Assessment of lncRNA biomarkers based on NETs for prognosis and therapeutic response in ovarian cancer.

Ovarian cancer (OC) usually progresses rapidly and is associated with high mortality, while a reliable clinical factor for OC patients to predict prognosis is currently lacking. Recently, the pathogenic role of neutrophils releasing neutrophil extracellular traps (NETs) in various cancers including OC has gradually been recognized. The study objective was to determine whether NETs-related biomarkers can be used to accurately predict the prognosis and guide clinical decision-making in OC. In this study, we utilized univariate and multivariate Cox regression to identify key prognostic features and developed a model with six NETs-related lncRNAs, selected via LASSO regression. The model's predictive capability was assessed through Kaplan-Meier, ROC, and Cox analyses. To understand the model's mechanisms, we conducted GO term analysis, KEGG pathway enrichment, and GSEA. We also analyzed gene mutation status, tumor mutation load, survival rates, and model correlation. Additionally, we compared immune functions, immune checkpoint expression, and chemotherapy sensitivity between risk groups. Besides, we validated the model's predictive value using test data and tissues acquired from our institution. Finally, we performed in vitro and in vivo experiments to confirm the expression of model lncRNAs and the cellular level function of GAS5. We developed a model using six NETs-associated lncRNAs: GAS5, GBP1P1, LINC00702, LINC01933, LINC02362, and ZNF687-AS1. The model's predictive performance, evaluated via ROC curve, was compared with traditional clinicopathological features. GO process analysis highlighted molecular functions related to antigen binding and immune system biological processes. Variations were observed in transcription regulators affecting immune response, inflammation, cytotoxicity, and regulation. We also predicted IC50 values for chemotherapeutic drugs (bexarotene, bicalutamide, embelin, GDC0941, and thapsigargin) in high- and low-risk groups, finding higher IC50 values in low-risk patients. The risk model's robustness was validated using OC cells, tissues, and clinical datas.