4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Low ACADM expression predicts poor prognosis and suppressive tumor microenvironment in clear cell renal cell carcinoma

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Clear cell renal cell carcinoma (ccRCC) represents a highly frequent renal cancer subtype. However, medium-chain acyl-CoA dehydrogenase (ACADM) encodes an important enzyme responsible for fatty acid β-oxidation (FAO) and its association with prognosis and immunity in cancers has rarely been reported. Therefore, the present work focused on exploring ACADM’s expression and role among ccRCC cases. We used multiple public databases and showed the hypo levels of ACADM protein and mRNA within ccRCC. Additionally, we found that ACADM down-regulation showed a remarkable relation to the advanced stage, high histological grade, as well as dismal prognostic outcome. As suggested by Kaplan–Meier curve analysis, cases showing low ACADM levels displayed shorter overall survival (OS) as well as disease-free survival (DFS). Moreover, according to univariate/multivariate Cox regression, ACADM-mRNA independently predicted the prognosis of ccRCC. In addition, this work conducted immunohistochemistry for validating ACADM protein expression and its prognostic role in ccRCC samples. KEGG and GO analyses revealed significantly enriched genes related to ACADM expression during fatty acid metabolism. The low-ACADM group with more regulatory T-cell infiltration showed higher expression of immune negative regulation genes and higher TIDE scores, which might contribute to poor response to immunotherapies. In conclusion, our results confirmed that downregulated ACADM predicted a poor prognosis for ccRCC and a poor response to immunotherapy. Our results provide important data for developing immunotherapy for ccRCC.

          Related collections

          Most cited references53

          • Record: found
          • Abstract: found
          • Article: not found

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Proteomics. Tissue-based map of the human proteome.

            Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body. Copyright © 2015, American Association for the Advancement of Science.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

              Abstract Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
                Bookmark

                Author and article information

                Contributors
                ddwgb@aliyun.com
                longhuiming@vip.sina.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                25 April 2024
                25 April 2024
                2024
                : 14
                : 9533
                Affiliations
                [1 ]Department of Urology, The Affiliated Lihuili Hospital, Ningbo University, ( https://ror.org/03et85d35) Ningbo, China
                [2 ]Departments of Urology, Ningbo Medical Center Lihuili Hospital, ( https://ror.org/030zcqn97) Ningbo, Zhejiang China
                [3 ]Ningbo Institute for Medicine and Biomedical Engineering Combined Innovation, The Affiliated Lihuili Hospital, Ningbo University, ( https://ror.org/03et85d35) Ningbo, China
                [4 ]School of Information Engineering, Nanchang University, ( https://ror.org/042v6xz23) Nanchang, China
                [5 ]Department of Urology, Ningbo Yinzhou No.2 Hospital, Ningbo, China
                Article
                59746
                10.1038/s41598-024-59746-5
                11045743
                38664460
                43a60cd6-88c6-4583-99c9-0eb62cd96ce2
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 April 2023
                : 15 April 2024
                Funding
                Funded by: Natural Science Foundation of Ningbo
                Award ID: 202003N4255
                Award Recipient :
                Funded by: Department of Health of Zhejiang Province
                Award ID: 2019KY603
                Award Recipient :
                Funded by: Medicine and Health Project of Zhejiang Province
                Award ID: 2020KY858
                Award Recipient :
                Funded by: Natural Science Foundation of Ningbo Municipality
                Award ID: 2021J281
                Award Recipient :
                Funded by: Key Cultivating Discipline of LihHuiLi Hospital
                Award ID: 2022-P09
                Award Recipient :
                Funded by: Ningbo Key Clinical Speciality Construction Project
                Award ID: 2023-BZZ
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                cancer,tumour biomarkers,tumour-suppressor proteins,urological cancer,computational biology and bioinformatics,data mining,gene ontology,genetics,gene expression,genetic markers

                Comments

                Comment on this article