Establishment of a Prognostic Necroptosis-Related lncRNA Signature inOvarian Cancer

Introduction: Ovarian Cancer (OC) was known for its high mortality rate among gynecological malignancies, often resulting in a poor prognosis. This study sought to identify prognostic necroptosis-related long non-coding RNAs (lncRNAs) (NRlncRNAs) with prognostic potential and to construct a reliable risk prediction model for OC patients. Method: The transcriptome and clinic data were sourced from TCGA and GTEx databases. Initially, NRlncRNAs were discovered by assessing gene correlations and evaluating differences in gene expression. Subsequently, Cox regression and LASSO methods were employed to develop the NRlncRNAs risk model, which was further validated through survival analysis, ROC curves, Cox regression, and nomograms across both the test and entire datasets. Results: Multivariate Cox analysis revealed that the risk score based on 14 NRlncRNAs can independently predict the prognosis of OC. The low-risk group demonstrated significantly higher immune cell infiltration scores and lower tumor immune dysfunction, exclusion, and TIDE scores, as well as an increased number of neoantigens and higher TMB. Notably, the low-risk group also exhibited an elevated HRD score. Conclusion: The model's predictive accuracy was further substantiated through ROC analysis, showing superior performance compared to many existing models.Finally, the expression levels of 14 NRlncRNAs were confirmed using the qRT-PCR in two OC cell lines. These findings suggested that the NRlncRNAs risk model could serve as a more precise indicator for forecasting immune response and outcomes of targeted treatments in OC. result: The results of multivariate Cox analysis indicated that the risk score calculated by 14 NRlncRNAs could serve as an independent prognostic factor in OC. The low-risk group owned higher scores of the immune cells infiltrations, which also tended to exhibit a lower tumor immune dysfunction, exclusion, and tumor immune dysfunction and exclusion (TIDE) score, respectively, a higher neoantigen number, and tumor mutation burden (TMB) level. Notably, a higher homologous recombination deficiency (HRD) score was also observed in the low-risk group. The ROC analysis showed the well-predictive power of the model. Paramountly, our model performance exceeded many previously published models. Finally, the expressions of 14 NRlncRNAs were validated by quantitative real-time PCR (qRT-PCR) in two OC cell lines