Literature DB >> 11406537

Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling.

J Luo1, D J Duggan, Y Chen, J Sauvageot, C M Ewing, M L Bittner, J M Trent, W B Isaacs.   

Abstract

Critical aspects of the biology and molecular basis for prostate malignancy remain poorly understood. To reveal fundamental differences between benign and malignant growth of prostate cells, we performed gene expression profiling of primary human prostate cancer and benign prostatic hyperplasia (BPH) using cDNA microarrays consisting of 6500 human genes. Frozen prostate specimens were processed to facilitate extraction of RNA from regions of tissue enriched in either benign or malignant epithelial cell growth within a given specimen. Gene expression in each of the 16 prostate cancer and nine BPH specimens was compared with a common reference to generate normalized measures for each gene across all of the samples. Using an analysis of complete pairwise comparisons of expression profiles among all of the samples, we observed clearly discernable patterns of overall gene expression that differentiated prostate cancer from BPH. Further analysis of the data identified 210 genes with statistically significant differences in expression between prostate cancer and BPH. These genes include many not recognized previously as differentially expressed in prostate cancer and BPH, including hepsin, which codes for a transmembrane serine protease. This study reveals for the first time that significant and widespread differences in gene expression patterns exist between benign and malignant growth of the prostate gland. Gene expression analysis of prostate tissues should help to disclose the molecular mechanisms underlying prostate malignant growth and identify molecular markers for diagnostic, prognostic, and therapeutic use.

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Year:  2001        PMID: 11406537

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  123 in total

Review 1.  Navigating the human transcriptome.

Authors:  R L Strausberg; G J Riggins
Journal:  Proc Natl Acad Sci U S A       Date:  2001-10-09       Impact factor: 11.205

2.  An empirical Bayes' approach to joint analysis of multiple microarray gene expression studies.

Authors:  Lingyan Ruan; Ming Yuan
Journal:  Biometrics       Date:  2011-04-22       Impact factor: 2.571

3.  Analysis of repeatability in spotted cDNA microarrays.

Authors:  Tor-Kristian Jenssen; Mette Langaas; Winston P Kuo; Birgitte Smith-Sørensen; Ola Myklebost; Eivind Hovig
Journal:  Nucleic Acids Res       Date:  2002-07-15       Impact factor: 16.971

4.  Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer.

Authors:  Debashis Ghosh; Terrence R Barette; Dan Rhodes; Arul M Chinnaiyan
Journal:  Funct Integr Genomics       Date:  2003-07-22       Impact factor: 3.410

5.  Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression.

Authors:  Daniel R Rhodes; Jianjun Yu; K Shanker; Nandan Deshpande; Radhika Varambally; Debashis Ghosh; Terrence Barrette; Akhilesh Pandey; Arul M Chinnaiyan
Journal:  Proc Natl Acad Sci U S A       Date:  2004-06-07       Impact factor: 11.205

6.  CONFAC: automated application of comparative genomic promoter analysis to DNA microarray datasets.

Authors:  Suresh Karanam; Carlos S Moreno
Journal:  Nucleic Acids Res       Date:  2004-07-01       Impact factor: 16.971

7.  Coexpression analysis of human genes across many microarray data sets.

Authors:  Homin K Lee; Amy K Hsu; Jon Sajdak; Jie Qin; Paul Pavlidis
Journal:  Genome Res       Date:  2004-06       Impact factor: 9.043

8.  ONCOMINE: a cancer microarray database and integrated data-mining platform.

Authors:  Daniel R Rhodes; Jianjun Yu; K Shanker; Nandan Deshpande; Radhika Varambally; Debashis Ghosh; Terrence Barrette; Akhilesh Pandey; Arul M Chinnaiyan
Journal:  Neoplasia       Date:  2004 Jan-Feb       Impact factor: 5.715

Review 9.  [Tissue microarrays. High-throughput procedures to verify potential biomarkers].

Authors:  R Kuefer; M D Hofer; J E Gschwend; M A Rubin
Journal:  Urologe A       Date:  2004-06       Impact factor: 0.639

10.  Module-based prediction approach for robust inter-study predictions in microarray data.

Authors:  Zhibao Mi; Kui Shen; Nan Song; Chunrong Cheng; Chi Song; Naftali Kaminski; George C Tseng
Journal:  Bioinformatics       Date:  2010-08-17       Impact factor: 6.937

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