Ni Jie, Wu Yang, Qi Feng, Li Xiao, Yu Shaorong, Liu Siwen, Feng Jifeng, Zheng Yuxiao
Department of Medical Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.
Research Center for Clinical Oncology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, China.
Front Oncol. 2020 Jan 21;9:1509. doi: 10.3389/fonc.2019.01509. eCollection 2019.
To identify genes of prognostic value which associated with tumor microenvironment (TME) in acute myeloid leukemia (AML). Level 3 AML patients gene transcriptome profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Clinical characteristics and survival data were extracted from the Genomic Data Commons (GDC) tool. Then, limma package was utilized for normalization processing. ESTIMATE algorithm was used for calculating immune, stromal and ESTIMATE scores. We examined the distribution of these scores in Cancer and Acute Leukemia Group B (CALGB) cytogenetics risk category. Kaplan-Meier (K-M) curves were used to evaluate the relationship between immune scores, stromal scores, ESTIMATE scores and overall survival. We performed clustering analysis and screened differential expressed genes (DEGs) by using heatmaps, volcano plots and Venn plots. After pathway enrichment analysis and gene set enrichment analysis (GESA), protein-protein interaction (PPI) network was constructed and hub genes were screened. We explore the prognostic value of hub genes by calculating risk scores (RS) and processing survival analysis. Finally, we verified the expression level, association of overall survival and gene interactions of hub genes in the Vizome database. We enrolled 173 AML samples from TCGA database in our study. Higher immune score was associated with higher risk rating in CALGB cytogenetics risk category ( = 0.0396) and worse overall survival outcomes ( = 0.0224). In Venn plots, 827 intersect genes were screened with differential analysis. Functional enrichment clustering analysis revealed a significant association between intersect genes and the immune response. After PPI network, 18 TME-related hub genes were identified. RS was calculated and the survival analysis results revealed that high RS was related with poor overall survival ( < 0.0001). Besides, the survival receiver operating characteristic curve (ROC) showed superior predictive accuracy (area under the curve = 0.725). Finally, the heatmap from Vizome database demonstrated that 18 hub genes showed high expression in patient samples. We identified 18 TME-related genes which significantly associated with overall survival in AML patients from TCGA database.
为了鉴定急性髓系白血病(AML)中与肿瘤微环境(TME)相关的具有预后价值的基因。从癌症基因组图谱(TCGA)数据库下载3级AML患者的基因转录组图谱。从基因组数据共享库(GDC)工具中提取临床特征和生存数据。然后,利用limma软件包进行标准化处理。使用ESTIMATE算法计算免疫、基质和ESTIMATE评分。我们研究了这些评分在癌症和急性白血病B组(CALGB)细胞遗传学风险分类中的分布情况。采用Kaplan-Meier(K-M)曲线评估免疫评分、基质评分、ESTIMATE评分与总生存期之间的关系。我们进行了聚类分析,并通过热图、火山图和维恩图筛选差异表达基因(DEG)。经过通路富集分析和基因集富集分析(GESA)后,构建了蛋白质-蛋白质相互作用(PPI)网络并筛选出枢纽基因。我们通过计算风险评分(RS)并进行生存分析来探索枢纽基因的预后价值。最后,我们在Vizome数据库中验证了枢纽基因的表达水平、总生存期的相关性以及基因相互作用。我们在研究中纳入了来自TCGA数据库的173个AML样本。较高的免疫评分与CALGB细胞遗传学风险分类中的较高风险评级(P = 0.0396)和较差的总生存结果(P = 0.0224)相关。在维恩图中,通过差异分析筛选出827个交集基因。功能富集聚类分析显示交集基因与免疫反应之间存在显著关联。经过PPI网络分析,鉴定出18个与TME相关的枢纽基因。计算RS并进行生存分析,结果显示高RS与较差的总生存期相关(P < 0.0001)。此外,生存受试者工作特征曲线(ROC)显示出卓越的预测准确性(曲线下面积 = 0.725)。最后,来自Vizome数据库的热图表明18个枢纽基因在患者样本中高表达。我们从TCGA数据库中鉴定出18个与TME相关的基因,这些基因与AML患者的总生存期显著相关。