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Delineation of prognostic biomarkers in prostate cancer

Author

Listed:
  • Saravana M. Dhanasekaran

    (University of Michigan Medical School)

  • Terrence R. Barrette

    (University of Michigan Medical School)

  • Debashis Ghosh

    (University of Michigan Medical School)

  • Rajal Shah

    (University of Michigan Medical School)

  • Sooryanarayana Varambally

    (University of Michigan Medical School)

  • Kotoku Kurachi

    (University of Michigan Medical School)

  • Kenneth J. Pienta

    (University of Michigan Medical School
    University of Michigan Medical School
    University of Michigan Medical School)

  • Mark A. Rubin

    (University of Michigan Medical School
    University of Michigan Medical School
    University of Michigan Medical School)

  • Arul M. Chinnaiyan

    (University of Michigan Medical School
    University of Michigan Medical School
    University of Michigan Medical School)

Abstract

Prostate cancer is the most frequently diagnosed cancer in American men1,2. Screening for prostate-specific antigen (PSA) has led to earlier detection of prostate cancer3, but elevated serum PSA levels may be present in non-malignant conditions such as benign prostatic hyperlasia (BPH). Characterization of gene-expression profiles that molecularly distinguish prostatic neoplasms may identify genes involved in prostate carcinogenesis, elucidate clinical biomarkers, and lead to an improved classification of prostate cancer4,5,6. Using microarrays of complementary DNA, we examined gene-expression profiles of more than 50 normal and neoplastic prostate specimens and three common prostate-cancer cell lines. Signature expression profiles of normal adjacent prostate (NAP), BPH, localized prostate cancer, and metastatic, hormone-refractory prostate cancer were determined. Here we establish many associations between genes and prostate cancer. We assessed two of these genes—hepsin, a transmembrane serine protease, and pim-1, a serine/threonine kinase—at the protein level using tissue microarrays consisting of over 700 clinically stratified prostate-cancer specimens. Expression of hepsin and pim-1 proteins was significantly correlated with measures of clinical outcome. Thus, the integration of cDNA microarray, high-density tissue microarray, and linked clinical and pathology data is a powerful approach to molecular profiling of human cancer.

Suggested Citation

  • Saravana M. Dhanasekaran & Terrence R. Barrette & Debashis Ghosh & Rajal Shah & Sooryanarayana Varambally & Kotoku Kurachi & Kenneth J. Pienta & Mark A. Rubin & Arul M. Chinnaiyan, 2001. "Delineation of prognostic biomarkers in prostate cancer," Nature, Nature, vol. 412(6849), pages 822-826, August.
    • Handle: RePEc:nat:nature:v:412:y:2001:i:6849:d:10.1038_35090585
    DOI: 10.1038/35090585
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    Citations

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    Cited by:

    1. Ming Yi & Ruoqing Zhu & Robert M Stephens, 2018. "GradientScanSurv—An exhaustive association test method for gene expression data with censored survival outcome," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-28, December.
    2. Debashis Ghosh & Arul Chinnaiyan, 2004. "Semiparametric methods for identification of tumor progression genes from microarray data," The University of Michigan Department of Biostatistics Working Paper Series 1039, Berkeley Electronic Press.
    3. Chakraborty, Sounak, 2009. "Bayesian binary kernel probit model for microarray based cancer classification and gene selection," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4198-4209, October.
    4. Debashis Ghosh, 2003. "Penalized Discriminant Methods for the Classification of Tumors from Gene Expression Data," Biometrics, The International Biometric Society, vol. 59(4), pages 992-1000, December.
    5. Marco Bolis & Daniela Bossi & Arianna Vallerga & Valentina Ceserani & Manuela Cavalli & Daniela Impellizzieri & Laura Di Rito & Eugenio Zoni & Simone Mosole & Angela Rita Elia & Andrea Rinaldi & Ricar, 2021. "Dynamic prostate cancer transcriptome analysis delineates the trajectory to disease progression," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    6. Sigrun Helga Lund & Daniel Fannar Gudbjartsson & Thorunn Rafnar & Asgeir Sigurdsson & Sigurjon Axel Gudjonsson & Julius Gudmundsson & Kari Stefansson & Gunnar Stefansson, 2014. "A Method for Detecting Long Non-Coding RNAs with Tiled RNA Expression Microarrays," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
    7. Debashis Ghosh & Arul Chinnaiyan, 2004. "Covariate adjustment in the analysis of microarray data from clinical studies," The University of Michigan Department of Biostatistics Working Paper Series 1030, Berkeley Electronic Press.
    8. Debashis Ghosh & Arul Chinnaiyan, 2004. "Classification and selection of biomarkers in genomic data using LASSO," The University of Michigan Department of Biostatistics Working Paper Series 1041, Berkeley Electronic Press.
    9. Ke Chen & Guo Li & Xiaoqun Gong & Qinjuan Ren & Junying Wang & Shuang Zhao & Ling Liu & Yuxing Yan & Qingshan Liu & Yang Cao & Yaoyao Ren & Qiong Qin & Qi Xin & Shu-Lin Liu & Peiyu Yao & Bo Zhang & Ji, 2024. "Atomic-scale strain engineering of atomically resolved Pt clusters transcending natural enzymes," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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