Analysis of signature genes and association with immune cells infiltration in pediatric septic shock.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36439140", "kind": "science-record", "revision": 4, "content_hash": "sha256:d6b0bedc39acd4bb94c72bd8b5a2c4fd7630f8d80def9bf4a3c00fec96c2f6c4", "created_at": "2026-11-01T19:00:54Z", "updated_at": "2026-11-01T22:50:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of signature genes and association with immune cells infiltration in pediatric septic shock.", "doi": "10.3389/fimmu.2022.1056750", "pmid": "36439140", "authors": ["Jiajie Fan", "Shanshan Shi", "Yunxiang Qiu", "Mingnan Liu", "Qiang Shu"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: Early diagnosis of septic shock in children is critical for prognosis. This study committed to investigate the signature genes and their connection with immune cells in pediatric septic shock.\nMethods: We screened a dataset of children with septic shock from the GEO database and analyzed differentially expressed genes (DEGs). Functional enrichment analysis was performed for these DEGs. Weighted gene co-expression network analysis (WCGNA) was used to screen the key modules. Least absolute shrinkage and selection operator (LASSO) and random forest analysis were finally applied to identify the signature genes. Then gene set enrichment analysis (GSEA) was exerted to explore the signaling pathways related to the hub genes. And the immune cells infiltration was subsequently classified via using CIBERSORT.\nResults: A total of 534 DEGs were screened from GSE26440. The data then was clustered into 17 modules via WGCNA, which MEgrey module was significantly related to pediatric septic shock (cor=-0.62, p<0.0001). LASSO and random forest algorithms were applied to select the signature genes, containing UPP1, S100A9, KIF1B, S100A12, SLC26A8. The receiver operating characteristic curve (ROC) of these signature genes was 0.965, 0.977, 0.984, 0.991 and 0.989, respectively, which were verified in the external dataset from GSE13904. GSEA analysis showed these signature genes involve in positively correlated fructose and mannose metabolism and starch and sucrose metabolism signaling pathway. CIBERSORT suggested these signature genes may participate in immune cells infiltration.\nConclusion: UPP1, S100A9, KIF1B, S100A12, SLC26A8 emerge remarkable diagnostic performance in pediatric septic shock and involved in immune cells infiltration.", "claims": [{"id": "claim-method", "statement": "Using R software’s limma package (19), differentially expressed genes (DEGs) between septic shock cohort and control cohort were analyzed, with the following criterion: adjust p value <0.05 and |log fold change (FC)| > 1. Based on the scale-free topology criterion, the co-expression network in the GSE26440 cohort was constructed by weighted gene co-expression network analysis (WGCNA) (21). LASSO analysis was implemented using the glmnet package with penalty parameters for 10-fold cross-validation, which is superior to regression analysis method in evaluating high-dimensional data (22). Moreover, we applied the R package “randomforest” to classify the DEGs for hub genes. The area under curve (AUC) of the receiver operating characteristic curve (ROCs) was used to assess the diagnostic efficiency of these signature genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36439140", "locator": "sections[4] \"Identification of DEGs\" paragraph 2", "quote": "Using R software’s limma package (19), differentially expressed genes (DEGs) between septic shock cohort and control cohort were analyzed, with the following criterion: adjust p value <0.05 and |log fold change (FC)| > 1."}, {"asset": "source:pmid:36439140", "locator": "sections[6] \"Weighted gene co-expression network analysis\" paragraph 2", "quote": "Based on the scale-free topology criterion, the co-expression network in the GSE26440 cohort was constructed by weighted gene co-expression network analysis (WGCNA) (21)."}, {"asset": "source:pmid:36439140", "locator": "sections[7] \"Signature gene identification\" paragraph 3", "quote": "LASSO analysis was implemented using the glmnet package with penalty parameters for 10-fold cross-validation, which is superior to regression analysis method in evaluating high-dimensional data (22)."}, {"asset": "source:pmid:36439140", "locator": "sections[7] \"Signature gene identification\" paragraph 3", "quote": "Moreover, we applied the R package “randomforest” to classify the DEGs for hub genes."}, {"asset": "source:pmid:36439140", "locator": "sections[7] \"Signature gene identification\" paragraph 3", "quote": "The area under curve (AUC) of the receiver operating characteristic curve (ROCs) was used to assess the diagnostic efficiency of these signature genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36439140", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The receiver operating characteristic curve (ROC) of these signature genes was 0.965, 0.977, 0.984, 0.991 and 0.989, respectively, which were verified in the external dataset from GSE13904.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36439140", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The receiver operating characteristic curve (ROC) of these signature genes was 0.965, 0.977, 0.984, 0.991 and 0.989, respectively, which were verified in the external dataset from GSE13904."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The receiver operating characteristic curve (ROC) of these signature genes was 0.965, 0.977, 0.984, 0.991 and 0.989, respectively, which were verified in the external dataset from GSE13904.", "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:36439140"], "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": "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:GSE13904"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36439140", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.1056750", "sha256": "sha256:48fdea88dd547eaea13b41ef446fbc04c50906b171ec3457395f2b895b8c0f9d", "bytes": 30769, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.1056750", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9686439", "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 original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/supplementary material. 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Based on the scale-free topology criterion, the co-expression network in the GSE26440 cohort was constructed by weighted gene co-expression network analysis (WGCNA) (21). LASSO analysis was implemented using the glmnet package with penalty parameters for 10-fold cross-validation, which is superior to regression analysis method in evaluating high-dimensional data (22). Moreover, we applied the R package “randomforest” to classify the DEGs for hub genes. The area under curve (AUC) of the receiver operating characteristic curve (ROCs) was used to assess the diagnostic efficiency of these signature genes."}}, {"id": "method-2", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "random forest"}}], "targets": [], "gaps": []}, "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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