〔Abstract〕 To construct a prognostic model for gastric cancer using NETosis-related long non-coding RNAs (lncRNAs) and to investigate their expression and functional roles in gastric cancer progression. Methods Data from gastric cancer patients were obtained from the TCGA database, and 85 NETosis-related genes were identified from the GeneCards database. NETosis-associated lncRNAs were screened using Pearson correlation analysis. A lncRNA-based prognostic model was constructed and evaluated through survival analysis, ROC curves,C-index, and nomogram analysis. Differences in gene set enrichment between high- and low-risk groups were further explored. Additionally, RT-qPCR was performed to validate lncRNA expression in gastric cancer patients,and the CCLE database was utilized to investigate lncRNA expression across various tumor cell lines. Results A risk prognostic model for gastric cancer was constructed using 12 lncRNAs. Based on risk scores, samples were stratified into high- and low-risk groups across multiple datasets. Validation results demonstrated significantly worse survival outcomes in the high-risk group compared to the low-risk group, with excellent predictive performance of the model (AUC = 0.758, 95% CI: 0.688-0.828). Gene set enrichment analysis revealed that the high-risk group was enriched in gene sets strongly associated with inflammatory progression, whereas the low-risk group showed enrichment in gene sets related to normal DNA function. RT-qPCR confirmed differential expression of lncRNAs between tumor and adjacent normal tissues (P < 0.001). Furthermore, analysis of the CCLE database indicated substantial variations in lncRNA expression across different tumor cell lines. Conclusion The prognostic model constructed with 12 lncRNAs demonstrates potential for assessing prognosis and immune status in gastric cancer patients. Gene enrichment analysis provides insights into further mechanistic studies on NETosis. The differential expression of lncRNAs between cancerous and normal tissues, as well as across tumor cell lines, may inform novel subtype classification and therapeutic strategies.