Identification of a novel prognostic gene signature in high-grade lung neuroendocrine tumors

Abstract Background The prognosis of high-grade LNETs patients was poor. Although previous studies have tried to explore molecular markers to stratify LNETs and to seek the therapeutic targets, there was little improvement in the treatment strategy and overall survival of high-grade LNET patients. Materials and Methods In our study, gene expression data and clinical features of LNETs patients were extracted from GSE30219 dataset, which was further divided into training cohort and validation cohort. Univariate cox regression analysis and LASSO method were used to construct a prognostic risk model. The predictive value of prognostic gene signature was evaluated by ROC curve analysis. Results An eight-gene signature predicting survival was constructed in training cohort and was further validated in validation cohort. This gene signature performed well in both early-stage and advanced-stage high-grade LNET patients. Moreover, multivariate cox regression analysis revealed that this gene signature was the only independent risk factor in high-grade LNET patients, while age, gender, pathological stage, T stage, N stage and M stage were not. GO analysis and KEGG pathway analysis of DEGs and GSEA analysis based on subgroups stratified by gene signature in high-grade LNET patients revealed the high activity of tumor cell division, proliferation and metabolization. Conclusion We identified a novel and reliable gene signature risk model for predicting prognosis in high-grade LNET patients, which could stratify high-risk LNETs patients and might provide new targets.