Differential Expression and Bioinformatics Analysis of circRNA in Non-small Cell Lung Cancer

Abstract

Circular RNA (CircRNA) plays an important role in tumorigenesis and progression of non-small cell lung cancer (NSCLC), but the pathogenesis of NSCLC caused by circRNA has not been fully elucidated. This study aimed to investigate differentially expressed circRNAs and identify the underlying pathogenesis hub genes of NSCLC by comprehensive bioinformatics analysis. Data of gene expression microarrays (GSE101586, GSE101684, and GSE112214) were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs (DECs) were obtained by the “limma” package of R programs and the overlapping operation was implemented of DECs. CircBase database and Cancer-Specific CircRNA database (CSCD) were used to find miRNAs binding to DECs. Target genes of the found miRNAs were identified utilizing Perl programs based on miRDB, miRTarBase, and TargetScan databases. Functional and enrichment analyses of selected target genes were performing using the “cluster profiler” package. Protein-protein interaction (PPI) network was constructed by the Search Tool for the STRING database and module analysis of selected hub genes was performed by Cytoscape 3.7.1. Survival analysis of hub genes were performed by Gene Expression Profiling Interactive Analysis (GEPIA). Respectively, 1 DEC, 249 DECs, and 101 DECs were identified in GSE101586, GSE101684, and GSE112214. A total of eight overlapped circRNAs, 43 miRNAs and 427 target genes were identified. Gene Ontology (GO) enrichment analysis showed these target genes were enriched in biological processes of regulation of histone methylation, Ras protein signal transduction and covalent chromatin modification etc. Pathway enrichment analysis showed these target genes are mainly involved in AMPK signaling pathway, signaling pathways regulating pluripotency of stem cells and insulin signaling pathway etc. A PPI network was constructed based on 427 target genes of the 43 miRNAs. Ten hub genes were found, of which the expression of MYLIP, GAN, and CDC27 were significantly related to NSCLC patient prognosis. Our study provide a deeper understanding the circRNAs-miRNAs-target genes by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of NSCLC. MYLIP, GAN, and CDC27 genes might serve as novel biomarker for precise treatment and prognosis of NSCLC in the future.

Circular RNA circ_0003028 contributes to tumorigenesis by regulating GOT2 via miR-1298-5p in non-small cell lung cancer

“…Since circRNAs have remarkable stability and high abundance, emphasizing the importance of circRNAs in human diseases diagnosis and prognosis, including cancer [8,9]. In fact, a number of articles have documented that the abnormal expression of circRNAs has an inextricable correlation in the tumorigenesis and progression of NSCLC [10]. For example, Li et al displayed that the forced expression of hsa_-circ_0046263 could aggravate the malignancy of the tumor by boosting carcinogenesis and metastasis in NSCLC cells [11].…”

A Novel circRNA–miRNA–mRNA Hub Regulatory Network in Lung Adenocarcinoma

“…In GO and KEGG functional enrichment analysis, p < 0.05 was given to imply a statistically significant difference. What is more, GO enrichment analysis consists of cellular component (CC), molecular function (MF), and biological process (BP), which, respectively, demonstrate the biological functions of genes at different levels ( Zhang L. et al, 2019 ; Sun et al, 2020 ). KEGG pathway enrichment analysis is applied to assess the enrichment degree of genes in different pathways.…”

A mRNA-miRNA-lncRNA regulatory network related to potential pathogenesis and prognostic markers of Non- small cell lung cancer

“…The Search Tool for the Retrieval of Interacting Genes database (STRING, https://string-db.org/) contains more than 1100 kinds of proteins and more than 52 million kinds of interactions 21 . By importing the o cial name of DEGs into it, the TSV format of "protein-protein interaction" (PPI) network was obtained, and the relevant results were visualized via the plug-in cytoHubba in Cytoscape 3.7.1 software 22 . Finally, the HUB genes were screened according to the degree.…”