23 May 2017 : Clinical Research
Clinical Value and Prospective Pathway Signaling of MicroRNA-375 in Lung Adenocarcinoma: A Study Based on the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Bioinformatics Analysis
Ting-qing Gan1ABDG, Wen-jie Chen2BC, Hui Qin2CD, Su-ning Huang3E, Li-hua Yang1CDF, Ye-ying Fang3DF, Lin-jiang Pan3DF*, Zu-yun Li2G, Gang Chen2ABCDEFDOI: 10.12659/MSM.901460
Med Sci Monit 2017; 23:2453-2464
Abstract
BACKGROUND: Lung adenocarcinoma (LUAD) is the most frequent lung cancer. MicroRNAs (miRNAs) are believed to have fundamental roles in tumorigenesis of LUAD. Although miRNAs are broadly recognized in LUAD, the role of microRNA-375 in LUAD is still not fully elucidated.
MATERIAL AND METHODS: We evaluated the significance of miR-375 expression in LUAD by using analysis of a public dataset from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and a literature review. Furthermore, we investigated the biological function of miR-375 by gene ontology enrichment and target prediction analysis.
RESULTS: MiR-375 expression was significantly higher in LUAD by TCGA data compared to normal lung tissue (p<0.0001). In addition, a common pattern of upregulation for miR-375 in LUAD was found in our review of the literature. A total of 682 genes, both LUAD-related and miR-375-related, were obtained from the analytical integration. Critical pathways were unveiled in the network analysis of the overlaps, such as pentose and glucuronate interconversions, ascorbate and aldarate metabolism, and starch and sucrose metabolism. Furthermore, we identified covert miR-375 associated genes that might participate in LUAD by network analysis, such as FGF2 (fibroblast growth factor 2), PAX6 (paired box 6), and RHOJ. The expression of these three genes were all downregulated in LUAD. Finally, FGF2 was revealed to be negatively correlated with miR-375 in LUAD (r=–0.1821, p=0.0001).
CONCLUSIONS: Overall, our study provides evidence that miR-375 is essential for the progression of LUAD.
Keywords: Computational Biology
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