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. 2022 Jan 16:15:345-362.
doi: 10.2147/JIR.S349186. eCollection 2022.

Pyroptosis-Mediated Molecular Subtypes are Characterized by Distinct Tumor Microenvironment Infiltration Characteristics in Breast Cancer

Lijun Xu  1 , Yaomin Hu  1 , Wenwen Liu  1
Affiliations

Pyroptosis-Mediated Molecular Subtypes are Characterized by Distinct Tumor Microenvironment Infiltration Characteristics in Breast Cancer

Lijun Xu et al. J Inflamm Res. .

Abstract

Background: Numerous reports have highlighted that pyroptosis is closely linked to tumorigenesis and drug resistance of tumors. However, the potential role of pyroptosis in regulating immune cell infiltration in tumor microenvironment (TME) remains unclear.

Methods: Here, we performed consensus clustering analysis based on the expression of 10 typical pyroptosis-related regulators (PRRs) to construct pyroptosis-mediated tumor pattern clusters and pyroptosis-related gene signature in breast cancer (BC). GSVA combined with ssGSEA methods were applied to evaluate the differences in biological pathway and immune cell infiltration level, respectively. The PCA method was employed to construct the pyro-score to quantify the pyroptosis pattern level of individual BC patient.

Results: We determined three distinct pyro-clusters among 1852 BC samples, which exhibited different survival outcomes and enriched biological processes. The TME features demonstrated that these three clusters corresponded to three established immune profiles: immune-desert, immune-excluded and immune-inflamed phenotype, respectively. Based on pyroptosis-related signature genes, we constructed the pyro-score and stratified BC patients into high and low pyro-score group. Patients with high pyro-score exhibited favorable outcome and increased infiltration of immune cells. Further investigation revealed that high pyro-score was also related to high expression of immunosuppressive molecules, decreased tumor mutation burden (TMB) and high rate of mutation in significantly mutated genes (SMGs) (eg, PIK3CA and CDH1).

Conclusion: This research emphasizes the indispensable role of pyroptosis in TME complexity and diversity. Assessing the pyroptosis pattern level of individual BC patient will assist us in better understanding TME features and directing more effective immunotherapeutic approaches.

Keywords: breast cancer; immune landscape; immunotherapy; pyroptosis; tumor microenvironment.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Landscape of genetic and transcriptomic variation of pyroptosis-related regulators in breast cancer. (A) 26 of the 986 BC patients experienced genetic alterations of 10 PRRs, with a frequency of 2.64%, mostly including missense mutation and nonsense mutation. (B) The CNV frequency of 10 PRRs was prevalent. The column represented the alteration frequency. The deletion frequency, green dot; The amplification frequency, pink dot. (C) PCA of 10 PRRs to distinguish BC from normal samples. (D) The difference of mRNA expression levels of 10 PRRs between BC and normal samples. The asterisks represented the statistical P-value (**P < 0.01; ***P < 0.001).
Figure 2
Figure 2
Tumor pattern clusters mediated by pyroptosis-related regulators. (A) The interaction of expression on 10 PRRs in BC. The lines connecting PRRs represented their interaction with each other. The size of each circle represented the prognosis effect of each regulator and scaled by P-value. Protective factors for patients’ survival were indicated by a green dot in the circle center and risk factors indicated by the purple dot in the circle center. (B) Kaplan–Meier curves of OS for 1834 BC patients in one meta cohort with different pyro-clusters. The numbers of patients in pyro-cluster A, B and C were 762, 688 and 384, respectively. (C) Unsupervised clustering of PRRs in one meta cohort. The pyro-cluster, gender, ER+, PR+, HER2+, phenotype, stage and survival status were used as patient annotations. Red represented the high expression of regulators and blue represented low expression.
Figure 3
Figure 3
Biological pathway and tumor microenvironment characteristics in distinct pyro-clusters. (A–C) Heatmap showed the GSVA score of representative Hallmark pathways in distinct pyro-clusters. The TCGA-BRCA and GEO cohort (GSE20685, GSE20711, GSE42568, GSE58812, GSE88770) compositions were used as sample annotations. (D) Difference in the immune-activation related gene expression among three pyro-clusters. (E) The fraction of tumor-infiltrating cells in three pyro-clusters using the ssGSEA. Within each group, the scattered dots represented TME cell expression values. The thick line represented the median value. The bottom and top of the boxes were the 25th and 75th percentiles (interquartile range). The statistical difference of three pyro-clusters was compared through the Kruskal–Wallis H-test. (F) Difference in the TGF/beta-EMT pathway-related gene expression among three pyro-clusters. (G) Difference in the immune-checkpoint related gene expression among three pyro-clusters. (H) PCA of PRRs to distinguish between pyro-cluster A, B and C. ***P < 0.001, ns, not significant.
Figure 4
Figure 4
Construction of pyro-gene signature and functional annotation. (A) 2632 pyroptosis-related DEGs between three pyro-clusters were shown in the Venn diagram. (B and C) Functional annotation for pyroptosis-related signature genes using GO and KEGG enrichment analysis. (D) The expression of PRRs in three pyro-gene clusters. The upper and lower ends of the boxes represented an interquartile range of values. The lines in the boxes represented the median value. The asterisks represented the statistical P-value The one-way ANOVA test was used to test the statistical differences among three pyro-gene clusters. (E) The survival curves of the pyro-gene clusters were estimated by the Kaplan-Meier plotter (P <0.001). ***P < 0.001.
Figure 5
Figure 5
Construction of the pyroptosis-mediated prognostic model. (A and B) Ranked dot and scatter plots showing the risk score distribution and patient survival status. (C) Kaplan–Meier analysis of the OS between the two groups in the all, discovery and validation dataset, respectively. (D) ROC curves to predict the sensitivity and specificity of 1-, 3- and 5-year survival according to the prognostic model in the all, discovery and validation dataset, respectively.
Figure 6
Figure 6
Construction of pyro-score and exploration of its clinical features. (A) Alluvial diagram of pyro-clusters in groups with different pyro-gene clusters, and pyro-score. (B) Distribution of pyro-score in the different pyro-clusters and pyro-gene clusters. (C) Correlations between pyro-score and the immune landscape using Spearman analysis. The negative correlation was marked with blue and positive correlation with red. The star signified the statistical difference (*P-value <0.05). (D) Kaplan–Meier curves for patients with high and low pyro-score subgroups. (E) Bar plot showing pyro-score in groups with ER+, PR+, HER2+, phenotypes. The differences between different groups were compared through the Kruskal–Wallis test.
Figure 7
Figure 7
Characteristics of pyro-score in tumor somatic mutation and immunotherapies. (A and B) Correlation analysis between pyro-score and TMB. (C) Relative distribution of TMB in high versus low pyro-score group. (D) Kaplan–Meier curves for high and low TMB patient groups (P= 0.001). (E) Kaplan–Meier curves for subgroup patients stratified by both pyro-score and TMB (P< 0.001). (F and G) Mutational landscape of SMGs in TCGA-BRCA stratified by high (left panel) versus low pyro-score (right panel) groups. Individual patients were represented in each column. The upper bar plot showed TMB, the right bar plot showed the mutation frequency of each gene in separate pyro-score groups. (H) Relative distribution of immunosuppressive molecules expression in high pyro-score versus low pyro-score groups. (I) Relative distribution of immunotherapeutic efficacy in high pyro-score versus low pyro-score groups.
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References

    1. Fang Y, Tian S, Pan Y, et al. Pyroptosis: a new frontier in cancer. Biomed Pharmacother. 2020;121:109595. doi:10.1016/j.biopha.2019.109595 - DOI - PubMed
    1. Kovacs SB, Miao EA. Gasdermins: effectors of Pyroptosis. Trends Cell Biol. 2017;27(9):673–684. doi:10.1016/j.tcb.2017.05.005 - DOI - PMC - PubMed
    1. Shi J, Gao W, Shao F. Pyroptosis: gasdermin-mediated programmed necrotic cell death. Trends Biochem Sci. 2017;42(4):245–254. doi:10.1016/j.tibs.2016.10.004 - DOI - PubMed
    1. Xia X, Wang X, Cheng Z, et al. The role of pyroptosis in cancer: pro-cancer or pro-”host”?. Cell Death Dis. 2019;10(9):650. doi:10.1038/s41419-019-1883-8 - DOI - PMC - PubMed
    1. Malireddi RKS, Kesavardhana S, Kanneganti TD. ZBP1 and TAK1: master regulators of NLRP3 inflammasome/ Pyroptosis, Apoptosis, and Necroptosis (PAN-optosis). Front Cell Infect Microbiol. 2019;9:406. doi:10.3389/fcimb.2019.00406 - DOI - PMC - PubMed

Grants and funding

This study was supported by National Scientific Foundation of China (No. 81870554) and Foundation from Renji Hospital, School of Medicine, Shanghai Jiaotong University (2019NYLYCP0102).