The role of pyroptosis-related lncRNA risk signature in ovarian cancer prognosis and immune system

Abstract

Ovarian cancer is a leading cause of death in females with gynecologic cancers. Pyroptosis is a relatively new discovered programmed cell death that is believed to be associated with inflammation. However, studies on pyroptosis-related lncRNAs in ovarian cancer are limited. In this study, we identified 29 pyroptosis-related genes and screened out 72 pyroptosis-related lncRNAs. Furthermore, the 72 lncRNAs were eliminated to 2 survival-related lncRNAs using Cox regression and Lasso regression to build an ovarian cancer prognostic prediction signature and were further validated on the test set. We adopted a riskscore from the two-gene signature, and the survival in low-risk group was higher than the high-risk group. Functional enrichment analysis indicated that the differentially expressed genes (DEGs) between two risk groups were associated with tumor immunity. This study implies that pyroptosis-related genes are closely related to tumor immunity and could be potential therapeutic factors for ovarian cancer treatment.

Keywords: Immune system; LncRNA; Ovarian cancer; Pyroptosis; Target.

Fig. 1

Flowchart of the data analysis in this study

Fig. 2

Networks and differential expression analysis of pyroptosis-related genes. A Volcano plot of the differential expression analysis of the pyroptosis related genes, where red represents upregulated genes and blue represents downregulated genes. B Heatmap of upregulated and downregulated pyroptosis-related genes in normal and tumor ovarian tissues. C Network of 29 pyroptosis-related genes. D The heatmap of the correlation of pyroptosis-related genes and lncRNAs, all with Pearson correlation |r| > 0.5, p < 0.05. E The boxplot of gene expression difference between normal and tumor tissues in pyroptosis-related genes. F An alluvial plot of pyroptosis-related genes and lncRNAs.

Fig. 3

Survival-related lncRNAs selection. A A forest plot of Univariate Cox regression coefficients that are correlated with survival time. B Plot of lambda selection in Lasso regression algorithm. C Lasso-Cox regression of pyroptosis survival-related lncRNAs. D Kalplan-Meier curves for lasso selected lncRNAs

Fig. 4

Distribution of gene expression in different groups. A PCA of all lncRNAs in tumor and normal tissues. B PCA of pyroptosis genes in tumor and normal tissues. C Distribution of patients who are classified as high and risk groups based on riskscore in the training set. D Distribution of patients who are classified as high- and low-risk groups based on riskscore in the test set. E Distribution of patients who are alive or dead in the training set. F Distribution of patients who are alive or dead in the test set

Fig. 5

Univariate and Multivariate Cox regression on riskscore. A A forest plot of univariate cox regression of riskscore and clinical information. B A forest plot of multivariate cox regression of riskscore. C Kaplan-Meier curve of riskscore on the training set. D ROC curves of riskscore in year-1, year-3, and year-5 on the training set. E ROC curves of riskscore in year-1, year-3, and year-5 on the test set. F A Nomogram predicting the survival rate at 1 year, 3 year and 5 year on training set. G A Nomogram predicting the survival rate at 1 year, 3 year and 5 year on test set

Fig. 6

Gene enrichment analysis in risk groups and target genes in pyroptosis-related lncRNAs. A GO enrichment analysis in risk groups. B KEGG enrichment in risk groups. C Target genes related to pyroptosis-related lncRNA, AC00814.1

Fig. 7

Comprehensive analysis in immune function analysis, immune cell composition and immune infiltration. A A heatmap for different immune functions in high- and low-risk groups. B A boxplot for different immune cells. C A boxplot for different immune cells in high- and low-risk groups. D Immune cell infiltration analysis