Construction and Analysis of a circRNA-Mediated ceRNA Network in Lung Adenocarcinoma
Background: Circular RNAs (circRNAs), a new class of regulatory noncoding RNAs, are involved in gene regulation and may play a role in cancer development. This study aimed to identify circRNAs involved in lung adenocarcinoma (LUAD) using bioinformatics analysis.
Methods: CircRNA (GSE101684, GSE101586), miRNA (GSE135918), and mRNA (GSE130779) microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed circRNAs (DECs), miRNAs (DEMs), and mRNAs (DEGs) in LUAD. Circinteractome and StarBase were used to predict miRNAs and mRNAs, respectively. A circRNA-miRNA-mRNA-ceRNA network was constructed. Patient survival was analyzed using UALCAN, and a sub-network was established.
Results: Hsa_circ_0072088 was identified as a differentially expressed (upregulated) circRNA in the two datasets. Intersection analysis identified hsa-miR-532-3p and hsa-miR-942 as the two miRNAs with the highest potential for binding to hsa_circ_0072088. Differential expression analysis and target gene prediction were performed to build a ceRNA network of hsa_circ_0072088 using Circinteractome/StarBase 3.0. Intersection analysis showed that TMEM52, IL24, POF1B, KIF1A, NHS, LBH, HIST2H2BE, ABCC3, PYCR1, CD79A, IGF2BP3, ANKRD17, GTSE1, MKI67, CLSPN, PLAU, LUC7L, MAGIX, GPATCH4, and ABAT were potential downstream mRNAs. The association between the expression level of 20 DEGs and LUAD patient survival was analyzed using UALCAN, which showed that IGF2BP3, MKI67, CD79A, and ABAT were related to patient survival.
Conclusion: The circRNA hsa_circ_0072088, the miRNAs hsa-miR-532-3p and hsa-miR-942-5p, and the genes IGF2BP3, MKI67, CD79A, and ABAT may serve as prognostic markers in LUAD.
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Posted 09 Jun, 2020
Construction and Analysis of a circRNA-Mediated ceRNA Network in Lung Adenocarcinoma
Posted 09 Jun, 2020
Background: Circular RNAs (circRNAs), a new class of regulatory noncoding RNAs, are involved in gene regulation and may play a role in cancer development. This study aimed to identify circRNAs involved in lung adenocarcinoma (LUAD) using bioinformatics analysis.
Methods: CircRNA (GSE101684, GSE101586), miRNA (GSE135918), and mRNA (GSE130779) microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed circRNAs (DECs), miRNAs (DEMs), and mRNAs (DEGs) in LUAD. Circinteractome and StarBase were used to predict miRNAs and mRNAs, respectively. A circRNA-miRNA-mRNA-ceRNA network was constructed. Patient survival was analyzed using UALCAN, and a sub-network was established.
Results: Hsa_circ_0072088 was identified as a differentially expressed (upregulated) circRNA in the two datasets. Intersection analysis identified hsa-miR-532-3p and hsa-miR-942 as the two miRNAs with the highest potential for binding to hsa_circ_0072088. Differential expression analysis and target gene prediction were performed to build a ceRNA network of hsa_circ_0072088 using Circinteractome/StarBase 3.0. Intersection analysis showed that TMEM52, IL24, POF1B, KIF1A, NHS, LBH, HIST2H2BE, ABCC3, PYCR1, CD79A, IGF2BP3, ANKRD17, GTSE1, MKI67, CLSPN, PLAU, LUC7L, MAGIX, GPATCH4, and ABAT were potential downstream mRNAs. The association between the expression level of 20 DEGs and LUAD patient survival was analyzed using UALCAN, which showed that IGF2BP3, MKI67, CD79A, and ABAT were related to patient survival.
Conclusion: The circRNA hsa_circ_0072088, the miRNAs hsa-miR-532-3p and hsa-miR-942-5p, and the genes IGF2BP3, MKI67, CD79A, and ABAT may serve as prognostic markers in LUAD.
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