Characterization of the circulating transcriptome expression profile and identification of novel miRNA biomarkers in hypertrophic cardiomyopathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37391825", "kind": "science-record", "revision": 4, "content_hash": "sha256:85aa221020484ef002df5eab04289ae2c0d6e20690c079d61971308f031fc6ac", "created_at": "2026-11-01T19:02:09Z", "updated_at": "2026-11-01T22:52:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Characterization of the circulating transcriptome expression profile and identification of novel miRNA biomarkers in hypertrophic cardiomyopathy.", "doi": "10.1186/s40001-023-01159-7", "pmid": "37391825", "authors": ["Lanyan Guo", "Yue Cai", "Bo Wang", "Fuyang Zhang", "Hang Zhao", "Liwen Liu", "Ling Tao"], "journal": "European journal of medical research", "year": 2023, "abstract": "BACKGROUND: Hypertrophic cardiomyopathy (HCM), one of the most common genetic cardiovascular diseases, but cannot be explained by single genetic factors. Circulating microRNAs (miRNAs) are stable and highly conserved. Inflammation and immune response participate in HCM pathophysiology, but whether the miRNA profile changes correspondingly in human peripheral blood mononuclear cells (PBMCs) with HCM is unclear. Herein, we aimed to investigate the circulating non-coding RNA (ncRNA) expression profile in PBMCs and identify potential miRNAs for HCM biomarkers.\nMETHODS: A Custom CeRNA Human Gene Expression Microarray was used to identify differentially expressed (DE) mRNAs, miRNAs, and ncRNAs (including circRNA and lncRNA) in HCM PBMCs. Weighted correlation network analysis (WGCNA) was used to identify HCM-related miRNA and mRNA modules. The mRNAs and miRNAs from the key modules were used to construct a co-expression network. Three separate machine learning algorithms (random forest, support vector machine, and logistic regression) were applied to identify potential biomarkers based on miRNAs from the HCM co-expression network. Gene Expression Omnibus (GEO) database (GSE188324) and experimental samples were used for further verification. Gene set enrichment analysis (GSEA) and competing endogenous RNA (ceRNA) network was used to determine the potential functions of the selected miRNAs in HCM.\nRESULTS: We identified 1194 DE-mRNAs, 232 DE-miRNAs and 7696 DE-ncRNAs in HCM samples compared with normal controls from the microarray data sets. WGCNA identified key miRNA modules and mRNA modules evidently associated with HCM. We constructed a miRNA‒mRNA co-expression network based on these modules. A total of three hub miRNAs (miR-924, miR-98 and miR-1) were identified by random forest, and the areas under the receiver operator characteristic curves of miR-924, miR-98 and miR-1 were 0.829, 0.866, and 0.866, respectively.\nCONCLUSIONS: We elucidated the transcriptome expression profile in PBMCs and identified three hub miRNAs (miR-924, miR-98 and miR-1) as potential biomarkers for HCM detection.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes (DEGs) in the microarray were screened using the R package limma.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37391825", "locator": "sections[4] \"Microarray data analysis\" paragraph 2", "quote": "Differentially expressed genes (DEGs) in the microarray were screened using the R package limma."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed in this study are included in this published article. All the data are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37391825", "locator": "sections[31] \"notes\" paragraph 1", "quote": "All data generated or analyzed in this study are included in this published article. All the data are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using the limma R package on microarray data, the study identified 1194 differentially expressed mRNAs, 232 differentially expressed miRNAs, and 7696 differentially expressed non-coding RNAs in HCM samples compared with normal controls.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37391825", "locator": "sections[15] \"Identification of DEGs\" paragraph 2", "quote": "To explore the profiles of differentially expressed (DE) mRNAs, miRNAs, and ncRNAs between HCM and NC, a total of 1194 mRNAs (549 upregulated and 645 downregulated), 232 miRNAs (212 upregulated and 20 downregulated) and 7696 ncRNAs (3757 upregulated and 3939 downregulated) were identified (Fig. 1A and Additional file 2: Table S2)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using the limma R package on microarray data, the study identified 1194 differentially expressed mRNAs, 232 differentially expressed miRNAs, and 7696 differentially expressed non-coding RNAs in HCM samples compared with normal controls.", "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:37391825"], "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:GSE188324"], "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:GSE188324"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37391825", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s40001-023-01159-7", "sha256": "sha256:ef97beb16449533c56524ed70e421577084c43e2814f5cf75bdb78735db2c39e", "bytes": 43135, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s40001-023-01159-7", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10314611", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE188324", "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=GSE188324", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE188324", "version": null, "note": "All data generated or analyzed in this study are included in this published article. All the data are available from the corresponding author on reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All data generated or analyzed in this study are included in this published article. All the data are available from the corresponding author on reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM5677783", "GSM5677784", "GSM5677785", "GSM5677786", "GSM5677787", "GSM5677788", "GSM5677789", "GSM5677790", "GSM5677791", "GSM5677792", "GSM5677793", "GSM5677794", "GSM5677795", "GSM5677796", "GSM5677797", "GSM5677798", "GSM5677799", "GSM5677800", "GSM5677801", "GSM5677802", "GSM5677803", "GSM5677804", "GSM5677805", "GSM5677806", "GSM5677807", "GSM5677808", "GSM5677809", "GSM5677810", "GSM5677811", "GSM5677812", "GSM5677813", "GSM5677814", "GSM5677815", "GSM5677816", "GSM5677817"], "sample_count": 35, "group_sizes": null, "group_sizes_basis": null, "platform": "30934", "taxon": "Homo sapiens", "title": "A novel miRNA screen identifies miRNA-4454 as a candidate biomarker for ventricular fibrosis in patients with hypertrophic cardiomyopathy", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE188324 not downloaded", "asset:data:geo:GSE188324 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Differentially expressed genes (DEGs) in the microarray were screened using the R package limma."}}, {"id": "method-2", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "random forest"}}, {"id": "method-3", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "support vector machine"}}, {"id": "method-4", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.7, "band": "medium", "quote": "logistic regression"}}, {"id": "method-5", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "χ2"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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