The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266003, China.
Department of General Surgery, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, 758 Hefei Road, Qingdao, 266035, Shandong, China.
BMC Gastroenterol. 2022 Jul 30;22(1):364. doi: 10.1186/s12876-022-02451-2.
Stromal cells play an important role in the process of tumor progression, but the relationship between stromal cells and metabolic reprogramming is not very clear in gastric cancer (GC).
Metabolism-related genes associated with stromal cells were identified in The Cancer Genome Atlas (TCGA) and GSE84437 datasets, and the two datasets with 804 GC patients were integrated into a training cohort to establish the prognostic signature. Univariate Cox regression analysis was used to screen for prognosis-related genes. A risk score was constructed by LASSO regression analysis combined with multivariate Cox regression analysis. The patients were classified into groups with high and low risk according to the median value. Two independent cohorts, GSE62254 (n = 300) and GSE15459 (n = 191), were used to externally verify the risk score performance. The CIBERSORT method was applied to quantify the immune cell infiltration of all included samples.
A risk score consisting of 24 metabolic genes showed good performance in predicting the overall survival (OS) of GC patients in both the training (TCGA and GSE84437) and testing cohorts (GSE62254 and GSE15459). As the risk score increased, the patients' risk of death increased. The risk score was an independent prognostic indicator in both the training and testing cohorts suggested by the univariate and multivariate Cox regression analyses. The patients were clustered into four subtypes according to the quantification of 22 kinds of immune cell infiltration (ICI). The proportion of ICI Cluster C with the best prognosis in the low-risk group was approximately twice as high as that in the high-risk group, and the risk score of ICI Cluster C was significantly lower than that of the other three subtypes.
Our study proposed the first scheme for prognostic risk classification of GC from the perspective of tumor stromal cells and metabolic reprogramming, which may contribute to the development of therapeutic strategies for GC.
基质细胞在肿瘤进展过程中起着重要作用，但胃癌（GC）中基质细胞与代谢重编程之间的关系尚不清楚。
在癌症基因组图谱（TCGA）和 GSE84437 数据集识别与基质细胞相关的代谢相关基因，将包含 804 例 GC 患者的两个数据集整合到训练队列中建立预后特征。采用单因素 Cox 回归分析筛选预后相关基因。采用 LASSO 回归分析结合多因素 Cox 回归分析构建风险评分。根据中位数将患者分为高风险和低风险组。使用两个独立的队列，GSE62254（n=300）和 GSE15459（n=191），对外验证风险评分的性能。采用 CIBERSORT 方法对所有纳入样本的免疫细胞浸润进行定量。
由 24 个代谢基因组成的风险评分在 TCGA 和 GSE84437 训练队列和 GSE62254 和 GSE15459 测试队列中均能较好地预测 GC 患者的总生存期（OS）。随着风险评分的增加，患者死亡的风险增加。单因素和多因素 Cox 回归分析提示风险评分是训练和测试队列中独立的预后指标。根据 22 种免疫细胞浸润（ICI）的定量，将患者聚类为四个亚型。低风险组中预后最好的 ICI 聚类 C 的比例约为高风险组的两倍，而 ICI 聚类 C 的风险评分明显低于其他三个亚型。
本研究从肿瘤基质细胞和代谢重编程的角度提出了 GC 预后风险分类的第一个方案，可能有助于制定 GC 的治疗策略。
