Identifying Potential Biomarkers in Colorectal Cancer and Developing Non-invasive Diagnostic Models Using Bioinformatics Approaches

Background: Colorectal cancer (CRC) is one of the most frequent causes of gastrointestinal tumors. Due to the invasiveness of the current diagnostic methods, there is an urgent need to develop non-invasive diagnostic approaches for CRC. The exact mechanisms and the most important genes associated wi...

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Autores principales:	Massoud Saidijam, Saeid Afshar, Amir Taherkhani
Formato:	article
Lenguaje:	EN
Publicado:	Hamadan University of Medical Sciences 2020
Materias:	biomarkers colorectal neoplasms genes machine learning micrornas protein interaction maps Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/cf89512a263941cb98c83cc8412f6c32
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spelling	oai:doaj.org-article:cf89512a263941cb98c83cc8412f6c322021-11-23T08:48:30ZIdentifying Potential Biomarkers in Colorectal Cancer and Developing Non-invasive Diagnostic Models Using Bioinformatics Approaches10.34172/ajmb.2020.152345-4113https://doaj.org/article/cf89512a263941cb98c83cc8412f6c322020-12-01T00:00:00Zhttp://ajmb.umsha.ac.ir/PDF/ajmb-8-99.pdfhttps://doaj.org/toc/2345-4113Background: Colorectal cancer (CRC) is one of the most frequent causes of gastrointestinal tumors. Due to the invasiveness of the current diagnostic methods, there is an urgent need to develop non-invasive diagnostic approaches for CRC. The exact mechanisms and the most important genes associated with the development of CRC are not fully demonstrated. Objectives: This study aimed to identify differentially expressed miRNAs (DEMs), key genes, and their regulators associated with the pathogenesis of CRC. The signaling pathways and biological processes (BPs) that were significantly affected in CRC were also indicated. Moreover, two non-invasive models were constructed for CRC diagnosis. Methods: The miRNA dataset GSE59856 was downloaded from the Gene Expression Omnibus (GEO) database and analyzed to identify DEMs in CRC patients compared with healthy controls (HCs). A protein-protein interaction (PPI) network was built and analyzed. Significant clusters in the PPI networks were identified, and the BPs and pathways associated with these clusters were studied. The hub genes in the PPI network, as well as their regulators were identified. Results: A total of 569 DEMs were demonstrated with the criteria of P value <0.001. A total of 110 essential genes and 30 modules were identified in the PPI network. Functional analysis revealed that 1005 BPs, 9 molecular functions (MFs), 14 cellular components (CCs), and 887 pathways were significantly affected in CRC. A total of 22 transcription factors (TFs) were demonstrated as the regulators of the hubs. Conclusion: Our results may provide new insight into the pathogenesis of CRC and advance the diagnostic and therapeutic methods of the disease. However, confirmation is required in the future. Massoud SaidijamSaeid AfsharAmir TaherkhaniHamadan University of Medical Sciencesarticlebiomarkerscolorectal neoplasmsgenesmachine learningmicrornasprotein interaction mapsMedical technologyR855-855.5ENAvicenna Journal of Medical Biochemistry, Vol 8, Iss 2, Pp 99-111 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	biomarkers colorectal neoplasms genes machine learning micrornas protein interaction maps Medical technology R855-855.5
spellingShingle	biomarkers colorectal neoplasms genes machine learning micrornas protein interaction maps Medical technology R855-855.5 Massoud Saidijam Saeid Afshar Amir Taherkhani Identifying Potential Biomarkers in Colorectal Cancer and Developing Non-invasive Diagnostic Models Using Bioinformatics Approaches
description	Background: Colorectal cancer (CRC) is one of the most frequent causes of gastrointestinal tumors. Due to the invasiveness of the current diagnostic methods, there is an urgent need to develop non-invasive diagnostic approaches for CRC. The exact mechanisms and the most important genes associated with the development of CRC are not fully demonstrated. Objectives: This study aimed to identify differentially expressed miRNAs (DEMs), key genes, and their regulators associated with the pathogenesis of CRC. The signaling pathways and biological processes (BPs) that were significantly affected in CRC were also indicated. Moreover, two non-invasive models were constructed for CRC diagnosis. Methods: The miRNA dataset GSE59856 was downloaded from the Gene Expression Omnibus (GEO) database and analyzed to identify DEMs in CRC patients compared with healthy controls (HCs). A protein-protein interaction (PPI) network was built and analyzed. Significant clusters in the PPI networks were identified, and the BPs and pathways associated with these clusters were studied. The hub genes in the PPI network, as well as their regulators were identified. Results: A total of 569 DEMs were demonstrated with the criteria of P value <0.001. A total of 110 essential genes and 30 modules were identified in the PPI network. Functional analysis revealed that 1005 BPs, 9 molecular functions (MFs), 14 cellular components (CCs), and 887 pathways were significantly affected in CRC. A total of 22 transcription factors (TFs) were demonstrated as the regulators of the hubs. Conclusion: Our results may provide new insight into the pathogenesis of CRC and advance the diagnostic and therapeutic methods of the disease. However, confirmation is required in the future.
format	article
author	Massoud Saidijam Saeid Afshar Amir Taherkhani
author_facet	Massoud Saidijam Saeid Afshar Amir Taherkhani
author_sort	Massoud Saidijam
title	Identifying Potential Biomarkers in Colorectal Cancer and Developing Non-invasive Diagnostic Models Using Bioinformatics Approaches
title_short	Identifying Potential Biomarkers in Colorectal Cancer and Developing Non-invasive Diagnostic Models Using Bioinformatics Approaches
title_full	Identifying Potential Biomarkers in Colorectal Cancer and Developing Non-invasive Diagnostic Models Using Bioinformatics Approaches
title_fullStr	Identifying Potential Biomarkers in Colorectal Cancer and Developing Non-invasive Diagnostic Models Using Bioinformatics Approaches
title_full_unstemmed	Identifying Potential Biomarkers in Colorectal Cancer and Developing Non-invasive Diagnostic Models Using Bioinformatics Approaches
title_sort	identifying potential biomarkers in colorectal cancer and developing non-invasive diagnostic models using bioinformatics approaches
publisher	Hamadan University of Medical Sciences
publishDate	2020
url	https://doaj.org/article/cf89512a263941cb98c83cc8412f6c32
work_keys_str_mv	AT massoudsaidijam identifyingpotentialbiomarkersincolorectalcanceranddevelopingnoninvasivediagnosticmodelsusingbioinformaticsapproaches AT saeidafshar identifyingpotentialbiomarkersincolorectalcanceranddevelopingnoninvasivediagnosticmodelsusingbioinformaticsapproaches AT amirtaherkhani identifyingpotentialbiomarkersincolorectalcanceranddevelopingnoninvasivediagnosticmodelsusingbioinformaticsapproaches
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Identifying Potential Biomarkers in Colorectal Cancer and Developing Non-invasive Diagnostic Models Using Bioinformatics Approaches

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