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      Comprehensive gene and pathway analysis of cervical cancer progression

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          Abstract

          Cervical Cancer is one of the leading causes of cancer-associated mortality in women. The present study aimed to identify key genes and pathways involved in cervical cancer (CC) progression, via a comprehensive bioinformatics analysis. The GSE63514 dataset from the Gene Expression Omnibus database was analyzed for hub genes and cancer progression was divided into four phases (phases I–IV). Pathway enrichment, protein-protein interaction (PPI) and pathway crosstalk analyses were performed, to identify key genes and pathways using a criterion nodal degree ≥5. Gene pathway analysis was determined by mapping the key genes into the key pathways. Co-expression between key genes and their effect on overall survival (OS) time was assessed using The Cancer Genome Atlas database. A total of 3,446 differentially expressed genes with 107 hub genes were identified within the four phases. A total of 14 key genes with 11 key pathways were obtained, following extraction of ≥5 degree nodes from the PPI and pathway crosstalk networks. Gene pathway analysis revealed that CDK1 and CCNB1 regulated the cell cycle and were activated in phase I. Notably, the following terms, ‘pathways in cancer’, ‘focal adhesion’ and the ‘PI3K-Akt signaling pathway’ ranked the highest in phases II–IV. Furthermore, FN1, ITGB1 and MMP9 may be associated with metastasis of tumor cells. STAT1 was indicated to predominantly function at the phase IV via cancer-associated signaling pathways, including ‘pathways in cancer’ and ‘Toll-like receptor signaling pathway’. Survival analysis revealed that high ITGB1 and FN1 expression levels resulted in significantly worse OS. CDK1 and CCNB1 were revealed to regulate proliferation and differentiation through the cell cycle and viral tumorigenesis, while FN1 and ITGB1, which may be developed as novel prognostic factors, were co-expressed to induce metastasis via cancer-associated signaling pathways, including PI3K-Art signaling pathway, and focal adhesion in CC; however, the underlying molecular mechanisms require further research.

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          Most cited references102

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          Human papillomavirus and cervical cancer.

          Cervical cancer is caused by human papillomavirus infection. Most human papillomavirus infection is harmless and clears spontaneously but persistent infection with high-risk human papillomavirus (especially type 16) can cause cancer of the cervix, vulva, vagina, anus, penis, and oropharynx. The virus exclusively infects epithelium and produces new viral particles only in fully mature epithelial cells. Human papillomavirus disrupts normal cell-cycle control, promoting uncontrolled cell division and the accumulation of genetic damage. Two effective prophylactic vaccines composed of human papillomavirus type 16 and 18, and human papillomavirus type 16, 18, 6, and 11 virus-like particles have been introduced in many developed countries as a primary prevention strategy. Human papillomavirus testing is clinically valuable for secondary prevention in triaging low-grade cytology and as a test of cure after treatment. More sensitive than cytology, primary screening by human papillomavirus testing could enable screening intervals to be extended. If these prevention strategies can be implemented in developing countries, many thousands of lives could be saved. Copyright © 2013 Elsevier Ltd. All rights reserved.
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            Principles of CDK regulation.

            D Morgan (1995)
            As key regulators of the cell cycle, the cyclin-dependent kinases must be tightly regulated by extra- and intracellular signals. The activity of cyclin-dependent kinases is controlled by four highly conserved biochemical mechanisms, forming a web of regulatory pathways unmatched in its elegance and intricacy.
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              Development of human protein reference database as an initial platform for approaching systems biology in humans.

              Human Protein Reference Database (HPRD) is an object database that integrates a wealth of information relevant to the function of human proteins in health and disease. Data pertaining to thousands of protein-protein interactions, posttranslational modifications, enzyme/substrate relationships, disease associations, tissue expression, and subcellular localization were extracted from the literature for a nonredundant set of 2750 human proteins. Almost all the information was obtained manually by biologists who read and interpreted >300,000 published articles during the annotation process. This database, which has an intuitive query interface allowing easy access to all the features of proteins, was built by using open source technologies and will be freely available at http://www.hprd.org to the academic community. This unified bioinformatics platform will be useful in cataloging and mining the large number of proteomic interactions and alterations that will be discovered in the postgenomic era.
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                Author and article information

                Journal
                Oncol Lett
                Oncol Lett
                OL
                Oncology Letters
                D.A. Spandidos
                1792-1074
                1792-1082
                April 2020
                03 March 2020
                03 March 2020
                : 19
                : 4
                : 3316-3332
                Affiliations
                Department of Obstetrics and Gynecology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, P.R. China
                Author notes
                Correspondence to: Professor Wei Zhang, Department of Obstetrics and Gynecology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, Hubei 430071, P.R. China, E-mail: zw6676@ 123456163.com
                [*]

                Contributed equally

                Article
                OL-0-0-11439
                10.3892/ol.2020.11439
                7074609
                10592173
                30c4707e-8e76-4c23-b76b-52ad80326a93
                Copyright: © Yi 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 June 2019
                : 15 January 2020
                Categories
                Articles

                Oncology & Radiotherapy
                cervical cancer,bioinformatics analysis,diagnosis,progression
                Oncology & Radiotherapy
                cervical cancer, bioinformatics analysis, diagnosis, progression

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