黑色素瘤转移相关基因的生物信息学分析

Bioinformatic Analysis of Differentially Expressed Genes in Metastatic Melanoma

目的通过对与黑色素瘤转移相关的基因芯片进行生物信息学分析,为探索黑色素瘤转移的分子机制提供理论依据。方法从公共基因数据库(GEO)中下载与黑色素瘤转移相关的基因芯片数据,经数据预处理后,分别通过BRB-Array Tools软件、DAVID在线分析软件、STRING在线数据库和Cytoscape软件进行差异表达基因的筛选、功能注释、通路分析、蛋白互作网络分析并计算网络及各个节点的拓扑特性。结果 BRB分析筛选出196个黑色素瘤转移差异表达基因,其中上调133个,下调36个,DAVID分析发现其主要集中在角质形成细胞分化、组织细胞间黏附、肥大细胞分化等生物学过程和Rap1信号通路、p53信号通路、谷胱甘肽代谢等通路。STRING分析发现了蛋白质网络互作图中的9个关键基因,分别为KRT14、KRT16、KRT1、EGFR、KIT、DSP、DSG1、PKP1、KLK7。结论通过生物信息学的方法对黑色素瘤转移相关基因芯片数据进行2次挖掘,为进一步研究黑色素瘤转移的相关机制提供了理论依据。

Objective In order to provide theoretical base for the mechanism of the development of melanoma by screening differential genes related to this tumor based on bioinformaties analysis. Methods In this research, we chose melanoma microarray datasets from GEO. After pre-proeessing the data, we used unpaired t-test to screen differential genes. We used tools in DAVID Software for GO analysis and KEGG pathway analysis, imported STRING online database for protein-protein interaction network analysis, and computed the network topology through Cytoscape software. Results We found 169 differentially expressed genes in melanoma in which 133 were up-regulated and 36 were down-regulated. GO analysis indicated that most of genes were enriched in keratinocyte differentiation, cell adhesion, mast cell differentiation, Rapl signaling pathway and p53 signaling pathway. STRING software screened nine key genes including KRT14, KRT16, KRT1, EGFR, KIT, DSP, DSG1, PKP1 and KLKT. Conclusion The internal biological information in melanoma can be revealed by bioinformatic methods, providing direction for further research in metastatic melanoma.