Department of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
Guangdong Provincial Key Laboratory of Malignant Tumour Epigenetics and Gene Regulation, Breast Tumour Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
Br J Surg. 2018 Sep;105(10):1338-1348. doi: 10.1002/bjs.10871. Epub 2018 Apr 25.
Increasing evidence has indicated an association between immune infiltration in gastric cancer and clinical outcome. However, reliable prognostic signatures, based on systematic assessments of the immune landscape inferred from bulk tumour transcriptomes, have not been established. The aim was to develop an immune signature, based on the cellular composition of the immune infiltrate inferred from bulk tumour transcriptomes, to improve the prognostic predictions of gastric cancer.
Twenty-two types of immune cell fraction were estimated based on large public gastric cancer cohorts from the Gene Expression Omnibus using CIBERSORT. An immunoscore based on the fraction of immune cell types was then constructed using a least absolute shrinkage and selection operator (LASSO) Cox regression model.
Using the LASSO model, an immunoscore was established consisting of 11 types of immune cell fraction. In the training cohort (490 patients), significant differences were found between high- and low-immunoscore groups in overall survival across and within subpopulations with an identical TNM stage. Multivariable analysis revealed that the immunoscore was an independent prognostic factor (hazard ratio 1·92, 95 per cent c.i. 1·54 to 2·40). The prognostic value of the immunoscore was also confirmed in the validation (210) and entire (700) cohorts.
The proposed immunoscore represents a promising signature for estimating overall survival in patients with gastric cancer.
越来越多的证据表明，胃癌中的免疫浸润与临床结局之间存在关联。然而，基于对肿瘤转录组推断的免疫景观的系统评估，尚未建立可靠的预后标志物。本研究旨在开发一种基于肿瘤转录组推断的免疫浸润细胞组成的免疫标志物，以改善对胃癌的预后预测。
利用 CIBERSORT 基于来自基因表达综合数据库（GEO）的大型公共胃癌队列，估计了 22 种免疫细胞分数。然后，使用最小绝对收缩和选择算子（LASSO）Cox 回归模型，基于免疫细胞类型分数构建免疫评分。
使用 LASSO 模型，建立了一个由 11 种免疫细胞分数组成的免疫评分。在训练队列（490 例患者）中，在具有相同 TNM 分期的不同亚组中，高免疫评分组和低免疫评分组的总生存时间存在显著差异。多变量分析显示，免疫评分是一个独立的预后因素（危险比 1.92，95%置信区间 1.54 至 2.40）。免疫评分的预后价值在验证队列（210 例）和全队列（700 例）中也得到了证实。
提出的免疫评分是一种有前途的标志物，可用于估计胃癌患者的总生存时间。
Zotero 插件
