RESUMO
Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account. We introduce PerseusNet, the new Perseus network module for the biological analysis of proteomics data. Proteomics is commonly used to generate networks, e.g., with affinity purification experiments, but networks are also used to explore proteomics data. PerseusNet supports the biomedical researcher for both modes of data analysis with a multitude of activities. For affinity purification, a volcano-plot-based statistical analysis method for network generation is featured which is scalable to large numbers of baits. For posttranslational modifications of proteins, such as phosphorylation, a collection of dedicated network analysis tools helps in elucidating cellular signaling events. Co-expression network analysis of proteomics data adopts established tools from transcriptome co-expression analysis. PerseusNet is extensible through a plugin architecture in a multi-lingual way, integrating analyses in C#, Python, and R, and is freely available at http://www.perseus-framework.org .
Assuntos
Biologia Computacional/métodos , Mapeamento de Interação de Proteínas/estatística & dados numéricos , Processamento de Proteína Pós-Traducional , Proteoma/metabolismo , Software , Animais , Biologia Computacional/estatística & dados numéricos , Interpretação Estatística de Dados , Redes Reguladoras de Genes , Humanos , Camundongos , Células-Tronco Embrionárias Murinas/citologia , Células-Tronco Embrionárias Murinas/metabolismo , Células-Tronco Neurais/citologia , Células-Tronco Neurais/metabolismo , Complexo Repressor Polycomb 1/genética , Complexo Repressor Polycomb 1/metabolismo , Complexo Repressor Polycomb 2/genética , Complexo Repressor Polycomb 2/metabolismo , Proteoma/genética , Proteínas Supressoras de Tumor/genética , Proteínas Supressoras de Tumor/metabolismo , Ubiquitina Tiolesterase/genética , Ubiquitina Tiolesterase/metabolismo , Ubiquitina-Proteína Ligases/genética , Ubiquitina-Proteína Ligases/metabolismoRESUMO
The genomic and transcriptomic landscapes of breast cancer have been extensively studied, but the proteomes of breast tumors are far less characterized. Here, we use high-resolution, high-accuracy mass spectrometry to perform a deep analysis of luminal-type breast cancer progression using clinical breast samples from primary tumors, matched lymph node metastases, and healthy breast epithelia. We used a super-SILAC mix to quantify over 10,000 proteins with high accuracy, enabling us to identify key proteins and pathways associated with tumorigenesis and metastatic spread. We found high expression levels of proteins associated with protein synthesis and degradation in cancer tissues, accompanied by metabolic alterations that may facilitate energy production in cancer cells within their natural environment. In addition, we found proteomic differences between breast cancer stages and minor differences between primary tumors and their matched lymph node metastases. These results highlight the potential of proteomic technology in the elucidation of clinically relevant cancer signatures.