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pmid:35872921
Comprehensive Analysis of circRNA-miRNA-mRNA Regulatory Network and Novel Potential Biomarkers in Acute Myocardial Infarction.
Jiahe Wu · Chenze Li · Zhe Lei · Huanhuan Cai · Yushuang Hu · Yanfang Zhu · Tong Zhang · Haoyan Zhu · Jianlei Cao · Xiaorong Hu
Frontiers in cardiovascular medicine · 2022 doi:10.3389/fcvm.2022.850991
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Experiment
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Data
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Analysis
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Execution
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Complete file
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However, studies on the role of circRNA in acute myocardial infarction (AMI) are limited. This study was performed to explore novel circRNA-related regulatory networks in AMI, aiming to better understand the molecular mechanism of circRNAs involvement in AMI and provide basis for further scientific research and clinical decision-making.\nMethods: The AMI-related microarray datasets GSE160717 (circRNA), GSE31568 (miRNA), GSE61741 (miRNA), and GSE24519 (mRNA) were obtained from the Gene Expression Omnibus (GEO) database. After differential expression analysis, the regulatory relationships between these DERNAs were identified by online databases circBank, circInteractome, miRDB, miRWalk, Targetscan, and then two circRNA-miRNA-mRNA regulatory networks were constructed. Differentially expressed genes (DEGs) in this network were selected followed by enrichment analysis and protein-protein interaction (PPI) analysis. Hub genes were identified using Cytohubba plug-in of Cytoscape software. Hub genes and hub gene-related miRNAs were used for receiver operating characteristic curve (ROC) analysis to identify potential biomarkers. The relative expression levels of these biomarkers were further assessed by GSE31568 (miRNA) and GSE66360 (mRNA). Finally, on the basis of the above analysis, myocardial hypoxia model was constructed to verify the expression of Hub genes and related circRNAs.\nResults: A total of 83 DEcircRNAs, 109 CoDEmiRNAs and 1204 DEGs were significantly differentially expressed in these datasets. The up-regulated circRNAs and down-regulated circRNAs were used to construct a circRNA-miRNA-mRNA regulatory network respectively. These circRNA-related DEGs were mainly enriched in the terms of \"FOXO signaling pathway,\" \"T cell receptor signaling pathway,\" \"MAPK signaling pathway,\" \"Insulin resistance,\" \"cAMP signaling pathway,\" and \"mTOR signaling pathway.\" The top 10 hub genes ATP2B2, KCNA1, GRIN2A, SCN2B, GPM6A, CACNA1E, HDAC2, SRSF1, ANK2, and HNRNPA2B1 were identified from the PPI network. Hub genes GPM6A, SRSF1, ANK2 and hub gene-related circRNAs hsa_circ_0023461, hsa_circ_0004561, hsa_circ_0001147, hsa_circ_0004771, hsa_circ_0061276, and hsa_circ_0045519 were identified as potential biomarkers in AMI.\nConclusion: In this study, the potential circRNAs associated with AMI were identified and two circRNA-miRNA-mRNA regulatory networks were constructed. This study explored the mechanism of circRNA involvement in AMI and provided new clues for the selection of new diagnostic markers and therapeutic targets for AMI.", "claims": [{"id": "claim-method", "statement": "preliminary differential expression analysis between AMI samples and control was performed using GEO2R.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35872921", "locator": "sections[4] \"Differential Expression Analysis\" paragraph 2", "quote": "preliminary differential expression analysis between AMI samples and control was performed using GEO2R."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35872921", "locator": "sections[25] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "There were 83 DEcircRNAs, including 50 upregulated and 33 downregulated, in GSE160717.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35872921", "locator": "sections[15] \"Identification of Differentially Expressed Genes in Acute Myocardial Infarction\" paragraph 2", "quote": "There were 83 DEcircRNAs, including 50 upregulated and 33 downregulated, in GSE160717."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "There were 83 DEcircRNAs, including 50 upregulated and 33 downregulated, in GSE160717.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35872921"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE160717"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE160717"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35872921", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcvm.2022.850991", "sha256": "sha256:d2c8d3eb76511d1316128a07fab1860e96403b9941ad6e4b6439a5697ece30fc", "bytes": 42831, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcvm.2022.850991", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9300925", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE160717", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE160717", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE160717", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1512138", "GSM1512139", "GSM1512140", "GSM1512141", "GSM1512142", "GSM1512143", "GSM1512144", "GSM1512145", "GSM1512146", "GSM1512147", "GSM1512148", "GSM1512149", "GSM1512150", "GSM1512151", "GSM1512152", "GSM1512153", "GSM1512154", "GSM1512155", "GSM1512156", "GSM1512157", "GSM1512158", "GSM1512159", "GSM1512160", "GSM1512161", "GSM1512162", "GSM1512163", "GSM1512164", "GSM1512165", "GSM1512166", "GSM1512167", "GSM1512168", "GSM1512169", "GSM1512170", "GSM1512171", "GSM1512172", "GSM1512173", "GSM1512174", "GSM1512175", "GSM1512176", "GSM1512177", "GSM1512178", "GSM1512179", "GSM1512180", "GSM1512181", "GSM1512182", "GSM1512183", "GSM1512184", "GSM1512185", "GSM1512186", "GSM1512187", "GSM1512188", "GSM1512189", "GSM1512190", "GSM1512191", "GSM1512192", "GSM1512193", "GSM1512194", 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