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多发性骨髓瘤关键基因和通路的生物信息学分析

Bioinformatics Analysis of the Key Genes and Pathways in Multiple Myeloma.

作者信息

Sheng Xinge, Wang Shuo, Huang Meijiao, Fan Kaiwen, Wang Jiaqi, Lu Quanyi

机构信息

Department of Hematology, Zhongshan Hospital Xiamen University, Xiamen, People's Republic of China.

Clinical Medicine Department, School of Medicine, Xiamen University, Xiamen, People's Republic of China.

出版信息

Int J Gen Med. 2022 Sep 5;15:6999-7016. doi: 10.2147/IJGM.S377321. eCollection 2022.

DOI:10.2147/IJGM.S377321
PMID:36090706
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9462443/
Abstract

OBJECTIVE

To study the differentially expressed genes between multiple myeloma and healthy whole blood samples by bioinformatics analysis, find out the key genes involved in the occurrence, development and prognosis of multiple myeloma, and analyze and predict their functions.

METHODS

The gene chip data GSE146649 was downloaded from the GEO expression database. The gene chip data GSE146649 was analyzed by R language to obtain the genes with different expression in multiple myeloma and healthy samples, and the cluster analysis heat map was constructed. At the same time, the protein-protein interaction (PPI) networks of these DEGs were established by STRING and Cytoscape software. The gene co-expression module was constructed by weighted correlation network analysis (WGCNA). The hub genes were identified from key gene and central gene. TCGA database was used to analyze the expression of differentially expressed genes in patients with multiple myeloma. Finally, the expression level of TNFSF11 in whole blood samples from patients with multiple myeloma was analyzed by RT qPCR.

RESULTS

We identified four genes (TNFSF11, FGF2, SGMS2, IGFBP7) as hub genes of multiple myeloma. Then, TCGA database was used to analyze the survival of TNFSF11, FGF2, SGMS2 and IGFBP7 in patients with multiple myeloma. Finally, the expression level of TNFSF11 in whole blood samples from patients with multiple myeloma was analyzed by RT qPCR.

CONCLUSION

The study suggests that TNFSF11, FGF2, SGMS2 and IGFBP7 are important research targets to explore the pathogenesis, diagnosis and treatment of multiple myeloma.

摘要

目的

通过生物信息学分析研究多发性骨髓瘤与健康全血样本之间的差异表达基因，找出参与多发性骨髓瘤发生、发展及预后的关键基因，并分析和预测其功能。

方法

从GEO表达数据库下载基因芯片数据GSE146649。用R语言对基因芯片数据GSE146649进行分析，以获得多发性骨髓瘤和健康样本中差异表达的基因，并构建聚类分析热图。同时，通过STRING和Cytoscape软件建立这些差异表达基因的蛋白质-蛋白质相互作用（PPI）网络。采用加权基因共表达网络分析（WGCNA）构建基因共表达模块。从关键基因和中心基因中鉴定出枢纽基因。利用TCGA数据库分析多发性骨髓瘤患者中差异表达基因的表达情况。最后，通过RT-qPCR分析多发性骨髓瘤患者全血样本中TNFSF11的表达水平。

结果

我们鉴定出四个基因（TNFSF11、FGF2、SGMS2、IGFBP7）作为多发性骨髓瘤的枢纽基因。然后，利用TCGA数据库分析TNFSF11、FGF2、SGMS2和IGFBP7在多发性骨髓瘤患者中的生存情况。最后，通过RT-qPCR分析多发性骨髓瘤患者全血样本中TNFSF11的表达水平。

结论

该研究表明TNFSF11、FGF2、SGMS2和IGFBP7是探索多发性骨髓瘤发病机制、诊断和治疗的重要研究靶点。

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