Survival analysis and functional annotation of long non-coding RNAs in lung adenocarcinoma.
Abbas SalavatyZahra RezvaniAli NajafiPublished in: Journal of cellular and molecular medicine (2019)
Long non-coding RNAs (lncRNAs) are a subclass of non-protein coding transcripts that are involved in several regulatory processes and are considered as potential biomarkers for almost all cancer types. This study aims to investigate the prognostic value of lncRNAs for lung adenocarcinoma (LUAD), the most prevalent subtype of lung cancer. To this end, the processed data of The Cancer Genome Atlas LUAD were retrieved from GEPIA and circlncRNAnet databases, matched with each other and integrated with the analysis results of a non-small cell lung cancer plasma RNA-Seq study. Then, the data were filtered in order to separate the differentially expressed lncRNAs that have a prognostic value for LUAD. Finally, the selected lncRNAs were functionally annotated using a bioinformatic and systems biology approach. Accordingly, we identified 19 lncRNAs as the novel LUAD prognostic lncRNAs. Also, based on our results, all 19 lncRNAs might be involved in lung cancer-related biological processes. Overall, we suggested several novel biomarkers and drug targets which could help early diagnosis, prognosis and treatment of LUAD patients.
Keyphrases
- long non coding rna
- rna seq
- network analysis
- genome wide identification
- genome wide analysis
- single cell
- poor prognosis
- papillary thyroid
- end stage renal disease
- chronic kidney disease
- ejection fraction
- big data
- newly diagnosed
- transcription factor
- squamous cell carcinoma
- magnetic resonance imaging
- prognostic factors
- small molecule
- artificial intelligence
- protein protein
- free survival
- image quality