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基于干性标志物的网络分析鉴定和验证预测肝细胞癌患者预后的新基因特征。

Identification and validation of a new gene signature predicting prognosis of hepatocellular carcinoma patients by network analysis of stemness indices.

机构信息

Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, Ministry of Education, Shanghai, China.

Department of General Surgery, Zhongshan Hospital, Fudan University, Shanghai, China.

出版信息

Expert Rev Gastroenterol Hepatol. 2021 Jun;15(6):699-709. doi: 10.1080/17474124.2021.1845142. Epub 2021 Feb 22.

DOI:10.1080/17474124.2021.1845142
PMID:33131341
Abstract

: Stem cells play an important role in hepatocellular carcinoma (HCC). However, their precise effect on HCC tumorigenesis and progression remains unclear. The present study aimed to characterize stem cell-related gene expression in HCC.: The mRNA expression-based stemness index (mRNAsi) was used to analyze the clinical characteristics and prognosis of HCC patients. The weighted gene co-expression network analysis (WGCNA) was used to construct a gene co-expression network of 374 HCC patients. Finally, six genes were used to construct the prognosis signature.: HCC patients had a higher mRNAsi score than healthy people, suggesting poor prognosis. Two gene modules highly related to mRNAsi were identified. Multivariate Cox analysis was carried out to establish a Cox proportional risk regression model. The risk score for each patient was the sum of the product of each gene expression and its coefficient. Survival analysis suggested that the low-risk group had a significantly better prognosis.: The established six-gene signature was able to predict patient prognosis accurately. This new signature should be verified in prospective studies in order to determine patient prognosis in clinical decision-making.

摘要

: 干细胞在肝细胞癌(HCC)中发挥着重要作用。然而,它们对 HCC 肿瘤发生和进展的确切影响仍不清楚。本研究旨在分析 HCC 中与干细胞相关的基因表达特征。: 使用基于 mRNA 表达的干性指数(mRNAsi)分析 HCC 患者的临床特征和预后。采用加权基因共表达网络分析(WGCNA)构建了 374 例 HCC 患者的基因共表达网络。最后,使用六个基因构建了预后特征。: HCC 患者的 mRNAsi 评分高于健康人,提示预后不良。鉴定出两个与 mRNAsi 高度相关的基因模块。进行多变量 Cox 分析以建立 Cox 比例风险回归模型。每位患者的风险评分是每个基因表达与其系数乘积的总和。生存分析表明,低风险组的预后明显更好。: 所建立的六基因特征能够准确预测患者的预后。为了在临床决策中确定患者的预后,该新特征应在前瞻性研究中进行验证。

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