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目的:从分子水平揭示雄激素非依赖型前列腺癌(AIPC)的发病机制,为临床诊疗提供新思路。方法:在公共基因芯片数据库(GEO)中下载前列腺癌的相关基因芯片数据,使用BRB-ArrayTools软件对其进行数据挖掘分析,筛选AIPC相关基因;并利用GATHER在线分析工具进行深入的生物信息学分析。此外,又利用基因集富集分析(GSEA)软件对上述基因芯片数据集进行了基因富集分析。结果:BRB分析发现在AIPC中有87个差异表达基因,其中上调23个,下调64个。这些差异基因的功能主要集中在细胞信号转导、细胞粘附、粘附斑、细胞外基质(ECM)受体相互作用等功能中。GSEA分析发现AIPC相关基因主要在ECM受体相互作用、SONIC_HEDGEHOG以及HDACI_COLON_TSABUT_DN功能基因集中富集。结论:应用BRB-ArrayTools和GSEA分析发现,ECM受体相互作用、细胞粘附过程及Hedgehog信号通路之间的网络调控系统可能与AIPC的发生密切相关。利用生物信息学的方法能有效分析基因芯片数据并获取生物内在信息,为确定雄激素非依赖型前列腺癌的早期诊断标志与治疗靶点提供新的思路。
Objective: To reveal the pathogenesis of androgen independent prostate cancer (AIPC) at the molecular level and to provide new ideas for clinical diagnosis and treatment. Methods: The gene chip data of prostate cancer was downloaded from the public gene chip database (GEO), and the data were analyzed by using BRB-ArrayTools software to screen the AIPC related genes. The bioinformatics analysis was performed by GATHER online analysis tool . In addition, the GeneChip enrichment analysis (GSEA) software was used to perform gene enrichment analysis on the above gene chip datasets. RESULTS: BRB analysis found 87 differentially expressed genes in AIPC, including 23 up-regulated and 64 down-regulated. The function of these differentially expressed genes is mainly in the functions of cell signal transduction, cell adhesion, adhesion spots and extracellular matrix (ECM) receptor interaction. GSEA analysis found that AIPC related genes mainly concentrated in ECM receptor interaction, SONIC_HEDGEHOG and HDACI_COLON_TSABUT_DN functional gene concentration. Conclusion: The results of BRB-ArrayTools and GSEA showed that the network regulation system between ECM receptor interaction, cell adhesion process and Hedgehog signaling pathway may be closely related to the occurrence of AIPC. Bioinformatics methods can effectively analyze gene chip data and obtain biological intrinsic information, providing a new idea for the determination of early diagnostic markers and therapeutic targets of androgen-independent prostate cancer.