Bioinformatic analysis of brucellosis and construction of a diagnostic model based on key genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41053229", "kind": "science-record", "revision": 4, "content_hash": "sha256:834f97057b148b7838b6f684a5717234d9be88e871eece0c4919a94a1a86b3e1", "created_at": "2026-11-01T20:31:06Z", "updated_at": "2026-11-01T22:57:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatic analysis of brucellosis and construction of a diagnostic model based on key genes.", "doi": "10.1038/s41598-025-18426-8", "pmid": "41053229", "authors": ["Xia Wang", "Qing-Cao Fei", "Le Zhang", "Xiao-Rong Mao", "Zi-Min Ma", "Li-Na Ma", "Xiang-Chun Ding"], "journal": "Scientific reports", "year": 2025, "abstract": "This study aims to identify and validate key genes associated with brucellosis. Due to diagnostic challenges, we focused on a bioinformatics-driven approach to construct a robust diagnostic model, providing a theoretical basis for clinical diagnosis. We specifically investigated Prosaposin-related genes (PRGs) due to their role in host-pathogen interactions. The brucellosis dataset GSE69597 was downloaded from the GEO database. After processing, differentially expressed genes were identified and intersected with PRGs to obtain Prosaposin-Related Differentially Expressed Genes (PRDEGs). We employed Random Forest and LASSO regression to screen for key genes and construct a multivariate logistic regression model. Model performance was evaluated using ROC curves. Finally, the expression of the key genes was validated by qPCR in an independent cohort of clinical peripheral blood samples (16 patients, 11 controls). A total of 19 PRDEGs were identified, from which 5 key genes (SKAP2, EIF2B1, PRKAB1, IRF8, RPN1) were selected. The logistic regression model based on these genes demonstrated good diagnostic performance with an Area Under the Curve (AUC) value of 0.844. Crucially, qPCR validation confirmed that the expression of all five genes was significantly different between patients and controls (all p < 0.05), consistent with the bioinformatic findings. Through comprehensive bioinformatic analysis and experimental validation, this study identified key genes closely associated with brucellosis and constructed an effective diagnostic model. This work provides new insights and a promising foundation for the early diagnosis and targeted therapy of brucellosis.", "claims": [{"id": "claim-method", "statement": "We employed Random Forest and LASSO regression to screen for key genes and construct a multivariate logistic regression model. Model performance was evaluated using ROC curves.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41053229", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We employed Random Forest and LASSO regression to screen for key genes and construct a multivariate logistic regression model. Model performance was evaluated using ROC curves."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as supplemental information. Gene expression data from the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) include the following datasets: GSE 69597.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41053229", "locator": "sections[32] \"Data availability\" paragraph 2", "quote": "Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as supplemental information. Gene expression data from the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) include the following datasets: GSE 69597."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The logistic regression model based on these genes demonstrated good diagnostic performance with an Area Under the Curve (AUC) value of 0.844.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41053229", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The logistic regression model based on these genes demonstrated good diagnostic performance with an Area Under the Curve (AUC) value of 0.844."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The logistic regression model based on these genes demonstrated good diagnostic performance with an Area Under the Curve (AUC) value of 0.844.", "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:41053229"], "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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Crucially, qPCR validation confirmed that the expression of", "mentally validated in clinical samples using qPCR. Clinical sample validation of key genes To", "through bioinformatic analysis, we performed qPCR analysis using peripheral blood samples from"], "order_basis": "paper_text_order:880", "id": "assay:1"}, {"role": "data_generation", "definition_id": "clinical_cohort", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["we performed qPCR analysis using peripheral blood samples from 16 brucellosis patients and 11 healthy contr", "IF2B1, PRKAB1, IRF8, and RPN1) in peripheral blood samples from brucellosis patients (n = 16) compared to he"], "order_basis": "paper_text_order:4176", "id": "assay:2"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We employed Random Forest and LASSO regression to screen for key genes and construct a multivariate logistic regression model. 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