Department of Nuclear Accident Medical Emergency, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China.
Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China.
Biomed Res Int. 2021 Apr 5;2021:5524486. doi: 10.1155/2021/5524486. eCollection 2021.
Dabrafenib resistance is a significant problem in melanoma, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism of drug resistance of dabrafenib and to explore the key genes and pathways that mediate drug resistance in melanoma. GSE117666 was downloaded from the Gene Expression Omnibus (GEO) database and 492 melanoma statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Besides, differentially expressed miRNAs (DEMs) were identified by taking advantage of the R software and GEO2R. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and FunRich was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify potential pathways and functional annotations linked with melanoma chemoresistance. 9 DEMs and 872 mRNAs were selected after filtering. Then, target genes were uploaded to Metascape to construct protein-protein interaction (PPI) network. Also, 6 hub mRNAs were screened after performing the PPI network. Furthermore, a total of 4 out of 9 miRNAs had an obvious association with the survival rate ( < 0.05) and showed a good power of risk prediction model of over survival. The present research may provide a deeper understanding of regulatory genes of dabrafenib resistance in melanoma.
达布拉非尼耐药是黑色素瘤的一个重大问题,其潜在的分子机制尚不清楚。本研究旨在研究达布拉非尼耐药的分子机制,并探讨介导黑色素瘤耐药的关键基因和途径。从基因表达综合数据库(GEO)下载 GSE117666,从癌症基因组图谱(TCGA)数据库下载 492 例黑色素瘤统计数据。此外,利用 R 软件和 GEO2R 鉴定差异表达的 miRNAs(DEMs)。使用数据库注释、可视化和综合发现(DAVID)和 FunRich 进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)通路富集分析,以鉴定与黑色素瘤化疗耐药相关的潜在通路和功能注释。过滤后选择了 9 个 DEM 和 872 个 mRNAs。然后,将靶基因上传到 Metascape 构建蛋白质-蛋白质相互作用(PPI)网络。之后,在进行 PPI 网络后筛选出 6 个 hub mRNAs。此外,9 个 miRNA 中有 4 个与生存率(<0.05)有明显关联,并且具有良好的过生存风险预测模型的能力。本研究可能为黑色素瘤中达布拉非尼耐药的调控基因提供更深入的了解。