The Cancer Genome Atlas (TCGA) based m6A methylation-related genes predict prognosis in hepatocellular carcinoma.
Jun LiuGuili SunShangling PanMengbin QinRong OuyangZhongzhuan LiJie'an HuangPublished in: Bioengineered (2021)
The current study aims to investigate the significance of N 6-methyladenosine (m6A) methylation-related genes in the clinical prognosis of hepatocellular carcinoma (HCC) using bioinformatics analyses based on The Cancer Genome Atlas (TCGA) database. Transcriptome data and corresponding clinical data on m6A methylation-related genes (including 15 genes) were obtained from TCGA database. Differential expression of 15 genes was identified. Survival curves of subgroups based on m6A methylation-related gene expression levels were plotted. We selected potential predictive genes and analyzed their prognostic values using bioinformatics methods. Eleven genes (METTL3, YTHDF1, YTHDF2, YTHDF3, YTHDC1, YTHDC2, FTO, KIAA1429, HNRNPC, HNRNPA2B1, and RBM15) were found to be overexpressed in HCC. Of these, five genes had worse survival (P < 0.05). There was a significant difference in the survival rate between subgroups with different expression levels of m6A. We selected five potential predictors (METTL3, KIAA1429, ZC3H13, YTHDF1, and YTHDF2) that met the independent predictive value. ZC3H13 was upregulated in patients with high cancer risk, whereas METTL3, KIAA1429, YTHDF1, and YTHDF2 were downregulated. In summary, we found that the expression levels of m6A methylation-related genes were different in patients with HCC and correlated with survival and prognosis. This implies that m6A methylation-related genes may be promising prognostic indicators or therapeutic targets for HCC.
Keyphrases
- genome wide
- dna methylation
- gene expression
- poor prognosis
- free survival
- papillary thyroid
- single cell
- genome wide identification
- bioinformatics analysis
- electronic health record
- squamous cell
- machine learning
- tyrosine kinase
- risk assessment
- deep learning
- childhood cancer
- human health
- adverse drug
- data analysis
- rna seq