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      Identification of key genes involved in the metastasis of clear cell renal cell carcinoma

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          Abstract

          Clear cell renal cell carcinoma (ccRCC) is the most common and lethal renal malignant tumor in adults. The aim of the present study was to identify the key genes involved in ccRCC metastasis. Expression profiling data for ccRCC patients with metastasis and without metastasis were obtained from The Cancer Genome Atlas database. The datasets were used to identify differentially expressed genes (DEGs) between the metastasis group and the non-metastasis group using the DESeq2 package. Function enrichment analyses of DEGs were performed. The protein-protein interaction (PPI) network was constructed and analyzed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape for further analysis of the identified hub genes. A total of 472 DEGs were identified, including 247 that were upregulated and 225 that were downregulated in the metastasis group. Gene Ontology enrichment analysis revealed that DEGs were mainly enriched in cell transmembrane movement and mitotic cell cycle process. Kyoto Encyclopedia of Genes Genomes pathway analysis revealed that the DEGs were mainly involved in the ‘cell cycle’ (hsa04110), ‘collecting duct acid secretion’ (hsa04966), ‘complement and coagulation cascades’ (hsa04610) and ‘aldosterone-regulated sodium reabsorption’ (hsa04960) pathways. Using the PPI network, 35 hub genes were identified, and the majority of them were upregulated in ccRCC tissue compared with normal kidney tissue. The expression levels of certain hub genes (CDKN3, TPX2, BUB1B, CDCA8, UBE2C, NDC80, RRM2, NCAPG, NCAPH, PTTG1, FAM64A, ANLN, KIF4A, CEP55, CENPF, KIF20A, ASPM and HJURP) were significantly associated with overall survival and recurrence-free survival in ccRCC. The present study has identified key genes associated with the metastasis of ccRCC.

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          Most cited references18

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          The KEGG database.

          KEGG (http://www.genome.ad.jp/kegg/) is a suite of databases and associated software for understanding and simulating higher-order functional behaviours of the cell or the organism from its genome information. First, KEGG computerizes data and knowledge on protein interaction networks (PATHWAY database) and chemical reactions (LIGAND database) that are responsible for various cellular processes. Second, KEGG attempts to reconstruct protein interaction networks for all organisms whose genomes are completely sequenced (GENES and SSDB databases). Third, KEGG can be utilized as reference knowledge for functional genomics (EXPRESSION database) and proteomics (BRITE database) experiments. I will review the current status of KEGG and report on new developments in graph representation and graph computations.
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            Resistance to Systemic Therapies in Clear Cell Renal Cell Carcinoma: Mechanisms and Management Strategies

            Renal cell carcinoma (RCC) is the most common form of kidney cancer. It is categorized into various subtypes, with clear cell RCC (ccRCC) representing about 85% of all RCC tumors. The lack of sensitivity to chemotherapy and radiation therapy prompted research efforts into novel treatment options. The development of targeted therapeutics, including multi-targeted tyrosine kinase inhibitors (TKIs) and mTOR inhibitors, has been a major breakthrough in ccRCC therapy. More recently, other therapeutic strategies, including immune checkpoint inhibitors, have emerged as effective treatment options against advanced ccRCC. Furthermore, recent advances in disease biology, tumor microenvironment, and mechanisms of resistance formed the basis for attempts to combine targeted therapies with newer generation immunotherapies to take advantage of possible synergy. This review focuses on the current status of basic, translational, and clinical studies on mechanisms of resistance to systemic therapies in ccRCC.
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              Hubba: hub objects analyzer—a framework of interactome hubs identification for network biology

              One major task in the post-genome era is to reconstruct proteomic and genomic interacting networks using high-throughput experiment data. To identify essential nodes/hubs in these interactomes is a way to decipher the critical keys inside biochemical pathways or complex networks. These essential nodes/hubs may serve as potential drug-targets for developing novel therapy of human diseases, such as cancer or infectious disease caused by emerging pathogens. Hub Objects Analyzer (Hubba) is a web-based service for exploring important nodes in an interactome network generated from specific small- or large-scale experimental methods based on graph theory. Two characteristic analysis algorithms, Maximum Neighborhood Component (MNC) and Density of Maximum Neighborhood Component (DMNC) are developed for exploring and identifying hubs/essential nodes from interactome networks. Users can submit their own interaction data in PSI format (Proteomics Standards Initiative, version 2.5 and 1.0), tab format and tab with weight values. User will get an email notification of the calculation complete in minutes or hours, depending on the size of submitted dataset. Hubba result includes a rank given by a composite index, a manifest graph of network to show the relationship amid these hubs, and links for retrieving output files. This proposed method (DMNC || MNC) can be applied to discover some unrecognized hubs from previous dataset. For example, most of the Hubba high-ranked hubs (80% in top 10 hub list, and >70% in top 40 hub list) from the yeast protein interactome data (Y2H experiment) are reported as essential proteins. Since the analysis methods of Hubba are based on topology, it can also be used on other kinds of networks to explore the essential nodes, like networks in yeast, rat, mouse and human. The website of Hubba is freely available at http://hub.iis.sinica.edu.tw/Hubba.
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                Author and article information

                Journal
                Oncol Lett
                Oncol Lett
                OL
                Oncology Letters
                D.A. Spandidos
                1792-1074
                1792-1082
                May 2019
                08 March 2019
                08 March 2019
                : 17
                : 5
                : 4321-4328
                Affiliations
                [1 ]Department of Medical Oncology, Affiliated Langdong Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
                [2 ]Department of Medical Oncology, The First People's Hospital of Nanning, Nanning, Guangxi Zhuang Autonomous Region 530022, P.R. China
                Author notes
                Correspondence to: Dr Xueqiong Han, Department of Medical Oncology, The First People's Hospital of Nanning, 89 Qixing Road, Nanning, Guangxi Zhuang Autonomous Region 530022, P.R. China, E-mail: sabrinahan@ 123456163.com
                Dr Zihai Xu, Department of Medical Oncology, Affiliated Langdong Hospital of Guangxi Medical University, 60 Xiangbin Road, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China, E-mail: zihai_xu@ 123456163.com
                [*]

                Contributed equally

                Article
                OL-0-0-10130
                10.3892/ol.2019.10130
                6447949
                30988807
                b1ea6aa4-10f3-46e8-bd2d-080edcc5200c
                Copyright: © Wei 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
                : 23 May 2018
                : 01 February 2019
                Categories
                Articles

                Oncology & Radiotherapy
                clear cell renal cell carcinoma,metastasis,hub genes
                Oncology & Radiotherapy
                clear cell renal cell carcinoma, metastasis, hub genes

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