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. 2021 Apr 7;7(1):71.
doi: 10.1038/s41420-021-00451-x.

A novel defined pyroptosis-related gene signature for predicting the prognosis of ovarian cancer

Ying Ye #  1   2 , Qinjin Dai #  3 , Hongbo Qi  4   5
Affiliations

A novel defined pyroptosis-related gene signature for predicting the prognosis of ovarian cancer

Ying Ye et al. Cell Death Discov. .

Abstract

Ovarian cancer (OC) is a highly malignant gynaecological tumour that has a very poor prognosis. Pyroptosis has been demonstrated in recent years to be an inflammatory form of programmed cell death. However, the expression of pyroptosis-related genes in OC and their correlations with prognosis remain unclear. In this study, we identified 31 pyroptosis regulators that were differentially expressed between OC and normal ovarian tissues. Based on these differentially expressed genes (DEGs), all OC cases could be divided into two subtypes. The prognostic value of each pyroptosis-related gene for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 7-gene signature was built and classified all OC patients in the TCGA cohort into a low- or high-risk group. OC patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, OC patients from a Gene Expression Omnibus (GEO) cohort were divided into two risk subgroups, and the low-risk group had increased overall survival (OS) time (P = 0.014). Combined with the clinical characteristics, the risk score was found to be an independent factor for predicting the OS of OC patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses indicated that immune-related genes were enriched and that the immune status was decreased in the high-risk group. In conclusion, pyroptosis-related genes play important roles in tumour immunity and can be used to predict the prognosis of OCs.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Expressions of the 33 pyroptosis-related genes and the interactions among them.
A Heatmap (green: low expression level; red: high expression level) of the pyroptosis-related genes between the normal (N, brilliant blue) and the tumour tissues (T, red). P values were showed as: **P < 0.01; ***P < 0.001. B PPI network showing the interactions of the pyroptosis-related genes (interaction score = 0.9). C The correlation network of the pyroptosis-related genes (red line: positive correlation; blue line: negative correlation. The depth of the colours reflects the strength of the relevance).
Fig. 2
Fig. 2. Tumour classification based on the pyroptosis-related DEGs.
A 379 OC patients were grouped into two clusters according to the consensus clustering matrix (k = 2). B Heatmap and the clinicopathologic characters of the two clusters classified by these DEGs (G1, G2, and G3 are the degree of tumour differentiation. G1: High differentiated; G2: Moderate differentiated; G3: Poor differentiated). C Kaplan–Meier OS curves for the two clusters.
Fig. 3
Fig. 3. Construction of risk signature in the TCGA cohort.
A Univariate cox regression analysis of OS for each pyroptosis-related gene, and 7 genes with P < 0.2. B LASSO regression of the 7 OS-related genes. C Cross-validation for tuning the parameter selection in the LASSO regression. D Distribution of patients based on the risk score. E PCA plot for OCs based on the risk score. F The survival status for each patient (low-risk population: on the left side of the dotted line; high-risk population: on the right side of the dotted line). G Kaplan–Meier curves for the OS of patients in the high- and low-risk groups. H ROC curves demonstrated the predictive efficiency of the risk score.
Fig. 4
Fig. 4. Validation of the risk model in the GEO cohort.
A Distribution of patients in the GEO cohort based on the median risk score in the TCGA cohort. B PCA plot for OCs. C The survival status for each patient (low-risk population: on the left side of the dotted line; high-risk population: on the right side of the dotted line). D Kaplan–Meier curves for comparison of the OS between low- and high-risk groups. E Time-dependent ROC curves for OCs.
Fig. 5
Fig. 5. Univariate and multivariate Cox regression analyses for the risk score.
A Univariate analysis for the TCGA cohort (grade: the degree of tumour differentiation, G1 to G3). B Multivariate analysis for the TCGA cohort. C Univariate analysis for the GEO cohort (FIGO stage: I to IV). D Multivariate analysis for the GEO cohort. E Heatmap (green: low expression; red: high expression) for the connections between clinicopathologic features and the risk groups (*P < 0.05).
Fig. 6
Fig. 6. Functional analysis based on the DEGs between the two-risk groups in the TCGA cohort.
A Bubble graph for GO enrichment (the bigger bubble means the more genes enriched, and the increasing depth of red means the differences were more obvious; q-value: the adjusted p-value). B Barplot graph for KEGG pathways (the longer bar means the more genes enriched, and the increasing depth of red means the differences were more obvious).
Fig. 7
Fig. 7. Comparison of the ssGSEA scores for immune cells and immune pathways.
A, B Comparison of the enrichment scores of 16 types of immune cells and 13 immune-related pathways between low- (green box) and high-risk (red box) group in the TCGA cohort. C, D Comparison of the tumour immunity between low- (blue box) and high-risk (red box) group in the GEO cohort. P values were showed as: ns not significant; *P < 0.05; **P < 0.01; ***P < 0.001.
Fig. 8
Fig. 8. Workflow diagram.
The specific workflow graph of data analysis.
See this image and copyright information in PMC

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References

    1. Siegel R, Miller K, Jemal A. Cancer statistics, 2019. CA. 2019;69:7–34. - PubMed
    1. Bast R, Hennessy B, Mills G. The biology of ovarian cancer: new opportunities for translation. Nat. Rev. Cancer. 2009;9:415–428. doi: 10.1038/nrc2644. - DOI - PMC - PubMed
    1. Kuroki L, Guntupalli S. Treatment of epithelial ovarian cancer. BMJ. 2020;371:m3773. doi: 10.1136/bmj.m3773. - DOI - PubMed
    1. Lee J, et al. Changes in ovarian cancer survival during the 20 years before the era of targeted therapy. BMC Cancer. 2018;18:601. doi: 10.1186/s12885-018-4498-z. - DOI - PMC - PubMed
    1. Kovacs S, Miao E. Gasdermins: effectors of Pyroptosis. Trends Cell Biol. 2017;27:673–684. doi: 10.1016/j.tcb.2017.05.005. - DOI - PMC - PubMed

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