Cao Xueyan, Zhang Qingquan, Zhu Yu, Huo Xiaoqing, Bao Junze, Su Min
Department of Obstetrics and Gynecology, Affiliated Hospital of Nantong University, Nantong, China.
Medical College, Nantong University, Nantong, China.
Front Oncol. 2022 Feb 24;12:780950. doi: 10.3389/fonc.2022.780950. eCollection 2022.
Pyroptosis is regulated by long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of pyroptosis-related lncRNAs (PRLs) in OC is crucial for developing therapeutic strategies and survival prediction.
Based on public database raw data, mutations in the landscape of pyroptosis-related genes (PRGs) in patients with OC were investigated thoroughly. PRLs were identified by calculating Pearson correlation coefficients. Cox and LASSO regression analyses were performed on PRLs to screen for lncRNAs participating in the risk signature. Furthermore, receiver operating characteristic (ROC) curves, Kaplan-Meier survival analyses, decision curve analysis (DCA) curves, and calibration curves were used to confirm the clinical benefits. To assess the ability of the risk signature to independently predict prognosis, it was included in a Cox regression analysis with clinicopathological parameters. Two nomograms were constructed to facilitate clinical application. In addition, potential biological functions of the risk signature were investigated using gene function annotation. Subsequently, immune-related landscapes and mutations were compared in different risk groups using diverse bioinformatics algorithms. Finally, we conducted a meta-analysis and assays on alternative lncRNAs.
A total of 374 patients with OC were randomized into training and validation cohorts (7:3). A total of 250 PRLs were selected from all the lncRNAs. Subsequently, a risk signature (DICER1-AS1, MIR600HG, AC083880.1, AC109322.1, AC007991.4, IL6R-AS1, AL365361.1, and AC022098.2) was constructed to distinguish the risk of patient survival. The ROC curve, K-M analysis, DCA curve, and calibration curve indicated excellent predictive performance for determining overall survival (OS) based on the risk signature in each cohort ( < 0.05). The Cox regression analysis indicated that the risk signature was an independent prognostic factor for OS ( < 0.05). Moreover, significant differences in the immune response and mutations were identified in different groups distinguished by the risk signature ( < 0.05). Interestingly, assays showed that an alternative lncRNA () could promote OC cell proliferation.
The PRL risk signature could independently predict overall survival and guide treatment in patients with OC.
焦亡由卵巢癌（OC）中的长链非编码RNA（lncRNA）调控。因此，全面分析OC中与焦亡相关的lncRNA（PRL）对于制定治疗策略和生存预测至关重要。
基于公共数据库原始数据，深入研究OC患者焦亡相关基因（PRG）图谱中的突变情况。通过计算皮尔逊相关系数来鉴定PRL。对PRL进行Cox和LASSO回归分析，以筛选参与风险特征的lncRNA。此外，使用受试者工作特征（ROC）曲线、Kaplan-Meier生存分析、决策曲线分析（DCA）曲线和校准曲线来确认临床益处。为评估风险特征独立预测预后的能力，将其纳入包含临床病理参数的Cox回归分析中。构建两个列线图以促进临床应用。另外，使用基因功能注释研究风险特征的潜在生物学功能。随后，使用多种生物信息学算法比较不同风险组中的免疫相关图谱和突变情况。最后，我们对替代lncRNA进行了荟萃分析和实验。
总共374例OC患者被随机分为训练组和验证组（7:3）。从所有lncRNA中总共筛选出250个PRL。随后，构建了一个风险特征（DICER1-AS1、MIR600HG、AC083880.1、AC109322.1、AC007991.4、IL6R-AS1、AL365361.1和AC022098.2）以区分患者生存风险。ROC曲线、K-M分析、DCA曲线和校准曲线表明，基于各队列中的风险特征来确定总生存期（OS）具有出色的预测性能（<0.05）。Cox回归分析表明，风险特征是OS的独立预后因素（<0.05）。此外，在以风险特征区分的不同组中，免疫反应和突变存在显著差异（<0.05）。有趣的是，实验表明一种替代lncRNA（）可促进OC细胞增殖。
PRL风险特征可独立预测OC患者的总生存期并指导治疗。
