Development and validation of M2 macrophage-related genes in a prognostic model of lung adenocarcinoma based on bulk RNA and ScRNA datasets.

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"6add621ca15b659ac05188874ace932c8ec2b81fc6b9d472aa99b6b49799c157", "source_hash": "0e3a82341619e85d6418388acd465efebd3a004c73ec85b6cfcc467cafd5900b", "raw_response_sha256": "sha256:2b0049667127d88d848eb156bfc5f9da03ea48b67e32cf28463cc6ec89cf204d", "raw_response_chars": 6252}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "3f018d578c07103d73a60de9ca76949d95194dfc276295dfca6e003a8796ac1a", "source_hash": "0e3a82341619e85d6418388acd465efebd3a004c73ec85b6cfcc467cafd5900b", "raw_response_sha256": "sha256:c114070973eafa287b2a59f7ffab2c600725e3bf3b56e5ee955516a9dfff80f4", "raw_response_chars": 51665}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "f3215bc65a28ed6aba4c0adfeb7ca425c100a3e2504c8f1044c07f0656fdb09f", "source_hash": "0e3a82341619e85d6418388acd465efebd3a004c73ec85b6cfcc467cafd5900b", "raw_response": "{\"parent_value\":4,\"unit\":\"key M2 macrophage–linked genes\",\"evidence\":\"We identified four key M2 macrophage–linked genes: TIMP1, CAV2, MIF, and SELENBP1.\",\"target_result\":\"The paper identifies four key M2 macrophage–linked genes: TIMP1, CAV2, MIF, and SELENBP1.\",\"number_stated\":true}", "reading": {"parent_value": 4, "unit": "key M2 macrophage–linked genes", "evidence": "We identified four key M2 macrophage–linked genes: TIMP1, CAV2, MIF, and SELENBP1.", "target_result": "The paper identifies four key M2 macrophage–linked genes: TIMP1, CAV2, MIF, and SELENBP1.", "number_stated": true, "source_locator": "chars 1157-1239 of source sha256 0e3a82341619e85d6418388acd465efebd3a004c73ec85b6cfcc467cafd5900b"}, "recorded_at": "2026-10-31T15:35:05.302129+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "3f344915b37aa1863a3a90298a35a9d70d9a93e351e85df5ef157995b8d080a7", "source_hash": "0e3a82341619e85d6418388acd465efebd3a004c73ec85b6cfcc467cafd5900b", "raw_response": "<think>We need answer JSON only. Need identify single numerical result headline stated for main finding / registered target. Source abstract: four key genes, survival high-risk lower across datasets p values. \"headline numerical result\" perhaps four? User asks replication of registered target has to reproduce, headline number only, value paper itself states for registered target. Registered target: headline numerical result paper states for main finding. Need infer likely number 4 key genes? Main finding identifies four key M2 macrophage-linked genes. Evidence exact contiguous quote containing number, whole clause. Unit perhaps \"genes\" or empty? They demand unit paper prints. Quote: \"We identified four key M2 macrophage–linked genes: TIMP1, CAV2, MIF, and SELENBP1.\" Number written \"four\", but parent_value number 4. Unit paper prints? It prints \"key M2 macrophage–linked genes\", could unit \"genes\". Headline numerical result perhaps this. But perhaps p=1.2 × 10–4 is headline numerical result for prognosis. Let's carefully parse wording.\n\nThey ask \"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.\" Registered target deliberately generic: \"the headline numerical result the paper states for its main finding.\" Need select from supplied text. The paper's main finding likely four key genes. Yet \"headline numerical result\" could be survival P in TCGA-LUAD = 1.2×10^-4. Which is numerical performance/result of prognostic model. Replication must reproduce headline p-value? Maybe target extraction task where registered target in hidden terms, and asks identify single numerical result. We need determine.\n\nCould be \"We identified four key M2 macrophage–linked genes\" central headline. Number \"four\" explicitly. Unit genes. Evidence quote exact. target_result describe. number_stated true. parent_value 4, unit \"genes\". Is \"four\" a numerical result? Yes.\n\nAlternatively, the abstract's concrete headline numerical result: \"Survival durations in the high-riskscore cluster were lower across the TCGA-LUAD (P = 1.2 × 10–4), GSE14814 (P = 0.02), and GSE37745 (P = 0.01) data sets.\" There are three numbers, not single. Registered target singular perhaps main finding refers risk model and headline number could p=1.2e-4 (TCGA primary dataset), but the text lists three. They ask single numerical result perhaps use \"four key genes\" because unique main count.\n\nNeed obey \"headline number only\" perhaps target_result should only number? Schema target_result string. Evidence quote exact. We can say \"Four M2 macrophage–linked genes (TIMP1, CAV2, MIF, and SELENBP1).\" But report headline number only perhaps unit/evidence enough. target_result maybe \"4 genes\". Ensure no invented. They specifically unit must be unit paper prints. \"genes\" printed. Exact quote contains word four, though numerical value converted from word to digit? \"value paper itself states\", parent_value must number JSON. That's okay, four is numerical value stated lexically. They said no value calculated/converted. Translating four to 4 may technically conversion? JSON requires number, so unavoidable. Likely accepted.\n\nCould unit be \"M2 macrophage–linked genes\" exact printed unit. Usually unit field expected e.g. \"genes\". Use \"genes\". Evidence exact full clause: \"We identified four key M2 macrophage–linked genes: TIMP1, CAV2, MIF, and SELENBP1.\" Contiguous exact punctuation? In source yes. Target result maybe \"The study identified four key M2 macrophage–linked genes: TIMP1, CAV2, MIF, and SELENBP1.\" This is paraphrase outside evidence, permitted? User asks target_result what text states if no number. For number stated perhaps target_result likely label. \"headline number only\" might mean target_result should simply say \"4 genes\". Need JSON valid.\n\nLet's assess whether unit empty could be required because \"four\" is count and unit \"genes\" explicit. no.\n\nCould \"four key genes\" be main finding, yes. Conclusion reiterates model constructed on basis of TIMP1, CAV2, MIF, SELENBP1 but without number. Abstract starts \"We identified four key...\" It's headline. The p-values are supporting findings. The requested \"single numerical result\" likely points exactly to count four.\n\nNeed not mention source DOI. Return JSON object only. Evidence exact contiguous. number_stated true. target_result maybe \"4 genes\". They said report headline number only; perhaps target_result should be \"4 genes\". Parent value 4, unit genes. Let's produce.</think>\n\n{\"parent_value\":4,\"unit\":\"genes\",\"evidence\":\"We identified four key M2 macrophage–linked genes: TIMP1, CAV2, MIF, and SELENBP1.\",\"target_result\":\"4 genes\",\"number_stated\":true}", "reading": {"parent_value": 4, "unit": "genes", "evidence": "We identified four key M2 macrophage–linked genes: TIMP1, CAV2, MIF, and SELENBP1.", "target_result": "4 genes", "number_stated": true, "source_locator": "chars 1157-1239 of source sha256 0e3a82341619e85d6418388acd465efebd3a004c73ec85b6cfcc467cafd5900b"}, "recorded_at": "2026-10-31T15:35:11.469441+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "The prognostic model identified four M2 macrophage–linked genes (TIMP1, CAV2, MIF, and SELENBP1); survival durations were lower in the high-risk-score group in TCGA-LUAD (P = 1.2 × 10–4), GSE14814 (P = 0.02), and GSE37745 (P = 0.01).", "minimax_alt": "We identified four key M2 macrophage–linked genes: TIMP1, CAV2, MIF, and SELENBP1. Survival durations in the high-riscore cluster were lower across the TCGA-LUAD (P = 1.2 × 10–4), GSE14814 (P = 0.02), and GSE37745 (P = 0.01) data sets.", "decisive": false}, "analysis_methods": {"minimax": ["Differential expression analysis", "Single-cell RNA sequencing quality control", "Batch-effect removal", "Cell annotation", "Cross-analysis of M2 macrophage-associated differentially expressed genes", "Machine learning for risk-model construction", "Risk-score computation", "Median-based high- and low-risk grouping", "Immune infiltration assessment using the Estimate and MCPcounter R packages", "Gene set enrichment analysis (GSEA; version 4.1.0)", "Drug-sensitivity prediction using the oncoPredict R package and GDSC"], "minimax_alt": ["Differentially expressed gene analysis", "Single-cell RNA-seq quality control, batch-effect removal, and annotation", "Cross-analysis of M2 macrophage–linked genes", "Machine-learning construction of a gene-based risk model", "Bulk RNA-seq evaluation of the risk model", "Risk-score stratification into low- and high-risk groups", "Survival comparison between risk groups (statistical test not specified in the supplied text)", "Immune infiltration analysis using the “Estimate” and “MCPcounter” R packages", "Gene set enrichment analysis (GSEA; version 4.1.0)", "Drug-sensitivity analysis using the “oncoPredict” R package and GDSC half-maximal drug suppression concentrations"], "decisive": false}, "unit": {"minimax": "key M2 macrophage–linked genes", "minimax_alt": "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:a30ca185d677c7dd03dea3c00d465bedafd2a77fe8aab1fa8284af30b1918268", "type": "sep_package", "label": "SEP v1 package for pmid:40100580"}], "activities": [{"id": "activity:conversion:pmid:40100580", "type": "conversion", "used": ["package:a30ca185d677c7dd03dea3c00d465bedafd2a77fe8aab1fa8284af30b1918268"], "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:40100580", "parent_hash": "sha256:05e959bb4ae42e04153dcbe97dd103e052809504d28c1b630cda33c6c11df1a6", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40100580 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:40100580' does not resolve to a declared asset", "targets"]}