Schneider, P. Toy, R. Lin, A. Newton, Y. Zhu, S. Finster, D. Vogel, B. Mintzer, H. Dinter, et al.
Antibodies neutralizing hepsin protease activity do not impact cell growth but inhibit invasion of prostate and ovarian tumor cells in culture.
Cancer Res., April 1, 2006; 66(7): 3611 - 3619.
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D. K. Vanaja, K. V. Ballman, B. W. Morlan, J. C. Cheville, R. M. Neumann, M. M. Lieber, D. J. Tindall, and C. Y.F. Young
PDLIM4 Repression by Hypermethylation as a Potential Biomarker for Prostate Cancer
Clin. Cancer Res., February 15, 2006; 12(4): 1128 - 1136.
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Epithelial-Restricted Gene Profile of Primary Cultures from Human Prostate Tumors: A Molecular Approach to Predict Clinical Behavior of Prostate Cancer
Mol. Cancer Res., February 1, 2006; 4(2): 79 - 92.
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CarcinogenesisHome page
S. P. Duggan, W. M. Gallagher, E. J.P. Fox, M. M. Abdel-Latif, J. V. Reynolds, and D. Kelleher
Low pH induces co-ordinate regulation of gene expression in oesophageal cells
Carcinogenesis, February 1, 2006; 27(2): 319 - 327.
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E. M. Vestergaard, M. Borre, S. S. Poulsen, E. Nexo, and N. Torring
Plasma Levels of Trefoil Factors are Increased in Patients with Advanced Prostate Cancer
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J. Koopmann, C. N. W. Rosenzweig, Z. Zhang, M. I. Canto, D. A. Brown, M. Hunter, C. Yeo, D. W. Chan, S. N. Breit, and M. Goggins
Serum Markers in Patients with Resectable Pancreatic Adenocarcinoma: Macrophage Inhibitory Cytokine 1 versus CA19-9
Clin. Cancer Res., January 15, 2006; 12(2): 442 - 446.
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D. A. Brown, C. Stephan, R. L. Ward, M. Law, M. Hunter, A. R. Bauskin, J. Amin, K. Jung, E. P. Diamandis, G. M. Hampton, et al.
Measurement of Serum Levels of Macrophage Inhibitory Cytokine 1 Combined with Prostate-Specific Antigen Improves Prostate Cancer Diagnosis
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ScienceHome page
S. A. Tomlins, D. R. Rhodes, S. Perner, S. M. Dhanasekaran, R. Mehra, X.-W. Sun, S. Varambally, X. Cao, J. Tchinda, R. Kuefer, et al.
Recurrent Fusion of TMPRSS2 and ETS Transcription Factor Genes in Prostate Cancer
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I. M. Berquin, Y. Min, R. Wu, H. Wu, and Y. Q. Chen
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BioinformaticsHome page
A. C. Tan, D. Q. Naiman, L. Xu, R. L. Winslow, and D. Geman
Simple decision rules for classifying human cancers from gene expression profiles
Bioinformatics, October 15, 2005; 21(20): 3896 - 3904.
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L. Xu, A. C. Tan, D. Q. Naiman, D. Geman, and R. L. Winslow
Robust prostate cancer marker genes emerge from direct integration of inter-study microarray data
Bioinformatics, October 15, 2005; 21(20): 3905 - 3911.
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C. J.M. Best, J. W. Gillespie, Y. Yi, G. V.R. Chandramouli, M. A. Perlmutter, Y. Gathright, H. S. Erickson, L. Georgevich, M. A. Tangrea, P. H. Duray, et al.
Molecular Alterations in Primary Prostate Cancer after Androgen Ablation Therapy
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X. Wang, J. Yu, A. Sreekumar, S. Varambally, R. Shen, D. Giacherio, R. Mehra, J. E. Montie, K. J. Pienta, M. G. Sanda, et al.
Autoantibody Signatures in Prostate Cancer
N. Engl. J. Med., September 22, 2005; 353(12): 1224 - 1235.
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Molecular Markers of Early Pancreatic Cancer
J. Clin. Oncol., July 10, 2005; 23(20): 4524 - 4531.
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A. R. Bauskin, D. A. Brown, S. Junankar, K. K. Rasiah, S. Eggleton, M. Hunter, T. Liu, D. Smith, T. Kuffner, G. J. Pankhurst, et al.
The Propeptide Mediates Formation of Stromal Stores of PROMIC-1: Role in Determining Prostate Cancer Outcome
Cancer Res., March 15, 2005; 65(6): 2330 - 2336.
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Primary cell cultures as models of prostate cancer development
Endocr. Relat. Cancer, March 1, 2005; 12(1): 19 - 47.
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A. Santamaria, E. Castellanos, V. Gomez, P. Benedit, J. Renau-Piqueras, J. Morote, J. Reventos, T. M. Thomson, and R. Paciucci
PTOV1 Enables the Nuclear Translocation and Mitogenic Activity of Flotillin-1, a Major Protein of Lipid Rafts
Mol. Cell. Biol., March 1, 2005; 25(5): 1900 - 1911.
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S. J. Baek, J.-S. Kim, F. R. Jackson, T. E. Eling, M. F. McEntee, and S.-H. Lee
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Carcinogenesis, December 1, 2004; 25(12): 2425 - 2432.
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J. P. Hobson, S. Netzel-Arnett, R. Szabo, S. M. Rehault, F. C. Church, D. K. Strickland, D. A. Lawrence, T. M. Antalis, and T. H. Bugge
Mouse DESC1 Is Located within a Cluster of Seven DESC1-like Genes and Encodes a Type II Transmembrane Serine Protease That Forms Serpin Inhibitory Complexes
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B. E. Nicholson, H. F. Frierson, M. R. Conaway, J. M. Seraj, M. A. Harding, G. M. Hampton, and D. Theodorescu
Profiling the Evolution of Human Metastatic Bladder Cancer
Cancer Res., November 1, 2004; 64(21): 7813 - 7821.
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M. Bibikova, D. Talantov, E. Chudin, J. M. Yeakley, J. Chen, D. Doucet, E. Wickham, D. Atkins, D. Barker, M. Chee, et al.
Quantitative Gene Expression Profiling in Formalin-Fixed, Paraffin-Embedded Tissues Using Universal Bead Arrays
Am. J. Pathol., November 1, 2004; 165(5): 1799 - 1807.
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T. J. Jang, H. J. Kang, J. R. Kim, and C. H. Yang
Non-steroidal anti-inflammatory drug activated gene (NAG-1) expression is closely related to death receptor-4 and -5 induction, which may explain sulindac sulfide induced gastric cancer cell apoptosis
Carcinogenesis, October 1, 2004; 25(10): 1853 - 1858.
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R. Foley, D. Hollywood, and M. Lawler
Molecular pathology of prostate cancer: the key to identifying new biomarkers of disease
Endocr. Relat. Cancer, September 1, 2004; 11(3): 477 - 488.
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P. K. Cheung, B. Woolcock, H. Adomat, M. Sutcliffe, T. C. Bainbridge, E. C. Jones, D. Webber, T. Kinahan, M. Sadar, M. E. Gleave, et al.
Protein Profiling of Microdissected Prostate Tissue Links Growth Differentiation Factor 15 to Prostate Carcinogenesis
Cancer Res., September 1, 2004; 64(17): 5929 - 5933.
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S. Ashida, H. Nakagawa, T. Katagiri, M. Furihata, M. Iiizumi, Y. Anazawa, T. Tsunoda, R. Takata, K. Kasahara, T. Miki, et al.
Molecular Features of the Transition from Prostatic Intraepithelial Neoplasia (PIN) to Prostate Cancer: Genome-wide Gene-expression Profiles of Prostate Cancers and PINs
Cancer Res., September 1, 2004; 64(17): 5963 - 5972.
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D. E.H. Afar, V. Bhaskar, E. Ibsen, D. Breinberg, S. M. Henshall, J. G. Kench, M. Drobnjak, R. Powers, M. Wong, F. Evangelista, et al.
Preclinical validation of anti-TMEFF2-auristatin E-conjugated antibodies in the treatment of prostate cancer
Mol. Cancer Ther., August 1, 2004; 3(8): 921 - 932.
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EndocrinologyHome page
A. M. Velasco, K. A. Gillis, Y. Li, E. L. Brown, T. M. Sadler, M. Achilleos, L. M. Greenberger, P. Frost, W. Bai, and Y. Zhang
Identification and Validation of Novel Androgen-Regulated Genes in Prostate Cancer
Endocrinology, August 1, 2004; 145(8): 3913 - 3924.
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Y. P. Yu, D. Landsittel, L. Jing, J. Nelson, B. Ren, L. Liu, C. McDonald, R. Thomas, R. Dhir, S. Finkelstein, et al.
Gene Expression Alterations in Prostate Cancer Predicting Tumor Aggression and Preceding Development of Malignancy
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Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression
PNAS, June 22, 2004; 101(25): 9309 - 9314.
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Humoral Immune Response to {alpha}-Methylacyl-CoA Racemase and Prostate Cancer
J Natl Cancer Inst, June 2, 2004; 96(11): 834 - 843.
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M. A. Rubin, S. Varambally, R. Beroukhim, S. A. Tomlins, D. R. Rhodes, P. L. Paris, M. D. Hofer, M. Storz-Schweizer, R. Kuefer, J. A. Fletcher, et al.
Overexpression, Amplification, and Androgen Regulation of TPD52 in Prostate Cancer
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How Are We Going to Discover New Cancer Biomarkers? A Proteomic Approach for Bladder Cancer
Clin. Chem., May 1, 2004; 50(5): 793 - 795.
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Mass Spectrometry as a Diagnostic and a Cancer Biomarker Discovery Tool: Opportunities and Potential Limitations
Mol. Cell. Proteomics, April 1, 2004; 3(4): 367 - 378.
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Serum Macrophage Inhibitory Cytokine 1 as a Marker of Pancreatic and Other Periampullary Cancers
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Elevated {alpha}-Methylacyl-CoA Racemase Enzymatic Activity in Prostate Cancer
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PNAS, January 20, 2004; 101(3): 811 - 816.
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PNAS, January 13, 2004; 101(2): 615 - 620.
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{alpha}-Methylacyl CoA racemase (P504S): overview and potential uses in diagnostic pathology as applied to prostate needle biopsies
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The homodimer of prostate-specific membrane antigen is a functional target for cancer therapy
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A Prognostic Test for Adenocarcinoma of the Lung From Gene Expression Profiling Data
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Role of Decreased Levels of Lipid Phosphate Phosphatase-1 in Accumulation of Lysophosphatidic Acid in Ovarian Cancer
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Macrophage Inhibitory Cytokine-1 Induces the Invasiveness of Gastric Cancer Cells by Up-Regulating the Urokinase-type Plasminogen Activator System
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Mol Cancer ResHome page
S. Rossi, E. Graner, P. Febbo, L. Weinstein, N. Bhattacharya, T. Onody, G. Bubley, S. Balk, and M. Loda
Fatty Acid Synthase Expression Defines Distinct Molecular Signatures in Prostate Cancer
Mol. Cancer Res., August 1, 2003; 1(10): 707 - 715.
[Abstract] [Full Text] [PDF]


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Am. J. Pathol.Home page
M. Grzmil, P. Thelen, B. Hemmerlein, S. Schweyer, S. Voigt, D. Mury, and P. Burfeind
Bax Inhibitor-1 Is Overexpressed in Prostate Cancer and Its Specific Down-Regulation by RNA Interference Leads to Cell Death in Human Prostate Carcinoma Cells
Am. J. Pathol., August 1, 2003; 163(2): 543 - 552.
[Abstract] [Full Text] [PDF]


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Cancer Res.Home page
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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INT J SURG PATHOLHome page
K. A. Iczkowski and C. G. Pantazis
Overexpression of NSAID-Activated Gene Product in Prostate Cancer
International Journal of Surgical Pathology, July 1, 2003; 11(3): 159 - 166.
[Abstract] [PDF]


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Clin. Cancer Res.Home page
D. A. Brown, R. L. Ward, P. Buckhaults, T. Liu, K. E. Romans, N. J. Hawkins, A. R. Bauskin, K. W. Kinzler, B. Vogelstein, and S. N. Breit
MIC-1 Serum Level and Genotype: Associations with Progress and Prognosis of Colorectal Carcinoma
Clin. Cancer Res., July 1, 2003; 9(7): 2642 - 2650.
[Abstract] [Full Text] [PDF]


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Cancer Res.Home page
G. L. Shen-Ong, Y. Feng, and D. A. Troyer
Expression Profiling Identifies a Novel {alpha}-Methylacyl-CoA Racemase Exon with Fumarate Hydratase Homology
Cancer Res., June 15, 2003; 63(12): 3296 - 3301.
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JNCI J Natl Cancer InstHome page
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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Cancer Res.Home page
S. Singh, S. Sadacharan, S. Su, A. Belldegrun, S. Persad, and G. Singh
Overexpression of Vimentin: Role in the Invasive Phenotype in an Androgen-independent Model of Prostate Cancer
Cancer Res., May 1, 2003; 63(9): 2306 - 2311.
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JNCI J Natl Cancer InstHome page
G. J. Gordon, R. V. Jensen, L.-L. Hsiao, S. R. Gullans, J. E. Blumenstock, W. G. Richards, M. T. Jaklitsch, D. J. Sugarbaker, and R. Bueno
Using Gene Expression Ratios to Predict Outcome Among Patients With Mesothelioma
J Natl Cancer Inst, April 16, 2003; 95(8): 598 - 605.
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Proc. Natl. Acad. Sci. USAHome page
J. B. Welsh, L. M. Sapinoso, S. G. Kern, D. A. Brown, T. Liu, A. R. Bauskin, R. L. Ward, N. J. Hawkins, D. I. Quinn, P. J. Russell, et al.
Large-scale delineation of secreted protein biomarkers overexpressed in cancer tissue and serum
PNAS, March 18, 2003; 100(6): 3410 - 3415.
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Am. J. Pathol.Home page
A. Santamaria, P. L. Fernandez, X. Farre, P. Benedit, J. Reventos, J. Morote, R. Paciucci, and T. M. Thomson
PTOV-1, a Novel Protein Overexpressed in Prostate Cancer, Shuttles between the Cytoplasm and the Nucleus and Promotes Entry into the S Phase of the Cell Division Cycle
Am. J. Pathol., March 1, 2003; 162(3): 897 - 905.
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HypertensionHome page
P. Philip-Couderc, F. Smih, M. Pelat, C. Vidal, P. Verwaerde, A. Pathak, S. Buys, M. Galinier, J.-M. Senard, and P. Rouet
Cardiac Transcriptome Analysis in Obesity-Related Hypertension
Hypertension, March 1, 2003; 41(3): 414 - 421.
[Abstract] [Full Text] [PDF]


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Clin. Cancer Res.Home page
V.-M. Wasenius, S. Hemmer, E. Kettunen, S. Knuutila, K. Franssila, and H. Joensuu
Hepatocyte Growth Factor Receptor, Matrix Metalloproteinase-11, Tissue Inhibitor of Metalloproteinase-1, and Fibronectin Are Up-Regulated in Papillary Thyroid Carcinoma: A cDNA and Tissue Microarray Study
Clin. Cancer Res., January 1, 2003; 9(1): 68 - 75.
[Abstract] [Full Text] [PDF]


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Am. J. Pathol.Home page
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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Cancer Res.Home page
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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Cancer Res.Home page
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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Cancer Res.Home page
J. J. Gildea, M. J. Seraj, G. Oxford, M. A. Harding, G. M. Hampton, C. A. Moskaluk, H. F. Frierson, M. R. Conaway, and D. Theodorescu
RhoGDI2 Is an Invasion and Metastasis Suppressor Gene in Human Cancer
Cancer Res., November 15, 2002; 62(22): 6418 - 6423.
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Am. J. Pathol.Home page
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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Am. J. Pathol.Home page
H. F. Frierson Jr, A. K. El-Naggar, J. B. Welsh, L. M. Sapinoso, A. I. Su, J. Cheng, T. Saku, C. A. Moskaluk, and G. M. Hampton
Large Scale Molecular Analysis Identifies Genes with Altered Expression in Salivary Adenoid Cystic Carcinoma
Am. J. Pathol., October 1, 2002; 161(4): 1315 - 1323.
[Abstract] [Full Text] [PDF]


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J. Biol. Chem.Home page
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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