Abstract
Ovarian cancer (OC), a predominant gynecological malignancy, has consistently showcased grim prognostic outcomes. This investigation delves into the emerging field of pyroptosis and the intricacies of long non-coding RNAs (lncRNAs), specifically the lesser-studied pyroptosis-related lncRNAs (PRlncRNAs), and their roles in OC prognosis. By harnessing transcriptome, and clinic data from the genotype-tissue expression (GTEx) and the cancer genome Atlas (TCGA), we formulated a unique PRlncRNAs risk model consisting of five prognostic lncRNAs by Cox regression and least absolute shrinkage and selection operator (LASSO) regression. Next, the Kaplan-Meier analysis, receiver operating characteristic (ROC) curve, nomogram, and calibration were implemented to verify and evaluate the model. The model also showed general applicability in pan-cancer analysis. Remarkably, our model, upon rigorous validation, outperformed 16 pre-existing counterparts, offering a promising avenue for prognosis prediction. The risk score was used to classify patients into high and low-risk subgroups. The low-risk group showed improved overall survival (OS) and progression-free survival (PFS). The risk score was proved to be an independent prognosis factor. The low-risk group patients also exhibited a higher immune infiltration score and homologous recombination deficiency (HRD) score. Moreover, consensus clustering analysis was utilized to categorize OC patients into three distinct groups, predicated on the expression of the five prognostic lncRNAs. Patients within the third cluster exhibited noteworthy traits, encompassing elevated survival, heightened immune checkpoint expression, and the HRD score. Finally, the expressions of five PRlncRNAs were validated by quantitative real-time PCR (qRT-PCR) in OC cell lines and tissues. In conclusion, the risk model based on the five PRlncRNAs might function as prognostic biomarkers to predict the immune and target drug treatment in OC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s43657-024-00173-x.