Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study
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Frank Emmert-Streib | Yasir Rahmatallah | Boris Zybailov | Ancha Baranova | Galina Glazko | G. Glazko | F. Emmert-Streib | A. Baranova | B. Zybailov | Y. Rahmatallah
[1] Frank Emmert-Streib,et al. Comparative evaluation of gene set analysis approaches for RNA-Seq data , 2014, BMC Bioinformatics.
[2] G. Glazko,et al. Ensuring the statistical soundness of competitive gene set approaches: gene filtering and genome-scale coverage are essential , 2013, Nucleic acids research.
[3] Ben S. Wittner,et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1 , 2009, Nature.
[4] Z. Werb,et al. The extracellular matrix: A dynamic niche in cancer progression , 2012, The Journal of cell biology.
[5] M. Imieliński,et al. In Situ Proteomic Analysis of Human Breast Cancer Epithelial Cells Using Laser Capture Microdissection: Annotation by Protein Set Enrichment Analysis and Gene Ontology* , 2010, Molecular & Cellular Proteomics.
[6] Jeffrey R. Whiteaker,et al. Proteogenomic characterization of human colon and rectal cancer , 2014, Nature.
[7] Antoine M. van Oijen,et al. Real-time single-molecule observation of rolling-circle DNA replication , 2009, Nucleic acids research.
[8] Frank Emmert-Streib,et al. Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline , 2015, Briefings Bioinform..
[9] C. Ko,et al. Colon cancer survival rates with the new American Joint Committee on Cancer sixth edition staging. , 2005, Journal of the National Cancer Institute.
[10] Atul J. Butte,et al. Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges , 2012, PLoS Comput. Biol..
[11] William M. Grady,et al. Epigenetic Alterations in Colorectal Cancer: Emerging Biomarkers. , 2015, Gastroenterology.
[12] Darryl Shibata,et al. Ubiquitous somatic mutations in simple repeated sequences reveal a new mechanism for colonic carcinogenesis , 1993, Nature.
[13] A. Duval,et al. Immunotherapy and patients treated for cancer with microsatellite instability. , 2017, Bulletin du cancer.
[14] P. Shannon,et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.
[15] K. Kinzler,et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints , 2015, Journal of Immunotherapy for Cancer.
[16] J. Beaulieu,et al. Integrin-linked kinase regulates migration and proliferation of human intestinal cells under a fibronectin-dependent mechanism , 2010, Journal of cellular physiology.
[17] Martin Eisenacher,et al. Detection of Patient Subgroups with Differential Expression in Omics Data: A Comprehensive Comparison of Univariate Measures , 2013, PloS one.
[18] Frank Emmert-Streib,et al. GSAR: Bioconductor package for Gene Set analysis in R , 2017, BMC Bioinformatics.
[19] Z. Kokot,et al. Mass spectrometry-based proteomics techniques and their application in ovarian cancer research , 2018, Journal of Ovarian Research.
[20] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[21] J. Friedman,et al. Multivariate generalizations of the Wald--Wolfowitz and Smirnov two-sample tests , 1979 .
[22] G. Orphanides,et al. Subtypes of primary colorectal tumors correlate with response to targeted treatment in colorectal cell lines , 2012, BMC Medical Genomics.
[23] J. Guinney,et al. Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer , 2017, Nature Reviews Cancer.
[24] F. V. Winck,et al. Functional annotation and biological interpretation of proteomics data. , 2015, Biochimica et biophysica acta.
[25] Hong Yan,et al. Molecular subtyping of cancer: current status and moving toward clinical applications , 2019, Briefings Bioinform..
[26] M. Pino,et al. The chromosomal instability pathway in colon cancer. , 2010, Gastroenterology.
[27] Michael A. Freitas,et al. Tag-Count Analysis of Large-Scale Proteomic Data. , 2016, Journal of proteome research.
[28] Frank Emmert-Streib,et al. Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets , 2013, Bioinform..
[29] Tomas Kalina,et al. MetaMass, a tool for meta-analysis of subcellular proteomics data , 2016, Nature Methods.
[30] Korbinian Strimmer,et al. BMC Bioinformatics BioMed Central Methodology article A general modular framework for gene set enrichment analysis , 2009 .
[31] J. Galon,et al. Correlation between Density of CD8+ T-cell Infiltrate in Microsatellite Unstable Colorectal Cancers and Frameshift Mutations: A Rationale for Personalized Immunotherapy. , 2015, Cancer research.
[32] Brad T. Sherman,et al. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.
[33] A. Jemal,et al. Cancer statistics, 2012 , 2012, CA: a cancer journal for clinicians.
[34] Jeffrey S. Morris,et al. The Consensus Molecular Subtypes of Colorectal Cancer , 2015, Nature Medicine.
[35] Qi Liu,et al. Gene-set analysis and reduction , 2008, Briefings Bioinform..
[36] Marco Y. Hein,et al. The Perseus computational platform for comprehensive analysis of (prote)omics data , 2016, Nature Methods.
[37] Henryk Maciejewski,et al. Gene set analysis methods: statistical models and methodological differences , 2013, Briefings Bioinform..
[38] Ruedi Aebersold,et al. Complex‐centric proteome profiling by SEC‐SWATH‐MS , 2019, Nature Protocols.
[39] Baofeng Yang,et al. PEP06 polypeptide 30 exerts antitumour effect in colorectal carcinoma via inhibiting epithelial–mesenchymal transition , 2018, British journal of pharmacology.
[40] Lewis C Cantley,et al. A colorectal cancer classification system that associates cellular phenotype and responses to therapy , 2013, Nature Medicine.
[41] Ashley C. Brown,et al. Synergistic effects of particulate matter and substrate stiffness on epithelial-to-mesenchymal transition. , 2014, Research report.
[42] J. Guinney,et al. Erratum: Consensus molecular subtypes and the evolution of precision medicine in colorectal cancer (Nature reviews. Cancer (2017) 17 2 (79-92)) , 2017 .
[43] Di Wu,et al. ROAST: rotation gene set tests for complex microarray experiments , 2010, Bioinform..
[44] Kris Laukens,et al. Bioinformatics approaches for the functional interpretation of protein lists: From ontology term enrichment to network analysis , 2015, Proteomics.
[45] Pablo Tamayo,et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[46] Seon-Young Kim,et al. Gene-set approach for expression pattern analysis , 2008, Briefings Bioinform..
[47] Peter Bühlmann,et al. Analyzing gene expression data in terms of gene sets: methodological issues , 2007, Bioinform..
[48] P. Roche,et al. The ins and outs of MHC class II-mediated antigen processing and presentation , 2015, Nature Reviews Immunology.
[49] R. Tibshirani,et al. On testing the significance of sets of genes , 2006, math/0610667.
[50] Klaus Jung,et al. Set-Based Test Procedures for the Functional Analysis of Protein Lists from Differential Analysis. , 2016, Methods in molecular biology.
[51] Daniel B. McClatchy,et al. PSEA-Quant: A Protein Set Enrichment Analysis on Label-Free and Label-Based Protein Quantification Data , 2014, Journal of proteome research.
[52] Christopher J. Ott,et al. A chemical probe toolbox for dissecting the cancer epigenome , 2017, Nature Reviews Cancer.
[53] Adam C. Wilkinson,et al. Branched-chain amino acid metabolism in cancer , 2017, Current opinion in clinical nutrition and metabolic care.
[54] I. Fournier,et al. Translating epithelial mesenchymal transition markers into the clinic: Novel insights from proteomics , 2016, EuPA open proteomics.
[55] A. Vazquez,et al. Cancer metabolism at a glance , 2016, Journal of Cell Science.
[56] Matthew E. Ritchie,et al. limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.
[57] Mira Ayadi,et al. Gene Expression Classification of Colon Cancer into Molecular Subtypes: Characterization, Validation, and Prognostic Value , 2013, PLoS medicine.
[58] Brad T. Sherman,et al. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.
[59] E. Marcotte,et al. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses , 2012, Nature Reviews Genetics.
[60] F. Gao,et al. Molecular subtyping of colorectal cancer: Recent progress, new challenges and emerging opportunities. , 2019, Seminars in cancer biology.
[61] Mohammad Al Hasan,et al. Pathway and network analysis in proteomics. , 2014, Journal of theoretical biology.
[62] L. Rodrigues,et al. Colorectal Cancer Cells Increase the Production of Short Chain Fatty Acids by Propionibacterium freudenreichii Impacting on Cancer Cells Survival , 2018, Front. Nutr..
[63] Roberto Romero,et al. A Comparison of Gene Set Analysis Methods in Terms of Sensitivity, Prioritization and Specificity , 2013, PloS one.
[64] Xin Wang,et al. Dissecting cancer heterogeneity--an unsupervised classification approach. , 2013, The international journal of biochemistry & cell biology.
[65] B. Bogen,et al. CD4+ T-cell-Mediated Rejection of MHC Class II-Positive Tumor Cells Is Dependent on Antigen Secretion and Indirect Presentation on Host APCs. , 2018, Cancer research.
[66] Frank Emmert-Streib,et al. Pathway Analysis of Expression Data: Deciphering Functional Building Blocks of Complex Diseases , 2011, PLoS Comput. Biol..
[67] Florian Markowetz,et al. Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions , 2013, Nature Medicine.
[68] S. Schwartz,et al. EMT blockage strategies: Targeting Akt dependent mechanisms for breast cancer metastatic behaviour modulation. , 2015, Current gene therapy.
[69] D. Sargent,et al. Prediction of overall survival in stage II and III colon cancer beyond TNM system: a retrospective, pooled biomarker study , 2017, Annals of oncology : official journal of the European Society for Medical Oncology.
[70] Hae-Yun Jung,et al. Molecular Pathways: Linking Tumor Microenvironment to Epithelial–Mesenchymal Transition in Metastasis , 2014, Clinical Cancer Research.
[71] M. Mohamadzadeh,et al. Microbiota impact on the epigenetic regulation of colorectal cancer. , 2013, Trends in molecular medicine.
[72] David Managadze,et al. Generalized Portrait of Cancer Metabolic Pathways Inferred from a List of Genes Overexpressed in Cancer , 2014, Genetics research international.
[73] M. Daly,et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes , 2003, Nature Genetics.
[74] Yu Shyr,et al. Melanoma-specific MHC-II expression represents a tumour-autonomous phenotype and predicts response to anti-PD-1/PD-L1 therapy , 2016, Nature Communications.
[75] Sabine Tejpar,et al. Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer , 2013, The Journal of pathology.
[76] P. Park,et al. Discovering statistically significant pathways in expression profiling studies. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[77] Andreas Schlicker,et al. Colorectal cancer intrinsic subtypes predict chemotherapy benefit, deficient mismatch repair and epithelial-to-mesenchymal transition , 2013, International journal of cancer.
[78] J. Breslow,et al. Intracellular Cholesterol Transport , 2004, Arteriosclerosis, thrombosis, and vascular biology.