A novel mast cell marker gene-related prognostic signature to predict prognosis and reveal the immune landscape in head and neck squamous cell carcinoma.

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"2.17", "4", "2.06", "11", "2.13", ""], ["Vital status", "", "", "", "", "", "", "0.896"], ["Alive", "184", "47.14", "112", "57.73", "296", "57.36", ""], ["Dead", "138", "42.86", "82", "42.27", "220", "42.64", ""]], "row_count": 26, "column_count": 8, "numeric_cells": 119, "header_rows": 2, "locator": "section[12]/table-wrap[1]#T1"}}, {"id": "asset:table:pmid:40703522:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:5636d70cb7c51ad7f467eb3654e5eeb9e0971545b5b326a7e53193493731158d", "bytes": 930, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[12]/table-wrap[2]#T2", "version": null, "note": "Fourteen MRGs associated with prognosis in HNSCC patients.", "structure": {"pmcid": "PMC12283675", "section_index": 12, "section_title": "14-MRGs prognostic signature construction and validation", "row_count": 15, "column_count": 4}, "table": {"label": "Table 2", "caption": "Fourteen MRGs associated with prognosis in HNSCC patients.", "rows": [["Gene", "Coefficient", "HR(95%CI)", "P value"], ["AREG", "0.405", "1.499 (1.004-2.240)", "0.048"], ["CD82", "0.733", "2.081 (1.410-3.070)", "0.000"], ["DAPK1", "0.885", "2.422 (1.387-4.230)", "0.002"], ["FDX1", "0.596", "1.815 (1.199-2.748)", "0.005"], ["GLUL", "0.853", "2.347 (1.405-3.918)", "0.001"], ["HS3ST1", "0.561", "1.752 (1.008-3.046)", "0.047"], ["LAT", "-0.749", "0.473 (0.275-0.813)", "0.007"], ["LIF", "0.893", "2.443 (1.574-3.792)", "0.000"], ["PTPN7", "-0.489", "0.613 (0.415-0.908)", "0.014"], ["RASGEF1B", "-0.542", "0.582 (0.389-0.871)", "0.008"], ["SLC18A2", "-0.620", "0.538 (0.339-0.854)", "0.009"], ["TBC1D14", "-0.731", "0.481 (0.325-0.713)", "0.000"], ["TMOD1", "0.550", "1.733 (1.144-2.623)", "0.009"], ["TPSD1", "-0.540", "0.583 (0.396-0.857)", "0.006"]], "row_count": 15, "column_count": 4, "numeric_cells": 46, "header_rows": 1, "locator": "section[12]/table-wrap[2]#T2"}}], "gaps": ["asset:source:pmid:40703522 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:40703522", "asset:table:pmid:40703522:1", "asset:table:pmid:40703522:2"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40703522"]}, "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": "405a5cd60ab6531b709bd23cec87d41921b2d171636b5eab2e30a2b47e1ed23b", "source_hash": "f35adf1c21e8c24dd0e6048a641b6228756c7275132296eb3d12d670c62e857d", "raw_response_sha256": "sha256:df2ad3ec0af0584be6e04261f2761f7d491b9168e2baeac831b1579636e45e0c", "raw_response_chars": 3889}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "c81b0e3b6c05c9138e6500d1d029ae16693892a7571022b8ed609391b706d937", "source_hash": "f35adf1c21e8c24dd0e6048a641b6228756c7275132296eb3d12d670c62e857d", "raw_response_sha256": "sha256:47707d99160e1a827656a0b88a1f185b0ecfc209359ec0ba7775b5aa5b8b51b2", "raw_response_chars": 60796}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "95d5b6fe906d5cbcfc8fc5b01b2525b6695164f1696648afa9c338a925851a4a", "source_hash": "f35adf1c21e8c24dd0e6048a641b6228756c7275132296eb3d12d670c62e857d", "raw_response": "{\"parent_value\":14,\"unit\":\"MRGs\",\"evidence\":\"The 14-MRGs prognostic signature classified patients into high- and low-risk groups, and the overall survival (OS) of the low-risk group was significantly higher than that of the high-risk group (p < 0.05).\",\"target_result\":\"The paper’s main finding is a prognostic signature based on 14 mast cell marker genes.\",\"number_stated\":true}", "reading": {"parent_value": 14, "unit": "MRGs", "evidence": "The 14-MRGs prognostic signature classified patients into high- and low-risk groups, and the overall survival (OS) of the low-risk group was significantly higher than that of the high-risk group (p < 0.05).", "target_result": "The paper’s main finding is a prognostic signature based on 14 mast cell marker genes.", "number_stated": true, "source_locator": "chars 1289-1495 of source sha256 f35adf1c21e8c24dd0e6048a641b6228756c7275132296eb3d12d670c62e857d"}, "recorded_at": "2026-10-31T15:22:12.727017+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "711dc979a13da0a3dc43a93bf599d6029a3cc301442eb025e564db401e40351c", "source_hash": "f35adf1c21e8c24dd0e6048a641b6228756c7275132296eb3d12d670c62e857d", "raw_response": "<think>We need carefully identify single numerical result headline main finding registered target. Need JSON only. We must reason ambiguities. Source title novel mast cell marker gene-related prognostic signature ... abstract: 14-MRGs prognostic signature. Main finding likely 14-MRGs prognostic signature. User asks \"single numerical result that a replication of the registered target has to reproduce. Report headline number only: value paper itself states for registered target, not calculated converted averaged reconstructed. unit unit paper prints. Evidence exact contiguous quote contains number. Attribution may parenthetical list respectively words naming target directly. When how text links quote whole clause. If no number registered target false...\" We need decide headline numerical result main finding likely 14? Or areas under ROC 0.740 0.737 0.707? We need parse wording. \"REGISTERED TARGET: the headline numerical result the paper states for its main finding\" Perhaps task built to identify main outcome e.g 14 MRGs? Need think deeply. We should extract main numerical result. Paper main finding is prognostic signature based on 14 mast cell related genes. Number 14. Unit? \"14-MRGs\" unit perhaps \"MRGs\" or \"mast cell marker genes\"? The paper prints \"14-MRGs\". They ask unit the paper prints, could unit empty because number is count? Could unit \"MRGs\". Headline number 14 with unit \"MRGs\" perhaps. Evidence exact quote from abstract includes linkage \"The 14-MRGs prognostic signature classified patients into high- and low-risk groups, and the overall survival (OS) of the low-risk group was significantly higher than that of the high-risk group (p < 0.05).\" Or conclusion \"We combined scRNA-seq data and bulk RNA-seq data to construct a 14-MRGs-based prognostic model capable of well predicting the prognosis of HNSCC patients.\" That directly target main finding. parent_value 14, unit \"MRGs\", evidence full sentence. target_result description maybe \"The paper states a 14-MRGs-based prognostic model for predicting HNSCC prognosis.\" number_stated true.\n\nBut is intended main finding 14-MRGs or AUC? Need not rush. We have huge token budget but can be concise final. Let's inspect instruction semantics: They designed prompt for numerical result extraction with target described vague \"the headline numerical result the paper states for its main finding\". Registered target might aim main finding e.g \"the areas under ROC curves 0.740, 0.737 and 0.707\" cannot single number? They explicitly single numerical result and number_stated false if no number. Maybe main finding is risk group OS higher and no effect size; then false? Need infer intended from source. It seems generic registered target phrase, perhaps benchmark tasks ask primary endpoint extraction with \"headline numerical result the paper itself states for its main finding\". We need choose likely. Many previous similar tasks? We have only prompt. Could reason based main result abstract and conclusion. Abstract states:\n\"We identified mast cell marker genes ... construct prognostic signature ...\"\n\"The 14-MRGs prognostic signature classified patients into high- and low-risk groups, and the overall survival (OS) of the low-risk group was significantly higher than that of the high-risk group (p < 0.05). The areas under the ROC curves of the nomogram were 0.740, 0.737 and 0.707 at 1-, 3-, and 5-year, and they also showed better detection efficacy in the validation group than other independent predictors...\"\n\"We combined ... construct a 14-MRGs-based prognostic model...\"\nThe primary finding perhaps 14 gene model, with 14 count. Headline numerical result is 14 genes. We can quote conclusion to attach. Unit \"MRGs\" printed. They want \"value paper itself states for registered target\" no computed etc. 14 directly stated. Should use exact contiguous quote sentence. Could use \"In this study, we applied scRNA-seq technology to identify MRGs, constructed a 14-MRGs prognostic model for HNSCC, and revealed the immune infiltration and landscape.\" That's clear names model number. unit \"MRGs\" (mast-cell-related genes). Evidence whole clause. parent_value 14.\n\nCould alternatively report AUC \"0.740\" no unit, first 1-year. They request \"single numerical result\" perhaps 0.740 with unit no unit. But target main finding could be \"prognostic performance of nomogram\" and AUC triple impossible one. They would likely use target \"one-year AUC\" if so. Instead target broad main finding, so unique number identifying model 14. I think correct.\n\nNeed decide target_result wording. Could state \"The paper's main finding is a 14-MRGs prognostic signature/model that separates patients into high- and low-risk groups, with longer overall survival in the low-risk group.\" Evidence selected exact sentence with 14. They request \"Report headline number only: ... value...\" Might imply target_result should be succinct just headline number and unit, no other numbers. Usually schema target_result maybe textual explanation. Could use \"14 MRGs\" or \"A prognostic signature based on 14 mast-cell-related genes.\" Better.\n\nUnit must unit paper prints. \"MRGs\" appears in hyphen \"14-MRGs\". Unit \"MRGs\" appropriate, not \"genes\" because paper abbreviates MRGs. Could write \"MRGs\" exactly. Evidence \"We combined scRNA-seq data and bulk RNA-seq data to construct a 14-MRGs-based prognostic model capable of well predicting the prognosis of HNSCC patients.\" Exact contiguous from source. Good.\n\nBut wait instruction \"The unit must be the unit the paper prints.\" Counts often unit absent; \"14-MRGs\" could \"MRGs\". We should output unit \"MRGs\" and value 14. Is \"14-MRGs\" an adjectival compound not unit? Still gene count unit MRGs. Can choose unit \"MRGs\". Main result may \"14\" count.\n\nMaybe \"14-MRGs\" headline number is number of selected model genes and not \"numerical result\" in sense result? They could consider number of model genes as core signature characteristic, so yes.\n\nNeed abide developer claims only source. Our target_result claims directly supported. If mention low-risk OS, evidence sentence perhaps includes. Could pick abstract full sentence to support all. Evidence:\n\"The 14-MRGs prognostic signature classified patients into high- and low-risk groups, and the overall survival (OS) of the low-risk group was significantly higher than that of the high-risk group (p < 0.05).\"\nThis attaches number directly to prognostic signature, supports main finding and survival. target_result \"A 14-MRGs prognostic signature that classifies HNSCC patients into high- and low-risk groups, with significantly higher overall survival in the low-risk group.\" Evidence has p value 0.05 but no issue exact contains multiple numbers though headline only 14. They say \"Report headline number only\" could not include p in target_result but evidence required quote includes p. Could avoid p by conclusion sentence with only 14 and HNSCC (no numeric). Good.\n\nEvidence sentence conclusion has 14-MRGs and no other numeric. Use that. \"We combined scRNA-seq data and bulk RNA-seq data to construct a 14-MRGs-based prognostic model capable of well predicting the prognosis of HNSCC patients.\" This exactly source. target_result \"A 14-MRGs-based prognostic model for predicting the prognosis of HNSCC patients.\" output JSON no markdown.\n\nDouble check source exact includes that exact sentence yes Abstract last and [Conclusion] first. \"14-MRGs-based\" yes. unit \"MRGs\" exact printed. parent_value 14.\n\nCould target_result \"The main finding is a prognostic signature based on 14 mast cell marker genes.\" MRG acronym defined \"prognostically relevant mast cell-related genes (MRGs)\" and title \"mast cell marker gene-related\". Use \"14-MRGs-based prognostic model\" exact term to avoid invented interpretation.\n\nWe should not mention ROC. Final.</think>\n\n{\"parent_value\":14,\"unit\":\"MRGs\",\"evidence\":\"We combined scRNA-seq data and bulk RNA-seq data to construct a 14-MRGs-based prognostic model capable of well predicting the prognosis of HNSCC patients.\",\"target_result\":\"A 14-MRGs-based prognostic model for predicting the prognosis of HNSCC patients.\",\"number_stated\":true}", "reading": {"parent_value": 14, "unit": "MRGs", "evidence": "We combined scRNA-seq data and bulk RNA-seq data to construct a 14-MRGs-based prognostic model capable of well predicting the prognosis of HNSCC patients.", "target_result": "A 14-MRGs-based prognostic model for predicting the prognosis of HNSCC patients.", "number_stated": true, "source_locator": "chars 2055-2209 of source sha256 f35adf1c21e8c24dd0e6048a641b6228756c7275132296eb3d12d670c62e857d"}, "recorded_at": "2026-10-31T15:22:31.335158+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "The nomogram had areas under the ROC curves of 0.740, 0.737, and 0.707 at 1, 3, and 5 years, respectively.", "minimax_alt": "The nomogram's areas under the ROC curves were 0.740 at 1 year, 0.737 at 3 years, and 0.707 at 5 years.", "decisive": false}, "analysis_methods": {"minimax": ["single-cell RNA sequencing (scRNA-seq)", "Cox regression", "LASSO regression", "receiver operating characteristic (ROC) analysis", "calibration curves", "Kaplan-Meier survival curves", "log-rank test", "Wilcoxon test", "Spearman correlation analysis", "CIBERSORT deconvolution", "CIBERSORT-ABS", "XCELL", "TIMER", "QUANTISEQ", "MCPCOUNTER", "EPIC", "ESTIMATE", "Tumor immune dysfunction and exclusion (TIDE)", "oncoPredict"], "minimax_alt": ["single-cell RNA sequencing (scRNA-seq)", "bulk RNA-seq", "Cox regression", "LASSO regression", "receiver operating characteristic (ROC) curves", "calibration curves", "Kaplan-Meier survival curves", "log-rank tests", "C-index", "X-tile cut-off calculation"], "decisive": false}, "statistical_method": {"minimax": "receiver operating characteristic (ROC) analysis", "minimax_alt": "receiver operating characteristic (ROC) curves", "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:bf1075346e394eebd3871635bfee77f8441a9471b818f0b90c8454ec8a34692a", "type": "sep_package", "label": "SEP v1 package for pmid:40703522"}], "activities": [{"id": "activity:conversion:pmid:40703522", "type": "conversion", "used": ["package:bf1075346e394eebd3871635bfee77f8441a9471b818f0b90c8454ec8a34692a"], "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:40703522", "parent_hash": "sha256:f85b2486c7483ca88a429b2706047591dc38d5e16d58bf24180910b56ac57850", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40703522 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:40703522' does not resolve to a declared asset", "targets"]}