Identification of anoikis-related genes to develop a risk model and predict the prognosis and tumor microenvironment in rectal adenocarcinoma.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40901678", "kind": "science-record", "revision": 1, "content_hash": "sha256:af5988ad362c35154d6e109e312949bd3ec4a65d9ffa75e1d01d5cdb2702ba87", "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": "Identification of anoikis-related genes to develop a risk model and predict the prognosis and tumor microenvironment in rectal adenocarcinoma.", "doi": "10.3389/fgene.2025.1604541", "pmid": "40901678", "authors": null, "journal": "Frontiers in genetics", "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": 6.0, "unit": "prognostic genes", "tolerance": null, "source": "chars 875-962 of source sha256 7df54a0e3979f34c2e8868399a313639c91c0ab15323e5beac9aee02a41ac4a6"}, "evidence": [{"asset": "source:pmid:40901678", "locator": "chars 875-962 of source sha256 7df54a0e3979f34c2e8868399a313639c91c0ab15323e5beac9aee02a41ac4a6", "quote": "The risk model included 6 prognostic genes (ALDH1A1, BRCA1, GSN, KRT17, SCD, and SNCG)."}]}], "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:40901678"], "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:40901678", "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:40901678' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40901678", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2025.1604541", "sha256": "sha256:7df54a0e3979f34c2e8868399a313639c91c0ab15323e5beac9aee02a41ac4a6", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2025.1604541", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}], "gaps": ["asset:source:pmid:40901678 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:40901678"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40901678"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "passed", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "c68b7979c2cbcc45fc00b865ac84801198f7239c4d55c2931c91b5f8e91f53db", "source_hash": "7df54a0e3979f34c2e8868399a313639c91c0ab15323e5beac9aee02a41ac4a6", "raw_response_sha256": "sha256:ca803a31702c8d4982b418170951818b63aba16e695cc907d697e0e950c2eb02", "raw_response_chars": 3755}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "39ebb3afaa5a2db6f88040fb5d8e03d03fe782e6616f1b5d780bcaf4a446731b", "source_hash": "7df54a0e3979f34c2e8868399a313639c91c0ab15323e5beac9aee02a41ac4a6", "raw_response_sha256": "sha256:d10aa6f8c268f009f78e97c4d45c25a382f171a6be56588d0ec81828c2b0a26b", "raw_response_chars": 73414}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "cfca79941bf038f98a0762ff418200bc31fd7cfe1fc9df8dce69d3d18d1cbb9f", "source_hash": "7df54a0e3979f34c2e8868399a313639c91c0ab15323e5beac9aee02a41ac4a6", "raw_response": "{\"parent_value\":6,\"unit\":\"prognostic genes\",\"evidence\":\"The risk model included 6 prognostic genes (ALDH1A1, BRCA1, GSN, KRT17, SCD, and SNCG).\",\"target_result\":\"The risk model included 6 prognostic genes: ALDH1A1, BRCA1, GSN, KRT17, SCD, and SNCG.\",\"number_stated\":true}", "reading": {"parent_value": 6, "unit": "prognostic genes", "evidence": "The risk model included 6 prognostic genes (ALDH1A1, BRCA1, GSN, KRT17, SCD, and SNCG).", "target_result": "The risk model included 6 prognostic genes: ALDH1A1, BRCA1, GSN, KRT17, SCD, and SNCG.", "number_stated": true, "source_locator": "chars 875-962 of source sha256 7df54a0e3979f34c2e8868399a313639c91c0ab15323e5beac9aee02a41ac4a6"}, "recorded_at": "2026-10-31T15:34:40.480316+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "7d59a44513ec95deb578b6bcdd0e2687637d70fc0c1ff454f885461b75057347", "source_hash": "7df54a0e3979f34c2e8868399a313639c91c0ab15323e5beac9aee02a41ac4a6", "raw_response": "<think>We need answer JSON object only. Need interpret task carefully. Need find single numerical result replication registered target headline numerical result paper states for main finding. Target vague \"headline numerical result the paper states for its main finding.\" Need identify likely number: risk model included 6 prognostic genes? Or AUC values? They ask single numerical result paper itself states for registered target. We need infer \"headline numerical result\" main finding. Source abstract: model included 6 prognostic genes, Kaplan-Meier P values, AUCs. Which single numerical result headline? The \"risk model included 6 prognostic genes\" perhaps main finding numerical. Or perhaps model predicts prognosis with AUC? They insist single numerical result, headline number only, value paper states, unit paper prints. Registered target isn't specific metric beyond headline numerical result. Need determine likely desired extraction.\n\nLet's parse wording: \"Read supplied paper text and find the single numerical result that a replication of the registered target has to reproduce. Report the 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. ... Attribution may be carried by the text's own wording: a number may be attached to the target by a parenthetical list, by the word 'respectively', or by words that name the target directly. When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves. If the text states no number for registered target, set number_stated false ...\"\n\nRegistered target: \"the headline numerical result the paper states for its main finding\". Main finding is ARGs-based prognostic risk model. Headline numerical result could be 6 prognostic genes. This is single headline number directly in abstract. AUC results multiple numbers; perhaps headline numerical result for model's predictive ability would be AUC 0.962 etc, but \"single numerical result\" perhaps 6 prognostic genes, likely.\n\nNeed output parent_value 6, unit perhaps \"prognostic genes\" (paper prints no physical unit, but count unit \"prognostic genes\"). Evidence exact contiguous quote containing number: \"The risk model included 6 prognostic genes (ALDH1A1, BRCA1, GSN, KRT17, SCD, and SNCG).\" Unit could \"prognostic genes\". target_result maybe \"The risk model included 6 prognostic genes.\" number_stated true.\n\nBut let's deeply assess. Could \"headline numerical result\" refer to count six, likely benchmark question generated to extract \"parent value\" from paper. Perhaps article's central result is risk model consisting six genes. They expect parent_value maybe 6, unit \"genes\", evidence sentence. The target's wording intentionally generic because target registry likely asks headline number. Could be AUC for main finding? Let's inspect title/abstract. Main numerical result likely model included 6 genes. They may ask \"single numerical result that a replication ... has to reproduce\" and the headline result is \"6 prognostic genes\". The abstract's first Results sentence says exactly. Rest metrics are supporting. The model's \"predictive ability\" has nine AUCs, no one single headline. So 6 is most salient standalone headline number.\n\nNeed JSON valid, no markdown. \"parent_value\":6, \"unit\":\"prognostic genes\" or \"genes\". The unit must be unit paper prints. The paper phrase \"6 prognostic genes\"; use \"prognostic genes\" perhaps. Evidence exact contiguous quotation, no shortening. We can quote full sentence. target_result perhaps \"The risk model included 6 prognostic genes.\" Should this quote? Fine. Ensure evidence exact. The evidence must contain number, and attribution attached. Full sentence does. Unit perhaps \"genes\" is printed in phrase, but requested unit paper prints. Since counts generally no explicit unit, \"prognostic genes\" makes sense. Could use \"genes\". target_result describe. Let's ensure no extra props.</think>\n\n{\"parent_value\":6,\"unit\":\"prognostic genes\",\"evidence\":\"The risk model included 6 prognostic genes (ALDH1A1, BRCA1, GSN, KRT17, SCD, and SNCG).\",\"target_result\":\"The risk model included 6 prognostic genes.\",\"number_stated\":true}", "reading": {"parent_value": 6, "unit": "prognostic genes", "evidence": "The risk model included 6 prognostic genes (ALDH1A1, BRCA1, GSN, KRT17, SCD, and SNCG).", "target_result": "The risk model included 6 prognostic genes.", "number_stated": true, "source_locator": "chars 875-962 of source sha256 7df54a0e3979f34c2e8868399a313639c91c0ab15323e5beac9aee02a41ac4a6"}, "recorded_at": "2026-10-31T15:34:43.731791+00:00"}], "agreement": true, "disagreements": {"analysis_methods": {"minimax": ["Differential analysis", "Cox regression analysis", "Kaplan-Meier curve analysis", "Log-rank test", "ROC curve analysis"], "minimax_alt": ["Differential analysis", "Cox regression analysis", "Kaplan-Meier curve analysis", "Log-rank test", "ROC curve analysis", "Nomogram", "External validation in the GSE87211 dataset"], "decisive": false}}, "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": "passed", "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.passed"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:fa348d7a87901500ca348a12782e5c49bdd121e1ef97efb7d96be6e3a2b2984e", "type": "sep_package", "label": "SEP v1 package for pmid:40901678"}], "activities": [{"id": "activity:conversion:pmid:40901678", "type": "conversion", "used": ["package:fa348d7a87901500ca348a12782e5c49bdd121e1ef97efb7d96be6e3a2b2984e"], "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": "ca286c5a-c590-47f6-bdb4-d2f03f7fe01f", "parent_hash": "fa348d7a87901500ca348a12782e5c49bdd121e1ef97efb7d96be6e3a2b2984e", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40901678 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.passed", "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:40901678' does not resolve to a declared asset", "targets"]}