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Bioinformatics Analysis: The Regulatory Network of hsa_circ_0007843 and hsa_circ_0007331 in Colon Cancer

OBJECTIVE: To analyze the molecular regulation network of circular RNA (circRNA) in colon cancer (CC) by bioinformatics method. METHODS: hsa_circ_0007843 and hsa_circ_0007331 proved to be associated with CC in previous studies were chosen as the research object. ConSite database was used to predict...

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Autores principales: Han, Zeping, Chen, Huafang, Guo, Zhonghui, Zhu, Jianxia, Xie, Xingyi, Li, Yuguang, He, Jinhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324362/
https://www.ncbi.nlm.nih.gov/pubmed/34337040
http://dx.doi.org/10.1155/2021/6662897
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author Han, Zeping
Chen, Huafang
Guo, Zhonghui
Zhu, Jianxia
Xie, Xingyi
Li, Yuguang
He, Jinhua
author_facet Han, Zeping
Chen, Huafang
Guo, Zhonghui
Zhu, Jianxia
Xie, Xingyi
Li, Yuguang
He, Jinhua
author_sort Han, Zeping
collection PubMed
description OBJECTIVE: To analyze the molecular regulation network of circular RNA (circRNA) in colon cancer (CC) by bioinformatics method. METHODS: hsa_circ_0007843 and hsa_circ_0007331 proved to be associated with CC in previous studies were chosen as the research object. ConSite database was used to predict the transcription factors associated with circRNA, and the CC-associated transcription factors were screened out after intersection. The CircInteractome database was used to predict the RNA-binding proteins (RBPs) interacting with circRNAs and screen out the CC-associated RBPs after an intersection. Furthermore, the CircInteractome database was used to predict the miRNAs interrelated with circRNAs, and the HMDD v3.2 database was used to search for miRNAs associated with CC. The target mRNAs of miRNA were predicted by the miRWalk v3.0 database. CC-associated target genes were screened out from the GeneCards database, and the upregulated genes were enriched and analyzed by the FunRich 3.1.3 software. Finally, the molecular regulatory network diagram of circRNA in CC was plotted. RESULTS: The ConSite database predicted a total of 14 common transcription factors of hsa_circ_0007843 and hsa_circ_0007331, among which Snail, SOX17, HNF3, C-FOS, and RORα-1 were related to CC. The CircInteractome database predicted that the RBPs interacting with these two circRNAs were AGO2 and EIF4A3, and both of them were related to CC. A total of 17 miRNAs interacting with hsa_circ_0007843 and hsa_circ_0007331 were predicted by CircInteractome database. miR-145-5p, miR-21, miR-330-5p, miR-326, and miR-766 were associated with CC according to the HMDDv3.2 database. miR-145-5p and miR-330-5p, lowly expressed in CC, were analyzed in the follow-up study. A total of 676 common target genes of these two miRNAs were predicted by the miRWalk3.0 database. And 57 target genes were involved in the occurrence and development of CC from the GeneCards database, with 23 genes downregulated and 34 genes upregulated. Additionally, GO analysis showed that the 34 upregulated genes were mainly enriched in biological processes such as signal transduction and cell communication. KEGG pathway analysis showed that the upregulated genes were closely related to integrin, ErbB receptor, and ALK1 signal pathways. Finally, a complete regulatory network of hsa_circ_0007843 and hsa_circ_0007331 in CC was proposed, whereby each one of the participants was either directly or indirectly associated and whose deregulation may result in CC progression. CONCLUSION: Predicting the molecular regulatory network of circRNAs by bioinformatics provides a new theoretical basis for further occurrence and development pathogenesis of CC and good guidance for future experimental research.
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spelling pubmed-83243622021-07-31 Bioinformatics Analysis: The Regulatory Network of hsa_circ_0007843 and hsa_circ_0007331 in Colon Cancer Han, Zeping Chen, Huafang Guo, Zhonghui Zhu, Jianxia Xie, Xingyi Li, Yuguang He, Jinhua Biomed Res Int Research Article OBJECTIVE: To analyze the molecular regulation network of circular RNA (circRNA) in colon cancer (CC) by bioinformatics method. METHODS: hsa_circ_0007843 and hsa_circ_0007331 proved to be associated with CC in previous studies were chosen as the research object. ConSite database was used to predict the transcription factors associated with circRNA, and the CC-associated transcription factors were screened out after intersection. The CircInteractome database was used to predict the RNA-binding proteins (RBPs) interacting with circRNAs and screen out the CC-associated RBPs after an intersection. Furthermore, the CircInteractome database was used to predict the miRNAs interrelated with circRNAs, and the HMDD v3.2 database was used to search for miRNAs associated with CC. The target mRNAs of miRNA were predicted by the miRWalk v3.0 database. CC-associated target genes were screened out from the GeneCards database, and the upregulated genes were enriched and analyzed by the FunRich 3.1.3 software. Finally, the molecular regulatory network diagram of circRNA in CC was plotted. RESULTS: The ConSite database predicted a total of 14 common transcription factors of hsa_circ_0007843 and hsa_circ_0007331, among which Snail, SOX17, HNF3, C-FOS, and RORα-1 were related to CC. The CircInteractome database predicted that the RBPs interacting with these two circRNAs were AGO2 and EIF4A3, and both of them were related to CC. A total of 17 miRNAs interacting with hsa_circ_0007843 and hsa_circ_0007331 were predicted by CircInteractome database. miR-145-5p, miR-21, miR-330-5p, miR-326, and miR-766 were associated with CC according to the HMDDv3.2 database. miR-145-5p and miR-330-5p, lowly expressed in CC, were analyzed in the follow-up study. A total of 676 common target genes of these two miRNAs were predicted by the miRWalk3.0 database. And 57 target genes were involved in the occurrence and development of CC from the GeneCards database, with 23 genes downregulated and 34 genes upregulated. Additionally, GO analysis showed that the 34 upregulated genes were mainly enriched in biological processes such as signal transduction and cell communication. KEGG pathway analysis showed that the upregulated genes were closely related to integrin, ErbB receptor, and ALK1 signal pathways. Finally, a complete regulatory network of hsa_circ_0007843 and hsa_circ_0007331 in CC was proposed, whereby each one of the participants was either directly or indirectly associated and whose deregulation may result in CC progression. CONCLUSION: Predicting the molecular regulatory network of circRNAs by bioinformatics provides a new theoretical basis for further occurrence and development pathogenesis of CC and good guidance for future experimental research. Hindawi 2021-07-23 /pmc/articles/PMC8324362/ /pubmed/34337040 http://dx.doi.org/10.1155/2021/6662897 Text en Copyright © 2021 Zeping Han et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Han, Zeping
Chen, Huafang
Guo, Zhonghui
Zhu, Jianxia
Xie, Xingyi
Li, Yuguang
He, Jinhua
Bioinformatics Analysis: The Regulatory Network of hsa_circ_0007843 and hsa_circ_0007331 in Colon Cancer
title Bioinformatics Analysis: The Regulatory Network of hsa_circ_0007843 and hsa_circ_0007331 in Colon Cancer
title_full Bioinformatics Analysis: The Regulatory Network of hsa_circ_0007843 and hsa_circ_0007331 in Colon Cancer
title_fullStr Bioinformatics Analysis: The Regulatory Network of hsa_circ_0007843 and hsa_circ_0007331 in Colon Cancer
title_full_unstemmed Bioinformatics Analysis: The Regulatory Network of hsa_circ_0007843 and hsa_circ_0007331 in Colon Cancer
title_short Bioinformatics Analysis: The Regulatory Network of hsa_circ_0007843 and hsa_circ_0007331 in Colon Cancer
title_sort bioinformatics analysis: the regulatory network of hsa_circ_0007843 and hsa_circ_0007331 in colon cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324362/
https://www.ncbi.nlm.nih.gov/pubmed/34337040
http://dx.doi.org/10.1155/2021/6662897
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