Literature DB >> 17579771

Transcriptomic signatures in breast cancer.

Jianjiang Fu1, Stefanie S Jeffrey.   

Abstract

High throughput DNA microarray technology has been broadly applied to the study of breast cancer to classify molecular subtypes, to predict outcome, survival, response to treatment, and for the identification of novel therapeutic targets. Although results are promising, this technology will not have a full impact on routine clinical practice until there is further standardization of techniques and optimal clinical trial design. Due to substantial disease heterogeneity and the number of genes being analyzed, collaborative, multi-institutional studies are required to accrue enough patients for sufficient statistical power. Newer bioinformatic approaches are being developed to assist with the analysis of this important data.

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Year:  2007        PMID: 17579771     DOI: 10.1039/b618163e

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  4 in total

Review 1.  DNA microarray-based gene expression profiling of estrogenic chemicals.

Authors:  Ryoiti Kiyama; Yun Zhu
Journal:  Cell Mol Life Sci       Date:  2014-01-08       Impact factor: 9.261

2.  Enhancer reprogramming driven by high-order assemblies of transcription factors promotes phenotypic plasticity and breast cancer endocrine resistance.

Authors:  Mingjun Bi; Zhao Zhang; Yi-Zhou Jiang; Pengya Xue; Hu Wang; Zhao Lai; Xiaoyong Fu; Carmine De Angelis; Yue Gong; Zhen Gao; Jianhua Ruan; Victor X Jin; Elisabetta Marangoni; Elodie Montaudon; Christopher K Glass; Wei Li; Tim Hui-Ming Huang; Zhi-Ming Shao; Rachel Schiff; Lizhen Chen; Zhijie Liu
Journal:  Nat Cell Biol       Date:  2020-05-18       Impact factor: 28.824

3.  Ratiometric Array of Conjugated Polymers-Fluorescent Protein Provides a Robust Mammalian Cell Sensor.

Authors:  Subinoy Rana; S Gokhan Elci; Rubul Mout; Arvind K Singla; Mahdieh Yazdani; Markus Bender; Avinash Bajaj; Krishnendu Saha; Uwe H F Bunz; Frank R Jirik; Vincent M Rotello
Journal:  J Am Chem Soc       Date:  2016-03-23       Impact factor: 15.419

Review 4.  Computational prognostic indicators for breast cancer.

Authors:  Xinan Yang; Xindi Ai; John M Cunningham
Journal:  Cancer Manag Res       Date:  2014-07-12       Impact factor: 3.989

  4 in total

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