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基于胃癌患者的癌症免疫疗法和免疫激活的综合分析。

Comprehensive Analysis Based on the Cancer Immunotherapy and Immune Activation of Gastric Cancer Patients.

机构信息

Department of Oncology, Zhongda Hospital, Southeast University, Nanjing 210009, China.

Department of General Surgery, Jining Hospital, Nanjing University, Nanjing, Jiangsu, China.

出版信息

Genet Res (Camb). 2023 Mar 6;2023:4674536. doi: 10.1155/2023/4674536. eCollection 2023.

Abstract

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.

摘要

在侵袭性和预后方面,免疫细胞在胃癌(GC)的微环境中起着重要作用。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。本研究旨在基于免疫状态分型开发一种用于分层胃癌风险的遗传特征。使用 TCGA 数据进行基因表达和临床特征分析。应用 ssGSEA 算法对胃癌队列进行分型。进行多变量和单变量 Cox 回归以及套索回归以确定与胃癌预后相关的基因。最后,我们使用免疫相关基因生成了一个 6 基因预后预测模型。进一步的分析表明,该预后预测模型与 GC 患者的预后密切相关。包含遗传特征和危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多个与特定免疫细胞亚群相关的免疫标志物显著相关。根据这些结果,患者的结局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可有助于改善胃癌的风险分层。可以根据这些特征指导免疫疗法和随访的临床决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f2e0/10010888/fd31d4bdffb2/GR2023-4674536.001.jpg

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