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23 April 2019 : Laboratory Research  

Long Non-Coding RNA ZFAS1 as a Novel Potential Biomarker for Predicting the Prognosis of Thyroid Cancer

Chuan-Gang Han1ABCDEFG, Yan Huang1ABCDEF, Lu Qin2ABCDEFG*

DOI: 10.12659/MSM.912921

Med Sci Monit 2019; 25:2984-2992

Abstract

BACKGROUND: Thyroid cancer is a type of endocrine cancers with rapidly increased incidence. Recent studies have indicated long non-coding RNAs (lncRNAs) played crucial roles in thyroid cancer tumorigenesis and progression. However, the roles of most lncRNAs in thyroid cancer were still unclear.

MATERIAL AND METHODS: We used TCGA (The Cancer Genome Atlas), GSE50901, GSE29265, and GSE33630 datasets to analyze the expression pattern of ZFAS1 (ZNFX1 antisense RNA 1). The correlation between ZFAS1 and clinicopathological features in thyroid cancer was analyzed. Cell proliferation and cell cycle assays were used to validate the roles of ZFAS1 in thyroid cancer cell lines. DAVID (the database for annotation, visualization and integrated discover) system was used to perform GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis. The starBase datasets and Cytoscape was used to perform ceRNA (competitive endogenous RNA) network.

RESULTS: We demonstrated ZFAS1 was highly expressed in thyroid cancer compared to normal samples. Moreover, upregulation of ZFAS1 was positively correlated with clinicopathological features and poor prognosis in thyroid cancer. Functional validation showed knockdown of ZFAS1 suppressed cell proliferation and cell cycle in thyroid cancer cells. Bioinformatics analysis showed ZFAS1 was associated with translation, rRNA processing, intra-Golgi vesicle-mediated transport, ribosome, and ubiquitin-mediated proteolysis.

CONCLUSIONS: Our study suggested ZFAS1 could serve as a biomarker for thyroid cancer.

Keywords: Biological Markers, Parathyroid Neoplasms, RNA, Long Noncoding, Biomarkers, Tumor, carcinogenesis, Cell Cycle, Databases, Genetic, Disease Progression, Epithelial-mesenchymal transition, gene ontology, Neoplasm Invasiveness, Prognosis

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750