Polyamine metabolism and immune related genes as prognostic features in breast cancer: a novel risk model approach.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40687245", "kind": "science-record", "revision": 2, "content_hash": "sha256:d4424fced909c313d18a38921d2a74d4bf21375de55e0f55299ca0f72ef08512", "created_at": "2026-11-01T10:25:51Z", "updated_at": "2026-11-01T14:01:44Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Polyamine metabolism and immune related genes as prognostic features in breast cancer: a novel risk model approach.", "doi": "10.21037/tcr-2024-2505", "pmid": "40687245", "authors": null, "journal": "Translational cancer research", "year": 2025, "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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"section_title": "PSME2, PSMB8 and PSMD14 were deemed prognostic genes for BRCA patients", "row_count": 4, "column_count": 2}, "table": {"label": "Table 1", "caption": "LASSO regression coefficient", "rows": [["Gene", "Coefficient"], ["PSME2", "−0.36229220"], ["PSMD14", "0.43677028"], ["PSMB8", "−0.06306154"]], "row_count": 4, "column_count": 2, "numeric_cells": 4, "header_rows": 1, "locator": "section[22]/table-wrap[1]#t1"}}], "gaps": ["asset:source:pmid:40687245 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:40687245", "asset:table:pmid:40687245:1"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40687245"]}, "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": "d12428d14f265f1a0b1e5d577da66800753d940c834e8b944babe7c4a857d484", "source_hash": "52e4dea36e57d8a8f61745b524fda4b2c65d35c90b366997ec9f0784e868bcbe", "raw_response_sha256": "sha256:546509ad85445e22c15de11658c3270817bdd3c3107ee1b43a387c5979a09d45", "raw_response_chars": 5241}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "445ba4719e2e9bdbd1e69ca4369f19f22ceec9ac27670ee1691af8e3e87c5d0e", "source_hash": "52e4dea36e57d8a8f61745b524fda4b2c65d35c90b366997ec9f0784e868bcbe", "raw_response_sha256": "sha256:eb08b0afb717fee99ed53f57a3c8a33ea83c9c5b0897163592eebb4f8ab53b93", "raw_response_chars": 69138}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "62971a88d09ca932c18261c6b2aabffa9ae8897f07215b6ed75b8ef42727ff24", "source_hash": "52e4dea36e57d8a8f61745b524fda4b2c65d35c90b366997ec9f0784e868bcbe", "raw_response": "{\"parent_value\":10,\"unit\":\"candidate genes\",\"evidence\":\"Among 9,558 DEGs, 1,793 IRGs, and 59 PMRGs, 10 candidate genes were identified, with PSME2, PSMB8, and PSMD14 selected as prognostic genes.\",\"target_result\":\"Ten candidate genes were identified, and PSME2, PSMB8, and PSMD14 were selected as prognostic genes.\",\"number_stated\":true}", "reading": {"parent_value": 10, "unit": "candidate genes", "evidence": "Among 9,558 DEGs, 1,793 IRGs, and 59 PMRGs, 10 candidate genes were identified, with PSME2, PSMB8, and PSMD14 selected as prognostic genes.", "target_result": "Ten candidate genes were identified, and PSME2, PSMB8, and PSMD14 were selected as prognostic genes.", "number_stated": true, "source_locator": "chars 1120-1259 of source sha256 52e4dea36e57d8a8f61745b524fda4b2c65d35c90b366997ec9f0784e868bcbe"}, "recorded_at": "2026-10-31T15:21:56.059979+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "ce3b56443566290aca8d2e2196540db904c49f3fde446b8c606c5be9b1e2bdb4", "source_hash": "52e4dea36e57d8a8f61745b524fda4b2c65d35c90b366997ec9f0784e868bcbe", "raw_response": "<think>We need respond JSON object only according schema extracting claims from supplied source. Need find single numerical result replication registered target headline number main finding. Source seems no headline numerical result for risk model; has counts 9558 DEGs, 1793 IRGs, 59 PMRGs, 10 candidate genes, 3 genes etc. No stated performance metric (HR, AUC etc). Registered target \"the headline numerical result the paper states for its main finding\". Likely number_stated false. Need leave parent_value 0 unit/evidence empty and say in target_result what text does state instead. Need decide exact wording and no invented source. We should provide JSON: {\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"The text states that a risk model and nomogram based on PSME2, PSMB8, and PSMD14 were developed to assess breast cancer (BRCA) prognosis, but it gives no single numerical result for the main finding.\",\"number_stated\":false}. Need be careful requirement \"You extract claims only from supplied source text\". target_result can paraphrase? They ask say what text does state instead. We can state no numerical result for main finding and describe qualitative. Evidence empty as instructed. Unit empty. JSON only.\n\nLet's thoroughly think if there is an intended number. Source abstract has no numbers except counts. They might treat \"10 candidate genes\" as headline numerical result? Main finding is model based on PSME2, PSMB8, PSMD14. There isn't model performance. Target \"headline numerical result the paper states for its main finding\" likely expects false. Many benchmark tasks register broad targets. If no numeric target, false. The source explicitly has 10 candidate genes, 3 prognostic genes, 9558 etc. But registered target headline numerical result main finding = risk model/ nomogram, no numerical performance. Could the correct answer be 10 candidate genes? That is a numerical result for part of the finding but not main model performance. Prompt says \"single numerical result that a replication of the registered target has to reproduce... value paper itself states for registered target\". Registered target vague \"headline numerical result ... main finding\". The abstract's main finding qualitative. No single headline numeric. Thus false.\n\nWe should keep target_result concise with source-grounded things: \"The text states that a risk model and nomogram based on PSME2, PSMB8, and PSMD14 were developed to assess breast cancer prognosis; it reports no numerical performance result for this main finding.\" This uses source wording and says absence. Don't quote evidence since required empty. Could quote within target_result exact? Not necessary. Maybe target_result should specify counts? \"The text reports a qualitative main finding—a risk model and nomogram based on PSME2, PSMB8, and PSMD14 for BRCA prognosis—and gives no numerical result for it.\" That's fine. It does mention 10 candidate genes in results, but no need.\n\nNeed return valid JSON not markdown. parent_value number 0. unit string empty. evidence empty. boolean false.\n\nWe have 32491 tokens budget but simple. Final JSON exact.</think>\n\n{\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"The text states that a risk model and nomogram based on PSME2, PSMB8, and PSMD14 were developed to assess breast cancer prognosis, but it reports no numerical performance result for this main finding.\",\"number_stated\":false}", "reading": {"parent_value": 0, "unit": "", "evidence": "", "target_result": "The text states that a risk model and nomogram based on PSME2, PSMB8, and PSMD14 were developed to assess breast cancer prognosis, but it reports no numerical performance result for this main finding.", "number_stated": false, "source_locator": null}, "recorded_at": "2026-10-31T15:22:04.862222+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "PSME2, PSMB8, and PSMD14 (three genes) were selected as prognostic genes and used to develop a breast cancer risk model and nomogram.", "minimax_alt": "Among 9,558 DEGs, 1,793 IRGs, and 59 PMRGs, 10 candidate genes were identified, with PSME2, PSMB8, and PSMD14 selected as prognostic genes.", "decisive": false}, "analysis_methods": {"minimax": ["Differential expression analysis using DESeq2", "Gene Ontology and KEGG enrichment analysis using clusterProfiler", "Protein-protein interaction network analysis using STRING", "Wilcoxon’s test", "Univariate Cox regression analysis", "Multivariate Cox regression analysis", "Nomogram modeling using rms", "Kaplan-Meier analysis", "ROC curve analysis", "Decision curve analysis", "Gene set enrichment analysis (GSEA) using clusterProfiler", "Tumor mutation burden analysis using maftools", "Genomics of Drug Sensitivity in Cancer (GDSC) drug-sensitivity analysis"], "minimax_alt": ["DESeq2 differential expression analysis", "Intersection of differentially expressed genes with IRGs and PMRGs", "Least absolute shrinkage and selection operator (LASSO) Cox regression", "Kaplan-Meier survival analysis", "Univariate and multivariate Cox regression", "Nomogram modeling with calibration, ROC, and decision curve analyses", "Gene set enrichment analysis (GSEA)"], "decisive": false}, "statistical_method": {"minimax": "A least absolute shrinkage and selection operator (LASSO) Cox regression model was used to screen prognostic genes.", "minimax_alt": "Least absolute shrinkage and selection operator (LASSO) Cox regression model", "decisive": true}, "number_stated": {"minimax": true, "minimax_alt": false, "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:d61cab1d203927f4c0d0cdeaf6d1acfa9429ce3fc6e06aa1d6a957cad5b07645", "type": "sep_package", "label": "SEP v1 package for pmid:40687245"}], "activities": [{"id": "activity:conversion:pmid:40687245", "type": "conversion", "used": ["package:d61cab1d203927f4c0d0cdeaf6d1acfa9429ce3fc6e06aa1d6a957cad5b07645"], "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": "pmid:40687245", "parent_hash": "sha256:c45327b815a46298f6842bb189fe3fa1a9d27709bb2b0e16cfaae7fd1396c55a", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40687245 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:40687245' does not resolve to a declared asset", "targets"]}