Anoikis-related genes predicts prognosis and therapeutic response in renal cell carcinoma.

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16) treated by nivolumab", "RNA-seq"], ["CHECKMATE-010", "ccRCC patients (n = 45) treated by nivolumab", "RNA-seq"], ["CHECKMATE-025", "ccRCC patients (n = 250) treated by nivolumab and everolimus", "RNA-seq"], ["GSE22541", "Primary and pulmonary metastasis of ccRCC (n = 68)", "RNA-seq"], ["GSE85258", "Primary and pulmonary metastasis of ccRCC (n = 29)", "RNA-seq"], ["GSE113204", "Primary, pulmonary metastasis and tumour thrombus of ccRCC (n = 14)", "RNA-seq"], ["GSE159115", "ccRCC patients (n = 7)", "scRNA-seqe"]], "row_count": 12, "column_count": 3, "numeric_cells": 21, "header_rows": 1, "locator": "section[20]/table-wrap[1]#t0001"}}], "gaps": ["asset:source:pmid:40830065 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": 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"5540c1ed9e37c944674dbf5ae297aa013a92d4650ebe16903acdf0bbccd761e1", "raw_response_sha256": "sha256:7094f55cb9954cc564870325f81ef4403438b14be18f99cbc2383ae08e704ff1", "raw_response_chars": 53846}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "b10ba375fdeba416ff106ae7a72983f74ce0c890fa714e714bad19aad0e59ae9", "source_hash": "5540c1ed9e37c944674dbf5ae297aa013a92d4650ebe16903acdf0bbccd761e1", "raw_response": "{\"parent_value\":0.79,\"unit\":\"AUC\",\"evidence\":\"Using Cox regression and machine learning, our model achieved a 5-year area under curve (AUC) of 0.79, surpassing existing models in predictive performance.\",\"target_result\":\"5-year area under curve (AUC) of 0.79\",\"number_stated\":true}", "reading": {"parent_value": 0.79, "unit": "AUC", "evidence": "Using Cox regression and machine learning, our model achieved a 5-year area under curve (AUC) of 0.79, surpassing existing models in predictive performance.", "target_result": "5-year area under curve (AUC) of 0.79", "number_stated": true, "source_locator": "chars 1324-1480 of source sha256 5540c1ed9e37c944674dbf5ae297aa013a92d4650ebe16903acdf0bbccd761e1"}, "recorded_at": "2026-10-31T15:30:34.998324+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "1b7c0a8389cd3639cefd96008900205ca23e2b602b95203ac5fb437009d56e66", "source_hash": "5540c1ed9e37c944674dbf5ae297aa013a92d4650ebe16903acdf0bbccd761e1", "raw_response": "<think>We need answer JSON object only. Need identify single numerical result replication registered target headline numerical result paper states for main finding. Likely 5-year AUC 0.79. Need exact contiguous quote containing number, likely sentence: \"Using Cox regression and machine learning, our model achieved a 5-year area under curve (AUC) of 0.79, surpassing existing models in predictive performance.\" Unit paper prints? AUC dimensionless, perhaps unit \"AUC\" or \"\"? Asked unit must be unit paper prints. Headline number only value. parent_value 0.79. unit perhaps \"AUC\" because metric labeled area under curve (AUC), no physical unit. Need evidence exact contiguous quote whole clause attribution. target_result perhaps \"5-year area under curve (AUC) of 0.79\". number_stated true. Ensure valid JSON, no extra. But perhaps headline result could be 59% anoikis? Let's parse request carefully: REGISTERED TARGET: \"the headline numerical result the paper states for its main finding\". Main finding perhaps abstract/result summary includes model achieved 5-year AUC 0.79; MMP9 HR 1.5; functional effects. Which is \"headline numerical result\" singular? Likely central headline metric is AUC 0.79. User wants single numerical result replication of registered target has to reproduce. The source says model achieved 5-year AUC of 0.79, likely intended. Need exact quote no shortening/ellipsis/paraphrase. Evidence can whole sentence. Unit must be unit paper prints. Since AUC is metric not unit. Could set unit \"AUC\" perhaps. But perhaps unit should be \"area under curve (AUC)\" as printed. They say unit must be unit paper prints, often extraction task expects number and unit like \"5-year AUC\" perhaps unit \"AUC\". Let's inspect wording. \"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.\" Registered target broad. Number 0.79. The unit associated is likely AUC. Evidence exact quote. Target result text perhaps \"5-year area under curve (AUC) of 0.79\". Could parent_value 0.79, unit \"AUC\". Is \"5-year\" part unit? Usually target result asks result for parent? Here \"parent_value\" schema perhaps generic. They might want \"5-year AUC\" as unit? Let's think likely benchmark design. They give a registered target and ask numerical result. Source text. Need infer headline numerical result. Could be \"5-year area under curve (AUC) of 0.79.\" They expect value 0.79, unit \"AUC\" (dimensionless), evidence. target_result perhaps wording.\n\nNeed obey \"headline number only\" perhaps target_result should only mention 0.79? Yet evidence necessarily full quote. JSON fields. We can state unit \"AUC\" and target_result \"5-year area under curve (AUC) of 0.79\". Good.\n\nExact contiguous quotation: sentence appears twice identically. Quote whole sentence:\n\"Using Cox regression and machine learning, our model achieved a 5-year area under curve (AUC) of 0.79, surpassing existing models in predictive performance.\"\nThat is exact. Attribution via \"our model achieved...\" directly. Could quote clause rather than full sentence, but whole sentence safest. No ellipsis. JSON escaped apostrophes not issue.\n\nCould unit be \"5-year AUC\"? If field unit expected unit printed, \"AUC\" is dimensionless. The 5-year is time horizon not unit. Maybe empty unit because no unit, but instruction says unit must be unit paper prints. For AUC, conventional unit \"AUC\". We can use \"AUC\". Number is 0.79.\n\nLet's ensure no markdown.</think>\n\n{\"parent_value\":0.79,\"unit\":\"AUC\",\"evidence\":\"Using Cox regression and machine learning, our model achieved a 5-year area under curve (AUC) of 0.79, surpassing existing models in predictive performance.\",\"target_result\":\"5-year area under curve (AUC) of 0.79\",\"number_stated\":true}", "reading": {"parent_value": 0.79, "unit": "AUC", "evidence": "Using Cox regression and machine learning, our model achieved a 5-year area under curve (AUC) of 0.79, surpassing existing models in predictive performance.", "target_result": "5-year area under curve (AUC) of 0.79", "number_stated": true, "source_locator": "chars 1324-1480 of source sha256 5540c1ed9e37c944674dbf5ae297aa013a92d4650ebe16903acdf0bbccd761e1"}, "recorded_at": "2026-10-31T15:30:40.292386+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "The ARG-based prognostic model achieved a 5-year area under curve (AUC) of 0.79.", "minimax_alt": "The ARG-based prognostic model achieved a 5-year area under curve (AUC) of 0.79 for predicting survival in RCC.", "decisive": false}, "physical_methods": {"minimax": ["In vitro anoikis assays using poly-HEMA-treated 786-O and Achn renal carcinoma cell lines", "MMP9 knockdown using lentivirus", "Wound-healing migration assay", "Transwell invasion assay", "Western blot analysis of MMP9 and cleaved caspase 3", "RT-qPCR analysis of MMP9 expression", "Flow cytometric analysis of apoptosis using Annexin V and DAPI"], "minimax_alt": ["in vitro experiments in 786 O and Achn RCC cell lines", "lentivirus-mediated MMP9 knockdown", "poly-HEMA treatment for anoikis assays", "wound-healing assay", "transwell invasion assay", "western blot analysis", "RT-qPCR", "Annexin V and DAPI flow cytometric analysis"], "decisive": false}, "analysis_methods": {"minimax": ["Differential expression analysis with DESeq2", "Median-split survival screening with log-rank tests", "LASSO regression", "SVM-RFE", "Random forest", "Cox regression", "Kaplan-Meier survival analysis", "Time-dependent ROC analysis with TimeROC", "Gene Ontology and KEGG over-representation analysis", "GSVA", "GSEA", "Monocle 2 pseudotime analysis", "NicheNet cell-cell interaction analysis", "pySCENIC transcription factor analysis"], "minimax_alt": ["Cox regression", "machine learning using LASSO, SVM-RFE, and random forest", "receiver operating characteristic (ROC) curves with AUC calculated by R package ‘TimeROC’", "log-rank test and Kaplan-Meier curves", "DESeq2 differential expression analysis", "Gene Set Variation Analysis (GSVA)", "Gene Set Enrichment Analysis (GSEA)", "Monocle 2 pseudotime analysis", "NicheNet ligand-receptor-target network analysis", "pySCENIC regulon analysis", "Student’s t-test or one-way ANOVA for experimental comparisons"], "decisive": false}, "statistical_method": {"minimax": "Receiver operating characteristic (ROC) curves were used, with AUC calculated by the TimeROC R package.", "minimax_alt": "Cox regression and machine learning", "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; 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