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      Molecular Characteristics, Clinical Implication, and Cancer Immunity Interactions of Pyroptosis-Related Genes in Breast Cancer

      research-article
      Author(s): 1 , 2 , 3 , *
      Publication date (Electronic):
      Journal: Frontiers in Medicine
      Publisher: Frontiers Media S.A.
      Keywords: breast cancer, pyroptosis, signature, prognosis, immune, nomogram

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          Abstract

          Objective: Pyroptosis represents an emerging inflammatory form of programmed cell death. Herein, specific functions and clinical implications of pyroptosis-related genes were systematically characterized in breast cancer.

          Methods: Expression, somatic mutation and copy number variation of 33 pyroptosis-related genes were assessed in breast cancer from TCGA dataset. Their interactions, biological functions and prognostic values were then observed. By stepwise Cox regression analysis, a pyroptosis-related gene signature was generated. The predictive efficacy in survival was examined by survival analyses, ROCs, univariate and multivariate analyses and subgroup analyses. Associations between risk score (RS) and cancer immunity cycle, HLA, immune cell infiltrations, and immune checkpoints were analyzed.

          Results: Most of pyroptosis-related genes were abnormally expressed in breast cancer. CASP8, NLRC4, NLRP3, NLRP2, PLCG1, NLRP1, NLRP7, SCAF11, GSDMC, and NOD1 occurred somatic mutations as well as most of them had high frequency of CNV. There were closely interactions between them. These genes were distinctly enriched in immune-related processes. A three-gene signature was generated, containing IL-18, GSDMC, and TIRAP. High RS predicted poorer overall survival, progression, and recurrence. After verification, this RS was an independent and sensitive predictive index. This RS was negatively correlated to cancer immunity cycle. Also, low RS was characterized by high HLA, immune cell infiltrations and immune checkpoints. A nomogram including age and RS was generated for accurately predicting 5-, 8-, and 10-year survival probabilities.

          Conclusion: Pyroptosis-related genes exert key roles in cancer immunity and might be applied as a prognostic factor of breast cancer.

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          Most cited references41

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          A. Subramanian, P. Tamayo, V. K. Mootha … (2005)
          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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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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              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
                Journal
                Journal ID (nlm-ta): Front Med (Lausanne)
                Journal ID (iso-abbrev): Front Med (Lausanne)
                Journal ID (publisher-id): Front. Med.
                Title: Frontiers in Medicine
                Publisher: Frontiers Media S.A.
                ISSN (Electronic): 2296-858X
                Publication date (Electronic): 13 September 2021
                Publication date Collection: 2021
                Volume: 8
                Electronic Location Identifier: 702638
                Affiliations
                [1] 1Internal Medicine II, The Third Affiliated Hospital of Shandong First Medical University (Affiliated Hospital of Shandong Academy of Medical Sciences) , Jinan, China
                [2] 2Department of Gastrointestinal Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University , Jinan, China
                [3] 3Department of Medical Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences , Jinan, China
                Author notes

                Edited by: Fu Wang, Xi'an Jiaotong University, China

                Reviewed by: Chunlin Ou, Central South University, China; Lijie Chen, ShengJing Hospital of China Medical University, China

                *Correspondence: Ling Qiang doctorqqll@ 123456126.com

                This article was submitted to Precision Medicine, a section of the journal Frontiers in Medicine

                Article
                DOI: 10.3389/fmed.2021.702638
                PMC ID: 8473741
                PubMed ID: 34589498
                SO-VID: 15c514a3-fa3c-4419-a3c5-db041462c7d7
                Copyright © Copyright © 2021 Xu, Ji and Qiang.
                License:

                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 : 29 April 2021
                Date accepted : 17 August 2021
                Page count
                Figures: 9, Tables: 1, Equations: 0, References: 41, Pages: 15, Words: 6756
                Categories
                Subject: Medicine
                Subject: Original Research

                Keywords: breast cancer,pyroptosis,signature,prognosis,immune,nomogram
                Data availability:
                Keywords: breast cancer, pyroptosis, signature, prognosis, immune, nomogram

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