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Crosstalk Between MicroRNAs and Circular RNAs in Human Diseases: A Bibliographic Study

ABSTRACT: Background: Crosstalk of circular RNAs (circRNAs) and microRNAs (miRNAs) refers to the communication and co-regulation between them. circRNAs can act as miRNAs sponges, and miRNAs can mediate circRNAs. They interact to regulate gene expression and participate in the occurrence and development of various human diseases. Methods: Publications on the crosstalk between miRNAs and circRNAs in human diseases were collected from Web of Science. The collected material was limited to English articles and reviews. CiteSpace and Microsoft Excel were used for bibliographic analysis. Results: A total of 1,013 papers satisfied the inclusion criteria. The publication outputs and types of researched diseases were analyzed, and bibliographic analysis was used to characterize the most active journals, countries, institutions, keywords, and references. The annual number of publications remarkably increased from 2011 to 2020. Neoplasm was the main research hotspot (n = 750 publications), and Biochemical and Biophysical Research Communications published the largest number of papers (n = 64) on this topic. Nanjing Medical University ranked first among institutions actively engaged in this field by publishing 72 papers, and China contributed 96.84% of the 1,013 papers (n = 981 publications) analyzed. Burst keywords in recent years included glioblastoma, miR-7, skeletal muscle, and non-coding RNA. Conclusion: Crosstalk between miRNAs and circRNAs in human diseases is a popular research topic. This study provides important clues on research trends and frontiers.

SUBMITTER: Chen Y

PROVIDER: S-EPMC8558455 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:MicroRNAs (miRNAs/miRs) are small non‑coding RNAs (ncRNAs) that regulate gene expression. Emerging knowledge has suggested that miRNAs have a role in the pathogenesis of metabolic disorders, supporting the hypothesis that miRNAs may represent potential biomarkers or targets for this set of diseases. However, the current evidence is often controversial. Therefore, the aim of the present study was to determine the associations between miRNAs‑target genes, miRNA‑long ncRNAs (lncRNAs), and miRNAs‑small molecules in human metabolic diseases, including obesity, type 2 diabetes and non‑alcoholic fatty liver disease. The metabolic disease‑related miRNAs were obtained from the Human MicroRNA Disease Database (HMDD) and miR2Disease database. A search on the databases Matrix Decomposition and Heterogeneous Graph Inference (MDHGI) and DisGeNET were also performed. miRNAs target genes were obtained from three independent sources: Microcosm, TargetScan and miRTarBase. The interactions between miRNAs‑lncRNA and miRNA‑small molecules were performed using the miRNet web tool. The network analyses were performed using Cytoscape software. As a result, a total of 20 miRNAs were revealed to be associated with metabolic disorders in the present study. Notably, 6 miRNAs (miR‑17‑5p, miR‑29c‑3p, miR‑34a‑5p, miR‑103a‑3p, miR‑107 and miR‑132‑3p) were found in the four resources (HMDD, miR2Disease, MDHGI, and DisGeNET) used for these analyses, presenting a stronger association with the diseases. Furthermore, the target genes of these miRNAs participate in several pathways previously associated with metabolic diseases. In addition, interactions between miRNA‑lncRNA and miRNA‑small molecules were also found, suggesting that some molecules can modulate gene expression via such an indirect way. Thus, the results of this data mining and integration analysis provide further information on the possible molecular basis of the metabolic disease pathogenesis as well as provide a path to search for potential biomarkers and therapeutic targets concerning metabolic diseases.

Project description:Aging is a complicated process characterized by progressive and extensive changes in physiological homeostasis at the organismal, tissue, and cellular levels. In modern society, age estimation is essential in a large variety of legal rights and duties. Accumulating evidence suggests roles for microRNAs (miRNAs) and circular RNAs (circRNAs) in regulating numerous processes during aging. Here, we performed circRNA sequencing in two age groups and analyzed microarray data of 171 healthy subjects (17-104 years old) downloaded from Gene Expression Omnibus (GEO) and ArrayExpress databases with integrated bioinformatics methods. A total of 1,403 circular RNAs were differentially expressed between young and old groups, and 141 circular RNAs were expressed exclusively in elderly samples while 10 circular RNAs were expressed only in young subjects. Based on their expression pattern in these two groups, the circular RNAs were categorized into three classes: age-related expression between young and old, age-limited expression-young only, and age-limited expression-old only. Top five expressed circular RNAs among three classes and a total of 18 differentially expressed microRNAs screened from online databases were selected to validate using RT-qPCR tests. An independent set of 200 blood samples (20-80 years old) was used to develop age prediction models based on 15 age-related noncoding RNAs (11 microRNAs and 4 circular RNAs). Different machine learning algorithms for age prediction were applied, including regression tree, bagging, support vector regression (SVR), random forest regression (RFR), and XGBoost. Among them, random forest regression model performed best in both training set (mean absolute error = 3.68 years, r = 0.96) and testing set (MAE = 6.840 years, r = 0.77). Models using one single type of predictors, circular RNAs-only or microRNAs-only, result in bigger errors. Smaller prediction errors were shown in males than females when constructing models according to different-sex separately. Putative microRNA targets (430 genes) were enriched in the cellular senescence pathway and cell homeostasis and cell differentiation regulation, indirectly indicating that the microRNAs screened in our study were correlated with development and aging. This study demonstrates that the noncoding RNA aging clock has potential in predicting chronological age and will be an available biological marker in routine forensic investigation to predict the age of biological samples.

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Crosstalk Between MicroRNAs and Circular RNAs in Human Diseases: A Bibliographic Study

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