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Integrated Bioinformatics Analysis of the Hub Genes Involved in Irinotecan Resistance in Colorectal Cancer
Different drug combinations including irinotecan remain some of the most important therapeutic modalities in treating colorectal cancer (CRC). However, chemotherapy often leads to the acquisition of cancer drug resistance. To bridge the gap between in vitro and in vivo models, we compared the mRNA e...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9312838/ https://www.ncbi.nlm.nih.gov/pubmed/35885025 http://dx.doi.org/10.3390/biomedicines10071720 |
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author | Kryczka, Jakub Boncela, Joanna |
author_facet | Kryczka, Jakub Boncela, Joanna |
author_sort | Kryczka, Jakub |
collection | PubMed |
description | Different drug combinations including irinotecan remain some of the most important therapeutic modalities in treating colorectal cancer (CRC). However, chemotherapy often leads to the acquisition of cancer drug resistance. To bridge the gap between in vitro and in vivo models, we compared the mRNA expression profiles of CRC cell lines (HT29, HTC116, and LoVo and their respective irinotecan-resistant variants) with patient samples to select new candidate genes for the validation of irinotecan resistance. Data were downloaded from the Gene Expression Omnibus (GEO) (GSE42387, GSE62080, and GSE18105) and the Human Protein Atlas databases and were subjected to an integrated bioinformatics analysis. The protein–protein interaction (PPI) network of differently expressed genes (DEGs) between FOLFIRI-resistant and -sensitive CRC patients delivered several potential irinotecan resistance markers: NDUFA2, SDHD, LSM5, DCAF4, COX10 RBM8A, TIMP1, QKI, TGOLN2, and PTGS2. The chosen DEGs were used to validate irinotecan-resistant cell line models, proving their substantial phylogenetic heterogeneity. These results indicated that in vitro models are highly limited and favor different mechanisms than in vivo, patient-derived ones. Thus, cell lines can be perfectly utilized to analyze specific mechanisms on their molecular levels but cannot mirror the complicated drug resistance network observed in patients. |
format | Online Article Text |
id | pubmed-9312838 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93128382022-07-26 Integrated Bioinformatics Analysis of the Hub Genes Involved in Irinotecan Resistance in Colorectal Cancer Kryczka, Jakub Boncela, Joanna Biomedicines Article Different drug combinations including irinotecan remain some of the most important therapeutic modalities in treating colorectal cancer (CRC). However, chemotherapy often leads to the acquisition of cancer drug resistance. To bridge the gap between in vitro and in vivo models, we compared the mRNA expression profiles of CRC cell lines (HT29, HTC116, and LoVo and their respective irinotecan-resistant variants) with patient samples to select new candidate genes for the validation of irinotecan resistance. Data were downloaded from the Gene Expression Omnibus (GEO) (GSE42387, GSE62080, and GSE18105) and the Human Protein Atlas databases and were subjected to an integrated bioinformatics analysis. The protein–protein interaction (PPI) network of differently expressed genes (DEGs) between FOLFIRI-resistant and -sensitive CRC patients delivered several potential irinotecan resistance markers: NDUFA2, SDHD, LSM5, DCAF4, COX10 RBM8A, TIMP1, QKI, TGOLN2, and PTGS2. The chosen DEGs were used to validate irinotecan-resistant cell line models, proving their substantial phylogenetic heterogeneity. These results indicated that in vitro models are highly limited and favor different mechanisms than in vivo, patient-derived ones. Thus, cell lines can be perfectly utilized to analyze specific mechanisms on their molecular levels but cannot mirror the complicated drug resistance network observed in patients. MDPI 2022-07-16 /pmc/articles/PMC9312838/ /pubmed/35885025 http://dx.doi.org/10.3390/biomedicines10071720 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kryczka, Jakub Boncela, Joanna Integrated Bioinformatics Analysis of the Hub Genes Involved in Irinotecan Resistance in Colorectal Cancer |
title | Integrated Bioinformatics Analysis of the Hub Genes Involved in Irinotecan Resistance in Colorectal Cancer |
title_full | Integrated Bioinformatics Analysis of the Hub Genes Involved in Irinotecan Resistance in Colorectal Cancer |
title_fullStr | Integrated Bioinformatics Analysis of the Hub Genes Involved in Irinotecan Resistance in Colorectal Cancer |
title_full_unstemmed | Integrated Bioinformatics Analysis of the Hub Genes Involved in Irinotecan Resistance in Colorectal Cancer |
title_short | Integrated Bioinformatics Analysis of the Hub Genes Involved in Irinotecan Resistance in Colorectal Cancer |
title_sort | integrated bioinformatics analysis of the hub genes involved in irinotecan resistance in colorectal cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9312838/ https://www.ncbi.nlm.nih.gov/pubmed/35885025 http://dx.doi.org/10.3390/biomedicines10071720 |
work_keys_str_mv | AT kryczkajakub integratedbioinformaticsanalysisofthehubgenesinvolvedinirinotecanresistanceincolorectalcancer AT boncelajoanna integratedbioinformaticsanalysisofthehubgenesinvolvedinirinotecanresistanceincolorectalcancer |