Identification of prognostic biomarkers of hepatocellular carcinoma via long noncoding RNA expression and copy number alterations.
Weibo DuWenbiao ChenZheyue ShuDairong XiangKefan BiYingfeng LuXujun ZhangLanjuan LiHongyan DiaoPublished in: Epigenomics (2020)
Aim: This study aimed to identify long noncoding RNAs (lncRNAs) with potential to be prognostic biomarkers of hepatocellular carcinoma (HCC) by analyzing copy number alterations (CNAs). Methods: RNA Sequencing data of 369 HCC patients was downloaded from The Cancer Genome Atlas database and analyzed with a series of systematic bioinformatics methods. Results: LncRNA-CNA association analysis revealed that many lncRNAs were located in sites frequently amplified or deleted. Three upregulated lncRNAs (LINC00689, SNHG20 and MAFG-AS1) with copy amplification and one downregulated lncRNA TMEM220-AS1 with copy deletion were associated with poor prognosis of HCC. Conclusion: This study reveals that differentially expressed lncRNAs correlate with CNAs in HCC. Moreover, the differentially expressed lncRNAs and their copy amplification/deletions could be promising prognostic biomarkers of HCC.
Keyphrases
- poor prognosis
- copy number
- long noncoding rna
- long non coding rna
- mitochondrial dna
- genome wide
- single cell
- network analysis
- dna methylation
- genome wide analysis
- end stage renal disease
- genome wide identification
- newly diagnosed
- emergency department
- ejection fraction
- squamous cell carcinoma
- machine learning
- gene expression
- papillary thyroid
- risk assessment
- patient reported outcomes
- nucleic acid
- deep learning
- adverse drug