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      High Endothelin Receptor Type A Expression as an Independent Prognostic Biomarker and Correlated with Immune Infiltrates in Stomach Adenocarcinoma

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          Abstract

          Background

          Stomach adenocarcinoma (STAD) is the most common gastrointestinal cancer and is associated with high mortality worldwide. Endothelin receptor type A (EDNRA) is associated with guanine-nucleotide-binding (G) proteins and plays important roles in cellular processes and various diseases.

          Purpose

          To investigate the prognosis value of EDNRA expression and its correlation with immune infiltrates in patients with STAD.

          Methods

          The association between clinical characteristics and EDNRA expression in STAD was analyzed using the Wilcoxon signed-rank test and logistic regression. The Kaplan–Meier plotter analysis and Cox regression were constructed to evaluate the influence of EDNRA on prognosis, and a receiver operating characteristic (ROC) curve and nomogram were constructed. Gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were conducted to analyze the correlation between EDNRA and immune infiltrates. In addition, Oncomine, TIMER databases and qRT-PCR of STAD cell lines were used to verify the EDNRA expression in STAD.

          Results

          Our results revealed that EDNRA expression was significantly higher in patients with STAD than normal gastric tissues, and the results have been confirmed by RT-qPCR. KM-plotter analysis revealed that patients with STAD had shorter OS, FP, and PPS (P<0.001). Multivariate Cox analysis further confirmed that high EDNRA expression was an independent risk factor for OS in patients with STAD. Moreover, other clinicopathologic features were related with worse prognosis in STAD, including age, lymph nodes metastases and primary outcome. More importantly, ROC analysis also confirmed the diagnostic value, and a prognostic nomogram involving age, T, M, N classification, pathologic stage, residual tumor and EDNRA was constructed. GSEA revealed that high EDNRA expression was correlated with immunoregulatory interactions between lymphoid and non lymphoid cells pathways, natural killer cell activation involved in immune response, interleukin 1 receptor binding and pathways in cancer, and ssGSEA showed that EDNRA is correlated with macrophages and NK cells.

          Conclusion

          Collectively, EDNRA can be an independent prognostic biomarker and correlated with immune infiltration in stomach adenocarcinoma.

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          Most cited references29

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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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            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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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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                Author and article information

                Journal
                Cancer Manag Res
                Cancer Manag Res
                cmar
                cancmanres
                Cancer Management and Research
                Dove
                1179-1322
                28 June 2021
                2021
                : 13
                : 5013-5026
                Affiliations
                [1 ]The First Clinical Medical College, Guangzhou University of Chinese Medicine , Guangzhou, 510000, People’s Republic of China
                [2 ]Department of Integrated Traditional Chinese & Western Medicine, The Second Xiangya Hospital, Central South University , Changsha, Hunan, 410011, People’s Republic of China
                [3 ]Department of Gastroenterology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine , Guangzhou, 510000, People’s Republic of China
                Author notes
                Correspondence: Peiwu Li; Yi Wen Email doctorlipw@gzucm.edu.cn; 421491922@qq.com
                Article
                313078
                10.2147/CMAR.S313078
                8254415
                34234547
                40ddbfd9-9ec9-4fe1-9252-156760ded2c9
                © 2021 Yan et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                Page count
                Figures: 9, Tables: 6, References: 29, Pages: 14
                Funding
                Funded by: Guangdong natural science fund project, China;
                Funded by: Major research project of Guangzhou University of Chinese medicine, China;
                This study was supported by D3-2-4 Young scholar of Qihuang, No. 08004001004003002004; Guangdong natural science fund project, China (2019), No.2019A1515011145; Major research project of Guangzhou University of Chinese medicine, China, No. A1-AFD018201A51; The first affiliated hospital of Guangzhou University of Chinese medicine “Innovative Strong Hospital ” clinical research project, China (2019), No.2019IIT19;Liu Fengbin, Guangdong famous traditional Chinese medicine inheritance studio (Guangdong traditional Chinese medicine office [2020] no. 1).
                Categories
                Original Research

                Oncology & Radiotherapy
                ednra,stad,bioinformatics,prognosis
                Oncology & Radiotherapy
                ednra, stad, bioinformatics, prognosis

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