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      Y disruption, autosomal hypomethylation and poor male lung cancer survival

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          Abstract

          Lung cancer is the most frequent cause of cancer death worldwide. It affects more men than women, and men generally have worse survival outcomes. We compared gene co-expression networks in affected and unaffected lung tissue from 126 consecutive patients with Stage IA–IV lung cancer undergoing surgery with curative intent. We observed marked degradation of a sex-associated transcription network in tumour tissue. This disturbance, detected in 27.7% of male tumours in the discovery dataset and 27.3% of male tumours in a further 123-sample replication dataset, was coincident with partial losses of the Y chromosome and extensive autosomal DNA hypomethylation. Central to this network was the epigenetic modifier and regulator of sexually dimorphic gene expression, KDM5D. After accounting for prognostic and epidemiological covariates including stage and histology, male patients with tumour KDM5D deficiency showed a significantly increased risk of death (Hazard Ratio [HR] 3.80, 95% CI 1.40–10.3, P = 0.009). KDM5D deficiency was confirmed as a negative prognostic indicator in a further 1100 male lung tumours (HR 1.67, 95% CI 1.4–2.0, P = 1.2 × 10 –10). Our findings identify tumour deficiency of KDM5D as a prognostic marker and credible mechanism underlying sex disparity in lung cancer.

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          BEDTools: a flexible suite of utilities for comparing genomic features

          Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.

              Genome-scale studies have revealed extensive, cell type-specific colocalization of transcription factors, but the mechanisms underlying this phenomenon remain poorly understood. Here, we demonstrate in macrophages and B cells that collaborative interactions of the common factor PU.1 with small sets of macrophage- or B cell lineage-determining transcription factors establish cell-specific binding sites that are associated with the majority of promoter-distal H3K4me1-marked genomic regions. PU.1 binding initiates nucleosome remodeling, followed by H3K4 monomethylation at large numbers of genomic regions associated with both broadly and specifically expressed genes. These locations serve as beacons for additional factors, exemplified by liver X receptors, which drive both cell-specific gene expression and signal-dependent responses. Together with analyses of transcription factor binding and H3K4me1 patterns in other cell types, these studies suggest that simple combinations of lineage-determining transcription factors can specify the genomic sites ultimately responsible for both cell identity and cell type-specific responses to diverse signaling inputs. Copyright 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                w.cookson@imperial.ac.uk
                m.moffatt@imperial.ac.uk
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                14 June 2021
                14 June 2021
                2021
                : 11
                : 12453
                Affiliations
                [1 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, National Heart and Lung Institute, , Imperial College London, ; London, SW3 6LY UK
                [2 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biostatistics, , Harvard T.H. Chan School of Public Health, ; Boston, MA 02115 USA
                [3 ]GRID grid.38142.3c, ISNI 000000041936754X, Program in Genetic Epidemiology and Statistical Genetics, , Harvard T.H. Chan School of Public Health, ; Boston, MA 02115 USA
                [4 ]GRID grid.13097.3c, ISNI 0000 0001 2322 6764, Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, , King’s College London, ; Lambeth Palace Road, London, SE1 7EH UK
                [5 ]McGill Genome Centre, Montréal, QC H3A 0G1 Canada
                [6 ]GRID grid.5072.0, ISNI 0000 0001 0304 893X, Royal Marsden Hospital NHS Foundation Trust, ; London and Surrey, UK
                [7 ]GRID grid.18886.3f, ISNI 0000 0001 1271 4623, The Institute of Cancer Research, ; 123 Old Brompton Road, London, SW7 3RP UK
                [8 ]GRID grid.439338.6, ISNI 0000 0001 1114 4366, Department of Thoracic Surgery, , Royal Brompton Hospital, ; Sydney Street, London, SW3 6NP UK
                [9 ]GRID grid.421662.5, ISNI 0000 0000 9216 5443, Department of Histopathology, , Royal Brompton and Harefield NHS Foundation Trust, ; London, UK
                [10 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Department of Human Genetics, , McGill University, ; Montréal, QC Canada
                Article
                91907
                10.1038/s41598-021-91907-8
                8203787
                34127738
                dc5a39a6-0536-4a79-be3a-b5e7e850ce42
                © 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
                : 9 December 2020
                : 26 May 2021
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                © The Author(s) 2021

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                non-small-cell lung cancer,gene expression
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                non-small-cell lung cancer, gene expression

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