Risk signature of NETosis-related subtype predicts prognosis and evaluates immunotherapy effectiveness in gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39525014", "kind": "science-record", "revision": 3, "content_hash": "sha256:6724a07b3e45d689697e4225e47339a1bb979c65abb2f31586b1e72fd1dcb484", "created_at": "2026-11-01T20:29:47Z", "updated_at": "2026-11-01T22:13:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Risk signature of NETosis-related subtype predicts prognosis and evaluates immunotherapy effectiveness in gastric cancer.", "doi": "10.21037/tcr-24-377", "pmid": "39525014", "authors": ["Ruyue Chen", "Zengwu Yao", "Lixin Jiang"], "journal": "Translational cancer research", "year": 2024, "abstract": "Background: Gastric cancer (GC) has a high incidence and mortality rate with a poor prognosis, so it is crucial to search for new biomarkers. The role of NETosis, a newly identified type of programmed cell death, in GC and its underlying mechanisms have yet to be explored and still require thorough investigation. Our research seeks to enhance our comprehension of NETosis and may offer novel approaches for treating GC.\nMethods: Utilizing The Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) dataset for training and the GSE84433 dataset for validation, our study delved into the associations between NETosis-related genes and the clinical risk of GC. Through comprehensive clustering, enrichment, and immune infiltration analyses, we evaluated the prognostic relevance of these NETosis genes in vivo. Furthermore, we devised a NETosis-related risk signature (NRRS) to assess its implications in risk stratification, survival prognosis, immune infiltration, and drug sensitivity. The NRRS's accuracy was validated by immunohistochemical staining.\nResults: By applying consensus clustering to data from 62 NETosis-related genes, we categorized patients into two distinct subgroups, C1 and C2. These subgroups demonstrated significant differences. Following this, we developed the NRRS using the least absolute shrinkage and selection operator (LASSO) regression analysis. This process involved the selection of the following genes: CXCR4, NRP1, PDCD1, CTLA4, AKR1B1, SERPINE1, RGS2, SLCO2A1, TNFAIP2, RNASE1, DOC2B, APOD, ENTPD2, and CCL24. High-risk and low-risk groups can be accurately distinguished. We further verify in the verification set. These results suggest that NETosis is related to the microenvironment of GC. Our designed NRRS can predict the survival of patients with GC.\nConclusions: We emphasized the relationship between NETosis and GC. We built and validated the value of NRRS. This contributes to deepening our view of NETosis and potentially provides new strategies for GC treatment.", "claims": [{"id": "claim-method", "statement": "We used the LASSO Cox method for regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39525014", "locator": "sections[17] \"Development and verification of the NETosis-associated risk signature (NRRS)\" paragraph 2", "quote": "We used the LASSO Cox method for regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "In the training cohort, we obtained transcriptomic data from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database’s stomach adenocarcinoma (STAD) dataset, encompassing 375 tumor samples and 32 normal tissue samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39525014", "locator": "sections[4] \"Data collection\" paragraph 2", "quote": "In the training cohort, we obtained transcriptomic data from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database’s stomach adenocarcinoma (STAD) dataset, encompassing 375 tumor samples and 32 normal tissue samples."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The NETosis-related risk signature was an effective tool for predicting survival in gastric cancer patients, with one-, three-, and five-year AUC values of 0.76, 0.81, and 0.85.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39525014", "locator": "sections[18] \"The association of NRRS with prognosis\" paragraph 2", "quote": "In the receiver operating characteristic ROC analysis, the AUC values for the one-, three-, and five-year periods were 0.76, 0.81, and 0.85 (Figure 5D)."}, {"asset": "source:pmid:39525014", "locator": "sections[18] \"The association of NRRS with prognosis\" paragraph 2", "quote": "Therefore, these results indicate that the NRRS is an effective tool for predicting the survival rate of GC patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The NETosis-related risk signature was an effective tool for predicting survival in gastric cancer patients, with one-, three-, and five-year AUC values of 0.76, 0.81, and 0.85.", "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:39525014"], "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:GSE84433"], "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:GSE84433"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39525014", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-24-377", "sha256": "sha256:aadfde2fdbf7546a11fd6e3ba0739d3141802e3973345ed9595b7defb37706e7", "bytes": 36120, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tcr-24-377", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11543040", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE84433", "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=GSE84433", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE84433", "version": null, "note": "In the training cohort, we obtained transcriptomic data from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database’s stomach adenocarcinoma (STAD) dataset, encompassing 375 tumor samples and 32 normal tissue samples.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "In the training cohort, we obtained transcriptomic data from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database’s stomach adenocarcinoma (STAD) dataset, encompassing 375 tumor samples and 32 normal tissue samples.", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM2235695", "GSM2235696", "GSM2235697", "GSM2235698", "GSM2235699", "GSM2235700", "GSM2235701", "GSM2235702", "GSM2235703", "GSM2235704", "GSM2235705", "GSM2235706", "GSM2235707", "GSM2235708", "GSM2235709", "GSM2235710", "GSM2235711", "GSM2235712", "GSM2235713", "GSM2235714", "GSM2235715", "GSM2235716", "GSM2235717", "GSM2235718", "GSM2235719", "GSM2235720", "GSM2235721", "GSM2235722", "GSM2235723", "GSM2235724", "GSM2235725", "GSM2235726", "GSM2235727", "GSM2235728", "GSM2235729", "GSM2235730", "GSM2235731", "GSM2235732", "GSM2235733", "GSM2235734", "GSM2235735", "GSM2235736", "GSM2235737", "GSM2235738", "GSM2235739", "GSM2235740", "GSM2235741", "GSM2235742", "GSM2235743", "GSM2235744", "GSM2235745", "GSM2235746", "GSM2235747", "GSM2235748", "GSM2235749", "GSM2235750", "GSM2235751", "GSM2235752", "GSM2235753", "GSM2235754", "GSM2235755", "GSM2235756", "GSM2235757", "GSM2235758", "GSM2235759", "GSM2235760", "GSM2235761", "GSM2235762", "GSM2235763", "GSM2235764", "GSM2235765", "GSM2235766", "GSM2235767", "GSM2235768", "GSM2235769", "GSM2235770", "GSM2235771", "GSM2235772", "GSM2235773", "GSM2235774", "GSM2235775", "GSM2235776", "GSM2235777", "GSM2235778", "GSM2235779", "GSM2235780", "GSM2235781", "GSM2235782", "GSM2235783", "GSM2235784", "GSM2235785", "GSM2235786", "GSM2235787", "GSM2235788", "GSM2235789", "GSM2235790", "GSM2235791", "GSM2235792", "GSM2235793", "GSM2235794", "GSM2235795", "GSM2235796", "GSM2235797", "GSM2235798", "GSM2235799", "GSM2235800", "GSM2235801", "GSM2235802", "GSM2235803", "GSM2235804", "GSM2235805", "GSM2235806", "GSM2235807", "GSM2235808", "GSM2235809", "GSM2235810", "GSM2235811", "GSM2235812", "GSM2235813", "GSM2235814", "GSM2235815", "GSM2235816", "GSM2235817", "GSM2235818", "GSM2235819", "GSM2235820", "GSM2235821", "GSM2235822", "GSM2235823", "GSM2235824", "GSM2235825", "GSM2235826", "GSM2235827", "GSM2235828", "GSM2235829", "GSM2235830", "GSM2235831", "GSM2235837", "GSM2235838", "GSM2235839", "GSM2235840", "GSM2235841", "GSM2235842", "GSM2235843", "GSM2235844", "GSM2235845", "GSM2235846", "GSM2235847", "GSM2235848", "GSM2235849", "GSM2235850", "GSM2235851", "GSM2235852", "GSM2235853", "GSM2235854", "GSM2235855", "GSM2235862", "GSM2235863", "GSM2235864", "GSM2235865", "GSM2235866", "GSM2235867", "GSM2235878", "GSM2235879", "GSM2235880", "GSM2235881", "GSM2235882", "GSM2235883", "GSM2235884", "GSM2235885", "GSM2235886", "GSM2235887", "GSM2235888", "GSM2235889", "GSM2235899", "GSM2235900", "GSM2235901", "GSM2235902", "GSM2235903", "GSM2235904", "GSM2235905", "GSM2235906", "GSM2235907", "GSM2235908", "GSM2235909", "GSM2235910", "GSM2235921", "GSM2235922", "GSM2235923", "GSM2235924", "GSM2235925", "GSM2235926", "GSM2235927", "GSM2235928", "GSM2235929", "GSM2235930", "GSM2235931", "GSM2235932", "GSM2235933", "GSM2235934", "GSM2235935", "GSM2235936", "GSM2235937", "GSM2235938", "GSM2235939", "GSM2235940", "GSM2235941", "GSM2235942", "GSM2235943", "GSM2235944", "GSM2235945", "GSM2235946", "GSM2235947", "GSM2235948", "GSM2235949", "GSM2235950", "GSM2235951", "GSM2235952", "GSM2235953", "GSM2235954", "GSM2235955", "GSM2235956", "GSM2235957", "GSM2235958", "GSM2235959", "GSM2235960", "GSM2235961", "GSM2235962", "GSM2235963", "GSM2235964", "GSM2235965", "GSM2235966", "GSM2235967", "GSM2235968", "GSM2235969", "GSM2235970", "GSM2235971", "GSM2235972", "GSM2235973", "GSM2235974", "GSM2235975", "GSM2235976", "GSM2235977", "GSM2235978", "GSM2235979", "GSM2235980", "GSM2235981", "GSM2235982", "GSM2235983", "GSM2235984", "GSM2235985", "GSM2235986", "GSM2235987", "GSM2235988", "GSM2235991", "GSM2235992", "GSM2235993", "GSM2235994", "GSM2235995", "GSM2235996", "GSM2235997", "GSM2235998", "GSM2235999", "GSM2236000", "GSM2236001", "GSM2236002", "GSM2236003", "GSM2236004", "GSM2236005", "GSM2236006", "GSM2236007", "GSM2236008", "GSM2236009", "GSM2236010", "GSM2236011", "GSM2236012", "GSM2236013", "GSM2236014", "GSM2236015", "GSM2236016", "GSM2236017", "GSM2236018", "GSM2236019", "GSM2236020", "GSM2236021", "GSM2236022", "GSM2236023", "GSM2236024", "GSM2236025", "GSM2236026", "GSM2236027", "GSM2236028", "GSM2236029", "GSM2236030", "GSM2236031", "GSM2236032", "GSM2236033", "GSM2236034", "GSM2236036", "GSM2236037", "GSM2236038", "GSM2236039", "GSM2236040", "GSM2236041", "GSM2236042", "GSM2236043", "GSM2236044", "GSM2236045", "GSM2236046", "GSM2236047", "GSM2236048", "GSM2236049", "GSM2236050", "GSM2236051", "GSM2236052", "GSM2236053", "GSM2236054", "GSM2236055", "GSM2236056", "GSM2236057", "GSM2236058", "GSM2236059", "GSM2236060", "GSM2236061", "GSM2236062", "GSM2236063", "GSM2236064", "GSM2236065", "GSM2236066", "GSM2236067", "GSM2236068", "GSM2236069", "GSM2236070", "GSM2236071", "GSM2236072", "GSM2236073", "GSM2236074", "GSM2236075", "GSM2236076", "GSM2236077", "GSM2236078", "GSM2236079", "GSM2236080", "GSM2236082", "GSM2236083", "GSM2236084", "GSM2236085", "GSM2236086", "GSM2236087", "GSM2236088", "GSM2236089", "GSM2236090", "GSM2236091", "GSM2236092", "GSM2236093", "GSM2236094", "GSM2236095"], "sample_count": 357, "group_sizes": null, "group_sizes_basis": null, "platform": "6947", "taxon": "Homo sapiens", "title": "Molecular subtypes in gastric cancer. [II]", "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:GSE84433 not downloaded", "asset:data:geo:GSE84433 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "We used the LASSO Cox method for regression analysis."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Kaplan-Meier"}}, {"id": "method-3", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "Limma"}}], "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": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "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": ["GSE84433"], "resolved_accessions": ["GSE84433"], "statements": ["In the training cohort, we obtained transcriptomic data from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database’s stomach adenocarcinoma (STAD) dataset, encompassing 375 tumor samples and 32 normal tissue samples."], "basis": "The paper names GSE84433 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": ["cox_ph"], "assets": ["source:pmid:39525014", "data:geo:GSE84433"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE84433"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:38:05Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 10943, "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:39525014", "type": "paper", "label": "pmid:39525014"}, {"id": "source:pmid:39525014", "type": "manuscript", "label": "PMID 39525014 full text"}, {"id": "data:geo:GSE84433", "type": "dataset", "label": "GEO GSE84433"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39525014", "type": "paper", "label": "pmid:39525014"}, {"id": "source:pmid:39525014", "type": "manuscript", "label": "PMID 39525014 full text"}, {"id": "data:geo:GSE84433", "type": "dataset", "label": "GEO GSE84433"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39525014", "type": "extraction", "used": ["source:pmid:39525014"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE84433", "type": "accession_resolution", "used": ["data:geo:GSE84433"], "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:39525014", "parent_hash": "sha256:7a8e3c7be9306767288a68e3a0e6d1224f7343ad590dfade659b9704d219afd5", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE84433 licence not read", "asset:data:geo:GSE84433 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["prognostic relevance of these NETosis genes in vivo. Furthermore, we devised a NETosis-related r"], "order_basis": "paper_text_order:879", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["tivity. The NRRS's accuracy was validated by immunohistochemical staining. Results: By applying consensus clu"], "order_basis": "paper_text_order:1102", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["We used the LASSO Cox method for regression analysis."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}