Dataset Information

Construction of circRNA-based ceRNA network to reveal the role of circRNAs in the progression and prognosis of metastatic clear cell renal cell carcinoma.

ABSTRACT: CircRNAs are now under hot discussion as novel promising bio-markers for patients with clear cell renal cell carcinoma. The purpose of our study is to identify several circRNAs related to the metastasis and progression of clear cell renal cell carcinoma, and to further investigate the mechanism of their influence on tumor progression. The transcriptome data of ccRCC and clinical characteristics used in this study were downloaded from the The Cancer Genome Atlas and Gene Expression Omnibus database. A total of 114 circRNAs were found to be related to tumor initiation, progression and metastasis after the intersection. In addition, 14 miRNAs and 201 eligible mRNAs were selected as targets gene, respectively. CeRNA network was constructed based on 8 circRNAs, 14 miRNAs, and 201 mRNAs. Besides, another 6 hub genes were identified via the PPI network. It should be noted that only TRIM2 was confirmed as an independent prognostic factor, which was simultaneously significantly related to both clinical stage and pathological grade in clinical cohorts. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analysis indicated the possible function of TRIM2 in ccRCC progression, such as ubiquitin mediated protein hydrolysis, cell adhesion molecules, Th17 cell differentiation signaling pathway and so on. Gene set enrichment analysis analysis revealed that TRIM2 may be involved in ubiquitin mediated proteolysis, apoptosis, autophagy and citrate cycle TCA cycle. Hub circ_RNAs expressions were validated in ccRCC tissues and cell lines. Our study revealed that the hsa_circ_0002286 / has-mir-222-5p / TRIM2 axis played a critical role in the progression of ccRCC. Specifically, it may inhibit the metastasis and progression of ccRCC, which could serve as a potential therapeutic target.

SUBMITTER: Wei X

PROVIDER: S-EPMC7762515 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Construction of circRNA-based ceRNA network to reveal the role of circRNAs in the progression and prognosis of metastatic clear cell renal cell carcinoma.

Wei Xiyi X Dong Yuxiang Y Chen Xinglin X Ren Xiaohan X Ren Xiaohan X Li Guangyao G Wang Yamin Y Wang Yichun Y Zhang Tongtong T Wang Shangqian S Qin Chao C Song Ninghong N

Aging 20201120 23

CircRNAs are now under hot discussion as novel promising bio-markers for patients with clear cell renal cell carcinoma. The purpose of our study is to identify several circRNAs related to the metastasis and progression of clear cell renal cell carcinoma, and to further investigate the mechanism of their influence on tumor progression. The transcriptome data of ccRCC and clinical characteristics used in this study were downloaded from the The Cancer Genome Atlas and Gene Expression Omnibus databa ...[more]

PMID: 33223511

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Project description:BackgroundCircular RNAs (circRNAs) are now under hot discussion as novel promising biomarkers for patients with hepatocellular carcinoma (HCC). The purpose of our study is to identify several competing endogenous RNA (ceRNA) networks related to the prognosis and progression of HCC and to further investigate the mechanism of their influence on tumor progression.MethodsFirst, we obtained gene expression data related to liver cancer from The Cancer Genome Atlas (TCGA) database (http://www.portal.gdc.cancer.gov/), including microRNA (miRNA) sequence, RNA sequence, and clinical information. A co-expression network was constructed through the Weighted Correlation Network Analysis (WGCNA) software package in R software. The differentially expressed messenger RNAs (DEmRNAs) in the key module were analyzed with the Database for Annotation Visualization and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/summary.jsp) to perform functional enrichment analysis including Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The data of miRNA expression and clinical information downloaded from TCGA were utilized for survival analysis to detach the prognostic value of the DEmiRNAs of the key module.ResultsThe 201 differentially expressed miRNAs (DEmiRNAs) and 3,783 DEmRNAs were preliminarily identified through differential expression analysis. The co-expression networks of DEmiRNAs and DEmRNAs were constructed with WGCNA. Further analysis confirmed four miRNAs in the most significant module (blue module) were associated with the overall survival (OS) of patients with liver cancer, including hsa-miR-92b-3p, hsa-miR-122-3p, hsa-miR-139-5p, and hsa-miR-7850-5p. DAVID was used for functional enrichment analysis of 286 co-expressed mRNAs. The GO analysis results showed that the top enriched GO terms were oxidation-reduction process, extracellular exosome, and iron ion binding. In KEGG pathway analysis, the top three enriched terms included metabolic pathways, fatty acid degradation, and valine, leucine, and isoleucine degradation. In addition, we intersected the miRNA-mRNA interaction prediction results with the differentially expressed and prognostic mRNAs. We found that hsa-miR-92b-3p can be related to CPEB3 and ACADL. By overlapping the data of predicted circRNAs by circBank and differentially expressed circRNAs of GSE94508, we screened has_circ_0077210 as the upstream regulatory molecule of hsa-miR-92b-3p. Hsa_circ_0077210/hsa-miR-92b-3p/cytoplasmic polyadenylation element binding protein-3 (CPEB3) and acyl-Coenzyme A dehydrogenase, long chain (ACADL) were validated in HCC tissue.ConclusionOur research provides a mechanistic elucidation of the unknown ceRNA regulatory network in HCC. Hsa_circ_0077210 might serve a momentous therapeutic role to restrain the occurrence and development of HCC.

Project description:Background: Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patient's quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear. Methods: We constructed a ceRNA network associated with KIRC by analyzing the long non-coding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from The Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through "The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts." Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism. Results: We established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [area under curves (AUCs) of 1, 3, and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747, and 0.772; AUCs of 1, 3, and 5-year survivals in nomogram based on immune cells: 0.603, 0.642, and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells. Conclusion: Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.

Project description:IntroductionCircRNA is closely correlated with a wide variety of processes of acute myeloid leukemia (AML), whereas the novel circRNAs, their molecular mechanism and the specific function they played in AML should be explored in depth.MethodsThe microarray chip data of AML patients and normal samples in the Gene Expression Omnibus (GEO) database were selected to differentially expressed (DE) circRNA, miRNA, and mRNA genes. The miRNA gene was the intersection of the circRNA target gene predicted using CSCD and the miRNA gene screened from AML patients, while the mRNA gene was the intersection of the target gene mRNA of miRNA predicted using miRanda and miRTarBase software and the mRNA gene screened from AML patients. The hub mRNAs related to survival were further screened through Cox proportional hazard regression. CircRNA/miRNA/mRNA interaction network was constructed by using Cytoscape software.10 circRNAs and 6 miRNAs in bone marrow mononuclear cells (BMMNCs) of AML patients (n=43) and healthy controls (n=35) were determined by RT-qPCR. Correlations between them were analyzed by Pearson correlation coefficient.Results10 circRNAs, 6 miRNAs, and 33 mRNAs were identified. Subsequently, the network of circRNAs, miRNAs, and hub genes was built using Cystoscope. Four key circRNAs, seven hub genes and their regulatory pathways were identified. The result of RT-qPCRs showed that hsa_circ_0009581 and hsa_circ_0005273 were significantly upregulated in AML patients while hsa_circ_0000497 and hsa_circ_0001947 were significantly downregulated. Hsa-miR-150-5p was significantly downregulated; hsa-miR-454-3p was upregulated in AML patients. Hsa_circ_0009581 and hsa-miR-150-5p; hsa_ circ_0001947 and hsa-miR-454-3p were inversely correlated using Pearson's correlation coefficient.ConclusionThis study suggests that differentially expressed circRNAs take on a critical significance to AML development and may be the effective therapeutic targets. We suppose that hsa_circ_0009581 promotes leukemia development through hsa-miR-150-5p and hsa_circ_0001947 through hsa-miR-454-3p. hsa_circ_0001947 and hsa_circ_0009581 may provide new directions in the pathogenesis of AML.

Project description:Background and purposeAccumulating evidence suggests that circular RNAs (circRNAs) are involved in immune and inflammatory processes after acute ischemic stroke (AIS). However, the roles of circRNA-mediated competing endogenous RNA (ceRNA) in modulating immune inflammation of AIS have not yet been determined. This study aimed to construct a circRNA-mediated immune-related ceRNA network and identify novel circRNAs in AIS.MethodsMicroarray data were downloaded from the GEO database and further analysed by R software. Then, we constructed a circRNA-mediated ceRNA network based on interaction information from the bioinformatics database. A topological property analysis of the ceRNA network was conducted to screen novel circRNAs. Finally, we further applied quantitative real-time polymerase chain reaction (qRT-PCR) to two independent sets.ResultsWe constructed an AIS immune-related ceRNA (AISIRC) network containing immune-related genes (IRGs), miRNAs, and circRNAs. Additionally, we extracted the subnetwork from the AISIRC network and screened six immune-related circRNAs. After identification and validation, we finally confirmed that plasma levels of circPTP4A2 and circTLK2 were significantly increased in AIS patients compared with both healthy control subjects (HCs) and transient ischemic attack (TIA) patients. Logistic regression and receiver-operating characteristic (ROC) curve analyses demonstrated that these two circRNAs may function as predictive and discriminative biomarkers for AIS. We also confirmed that plasma levels of circPTP4A2 were elevated in TIA patients compared with HCs and might be an independent risk factor for predicting TIA. Longitudinal analysis of circRNA expression up to 90 days after AIS indicated that the ability of circPTP4A2 and circTLK2 to monitor AIS dynamics was highly desirable.ConclusionIn summary, the circRNA-mediated immune-related ceRNA network was successfully constructed, and two circulating circRNAs (circPTP4A2 and circTLK2) improved sensitivity for the diagnosis of AIS and could be considered diagnostic biomarkers.

Project description:Background:Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial. Methods:Raw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Tumor samples were divided into two sets. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. The gene-based risk score was developed based on the Cox coefficient of the individual gene. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival analysis were used to assess its prognostic power. GSE29609 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to screen independent prognostic parameters to construct a nomogram. The predictive power of the nomogram was revealed by time-dependent ROC curves and the calibration plot and verified in the validation set. Finally, Functional enrichment analysis of DEGs and 5 novel genes were performed to suggest the potential biological pathways. Results:PADI1, ATP6V0D2, DPP6, C9orf135 and PLG were screened to be significantly related to the prognosis of ccRCC patients. The risk score effectively stratified the patients into high-risk group with poor overall survival (OS) based on survival analysis. AJCC-stage, age, recurrence and risk score were regarded as independent prognostic parameters by Cox regression analysis and were used to construct a nomogram. Time-dependent ROC curves showed the nomogram performed best in 1-, 3- and 5-year survival predictions compared with AJCC-stage and risk score in validation sets. The calibration plot showed good agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis suggested several enriched biological pathways related to cancer. Conclusions:In our study, we constructed a gene-based model integrating clinical prognostic parameters to predict prognosis of ccRCC well, which might provide a reliable prognosis assessment tool for clinician and aid treatment decision-making in the clinic.

Project description:BackgroundA rising amount of data demonstrates that the epithelial-mesenchymal transition (EMT) in clear cell renal cell carcinomas (ccRCC) is connected with the advancement of the cancer. In order to understand the role of EMT in ccRCC, it is critical to integrate molecules involved in EMT into prognosis prediction. The objective of this project was to establish a prognosis prediction model using genes associated with EMT in ccRCC.MethodsWe acquired the mRNA expression profiles and clinical information about ccRCC from TCGA database. In this study, we measured differentially expressed EMT-related genes (DEEGs) by two comparison groups (tumor versus normal tissues; "stages I-II" versus "stages III-IV" tumor tissues). Based on classification and regression random forest models, we identified the most important DEEGs in predicting prognosis. Afterwards, a risk-score model was created using the identified important DEEGs. The prediction ability of the risk-score model was calculated by the area under the curve (AUC). A nomogram for prognosis prediction was built using the risk-score in combination with clinical factors.ResultsAmong the 72 DEEGs, the classification and regression random forest models identified six hub genes (DKK1, DLX4, IL6, KCNN4, RPL22L1, and SPDEF), which exhibited the highest importance values in both models. Through the expression of these six hub genes, a novel risk-score was developed for the prognosis prediction of ccRCC. ROC curves showed the risk-score performed well in both the training (0.749) and testing (0.777) datasets. According to the survival analysis, individuals who were separated into high/low-risk groups had statistically different outcomes in terms of prognosis. Besides, the risk-score model also showed outstanding ability in assessing the progression of ccRCC after treatment. In terms of nomogram, the concordance index (C-index) was 0.79. Additionally, we predicted the differences in response to chemotherapy drugs among patients from low- and high-risk groups.ConclusionGene signatures related to EMT could be useful in predicting ccRCC prognosis.

Dataset Information

Construction of circRNA-based ceRNA network to reveal the role of circRNAs in the progression and prognosis of metastatic clear cell renal cell carcinoma.

Publications

Construction of circRNA-based ceRNA network to reveal the role of circRNAs in the progression and prognosis of metastatic clear cell renal cell carcinoma.

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