Bioinformatics analysis of the circRNA-miRNA-mRNA network for non-small cell lung cancer.
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ABSTRACT:
SUBMITTER: Cai X
PROVIDER: S-EPMC7294496 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
Bioinformatics analysis of the circRNA-miRNA-mRNA network for non-small cell lung cancer.
Cai Xueying X Lin Lixuan L Zhang Qiuhua Q Wu Weixin W Su An A
The Journal of international medical research 20200601 6
<h4>Objective</h4>Non-small cell lung cancer (NSCLC) accounts for approximately 80% of all lung cancers, but its pathogenesis has not been fully elucidated. Therefore, it is valuable to explore the pathogenesis of NSCLC to improve diagnosis and identify novel treatment biomarkers.<h4>Methods</h4>Circular (circ)RNA, micro (mi)RNA, and gene expression datasets of NSCLC were analyzed to identify those that were differentially expressed between tumor and healthy tissues. Common genes were found and ...[more]
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Project description:BackgroundThe underlying circular RNAs (circRNAs)-related competitive endogenous RNA (ceRNA) mechanisms of pathogenesis and prognosis in non-small cell lung cancer (NSCLC) remain unclear.MethodsDifferentially expressed circRNAs (DECs) in two Gene Expression Omnibus datasets (GSE101684 and GSE112214) were identified by utilizing R package (Limma). Circinteractome and StarBase databases were used to predict circRNA associated-miRNAs and mRNAs, respectively. Then, protein-protein interaction (PPI) network of hub genes and ceRNA network were constructed by STRING and Cytoscape. Also, analyses of functional enrichment, genomic mutation and diagnostic ROC were performed. TIMER database was used to analyze the association between immune infiltration and target genes. Kaplan-Meier analysis, cox regression and the nomogram prediction model were used to evaluate the prognostic value of target genes. Finally, the expression of potential circRNAs and target genes was validated in cell lines and tissues by quantitative real-time PCR (qRT-PCR) and Human Protein Atlas (HPA) database.ResultsIn this study, 15 DECs were identified between NSCLC tissues and adjacent-normal tissues in two GEO datasets. Following the qRT-PCR corroboration, 7 DECs (hsa_circ_0002017, hsa_circ_0069244, hsa_circ_026337, hsa_circ_0002346, hsa_circ_0007386, hsa_circ_0008234, hsa_circ_0006857) were dramatically downregulated in A549 and SK-MES-1 compared with HFL-1 cells. Then, 12 circRNA-sponged miRNAs were screened by Circinteractome and StarBase, especially, hsa-miR-767-3p and hsa-miR-767-5p were significantly up-regulated and relevant to the prognosis. Utilizing the miRDB and Cytoscape, 12 miRNA-target genes were found. Functional enrichment, genomic mutation and diagnostic analyses were also performed. Among them, FNBP1, AKT3, HERC1, COL4A1, TOLLIP, ARRB1, FZD4 and PIK3R1 were related to the immune infiltration via TIMER database. The expression of ARRB1, FNBP1, FZD4, and HERC1 was correlated with poor overall survival (OS) in NSCLC patients by cox regression and nomogram. Furthermore, the hub-mRNAs were validated in cell lines and tissues.ConclusionWe constructed the circRNA-miRNA-mRNA network that might provide novel insights into the pathogenesis of NSCLC and reveal promising immune infiltration and prognostic biomarkers.
| S-EPMC8605517 | biostudies-literature
Project description:Lung cancer (LC) is one of the most frequently diagnosed cancers and the leading cause of cancer death worldwide, and most LCs are non-small cell lung cancer (NSCLC). Radiotherapy is one of the most effective treatments for patients with lung cancer, either alone or in combination with other treatment methods. However, radiotherapy responses vary considerably among NSCLC patients. The efficacy of radiotherapy is influenced by several factors, among which autophagy is of importance. Autophagy is induced by radiotherapy and also influences cell responses to radiation. We explored the clinical significance of autophagy-related genes (ARGs) and gene sets (ARGSs) and the underlying mechanism in NSCLC patients treated with radiotherapy. First, differentially expressed ARGs (SNCA, SESN3, DAPL1, and ELAPOR1) and miRNAs (miR-205-5p, miR-26a-1-3p, miR-6510-3p, miR-194-3p, miR-215-5p, and miR-375-3p) were identified between radiotherapy-resistant and radiotherapy-sensitive groups. An autophagy-related radiosensitivity risk signature (ARRS) by nine ARmRNAs/miRNAs and an autophagy-related overall survival risk signature (AROS) by three ARmRNAs were then constructed with estimated AUCs of 0.8854 (95% CI: 0.8131-0.9576) and 0.7901 (95% CI: 0.7168-0.8685), respectively. The correlations between ARGSs or prognostic signatures and clinicopathological factors, short-term radiotherapy responses (radiotherapy sensitivity), long-term radiotherapy responses (overall survival), and immune characteristics were analyzed. Both ARGSs and prognostic signatures were related to immune checkpoint inhibitors (ICIs), infiltration of tumor-infiltrating immune cells (TIICs), and the activity of the cancer immune cycle. Finally, after target prediction and correlation analysis, circRNA (hsa_circ_0019709, hsa_circ_0081983, hsa_circ_0112354, hsa_circ_0040569, hsa_circ_0135500, and hsa_circ_0098966)-regulated miRNA/ARmRNA axes (miR-194-3p/SESN3, miR-205-5p/ELAPOR1, and miR-26a-1-3p/SNCA) were considered potential modulatory mechanisms by influencing the regulation of autophagy, macroautophagy, and chaperone-mediated autophagy.
| S-EPMC8458766 | biostudies-literature
Project description: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.
| S-EPMC7732606 | biostudies-literature
Project description:Atrial fibrillation (AF) is a chronic and progressive disease, with advancing age, the morbidity of which will increase exponentially. Circular ribonucleic acids (RNAs; circRNAs) have gained a growing attention in the development of AF in recent years. The purpose of this study is to explore the mechanism of circRNA regulation in AF, in particular, the intricate interactions among circRNA, microRNA (miRNA), and messenger RNA (mRNA). Three datasets (GSE129409, GSE68475, and GSE79768) were obtained from the Gene Expression Omnibus database to screen differentially expressed (DE) circRNAs, DE miRNAs, and DE mRNAs in AF, respectively. Based on circRNA-miRNA pairs and miRNA-mRNA pairs, a competing endogenous RNAs (ceRNAs) network was built. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis of DE mRNAs in the network were performed and protein-protein interaction (PPI) networks were established to identify hub genes. Finally, a circRNA-miRNA-hub gene subnetwork was constructed. A total of 103 DE circRNAs, 16 DE miRNAs, and 110 DE mRNAs were screened in AF. Next, ceRNAs network in AF was constructed with 3 upregulated circRNAs, 2 downregulated circRNAs, 2 upregulated miRNAs, 2 downregulated miRNAs, 17 upregulated mRNAs, and 24 downregulated mRNAs. Thirty GO terms and 6 KEGG pathways were obtained. Besides, 6 hub genes (C-X-C chemokine receptor type 4 [CXCR4], C-X-C chemokine receptor type 2 [CXCR2], C-X-C motif chemokine 11 [CXCL11], neuromedin-U, B1 bradykinin receptor, and complement C3) were screened from constructing a PPI network. Finally, a circRNA-miRNA-hub gene subnetwork with 10 regulatory axes was constructed to describe the interactions among the differential circRNAs, miRNA, and hub genes. We speculated that hsa_circRNA_0056281/hsa_circRNA_0006665 -hsa-miR-613-CXCR4/CXCR2/CXCL11 regulatory axes and hsa_circRNA_0003638-hsa-miR-1207-3p-CXCR4 regulatory axis may be associated with the pathogenesis of AF.
| S-EPMC9410607 | biostudies-literature
Project description:Gastric cancer (GC) is one of the most common malignancies in the world, with morbidity and mortality ranking second among all cancers. Accumulating evidences indicate that circular RNAs (circRNAs) are closely correlated with tumorigenesis. However, the mechanisms of circRNAs still remain unclear. This study is aimed at determining hub genes and circRNAs and analyzing their potential biological functions in GC. Expression profiles of mRNAs and circRNAs were downloaded from the Gene Expression Omnibus (GEO) data sets of GC and paracancer tissues. Differentially expressed genes (DEGs) and differentially expressed circRNAs (DE-circRNAs) were identified. The target miRNAs of DE-circRNAs and the bidirectional interaction between target miRNAs and DEGs were predicted. Functional analysis was performed, and the protein-protein interaction (PPI) network and the circRNA-miRNA-mRNA network were established. A total of 456 DEGs and 2 DE-circRNAs were identified with 3 mRNA expression profiles and 2 circRNA expression profiles. GO analysis indicated that DEGs were mainly enriched in extracellular matrix and cell adhesion, and KEGG confirmed that DEGs were mainly associated with focal adhesion, the PI3K-Akt signaling pathway, extracellular matrix- (ECM)- receptor interaction, and gastric acid secretion. 15 hub DEGs (BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, UBE2C, CCNB1, CD44, CXCL8, COL3A1, COL5A2, and THBS1) were identified from the PPI network. Furthermore, the survival analysis indicate that GC patients with a high expression of the following 9 hub DEGs, namely, BGN, COL1A1, COL1A2, FBN1, FN1, SPARC, SPP1, TIMP1, and UBE2C, had significantly worse overall survival. The circRNA-miRNA-mRNA network was constructed based on 1 circRNA, 15 miRNAs, and 45 DEGs. In addition, the 45 DEGs included 5 hub DEGs. These results suggested that hub DEGs and circRNAs could be implicated in the pathogenesis and development of GC. Our findings provide novel evidence on the circRNA-miRNA-mRNA network and lay the foundation for future research of circRNAs in GC.
| S-EPMC7463386 | biostudies-literature
Project description:In recent years, increasing evidence shows that circular RNA (circRNA) disorder is closely related to tumorigenesis and cancer progression. However, the regulatory functions of most circRNAs in bladder cancer (BCa) remain unclear. This study was aimed at exploring the molecular regulatory mechanism of circRNAs in BCa. We obtained four datasets of circRNA, microRNA (miRNA), and messenger (mRNA) expression profiles from the Gene Expression Omnibus and The Cancer Genome Atlas microarray databases and identified 434, 367, and 4799/4841 differentially expressed circRNAs, miRNAs, and mRNAs, respectively. With these differentially expressed RNAs, we established a circRNA-miRNA-mRNA targeted interaction network. A total of 18, 24, and 51 central circRNAs, miRNAs, and mRNAs were identified, respectively. Among them, the top 10 mRNAs that had high connectivity with other circRNAs and miRNAs were regarded as hub genes. We detected the expression levels of these 10 mRNAs in 16 pairs of BCa tissues and adjacent normal tissues through quantitative real-time polymerase chain reaction. The differentially expressed mRNAs and central mRNAs were enriched in the processes and pathways that are associated with the growth, differentiation, proliferation, and apoptosis of tumor cells. The outstanding genes (CDCA4, GATA6, LATS2, RHOB, ZBTB4, and ZFPM2) also interacted with numerous drugs, indicating their potency as biomarkers and drug targets. The findings of this study provide a deep understanding of the circRNA-related competitive endogenous RNA regulatory mechanism in BCa pathogenesis.
| S-EPMC8610721 | biostudies-literature
Project description:AimThe aim of this study was to integrate both coding and non-coding available microarray data in the development of colorectal cancer (CRC) with bioinformatics analyses to attain a more inclusive pathobiologic map of their molecular interactions and functions.BackgroundIdentification of competing endogenous RNAs (ceRNAs), especially circRNAs, has become a new hotspot in cancer research, although their roles and underlying mechanisms in CRC development remain mostly unknown.MethodsMicroarray data was retrieved from the Gene Expression Omnibus (GEO) database and analyzed. Several bioinformatics tools and databases were applied for further elucidation. Principal component analysis (PCA) was run separately for four datasets. The dysregulated circRNA-miRNA-mRNA, co-expression, and protein-protein interaction (PPI) networks were established.ResultsPCA discloses colorectal tumors; normal tissue can be distinguished not only by mRNAs expression profile, but also by both circRNA and miRNA expression profiles. In this study, 14 DE mRNAs, 85 DE miRNAs, and 36 DE circRNAs were identified in CRC tissue and compared with normal tissue. Taking their potential interactions into account, a circRNA-miRNA-mRNA network was constructed. The results disclosed some DE circRNAs with potential oncogenic (circ_0014879) or tumor suppressive (circ_0001666 and circ_0000977) effects. Finally, the PPI network suggests pivotal roles for DOCK2 and PTPRC dysregulation in the progression of CRC, possibly by facilitating tumor escape from immune surveillance.ConclusionThe current study proposes a novel regulatory network consisting of DE circRNAs, miRNAs, and mRNAs in CRC development that highlights the roles of DE circRNAs at the upstream of oncotranscriptomic cascade in CRC development, suggesting their potential to be utilized as both prognostic and therapeutic biomarkers.
| S-EPMC8101520 | biostudies-literature
Project description:Background: Acute myocardial infarction (AMI) is one of the main fatal diseases of cardiovascular diseases. Circular RNA (circRNA) is a non-coding RNA (ncRNA), which plays a role in cardiovascular disease as a competitive endogenous RNA (ceRNA). However, their role in AMI has not been fully clarified. This study aims to explore the mechanism of circRNA-related ceRNA network in AMI, and to identify the corresponding immune infiltration characteristics. Materials and Methods: The circRNA (GSE160717), miRNA (GSE24548), and mRNA (GSE60993) microarray datasets of AMI were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs (DEcircRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were identified by the "limma" package. After integrating the circRNA, miRNA and mRNA interaction, we constructed a circRNA-miRNA-mRNA network. The "clusterProfiler" package and String database were used for functional enrichment analysis and protein-protein interaction (PPI) analysis, respectively. After that, we constructed a circRNA-miRNA-hub gene network and validated the circRNAs and mRNAs using an independent dataset (GSE61144) as well as qRT-PCR. Finally, we used CIBERSORTx database to analyze the immune infiltration characteristics of AMI and the correlation between hub genes and immune cells. Results: Using the "limma" package of the R, 83 DEcircRNAs, 54 DEmiRNAs, and 754 DEmRNAs were identified in the microarray datasets of AMI. Among 83 DEcircRNAs, there are 55 exonic DEcircRNAs. Then, a circRNA-miRNA-mRNA network consists of 21 DEcircRNAs, 11 DEmiRNAs, and 106 DEmRNAs were predicted by the database. After that, 10 hub genes from the PPI network were identified. Then, a new circRNA-miRNA-hub gene network consists of 14 DEcircRNAs, 7 DEmiRNAs, and 9 DEmRNAs was constructed. After that, three key circRNAs (hsa_circ_0009018, hsa_circ_0030569 and hsa_circ_0031017) and three hub genes (BCL6, PTGS2 and PTEN) were identified from the network by qRT-PCR. Finally, immune infiltration analysis showed that hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI. Conclusion: Our study constructed a circRNA-related ceRNA networks in AMI, consists of hsa_circ_0031017/hsa-miR-142-5p/PTEN axis, hsa_circ_0030569/hsa-miR-545/PTGS2 axis and hsa_circ_0009018/hsa-miR-139-3p/BCL6 axis. These three hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI. It helps improve understanding of AMI mechanism and provides future potential therapeutic targets.
| S-EPMC9002054 | biostudies-literature
Project description:Background: Osteoarthritis (OA) is a degenerative joint disease that seriously affects the quality of people. Unfortunately, the pathogenesis of OA has not been fully known. Therefore, this study aimed to construct a ceRNA regulatory network related to OA to explore the pathogenesis of OA. Methods: Differentially expressed circRNAs (DEcircRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were obtained from the Gene Expression Omnibus microarray data (GSE175959, GSE105027, and GSE169077). The miRNA response elements and target mRNAs were identified using bioinformatics approaches. Additionally, a circRNA-miRNA-mRNA network was established using Cytoscape version 3.8.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of mRNAs in the network were conducted to explore the possible mechanisms underlying OA development. Protein-protein interaction (PPI) analysis was performed to determine the hub genes. Based on the hub genes, a sub network was constructed using Cytoscape 3.8.0 version. Finally, connectivity map (CMap) and drug-gene interaction database (DGIdb) analyses were performed to identify the potential therapeutic targets for OA. Results: Altogether, five DEcircRNAs, 89 DEmiRNAs, and 345 DEmRNAs were identified. Moreover, a circRNA-miRNA-mRNA network was established using three circRNAs, seven miRNAs, and 37 mRNAs. GO and KEGG analyses demonstrated that the mRNAs in the network could be related to the occurrence and development of OA. PPI analysis was performed and six key genes, namely serpin family H member 1 [SERPINH1], collagen type VIII alpha 2 chain [COL8A2], collagen type XV alpha 1 chain [COL15A1], collagen type VI alpha 3 chain [COL6A3], collagen type V alpha 1 chain [COL5A1], and collagen type XI alpha 1 chain [COL11A1], were identified. Furthermore, a circRNA-miRNA-hub gene subnetwork was established in accordance with two circRNAs (hsa_circ_0075320 and hsa_circ_0051428), two miRNAs (hsa-miR-6124 and hsa-miR-1207-5p), and six hub genes (COL11A1, SERPINH1, COL6A3, COL5A1, COL8A2, and COL15A1). Finally, three chemicals (noscapine, diazepam, and TG100-115) based on CMap analysis and two drugs (collagenase Clostridium histolyticum and ocriplasmin) based on DGIdb were discovered as potential treatment options for OA. Conclusion: This study presents novel perspectives on the pathogenesis and treatment of OA based on circRNA-related competitive endogenous RNA regulatory networks.
| S-EPMC9523487 | biostudies-literature
Project description:The growing evidence suggests that circular RNAs (circRNAs) have significant associations with tumor occurrence and progression, yet the regulatory mechanism of circRNAs in lung adenocarcinoma (LUAD) remains unclear. This study clarified the potentially regulatory network and functional mechanism of circRNAs in LUAD. The expression data of circRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) were obtained from the Gene Expression Omnibus (GEO) database. Relying on GSE101586, GSE101684, and GSE112214, we identified differentially expressed circRNAs (DEcircRNAs). Depending on GSE135918 and GSE32863, we screened out differentially expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs), respectively. Then, a novel competing endogenous RNA (ceRNA) regulatory network related to LUAD was constructed. We also revealed biological processes and signal pathways regulated by these DEcircRNAs. Based on gene expression data and survival information of LUAD patients in The Cancer Genome Atlas (TCGA) and GEO, we implemented survival analysis to select DEmRNAs related to prognosis and build a novel circRNA-miRNA-mRNA hub regulatory network. Meanwhile, quantitative real-time PCR (qRT-PCR) was utilized to validate DEcircRNAs in the ceRNA hub regulatory network. As a result, a total of 8 DEcircRNAs, 19 DEmiRNAs, and 85 DEmRNAs were identified. The novel ceRNA regulatory network included 5 circRNAs, 8 miRNAs, and 22 mRNAs. The final ceRNA hub regulatory network contained two circRNAs, two miRNAs, and two mRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the five DEcircRNAs may affect LUAD onset and progression through Wnt signaling pathway and Hippo signaling pathway. All in all, this study revealed the regulatory network and functional mechanism of circRNA-related ceRNAs in LUAD.
| S-EPMC8292838 | biostudies-literature