Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes.

IF 2.1 4区生物学 Q4 GENETICS & HEREDITY

Genetics research Pub Date : 2022-06-29 eCollection Date: 2022-01-01 DOI:10.1155/2022/1273153

Xiaoju Bao, Jingyue Qiu, Qin Xuan, Xinming Ye

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Abstract

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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糖尿病运动相关生物标志物的生物信息学分析。

背景:运动是一种有规律的行为活动，不仅有助于减肥，而且可以降低患心脑血管疾病的风险。糖尿病是一种危害人类健康的常见病。研究表明，经常运动可以提高糖尿病患者的胰岛素敏感性，在辅助治疗中具有重要作用。方法:从Gene Expression Omnibus (GEO)数据库下载GSE101931数据集，从GSE101931数据集中获得10个样本，其中5个为运动前样本，5个为运动后样本，使用GEO2R筛选热图显示的差异表达基因(deg)。然后，利用metscape对基因本体(GO)功能中的DEGs进行富集分析，并利用基因集富集分析(GSEA)对DEGs的京都基因与基因组百科全书(KEGG)途径进行分析。下一步，绘制蛋白-蛋白相互作用(PPI)网络图谱，并通过metscape对枢纽基因进行鉴定。最后，对数据集中中心基因的表达进行了分析。结果:从这些数据中共鉴定出116个上调的deg和1017个下调的deg。这些deg主要富集在血小板源性生长因子受体信号通路和mRNA加工中。然后，GSEA分析显示，运动后糖尿病前期样本中有6条KEGG通路相关，即ABC转运蛋白、局灶粘连、MAPK信号通路、朊病毒疾病、黑色素生成和间隙连接。之后，通过箱形图分析，运动后三个枢纽基因(HSPA8、STIP1和HSPH1)高表达。结论:大量的研究结果证实了运动与糖尿病之间存在一定的联系，这为将运动引入糖尿病患者的治疗中提供了有利的依据。

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来源期刊

Genetics research 生物-遗传学

自引率

6.70%

发文量

审稿时长

>12 weeks

期刊介绍： Genetics Research is a key forum for original research on all aspects of human and animal genetics, reporting key findings on genomes, genes, mutations and molecular interactions, extending out to developmental, evolutionary, and population genetics as well as ethical, legal and social aspects. Our aim is to lead to a better understanding of genetic processes in health and disease. The journal focuses on the use of new technologies, such as next generation sequencing together with bioinformatics analysis, to produce increasingly detailed views of how genes function in tissues and how these genes perform, individually or collectively, in normal development and disease aetiology. The journal publishes original work, review articles, short papers, computational studies, and novel methods and techniques in research covering humans and well-established genetic organisms. Key subject areas include medical genetics, genomics, human evolutionary and population genetics, bioinformatics, genetics of complex traits, molecular and developmental genetics, Evo-Devo, quantitative and statistical genetics, behavioural genetics and environmental genetics. The breadth and quality of research make the journal an invaluable resource for medical geneticists, molecular biologists, bioinformaticians and researchers involved in genetic basis of diseases, evolutionary and developmental studies.