Hypertrophic cardiomyopathy induces changes in the tryptophan-5-hydroxylase, serotonin transporter and serotonergic receptors expressions

Manjarrez-Gutiérrez, Gabriel; Valero-Elizondo, Guillermo; Serrano-Hernández, Yesenia; Mondragón-Herrera, José Antonio; Mansilla-Olivares, Armando

doi:10.24875/gmm.m22000717

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with NCs, HCM patients showed more abnormalities in inflammatory signaling, metabolic pathways and calcium pathways. Previous studies have shown that abnormalities of such pathways in cardiac muscle tissue play an important role in the development of HCM, presenting consistent changes in PBMCs and myocardial tissue in HCM [ 32 ]. Furthermore, DE-miRNAs in PBMCs are more relevant to HCM-related pathogenic pathways than DE-mRNAs, which suggests the importance of circulating miRNA alterations for the pathogenesis of HCM and their potential as diagnostic biomarkers and therapeutic targets.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

Background Hypertrophic 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. Methods A 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. Results We 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. Conclusions We 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.

show abstract

Section: Discussionmentioning

confidence: 99%