Characterization of the circulating transcriptome expression profile and identification of novel miRNA biomarkers in hypertrophic cardiomyopathy

European Journal of Medical Research

Research

Yue Cai¹  Hang Zhao¹  Ling Tao¹  Lanyan Guo¹  Fuyang Zhang¹  Bo Wang²  Liwen Liu² 

[1]Department of Cardiology, Xijing Hospital, the Fourth Military Medical University, 127 Changle West Road, 710032, Xi’an, Shaan Xi, China

[2]Department of Ultrasound, Xijing Hospital, the Fourth Military Medical University, 127 Changle West Road, 710032, Xi’an, Shaan Xi, China

关键词: Hypertrophic cardiomyopathy;    miRNAs;    Weighted correlation network analysis;    Gene set enrichment analysis;    Competing endogenous RNA network;   

DOI  :  10.1186/s40001-023-01159-7

 received in 2023-04-10, accepted in 2023-06-07,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundHypertrophic cardiomyopathy (HCM), one of the most common genetic cardiovascular diseases, but cannot be explained by single genetic factors. Circulating microRNAs (miRNAs) are stable and highly conserved. Inflammation and immune response participate in HCM pathophysiology, but whether the miRNA profile changes correspondingly in human peripheral blood mononuclear cells (PBMCs) with HCM is unclear. Herein, we aimed to investigate the circulating non-coding RNA (ncRNA) expression profile in PBMCs and identify potential miRNAs for HCM biomarkers.MethodsA Custom CeRNA Human Gene Expression Microarray was used to identify differentially expressed (DE) mRNAs, miRNAs, and ncRNAs (including circRNA and lncRNA) in HCM PBMCs. Weighted correlation network analysis (WGCNA) was used to identify HCM-related miRNA and mRNA modules. The mRNAs and miRNAs from the key modules were used to construct a co-expression network. Three separate machine learning algorithms (random forest, support vector machine, and logistic regression) were applied to identify potential biomarkers based on miRNAs from the HCM co-expression network. Gene Expression Omnibus (GEO) database (GSE188324) and experimental samples were used for further verification. Gene set enrichment analysis (GSEA) and competing endogenous RNA (ceRNA) network was used to determine the potential functions of the selected miRNAs in HCM.ResultsWe identified 1194 DE-mRNAs, 232 DE-miRNAs and 7696 DE-ncRNAs in HCM samples compared with normal controls from the microarray data sets. WGCNA identified key miRNA modules and mRNA modules evidently associated with HCM. We constructed a miRNA‒mRNA co-expression network based on these modules. A total of three hub miRNAs (miR-924, miR-98 and miR-1) were identified by random forest, and the areas under the receiver operator characteristic curves of miR-924, miR-98 and miR-1 were 0.829, 0.866, and 0.866, respectively.ConclusionsWe elucidated the transcriptome expression profile in PBMCs and identified three hub miRNAs (miR-924, miR-98 and miR-1) as potential biomarkers for HCM detection.

【 授权许可】

CC BY   
© The Author(s) 2023

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