整合分析与 CD16+ 单核细胞中骨质疏松症相关的基因。

Integrative Analyses of Genes Associated With Osteoporosis in CD16+ Monocyte.

机构信息

Department of Orthopedics, The Second People's Hospital of Hefei, Hefei, China.

Department of Endocrinology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

出版信息

Front Endocrinol (Lausanne). 2021 Jan 21;11:581878. doi: 10.3389/fendo.2020.581878. eCollection 2020.

DOI:10.3389/fendo.2020.581878

PMID:33551990

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7859337/

Abstract

BACKGROUND

Osteoporosis is a metabolic bone disease characterized by decreased bone mineral density and abnormal bone quality. Monocytes can secret cytokines for bone resorption, resulting in bone mass loss. However, the mechanism by which monocytes subpopulations lead to osteoporosis remains unclear. The aim of this study was to identify genes associated with osteoporosis in monocytes subsets.

METHODS

Three microarray datasets including GSE7158 (transcription of low/high-peak bone mass), GSE101489 (transcription of CD16+/CD16- monocyte) and GSE93883 (miRNA expression profile of primary osteoporosis) were derived from the Gene Expression Omnibus (GEO) database and analyzed with GEO2R tool to identify differentially expressed genes (DEGs). Functional enrichment was analyzed using Metascape database and GSEA software. STRING was utilized for the Protein-Protein Interaction Network construct. The hub genes were screened out using the Cytoscape software. Related miRNAs were predicted in miRWalk, miRDB, and TargetScan databases.

RESULTS

Total 368 DEGs from GSE7158 were screened out, which were mostly enriched in signaling, positive regulation of biological process and immune system process. The hub genes were clustered into two modules by PPI network analysis. We identified 15 overlapping DGEs between GSE101489 and GSE7158 microarray datasets. Moreover, all of them were up-regulated genes in both datasets. Then, nine key genes were screened out from above 15 overlapping DEGs using Cytoscape software. It is a remarkable fact that the nine genes were all in one hub gene module of GSE7158. Additionally, 183 target miRNAs were predicted according to the above nine DEGs. After cross-verification with miRNA express profile dataset for osteoporosis (GSE93883), 12 DEmiRNAs were selected. Finally, a miRNA-mRNA network was constructed with the nine key genes and 12 miRNAs, which were involved in osteoporosis.

CONCLUSION

Our analysis results constructed a gene expression framework in monocyte subsets for osteoporosis. This approach could provide a novel insight into osteoporosis.

摘要

背景

骨质疏松症是一种代谢性骨病，其特征是骨矿物质密度降低和骨质量异常。单核细胞可以分泌细胞因子进行骨吸收，导致骨量丢失。然而，单核细胞亚群导致骨质疏松症的机制尚不清楚。本研究旨在鉴定单核细胞亚群中与骨质疏松症相关的基因。

方法

从基因表达综合数据库（GEO）中提取了三个微阵列数据集，包括 GSE7158（高低骨量峰的转录）、GSE101489（CD16+/CD16-单核细胞的转录）和 GSE93883（原发性骨质疏松症的 miRNA 表达谱），并使用 GEO2R 工具进行分析，以识别差异表达基因（DEGs）。使用 Metascape 数据库和 GSEA 软件进行功能富集分析。使用 STRING 构建蛋白质-蛋白质相互作用网络。使用 Cytoscape 软件筛选出枢纽基因。在 miRWalk、miRDB 和 TargetScan 数据库中预测相关 miRNAs。

结果

从 GSE7158 筛选出 368 个 DEGs，主要富集于信号转导、生物过程的正调控和免疫系统过程。通过 PPI 网络分析将枢纽基因聚类为两个模块。我们在 GSE101489 和 GSE7158 微阵列数据集之间鉴定出 15 个重叠的 DGEs。此外，这两个数据集均为上调基因。然后，使用 Cytoscape 软件从上述 15 个重叠 DEGs 中筛选出 9 个关键基因。值得注意的是，这 9 个基因均位于 GSE7158 的一个枢纽基因模块中。此外，根据上述 9 个 DEGs 预测出 183 个靶 miRNAs。与骨质疏松症 miRNA 表达谱数据集（GSE93883）交叉验证后，选择了 12 个 DEmiRNAs。最后，构建了一个包含 9 个关键基因和 12 个 miRNAs 的 miRNA-mRNA 网络，这些基因和 miRNAs 均参与了骨质疏松症。