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MicroRNA expression profile and identification of novel microRNA biomarkers for metabolic syndrome.

ABSTRACT: The lack of efficient biomarkers is the main reason for the inaccurate early diagnosis and poor treatment outcomes of patients with metabolic syndrome (MetS). The current study aimed to identify several novel microRNA (miRNA) biomarkers for metabolic syndrome via high-throughput sequencing and comprehensive bioinformatics analysis. Through high-throughput sequencing and differentially expressed miRNA (DEM) analysis, we first identified two upregulated and 36 downregulated DEMs in the plasma samples of patients with MetS compared to the healthy volunteers. Additionally, we also predicted 379 potential target genes and subsequently carried out enrichment analysis and protein-protein interaction network analysis to investigate the signaling pathways and functions of the identified DEMs as well as the interactions between their target genes. Furthermore, we selected two upregulated and top 10 downregulated DEMs with the highest |log2FC| values as the key microRNAs, which may serve as potential biomarkers for MetS. RT-qPCR was performed to validated these result. Finally, hsa-miR-526b-5p, hsa-miR-6516-5p was identified as the novel biomarkers for MetS.

SUBMITTER: Liu G

PROVIDER: S-EPMC8806888 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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MicroRNA expression profile and identification of novel microRNA biomarkers for metabolic syndrome.

Liu Guanzhi G Lei Yutian Y Luo Sen S Huang Zhuo Z Chen Chen C Wang Kunzheng K Yang Pei P Huang Xin X

Bioengineered 20211201 1

The lack of efficient biomarkers is the main reason for the inaccurate early diagnosis and poor treatment outcomes of patients with metabolic syndrome (MetS). The current study aimed to identify several novel microRNA (miRNA) biomarkers for metabolic syndrome via high-throughput sequencing and comprehensive bioinformatics analysis. Through high-throughput sequencing and differentially expressed miRNA (DEM) analysis, we first identified two upregulated and 36 downregulated DEMs in the plasma samp ...[more]

PMID: 34269146

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Project description:Laryngeal carcinoma is still a worldwide burden that has shown no significant improvement during the last few decades regarding definitive treatment strategies. The lack of suitable biomarkers for personalized treatment protocols and delineating field cancerization prevents further progress in clinical outcomes. In the light of this perspective, MicroRNAs could be promising biomarkers both in terms of diagnostic and prognostic value. The aim of this prospective study is to find strong prognostic microRNA biomarkers for advanced laryngeal carcinoma and molecular signatures of field cancerization. Sixty patients were enrolled and four samples were collected from each patient: tumor surface and depth, peritumor normal mucosa, and control distant laryngeal mucosa. Initially, a global microRNA profile was conducted in twelve patients from the whole cohort and subsequently, we validated a selected group of 12 microRNAs with RT-qPCR. The follow-up period was 24 months (SD ± 13 months). Microarray expression profile revealed 59 dysregulated microRNAs. The validated expression levels of miR-93-5p (χ2(2) = 4.68, log-rank p = 0.03), miR-144-3p (χ2(2) = 4.53, log-rank p = 0.03) and miR-210-3p (χ2(2) = 4.53, log-rank p = 0.03) in tumor samples exhibited strong association with recurrence-free survival as higher expression levels of these genes predict worse outcome. Tumor suppressor genes miR-144-3p (mean rank 1.58 vs 2.14 vs 2.29, p = 0.000) and miR-145-5p (mean rank 1.57 vs 2.15 vs 2.28, p = 0.000) were significantly dysregulated in peritumor mucosa with a pattern of expression consistent with paired tumor samples thus revealing a signature of field cancerization in laryngeal carcinoma. Additionally, miR-1260b, miR-21-3p, miR-31-3p and miR-31-5p were strongly associated with tumor grade. Our study reports the first global microRNA profile specifically in advanced laryngeal carcinoma that includes survival analysis and investigates the molecular signature of field cancerization. We report two strong biomarkers of field cancerization and three predictors for recurrence in advance stage laryngeal cancer.

Project description: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.

Project description:BackgroundSmall cell lung cancer (SCLC) is an aggressive and recalcitrant cancer. In recent years, studies focused on the abnormal expression of microRNA which has proven valuable in terms of prognosis, diagnosis and treatment in SCLC. To address the limitations of independent studies data, a meta-analysis seems necessary for further exploration of microRNA as biological target and regulatory factor in SCLC.MethodsWe performed comprehensive literature retrieval in GEO database and EBI ArrayExpress database. The microRNA expression data was extracted from 4 related researches (GSE15008, GSE74190, GSE19945, GSE77380), which was obtained from GEO database. In each included study, the R. Affymetrix Expression Console's Limma package and RMA algorithms were used to screen for raw data for gene chip quality control, standardization, log2 conversion and differential expression of the gene chip, respectively. Significant microRNA meta-signatures were identified by Robust Rank Aggregation method. Subsequently, gene ontology (GO) enrichment analysis and pathway analysis were performed using bioinformatics tools.ResultsWe found a significant microRNA meta-signature of six up-regulated (hsa-miR-182-5p, hsa-miR-96-5p, hsa-miR-7-5p, hsa-miR-301b-3p, hsa-miR-130b-3p, hsa-miR-210-3p) and four down-regulated (hsa-miR-126-3p, hsa-miR-451a, hsa-miR-145-5p, hsa-miR-486-5p) microRNA s in meta-analysis approaches. GO analysis showed that target gene of meta-signatures microRNA was mainly enriched in endosome, chordate embryonic development and transforming growth factor beta receptor. The related functional gene of microRNA meta signature synergistically targeting SCLC signaling pathway was confirmed by enrichment analysis. In particular, neurotrophin and TGF-beta signaling pathway play the most important roles in the pathway network.ConclusionsOur study identified 10 highly significant and consistently dysregulated microRNA s from 4 datasets, which offering convincing molecular targets and regulatory factors in future research of SCLC.

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MicroRNA expression profile and identification of novel microRNA biomarkers for metabolic syndrome.

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MicroRNA expression profile and identification of novel microRNA biomarkers for metabolic syndrome.

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