MiRGOFS: a GO-based functional similarity measurement for miRNAs, with applications to the prediction of miRNA subcellular localization and miRNA–disease association

Yang, Yang; Fu, Xiuwei; Qu, Wenhao; Xiao, Yiqun; Shen, Hong‐Bin

doi:10.1093/bioinformatics/bty343

Cited by 53 publications

(25 citation statements)

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“…The results of a KEGG analysis provides insights into the higher biological functions of cells. KEGG analysis can provide the most important biochemical metabolic pathways and signal transduction pathways involved in protein (Yang et al, 2018). COG annotates the functions of homologous proteins and includes both the COG database (clusters of homologous proteins from prokaryotes) and the KOG database (clusters of homologous proteins in eukaryotes) (Tatusov, Koonin & Lipman, 1997).…”

Section: Gene Ontology (Go) Kyoto Encyclopedia Of Genes and Genomes mentioning

confidence: 99%

Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics

Qin

et al. 2020

PeerJ

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Background Although asthma is one of the most common chronic, noncommunicable diseases worldwide, the pathogenesis of childhood asthma is not yet clear. Genetic factors and environmental factors may lead to airway immune-inflammation responses and an imbalance of airway nerve regulation. The aim of the present study was to determine which serum proteins are differentially expressed between children with or without asthma and to ascertain the potential roles that these differentially expressed proteins (DEPs) may play in the pathogenesis of childhood asthma. Methods Serum samples derived from four children with asthma and four children without asthma were collected. The DEPs were identified by using isobaric tags for relative and absolute quantitation (iTRAQ) combined with liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses. Using biological information technology, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Cluster of Orthologous Groups of Proteins (COG) databases and analyses, we determined the biological processes associated with these DEPs. Key protein glucose-6-phosphate dehydrogenase (G6PD) was verified by enzyme linked immunosorbent assay (ELISA). Results We found 46 DEPs in serum samples of children with asthma vs. children without asthma. Among these DEPs, 12 proteins were significantly (>1.5 fold change) upregulated and 34 proteins were downregulated. The results of GO analyses showed that the DEPs were mainly involved in binding, the immune system, or responding to stimuli or were part of a cellular anatomical entity. In the KEGG signaling pathway analysis, most of the downregulated DEPs were associated with cardiomyopathy, phagosomes, viral infections, and regulation of the actin cytoskeleton. The results of a COG analysis showed that the DEPs were primarily involved in signal transduction mechanisms and posttranslational modifications. These DEPs were associated with and may play important roles in the immune response, the inflammatory response, extracellular matrix degradation, and the nervous system. The downregulated of G6PD in the asthma group was confirmed using ELISA experiment. Conclusion After bioinformatics analyses, we found numerous DEPs that may play important roles in the pathogenesis of childhood asthma. Those proteins may be novel biomarkers of childhood asthma and may provide new clues for the early clinical diagnosis and treatment of childhood asthma.

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Section: Gene Ontology (Go) Kyoto Encyclopedia Of Genes and Genomes mentioning

confidence: 99%

Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics

Qin

et al. 2020

PeerJ

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“…Let-7 family is actively involved in diverse biological processes, including development, carcinogenesis, and autoimmunity, but the precise role of these miRNAs in lupus pathogenesis remains unclear. Considering of the miRNA functional similarity by miRNA functional networks study (16,17), and the important role of let-7 family in immune system (18), together with the possible effect of let-7f in regulating TGF-β1 receptor 1 (TGFBR1) suggested by bioinformatics analysis, it is very likely that let-7f could be functionally similar to miR-663 or clustered as a functional sets with other miRNAs such as miR-663, which might also be involved in lupus pathogenesis mediated by regulating T cells.…”

Section: Introductionmentioning

confidence: 99%

Reduced Let-7f in Bone Marrow-Derived Mesenchymal Stem Cells Triggers Treg/Th17 Imbalance in Patients With Systemic Lupus Erythematosus

et al. 2020

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Systemic lupus erythematosus (SLE) patients exist an imbalance between regulatory T (Treg) and T helper 17 cells (Th17), which might be contributed by defective immune regulation of bone marrow derived mesenchymal stem cells (BM-MSCs) from SLE patients. Our microRNA array analysis showed markedly down-regulated expression levels of microRNA let-7f in BM-MSCs from SLE patients compared to those from normal controls (NOR). To explore the role of let-7f in the disease pathogenesis, we showed that expression levels of let-7f in SLE BM-MSCs were negatively associated with SLE disease activity, and the predicted let-7 family targeted gene expression of interlukin-6 (IL-6) was significantly higher in BM-MSCs from SLE patients compared to normal controls (NOR). Transient transfection of BM-MSCs with let-7f mimics or inhibitors showed reduced levels of let-7f impaired the proliferation rate of BM-MSCs, BM-MSC-mediated downregulation of Th17 cells and upregulation of Treg cells, increased the apoptosis rate of BM-MSCs through targeting IL-6 and activating signal transducers and activators of transcription-3 (STAT3) pathway, but had no significant effect on the differentiation of Th1 and Th2. Our findings showed a key role of let-7f in the imbalance of Treg/Th17 mediated by SLE BM-MSCs, suggesting the potential of manipulating let-7f expression in BM-MSCs for treating SLE patients.Keywords: let-7f miRNA, systemic lupus erythematosus, bone marrow derived mesenchymal stem cell, interlukin 6, regulatory T cell (Treg), T help cell 17 (Th17)

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“…In order to assess performance of EKRRMDA, LOOCV and 5-fold cross validation were carried out based on the known miRNA-disease associations from the HMDD V2.0 database (Li et al, 2014), and prediction performance was measured in terms of AUC. Cross validation experiments and AUC measurement were usually used for evaluating methods of miRNA-disease association prediction in many previous important studies (Chen and Huang, 2017;Xiao et al, 2017;You et al, 2017;Chen et al, 2018e;Yang et al, 2018). Particularly, LOOCV was divided into global LOOCV and local LOOCV.…”

Section: Performance Evaluationmentioning

confidence: 99%

A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression

Peng

Zhou

Chen

et al. 2020

Front. Bioeng. Biotechnol.

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As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification of associations between miRNAs and diseases. Identifying such associations purely via experiments is costly and demanding, which prompts researchers to develop computational methods to complement the experiments. In this paper, a novel prediction model named Ensemble of Kernel Ridge Regression based MiRNA-Disease Association prediction (EKRRMDA) was developed. EKRRMDA obtained features of miRNAs and diseases by integrating the disease semantic similarity, the miRNA functional similarity and the Gaussian interaction profile kernel similarity for diseases and miRNAs. Under the computational framework that utilized ensemble learning and feature dimensionality reduction, multiple base classifiers that combined two Kernel Ridge Regression classifiers from the miRNA side and disease side, respectively, were obtained based on random selection of features. Then average strategy for these base classifiers was adopted to obtain final association scores of miRNA-disease pairs. In the global and local leave-one-out cross validation, EKRRMDA attained the AUCs of 0.9314 and 0.8618, respectively. Moreover, the model's average AUC with standard deviation in 5-fold cross validation was 0.9275 ± 0.0008. In addition, we implemented three different types of case studies on predicting miRNAs associated with five important diseases. As a result, there were 90% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 86% (Lymphoma), 98% (Lung Neoplasms), and 96% (Breast Neoplasms) of the top 50 predicted miRNAs verified to have associations with these diseases.

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MiRGOFS: a GO-based functional similarity measurement for miRNAs, with applications to the prediction of miRNA subcellular localization and miRNA–disease association

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 53 publications

References 58 publications

Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics

Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics

Reduced Let-7f in Bone Marrow-Derived Mesenchymal Stem Cells Triggers Treg/Th17 Imbalance in Patients With Systemic Lupus Erythematosus

A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression

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