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pmid:41021615

Uncovering biomarkers and molecular pathways linking NAFLD and AIS: Insights from bioinformatic analysis and experiment.

Junjie Su · Fenglei Sun

PloS one · 2025 doi:10.1371/journal.pone.0333719

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

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title
Uncovering biomarkers and molecular pathways linking NAFLD and AIS: Insights from bioinformatic analysis and experiment.
doi
10.1371/journal.pone.0333719
pmid
41021615
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[0]
Junjie Su
[1]
Fenglei Sun
journal
PloS one
year
2025
abstract
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) and acute ischemic stroke (AIS) are both closely related to chronic inflammation and metabolic disorders, but the molecular mechanisms between them are not yet clear. This study aims to explore the common molecular characteristics between NAFLD and AIS using bioinformatics methods. METHODS: We integrated transcriptomic data from GEO (GSE89632 for NAFLD and GSE16561 for AIS) to identify shared molecular signatures. Differentially expressed genes (DEGs) were screened using the limma package, and co-expression modules were identified via WGCNA. Functional enrichment and immune infiltration analyses were conducted using standard bioinformatics tools. Key genes were selected through LASSO and random forest algorithms, and candidate drugs were predicted using the CMap database. Experimental validation included qPCR, immunohistochemistry, and drug intervention in HepG2 and HMC3 cell models, as well as a high-fat diet-induced NAFLD mouse model. RESULTS: Through differential expression analysis, 65 common DEGs were identified in both NAFLD and AIS. Functional enrichment analysis showed that these genes mainly involve signal pathways related to immune regulation and inflammatory responses. Immune infiltration analysis showed a significant increase in monocytes, B cells, and plasma cells in NAFLD patients. Ultimately, four potential biomarkers were screened out using LASSO regression and random forest algorithms: CEBPD, SOCS2, THBS1, and IFIT2. Using cMAP, 10 candidate therapeutic drugs, including lamotrigine, were identified. Expression of CEBPD and SOCS2 was consistently upregulated in NAFLD clinical datasets, FFA-treated HepG2 cells, and liver tissues of HFD-induced NAFLD mice, but not in AIS datasets or OGD-treated microglial cells. Furthermore, lamotrigine, cinnarizine, and lenvatinib significantly suppressed FFA-induced CEBPD and SOCS2 expression in HepG2 cells (p < 0.05), supporting their potential regulatory effects. CONCLUSIONS: This study identified common DEGs between NAFLD and AIS and experimentally validated CEBPD as a potential marker, revealing possible common molecular mechanisms between the two diseases, providing new directions for future diagnosis and treatment.
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Using the limma package in RStudio, differential analysis was performed with the lmFit and eBayes functions.
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Using the limma package in RStudio, differential analysis was performed with the lmFit and eBayes functions.
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All datasets files are available from the GEO (https://www.ncbi.nlm.nih.gov/geo/) database (accession numbers GSE89632, GSE16561,GSE58979,GSE63067,GSE22255).
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Through differential expression analysis, 65 common DEGs were identified in both NAFLD and AIS.
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The study identified 65 common differentially expressed genes between NAFLD and AIS.
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This study aims to explore the common molecular characteristics between NAFLD and AIS using bioinformatics methods.\nMETHODS: We integrated transcriptomic data from GEO (GSE89632 for NAFLD and GSE16561 for AIS) to identify shared molecular signatures. Differentially expressed genes (DEGs) were screened using the limma package, and co-expression modules were identified via WGCNA. Functional enrichment and immune infiltration analyses were conducted using standard bioinformatics tools. Key genes were selected through LASSO and random forest algorithms, and candidate drugs were predicted using the CMap database. Experimental validation included qPCR, immunohistochemistry, and drug intervention in HepG2 and HMC3 cell models, as well as a high-fat diet-induced NAFLD mouse model.\nRESULTS: Through differential expression analysis, 65 common DEGs were identified in both NAFLD and AIS. Functional enrichment analysis showed that these genes mainly involve signal pathways related to immune regulation and inflammatory responses. Immune infiltration analysis showed a significant increase in monocytes, B cells, and plasma cells in NAFLD patients. Ultimately, four potential biomarkers were screened out using LASSO regression and random forest algorithms: CEBPD, SOCS2, THBS1, and IFIT2. Using cMAP, 10 candidate therapeutic drugs, including lamotrigine, were identified. Expression of CEBPD and SOCS2 was consistently upregulated in NAFLD clinical datasets, FFA-treated HepG2 cells, and liver tissues of HFD-induced NAFLD mice, but not in AIS datasets or OGD-treated microglial cells. Furthermore, lamotrigine, cinnarizine, and lenvatinib significantly suppressed FFA-induced CEBPD and SOCS2 expression in HepG2 cells (p < 0.05), supporting their potential regulatory effects.\nCONCLUSIONS: This study identified common DEGs between NAFLD and AIS and experimentally validated CEBPD as a potential marker, revealing possible common molecular mechanisms between the two diseases, providing new directions for future diagnosis and treatment.", "claims": [{"id": "claim-method", "statement": "Using the limma package in RStudio, differential analysis was performed with the lmFit and eBayes functions.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41021615", "locator": "sections[4] \"2.2 Differential expression gene analysis\" paragraph 2", "quote": "Using the limma package in RStudio, differential analysis was performed with the lmFit and eBayes functions."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All datasets files are available from the GEO (https://www.ncbi.nlm.nih.gov/geo/) database (accession numbers GSE89632, GSE16561,GSE58979,GSE63067,GSE22255).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41021615", "locator": "sections[30] \"Data Availability\" paragraph 2", "quote": "All datasets files are available from the GEO (https://www.ncbi.nlm.nih.gov/geo/) database (accession numbers GSE89632, GSE16561,GSE58979,GSE63067,GSE22255)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified 65 common differentially expressed genes between NAFLD and AIS.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41021615", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Through differential expression analysis, 65 common DEGs were identified in both NAFLD and AIS."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified 65 common differentially expressed genes between NAFLD and AIS.", "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:41021615"], "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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