LAUSR

Background Cervical cancer (CC) is a common gynecological malignant tumor. Ferroptosis is a new type of programmed cell death, which plays a crucial part in cancer. However, current knowledge regarding ferroptosis-related long noncoding RNAs (lncRNAs) in CC is still limited. Therefore, our aim is to identify ferroptosis-related lncRNAs, build a steady prediction model, and improve the prediction value of CC. Methods We obtained RNA expression and ferroptosis-related gene data of female CC patients from TCGA and FerrDb databases, respectively. Then, the ferroptosis-related lncRNAs were obtained by the limma R package and Cytoscape 3.7.1. We constructed the prediction model by Cox regression analysis. Finally, the prediction model was verified by the median risk score, Kaplan–Meier analysis, the time-dependent receiver operating characteristic (ROC) curve, clinical features, and immunoinfiltration analysis. Results We acquired 1393 ferroptosis-related lncRNAs. The ferroptosis-related lncRNA signature was obtained by multivariate Cox regression analysis, and the patients were divided into a high-risk group and a low-risk group. The prognosis of the high-risk group was worse than that of the low-risk group. We found that the risk score can be used as an independent prognostic index by multivariate Cox regression analysis. The area under the time-dependent ROC curve reached 0.847 at 1 year, 0.906 at 2 years, 0.807 at 3 years, and 0.724 at 5 years in the training cohort. Principal component analysis showed that the diffusion directions of the two groups were different. Gene set enrichment analysis indicated that lncRNAs of two groups may be involved in tumorigenesis. Further analysis showed that high-risk groups were related to immune-related pathways. Ferroptosis-related lncRNAs are related to the proportion of tumor-infiltrating immune cells in CC. Conclusion We have constructed a ferroptosis-related lncRNA prediction model. The prognostic model had important clinical significance, including improving the predictive value and guiding the individualized treatment of CC patients.