Identification of metabolism related biomarkers in obesity based on adipose bioinformatics and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39482740", "kind": "science-record", "revision": 4, "content_hash": "sha256:71f89599643bd0165a75abf0785a7437f8686c6f03a081b8f2af22c9398ac7a8", "created_at": "2026-11-01T20:29:44Z", "updated_at": "2026-11-01T22:55:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of metabolism related biomarkers in obesity based on adipose bioinformatics and machine learning.", "doi": "10.1186/s12967-024-05615-8", "pmid": "39482740", "authors": ["Yanping Wang", "Honglin Wang", "Xingrui Yu", "Qinan Wu", "Xinlu Lv", "Xuelian Zhou", "Yong Chen", "Shan Geng"], "journal": "Journal of translational medicine", "year": 2024, "abstract": "BACKGROUND: Obesity has emerged as a growing global public health concern over recent decades. Obesity prevalence exhibits substantial global variation, ranging from less than 5% in regions like China, Japan, and Africa to rates exceeding 75% in urban areas of Samoa.\nAIM: To examine the involvement of metabolism-related genes.\nMETHODS: Gene expression datasets GSE110729 and GSE205668 were accessed from the GEO database. DEGs between obese and lean groups were identified through DESeq2. Metabolism-related genes and pathways were detected using enrichment analysis, WGCNA, Random Forest, and XGBoost. The identified signature genes were validated by real-time quantitative PCR (qRT-PCR) in mouse models.\nRESULTS: A total of 389 genes exhibiting differential expression were discovered, showing significant enrichment in metabolic pathways, particularly in the propanoate metabolism pathway. The orangered4 module, which exhibited the highest correlation with propanoate metabolism, was identified using Weighted Correlation Network Analysis (WGCNA). By integrating the DEGs, WGCNA results, and machine learning methods, the identification of two metabolism-related genes, Storkhead Box 1 (STOX1), NACHT and WD repeat domain-containing protein 2(NWD2) was achieved. These signature genes successfully distinguished between obese and lean individuals. qRT-PCR analysis confirmed the downregulation of STOX1 and NWD2 in mouse models of obesity.\nCONCLUSION: This study has analyzed the available GEO dataset in order to identify novel factors associated with obesity metabolism and found that STOX1 and NWD2 may serve as diagnostic biomarkers.", "claims": [{"id": "claim-method", "statement": "The \"DESeq2\" package in R was utilized to perform differential analysis on the GSE110729 dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39482740", "locator": "sections[4] \"Differential analysis\" paragraph 2", "quote": "The \"DESeq2\" package in R was utilized to perform differential analysis on the GSE110729 dataset."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated during the study are included in the article and Additional file 1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39482740", "locator": "sections[27] \"notes\" paragraph 1", "quote": "All data generated during the study are included in the article and Additional file 1."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In the training dataset, the model demonstrated an area under the curve (AUC) of 1 (95% confidence interval 1–1), whereas in the validation dataset, the AUC was 0.799 (95% CI 0.727–1) (Fig. 3A, D).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "In the training dataset, the model demonstrated an area under the curve (AUC) of 1 (95% confidence interval 1–1), whereas in the validation dataset, the AUC was 0.799 (95% CI 0.727–1) (Fig. 3A, D)."}, "evidence": [{"asset": "source:pmid:39482740", "locator": "sections[18] \"Construction and evaluation of machine learning models\" paragraph 2", "quote": "In the training dataset, the model demonstrated an area under the curve (AUC) of 1 (95% confidence interval 1–1), whereas in the validation dataset, the AUC was 0.799 (95% CI 0.727–1) (Fig. 3A, D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In the training dataset, the model demonstrated an area under the curve (AUC) of 1 (95% confidence interval 1–1), whereas in the validation dataset, the AUC was 0.799 (95% CI 0.727–1) (Fig. 3A, D).", "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:39482740"], "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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RESULTS: A total"], "order_basis": "paper_text_order:722", "id": "assay:1"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["d by real-time quantitative PCR (qRT-PCR) in mouse models. RESULTS: A total of 389 genes exhibi", "rmed the downregulation of STOX1 and NWD2 in mouse models of obesity. CONCLUSION: This study ha", "eek-old male C57BL/6J (WT), ob/ob, and DB/DB mice were sourced from Shulaibao (Wuhan) Biotechnology", "laibao (Wuhan) Biotechnology Co., Ltd. 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