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pmid:41399382

Integration of Single-cell and Bulk Transcriptome Analyses Unravels a Macrophage-based Gene Signature for Prognostication and Treatment in Triple-negative Breast Cancer.

International journal of medical sciences · 2026 doi:10.7150/ijms.120593

Paper

partial

Experiment

partial

Data

partial

Analysis

absent

Execution

absent

Execution evidence has not yet been added to this .science file. Explore its methods, data and open questions to plan the next step.
Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:41399382
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science-record
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paper object · 10 fields
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partial
title
Integration of Single-cell and Bulk Transcriptome Analyses Unravels a Macrophage-based Gene Signature for Prognostication and Treatment in Triple-negative Breast Cancer.
doi
10.7150/ijms.120593
pmid
41399382
authors
null
journal
International journal of medical sciences
year
2026
abstract
null
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the headline numerical result the paper states for its main finding
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chars 1690-1810 of source sha256 906d70ca5b5d3d2c56b4a6ccca31a3ddcdfc738b6d918cdb2a0d7ad60ac2bdee
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chars 1690-1810 of source sha256 906d70ca5b5d3d2c56b4a6ccca31a3ddcdfc738b6d918cdb2a0d7ad60ac2bdee
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This signature demonstrated reliable performance in external validation, particularly for one-year survival (AUC > 0.9).
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abstract
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experiment object · 8 fields
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partial
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the headline numerical result the paper states for its main finding
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other
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null
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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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the source data with its provenance
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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:41399382
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This signature demonstrated reliable performance in external validation, particularly for one-year survival (AUC > 0.9).
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<think>We need answer JSON only. Need identify single numerical result headline main finding. Source abstract. Likely AUC > 0.9 particularly one-year survival. Need report headline number only, value paper states, unit printed. "AUC > 0.9". Parent_value numeric 0.9, unit perhaps "AUC". Evidence exact contiguous quote containing number, and attribution whole clause. Could quote: "This signature demonstrated reliable performance in external validation, particularly for one-year survival (AUC > 0.9)." Number represented 0.9. Unit must paper prints: AUC likely unit "AUC" perhaps parent_value 0.9 and unit "AUC". Evidence exact contiguous quote containing number, and attribution whole clause. Could quote: "This signature demonstrated reliable performance in external validation, particularly for one-year survival (AUC > 0.9)." Number represented 0.9. Unit must paper prints: AUC likely unit "AUC" perhaps parent_value 0.9 and unit "AUC". target_result perhaps "one-year survival AUC > 0.9 in external validation". Need exact evidence. Is headline numerical result maybe AUC > 0.9. It asks single numerical result replication target. Yes. Need make JSON valid. No markdown. "unit": "AUC" (unit printed? AUC is metric, not unit, but schema string). Could perhaps unit "". Yet requirement says unit must be unit paper prints. AUC is dimensionless and paper prints it as label. They probably expect "AUC". target_result should state. Evidence must exact contiguous quotation from source and do not shorten. Quote full sentence. Exact text: "This signature demonstrated reliable performance in external validation, particularly for one-year survival (AUC > 0.9)." Good. Potential issue > relation omitted in parent numeric. Could target_result include "AUC > 0.9". Fine. parent_value 0.9. JSON number cannot encode inequality. Unit "AUC". Need no extra.</think> {"parent_value":0.9,"unit":"AUC","evidence":"This signature demonstrated reliable performance in external validation, particularly for one-year survival (AUC > 0.9).","target_result":"One-year survival AUC > 0.9 in external validation.","number_stated":true}
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minimax
One-year survival prediction by the macrophage-based gene signature achieved an AUC > 0.9 in external validation.
minimax_alt
The macrophage-based gene signature achieved an AUC > 0.9 for one-year survival in external validation.
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[1]
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[5]
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[0]
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CIBERSORTX immune infiltration analysis
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Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis
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Gene set enrichment analysis (GSEA)
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SCENIC transcription-factor analysis
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limma differential-expression analysis
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[10]
Least absolute shrinkage and selection operator (LASSO) regression using glmnet
[11]
1000-iteration Bootstrap resampling for optimism-corrected performance metrics
[12]
Calibration curve analysis using the rms package
[13]
Decision Curve Analysis (DCA)
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Receiver operator characteristic (ROC) analysis using the pROC package
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Kaplan-Meier survival analysis with log-rank test
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Student's t test
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One-way analysis of variance
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Pearson correlation test
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Spearman correlation test
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statistical_method object · 3 fields
minimax
Receiver operator characteristic (ROC) analysis
minimax_alt
Receiver operator characteristic (ROC) curve analysis using the pROC package
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"reading": {"parent_value": 0.9, "unit": "AUC", "evidence": "This signature demonstrated reliable performance in external validation, particularly for one-year survival (AUC > 0.9).", "target_result": "AUC > 0.9 for one-year survival in external validation", "number_stated": true, "source_locator": "chars 1690-1810 of source sha256 906d70ca5b5d3d2c56b4a6ccca31a3ddcdfc738b6d918cdb2a0d7ad60ac2bdee"}, "recorded_at": "2026-10-31T15:15:34.056930+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "41fe4c1260199015eb121933a6e71eda66320c98d129b39da69e670625d24ce6", "source_hash": "906d70ca5b5d3d2c56b4a6ccca31a3ddcdfc738b6d918cdb2a0d7ad60ac2bdee", "raw_response": "<think>We need answer JSON only. Need identify single numerical result headline main finding. Source abstract. Likely AUC > 0.9 particularly one-year survival. Need report headline number only, value paper states, unit printed. \"AUC > 0.9\". Parent_value numeric 0.9, unit perhaps \"AUC\". Evidence exact contiguous quote containing number, and attribution whole clause. Could quote: \"This signature demonstrated reliable performance in external validation, particularly for one-year survival (AUC > 0.9).\" Number represented 0.9. Unit must paper prints: AUC likely unit \"AUC\" perhaps parent_value 0.9 and unit \"AUC\". Evidence exact contiguous quote containing number, and attribution whole clause. Could quote: \"This signature demonstrated reliable performance in external validation, particularly for one-year survival (AUC > 0.9).\" Number represented 0.9. Unit must paper prints: AUC likely unit \"AUC\" perhaps parent_value 0.9 and unit \"AUC\". target_result perhaps \"one-year survival AUC > 0.9 in external validation\". Need exact evidence. Is headline numerical result maybe AUC > 0.9. It asks single numerical result replication target. Yes.\n\nNeed make JSON valid. No markdown. \"unit\": \"AUC\" (unit printed? AUC is metric, not unit, but schema string). Could perhaps unit \"\". Yet requirement says unit must be unit paper prints. AUC is dimensionless and paper prints it as label. They probably expect \"AUC\". target_result should state. Evidence must exact contiguous quotation from source and do not shorten. Quote full sentence. Exact text: \"This signature demonstrated reliable performance in external validation, particularly for one-year survival (AUC > 0.9).\" Good.\n\nPotential issue > relation omitted in parent numeric. Could target_result include \"AUC > 0.9\". Fine. parent_value 0.9. JSON number cannot encode inequality. Unit \"AUC\". Need no extra.</think>\n\n{\"parent_value\":0.9,\"unit\":\"AUC\",\"evidence\":\"This signature demonstrated reliable performance in external validation, particularly for one-year survival (AUC > 0.9).\",\"target_result\":\"One-year survival AUC > 0.9 in external validation.\",\"number_stated\":true}", "reading": {"parent_value": 0.9, "unit": "AUC", "evidence": "This signature demonstrated reliable performance in external validation, particularly for one-year survival (AUC > 0.9).", "target_result": "One-year survival AUC > 0.9 in external validation.", "number_stated": true, "source_locator": "chars 1690-1810 of source sha256 906d70ca5b5d3d2c56b4a6ccca31a3ddcdfc738b6d918cdb2a0d7ad60ac2bdee"}, "recorded_at": "2026-10-31T15:15:37.587435+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "One-year survival prediction by the macrophage-based gene signature achieved an AUC > 0.9 in external validation.", "minimax_alt": "The macrophage-based gene signature achieved an AUC > 0.9 for one-year survival in external validation.", "decisive": false}, "analysis_methods": {"minimax": ["Single-cell transcriptomic analysis", "Bulk transcriptome analysis", "Principal component analysis", "Cell clustering and annotation", "Uniform manifold approximation and projection", "CIBERSORTX immune infiltration analysis", "Gene Ontology enrichment analysis", "KEGG pathway enrichment analysis", "Gene set enrichment analysis", "SCENIC transcription-factor analysis", "limma differential expression analysis", "Univariate Cox regression", "Least absolute shrinkage and selection operator (LASSO) analysis", "Kaplan-Meier survival analysis", "Log-rank test", "Receiver operator characteristic curve analysis", "Bootstrap resampling", "Calibration curve analysis", "Decision Curve Analysis (DCA)"], "minimax_alt": ["Single-cell transcriptome analysis", "Bulk transcriptome analysis", "Principal component analysis (PCA)", "Uniform manifold approximation and projection (UMAP)", "CIBERSORTX immune infiltration analysis", "Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis", "Gene set enrichment analysis (GSEA)", "SCENIC transcription-factor analysis", "limma differential-expression analysis", "Univariate Cox regression", "Least absolute shrinkage and selection operator (LASSO) regression using glmnet", "1000-iteration Bootstrap resampling for optimism-corrected performance metrics", "Calibration curve analysis using the rms package", "Decision Curve Analysis (DCA)", "Receiver operator characteristic (ROC) analysis using the pROC package", "Kaplan-Meier survival analysis with log-rank test", "Student's t test", "One-way analysis of variance", "Pearson correlation test", "Spearman correlation test"], "decisive": false}, "statistical_method": {"minimax": "Receiver operator characteristic (ROC) analysis", "minimax_alt": "Receiver operator characteristic (ROC) curve analysis using the pROC package", "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:c0502057a3ae71b4b4c7b5e019ef7a990fce95feb2ac36c2826b3d4dd4353dc8", "type": "sep_package", "label": "SEP v1 package for pmid:41399382"}], "activities": [{"id": "activity:conversion:pmid:41399382", "type": "conversion", "used": ["package:c0502057a3ae71b4b4c7b5e019ef7a990fce95feb2ac36c2826b3d4dd4353dc8"], "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": "5f9c8707-7f52-44dc-845e-f2bb0f447711", "parent_hash": "c0502057a3ae71b4b4c7b5e019ef7a990fce95feb2ac36c2826b3d4dd4353dc8", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:41399382 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:41399382' does not resolve to a declared asset", "targets"]}