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pmid:38606153
Potential diagnostic markers shared between non-alcoholic fatty liver disease and atherosclerosis determined by machine learning and bioinformatic analysis.
Lihong Wang · Wenhui He · Xilin Wang · Jianrong Wang · Xiaojuan Wei · Dongzhi Wu · Yundan Wu
Frontiers in medicine · 2024 doi:10.3389/fmed.2024.1322102
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Experiment
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Data
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Analysis
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Execution
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Complete file
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The progression of NAFLD co-expression gene modules were recognized via weighted gene co-expression network analysis (WGCNA). We screened for differentially expressed genes (DEGs) associated with AS and identified common genes associated with NAFLD and AS using Venn diagrams. We investigated the most significant core genes between NAFLD and AS using machine learning algorithms. We then constructed a diagnostic model by creating a nomogram and evaluating its performance using ROC curves. Furthermore, the CIBERSORT algorithm was utilized to explore the immune cell infiltration between the two diseases, and evaluate the relationship between diagnostic genes and immune cells.\nResults: The WGCNA findings associated 1,129 key genes with NAFLD, and the difference analysis results identified 625 DEGs in AS, and 47 genes that were common to both diseases. We screened the core RPS6KA1 and SERPINA3 genes associated with NAFLD and AS using three machine learning algorithms. A nomogram and ROC curves demonstrated that these genes had great clinical meaning. We found differential expression of RPS6KA1 in patients with steatosis and NASH, and of SERPINA3 only in those with NASH compared with normal individuals. Immune infiltration findings revealed that macrophage and mast cell infiltration play important roles in the development of NAFLD and AS. Notably, SERPINA3 correlated negatively, whereas RPS6KA1 correlated positively with macrophages and mast cells.\nConclusion: We identified RPS6KA1 and SERPINA3 as potential diagnostic markers for NAFLD and AS. The most promising marker for a diagnosis of NAFLD and AS might be RPS6KA1, whereas SERPINA3 is the most closely related gene for NASH and AS. We believe that further exploration of these core genes will reveal the etiology and a pathological relationship between NAFLD and AS.", "claims": [{"id": "claim-method", "statement": "We constructed a gene co-expression network for NAFLD by the WGCNA version 1.72.1 package in R version 4.3.1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38606153", "locator": "sections[4] \"Gene expression analyzed using WGCNA\" paragraph 2", "quote": "We constructed a gene co-expression network for NAFLD by the WGCNA version 1.72.1 package in R version 4.3.1."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38606153", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The analysis identified 47 genes common to NAFLD and AS.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38606153", "locator": "sections[15] \"Identification of DEGs in AS\" paragraph 2", "quote": "The Venn diagram revealed 47 intersecting genes between AS and NAFLD (Figure 2F). Thus, these two diseases have 47 genes in common."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The analysis identified 47 genes common to NAFLD and AS.", "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:38606153"], "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. 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