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一种用于卵巢癌预后预测的新型六基因特征

A Novel Six-Gene Signature for Prognosis Prediction in Ovarian Cancer.

作者信息

Pan Xin, Ma Xiaoxin

机构信息

Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.

出版信息

Front Genet. 2020 Oct 15;11:1006. doi: 10.3389/fgene.2020.01006. eCollection 2020.

Abstract

Ovarian cancer (OC) is the most malignant tumor in the female reproductive tract. Although abundant molecular biomarkers have been identified, a robust and accurate gene expression signature is still essential to assist oncologists in evaluating the prognosis of OC patients. In this study, samples from 367 patients in The Cancer Genome Atlas (TCGA) database were subjected to mRNA expression profiling. Then, we used a gene set enrichment analysis (GSEA) to screen genes correlated with epithelial-mesenchymal transition (EMT) and assess their prognostic power with a Cox proportional regression model. Six genes (TGFBI, SFRP1, COL16A1, THY1, PPIB, BGN) associated with overall survival (OS) were used to construct a risk assessment model, after which the patients were divided into high-risk and low-risk groups. The six-gene signature was an independent prognostic biomarker of OS for OC patients based on the multivariate Cox regression analysis. In addition, the six-gene model was validated with samples from the Gene Expression Omnibus (GEO) database. In summary, we established a six-gene signature relevant to the prognosis of OC, which might become a therapeutic tool with clinical applications in the future.

摘要

卵巢癌(OC)是女性生殖道中最恶性的肿瘤。尽管已经鉴定出大量分子生物标志物,但强大而准确的基因表达特征对于协助肿瘤学家评估OC患者的预后仍然至关重要。在本研究中,对癌症基因组图谱(TCGA)数据库中367例患者的样本进行了mRNA表达谱分析。然后,我们使用基因集富集分析(GSEA)筛选与上皮-间质转化(EMT)相关的基因,并通过Cox比例回归模型评估其预后能力。使用与总生存期(OS)相关的六个基因(TGFBI、SFRP1、COL16A1、THY1、PPIB、BGN)构建风险评估模型,之后将患者分为高风险和低风险组。基于多变量Cox回归分析,六基因特征是OC患者OS的独立预后生物标志物。此外,使用来自基因表达综合数据库(GEO)的样本对六基因模型进行了验证。总之,我们建立了一个与OC预后相关的六基因特征,这可能在未来成为一种具有临床应用价值的治疗工具。

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