A novel m6A-related prognostic signature for predicting the overall survival of hepatocellular carcinoma patients.

IF 1.9 4区 生物学 Q4 CELL BIOLOGY IET Systems Biology Pub Date : 2022-02-01 Epub Date: 2021-10-14 DOI:10.1049/syb2.12036
Shiyang Xie, Yaxuan Wang, Jin Huang, Guang Li
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引用次数: 3

Abstract

Liver hepatocellular carcinoma (LIHC) comprises most cases of liver cancer with a poor prognosis. N6 -methyladenosine (m6A) plays important biological functions in cancers. Thus, the present research was aimed to determine biomarkers of m6A regulators that could effectively predict the prognosis of LIHC patients. Based on the data collected from the Cancer Genome Atlas (TCGA) database, the correlation between the mRNA expression levels and copy number variation (CNV) patterns were determined. Higher mRNA expression resulted from the increasing number of 9 genes. Using the univariate Cox regression analysis, 11 m6A regulators that had close correlations with the LIHC prognosis were identified. In addition, under the support of the multivariate Cox regression models and the least absolute shrinkage and selection operator, a 4-gene (YTHDF2, IGF2BP3, KIAA1429, and ALKBH5) signature of m6A regulators was constructed. This signature was expected to present a prognostic value in LIHC (log-rank test p value < 0.0001). The GSE76427 (n = 94) and ICGC-LIRI-JP (n = 212) datasets were used to validate the prognostic signature, suggesting strong power to predict patients' prognosis for LIHC. To sum up, genetic alterations in m6A regulatory genes were identified as reliable and effective biomarkers for predicting the prognosis of LIHC patients.

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预测肝细胞癌患者总生存期的一种新的m6a相关预后特征。
肝细胞癌(LIHC)包括大多数预后不良的肝癌病例。N6 -甲基腺苷(m6A)在癌症中发挥着重要的生物学功能。因此,本研究旨在确定能够有效预测LIHC患者预后的m6A调节因子的生物标志物。基于从癌症基因组图谱(TCGA)数据库收集的数据,确定mRNA表达水平与拷贝数变异(CNV)模式之间的相关性。9个基因数量增加导致mRNA表达量增加。通过单变量Cox回归分析,确定了11个与LIHC预后密切相关的m6A调节因子。此外,在多元Cox回归模型和最小绝对收缩选择算子的支持下,构建了m6A调节因子的4基因(YTHDF2、IGF2BP3、KIAA1429和ALKBH5)特征。预计该特征在LIHC (log-rank检验p值)中具有预后价值
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来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
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