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pmid:37063958
Identification of common signature genes and pathways underlying the pathogenesis association between nonalcoholic fatty liver disease and atherosclerosis.
Shuangyang Mo · Yingwei Wang · Xin Yuan · Wenhong Wu · Huaying Zhao · Haixiao Wei · Haiyan Qin · Haixing Jiang · Shanyu Qin
Frontiers in cardiovascular medicine · 2023 doi:10.3389/fcvm.2023.1142296
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Experiment
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Complete file
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The constantly emerging evidence indicates a close association between nonalcoholic fatty liver disease (NAFLD) and AS. However, the exact molecular mechanisms underlying the correlation between these two diseases remain unclear. This study proposed exploring the common signature genes, pathways, and immune cells among AS and NAFLD.\nMethods: The common differentially expressed genes (co-DEGs) with a consistent trend were identified via bioinformatic analyses of the Gene Expression Omnibus (GEO) datasets GSE28829 and GSE49541, respectively. Further, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. We utilized machine learning algorithms of lasso and random forest (RF) to identify the common signature genes. Then the diagnostic nomogram models and receiver operator characteristic curve (ROC) analyses were constructed and validated with external verification datasets. The gene interaction network was established via the GeneMANIA database. Additionally, gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune infiltration analysis were performed to explore the co-regulated pathways and immune cells.\nResults: A total of 11 co-DEGs were identified. GO and KEGG analyses revealed that co-DEGs were mainly enriched in lipid catabolic process, calcium ion transport, and regulation of cytokine. Moreover, three common signature genes (PLCXD3, CCL19, and PKD2) were defined. Based on these genes, we constructed the efficiently predictable diagnostic models for advanced AS and NAFLD with the nomograms, evaluated with the ROC curves (AUC = 0.995 for advanced AS, 95% CI 0.971-1.0; AUC = 0.973 for advanced NAFLD, 95% CI 0.938-0.998). In addition, the AUC of the verification datasets had a similar trend. The NOD-like receptors (NLRs) signaling pathway might be the most crucial co-regulated pathway, and activated CD4 T cells and central memory CD4 T cells were significantly excessive infiltration in advanced NAFLD and AS.\nConclusion: We identified three common signature genes (PLCXD3, CCL19, and PKD2), co-regulated pathways, and shared immune features of NAFLD and AS, which might provide novel insights into the molecular mechanism of NAFLD complicated with AS.", "claims": [{"id": "claim-method", "statement": "The differently expressed genes (DEGs) were identified from normalized and preprocessed data via the GEO2R tool (23).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37063958", "locator": "sections[4] \"Identification of co-DEGs\" paragraph 2", "quote": "The differently expressed genes (DEGs) were identified from normalized and preprocessed data via the GEO2R tool (23)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28829; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49541; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43292; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48452.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37063958", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE28829; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49541; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43292; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48452."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 11 co-DEGs were identified.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37063958", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 11 co-DEGs were identified."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A total of 11 co-DEGs 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:37063958"], "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:GSE28829"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:GSE28829"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37063958", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcvm.2023.1142296", "sha256": "sha256:4197675994849ce031a39c1003da6582fbc3ade2e60eb4b7b03299f83288a28c", "bytes": 46140, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcvm.2023.1142296", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10098172", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE28829", "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=GSE28829", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE28829", "version": null, "note": "Publicly available datasets were analyzed in this study. 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