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      Downregulation of HNRNPA1 induced neoantigen generation via regulating alternative splicing

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          Abstract

          Background

          Immunotherapies effectively treat human malignancies, but the low response and resistance are major obstacles. Neoantigen is an emerging target for tumor immunotherapy that can enhance anti-tumor immunity and improve immunotherapy. Aberrant alternative splicing is an important source of neoantigens. HNRNPA1, an RNA splicing factor, was found to be upregulated in the majority of tumors and play an important role in the tumor immunosuppressive microenvironment.

          Methods

          Whole transcriptome sequencing was performed on shHNRNPA1 SKOV3 cells and transcriptomic data of shHNRNPA1 HepG2, MCF-7M, K562, and B-LL cells were downloaded from the GEO database. Enrichment analysis was performed to elucidate the mechanisms underlying the activation of anti-tumor immunity induced by HNRNPA1 knockdown. mRNA alternative splicing was analyzed and neoantigens were predicted by JCAST v.0.3.5 and Immune epitope database. The immunogenicity of candidate neoantigens was calculated by Class I pMHC Immunogenicity and validated by the IFN-γ ELISpot assay. The effect of shHNRNPA1 on tumor growth and immune cells in vivo was evaluated by xenograft model combined with immunohistochemistry.

          Results

          HNRNPA1 was upregulated in a majority of malignancies and correlated with immunosuppressive status of the tumor immune microenvironment. Downregulation of HNRNPA1 could induce the activation of immune-related pathways and biological processes. Disruption of HNRNPA1 resulted in aberrant alternative splicing events and generation of immunogenic neoantigens. Downregulation of HNRNPA1 inhibited tumor growth and increased CD8 + T cell infiltration in vivo.

          Conclusion

          Our study demonstrated that targeting HNRNPA1 could produce immunogenic neoantigens that elicit anti-tumor immunity by inducing abnormal mRNA splicing. It suggests that HNRNPA1 may be a potential target for immunotherapy.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s10020-024-00849-0.

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          Most cited references36

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            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.
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              Robust enumeration of cell subsets from tissue expression profiles

              We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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                Author and article information

                Contributors
                mdcheng18@tongji.edu.cn
                lshup@tongji.edu.cn
                Journal
                Mol Med
                Mol Med
                Molecular Medicine
                BioMed Central (London )
                1076-1551
                1528-3658
                12 June 2024
                12 June 2024
                2024
                : 30
                : 85
                Affiliations
                [1 ]GRID grid.24516.34, ISNI 0000000123704535, Department of Obstetrics and Gynecology, Shanghai Tenth People’s Hospital, School of Medicine, , Tongji University, ; Shanghai, 200072 China
                [2 ]Anhui University of Science and Technology, ( https://ror.org/00q9atg80) Huainan, 232001 China
                Author information
                http://orcid.org/0000-0002-9276-7086
                Article
                849
                10.1186/s10020-024-00849-0
                11167825
                38867190
                c22ab07d-35a9-49f7-a9cc-a4000754d1c3
                © 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
                : 5 February 2024
                : 28 May 2024
                Funding
                Funded by: Climbing talent program
                Award ID: 2021SYPDRC006
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100012226, Fundamental Research Funds for the Central Universities;
                Award ID: 22120220601
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 82103337
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Feinstein Institute for Medical Research 2024

                alternative splicing,cancer immunology,hnrnpa1,neoantigen

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