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 interference (CRISPRi) method and patient-derived tumor xenograft (PDTX) model to evaluate the therapeutic potential of targeting FAM83H-AS1.Results
The results suggest that FAM83H-AS1 is a potential oncogenic driver due to chromosome 8q24 amplification. Upregulation of FAM83H-AS1 results in poor prognosis of LUAD patients in both Jiangsu Cancer Hospital (JSCH) and TCGA cohorts. Functional assays revealed that FAM83H-AS1 promotes malignant progression and inhibits apoptosis. Mechanistically, FAM83H-AS1 binds HNRNPK to enhance the translation of antiapoptotic oncogenes RAB8B and RAB14. Experiments using CRISPRi-mediated xenografts and PDTX models indicated that targeting FAM83H-AS1 inhibited LUAD progression in vivo.Conclusions
Our work demonstrates that FAM83H-AS1 is a noncoding oncogenic driver that inhibits LUAD apoptosis via the FAM83H-AS1-HNRNPK-RAB8B/RAB14 axis, which highlights the importance and potential roles that FAM83H-AS1 may serve as a novel therapeutic target for LUAD.
SUBMITTER: Wang S
PROVIDER: S-EPMC7882096 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
Clinical and translational medicine 20210201 2
<h4>Background</h4>Little is known about noncoding oncogenes of lung adenocarcinoma (LUAD), and these potential drivers might provide novel therapeutic targets.<h4>Methods</h4>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 ...[more]