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      Identification of co-expression modules and hub genes of retinoblastoma via co-expression analysis and protein-protein interaction networks

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          Abstract

          Retinoblastoma is a common intraocular malignant tumor in children. However, the molecular and genetic mechanisms of retinoblastoma remain unclear. The gene expression dataset GSE110811 was retrieved from Gene Expression Omnibus. After preprocessing, coexpression modules were constructed by weighted gene coexpression network analysis (WGCNA), and modules associated with clinical traits were identified. In addition, functional enrichment analysis was performed for genes in the indicated modules, and protein-protein interaction (PPI) networks and subnetworks were constructed based on these genes. Eight coexpression modules were constructed through WGCNA. Of these, the yellow module had the highest association with severity and age (r=0.82 and P=3e-07; r=0.72 and P=3e-05). The turquoise module had the highest association with months (r=−0.63 and P=5e-04). The genes in the two modules participate in multiple pathways of retinoblastoma, and by combining the PPI network and subnetworks; 10 hub genes were identified in the two modules. The present study identified coexpression modules and hub genes associated with clinical traits of retinoblastoma, providing novel insight into retinoblastoma progression.

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          Most cited references39

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          Cytoscape: software for visualization and analysis of biological networks.

          Substantial progress has been made in the field of "omics" research (e.g., Genomics, Transcriptomics, Proteomics, and Metabolomics), leading to a vast amount of biological data. In order to represent large biological data sets in an easily interpretable manner, this information is frequently visualized as graphs, i.e., a set of nodes and edges. Nodes are representations of biological molecules and edges connect the nodes depicting some kind of relationship. Obviously, there is a high demand for computer-based assistance for both visualization and analysis of biological data, which are often heterogeneous and retrieved from different sources. This chapter focuses on software tools that assist in visual exploration and analysis of biological networks. Global requirements for such programs are discussed. Utilization of visualization software is exemplified using the widely used Cytoscape tool. Additional information about the use of Cytoscape is provided in the Notes section. Furthermore, special features of alternative software tools are highlighted in order to assist researchers in the choice of an adequate program for their specific requirements.
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            Co-expression modules construction by WGCNA and identify potential prognostic markers of uveal melanoma

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              Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis

              Since the five-year survival rate is less than 5%, pancreatic ductal adenocarcinoma (PDAC) remains the 4th cause of cancer-related death. Although PDAC has been repeatedly researched in recent years, it is still predicted to be the second leading cause of cancer death by year 2030. In our study, the differentially expressed genes in dataset GSE62452 were used to construct a co-expression network by WGCNA. The yellow module related to grade of PDAC was screened. Combined with co-expression network and PPI network, 36 candidates were screened. After survival and regression analysis by using GSE62452 and TCGA dataset, we identified 10 real hub genes (CCNA2, CCNB1, CENPF, DLGAP5, KIF14, KIF23, NEK2, RACGAP1, TPX2 and UBE2C) tightly related to progression of PDAC. According to Oncomine database and The Human Protein Atlas (HPA), we found that all real hub genes were overexpressed in pancreatic carcinoma compared with normal tissues on transcriptional and translational level. ROC curve was plotted and AUC was calculated to distinguish recurrent and non-recurrent PDAC and every AUC of the real hub gene was greater than 0.5. Finally, functional enrichment analysis and gene set enrichment (GSEA) was performed and both of them showed the cell cycle played a vital role in PDAC.
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                Author and article information

                Journal
                Mol Med Rep
                Mol Med Rep
                Molecular Medicine Reports
                D.A. Spandidos
                1791-2997
                1791-3004
                August 2020
                27 May 2020
                27 May 2020
                : 22
                : 2
                : 1155-1168
                Affiliations
                [1 ]Department of Orthopedics, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, P.R. China
                [2 ]Department of Neurovascular Surgery, The Third Medical Centre, Chinese PLA (People's Liberation Army) General Hospital, Beijing 100039, P.R. China
                Author notes
                Correspondence to: Professor Gengsheng Mao, Department of Neurovascular Surgery, The Third Medical Centre, Chinese PLA (People's Liberation Army) General Hospital, 69 Yongding Road, Beijing 100039, P.R. China, E-mail: mclxmgs@ 123456126.com
                Article
                MMR-22-02-1155
                10.3892/mmr.2020.11189
                7339782
                32468072
                0082da90-e7ef-408c-9b15-87e8f194918d
                Copyright: © Mao 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
                : 24 July 2019
                : 01 April 2020
                Categories
                Articles

                retinoblastoma,coexpression modules,hub genes,weighted gene coexpression network analysis,protein-protein interaction networks

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