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      ZDHHC9: a promising therapeutic target for triple-negative breast cancer through immune modulation and immune checkpoint blockade resistance

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          Abstract

          Background

          Triple-negative breast cancer (TNBC) is a subtype of breast cancer with limited treatment options and poor prognosis. This study aimed to identify potential therapeutic targets based on the expression profiles of differentially expressed genes (DEGs) in TNBC.

          Methods

          The Limma package was used to identify DEGs in TCGA and GEO datasets. Immunohistochemical (IHC) analysis and western blotting were used to determine the expression of ZDHHC9 in TNBC tissues. Flow cytometry assay and tissue immunofluorescence analysis were used to detect infiltration of multiple immune cells in tumor tissue at different levels of ZDHHC9 expression.

          Results

          ZDHHC9 was identified as a key factor associated with resistance to ICB therapy through weighted gene co-expression network analysis (WGCNA) and single-cell RNA sequencing (scRNA-seq). Subsequently, immunohistochemical (IHC) analysis and western blotting verified that ZDHHC9 expression was elevated in TNBC cancer tissues and that elevated expression of ZDHHC9 was associated with the poor survival of patients with TNBC. Analysis of data from several public datasets revealed that patients with high ZDHHC9 expression had an increased proportion of Ki-67 + breast cancer cells and tended to be basal-like breast cancer. In addition, in vitro and in vivo experiments demonstrated that high expression of ZDHHC9 significantly predicted the efficacy and responsiveness of immunotherapy in TNBC.

          Conclusion

          These findings suggest that ZDHHC9 is a valuable marker for guiding the classification, diagnosis and prognosis of TNBC and developing specific targeted therapies.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s12672-023-00790-4.

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          Most cited references32

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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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            Cancer statistics, 2022

            Rebecca Siegel, Kimberly D Miller, Hannah Fuchs … (2022)
            Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths in the United States and compiles the most recent data on population-based cancer occurrence and outcomes. Incidence data (through 2018) were collected by the Surveillance, Epidemiology, and End Results program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2019) were collected by the National Center for Health Statistics. In 2022, 1,918,030 new cancer cases and 609,360 cancer deaths are projected to occur in the United States, including approximately 350 deaths per day from lung cancer, the leading cause of cancer death. Incidence during 2014 through 2018 continued a slow increase for female breast cancer (by 0.5% annually) and remained stable for prostate cancer, despite a 4% to 6% annual increase for advanced disease since 2011. Consequently, the proportion of prostate cancer diagnosed at a distant stage increased from 3.9% to 8.2% over the past decade. In contrast, lung cancer incidence continued to decline steeply for advanced disease while rates for localized-stage increased suddenly by 4.5% annually, contributing to gains both in the proportion of localized-stage diagnoses (from 17% in 2004 to 28% in 2018) and 3-year relative survival (from 21% to 31%). Mortality patterns reflect incidence trends, with declines accelerating for lung cancer, slowing for breast cancer, and stabilizing for prostate cancer. In summary, progress has stagnated for breast and prostate cancers but strengthened for lung cancer, coinciding with changes in medical practice related to cancer screening and/or treatment. More targeted cancer control interventions and investment in improved early detection and treatment would facilitate reductions in cancer mortality.
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              The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data.

              Ethan Cerami, Jianjiong Gao, Ugur Dogrusoz … (2012)
              The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications. © 2012 AACR.
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                Author and article information

                Contributors
                Zhongsheng Tong: tongzhongsheng@tjmuch.com
                Yanfang Yang: tjyangyf@126.com
                Journal
                Journal ID (nlm-ta): Discov Oncol
                Journal ID (iso-abbrev): Discov Oncol
                Title: Discover. Oncology
                Publisher: Springer US (New York )
                ISSN (Electronic): 2730-6011
                Publication date (Electronic): 24 October 2023
                Publication date PMC-release: 24 October 2023
                Publication date Collection: December 2023
                Volume: 14
                Electronic Location Identifier: 191
                Affiliations
                [1 ]GRID grid.265021.2, ISNI 0000 0000 9792 1228, Key Laboratory of Breast Cancer Prevention and Therapy, , Tianjin Medical University, Ministry of Education, ; North Huanhu West Road, Tianjin, 300060 China
                [2 ]GRID grid.411918.4, ISNI 0000 0004 1798 6427, National Clinical Research Center for Cancer, , Tianjin Cancer Hospital Airport Hospital, ; Tianjin, 300060 China
                [3 ]Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, ( https://ror.org/0152hn881) Tianjin, 300060 China
                Article
                Publisher ID: 790
                DOI: 10.1007/s12672-023-00790-4
                PMC ID: 10597932
                PubMed ID: 37875591
                SO-VID: 6cbef1e8-2c37-4e67-b490-4cbb16f77187
                Copyright © © Springer Science+Business Media, LLC 2023
                License:

                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
                Date received : 6 May 2023
                Date accepted : 14 September 2023
                Funding
                Funded by: Science & Technology Development Fund of Tianjin Education Commission for Higher Education
                Award ID: 2022KJ221
                Award Recipient :
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © Springer Science+Business Media, LLC 2023

                Keywords: tnbc,zdhhc9,immune biomarkers,immunotherapy resistance
                Data availability:
                Keywords: tnbc, zdhhc9, immune biomarkers, immunotherapy resistance

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