A Four-Methylated lncRNAs-Based Prognostic Signature for Hepatocellular Carcinoma.
Le-En LiaoDan-Dan HuYun ZhengPublished in: Genes (2020)
Currently, an increasing number of studies suggest that long non-coding RNAs (lncRNAs) and methylation-regulated lncRNAs play a critical role in the pathogenesis of various cancers including hepatocellular carcinoma (HCC). Therefore, methylated differentially expressed lncRNAs (MDELs) may be critical biomarkers of HCC. In this study, 63 MDELs were identified by screening The Cancer Genome Atlas (TCGA) HCC lncRNAs expression data set and lncRNAs methylation data set. Based on univariate and multivariate survival analysis, four MDELs (AC025016.1, LINC01164, LINC01183 and LINC01269) were selected to construct the survival prognosis prediction model. Through the PI formula, the study indicates that our new prediction model performed well and is superior to the traditional staging method. At the same time, compared with the previous prediction models reported in the literature, the results of time-dependent receiver operating characteristic (ROC) curve analysis show that our 4-MDELs model predicted overall survival (OS) stability and provided better prognosis. In addition, we also applied the prognostic model to Cancer Cell Line Encyclopedia (CCLE) cell lines and classified different hepatoma cell lines through the model to evaluate the sensitivity of different hepatoma cell lines to different drugs. In conclusion, we have established a new risk scoring system to predict the prognosis, which may have a very important guiding significance for the individualized treatment of HCC patients.
Keyphrases
- long non coding rna
- poor prognosis
- network analysis
- genome wide analysis
- cell proliferation
- genome wide identification
- papillary thyroid
- genome wide
- long noncoding rna
- end stage renal disease
- dna methylation
- ejection fraction
- systematic review
- squamous cell
- electronic health record
- chronic kidney disease
- newly diagnosed
- squamous cell carcinoma
- big data
- free survival
- lymph node
- prognostic factors
- patient reported
- deep learning
- transcription factor
- lymph node metastasis
- young adults
- human milk
- smoking cessation