Revelation of prognosis and tumor microenvironment of colorectal cancer based on genes related to antibody-dependent cellular phagocytosis and single-cell landscape.

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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:40841999' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40841999", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12014-025-09553-5", "sha256": "sha256:c1f4e4e22d37c05464f50ee0247c877e93a496a2dfac2c2dbeefabe8159852e0", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12014-025-09553-5", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}], "gaps": ["asset:source:pmid:40841999 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:40841999"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40841999"]}, "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": "0a0261f1f8d12bb77d8b32926b36322a81624e57a35044b30367720a06a9d1b6", "source_hash": "c1f4e4e22d37c05464f50ee0247c877e93a496a2dfac2c2dbeefabe8159852e0", "raw_response_sha256": "sha256:fd1ab165c0684ff9201a37bac3580f9839bed95041b9b3f5f648fba7110df597", "raw_response_chars": 3703}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "c88effefb3f8183d8b5886fc2d177f51af6b06914661ef494cb46781ec284522", "source_hash": "c1f4e4e22d37c05464f50ee0247c877e93a496a2dfac2c2dbeefabe8159852e0", "raw_response_sha256": "sha256:8272df1d0c01d8815194787fed4129ff204689650c5bd1f8eac9e3c7f3068115", "raw_response_chars": 70605}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "f0be83fce6de94ba7de1fac5ce45f167a39636e437898d307e106c43cd2cce8d", "source_hash": "c1f4e4e22d37c05464f50ee0247c877e93a496a2dfac2c2dbeefabe8159852e0", "raw_response": "{\"parent_value\":7,\"unit\":\"feature genes\",\"evidence\":\"This project created a prognostic model based on 7 feature genes using the TCGA training set.\",\"target_result\":\"The paper states that the prognostic model was based on 7 feature genes.\",\"number_stated\":true}", "reading": {"parent_value": 7, "unit": "feature genes", "evidence": "This project created a prognostic model based on 7 feature genes using the TCGA training set.", "target_result": "The paper states that the prognostic model was based on 7 feature genes.", "number_stated": true, "source_locator": "chars 1659-1752 of source sha256 c1f4e4e22d37c05464f50ee0247c877e93a496a2dfac2c2dbeefabe8159852e0"}, "recorded_at": "2026-10-31T15:31:53.879620+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "b8587c2e7217e3c98a881611f91cc53a00881917a71a2d95695216e6daa6b157", "source_hash": "c1f4e4e22d37c05464f50ee0247c877e93a496a2dfac2c2dbeefabe8159852e0", "raw_response": "<think>We need answer JSON only. Need identify single numerical result replication registered target headline numerical result main finding. Source abstract/results says prognostic model based on 7 feature genes, genes across 8 cell clusters. Which is \"headline numerical result\" likely 7? Need carefully infer. User asks target generic: headline numerical result paper states for main finding. Need report value paper itself states for registered target, exact contiguous quote with number and attribution.\n\nPotential candidates:\n- \"This project created a prognostic model based on 7 feature genes using the TCGA training set.\" This is main finding headline, perhaps model based on 7 genes.\n- \"the 7 feature genes were examined to be expressed across 8 cell clusters...\" Main findings.\nCould headline numerical result be AUC/C-index/HR absent? Source truncated perhaps no model performance numbers. Registered target likely benchmark asks extract headline numerical result, expected 7 feature genes perhaps unit \"feature genes\". Need parse wording. They explicitly instruct \"single numerical result that a replication ... has to reproduce.\" Main finding is prognostic model based on 7 feature genes. Thus number 7, unit \"feature genes\". Exact quote sentence.\n\nNeed output fields:\nparent_value 7\nunit \"feature genes\"\nevidence exact contiguous quotation: \"This project created a prognostic model based on 7 feature genes using the TCGA training set.\"\ntarget_result perhaps \"The prognostic model was created based on 7 feature genes using the TCGA training set.\" But they demand headline number only maybe target_result perhaps target description. Schema includes target_result. We can put \"Prognostic model based on 7 feature genes using the TCGA training set.\" Yet instruction report headline number only could imply parent_value only, unit, evidence, target_result. JSON necessarily. Maybe target_result can state what text does state instead. number_stated true.\n\nNeed ensure evidence quote contains number. Exact punctuation. Unit printed \"feature genes\". Is \"7 feature genes\" unit. Yes.\n\nCould target be 8 cell clusters? Main finding says 7 model genes and 8 clusters. Which is \"headline numerical result\"? Let's investigate likely task conventions. Registered target phrased \"the headline numerical result the paper states for its main finding.\" They expect identify a numerical result. In abstracts, often first results sentence says \"created prognostic model based on 7 feature genes\". That's headline. The question is perhaps generated around \"registered target\" that is semantically generic, and source text may have key number. We need select likely number. Let's inspect abstract. Title. Abstract Background etc. Results:\n\"This project created a prognostic model based on 7 feature genes using the TCGA training set. The K-M survival curves and ROC curves indicated that the model, as well as the nomogram, was capable of accurately predicting prognosis for CRC patients. Based on scRNA-seq data analysis, the 7 feature genes were examined to be expressed across 8 cell clusters (...). ...\"\nConclusion. Main numerical headline = model based on 7 feature genes. Definitely.\n\nShould evidence quote whole clause. Whole sentence exact. target_result perhaps \"A prognostic model based on 7 feature genes was created using the TCGA training set.\" Fine.\n\n\"Report the headline number only: the value the paper itself states for the registered target\" Means don't include ancillary numbers. Evidence necessarily one sentence with 7 and perhaps no other number. It has TCGA (not numeric). Good.\n\nJSON valid, no markdown.</think>\n\n{\"parent_value\":7,\"unit\":\"feature genes\",\"evidence\":\"This project created a prognostic model based on 7 feature genes using the TCGA training set.\",\"target_result\":\"A prognostic model based on 7 feature genes was created using the TCGA training set.\",\"number_stated\":true}", "reading": {"parent_value": 7, "unit": "feature genes", "evidence": "This project created a prognostic model based on 7 feature genes using the TCGA training set.", "target_result": "A prognostic model based on 7 feature genes was created using the TCGA training set.", "number_stated": true, "source_locator": "chars 1659-1752 of source sha256 c1f4e4e22d37c05464f50ee0247c877e93a496a2dfac2c2dbeefabe8159852e0"}, "recorded_at": "2026-10-31T15:31:54.266801+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "A prognostic model based on 7 feature genes was developed using the TCGA training set and evaluated with Kaplan-Meier survival curves and ROC curves.", "minimax_alt": "The headline numerical result was a prognostic model based on 7 feature genes created using the TCGA training set; Kaplan-Meier survival curves and ROC curves indicated that the model and nomogram accurately predicted prognosis for CRC patients.", "decisive": false}, "analysis_methods": {"minimax": ["univariate Cox regression analysis", "LASSO Cox regression analysis", "multivariate Cox regression analysis", "Kaplan-Meier survival analysis", "receiver operating characteristic (ROC) curve analysis"], "minimax_alt": ["univariate Cox regression", "LASSO Cox regression", "multivariate Cox regression", "nomogram construction based on clinical information and risk scoring", "Kaplan-Meier (K-M) survival analysis", "receiver operation characteristic (ROC) curve analysis", "AUCell R package scoring of individual single cells"], "decisive": false}, "statistical_method": {"minimax": "multivariate Cox regression analysis", "minimax_alt": "Univariate Cox and LASSO Cox regression analyses were used to screen genes, multivariate Cox regression analysis was used to construct the prognostic model, and Kaplan-Meier (K-M) survival curves and receiver operation characteristic (ROC) curves were used to evaluate its prognostic value.", "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:15a81dbbdd319086a74bb6909617ea454d5e7e8423194082ece3530564d1ecc4", "type": "sep_package", "label": "SEP v1 package for pmid:40841999"}], "activities": [{"id": "activity:conversion:pmid:40841999", "type": "conversion", "used": ["package:15a81dbbdd319086a74bb6909617ea454d5e7e8423194082ece3530564d1ecc4"], "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": "ceb135e4-49a2-4951-92ee-60432b1651df", "parent_hash": "15a81dbbdd319086a74bb6909617ea454d5e7e8423194082ece3530564d1ecc4", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40841999 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:40841999' does not resolve to a declared asset", "targets"]}