■趋化细胞因子在急性髓性白血病(AML)的发展中起着至关重要的作用。因此,研究AML中趋化细胞因子相关基因(CCRGs)的作用机制至关重要.
■使用TCGA-AML,GSE114868和GSE12417数据集,差异表达分析鉴定了差异表达的CCRG(DE-CCRG)。通过将AML和对照组之间的差异表达基因(DEG)与CCRG重叠来筛选这些基因。随后,进行了功能富集分析和蛋白质-蛋白质相互作用(PPI)网络的构建,以探索DE-CCRGs的功能。单变量Cox回归,最小绝对收缩和选择运算符(LASSO),和多变量Cox回归分析确定了相关的预后基因,并建立了预后模型。进行预后基因的生存分析,其次是功能相似性分析,免疫分析,富集分析,和药物预测分析。
■差异表达分析显示6,743DEGs,其中29个DE-CCRG被选择用于本研究。功能富集分析表明,DE-CCRG主要参与趋化细胞因子相关功能和途径。六个预后基因(CXCR3,CXCR2,CXCR6,CCL20,CCL4和CCR2)被鉴定并纳入风险模型。使用GSE12417数据集验证了模型的性能。生存分析显示预后基因高表达组和低表达组之间AML总生存(OS)存在显著差异,提示预后基因可能与患者生存显著相关。此外,在两个风险组之间鉴定出9种不同的免疫细胞.相关性分析显示,CCR2与单核细胞呈最显著正相关,与静息肥大细胞呈最显著负相关。高危人群肿瘤免疫功能紊乱和排除评分较低。
■CXCR3、CXCR2、CXCR6、CCL20、CCL4和CCR2被鉴定为与AML和肿瘤免疫微环境相关的预后基因。这些发现为AML的预防和治疗提供了新的见解。
UNASSIGNED: Chemotactic cytokines play a crucial role in the development of acute myeloid leukemia (AML). Thus, investigating the mechanisms of chemotactic cytokine-related genes (CCRGs) in AML is of paramount importance.
UNASSIGNED: Using the TCGA-AML, GSE114868, and GSE12417 datasets, differential expression analysis identified differentially expressed CCRGs (DE-CCRGs). These genes were screened by overlapping differentially expressed genes (DEGs) between AML and control groups with CCRGs. Subsequently, functional enrichment analysis and the construction of a protein-protein interaction (PPI) network were conducted to explore the functions of the DE-CCRGs. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses identified relevant prognostic genes and developed a prognostic model. Survival analysis of the prognostic gene was performed, followed by functional similarity analysis, immune analysis, enrichment analysis, and drug prediction analysis.
UNASSIGNED: Differential expression analysis revealed 6,743 DEGs, of which 29 DE-CCRGs were selected for this study. Functional enrichment analysis indicated that DE-CCRGs were primarily involved in chemotactic cytokine-related functions and pathways. Six prognostic genes (CXCR3, CXCR2, CXCR6, CCL20, CCL4, and CCR2) were identified and incorporated into the risk model. The model\'s performance was validated using the GSE12417 dataset. Survival analysis showed significant differences in AML overall survival (OS) between prognostic gene high and low expression groups, indicating that prognostic gene might be significantly associated with patient survival. Additionally, nine different immune cells were identified between the two risk groups. Correlation analysis revealed that CCR2 had the most significant positive correlation with monocytes and the most significant negative correlation with resting mast cells. The tumor immune dysfunction and exclusion score was lower in the high-risk group.
UNASSIGNED: CXCR3, CXCR2, CXCR6, CCL20, CCL4, and CCR2 were identified as prognostic genes correlated to AML and the tumor immune microenvironment. These findings offerred novel insights into the prevention and treatment of AML.