Comprehensive analysis of miRNA-mRNA regulatory network and potential drugs in chronic chagasic cardiomyopathy across human and mouse.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34844599", "kind": "science-record", "revision": 4, "content_hash": "sha256:3e8f636d6551f57f7baf6d430906988f3532ad2ebdcff49b8107fad6c4101c0f", "created_at": "2026-11-01T17:36:32Z", "updated_at": "2026-11-01T22:05:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of miRNA-mRNA regulatory network and potential drugs in chronic chagasic cardiomyopathy across human and mouse.", "doi": "10.1186/s12920-021-01134-3", "pmid": "34844599", "authors": ["Jiahe Wu", "Jianlei Cao", "Yongzhen Fan", "Chenze Li", "Xiaorong Hu"], "journal": "BMC medical genomics", "year": 2021, "abstract": "BACKGROUND: Chronic chagasic cardiomyopathy (CCC) is the leading cause of heart failure in Latin America and often causes severe inflammation and fibrosis in the heart. Studies on myocardial function and its molecular mechanisms in patients with Chronic chagasic cardiomyopathy are very limited. In order to understand the development and progression of Chronic chagasic cardiomyopathy and find targets for its diagnosis and treatment, the field needs to better understand the exact molecular mechanisms involved in these processes.\nMETHODS: The mRNA microarray datasets GSE84796 (human) and GSE24088 (mouse) were obtained from the Gene Expression Omnibus (GEO) database. Homologous genes between the two species were identified using the online database mining tool Biomart, followed by differential expression analysis, gene enrichment analysis and protein-protein interaction (PPI) network construction. Cytohubba plug-in of Cytoscape software was used to identify Hub gene, and miRNet was used to construct the corresponding miRNA-mRNA regulatory network. miRNA-related databases: miRDB, Targetscan and miRWalk were used to further evaluate miRNAs in the miRNA-mRNA network. Furthermore, Comparative Toxicogenomics Database (CTD) and L1000 Platform were used to identify hub gene-related drugs.\nRESULTS: A total of 86 homologous genes were significantly differentially expressed in the two datasets, including 73 genes with high expression and 13 genes with low expression. These differentially expressed genes were mainly enriched in the terms of innate immune response, signal transduction, protein binding, Natural killer cell mediated cytotoxicity, Tuberculosis, Chemokine signaling pathway, Chagas disease and PI3K-Akt signaling pathway. The top 10 hub genes LAPTM5, LCP1, HCLS1, CORO1A, CD48, TYROBP, RAC2, ARHGDIB, FERMT3 and NCF4 were identified from the PPI network. A total of 122 miRNAs were identified to target these hub genes and 30 of them regulated two or more hub genes at the same time. miRDB, Targetscan and miRWalk were further analyzed and screened out hsa-miR-34c-5p, hsa-miR-34a-5p and hsa-miR-16-5p as miRNAs regulating these hub genes. Finally, Progesterone, Flutamide, Nimesulide, Methotrexate and Temozolomide were identified to target these hub genes and might be targeted therapies for Chronic chagasic cardiomyopathy.\nCONCLUSIONS: In this study, the potential genes associated with Chronic chagasic cardiomyopathy are identified and a miRNA-mRNA regulatory network is constructed. This study explores the molecular mechanisms of Chronic chagasic cardiomyopathy and provides important clues for finding new therapeutic targets.", "claims": [{"id": "claim-method", "statement": "The gene expression dataset was downloaded from the abovementioned platform and GEO2R was adopted to identify the DEGs between CCC samples and control samples. The adjusted P-values (adj. P) and log-fold change (FC) in expression were determined. Benjamini–hochberg method with default values was used to correct adj. P for potential false positive results. Adj. P < 0.05 and |logFC|> 1.0 was set as the specific cut-off criteria of DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34844599", "locator": "sections[4] \"Identification of DEGs in chronic chagasic cardiomyopathy\" paragraph 2", "quote": "The gene expression dataset was downloaded from the abovementioned platform and GEO2R was adopted to identify the DEGs between CCC samples and control samples. The adjusted P-values (adj. P) and log-fold change (FC) in expression were determined. Benjamini–hochberg method with default values was used to correct adj. P for potential false positive results. Adj. P < 0.05 and |logFC|> 1.0 was set as the specific cut-off criteria of DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability: Data is available at NCBI GEO: GSE84796 and GSE24088.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34844599", "locator": "sections[26] \"notes\" paragraph 1", "quote": "The following information was supplied regarding data availability: Data is available at NCBI GEO: GSE84796 and GSE24088."}], "state": "complete"}, {"id": "claim-target-result", "statement": "These identified homologous genes were intersected with genes that had obvious common differential expression in the two datasets and 86 Homologous DEGs (Additional file 3), including 73 upregulated and 13 downregulated genes, were identified.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34844599", "locator": "sections[12] \"Identification of homologous DEGs between human and mouse\" paragraph 2", "quote": "These identified homologous genes were intersected with genes that had obvious common differential expression in the two datasets and 86 Homologous DEGs (Additional file 3), including 73 upregulated and 13 downregulated genes, were identified."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "These identified homologous genes were intersected with genes that had obvious common differential expression in the two datasets and 86 Homologous DEGs (Additional file 3), including 73 upregulated and 13 downregulated genes, were identified.", "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:34844599"], "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:GSE24088"], "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:GSE24088"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34844599", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-021-01134-3", "sha256": "sha256:4e4b6941ed7b113756c3af4d76042c17ecaa43b9262b4a08fa613cdd6377a9f5", "bytes": 34451, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12920-021-01134-3", "version": null, "note": "Full article fetched from PMC; 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The adjusted P-values (adj. P) and log-fold change (FC) in expression were determined. Benjamini–hochberg method with default values was used to correct adj. P for potential false positive results. Adj. 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