Immune molecule diagnostics in colorectal cancer: CCL2 and CXCL11.

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null, "sha256": "sha256:2cbb2cf5b0b38ff6e9f97c56da667a8ac54c34722028d11de7ceabd9c4e4dff5", "bytes": 725, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[12]/table-wrap[1]#j_biol-2025-1147_tab_001", "version": null, "note": "Immune cell infiltration showed statistically significant differences among the four groups", "structure": {"pmcid": "PMC12355365", "section_index": 12, "section_title": "Landscape of immune cell infiltration in SSA and TSA", "row_count": 14, "column_count": 2}, "table": {"label": "Table 1", "caption": "Immune cell infiltration showed statistically significant differences among the four groups", "rows": [["Immune cells", "P values"], ["Activated mast cells", "1.53 × 10−13"], ["Resting mast cells", "1.82 × 10−16"], ["Activated dendritic cells", "0.01"], ["Resting dendritic cells", "1.73 × 10−7"], ["Macrophages", ""], ["M0", "1.72 × 10−8"], ["M1", "4.43 × 10−9"], ["M2", "1.28 × 10−5"], ["Regulatory T cells (Tregs)", "0.02"], ["Follicular Helper T cells", "0.003"], ["Activated memory T CD4 cells", "0.0002"], ["Plasma cells", "7.92 × 10−11"], ["Naive B cells", "0.01"]], "row_count": 14, "column_count": 2, "numeric_cells": 12, "header_rows": 1, "locator": "section[12]/table-wrap[1]#j_biol-2025-1147_tab_001"}}, {"id": "asset:table:pmid:40822974:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:b9d6b405ba748ab2b1bae3c4b586a13a793a40fa5976d4a4b695b7a384712675", "bytes": 834, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[14]/table-wrap[1]#j_biol-2025-1147_tab_002", "version": null, "note": "Ten different algorithms for hub genes that had the highest scores by CytoHubba", "structure": {"pmcid": "PMC12355365", "section_index": 14, "section_title": "PPI network for key immune-related gene selection", "row_count": 11, "column_count": 6}, "table": {"label": "Table 2", "caption": "Ten different algorithms for hub genes that had the highest scores by CytoHubba", "rows": [["Algorithm", "Gene1", "Gene2", "Gene3", "Gene4", "Gene5"], ["MCC", "CXCL14", "CCL24", "CXCL12", "CCL19", "CXCL11"], ["DMNC", "CCL2", "CXCR4", "CXCL12", "CCL19", "CXCL11"], ["MNC", "CCL2", "CXCL11", "CXCR4", "CXCL12", "CCL19"], ["Degree", "CXCL11", "CCL2", "CXCR4", "CXCL3", "CCL19"], ["EPC", "CXCL11", "CCL2", "CXCR4", "CCL24", "LGR5"], ["BottleNeck", "CCL11", "CCL2", "CXCR4", "CCL24", "CXCL14"], ["EcCentricity", "CXCL11", "CCL2", "CXCR4", "CXCL14", "CXCL12"], ["Closeness", "CXCL11", "CCL2", "CXCR4", "CXCL14", "CXCL12"], ["Radiality", "CXCL11", "CCL2", "CXCL14", "RETNLB", "MX2"], ["Betweenness", "CCL2", "CXCL11", "RETNLB", "CXCR4", "CXCL14"]], "row_count": 11, "column_count": 6, "numeric_cells": 28, "header_rows": 1, "locator": "section[14]/table-wrap[1]#j_biol-2025-1147_tab_002"}}], "gaps": ["asset:source:pmid:40822974 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": {"dsl_operations": [], "assets": ["source:pmid:40822974", "asset:table:pmid:40822974:1", "asset:table:pmid:40822974:2"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40822974"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "6b612d15a9fbd0ddcbb6b4de8e7be9039d448cf95d15c8f76959478ea05bbc6d", "source_hash": "f45c60e81b4619b2d57eea50b6272aba1f4c3149a5e3080a3b39b14ad1e90e1c", "raw_response_sha256": "sha256:f0d0555ca96fad6b0524208e18e0e85b4a5b0201048cacc79773d56995380dc3", "raw_response_chars": 20076}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "611067995bdfbeef70b71c077d8d1d072238b1015edca8205da2ba038072ddcb", "source_hash": "f45c60e81b4619b2d57eea50b6272aba1f4c3149a5e3080a3b39b14ad1e90e1c", "raw_response": "{\"parent_value\":45,\"unit\":\"DEGs\",\"evidence\":\"A total of 45 DEGs (34 upregulated, 11 downregulated) were identified, with CCL2 and CXCL11 emerging as key hub genes.\",\"target_result\":\"A total of 45 DEGs (34 upregulated, 11 downregulated) were identified.\",\"number_stated\":true}", "reading": {"parent_value": 45, "unit": "DEGs", "evidence": "A total of 45 DEGs (34 upregulated, 11 downregulated) were identified, with CCL2 and CXCL11 emerging as key hub genes.", "target_result": "A total of 45 DEGs (34 upregulated, 11 downregulated) were identified.", "number_stated": true, "source_locator": "chars 1031-1149 of source sha256 f45c60e81b4619b2d57eea50b6272aba1f4c3149a5e3080a3b39b14ad1e90e1c"}, "recorded_at": "2026-10-31T15:29:39.333755+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "60f4237f7b3a3fe8736d581eca4ad4f33b22fb4f3a85d73ceac1274fa1589401", "source_hash": "f45c60e81b4619b2d57eea50b6272aba1f4c3149a5e3080a3b39b14ad1e90e1c", "raw_response": "<think>We need answer JSON only, extract claims source. Need determine single numerical result headline main finding registered target. Source gives \"A total of 45 DEGs (34 upregulated, 11 downregulated) were identified, with CCL2 and CXCL11 emerging as key hub genes.\" Need decide headline number 45 likely target numerical result. Unit DEGs. Need quote exact contiguous containing number and linking clause whole. User stresses value paper itself states main finding, no calc. This source abstract seems actual article maybe AUC values omitted. Headline numerical result likely 45 DEGs with breakdown. Need output parent_value 45 unit \"DEGs\" evidence full clause \"A total of 45 DEGs (34 upregulated, 11 downregulated) were identified, with CCL2 and CXCL11 emerging as key hub genes.\" target_result maybe \"45 DEGs (34 upregulated, 11 downregulated) were identified, with CCL2 and CXCL11 emerging as key hub genes.\" number_stated true. Need deliberate if target is headline result for main finding \"CCL2 and CXCL11\" maybe diagnostic AUC missing. Only numbers abstract 45 (34 up 11 down), datasets, immune 22, etc. They ask single numerical result a replication registered target has to reproduce. They likely expect 45 DEGs? Main finding could be diagnostic AUC but no value in source. We should not false because number stated 45. Need parse wording \"the headline numerical result the paper states for its main finding.\" Which is 45 DEGs. The abstract source likely truncated actual AUCs and method counts. Their benchmark could expect false if target is AUC? Registered target generic. They likely extraction instruction target numerical result from source. Usually tasks ask to find headline numerical result main finding, and number stated is 45. We need ensure unit exact \"DEGs\" yes. Evidence contiguous full sentence. Could target_result \"A total of 45 DEGs (34 upregulated, 11 downregulated) were identified.\" Parent 45. Could include key hubs. No invented source. JSON valid no code fences likely.\n\nNeed consider unit empty if no headline number? They say if no number for registered target false and what text states instead. Source states numerical result 45 DEGs. It's main finding discovery. Yes.\n\nWe should deliver only JSON object.</think>\n\n{\"parent_value\":45,\"unit\":\"DEGs\",\"evidence\":\"A total of 45 DEGs (34 upregulated, 11 downregulated) were identified, with CCL2 and CXCL11 emerging as key hub genes.\",\"target_result\":\"A total of 45 DEGs (34 upregulated, 11 downregulated) were identified, with CCL2 and CXCL11 emerging as key hub genes.\",\"number_stated\":true}", "reading": {"parent_value": 45, "unit": "DEGs", "evidence": "A total of 45 DEGs (34 upregulated, 11 downregulated) were identified, with CCL2 and CXCL11 emerging as key hub genes.", "target_result": "A total of 45 DEGs (34 upregulated, 11 downregulated) were identified, with CCL2 and CXCL11 emerging as key hub genes.", "number_stated": true, "source_locator": "chars 1031-1149 of source sha256 f45c60e81b4619b2d57eea50b6272aba1f4c3149a5e3080a3b39b14ad1e90e1c"}, "recorded_at": "2026-10-31T15:29:56.045369+00:00"}], "agreement": false, "disagreements": [], "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": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "reason": "a quoted statement is absent from the supplied text", "raw_response": "{\n  \"target_result\": \"A total of 45 DEGs (34 upregulated, 11 downregulated) were identified, with CCL2 and CXCL11 emerging as key hub genes.\",\n  \"work_type\": \"analysis_plus_physical_validation\",\n  \"physical_methods\": [\n    \"Immunohistochemistry\",\n    \"Grayscale quantification of stained tissue sections\",\n    \"Systematic sampling of minimum five sample points within identical regions of interest\"\n  ],\n  \"analysis_methods\": [\n    \"RNA sequencing\",\n    \"GEOquery\",\n    \"CIBERSORTx\",\n    \"CIBERSORT\",\n    \"limma differential expression analysis\",\n    \"Gene Ontology enrichment analysis\",\n    \"KEGG pathway enrichment analysis\",\n    \"clusterProfiler\",\n    \"STRING protein-protein interaction network analysis\",\n    \"Cytoscape sub-cluster analysis\",\n    \"Receiver operating characteristic analysis\",\n    \"Area under the curve estimation\",\n    \"Analysis of variance\",\n    \"Student's t-test\",\n    \"Pearson's correlation analysis\",\n    \"One-way ANOVA with post hoc Tukey's test\"\n  ],\n  \"statistical_method\": \"Differential expression analysis of immune-related genes was performed with the “limma” package.\",\n  \"method_evidence\": \"Differential expression analysis of immune-related genes was performed with the “limma” package.\",\n  \"repositories\": [\n    {\n      \"repository\": \"Gene Expression Omnibus (GEO)\",\n      \"accession\": \"GSE117606\",\n      \"data_description\": \"RNA sequencing data used to distinguish TSA from SSA.\",\n      \"evidence\": \"This study developed an immune molecule-based model to distinguish TSA from SSA using RNA sequencing data from the GEO datasets (GSE117606, GSE45270, GSE117607).\",\n      \"publicly_downloadable\": false\n    },\n    {\n      \"repository\": \"Gene Expression Omnibus (GEO)\",\n      \"accession\": \"GSE45270\",\n      \"data_description\": \"RNA sequencing data used to distinguish TSA from SSA.\",\n      \"evidence\": \"This study developed an immune molecule-based model to distinguish TSA from SSA using RNA sequencing data from the GEO datasets (GSE117606, GSE45270, GSE117607).\",\n      \"publicly_downloadable\": false\n    },\n    {\n      \"repository\": \"Gene Expression Omnibus (GEO)\",\n      \"accession\": \"GSE117607\",\n      \"data_description\": \"RNA sequencing data used to distinguish TSA from SSA.\",\n      \"evidence\": \"This study developed an immune molecule-based model to distinguish TSA from SSA using RNA sequencing data from the GEO datasets (GSE117606, GSE45270, GSE117607).\",\n      \"publicly_downloadable\": false\n    }\n  ],\n  \"availability_statement\": \"The paper identifies three GEO datasets, but the supplied text does not explicitly state whether the data can be downloaded without an application, embargo, or request, nor does it state that the data are available on request.\",\n  \"data_on_request\": false,\n  \"source_claims\": [\n    \"A total of 45 DEGs (34 upregulated, 11 downregulated) were identified, with CCL2 and CXCL11 emerging as key hub genes.\",\n    \"Their diagnostic potential was validated through receiver operating characteristic analysis in GEO datasets and clinical samples, while immunohistochemistry revealed decreased expression of CCL2 and CXCL11 in SSA compared to TSA and normal tissues, indicating their role in SSA pathogenesis and potential as molecular diagnostic markers.\",\n    \"The diagnostic value of CCL2 is superior to that of CXCL11, while the diagnostic value of CXCL11 requires further experimental verification.\",\n    \"CCL2 is highly promising as a potential diagnostic model for SSA and TSA; the potential diagnostic value of CXCL11 needs to be further verified.\",\n    \"The diagnostic value of gene expression in patients with SSAs was analyzed using receiver operating characteristic (ROC) curves, with the area under curve (AUC) being used to estimate the diagnostic value.\"\n  ],\n  \"missing_information\": [\n    \"No numerical AUC, sensitivity, specificity, confidence interval, or other numerical diagnostic-performance result is provided.\",\n    \"The paper does not provide a numerical headline result for the comparative diagnostic performance of CCL2 versus CXCL11.\",\n    \"The supplied text does not explicitly describe the public download conditions for the GEO datasets.\",\n    \"The supplied text does not state that any data are available on request.\"\n  ],\n  \"evidence_spans\": [\n    \"This study developed an immune molecule-based model to distinguish TSA from SSA using RNA sequencing data from the GEO datasets (GSE117606, GSE45270, GSE117607).\",\n    \"Differential expression analysis of immune-related genes was performed with the “limma” package.\",\n    \"A total of 45 DEGs (34 upregulated, 11 downregulated) were identified, with CCL2 and CXCL11 emerging as key hub genes.\",\n    \"Their diagnostic potential was validated through receiver operating characteristic analysis in GEO datasets and clinical samples, while immunohistochemistry revealed decreased expression of CCL2 and CXCL11 in SSA compared to TSA and normal tissues, indicating their role in SSA pathogenesis and potential as molecular diagnostic markers.\",\n    \"The diagnostic value of CCL2 is superior to that of CXCL11, while the diagnostic value of CXCL11 requires further experimental verification.\",\n    \"The diagnostic value of gene expression in patients with SSAs was analyzed using receiver operating characteristic (ROC) curves, with the area under curve (AUC) being used to estimate the diagnostic value.\",\n    \"Statistical comparison: Intergroup comparisons were conducted using one-way ANOVA with post hoc Tukey’s test. 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