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Selection of key genes for dilated cardiomyopathy based on machine learning algorithms and assessment of diagnostic accuracy

BACKGROUND: The mechanisms of the occurrence and progression of dilated cardiomyopathy are still unclear and further exploration is needed. The upgrading of programming languages and the improvement of biological databases have created conditions for us to explore the structural and functional infor...

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Autores principales: Chen, Tingting, Xuan, Xiulin, Ni, Jiajia, Jiang, Shuyin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10482651/
https://www.ncbi.nlm.nih.gov/pubmed/37691671
http://dx.doi.org/10.21037/jtd-23-1086
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author Chen, Tingting
Xuan, Xiulin
Ni, Jiajia
Jiang, Shuyin
author_facet Chen, Tingting
Xuan, Xiulin
Ni, Jiajia
Jiang, Shuyin
author_sort Chen, Tingting
collection PubMed
description BACKGROUND: The mechanisms of the occurrence and progression of dilated cardiomyopathy are still unclear and further exploration is needed. The upgrading of programming languages and the improvement of biological databases have created conditions for us to explore the structural and functional information of biological molecules at the nucleic acid and protein levels, screen key pathogenic genes, and elucidate pathogenic mechanisms. This study aimed to screen key pathogenic genes using machine learning algorithms and explore the correlation between key genes and immune microenvironment through transcriptome sequencing data sets of myocardial samples from patients with dilated cardiomyopathy, providing new ideas for elucidating the pathogenesis of the disease. METHODS: The transcriptome sequencing data sets of heart tissue from patients with dilated cardiomyopathy were downloaded from the Gene Expression Omnibus (GEO) database (GSE29819 and GSE21610). Differentially expressed genes (DEGs) were screened between pathological and normal tissues. The key genes were screened using least absolute shrinkage and selection operator (LASSO) regression analysis and random forest tree algorithms. The diagnostic efficiency of the key genes for the disease was evaluated using the receiver operating characteristic (ROC) curve. RESULTS: Compared with the normal heart tissue (control group) samples, there were 213 DEGs in the heart tissue samples of patients with dilated cardiomyopathy (treat group), including 101 upregulated and 102 downregulated genes. CCL5 and CTGF were highly expressed in the treat group compared to the control group. The ROC curve showed that the areas under the curve (AUCs) of CCL5 and CTGF were 0.821 and 0.902, respectively (P<0.05). In the treat group samples, CCL5 was positively correlated with the infiltration content of most immune cell subtypes. CONCLUSIONS: CCL5 and CTGF are key disease-causing genes in dilated cardiomyopathy and have good diagnostic efficiency for the disease. CCL5 and CTGF may be related to immune cell enrichment and myocardial fibrosis, respectively.
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spelling pubmed-104826512023-09-08 Selection of key genes for dilated cardiomyopathy based on machine learning algorithms and assessment of diagnostic accuracy Chen, Tingting Xuan, Xiulin Ni, Jiajia Jiang, Shuyin J Thorac Dis Original Article BACKGROUND: The mechanisms of the occurrence and progression of dilated cardiomyopathy are still unclear and further exploration is needed. The upgrading of programming languages and the improvement of biological databases have created conditions for us to explore the structural and functional information of biological molecules at the nucleic acid and protein levels, screen key pathogenic genes, and elucidate pathogenic mechanisms. This study aimed to screen key pathogenic genes using machine learning algorithms and explore the correlation between key genes and immune microenvironment through transcriptome sequencing data sets of myocardial samples from patients with dilated cardiomyopathy, providing new ideas for elucidating the pathogenesis of the disease. METHODS: The transcriptome sequencing data sets of heart tissue from patients with dilated cardiomyopathy were downloaded from the Gene Expression Omnibus (GEO) database (GSE29819 and GSE21610). Differentially expressed genes (DEGs) were screened between pathological and normal tissues. The key genes were screened using least absolute shrinkage and selection operator (LASSO) regression analysis and random forest tree algorithms. The diagnostic efficiency of the key genes for the disease was evaluated using the receiver operating characteristic (ROC) curve. RESULTS: Compared with the normal heart tissue (control group) samples, there were 213 DEGs in the heart tissue samples of patients with dilated cardiomyopathy (treat group), including 101 upregulated and 102 downregulated genes. CCL5 and CTGF were highly expressed in the treat group compared to the control group. The ROC curve showed that the areas under the curve (AUCs) of CCL5 and CTGF were 0.821 and 0.902, respectively (P<0.05). In the treat group samples, CCL5 was positively correlated with the infiltration content of most immune cell subtypes. CONCLUSIONS: CCL5 and CTGF are key disease-causing genes in dilated cardiomyopathy and have good diagnostic efficiency for the disease. CCL5 and CTGF may be related to immune cell enrichment and myocardial fibrosis, respectively. AME Publishing Company 2023-08-23 2023-08-31 /pmc/articles/PMC10482651/ /pubmed/37691671 http://dx.doi.org/10.21037/jtd-23-1086 Text en 2023 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Chen, Tingting
Xuan, Xiulin
Ni, Jiajia
Jiang, Shuyin
Selection of key genes for dilated cardiomyopathy based on machine learning algorithms and assessment of diagnostic accuracy
title Selection of key genes for dilated cardiomyopathy based on machine learning algorithms and assessment of diagnostic accuracy
title_full Selection of key genes for dilated cardiomyopathy based on machine learning algorithms and assessment of diagnostic accuracy
title_fullStr Selection of key genes for dilated cardiomyopathy based on machine learning algorithms and assessment of diagnostic accuracy
title_full_unstemmed Selection of key genes for dilated cardiomyopathy based on machine learning algorithms and assessment of diagnostic accuracy
title_short Selection of key genes for dilated cardiomyopathy based on machine learning algorithms and assessment of diagnostic accuracy
title_sort selection of key genes for dilated cardiomyopathy based on machine learning algorithms and assessment of diagnostic accuracy
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10482651/
https://www.ncbi.nlm.nih.gov/pubmed/37691671
http://dx.doi.org/10.21037/jtd-23-1086
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