{"title":"A novel m6A-related prognostic signature for predicting the overall survival of hepatocellular carcinoma patients.","authors":"Shiyang Xie, Yaxuan Wang, Jin Huang, Guang Li","doi":"10.1049/syb2.12036","DOIUrl":null,"url":null,"abstract":"<p><p>Liver hepatocellular carcinoma (LIHC) comprises most cases of liver cancer with a poor prognosis. N<sup>6</sup> -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.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"16 1","pages":"1-17"},"PeriodicalIF":1.9000,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8849219/pdf/","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Systems Biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1049/syb2.12036","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/10/14 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
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.
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.