Inflammatory microRNAs in cardiovascular pathology: another brick in the wall

Zapata-Martínez, Laura; Águila, Sonia; Reyes-García, Ascensión M. de los; Carrillo-Tornel, Salvador; Lozano, María Luisa; Gónzález-Conejero, Rocío; Martı́nez, Constantino

doi:10.3389/fimmu.2023.1196104

Cited by 7 publications

(1 citation statement)

References 139 publications

(196 reference statements)

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“…The related research on circulating and extracellular vesicle-derived microRNAs as biomarkers for bone-related maladies was developed by Huber et al ( 2 ). Additionally, Zapata-Martinez et al ( 3 ) proposed the involvement of inflammatory microRNAs in cardiovascular pathology. The role of miroRNA-21-containing microvesicles derived from renal tubular epithelial cells in cardiac hypertrophy was developed by Di et al ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

DEJKMDR: miRNA-disease association prediction method based on graph convolutional network

Gao,

Kuang,

Duan

et al. 2023

Front. Med.

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Numerous studies have shown that miRNAs play a crucial role in the investigation of complex human diseases. Identifying the connection between miRNAs and diseases is crucial for advancing the treatment of complex diseases. However, traditional methods are frequently constrained by the small sample size and high cost, so computational simulations are urgently required to rapidly and accurately forecast the potential correlation between miRNA and disease. In this paper, the DEJKMDR, a graph convolutional network (GCN)-based miRNA-disease association prediction model is proposed. The novelty of this model lies in the fact that DEJKMDR integrates biomolecular information on miRNA and illness, including functional miRNA similarity, disease semantic similarity, and miRNA and disease similarity, according to their Gaussian interaction attribute. In order to minimize overfitting, some edges are randomly destroyed during the training phase after DropEdge has been used to regularize the edges. JK-Net, meanwhile, is employed to combine various domain scopes through the adaptive learning of nodes in various placements. The experimental results demonstrate that this strategy has superior accuracy and dependability than previous algorithms in terms of predicting an unknown miRNA-disease relationship. In a 10-fold cross-validation, the average AUC of DEJKMDR is determined to be 0.9772.

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Section: Introductionmentioning

confidence: 99%

DEJKMDR: miRNA-disease association prediction method based on graph convolutional network

Gao,

Kuang,

Duan

et al. 2023

Front. Med.

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Artificial Intelligence and MicroRNA: Role in Cancer Evolution

Koroliouk,

Mattei,

Zoziuk

et al. 2024

Lecture Notes in Electrical Engineering

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Recent advances in mechanistic studies of heart failure with preserved ejection fraction and its comorbidities—Role of microRNAs

Li,

Sun,

Wang

et al. 2023

Eur J Clin Investigation

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BackgroundHeart failure with preserved ejection fraction (HFpEF) is a multifaceted syndrome with a complex aetiology commonly associated with comorbidities such as diabetes mellitus, obesity, hypertension and renal disease. Various diseases induce systemic, chronic and low‐grade inflammation; microvascular dysfunction; metabolic stress; tissue ischemia; and fibrosis, leading to HFpEF. An effective treatment for HFpEF is lacking, largely owing to its pathophysiological heterogeneity. Recent studies have revealed that microRNAs (miRNAs) play crucial roles in regulating the pathogenesis of HFpEF and its comorbidities.MethodsThis narrative review included original articles and reviews published over the past 20 years found through ‘PubMed’ and ‘Web of Science’. The search terms included “HFpEF,” “MicroRNAs,” “comorbidities,” “Microvascular Dysfunction (MVD),” “inflammation,” “pathophysiology,” “endothelial dysfunction,” “energy metabolism abnormalities” “cardiac fibrosis” and “treatment.”ResultsInflammation, MVD, abnormal energy metabolism, myocardial hypertrophy and myocardial fibrosis are important pathophysiological mechanisms underlying HFpEF. As gene expression regulators, miRNAs may contribute to the pathophysiology of HFpEF and are expected to serve in the stratification of patients with HFpEF and as prognostic indicators for monitoring treatment responses.ConclusionsA customized strategy based on miRNAs has emerged as an effective treatment for HFpEF. In this review, we discuss recent research surrounding miRNAs and HFpEF and propose potential miRNA targets for the pathophysiology of HFpEF and its comorbidities. Although current research concerning miRNAs and their therapeutic potential is in its early stages, miRNA‐based diagnostics and therapeutics hold great promise in the future.

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Inflammatory microRNAs in cardiovascular pathology: another brick in the wall

Cited by 7 publications

References 139 publications

DEJKMDR: miRNA-disease association prediction method based on graph convolutional network

DEJKMDR: miRNA-disease association prediction method based on graph convolutional network

Artificial Intelligence and MicroRNA: Role in Cancer Evolution

Recent advances in mechanistic studies of heart failure with preserved ejection fraction and its comorbidities—Role of microRNAs

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