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pmid:34532380
Diagnostic biomarkers and potential drug targets for coronary artery disease as revealed by systematic analysis of lncRNA characteristics.
Ziqi Chen · Dawang Zhou · Xiaocong Zhang · Qian Wu · Guifu Wu
Annals of translational medicine · 2021 doi:10.21037/atm-21-3276
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Experiment
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Complete file
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Therefore, the current study aimed to investigate lncRNA-based prognostic biomarkers for CAD.\nMETHODS: The expression profiles of lncRNA and messenger RNA (mRNA) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed lncRNA (DElncRNAs) and DEmRNAs were identified from CAD and normal samples, and weighted gene co-expression network analysis (WGCNA) was conducted. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to investigate the principal functions of significantly dysregulated genes. The potential drugs of new CAD-specific genes were identified by network distance method. Receiver operating characteristic (ROC) was used to verify the classification performance of genes.\nRESULTS: A total of 512 differentially expressed genes (DEGs) and 308 DElncRNAs were identified from GSE113079 dataset to classify CAD samples. Through WGCNA co-expression analysis, 24 co-expression modules were obtained. A total of 187 DElncRNAs and 253 DEGs were determined from 7 modules correlated with CAD. Functional enrichment analysis showed that these DEGs were mainly related to inflammatory and immune-related pathways. Furthermore, 36 regulatory pairs of significantly shared micro RNAs (miRNAs) were identified as dysregulated lncRNA-mRNA (LRM-CAD), which contained 11 lncRNAs and 33 genes. Compared with a single lncRNA or gene, LRM-CAD showed stronger classification performance [average area under the curve (AUC) =0.958]. We screened 3 potential therapeutic drugs, DB09105, DB12371, and DB12612, a by binding drug-target gene interaction network. Molecular docking verified that the S1PR1 gene bound relatively closely to DB12371 and DB12612. The ROC analysis on external data sets showed that S1PR1, AC012640.4, and S1PR1-AC012640.4 could effectively distinguish CAD samples from control samples.\nCONCLUSIONS: We provided a transcriptome overview of abnormally expressed lncRNAs in CAD patients and identified novel biomarkers for diagnosing CAD.", "claims": [{"id": "claim-method", "statement": "The R software package Limma (19) was used to screen differentially expressed genes (DEGs) and lncRNAs between CAD and control samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34532380", "locator": "sections[4] \"Differential expression analysis and weighted co-expression network\" paragraph 2", "quote": "The R software package Limma (19) was used to screen differentially expressed genes (DEGs) and lncRNAs between CAD and control samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The discovered Gene Expression data were downloaded from the GEO, numbers GSE64566, GSE20681, and a standardized data set were downloaded. 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Regulatory interactions between miRNA-lncRNA were downloaded from starBase (http://starbase.sysu.edu.cn/) and miRcode (http://www.mircode.org/). 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE113079"], "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:GSE113079"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34532380", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/atm-21-3276", "sha256": "sha256:91dd1bac42f73e4024202326330e96f6f7a440fa3c4c8a4483c49c3545376fbf", "bytes": 37464, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/atm-21-3276", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8421950", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE113079", "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=GSE113079", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE113079", "version": null, "note": "The discovered Gene Expression data were downloaded from the GEO, numbers GSE64566, GSE20681, and a standardized data set were downloaded. 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