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. 2022 Jul 25:12:948169.
doi: 10.3389/fonc.2022.948169. eCollection 2022.

Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer

Ying Zhou  1   2 , Jianfeng Zheng  3 , Mengru Bai  1   2 , Yuzhen Gao  4 , Nengming Lin  1   5
Affiliations

Effect of Pyroptosis-Related Genes on the Prognosis of Breast Cancer

Ying Zhou et al. Front Oncol. .

Abstract

Backgrounds: Pyroptosis, a newly pattern of specific programmed cell death, has been reported to participate in several cancers. However, the value of pyroptosis in breast cancer (BRCA) is still not clear.

Methods: Herein, we analyzed the data of BRCA from both The Cancer Genome Atlas (TCGA) and GSEA MSigDB database. Based on the obtained pyroptosis-related genes (PRGs), we searched the interactions by STRING. After that, we performed clustering analysis by ConsensusClusterPlus. The PRGs with significant prognostic value were then screened through univariate cox regression and further evaluate by constructing a risk model by least absolute shrinkage and selection operator (LASSO) Cox regression. The immune and sensitivity to drugs were also predicted by comprehensive algorithms. Finally, real-time quantitative PCR (qPCR) was performed on two of the screened signature PRGs.

Results: A total of 49 PRGs were obtained from public database and 35 of them were significantly differentially expressed genes (DEGs). Cluster analysis was then performed to explore the relationship between DEGs with overall survival. After that, 6 optimal PRGs (GSDMC, IL-18, CHMP3, TP63, GZMB and CHMP6) were screened out to construct a prognostic signature, which divide BRCA patients into two risk groups. Risk scores were then confirmed to be independent prognostic factors in BRCA. Functional enrichment analyses showed that the signature were obviously associated with tumor-related and immune-associated pathways. 79 microenvironmental cells and 11 immune checkpoint genes were found disparate in two groups. Besides, tumor immune dysfunction and exclusion (TIDE) scores revealed that patients with higher risk scores are more sensitive to immune checkpoint blockade treatment. Patients in the low-risk group were more sensitive to Cytarabine, Docetaxel, Gefitinib, Paclitaxel, and Vinblastine. Inversely, patients in the high-risk group were more sensitive to Lapatinib. Finally, we found that, CHMP3 were down-regulated in both BRCA tissues and cell lines, while IL-18 were up-regulated.

Conclusion: PRGs play important roles in BRCA. Our study fills the gaps of 6 selected PRGs in BRCA, which were worthy for the further study as predict potential biomarkers and therapeutic targets.

Keywords: PRGs; algorithm; bioinformatic analyses; breast cancer; programmed cell death.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram of our study.
Figure 2
Figure 2
Differential analysis and network. (A) Heatmap showing the expression levels of pyroptosis-related genes with significant expression levels in breast cancer tumors and control samples. Red: up regulated; blue: down regulated. (B) Protei-protein interaction. Blue, red, and white nodes represent genes that are significantly down-regulated, up-regulated, and insignificantly differentially expressed in tumor tissue, respectively. (C) co-expression network based on pyroptosis-related genes. Blue, red, and white nodes represent significantly down-regulated, up-regulated, and insignificantly differentially expressed genes in tumor tissues, respectively. Red and green junctions represent significantly positive and negative correlations.
Figure 3
Figure 3
Subtype analysis. (A) Cluster diagram for subtype analysis of breast cancer samples. The intragroup correlations were the highest and the inter-group correlations were low when k=2. (B) The Kaplan-Meier analysis for the three different subtypes. Subtype 1 had the best prognosis, while Subtype 3 had the worst prognosis. (C) The distribution map of pyroptosis- related genes and clinicopathologic characters in the three subtypes.
Figure 4
Figure 4
The survival status and ROC curves. (A-C) The survival status for each patient and the time-dependent ROC curve in TCGA (A), GSE42568 (B), and GSE20685 (C).
Figure 5
Figure 5
The Kaplan-Meier analysis based on the prognostic signature. The Kaplan-Meier analysis for TCGA (A), GSE42568 (B), and GSE20685 (C). The blue and red curves represent low- or high-risk samples, respectively.
Figure 6
Figure 6
The univariate and multivariate Cox regression analysis for prognostic signature. (A) Forest plot for the prognosis of clinicopathologic characters. (B) Heatmap for the connections between the expression levels of six optimal PRGs and the distribution of the independent prognostic factors. Red: up regulated; blue: down regulated. *p < 0.05, ***p < 0.001.
Figure 7
Figure 7
Differences in functional pathways between the risk groups. (A) Biological processes (BP). (B) KEGG signaling pathways. The horizontal axis represents the number of significantly differentially expressed genes, the vertical axis represents the item name, the size of the dots represents the number of DEGs, the color of the dots represents the enrichment significance, and the closer the color is to red, the higher the significance.
Figure 8
Figure 8
Immune analysis. (A) Comparison of the immune score and ESTIMATE score between the two risk groups. (B-E) Comparison of immune cells between the two risk groups based on Cibersort (B), MCPcount (C), ssGSEA (D) and xCELL (E). Expression distribution of 13 immune checkpoint genes between the two risk groups (F). TIDE score between the two risk groups (G).
Figure 9
Figure 9
Sensitivity of chemotherapy drugs. (A–F) Difference in the estimated IC50 levels of Lapatinib (A), Cytarabine (B), Docetaxel (C), Gefitinib (D), Paclitaxel (E), and Vinblastine (F). Data are shown as means ± S.D. ns: not significant, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Figure 10
Figure 10
Real-time qPCR and HPA analysis. The expression of two candidate PRGs including IL-18 (A) and CHMP3 (B) in both normal breast cell line (MCF-10A) and breast cancer cell lines (MDA-MB-231, MDA-MB-453 and MCF-7) were checked by qPCR analysis. IHC staining for IL-18 (C) and CHMP3 (D) from HPA database.
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