Comprehensive analysis of a TNF family based-signature in diffuse gliomas with regard to prognosis and immune significance

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Abstract

Background

Several studies have shown that members of the tumor necrosis factor (TNF) family play an important role in cancer immunoregulation, and trials targeting these molecules are already underway. Our study aimed to integrate and analyze the expression patterns and clinical significance of TNF family-related genes in gliomas.

Methods

A total of 1749 gliomas from 4 datasets were enrolled in our study, including the Cancer Genome Atlas (TCGA) dataset as the training cohort and the other three datasets (CGGA, GSE16011, and Rembrandt) as validation cohorts. Clinical information, RNA expression data, and genomic profile were collected for analysis. We screened the signature gene set by Cox proportional hazards modelling. We evaluated the prognostic value of the signature by Kaplan–Meier analysis and timeROC curve. Gene Ontology (GO) and Gene set enrichment analysis (GSEA) analysis were performed for functional annotation. CIBERSORT algorithm and inflammatory metagenes were used to reveal immune characteristics.

Results

In gliomas, the expression of most TNF family members was positively correlated. Univariate analysis showed that most TNF family members were related to the overall survival of patients. Then through the LASSO regression model, we developed a TNF family-based signature, which was related to clinical, molecular, and genetic characteristics of patients with glioma. Moreover, the signature was found to be an independent prognostic marker through survival curve analysis and Cox regression analysis. Furthermore, a nomogram prognostic model was constructed to predict individual survival rates at 1, 3 and 5 years. Functional annotation analysis revealed that the immune and inflammatory response pathways were enriched in the high-risk group. Immunological analysis showed the immunosuppressive status in the high-risk group.

Conclusions

We developed a TNF family-based signature to predict the prognosis of patients with glioma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12964-021-00814-y.

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Most cited references 50

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The blockade of immune checkpoints in cancer immunotherapy.

Drew M Pardoll (2012)

Among the most promising approaches to activating therapeutic antitumour immunity is the blockade of immune checkpoints. Immune checkpoints refer to a plethora of inhibitory pathways hardwired into the immune system that are crucial for maintaining self-tolerance and modulating the duration and amplitude of physiological immune responses in peripheral tissues in order to minimize collateral tissue damage. It is now clear that tumours co-opt certain immune-checkpoint pathways as a major mechanism of immune resistance, particularly against T cells that are specific for tumour antigens. Because many of the immune checkpoints are initiated by ligand-receptor interactions, they can be readily blocked by antibodies or modulated by recombinant forms of ligands or receptors. Cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) antibodies were the first of this class of immunotherapeutics to achieve US Food and Drug Administration (FDA) approval. Preliminary clinical findings with blockers of additional immune-checkpoint proteins, such as programmed cell death protein 1 (PD1), indicate broad and diverse opportunities to enhance antitumour immunity with the potential to produce durable clinical responses.

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Robust enumeration of cell subsets from tissue expression profiles

Aaron Newman, Chih Long Liu, Michael Green … (2015)

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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Is Open Access

GSVA: gene set variation analysis for microarray and RNA-Seq data

Sonja Hänzelmann, Robert Castelo, Justin Guinney (2013)

Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

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Author and article information

Contributors

Qiang-Wei Wang: qiangwei1217@126.com

Wei-Wei Lin: lindw@zju.edu.cn

Yong-Jian Zhu:

ORCID: http://orcid.org/0000-0002-5977-3257

neurosurgery@zju.edu.cn , neurosurgery_zyj@163.com

Journal

Journal ID (nlm-ta): Cell Commun Signal

Journal ID (iso-abbrev): Cell Commun Signal

Title: Cell Communication and Signaling : CCS

Publisher: BioMed Central (London )

ISSN (Electronic): 1478-811X

Publication date (Electronic): 9 January 2022

Publication date PMC-release: 9 January 2022

Publication date Collection: 2022

Volume: 20

Electronic Location Identifier: 6

Affiliations

[1 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Department of Neurosurgery, The Second Affiliated Hospital, , Zhejiang University School of Medicine, ; No. 88 Jiefang Road, Hangzhou, 310009 China

[2 ]Chinese Glioma Genome Atlas Network (CGGA) and Asian Glioma Genome Atlas Network (AGGA), Beijing, 100070 China

Author information

Yong-Jian Zhu http://orcid.org/0000-0002-5977-3257

Article

Publisher ID: 814

DOI: 10.1186/s12964-021-00814-y

PMC ID: 8744324

PubMed ID: 35000592

SO-VID: 9601c76a-7b72-4201-b411-a17111875cd5

License:

Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 29 July 2021

Date accepted : 2 December 2021

Funding

Funded by: provincial key r&d program, science and technology department of zhejiang province

Award ID: 2017C03018

Award Recipient : Yong-Jian Zhu

Custom metadata

ScienceOpen disciplines: Cell biology

Keywords: glioma,tumor necrosis factor,prognosis,tumor immunity

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ScienceOpen disciplines: Cell biology

Keywords: glioma, tumor necrosis factor, prognosis, tumor immunity

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Comprehensive analysis of a TNF family based-signature in diffuse gliomas with regard to prognosis and immune significance

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Abstract

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Conclusions

Supplementary Information

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Most cited references 50

The blockade of immune checkpoints in cancer immunotherapy.

Robust enumeration of cell subsets from tissue expression profiles

GSVA: gene set variation analysis for microarray and RNA-Seq data

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Cited by 12

Most referenced authors 1,004