Ontology highlight
ABSTRACT: Background
Little is known about noncoding oncogenes of lung adenocarcinoma (LUAD), and these potential drivers might provide novel therapeutic targets.Methods
Since abnormally overexpression of oncogenic drivers is induced by genomic variation, we here utilized genomic, transcriptomic, and clinical prognosis data of The Cancer Genome Atlas (TCGA) LUAD datasets to discover novel drivers from long noncoding RNAs. We further used zebrafish models to validate the biological function of candidates in vivo. The full length of FAM83H-AS1 was obtained by rapid amplification of the cDNA ends assay. RNA pull-down, RNA immunoprecipitation, quantitative mass spectrometry, and RNA sequencing assays were conducted to explore the potential mechanisms. Additionally, we used CRISPR interfe
SUBMITTER: Wang S
PROVIDER: S-EPMC7882096 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature