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      Identification of novel candidate genes involved in the progression of emphysema by bioinformatic methods

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          Abstract

          Purpose

          By reanalyzing the gene expression profile GSE76925 in the Gene Expression Omnibus database using bioinformatic methods, we attempted to identify novel candidate genes promoting the development of emphysema in patients with COPD.

          Patients and methods

          According to the Quantitative CT data in GSE76925, patients were divided into mild emphysema group (%LAA-950<20%, n=12) and severe emphysema group (%LAA-950>50%, n=11). Differentially expressed genes (DEGs) were identified using Agilent GeneSpring GX v11.5 (corrected P-value <0.05 and |Fold Change|>1.3). Known driver genes of COPD were acquired by mining literatures and retrieving databases. Direct protein–protein interaction network (PPi) of DEGs and known driver genes was constructed by STRING.org to screen the DEGs directly interacting with driver genes. In addition, we used STRING.org to obtain the first-layer proteins interacting with DEGs’ products and constructed the indirect PPi of these interaction proteins. By merging the indirect PPi with driver genes’ PPi using Cytoscape v3.6.1, we attempted to discover potential pathways promoting emphysema’s development.

          Results

          All the patients had COPD with severe airflow limitation (age=62±8, FEV 1%=28±12). A total of 57 DEGs (including 12 pseudogenes) and 135 known driving genes were identified. Direct PPi suggested that GPR65, GNB4, P2RY13, NPSR1, BCR, BAG4, and IMPDH2 were potential pathogenic genes. GPR65 could regulate the response of immune cells to the acidic microenvironment, and NPSR1’s expression on eosinophils was associated with asthma’s severity and IgE level. Indirect merging PPi demonstrated that the interacting network of TP53, IL8, CCR2, HSPA1A, ELANE, PIK3CA was associated with the development of emphysema. IL8, ELANE, and PIK3CA were molecules involved in the pathological mechanisms of emphysema, which also in return proved the role of TP53 in emphysema.

          Conclusion

          Candidate genes such as GPR65, NPSR1, and TP53 may be involved in the progression of emphysema.

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          Most cited references 50

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          TRANSFAC: transcriptional regulation, from patterns to profiles.

          The TRANSFAC database on eukaryotic transcriptional regulation, comprising data on transcription factors, their target genes and regulatory binding sites, has been extended and further developed, both in number of entries and in the scope and structure of the collected data. Structured fields for expression patterns have been introduced for transcription factors from human and mouse, using the CYTOMER database on anatomical structures and developmental stages. The functionality of Match, a tool for matrix-based search of transcription factor binding sites, has been enhanced. For instance, the program now comes along with a number of tissue-(or state-)specific profiles and new profiles can be created and modified with Match Profiler. The GENE table was extended and gained in importance, containing amongst others links to LocusLink, RefSeq and OMIM now. Further, (direct) links between factor and target gene on one hand and between gene and encoded factor on the other hand were introduced. The TRANSFAC public release is available at http://www.gene-regulation.com. For yeast an additional release including the latest data was made available separately as TRANSFAC Saccharomyces Module (TSM) at http://transfac.gbf.de. For CYTOMER free download versions are available at http://www.biobase.de:8080/index.html.
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            Network-based classification of breast cancer metastasis

            Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.
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              The genetic association database.

               S Wang,  K Barnes,  K. Becker (2004)
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                Author and article information

                Journal
                Int J Chron Obstruct Pulmon Dis
                Int J Chron Obstruct Pulmon Dis
                International Journal of COPD
                International Journal of Chronic Obstructive Pulmonary Disease
                Dove Medical Press
                1176-9106
                1178-2005
                2018
                14 November 2018
                : 13
                : 3733-3747
                Affiliations
                Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China, jingatlas@ 123456hotmail.com
                Author notes
                Correspondence: Jing Zhang, Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai 200032, China, Tel +86 186 1688 1189, Fax +86 21 6406 1990, Email jingatlas@ 123456hotmail.com
                Article
                copd-13-3733
                10.2147/COPD.S183100
                6241693
                © 2018 Hu et al. This work is published and licensed by Dove Medical Press Limited

                The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.

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                Original Research

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