A ferroptosis-related lncRNA signature predicts prognosis in ovarian cancer patients

BACKGROUND: Ovarian cancer (OC) is a common gynecological malignant tumor with poor prognosis. Ferroptosis is an iron-dependent modality of regulated cell death. The purpose of this study was to determine the prognostic ability of ferroptosis-related long non-coding RNAs (lncRNAs) in OC patients and construct a ferroptosis-related lncRNA prognostic model. METHODS: The Cancer Genome Atlas (TCGA) and FerrDb databases were used to collect RNA sequencing data of OC patients and ferroptosis-related genes, respectively. OC patients were randomly assigned to the training or testing set. Pearson correlation analysis was used to identify ferroptosis-related lncRNAs. Univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate regression analyses were performed in the training set to develop a predictive model. The model was validated in the testing set and entire set. Survival analysis, receiver operating characteristic curves, independent prognostic factor analysis, and correlation analysis with clinical features were performed to evaluate the predictive value of the model. A nomogram was established to predict the survivability of OC patients over 1, 3, and 5 years. The distribution of distinct groups was investigated using principal component analysis, and the underlying the biological functions were explored using gene set enrichment analysis. RESULTS: Eleven ferroptosis-related lncRNAs were determined to establish the prognostic model. Patients in the high-risk group had poor prognosis compared with the low-risk group in the training, testing and entire sets. The area under the receiver operating characteristic curve corresponding to 1-, 3-, and 5-year survival were 0.731, 0.796, and 0.805 in the training set; 0.704, 0.597, and 0.655 in the testing set; and 0.715, 0.691, and 0.736, in the entire set, respectively. The risk score correlated with age and grade. The risk score was also an independent prognostic factor in OC. A nomogram with high C-index (0.68) was constructed. An intuitive observation of the principal component analysis revealed a distinction between high- and low-risk groups, and gene set enrichment analysis indicated that cancer-related pathways were enriched in the high-risk group. CONCLUSIONS: The signature composed of 11 ferroptosis-related lncRNAs accurately predicted the prognosis of OC patients.