Liu Yu, Zhou Hao, Zheng Ji, Zeng Xiaojun, Yu Wenjing, Liu Wei, Huang Guorong, Zhang Yang, Fu Weiling
Department of Laboratory Medicine, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
Department of Urology, First Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
Front Oncol. 2020 Jul 31;10:1008. doi: 10.3389/fonc.2020.01008. eCollection 2020.
Cancer, especially malignant tumors with poor prognosis, has become a major hazard to human life and health. The tumor microenvironment is gaining increasing attention from researchers, as it offers a new focus for tumor diagnosis, therapy, and prognosis. The numbers of immune and stromal cells, which are major components of the tumor microenvironment, could be determined from RNA-seq data with the Estimation of STromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. To explore the effects of immune and stromal cells on tumor prognosis, we analyzed associations between overall survival and immune/stromal scores for 20 malignant tumor types based on The Cancer Genome Atlas (TCGA) data. For six of the 20 tumor types, we observed statistically significant associations. Furthermore, to better explain the predictive ability of these scores, differentially expressed genes (DEGs) were identified in groups of cases with high or low immune or stromal scores for each of these six malignant tumor types. In addition, a list of immune-related genes was screened to identify prognostic predictors for one or more tumor types. Thus, multi-database joint analysis can provide a new approach to the assessment of tumor prognosis and allow the identification of related genes that may be new biomarkers for tumor metastasis and prognosis.
癌症,尤其是预后较差的恶性肿瘤,已成为危害人类生命健康的主要因素。肿瘤微环境正日益受到研究人员的关注,因为它为肿瘤诊断、治疗和预后提供了新的关注点。利用表达数据估计恶性肿瘤中的基质和免疫细胞(ESTIMATE)算法,可以从RNA测序数据中确定作为肿瘤微环境主要成分的免疫细胞和基质细胞的数量。为了探究免疫细胞和基质细胞对肿瘤预后的影响,我们基于癌症基因组图谱(TCGA)数据,分析了20种恶性肿瘤类型的总生存期与免疫/基质评分之间的关联。在这20种肿瘤类型中,我们观察到有6种存在统计学上的显著关联。此外,为了更好地解释这些评分的预测能力,我们针对这6种恶性肿瘤类型,在免疫或基质评分高或低的病例组中鉴定了差异表达基因(DEG)。另外,筛选了一份免疫相关基因列表,以确定一种或多种肿瘤类型的预后预测指标。因此,多数据库联合分析可为评估肿瘤预后提供一种新方法,并有助于鉴定可能成为肿瘤转移和预后新生物标志物的相关基因。