AP001056.1, A Prognosis-Related Enhancer RNA in Squamous Cell Carcinoma of the Head and Neck.
Xiaolian GuLixiao WangLinda BoldrupPhilip J CoatesRobin FahraeusNicola SgaramellaTorben WilmsKarin NylanderPublished in: Cancers (2019)
A growing number of long non-coding RNAs (lncRNAs) have been linked to squamous cell carcinoma of the head and neck (SCCHN). A subclass of lncRNAs, termed enhancer RNAs (eRNAs), are derived from enhancer regions and could contribute to enhancer function. In this study, we developed an integrated data analysis approach to identify key eRNAs in SCCHN. Tissue-specific enhancer-derived RNAs and their regulated genes previously predicted using the computational pipeline PreSTIGE, were considered as putative eRNA-target pairs. The interactive web servers, TANRIC (the Atlas of Noncoding RNAs in Cancer) and cBioPortal, were used to explore the RNA levels and clinical data from the Cancer Genome Atlas (TCGA) project. Requiring that key eRNAs should show significant associations with overall survival (Kaplan⁻Meier log-rank test, p < 0.05) and the predicted target (correlation coefficient r > 0.4, p < 0.001), we identified five key eRNA candidates. The most significant survival-associated eRNA was AP001056.1 with ICOSLG encoding an immune checkpoint protein as its regulated target. Another 1640 genes also showed significant correlation with AP001056.1 (r > 0.4, p < 0.001), with the "immune system process" being the most significantly enriched biological process (adjusted p < 0.001). Our results suggest that AP001056.1 is a key immune-related eRNA in SCCHN with a positive impact on clinical outcome.
Keyphrases
- transcription factor
- genome wide identification
- squamous cell carcinoma
- data analysis
- binding protein
- long non coding rna
- papillary thyroid
- genome wide
- lymph node metastasis
- squamous cell
- single cell
- genome wide analysis
- poor prognosis
- quality improvement
- dna methylation
- drug induced
- computed tomography
- machine learning
- small molecule
- amino acid
- network analysis