ArticleBMC medical genomics2020
HDNA methylation data-based molecular subtype classification related to the prognosis of patients with hepatocellular carcinoma.
Article in BMC medical genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- DNA methylation-based classification of hematolymphoid neoplasms.Blood advances · 2026Article
- MJDs family members: Potential prognostic targets and immune-associated biomarkers in hepatocellular carcinoma.Frontiers in genetics · 2022Article
- Epigenetic modifications precede molecular alterations and drive human hepatocarcinogenesis.JCI insight · 2021Article
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundDNA methylation is a common chemical modification of DNA in the carcinogenesis of hepatocellular carcinoma (HCC).
methodsIn this bioinformatics analysis, 348 liver cancer samples were collected from the Cancer Genome Atlas (TCGA) database to analyse specific DNA methylation sites that affect the prognosis of HCC patients.
results10,699 CpG sites (CpGs) that were significantly related to the prognosis of patients were clustered into 7 subgroups, and the samples of each subgroup were significantly different in various clinical pathological data. In addition, by calculating the level of methylation sites in each subgroup, 119 methylation sites (corresponding to 105 genes) were selected as specific methylation sites within the subgroups. Moreover, genes in the corresponding promoter regions in which the above specific methylation sites were located were subjected to signalling pathway enrichment analysis, and it was discovered that these genes were enriched in the biological pathways that were reported to be closely correlated with HCC. Additionally, the transcription factor enrichment analysis revealed that these genes were mainly enriched in the transcription factor KROX. A naive Bayesian classification model was used to construct a prognostic model for HCC, and the training and test data sets were used for independent verification and testing.
conclusionThis classification method can well reflect the heterogeneity of HCC samples and help to develop personalized treatment and accurately predict the prognosis of patients.
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