Identification and analysis of exosome-associated signatures in pediatric sepsis by integrated bioinformatics analysis and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41527570", "kind": "science-record", "revision": 3, "content_hash": "sha256:bd03ef7fc7680d84bf16a4dba12aa7d4ed66d50278930973c9ef0ddb34bca641", "created_at": "2026-11-01T20:31:29Z", "updated_at": "2026-11-01T22:15:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and analysis of exosome-associated signatures in pediatric sepsis by integrated bioinformatics analysis and machine learning.", "doi": "10.7717/peerj.20555", "pmid": "41527570", "authors": ["Junming Huang", "Lichuan Lai", "Jinji Chen", "Xiaotao Su"], "journal": "PeerJ", "year": 2026, "abstract": "Background: Pediatric sepsis (PS) is a critical condition characterized by life-threatening organ dysfunction and immune dysregulation, including exosome-mediated immune modulation, often linked to infections. Investigating the role of exosome-related genes (ERGs) in the pathogenesis of PS is essential for identifying significant diagnostic and therapeutic targets.\nMethods: Four datasets, namely GSE66099 (training set) and GSE13904, GSE26378, and GSE26440 (validation sets), were retrieved from the Gene Expression Omnibus (GEO). The differential expression of 56 ERGs was analyzed, followed by consensus clustering to identify distinct exosome-related patterns in PS. Weighted gene co-expression network analysis (WGCNA) was utilized to identify PS-related genes (SRGs). Additionally, the immune microenvironment was assessed, and diagnostic models were developed employing specific machine learning algorithms.\nResults: The differential expression analysis identified 21 ERGs that exhibited significant alterations in PS. Consensus clustering revealed two distinct subtypes of PS based on the expression pattern of ERGs. WGCNA identified several hub genes involved in exosome function and PS, with immune-related pathways, including phagocytosis and NF-κB signaling, showing significant enrichment. These genes were leveraged to construct machine learning models, which demonstrated a high diagnostic accuracy, with an area under the curve (AUC) > 0.995. The analysis identified CD177, GYG1, IRAK3, MCEMP1, and TLR5 as key biomarkers. Furthermore, external validation confirmed the superior performance of the constructed model.\nConclusion: This study elucidated the role of ERGs in PS, and highlights the significance of immune dysregulation in the pathogenesis of the disease. The developed diagnostic models represent promising tools for the early detection and prognosis prognostic of PS.", "claims": [{"id": "claim-method", "statement": "Among them, RF proved to be the most reliable diagnostic model for PS, as evidenced by its predictive performance and stability.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41527570", "locator": "sections[16] \"Diagnostic model construction using machine learning\" paragraph 2", "quote": "Among them, RF proved to be the most reliable diagnostic model for PS, as evidenced by its predictive performance and stability."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:The code and raw data are available in the Supplemental Files, at GEO (GSE66099, GSE13904, GSE26378, and GSE26440), and GitHub (https://github.com/gxmuhjm/exosome_sepsis.git).Huang, J. (2025). codes and raw data in the manuscript. Zenodo. https://doi.org/10.5281/zenodo.17626895.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41527570", "locator": "sections[21] \"Additional Information and Declarations\" paragraph 5", "quote": "The following information was supplied regarding data availability:The code and raw data are available in the Supplemental Files, at GEO (GSE66099, GSE13904, GSE26378, and GSE26440), and GitHub (https://github.com/gxmuhjm/exosome_sepsis.git).Huang, J. (2025). codes and raw data in the manuscript. Zenodo. https://doi.org/10.5281/zenodo.17626895."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The three machine-learning models exhibited excellent discriminative performance, with an area under the curve (AUC) exceeding 0.995.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41527570", "locator": "sections[16] \"Diagnostic model construction using machine learning\" paragraph 2", "quote": "The three models exhibited excellent discriminative performance, with an area under the curve (AUC) exceeding 0.995."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The three machine-learning models exhibited excellent discriminative performance, with an area under the curve (AUC) exceeding 0.995.", "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:41527570"], "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:GSE13904"], "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:machine_learning_random_forest", "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:GSE13904"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41527570", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.20555", "sha256": "sha256:626dcd60891b923dc39507af9696d6351bcb8ef79dc5e110719f14e152fa537f", "bytes": 33972, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.20555", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12790779", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE13904", "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=GSE13904", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE13904", "version": null, "note": "The following information was supplied regarding data availability:The code and raw data are available in the Supplemental Files, at GEO (GSE66099, GSE13904, GSE26378, and GSE26440), and GitHub (https://github.com/gxmuhjm/exosome_sepsis.git).Huang, J. (2025). codes and raw data in the manuscript. Zenodo. https://doi.org/10.5281/zenodo.17626895.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The following information was supplied regarding data availability:The code and raw data are available in the Supplemental Files, at GEO (GSE66099, GSE13904, GSE26378, and GSE26440), and GitHub (https://github.com/gxmuhjm/exosome_sepsis.git).Huang, J. (2025). codes and raw data in the manuscript. 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(2025). codes and raw data in the manuscript. Zenodo. https://doi.org/10.5281/zenodo.17626895.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The following information was supplied regarding data availability:The code and raw data are available in the Supplemental Files, at GEO (GSE66099, GSE13904, GSE26378, and GSE26440), and GitHub (https://github.com/gxmuhjm/exosome_sepsis.git).Huang, J. (2025). codes and raw data in the manuscript. 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(2025). codes and raw data in the manuscript. Zenodo. https://doi.org/10.5281/zenodo.17626895.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The following information was supplied regarding data availability:The code and raw data are available in the Supplemental Files, at GEO (GSE66099, GSE13904, GSE26378, and GSE26440), and GitHub (https://github.com/gxmuhjm/exosome_sepsis.git).Huang, J. (2025). codes and raw data in the manuscript. 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(2025). codes and raw data in the manuscript. Zenodo. https://doi.org/10.5281/zenodo.17626895.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The following information was supplied regarding data availability:The code and raw data are available in the Supplemental Files, at GEO (GSE66099, GSE13904, GSE26378, and GSE26440), and GitHub (https://github.com/gxmuhjm/exosome_sepsis.git).Huang, J. (2025). codes and raw data in the manuscript. 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"GSM1617767"], "sample_count": 276, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Unique Patients from the Genomics of Pediatric SIRS and Septic Shock Investigators (GPSSSI)", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE13904 not downloaded", "asset:data:geo:GSE13904 licence not read", "asset:data:geo:GSE26378 not downloaded", "asset:data:geo:GSE26378 licence not read", "asset:data:geo:GSE26440 not downloaded", "asset:data:geo:GSE26440 licence not read", "asset:data:geo:GSE66099 not downloaded", "asset:data:geo:GSE66099 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], 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"blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": false, "required_operations": [], "missing_operations": ["machine_learning_random_forest"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_forest."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE66099", "GSE13904", "GSE26378", "GSE26440"], "resolved_accessions": ["GSE66099", "GSE13904", "GSE26378", "GSE26440"], "statements": ["The following information was supplied regarding data availability:The code and raw data are available in the Supplemental Files, at GEO (GSE66099, GSE13904, GSE26378, and GSE26440), and GitHub (https://github.com/gxmuhjm/exosome_sepsis.git).Huang, J. (2025). codes and raw data in the manuscript. Zenodo. https://doi.org/10.5281/zenodo.17626895."], "basis": "The paper names GSE66099, GSE13904, GSE26378, GSE26440 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:41527570", "data:geo:GSE13904"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE13904"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:09:50Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9025, "completion_tokens": 1360, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:41527570", "type": "paper", "label": "pmid:41527570"}, {"id": "source:pmid:41527570", "type": "manuscript", "label": "PMID 41527570 full text"}, {"id": "data:geo:GSE13904", "type": "dataset", "label": "GEO GSE13904"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:41527570", "type": "paper", "label": "pmid:41527570"}, {"id": "source:pmid:41527570", "type": "manuscript", "label": "PMID 41527570 full text"}, {"id": "data:geo:GSE13904", "type": "dataset", "label": "GEO GSE13904"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-41527570", "type": "extraction", "used": ["source:pmid:41527570"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE13904", "type": "accession_resolution", "used": ["data:geo:GSE13904"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:41527570", "parent_hash": "sha256:1cf0f013ca86ebfaab90868a571c7a70d08e0f10a7157940032618b848a1dfad", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE13904 licence not read", "asset:data:geo:GSE13904 not downloaded", "asset:data:geo:GSE26378 licence not read", "asset:data:geo:GSE26378 not downloaded", "asset:data:geo:GSE26440 licence not read", "asset:data:geo:GSE26440 not downloaded", "asset:data:geo:GSE66099 licence not read", "asset:data:geo:GSE66099 not downloaded", "execution.not_run", "method.machine_learning_random_forest", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:machine_learning_random_forest"], "assays": [{"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": ["Among them, RF proved to be the most reliable diagnostic model for PS, as evidenced by its predictive performance and stability."], "order_basis": "analysis_only", "id": "assay:1"}]}