P37.24 Identification of microRNAs in Non-Small-Cell Lung Cancer Based on Bioinformation Analysis

Abstract

This project is to identify more accurate and effective biomarkers for the diagnosis and clinical treatment of NSCLC by using comprehensive bioinformatics analysis and to reveal the tumorigenesis progression of NSCLC. The miRNA and mRNA microarray datasets GSE102286, GSE56036, GSE25508, and GSE101929 were downloaded from the GEO database. We used GEO2R to screen out differential miRNAs (DEMs) and miRTarBase to predict potential target genes. The Funrich tool was used for functional annotation and pathway enrichment analysis of these potential DEmiRNAs targets. Protein–protein interaction (PPI) network and ceRNA network were established and visualized by STRING database and Cytoscape software. A total of 77 DEmiRNAs were identified in the three miRNA datasets, among which 39 were up-regulated and 38 were down-regulated. After the intersection of DEmiRNAs of the three datasets, the up-regulated Has-miR-21 and down-regulated Has-miR-30a were obtained respectively. The number of predicted potential target genes of up-regulated miRNA was 685 and that of down-regulated miRNA was 904. The enriched pathways contained pathways in Integrin family cell surface interactions, TRAIL signaling pathway, and MAPK signaling pathway. In the PPI network, the top 10 hub nodes with higher degrees were identified as hub genes, such as MYC and TP53. In NSCLC tissues, the expressions of BRCA1, EGFR and UBE2N, the three targets of Has-miR-21, were significantly higher than those in normal tissues, while the expression of PTEN gene was significantly down-regulated. The expression of miRNA was negatively correlated with the expression of target genes. Therefore, PTEN gene may be the most likely target for Has-miR-21, while HDAC1 gene may be the most likely target for Has-miR-30a. In addition, MYC, SKP2 and EGFR are the hubs of ceRNA network. This study demonstrated that screening of DEmiRNAs, DElncRNAs and enriched pathways of NSCLC by bioinformatics comprehensive analysis could be helpful to explore the occurrence and development of NSCLC and may provide effective targets for the treatment of NSCLC.