Mi Tao, Jin Liming, Zhang Zhaoxia, Wang Jinkui, Li Mujie, Zhanghuang Chenghao, Tan Xiaojun, Wang Zhang, Tian Xiaomao, Xiang Bin, He Dawei
Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China.
Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China.
Front Oncol. 2022 Apr 22;12:882714. doi: 10.3389/fonc.2022.882714. eCollection 2022.
To investigate the role of chemokines in Wilms tumours, especially their chemotaxis to immune cells and the role of DNA methylation in regulating the expression level of chemokines.
RNAseqV2 gene expression and clinical data were downloaded from the TARGET database. DNA methylation data were downloaded from the GEO and cBioPortal database. The difference analysis and Kaplan-Meier(KM) analysis of chemokines were performed by edgeR package. Then predictive model based on chemokines was constructed by lasso regression and multivariate COX regression. ROC curve, DCA curve, Calibration curve, and Nomogram were used to evaluate the prognostic model. MCPcounter and Cibersort algorithm was used to calculate the infiltration of immune cells in Wilms tumour and para-tumour samples. Then the difference analysis of the immune cells was performed. The relationship between chemokines and immune cells were calculated by Pearson correlation. In addition, DNA methylation differences between Wilms tumour and para-tumour samples was performed. The correlation between DNA methylation and mRNA expression was calculated by Pearson correlation. Western blot(WB)and immunofluorescence were used to confirm the differential expression of CX3CL1 and T cells, and the correlation between them.
A total of 16 chemokines were differentially expressed in tumour and para-tumour samples. A total of seven chemokines were associated with survival. CCL2 and CX3CL1 were positively correlated with prognosis, while high expression of CCL3, CCL8, CCL15, CCL18 and CXCL9 predicted poor prognosis. By lasso regression and multivariate COX regression, CCL3, CCL15, CXCL9 and CX3CL1 were finally included to construct a prediction model. The model shows good prediction ability. MCPcounter and Cibersort algorithm both showed that T cells were higher in para-tumour tissues than cancer tissues. Correlation analysis showed that CX3CL1 had a strong correlation with T cells. These were verified by Weston blot and immunofluorescence. DNA methylation analysis showed that various chemokines were different in para-tumours and tumours. CX3CL1 was hypermethylated in tumours, and the degree of methylation was negatively correlated with mRNA expression.
探讨趋化因子在肾母细胞瘤中的作用,尤其是它们对免疫细胞的趋化作用以及DNA甲基化在调节趋化因子表达水平中的作用。
从TARGET数据库下载RNAseqV2基因表达和临床数据。从GEO和cBioPortal数据库下载DNA甲基化数据。通过edgeR软件包对趋化因子进行差异分析和Kaplan-Meier(KM)分析。然后通过套索回归和多变量COX回归构建基于趋化因子的预测模型。使用ROC曲线、DCA曲线、校准曲线和列线图评估预后模型。使用MCPcounter和Cibersort算法计算肾母细胞瘤和癌旁组织样本中免疫细胞的浸润情况。然后对免疫细胞进行差异分析。通过Pearson相关性计算趋化因子与免疫细胞之间的关系。此外,对肾母细胞瘤和癌旁组织样本进行DNA甲基化差异分析。通过Pearson相关性计算DNA甲基化与mRNA表达之间的相关性。使用蛋白质免疫印迹法(WB)和免疫荧光法证实CX3CL1和T细胞的差异表达以及它们之间的相关性。
肿瘤组织和癌旁组织样本中共有16种趋化因子表达存在差异。共有7种趋化因子与生存相关。CCL2和CX3CL1与预后呈正相关,而CCL3、CCL8、CCL15、CCL18和CXCL9的高表达预示预后不良。通过套索回归和多变量COX回归,最终纳入CCL3、CCL15、CXCL9和CX3CL1构建预测模型。该模型显示出良好的预测能力。MCPcounter和Cibersort算法均显示癌旁组织中的T细胞高于癌组织。相关性分析表明CX3CL1与T细胞有很强的相关性。这些结果通过蛋白质免疫印迹法和免疫荧光法得到验证。DNA甲基化分析表明,各种趋化因子在癌旁组织和肿瘤组织中存在差异。CX3CL1在肿瘤组织中发生高甲基化,甲基化程度与mRNA表达呈负相关。