A Pyroptosis-Related Gene Signature for Predicting Survival in Glioblastoma

Overview

Journal Front Oncol

Specialty Oncology

Date 2021 Sep 6

PMID 34485134

Citations 39

Authors

Xin-Yu Li

Lu-Yu Zhang

Xue-Yuan Li

Xi-Tao Yang

Li-Xin Su

Affiliations

Soon will be listed here.

Abstract

Background: In this study, a prognostic model based on pyroptosis-related genes was established to predict overall survival (OS) in patients with glioblastoma (GBM).

Methods: The gene expression data and clinical information of GBM patients were obtained from The Cancer Genome Atlas (TCGA), and bioinformatics analysis of differentially expressed genes was performed. LASSO Cox regression model was used to construct a three-pyroptosis-related gene signature, and validation was performed using an experimental cohort.

Results: A total of three pyroptosis-related genes (, , and ) were used to construct a survival prognostic model, and experimental validation was performed using an experimental cohort. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curves (AUC) was 0.921, 0.840, and 0.905 at 1, 3, and 5 years, respectively. Functional analysis revealed that T-cell activation, regulation of T-cell activation, leukocyte cell-cell adhesion, and positive regulation of cell adhesion among other immune-related functions were enriched, and immune-related processes were different between the two risk groups.

Conclusion: In this study, a novel prognostic model based on three pyroptosis-related genes is constructed and used to predict the prognosis of GBM patients. The model can accurately and conveniently predict the 1-, 3-, and 5-year OS of GBM patients.

Citing Articles

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

Wang H, Yan L, Liu L, Lu X, Chen Y, Zhang Q PeerJ. 2023; 11:e16412.

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Liu J, Wu R, Yuan S, Kelleher R, Chen S, Chen R Pharmaceuticals (Basel). 2023; 16(11).

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Liu H, Zeng Z, Sun P Aging (Albany NY). 2023; 15(18):9842-9857.

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