基于生物信息学分析转移性嗜铬细胞瘤的机制  

Bioinformatics analysis of mechanism of metastatic pheochromocytoma

作　　者：兰东阳 栾永坤 任翼麟 刘统虎 王宇[2] 王启昕 王智宇[1] 高宇奎 闫泽晨[1] Lan Dongyang;Luan Yongkun;Ren Yilin;Liu Tonghu;Wang Yu;Wang Qixin;Wang Zhiyu;Gao Yukui;Yan Zechen(Department of Urology,the First Affiliated Hospital of Zhengzhou University,Zhengzhou 450052,China;BGI College&Henan Institute of Medical and Pharmaceutical Sciences,Zhengzhou University,Zhengzhou 450001,China;Department of Urology,the First Affiliated Hospital of Wannan Medical College,Wuhu 241001,China)

机构地区：[1]郑州大学第一附属医院泌尿外科,郑州450052 [2]郑州大学华大基因学院,郑州450001 [3]皖南医学院第一附属医院泌尿外科,芜湖241001

出　　处：《中华实验外科杂志》2023年第4期758-761,共4页Chinese Journal of Experimental Surgery

基　　金：河南省医学科技攻关项目(2020zd-02)。

摘　　要：目的通过生物信息学分析鉴定转移性嗜铬细胞瘤的关键基因及分析相关的生物学通路,为临床诊断及治疗提供参考。方法从GEO数据库中下载51例样本(转移性嗜铬细胞瘤11例,对照组40例)的RNA表达谱数据,利用加权基因共表达网络分析(WGCNA)分析识别与转移性嗜铬细胞瘤最为相关的模块。提取与转移性嗜铬细胞瘤密切相关的模块基因进行基因本体(GO)和京都基因与基因组百科全书(KEGG)富集分析。接着,以基因显著性(GS)>0.4;成员之间相关性(MM)>0.6为阈值对模块基因进行初步筛选。为了防止过拟合,Lasso回归算法对初步鉴定的基因进一步筛选。最后采用受试者工作特征(ROC)曲线计算数据集的曲线下面积(AUC)值。结果通过对数据集进行WGCNA分析,共获得24个基因模块,其中浅蓝色基因模块与转移性嗜铬细胞瘤相关性最高(r=0.65),共包含218个基因。GO分析结果提示模块基因主要富集于细胞连接、细胞黏附分子等生物学功能;KEGG分析提示模块基因主要集中在"Wnt信号通路"等信号通路。对浅蓝色模块初步筛选,得到候选关键基因77个。通过Lasso回归算法进一步分析,鉴定出2个关键基因,即IRX3、KCNG3。ROC曲线结果提示IRX3、KCNG3在数据集中AUC值分别为0.897、0.878。结论利用生物信息学分析揭示与嗜铬细胞瘤转移相关的关键基因IRX3、KCNG3,为转移性嗜铬细胞瘤临床诊断和治疗提供基础。Objective Bioinformatics analysis was conducted to identify the key gene of metastatic pheochromocytoma and analyze the related biological pathways for clinical diagnosis and treatment.Methods The RNA expression profile data of 51 samples(metastatic pheochromocytoma:11 cases;control:40 cases)were downloaded in the GEO database,and then weighted gene co-expression network analysis(WGCNA)analysis was used to identify the modules most associated with metastatic pheochromocytoma.The module genes closely associated with metastatic pheochromocytoma were extracted for gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis.Then,the module genes were initially screened with threshold values of gene significance(GS)>0.4;module membership(MM)>0.6.The Lasso regression algorithm was used to screen the initially identified genes further for preventing over-fitting.Finally,receiver operating characteristic(ROC)curves were used to calculate the area under curve(AUC)values of the dataset.Results A total of 24 gene modules were obtained by WGCNA analysis of the dataset,and there was a highest correlation between the lightcya gene module and metastatic pheochromocytoma(r=0.65),containing a total of 218 genes.GO analysis results suggested that the module genes were mainly enriched in cell junction assembly,regulation of cell junction assembly and other biological functions.KEGG analysis suggested that the module genes were focused on signaling pathways such as"Wnt signaling pathway".The initial screening of the light blue module obtained a total of 77 candidate key genes.Lasso regression algorithm was used to further analyze and identify 2 key genes,namely IRX3,KCNG3.The ROC curve results suggested that IRX3 and KCNG3 had AUC values of 0.897 and 0.878 in the dataset,respectively.Conclusion Bioinformatics analysis was applied to reveal the key genes IRX3 and KCNG3 associated with metastasis of pheochromocytoma,which provides directions for the clinical diagnosis and treatment of metastatic pheochromocyt

关 键 词：嗜铬细胞瘤 生物信息学 关键基因 机器学习 

分 类 号：Q811.4[生物学—生物工程] R736.6[医药卫生—肿瘤]