ArticleMolecular & cellular proteomics : MCP2012
Integrated proteomic, transcriptomic, and biological network analysis of breast carcinoma reveals molecular features of tumorigenesis and clinical relapse.
Article in Molecular & cellular proteomics : MCP, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- The hypoxic ECM and neutrophils in MIBC immunotherapy resistance.Nature reviews. Urology · 2026Review
- Vitellogenin accumulation leads to reproductive senescence by impairing lysosomal function.Science China. Life sciences · 2023Article
- 3PNMF-MKL: A non-negative matrix factorization-based multiple kernel learning method for multi-modal data integration and its application to gene signature detection.Frontiers in genetics · 2023Article
- TOR functions as a molecular switch connecting an iron cue with host innate defense against bacterial infection.PLoS genetics · 2021Article
- Shared and distinct lipid-lipid interactions in plasma and affected tissues in a diabetic mouse model.Journal of lipid research · 2018Article
- Article
- Inhibition of the hexosamine biosynthetic pathway promotes castration-resistant prostate cancer.Nature communications · 2016Article
- Integrative topological analysis of mass spectrometry data reveals molecular features with clinical relevance in esophageal squamous cell carcinoma.Scientific reports · 2016Article
- A non-negative matrix factorization method for detecting modules in heterogeneous omics multi-modal data.Bioinformatics (Oxford, England) · 2016Article
- Analyses that combine 'omics' show cell targets in detail.Nature medicine · 2015Article
- mir-233 modulates the unfolded protein response in C. elegans during Pseudomonas aeruginosa infection.PLoS pathogens · 2015Article
- Bioinformatics for cancer immunotherapy target discovery.Cancer immunology, immunotherapy : CII · 2014Review
- Ezrin expression and cell survival regulation in colorectal cancer.Cellular signalling · 2014Article
- Pathway-centric integrative analysis identifies RRM2 as a prognostic marker in breast cancer associated with poor survival and tamoxifen resistance.Neoplasia (New York, N.Y.) · 2014Article
- Metabolomic profiling identifies biochemical pathways associated with castration-resistant prostate cancer.Journal of proteome research · 2014Article
- Tumor antigens as proteogenomic biomarkers in invasive ductal carcinomas.BMC medical genomics · 2014Article
- Pathway analysis and transcriptomics improve protein identification by shotgun proteomics from samples comprising small number of cells--a benchmarking study.BMC genomics · 2014Article
- Moving H5N1 studies into the era of systems biology.Virus research · 2013Review
- Inter-individual differences in response to dietary intervention: integrating omics platforms towards personalised dietary recommendations.The Proceedings of the Nutrition Society · 2013Article
- Integrated analysis of transcriptomic and proteomic data.Current genomics · 2013Article
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Authors and funding
6 authors.
Funding
Abstract
Gene and protein expression changes observed with tumorigenesis are often interpreted independently of each other and out of context of biological networks. To address these limitations, this study examined several approaches to integrate transcriptomic and proteomic data with known protein-protein and signaling interactions in estrogen receptor positive (ER+) breast cancer tumors. An approach that built networks from differentially expressed proteins and identified among them networks enriched in differentially expressed genes yielded the greatest success. This method identified a set of genes and proteins linking pathways of cellular stress response, cancer metabolism, and tumor microenvironment. The proposed network underscores several biologically intriguing events not previously studied in the context of ER+ breast cancer, including the overexpression of p38 mitogen-activated protein kinase and the overexpression of poly(ADP-ribose) polymerase 1. A gene-based expression signature biomarker built from this network was significantly predictive of clinical relapse in multiple independent cohorts of ER+ breast cancer patients, even after correcting for standard clinicopathological variables. The results of this study demonstrate the utility and power of an integrated quantitative proteomic, transcriptomic, and network analysis approach to discover robust and clinically meaningful molecular changes in tumors.
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