Identification of Genes Periodically Expressed in the Human Cell Cycle and Their Expression in Tumors
Michael L. Whitfield,Gavin Sherlock,Alok J. Saldanha,John I. Murray,Catherine A. Ball,Karen E. Alexander,John C. Matese,Charles M. Perou,Myra M. Hurt,Patrick O. Brown,David Botstein +10 more
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TLDR
The genome-wide program of gene expression during the cell division cycle in a human cancer cell line (HeLa) was characterized using cDNA microarrays to provide a comprehensive catalog of cell cycle regulated genes that can serve as a starting point for functional discovery.Abstract:
The genome-wide program of gene expression during the cell division cycle in a human cancer cell line (HeLa) was characterized using cDNA microarrays. Transcripts of >850 genes showed periodic variation during the cell cycle. Hierarchical clustering of the expression patterns revealed coexpressed groups of previously well-characterized genes involved in essential cell cycle processes such as DNA replication, chromosome segregation, and cell adhesion along with genes of uncharacterized function. Most of the genes whose expression had previously been reported to correlate with the proliferative state of tumors were found herein also to be periodically expressed during the HeLa cell cycle. However, some of the genes periodically expressed in the HeLa cell cycle do not have a consistent correlation with tumor proliferation. Cell cycle-regulated transcripts of genes involved in fundamental processes such as DNA replication and chromosome segregation seem to be more highly expressed in proliferative tumors simply because they contain more cycling cells. The data in this report provide a comprehensive catalog of cell cycle regulated genes that can serve as a starting point for functional discovery. The full dataset is available at http://genome-www.stanford.edu/Human-CellCycle/HeLa/.read more
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MicroRNA expression profiles classify human cancers
Jun Lu,Gad Getz,Eric A. Miska,Eric A. Miska,Ezequiel Alvarez-Saavedra,Justin Lamb,David Peck,Alejandro Sweet-Cordero,Alejandro Sweet-Cordero,Benjamin L. Ebert,Benjamin L. Ebert,Raymond H. Mak,Raymond H. Mak,Adolfo A. Ferrando,James R. Downing,Tyler Jacks,H. Robert Horvitz,H. Robert Horvitz,Todd R. Golub,Todd R. Golub,Todd R. Golub +20 more
TL;DR: A new, bead-based flow cytometric miRNA expression profiling method is used to present a systematic expression analysis of 217 mammalian miRNAs from 334 samples, including multiple human cancers, and finds the miRNA profiles are surprisingly informative, reflecting the developmental lineage and differentiation state of the tumours.
Journal ArticleDOI
Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1
Roel G.W. Verhaak,Katherine A. Hoadley,Elizabeth Purdom,Victoria Wang,Yuan-yuan Qi,Matthew D. Wilkerson,C. Ryan Miller,Li Ding,Todd R. Golub,Jill P. Mesirov,Gabriele Alexe,Michael S. Lawrence,Michael O'Kelly,Pablo Tamayo,Barbara A. Weir,Stacey Gabriel,Wendy Winckler,Supriya Gupta,Lakshmi Jakkula,Heidi S. Feiler,J. Graeme Hodgson,C. David James,Jann N. Sarkaria,Cameron Brennan,Ari B. Kahn,Paul T. Spellman,Richard K. Wilson,Terence P. Speed,Terence P. Speed,Joe W. Gray,Matthew Meyerson,Gad Getz,Charles M. Perou,Charles M. Perou,D. Neil Hayes +34 more
TL;DR: A robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes is described and multidimensional genomic data is integrated to establish patterns of somatic mutations and DNA copy number.
Journal ArticleDOI
Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets
Evan Z. Macosko,Evan Z. Macosko,Anindita Basu,Anindita Basu,Rahul Satija,Rahul Satija,James Nemesh,James Nemesh,Karthik Shekhar,Melissa Goldman,Melissa Goldman,Itay Tirosh,Allison R. Bialas,Nolan Kamitaki,Nolan Kamitaki,Emily M. Martersteck,John J. Trombetta,David A. Weitz,Joshua R. Sanes,Alex K. Shalek,Alex K. Shalek,Alex K. Shalek,Aviv Regev,Aviv Regev,Aviv Regev,Steven A. McCarroll,Steven A. McCarroll +26 more
TL;DR: Drop-seq will accelerate biological discovery by enabling routine transcriptional profiling at single-cell resolution by separating them into nanoliter-sized aqueous droplets, associating a different barcode with each cell's RNAs, and sequencing them all together.
Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1
Roel G.W. Verhaak,Katherine A. Hoadley,Elizabeth Purdom,Victoria Wang,Yuan-yuan Qi,Matthew D. Wilkerson,C. Ryan Miller,Li Ding,Todd R. Golub,Jill P. Mesirov,Gabriele Alexe,Michael S. Lawrence,Michael O'Kelly,Pablo Tamayo,Barbara A. Weir,Stacey Gabriel,Wendy Winckler,Supriya Gupta,Lakshmi Jakkula,Heidi S. Feiler,J. Graeme Hodgson,C. David James,Jann N. Sarkaria,Cameron Brennan,Ari B. Kahn,Paul T. Spellman,Richard K. Wilson,Terence P. Speed,Terence P. Speed,Joe W. Gray,Matthew Meyerson,Gad Getz,Charles M. Perou,Charles M. Perou,D. Neil Hayes +34 more
TL;DR: The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM) and proposed a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes as discussed by the authors.
Journal ArticleDOI
Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study.
Lisa A. Carey,Charles M. Perou,Chad A. Livasy,Lynn G. Dressler,David Cowan,Kathleen Conway,Gamze Karaca,Melissa A. Troester,Chiu Kit Tse,Sharon N. Edmiston,Sandra L. Deming,Joseph Geradts,Maggie C.U. Cheang,Torsten O. Nielsen,Patricia G. Moorman,H. Shelton Earp,Robert C. Millikan +16 more
TL;DR: Basal-like breast tumors occurred at a higher prevalence among premenopausal African American patients compared with postmenopausal American and non-African American patients in this population-based study, and their associations with tumor size, axillary nodal status, mitotic index, nuclear pleomorphism, combined grade, p53 mutation status, and breast cancer-specific survival were examined.
References
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Cancer : Principles and Practice of Oncology
TL;DR: Part I: Molecular Biology of Cancer Molecular Methods in Oncology Section 1. Amplification Techniques Section 2. RNA Interference Section 3. cDNA arrays Section 4. Tissue arrays Section 5. Cytogenetics Section 6. Bioinformatics Genomics and Proteomics Molecular Targets in oncology.
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