Identification and Validation of a Gene Signature for Lower-Grade Gliomas Based on Pyroptosis-Related Genes to Predict Survival and Response to Immune Checkpoint Inhibitors

Abstract

Pyroptosis plays a critical role in the immune response to immune checkpoint inhibitors (ICIs) by mediating the tumor immune microenvironment. However, the impact of pyroptosis-related biomarkers on the prognosis and efficacy of ICIs in patients with lower-grade gliomas (LGGs) is unclear. An unsupervised clustering analysis identified pyroptosis-related subtypes (PRSs) based on the expression profile of 47 pyroptosis-related genes in The Cancer Genome Atlas-LGG cohort. A PRS gene signature was established using univariate Cox regression, random survival forest, least absolute shrinkage and selection operator, and stepwise multivariable Cox regression analyses. The predictive power of this signature was validated in the Chinese Glioma Genome Atlas database. We also investigated the differences between high- and low-risk groups in terms of the tumor immune microenvironment, tumor mutation, and response to target therapy and ICIs. The PRS gene signature comprised eight PRS genes, which independently predicted the prognosis of LGG patients. High-risk patients had a worse overall survival than did the low-risk patients. The high-risk group also displayed a higher proportion of M1 macrophages and CD8⁺ T cells and higher immune scores, tumor mutational burden, immunophenoscore, IMmuno-PREdictive Score, MHC I association immune score, and T cell-inflamed gene expression profile scores, but lower suppressor cells scores, and were more suitable candidates for ICI treatment. Higher risk scores were more frequent in patients who responded to ICIs using data from the ImmuCellAI website. The presently established PRS gene signature can be validated in melanoma patients treated with real ICI treatment. This signature is valuable in predicting prognosis and ICI treatment of LGG patients, pending further prospective verification.

Figure 1

The experimental flow chart in this study.

Figure 2

Identification and characteristics of PRS. (a) Consensus matrix heatmap of two subtypes (k = 2). (b) The correlation between CDF and consensus index under consensus CDF curve when k = 2–7. (c) The relative change in area under the CDF curve when k = 2–7. (d) Kaplan-Meier survival analysis of OS between Cluster1 and Cluster2. (e) The volcano plot of differentially expressed PRS genes. (f) GO enrichment analysis of differentially expressed PRS genes. (g) KEGG pathway enrichment analysis of differentially expressed PRS genes. BP, biology process; CC, cellular component; MF, molecular function.

Figure 3

The construction of PRS gene signature. (a) The change of error rate with the number of trees in the RSF model. (b) The relative importance score distribution of PRS genes in the RSF model. (c) LASSO coefficient profiles of the 204 prognostic PRS genes. (d) Partial likelihood deviance of genes revealed by LASSO. (e) Forest plot of each gene in eight-gene PRS signature after stepwise multivariate Cox regression analysis.

Figure 4

The validation of an eight-gene PRS signature. (a) Alluvial diagram of different subtypes with different risk scores and survival outcomes. (b) Kaplan-Meier survival curves of OS between the high-risk group and low-risk group in TCGA. (c) Kaplan-Meier survival curves of OS between the high-risk group and low-risk group in CGGA. (d) The distribution of risk scores in TCGA. (e) The distribution of risk scores in CGGA. (f) The time-dependent ROC curves of 1-, 3-, and 5-year in TCGA. (g) The relationship between survival status, survival time, and risk score in TCGA. (h) The relationship between survival status, survival time, and risk score in CGGA. (i) The time-dependent ROC curves of 1-, 3-, and 5-year in CGGA.

Figure 5

The eight-gene PRS signature and clinical features. (a) The forest plot of univariate Cox regression analysis in TCGA. (b) The forest plot of multivariate Cox regression analysis in TCGA. (c) The forest plot of univariate Cox regression analysis in CGGA. (d) The forest plot of multivariate Cox regression analysis in CGGA. (e-g) Risk score in LGG patients with different age, gender, and grade groups in TCGA. (h-j) Risk score in LGG patients with different age, gender, and grade groups in CGGA.

Figure 6

The relationship between tumor-infiltrating immune cells and PRS gene signature. (a) The proportion of tumor-infiltrating immune cells between the low-risk group and high-risk group in TCGA. (b) The proportion of tumor-infiltrating immune cells between the low-risk group and high-risk group in CGGA. Data in (a-b) were analyzed by Wilcoxon test; ns, no significance; ^∗p < 0.05, ^∗∗p < 0.01, and ^∗∗∗p < 0.001.

Figure 7

The relationship between TME and OS, and PRS gene signature. (a-b) Kaplan-Meier survival analysis of immune score and stromal score in TCGA. (c-d) Kaplan-Meier survival analysis of immune score and stromal score in CGGA. (e-f) The immune score and stromal score between the high-risk group and low-risk group in TCGA. (g-h) The immune score and stromal score between the high-risk group and low-risk group in CGGA. Data in (e-h) were analyzed by Wilcoxon test; ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001, and ^∗∗∗∗p < 0.0001.

Figure 8

The relationship between tumor mutation and PRS gene signature. (a) Mutation profile of top 10 mutated genes in the low-risk group. (b) Mutation profile of top 10 mutated genes in the high-risk group. (c) Kaplan-Meier survival analysis between patients with wild-type IDH1 and mutant IDH1. (d) Kaplan-Meier survival analysis between patients with wild-type CIC and mutant CIC. (e) Kaplan-Meier survival analysis between the low-TMB group and high-TMB group. (f) The TMB between the low-risk group and high-risk group.

Figure 9

LGG patients' response to ICIs and PRS gene signature. (a) The expressions of 11 ICs between the low-risk group and high-risk group in TCGA. (b) The expressions of 11 ICs between the low-risk group and high-risk group in CGGA. (c-e) The MHC score, EC score, and SC score between the low-risk group and high-risk group. (f) Risk score in LGG patients with a different ICI response status. (g-j) The MIAS, GEP score, IPS, and IMPRES between the low-risk group and high-risk group. Data in (a-b) were analyzed by Wilcoxon test; ^∗p < 0.05, ^∗∗p < 0.01, ^∗∗∗p < 0.001, and ^∗∗∗∗p < 0.0001.

Figure 10

The role of PRS gene signature in the prediction of immunotherapeutic benefits and targeted therapy. (a) Kaplan-Meier survival curves for 49 melanoma patients with high and low PRG scores in GSE91061. (b) Rate of 49 melanoma patients' clinical responses to ICI treatment in high and low PRG scores in GSE91061. (c) PRG score in 49 melanoma patients with a different ICI response status in GSE91061. (d) Lapatinib IC50 value of LGG patients between the high-risk group and low-risk group.