Gene expression profiles and pathway enrichment analysis of human osteosarcoma cells exposed to sorafenib.
Gene expression profiles and pathway enrichment analysis of human osteosarcoma cells exposed to sorafenib.
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DOI：
10.1002/2211-5463.12428
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发表时间：
2018-05
期刊：
影响因子：
2.6
通讯作者：
Zhu J
中科院分区：
文献类型：
--
作者：
Dai Z;Tang H;Pan Y;Chen J;Li Y;Zhu J
索拉非尼是一种酪氨酸激酶受体抑制剂，用于治疗各种癌症，包括肝细胞癌、肾细胞癌和甲状腺癌。它已被证明可以改变与骨肉瘤相关的各种靶点，但详细的机制仍不清楚。为了鉴定人骨肉瘤细胞暴露于索拉非尼期间的关键基因、富集的途径和重要模块，基因表达谱的数据（http：www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc= GSE 53155）从GEO数据库下载。R bioconductor软件包共鉴定出61个差异表达基因（DEG）。使用大卫数据库进行DEG的功能和富集分析。这些结果表明DEGs在炎症免疫反应和血管生成的生物学过程、分子功能和KEGG通路中富集。以IL 8、CXCL 2、PTGS 2、FOS、CXCL 1、C3、EHMT 2和PGF 8个基因为枢纽，构建了一个蛋白质-蛋白质相互作用网络。随后，mcode只识别出一个簇，该簇由6个节点（CXCL 1，CXCL 2，PTGS 2，FOS，C3和PGF）和9条边组成。PGF是该簇中的种子基因。总之，这些数据挖掘和整合的结果将有助于揭示索拉非尼抑制骨肉瘤的新机制和靶点。
Sorafenib is an inhibitor of a variety of tyrosine kinase receptors used to treat various cancers including hepatocellular, renal cell and thyroid carcinoma. It has been shown to change various targets associated with osteosarcoma, but the detailed mechanism remains unclear. In order to identify key genes, enriched pathways and important modules during the exposure of human osteosarcoma cells to sorafenib, data for gene expression profiles (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53155) were downloaded from the GEO database. In total, 61 differentially expressed genes (DEGs) were identified by the R bioconductor packages. Functional and enrichment analyses of DEGs were performed using the DAVID database. These revealed that DEGs were enriched in biological processes, molecular function and KEGG pathway of inflammatory immune response and angiogenesis. A protein–protein interaction network was constructed by string and visualized in cytoscape, and eight genes were selected as hubs: IL8,CXCL2,PTGS2,FOS,CXCL1, C3,EHMT2 and PGF. Subsequently, only one cluster was identified by mcode, which consisted of six nodes (CXCL1,CXCL2,PTGS2,FOS, C3 and PGF) and nine edges. PGF was the seed gene in this cluster. In conclusion, the results of this data mining and integration should help in revealing new mechanisms and targets of sorafenib in inhibiting osteosarcoma.
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