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pmid:40873584

Machine learning identifies PYGM as a macrophage polarization-linked metabolic biomarker in rectal cancer prognosis.

Frontiers in immunology · 2025 doi:10.3389/fimmu.2025.1639303

Paper

partial

Experiment

partial

Data

partial

Analysis

absent

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:40873584
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science-record
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title
Machine learning identifies PYGM as a macrophage polarization-linked metabolic biomarker in rectal cancer prognosis.
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10.3389/fimmu.2025.1639303
pmid
40873584
authors
null
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Frontiers in immunology
year
2025
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null
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the headline numerical result the paper states for its main finding
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the headline numerical result the paper states for its main finding
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The number a replication must land on, and the sentence it came from.
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the published estimate, its unit and its interval
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no owned computational capability is bound to this experiment
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Re-run the paper's analysis on the source data and produce the registered result. | SEP input not carried into the asset list: materials:the source data behind the published result in pmid:40873584
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The text states that PYGM is upregulated in rectal cancer and promotes tumor cell proliferation, migration, and M2 macrophage polarization, but it gives no numerical result for this main finding.
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No headline numerical result is stated in the supplied text. The main finding is that PYGM is upregulated in rectal cancer and promotes tumor cell proliferation, migration, and M2 macrophage polarization.
minimax_alt
unclear — the supplied text reports qualitative MPGS/PYGM findings, two cohorts (363 samples from GSE87211 and 177 from TCGA), and three hub genes (PYGM, MAOB, TIMP1), but states no headline numerical effect estimate or prognostic performance metric for MPGS or PYGM.
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oncoPredict
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prophetic
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extreme gradient boosting (XGBoost)
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10-fold cross-validation repeated 3 times
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single-cell RNA sequencing
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CIBERSORT
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single-sample GSEA (ssGSEA)
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TIDE
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Need determine no headline numerical result stated. Source has sample sizes 363,177, thresholds, etc but no main finding performance metric like AUC, HR, P. Registered target headline numerical result main finding. Need false, parent 0 unit evidence \"\", target_result says text states qualitative findings, no single numerical result for main finding. Need exact schema. Could potentially number 363 etc? User asks single numerical result replication target has to reproduce. The paper text supplied lacks actual results numbers. Thus false. Need ensure target_result concise. JSON valid.</think>\n\n{\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"The text states that PYGM is upregulated in rectal cancer and promotes tumor cell proliferation, migration, and M2 macrophage polarization, but it gives no numerical result for this main finding.\",\"number_stated\":false}", "reading": {"parent_value": 0, "unit": "", "evidence": "", "target_result": "The text states that PYGM is upregulated in rectal cancer and promotes tumor cell proliferation, migration, and M2 macrophage polarization, but it gives no numerical result for this main finding.", "number_stated": false, "source_locator": null}, "recorded_at": "2026-10-31T15:32:58.933132+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "No headline numerical result is stated in the supplied text. The main finding is that PYGM is upregulated in rectal cancer and promotes tumor cell proliferation, migration, and M2 macrophage polarization.", "minimax_alt": "unclear — the supplied text reports qualitative MPGS/PYGM findings, two cohorts (363 samples from GSE87211 and 177 from TCGA), and three hub genes (PYGM, MAOB, TIMP1), but states no headline numerical effect estimate or prognostic performance metric for MPGS or PYGM.", "decisive": false}, "physical_methods": {"minimax": ["Immunohistochemistry of clinical specimens", "In vitro cellular assays"], "minimax_alt": ["immunohistochemistry of clinical specimens", "clinical tissue analyses", "in vitro cellular assays"], "decisive": false}, "analysis_methods": {"minimax": ["Weighted gene co-expression network analysis (WGCNA)", "Differential expression analysis using limma", "Univariate Cox regression", "LASSO Cox regression", "Support vector machine with Recursive Feature Elimination", "Random forest with 500 trees", "Extreme gradient boosting (XGBoost)", "10-fold cross-validation repeated 3 times", "Gene Ontology analysis", "Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis", "Gene set enrichment analysis (GSEA)", "CIBERSORT", "QuanTIseq", "Single-sample gene set enrichment analysis (ssGSEA)", "TIDE algorithm", "oncoPredict", "prophetic", "CellChat", "Wilcoxon rank-sum test"], "minimax_alt": ["weighted gene co-expression network analysis (WGCNA)", "limma differential expression analysis", "Univariate Cox regression", "LASSO Cox regression", "support vector machine with Recursive Feature Elimination", "random forest", "extreme gradient boosting (XGBoost)", "10-fold cross-validation repeated 3 times", "single-cell RNA sequencing", "CIBERSORT", "QuanTIseq", "single-sample GSEA (ssGSEA)", "TIDE", "Wilcoxon rank-sum test"], "decisive": false}, "statistical_method": {"minimax": "", "minimax_alt": "Univariate Cox regression", "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:6982e34d56294406f8ac1a02c25f7f8afe21cf2e16de363f77c1fff60186a38e", "type": "sep_package", "label": "SEP v1 package for pmid:40873584"}], "activities": [{"id": "activity:conversion:pmid:40873584", "type": "conversion", "used": ["package:6982e34d56294406f8ac1a02c25f7f8afe21cf2e16de363f77c1fff60186a38e"], "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": "69fbb6a4-8374-4789-b845-579625849f8b", "parent_hash": "6982e34d56294406f8ac1a02c25f7f8afe21cf2e16de363f77c1fff60186a38e", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40873584 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:40873584' does not resolve to a declared asset", "targets"]}