Abstract
Cuproptosis is a newly discovered non-apoptotic form of cell death that may be related to the development of tumors. Nonetheless, the potential role of cuproptosis-related lncRNAs in tumor microenvironment (TME) formation and patient-tailored treatment optimization of gastric cancer (GC) is still unclear. In this study, the six-lncRNA signature was constructed to quantify the molecular patterns of GC using LASSO-Cox regression model. Receiver operating characteristic (ROC) curves, C-index curves, independent prognostic analysis and principal component analysis (PCA) were conducted to verify and evaluate the model. The results showed that this risk model was accurate and reliable in predicting GC patient survival. In addition, two distinct subgroups were identified based on the risk model, which showed significant difference in biological functions of the associated genes, TME scores, characteristics of infiltrating immune cells and immunotherapy responses. We found that the high-risk subgroup was associated with immune activation and tumor-related pathways. Furthermore, compared with the low-risk subgroup, the high-risk subgroup had higher TME scores, richer immune cell infiltration and a better immunotherapy response. To accurately identify immune cold tumors and hot tumors, all samples of GC were divided into four distinct clusters by consensus clustering. Among them, Cluster 3 was identified as an immune hot tumor and was more sensitive to immunotherapy. Overall, this study demonstrates that cuproptosis-related lncRNAs could accurately predict the prognosis of patients with GC, help make a distinction between immune cold tumors and hot tumors and provide a basis for the precision medicine of GC.