Neutrophil extracellular trap genes predict immunotherapy response in gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39296112", "kind": "science-record", "revision": 4, "content_hash": "sha256:9366afb5561f04819156b6c073d0875030b061eba07a3eb6dc8269ff2d31c9c6", "created_at": "2026-11-01T20:29:28Z", "updated_at": "2026-11-01T22:55:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Neutrophil extracellular trap genes predict immunotherapy response in gastric cancer.", "doi": "10.1016/j.heliyon.2024.e37357", "pmid": "39296112", "authors": ["Ningjie Sun", "Junjie Jiang", "Biying Chen", "Yiran Chen", "Haiming Wu", "Haiyong Wang", "Jianfeng Chen"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Neutrophil extracellular trap (NET) is associated with host response, tumorigenesis, and immune dysfunction. However, the link between NET and the tumor microenvironment (TME) of gastric cancer (GC) remains unclear. Our study aims to characterize the expression patterns of NET-related genes and their relationships with clinicopathological characteristics, prognosis, TME features, and immunotherapy efficacy in GC cohorts.\nMethods: Transcriptomic and single-cell RNA sequencing profiles of GC with annotated clinicopathological data were obtained from TCGA-STAD (n = 415), GSE62254 (n = 300), GSE15459 (n = 192), and GSE183904 (n = 26). The consensus cluster algorithm was used to classify tumor samples into different NET-related clusters. A NET-related signature was constructed using LASSO regression and verified in four immunotherapy cohorts. ROC and Kaplan-Meier analyses were conducted to evaluate the predictive and prognostic value of the model for immunotherapy efficacy.\nResults: This study identified two NET-related clusters with distinct clinicopathological features, prognosis, and TME landscapes. The high NET-related cluster, characterized by increased NET-related gene expression, exhibited more aggressive behavior and a worse prognosis (HR = 1.63, P = 0.004) than the low NET-related cluster. DEGs were primarily involved in the chemokine/cytokine-associated pathways. Moreover, the high NET-related cluster had significantly higher levels of TME scores, immune infiltration, and immune effectors (all P < 0.001). The NET-related signature displayed a high predictive accuracy for immunotherapy response (AUC = 0.939, P < 0.001). Furthermore, patients with high NET-related scores consistently harbored a more favorable prognosis in different immunotherapy cohorts (all P < 0.05).\nConclusions: This study identified the NET-related signature as a robust model for predicting immunotherapy response in GC, which can help clinicians make appropriate immunotherapy decisions.", "claims": [{"id": "claim-method", "statement": "A NET-related signature was constructed using LASSO regression and verified in four immunotherapy cohorts. ROC and Kaplan-Meier analyses were conducted to evaluate the predictive and prognostic value of the model for immunotherapy efficacy.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39296112", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "A NET-related signature was constructed using LASSO regression and verified in four immunotherapy cohorts. ROC and Kaplan-Meier analyses were conducted to evaluate the predictive and prognostic value of the model for immunotherapy efficacy."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The TCGA-STAD dataset was downloaded from the cBioportal database (https://www.cbioportal.org/). The microarray datasets (GSE62254, GSE15459) and the scRNA-seq dataset (GSE183904) were downloaded from Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The data of the Kim, Gide, Liu, and Mariathasan cohorts were acquired from the corresponding publications and article supplementary materials. All the codes and datasets can be obtained from the supplementary materials and the Github website (https://github.com/jiangjunjie0415/Neutrophil-Extracellular-Trap).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39296112", "locator": "sections[22] \"Data availability\" paragraph 2", "quote": "The TCGA-STAD dataset was downloaded from the cBioportal database (https://www.cbioportal.org/). The microarray datasets (GSE62254, GSE15459) and the scRNA-seq dataset (GSE183904) were downloaded from Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The data of the Kim, Gide, Liu, and Mariathasan cohorts were acquired from the corresponding publications and article supplementary materials. All the codes and datasets can be obtained from the supplementary materials and the Github website (https://github.com/jiangjunjie0415/Neutrophil-Extracellular-Trap)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The NET-related signature displayed a high predictive accuracy for immunotherapy response (AUC = 0.939, P < 0.001).", "kind": "quantitative", "target": {"value": 0.939, "unit": "auc", "tolerance": null, "source": "The NET-related signature displayed a high predictive accuracy for immunotherapy response (AUC = 0.939, P < 0.001)."}, "evidence": [{"asset": "source:pmid:39296112", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The NET-related signature displayed a high predictive accuracy for immunotherapy response (AUC = 0.939, P < 0.001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The NET-related signature displayed a high predictive accuracy for immunotherapy response (AUC = 0.939, P < 0.001).", "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:39296112"], "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:GSE15459"], "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:GSE15459"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39296112", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e37357", "sha256": "sha256:99759cd2081d566b8be6ebab5ee6ce6e0e74410f439a90bedea6b44c0b4511d2", "bytes": 46773, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e37357", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11409185", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE15459", "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=GSE15459", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE15459", "version": null, "note": "The TCGA-STAD dataset was downloaded from the cBioportal database (https://www.cbioportal.org/). 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The data of the Kim, Gide, Liu, and Mariathasan cohorts were acquired from the corresponding publications and article supplementary materials. 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The microarray datasets (GSE62254, GSE15459) and the scRNA-seq dataset (GSE183904) were downloaded from Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The data of the Kim, Gide, Liu, and Mariathasan cohorts were acquired from the corresponding publications and article supplementary materials. All the codes and datasets can be obtained from the supplementary materials and the Github website (https://github.com/jiangjunjie0415/Neutrophil-Extracellular-Trap).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The TCGA-STAD dataset was downloaded from the cBioportal database (https://www.cbioportal.org/). The microarray datasets (GSE62254, GSE15459) and the scRNA-seq dataset (GSE183904) were downloaded from Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The data of the Kim, Gide, Liu, and Mariathasan cohorts were acquired from the corresponding publications and article supplementary materials. 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The microarray datasets (GSE62254, GSE15459) and the scRNA-seq dataset (GSE183904) were downloaded from Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The data of the Kim, Gide, Liu, and Mariathasan cohorts were acquired from the corresponding publications and article supplementary materials. All the codes and datasets can be obtained from the supplementary materials and the Github website (https://github.com/jiangjunjie0415/Neutrophil-Extracellular-Trap).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The TCGA-STAD dataset was downloaded from the cBioportal database (https://www.cbioportal.org/). The microarray datasets (GSE62254, GSE15459) and the scRNA-seq dataset (GSE183904) were downloaded from Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The data of the Kim, Gide, Liu, and Mariathasan cohorts were acquired from the corresponding publications and article supplementary materials. 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