A Novel pyroptosis-related signature for predicting prognosis and evaluating tumor immune microenvironment in ovarian cancer - Jiani Yang; Chao Wang; Yue Zhang; Shanshan Cheng; Yanna Xu; Yu Wang (BMC, 2023)
Abstract Ovarian cancer (OV) is the most fatal gynecological malignant tumor worldwide, with high recurrence rates and great heterogeneity. Pyroptosis is a newly-acknowledged inflammatory form of cell death with an essential role in cancer progression, though studies focusing on prognostic patterns of pyroptosis in OV are still lacking. Our research filtered 106 potential pyroptosis-related genes (PRGs) among the 6406 differentially expressed genes (DEGs) between the 376 TCGA-OV samples and 180 normal controls. Through the LASSO-Cox analysis, the 6-gene prognostic signature, namely CITED2, EXOC6B, MIA2, NRAS, SETBP1, and TRPV46, was finally distinguished. Then, the K-M survival analysis and time-dependent ROC curves demonstrated the promising prognostic value of the 6-gene signature (p-value < 0.0001). Furthermore, based on the signature and corresponding clinical features, we constructed and validated a nomogram model for 1-year, 2-year, and 3-year OV survival, with reliable prognostic values in TCGA-OV (p-value < 0.001) and ICGC-OV cohort (p-value = 0.040). Pathway analysis enriched several critical pathways in cancer, refer to the pyroptosis-related signature, while the m6A analysis indicated greater m6A level in high-risk group. We assessed tumor immune microenvironment through the CIBERSORT algorithm, which demonstrated the upregulation of M1 Macrophages and activated DCs and high expression of key immune checkpoint molecules (CTLA4, PDCD1LG2, and HAVCR2) in high-risk group. Interestingly, the high-risk group exhibited poor sensitivity towards immunotherapy and better sensitivity towards chemotherapies, including Vinblastine, Docetaxel, and Sorafenib. Briefly, the pyroptosis-related signature was a promising tool to predict prognosis and evaluate immune responses, in order to assist decision-making for OV patients in the realm of precision medicine.