Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes

Background. Exercise is a regular behavioral activity that not only helps to lose weight but also reduces the risk of cardiovascular and cerebrovascular diseases. Diabetes is a common disease that plagues human health. It is shown that regular exercise can improve the insulin sensitivity of diabetic...

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Main Authors: Xiaoju Bao, Jingyue Qiu, Qin Xuan, Xinming Ye
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Genetics Research
Online Access:http://dx.doi.org/10.1155/2022/1273153
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author Xiaoju Bao
Jingyue Qiu
Qin Xuan
Xinming Ye
author_facet Xiaoju Bao
Jingyue Qiu
Qin Xuan
Xinming Ye
author_sort Xiaoju Bao
collection DOAJ
description Background. Exercise is a regular behavioral activity that not only helps to lose weight but also reduces the risk of cardiovascular and cerebrovascular diseases. Diabetes is a common disease that plagues human health. It is shown that regular exercise can improve the insulin sensitivity of diabetic patients and have an important function in adjuvant therapy. Methods. We downloaded the GSE101931 dataset from the Gene Expression Omnibus (GEO) database, 10 samples were obtained from the GSE101931 dataset, including 5 before exercise and 5 postexercise samples, and GEO2R was used to screen the differentially expressed genes (DEGs) exhibited by a heat map. Then, the enrichment analysis of DEGs in Gene Ontology (GO) function was analyzed by Metascape, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of DEGs was also analyzed by gene set enrichment analysis (GSEA). Next, the protein-protein interaction (PPI) network maps were drawn, and the hub genes were identified through Metascape. Finally, the expressions of the hub genes in the dataset were analyzed. Results. Totally, 116 upregulated DEGs and 1017 downregulated DEGs were identified from these data. These DEGs were mainly enriched in the platelet-derived growth factor receptor signaling pathway and mRNA processing. Then, the GSEA analysis showed that 6 KEGG pathways were associated with postexercise prediabetic samples, namely, ABC transporters, focal adhesion, MAPK signaling pathway, prion diseases, melanogenesis, and gap junction. Afterward, three hub genes (HSPA8, STIP1, and HSPH1) were highly expressed after exercise through the box plot analysis. Conclusion. A myriad of research results confirms that there is a certain connection between exercise and diabetes, which provides a favorable basis for emerging exercise into the treatment of diabetic patients.
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spelling doaj-art-b8880fed436c48798fd94710cc1aae942025-02-03T05:51:01ZengWileyGenetics Research1469-50732022-01-01202210.1155/2022/1273153Bioinformatics Analysis of Exercise-Related Biomarkers in DiabetesXiaoju Bao0Jingyue Qiu1Qin Xuan2Xinming Ye3School of Sports Science and EngineeringSchool of Physical Science and EngineeringSchool of Sports Science and EngineeringSchool of Sports Science and EngineeringBackground. Exercise is a regular behavioral activity that not only helps to lose weight but also reduces the risk of cardiovascular and cerebrovascular diseases. Diabetes is a common disease that plagues human health. It is shown that regular exercise can improve the insulin sensitivity of diabetic patients and have an important function in adjuvant therapy. Methods. We downloaded the GSE101931 dataset from the Gene Expression Omnibus (GEO) database, 10 samples were obtained from the GSE101931 dataset, including 5 before exercise and 5 postexercise samples, and GEO2R was used to screen the differentially expressed genes (DEGs) exhibited by a heat map. Then, the enrichment analysis of DEGs in Gene Ontology (GO) function was analyzed by Metascape, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of DEGs was also analyzed by gene set enrichment analysis (GSEA). Next, the protein-protein interaction (PPI) network maps were drawn, and the hub genes were identified through Metascape. Finally, the expressions of the hub genes in the dataset were analyzed. Results. Totally, 116 upregulated DEGs and 1017 downregulated DEGs were identified from these data. These DEGs were mainly enriched in the platelet-derived growth factor receptor signaling pathway and mRNA processing. Then, the GSEA analysis showed that 6 KEGG pathways were associated with postexercise prediabetic samples, namely, ABC transporters, focal adhesion, MAPK signaling pathway, prion diseases, melanogenesis, and gap junction. Afterward, three hub genes (HSPA8, STIP1, and HSPH1) were highly expressed after exercise through the box plot analysis. Conclusion. A myriad of research results confirms that there is a certain connection between exercise and diabetes, which provides a favorable basis for emerging exercise into the treatment of diabetic patients.http://dx.doi.org/10.1155/2022/1273153
spellingShingle Xiaoju Bao
Jingyue Qiu
Qin Xuan
Xinming Ye
Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes
Genetics Research
title Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes
title_full Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes
title_fullStr Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes
title_full_unstemmed Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes
title_short Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes
title_sort bioinformatics analysis of exercise related biomarkers in diabetes
url http://dx.doi.org/10.1155/2022/1273153
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AT jingyueqiu bioinformaticsanalysisofexerciserelatedbiomarkersindiabetes
AT qinxuan bioinformaticsanalysisofexerciserelatedbiomarkersindiabetes
AT xinmingye bioinformaticsanalysisofexerciserelatedbiomarkersindiabetes