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      SPOCK2 Serves as a Potential Prognostic Marker and Correlates With Immune Infiltration in Lung Adenocarcinoma

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          Abstract

          Lung adenocarcinoma (LUAD) is one of the major types of lung cancer. Tumor-infiltrating immune cells (TIICs) are positively associated with overall survival (OS) in LUAD. The SPARC/osteonectin, cwcv and kazal-like domains proteoglycan 2 (SPOCK2) is a complex type of secreted proteoglycan involved in forming a protective barrier against viral infection. The purpose of this study was to investigate the relationship between SPOCK2 and TIICs and the prognostic role of SPOCK2 in LUAD. The GEPIA2, GEO, CPTAC, and HPA databases were analyzed to examine both the mRNA and protein expression of SPOCK2 in LUAD. GEPIA2 and the Kaplan-Meier Plotter (KM Plotter) were used to evaluate the prognostic value of SPOCK2 in LUAD patients. TCGA data were examined for a correlation between SPOCK2 expression and clinical characteristics. Gene enrichment analyses were performed to explore the underlying mechanism of SPOCK2 based on LinkedOmics. RegNetwork was used to predict the regulators of SPOCK2. The correlation between SPOCK2 and TIICs, including immune infiltration level and relative proportion was investigated via TIMER. KM Plotter was also used to evaluate the prognostic role of SPOCK2 expression in LUAD with enriched and decreased TIIC subgroups. We found SPOCK2 was significantly downregulated in LUAD compared with that in non-tumor controls and was correlated with clinical parameters. Moreover, a high SPOCK2 expression level predicted better survival. The SPOCK2-associated regulatory network was constructed. SPOCK2 influenced the TIIC infiltration level and relative proportion in LUAD. Furthermore, a high SPOCK2 expression level was associated with a favorable prognosis in enriched CD4 + T cells and macrophage subgroups in LUAD. In conclusion, a high level of SPOCK2 expression predicted favorable prognosis and was significantly correlated with TIICs in LUAD. Therefore, the expression of SPOCK2 may affect the prognosis of LUAD partly due to TIICs.

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          Most cited references37

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          Hallmarks of Cancer: The Next Generation

          Douglas Hanahan, Robert A. Weinberg (2011)
          The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)
            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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              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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                Author and article information

                Contributors
                Journal
                Journal ID (nlm-ta): Front Genet
                Journal ID (iso-abbrev): Front Genet
                Journal ID (publisher-id): Front. Genet.
                Title: Frontiers in Genetics
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 1664-8021
                Publication date (Electronic): 10 November 2020
                Publication date Collection: 2020
                Volume: 11
                Electronic Location Identifier: 588499
                Affiliations
                [1] 1Department of Pathology, College of Basic Medical Sciences, China Medical University , Shenyang, China
                [2] 2Department of Pathology, The First Affiliated Hospital of China Medical University , Shenyang, China
                [3] 3Department of Chronic Disease Epidemiology, Yale School of Public Health, Yale University , New Haven, CT, United States
                Author notes

                Edited by: Hongwei Wang, Sun Yat-sen University, China

                Reviewed by: Stefano Forte, Mediterranean Institute of Oncology (IOM), Italy; Manal Said Fawzy, Suez Canal University, Egypt

                *Correspondence: Qingchang Li, qcli@ 123456cmu.edu.cn

                This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics

                Article
                DOI: 10.3389/fgene.2020.588499
                PMC ID: 7683796
                PubMed ID: 33244319
                SO-VID: 3cf439a2-f803-42de-b34e-c219317f8bbc
                Copyright © Copyright © 2020 Zhao, Cheng, Gai, Zhang, Du and Li.
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                Date received : 29 July 2020
                Date accepted : 21 October 2020
                Page count
                Figures: 6, Tables: 3, Equations: 0, References: 37, Pages: 12, Words: 0
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 81672964
                Award ID: 81874214
                Categories
                Subject: Genetics
                Subject: Original Research

                ScienceOpen disciplines: Genetics
                Keywords: spock2,tumor-infiltrating immune cell,prognosis,lung adenocarcinoma,bioinformatics
                Data availability:
                ScienceOpen disciplines: Genetics
                Keywords: spock2, tumor-infiltrating immune cell, prognosis, lung adenocarcinoma, bioinformatics

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