A Charcteristic Model of Cuproptosis-Associated Long Noncoding RNAs to Predict Prognosis and the Immunotherapy Response in Patients with Gastric Cancer

Background Cuproptosis and lncRNAs are the focus of current research, which had been confirmed to play an important role in the development of tumor. Nevertheless, effects of cuproptosis-related lncRNAs on GC have been limited. The purpose of this study was to determine a characteristic model of cuproptosis-related lncRNAs to predict their prognostic role and therapeutic response in GC. Methods Transcriptome profiling data and clinical data from GC patients were available from the TCGA database. GC patients were randomly assigned to the training group and the testing group. In the training group, a risk prognostic model of cuproptosis-related lncRNAs was constructed by co-expression, univalent analysis, LASSO analysis and multivariate analysis, and the model was verified in the testing group. Supported by the risk score and clinical characteristics, we subsequently developed a mixed nomogram. Finally,immunotherapy response was evaluated by TMB and TIDE score. Results Based on previous literature, we collected 19 genes related to cuproptosis. Subsequently, 140 cuproptosis-related lncRNAs were available for consultation as a result of co-expression. The Cox model revealed and constructed five cuproptosis-related lncRNAs prognostic models(AC243829.4, AL353622.1, CMAHP, LINC01579, MIR194_2HG). Patients were subdivided into high-risk and low-risk groups based on a median risk score. The Kaplan-Meier survival curve and receiver operating characteristic curve revealed that the model had accurate predictive ability. Univariate and multivariate Cox regression analyses showed that the risk score was an independent prognostic factor. In addition, we used multivariate Cox regression to construct a mixed nomogram of the predictive power of prognostic markers. Most notably, patients with lower risk score had increased TMB score and decreased TIDE score, suggesting that these patients exhibited a better immune checkpoint blockade response. Conclusions The prognostic characteristic model based on cuproptosis-related lncRNAs has the potential to be laid down the survival, immune environment and immunotherapy efficacy of GC, which is helpful for clinical prediction and individualized treatment.