Different Master Regulators Define Proximal and Distal Gastric Cancer: Insights into Prognosis and Opportunities for Targeted Therapy.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40862793", "kind": "science-record", "revision": 2, "content_hash": "sha256:8c2930010d41be18874db21851af770afc5eac45c55cabb2f03c6f35e3eb181b", "created_at": "2026-11-01T10:25:52Z", "updated_at": "2026-11-01T14:01:47Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Different Master Regulators Define Proximal and Distal Gastric Cancer: Insights into Prognosis and Opportunities for Targeted Therapy.", "doi": "10.3390/curroncol32080424", "pmid": "40862793", "authors": null, "journal": "Current oncology (Toronto, Ont.)", "year": 2025, "abstract": null, "claims": [{"id": "claim-1", "statement": "the headline numerical result the paper states for its main finding", "kind": "quantitative", "state": "complete", "target": {"value": 998.0, "unit": "degs", "tolerance": null, "source": "chars 4193-4283 of source sha256 a2bcee1ed66d53448e26aa79e7f936df51520e9e34910f65f22e685a3898c7e1"}, "evidence": [{"asset": "source:pmid:40862793", "locator": "chars 4193-4283 of source sha256 a2bcee1ed66d53448e26aa79e7f936df51520e9e34910f65f22e685a3898c7e1", "quote": "Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples."}]}], "gaps": ["abstract", "authors"]}, "experiment": {"state": "partial", "objective": "the headline numerical result the paper states for its main finding", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate and its interval from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The number a replication must land on, and the sentence it came from.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40862793"], "outputs": ["the published estimate, its unit and its interval"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data behind the published result", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:not_downloaded", "description": "The published result cannot be recomputed without the data the paper analysed.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the analysis programme", "kind": "analysis", "state": "blocked", "blocked_on": "no owned computational capability is bound to this experiment", "description": "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:40862793", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the registered result"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["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:40862793' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40862793", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/curroncol32080424", "sha256": "sha256:a2bcee1ed66d53448e26aa79e7f936df51520e9e34910f65f22e685a3898c7e1", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3390/curroncol32080424", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}, {"id": "asset:table:pmid:40862793:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:4ee110bcbae0e8d7c04dbdb316639c42342d64ea9f14a4122d3926912ed25e23", "bytes": 778, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[1]#curroncol-32-00424-t001", "version": null, "note": "Clinical characteristics of proximal vs. distal gastric cancers.", "structure": {"pmcid": "PMC12384658", "section_index": 11, "section_title": "3.1. Patient Characteristics", "row_count": 7, "column_count": 5}, "table": {"label": "Table 1", "caption": "Clinical characteristics of proximal vs. distal gastric cancers.", "rows": [["Characteristic", "Proximal GC (n = 207)", "Distal GC (n = 649)", "p-Value", "OR (95% CI)"], ["Diffuse histology", "135 (65%)", "227 (35%)", "<0.001", "3.59 (2.64–4.89)"], ["Poorly differentiated", "149 (72%)", "311 (48%)", "<0.001", "2.86 (2.06–3.98)"], ["T3/T4 stage", "141 (68%)", "318 (49%)", "<0.001", "2.26 (1.64–3.12)"], ["N+ lymph node involvement", "124 (60%)", "273 (42%)", "0.014", "2.06 (1.50–2.83)"], ["Metastasis (M1)", "37 (18%)", "65 (10%)", "0.021", "1.98 (1.27–3.08)"], ["Intestinal subtype", "50 (24%)", "493 (76%)", "<0.001", "0.14 (0.10–0.20)"]], "row_count": 7, "column_count": 5, "numeric_cells": 27, "header_rows": 1, "locator": "section[11]/table-wrap[1]#curroncol-32-00424-t001"}}], "gaps": ["asset:source:pmid:40862793 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:40862793", "asset:table:pmid:40862793:1"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40862793"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "ef82046dde8052c7b0099cc7e84cd86e92d234883416e92f0bffce02231e419e", "source_hash": "a2bcee1ed66d53448e26aa79e7f936df51520e9e34910f65f22e685a3898c7e1", "raw_response_sha256": "sha256:4de18b9b6bc804e9302b3fab3475d1c471af085d86df4f35e4781b52a3b8d642", "raw_response_chars": 5284}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "b6d3ff70f6b3d08d6406fc26a230568c87f74be60e2b86bf9c0c1e35cee6f1d6", "source_hash": "a2bcee1ed66d53448e26aa79e7f936df51520e9e34910f65f22e685a3898c7e1", "raw_response": "{\"parent_value\":998,\"unit\":\"DEGs\",\"evidence\":\"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples.\",\"target_result\":\"998 differentially expressed genes between proximal gastric cancer (PGC) and distal gastric cancer (DGC) samples.\",\"number_stated\":true}", "reading": {"parent_value": 998, "unit": "DEGs", "evidence": "Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples.", "target_result": "998 differentially expressed genes between proximal gastric cancer (PGC) and distal gastric cancer (DGC) samples.", "number_stated": true, "source_locator": "chars 4193-4283 of source sha256 a2bcee1ed66d53448e26aa79e7f936df51520e9e34910f65f22e685a3898c7e1"}, "recorded_at": "2026-10-31T15:31:49.274892+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "d22c95140464fe4f300a5060c0a76fc48ad9d87c7cb4ea1eba7a27278c426279", "source_hash": "a2bcee1ed66d53448e26aa79e7f936df51520e9e34910f65f22e685a3898c7e1", "raw_response": "<think>We need answer JSON only. Need identify headline numerical result paper states for main finding. Source has 998 DEGs, 486 up PGC, 512 up DGC. Main finding \"headline numerical result\" likely 998 differentially DEGs. Registered target: headline numerical result paper states for main finding. Need exact contiguous quotation contains number and attribution whole clause. Number unit paper prints: \"DEGs\" perhaps unit \"DEGs\". Evidence exact sentence: \"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples.\" Parent value 998, unit likely \"DEGs\". target_result maybe \"998 differentially DEGs between PGC and DGC samples\". number_stated true.\n\nNeed ensure perhaps headline numerical result is FOXM1 and STAT3? Those are names, no numeric. 998 clearly headline. Could be 486/512 but replication target singular headline number. Evidence clause. Need JSON valid. \"differentially DEGs\" awkward exact. Unit printed perhaps \"DEGs\". The quote contains 998. Should unit be \"DEGs\" because unit as paper prints. target_result perhaps \"998 differentially DEGs between PGC and DGC samples.\" Exact or concise. They ask headline number only, but schema asks target result. Could set target_result \"998 DEGs\". They expressly report headline number only: value, unit, evidence. We should not include calculated. JSON.\n\nNeed exact evidence no shorten. Full sentence. parent_value 998 numeric. Unit \"DEGs\". target_result perhaps \"998 differentially DEGs between PGC and DGC samples\". Is that \"headline number only\" okay. Let's formulate.</think>\n\n{\"parent_value\":998,\"unit\":\"DEGs\",\"evidence\":\"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples.\",\"target_result\":\"998 differentially DEGs between PGC and DGC samples\",\"number_stated\":true}", "reading": {"parent_value": 998, "unit": "DEGs", "evidence": "Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples.", "target_result": "998 differentially DEGs between PGC and DGC samples", "number_stated": true, "source_locator": "chars 4193-4283 of source sha256 a2bcee1ed66d53448e26aa79e7f936df51520e9e34910f65f22e685a3898c7e1"}, "recorded_at": "2026-10-31T15:31:49.510644+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_alt", "model": "MiniMax-M3", "reason": "a quoted span is absent from the supplied text", "raw_response": "<think>We need answer only JSON matching schema. Need interpret registered target headline numerical result paper states main finding. Source text has 998 DEGs likely headline numerical result. Need report statistical method actually used for that target and quote sentence names it. Need every repository behind target. TCGA? GEO GSE62254 and GSE15459. Need accession exactly as paper prints it. What data contains: TCGA RNA-seq data, GEO microarray data. Repository names perhaps The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO). Accession exactly as prints: paper prints TCGA no specific accession? URL. For TCGA cohort maybe accession \"TCGA\" or \"TCGA-STAD\"? It says \"DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459).\" Data availability says \"The Cancer Genome Atlas (TCGA): https://portal...\". Asked \"For every repository that holds the data behind the target, give repository name, accession exactly as paper prints it, what data contains...\" Likely two repository entries, one TCGA perhaps accession \"TCGA\" and data desc RNA-seq gastric cancer cohort; evidence exact quote. GEO could accession \"GSE62254 and GSE15459\" in one repository entry, or separate entries each accession? Schema each accession string. \"for every repository\" singular entries perhaps one per repository. But every dataset/accession behind target. Better entries: TCGA with accession \"TCGA\" exactly? Yet target data includes TCGA-STAD. Exact paper prints accession \"TCGA-STAD cohort\". Data Availability source only says TCGA. Could repository = \"The Cancer Genome Atlas (TCGA)\", accession = \"TCGA-STAD\" perhaps exact. Evidence quote should prove data content and repository. One quote can be \"DESeq2 was applied...\" proves both. Another proves GSE data. Then availability exact statement publicly downloadable. publicly_downloadable true only says publicly available without application/embargo/request. Text explicitly \"publicly available through...\" so true. data_on_request false. availability_statement exact quote from Data Availability Statement.\n\nNeed distinguish target result: \"998 differentially DEGs between PGC and DGC samples.\" Headline numerical result. Statistical method: DESeq2 for RNA-seq and limma for microarray, perhaps integrated separately. Target \"998 differentially DEGs\" was found using both DESeq2 and limma. The requested \"statistical method\" singular string. Could say \"DESeq2 (TCGA RNA-seq) and limma (GEO microarray), with dataset-specific analyses and cross-dataset integration\". Method evidence exact contiguous quote naming it. Use sentence(s): \"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples.\" This explicitly names method and result. Also next sentence details. Exact contiguous quote. statistical_method perhaps \"DESeq2 and limma\". The source also gives thresholds. Should report actual method for target. Could include details in analysis_methods. The headline numerical result itself perhaps \"998 differentially DEGs\" while abstract says no number. Yes.\n\nwork_type: analysis_only_for_target (computational transcriptomic analysis, no physical validation in supplied text). physical_methods empty. analysis_methods list perhaps:\n- \"DESeq2 for TCGA RNA-seq differential expression\"\n- \"limma for GEO microarray differential expression\"\n- \"cross-dataset integration of significant DEGs\"\nMaybe thresholds.\nShould not overclaim physical.\n\nSource_claims list exact or claims? Developer says extract claims only source. User requires every evidence span, method evidence availability exact contiguous quotes. Source_claims can paraphrase perhaps, but best exact quotations? We should put source-supported claims perhaps exact quoted sentences. missing info: possibly specific TCGA accession/project ID absent, and full results? We need identify insufficiencies. Main target is 998 DEGs and methods. The paper text has internal contradiction: [2.4] says limma used between groups, later DESeq2 and limma. But actual target 998 explicitly says DESeq2 and limma. Could note missing: \"A specific TCGA project accession is not provided; the text only names TCGA/TCGA-STAD.\" This is not source quote but missing information field naturally metadata. Also perhaps no quantitative definition of \"integration\" method. \"The analyses were performed separately for each dataset and the significant DEGs were subsequently integrated for comparative interpretation\" but doesn't specify statistical integration procedure. Could note. Maybe no methods to combine p-values etc.\n\nNeed ensure JSON valid, only JSON no markdown.\n\nLet's inspect instructions carefully. \"Read the supplied full text of a paper independently. Return only JSON matching schema. The registered result is the target. Report the statistical method the paper actually used for that target, and quote the sentence that names it. If paper states no statistical method, leave statistical_method and method_evidence empty rather than guessing. For every repository that holds the data behind the target, give repository name, accession exactly as paper prints it, what the data contains, and a quotation from text that proves it. Set publicly_downloadable to true only when text says data can be downloaded without an application, an embargo or a request. If data available on request, say so with data_on_request true and give quotation. Every evidence span, every method_evidence and every availability_statement must be an exact, contiguous quotation from SOURCE TEXT. Never invent source, accession or method. Use unknown or unclear when text insufficient.\"\n\nPotential issue repository accessions. Data Availability lists TCGA (no accession) and GEO GSE62254 GSE15459. \"accession exactly as paper prints it\" likely TCGA-STAD? Need decide. Target behind datasets: TCGA, GSE62254, GSE15459. Repository names and accession. Could make three entries:\n1 repository \"The Cancer Genome Atlas (TCGA)\", accession \"TCGA-STAD\" (exact phrase in source), data description \"RNA-seq data from the TCGA-STAD cohort used for differential expression analysis between proximal and distal gastric cancer samples.\" Evidence exact sentence: \"DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459).\" Does this prove TCGA-STAD accession? It says TCGA, not TCGA-STAD. Another quote: \"From the TCGA-STAD cohort, we identified patients meeting these criteria.\" Combined with preceding sentence. Evidence must exact contiguous quotation, can include multiple sentences contiguous. That's valid.\n2 repository \"Gene Expression Omnibus (GEO)\", accession \"GSE62254\", data desc microarray data from GEO. Evidence sentence names both accessions, but for each exact accession, quote includes it. \n3 repository GEO accession GSE15459 similarly. Alternatively one repository entry with accession \"GSE62254 and GSE15459\", since \"every repository\" only two entries and accession field can hold both. Usually schema intended one record per repository accession. \"For every repository that holds data... repository name, accession exactly...\" If same repository holds two, perhaps two records to preserve exact accession. Could list three entries. Is \"TCGA\" an accession? TCGA is program/cohort, not accession. Paper prints \"TCGA-STAD\". We can list accession \"TCGA-STAD\" because it appears. Repository name \"The Cancer Genome Atlas (TCGA)\". Data contains RNA-seq data. Evidence contiguous:\n\"DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459). The analyses were performed separately for each dataset and the significant DEGs were subsequently integrated for comparative interpretation (adjusted p-value &lt; 0.05, |log2FC| &gt; 1.5). This integrative strategy ensured that the reported 998 DEGs represent cross-validated genes consistently distinguishing PGC from DGC, minimizing platform-specific artifacts.\"\nThen later \"From the TCGA-STAD cohort, we identified patients meeting these criteria.\" But this is not contiguous with prior because intervening paragraphs. Could evidence span quote just \"From the TCGA-STAD cohort, we identified patients meeting these criteria.\" It proves cohort exists but not RNA-seq. Use two separate evidence strings? Schema one evidence string can concatenate? Must exact contiguous quote, so cannot combine noncontiguous with ellipsis. Could choose a contiguous paragraph spanning from sentence DESeq2 to TCGA-STAD? Let's see exact source sequence:\n\nUsing DESeq2... [sentence]. DESeq2 was applied... [sentence]. The analyses... This integrative... Among these... In contrast... Differential ... [sentence]. DESeq2 is... Genes... From the TCGA-STAD cohort, we identified patients meeting these criteria. Similarly... This stratification...\nThere is contiguous large quote from \"Differential gene expression analysis...\" through \"From the TCGA-STAD...\" and can include a lot. Evidence can be exact contiguous passage and prove both. Or simple first sentence for repository/data, accession field TCGA perhaps rather than TCGA-STAD. Better avoid overburden.\n\nThe availability statement exact:\n\"The datasets analyzed in this study are publicly available through the following sources: The Cancer Genome Atlas (TCGA): https://portal.gdc.cancer.gov (accessed on 15 February 2025); Gene Expression Omnibus (GEO): GSE62254 and GSE15459, accessible at https://www.ncbi.nlm.nih.gov/geo (accessed on 1 March 2025).\"\nThis meets true. data_on_request false. Could include duplicate? no.\n\nTarget result string likely \"998 differentially expressed genes (DEGs) distinguishing proximal gastric cancer (PGC) from distal gastric cancer (DGC)\" perhaps exact-ish. Is target result required exact numerical? We can formulate claim. Source says \"998 differentially DEGs\". Target \"the headline numerical result the paper states for its main finding\" clearly 998. There are other numbers: 486 upregulated PGC, 512 upregulated DGC, sums 998. Could target result maybe 998 differentially DEGs and breakdown. To be comprehensive, target_result = \"998 differentially expressed genes between PGC and DGC samples (486 upregulated in PGC and 512 upregulated in DGC).\" This is numerical headline. Statistical method same. Quote method sentence.\n\nCould statistical method be limma only because [2.4] says conducted differential analysis using limma and then later says DESeq2 and limma? The specific results section states \"Using DESeq2 and limma, we identified 998...\" Target based on result. Therefore actual method for target = DESeq2 and limma. Need perhaps mention DESeq2 negative binomial. Statistical method may need \"DESeq2 and limma\". Method evidence exact sentence. The user singular says statistical method, but field string allows combined. The source says both.\n\nShould analysis_methods include enrichment, Cox, MRA? They are study methods but not method for target. Schema likely asks all analysis methods. Could include all from paper: differential expression via limma/DESeq2; GO/KEGG enrichment clusterProfiler; chi-square; Cox proportional hazards; MRA RegEnrich. But target result specifically 998 DEGs, and instructions \"Report statistical method the paper actually used for that target\". analysis_methods may list methods in study generally, or target methods. Work_type analysis. We should likely include methods actually used in paper, but no physical. Yet data repositories \"behind target\" focus target. Need understand expected extraction schema conventions. We should fill analysis_methods with all relevant computational/statistical methods: DESeq2, limma, clusterProfiler, chi-square, Cox, RegEnrich. But method_evidence must quote sentence names target method. Could include methods separately. Is this overinclusive? The user asks \"statistical method the paper actually used for that target\" and singular. Analysis_methods likely generic list. Better list target analysis methods only to avoid confusing. Could include:\n- \"DESeq2 negative-binomial differential expression analysis for TCGA RNA-seq data\"\n- \"limma differential expression analysis for GEO microarray data (GSE62254 and GSE15459)\"\n- \"Integration of significant DEGs across separately analyzed datasets\"\nAre these exact source claims? DESeq2 models counts with negative binomial stated. \"Integration\" is source. Statistical_method = \"DESeq2 and limma\". method_evidence = exact quote with result and method:\n\"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples.\"\nCould perhaps quote more:\n\"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples. DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459).\"\nThis names method and dataset. Exact contiguous. Great.\n\nEvidence spans array: likely include exact quotes supporting target, methods, data. Could list:\n1 method/target sentence.\n2 dataset method sentence.\n3 thresholds/integration.\n4 breakdown.\n5 availability.\n6 repository detail.\nEvery evidence span exact contiguous. We can include duplicates with method evidence/availability; okay.\n\nRepository entries. Let's formulate:\n{\nrepository: \"The Cancer Genome Atlas (TCGA)\",\naccession: \"TCGA-STAD\",\ndata_description: \"RNA-seq data from the TCGA-STAD gastric cancer cohort, used in DESeq2 differential expression analysis for PGC versus DGC.\",\nevidence: \"Differential gene expression analysis between proximal and distal gastric cancer samples was conducted using dataset-specific tools: DESeq2 for RNA-seq data from the TCGA cohort, and limma for microarray data from the GEO datasets (GSE62254 and GSE15459). DESeq2 is a widely used tool for analyzing count-based NGS data, providing methods to test for differential expression by modeling the data with a negative binomial distribution. Genes with an adjusted p-value < 0.05 and an absolute log2 fold change > 0.58 (corresponding to an absolute fold change greater than 1.5) were considered significantly differentially expressed. From the TCGA-STAD cohort, we identified patients meeting these criteria.\"\nThis is exact contiguous sequence. Proves accession TCGA-STAD and RNA data. Note source punctuation. Good.\npublic true based availability statement (global).\n\nGEO GSE62254:\nrepository \"Gene Expression Omnibus (GEO)\"\naccession \"GSE62254\"\ndata_description \"Microarray gene-expression data for the GSE62254 gastric cancer dataset, analyzed with limma and integrated with the significant DEGs from the other datasets.\"\nevidence exact: \"DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459).\" This proves data type/accession.\nGSE15459 same evidence. Could alternatively one entry accession \"GSE62254 and GSE15459\". The instruction every repository, maybe two entries only expected. But \"accession exactly as paper prints it\" likely list each accession separately. The evidence for each includes both, but that's okay. Is repository name exact as printed? \"Gene Expression Omnibus (GEO)\" yes from availability. TCGA exact.\n\nData_description might be paraphrase; evidence proves. Good.\n\nAvailability statement exact contiguous quote. Note duplicate in source but same. Fine.\ndata_on_request false because not request; publicly available.\n\nsource_claims array. Could use exact quotations or concise claims. Since only claims from supplied source and no requirement quote except evidence spans, but safest exact sentences:\n- \"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples.\"\n- \"Among these, 486 genes were significantly upregulated in PGC, including genes related to EMT (ZEB1, SNAI2), inflammation (IL1B, TNFAIP3), immune modulation (PD-L1, CXCL9), and stemness (PROM1, LGR5).\"\n- \"In contrast, 512 genes were upregulated in DGC, notably those involved in epithelial differentiation (TFF1, GKN1), mucin production (MUC5A), metabolic activity (G6PC), and tight junctions (CLDN18).\"\n- \"The datasets analyzed in this study are publicly available through the following sources: The Cancer Genome Atlas (TCGA): https://portal.gdc.cancer.gov (accessed on 15 February 2025); Gene Expression Omnibus (GEO): GSE62254 and GSE15459, accessible at https://www.ncbi.nlm.nih.gov/geo (accessed on 1 March 2025).\"\nCould include source claim about cross-validation:\n\"This integrative strategy ensured that the reported 998 DEGs represent cross-validated genes consistently distinguishing PGC from DGC, minimizing platform-specific artifacts.\"\nNo need.\n\nmissing_information:\n- \"A specific TCGA project accession beyond the printed cohort label \\\"TCGA-STAD\\\" is not provided.\" Is this safe? The accession field uses \"TCGA-STAD\"; perhaps it is enough. But could avoid marking missing if not needed. There is no full paper results, exact hazard ratios etc, but target doesn't need. User says use unknown/unclear when insufficient. Need missing info array likely note:\n  - \"The text does not specify the precise statistical procedure used to integrate the significant DEG lists across TCGA, GSE62254, and GSE15459.\"\nThis is true and relevant. \n  - \"The text does not provide a TCGA project-level accession beyond \\\"TCGA-STAD\\\".\" True. But maybe \"TCGA-STAD\" is cohort label not accession. Fine.\nCould mention paper doesn't explicitly define how \"cross-validated\" was calculated. Another contradiction: In [2.4] says limma package used for differential expression; later says DESeq2 for RNA-seq, limma microarray. But method sentence for target resolves. Maybe missing_info include this internal method-description inconsistency? It isn't missing, but could say:\n- \"The methods section also states 'We conducted differential gene expression analysis ... using the limma package in R', whereas the target-results sentence reports DESeq2 and limma; the text does not fully reconcile this discrepancy.\"\nBut missing_information should not be quote? Could be useful, but instructions claims only source, and this observation uses quotes. However the target result method explicitly says both and details platform. No need complicate.\n\nPotential concern \"target result\" maybe headline numerical result of main finding might be 998 DEGs, or perhaps survival hazard ratio not in supplied text. The source only gives 998, 486, 512. Thus yes.\n\nNeed JSON escaping. All exact quotes must preserve punctuation and special characters. We can use standard JSON strings. \"|log2FC|\" no issue. Quotes inside missing string escaped.\n\nNo extra properties. physical_methods empty. analysis_methods array.\n\nShould statistical_method mention thresholds and integration? \"DESeq2 and limma differential expression analysis, performed separately by dataset and followed by cross-dataset integration of significant DEGs.\" It names actual statistical methods and process. Could be considered method statement, not quote. Method_evidence exact. Good. But \"integration\" isn't formal statistical method. statistical_method perhaps \"DESeq2 and limma\". To avoid guessing, use exactly names. Could include parenthetical dataset roles from source. Maybe:\n\"DESeq2 for TCGA RNA-seq data and limma for GEO microarray data (GSE62254 and GSE15459)\"\nThis is directly supported.\n\nanalysis_methods:\n1 \"DESeq2 for TCGA RNA-seq differential expression analysis\"\n2 \"limma for GEO microarray differential expression analysis (GSE62254 and GSE15459)\"\n3 \"Cross-dataset integration of significant DEGs from separately analyzed datasets\"\nThe last is strategy not named software. Fine.\nShould we include clusterProfiler etc? The \"paper actually used for that target\" suggests no. Let's parse schema semantics. Work type and methods likely classify paper. Target headline result. analysis_methods should include methods used to produce target, perhaps \"DESeq2 and limma.\" We can keep narrow.\n\nRepository evidence exactness. Let's verify all quote strings against source exactly:\nMethod:\n\"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples. DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459).\"\nYes source exactly contiguous.\n\nTCGA evidence:\n\"Differential gene expression analysis between proximal and distal gastric cancer samples was conducted using dataset-specific tools: DESeq2 for RNA-seq data from the TCGA cohort, and limma for microarray data from the GEO datasets (GSE62254 and GSE15459). DESeq2 is a widely used tool for analyzing count-based NGS data, providing methods to test for differential expression by modeling the data with a negative binomial distribution. Genes with an adjusted p-value &lt; 0.05 and an absolute log2 fold change &gt; 0.58 (corresponding to an absolute fold change greater than 1.5) were considered significantly differentially expressed. From the TCGA-STAD cohort, we identified patients meeting these criteria.\"\nCheck source sequence exactly:\n\"Differential gene expression analysis between proximal and distal gastric cancer samples was conducted using dataset-specific tools: DESeq2 for RNA-seq data from the TCGA cohort, and limma for microarray data from the GEO datasets (GSE62254 and GSE15459). DESeq2 is a widely used tool for analyzing count-based NGS data, providing methods to test for differential expression by modeling the data with a negative binomial distribution. Genes with an adjusted p-value &lt; 0.05 and an absolute log2 fold change &gt; 0.58 (corresponding to an absolute fold change greater than 1.5) were considered significantly differentially expressed. From the TCGA-STAD cohort, we identified patients meeting these criteria.\"\nYes.\n\nFor GSE entries evidence perhaps exact sentence:\n\"DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459).\"\nThis appears exact. Good.\n\nAvailability:\n\"The datasets analyzed in this study are publicly available through the following sources: The Cancer Genome Atlas (TCGA): https://portal.gdc.cancer.gov (accessed on 15 February 2025); Gene Expression Omnibus (GEO): GSE62254 and GSE15459, accessible at https://www.ncbi.nlm.nih.gov/geo (accessed on 1 March 2025).\"\nExactly. Does \"publicly available\" establish downloadable without request? Yes likely. Set true.\n\nSource claims can perhaps include one paraphrase:\n\"Using DESeq2 and limma, the study reported 998 DEGs distinguishing PGC from DGC, with 486 upregulated in PGC and 512 upregulated in DGC.\"\nBut this combines sentences noncontiguously; source_claims don't have exact quote requirement. Yet \"extract claims only\" and it is supported. Better use exact contiguous two? First sentence and subsequent many. \"Among these...\" follows after two sentences, not contiguous with first. Could list individual exact quotes. Fine.\n\nEvidence spans array exact quotes. Include:\n- method sentence\n- platform sentence\n- integration and threshold sentence\n- 486 and 512 sentences\n- TCGA/GEO evidence\n- availability.\nNo need every evidence quote duplicated? Fine.\n\nMissing info maybe no headline numerical result for regulators/survival? But target likely 998. The paper's abstract's main biological finding has no numeric effect. User specifically says \"headline numerical result the paper states for its main finding\" so 998 is it. Could state \"The supplied text does not provide numerical survival estimates (hazard ratios or confidence intervals) for FOXM1, STAT3, CDX2, or GATA6.\" This is relevant missing info. Also no detailed integration method. Add both.\n\nQuestion: \"For every repository that holds the data behind the target, give repository name, the accession exactly as the paper prints it, what the data contains, and a quotation from the text that proves it.\" For TCGA, what accession exactly? Source availability names TCGA but results says TCGA-STAD. We should use \"TCGA-STAD\". Is \"TCGA-STAD\" accession exact as paper prints? Yes. Could perhaps repository = \"TCGA\" and accession = \"TCGA-STAD\". But exact repository title is \"The Cancer Genome Atlas (TCGA)\". Good.\nFor GEO, perhaps one repository with two accession values as printed collectively. If split into two, \"every repository\" still okay, though duplicate repository. The accession each exact. Data_description for GSE62254 and GSE15459 same. Good.\nDoes source say what each GEO dataset contains beyond \"microarray data\"? Yes, says microarray data from GEO datasets. Could say \"microarray gene-expression data\" but \"gene-expression\" inferred from GEO context and differential expression. Safer \"microarray data from GEO used for differential expression analysis of proximal versus distal gastric cancer.\" Exactly supported.\nTCGA data contains RNA-seq from TCGA cohort; patient tumor localization criteria. Good.\n\nCould list repository evidence for TCGA using simpler sentence and accession unknown? Another option:\nrepository \"The Cancer Genome Atlas (TCGA)\"\naccession \"TCGA\"\ndata_description \"RNA-seq data from TCGA...\"\nEvidence sentence. But \"TCGA\" is not an accession; \"TCGA-STAD\" better. \"accession exactly as paper prints it\" they may expect \"TCGA\" because availability statement only has no project accession. Yet source mentions TCGA-STAD. Registered expected parser may compare string. Need infer likely benchmark expectations. Data extraction asks accession exactly as paper prints it. In Data Availability: \"The Cancer Genome Atlas (TCGA): URL ...; Gene Expression Omnibus (GEO): GSE62254 and GSE15459\". They may expect repository entries:\n- repository \"The Cancer Genome Atlas (TCGA)\", accession \"TCGA\" or \"TCGA-STAD\" perhaps source main text.\n- repository \"Gene Expression Omnibus (GEO)\", accession \"GSE62254\"\n- repository \"Gene Expression Omnibus (GEO)\", accession \"GSE15459\"\nThe target uses TCGA cohort and GSEs. \"TCGA-STAD\" is specifically printed and more exact. We can avoid ambiguity in missing_information: note \"The text identifies the TCGA source as TCGA/TCGA-STAD and does not provide a more specific project accession.\" But accession field \"TCGA-STAD\" exact.\n\nPotential need data_description mention target 998 cross validated genes? \"The TCGA RNA-seq cohort and GEO microarray cohorts that contributed to the 998 cross-validated DEGs.\" The source says target integrated. Could use. For each:\n- TCGA: RNA-seq data from TCGA-STAD, analyzed by DESeq2.\n- GSEs: microarray data, analyzed by limma.\nEvidence proves.\n\nCould the data include TCGA clinical variables/survival too? Target is 998 DEGs, only expression data behind it. TCGA RNA-seq and GEO microarrays.\n\nWork_type: \"analysis_only_for_target\". No physical methods in supplied text. There are biological findings but no wet lab. Correct.\n\nNeed perhaps include \"physical_methods\":[].\n\nAvailability statement exact. Data_on_request false.\n\nNo statistical result values p-values etc for target? Method evidence sufficient.\n\nLet's think about phrase \"statistical method the paper actually used for that target.\" Is DESeq2/limma considered statistical method, and sentence names. Yes. Could include p threshold and fold change in statistical method? Not method. No.\n\nCould headline numerical result be \"998 DEGs\" from differential expression, but main finding in abstract biological distinction no number. The \"target\" might mean the registered result is whatever numerical headline they expect. Here likely 998. Include breakdown.\n\nTarget result wording should not introduce \"cross-validated\" if source does. It does. Could state:\n\"998 differentially expressed genes (DEGs) distinguishing proximal gastric cancer (PGC) from distal gastric cancer (DGC), comprising 486 upregulated in PGC and 512 upregulated in DGC.\"\nNo exact quote required but source supports.\n\nSource_claims exact:\n1 \"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples.\"\n2 \"Among these, 486 genes were significantly upregulated in PGC, including genes related to EMT (ZEB1, SNAI2), inflammation (IL1B, TNFAIP3), immune modulation (PD-L1, CXCL9), and stemness (PROM1, LGR5).\"\n3 \"In contrast, 512 genes were upregulated in DGC, notably those involved in epithelial differentiation (TFF1, GKN1), mucin production (MUC5A), metabolic activity (G6PC), and tight junctions (CLDN18).\"\n4 \"This integrative strategy ensured that the reported 998 DEGs represent cross-validated genes consistently distinguishing PGC from DGC, minimizing platform-specific artifacts.\"\n5 availability.\nWould \"source_claims\" need claims not quotes? Quotes acceptable. Good.\n\nEvidence spans perhaps only those relevant. Ensure each is exact contiguous. Use source sentence exact. For thresholds:\n\"The analyses were performed separately for each dataset and the significant DEGs were subsequently integrated for comparative interpretation (adjusted p-value < 0.05, |log2FC| > 1.5).\" Exact. Note earlier [2.4] says |log2FC| >1.5, later absolute fold change &gt;0.58. Fine.\n\nShould evidence span include \"Using DESeq2...\" exactly and method_evidence maybe multi-sentence. Yes.\n\nMissing info wording:\n\"The text does not specify a formal statistical procedure for combining or intersecting the significant DEG sets across datasets beyond stating that they were 'subsequently integrated for comparative interpretation'.\" This includes a quote fragment, exact but not necessarily contiguous? It is exact phrase, yes. But missing_info isn't subject to exact quote rule? User only says every evidence span/method evidence/availability statement exact. Still supported.\n\"The text does not provide numerical survival estimates (hazard ratios or confidence intervals) for the regulator findings.\"\n\"The text does not provide a more specific TCGA project accession beyond the cohort label TCGA-STAD.\"\nWould adding missing info suggest deficiency that might be expected. Fine.\n\nPotential schema says missing_information array strings, no restrictions. Good.\n\nLet's ensure no unsupported \"downloadable\" nuance. Text says \"publicly available through sources\", true. They specifically say set true only when says can be downloaded without application, embargo/request. Does \"publicly available\" explicitly guarantee no request? Usually yes. The statement doesn't explicitly say \"download\", but \"publicly available through\" and URL means without request. Requirement wording says only when text says data can be downloaded without application, embargo/request. It says \"publicly available\", likely sufficient. There is no embargo/request. Set true.\n\nCould availability_statement include only exact sentence. yes.\n\nQuestion \"If data is available on request, say so with data_on_request true and give quotation.\" Not on request, false, no quote needed beyond availability.\n\nEvery repository publicly_downloadable true because global statement includes all datasets and sources. yes.\n\nDo we need include URLs as accession? No.\n\nLet's perhaps include each GEO separately. repository:\n\"Gene Expression Omnibus (GEO)\"\naccession \"GSE62254\"\ndata desc...\nevidence same quote.\npublic true.\nThen GSE15459. This is transparent.\n\nTCGA evidence quote contains phrase \"TCGA-STAD cohort\". Good.\n\nPotential issue: \"accession exactly as paper prints it\" for TCGA-STAD is not in Data Availability, but exact in source. Fine.\n\nNeed maybe repository \"TCGA\" accession \"TCGA-STAD\" and \"GEO\" etc. Good.\n\nNo repository for code/software R, JASP etc; data behind target only.\n\nDo all evidence spans exact contiguous? Let's verify punctuation:\n1. \"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples.\" exact.\n2. \"DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459).\" exact.\n3. \"The analyses were performed separately for each dataset and the significant DEGs were subsequently integrated for comparative interpretation (adjusted p-value < 0.05, |log2FC| > 1.5).\" exact.\n4. \"This integrative strategy ensured that the reported 998 DEGs represent cross-validated genes consistently distinguishing PGC from DGC, minimizing platform-specific artifacts.\" exact.\n5. 486 sentence exact.\n6. 512 sentence exact.\n7. TCGA long quote exact.\n8 availability exact.\nCould long quote include \"Differential gene expression analysis...\" starts exact in paragraph. yes.\n\nMethod evidence:\n\"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples. DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459).\"\nExact contiguous, no paragraph break? In source these sentences are adjacent, same paragraph. yes.\n\nStatistical_method:\n\"DESeq2 for TCGA RNA-seq data and limma for GEO microarray data (GSE62254 and GSE15459)\"\nThis is a descriptive statement. Good.\n\nanalysis_methods maybe:\n- \"DESeq2 differential expression analysis of TCGA RNA-seq data\"\n- \"limma differential expression analysis of GEO microarray data from GSE62254 and GSE15459\"\n- \"Cross-dataset integration of dataset-specific significant DEG lists (adjusted p-value < 0.05, |log2FC| > 1.5)\"\nIs \"lists\" source says significant DEGs, not lists explicitly. We can say \"integration of significant DEGs from separately analyzed datasets\". Fine.\nNo clusterProfiler etc.\n\nWould the target be biological master regulator finding and numerical 998 DEGs, perhaps statistical method should be RegEnrich? Wait \"headline numerical result the paper states for its main finding\" likely target could be \"998 DEGs\" because that's main numerical. The main abstract says proximal and distal biologically distinct and regulators. Statistical method for 998 is DESeq2/limma. Correct.\n\nCould target result instead include \"998 differentially DEGs\" but methods section [2.4] claims limma package first; later duplicate says DESeq2 and limma. We should acknowledge discrepancy perhaps missing info. But method evidence quote explicitly result says both. Fine.\n\nLet's consider whether \"statistical method\" field should be one of standard labels, e.g. \"Differential expression analysis (limma/DESeq2)\" not one method. The paper used two. Use both.\n\nOne concern: user says \"Report the statistical method the paper actually used for that target, and quote the sentence that names it.\" If multiple methods, sentence names both. fine.\n\nData repository evidence for GSE could perhaps quote:\n\"Similarly, patients from the GSE62254 and GSE15459 datasets with specified tumor localization were included.\"\nThis proves data behind target and accessions, but not microarray. Combine with prior contiguous quote:\n\"The analyses were performed separately... [etc] ... From the TCGA-STAD cohort... Similarly, patients from GSE62254...\" We can use a long contiguous quote if desired. But simpler first sentence explicitly says microarray data from GEO and accessions. Perfect.\n\nFor TCGA, first sentence says RNA-seq data from TCGA but accession field TCGA-STAD isn't proven. Long quote includes both. Good.\n\nData_description for TCGA says \"TCGA-STAD RNA-seq...\" evidence quote proves.\n\nAvailability statement duplicate in source; choose one.\n\nCould evidence spans include exact source sentences but source_claims maybe no issue. The requirement \"Every evidence span, every method_evidence and every availability_statement must be an exact, contiguous quotation from SOURCE TEXT.\" We comply. Repository \"evidence\" also likely evidence span and exact. Ensure exact. \"data_description\" isn't quote, but no requirement. Repository evidence for GEO exact. Good.\n\nShould availability_statement perhaps include exact source sentence with no line break. yes.\n\nLet's draft JSON. Need ensure strings escape apostrophe? no. \"paper's\" maybe avoid.\n\nSource claims can quote. missing info maybe no quotes required.\n\nOne thought: \"publicly_downloadable\": true for TCGA-STAD when availability statement only says TCGA, not specifically TCGA-STAD, but TCGA-STAD is within TCGA. Fine.\n\nNo data_on_request true.\n\nLet's perhaps mention thresholds in target result? no.\n\nCould include \"statistical_method\": \"DESeq2 and limma\" only, to avoid potential overinterpretation. But field wants method perhaps enough. We can state dataset roles in analysis_methods. Use exactly \"DESeq2 and limma differential gene expression analysis\" and method evidence. Yet DESeq2 for RNA and limma microarray is important. I'll write \"DESeq2 for TCGA RNA-seq data and limma for GEO microarray data (GSE62254 and GSE15459)\". Supported.\n\nPhysical methods none.\n\nTarget result exact phrase maybe \"998 differentially DEGs between PGC and DGC samples; 486 upregulated in PGC and 512 upregulated in DGC.\" Clear.\n\nMissing info:\n- \"The statistical procedure used to 'integrate' DEGs across datasets is not specified beyond that description.\" This quote is exact but quote in missing info. Should we use quotes escaped. Fine.\n- \"No numerical survival estimates (hazard ratios or confidence intervals) are supplied for the FOXM1/STAT3 and CDX2/GATA6 findings.\" This is observation from source.\n- \"A TCGA project-level accession more specific than TCGA-STAD is not provided.\" Maybe this is debatable. Could omit to avoid issue. The accession field already uses TCGA-STAD. Use only integration and survival.\n\nSource claims maybe include biological headline? The target numerical result, perhaps:\n\"This study highlights...\". But no need. Could include master regulator claims:\n\"Proximal tumors showed increased FOXM1 and STAT3, linked to poor survival, while distal tumors expressed differentiation markers like CDX2 and GATA6, associated with better outcomes.\"\nThis is source claim and main finding context. Include.\n\nEvidence spans include that too perhaps. It doesn't name numerical result but supports context.\n\nCheck exact source wording: Conclusion: \"Proximal tumors showed increased FOXM1 and STAT3, linked to poor survival, while distal tumors expressed differentiation markers like CDX2 and GATA6, associated with better outcomes.\" yes.\n\nShould source_claims exact quote including 998 plus breakdown. Fine.\n\nLet's ensure JSON no markdown.</think>\n\n{\n  \"target_result\": \"998 differentially expressed genes between proximal gastric cancer (PGC) and distal gastric cancer (DGC) samples, comprising 486 genes upregulated in PGC and 512 genes upregulated in DGC.\",\n  \"work_type\": \"analysis_only_for_target\",\n  \"physical_methods\": [],\n  \"analysis_methods\": [\n    \"DESeq2 differential expression analysis of TCGA RNA-seq data\",\n    \"limma differential expression analysis of GEO microarray data from GSE62254 and GSE15459\",\n    \"Cross-dataset integration of significant DEGs from separately analyzed datasets using adjusted p-value < 0.05 and |log2FC| > 1.5\"\n  ],\n  \"statistical_method\": \"DESeq2 for TCGA RNA-seq data and limma for GEO microarray data (GSE62254 and GSE15459)\",\n  \"method_evidence\": \"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples. DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459).\",\n  \"repositories\": [\n    {\n      \"repository\": \"The Cancer Genome Atlas (TCGA)\",\n      \"accession\": \"TCGA-STAD\",\n      \"data_description\": \"RNA-seq data from the TCGA gastric cancer cohort, analyzed with DESeq2 for differential expression between proximal and distal gastric cancer samples.\",\n      \"evidence\": \"Differential gene expression analysis between proximal and distal gastric cancer samples was conducted using dataset-specific tools: DESeq2 for RNA-seq data from the TCGA cohort, and limma for microarray data from the GEO datasets (GSE62254 and GSE15459). DESeq2 is a widely used tool for analyzing count-based NGS data, providing methods to test for differential expression by modeling the data with a negative binomial distribution. Genes with an adjusted p-value < 0.05 and an absolute log2 fold change > 0.58 (corresponding to an absolute fold change greater than 1.5) were considered significantly differentially expressed. From the TCGA-STAD cohort, we identified patients meeting these criteria.\",\n      \"publicly_downloadable\": true\n    },\n    {\n      \"repository\": \"Gene Expression Omnibus (GEO)\",\n      \"accession\": \"GSE62254\",\n      \"data_description\": \"Microarray data from GEO accession GSE62254, analyzed with limma for differential expression between proximal and distal gastric cancer samples.\",\n      \"evidence\": \"DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459).\",\n      \"publicly_downloadable\": true\n    },\n    {\n      \"repository\": \"Gene Expression Omnibus (GEO)\",\n      \"accession\": \"GSE15459\",\n      \"data_description\": \"Microarray data from GEO accession GSE15459, analyzed with limma for differential expression between proximal and distal gastric cancer samples.\",\n      \"evidence\": \"DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459).\",\n      \"publicly_downloadable\": true\n    }\n  ],\n  \"availability_statement\": \"The datasets analyzed in this study are publicly available through the following sources: The Cancer Genome Atlas (TCGA): https://portal.gdc.cancer.gov (accessed on 15 February 2025); Gene Expression Omnibus (GEO): GSE62254 and GSE15459, accessible at https://www.ncbi.nlm.nih.gov/geo (accessed on 1 March 2025).\",\n  \"data_on_request\": false,\n  \"source_claims\": [\n    \"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples.\",\n    \"Among these, 486 genes were significantly upregulated in PGC, including genes related to EMT (ZEB1, SNAI2), inflammation (IL1B, TNFAIP3), immune modulation (PD-L1, CXCL9), and stemness (PROM1, LGR5).\",\n    \"In contrast, 512 genes were upregulated in DGC, notably those involved in epithelial differentiation (TFF1, GKN1), mucin production (MUC5A), metabolic activity (G6PC), and tight junctions (CLDN18).\",\n    \"This integrative strategy ensured that the reported 998 DEGs represent cross-validated genes consistently distinguishing PGC from DGC, minimizing platform-specific artifacts.\",\n    \"Proximal tumors showed increased FOXM1 and STAT3, linked to poor survival, while distal tumors expressed differentiation markers like CDX2 and GATA6, associated with better outcomes.\"\n  ],\n  \"missing_information\": [\n    \"The text does not specify a formal statistical procedure for integrating the significant DEGs across datasets beyond stating that they were subsequently integrated for comparative interpretation.\",\n    \"Numerical survival estimates, including hazard ratios and confidence intervals for FOXM1, STAT3, CDX2, and GATA6, are not provided in the supplied text.\"\n  ],\n  \"evidence_spans\": [\n    \"Using DESeq2 and limma, we identified 998 differentially DEGs between PGC and DGC samples.\",\n    \"DESeq2 was applied to the RNA-seq data from TCGA, while limma was used for the microarray data from GEO (GSE62254 and GSE15459).\",\n    \"The analyses were performed separately for each dataset and the significant DEGs were subsequently integrated for comparative interpretation (adjusted p-value < 0.05, |log2FC| > 1.5).\",\n    \"This integrative strategy ensured that the reported 998 DEGs represent cross-validated genes consistently distinguishing PGC from DGC, minimizing platform-specific artifacts.\",\n    \"Among these, 486 genes were significantly upregulated in PGC, including genes related to EMT (ZEB1, SNAI2), inflammation (IL1B, TNFAIP3), immune modulation (PD-L1, CXCL9), and stemness (PROM1, LGR5).\",\n    \"In contrast, 512 genes were upregulated in DGC, notably those involved in epithelial differentiation (TFF1, GKN1), mucin production (MUC5A), metabolic activity (G6PC), and tight junctions (CLDN18).\",\n    \"Proximal tumors showed increased FOXM1 and STAT3, linked to poor survival, while distal tumors expressed differentiation markers like CDX2 and GATA6, associated with better outcomes.\",\n    \"The datasets analyzed in this study are publicly available through the following sources: The Cancer Genome Atlas (TCGA): https://portal.gdc.cancer.gov (accessed on 15 February 2025); Gene Expression Omnibus (GEO): GSE62254 and GSE15459, accessible at https://www.ncbi.nlm.nih.gov/geo (accessed on 1 March 2025).\"\n  ]\n}"}]}}, {"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:b92dc98ce2815eb4d8b4db2422eccb754de1545b65ecece2949e0abafe6bf07c", "type": "sep_package", "label": "SEP v1 package for pmid:40862793"}], "activities": [{"id": "activity:conversion:pmid:40862793", "type": "conversion", "used": ["package:b92dc98ce2815eb4d8b4db2422eccb754de1545b65ecece2949e0abafe6bf07c"], "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": "pmid:40862793", "parent_hash": "sha256:c22df3715506b6984ff39460b50b213aa554d9313f694681f30eb865ecb8e3a8", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40862793 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:40862793' does not resolve to a declared asset", "targets"]}