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Research Paper

Identification of hsa_circ_0002024 as a prognostic competing endogenous RNA (ceRNA) through the hsa_miR_129-5p/Anti-Silencing Function 1B Histone Chaperone (ASF1B) axis in renal cell carcinoma

, , & ORCID Icon
Pages 6579-6593 | Received 20 Jun 2021, Accepted 25 Aug 2021, Published online: 13 Sep 2021

Figures & data

Figure 1. Flow chart of the present study

GEO, Gene Expression Omnibus; TCGA, The Cancer Genome Atlas; circRNAs, circular RNAs; miRNAs, microRNAs; mRNAs, messenger RNAs; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; DEcircRNAs, differentially expressed circRNAs; DEmiRNAs, differentially expressed miRNAs; DEmRNAs, differentially expressed mRNAs; proDEmRNA, prognostic differentially expressed mRNAs; WGCNA, weighted gene coexpression network analysis; GO, Gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GEPIA, Gene Expression Profiling Interactive Analysis; q-PCR, quantitative polymerase chain reaction; ceRNA, competing endogenous RNA; ASF1B, Anti-Silencing Function 1B Histone Chaperone.
Figure 1. Flow chart of the present study

Table 1. Clinical characteristics of GSE108735

Figure 2. Identified DERNAs from GEO and TCGA. (a) 113 upregulated differentially expressed circular RNAs were identified from GSE108735. (b) 56 differentially expressed microRNAs with 39 upregulated ones and 17 downregulated ones were identified from The Cancer Genome Atlas (TCGA). (c) 3807 differentially expressed messenger RNAs with 2865 upregulated ones and 942 downregulated ones were identified from TCGA

Figure 2. Identified DERNAs from GEO and TCGA. (a) 113 upregulated differentially expressed circular RNAs were identified from GSE108735. (b) 56 differentially expressed microRNAs with 39 upregulated ones and 17 downregulated ones were identified from The Cancer Genome Atlas (TCGA). (c) 3807 differentially expressed messenger RNAs with 2865 upregulated ones and 942 downregulated ones were identified from TCGA

Figure 3. WGCNA of the DEmRNAs. (a) Analysis of network topology for different soft-thresholding powers. (b) 11 coexpression gene modules of more than 20 genes each were demonstrated in the clustering dendrogram with assigned module colors. (c) In the correlation of mRNA coexpression network modules with clinical prognostic factors of RCC, red module had a significant positive correlation with tumor malignancy (grade and stage) and negative correlation with survival time

Figure 3. WGCNA of the DEmRNAs. (a) Analysis of network topology for different soft-thresholding powers. (b) 11 coexpression gene modules of more than 20 genes each were demonstrated in the clustering dendrogram with assigned module colors. (c) In the correlation of mRNA coexpression network modules with clinical prognostic factors of RCC, red module had a significant positive correlation with tumor malignancy (grade and stage) and negative correlation with survival time

Figure 4. GO annotation analysis and KEGG pathway enrichment analysis of proDEmRNAs. (a)Terms enriched in biological processes of Gene ontology (GO) enrichment analysis were as follows: nuclear division, organelle fission, mitotic nuclear division, chromosome segregation, regulation of mitotic nuclear division, regulation of nuclear division, sister chromatid segregation, nuclear chromosome segregation, mitotic sister chromatid segregation, regulation of mitotic cell cycle phase transition, cell cycle checkpoint, cytokinesis, regulation of cell cycle phase transition, cell cycle G2/M phase transition, positive regulation of cell cycle process, negative regulation of mitotic cell cycle, negative regulation of cell cycle process, positive regulation of cell cycle. (b) Terms enriched in cellular components of GO enrichment analysis were as follows: condensed chromosome, spindle, microtubule. (c) Pathways enriched in Kyoto Encyclopedia of Genes and Genomes analysis were cell cycle, oocyte meiosis and progesterone-mediated oocyte maturation

Figure 4. GO annotation analysis and KEGG pathway enrichment analysis of proDEmRNAs. (a)Terms enriched in biological processes of Gene ontology (GO) enrichment analysis were as follows: nuclear division, organelle fission, mitotic nuclear division, chromosome segregation, regulation of mitotic nuclear division, regulation of nuclear division, sister chromatid segregation, nuclear chromosome segregation, mitotic sister chromatid segregation, regulation of mitotic cell cycle phase transition, cell cycle checkpoint, cytokinesis, regulation of cell cycle phase transition, cell cycle G2/M phase transition, positive regulation of cell cycle process, negative regulation of mitotic cell cycle, negative regulation of cell cycle process, positive regulation of cell cycle. (b) Terms enriched in cellular components of GO enrichment analysis were as follows: condensed chromosome, spindle, microtubule. (c) Pathways enriched in Kyoto Encyclopedia of Genes and Genomes analysis were cell cycle, oocyte meiosis and progesterone-mediated oocyte maturation

Figure 5. Overlapping interactions derived from the intersection analysis of TargetScan, miRanda and RNAhybird. (a) Networks of differentially expressed circular RNAs (DEcircRNAs) to differentially expressed microRNAs (DEmiRNAs) contained 1174 interactions in TargetScan, 1532 interactions in miRanda and 307 interactions in RNAhybird. A total of 39 overlapping interactions of DEcircRNAs to DEmiRNAs were identified with Venn intersection analysis. (b) Networks of DEmiRNAs to prognostic differentially expressed messenger RNAs (proDEmRNAs) contained 871 interactions in TargetScan, 1136 interactions in miRanda and 305 interactions in RNAhybird. A total of 120 overlapping interactions of DEmiRNAs to proDEmRNAs were identified with Venn intersection analysis

Figure 5. Overlapping interactions derived from the intersection analysis of TargetScan, miRanda and RNAhybird. (a) Networks of differentially expressed circular RNAs (DEcircRNAs) to differentially expressed microRNAs (DEmiRNAs) contained 1174 interactions in TargetScan, 1532 interactions in miRanda and 307 interactions in RNAhybird. A total of 39 overlapping interactions of DEcircRNAs to DEmiRNAs were identified with Venn intersection analysis. (b) Networks of DEmiRNAs to prognostic differentially expressed messenger RNAs (proDEmRNAs) contained 871 interactions in TargetScan, 1136 interactions in miRanda and 305 interactions in RNAhybird. A total of 120 overlapping interactions of DEmiRNAs to proDEmRNAs were identified with Venn intersection analysis

Figure 6. The circRNA-miRNA-mRNA ceRNA network

The competing endogenous RNA network was constructed with 13 upregulated circular RNA RNAs, 8 downregulated microRNAs and 21 upregulated messenger RNAs, in which the identified prognostic axis of hsa_circ_0002024/hsa_miR_129-5p/Anti-Silencing Function 1B Histone Chaperone (ASF1B) is marked with a dashed ellipse.
Figure 6. The circRNA-miRNA-mRNA ceRNA network

Figure 7. Pooled comparative analysis of the mRNA expression in Oncomine database

* The rank for a gene is the median rank for that gene across each of the analyses. †The p-Value for a gene is its p-value for the median-ranked analysis.1. Hereditary Clear Cell Renal Cell Carcinoma vs. Normal; Beroukhim Renal, Cancer Res, 20092. Non-Hereditary Clear Cell Renal Cell Carcinoma vs. Normal; Beroukhim Renal, Cancer Res, 20093. Clear Cell Sarcoma of the Kidney vs. Normal; Cutcliffe Renal, Clin Cancer Res, 20054. Clear Cell Renal Cell Carcinoma vs. Normal; Gumz Renal, Clin Cancer Res, 20075. Chromophobe Renal Cell Carcinoma vs. Normal; Higgins Renal, Am J Pathol, 20036. Clear Cell Renal Cell Carcinoma vs. Normal; Higgins Renal, Am J Pathol, 20037. Granular Renal Cell Carcinoma vs. Normal; Higgins Renal, Am J Pathol, 20038. Papillary Renal Cell Carcinoma vs. Normal; Higgins Renal, Am J Pathol, 20039. Chromophobe Renal Cell Carcinoma vs. Normal; Jones Renal, Clin Cancer Res, 200510. Clear Cell Renal Cell Carcinoma vs. Normal; Jones Renal, Clin Cancer Res, 200511. Papillary Renal Cell Carcinoma vs. Normal; Jones Renal, Clin Cancer Res, 200512. Clear Cell Renal Cell Carcinoma vs. Normal; Lenburg Renal, BMC Cancer, 200313. Chromophobe Renal Cell Carcinoma vs. Normal; Yusenko Renal, BMC Cancer, 200914. Clear Cell Renal Cell Carcinoma vs. Normal; Yusenko Renal, BMC Cancer, 200915. Papillary Renal Cell Carcinoma vs. Normal; Yusenko Renal, BMC Cancer, 2009
Figure 7. Pooled comparative analysis of the mRNA expression in Oncomine database

Figure 8. Box-plots of mRNA expression between RCC and normal kidney in GEPIA database

The message RNAs expression of Anti-Silencing Function 1B Histone Chaperone (ASF1B), Ribonucleotide Reductase Regulatory Subunit M2 (RRM2) and Forkhead Box M1 (FOXM1) between renal cell carcinoma (RCC) and normal kidney in Gene Expression Profiling Interactive Analysis (GEPIA) database using data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) was demonstrated. Fold change > 1.5 and p value < 0.01 were considered significant and only ASF1B and FOXM1 showed significant overexpression in clear cell RCC, papillary RCC and chromophobe RCC.
Figure 8. Box-plots of mRNA expression between RCC and normal kidney in GEPIA database

Figure 9. Relative expression of ASF1B and FOXM1 in q-PCR

In comparison with normal kidney cells 293 T, Anti-Silencing Function 1B Histone Chaperone (ASF1B) was with higher message RNA (mRNA) abundance in A498, 786-O and ACHN, and Forkhead Box M1 (FOXM1) was with higher mRNA abundance in 786-O and ACHN. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Figure 9. Relative expression of ASF1B and FOXM1 in q-PCR

Figure 10. Survival analysis of the significant mRNAs and correlative miRNAs in the ceRNA network

Anti-Silencing Function 1B Histone Chaperone (ASF1B), Forkhead Box M1 (FOXM1) and hsa−miR−1254 had significant negative correlations with overall survival time, while hsa_miR_129-5p had significant positive correlation with overall survival time.
Figure 10. Survival analysis of the significant mRNAs and correlative miRNAs in the ceRNA network
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Data availability statement

The datasets analyzed for this study can be found in the GEO database (www.ncbi.nlm.nih.gov/geo), TCGA database (cancergenome.nih.gov), Oncomines database (www.oncomine.org) and GEPIA database (gepia.cancer-pku.cn).