Development and validation of a prognostic model based on T cell signature genes in colon cancer using single-cell RNA sequencing.

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[], "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:41483215' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41483215", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s10238-025-01994-4", "sha256": "sha256:6a41760af75f32faa9dbca16bfecf64dedd1bcaa806936a782a3539cb2c5be19", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s10238-025-01994-4", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}, {"id": "asset:table:pmid:41483215:1", "role": "table", "format": "jats-table", "media_type": 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"0.91 (0.69–1.20,p = 0.511)", ""], ["M", "Mean ± SD", "0.3 ± 0.4", "3.37 (2.26–5.03,p < 0.001)", "1.83 (1.06–3.15,p = 0.030)"], ["N", "Mean ± SD", "0.6 ± 0.8", "1.98 (1.57–2.49,p < 0.001)", "1.24 (0.86–1.78,p = 0.249)"], ["T", "Mean ± SD", "2.9 ± 0.6", "2.80 (1.88–4.17,p < 0.001)", "1.79 (1.13–2.86,p = 0.014)"], ["Stage", "Mean ± SD", "2.4 ± 0.9", "2.18 (1.74–2.73,p < 0.001)", "1.32 (0.86–2.02,p = 0.201)"], ["Riskscore", "Mean ± SD", "1.2 ± 0.9", "1.80 (1.53–2.12,p < 0.001)", "1.54 (1.30–1.83,p < 0.001)"]], "row_count": 9, "column_count": 5, "numeric_cells": 22, "header_rows": 1, "locator": "section[19]/table-wrap[1]#Tab1"}}], "gaps": ["asset:source:pmid:41483215 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:41483215", "asset:table:pmid:41483215:1"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:41483215"]}, "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": "826767af425496c0a88a64ad191ec1f3a6875bc9ff200d975afc38a3e8d98861", "source_hash": "6a41760af75f32faa9dbca16bfecf64dedd1bcaa806936a782a3539cb2c5be19", "raw_response_sha256": "sha256:7549cb7a7b477751a51e18d2502ce3c108c390ffecf4518dcb9cc7b0e86edf2b", "raw_response_chars": 4210}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "8c869d9f92bbddf7cfb1e7eaed16a80d0db561ca94ae1861ecee5ed1e2e3ceb0", "source_hash": "6a41760af75f32faa9dbca16bfecf64dedd1bcaa806936a782a3539cb2c5be19", "raw_response_sha256": "sha256:a03819e179a895bf2578da45aceb3fb230a4db29c52348501d51d94f87c35aaf", "raw_response_chars": 70864}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "d1029ae8dbbe22299a19ef75ce500ffe6a31c72eccbddba927656ffe3b498b97", "source_hash": "6a41760af75f32faa9dbca16bfecf64dedd1bcaa806936a782a3539cb2c5be19", "raw_response": "{\"parent_value\":7,\"unit\":\"genes\",\"evidence\":\"A prognostic model was developed based on seven genes, encompassing UBE2N, TUBA1C, FXR1, CBLB, YTHDC1, GPRIN3, and AGPAT2.\",\"target_result\":\"The prognostic model was developed based on seven genes.\",\"number_stated\":true}", "reading": {"parent_value": 7, "unit": "genes", "evidence": "A prognostic model was developed based on seven genes, encompassing UBE2N, TUBA1C, FXR1, CBLB, YTHDC1, GPRIN3, and AGPAT2.", "target_result": "The prognostic model was developed based on seven genes.", "number_stated": true, "source_locator": "chars 1121-1243 of source sha256 6a41760af75f32faa9dbca16bfecf64dedd1bcaa806936a782a3539cb2c5be19"}, "recorded_at": "2026-10-31T15:18:00.512055+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "74abdb650a75272ac60bc28af092122cb4a4f61c5948b9956d0197d47d814927", "source_hash": "6a41760af75f32faa9dbca16bfecf64dedd1bcaa806936a782a3539cb2c5be19", "raw_response": "<think>We need answer JSON only. Need interpret \"single numerical result that a replication of registered target has to reproduce\", target headline numerical result paper states for main finding. Main finding prognostic model, likely AUC training cohort values? But asks single numerical result singular headline number. Need infer perhaps seven genes? Registered target \"the headline numerical result the paper itself states for its main finding\". Source abstract gives model based on seven genes; AUC 3,5,7 years 0.676, 0.715, 0.721. Which single numerical result headline? They insist one number, evidence exact contiguous quote containing number; attribution may attached list/respectively or direct words. Perhaps expected number is 0.715? Main result perhaps AUC? Need identify \"single numerical result\" likely title/abstract headline metric. We need decide.\n\nQuestion wording perhaps benchmark for extracting parent value from paper, registered target vague generic. \"the headline numerical result the paper itself states for its main finding.\" Usually want one headline number, maybe number of genes? \"A prognostic model was developed based on seven genes\" is headline main finding. Replication of registered target needs reproduce 7? But likely target asks main finding and number could be 0.715? Let's parse.\n\nThey explicitly explain attribution mechanisms:\n- number may be attached to target by parenthetical list\n- by word respectively\n- or words that name target directly.\n- If linked that way, quote whole clause.\nThis suggests source often presents multiple values and registered target disambiguates one. Here target is generic \"headline numerical result ... main finding\". We need choose perhaps \"seven\" as model result, since no specified cohort/time point. Yet parent_value numerical, unit maybe \"genes\". Evidence exact quote: \"A prognostic model was developed based on seven genes, encompassing...\" Target_result perhaps \"The prognostic model was developed based on seven genes.\" Number stated true; parent_value 7; unit \"genes\". But they request \"headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed.\" The main finding is model based on seven genes. Single numerical result that replication must reproduce = 7 genes. However \"headline numerical result\" commonly efficacy metric (AUC), not sample size or dimensional model.\n\nCould be AUC highest 0.951 in GSE17537 at 7 years, but not headline? External validation shows \"consistent predictive performance\", yet one 0.951 stands out. But replication target would likely reproduce model's AUC at specific horizon, perhaps 0.721 in training at 7 years. Yet target does not specify which. Could report entire set? Schema only one number and says single numerical result. Need identify likely intended from abstract. Maybe \"the headline numerical result the paper itself states for its main finding\" = model's AUC. Abstract first reports 0.676, 0.715, 0.721 at 3,5,7 years. No single. Could choose seven genes because only singular number linked directly to model.\n\nLet's inspect exact request semantics: \"Read the supplied paper text and find the single numerical result that a replication of the registered target has to reproduce.\" Registered target given: \"the headline numerical result the paper itself states for its main finding\". Perhaps they expect answer based on target class, perhaps headline statistic. Main finding paper: prognostic model based on seven genes and AUC. What \"single numerical result\" likely headline? Could be 0.715, as AUC at 5 years is often standard headline performance. But source says at 3,5,7 years, and perhaps in full paper a conclusion sentence omitted? The supplied source includes abstract and methods snippets. We need not infer 5-year unless explicitly target. The only singular result is \"seven genes.\" Wording \"based on seven genes\" directly attaches count to main finding model. Thus likely answer 7 genes.\n\nNeed ensure parent_value number 7, unit paper prints. Paper says \"seven genes\", not numeral but number. Unit \"genes\". Evidence exact contiguous quotation: \"A prognostic model was developed based on seven genes, encompassing UBE2N, TUBA1C, FXR1, CBLB, YTHDC1, GPRIN3, and AGPAT2.\" That's full clause perhaps sentence. Exact. target_result should concise identify. number_stated true. JSON valid. They ask \"headline number only\" perhaps target_result maybe \"7 genes\" rather than extra. Could say \"The prognostic model was developed based on seven genes.\" Schema parent_value etc.\n\nCould unit be \"genes\" exactly printed. yes.\n\nLet's consider whether \"headline numerical result\" may instead be \"0.721\" at 7 years. In abstract, AUC results are key headline numerical outcomes. Replication of registered target might reproduce model performance. But no target metric designated. Maybe benchmark source title/abstract asks identify \"single numerical result\", and perhaps they select main finding number based on generic registered target. Could be intended to choose strongest headline metric, perhaps 0.715? Let's reason through likely task generation.\n\nUser template: \"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. The evidence must be an exact, contiguous quotation from SOURCE TEXT that contains the number; do not shorten it, do not add an ellipsis and do not paraphrase it. 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 the registered target, set number_stated to false, leave parent_value at 0, leave unit and evidence empty, and say in target_result what the text does state instead. Never invent a source.\nREGISTERED TARGET: the headline numerical result the paper itself states for its main finding\"\n\nThis seems user explicitly asks abstractive extraction where target is semantically \"headline numerical result main finding\". Maybe expected to decide number from abstract. They emphasize \"number may be attached ... respectively\" likely because abstract AUC has lists. They might expect some one value selected as headline. Which? Maybe main finding is \"AUC values ...\" and three at 3, 5, 7 years; perhaps target singular means list? But schema parent_value singular, no arrays. Could \"headline\" refer first number in abstract (0.676)? Yet no.\n\nPotential intended answer perhaps 7 genes because it's literally single number and directly linked. Why mention parenthetical/respectively then generic boilerplate applies across tasks. The model based on seven genes is a \"single numerical result\" and replication has to reproduce gene count? A replication would have to identify same seven genes, not merely count 7. But target says headline numerical result, could count as headline model architecture. Yet replication target likely primary endpoint is AUC, not number of genes. Still only unambiguous singular headline number.\n\nCould report 0.715 as central 5-year AUC? But that is calculated selection of middle value, not paper naming it headline. 7-year is commonly primary? For colon cancer prognosis, 5-year AUC likely headline. Abstract reports all horizons, no indication one is headline. User says \"value the paper itself states for the registered target\"—we cannot arbitrarily select 5-year. If target is the main finding (model), all three AUC values are stated, violating single. Number of model genes directly attached to finding and singular. Thus 7 genes.\n\nNeed perhaps target_result \"The prognostic model was developed based on seven genes.\" evidence whole sentence. Unit exact maybe \"genes\". JSON only no markdown. Ensure evidence exact contiguous from source. Let's formulate.\n\nCould parent_value 7 (numeric not seven). Unit \"genes\". target_result perhaps \"The prognostic model was developed based on seven genes.\" This paraphrases but target_result can. Evidence exact.\n\nLet's answer.</think>\n\n{\"parent_value\":7,\"unit\":\"genes\",\"evidence\":\"A prognostic model was developed based on seven genes, encompassing UBE2N, TUBA1C, FXR1, CBLB, YTHDC1, GPRIN3, and AGPAT2.\",\"target_result\":\"The prognostic model was developed based on seven genes.\",\"number_stated\":true}", "reading": {"parent_value": 7, "unit": "genes", "evidence": "A prognostic model was developed based on seven genes, encompassing UBE2N, TUBA1C, FXR1, CBLB, YTHDC1, GPRIN3, and AGPAT2.", "target_result": "The prognostic model was developed based on seven genes.", "number_stated": true, "source_locator": "chars 1121-1243 of source sha256 6a41760af75f32faa9dbca16bfecf64dedd1bcaa806936a782a3539cb2c5be19"}, "recorded_at": "2026-10-31T15:18:12.142118+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "A seven-gene prognostic model comprising UBE2N, TUBA1C, FXR1, CBLB, YTHDC1, GPRIN3, and AGPAT2; the training-cohort AUC values at 3, 5, and 7 years were 0.676, 0.715, and 0.721, respectively.", "minimax_alt": "A prognostic model was developed based on seven genes, encompassing UBE2N, TUBA1C, FXR1, CBLB, YTHDC1, GPRIN3, and AGPAT2. The area under the ROC curve (AUC) for the training cohort at 3, 5, and 7 years reached 0.676, 0.715, and 0.721, respectively. External validation using three GEO datasets demonstrated consistent predictive performance of the model. The AUC values at 3, 5, and 7 years were 0.632, 0.617, and 0.582 in GSE39582, 0.689, 0.755, and 0.951 in GSE17537, and 0.667, 0.653, and 0.649 in GSE161158.", "decisive": false}, "analysis_methods": {"minimax": ["least absolute shrinkage and selection operator (LASSO) regression", "LASSO-Cox regression", "multivariate Cox proportional hazards modeling", "receiver operating characteristic (ROC) curve analysis"], "minimax_alt": ["Differential gene expression analysis", "single-cell RNA sequencing (scRNA-seq)", "intersectional analysis", "least absolute shrinkage and selection operator (LASSO) regression", "multivariate Cox proportional hazards models", "receiver operating characteristic (ROC) curves", "Cox regression and LASSO-Cox regression analyses", "KM survival curves and log-rank test"], "decisive": false}, "statistical_method": {"minimax": "receiver operating characteristic (ROC) curves", "minimax_alt": "LASSO regression and multivariate Cox proportional hazards models for prognostic-model development; receiver operating characteristic (ROC) curve analysis for AUC evaluation", "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:f39a0f06d0d86b1c40af9720c6d5a7230947a6daaec197cef8c69f2f12210165", "type": "sep_package", "label": "SEP v1 package for pmid:41483215"}], "activities": [{"id": "activity:conversion:pmid:41483215", "type": "conversion", "used": ["package:f39a0f06d0d86b1c40af9720c6d5a7230947a6daaec197cef8c69f2f12210165"], "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:41483215", "parent_hash": "sha256:90852dff032ee46960692a43ed30a67f052c45bd8204891c4d4c9ee0005304b5", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:41483215 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:41483215' does not resolve to a declared asset", "targets"]}