Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer - Cao, Xueyan; Zhang, Qingquan; Zhu, Yu; Huo, Xiaoqing; Bao, Junze; Su, Min (Frontiers in Oncology, 2022)
Background: Pyroptosis may be regulated by Long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, comprehensive analysis of pyroptosis-related lncRNAs (PRLs) in OC is crucial for proposing therapeutic strategies and survival prediction. Methods: Based on public database raw-data, mutations in the landscape of pyroptosis-related genes (PRGs) in OC patients was investigated thoroughly. PRLs was identified by calculating pearson correlation coefficients. Cox and LASSO regression analysis were performed on the PRLs in order to screen the lncRNAs participating in risk signature. Further, receiver operator characteristic (ROC) curve, Kaplan-Meier survival analysis, decision curve analysis (DCA) curve and calibration curve were used to confirm the clinical benefits. To assess ability of risk signature to independently predict prognosis, it was included in Cox regression analysis with clinicopathological parameters. Meanwhile, two nomograms were constructed to facilitate clinical application. In addition, the potential biological functions of the risk signature were investigated by genes function annotation. Subsequently, immune related landscape and BRCA1/2 mutations were compared in different risk groups using diversiform bioinformatics algorithms. Finally, we conducted Meta-analysis and vitro assays about a alternative lncRNA. Results: Firstly, the 374 OC patients were randomized into a training and validation cohort (7:3). A total of 250 PRLs were selected from all lncRNAs. Subsequently, a risk signature (DICER1-AS1, MIR600HG, AC083880.1, AC109322.1, AC007991.4, IL6R-AS1, AL365361.1, AC022098.2) was constructed to distinguish the survival status risk of patients. It is worth mentioning that ROC curve, K-M analysis, DCA curve and calibration curve indicated excellent predictive performance for overall survival (OS) of risk signature in each cohort (p < 0.05). The Cox regression analysis indicated that risk signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in immune response and BRCA1 mutations were excavated in different groups distinguished by risk signature (p < 0.05). Interestingly, vitro assays showed that an alternative lncRNA (DICER1-AS1) could promote proliferation of OC cells. Conclusion: The PRLs risk signature could independently predict overall survival and guide treatment in OC patients.