Immunogenic cell death-related risk signature predicts prognosis and characterizes the tumour microenvironment in lower-grade glioma

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Abstract

Lower-grade glioma (LGG) is a common malignant primary tumour in the central nervous system, and most patients eventually develop highly aggressive gliomas despite comprehensive traditional treatment. Tumour molecular subtypes and prognostic biomarkers play a crucial role in LGG diagnosis and treatment. Therefore, the identification of novel biomarkers in LGG patients is crucial for predicting the prognosis of glioma. Immunogenic cell death (ICD) is defined as regulated cell death that is sufficient to activate the adaptive immune response of immunocompetent hosts. The combination of ICD and immunotherapy might exert a greater and more persistent antitumour effect in gliomas. In our study, we explored the expression, function, and genetic alterations of 34 ICD-related genes. Using 12 ICD-related genes, including IL17RA, IL1R1, EIF2AK3, CD4, PRF1, CXCR3, CD8A, BAX, PDIA3, CASP8, MYD88, and CASP1, we constructed and validated an ICD-related risk signature via least absolute shrinkage and selection operator (LASSO) Cox regression analysis. All the information was obtained from public databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and the Chinese Glioma Genome Atlas (CGGA) databases. Our results revealed that ICD-high risk groups have a poor prognosis and might be more sensitive to immune checkpoint blockade (ICB) immunotherapy. In addition, ICD-high risk groups were associated with 1p19q noncodeletion, higher WHO grade, wild type IDH, and an immunosuppressive tumour microenvironment. We verified the prognostic value of 12 ICD-related genes in TCGA and CGGA databases. Immunohistochemistry was performed to verify the expression of several ICD-related genes at the protein level. Our study provides a novel and comprehensive perspective to elucidate the underlying mechanisms of LGG prognosis and direction for future individualized cancer immunotherapy.

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The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

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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Is Open Access

limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Contributors

Jiayang Cai: URI : https://loop.frontiersin.org/people/1925884

Yuanyuan Hu: URI : https://loop.frontiersin.org/people/1991967

Zhang Ye: URI : https://loop.frontiersin.org/people/1861757

Liguo Ye: URI : https://loop.frontiersin.org/people/1106354

Lun Gao: URI : https://loop.frontiersin.org/people/1041335

Yixuan Wang: URI : https://loop.frontiersin.org/people/1438903

Qian sun: URI : https://loop.frontiersin.org/people/1440649

Qianxue Chen: URI : https://loop.frontiersin.org/people/717605

Journal

Journal ID (nlm-ta): Front Immunol

Journal ID (iso-abbrev): Front Immunol

Journal ID (publisher-id): Front. Immunol.

Title: Frontiers in Immunology

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1664-3224

Publication date (Electronic): 17 October 2022

Publication date Collection: 2022

Volume: 13

Electronic Location Identifier: 1011757

Affiliations

[1] ¹ Department of Neurosurgery, Renmin Hospital of Wuhan University , Wuhan, China

[2] ² Central Laboratory, Renmin Hospital of Wuhan University , Wuhan, China

[3] ³ Department of Ophthalmology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology , Wuhan, China

Author notes

Edited by: Lucy Frances Stead, University of Leeds, United Kingdom

Reviewed by: Changgang Sun, Affiliated Hospital of Weifang Medical University, China; Ding Hu, Qingdao University, China; Jin Wang, Jinan University, China

*Correspondence: Qianxue Chen, chenqx666@ 123456whu.edu.cn ; Ji’an Yang, jayang@ 123456whu.edu.cn

†These authors share first authorship

This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Article

DOI: 10.3389/fimmu.2022.1011757

PMC ID: 9618960

PubMed ID: 36325335

SO-VID: a5d381b3-9d8b-44ba-a8fb-0faf8a0a7f8e

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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 04 August 2022

Date accepted : 30 September 2022

Page count

Figures: 13, Tables: 0, Equations: 1, References: 35, Pages: 18, Words: 5869

Funding

Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;

Immunogenic cell death-related risk signature predicts prognosis and characterizes the tumour microenvironment in lower-grade glioma

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Cancer Immunotherapy

Most cited references 35

Hallmarks of Cancer: The Next Generation

clusterProfiler: an R package for comparing biological themes among gene clusters.

limma powers differential expression analyses for RNA-sequencing and microarray studies

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