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Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer

Bevacizumab combined with cytotoxic chemotherapy is the backbone of metastatic colorectal cancer (mCRC) therapy; however, its treatment efficacy is hampered by therapeutic resistance. Therefore, understanding the mechanisms underlying bevacizumab resistance is crucial to increasing the therapeutic e...

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Autores principales: Makondi, Precious Takondwa, Lee, Chia-Hwa, Huang, Chien-Yu, Chu, Chi-Ming, Chang, Yu-Jia, Wei, Po-Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5771567/
https://www.ncbi.nlm.nih.gov/pubmed/29342159
http://dx.doi.org/10.1371/journal.pone.0189582
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author Makondi, Precious Takondwa
Lee, Chia-Hwa
Huang, Chien-Yu
Chu, Chi-Ming
Chang, Yu-Jia
Wei, Po-Li
author_facet Makondi, Precious Takondwa
Lee, Chia-Hwa
Huang, Chien-Yu
Chu, Chi-Ming
Chang, Yu-Jia
Wei, Po-Li
author_sort Makondi, Precious Takondwa
collection PubMed
description Bevacizumab combined with cytotoxic chemotherapy is the backbone of metastatic colorectal cancer (mCRC) therapy; however, its treatment efficacy is hampered by therapeutic resistance. Therefore, understanding the mechanisms underlying bevacizumab resistance is crucial to increasing the therapeutic efficacy of bevacizumab. The Gene Expression Omnibus (GEO) database (dataset, GSE86525) was used to identify the key genes and pathways involved in bevacizumab-resistant mCRC. The GEO2R web tool was used to identify differentially expressed genes (DEGs). Functional and pathway enrichment analyses of the DEGs were performed using the Database for Annotation, Visualization, and Integrated Discovery(DAVID). Protein–protein interaction (PPI) networks were established using the Search Tool for the Retrieval of Interacting Genes/Proteins database(STRING) and visualized using Cytoscape software. A total of 124 DEGs were obtained, 57 of which upregulated and 67 were downregulated. PPI network analysis showed that seven upregulated genes and nine downregulated genes exhibited high PPI degrees. In the functional enrichment, the DEGs were mainly enriched in negative regulation of phosphate metabolic process and positive regulation of cell cycle process gene ontologies (GOs); the enriched pathways were the phosphoinositide 3-kinase-serine/threonine kinase signaling pathway, bladder cancer, and microRNAs in cancer. Cyclin-dependent kinase inhibitor 1A(CDKN1A), toll-like receptor 4 (TLR4), CD19 molecule (CD19), breast cancer 1, early onset (BRCA1), platelet-derived growth factor subunit A (PDGFA), and matrix metallopeptidase 1 (MMP1) were the DEGs involved in the pathways and the PPIs. The clinical validation of the DEGs in mCRC (TNM clinical stages 3 and 4) revealed that high PDGFA expression levels were associated with poor overall survival, whereas high BRCA1 and MMP1 expression levels were associated with favorable progress free survival(PFS). The identified genes and pathways can be potential targets and predictors of therapeutic resistance and prognosis in bevacizumab-treated patients with mCRC.
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spelling pubmed-57715672018-01-23 Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer Makondi, Precious Takondwa Lee, Chia-Hwa Huang, Chien-Yu Chu, Chi-Ming Chang, Yu-Jia Wei, Po-Li PLoS One Research Article Bevacizumab combined with cytotoxic chemotherapy is the backbone of metastatic colorectal cancer (mCRC) therapy; however, its treatment efficacy is hampered by therapeutic resistance. Therefore, understanding the mechanisms underlying bevacizumab resistance is crucial to increasing the therapeutic efficacy of bevacizumab. The Gene Expression Omnibus (GEO) database (dataset, GSE86525) was used to identify the key genes and pathways involved in bevacizumab-resistant mCRC. The GEO2R web tool was used to identify differentially expressed genes (DEGs). Functional and pathway enrichment analyses of the DEGs were performed using the Database for Annotation, Visualization, and Integrated Discovery(DAVID). Protein–protein interaction (PPI) networks were established using the Search Tool for the Retrieval of Interacting Genes/Proteins database(STRING) and visualized using Cytoscape software. A total of 124 DEGs were obtained, 57 of which upregulated and 67 were downregulated. PPI network analysis showed that seven upregulated genes and nine downregulated genes exhibited high PPI degrees. In the functional enrichment, the DEGs were mainly enriched in negative regulation of phosphate metabolic process and positive regulation of cell cycle process gene ontologies (GOs); the enriched pathways were the phosphoinositide 3-kinase-serine/threonine kinase signaling pathway, bladder cancer, and microRNAs in cancer. Cyclin-dependent kinase inhibitor 1A(CDKN1A), toll-like receptor 4 (TLR4), CD19 molecule (CD19), breast cancer 1, early onset (BRCA1), platelet-derived growth factor subunit A (PDGFA), and matrix metallopeptidase 1 (MMP1) were the DEGs involved in the pathways and the PPIs. The clinical validation of the DEGs in mCRC (TNM clinical stages 3 and 4) revealed that high PDGFA expression levels were associated with poor overall survival, whereas high BRCA1 and MMP1 expression levels were associated with favorable progress free survival(PFS). The identified genes and pathways can be potential targets and predictors of therapeutic resistance and prognosis in bevacizumab-treated patients with mCRC. Public Library of Science 2018-01-17 /pmc/articles/PMC5771567/ /pubmed/29342159 http://dx.doi.org/10.1371/journal.pone.0189582 Text en © 2018 Makondi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Makondi, Precious Takondwa
Lee, Chia-Hwa
Huang, Chien-Yu
Chu, Chi-Ming
Chang, Yu-Jia
Wei, Po-Li
Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer
title Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer
title_full Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer
title_fullStr Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer
title_full_unstemmed Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer
title_short Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer
title_sort prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5771567/
https://www.ncbi.nlm.nih.gov/pubmed/29342159
http://dx.doi.org/10.1371/journal.pone.0189582
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