Network‐based classification of breast cancer metastasis
Open accessAbstract
Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
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Molecular profiling methods provide large amounts of information about dynamics of cellular pathway components and increase power of pathway analysis techniques.
“In addition, molecular profiling methods, such as RNA profiling using microarrays or protein quantification using mass spectrometry, provide large amounts of information about the dynamics of cellular pathway components and increase the power of pathway analysis techniques18,19.”
From The BioPAX community standard for pathway data sharing (2010) · cites this paper as [19]
A number of recent papers have used pathway analysis workflow to highlight genes and pathways active in specific model organisms or diseased tissues.
“A number of recent papers have used this pathway analysis workflow to highlight genes and pathways that are active in specific model organisms or diseased tissues, such as breast cancer, using gene and protein expression, copy number variants (CNVs) and SNPs19,44-49.”
From The BioPAX community standard for pathway data sharing (2010) · cites this paper as [19]
Network connections can nominate a set of genes whose input data should be aggregated to create a composite feature, such as pooling rarely altered genes in cancer into a single pathway that is recurrently altered across a patient population.
“Network connections can also nominate a set of genes whose input data should be aggregated to create a composite feature, such as pooling of rarely altered genes in cancer into a single pathway, which, viewed as a single aggregate, is recurrently altered across a patient population (Alvarez et al., 2016; Chuang et al., 2007).”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Chuang et al., 2007
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- Authors
- Chuang, H., Lee, E., Liu, Y., Lee, D., & Ideker, T.
- Affiliations
- University of California San Diego (US)
- Korea Advanced Institute of Science and Technology (KR)
- Corresponding author
- Ideker, Trey
- Venue
- Molecular Systems Biology
- Published
- 2007-10-16
- Volume
- 3
- Issue
- 1
- Pages
- 140-140
- ISSN
- 1744-4292 (ISSN-L)
- PMC
- PMC2063581
- OpenAlex
- W2154947819
- Cited by
- 1,431
- References
- 52
- Citations by year
- 2026: 10 · 2025: 7 · 2024: 17 · 2023: 17 · 2022: 30 · 2021: 30 · 2020: 50 · 2019: 70 · 2018: 73 · 2017: 90 · 2016: 109 · 2015: 120 · 2014: 129 · 2013: 153 · 2012: 141
- FWCI
- 33.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Molecular Systems Biology (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health
- National Institute of Environmental Health Sciences (R01ES014811, ES14811-01)
- Topics
- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- breast cancer metastasis, protein-protein interaction network, metastasis classification, tumor progression pathways, differentially expressed genes, network-based biomarkers, gene expression profiling
- MeSH terms
- Breast Neoplasms, Female, Humans, Neoplasm Metastasis, Neoplasm Proteins, Biomarkers, Tumor, Gene Expression Regulation, Neoplastic, Computational Biology, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling
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