肿瘤细胞治疗研究
英文原题：Tumor microenvironment characterization in esophageal cancer identifies prognostic relevant immune cell subtypes and gene signatures.
Tumor microenvironment characterization in esophageal cancer identifies prognostic relevant immune cell subtypes and gene signatures.
分数与星级只用于站内排序 —— 不代表疗效、安全性或个人适用性。
食管癌（ESCA）是消化系统常见的恶性肿瘤，死亡率高、预后差。肿瘤微环境（TME）在ESCA的肿瘤发生、进展和治疗耐药中发挥重要作用，但其在预测临床结局中的作用尚未完全阐明。在本研究中，我们使用基因集变异分析（GSVA）全面评估了164例ESCA患者的TME浸润模式，并识别出4种与ESCA患者预后相关的关键免疫细胞（自然杀伤T细胞、未成熟B细胞、NK 细胞和1型辅助性T细胞）。此外，根据TME模式定义了2个具有不同临床结局的TME组。根据两个TME组之间的表达基因集，我们基于单样本基因集富集分析（ssGSEA）算法构建了一个计算TMEscore的模型。TMEscore系统地将TME组与基因组特征和临床病理特征相关联。总之，我们的数据提供了一种新的TMEscore，可作为预测ESCA临床结局的可靠指标。
Esophageal cancer (ESCA) is a common malignancy in the digestive system with a high mortality rate and poor prognosis. Tumor microenvironment (TME) plays an important role in the tumorigenesis, progression and therapy resistance of ESCA, whereas its role in predicting clinical outcomes has not been fully elucidated. In this study, we comprehensively estimated the TME infiltration patterns of 164 ESCA patients using Gene Set Variation Analysis (GSVA) and identified 4 key immune cells (natural killer T cell, immature B cell, natural killer cell, and type 1 T helper cell) associated with the prognosis of ESCA patients.
Besides, two TME groups were defined based on the TME patterns with different clinical outcomes. According to the expression gene set between two TME groups, we built a model to calculate TMEscore based on the single-sample gene-set enrichment analysis (ssGSEA) algorithm. TMEscore systematically correlated the TME groups with genomic characteristics and clinicopathologic features.
In conclusion, our data provide a novel TMEscore which can be regarded as a reliable index for predicting the clinical outcomes of ESCA.
MEMBER ACCOUNT
登录成功会直接打开下一页。
