胃癌肿瘤突变负荷预测标志物的鉴定:基于TCGA数据和机器学习算法  

Identification of diagnostic signatures associated with tumor mutation burden in gastric cancer based on TCGA and machine-learning methods

作　　者：徐龙文 聂元华 白子龙 常东民[1] XU Longwen;NIE Yuanhua;BAI Zilong;CHANG Dongmin(Department of Surgical Oncology,The First Affiliated Hospital of Xi’an Jiaotong University,Xi’an 710061,China)

机构地区：[1]西安交通大学第一附属医院肿瘤外科,陕西西安710061

出　　处：《西安交通大学学报（医学版）》2023年第5期755-763,共9页Journal of Xi’an Jiaotong University（Medical Sciences）

基　　金：陕西省科学技术基金资助项目(S2018-YFZDSF-0102)。

摘　　要：目的筛选和鉴定具有预测胃癌肿瘤突变负荷(TMB)分类的微小RNA(microRNAs),从而为胃癌患者免疫治疗提供参考依据。方法从TCGA数据库中下载胃癌肿瘤样本的mRNA、microRNA表达谱数据和体细胞突变数据。利用R语言的“limma”包筛选高-、低-TMB组间差异表达的miRNAs。使用DIANA-miRPath v3.0数据库分析这些差异miRNAs可能参与的生物学功能。此外,采用随机森林(RF)和支持向量机递归特征消除(SVM-RFE)算法筛选出能预测TMB分类的miRNAs,然后利用曲线下面积(AUC)来评估其区分能力,并在其他癌种中进一步验证。最后,采用CIBERSORT算法评估不同TMB分组中免疫细胞浸润情况。结果共鉴定出56个差异表达miRNAs(DE-miRNAs)。功能富集分析显示,这些DE-miRNAs主要富集在肿瘤发生发展等信号通路(如ErbB信号和MAPK信号通路)和免疫相关生物学过程(如免疫系统过程和Toll样受体信号通路)。RF和SVM-RFE算法共同鉴定出10个诊断特征的miRNAs,其中只有hsa-miR-210-3p被认为是与TMB分类最相关的预测生物标志物,因其具有较高的诊断效能,其AUC值在训练集、测试集和总集中分别为0.822、0.721和0.793,并在多个癌种中得到验证。此外,CIBERSORT计算分析提示高/低TMB组间存在免疫细胞浸润差异,并且hsa-miR-210-3p与免疫检查点相关基因和错配修复相关基因的表达呈显著正相关。结论本研究成功鉴定出hsa-miR-210-3p为TMB分类的预测生物标志物,该生物标志物可有效预测胃癌及多种其他癌症患者中的TMB值,并可能为免疫治疗提供一定指导。Objective To select and identify miRNA signatures to predict TMB level in gastric cancer based on The Cancer Genome Atlas(TCGA)database and machine learning methods.Methods MiRNA expression and somatic mutation profiles of gastric cancer(GC)were downloaded from TCGA database.R“limma”package was performed to select differentially expressed miRNAs between high-TMB and low-TMB groups.Two machine learning algorisms,random forest(RF),and Support Vector Machine-Recursive Feature Elimination were utilized to identify miRNAs with the highest discriminative ability.ROC was used to test the predictive ability of these signatures in multiple datasets.Besides,immune cells of different TMB levels were compared by the CIBERSORT method.Results A total of 56 differentially expressed miRNAs(DE-miRNAs)were filtered.Functional enrichment analysis showed that these DE miRNAs are mainly enriched in signaling pathways related to tumor occurrence and development as well as immunity-related biological processes.The RF and SVM-RFE algorithms jointly identified 10 diagnostic features of miRNAs,among which only hsa-miR-210-3p is considered the most relevant predictive biomarker for TMB classification.The AUC value of hsa-miR-210-3p in the training,testing,and total sets is 0.822,0.721,and 0.793,respectively,and has been validated in other cancer types.Besides,CIBERSORT analysis suggests differences in immune cell infiltration between high-and low-TMB groups.Meanwhile,there is a significant positive correlation between the expression of immune checkpoint related genes and mismatch repair related genes and hsa-miR-210-3p.Conclusion This study successfully identified hsa-miR-210-3p as a predictive biomarker for TMB classification,which can effectively predict TMB values in gastric cancer and other cancer patients and may provide some guidance for immunotherapy.

关 键 词：胃癌 肿瘤突变负荷 微小RNA 癌症基因组图谱 免疫治疗 

分 类 号：R735[医药卫生—肿瘤]