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Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer

Colorectal cancer represents a leading cause of cancer-related morbidity and mortality. Despite improvements, chemotherapy remains the backbone of colorectal cancer treatment. The aim of this study is to investigate the variation of circulating microRNA expression profiles and the response to irinot...

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Autores principales: Pliakou, Evangelia, Lampropoulou, Dimitra Ioanna, Dovrolis, Nikolas, Chrysikos, Dimosthenis, Filippou, Dimitrios, Papadimitriou, Christos, Vezakis, Antonios, Aravantinos, Gerasimos, Gazouli, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9820223/
https://www.ncbi.nlm.nih.gov/pubmed/36613487
http://dx.doi.org/10.3390/ijms24010046
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author Pliakou, Evangelia
Lampropoulou, Dimitra Ioanna
Dovrolis, Nikolas
Chrysikos, Dimosthenis
Filippou, Dimitrios
Papadimitriou, Christos
Vezakis, Antonios
Aravantinos, Gerasimos
Gazouli, Maria
author_facet Pliakou, Evangelia
Lampropoulou, Dimitra Ioanna
Dovrolis, Nikolas
Chrysikos, Dimosthenis
Filippou, Dimitrios
Papadimitriou, Christos
Vezakis, Antonios
Aravantinos, Gerasimos
Gazouli, Maria
author_sort Pliakou, Evangelia
collection PubMed
description Colorectal cancer represents a leading cause of cancer-related morbidity and mortality. Despite improvements, chemotherapy remains the backbone of colorectal cancer treatment. The aim of this study is to investigate the variation of circulating microRNA expression profiles and the response to irinotecan-based treatment in metastatic colorectal cancer and to identify relevant target genes and molecular functions. Serum samples from 95 metastatic colorectal cancer patients were analyzed. The microRNA expression was tested with a NucleoSpin miRNA kit (Machnery-Nagel, Germany), and a machine learning approach was subsequently applied for microRNA profiling. The top 10 upregulated microRNAs in the non-responders group were hsa-miR-181b-5p, hsa-miR-10b-5p, hsa-let-7f-5p, hsa-miR-181a-5p, hsa-miR-181d-5p, hsa-miR-301a-3p, hsa-miR-92a-3p, hsa-miR-155-5p, hsa-miR-30c-5p, and hsa-let-7i-5p. Similarly, the top 10 downregulated microRNAs were hsa-let-7d-5p, hsa-let-7c-5p, hsa-miR-215-5p, hsa-miR-143-3p, hsa-let-7a-5p, hsa-miR-10a-5p, hsa-miR-142-5p, hsa-miR-148a-3p, hsa-miR-122-5p, and hsa-miR-17-5p. The upregulation of microRNAs in the miR-181 family and the downregulation of those in the let-7 family appear to be mostly involved with non-responsiveness to irinotecan-based treatment.
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spelling pubmed-98202232023-01-07 Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer Pliakou, Evangelia Lampropoulou, Dimitra Ioanna Dovrolis, Nikolas Chrysikos, Dimosthenis Filippou, Dimitrios Papadimitriou, Christos Vezakis, Antonios Aravantinos, Gerasimos Gazouli, Maria Int J Mol Sci Article Colorectal cancer represents a leading cause of cancer-related morbidity and mortality. Despite improvements, chemotherapy remains the backbone of colorectal cancer treatment. The aim of this study is to investigate the variation of circulating microRNA expression profiles and the response to irinotecan-based treatment in metastatic colorectal cancer and to identify relevant target genes and molecular functions. Serum samples from 95 metastatic colorectal cancer patients were analyzed. The microRNA expression was tested with a NucleoSpin miRNA kit (Machnery-Nagel, Germany), and a machine learning approach was subsequently applied for microRNA profiling. The top 10 upregulated microRNAs in the non-responders group were hsa-miR-181b-5p, hsa-miR-10b-5p, hsa-let-7f-5p, hsa-miR-181a-5p, hsa-miR-181d-5p, hsa-miR-301a-3p, hsa-miR-92a-3p, hsa-miR-155-5p, hsa-miR-30c-5p, and hsa-let-7i-5p. Similarly, the top 10 downregulated microRNAs were hsa-let-7d-5p, hsa-let-7c-5p, hsa-miR-215-5p, hsa-miR-143-3p, hsa-let-7a-5p, hsa-miR-10a-5p, hsa-miR-142-5p, hsa-miR-148a-3p, hsa-miR-122-5p, and hsa-miR-17-5p. The upregulation of microRNAs in the miR-181 family and the downregulation of those in the let-7 family appear to be mostly involved with non-responsiveness to irinotecan-based treatment. MDPI 2022-12-20 /pmc/articles/PMC9820223/ /pubmed/36613487 http://dx.doi.org/10.3390/ijms24010046 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
Pliakou, Evangelia
Lampropoulou, Dimitra Ioanna
Dovrolis, Nikolas
Chrysikos, Dimosthenis
Filippou, Dimitrios
Papadimitriou, Christos
Vezakis, Antonios
Aravantinos, Gerasimos
Gazouli, Maria
Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer
title Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer
title_full Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer
title_fullStr Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer
title_full_unstemmed Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer
title_short Circulating miRNA Expression Profiles and Machine Learning Models in Association with Response to Irinotecan-Based Treatment in Metastatic Colorectal Cancer
title_sort circulating mirna expression profiles and machine learning models in association with response to irinotecan-based treatment in metastatic colorectal cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9820223/
https://www.ncbi.nlm.nih.gov/pubmed/36613487
http://dx.doi.org/10.3390/ijms24010046
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