Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms

Tendinopathy is a complex multifaceted tendinopathy often associated with overuse and with its high prevalence resulting in significant health care costs. At present, the pathogenesis and effective treatment of tendinopathy are still not sufficiently elucidated. The purpose of this research is to in...

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Autores principales:	Ya xi Zhu, Jia qiang Huang, Yu yang Ming, Zhao Zhuang, Hong Xia
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0266f1035d3b4b8ea79ed7fe7b4c8c87
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spelling	oai:doaj.org-article:0266f1035d3b4b8ea79ed7fe7b4c8c872021-11-04T07:42:10ZScreening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms1932-6203https://doaj.org/article/0266f1035d3b4b8ea79ed7fe7b4c8c872021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8555777/?tool=EBIhttps://doaj.org/toc/1932-6203Tendinopathy is a complex multifaceted tendinopathy often associated with overuse and with its high prevalence resulting in significant health care costs. At present, the pathogenesis and effective treatment of tendinopathy are still not sufficiently elucidated. The purpose of this research is to intensely explore the genes, functional pathways, and immune infiltration characteristics of the occurrence and development of tendinopathy. The gene expression profile of GSE106292, GSE26051 and GSE167226 are downloaded from GEO (NCBI comprehensive gene expression database) and analyzed by WGCNA software bag using R software, GSE26051, GSE167226 data set is combined to screen the differential gene analysis. We subsequently performed gene enrichment analysis of Gene Ontology (GO) and "Kyoto Encyclopedia of Genes and Genomes" (KEGG), and immune cell infiltration analysis. By constructing the LASSO regression model, Support vector machine (SVM-REF) and Gaussian mixture model (GMMs) algorithms are used to screen, to identify early diagnostic genes. We have obtained a total of 171 DEGs through WGCNA analysis and differentially expressed genes (DEGs) screening. By GO and KEGG enrichment analysis, it is found that these dysregulated genes were related to mTOR, HIF-1, MAPK, NF-κB and VEGF signaling pathways. Immune infiltration analysis showed that M1 macrophages, activated mast cells and activated NK cells had infiltration significance. After analysis of THE LASSO SVM-REF and GMMs algorithms, we found that the gene MACROD1 may be a gene for early diagnosis. We identified the potential of tendon disease early diagnosis way and immune gene regulation MACROD1 key infiltration characteristics based on comprehensive bioinformatics analysis. These hub genes and functional pathways may as early biomarkers of tendon injuries and molecular therapy level target is used to guide drug and basic research.Ya xi ZhuJia qiang HuangYu yang MingZhao ZhuangHong XiaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ya xi Zhu Jia qiang Huang Yu yang Ming Zhao Zhuang Hong Xia Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms
description	Tendinopathy is a complex multifaceted tendinopathy often associated with overuse and with its high prevalence resulting in significant health care costs. At present, the pathogenesis and effective treatment of tendinopathy are still not sufficiently elucidated. The purpose of this research is to intensely explore the genes, functional pathways, and immune infiltration characteristics of the occurrence and development of tendinopathy. The gene expression profile of GSE106292, GSE26051 and GSE167226 are downloaded from GEO (NCBI comprehensive gene expression database) and analyzed by WGCNA software bag using R software, GSE26051, GSE167226 data set is combined to screen the differential gene analysis. We subsequently performed gene enrichment analysis of Gene Ontology (GO) and "Kyoto Encyclopedia of Genes and Genomes" (KEGG), and immune cell infiltration analysis. By constructing the LASSO regression model, Support vector machine (SVM-REF) and Gaussian mixture model (GMMs) algorithms are used to screen, to identify early diagnostic genes. We have obtained a total of 171 DEGs through WGCNA analysis and differentially expressed genes (DEGs) screening. By GO and KEGG enrichment analysis, it is found that these dysregulated genes were related to mTOR, HIF-1, MAPK, NF-κB and VEGF signaling pathways. Immune infiltration analysis showed that M1 macrophages, activated mast cells and activated NK cells had infiltration significance. After analysis of THE LASSO SVM-REF and GMMs algorithms, we found that the gene MACROD1 may be a gene for early diagnosis. We identified the potential of tendon disease early diagnosis way and immune gene regulation MACROD1 key infiltration characteristics based on comprehensive bioinformatics analysis. These hub genes and functional pathways may as early biomarkers of tendon injuries and molecular therapy level target is used to guide drug and basic research.
format	article
author	Ya xi Zhu Jia qiang Huang Yu yang Ming Zhao Zhuang Hong Xia
author_facet	Ya xi Zhu Jia qiang Huang Yu yang Ming Zhao Zhuang Hong Xia
author_sort	Ya xi Zhu
title	Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms
title_short	Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms
title_full	Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms
title_fullStr	Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms
title_full_unstemmed	Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms
title_sort	screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/0266f1035d3b4b8ea79ed7fe7b4c8c87
work_keys_str_mv	AT yaxizhu screeningofkeybiomarkersoftendinopathybasedonbioinformaticsandmachinelearningalgorithms AT jiaqianghuang screeningofkeybiomarkersoftendinopathybasedonbioinformaticsandmachinelearningalgorithms AT yuyangming screeningofkeybiomarkersoftendinopathybasedonbioinformaticsandmachinelearningalgorithms AT zhaozhuang screeningofkeybiomarkersoftendinopathybasedonbioinformaticsandmachinelearningalgorithms AT hongxia screeningofkeybiomarkersoftendinopathybasedonbioinformaticsandmachinelearningalgorithms
_version_	1718445001680093184

Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms

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