A novel defined risk signature based on pyroptosis-related genes can predict the prognosis of prostate cancer

• Main Authors: Ding Hu (Author), Qingfei Cao (Author), Ming Tong (Author), Chundong Ji (Author), Zizhi Li (Author), Weichao Huang (Author), Yanyang Jin (Author), Guangquan Tong (Author), Yutao Wang (Author), Pengfei Li (Author), Huashan Zhang (Author)
• Format: Book
• Published: BMC, 2022-02-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12920-022-01172-5; 1755-8794
• Keywords: Pyroptosis; TCGA; GEO; Prostate cancer; Prognostic signature; Immune infiltration; Internal medicine; RC31-1245; Genetics; QH426-470
• Source: BMC Medical Genomics, Vol 15, Iss 1, Pp 1-17 (2022)

Abstract Background Pyroptosis can not only inhibit the occurrence and development of tumors but also develop a microenvironment conducive to cancer growth. However, pyroptosis research in prostate cancer (PCa) has rarely been reported. Methods The expression profile and corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients were divided into different clusters using consensus clustering analysis, and differential genes were obtained. We developed and validated a prognostic biomarker for biochemical recurrence (BCR) of PCa using univariate Cox analysis, Lasso-Cox analysis, Kaplan-Meier (K-M) survival analysis, and time-dependent receiver operating characteristics (ROC) curves. Results The expression levels of most pyroptosis-related genes (PRGs) are different not only between normal and tumor tissues but also between different clusters. Cluster 2 patients have a better prognosis than cluster 1 patients, and there are significant differences in immune cell content and biological pathway between them. Based on the classification of different clusters, we constructed an eight genes signature that can independently predict the progression-free survival (PFS) rate of a patient, and this signature was validated using a GEO data set (GSE70769). Finally, we established a nomogram model with good accuracy. Conclusions In this study, PRGs were used as the starting point and based on the expression profile and clinical data, a prognostic signature with a high predictive value for biochemical recurrence (BCR) following radical prostatectomy (RP) was finally constructed, and the relationship between pyroptosis, immune microenvironment, and PCa was explored, providing important clues for future research on pyroptosis and immunity.