利用生物信息学鉴定影响卵巢癌预后的TGF-β信号通路相关基因

Identification of TGF-βSignaling Related Genes in the Prognosis of Ovarian Cancer by Bioinformatics

目的探讨转化生长因子(TGF-β)信号通路相关基因在卵巢癌中的预后价值。方法下载癌症基因组图谱(TCGA)和基因表达集锦(GEO)数据库中的卵巢癌患者转录组测序数据,利用TGF-β信号通路相关基因的表达将卵巢癌患者分组。通过差异表达分析和单因素Cox回归分析鉴定影响卵巢癌预后的TGF-β信号通路相关基因。计算预后基因评分,比较高、低评分组中卵巢癌患者的生存情况,利用多因素Cox回归分析卵巢癌的独立预后因素。结果一致性聚类分析结果表明,根据TGF-β信号通路相关基因可将卵巢癌患者分为两组,两组患者的免疫微环境有差异。差异表达分析和单因素Cox回归分析结果表明GMPR、PIEZO1、EMP1、CXCL13、GADD45B、SORCS2、FOSL2、PODN、LYNX1和SLC38A5是卵巢癌的预后基因,且预后评分是卵巢癌的独立预后因素。结论TGF-β信号通路相关基因可将卵巢癌患者划分为不同的免疫亚型,并可作为卵巢癌的预后标志物。

Objective To investigate the prognostic value of transforming growth factor-β(TGF-β)signaling related genes in the ovarian cancer.Methods The transcriptome data of ovarian cancer patients were downloaded from the Cancer Genome Atlas(TCGA)and gene expression omnibus(GEO)database.Based on the expressions of TGF-βsignaling related genes,the ovarian cancer patients were divided into different groups.Differential expression and univariate Cox regression analyses were performed to identify prognostic TGF-βsignaling related genes.The prognostic score was calculated,and the survival of patients was compared between high and low score group.Multivariate Cox regression analysis was carried out to identify independent prognostic factors.Results Consistent cluster analysis showed that ovarian cancer patients could be divided into two groups according to TGF-βsignaling pathway related genes,and the immune microenvironment of the two groups was different.Differential expression analysis and single-factor Cox regression analysis showed that GMPR,PIEZO1,EMP1,CXCL13,GADD45B,SORCS2,FOSL2,PODN,LYNX1 and SLC38A5 were identified as prognostic genes,and the prognostic score was an independent prognostic factor in ovarian cancer.Conclusion TGF-βsignaling related genes are able to divide ovarian cancer patients into different immune subtypes and act as prognostic biomarkers in ovarian cancer.