A module map showing conditional activity of expression modules in cancer
Open accessAbstract
DNA microarrays are widely used to study changes in gene expression in tumors, but such studies are typically system-specific and do not address the commonalities and variations between different types of tumor. Here we present an integrated analysis of 1,975 published microarrays spanning 22 tumor types. We describe expression profiles in different tumors in terms of the behavior of modules, sets of genes that act in concert to carry out a specific function. Using a simple unified analysis, we extract modules and characterize gene-expression profiles in tumors as a combination of activated and deactivated modules. Activation of some modules is specific to particular types of tumor; for example, a growth-inhibitory module is specifically repressed in acute lymphoblastic leukemias and may underlie the deregulated proliferation in these cancers. Other modules are shared across a diverse set of clinical conditions, suggestive of common tumor progression mechanisms. For example, the bone osteoblastic module spans a variety of tumor types and includes both secreted growth factors and their receptors. Our findings suggest that there is a single mechanism for both primary tumor proliferation and metastasis to bone. Our analysis presents multiple research directions for diagnostic, prognostic and therapeutic studies.
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GBA approaches are widely used to identify new potential disease genes if a substantial proportion of the genes within a module are associated with a particular disease.
“GBA approaches are also widely used to identify new potential disease genes if a substantial proportion of the genes within a module are associated with a particular disease [26, 121–126] (Figure 1).”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [122]
The paper describes deriving gene-expression modules and associating their activity with cancer-related clinical conditions (Abstract; no page supplied). Our copy does not address the claimed guilt-by-association practice of identifying new disease genes because a substantial proportion of genes in their module are already disease-associated (searched for candidate disease genes and module-based prediction). (LLM, conf=0.72)
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- Authors
- Segal, E., Friedman, N., Koller, D., & Regev, A.
- Affiliations
- Rockefeller University (US)
- Hebrew University of Jerusalem (IL)
- Stanford University (US)
- Harvard University (US)
- Corresponding author
- Koller, Daphne
- Regev, Aviv
- Venue
- Nature Genetics
- Published
- 2004-09-26
- Volume
- 36
- Issue
- 10
- Pages
- 1090-1098
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC2271138
- OpenAlex
- W2127053639
- Cited by
- 685
- References
- 58
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 6 · 2023: 8 · 2022: 9 · 2021: 14 · 2020: 12 · 2019: 11 · 2018: 13 · 2017: 16 · 2016: 21 · 2015: 30 · 2014: 42 · 2013: 44 · 2012: 44
- FWCI
- 20.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- National Human Genome Research Institute (R01HG003191, R01HG002189)
- National Institute of General Medical Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- gene modules, tumor gene expression profiles, cancer, acute lymphoblastic leukemia, bone metastasis, tumor cell proliferation, secreted growth factors, growth factor receptors
- MeSH terms
- Female, Multigene Family, Humans, Male, Neoplasms, Oncogenes, Organ Specificity, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Databases, Genetic
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