Identification of a pyroptosis related gene signature for predicting prognosis and estimating tumor immune microenvironment in bladder cancer

Abstract

Background: Recently, there are growing evidence indicated that pyroptosis play a critical role in the incidence of many diseases. Here, we aimed to identify the specific function and prognosis predictive of pyroptosis-related genes (PRGs) in bladder cancer (BLCA) patients.

Methods: The gene expression and corresponding clinical data of BLCA patients were obtained from The Cancer Genome Atlas (TCGA), and the expression level of PRGs was identified between normal and tumor tissues. Furthermore, univariate Cox regression was conducted to filter the PRGs related to overall survival, and LASSO Cox regression was subsequently conducted to establish the PRGs risk model. Besides, the correlation of risk score with patients' clinical features, tumor mutational burden (TMB) as well as tumor microenvironment (TME) was also investigated.

Results: A total of 6 PRGs was used to establish the risk prognostic model. According the median value of risk score, the patients were classified into low- and high-risk subgroup. Kaplan-Meier survival analysis revealed that the BLCA patients in low-risk group exhibited a better survival prognosis compared with high-risk group. More important, after adjusting for age, gender, tumor grade, and clinical stage, the risk score resulted as an independent factor affecting the clinical prognosis of BLCA patients. In addition, the PRGs risk signature was also correlated with immune cell infiltration, TMB and TME.

Conclusions: The present study offered a novel PRGs risk model to access the clinical prognosis of BLCA and provided new insight for future study to improve overall survival and responses to cancer therapy targeting pyroptosis.

Keywords: Bladder cancer (BLCA); biomarker; prognosis; pyroptosis.

Figure 1

Different expression level of PRGs and interactions among them. (A) Different expression level of PRGs between adjacent normal tissues (n=19) and bladder tumor tissues (n=411). P values were notated as follow: (B) Heatmap of different expression level of PRGs between adjacent normal and bladder tumor tissues. (C) The correlation network of different expression PRGs. PRG, pyroptosis-related gene. *, P<0.05; **, P<0.01; ***, P<0.001.

Figure 2

Establishment of risk model based on PRGs in TCGA training cohort. (A) Forest plots for univariate Cox regression analysis. (B) LASSO regression of the overall survival related 8 PRGs. (C) Distribution of each BLCA samples with different risk score (up); distribution of survival status for each sample with different risk score (dead or alive, middle); expression level of 6 PRGs and clinical features in high- and low-risk subgroups (down). (D) The survival status curves for BLCA samples with different risk score in training cohort. (E) PCA plot for each BLCA samples with different risk score in training cohort. (F) ROC curves tested the specificity and sensitivity of the risk score model. (G) Univariate analysis for the overall survival of BLCA samples in TCGA training cohort. (H) Multivariate analysis for the overall survival of BLCA samples in TCGA training cohort. **, P<0.01; ***, P<0.001. PRG, pyroptosis-related gene; TCGA, The Cancer Genome Atlas; BLCA, bladder cancer; PCA, principal component analysis; ROC, receiver operating characteristic; AUC, area under the ROC curve.

Figure 3

Validation of the PRGs risk mode in TCGA testing cohort. (A) The survival status curves for BLCA samples with different risk score in testing cohort. (B) Distribution of samples with different risk score (up); distribution of survival status for each sample with different risk score (dead or alive, middle); expression level of 6 PRGs and clinical features in high- and low-risk subgroups (down). (C) ROC curves tested the sensitivity and specificity of the risk score model. (D) PCA plot for each BLCA samples with different risk score in testing cohort. (E) Univariate analysis for the prognosis of BLCA samples in testing cohort. (F) Multivariate analysis for the prognosis of BLCA samples in testing cohort. **, P<0.01. PRG, pyroptosis-related gene; TCGA, The Cancer Genome Atlas; BLCA, bladder cancer; PCA, principal component analysis; ROC, receiver operating characteristic; AUC, area under the ROC curve.

Figure 4

Validation of the PRGs risk model TCGA entire cohort. (A) The survival status curves for BLCA samples with different risk score in TCGA BLCA entire cohort. (B) Distribution of samples with different the risk score (up); distribution of survival status for each sample with different risk score (dead or alive, middle); expression level of 6 PRGs and clinical features in high- and low-risk subgroups (down). (C) ROC curves tested the specificity and sensitivity of the risk score model. (D) PCA plot for each BLCA samples with different risk score in entire cohort. (E) Univariate analysis for the prognosis of BLCA samples in entire cohort. (F) Multivariate analysis for the prognosis of BLCA samples in entire cohort. *, P<0.05; ***, P<0.001. PRG, pyroptosis-related gene; TCGA, The Cancer Genome Atlas; BLCA, bladder cancer; PCA, principal component analysis; ROC, receiver operating characteristic; AUC, area under the ROC curve.

Figure 5

Expression of risk score in BLCA patients stratified by different clinical characteristics (age, gender, grade and stage). (A) Age at diagnosis; (B) patients’ gender; (C) tumor grade; (D) tumor clinical stage; (E) tumor T stage; (F) tumor N stage. BLCA, bladder cancer.

Figure 6

Functional analyses of DEGs in high- and low-risk score subtypes in TCGA BLCA entire cohort. (A) Comparative analysis of the top 5 enrichment in biological processes, cellular components and molecular functions for DEGs based on the risk score. (B) Comparative analysis of the top 20 enriched KEGG pathways for DEGs. (C) Comparative analysis of the top 8 significantly enriched pathways of GSEA results. MHC, main histocompatibility complex; ECM, extracellular matrix; IgA, immunoglobin A; DEG, differentially expressed gene; TCGA, The Cancer Genome Atlas; BLCA, bladder cancer; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, gene set enrichment analysis.

Figure 7

Correlation of the PRGs risk score with TMB and TME in TCGA BLCA entire cohort. (A) Waterfall showing the top 20 mutated genes in low-risk subgroup. (B) Waterfall showing the top 20 mutated genes in high-risk subgroup. (C) Boxplots for the comparison of TMB between low- and high-risk subgroup. (D) The correlation between TMB and PRGs risk score in TCGA BLCA cohort. Violin plots for the comparison of estimate score (E), immune score (F), stromal score (G) and tumor purity (H) between low- and high-risk subgroup. Comparison of the immune cells ssGSEA scores (I) and immune related function (J) between high- and low-risk subgroup. (K) The heatmap of the estimate, immune, stromal score, tumor purity and ssGSEA scores of immune cells between low- and high-risk subgroup. (L) The heatmap of the estimate, immune, stromal score, tumor purity and ssGSEA scores of immune related function between low- and high-risk subgroup. *, P<0.05; **, P<0.01; ***, P<0.001. PRG, pyroptosis-related gene; TMB, tumor mutational burden; TME, tumor microenvironment; TCGA, The Cancer Genome Atlas; BLCA, bladder cancer; ssGSEA, single sample gene set enrichment analysis; aDC, activated dendritic cell; DC, dendritic cells; iDC, immature dendritic cell; NK, natural killer; pDCs, plasmacytoid dendritic cells; Tfh, T follicular helper; Th2, T helper 2; TIL, tumor-infiltrating lymphocyte; APC, antigen presenting cell; CCR, cytokine-cytokine receptor; HLA, human leukocyte antigen; MHC, main histocompatibility complex; IFN, interferon.

Figure 8

Immune cell infiltrations analyses between high- and low-risk subgroup in TCGA entire cohort. (A) Overall view of infiltration proportion for 22 types of immune cell. (B) Boxplots for 22 types of immune cell infiltrations between high- and low-risk score patients. (C) Overall view of correlation with PRGs risk score and infiltration levels of immune cell. Correlation of PRGs risk score and immune cell infiltrations for macrophages M0 (D), macrophages M2 (E), T cells CD4 memory activated (F), T cells CD8 (G), T cells follicular helper (H) and T cells regulatory (Tregs) (I). *, P<0.05; **, P<0.01; ***, P<0.001. NK, natural killer; TCGA, The Cancer Genome Atlas; PRG, pyroptosis-related gene.

Figure 9

Correlation of overall survival with immune cell infiltrations in BLCA entire cohort. (A) B cells memory; (B) macrophages M0; (C) macrophages M2; (D) mast cells resting; (E) neutrophils; (F) plasma cells; (G) T cells CD4 memory activated; (H) T cells CD8; (I) T cells follicular helper. BLCA, bladder cancer.