Construction of a PANoptosis-related Prognostic Signature for Predicting Prognosis, Tumor Microenvironment, and Immune Response in Ovarian Cancer

Background: The PANoptosis pathway is a recently identified mechanism of cellular death that involves the interaction and synchronization among cellular pyroptosis, apoptosis, and necrosis. More and more evidence suggests that PANoptosis is involved in the development and treatment of cancer. However, a comprehensive understanding of the influence of PANoptosis genes on prognostic value, tumor microenvironment characteristics, and therapeutic outcomes in patients with ovarian cancer (OC) remains incomplete. Objective: The present work was designed to devise a PANoptosis signature for OC prognosis and explore its potential molecular function. Methods: For this study, we obtained RNA sequencing and clinical data for ovarian cancer from the Cancer Genome Atlas (TCGA) and the GSE32062 cohort. Somatic variants of PANoptosis-related genes (PRGs) in OC were analyzed using GSCA. TCGA-OC and GSE32062 were used to construct training and validation cohorts for the model. Differential expression and correlation analyses were performed following the screening of genes with prognostic ability using univariate Cox analysis. Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed to construct PRG signature based on genes that were differentially expressed and correlated with prognosis. CIBERSORT and ESTIMATE were used to analyze the relationship between the PRGs signature and immune infiltration. TIDE was used to analyze the relationship between the PRG signature and immune checkpoint genes. OncoPredict was used to analyze the relationship between the PRG signature and the drug sensitivity. Quantitative real-time PCR (qRT-PCR) was used to validate the expression of PRGs in OC. Results: The PRG signature was constructed using three prognostic genes (AIM2, APAF1, and ZBP1) in both TCGA-OC. The results showed that the PRGs signature had an AUC of 0.521, 0.546, and 0.598 in TCGA-OC and 0.620, 0.586, and 0.579 in GSE32062 to predict to predict OS at 1-, 3-, and 5-year intervals. Furthermore, a higher PRG signature risk score was significantly associated with shorter OS (HR = 1.693, 95% CI: 1.303 - 2.202, p = 8.34 × 10^-5 in TCGA-OC and HR = 1.63, 95% CI: 1.13 - 2.35, p = 0.009 in GSE32062). The risk score was identified as the independent prognostic factor for OC. Patients categorized according to their risk score exhibited notable variations in immune status, response to immunotherapy, and sensitivity to drugs. AIM2, APAF1, and ZBP1 were significantly aberrantly expressed in OC cell lines. Conclusion: The PRG signature has the potential to serve as a prognostic predictor for OC and to provide new insights into OC treatment. other: The findings emphasize the importance of PRGs in the advancement and growth of OC and establish a foundation for implementing PRG signatures as a therapeutic strategy for patients with OC.