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用于识别非梗阻性无精子症潜在生物标志物和相关通路的整合生物信息学方法

Integrative bioinformatics approaches for identifying potential biomarkers and pathways involved in non-obstructive azoospermia.

作者信息

Hu Tengfei, Luo Shaoge, Xi Yu, Tu Xuchong, Yang Xiaojian, Zhang Hui, Feng Jiarong, Wang Chunlin, Zhang Yan

机构信息

Department of Infertility and Sexual Medicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Department of Andrology, Ruikang Hospital Affiliated to Guangxi University of Traditional Chinese Medicine, Nanning, China.

出版信息

Transl Androl Urol. 2021 Jan;10(1):243-257. doi: 10.21037/tau-20-1029.

Abstract

BACKGROUND

Non-obstructive azoospermia (NOA) is a disease related to spermatogenic disorders. Currently, the specific etiological mechanism of NOA is unclear. This study aimed to use integrated bioinformatics to screen biomarkers and pathways involved in NOA and reveal their potential molecular mechanisms.

METHODS

GSE145467 and GSE108886 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NOA tissues and matched obstructive azoospermia (OA) tissues were identified using the GEO2R tool. Common DEGs in the two datasets were screened out by the VennDiagram package. For the functional annotation of common DEGs, DAVID v.6.8 was used to perform Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. In accordance with data collected from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, a protein-protein interaction (PPI) network was constructed by Cytoscape. Cytohubba in Cytoscape was used to screen the hub genes. Furthermore, the hub genes were validated based on a separate dataset, GSE9210. Finally, potential micro RNAs (miRNAs) of hub genes were predicted by miRWalk 3.0.

RESULTS

A total of 816 common DEGs, including 52 common upregulated and 764 common downregulated genes in two datasets, were screened out. Some of the more important of these pathways, including focal adhesion, PI3K-Akt signaling pathway, cell cycle, oocyte meiosis, AMP-activated protein kinase (AMPK) signaling pathway, FoxO signaling pathway, and Huntington disease, were involved in spermatogenesis. We further identified the top 20 hub genes from the PPI network, including , , , , , , , , , , , , , , , , , , , and , which were all downregulated genes. In addition, potential miRNAs of hub genes, including hsa-miR-3666, hsa-miR-130b-3p, hsa-miR-15b-5p, hsa-miR-6838-5p, and hsa-miR-195-5p, were screened out.

CONCLUSIONS

Taken together, the identification of the above hub genes, miRNAs and pathways will help us better understand the mechanisms associated with NOA, and provide potential biomarkers and therapeutic targets for NOA.

摘要

背景

非梗阻性无精子症(NOA)是一种与生精障碍相关的疾病。目前,NOA的具体病因机制尚不清楚。本研究旨在利用综合生物信息学方法筛选参与NOA的生物标志物和信号通路,并揭示其潜在的分子机制。

方法

从基因表达综合数据库(GEO)中获取GSE145467和GSE108886基因表达谱。使用GEO2R工具鉴定NOA组织与匹配的梗阻性无精子症(OA)组织之间的差异表达基因(DEG)。通过VennDiagram软件包筛选两个数据集中的共同DEG。对于共同DEG的功能注释,使用DAVID v.6.8进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)信号通路富集分析。根据从检索相互作用基因/蛋白质的搜索工具(STRING)数据库收集的数据,用Cytoscape构建蛋白质-蛋白质相互作用(PPI)网络。利用Cytoscape中的Cytohubba筛选枢纽基因。此外,基于另一个数据集GSE9210对枢纽基因进行验证。最后,通过miRWalk 3.0预测枢纽基因的潜在微小RNA(miRNA)。

结果

共筛选出816个共同DEG,其中包括两个数据集中52个共同上调基因和764个共同下调基因。其中一些更重要的信号通路,包括粘着斑、PI3K-Akt信号通路、细胞周期、卵母细胞减数分裂、AMP活化蛋白激酶(AMPK)信号通路、FoxO信号通路和亨廷顿病,均参与精子发生过程。我们进一步从PPI网络中鉴定出前20个枢纽基因,包括 , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,均为下调基因。此外,还筛选出枢纽基因的潜在miRNA,包括hsa-miR-3666, hsa-miR-130b-3p, hsa-miR-15b-5p, hsa-miR-6838-5p和hsa-miR-195-5p。

结论

综上所述,上述枢纽基因、miRNA和信号通路的鉴定将有助于我们更好地理解与NOA相关的机制,并为NOA提供潜在的生物标志物和治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b07/7844508/53e80d710e4e/tau-10-01-243-f1.jpg

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