Lysosome-Related Diagnostic Biomarkers for Pediatric Sepsis Integrated by Machine Learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38034045", "kind": "science-record", "revision": 4, "content_hash": "sha256:01f00e1ffe87655b146913079e13d341704857cf22135d79de5d551fad4b8490", "created_at": "2026-11-01T19:02:58Z", "updated_at": "2026-11-01T22:53:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Lysosome-Related Diagnostic Biomarkers for Pediatric Sepsis Integrated by Machine Learning.", "doi": "10.2147/JIR.S437110", "pmid": "38034045", "authors": ["Yang Yang", "Genhao Zhang"], "journal": "Journal of inflammation research", "year": 2023, "abstract": "Background: There is currently no biomarker that can reliably identify sepsis, despite recent scientific advancements. We systematically evaluated the value of lysosomal genes for the diagnosis of pediatric sepsis.\nMethods: Three datasets (GSE13904, GSE26378, and GSE26440) were obtained from the gene expression omnibus (GEO) database. LASSO regression analysis and random forest analysis were employed for screening pivotal genes to construct a diagnostic model between the differentially expressed genes (DEGs) and lysosomal genes. The efficacy of the diagnostic model for pediatric sepsis identification in the three datasets was validated through receiver operating characteristic curve (ROC) analysis. Furthermore, a total of 30 normal samples and 35 pediatric sepsis samples were gathered to detect the expression levels of crucial genes and assess the diagnostic model's efficacy in diagnosing pediatric sepsis in real clinical samples through real-time quantitative PCR (qRT-PCR).\nResults: Among the 83 differentially expressed genes (DEGs) related to lysosomes, four key genes (STOM, VNN1, SORT1, and RETN) were identified to develop a diagnostic model for pediatric sepsis. The expression levels of these four key genes were consistently higher in the sepsis group compared to the normal group across all three cohorts. The diagnostic model exhibited excellent diagnostic performance, as evidenced by area under the curve (AUC) values of 1, 0.971, and 0.989. Notably, the diagnostic model also demonstrated strong diagnostic ability with an AUC of 0.917 when applied to the 65 clinical samples, surpassing the efficacy of conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%).\nConclusion: A four-gene diagnostic model of lysosomal function was devised and validated, aiming to accurately detect pediatric sepsis cases and propose potential target genes for lysosomal intervention in affected children.", "claims": [{"id": "claim-method", "statement": "Moreover, the shared genes in the two machine learning algorithms were selected for logistic regression analysis to develop a diagnostic model for pediatric sepsis. Subsequently, the validity of the diagnostic model was evaluated using receiver operating characteristic curve (ROC) analysis in both the training and testing datasets and the area under the curve (AUC) was calculated to assess the predictive performance of the algorithm.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38034045", "locator": "sections[5] \"Lysosomal-Related Diagnostic Biomarkers Developing\" paragraph 2", "quote": "Moreover, the shared genes in the two machine learning algorithms were selected for logistic regression analysis to develop a diagnostic model for pediatric sepsis. Subsequently, the validity of the diagnostic model was evaluated using receiver operating characteristic curve (ROC) analysis in both the training and testing datasets and the area under the curve (AUC) was calculated to assess the predictive performance of the algorithm."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38034045", "locator": "sections[19] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A four-gene lysosomal diagnostic model comprising STOM, VNN1, SORT1, and RETN was developed and validated for identifying pediatric sepsis, with AUC values of 1, 0.971, and 0.989 in three cohorts and 0.917 in 65 clinical samples.", "kind": "quantitative", "target": {"value": 0.917, "unit": "auc", "tolerance": null, "source": "Notably, the diagnostic model also demonstrated strong diagnostic ability with an AUC of 0.917 when applied to the 65 clinical samples, surpassing the efficacy of conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%)."}, "evidence": [{"asset": "source:pmid:38034045", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Among the 83 differentially expressed genes (DEGs) related to lysosomes, four key genes (STOM, VNN1, SORT1, and RETN) were identified to develop a diagnostic model for pediatric sepsis."}, {"asset": "source:pmid:38034045", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The diagnostic model exhibited excellent diagnostic performance, as evidenced by area under the curve (AUC) values of 1, 0.971, and 0.989."}, {"asset": "source:pmid:38034045", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Notably, the diagnostic model also demonstrated strong diagnostic ability with an AUC of 0.917 when applied to the 65 clinical samples, surpassing the efficacy of conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A four-gene lysosomal diagnostic model comprising STOM, VNN1, SORT1, and RETN was developed and validated for identifying pediatric sepsis, with AUC values of 1, 0.971, and 0.989 in three cohorts and 0.917 in 65 clinical samples.", "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:38034045"], "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:38034045", "role": "manuscript", "format": "xml", "media_type": "application/xml", 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corresponding author upon reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM350139", "GSM350140", "GSM350141", "GSM350142", "GSM350143", "GSM350144", "GSM350145", "GSM350146", "GSM350147", "GSM350148", "GSM350149", "GSM350150", "GSM350151", "GSM350152", "GSM350153", "GSM350154", "GSM350155", "GSM350156", "GSM350157", "GSM350158", "GSM350159", "GSM350160", "GSM350161", "GSM350162", "GSM350163", "GSM350164", "GSM350165", "GSM350166", "GSM350167", "GSM350168", "GSM350169", "GSM350170", "GSM350171", "GSM350172", "GSM350173", "GSM350174", "GSM350175", "GSM350176", "GSM350177", "GSM350178", "GSM350179", "GSM350180", "GSM350181", "GSM350182", "GSM350183", "GSM350184", "GSM350185", "GSM350186", "GSM350187", 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"title": "Expression data from validation cohort of children with septic shock", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE26440", "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=GSE26440", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE26440", "version": null, "note": "The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.", 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Subsequently, the validity of the diagnostic model was evaluated using receiver operating characteristic curve (ROC) analysis in both the training and testing datasets and the area under the curve (AUC) was calculated to assess the predictive performance of the algorithm."}}, {"id": "method-2", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.8, "band": "high", "quote": "logistic regression"}}, {"id": "method-3", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "chi-square"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "chi_square_test", "expected_value": 0.917, "unit": "auc", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}], "gaps": ["method.machine_learning_random_forest"]}, "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": 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. 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