Analysis of machine learning based integration to identify the crosslink between inflammation and immune response in non-alcoholic fatty liver disease through bioinformatic analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39108890", "kind": "science-record", "revision": 4, "content_hash": "sha256:a7b5c1ad1c36b0b48f6042e3a04d15e94499db8962020387441f7d003990e992", "created_at": "2026-11-01T20:29:12Z", "updated_at": "2026-11-01T22:54:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of machine learning based integration to identify the crosslink between inflammation and immune response in non-alcoholic fatty liver disease through bioinformatic analysis.", "doi": "10.1016/j.heliyon.2024.e32783", "pmid": "39108890", "authors": ["Runzhi Yu", "Yiqin Huang", "Xiaona Hu", "Jie Chen"], "journal": "Heliyon", "year": 2024, "abstract": "Background: The prevalence of nonalcoholic fatty liver disease (NAFLD) is a major form of chronic liver disease. This study aimed to scrutinize the diagnostic biomarkers of NAFLD and their correlation with the immune microenvironment through bioinformatic analysis.\nMethods: To identify genes associated with nonalcoholic fatty liver disease (NAFLD), we obtained microarray datasets (GSE63067 and GSE89632) from the Gene Expression Omnibus (GEO) database. Machine learning techniques such as Support Vector Machine (SVM), Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) were used to identify key genes. We performed gene ontology analysis to identify the driver pathways of NAFLD. External datasets (merging GSE48452, GSE66676 and GSE135251) were used to validate the identified genes and confirm protein levels by Western blotting. The CIBERSORT algorithm and immune-related techniques, such as ssGSEA, were used to assess the level of infiltration of different immune cell types and their functions. Finally, Spearman's analysis confirmed the relationship between pivotal genes and immune cells.\nResults: Hub genes (BBOX1, FOSB, NR4A2, RAB26 and SOCS2) were identified as potential biomarkers. This study demonstrates that these hub genes are significantly dysregulated in NAFLD, suggesting that they may be useful as diagnostic indicators and possible targets for treatment. Also covered are their possible effects on inflammation, immune cell activation, and liver damage in NAFLD. A better understanding of the intricate relationship between metabolic inefficiency, immunological response, and liver pathology in NAFLD may be gained from this work, which can lead to the development of new diagnostic tools and clinical treatments.\nConclusion: The current study identified BBOX1, FOSB, NR4A2, RAB26 and SOCS2 as important diagnostic biomarkers for NAFLD. The study highlights the important function of immune cell infiltration in developing NAFLD. Their findings provide valuable molecular biological insights into the development of NAFLD and may lead to novel therapeutic strategies for treating this disease.", "claims": [{"id": "claim-method", "statement": "To determine the differential expression (DEGs) between NAFLD samples and control samples, we performed differential expression analysis using the R package “limma” with set criteria of | logFC| > 0.5 and p-value <0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39108890", "locator": "sections[4] \"Identification of differential expression genes\" paragraph 2", "quote": "To determine the differential expression (DEGs) between NAFLD samples and control samples, we performed differential expression analysis using the R package “limma” with set criteria of | logFC| > 0.5 and p-value <0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "This study utilized publicly available datasets, which can be accessed at the following link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63067 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89632 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135251 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48452 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66676.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39108890", "locator": "sections[28] \"Data availability statement\" paragraph 2", "quote": "This study utilized publicly available datasets, which can be accessed at the following link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63067 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89632 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135251 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48452 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66676."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Subsequently, 596 DEGs were identified, including 346 down-regulated genes and 250 up-regulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39108890", "locator": "sections[13] \"Screening of DEGs in NAFLD\" paragraph 2", "quote": "Subsequently, 596 DEGs were identified (Supplementary Table 2), including 346 down-regulated genes and 250 up-regulated genes, shown in the heat map (Fig. 1B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Subsequently, 596 DEGs were identified, including 346 down-regulated genes and 250 up-regulated genes.", "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:39108890"], "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:GSE135251"], "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:GSE135251"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39108890", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e32783", "sha256": 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"platform": "18573", "taxon": "Homo sapiens", "title": "TRANSCRIPTOMIC PROFILING ACROSS THE SPECTRUM OF NON-ALCOHOLIC FATTY LIVER DISEASE", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE48452", "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=GSE48452", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE48452", "version": null, "note": "This study utilized publicly available datasets, which can be accessed at the following link: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63067 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89632 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135251 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