Hypoxia-associated genes as predictors of outcomes in gastric cancer: a genomic approach.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40129974", "kind": "science-record", "revision": 1, "content_hash": "sha256:7161af8e4a2a08b1b93ce5abf683eea460785a1c8bd0869f2dea62c80c7a825c", "created_at": "2026-11-01T10:25:50Z", "updated_at": "2026-11-01T10:25:50Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Hypoxia-associated genes as predictors of outcomes in gastric cancer: a genomic approach.", "doi": "10.3389/fimmu.2025.1553477", "pmid": "40129974", "authors": null, "journal": "Frontiers in immunology", "year": 2025, "abstract": null, "claims": [{"id": "claim-1", "statement": "the headline numerical result the paper states for its main finding", "kind": "quantitative", "state": "complete", "target": {"value": 5.0, "unit": "h1-specific transcription factors", "tolerance": null, "source": "chars 1140-1365 of source sha256 50a61d69f0349e43c6cedb787e53a0ded80cf83b3748bc00ac2f794fd76e324e"}, "evidence": [{"asset": "source:pmid:40129974", "locator": "chars 1140-1365 of source sha256 50a61d69f0349e43c6cedb787e53a0ded80cf83b3748bc00ac2f794fd76e324e", "quote": "The prognostic model constructed by five H1-specific transcription factors EHF, EIF1AD, GLA, KEAPI, and MAGED2, was demonstrated efficacy in predicting overall survival (OS), with significantly worse OS in high-risk patients."}]}], "gaps": ["abstract", "authors"]}, "experiment": {"state": "partial", "objective": "the headline numerical result the paper states for its main finding", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate and its interval from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The number a replication must land on, and the sentence it came from.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40129974"], "outputs": ["the published estimate, its unit and its interval"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data behind the published result", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:not_downloaded", "description": "The published result cannot be recomputed without the data the paper analysed.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the analysis programme", "kind": "analysis", "state": "blocked", "blocked_on": "no owned computational capability is bound to this experiment", "description": "Re-run the paper's analysis on the source data and produce the registered result. | SEP input not carried into the asset list: materials:the source data behind the published result in pmid:40129974", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the registered result"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["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:40129974' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40129974", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2025.1553477", "sha256": "sha256:50a61d69f0349e43c6cedb787e53a0ded80cf83b3748bc00ac2f794fd76e324e", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2025.1553477", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}], "gaps": ["asset:source:pmid:40129974 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:40129974"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40129974"]}, "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": "0cb26ad17c3d5c4080dd2dd338f71167ff74728d49484fca0efd6d58d78f5d3c", "source_hash": "50a61d69f0349e43c6cedb787e53a0ded80cf83b3748bc00ac2f794fd76e324e", "raw_response_sha256": "sha256:74e1d18f5389e9e4bf50aee4336c1d7121c56f1a0623f38a977d884dcb2d5ebb", "raw_response_chars": 3879}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "d607631e26bb8e4c6384e6357c69623eb74afd4d47eaf723f66250a3bf5cad5f", "source_hash": "50a61d69f0349e43c6cedb787e53a0ded80cf83b3748bc00ac2f794fd76e324e", "raw_response_sha256": "sha256:314654c281c3663f81fe465812f670ce06d66b78d605b6c3264af76fb1e70f05", "raw_response_chars": 46461}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "18441fea4558cdb2b16d99b8a0f2a2735618526856661e8a715615d6e674512e", "source_hash": "50a61d69f0349e43c6cedb787e53a0ded80cf83b3748bc00ac2f794fd76e324e", "raw_response": "{\"parent_value\":5,\"unit\":\"H1-specific transcription factors\",\"evidence\":\"The prognostic model constructed by five H1-specific transcription factors EHF, EIF1AD, GLA, KEAPI, and MAGED2, was demonstrated efficacy in predicting overall survival (OS), with significantly worse OS in high-risk patients.\",\"target_result\":\"The prognostic model was constructed using five H1-specific transcription factors: EHF, EIF1AD, GLA, KEAPI, and MAGED2.\",\"number_stated\":true}", "reading": {"parent_value": 5, "unit": "H1-specific transcription factors", "evidence": "The prognostic model constructed by five H1-specific transcription factors EHF, EIF1AD, GLA, KEAPI, and MAGED2, was demonstrated efficacy in predicting overall survival (OS), with significantly worse OS in high-risk patients.", "target_result": "The prognostic model was constructed using five H1-specific transcription factors: EHF, EIF1AD, GLA, KEAPI, and MAGED2.", "number_stated": true, "source_locator": "chars 1140-1365 of source sha256 50a61d69f0349e43c6cedb787e53a0ded80cf83b3748bc00ac2f794fd76e324e"}, "recorded_at": "2026-10-31T15:35:44.657001+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "d8c720775e6a8937cba6279dfc9539116d49c731582effca2030b82a6f1b835f", "source_hash": "50a61d69f0349e43c6cedb787e53a0ded80cf83b3748bc00ac2f794fd76e324e", "raw_response": "<think>We need answer JSON only. Need interpret target: headline numerical result paper states for main finding. Supplied source has many numbers, but likely headline number is five H1-specific transcription factors? Need identify \"single numerical result that a replication of registered target has to reproduce.\" Registered target generic \"the headline numerical result the paper states for its main finding.\" Main finding prognostic model constructed by five genes. Headline number likely 5, unit perhaps \"genes\" or \"H1-specific transcription factors\". Need exact contiguous quotation containing number, and if attribution via list/respectively/words target direct, quote whole clause. Source says \"The prognostic model constructed by five H1-specific transcription factors EHF, EIF1AD, GLA, KEAPI, and MAGED2, was demonstrated efficacy in predicting overall survival (OS), with significantly worse OS in high-risk patients.\" Number is written word \"five\", but parent_value must number. Evidence exact quote with \"five\". Unit the paper prints: Does paper print a unit? It says \"five H1-specific transcription factors\"; unit could \"H1-specific transcription factors\". User asks unit must be unit paper prints, not calculated. Headline numerical result perhaps 5? Number stated yes, despite textual numeral \"five\", numerical result. parent_value 5. Evidence exact contiguous clause. target_result perhaps \"The prognostic model was constructed by five H1-specific transcription factors.\" Need make JSON valid. But consider perhaps headline result is survival metric (HR, P value) absent from source. Main abstract states model and perhaps no numerical outcome. The registered target asks \"headline numerical result the paper states for its main finding.\" Could be count 5 model genes. They want one numerical result. The supplied text headline finding: five H1-specific TF. likely answer 5 genes.\n\nNeed be careful exact evidence quote. They insist \"do not shorten it\", quote whole clause when attribution. Could quote sentence entire? \"The prognostic model constructed by five H1-specific transcription factors EHF, EIF1AD, GLA, KEAPI, and MAGED2, was demonstrated efficacy in predicting overall survival (OS), with significantly worse OS in high-risk patients.\" Whole clause perhaps sufficient. Unit \"H1-specific transcription factors\" as paper's wording. Parent number 5. target_result perhaps \"The prognostic model was constructed by five H1-specific transcription factors: EHF, EIF1AD, GLA, KEAPI, and MAGED2.\" No unsupported. JSON only.\n\nQuestion nuance: \"headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed. The unit must be the unit the paper prints.\" Maybe they expect numerical result from abstract, perhaps \"five\". Unit may be \"transcription factors\". Exact quote contains number word. JSON schema number_stated. Could parent_value 5.\n\nCould there be another headline number: top 1%, 106 and 85 TFs, 44 compounds, four hypo/four non-hypo. Main finding is prognostic model built using five genes, so 5.\n\nNeed perhaps no unit if count doesn't have printed unit? But \"five H1-specific transcription factors\" clearly unit. Use \"H1-specific transcription factors\". target_result should state what it is. Ensure evidence contiguous exact source. We can quote the sentence exactly. Avoid altered punctuation. Source uses \"The prognostic model constructed by five H1-specific transcription factors EHF, EIF1AD, GLA, KEAPI, and MAGED2, was demonstrated efficacy in predicting overall survival (OS), with significantly worse OS in high-risk patients.\" Use exact. target_result could reproduce concise. JSON escaping no issues.\n\nLet's formulate:\n{\n \"parent_value\":5,\n \"unit\":\"H1-specific transcription factors\",\n \"evidence\":\"The prognostic model constructed by five H1-specific transcription factors EHF, EIF1AD, GLA, KEAPI, and MAGED2, was demonstrated efficacy in predicting overall survival (OS), with significantly worse OS in high-risk patients.\",\n \"target_result\":\"The prognostic model consists of five H1-specific transcription factors: EHF, EIF1AD, GLA, KEAPI, and MAGED2.\",\n \"number_stated\":true\n}\nDoes \"headline number only\" mean target_result should maybe be \"5 H1-specific transcription factors\" rather than sentence. Fine. Need perhaps unit exactly \"H1-specific transcription factors\" and not lowercase. Great.</think>\n\n{\"parent_value\":5,\"unit\":\"H1-specific transcription factors\",\"evidence\":\"The prognostic model constructed by five H1-specific transcription factors EHF, EIF1AD, GLA, KEAPI, and MAGED2, was demonstrated efficacy in predicting overall survival (OS), with significantly worse OS in high-risk patients.\",\"target_result\":\"The prognostic model consists of five H1-specific transcription factors: EHF, EIF1AD, GLA, KEAPI, and MAGED2.\",\"number_stated\":true}", "reading": {"parent_value": 5, "unit": "H1-specific transcription factors", "evidence": "The prognostic model constructed by five H1-specific transcription factors EHF, EIF1AD, GLA, KEAPI, and MAGED2, was demonstrated efficacy in predicting overall survival (OS), with significantly worse OS in high-risk patients.", "target_result": "The prognostic model consists of five H1-specific transcription factors: EHF, EIF1AD, GLA, KEAPI, and MAGED2.", "number_stated": true, "source_locator": "chars 1140-1365 of source sha256 50a61d69f0349e43c6cedb787e53a0ded80cf83b3748bc00ac2f794fd76e324e"}, "recorded_at": "2026-10-31T15:35:50.192059+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "A prognostic model constructed from five H1-specific transcription factors—EHF, EIF1AD, GLA, KEAPI, and MAGED2—was reported to predict overall survival (OS), with significantly worse OS in high-risk patients.", "minimax_alt": "A five-gene prognostic model based on the H1-specific transcription factors EHF, EIF1AD, GLA, KEAPI, and MAGED2 predicted overall survival, with significantly worse OS in high-risk patients.", "decisive": false}, "physical_methods": {"minimax": ["qRT-PCR analysis"], "minimax_alt": ["Total RNA extraction", "quantitative real-time PCR (qRT-PCR)"], "decisive": false}, "analysis_methods": {"minimax": ["scRNA-seq analysis", "CHPF software", "WGCNA", "GO-BP enrichment analysis", "KEGG enrichment analysis", "LassoCox algorithm", "Cox regression analysis", "Kaplan-Meier survival analysis"], "minimax_alt": ["Single-cell RNA sequencing (scRNA-seq)", "CHPF software for predicting cellular hypoxia status", "UMAP and t-SNE dimensionality reduction and clustering", "WGCNA", "GO-BP and KEGG enrichment analysis", "GSVA activity-score analysis", "transcription factor analysis", "LassoCox algorithm for prognostic-model development", "Cox regression analysis", "Kaplan-Meier survival analysis"], "decisive": false}, "statistical_method": {"minimax": "Cox regression analysis and Kaplan-Meier survival analysis", "minimax_alt": "Cox regression analysis and Kaplan-Meier survival analysis using the survival package in R", "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:b8216902c1f4de56839ef15a804083439f7caee2b9050a5b603fb90de225d00f", "type": "sep_package", "label": "SEP v1 package for pmid:40129974"}], "activities": [{"id": "activity:conversion:pmid:40129974", "type": "conversion", "used": ["package:b8216902c1f4de56839ef15a804083439f7caee2b9050a5b603fb90de225d00f"], "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": "f78a4451-214a-4fb4-b872-06c025e0775d", "parent_hash": "b8216902c1f4de56839ef15a804083439f7caee2b9050a5b603fb90de225d00f", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40129974 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:40129974' does not resolve to a declared asset", "targets"]}