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      The underlying molecular mechanism and identification of transcription factor markers for laryngeal squamous cell carcinoma

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          ABSTRACT

          The screening and treatment of laryngeal squamous cell carcinoma (LSCC) still perplexes clinicians, making it necessary to explore new markers. To this end, this research examined the underlying molecular mechanism of LSCC based on high-throughput datasets ( n = 249) from multiple databases. It also identified transcription factors (TFs) independently associated with LSCC prognosis. Through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, differential expression genes of LSCC were deemed relevant to the extracellular matrix and its related structures or pathways, suggesting that the extracellular matrix plays an important role in LSCC. At the same time, several hub genes that may also have important roles in LSCC were identified via protein–protein interaction analysis, including CDC45, TPX2, AURKA, KIF2C, NUF, MUC1, MUC7, MUC4, MUC15, and MUC21. Eight unreported LSCC prognostic TFs – BCAT1, CHD4, FOXA2, GATA6, HNF1A, HOXB13, MAFF, and TCF4 – were screened via Kaplan–Meier curves. Cox analysis determined for the first time that HOXB13 expression and gender were independently associated with LSCC prognosis. Compared to control tissues, elevated expression of HOXB13 was found in LSCC tissues (standardized mean difference = 0.44, 95% confidence interval [0.13–0.76]). HOXB13 expression also makes it feasible to screen LSCC from non-LSCC (area under the curve = 0.77), and HOXB13 may play an essential role in LSCC by regulating HOXB7. In conclusion, HOXB13 may be a novel marker for LSCC clinical screening and treatment.

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          Most cited references67

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              Cytoscape: a software environment for integrated models of biomolecular interaction networks.

              Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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                Author and article information

                Journal
                Bioengineered
                Bioengineered
                Bioengineered
                Taylor & Francis
                2165-5979
                2165-5987
                4 January 2021
                2021
                4 January 2021
                : 12
                : 1
                : 208-224
                Affiliations
                [a ]Department of Otolaryngology, Liuzhou People’s Hospital of Guangxi; , Liuzhou, Guangxi Zhuang Autonomous Region, P.R. China
                [b ]Department of Radiotherapy, First Affiliated Hospital of Guangxi Medical University; , Nanning, Guangxi Zhuang Autonomous Region, P.R. China
                [c ]Department of Radiotherapy, Guangxi Medical University Cancer Hospital; , Nanning, Guangxi Zhuang Autonomous Region, P.R. China
                [d ]Department of Pathology, Liuzhou People’s Hospital; , Liuzhou, Guangxi Zhuang Autonomous Region, P.R. China
                Author notes
                CONTACT Wen-Bin Dai daiwenbin1973@ 123456163.com Department of Pathology, Liuzhou People’s Hospital; , Liuzhou, Guangxi Zhuang Autonomous Region 545006, P.R. China
                Lin Ruan ruan_lin_gxmuyfy@ 123456163.com Department of Radiotherapy, First Affiliated Hospital of Guangxi Medical University; , Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China
                [*]

                Contributed equally as co-first authors.

                Article
                1862527
                10.1080/21655979.2020.1862527
                8291796
                33315534
                66af69e1-9d2b-4344-97fd-66146ad661f8
                © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Figures: 10, Tables: 1, References: 67, Pages: 17
                Categories
                Research Article
                Research Paper

                Biomedical engineering
                laryngeal squamous cell carcinoma,transcription factor,homeobox b13,rna sequencing,prognosis

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