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可能对结核病的诊断和治疗至关重要。

, , and May Be Critical for the Diagnosis and Treatment of Tuberculosis.

机构信息

Department of Clinical Laboratory, Xiangya Hospital of Central South University, Changsha 410008, Hunan, China.

出版信息

Can Respir J. 2020 Jul 23;2020:4348371. doi: 10.1155/2020/4348371. eCollection 2020.

Abstract

BACKGROUND

Tuberculosis (TB) is usually caused by , which has the highest mortality rate among infectious diseases. This study is designed to identify the key genes affecting the diagnosis and treatment of TB.

METHODS

GSE54992, which included 39 peripheral blood mononuclear cell (PBMC) samples, was extracted from the Gene Expression Omnibus database. After the samples were classified into type and time groups by limma package, the differentially expressed genes (DEGs) were analyzed using the Analysis of Variance. Using pheatmap package, hierarchical cluster analysis was performed for the DEGs. Then, the key modules correlated with TB were selected using the WGCNA package. Finally, functional and pathway enrichment analyses were carried out using clusterProfiler package.

RESULTS

The DEGs in subclusters 3, 6, 7, and 8 were chosen for further analyses. Based on WGCNA analysis, blue and green modules in type group and pink module in time group were selected as key modules. From the key modules, 9 (including and ) hub genes in type group and 6 (including ) hub genes in time group were screened. Through pathway enrichment analysis, the TNF signaling pathway was enriched for the green module.

CONCLUSION

, , and might be key genes acting in the mechanisms of TB. Besides, the TNF signaling pathway might also be critical for the diagnosis and therapy of the disease.

摘要

背景

结核病(TB)通常由引起,其在传染病中的死亡率最高。本研究旨在确定影响结核病诊断和治疗的关键基因。

方法

从基因表达综合数据库中提取了 GSE54992,其中包含 39 个外周血单核细胞(PBMC)样本。使用 limma 包将样本按类型和时间组分类后,使用方差分析对差异表达基因(DEGs)进行分析。使用 pheatmap 包对 DEGs 进行层次聚类分析。然后,使用 WGCNA 包选择与 TB 相关的关键模块。最后,使用 clusterProfiler 包进行功能和途径富集分析。

结果

选择了亚群 3、6、7 和 8 中的 DEGs 进行进一步分析。基于 WGCNA 分析,选择了类型组中的蓝色和绿色模块以及时间组中的粉红色模块作为关键模块。从关键模块中,筛选出了类型组中的 9 个(包括和)和时间组中的 6 个(包括)的枢纽基因。通过途径富集分析,绿色模块中富集了 TNF 信号通路。

结论

、和可能是参与结核病机制的关键基因。此外,TNF 信号通路可能对该疾病的诊断和治疗也很关键。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae34/7396107/e8db6c73d152/CRJ2020-4348371.001.jpg

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