Construction of the bromodomain-containing protein-associated prognostic model in triple-negative breast cancer.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39827145", "kind": "science-record", "revision": 2, "content_hash": "sha256:f024fea73219ecc9cdd3c494d79e07cde96281ec086a2b46056252c326a1ff1e", "created_at": "2026-11-01T10:25:49Z", "updated_at": "2026-11-01T14:01:33Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Construction of the bromodomain-containing protein-associated prognostic model in triple-negative breast cancer.", "doi": "10.1186/s12935-025-03648-7", "pmid": "39827145", "authors": null, "journal": "Cancer cell international", "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": []}], "gaps": 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"section_index": 17, "section_title": "Constructed, evaluated, and validated a risk model", "row_count": 7, "column_count": 2}, "table": {"label": "Table 1", "caption": "Gene coefficients for six prognostic genes", "rows": [["Gene", "Coef"], ["KRT6A", "0.088900692"], ["PGF", "0.219005689"], ["ABCA1", "0.210816453"], ["EDNRB", "0.0638788218063777"], ["CTSD", "0.258686967"], ["GJA4", "0.06757986"]], "row_count": 7, "column_count": 2, "numeric_cells": 6, "header_rows": 1, "locator": "section[17]/table-wrap[1]#Tab1"}}], "gaps": ["asset:source:pmid:39827145 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:39827145", "asset:table:pmid:39827145:1"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:39827145"]}, "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": "54a1e638fde8b027a39fd9a28efe2eaaea1ba13dd9cf52de7785c20ec72dd2ec", "source_hash": "b1e5d3e02b454389abd95f25691355a145ce4425d8240e22df488822dabd7d1c", "raw_response_sha256": "sha256:5a76e3bd320a6ef431abdc0832e9c176226c15a6c4bf75e884544e03b758432d", "raw_response_chars": 4432}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "1ccfb0821c4a6875b1799ea7f918240efee98abaf6d1d73ee6aafbd9128a8903", "source_hash": "b1e5d3e02b454389abd95f25691355a145ce4425d8240e22df488822dabd7d1c", "raw_response_sha256": "sha256:f10842fa17d01fddee814cc5a871759049644462f396c6c062d884bc37cabea2", "raw_response_chars": 74421}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "85542c2c94c09f7aff7d6e34c45b7234f054c7dbfcd628a9b8a2d2a791c1060f", "source_hash": "b1e5d3e02b454389abd95f25691355a145ce4425d8240e22df488822dabd7d1c", "raw_response": "{\"parent_value\":120,\"unit\":\"DE-BRDRGs\",\"evidence\":\"A total of 120 DE-BRDRGs were identified by overlapping 605 DEGs1 from four key cell types, 10,776 DEGs2, and 4,497 DEGs3.\",\"target_result\":\"A total of 120 DE-BRDRGs were identified.\",\"number_stated\":true}", "reading": {"parent_value": 120, "unit": "DE-BRDRGs", "evidence": "A total of 120 DE-BRDRGs were identified by overlapping 605 DEGs1 from four key cell types, 10,776 DEGs2, and 4,497 DEGs3.", "target_result": "A total of 120 DE-BRDRGs were identified.", "number_stated": true, "source_locator": "chars 1625-1747 of source sha256 b1e5d3e02b454389abd95f25691355a145ce4425d8240e22df488822dabd7d1c"}, "recorded_at": "2026-10-31T15:21:16.491616+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "fcf6abd330115ff0f147c8770d1dcee842ddc5a42626df64d6c3758ccee3f172", "source_hash": "b1e5d3e02b454389abd95f25691355a145ce4425d8240e22df488822dabd7d1c", "raw_response": "<think>We need produce JSON object only per developer. Need extract single numerical result headline paper states for main finding registered target. Need scrutinize source. Main finding novel prognostic model BRDRGs TNBC. Headline numerical result likely six prognostic genes risk model comprising six genes. Source text says \"A risk model comprising six prognostic genes (KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4) was constructed.\" This is headline numerical result? Need decide target registered \"the headline numerical result the paper itself states for its main finding.\" They demand value paper states for registered target, unit printed, evidence exact contiguous quote containing number, attribution. Likely 6 genes as main finding. Abstract lacks numerical performance metrics. It has six risk model. Could be \"120 DE-BRDRGs\" or six prognostic genes. Need reason tasks generic extraction target main finding. They provide target vague but main finding numerical result likely six prognostic genes as model. Need find expected number/unit/evidence.\n\nWe should not overthink? Need answer JSON e.g.\n{\"parent_value\":6,\"unit\":\"prognostic genes\",\"evidence\":\"A risk model comprising six prognostic genes (KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4) was constructed.\",\"target_result\":\"The paper states that a risk model comprising six prognostic genes was constructed.\",\"number_stated\":true}\nUnit must unit paper prints: \"six prognostic genes\" number written not numerical digit. They say numerical result value paper states not calculated converted. Could parent_value 6, unit \"prognostic genes\", evidence includes six. Evidence number is six (numeric word). Could alternatively headline 120 DE-BRDRGs (de-BRDRGs). Need ensure target result.\n\nLet's examine title construction of model, abstract results risk model comprising six prognostic genes. \"single numerical result that a replication of the registered target has to reproduce\" likely primary result from paper maybe \"six prognostic genes\" is structure not performance. User deliberately target is \"headline numerical result ... main finding.\" No survival HR or AUC given, so maybe \"six prognostic genes\" or \"120 DE-BRDRGs\" with associated unit. Which is headline numerical result? We should infer from prominent abstract and methods. Main finding model built on six genes. Replication target would reproduce list of 6? Yes model comprising six genes. 120 DE-BRDRGs are upstream screen. Headline is six. Could \"total of 120 DE-BRDRGs were identified...\" be core analysis result. They might want 120 because this is single numerical result with exact \"total of 120\" and registered target main finding maybe number of DE-BRDRGs. But registered target wording in these tasks often generalized \"headline numerical result the paper itself states for its main finding\" and could refer to any main finding. We need decide.\n\nLet's consider prompts likely from benchmark designed extraction one headline number from article. They don't ask identify type, but registered target is semantic target. They include entire text and maybe no exact target metric specified. They expect choose headline numerical result. We need produce closest. Title risk model. Headline result six prognostic genes (KRT6A etc). \"single numerical result that a replication of registered target has to reproduce\" The target main finding is model. The number of prognostic genes is six. No performance values. Could also \"four key cell types\", \"12 immune function pathways\" etc. But six is main.\n\nNeed ensure evidence exact contiguous with number and attribution. \"A risk model comprising six prognostic genes (KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4) was constructed.\" Yes exact. Unit \"prognostic genes\" paper prints. number_stated true. target_result maybe \"The paper states that a risk model comprising six prognostic genes was constructed.\" Need not overly restate, no ambiguity. Could target_result \"Six prognostic genes (KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4) were used to construct the risk model.\" Is that adding content? No. Maybe \"The main risk model comprises six prognostic genes.\" Good.\n\nNeed adhere JSON valid. Should parent_value integer 6. Unit \"prognostic genes\". evidence one sentence. final only JSON. But before finalize let's deeply assess ambiguity as token budget huge but no need. We should consider what \"single numerical result\" means could be six prognostic genes count from abstract, good. The source says \"six prognostic genes\" spelled. They ask number value paper states not calculated converted. \"six\" -> 6 acceptable likely. Unit paper prints \"prognostic genes\". They may expect \"genes\" or \"prognostic genes\". Usually extraction tasks unit exact unit string. E.g \"risk model comprising six prognostic genes\". Unit \"prognostic genes\" fits.\n\nMaybe headline is \"120 DE-BRDRGs\" as their abstract: \"A total of 120 DE-BRDRGs were identified by overlapping 605 DEGs1 from four key cell types, 10,776 DEGs2, and 4,497 DEGs3.\" Then \"A risk model comprising six prognostic genes...\" It identifies primary target number \"six\" not 120. Main finding is construction of model, and replication requires six specific genes. 120 differentially expressed genes are intermediate. Good.\n\nWe can put target_result \"The paper states that the risk model comprises six prognostic genes: KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4.\" Does this violate only extract claims? All directly from source. Fine.\n\nQuestion \"Report the headline number only: the value the paper itself states for the registered target\" They want only number but also target_result and evidence. Should not include other numerical values in target_result. We'll only mention six. Fine.\n\nWe must not output markdown. final JSON.</think>\n\n{\"parent_value\":6,\"unit\":\"prognostic genes\",\"evidence\":\"A risk model comprising six prognostic genes (KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4) was constructed.\",\"target_result\":\"The paper states that the risk model comprises six prognostic genes: KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4.\",\"number_stated\":true}", "reading": {"parent_value": 6, "unit": "prognostic genes", "evidence": "A risk model comprising six prognostic genes (KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4) was constructed.", "target_result": "The paper states that the risk model comprises six prognostic genes: KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4.", "number_stated": true, "source_locator": "chars 1964-2067 of source sha256 b1e5d3e02b454389abd95f25691355a145ce4425d8240e22df488822dabd7d1c"}, "recorded_at": "2026-10-31T15:21:34.094479+00:00"}], "agreement": false, "disagreements": {"analysis_methods": {"minimax": ["univariate Cox regression", "least absolute shrinkage and selection operator (LASSO) regression", "multivariate Cox regression", "single-sample Gene Set Enrichment Analysis (ssGSEA)", "DESeq2 differential expression analysis", "Wilcoxon test", "Gene Set Enrichment Analysis (GSEA)", "Seurat", "Monocle3"], "minimax_alt": ["single-sample Gene Set Enrichment Analysis (ssGSEA)", "differential expression analysis using DESeq2", "Wilcoxon test", "intersection of DEG sets", "Gene Ontology (GO) enrichment analysis", "Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis", "protein-protein interaction (PPI) network analysis", "univariate Cox regression", "least absolute shrinkage and selection operator (LASSO) regression", "multivariate Cox regression", "Gene Set Enrichment Analysis (GSEA)", "principal component analysis (PCA)", "uniform manifold approximation and projection (UMAP)", "SingleR cell-type annotation", "CellChat", "nomogram analysis with calibration, ROC curves and decision curve analysis (DCA)", "maftools tumour mutational burden analysis", "TIDE analysis", "pRRophetic drug-sensitivity analysis", "Monocle3 pseudotime analysis"], "decisive": false}, "statistical_method": {"minimax": "univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses", "minimax_alt": "Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression", "decisive": true}, "parent_value": {"minimax": 120, "minimax_alt": 6, "decisive": true}, "unit": {"minimax": "DE-BRDRGs", "minimax_alt": "prognostic 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:266233a12a4a21ecad934f2b61e47591f4e36c780c7976bf3f3e40e64ced9e9e", "type": "sep_package", "label": "SEP v1 package for pmid:39827145"}], "activities": [{"id": "activity:conversion:pmid:39827145", "type": "conversion", "used": ["package:266233a12a4a21ecad934f2b61e47591f4e36c780c7976bf3f3e40e64ced9e9e"], "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:39827145", "parent_hash": "sha256:8f0a158c7961ff44f0d162539ad79e084023d363ce29e4664edc8c2df2801477", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:39827145 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:39827145' does not resolve to a declared asset", "targets"]}