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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Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
Role of Histone H3 Lysine 27 Methylation in Polycomb-Group Silencing
Cao, R., Wang, L., Wang, H., Xia, L., Erdjument-Bromage, H., Tempst, P., Jones, R. S., & Zhang, Y. (2002) · Science · doi:10.1126/science.1076997 · Cited by 3,775
The polycomb group protein EZH2 is involved in progression of prostate cancer
Varambally, S., Dhanasekaran, S. M., Zhou, M., Barrette, T. R., Kumar-Sinha, C., Sanda, M. G., Ghosh, D., Pienta, K. J., Sewalt, R. G. A. B., Otte, A. P., et al. (2002) · Nature · doi:10.1038/nature01075 · Cited by 2,717
Self-citationComparative Genomics of the Eukaryotes
Rubin, G. M., Yandell, M. D., Wortman, J. R., Gabor, G. L., Miklos, Nelson, C. R., Hariharan, I. K., Fortini, M. E., Li, P. W., Apweiler, R., et al. (2000) · Science · doi:10.1126/science.287.5461.2204 · Cited by 1,698
Kleer, C. G., Cao, Q., Varambally, S., Shen, R., Ota, I., Tomlins, S. A., Ghosh, D., Sewalt, R. G. A. B., Otte, A. P., Hayes, D. F., et al. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1933744100 · Cited by 1,649
Self-citationEZH2 is downstream of the pRB-E2F pathway, essential for proliferation and amplified in cancer.
Bracken, A. P., Pasini, D., Capra, M., Prosperini, E., Colli, E., & Helin, K. (2003) · The EMBO Journal · doi:10.1093/emboj/cdg542 · Cited by 1,196
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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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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
