Molecular Subtypes and CD4(+) Memory T Cell-Based Signature Associated With Clinical Outcomes in Gastric Cancer.
Molecular Subtypes and CD4(+) Memory T Cell-Based Signature Associated With Clinical Outcomes in Gastric Cancer.
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分子亚型和 CD4 记忆 T 细胞特征与胃癌临床结果相关
DOI：
10.3389/fonc.2020.626912
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发表时间：
2020
影响因子：
4.7
通讯作者：
Zong Z
中科院分区：
文献类型：
--
作者：
Ning ZK;Hu CG;Huang C;Liu J;Zhou TC;Zong Z
背景CD 4+记忆性T细胞是肿瘤微环境的重要组成部分，影响肿瘤的发生和发展。然而，目前还没有系统的分析CD 4+记忆T细胞在胃癌（GC）中的作用。方法从基因表达综合数据库（GEO）中检索并下载从微阵列获得的三个数据集和相应的GC患者临床数据。我们将具有标准注释的标准化基因表达数据上传到CIBERSORT门户网站，用于评估GC样品中免疫细胞的比例。通过WGCNA分析确定与CD 4+记忆T细胞相关的CD 4+记忆T细胞相关模块（CD 4 + MTRM）。单因素考克斯分析筛选CD 4+记忆T细胞相关基因（CD 4 + MTRG）。然后进行LASSO分析和多变量考克斯分析以构建预后基因签名，其效果通过Kaplan-Meier曲线和受试者操作特征（ROC）、Harrell一致性指数（C-指数）和决策曲线分析（DCA）来评估。最后根据CD 4 + MTRG建立预后列线图。结果胃癌患者CD 4+记忆性T细胞的高丰度与较好的生存率相关。采用CD 4 + MTRM对胃癌患者进行无监督聚类分析，共鉴定出10个CD 4 + MTRG。观察到三个集群之间的总体生存率、5种免疫检查点基因和17种免疫细胞存在显着差异。构建ten-CD 4 + MTRG标记以预测GC患者预后。10-CD 4 + MTRG标记可以将GC患者分为具有不同OS率的高风险组和低风险组。多因素考克斯分析提示，ten-CD 4 + MTRG是胃癌的独立危险因素。建立了包含该特征和临床变量的列线图，C指数为0.73（95%CI：0.697-0.763）。校正曲线和DCA显示了OS诺模图的高可信度。结论我们鉴定了3种分子亚型，10种CD 4 + MTRG，并生成了可靠预测GC OS的预后诺模图。这些发现对精确的预后预测和个体化靶向治疗具有重要意义。
Background CD4+ memory T cells are an important component of the tumor microenvironment (TME) and affect tumor occurrence and progression. Nevertheless, there has been no systematic analysis of the effect of CD4+ memory T cells in gastric cancer (GC). Methods Three datasets obtained from microarray and the corresponding clinical data of GC patients were retrieved and downloaded from the Gene Expression Omnibus (GEO) database. We uploaded the normalize gene expression data with standard annotation to the CIBERSORT web portal for evaluating the proportion of immune cells in the GC samples. The WGCNA was performed to identify the modules the CD4+ memory T cell related module (CD4+ MTRM) which was most significantly associated with CD4+ memory T cell. Univariate Cox analysis was used to screen prognostic CD4+ memory T cell-related genes (CD4+ MTRGs) in CD4+ MTRM. LASSO analysis and multivariate Cox analysis were then performed to construct a prognostic gene signature whose effect was evaluated by Kaplan-Meier curves and receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and decision curve analyses (DCA). A prognostic nomogram was finally established based on the CD4+ MTRGs. Result We observed that a high abundance of CD4+ memory T cells was associated with better survival in GC patients. CD4+ MTRM was used to stratify GC patients into three clusters by unsupervised clustering analysis and ten CD4+ MTRGs were identified. Overall survival, five immune checkpoint genes and 17 types of immunocytes were observed to be significantly different among the three clusters. A ten-CD4+ MTRG signature was constructed to predict GC patient prognosis. The ten-CD4+ MTRG signature could divide GC patients into high- and low-risk groups with distinct OS rates. Multivariate Cox analysis suggested that the ten-CD4+ MTRG signature was an independent risk factor in GC. A nomogram incorporating this signature and clinical variables was established, and the C-index was 0.73 (95% CI: 0.697–0.763). Calibration curves and DCA presented high credibility for the OS nomogram. Conclusion We identified three molecule subtypes, ten CD4+ MTRGs, and generated a prognostic nomogram that reliably predicts OS in GC. These findings have implications for precise prognosis prediction and individualized targeted therapy.
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