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      Functional analysis of gene expression profiling-based prediction in bladder cancer

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          Abstract

          The present study aimed to analyze the modification of gene expression in bladder cancer (BC) by identifying significant differentially expressed genes (DEGs) and functionally assess them using bioinformatics analysis. To achieve this, two microarray datasets, GSE24152 (which included 10 fresh tumor tissue samples from urothelial bladder carcinoma patients and 7 benign mucosa samples from the bladder), and GSE42089 (which included 10 tissues samples from urothelial cell carcinoma patients and 8 tissues samples from the normal bladder), were downloaded from the Gene Expression Omnibus database for further analysis. Differentially expressed genes (DEGs) were screened between benign the mucosa and control groups in GSE24152 and GSE42089 datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on overlapping DEGs identified in GSE24152 and GSE42089. Protein-protein interaction (PPI) networks and sub-networks were then constructed to identify key genes and main pathways. GO terms analysis was also performed for the selected clusters. In total, 1,325 DEGs in GSE24152 and 647 DEGs in GSE42089 were screened, in which 619 common DEGs were identified. The DEGs were mainly enriched in pathways and GO terms associated with mitotic and chromosome assembly, including nucleosome assembly, spindle checkpoint and DNA replication. In the interaction network, progesterone receptor ( PGR), MAF bZIP transcription factor G ( MAFG), cell division cycle 6 ( CDC6) and members of the minichromosome maintenance family ( MCMs) were identified as key genes. Histones were also considered to be significant factors in BC. Nucleosome assembly and sequence-specific DNA binding were the most significant clustered GO terms. In conclusion, the DEGs, including PGR, MAFG, CDC6 and MCMs, and those encoding the core histone family were closely associated with the development of BC via pathways associated with mitotic and chromosome assembly.

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          Cancer Statistics, 2008

          Each year, the American Cancer Society estimates the number of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival based on incidence data from the National Cancer Institute, Centers for Disease Control and Prevention, and the North American Association of Central Cancer Registries and mortality data from the National Center for Health Statistics. Incidence and death rates are age-standardized to the 2000 US standard million population. A total of 1,437,180 new cancer cases and 565,650 deaths from cancer are projected to occur in the United States in 2008. Notable trends in cancer incidence and mortality include stabilization of incidence rates for all cancer sites combined in men from 1995 through 2004 and in women from 1999 through 2004 and a continued decrease in the cancer death rate since 1990 in men and since 1991 in women. Overall cancer death rates in 2004 compared with 1990 in men and 1991 in women decreased by 18.4% and 10.5%, respectively, resulting in the avoidance of over a half million deaths from cancer during this time interval. This report also examines cancer incidence, mortality, and survival by site, sex, race/ethnicity, education, geographic area, and calendar year, as well as the proportionate contribution of selected sites to the overall trends. Although much progress has been made in reducing mortality rates, stabilizing incidence rates, and improving survival, cancer still accounts for more deaths than heart disease in persons under age 85 years. Further progress can be accelerated by supporting new discoveries and by applying existing cancer control knowledge across all segments of the population.
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            Detecting overlapping protein complexes in protein-protein interaction networks.

            We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.
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              BIND: the Biomolecular Interaction Network Database.

              The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD.
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                Author and article information

                Journal
                Oncol Lett
                Oncol Lett
                OL
                Oncology Letters
                D.A. Spandidos
                1792-1074
                1792-1082
                June 2018
                28 March 2018
                28 March 2018
                : 15
                : 6
                : 8417-8423
                Affiliations
                [1 ]Department of Radiology, The First Hospital of Jilin University, Changchun, Jilin 130021, P.R. China
                [2 ]Department of Geratology, The First Hospital of Jilin University, Changchun, Jilin 130021, P.R. China
                Author notes
                Correspondence to: Dr Ji-Yan Leng, Department of Geratology, The First Hospital of Jilin University, 71 Xinmin Street, Changchun, Jilin 130021, P.R. China, E-mail: lvvyanf@ 123456163.com
                Article
                OL-0-0-8370
                10.3892/ol.2018.8370
                5950606
                29805577
                a0611c15-19fd-46bf-90f4-83eebf4b26e2
                Copyright: © Wang et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

                History
                : 21 March 2016
                : 02 November 2017
                Categories
                Articles

                Oncology & Radiotherapy
                bladder cancer,differentially expressed genes,interaction network,clustering analysis

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