Exploration of pyroptosis-associated prognostic gene signature and lncRNA regulatory network in ovarian cancer.

Ovarian cancer (OC), is a tumor that poses a serious threat to women's health due to its high mortality rate and bleak prognosis. Pyroptosis, a type of programmed cell death, is important for determining the prognosis of a patient's prognosis for cancer and may represent a novel target for treatment. However, research into how prognosis is impacted by pyroptosis-related genes (PRGs) is poorly understood. In this study, a prognostic model was created using bioinformatic analysis of PRGs in OC. In OC, we discovered 18 pyroptosis regulators that were either up- or down-regulated. By analyzing prognoses, we developed a 9-genes based prognostic model. Each OC patient received a risk score that could be used to categorize them into two subgroups: those with high risk and/or low chance of survival and those with low risk and/or high chance of survival. Functional enrichment and immunoinfiltration analysis indicated that low expression of immune pathways in high-risk group may account for the decrease of survival possibility. In Multivariable cox regression studies, age, clinical stage and the prognostic model were discovered to be independent factors impacting the prognosis for OC. To forecast OC patient survival, a predictive nomogram was developed. Furthermore, we found a correlation between predictive PRGs and clinical stage, indicating that AIM2, CASP3, ZBP1 and CASP8 may play a role in the growth of tumor in OC. After detailed and complete bioinformatics analysis, the lncRNA RP11-186B7.4/hsa-miR-449a/CASP8/AIM2/ZBP1 regulatory axis was identified in OC. Our study may provide a novel approach for prognostic biomarkers and therapeutic targets of OC.