The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression
Open accessAbstract
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 metaprofiling, 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.
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Self-citationComparative Genomics of the Eukaryotes
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Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Coming of age: ten years of next-generation sequencing technologies
Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.49 · Cited by 4,352
Glinsky, G. V., Berezovska, O., & Glinskii, A. B. (2005) · Journal of Clinical Investigation · doi:10.1172/jci23412 · Cited by 916
Meta-analysis of age-related gene expression profiles identifies common signatures of aging
de Magalhães, J. P., Curado, J., & Church, G. M. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp073 · Cited by 737
A module map showing conditional activity of expression modules in cancer
Segal, E., Friedman, N., Koller, D., & Regev, A. (2004) · Nature Genetics · doi:10.1038/ng1434 · Cited by 685
Metadata
- Authors
- Rhodes, D. R., Yu, J., Shanker, K., Deshpande, N., Varambally, R., Ghosh, D., Barrette, T., Pandey, A., & Chinnaiyan, A. M.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2004-06-07
- Volume
- 101
- Issue
- 25
- Pages
- 9309-9314
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2124201592
- Cited by
- 1,019
- References
- 8
- FWCI
- 5.62
- Type
- journal article
- Language
- en
- Open access
- green
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- Rhodes, D. R., Yu, J., Shanker, K., Deshpande, N., Varambally, R., Ghosh, D., Barrette, T., Pandey, A., & Chinnaiyan, A. M. (2004). Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0401994101
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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