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      Cullin-5 (CUL5) as a potential prognostic marker in a pan-cancer analysis of human tumors

      research-article
      a , b , a , b , a , b , a , b , b , b , a , b
      Bioengineered
      Taylor & Francis
      CUL5, cancer, prognosis, immune infiltration

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          ABSTRACT

          There is some evidence supporting an association between Cullin-5 (CUL5) and cancer, but no research using pan-cancer analysis has been conducted previously. We therefore investigated the oncogenic role of CUL5 in 33 tumors from the Gene Expression Omnibus and The Cancer Genome Atlas databases. Many cancers reduce CUL5 levels, and the prognosis of certain cancers is vitally linked with CUL5 expression. CUL5 expression is associated with CD8 + T-cell infiltration levels in uveal melanomas and head and neck squamous cell carcinomas, and we observed a positive relationship between CUL5 and Tcm (T central memory) cells, and a negative relationship between T helper (Th) cells and pDC (plasmacytoid DC). CUL5 had negative associations with NK cells, NK CD56 bright cells, NK CD56 dim cells, Tregs, cytotoxic cells, and Th17 cells. Functions relating to protein processing and ubiquitin were included in the CUL5 functional mechanisms. The top 100 genes that are most strongly related to CUL5 were identified, and enrichment analysis indicated that the biological process with the closest relationship was neddylation, related pathways included the TGF-beta signaling pathway and intracellular receptor signaling pathway. CUL5 is related to biological cell behaviors such as chromosome segregation and positive regulation of chromosome organization. As the first study to perform a pan-cancer analysis of CUL5, the present findings will improve the understanding of the oncogenic role of CUL5 in different tumors.

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          Most cited references36

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          Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

          A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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            STRING v10: protein–protein interaction networks, integrated over the tree of life

            The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
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              GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis

              Abstract Introduced in 2017, the GEPIA (Gene Expression Profiling Interactive Analysis) web server has been a valuable and highly cited resource for gene expression analysis based on tumor and normal samples from the TCGA and the GTEx databases. Here, we present GEPIA2, an updated and enhanced version to provide insights with higher resolution and more functionalities. Featuring 198 619 isoforms and 84 cancer subtypes, GEPIA2 has extended gene expression quantification from the gene level to the transcript level, and supports analysis of a specific cancer subtype, and comparison between subtypes. In addition, GEPIA2 has adopted new analysis techniques of gene signature quantification inspired by single-cell sequencing studies, and provides customized analysis where users can upload their own RNA-seq data and compare them with TCGA and GTEx samples. We also offer an API for batch process and easy retrieval of the analysis results. The updated web server is publicly accessible at http://gepia2.cancer-pku.cn/.
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                Author and article information

                Journal
                Bioengineered
                Bioengineered
                Bioengineered
                Taylor & Francis
                2165-5979
                2165-5987
                20 August 2021
                2021
                20 August 2021
                : 12
                : 1
                : 5348-5360
                Affiliations
                [a ]Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University; , Zhengzhou, China
                [b ]Henan International Joint Laboratory of Glioma Metabolism and Microenvironment Research; , Zhengzhou, Henan, China
                Author notes
                CONTACT Xinjun Wang wangxj@ 123456zzu.edu.cn Department of Neurosurgery, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University; , Zhengzhou 450052, China
                Author information
                https://orcid.org/0000-0001-8139-2154
                Article
                1940042
                10.1080/21655979.2021.1940042
                8806887
                34415831
                5e67a676-3046-4ed8-8657-ccc6eebdf107
                © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Figures: 6, References: 36, Pages: 13
                Categories
                Research Article
                Research Paper

                Biomedical engineering
                cul5,cancer,prognosis,immune infiltration
                Biomedical engineering
                cul5, cancer, prognosis, immune infiltration

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