Literature DB >> 11304808

Identification of genes differentially expressed in benign prostatic hyperplasia.

A G DiLella1, T J Toner, C P Austin, B M Connolly.   

Abstract

Differences between benign prostatic hyperplasia (BPH) and normal prostate tissue at the level of mRNA expression provide an opportunity to identify candidate genes for this disease. A cDNA subtraction procedure was used to isolate differentially expressed genes in BPH. The subtraction was done by solution hybridization of BPH cDNA against excess normal prostate cDNA. We identified known, EST, and novel genes by sequence and database analysis of the subtracted cDNAs. Several of these cDNAs were used as probes in Northern blotting analysis to confirm over-expression of their corresponding mRNAs in BPH tissues. One highly upregulated sequence of interest shared identity with a known mRNA encoding human NELL2, a protein containing epidermal growth factor-like domains. NELL2 was not previously reported to be expressed in prostate and may code for a novel prostatic growth factor. In situ hybridization analysis of hyperplastic prostate specimens demonstrated that NELL2 mRNA expression is predominantly localized in basal cells of the epithelium. Disease-related changes in the levels of NELL2 may contribute to alterations in epithelial-stromal homeostasis in BPH. (J Histochem Cytochem 49:669-670, 2001)

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Year:  2001        PMID: 11304808     DOI: 10.1177/002215540104900517

Source DB:  PubMed          Journal:  J Histochem Cytochem        ISSN: 0022-1554            Impact factor:   2.479


  7 in total

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Authors:  Austin H Chen; Yin-Wu Tsau; Ching-Heng Lin
Journal:  BMC Genomics       Date:  2010-04-30       Impact factor: 3.969

2.  Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data.

Authors:  Enrico Glaab; Jaume Bacardit; Jonathan M Garibaldi; Natalio Krasnogor
Journal:  PLoS One       Date:  2012-07-11       Impact factor: 3.240

3.  Functional networks inference from rule-based machine learning models.

Authors:  Nicola Lazzarini; Paweł Widera; Stuart Williamson; Rakesh Heer; Natalio Krasnogor; Jaume Bacardit
Journal:  BioData Min       Date:  2016-09-05       Impact factor: 2.522

4.  RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers.

Authors:  Nicola Lazzarini; Jaume Bacardit
Journal:  BMC Bioinformatics       Date:  2017-06-30       Impact factor: 3.169

5.  TWIST2: A new candidate tumor suppressor in prostate cancer.

Authors:  Chengxiao Zhao; Wei Zhang; Xiaoquan Zhu; Yong Xu; Kuo Yang; Dong Wei; Siying Liang; Fan Zhao; Yaoguang Zhang; Xin Chen; Liang Sun; Huiping Yuan; Xiaohong Shi; Xin Wang; Ming Liu; Fan Yang; Jianye Wang; Ze Yang
Journal:  Prostate       Date:  2019-08-21       Impact factor: 4.104

6.  An integrated approach for identifying wrongly labelled samples when performing classification in microarray data.

Authors:  Yuk Yee Leung; Chun Qi Chang; Yeung Sam Hung
Journal:  PLoS One       Date:  2012-10-17       Impact factor: 3.240

7.  Transcriptional profiling of inductive mesenchyme to identify molecules involved in prostate development and disease.

Authors:  Griet Vanpoucke; Brigid Orr; O Cathal Grace; Ray Chan; George R Ashley; Karin Williams; Omar E Franco; Simon W Hayward; Axel A Thomson
Journal:  Genome Biol       Date:  2007       Impact factor: 13.583

  7 in total

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