Identification of Key circRNAs in Non-Small Cell Lung Cancer

Li, Lei; Sun, Di; Li, Xiaoping; Yang, Bo; Zhang, Weidong

doi:10.1016/j.amjms.2020.08.008

Cited by 28 publications

(22 citation statements)

References 30 publications

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“…However, Liu et al proposed the opposite conclusion that hsa_circ_0046264 was remarkably up-regulated in lung cancer patients, enhancing tumor growth, invasion, metastasis, and chemotherapy resistance (Liu et al, 2020). Hsa_circ_0049271 was down-regulated in non-small cell lung cancer, which was similar to the down-regulation we found in osteosarcoma (Li et al, 2021). The other 6 circRNAs had not been studied yet, which need further investigation, and they have the potential to be reliable biomarkers and therapeutic targets.…”

Section: Discussionsupporting

confidence: 80%

Construction of a circRNA-miRNA-mRNA Regulatory Network Reveals Potential Mechanism and Treatment Options for Osteosarcoma

Zhou

Wang

et al. 2021

Front. Genet.

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BackgroundOsteosarcoma is a common malignant primary bone tumor in adolescents and children. Numerous studies have shown that circRNAs were involved in the proliferation and invasion of various tumors. However, the role of circRNAs in osteosarcoma remains unclear. Here, we aimed to explore the regulatory network among circRNA-miRNA-mRNA in osteosarcoma.MethodsThe circRNA (GSE140256), microRNA (GSE28423), and mRNA (GSE99671) expression profiles of osteosarcoma were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs, miRNAs and mRNAs were identified. CircRNA-miRNA interactions and miRNA-mRNA interactions were determined by Circular RNA Interactome (CircInteractome) database and microRNA Data Integration Portal (mirDIP) database, respectively. Then, we constructed a regulatory network. Function enrichment analysis of miRNA and mRNA was performed by DIANA-miRPath v3.0 and Metascape database, respectively. mRNAs with significant prognostic value were identified based on expression profiles from The Cancer Genome Atlas (TCGA) database, and we constructed a subnetwork for them. To make the most of the network, we used the CLUE database to predict potential drugs for the treatment of osteosarcoma based on mRNA expression in the network. And we used the STITCH database to analyze and validate the interactions among these drugs and mRNAs, and to further screen for potential drugs.ResultsA total of 9 circRNAs, 19 miRNAs, 67 mRNAs, 54 pairs of circRNA-miRNA interactions and 110 pairs of miRNA-mRNA interactions were identified. A circRNA-miRNA-mRNA network was constructed. Function enrichment analysis indicated that these miRNAs and mRNAs in the network were involved in the process of tumorigenesis and immune response. Among these mRNAs, STC2 and RASGRP2 with significantly prognostic value were identified, and we constructed a subnetwork for them. Based on mRNA expression in the network, three potential drugs, quinacridine, thalidomide and zonisamide, were screened for the treatment of osteosarcoma. Among them, quinacridine and thalidomide have been proved to have anti-tumor effects in previous studies, while zonisamide has not been reported. And a corresponding drug-protein interaction network was constructed.ConclusionOverall, we constructed a circRNA-miRNA-mRNA regulatory network to investigate the possible mechanism in osteosarcoma, and predicted that quinacridine, thalidomide and zonisamide could be potential drugs for the treatment of osteosarcoma.

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Section: Discussionsupporting

confidence: 80%

Construction of a circRNA-miRNA-mRNA Regulatory Network Reveals Potential Mechanism and Treatment Options for Osteosarcoma

Zhou

Wang

et al. 2021

Front. Genet.

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“…For example, miR-494-3p in endometrial cancer cells inhibit PTEN expression in translation, activate the downstream phosphoinositide 3 kinase/protein kinase B (PI3K/AKT) signaling pathway, and enhance tumor cell proliferation, migration, and invasion. Conversely, the restoration of PTEN protein levels or inhibition of the PI3K/AKT pathway also eliminates the miR-494-3p-mediated pro-tumor effect [33]. The in uence of miR-494-3p on PTEN can promote the proliferation and metastasis of tumor cells.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis Predicts hsa_circ_0026337/miR-197-3p as a Potential Oncogenic ceRNA Network for Non-Small Cell Lung Cancers

Zhang

Kang

et al. 2022

ACAMC

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Background: Circular RNAs (circRNAs) play an essential role in developing tumors, but their role in non-small cell lung cancer (NSCLC) is unclear. Thus, the present study explored the possible molecular mechanism of circRNAs in NSCLC. Methods: Three circular RNA (circRNA) microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differential expressions of circRNAs (DECs) were identified in NSCLC tissue and compared to adjacent healthy tissue. The online cancer-specific circRNA database (CSCD) was used for the analysis of the DECs function. Protein-protein interaction (PPI) network, Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), Cytoscape, and UALCAN were used to predict the critical nodes and perform patient survival analysis, respectively. The interaction between the DECs, the predicted miRNAs, and hub genes was also determined. Finally, the circRNA-miRNA-mRNA network was established. Results: The expression of hsa_circ_0049271, hsa_circ_0026337, hsa_circ_0043256, and hsa_circ_0008234 was decreased in NSCLC tissues. The Encyclopedia of RNA Interactomes (ENCORI) and CSCD database results showed that hsa_circ_0026337 was found to sponge with miR-1193, miR-197-3p, miR-3605-5p, miR-433-3p, and miR-652-3p, and hsa_circ_0043256 to sponge with miR-1252-5p, miR-494-3p, and miR-558, respectively. Subsequently, 100 mRNAs were predicted to bind with these seven miRNA response elements (MREs). The GO analysis and KEGG pathway revealed that these 100 MREs might be involved in “histone deacetylase binding” and “cellular senescence”. PPI network and Cytoscape identified the top ten hub genes. Survival analysis data showed that the low expression of hsa_circ_0026337 was significantly associated with shortened survival time in NSCLC (P=0.037), which increased the expression level of hsa-miR-197-3p, thereby inhibiting the translation of specific proteins. Conclusion: This study examined the circRNA-miRNA-mRNA regulatory network associated with NSCLC and explored the potential functions of DECs in the network to elucidate the mechanisms underlying disease progression in NSCLC.

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“…Due to the rapid development of gene sequencing technology, a large amount of biological data has been accumulated and some studies have used multiple microarrays to analyse. For example, Li et al (2020) used the same public datasets as this study, they took the union of GSE101586, GSE101684, and GSE11214 to identify differentially expressed circRNAs among NSCLC and healthy individuals for early diagnosis of NSCLC. Liang et al (2020) used the intersection of GSE101586, GSE101684 microarrays and GSE104854 high-throughput sequencing data to exploring the aberrant circRNAs and their potential molecular function in LUAD.…”

Section: Discussionmentioning

confidence: 99%

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

Sun

et al. 2020

Front. Genet.

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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.

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Identification of Key circRNAs in Non-Small Cell Lung Cancer

Cited by 28 publications

References 30 publications

Construction of a circRNA-miRNA-mRNA Regulatory Network Reveals Potential Mechanism and Treatment Options for Osteosarcoma

Construction of a circRNA-miRNA-mRNA Regulatory Network Reveals Potential Mechanism and Treatment Options for Osteosarcoma

Bioinformatics Analysis Predicts hsa_circ_0026337/miR-197-3p as a Potential Oncogenic ceRNA Network for Non-Small Cell Lung Cancers

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

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