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      Progressive and Prognostic Performance of an Extracellular Matrix-Receptor Interaction Signature in Gastric Cancer

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      1 , 2 , 3 , 4 , 1 ,
      Disease Markers
      Hindawi

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          Abstract

          The role of an extracellular matrix- (ECM-) receptor interaction signature has not been fully clarified in gastric cancer. This study performed comprehensive analyses on the differentially expressed ECM-related genes, clinicopathologic features, and prognostic application in gastric cancer. The differentially expressed genes between tumorous and matched normal tissues in The Cancer Genome Atlas (TCGA) and validation cohorts were identified by a paired t-test. Consensus clusters were built to find the correlation between clinicopathologic features and subclusters. Then, the least absolute shrinkage and selection operator (lasso) method was used to construct a risk score model. Correlation analyses were made to reveal the relation between risk score-stratified subgroups and clinicopathologic features or significant signatures. In TCGA (26 pairs) and validation cohort (134 pairs), 25 ECM-related genes were significantly highly expressed and 11 genes were downexpressed in gastric cancer. ECM-based subclusters were slightly related to clinicopathologic features. We constructed a risk score model = 0.081∗log 2 (CD36) + 0.043∗log 2 (COL5A2) + 0.001∗log 2 (ITGB5) + 0.039∗log 2 (SDC2) + 0.135∗log 2 (SV2B) + 0.012∗log 2 (THBS1) + 0.068∗log 2 (VTN) + 0.023∗log 2 (VWF). The risk score model could well predict the outcome of patients with gastric cancer in both training ( n = 351, HR: 1.807, 95% CI: 1.292-2.528, P = 0.00046) and validation ( n = 300, HR: 1.866, 95% CI: 1.347-2.584, P = 0.00014) cohorts. Besides, risk score-based subgroups were associated with angiogenesis, cell adhesion molecules, complement and coagulation cascades, TGF-beta signaling, and mismatch repair-relevant signatures ( P < 0.0001). By univariate (1.845, 95% CI: 1.382-2.462, P < 0.001) and multivariate (1.756, 95% CI: 1.284-2.402, P < 0.001) analyses, we regarded the risk score as an independent risk factor in gastric cancer. Our findings revealed that ECM compositions became accomplices in the tumorigenesis, progression, and poor survival of gastric cancer.

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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

                Contributors
                Journal
                Dis Markers
                Dis Markers
                DM
                Disease Markers
                Hindawi
                0278-0240
                1875-8630
                2020
                29 October 2020
                : 2020
                : 8816070
                Affiliations
                1Department of Hematology and Medical Oncology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325000 Zhejiang Province, China
                2Department of Pharmacy, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000 Zhejiang Province, China
                3Second Clinical College of Medicine, Wenzhou Medical University, Wenzhou, 325000 Zhejiang Province, China
                4College of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325000 Zhejiang Province, China
                Author notes

                Academic Editor: Stamatios E. Theocharis

                Author information
                https://orcid.org/0000-0002-3200-4386
                Article
                10.1155/2020/8816070
                7647771
                33178362
                379d3ef9-ab9a-4d58-be5a-b761ab74adde
                Copyright © 2020 Xiangchou Yang et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 April 2020
                : 15 July 2020
                : 22 September 2020
                Funding
                Funded by: Wenzhou Municipal Science and Technology Bureau
                Award ID: 2019Y0216
                Categories
                Research Article

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