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      Identification of Cancer/Testis Antigens Related to Gastric Cancer Prognosis Based on Co-Expression Network and Integrated Transcriptome Analyses

      research-article
      Author(s): ,
      Publication date (Electronic):
      Journal: Advanced Biomedical Research
      Publisher: Wolters Kluwer - Medknow
      Keywords: Cancer/testis antigens, gastric cancer, prognostic, transcriptome

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          Abstract

          Background:

          Gastric cancer is a worldwide life-threatening cancer. The underlying cause of it is still unknown. We have noticed that some cancer/testis antigens (CTAs) are up-regulated in gastric cancer. The role of these genes in gastric cancer development is not fully understood. The main aim of the current study was to comprehensively investigate CTAs’ expression and function in stomach adenocarcinoma (STAD).

          Materials and Methods:

          A comprehensive list of CTA genes was compiled from different databases. Transcriptome profiles of STAD were downloaded from the cancer genome atlas (TCGA) database and analyzed. Differentially-expressed CTAs were identified. Pathway enrichment analysis, weighted gene correlation network analysis (WGCNA), and overall survival (OS) analysis were performed on differentially-expressed CTA genes.

          Results:

          Pathway enrichment analysis indicates that CTA genes are involved in protein binding, ribonucleic acid processing, and reproductive tissues. WGCNA showed that six differentially-expressed CTA genes, namely Melanoma antigen gene (MAGE) family member A3, A6, A12 and chondrosarcoma associated gene (CSAG) 1, 2, and 3, were correlated. Up-regulation of MAGEA11, MAGEC3, Per ARNT SIM domain containing 1 (PASD1), placenta-specific protein 1 (PLAC1) and sperm protein associated with the nucleus X-linked family member (SPANXB1) were significantly associated with lower OS of patients.

          Conclusion:

          MAGEA11, MAGEC3, PASD1, PLAC1, and SPANXB1 can be investigated as prognostic biomarkers in basic and clinical studies. Further functional experiments are needed to understand the exact interaction mechanisms of these genes.

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          Most cited references38

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          Hyuna Sung, Jacques Ferlay, Rebecca Siegel … (2021)
          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Paul Shannon, Andrew Markiel, Owen Ozier … (2003)
            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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              WGCNA: an R package for weighted correlation network analysis

              Peter Langfelder, Steve Horvath (2008)
              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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                Author and article information

                Journal
                Journal ID (nlm-ta): Adv Biomed Res
                Journal ID (iso-abbrev): Adv Biomed Res
                Journal ID (publisher-id): ABR
                Title: Advanced Biomedical Research
                Publisher: Wolters Kluwer - Medknow (India )
                ISSN (Electronic): 2277-9175
                Publication date Collection: 2023
                Publication date (Electronic): 25 February 2023
                Volume: 12
                Electronic Location Identifier: 52
                Affiliations
                [1]Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
                Author notes
                Address for correspondence: Dr. Parvaneh Nikpour, Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran. E-mail: pnikpour@ 123456med.mui.ac.ir
                Article
                Publisher ID: ABR-12-52
                DOI: 10.4103/abr.abr_400_21
                PMC ID: 10086657
                SO-VID: 315c4683-aec3-40a9-817d-816e721f0f04
                Copyright © Copyright: © 2023 Advanced Biomedical Research
                License:

                This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

                History
                Date received : 21 December 2021
                Date revision received : 06 February 2022
                Date accepted : 08 February 2022
                Categories
                Subject: Original Article

                ScienceOpen disciplines: Molecular medicine
                Keywords: cancer/testis antigens,gastric cancer,prognostic,transcriptome
                Data availability:
                ScienceOpen disciplines: Molecular medicine
                Keywords: cancer/testis antigens, gastric cancer, prognostic, transcriptome

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