When it comes to aggressiveness and prognosis, immune cells play an important role in the microenvironment of gastric cancer (GC). Currently, there is no well-established evidence that immune status typing is reliable as a prognostic tool for gastric cancer. This study aimed to develop a genetic signature based on immune status typing for the stratification of gastric cancer risk. TCGA data were used for gene expression and clinical characteristics analysis. A ssGSEA algorithm was applied to type the gastric cancer cohorts. A multivariate and univariate Cox regression and a lasso regression were conducted to determine which genes are associated with gastric cancer prognosis. Finally, we were able to produce a 6-gene prognostic prediction model using immune-related genes. Further analysis revealed that the prognostic prediction model is closely related to the prognosis of patients with GC. Nomograms incorporating genetic signatures and risk factors produced better calibration results. The relationship between the risk score and gastric cancer T stage was also significantly correlated with multiple immune markers related to specific immune cell subsets. According to these results, patients’ outcomes and tumor immune cell infiltration correlate with risk scores. In addition, immune cellular-based genetic signatures can contribute to improved risk stratification for gastric cancer. Clinical decisions regarding immunotherapy and followup can be guided by these features.

在侵袭性和预后方面，免疫细胞在胃癌（GC）的微环境中发挥着重要作用。目前，没有充分的证据表明免疫状态分型作为胃癌的预后工具是可靠的。本研究旨在开发基于免疫状态分型的遗传特征，用于胃癌风险分层。TCGA数据用于基因表达和临床特征分析。应用 ssGSEA 算法对胃癌队列进行分类。进行多变量和单变量 Cox 回归以及 lasso 回归以确定哪些基因与胃癌预后相关。最后，我们能够使用免疫相关基因生成 6 基因预后预测模型。进一步分析发现，预后预测模型与GC患者的预后密切相关。结合遗传特征和风险因素的列线图产生了更好的校准结果。风险评分与胃癌T分期之间的关系也与特定免疫细胞亚群相关的多种免疫标志物显着相关。根据这些结果，患者的结果和肿瘤免疫细胞浸润与风险评分相关。此外，基于免疫细胞的遗传特征有助于改善胃癌的风险分层。有关免疫治疗和随访的临床决策可以根据这些特征来指导。