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      Adipogenesis in triple-negative breast cancer is associated with unfavorable tumor immune microenvironment and with worse survival

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          Abstract

          Cancer-associated adipocytes are known to cause inflammation; however, the role of adipogenesis, the formation of adipocytes, in breast cancer is unclear. We hypothesized that intra-tumoral adipogenesis reflects a different cancer biology than abundance of intra-tumoral adipocytes. The Molecular Signatures Database Hallmark adipogenesis gene set of gene set variant analysis was used to quantify adipogenesis. Total of 5,098 breast cancer patients in multiple cohorts (training; GSE96058 ( n = 3273), validation; TCGA ( n = 1069), treatment response; GSE25066 ( n = 508) and GSE20194 ( n = 248)) were analyzed. Adipogenesis did not correlate with abundance of adipocytes. Adipogenesis was significantly lower in triple negative breast cancer (TNBC). Elevated adipogenesis was significantly associated with worse survival in TNBC, but not in the other subtypes. High adipogenesis TNBC was significantly associated with low homologous recombination deficiency, but not with mutation load. High adipogenesis TNBC enriched metabolism-related gene sets, but neither of cell proliferation- nor inflammation-related gene sets, which were enriched to adipocytes. High adipogenesis TNBC was infiltrated with low CD8 + T cells and high M2 macrophages. Although adipogenesis was not associated with neoadjuvant chemotherapy response, high adipogenesis TNBC was significantly associated with low expression of PD-L1 and PD-L2 genes, and immune checkpoint molecules index. In conclusion, adipogenesis in TNBC was associated with cancer metabolism and unfavorable tumor immune microenvironment, which is different from abundance of adipocytes.

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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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            Cancer statistics, 2020

            Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers.
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              GSVA: gene set variation analysis for microarray and RNA-Seq data

              Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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                Author and article information

                Contributors
                kazuaki.takabe@roswellpark.org
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                15 June 2021
                15 June 2021
                2021
                : 11
                : 12541
                Affiliations
                [1 ]GRID grid.240614.5, ISNI 0000 0001 2181 8635, Department of Surgical Oncology, , Roswell Park Comprehensive Cancer Center, ; Elm & Carlton Streets, Buffalo, NY 14263 USA
                [2 ]GRID grid.268441.d, ISNI 0000 0001 1033 6139, Department of Gastroenterological Surgery, , Yokohama City University Graduate School of Medicine, ; Yokohama, Kanagawa 236-0004 Japan
                [3 ]GRID grid.256342.4, ISNI 0000 0004 0370 4927, Department of Surgical Oncology, Graduate School of Medicine, , Gifu University, ; 1-1 Yanagido, Gifu, 501-1194 Japan
                [4 ]GRID grid.240614.5, ISNI 0000 0001 2181 8635, Department of Biostatistics & Bioinformatics, , Roswell Park Comprehensive Cancer Center, ; Buffalo, NY 14263 USA
                [5 ]GRID grid.260975.f, ISNI 0000 0001 0671 5144, Division of Digestive and General Surgery, , Niigata University Graduate School of Medical and Dental Sciences, ; Niigata, 951-8520 Japan
                [6 ]GRID grid.411582.b, ISNI 0000 0001 1017 9540, Department of Breast Surgery, , Fukushima Medical University School of Medicine, ; Fukushima, 960-1295 Japan
                [7 ]GRID grid.410793.8, ISNI 0000 0001 0663 3325, Department of Breast Surgery and Oncology, , Tokyo Medical University, ; Tokyo, 160-8402 Japan
                [8 ]GRID grid.273335.3, ISNI 0000 0004 1936 9887, Department of Surgery, Jacobs School of Medicine and Biomedical Sciences, , State University of New York, ; Buffalo, NY 14263 USA
                Author information
                http://orcid.org/0000-0003-2481-7739
                http://orcid.org/0000-0002-8546-7309
                http://orcid.org/0000-0002-4792-9998
                http://orcid.org/0000-0002-6435-4241
                Article
                91897
                10.1038/s41598-021-91897-7
                8206113
                34131208
                750f8ba1-1f31-49ae-96e1-ee863a983c58
                © The Author(s) 2021

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 18 January 2021
                : 30 May 2021
                Funding
                Funded by: Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)
                Award ID: R01CA160688
                Funded by: National Cancer Institute, USA cancer center support grant P30-CA016056
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                breast cancer,lipid signalling,tumour immunology,cancer genomics
                Uncategorized
                breast cancer, lipid signalling, tumour immunology, cancer genomics

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