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      Laryngeal Squamous Cell Carcinoma: Potential Molecular Mechanism and Prognostic Signature Based on Immune-Related Genes

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          Abstract

          Background

          Immune-related genes (IRGs) are closely related to the incidence and progression of tumors, potentially indicating that IRGs play an important role in laryngeal squamous cell carcinoma (LSCC).

          Material/Methods

          An RNA sequencing dataset containing 123 samples was collected from The Cancer Genome Atlas. Based on immune-related differentially expressed genes (IRDEGs), a potential molecular mechanism of LSCC was explored through analysis of information in the Gene Ontology (GO) resource and the Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interactions (PPIs). A regulatory network of transcriptional regulators and IRDEGs was constructed to explore the underlying molecular mechanism of LSCC at the upstream level. Candidates from IRDEGs for signature were screened via univariate Cox analysis and using the least absolute shrinkage and selection operator (LASSO) technique. The IRDEG signature of LSCC was constructed by using a multivariate Cox proportional hazards model.

          Results

          GO and KEGG analysis showed that IRDEGs may participate in the progression of LSCC through immune-related reactions. PPI analysis demonstrated that, among the IRDEGs in LSCC, the Kininogen 1; C-X-X motif chemokine ligand 10; elastase, neutrophil expressed; and LYZ genes are hub genes in the development of LSCC. At the upstream level, SPI1, SP140, signal transducer and activator of transcription 4, zinc finger E-box binding homeobox, and Ikaros family zinc finger 2 are the hub transcriptional regulators of IRDEGs. The risk score based on the IRDEG signature was able to distinguish prognosis in patients with LSCC and represents an independent prognostic risk factor for LSCC.

          Conclusions

          From the perspective of IRGs, we first constructed an IRDEG signature related to the prognosis of LSCC, which can be used as a novel marker to predict prognosis in patients with LSCC.

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          Most cited references61

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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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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Journal
                Med Sci Monit
                Med Sci Monit
                Medical Science Monitor
                Medical Science Monitor : International Medical Journal of Experimental and Clinical Research
                International Scientific Literature, Inc.
                1234-1010
                1643-3750
                2020
                26 December 2020
                21 October 2020
                : 26
                : e928185-1-e928185-26
                Affiliations
                [1 ]Department of Otolaryngology, Liuzhou People’s Hospital of Guangxi, Liuzhou, Guangxi, P.R. China
                [2 ]Department of Radiotherapy, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, P.R. China
                [3 ]Department of Radiotherapy, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, P.R. China
                [4 ]Department of Pathology, Liuzhou People’s Hospital, Liuzhou, Guangxi, P.R. China
                Author notes
                Corresponding Authors: Wen-Bin Dai, e-mail: daiwenbin1973@ 123456163.com , Lin Ruan, e-mail: ruan_lin_gxmuyfy@ 123456163.com
                [A]

                Study Design

                [B]

                Data Collection

                [C]

                Statistical Analysis

                [D]

                Data Interpretation

                [E]

                Manuscript Preparation

                [F]

                Literature Search

                [G]

                Funds Collection

                [*]

                Bin-Yu Mo and Guo-Sheng Li contributed equally to this work and are considered to be co-first authors

                Article
                928185
                10.12659/MSM.928185
                7772955
                33361747
                fa7aec50-f799-4c53-bf14-e7f76a89b03f
                © Med Sci Monit, 2020

                This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International ( CC BY-NC-ND 4.0)

                History
                : 25 August 2020
                : 04 October 2020
                Categories
                Database Analysis

                carcinoma, squamous cell,databases, nucleic acid,immunity,prognosis,transcriptome

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