Gene Co-Expression Modules as Clinically Relevant Hallmarks of Breast Cancer Diversity
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Summary
Overall, co-expression modules provide a high-level functional view of breast cancer that complements the “cancer hallmarks” and may form the basis for improved predictors and treatments.
- Type
- article
- Published
- 2014-02-07
- Cited by
- 117
- References
- 55
- Access
- Open access
- OpenAlex
- https://openalex.org/W1992468678
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:4639120
Keywords
Immune system, Biology, Cancer research, Carcinogenesis, Downregulation and upregulation
References
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- MCLUST Version 3: An R Package for Normal Mixture Modeling and Model-Based Clustering
- The Bimodality Index: A Criterion for Discovering and Ranking Bimodal Signatures from Cancer Gene Expression Profiling Data
- From molecular to modular cell biology
- Dissecting the dynamics of dysregulation of cellular processes in mouse mammary gland tumor
- The hallmarks of cancer
- Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data
- Motifs, modules and games in bacteria.
- The application of gene co-expression network reconstruction based on CNVs and gene expression microarray data in breast cancer
- The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
- Bimodal gene expression patterns in breast cancer
- Tissue architecture and function: dynamic reciprocity via extra- and intra-cellular matrices
- A robust classifier of high predictive value to identify good prognosis patients in ER-negative breast cancer
- Endocrine resistance in breast cancer: new roles for ErbB3 and ErbB4
- Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer
- Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability
- Genes that mediate breast cancer metastasis to the brain
- g:Profiler—a web server for functional interpretation of gene lists (2011 update)
- Gene expression profiling of response to mTOR inhibitor everolimus in pre-operatively treated post-menopausal women with oestrogen receptor-positive breast cancer
- Statistical Significance of Clustering using Soft Thresholding
Cited by
- Transcriptional Profiling of Breast Cancer Metastases Identifies Liver Metastasis–Selective Genes Associated with Adverse Outcome in Luminal A Primary Breast Cancer
- Clinical and molecular complexity of breast cancer metastases.
- Metastatic Breast Cancer: Biomolecular Characterization and Targeted Therapy
- Dissecting the Tumor Myeloid Compartment Reveals Rare Activating Antigen Presenting Cells, Critical for T cell Immunity
- Serial expression analysis of breast tumors during neoadjuvant chemotherapy reveals changes in cell cycle and immune pathways associated with recurrence and response
- GeneFriends: a human RNA-seq-based gene and transcript co-expression database
- Interferons and the Immunogenic Effects of Cancer Therapy
- Covariance-based analyses of biological pathways
- The dual role of asporin in breast cancer progression
- The Role of Proliferation in Determining Response to Neoadjuvant Chemotherapy in Breast Cancer: A Gene Expression-Based Meta-Analysis
- The Critical Role of CD103+ Dendritic Cells in Anti-Tumor T Cell Immunity
- Co-expression modules identified from published immune signatures reveal five distinct immune subtypes in breast cancer
- CrosstalkNet: mining large-scale bipartite co-expression networks to characterize epi-stroma crosstalk
- Gene co-expression network reconstruction: a review on computational methods for inferring functional information from plant-based expression data
- Coexpressed modular gene expression reveals inverse correlation between immune responsive transcription and aggressiveness in gastric tumours
- Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer
- Implementation and Application of Method for Differential Correlation Network Analysis.
- A moment-distance hybrid method for estimating a mixture of two symmetric densities
- Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics
- Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer
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