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      DC vaccines loaded with glioma cells killed by photodynamic therapy induce Th17 anti-tumor immunity and provide a four-gene signature for glioma prognosis

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          Abstract

          Gliomas, the most frequent type of primary tumor of the central nervous system in adults, results in significant morbidity and mortality. Despite the development of novel, complex, multidisciplinary, and targeted therapies, glioma therapy has not progressed much over the last decades. Therefore, there is an urgent need to develop novel patient-adjusted immunotherapies that actively stimulate antitumor T cells, generate long-term memory, and result in significant clinical benefits. This work aimed to investigate the efficacy and molecular mechanism of dendritic cell (DC) vaccines loaded with glioma cells undergoing immunogenic cell death (ICD) induced by photosens-based photodynamic therapy (PS-PDT) and to identify reliable prognostic gene signatures for predicting the overall survival of patients. Analysis of the transcriptional program of the ICD-based DC vaccine led to the identification of robust induction of Th17 signature when used as a vaccine. These DCs demonstrate retinoic acid receptor-related orphan receptor-γt dependent efficacy in an orthotopic mouse model. Moreover, comparative analysis of the transcriptome program of the ICD-based DC vaccine with transcriptome data from the TCGA-LGG dataset identified a four-gene signature (CFH, GALNT3, SMC4, VAV3) associated with overall survival of glioma patients. This model was validated on overall survival of CGGA-LGG, TCGA-GBM, and CGGA-GBM datasets to determine whether it has a similar prognostic value. To that end, the sensitivity and specificity of the prognostic model for predicting overall survival were evaluated by calculating the area under the curve of the time-dependent receiver operating characteristic curve. The values of area under the curve for TCGA-LGG, CGGA-LGG, TCGA-GBM, and CGGA-GBM for predicting five-year survival rates were, respectively, 0.75, 0.73, 0.9, and 0.69. These data open attractive prospects for improving glioma therapy by employing ICD and PS-PDT-based DC vaccines to induce Th17 immunity and to use this prognostic model to predict the overall survival of glioma patients.

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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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            Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                Dmitri.Krysko@UGent.be
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                21 December 2022
                21 December 2022
                December 2022
                : 13
                : 12
                : 1062
                Affiliations
                [1 ]GRID grid.28171.3d, ISNI 0000 0001 0344 908X, Institute of Biology and Biomedicine, , National Research Lobachevsky State University of Nizhny Novgorod, ; Nizhny Novgorod, Russia
                [2 ]GRID grid.5342.0, ISNI 0000 0001 2069 7798, Cell Death Investigation and Therapy (CDIT) Laboratory, Department of Human Structure and Repair, , Ghent University, ; Ghent, Belgium
                [3 ]GRID grid.28171.3d, ISNI 0000 0001 0344 908X, Institute of Information Technology, Mathematics and Mechanics, , National Research Lobachevsky State University of Nizhny Novgorod, ; Nizhny Novgorod, Russia
                [4 ]GRID grid.510942.b, Cancer Research Institute Ghent, ; Ghent, Belgium
                [5 ]GRID grid.5342.0, ISNI 0000 0001 2069 7798, 4Brain Team, Department of Head and Skin, , Ghent University, ; Ghent, Belgium
                [6 ]GRID grid.5342.0, ISNI 0000 0001 2069 7798, Radiobiology Research Group, Department of Human Structure and Repair, , Ghent University, ; Ghent, Belgium
                [7 ]GRID grid.5342.0, ISNI 0000 0001 2069 7798, IBiTech-MEDISIP-Infinity Laboratory, Department of Electronics and Information Systems, , Ghent University, ; Ghent, Belgium
                [8 ]GRID grid.5342.0, ISNI 0000 0001 2069 7798, Upper Airways Research Laboratory, Department of Head and Skin, , Ghent University, ; Ghent, Belgium
                [9 ]GRID grid.5342.0, ISNI 0000 0001 2069 7798, Center for Medical Genetics Ghent (CMGG), Department of Biomolecular Medicine, , Ghent University, ; Ghent, Belgium
                [10 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Laboratory of Cell Death Research & Therapy, Department of Cellular and Molecular Medicine, , KU Leuven, ; Leuven, Belgium
                [11 ]GRID grid.511459.d, VIB Center for Cancer Biology Research, ; Leuven, Belgium
                [12 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Laboratory of Cell Stress & Immunity (CSI), Department of Cellular & Molecular Medicine, , KU Leuven, ; Leuven, Belgium
                Author information
                http://orcid.org/0000-0001-9759-6477
                http://orcid.org/0000-0002-3917-9592
                http://orcid.org/0000-0003-4463-5035
                http://orcid.org/0000-0002-3988-5980
                http://orcid.org/0000-0003-4742-1665
                http://orcid.org/0000-0003-1314-2115
                http://orcid.org/0000-0002-9976-9922
                http://orcid.org/0000-0002-9692-2047
                Article
                5514
                10.1038/s41419-022-05514-0
                9767932
                36539408
                4cee8ead-dcc7-408b-890e-b7435145043d
                © The Author(s) 2022

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 June 2022
                : 12 December 2022
                : 12 December 2022
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                © The Author(s) 2022

                Cell biology
                cns cancer,preclinical research,cancer immunotherapy,cell death and immune response,prognostic markers

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