miR-22-3p as a potential biomarker for coronary artery disease based on integrated bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36105099", "kind": "science-record", "revision": 5, "content_hash": "sha256:5dc3abbe46c4a80f3eb728023c83cf42823e2c4d8ac1df32770b411000c6d995", "created_at": "2026-11-01T18:15:21Z", "updated_at": "2026-11-01T22:50:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "miR-22-3p as a potential biomarker for coronary artery disease based on integrated bioinformatics analysis.", "doi": "10.3389/fgene.2022.936937", "pmid": "36105099", "authors": ["Minghua Zhang", "Yan Hu", "Haoda Li", "Xiaozi Guo", "Junhui Zhong", "Sha He"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Coronary artery disease (CAD) is a common cardiovascular disease that has attracted attention worldwide due to its high morbidity and mortality. Recent studies have shown that abnormal microRNA (miRNA) expression is effective in CAD diagnoses and processes. However, the potential relationship between miRNAs and CAD remains unclear. Methods: Microarray datasets GSE105449 and GSE28858 were downloaded directly from the Gene Expression Omnibus (GEO) to identify miRNAs involved in CAD. Target gene prediction and enrichment analyses were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Results: There were nine differentially expressed miRNAs in CAD patients compared to the controls. A total of 352 genes were predicted and subjected to GO analysis, which showed that differentially expressed genes (DEGs) were mainly associated with axon guidance, neuron projection guidance, neuron-to-neuron synapses, and postsynaptic density. According to the KEGG pathway analysis, the most enriched pathways were those involved in transcriptional misregulation in cancer, growth hormone synthesis, secretion and action, endocrine resistance, axon guidance, and Cushing syndrome. Pathway analysis was mainly involved in the HIPPO and prion disease signaling pathways. Furthermore, a competing endogenous RNA (ceRNA) interaction network centered on miR-22-3p revealed eight related transcription factors in the cardiovascular system. The receiver operating characteristic (ROC) curve analysis suggested that miR-22-3p may be a better CAD predictor. Conclusion: The results indicate that miR-22-3p may function in pathophysiological CAD processes. Our study potentiates miR-22-3p as a specific biomarker for diagnosing CAD.", "claims": [{"id": "claim-method", "statement": "The GSE105449 and GSE28858 datasets were analyzed using the pROC package to construct a hsa-miR-22-3p molecular expression and prediction outcome model using the area under the ROC curve (AUC) to analyze prediction efficacy.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36105099", "locator": "sections[5] \"2.3 Receiver operating characteristic (ROC) analysis of the hsa-miR-22-3p gene\" paragraph 2", "quote": "The GSE105449 and GSE28858 datasets were analyzed using the pROC package to construct a hsa-miR-22-3p molecular expression and prediction outcome model using the area under the ROC curve (AUC) to analyze prediction efficacy."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. These data can be found here: https://www.ncbi.nlm.nih.gov/geo/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36105099", "locator": "sections[18] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. These data can be found here: https://www.ncbi.nlm.nih.gov/geo/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ROC curves of the GSE105449 and GSE28858 datasets were constructed with miR-22-3p as the center. The area under the AUC curve of GSE105449 was 0.719 (Figure 6A), whereas the area under the AUC curve of the GSE28858 validation set was 0.642 (Figure 6B).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36105099", "locator": "sections[15] \"3.4 ROC curve analysis\" paragraph 2", "quote": "ROC curves of the GSE105449 and GSE28858 datasets were constructed with miR-22-3p as the center. The area under the AUC curve of GSE105449 was 0.719 (Figure 6A), whereas the area under the AUC curve of the GSE28858 validation set was 0.642 (Figure 6B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ROC curves of the GSE105449 and GSE28858 datasets were constructed with miR-22-3p as the center. The area under the AUC curve of GSE105449 was 0.719 (Figure 6A), whereas the area under the AUC curve of the GSE28858 validation set was 0.642 (Figure 6B).", "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:36105099"], "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:GSE105449"], "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:GSE105449"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36105099", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.936937", "sha256": "sha256:691eb44a3b034ee3c5ccbea36891fdef736d00918bf073c57a112d1d5a7e5bc1", "bytes": 44337, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.936937", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9464939", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE105449", "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=GSE105449", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE105449", "version": null, "note": "Publicly available datasets were analyzed in this study. 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