Prediction model of clinical prognosis and immunotherapy efficacy of gastric cancer based on level of expression of cuproptosis-related genes

基于铜凋亡相关基因表达水平的胃癌临床预后及免疫治疗疗效预测模型

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Abstract

BACKGROUND: Gastric cancer is one of the most common malignancies in the world and ranks fourth among cancer-related causes of death. Gastric adenocarcinoma is the most common pathological type of gastric cancer; usually, this tumor is associated with distant metastasis upon first diagnosis and has a poor prognosis. Cuproptosis is a novel mechanism of cell death induced by copper, and is closely related to tumor progression, prognosis and immune response. However, the role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of gastric cancer has yet to be elucidated. METHODS: Gastric adenocarcinoma data were downloaded from the Cancer Genome Atlas (TCGA) database. Through bioinformatics analysis, a risk scoring model was constructed from cuproptosis gene-related lncRNA. Then, we investigated the relationship between prognosis and the TIME of gastric cancer according to clinical characteristics and risk score. RESULTS: Validation of the model showed that the overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group (P < 0.001) and that the risk score was an independent predictor of prognosis (P < 0.001). The new model was significantly correlated with the prognosis and TIME of patients with gastric cancer, including immune cell infiltration, tumor mutation burden (TMB) score, targeted drug sensitivity, and immune checkpoint gene expression. In addition, a prognostic nomogram was established based on the risk score (AC008915.2, AC011005.4, AC023511.1, AC139792.1, AL355312.2, LINC01094 and LINC02476). CONCLUSION: Our analysis revealed that the prognostic model of cuproptosis-related genes could effectively predict the prognosis of patients with gastric cancer and comprehensively establish the relationship between cuproptosis genes and tumor immunity. This may provide a new strategy for the precise treatment of gastric cancer.

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