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      Upregulation of OTX2-AS1 is Associated With Immune Infiltration and Predicts Prognosis of Gastric Cancer

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          Abstract

          Background

          It is unclear whether the long non-coding RNA (lncRNA) OTX2 antisense RNA 1 (OTX2-AS1) plays a pivotal role in gastric cancer (GC). An analysis of The Cancer Genome Atlas (TCGA) database data and bioinformatics was used to explore the relationship between OTX2-AS1 and GC in the current study.

          Methods

          We evaluated the relationship between clinical features and OTX2-AS1 expression, prognostic factors, and the significant involvement of OTX2-AS1 in function using various statistical methods, such as Kaplan–Meier method, Cox regression analysis, Gene Set Enrichment Analysis (GSEA), and immune infiltration analysis. GC cell lines were tested for OTX2-AS1 expression using qRT-PCR.

          Results

          A high level of OTX2-AS1 expression was significantly and negatively associated with Helicobacter pylori ( H pylori) infection in GC patients ( P = .006) and predicted a poorer overall survival (OS) (HR: 1.54; 95% CI: 1.10-2.14; P = .011), progression-free interval (PFI) (HR: 1.75; 95% CI: 1.22-2.51; P = .002) and disease-specific survival (DSS) (HR: 1.85; 95% CI: 1.21-2.85; P = .005) in GC patients. There was an independent correlation between OTX2-AS1 expression (HR: 1.771; 95% CI: 1.164-2.696; P = .008) and OS in patients with GC. There were differential enrichments for the OTX2-AS1 high expression phenotype in the olfactory transduction, G alpha (s) signaling events, keratinization, olfactory signaling pathway, and preimplantation embryo. OTX2-AS1 expression may be related to certain immune-infiltrating cells. Compared to gastric epithelial cells (GES-1), GC cell lines showed a significant increase in OTX2-AS1 expression.

          Conclusion

          There was a significant association between OTX2-AS1 expression in GC patients and poor survival, suggesting that it may be a useful biomarker for prognosis and immunotherapy outcome of stomach adenocarcinoma (STAD) in GC.

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          Most cited references36

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            GSVA: gene set variation analysis for microarray and RNA-Seq data

            Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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              Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods

              Estimates of the worldwide incidence and mortality from 36 cancers and for all cancers combined for the year 2018 are now available in the GLOBOCAN 2018 database, compiled and disseminated by the International Agency for Research on Cancer (IARC). This paper reviews the sources and methods used in compiling the cancer statistics in 185 countries. The validity of the national estimates depends upon the representativeness of the source information, and to take into account possible sources of bias, uncertainty intervals are now provided for the estimated sex- and site-specific all-ages number of new cancer cases and cancer deaths. We briefly describe the key results globally and by world region. There were an estimated 18.1 million (95% UI: 17.5-18.7 million) new cases of cancer (17 million excluding non-melanoma skin cancer) and 9.6 million (95% UI: 9.3-9.8 million) deaths from cancer (9.5 million excluding non-melanoma skin cancer) worldwide in 2018.
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                Author and article information

                Journal
                Technol Cancer Res Treat
                Technol Cancer Res Treat
                TCT
                sptct
                Technology in Cancer Research & Treatment
                SAGE Publications (Sage CA: Los Angeles, CA )
                1533-0346
                1533-0338
                6 February 2023
                2023
                : 22
                : 15330338231154091
                Affiliations
                [1 ]Medical Oncology Department, The First Medical Center, Ringgold 104607, universityChinese PLA General Hospital; , Beijing, China
                [2 ]School of Medicine, Ringgold 12538, universityNankai University; , Tianjin, China
                [3 ]Ringgold 590636, universityChosenMed Technology (Beijing) Co., Ltd; , Beijing, China
                Author notes
                [#]

                These authors contributed equally to this work.

                [*]Quan-li Han, Medical Oncology Department, The First Medical Center, Chinese PLA General Hospital, Beijing, China, 28 Fuxing Road, Haidian District, Beijing 100853, China. Email: hanquanli@ 123456301hospital.com.cn
                Author information
                https://orcid.org/0000-0002-5227-9643
                Article
                10.1177_15330338231154091
                10.1177/15330338231154091
                9905030
                36740995
                8c67d9db-801a-490c-80ff-e0dec5b5716f
                © The Author(s) 2023

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 12 August 2022
                : 22 December 2022
                : 12 January 2023
                Categories
                LncRNAs and CircRNAs: Biomarkers of Therapeutic Resistance and Tumor Progression
                Original Article
                Custom metadata
                ts19
                January-December 2023

                gastric cancer,otx2-as1,prognosis,immune infiltration,biomarker

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