Development of multiple cell death pattern features to predict prognosis and drug sensitivity in gastric adenocarcinoma.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41197447", "kind": "science-record", "revision": 1, "content_hash": "sha256:6be5f9d53cfaf0fbc2ee68c7902ef2dd694cfe1709bb254286ade00101423f3a", "created_at": "2026-11-01T10:25:53Z", "updated_at": "2026-11-01T10:25:53Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Development of multiple cell death pattern features to predict prognosis and drug sensitivity in gastric adenocarcinoma.", "doi": "10.1016/j.tranon.2025.102584", "pmid": "41197447", "authors": null, "journal": "Translational oncology", "year": 2026, "abstract": null, "claims": [{"id": "claim-1", "statement": "the headline numerical result the paper states for its main finding", "kind": "qualitative", "state": "blocked", "target": {"value": null, "unit": null, "tolerance": null, "source": null}, "evidence": []}], 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"source:pmid:41197447", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.tranon.2025.102584", "sha256": "sha256:b0c1561eeadfdc8d64b5aac1939e97634484c7e59c704569334ba455c8189c21", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.tranon.2025.102584", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}], "gaps": ["asset:source:pmid:41197447 not verified"]}, "analysis": {"state": "absent", "methods": [], "targets": [], "gaps": ["method", "targets"]}, "capability": {"state": "absent", "requirements": [], "gaps": ["capability"]}, "readiness": {"state": "partial", "tier": null, "blocking_axis": null, "blocked_on": null, "axes": {}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:41197447"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:41197447"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "43ea387aabec98b9dd3d7a0baa3b6bfba798d6e81cd93f80c6e7a5ded0390327", "source_hash": "b0c1561eeadfdc8d64b5aac1939e97634484c7e59c704569334ba455c8189c21", "raw_response_sha256": "sha256:47b1ae01ee2e6977d1a33b652a7b5e25f2633fb06f1712e8c8f4949b7bde695a", "raw_response_chars": 2932}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "d715f828e5c489cb5e43047ac1467a9f47995e48c0e779b7ba13105a27faae6b", "source_hash": "b0c1561eeadfdc8d64b5aac1939e97634484c7e59c704569334ba455c8189c21", "raw_response_sha256": "sha256:c211dca088dee5da0cbecf950dd14023337920f1ae6dbebaa238235afea912b3", "raw_response_chars": 45545}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "fabdaaa7bafa726d165520a6bfeb75b7757d3e1ec585015c3862fff4dfc6e8f9", "source_hash": "b0c1561eeadfdc8d64b5aac1939e97634484c7e59c704569334ba455c8189c21", "raw_response": "{\"parent_value\":6,\"unit\":\"RCD-related genes\",\"evidence\":\"Developed and validated a novel prognostic signature (PCD Score) integrating six key RCD-related genes.\",\"target_result\":\"The paper states that its PCD Score integrates six key RCD-related genes.\",\"number_stated\":true}", "reading": {"parent_value": 6, "unit": "RCD-related genes", "evidence": "Developed and validated a novel prognostic signature (PCD Score) integrating six key RCD-related genes.", "target_result": "The paper states that its PCD Score integrates six key RCD-related genes.", "number_stated": true, "source_locator": "chars 351-454 of source sha256 b0c1561eeadfdc8d64b5aac1939e97634484c7e59c704569334ba455c8189c21"}, "recorded_at": "2026-10-31T15:27:04.746171+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "c835788f3ba0cc7e8c4256a441c181cafbf4ca40fdd7503bcdeed1ef8ae2b261", "source_hash": "b0c1561eeadfdc8d64b5aac1939e97634484c7e59c704569334ba455c8189c21", "raw_response": "<think>We need answer JSON only. Need interpret registered target headline numerical result paper states for main finding. Source has many numbers: 26 genes prognostic relevance, two clusters, six-gene model, 190 DEGs. Asked \"single numerical result that a replication of the registered target has to reproduce. Report headline number only: value paper itself states for registered target, not calculated...\" Registered target is \"the headline numerical result the paper states for its main finding.\" Need identify likely headline number perhaps 190 differentially expressed genes? Main finding perhaps PCD score, validation predictive power, but no AUC/C-index number. Need extract claim only. We need decide number_stated perhaps false because no headline numerical performance result stated for main finding? Yet source explicitly says \"Twenty-six RCD-associated genes showed significant prognostic relevance\" and \"A total of 190 differentially expressed genes...\" Which is \"headline numerical result\" perhaps the main finding. Registered target wording generic. Need infer expected benchmark extraction. The task asks find single numerical result replication of registered target has to reproduce. \"REGISTERED TARGET: the headline numerical result the paper states for its main finding\". Source likely adapted paper and perhaps abstract omitted key numeric AUC? We must report headline number only. Need use exact contiguous quote containing number, unit paper prints. Which numerical result is the headline for main finding?\n\nLet's parse source. Title. Summary:\n- Established prognostic framework.\n- Identified two molecular clusters...\n- Developed and validated novel prognostic signature PCD Score integrating six key genes.\n- Revealed immune...\n- Predicted temozolomide.\nResults:\n- Twenty-six RCD-associated genes showed significant prognostic relevance.\n- Two clusters identified, B better OS.\n- signature genes...\n- validation strong predictive power.\n...\nThen supplemental detailed:\n- 190 DEGs identified between high/low based threshold.\n- Conclusion: validated six-gene model.\n\nQuestion specifically \"single numerical result that a replication of the registered target has to reproduce.\" Registered target is abstract target, not named metric. \"headline numerical result the paper itself states for its main finding\". Main finding = \"Twenty-six RCD-associated genes showed significant prognostic relevance.\" That likely headline numerical result. Could be 26 RCD-associated genes. Unit perhaps \"genes\". Evidence exact quote: \"Twenty-six RCD-associated genes showed significant prognostic relevance.\" parent_value 26, unit \"genes\". This sounds likely.\n\nBut perhaps main finding is PCD Score with six genes, headline numerical result = six key RCD-related genes. Yet \"a replication of registered target has to reproduce\" maybe extraction benchmark where target can be specific claim, and asks headline numerical result. Here registered target wording is intentionally semantic, likely answer from paper abstract/result. They want a number only and evidence. Need determine which is headline. \"the headline numerical result the paper states for its main finding\" singular. The first Results sentence is \"Twenty-six RCD-associated genes showed significant prognostic relevance.\" That's a numerical finding. The model itself integrates six genes, repeatedly emphasized. \"headline numerical result\" might be 26 genes discovered. Need assess likely expected.\n\nCould answer 26 genes with evidence exact sentence. They demand evidence exact contiguous quotation. Unit must paper prints. Paper prints \"Twenty-six\" word, but parent numeric 26. Unit paper prints \"genes\". They say value paper itself states, no converted. Number is spelled out \"Twenty-six\"—does numeric JSON 26 count converted? Usually extraction accepts number from word. \"The unit must be the unit the paper prints.\" Evidence exact. \"number the paper itself states\" perhaps can normalize spelled number to 26. parent_value number required. Fine.\n\nCould unit be \"RCD-associated genes\" rather than \"genes\"? They ask unit printed. Likely \"genes\". target_result perhaps \"Twenty-six RCD-associated genes showed significant prognostic relevance.\" Maybe number_stated true.\n\nNeed not mention alternatives. JSON valid, no markdown.\n\nLet's investigate clues. They explicitly mention \"Attribution may be carried by text's own wording: a number may be attached to target by a parenthetical list, by word respectively, or by words that name target directly. When that is how text links them, quote whole clause...\" Here target generic, and source sentence directly names \"Twenty-six RCD-associated genes\". The instruction designed to prevent fabricated metric from paper. \"registered target: headline numerical result paper states for its main finding.\" We need choose.\n\nCould \"headline numerical result\" refer 190 DEGs as a central Results subsection, but that's later and not main finding. Main finding is prognostic model based on 26 RCD genes. The abstract-like top says \"Twenty-six RCD-associated genes showed significant prognostic relevance.\" Thus 26 likely.\n\nMaybe there is an original paper with headline \"26 RCD-related genes associated with prognosis...\" and PCD score. Search mental recognition? Title perhaps actual Front Oncol / Scientific Reports. Could abstract include:\n\"Twenty-six RCD-related genes were screened for prognostic significance in STAD. Consensus clustering analysis divided patients into two molecular subtypes...\" \nMaybe key result has \"PCD Score accurately predicted overall survival (AUC = 0.7...)\" But supplied text omitted detailed values. They ask only source. Number 26 is first main numerical result.\n\nLet's ensure exact quote contiguous: \"Twenty-six RCD-associated genes showed significant prognostic relevance.\" Yes. Unit \"genes\". parent 26. target_result perhaps \"The paper reports 26 RCD-associated genes with significant prognostic relevance.\" But they said report headline number only perhaps target_result should be minimal, maybe \"Twenty-six RCD-associated genes showed significant prognostic relevance.\" Evidence same exact quote. Number stated true. Should target_result include number and claim. JSON.\n\nNeed maybe unit \"RCD-associated genes\" to be exact. Unit usually unit printed next to number: \"RCD-associated genes\". Parent_value 26. I think unit could be \"RCD-associated genes\". The prompt: \"The unit must be the unit the paper prints.\" If \"26 genes\", unit genes. They may expect \"genes\". Evidence.\n\nCould target_result be \"26 RCD-associated genes showed significant prognostic relevance.\" We can use exact source wording in evidence, and target result own wording. Since says report headline number only perhaps no extraneous. Let's answer.</think>\n\n{\"parent_value\":26,\"unit\":\"RCD-associated genes\",\"evidence\":\"Twenty-six RCD-associated genes showed significant prognostic relevance.\",\"target_result\":\"Twenty-six RCD-associated genes showed significant prognostic relevance.\",\"number_stated\":true}", "reading": {"parent_value": 26, "unit": "RCD-associated genes", "evidence": "Twenty-six RCD-associated genes showed significant prognostic relevance.", "target_result": "Twenty-six RCD-associated genes showed significant prognostic relevance.", "number_stated": true, "source_locator": "chars 1374-1446 of source sha256 b0c1561eeadfdc8d64b5aac1939e97634484c7e59c704569334ba455c8189c21"}, "recorded_at": "2026-10-31T15:27:12.587563+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "A prognostic signature (PCD Score) integrating six key RCD-related genes.", "minimax_alt": "A validated six-gene PCD Score prognostic signature (CTSV, GCSH, LZTS1, SERPINE1, DDIAS, TRAF2) for stratifying STAD patients by survival risk.", "decisive": false}, "analysis_methods": {"minimax": ["limma", "ConsensusClusterPlus", "Cox regression", "ROC curves", "C-index", "multivariate analyses", "ssGSEA", "GSVA", "oncoPredict"], "minimax_alt": ["Cox regression for risk scoring", "ROC curves, C-index, and multivariate analyses for prognostic validation", "limma for differential expression analysis", "ConsensusClusterPlus for unsupervised clustering", "ssGSEA for immune infiltration", "GSVA for pathway scoring", "clusterProfiler (v4.8.2) for GO and KEGG enrichment analysis", "maftools (v2.14.0) for somatic mutation profiling", "oncoPredict with GDSC pharmacogenomic data for drug-sensitivity prediction"], "decisive": false}, "parent_value": {"minimax": 6, "minimax_alt": 26, "decisive": true}, "unit": {"minimax": "RCD-related genes", "minimax_alt": "RCD-associated genes", "decisive": true}}, "agreement_basis": {"policy": "decisive_fields", "decisive_fields": ["work_type", "number_stated", "parent_value", "unit"]}, "blinded": true}, "human_critique": {"status": "not_run", "reviewer": null, "signed_at": null, "conflict_checked": false, "commentary": null}, "stages": [{"key": "target_materials", "status": "pending", "reason": null, "evidence": null}, {"key": "independent_readings", "status": "blocked", "reason": "The independent readings are incomplete or disagree; the source evidence must resolve the difference.", "evidence": {"provider_errors": []}}, {"key": "source_resolution", "status": "pending", "reason": null, "evidence": null}, {"key": "protocol_reconstruction", "status": "pending", "reason": null, "evidence": null}, {"key": "control_execution", "status": "pending", "reason": null, "evidence": null}, {"key": "control_comparison", "status": "pending", "reason": null, "evidence": null}, {"key": "publication", "status": "pending", "reason": null, "evidence": null}], "gaps": ["review.human_critique", "review.machine_critique.blocked"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:f2b68a2f5e1394bc4c7e88b2f4ca65abf2460a10b6011ffcbf71f2456199e378", "type": "sep_package", "label": "SEP v1 package for pmid:41197447"}], "activities": [{"id": "activity:conversion:pmid:41197447", "type": "conversion", "used": ["package:f2b68a2f5e1394bc4c7e88b2f4ca65abf2460a10b6011ffcbf71f2456199e378"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:minimax", "type": "model", "label": "minimax"}, {"id": "agent:minimax_alt", "type": "model", "label": "minimax_alt"}, {"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}], "source_run_id": "run-20261023T122449232913", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "4768e430-d8ed-4837-9f8c-2cbe16d2a9a9", "parent_hash": "f2b68a2f5e1394bc4c7e88b2f4ca65abf2460a10b6011ffcbf71f2456199e378", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:41197447 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.blocked", "step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:41197447' does not resolve to a declared asset", "targets"]}