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pmid:40108606
Fatty acid metabolism related gene MECR contributes to the progression of prostate cancer.
Cancer cell international · 2025 doi:10.1186/s12935-025-03738-6
Paper
partial
Experiment
partial
Data
partial
Analysis
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Execution
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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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Complete file
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"minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "173d2cb5ae91b1565aa47541659bdcc43d354c6d385deb09ea8f198a3612634f", "source_hash": "106e7afb2b98a56a724d9f914360bdf3bd3913d2a5d5de86722c03c17022f344", "raw_response": "{\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"The text states that MECR is significantly associated with clinical features, survival outcomes, and prognosis in prostate cancer, and that the in vitro and in vivo data indicated that MECR plays a role in prostate cancer proliferation, migration, and invasion, but it gives no headline numerical result for this main finding.\",\"number_stated\":false}", "reading": {"parent_value": 0, "unit": "", "evidence": "", "target_result": "The text states that MECR is significantly associated with clinical features, survival outcomes, and prognosis in prostate cancer, and that the in vitro and in vivo data indicated that MECR plays a role in prostate cancer proliferation, migration, and invasion, but it gives no headline numerical result for this main finding.", "number_stated": false, "source_locator": null}, "recorded_at": "2026-10-31T15:35:48.921520+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "535b40bec04a85b9e15b2325e3c0ad8a9f449b2c3f44fd119294f540825bc947", "source_hash": "106e7afb2b98a56a724d9f914360bdf3bd3913d2a5d5de86722c03c17022f344", "raw_response": "<think>We need answer JSON only. Need inspect source. User asks \"single numerical result that a replication of registered target has to reproduce. headline numerical result paper states for main finding.\" Source is abstract/results largely qualitative. Numerical values include 92 genes, cutoffs, pathways NES etc. Registered target is headline numerical result paper states for main finding. Likely no number for main finding. Need set number_stated false parent_value 0 unit/evidence empty, target_result says text states qualitative finding instead. Need exact JSON schema. Need be cautious: Maybe headline numerical result is 92? \"Dataset GSE6919 ... 92 human fatty acid metabolism-related genes were identified\". But registered target wording is \"the headline numerical result the paper states for its main finding\". The abstract/result says MECR associated etc but no specific numerical metric. They explicitly allow if no number for registered target. So false.\n\ntarget_result could say \"The text states that MECR expression is significantly associated with clinical features, survival outcomes, and prognosis in PCa, and that in vitro and in vivo data indicate a role in proliferation, migration, and invasion, but it gives no headline numerical result.\" Need no evidence since empty. Is target_result expected headline? \"say in target_result what the text does state instead.\" We can quote or summarize? No restriction perhaps. Better exact source wording perhaps: \"The text states that ...\". But maybe they demand only claims from source. We can use exact contiguous quotation? The evidence must empty if no number. target_result can state source wording. Ensure valid JSON and no markdown.</think>\n\n{\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"The text states that MECR expression is significantly associated with clinical features, survival outcomes, and prognosis in prostate cancer, and that in vitro and in vivo data indicate that MECR plays a role in proliferation, migration, and invasion; no headline numerical result is stated for this main finding.\",\"number_stated\":false}", "reading": {"parent_value": 0, "unit": "", "evidence": "", "target_result": "The text states that MECR expression is significantly associated with clinical features, survival outcomes, and prognosis in prostate cancer, and that in vitro and in vivo data indicate that MECR plays a role in proliferation, migration, and invasion; no headline numerical result is stated for this main finding.", "number_stated": false, "source_locator": null}, "recorded_at": "2026-10-31T15:35:51.316619+00:00"}], "agreement": true, "disagreements": {"target_result": {"minimax": "Differential analysis of MECR combined with GSEA enrichment analysis in the TCGA-PRAD cohort identified WP_PI3KAKT_SIGNALING_PATHWAY (NES = −1.370, p.adj = 0.046, FDR = 0.035), WP_ADIPOGENESIS (NES = −1.561, p.adj = 0.046, FDR = 0.035), and PID_AP1_PATHWAY (NES = −1.702, p.adj = 0.046, FDR = 0.035).", "minimax_alt": "unclear as a single headline numerical result for the overall MECR finding; the clearest explicit quantitative MECR result is that differential analysis of MECR single genes combined with GSEA in the TCGA-PRAD cohort identified WP_PI3KAKT_SIGNALING_PATHWAY (NES = −1.370, p.adj = 0.046, FDR = 0.035), WP_ADIPOGENESIS (NES = −1.561, p.adj = 0.046, FDR = 0.035), and PID_AP1_PATHWAY (NES = −1.702, p.adj = 0.046, FDR = 0.035).", "decisive": false}, "analysis_methods": {"minimax": ["R package ‘limma’ for differential expression analysis", "GSEA enrichment analysis", "univariate and multivariate Cox regression analyses", "independent t-test", "Kruskal Wallis test", "Spearman correlation analysis"], "minimax_alt": ["Differential-expression analysis of fatty acid metabolism-related genes using R package ‘limma’", "Z-score normalization", "Venn and Volcano diagram generation using ImageGP", "Gene Ontology (GO) and Kyoto Genome Encyclopedia (KEGG) enrichment analysis using ClusterProfiler", "Gene Set Enrichment Analysis (GSEA)", "TIMER differential-expression analysis", "TIMER immune-cell infiltration analysis", "cBioPortal mutational-profile analysis", "UALCAN methylation analysis", "Univariate and multivariate Cox regression using R package 'survival'", "Independent t-test", "Kruskal Wallis test", "Spearman correlation analysis", "R software analysis of correlations between gene expression, TMB, and MSI"], "decisive": false}}, "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": "passed", "reason": "The independent readings are incomplete or disagree; 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