Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression

Daniel R. Rhodes, Jianjun Yu, K. Shanker, Nandan Deshpande, Radhika Varambally, Debashis Ghosh, Terrence Barrette, Akhilesh Pandey, Arul M. Chinnaiyan

Research output: Contribution to journalArticle

Abstract

Many studies have used DNA microarrays to identify the gene expression signatures of human cancer, yet the critical features of these often unmanageably large signatures remain elusive. To address this, we developed a statistical method, comparative meta-profiling, which identifies and assesses the intersection of multiple gene expression signatures from a diverse collection of microarray data sets. We collected and analyzed 40 published cancer microarray data sets, comprising 38 million gene expression measurements from >3,700 cancer samples. From this, we characterized a common transcriptional profile that is universally activated in most cancer types relative to the normal tissues from which they arose, likely reflecting essential transcriptional features of neoplastic transformation. In addition, we characterized a transcriptional profile that is commonly activated in various types of undifferentiated cancer, suggesting common molecular mechanisms by which cancer cells progress and avoid differentiation. Finally, we validated these transcriptional profiles on independent data sets.

Original languageEnglish (US)
Pages (from-to)9309-9314
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume101
Issue number25
DOIs
StatePublished - Jun 22 2004

ASJC Scopus subject areas

  • Genetics
  • General

Fingerprint Dive into the research topics of 'Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression'. Together they form a unique fingerprint.

  • Cite this