Abstract
BackgroundFerroptosis and iron-metabolism are regulated by Long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of ferroptosis and iron-metabolism related lncRNAs (FIRLs) in OC is crucial for proposing therapeutic strategies and survival prediction.MethodsIn multi-omics data from OC patients, FIRLs were identified by calculating Pearson correlation coefficients with ferroptosis and iron-metabolism related genes (FIRGs). Cox-Lasso regression analysis was performed on the FIRLs to screen further the lncRNAs participating in FIRLs signature. In addition, all patients were divided into two robust risk subtypes using the FIRLs signature. Receiver operator characteristic (ROC) curve, Kaplan–Meier analysis, decision curve analysis (DCA), Cox regression analysis and calibration curve were used to confirm the clinical benefits of FIRLs signature. Meanwhile, two nomograms were constructed to facilitate clinical application. Moreover, the potential biological functions of the signature were investigated by genes function annotation. Finally, immune microenvironment, chemotherapeutic sensitivity, and the response of PARP inhibitors were compared in different risk groups using diversiform bioinformatics algorithms.ResultsThe raw data were randomized into a training set (n = 264) and a testing set (n = 110). According to Pearson coefficients between FIRGs and lncRNAs, 1075 FIRLs were screened for univariate Cox regression analysis, and then LASSO regression analysis was used to construct 8-FIRLs signature. It is worth mentioning that a variety of analytical methods indicated excellent predictive performance for overall survival (OS) of FIRLs signature (p < 0.05). The multivariate Cox regression analysis showed that FIRLs signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the abundance of immune cells, immune-related pathways, and drug response were excavated in different risk subtypes (p < 0.05).ConclusionThe FIRLs signature can independently predict overall survival and therapeutic effect in OC patients.
Highlights
Ovarian cancer (OC) is an important cause of gynaecological cancer-related death
There are many studies using Long non-coding RNAs (lncRNAs) expression to predict the prognosis of cancer patients, such as a risk score system based on co-expression network analysis [14], four prognosis-associated lncRNAs as biomarkers in ovarian cancer (OC) [15], and lncRNAs-associated ceRNA network [16]
We identified a FILRs signature based on 8-ferroptosis and iron-metabolism related lncRNAs (FIRLs) (AC138904.1, AP005205.2, AC007114.1, LINC00665, UBXN10-AS1, AC083880.1, LINC01558, and AL023583.1) that showed an ability to distinguish OC patients into different risk groups, and clinical benefits in survival prediction were confirmed
Summary
Ovarian cancer (OC) is an important cause of gynaecological cancer-related death. Ferroptosis has been discovered as a possible preventive or therapeutic strategy for cancer cell death, in resistant cancers to traditional therapies [5]. LncRNAs have been shown to play major regulatory roles in various disease processes, including OC [10, 11]. LncRNAs have been shown to play significant regulatory roles in various disease processes, including OC [12, 13]. It is necessary to investigate the clinical value of lncRNAs related to iron metabolism and ferroptosis and screen out hub lncRNAs for predicting OS in OC patients. Ferroptosis and iron-metabolism are regulated by Long non-coding RNAs (lncRNAs) in ovarian cancer (OC). A comprehensive analysis of ferroptosis and iron-metabolism related lncRNAs (FIRLs) in OC is crucial for proposing therapeutic strategies and survival prediction
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