A pyroptosis gene-based prognostic model for predicting survival in low-grade glioma

Hua Wang; Lin Yan; Lixiao Liu; Xianghe Lu; Yingyu Chen; Qian Zhang; Mengyu Chen; Lin Cai; Zhang'an Dai

doi:10.7717/peerj.16412

A pyroptosis gene-based prognostic model for predicting survival in low-grade glioma

PeerJ. 2023 Nov 13:11:e16412. doi: 10.7717/peerj.16412. eCollection 2023.

Authors

Hua Wang^#^{1

2}, Lin Yan^#³, Lixiao Liu⁴, Xianghe Lu^{1

2}, Yingyu Chen¹, Qian Zhang¹, Mengyu Chen¹, Lin Cai⁵, Zhang'an Dai^{1

2}

Affiliations

¹ Department of Neurosurgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
² Zhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
³ Department of Breast Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
⁴ Department of Gynaecology and Obstetrics, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
⁵ Department of Neurosurgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

^# Contributed equally.

Abstract

Background: Pyroptosis, a lytic form of programmed cell death initiated by inflammasomes, has been reported to be closely associated with tumor proliferation, invasion and metastasis. However, the roles of pyroptosis genes (PGs) in low-grade glioma (LGG) remain unclear.

Methods: We obtained information for 1,681 samples, including the mRNA expression profiles of LGGs and normal brain tissues and the relevant corresponding clinical information from two public datasets, TCGA and GTEx, and identified 45 differentially expressed pyroptosis genes (DEPGs). Among these DEPGs, nine hub pyroptosis genes (HPGs) were identified and used to construct a genetic risk scoring model. A total of 476 patients, selected as the training group, were divided into low-risk and high-risk groups according to the risk score. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves verified the accuracy of the model, and a nomogram combining the risk score and clinicopathological characteristics was used to predict the overall survival (OS) of LGG patients. In addition, a cohort from the Gene Expression Omnibus (GEO) database was selected as a validation group to verify the stability of the model. qRT-PCR was used to analyze the gene expression levels of nine HPGs in paracancerous and tumor tissues from 10 LGG patients.

Results: Survival analysis showed that, compared with patients in the low-risk group, patients in the high-risk group had a poorer prognosis. A risk score model combining PG expression levels with clinical features was considered an independent risk factor. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that immune-related genes were enriched among the DEPGs and that immune activity was increased in the high-risk group.

Conclusion: In summary, we successfully constructed a model to predict the prognosis of LGG patients, which will help to promote individualized treatment and provide potential new targets for immunotherapy.

Keywords: Pyroptosis; Individualized treatment; Low-grade gliomas; Prognosis; Risk scoring model.

MeSH terms

Glioma* / genetics
Humans
Nomograms
Prognosis
Pyroptosis*
Risk Factors

Grants and funding

This work was supported by the Wenzhou Municipal Science and Technology Bureau of China (grant number Y2020063). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.