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Literature DB >> 23788797

Genomic approaches in breast cancer research.

Abstract

Microarray technologies provide high-throughput analysis of genes that are differentially expressed in humans and other species, and thereby provide a means to measure how biological systems are altered during development or disease states. Within, we review how high-throughput genomic technologies have increased our understanding about the molecular complexity of breast cancer, identified distinct molecular phenotypes and how they can be used to increase the accuracy of predicted clinical outcome.

Entities: Disease Species

Keywords: breast cancer; genomics; histology; microarray; tumor

Mesh：

Year: 2013 PMID： 23788797 PMCID： PMC3776566 DOI： 10.1093/bfgp/elt019

Source DB: PubMed Journal: Brief Funct Genomics ISSN： 2041-2649 Impact factor: 4.241

37 in total

1. A pathway-based classification of human breast cancer.

Authors: Michael L Gatza; Joseph E Lucas; William T Barry; Jong Wook Kim; Quanli Wang; Matthew D Crawford; Michael B Datto; Michael Kelley; Bernard Mathey-Prevot; Anil Potti; Joseph R Nevins
Journal: Proc Natl Acad Sci U S A Date: 2010-03-24 Impact factor: 11.205

2. Genomics: driving cancer biology.

Authors: Gemma K Alderton
Journal: Nat Rev Cancer Date: 2011-02 Impact factor: 60.716

Review 3. BRCA1, BRCA2, and DNA damage response: collision or collusion?

Authors: H Zhang; G Tombline; B L Weber
Journal: Cell Date: 1998-02-20 Impact factor: 41.582

4. Molecular portraits of human breast tumours.

Authors: C M Perou; T Sørlie; M B Eisen; M van de Rijn; S S Jeffrey; C A Rees; J R Pollack; D T Ross; H Johnsen; L A Akslen; O Fluge; A Pergamenschikov; C Williams; S X Zhu; P E Lønning; A L Børresen-Dale; P O Brown; D Botstein
Journal: Nature Date: 2000-08-17 Impact factor: 49.962

Review 5. Gene expression profiling in breast cancer: classification, prognostication, and prediction.

Authors: Jorge S Reis-Filho; Lajos Pusztai
Journal: Lancet Date: 2011-11-19 Impact factor: 79.321

6. Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma.

Authors: Torsten O Nielsen; Forrest D Hsu; Kristin Jensen; Maggie Cheang; Gamze Karaca; Zhiyuan Hu; Tina Hernandez-Boussard; Chad Livasy; Dave Cowan; Lynn Dressler; Lars A Akslen; Joseph Ragaz; Allen M Gown; C Blake Gilks; Matt van de Rijn; Charles M Perou
Journal: Clin Cancer Res Date: 2004-08-15 Impact factor: 12.531

7. Ancient Greek and Greco-Roman methods in modern surgical treatment of cancer.

Authors: Niki Papavramidou; Theodossis Papavramidis; Thespis Demetriou
Journal: Ann Surg Oncol Date: 2010-03 Impact factor: 5.344

8. Whole-genome analysis informs breast cancer response to aromatase inhibition.

Authors: Matthew J Ellis; Li Ding; Dong Shen; Jingqin Luo; Vera J Suman; John W Wallis; Brian A Van Tine; Jeremy Hoog; Reece J Goiffon; Theodore C Goldstein; Sam Ng; Li Lin; Robert Crowder; Jacqueline Snider; Karla Ballman; Jason Weber; Ken Chen; Daniel C Koboldt; Cyriac Kandoth; William S Schierding; Joshua F McMichael; Christopher A Miller; Charles Lu; Christopher C Harris; Michael D McLellan; Michael C Wendl; Katherine DeSchryver; D Craig Allred; Laura Esserman; Gary Unzeitig; Julie Margenthaler; G V Babiera; P Kelly Marcom; J M Guenther; Marilyn Leitch; Kelly Hunt; John Olson; Yu Tao; Christopher A Maher; Lucinda L Fulton; Robert S Fulton; Michelle Harrison; Ben Oberkfell; Feiyu Du; Ryan Demeter; Tammi L Vickery; Adnan Elhammali; Helen Piwnica-Worms; Sandra McDonald; Mark Watson; David J Dooling; David Ota; Li-Wei Chang; Ron Bose; Timothy J Ley; David Piwnica-Worms; Joshua M Stuart; Richard K Wilson; Elaine R Mardis
Journal: Nature Date: 2012-06-10 Impact factor: 49.962

9. The Gene expression Grade Index: a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1-98 trial.

Authors: Christine Desmedt; Anita Giobbie-Hurder; Patrick Neven; Robert Paridaens; Marie-Rose Christiaens; Ann Smeets; Françoise Lallemand; Benjamin Haibe-Kains; Giuseppe Viale; Richard D Gelber; Martine Piccart; Christos Sotiriou
Journal: BMC Med Genomics Date: 2009-07-02 Impact factor: 3.063

Review 10. Comparing whole genomes using DNA microarrays.

Authors: David Gresham; Maitreya J Dunham; David Botstein
Journal: Nat Rev Genet Date: 2008-04 Impact factor: 53.242

9 in total

1. Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA.

Authors: Xing Chen
Journal: Sci Rep Date: 2015-08-17 Impact factor: 4.379

2. A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network.

Authors: Shunxian Zhou; Zhanwei Xuan; Lei Wang; Pengyao Ping; Tingrui Pei
Journal: Comput Math Methods Med Date: 2018-05-06 Impact factor: 2.238

Genomic approaches in breast cancer research.

1. A pathway-based classification of human breast cancer.

2. Genomics: driving cancer biology.

Review 3. BRCA1, BRCA2, and DNA damage response: collision or collusion?

4. Molecular portraits of human breast tumours.

Review 5. Gene expression profiling in breast cancer: classification, prognostication, and prediction.

6. Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma.

7. Ancient Greek and Greco-Roman methods in modern surgical treatment of cancer.

8. Whole-genome analysis informs breast cancer response to aromatase inhibition.

9. The Gene expression Grade Index: a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1-98 trial.

Review 10. Comparing whole genomes using DNA microarrays.

1. Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA.

2. A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network.

3. BRWLDA: bi-random walks for predicting lncRNA-disease associations.

4. A Novel Method for Predicting Disease-Associated LncRNA-MiRNA Pairs Based on the Higher-Order Orthogonal Iteration.

5. A Probabilistic Matrix Factorization Method for Identifying lncRNA-disease Associations.

6. lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering.

Review 7. Long non-coding RNAs and complex diseases: from experimental results to computational models.

8. Laplacian normalization and bi-random walks on heterogeneous networks for predicting lncRNA-disease associations.

9. IDLDA: An Improved Diffusion Model for Predicting LncRNA-Disease Associations.