Development and validation of an immune-related gene signature for the prognostic and immune landscape prediction in head and neck squamous cell carcinoma by integrated analysis of machine learning and Mendelian randomization.

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["Female", "133 (26.03)", "47 (17.60)"], ["Male", "378 (73.97)", "220 (82.40)"], ["Histologic grade", "", ""], ["G1–2", "359 (70.25)", "–"], ["G3–4", "130 (25.44)", "–"], ["Unknown", "22 (4.31)", "–"], ["T stage", "", ""], ["T1–2", "180 (35.23)", "114 (42.70)"], ["T3–4", "268 (52.45)", "153 (57.30)"], ["Unknown", "63 (12.33)", "–"], ["N stage", "", ""], ["N0", "172 (33.66)", "93 (34.83)"], ["N1–3", "239 (46.77)", "174 (65.17)"], ["Unknown", "100 (19.57)", "–"], ["Metastasis status", "", ""], ["Yes", "1 (0.20)", "–"], ["No", "181 (35.42)", "–"], ["Unknown", "329 (64.38)", "–"], ["Clinical stage", "", ""], ["Stage I–II", "97 (18.98)", "54 (20.22)"], ["Stage III–IV", "339 (66.34)", "213 (79.78)"], ["Unknown", "75 (14.68)", "–"], ["Survival status", "", ""], ["Dead", "214 (41.88)", "91 (34.08)"], ["Alive", "297 (58.12)", "176 (65.92)"]], "row_count": 30, "column_count": 3, "numeric_cells": 41, "header_rows": 1, "locator": "section[4]/table-wrap[1]#t1"}}], "gaps": ["asset:source:pmid:40950700 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:40950700", "asset:table:pmid:40950700:1"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40950700"]}, "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": "037aa7b59a0163df981f7a2e1ebe9e1fabc4b4ff4d1e83ae6c52b5dac8a43e61", "source_hash": "bf593054b21be92025c7c550b61793bbc6e61c81f15a72659525e813040ddc6c", "raw_response_sha256": "sha256:ad2787b47c15d38ff7576e3d1f747c3e7b57946aa16a4fd2eaac48350579da9a", "raw_response_chars": 5053}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "2627bb857708fdb860f511c902ca01e2e58249a6c9d7cab079532866de3978ad", "source_hash": "bf593054b21be92025c7c550b61793bbc6e61c81f15a72659525e813040ddc6c", "raw_response_sha256": "sha256:906f98643ed30b17a659d1c3ede080843d6c832f19e6eb8135306fd432207220", "raw_response_chars": 35131}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "1b17cdfe1d5dcc2a51556eb185bb58dc6cb9bd4236c388dafd747f4a5c2e497e", "source_hash": "bf593054b21be92025c7c550b61793bbc6e61c81f15a72659525e813040ddc6c", "raw_response": "{\"parent_value\":3.647,\"unit\":\"hazard ratio\",\"evidence\":\"The multivariate prognostic analysis showed that the risk score was an independent prognostic factor associated with HNSCC (hazard ratio =3.647, P<0.001).\",\"target_result\":\"The risk score was an independent prognostic factor associated with HNSCC (hazard ratio =3.647, P<0.001).\",\"number_stated\":true}", "reading": {"parent_value": 3.647, "unit": "hazard ratio", "evidence": "The multivariate prognostic analysis showed that the risk score was an independent prognostic factor associated with HNSCC (hazard ratio =3.647, P<0.001).", "target_result": "The risk score was an independent prognostic factor associated with HNSCC (hazard ratio =3.647, P<0.001).", "number_stated": true, "source_locator": "chars 1831-1985 of source sha256 bf593054b21be92025c7c550b61793bbc6e61c81f15a72659525e813040ddc6c"}, "recorded_at": "2026-10-31T15:35:39.273614+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "c0f53a9d282b6c6ea272ec50e740610f5328463a37adf080ccc3c8d52c728d7e", "source_hash": "bf593054b21be92025c7c550b61793bbc6e61c81f15a72659525e813040ddc6c", "raw_response": "<think>We need answer JSON object only. Need identify single numerical result headline for registered target = headline numerical result paper states for main finding. Need infer which number. Source has several candidates: 10 IRGs, AUCs 0.694, .731, .656, hazard ratio 3.647. Headline numerical result likely hazard ratio = 3.647? User asks \"single numerical result that a replication of the registered target has to reproduce\" and registered target \"the headline numerical result the paper itself states for the main finding.\" Need report headline number only, paper stated, not calculated. Evidence exact contiguous quote containing number. Likely 3.647, unit \"hazard ratio\" perhaps. Need decide.\n\nCould be \"Ten IRGs\" but headline numerical result? Main finding is novel immune-related gene signature; ten IRGs. A replication target likely signature's performance? The abstract highlights 10 IRGs and AUCs and HR. \"headline numerical result\" often asks identify a specific primary numerical outcome. Here likely hazard ratio 3.647. Need not overthink? Let's parse wording. \"REGISTERED TARGET: the headline numerical result the paper itself states for its main finding\". They want one number, unit. Evidence exact quote. The main finding includes signature; key finding no number. In Results first number \"Ten IRGs were identified...\" This is arguably the headline count of main finding. But \"headline numerical result\" likely refers to the number reported in abstract perhaps hazard ratio. Which one singular?\n\nLet's inspect title and abstract. Main finding: risk score independent prognostic factor associated with HNSCC (hazard ratio =3.647, P&lt;0.001). The headline quantitative performance likely HR. AUC list has 3 values, violates single number unless choose best .731? Instructions explicitly mention attribution via parenthetical list, respectively — perhaps designed to handle multi-value AUC sentence? Yet request \"single numerical result.\" Maybe they expect e.g. number can be attached by parenthetical list. Here perhaps target is \"the headline numerical result\" and candidate is HR 3.647. Parenthetical list mention generic benchmark instruction. Need output evidence whole clause when attribution indirect. Evidence could be exact sentence: \"The multivariate prognostic analysis showed that the risk score was an independent prognostic factor associated with HNSCC (hazard ratio =3.647, P&lt;0.001).\" Unit \"hazard ratio\". parent_value 3.647. target_result maybe concise \"Hazard ratio of 3.647 for the risk score as an independent prognostic factor associated with HNSCC.\" They say \"Report the headline number only\" likely target_result should just number/unit, no extraneous values. Need avoid mentioning P. Evidence necessarily includes P, okay. Unit paper prints? It prints \"hazard ratio\" perhaps unit dimensionless. Could say \"hazard ratio\" rather than \"HR\". JSON valid.\n\nBut let's thoroughly assess possibility that expected number is \"Ten\" / \"10 IRGs\". Registered target wording could point to \"the headline numerical result the paper itself states for its main finding\" and source's headline: \"Ten IRGs were identified and incorporated into the predictive signature.\" This may be more headline than HR. Yet replication of \"registered target\" requires reproduce the main finding, likely model composed of 10 genes. A headline number might be AUC at 1,3,5 years, but that's three. They insist single numerical result, perhaps choose \"hazard ratio = 3.647\". Why mention \"single\" if source has multiple? It's designed to disambiguate only one headline result. Hazard ratio explicitly called result in abstract.\n\nLet's understand task schema semantics: `parent_value` weird naming maybe standardized benchmark where target could be \"hazard ratio\" and parent_value numerical. They ask number stated. \"REGISTERED TARGET: the headline numerical result the paper itself states for its main finding.\" They may expect extraction based on target description rather than specific field. We need identify best.\n\nMaybe this benchmark asks \"replication of registered target\" where a registered report has one target? Here target is broad. Headline numerical result usually from abstract's conclusion? In abstracts, quantitative result frequently hazard ratio. The primary claim is prognostic signature; performance measures: 10 IRGs, AUC at years, HR. Which is headline? The first sentence in results says 10 IRGs; the \"headline numerical result\" could indeed be 10. But replication of main finding would need reproduce signature and its reported HR. Let's inspect abstract structure:\n- Objective\n- Methods data counts\n- Results: Ten IRGs... AUC ... KM ... multivariate ... HR=3.647...\n- Conclusion.\nMain quantitative claim probably HR because prognostic significance. The number 10 is model composition, not effect result.\nQuestion says \"headline numerical result\" singular, likely asks effect estimate. \"value the paper itself states for the registered target\" likely HR.\nEvidence must exact contiguous quotation containing number. We can quote full sentence, exact punctuation. Unit must paper prints. \"hazard ratio\" is label. Could set unit \"hazard ratio\". Is that a unit? Hazard ratio is dimensionless measure, but prompt allows unit as printed. Alternatively \"hazard ratio =3.647\" means value 3.647 and unit \"hazard ratio\". Usually output parent_value number and unit \"hazard ratio\". We should not use \"3.647\" in target_result plus unit perhaps.\n\nCould output:\n{\"parent_value\":3.647,\"unit\":\"hazard ratio\",\"evidence\":\"The multivariate prognostic analysis showed that the risk score was an independent prognostic factor associated with HNSCC (hazard ratio =3.647, P<0.001).\",\"target_result\":\"3.647 hazard ratio\",\"number_stated\":true}\nDoes \"headline number only\" mean target_result should only be number perhaps \"3.647\". They ask report headline number only, likely no extra. JSON has fields. Could set target_result \"3.647 (hazard ratio)\" but evidence enough. Maybe target_result is prose for if false; when true likely number/unit. Schema no guidance. To be safe target_result: \"3.647\" only. But then unit separately. Evidence sentence includes attribution. \"headline number only\" perhaps target_result \"3.647 hazard ratio\". They explicitly say report headline number only: value and unit. Let's make \"3.647 (hazard ratio)\"? Number only might mean don't give all AUCs. Fine.\n\nNeed ensure exact source quote. Source sentence exact: \"The multivariate prognostic analysis showed that the risk score was an independent prognostic factor associated with HNSCC (hazard ratio =3.647, P<0.001).\" Yes.\n\nCould there be space around equals? Printed \"hazard ratio =3.647\". Exact quote must preserve. Fine.\n\nLet's think if they instead expect 10 IRGs. We need maximize. Analyze linguistic cue \"headline numerical result the paper itself states for its main finding.\" If target were 10 genes, unit \"IRGs\", evidence \"Ten IRGs were identified and incorporated into the predictive signature.\" But parent_value needs number; \"Ten\" is textual numeral, but instruction \"single numerical result\" and \"number paper states\" could convert word to 10? They explicitly say don't convert; \"value the paper itself states\" perhaps if number printed as \"Ten\", parent_value 10 still numeric representation necessarily converted. They might want 10. Yet \"headline number\" typically 10 IRGs is first result. But source's \"main finding\" is the IRG signature. The count of IRGs is perhaps central headline. Hazard ratio is also central.\n\nCould phrase target result be \"the headline numerical result the paper itself states for its main finding\"—perhaps generated target mapping from scientific paper benchmark where registered target is \"headline numerical result\". They want identify one. The abstract's headline numeric claim often the central effect. For prognostic papers, hazard ratio is likely.\n\nLook at \"unit must be the unit the paper prints.\" Hazard ratio is not a physical unit but measure label. \"Ten IRGs\" has unit IRGs. AUC values have unit AUC. Which one seems likely given instruction on parenthetical list and \"respectively\"? Maybe they intentionally included that because source sentence with AUC list might tempt returning multiple values. The target could be AUC perhaps \"the area under ROC curves for overall survival at 1, 3, and 5 years\" but registered target generic, not specify year. Thus impossible to choose one among three, indicating not AUC.\n\nThey mention \"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.\" This generic instruction would apply to HR in parenthetical \"(hazard ratio =3.647, P&lt;0.001)\"—indeed direct naming within parentheses. For AUC sentence, each value attached by respectively and time points. But target perhaps could be \"headline numerical result\" as one of them? No.\n\nMaybe headline number is \"hazard ratio =3.647\" because it's directly parenthetical and singular. Good.\n\nNeed only JSON, no markdown. Let's ensure number_stated boolean. parent_value 3.647. unit perhaps \"hazard ratio\". target_result \"3.647\". But target_result should likely describe result? They say \"Report the headline number only\" so \"3.647\" is apt. Evidence has full exact quote. Unit separate.\n\nLet's answer.</think>\n\n{\"parent_value\":3.647,\"unit\":\"hazard ratio\",\"evidence\":\"The multivariate prognostic analysis showed that the risk score was an independent prognostic factor associated with HNSCC (hazard ratio =3.647, P<0.001).\",\"target_result\":\"3.647\",\"number_stated\":true}", "reading": {"parent_value": 3.647, "unit": "hazard ratio", "evidence": "The multivariate prognostic analysis showed that the risk score was an independent prognostic factor associated with HNSCC (hazard ratio =3.647, P<0.001).", "target_result": "3.647", "number_stated": true, "source_locator": "chars 1831-1985 of source sha256 bf593054b21be92025c7c550b61793bbc6e61c81f15a72659525e813040ddc6c"}, "recorded_at": "2026-10-31T15:35:47.323325+00:00"}], "agreement": true, "disagreements": {"target_result": {"minimax": "A 10-IRG prognostic signature was established; the risk score was an independent prognostic factor for HNSCC with a hazard ratio of 3.647 (P<0.001).", "minimax_alt": "The multivariate prognostic analysis showed that the risk score was an independent prognostic factor associated with HNSCC (hazard ratio =3.647, P<0.001).", "decisive": false}, "analysis_methods": {"minimax": ["machine learning algorithms", "Kaplan-Meier survival analysis", "multivariate prognostic analysis", "receiver operating characteristic curve analysis", "subgroup analysis", "Wilcoxon signed-rank test", "Chi-squared test", "Pearson correlation analysis", "gene set enrichment analysis", "single-sample gene set enrichment analysis", "CIBERSORT", "Tumor Immune Dysfunction and Exclusion score analysis", "Immunophenoscore analysis", "Mendelian randomization", "MR-Egger intercept examination", "Cochran Q test for heterogeneity", "leave-one-out sensitivity analysis"], "minimax_alt": ["machine learning algorithms", "area under the receiver operating characteristic curves", "Kaplan-Meier survival analysis", "multivariate prognostic analysis", "subgroup analysis", "Mendelian randomization (MR)", "gene set enrichment analysis (GSEA)", "single-sample GSEA (ssGSEA)", "CIBERSORT", "Wilcoxon signed-rank test", "Chi-squared test", "Pearson correlation analysis", "leave-one-out sensitivity analysis", "MR-Egger", "IVW", "Cochran Q test"], "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:5d83b9e8c7582e4866577ce291988a26ab166e50ba06c0ebfc74aeb4e309e92c", "type": "sep_package", "label": "SEP v1 package for pmid:40950700"}], "activities": [{"id": "activity:conversion:pmid:40950700", "type": "conversion", "used": ["package:5d83b9e8c7582e4866577ce291988a26ab166e50ba06c0ebfc74aeb4e309e92c"], "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:40950700", "parent_hash": "sha256:29f4ce93c8b092fd745817053090ff5f69499511426fea617731dbacb3be82aa", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40950700 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:40950700' does not resolve to a declared asset", "targets"]}