CD79A and GADD45A as novel immune-related biomarkers for respiratory syncytial virus severity in children: an integrated machine learning analysis and clinical validation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40677720", "kind": "science-record", "revision": 4, "content_hash": "sha256:bc54f5f6b5cafc6a8e02e029e810fcc6780865f77488084ac93d977444314fdd", "created_at": "2026-11-01T20:30:46Z", "updated_at": "2026-11-01T22:56:51Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "CD79A and GADD45A as novel immune-related biomarkers for respiratory syncytial virus severity in children: an integrated machine learning analysis and clinical validation.", "doi": "10.3389/fimmu.2025.1609183", "pmid": "40677720", "authors": ["Juan Juan Chen", "Zhang Ze Lu", "Yu Xin Jing", "Xing Mei Nong", "Yi Qin", "Jin Yang Huang", "Na Lin", "Jie Wei"], "journal": "Frontiers in immunology", "year": 2025, "abstract": "Background: Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory infections in children, yet biomarkers for assessing disease severity remain limited. Herein, we investigated the differential expression biomarkers between RSV infected hospitalized patients, healthy groups and RSV infected outpatients.\nMethods: Two publicly available transcriptomic datasets (GSE77087 and GSE188427) were retrieved from the Gene Expression Omnibus (GEO) database. The GSE77087 dataset comprised peripheral blood samples from 81 children with confirmed RSV infection (61 hospitalized and 20 outpatient) and 23 healthy controls. The GSE188427 dataset included 147 RSV-infected children (113 hospitalized and 34 outpatient) and 51 healthy controls. Genes with |log2 fold change (logFC)| > 0 and false discovery rate (FDR) < 0.05 were considered differentially expressed. Overlapping DEGs between the two datasets were identified using the VennDiagram package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted on the intersecting DEGs via the clusterProfiler package, with terms deemed significant at FDR < 0.05.The CIBERSORT algorithm was applied to estimate the relative proportions of 22 immune cell types in 228 RSV-infected samples. Potential drug interactions for hug genes were predicted using the Drug-Gene Interaction Database (DGIdb). Competing endogenous RNA (ceRNA) networks were constructed using the SpongeScan database to identify lncRNAs interacting with the target miRNAs. Networks were visualized using Cytoscape (v3.10.1).Finally, Machine Learning-Based Biomarker Selection and hub gene identification and validation.\nResults: Differential gene expression analysis revealed 81 overlapping genes between hospitalized and outpatient RSV-infected children. Machine learning models, particularly SVM (area under the curve, AUC = 0.950), prioritized CD79A and GADD45A as key predictors of hospitalization. CD79A was significantly downregulated in severe cases, correlating with impaired B-cell responses and cytotoxic immunity, while GADD45A, upregulated in severe infections, linked to oxidative stress and neutrophil-driven inflammation. Immune cell profiling highlighted distinct infiltration patterns, with severe cases showing elevated naïve B cells and M0 macrophages versus activated NK cells and M1 macrophages in mild cases. Clinical validation in 92 children confirmed CD79A suppression and GADD45A elevation in severe RSV (p < 0.001), aligning with younger age, lower weight, and respiratory distress. Functional enrichment implicated endoplasmic reticulum stress and neutrophil extracellular traps in disease progression. Drug-target predictions and ceRNA networks further revealed therapeutic potential.\nConclusion: These findings establish CD79A and GADD45A as clinically actionable biomarkers for RSV severity, offering insights into immune dysregulation and guiding personalized management strategies.", "claims": [{"id": "claim-method", "statement": "Differential genes expression (DEGs) analysis was conducted using the limma package (https://bioconductor.org/packages/release/bioc/html/limma.html), with a filtering criterion of absolute logFC > 0 and a false discovery rate (FDR) value < 0.05 to identify DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40677720", "locator": "sections[4] \"Differential gene expression analysis between inpatient and outpatient children\" paragraph 2", "quote": "Differential genes expression (DEGs) analysis was conducted using the limma package (https://bioconductor.org/packages/release/bioc/html/limma.html), with a filtering criterion of absolute logFC > 0 and a false discovery rate (FDR) value < 0.05 to identify DEGs."}], "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 in the article/supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40677720", "locator": "sections[24] \"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 in the article/supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Machine learning models, particularly SVM (area under the curve, AUC = 0.950), prioritized CD79A and GADD45A as key predictors of hospitalization.", "kind": "quantitative", "target": {"value": 0.95, "unit": "auc", "tolerance": null, "source": "Machine learning models, particularly SVM (area under the curve, AUC = 0.950), prioritized CD79A and GADD45A as key predictors of hospitalization."}, "evidence": [{"asset": "source:pmid:40677720", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Machine learning models, particularly SVM (area under the curve, AUC = 0.950), prioritized CD79A and GADD45A as key predictors of hospitalization."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Machine learning models, particularly SVM (area under the curve, AUC = 0.950), prioritized CD79A and GADD45A as key predictors of hospitalization.", "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:40677720"], "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:GSE188427"], "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:GSE188427"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40677720", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2025.1609183", "sha256": "sha256:3281cd11480914d306f54841471ea696093774a4c3c7f7b7ceb1a871c2d904b7", "bytes": 41857, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2025.1609183", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12267186", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE188427", "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=GSE188427", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE188427", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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No tolerance is published because the paper states none."}], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:40677720", "data:geo:GSE188427"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE188427"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:57:18Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10755, "completion_tokens": 1303, "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:40677720", "type": "paper", "label": "pmid:40677720"}, {"id": "source:pmid:40677720", "type": "manuscript", "label": "PMID 40677720 full text"}, {"id": "data:geo:GSE188427", "type": "dataset", "label": "GEO GSE188427"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40677720", "type": "paper", "label": "pmid:40677720"}, {"id": "source:pmid:40677720", "type": "manuscript", "label": "PMID 40677720 full text"}, {"id": "data:geo:GSE188427", "type": "dataset", "label": "GEO GSE188427"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40677720", "type": "extraction", "used": ["source:pmid:40677720"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE188427", "type": "accession_resolution", "used": ["data:geo:GSE188427"], "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:40677720", "parent_hash": "sha256:bfd4ea0e9a63fbee95e022b760298018396d792fbca6c439d83cbdf44233bc99", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE188427 licence not read", "asset:data:geo:GSE188427 not downloaded", "asset:data:geo:GSE77087 licence not read", "asset:data:geo:GSE77087 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.69, "evidence": ["versus−host disease, FoxO signaling pathway, apoptosis, and melanoma pathways ( Figure 2C ). These"], "order_basis": "paper_text_order:8737", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Differential genes expression (DEGs) analysis was conducted using the limma package (https://bioconductor.org/packages/release/bioc/html/limma.html), with a filtering criterion of absolute logFC > 0 and a false discovery rate (FDR) value < 0.05 to identify DEGs."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}