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      Hypoxia induced responses are reflected in the stromal proteome of breast cancer

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          Abstract

          Cancers are often associated with hypoxia and metabolic reprogramming, resulting in enhanced tumor progression. Here, we aim to study breast cancer hypoxia responses, focusing on secreted proteins from low-grade (luminal-like) and high-grade (basal-like) cell lines before and after hypoxia. We examine the overlap between proteomics data from secretome analysis and laser microdissected human breast cancer stroma, and we identify a 33-protein stromal-based hypoxia profile (33P) capturing differences between luminal-like and basal-like tumors. The 33P signature is associated with metabolic differences and other adaptations following hypoxia. We observe that mRNA values for 33P predict patient survival independently of molecular subtypes and basic prognostic factors, also among low-grade luminal-like tumors. We find a significant prognostic interaction between 33P and radiation therapy.

          Abstract

          The role of hypoxia and metabolic reprogramming in breast cancer remains to be explored. Here, the authors investigate the landscape of secreted proteins in response to hypoxia in breast cancer cell lines and identify a stromal-based hypoxia profile in breast cancer tissue.

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          Most cited references91

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

              Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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                Author and article information

                Contributors
                lars.akslen@uib.no
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                22 June 2023
                22 June 2023
                2023
                : 14
                : 3724
                Affiliations
                [1 ]GRID grid.7914.b, ISNI 0000 0004 1936 7443, Centre for Cancer Biomarkers CCBIO, Department of Clinical Medicine, Section for Pathology, , University of Bergen, ; Bergen, N-5021 Norway
                [2 ]GRID grid.412008.f, ISNI 0000 0000 9753 1393, Department of Pathology, , Haukeland University Hospital, ; Bergen, N-5021 Norway
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Cell Biology, , Harvard Medical School, ; Boston, MA USA
                [4 ]GRID grid.7914.b, ISNI 0000 0004 1936 7443, Department of Informatics, Computational Biology Unit, , University of Bergen, ; Bergen, Norway
                Author information
                http://orcid.org/0000-0002-8952-9757
                http://orcid.org/0000-0001-6251-5817
                http://orcid.org/0009-0000-2532-128X
                http://orcid.org/0009-0009-7145-1080
                http://orcid.org/0000-0002-5069-5715
                http://orcid.org/0000-0002-4291-413X
                http://orcid.org/0000-0001-6375-9477
                http://orcid.org/0000-0003-1086-821X
                http://orcid.org/0000-0003-2710-9543
                Article
                39287
                10.1038/s41467-023-39287-7
                10287711
                37349288
                5fa1860a-be66-4aab-9779-6fabed6673d5
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 2 December 2021
                : 7 June 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100005416, Norges Forskningsråd (Research Council of Norway);
                Award ID: 223250
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100005036, Universitetet i Bergen (University of Bergen);
                Funded by: The work was supported by the University of Bergen and the Research Council of Norway through its Centres of Excellence funding scheme (project number 223250)
                Funded by: FundRef https://doi.org/10.13039/100000057, U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS);
                Award ID: R01 GM132129
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                breast cancer,proteomics
                Uncategorized
                breast cancer, proteomics

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