应用生物信息学方法识别卵巢癌重要基因

发布时间：2018-01-17 16:21

本文关键词：应用生物信息学方法识别卵巢癌重要基因　出处：《肿瘤》2017年05期 　论文类型：期刊论文

【摘要】：目的:上皮细胞卵巢癌是目前最常见的妇科恶性肿瘤之一,其发病率每年都在升高。本次研究识别的重要基因能够预测上皮细胞卵巢癌患者的预后,阐释上皮细胞卵巢癌的发病机制。方法:本研究利用4对上皮细胞卵巢癌及其癌旁正常组织的转录组测序(RNA-Seq)数据进行差异基因的筛选,并应用Gene Ontology(GO)进行富集分析,在TCGA(The Cancer Genome Atlas)在线数据库中进行预后评估。结果:识别865个差异表达基因,通过功能注释识别13个有意义的GO类型,包括转录调控(regulation of transcription)、细胞黏附(cell adhesion)和生物黏附(biological adhesion)。结合卵巢癌TCGA在线数据库,发现2个重要基因(FBXO 5和CTNNB 1)在高表达时,不利于上皮细胞卵巢癌患者的生存;并且在独立的另外20对上皮细胞卵巢癌及其癌旁正常组织的样本中,采用PCR证实了这2个基因的差异表达情况。结论:本研究识别出的重要基因有助于更好地了解上皮细胞卵巢癌的发生和发展机制,可能成为预后判断的生物标志物。
[Abstract]:Objective: epithelial ovarian cancer is one of the most common gynecologic malignancies and its incidence is increasing every year. The important genes identified in this study can predict the prognosis of patients with epithelial ovarian cancer. Methods: in this study, four differentially expressed genes were screened from transcriptome sequencing data of epithelial ovarian carcinoma and its adjacent normal tissues. The enrichment analysis was carried out by using Gene Ontology GO. The prognosis was evaluated in the TCGA(The Cancer Genome Atlas online database. Results: 865 differentially expressed genes were identified. Functional annotations were used to identify 13 meaningful go types, including transcriptional regulation of transcription. Cell adhesion and biological adhesion. Combined with ovarian cancer TCGA online database. It was found that the high expression of FBXO _ 5 and CTNNB _ 1 were harmful to the survival of patients with epithelial ovarian cancer. And in a separate sample of another 20 pairs of epithelial ovarian cancer and its adjacent normal tissue. The differential expression of these two genes was confirmed by PCR. Conclusion: the important genes identified in this study are helpful to better understand the pathogenesis of epithelial ovarian cancer. May be a biomarker for prognostic judgment.
【作者单位】：云南省第二人民医院妇科;
【分类号】：R737.31
【正文快照】： 方法:本研究利用4对上皮细胞卵巢癌及其癌旁正常组织的转录组测序(RNA-Seq)数据进行差异基因的筛选,并应用Gene Ontology(GO)进行富集分析,在TCGA(The Cancer Genome Atlas)在线数据库中进行预后评估。结果:识别865个差异表达基因,通过功能注释识别13个有意义的GO类型,包括转

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