Background: A definitive preoperative diagnosis of lung adenocarcinoma (LUAD) is a clinical challenge. Informative and blood-based microRNAs (miRNAs) may provide new insights on LUAD screening and detection but are limited by suboptimal accuracy.
Methods: The raw expression levels of circulating miR-21, miR-155, miR-210, miR-126, miR-182, and miR-17 in LUAD cases and healthy controls from the Gene Expression Omnibus (GEO) database were obtained and analyzed to identify accurate diagnostic miRNAs biomarkers for LUAD detection. We applied a meta-analysis to determine the magnitude of statistically significant miRNAs in LUAD samples, based on estimation outcomes acquired by analyzing raw data. Furthermore, bioinformatics analysis was conducted to reveal the function of significant miRNAs in the comprehensive underlying mechanisms of LUAD biology.
Results: A total of 5 raw microarray datasets, including 87 LUAD samples and 83 healthy controls, were eligible. Through our analysis, the primary outcome measure was that circulating miR-17 showed a favorable accuracy in diagnosing LUAD, and the overall pooled area under the curve (AUC) value was 0.79. Furthermore, a total of 85 predicted target genes were chosen, and 29 gene ontology (GO) items and 3 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways showed a significant statistical difference, which demonstrated that the target genes are involved in the biological processes of LUAD. A protein-protein interaction (PPI) network analysis revealed 10 hub genes, namely CCND2, E2F3, TNRC6B, AGO1, AAK1, RAB5B, LDLR, FBXO21, UBE3C, and MYLIP, located in the center of the PPI network.
Conclusions: In conclusion, circulating miR-17 may be a quality diagnostic biomarker for LUAD screening, but confirmation by a large, rigorous clinical validation in a longitudinal setting is warranted.
Keywords: Lung adenocarcinoma (LUAD); bioinformatics; biomarker; miR-17.
2020 Translational Cancer Research. All rights reserved.