Integrative analysis of an endoplasmic reticulum stress‐related signature in multiple myeloma

内质网 基因 免疫系统 生物 多发性骨髓瘤 未折叠蛋白反应 信使核糖核酸 基因表达 转录因子 小RNA 基因本体论 细胞生物学 计算生物学 免疫学 遗传学
作者
Chengyu Wu,Mei Liu,Jia Liu,Mingyuan Jia,Xinyi Zeng,Ze Fu,Yanlai Geng,Ziqi He,Xian Zhang,Hua Yan
出处
期刊:Journal of Gene Medicine [Wiley]
卷期号:26 (1)
标识
DOI:10.1002/jgm.3595
摘要

Abstract Background Multiple myeloma (MM) is a malignancy in which plasma cells proliferate abnormally, and it remains incurable. The cells are characterized by high levels of endoplasmic reticulum stress (ERS) and depend on the ERS response for survival. Thus, we aim to find an ERS‐related signature of MM and assess its diagnostic value. Methods We downloaded three datasets of MM from the Gene Expression Omnibus database. After identifying ERS‐related differentially expressed genes (ERDEGs), we analyzed them using Gene Ontology enrichment analysis. A protein–protein interaction network, a transcription factor–mRNA network, a miRNA–mRNA network and a drug–mRNA network were constructed to explore the ERDEGs. The clinical application of these genes was identified by calculating the infiltration of immune cells and using receiver operating characteistic analyses. Finally, qPCR was performed to further confirm the roles of ERDEGs. Results We obtained nine ERDEGs of MM. Gene Ontology enrichment indicated that the ERDEGs played a role in the endoplasmic reticulum membrane. Additionally, the protein–protein interaction network showed interaction among the ERDEGs, and there were 20 proteins, 107 transcription factors, 42 drugs or molecular compounds and 51 miRNAs which were likely to interact with the nine genes. In addition, immune cell infiltration analyses showed that there was a strong correlation between the nine genes and immune cells, and these potential biomarkers exhibited good diagnostic values. Finally, the expression of ERDEGs in MM cells was different from that in healthy donor samples. Conclusion The nine ERS‐related genes, CR2, DHCR7, DNAJC3, KDELR2, LPL, OSBPL3, PINK1, VCAM1 and XBP1 are potential biomarkers of MM, and this supports further clinical development of the diagnosis and treatment of MM.
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