Background: Gastric cancer (GC) is a common malignant cancer with a poor prognosis. Ferroptosis has been shown to play crucial roles in GC development. Long non-coding RNAs (lncRNAs) is also associated with tumor progression in GC. This study aimed to screen the prognostic ferroptosis-related lncRNAs and to construct a prognostic risk model for GC. Methods: Ferroptosis-related lncRNAs from The Cancer Genome Atlas (TCGA) GC expression data was downloaded. First, single factor Cox proportional hazard regression analysis was used to select seven prognostic ferroptosis-related lncRNAs from TCGA database. And then, the selected lncRNAs were further included in the multivariate Cox proportional hazard regression analysis to establish the prognostic model. A nomogram was constructed to predict individual survival probability. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the risk model. Results: We constructed a prognostic ferroptosis-related lncRNA signature in this study. Kaplan-Meier curve analysis revealed a significantly better prognosis for the low-risk group than for the high-risk group (P = 2.036e-05). Multivariate Cox proportional risk regression analysis demonstrated that risk score was an independent prognostic factor [hazard ratio (HR) = 1.798, 95% confidence interval (CI) =1.410–2.291, P < 0.001]. A nomogram, receiver operating characteristic curve, and principal component analysis were used to predict individual prognosis. Finally, the expression levels of AP003392.1, AC245041.2, AP001271.1, and BOLA3-AS1 in GC cell lines and normal cell lines were tested by qRT-PCR. Conclusions: This risk model was shown to be a novel method for predicting prognosis for GC patients.

Neutrophils expelling neutrophil extracellular traps (NETs) during the process of NETosis play a critical role in tumor development. In this study, we aimed to identify a prognostic NETosis-related gene signature that could predict the immunetherapy response and explore the expression of these genes in hepatocellular carcinoma (HCC). Differentially expressed NETosis-related genes were identified from the GSE80489 dataset in the Gene Expression Omnibus (GEO), and a protein–protein interaction (PPI) network was constructed using Cytoscape and the STRING database. Gene Ontology (GO) enrichment analysis revealed associations with chemical stimuli and the positive regulation of cellular processes in HCC. A total of 37 differentially expressed NETosis-related genes were identified, and SNORA31, PMAIP1, GLA, CXCL1, and ADM were selected to construct a prognostic signature. In the training cohort, the low-risk group demonstrated significantly better overall survival (OS) compared to the high-risk group (P = 2.715e−06), and multivariable analysis confirmed that this signature was an independent prognostic factor for HCC (HR = 1.630, 95% CI = 1.295–2.052, P < 0.001). This signature also significantly predicted OS in validation cohorts (P = 9.704e−03) and was associated with HCC patients' OS according to the Kaplan–Meier Plotter. Moreover, it could predict the immunotherapy response in HCC patients. Finally, we investigated the expression of NETosis-related genes in single-cell subtypes and in the HepG2 cell line, providing further insights into their relevance in HCC. Overall, this NETosis-related gene signature serves as a prognostic indicator and a potential predictor for immunotherapy response in HCC.