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A Mixture Method for Robust Detection HCV Early Diagnosis Biomarker with ML Approach and Molecular Docking

Given the substantial correlation between early diagnosis and prolonged patient survival in HCV patients, it is vital to identify a reliable and accessible biomarker. The purpose of this research was to identify accurate miRNA biomarkers to aid in the early diagnosis of HCV and to identify key targe...

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Autores principales: Gholizadeh, Maryam, Łapczuk-Romańska, Joanna, Post, Mariola, Komaniecka, Nina, Mazlooman, Seyed Reza, Kaderali, Lars, Droździk, Marek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10138470/
https://www.ncbi.nlm.nih.gov/pubmed/37108370
http://dx.doi.org/10.3390/ijms24087207
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author Gholizadeh, Maryam
Łapczuk-Romańska, Joanna
Post, Mariola
Komaniecka, Nina
Mazlooman, Seyed Reza
Kaderali, Lars
Droździk, Marek
author_facet Gholizadeh, Maryam
Łapczuk-Romańska, Joanna
Post, Mariola
Komaniecka, Nina
Mazlooman, Seyed Reza
Kaderali, Lars
Droździk, Marek
author_sort Gholizadeh, Maryam
collection PubMed
description Given the substantial correlation between early diagnosis and prolonged patient survival in HCV patients, it is vital to identify a reliable and accessible biomarker. The purpose of this research was to identify accurate miRNA biomarkers to aid in the early diagnosis of HCV and to identify key target genes for anti-hepatic fibrosis therapeutics. The expression of 188 miRNAs in 42 HCV liver patients with different functional states and 23 normal livers were determined using RT-qPCR. After screening out differentially expressed miRNA (DEmiRNAs), the target genes were predicted. To validate target genes, an HCV microarray dataset was subjected to five machine learning algorithms (Random Forest, Adaboost, Bagging, Boosting, XGBoost) and then, based on the best model, importance features were selected. After identification of hub target genes, to evaluate the potency of compounds that might hit key hub target genes, molecular docking was performed. According to our data, eight DEmiRNAs are associated with early stage and eight DEmiRNAs are linked to a deterioration in liver function and an increase in HCV severity. In the validation phase of target genes, model evaluation revealed that XGBoost (AUC = 0.978) outperformed the other machine learning algorithms. The results of the maximal clique centrality algorithm determined that CDK1 is a hub target gene, which can be hinted at by hsa-miR-335, hsa-miR-140, hsa-miR-152, and hsa-miR-195. Because viral proteins boost CDK1 activation for cell mitosis, pharmacological inhibition may have anti-HCV therapeutic promise. The strong affinity binding of paeoniflorin (−6.32 kcal/mol) and diosmin (−6.01 kcal/mol) with CDK1 was demonstrated by molecular docking, which may result in attractive anti-HCV compounds. The findings of this study may provide significant evidence, in the context of the miRNA biomarkers, for early-stage HCV diagnosis. In addition, recognized hub target genes and small molecules with high binding affinity may constitute a novel set of therapeutic targets for HCV.
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spelling pubmed-101384702023-04-28 A Mixture Method for Robust Detection HCV Early Diagnosis Biomarker with ML Approach and Molecular Docking Gholizadeh, Maryam Łapczuk-Romańska, Joanna Post, Mariola Komaniecka, Nina Mazlooman, Seyed Reza Kaderali, Lars Droździk, Marek Int J Mol Sci Article Given the substantial correlation between early diagnosis and prolonged patient survival in HCV patients, it is vital to identify a reliable and accessible biomarker. The purpose of this research was to identify accurate miRNA biomarkers to aid in the early diagnosis of HCV and to identify key target genes for anti-hepatic fibrosis therapeutics. The expression of 188 miRNAs in 42 HCV liver patients with different functional states and 23 normal livers were determined using RT-qPCR. After screening out differentially expressed miRNA (DEmiRNAs), the target genes were predicted. To validate target genes, an HCV microarray dataset was subjected to five machine learning algorithms (Random Forest, Adaboost, Bagging, Boosting, XGBoost) and then, based on the best model, importance features were selected. After identification of hub target genes, to evaluate the potency of compounds that might hit key hub target genes, molecular docking was performed. According to our data, eight DEmiRNAs are associated with early stage and eight DEmiRNAs are linked to a deterioration in liver function and an increase in HCV severity. In the validation phase of target genes, model evaluation revealed that XGBoost (AUC = 0.978) outperformed the other machine learning algorithms. The results of the maximal clique centrality algorithm determined that CDK1 is a hub target gene, which can be hinted at by hsa-miR-335, hsa-miR-140, hsa-miR-152, and hsa-miR-195. Because viral proteins boost CDK1 activation for cell mitosis, pharmacological inhibition may have anti-HCV therapeutic promise. The strong affinity binding of paeoniflorin (−6.32 kcal/mol) and diosmin (−6.01 kcal/mol) with CDK1 was demonstrated by molecular docking, which may result in attractive anti-HCV compounds. The findings of this study may provide significant evidence, in the context of the miRNA biomarkers, for early-stage HCV diagnosis. In addition, recognized hub target genes and small molecules with high binding affinity may constitute a novel set of therapeutic targets for HCV. MDPI 2023-04-13 /pmc/articles/PMC10138470/ /pubmed/37108370 http://dx.doi.org/10.3390/ijms24087207 Text en © 2023 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
Gholizadeh, Maryam
Łapczuk-Romańska, Joanna
Post, Mariola
Komaniecka, Nina
Mazlooman, Seyed Reza
Kaderali, Lars
Droździk, Marek
A Mixture Method for Robust Detection HCV Early Diagnosis Biomarker with ML Approach and Molecular Docking
title A Mixture Method for Robust Detection HCV Early Diagnosis Biomarker with ML Approach and Molecular Docking
title_full A Mixture Method for Robust Detection HCV Early Diagnosis Biomarker with ML Approach and Molecular Docking
title_fullStr A Mixture Method for Robust Detection HCV Early Diagnosis Biomarker with ML Approach and Molecular Docking
title_full_unstemmed A Mixture Method for Robust Detection HCV Early Diagnosis Biomarker with ML Approach and Molecular Docking
title_short A Mixture Method for Robust Detection HCV Early Diagnosis Biomarker with ML Approach and Molecular Docking
title_sort mixture method for robust detection hcv early diagnosis biomarker with ml approach and molecular docking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10138470/
https://www.ncbi.nlm.nih.gov/pubmed/37108370
http://dx.doi.org/10.3390/ijms24087207
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