FOXP3 as a prognostic marker and therapeutic target in immunogenic cell death modulation for clear cell renal cell carcinoma.

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{"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "f5747ca32fe6f2a687fe6f5eaa1138b79b976c2ac23f11293dc87222fc20b514", "source_hash": "ef0ca94bfb80c40560513ace0d81acf61eb4f03179ce7ca95ffbfdca9acce840", "raw_response_sha256": "sha256:58b46d4f9edec5307dc07f29f847b1e84f638d3f42cdd2b625a907fc82c8edbc", "raw_response_chars": 48759}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "5f579c41bbe31e7afb3ab55fe8e14ba0f1cf321ee9885faacfa12a4be9d15ebe", "source_hash": "ef0ca94bfb80c40560513ace0d81acf61eb4f03179ce7ca95ffbfdca9acce840", "raw_response": "{\"parent_value\":0.781,\"unit\":\"AUC\",\"evidence\":\"Diagnostic ROC curve indicated that FOXP3 has good accuracy in distinguishing between cancer and adjacent normal tissues, with an AUC of 0.781 (Fig. 6B).\",\"target_result\":\"FOXP3 diagnostic ROC AUC\",\"number_stated\":true}", "reading": {"parent_value": 0.781, "unit": "AUC", "evidence": "Diagnostic ROC curve indicated that FOXP3 has good accuracy in distinguishing between cancer and adjacent normal tissues, with an AUC of 0.781 (Fig. 6B).", "target_result": "FOXP3 diagnostic ROC AUC", "number_stated": true, "source_locator": "chars 5043-5196 of source sha256 ef0ca94bfb80c40560513ace0d81acf61eb4f03179ce7ca95ffbfdca9acce840"}, "recorded_at": "2026-10-31T15:22:17.392092+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "e3fda92cbd0255e0780e1b749a3c15e3d4d8305a7f52ec2fd1f0cde8fdd81c28", "source_hash": "ef0ca94bfb80c40560513ace0d81acf61eb4f03179ce7ca95ffbfdca9acce840", "raw_response": "<think>We need answer JSON only. Need identify single numerical result replication registered target headline numerical result paper states for main finding. Source includes multiple numbers. Main finding perhaps FOXP3 AUC 0.781 as headline numerical result? Need infer. They ask \"registered target: the headline numerical result the paper states for its main finding.\" We need report value paper itself states, not calculated. Unit paper prints. Evidence exact contiguous quote containing number and attribution via direct wording. Candidate \"Diagnostic ROC curve indicated that FOXP3 has good accuracy in distinguishing between cancer and adjacent normal tissues, with an AUC of 0.781 (Fig. 6B).\" Headline numerical result likely AUC 0.781. Could perhaps 11 genes. But paper main finding FOXP3 as prognostic marker and therapeutic target; headline numerical result perhaps AUC 0.781. Need parent_value 0.781, unit maybe \"AUC\" or empty? They say unit must be unit paper prints. AUC is dimensionless and paper prints \"AUC of 0.781.\" Unit likely \"AUC\". Evidence whole clause exact. target_result description perhaps \"FOXP3 has an AUC of 0.781 for distinguishing...\" Need ensure source exact quotation no shorten. Exact contiguous quote. Could quote sentence. \"Diagnostic ROC curve indicated that FOXP3 has good accuracy in distinguishing between cancer and adjacent normal tissues, with an AUC of 0.781 (Fig. 6B).\" Yes.\n\nQuestion wording nuanced: \"find the single numerical result that a replication of the registered target has to reproduce.\" Maybe target registered is \"headline numerical result the paper states for its main finding.\" Need select one. Other salient numerical: 11 genes, 18 genes, 3464, p &lt;0.001, AUC .781. Main finding is FOXP3. Its quantitative headline is AUC .781. Unit printed maybe no conventional unit. Unit string could be \"AUC\". In schema parent_value numeric.\n\nNeed not include extra. target_result likely \"FOXP3's diagnostic ROC AUC was 0.781.\" But this paraphrases? target_result unrestricted. Evidence exact. number_stated true.\n\nCould unit be \"\" because AUC isn't a unit? User says \"The unit must be the unit the paper prints.\" Paper prints \"AUC\", not a unit exactly. They likely expect unit \"AUC\". We should state \"AUC\". Let's ensure JSON valid, no markdown.</think>\n\n{\"parent_value\":0.781,\"unit\":\"AUC\",\"evidence\":\"Diagnostic ROC curve indicated that FOXP3 has good accuracy in distinguishing between cancer and adjacent normal tissues, with an AUC of 0.781 (Fig. 6B).\",\"target_result\":\"FOXP3 had an AUC of 0.781 for distinguishing ccRCC from adjacent normal tissues.\",\"number_stated\":true}", "reading": {"parent_value": 0.781, "unit": "AUC", "evidence": "Diagnostic ROC curve indicated that FOXP3 has good accuracy in distinguishing between cancer and adjacent normal tissues, with an AUC of 0.781 (Fig. 6B).", "target_result": "FOXP3 had an AUC of 0.781 for distinguishing ccRCC from adjacent normal tissues.", "number_stated": true, "source_locator": "chars 5043-5196 of source sha256 ef0ca94bfb80c40560513ace0d81acf61eb4f03179ce7ca95ffbfdca9acce840"}, "recorded_at": "2026-10-31T15:22:18.342976+00:00"}], "agreement": true, "disagreements": {"target_result": {"minimax": "FOXP3 had an AUC of 0.781 for distinguishing ccRCC cancer tissue from adjacent normal tissue.", "minimax_alt": "FOXP3 achieved an AUC of 0.781 for distinguishing ccRCC cancer tissue from adjacent normal tissue.", "decisive": false}, "physical_methods": {"minimax": ["FOXP3 knockdown experiments in ccRCC cell lines"], "minimax_alt": ["FOXP3 knockdown in ccRCC cell lines", "Cell proliferation measurement", "Cell migration measurement"], "decisive": false}, "analysis_methods": {"minimax": ["consensus clustering", "principal component analysis (PCA)", "univariate Cox regression analysis", "Kaplan-Meier (KM) curve analysis", "receiver operating characteristic (ROC) curve analysis", "Gene Set Enrichment Analysis (GSEA)", "ESTIMATE", "immune cell infiltration analysis", "functional pathway enrichment analysis", "somatic mutation analysis using the maftools R package", "tumor mutational burden (TMB) analysis", "drug sensitivity analysis using the prophetic package"], "minimax_alt": ["Consensus clustering", "Principal component analysis (PCA) for ICD signature development", "Differential expression analysis using an adjusted p-value threshold of < 0.001", "Univariate Cox regression", "Receiver operating characteristic (ROC) curve analysis", "Kaplan-Meier (KM) survival analysis", "Gene set enrichment analysis (GSEA)", "ESTIMATE", "maftools somatic mutation profiling", "Tumor mutational burden comparison", "prophetic-package IC50 analysis", "TCIA immune-therapy response comparison"], "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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