Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40765579", "kind": "science-record", "revision": 3, "content_hash": "sha256:71f4741de4d42e4d88ddafe4a73d15dc7f6a1c698d49d78fcba50df7bcce2cc5", "created_at": "2026-11-01T20:30:51Z", "updated_at": "2026-11-01T22:14:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury.", "doi": "10.3389/fgene.2025.1561331", "pmid": "40765579", "authors": ["Chen Lin", "Meng Zheng", "Wensi Wu", "Zhishan Wang", "Guofeng Lu", "Shaodan Feng", "Xinlan Zhang"], "journal": "Frontiers in genetics", "year": 2025, "abstract": "Background: Sepsis frequently induces acute kidney injury (AKI), and the complex interplay between these two conditions worsens prognosis, prolongs hospitalization, and increases mortality. Despite therapeutic options such as antibiotics and supportive care, early diagnosis and treatment remain a challenge. Understanding the underlying molecular mechanisms linking sepsis and AKI is critical for the development of effective diagnostic tools and therapeutic strategies.\nMethods: We used two sepsis (GSE57065 and GSE28750) and three AKI (GSE30718, GSE139061, and GSE67401) datasets from the NCBI Gene Expression Omnibus (GEO) for model development and validation, and performed batch effect mitigation, differential gene, and functional enrichment analysis using R software packages. We assessed 113 combinations of 12 different algorithms to develop an internally and externally validated machine-learning model for diagnosing AKI. Finally, we used functional enrichment analysis to identify potential therapeutic agents for AKI.\nResults: We identified 556 and 725 DEGs associated with sepsis and AKI, respectively, with 28 overlapping genes suggesting shared pathways. Functional enrichment analysis revealed important associations of AKI with immune responses and cell adhesion processes. The immune infiltration analysis showed significant differences in immune cell presence between sepsis and AKI patients compared with the control group. The machine-learning models identified eight key genes (NR3C2, PLEKHO1, CEACAM1, CDC25B, HEPACAM2, VNN1, SLC2A3, RPL36) with potential for diagnosing AKI. The diagnostic performance of the model constructed in this way was excellent (area under the curve = 0.978), especially in the under 60 years and male patient subgroups. The diagnostic performance outperformed previous models in both the training and validation sets. In addition, cyclosporin A and nine other drugs were identified as potential agents for treating sepsis-associated AKI.\nConclusion: This study highlights the potential of integrating bioinformatics and machine-learning approaches to generate a new diagnostic model for sepsis-associated AKI using molecular crossovers with sepsis. The genes identified have potential to serve as biomarkers and therapeutic targets, providing avenues for future research aimed at enhancing sepsis-associated AKI diagnosis and treatment.", "claims": [{"id": "claim-method", "statement": "The performance of the models was assessed in different subgroups and compared with published models of key genes for sepsis AKI pathogenesis were compared (DeLong test, P < 0.05).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40765579", "locator": "sections[6] \"2.4 Machine-learning algorithms\" paragraph 2", "quote": "The performance of the models was assessed in different subgroups and compared with published models of key genes for sepsis AKI pathogenesis were compared (DeLong test, P < 0.05)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/, GSE57065 https://www.ncbi.nlm.nih.gov/geo/, GSE28750 https://www.ncbi.nlm.nih.gov/geo/, GSE30718 https://www.ncbi.nlm.nih.gov/geo/, GSE139061 https://www.ncbi.nlm.nih.gov/geo/, GSE67401.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40765579", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/, GSE57065 https://www.ncbi.nlm.nih.gov/geo/, GSE28750 https://www.ncbi.nlm.nih.gov/geo/, GSE30718 https://www.ncbi.nlm.nih.gov/geo/, GSE139061 https://www.ncbi.nlm.nih.gov/geo/, GSE67401."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The diagnostic performance of the model constructed in this way was excellent (area under the curve = 0.978), especially in the under 60 years and male patient subgroups.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40765579", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The diagnostic performance of the model constructed in this way was excellent (area under the curve = 0.978), especially in the under 60 years and male patient subgroups."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The diagnostic performance of the model constructed in this way was excellent (area under the curve = 0.978), especially in the under 60 years and male patient subgroups.", "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:40765579"], "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:GSE139061"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_mann_whitney", "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:GSE139061"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_mann_whitney"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40765579", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2025.1561331", "sha256": "sha256:232c6b509a31d37b874637c072683f6b06ec4ecd4d938dc340e1524431e98322", "bytes": 35696, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2025.1561331", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12321556", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE139061", "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=GSE139061", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE139061", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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