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      MYC Targets Scores Are Associated with Cancer Aggressiveness and Poor Survival in ER-Positive Primary and Metastatic Breast Cancer

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          Abstract

          MYC is one of the most studied oncogenes that is known to promote cell proliferation. We utilized MYC targets v1 and MYC targets v2 scores of gene set variation analysis and hypothesized that these scores correlate with tumor aggressiveness and survival outcomes. We examined a total of 3109 breast cancer patients from TCGA, METABRIC, and GSE124647 cohorts. In each cohort, the patients were divided into high- and low-score groups using the upper third value as the cut off. As expected, higher scores were related to increased cell proliferation and worse clinical and pathologic features. High MYC targets scores were associated with worse survival, specifically in primary ER-positive breast cancer, consistently in both TCGA and METABRIC cohorts. In ER-positive breast cancer, high MYC targets v1, but not v2 score, was associated with high mutation load, and high MYC targets v1 and v2 scores were both associated with increased infiltration of pro- and anti-cancerous immune cells. We found that high MYC scores were associated with worse survival in metastatic breast cancer. Our findings show that the MYC targets v1 and v2 scores are associated with tumor aggressiveness and poor prognosis in ER-positive primary tumors, as well as in metastatic breast cancer.

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          Most cited references46

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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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            Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

            The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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              The Molecular Signatures Database (MSigDB) hallmark gene set collection.

              The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                30 October 2020
                November 2020
                : 21
                : 21
                : 8127
                Affiliations
                [1 ]Breast Surgery, Department of Surgical Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, NY 14263, USA; amyschul@ 123456buffalo.edu (A.S.); Masanori.Oshi@ 123456RoswellPark.org (M.O.)
                [2 ]Department of Surgery, Jacobs School of Medicine and Biomedical Sciences, State University of New York, Buffalo, New York, NY 14263, USA
                [3 ]Department of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama 236-0004, Japan; endoit@ 123456yokohama-cu.ac.jp
                [4 ]Department of Surgery, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
                [5 ]Department of Breast Surgery and Oncology, Tokyo Medical University, Tokyo 160-8402, Japan
                [6 ]Department of Gastrointestinal Tract Surgery, Fukushima Medical University School of Medicine, Fukushima 960-1295, Japan
                Author notes
                [* ]Correspondence: kazuaki.takabe@ 123456roswellpark.org ; Tel.: +1-716-845-5540
                Author information
                https://orcid.org/0000-0002-1404-1570
                https://orcid.org/0000-0002-6435-4241
                Article
                ijms-21-08127
                10.3390/ijms21218127
                7663719
                33143224
                8ea12180-2142-4a1a-a157-4b98b5a6f5c8
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 01 October 2020
                : 29 October 2020
                Categories
                Article

                Molecular biology
                breast cancer,biomarker,myc,tumor gene expression
                Molecular biology
                breast cancer, biomarker, myc, tumor gene expression

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