背景:PANoptosis途径是最近发现的细胞死亡机制,涉及细胞焦亡之间的相互作用和同步,凋亡,和坏死。越来越多的证据表明,PANoptosis参与了癌症的发展和治疗。然而,全面了解PANoptosis基因对预后价值的影响,肿瘤微环境特征,卵巢癌(OC)患者的治疗结果仍然不完整。
目的:本工作旨在设计用于OC预后的PANoptosis特征并探索其潜在的分子功能。
方法:对于本研究,我们从癌症基因组图谱(TCGA)和GSE32062队列获得了卵巢癌的RNA测序和临床数据.使用GSCA分析了OC中PANoptosis相关基因(PRG)的体细胞变体。TCGA-OC和GSE32062用于构建模型的训练和验证队列。在使用单变量Cox分析筛选具有预后能力的基因之后进行差异表达和相关性分析。进行最小绝对收缩和选择算子(LASSO)回归以基于差异表达并与预后相关的基因构建PRG标签。使用CIBERSORT和ESTIMATE分析PRGs特征与免疫浸润之间的关系。使用TIDE分析PRG标记与免疫检查点基因之间的关系。使用OncoPredict分析PRG特征与药物敏感性之间的关系。定量实时PCR(qRT-PCR)用于验证PRG在OC中的表达。
结果:使用TCGA-OC中的三个预后基因(AIM2,APAF1和ZBP1)构建PRG标签。结果表明,PRGs特征在TCGA-OC中的AUC为0.521、0.546和0.598,在GSE32062中的AUC为0.620、0.586和0.579,以预测在1-3-,和5年的间隔。此外,较高的PRG特征风险评分与较短的OS显着相关(TCGA-OC中HR=1.693,95%CI:1.303-2.202,p=8.34×10^-5,GSE32062中HR=1.63,95%CI:1.13-2.35,p=0.009)。风险评分被确定为OC的独立预后因素。根据风险评分分类的患者在免疫状态方面表现出显著的变化,对免疫疗法的反应,和对药物的敏感性。AIM2、APAF1和ZBP1在OC细胞系中显著异常表达。
结论:PRG特征有可能作为OC的预后预测因子,并为OC治疗提供新的见解。
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.
The present work was designed to devise a PANoptosis signature for OC prognosis and explore its potential molecular function.
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 nd Selection Operator (LASSO) regression was performed to construct PRG signature based on genes that were differentially expressed and correlated with prognosis. CIBER-SORT 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.
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.
The PRG
signature has the potential to serve as a prognostic predictor for OC and to provide new insights into OC treatment.