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

Abstract

Background: Ovarian cancer (OC) usually progresses rapidly 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. Methods: In this research, we utilized both univariate and multivariate Cox regression analysis to detect important prognostic characteristics. A set of six lncRNAs related to NETs was used to build a model, and the feature selection was performed using the LASSO regression algorithm. The model's predictive capability was evaluated using Kaplan-Meier (K-M) analysis, receiver operating characteristic (ROC) analysis, as well as univariate and multivariate Cox analyses. In order to obtain a better understanding of the fundamental processes of the predictive model, we performed an analysis of gene ontology (GO) terms, enrichment analyses of KEGG pathways, and Gene set enrichment analysis (GSEA). Furthermore, we examined the mutation status of every gene in every sample using cascade diagrams and explored the correlation between tumor mutation load, rate of survival, and the model. In addition, we conducted a comparison of immune functions, the expression of targeted immune checkpoints, and the sensitivity to chemotherapeutic drugs in both low- and high-risk groups. Ultimately, we confirmed the predictive significance of our model by analyzing data from test sets as well as ovarian cancer cells and tissues acquired from our institution. Results: We built a model consisting of six lncRNAs associated with NETs, specifically GAS5, GBP1P1, LINC00702, LINC01933, LINC02362, and ZNF687-AS1. The ROC curve was used to evaluate the predictive performance of the models and compared with traditional clinicopathological features. The analysis of the GO process indicated that the predominant category was molecular function associated with antigen binding, along with several biological processes related to the immune system. Furthermore, variations were noted in the manifestation of transcription regulators linked to immune response, including the facilitation of inflammation, cytotoxic capabilities, and regulatory points. In addition, we made predictions for the IC50 values of chemotherapeutic drugs (bexarotene, bicalutamide, embelin, GDC0941, and thapsigargin) in both high-risk and low-risk groups. According to the findings, low-risk patients exhibited elevated IC50 values for all five medications. In the end, we confirmed the strength of the risk model by testing it on OC cells and tissues along with clinical data. Conclusion: We established a NETs-related lncRNA risk model, which has the potential to predict the prognosis and clinical response of OC patients. In the short term, the model could assist healthcare professionals in identifying patients who require individualized therapeutic approaches, including those who might gain advantages from immunotherapy.