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      getDEG: A Versatile Matlab Tool for Identifying Differentially Expressed Genes from High-Throughput Biomedical Data

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          Abstract

          The identification of differentially expressed genes (DEGs) is an important initial step for characterizing critical regulators and associated signaling profiles under specific conditions. Yet, a sophisticated computational tool to detect DEGs in a fully automatic manner is still lacking. Here we describe getDEG, a versatile Matlab program to fill this gap by offering efficient solutions of the most needed functions. Particularly, getDEG adopts user-designated statistical test and ranking method to prioritize probes/genes assayed. Furthermore, getDEG allows preliminary filtering by the machine detection p-value, and collapsing multiple probes to their associated gene. Taken together, getDEG is a powerful and automatic tool which satisfies basic and advanced needs in searching for most relevant candidates from microarray assays or other high-throughput screens. The tool getDEG and test examples are freely available online at https://sites.google.com/site/differentiallyexpressedgene.

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          Most cited references7

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          A module of negative feedback regulators defines growth factor signaling.

          Signaling pathways invoke interplays between forward signaling and feedback to drive robust cellular response. In this study, we address the dynamics of growth factor signaling through profiling of protein phosphorylation and gene expression, demonstrating the presence of a kinetically defined cluster of delayed early genes that function to attenuate the early events of growth factor signaling. Using epidermal growth factor receptor signaling as the major model system and concentrating on regulation of transcription and mRNA stability, we demonstrate that a number of genes within the delayed early gene cluster function as feedback regulators of immediate early genes. Consistent with their role in negative regulation of cell signaling, genes within this cluster are downregulated in diverse tumor types, in correlation with clinical outcome. More generally, our study proposes a mechanistic description of the cellular response to growth factors by defining architectural motifs that underlie the function of signaling networks.
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            A clinically relevant gene signature in triple negative and basal-like breast cancer

            Introduction Current prognostic gene expression profiles for breast cancer mainly reflect proliferation status and are most useful in ER-positive cancers. Triple negative breast cancers (TNBC) are clinically heterogeneous and prognostic markers and biology-based therapies are needed to better treat this disease. Methods We assembled Affymetrix gene expression data for 579 TNBC and performed unsupervised analysis to define metagenes that distinguish molecular subsets within TNBC. We used n = 394 cases for discovery and n = 185 cases for validation. Sixteen metagenes emerged that identified basal-like, apocrine and claudin-low molecular subtypes, or reflected various non-neoplastic cell populations, including immune cells, blood, adipocytes, stroma, angiogenesis and inflammation within the cancer. The expressions of these metagenes were correlated with survival and multivariate analysis was performed, including routine clinical and pathological variables. Results Seventy-three percent of TNBC displayed basal-like molecular subtype that correlated with high histological grade and younger age. Survival of basal-like TNBC was not different from non basal-like TNBC. High expression of immune cell metagenes was associated with good and high expression of inflammation and angiogenesis-related metagenes were associated with poor prognosis. A ratio of high B-cell and low IL-8 metagenes identified 32% of TNBC with good prognosis (hazard ratio (HR) 0.37, 95% CI 0.22 to 0.61; P < 0.001) and was the only significant predictor in multivariate analysis including routine clinicopathological variables. Conclusions We describe a ratio of high B-cell presence and low IL-8 activity as a powerful new prognostic marker for TNBC. Inhibition of the IL-8 pathway also represents an attractive novel therapeutic target for this disease.
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              NCBI GEO standards and services for microarray data.

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                Author and article information

                Contributors
                Journal
                Infectious Diseases and Translational Medicine
                Infect. Dis. Transl. Med.
                Infect. Dis. Transl. Med.
                International Biological and Medical Journals Publishing House Co., Limited (Room E16, 3/f, Yongda Commercial Building, No.97, Bonham Stand (Sheung Wan), HongKong )
                2411-2917
                31 October 2017
                31 October 2017
                : 3
                : 2
                : 43-50 (pp. )
                Affiliations
                From College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China; Center for Bioinformatics & Systems Biology, Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
                Author notes
                Correspondence to: Hua Tan, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. Email: Hua.Tan@uth.tmc.edu.
                Article
                10.11979/idtm.201702006
                47c765f4-8b14-4ffa-a8f9-ce6931420f4d

                This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Page count
                Figures: 7, Tables: 2, References: 10, Pages: 8
                Product
                Categories
                Original Research

                Medicine,Infectious disease & Microbiology
                Differentially expressed gene,Microarray,High-throughput biomedical data

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