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Bioinformatics-based identification of key genes and pathways associated with colorectal cancer diagnosis, treatment, and prognosis

Colorectal cancer (CRC) is known to display a high risk of metastasis and recurrence. The main objective of our investigation was to shed more light on CRC pathogenesis by screening CRC datasets for the identification of key genes and signaling pathways, possibly leading to new approaches for the di...

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Autores principales: Wang, Chaochao, Zhang, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9478217/
https://www.ncbi.nlm.nih.gov/pubmed/36123948
http://dx.doi.org/10.1097/MD.0000000000030619
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author Wang, Chaochao
Zhang, Li
author_facet Wang, Chaochao
Zhang, Li
author_sort Wang, Chaochao
collection PubMed
description Colorectal cancer (CRC) is known to display a high risk of metastasis and recurrence. The main objective of our investigation was to shed more light on CRC pathogenesis by screening CRC datasets for the identification of key genes and signaling pathways, possibly leading to new approaches for the diagnosis and treatment of CRC. We downloaded the colorectal cancer datasets from the Gene Expression Omnibus (GEO) database site. We used GEO2R to screen for differentially expressed genes (DEGs) of which those with a fold change >1 were considered as up-regulated and those with a fold change <-1 were considered as down-regulated on the basis of a P < .05. “Gene ontology (GO)” and “Kyoto Encyclopedia of Genes and Genomes (KEGG)” data were analyzed by the “DAVID” software. The online search tool “STRING” was used to search for interacting genes or proteins and we used Cytoscape (v3.8.0) to generate a PPI network map and to identify key genes. Finally, survival analysis and stage mapping of key genes were performed using “GEPIA” with the aim of elucidating their potential impact on CRC. Our study revealed 120 intersecting genes of which 55 were up- and 65 were downregulated, respectively. GO analysis revealed that these genes were involved in cell proliferation, exosome secretion, G2/M transition, cytosol, protein binding, and protein kinase activity. KEGG pathway analysis showed that these genes were involved in cell cycle and mineral absorption. The Cytoscape PPI map showed 17 nodes and 262 edges, and 10 hub genes were identified by top 10 degrees. Survival analysis demonstrated that the AURKA, CCNB1, and CCNA2 genes were strongly associated with the survival rate of CRC patients. In addition, CCNB1, CCNA2, CDK1, CKS2, MAD2L1, and DLGAP5 could be correlated to pathological CRC staging. In this research, we identified key genes that may explain the molecular mechanism of occurrence and progression of CRC but may also contribute to an improvement in the clinical staging and prognosis of CRC patients.
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spelling pubmed-94782172022-09-19 Bioinformatics-based identification of key genes and pathways associated with colorectal cancer diagnosis, treatment, and prognosis Wang, Chaochao Zhang, Li Medicine (Baltimore) Research Article Colorectal cancer (CRC) is known to display a high risk of metastasis and recurrence. The main objective of our investigation was to shed more light on CRC pathogenesis by screening CRC datasets for the identification of key genes and signaling pathways, possibly leading to new approaches for the diagnosis and treatment of CRC. We downloaded the colorectal cancer datasets from the Gene Expression Omnibus (GEO) database site. We used GEO2R to screen for differentially expressed genes (DEGs) of which those with a fold change >1 were considered as up-regulated and those with a fold change <-1 were considered as down-regulated on the basis of a P < .05. “Gene ontology (GO)” and “Kyoto Encyclopedia of Genes and Genomes (KEGG)” data were analyzed by the “DAVID” software. The online search tool “STRING” was used to search for interacting genes or proteins and we used Cytoscape (v3.8.0) to generate a PPI network map and to identify key genes. Finally, survival analysis and stage mapping of key genes were performed using “GEPIA” with the aim of elucidating their potential impact on CRC. Our study revealed 120 intersecting genes of which 55 were up- and 65 were downregulated, respectively. GO analysis revealed that these genes were involved in cell proliferation, exosome secretion, G2/M transition, cytosol, protein binding, and protein kinase activity. KEGG pathway analysis showed that these genes were involved in cell cycle and mineral absorption. The Cytoscape PPI map showed 17 nodes and 262 edges, and 10 hub genes were identified by top 10 degrees. Survival analysis demonstrated that the AURKA, CCNB1, and CCNA2 genes were strongly associated with the survival rate of CRC patients. In addition, CCNB1, CCNA2, CDK1, CKS2, MAD2L1, and DLGAP5 could be correlated to pathological CRC staging. In this research, we identified key genes that may explain the molecular mechanism of occurrence and progression of CRC but may also contribute to an improvement in the clinical staging and prognosis of CRC patients. Lippincott Williams & Wilkins 2022-09-16 /pmc/articles/PMC9478217/ /pubmed/36123948 http://dx.doi.org/10.1097/MD.0000000000030619 Text en Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Chaochao
Zhang, Li
Bioinformatics-based identification of key genes and pathways associated with colorectal cancer diagnosis, treatment, and prognosis
title Bioinformatics-based identification of key genes and pathways associated with colorectal cancer diagnosis, treatment, and prognosis
title_full Bioinformatics-based identification of key genes and pathways associated with colorectal cancer diagnosis, treatment, and prognosis
title_fullStr Bioinformatics-based identification of key genes and pathways associated with colorectal cancer diagnosis, treatment, and prognosis
title_full_unstemmed Bioinformatics-based identification of key genes and pathways associated with colorectal cancer diagnosis, treatment, and prognosis
title_short Bioinformatics-based identification of key genes and pathways associated with colorectal cancer diagnosis, treatment, and prognosis
title_sort bioinformatics-based identification of key genes and pathways associated with colorectal cancer diagnosis, treatment, and prognosis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9478217/
https://www.ncbi.nlm.nih.gov/pubmed/36123948
http://dx.doi.org/10.1097/MD.0000000000030619
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