In silico prediction of paradoxical effect for oxaliplatin in gastric cancer patients based on their transcriptomic profile.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40933599", "kind": "science-record", "revision": 2, "content_hash": "sha256:1e582949c3c3cfb0f8da0d52bdb10df2223c21b229ca142a0fa62243721dfa1a", "created_at": "2026-11-01T10:25:53Z", "updated_at": "2026-11-01T14:01:48Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "In silico prediction of paradoxical effect for oxaliplatin in gastric cancer patients based on their transcriptomic profile.", "doi": "10.4103/rps.rps_148_24", "pmid": "40933599", "authors": null, "journal": "Research in pharmaceutical sciences", "year": 2025, "abstract": null, "claims": [{"id": "claim-1", "statement": "the headline numerical result the paper states for its main finding", "kind": "qualitative", "state": "blocked", "target": {"value": null, "unit": null, "tolerance": null, "source": null}, 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{"pmcid": "PMC12419565", "section_index": 14, "section_title": "Survival analysis", "row_count": 26, "column_count": 6}, "table": {"label": "Table 1", "caption": "Survival-related genes in gastric cancer patients.", "rows": [["Gene", "Up/down-regulated", "|Log2FC*|", "Log-rank p", "Hazard ratio", "95% Confidence interval"], ["PDGFD", "Down", "1.27", "0.00008", "2.03", "1.43, 2.88"], ["SERPINE1", "Up", "2.21", "0.0005", "1.8", "1.31, 2.63"], ["PLCXD3", "Down", "1.98", "0.0006", "1.83", "1.30, 2.59"], ["PDK4", "Down", "2.74", "0.0009", "1.80", "1.27, 2.55"], ["RECK", "Down", "1.28", "0.0009", "1.80", "1.27, 2.55"], ["GHR", "Down", "2.17", "0.002", "1.72", "1.22, 2.43"], ["SLC16A7", "Down", "1.5", "0.002", "1.75", "1.23, 2.48"], ["COL4A5", "Down", "1.99", "0.003", "1.69", "1.19, 2.40"], ["PCDH7", "Down", "0.77", "0.008", "1.59", "1.13, 2.25"], ["ANGPT1", "Down", "1.5", "0.012", "1.55", "1.10, 2.20"], ["LRRC17", "Down", "0.69", "0.012", "1.55", "1.10, 2.19"], ["CTHRC1", "Up", "3.46", "0.01356", "1.54", "1.09, 2.17"], ["TEAD4", "Up", "1.49", "0.014", "0.65", "0.46, 0.92"], ["AFF3", "Down", "1.74", "0.015", "1.53", "1.09, 2.17"], ["PTGER3", "Down", "1.51", "0.016", "1.53", "1.08, 2.15"], ["CD36", "Down", "2.23", "0.020", "1.50", "1.06, 2.11"], ["ADAMTS1", "Down", "2.07", "0.022", "1.49", "1.06, 2.11"], ["JAM2", "Down", "1.96", "0.022", "1.49", "1.06, 2.11"], ["CLIC6", "Down", "1.46", "0.022", "1.50", "1.06, 2.12"], ["DMD", "Down", "1.96", "0.029", "1.47", "1.04, 2.07"], ["COL10A1", "Up", "6.87", "0.032", "1.46", "1.03, 2.05"], ["GPX3", "Down", "2.47", "0.038", "1.44", "1.02, 2.03"], ["ADH1B", "Down", "2.48", "0.040", "1.43", "1.02, 2.02"], ["CPE", "Down", "1.29", "0.040", "1.43", "1.02, 2.02"], ["SPP1", "Up", "3.95", "0.045", "1.42", "1.01, 2.00"]], "row_count": 26, "column_count": 6, "numeric_cells": 105, "header_rows": 1, "locator": "section[14]/table-wrap[1]#RPS.RPS_148_24-T1"}}, {"id": "asset:table:pmid:40933599:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:07daf5c47157a1336eccc799487bcbdd42cbf2f434896341f9f61582ac478b75", "bytes": 797, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[15]/table-wrap[1]#RPS.RPS_148_24-T2", "version": null, "note": "Comparison between the gastric cancer mRNA expression of genes associated with identified pathways, with the Comparative Toxicogenomics Database detected binary interactions between OXA and the same group of genes.", "structure": {"pmcid": "PMC12419565", "section_index": 15, "section_title": "Oxaliplatin-interacting genes", "row_count": 15, "column_count": 3}, "table": {"label": "Table 2", "caption": "Comparison between the gastric cancer mRNA expression of genes associated with identified pathways, with the Comparative Toxicogenomics Database detected binary interactions between OXA and the same group of genes.", "rows": [["Gene name", "OXA-mRNA interaction", "mRNA expression in gastric cancer"], ["BIRC5*", "Low", "High"], ["BLVRB**", "Low", "Low"], ["CAV1*", "High", "Low"], ["CDH2*", "High", "Low"], ["CDKN1A", "High/Low", "Low"], ["CDKN2A**", "High", "High"], ["IL6*", "High", "Low"], ["JUN*", "High", "Low"], ["MAPK3**", "Low", "Low"], ["PLAU**", "High", "High"], ["PTGS2**", "High", "High"], ["SERPINB2*", "High", "Low"], ["SERPINE1**", "High", "High"], ["TYMS*", "Low", "High"]], "row_count": 15, "column_count": 3, "numeric_cells": 0, "header_rows": 1, "locator": "section[15]/table-wrap[1]#RPS.RPS_148_24-T2"}}], "gaps": ["asset:source:pmid:40933599 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:40933599", "asset:table:pmid:40933599:1", "asset:table:pmid:40933599:2"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40933599"]}, "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": "9e4fae045a860a52920eab76f3c8730b69a07af166e71f8f06fb0a8e6764b445", "source_hash": "e274ee927538c21f339e925ae5cd3628ba881350ae27247e8b0aeb4f259cf15e", "raw_response_sha256": "sha256:d1515c5fc672afd0324bbe22e954b17f0c5def86a8c727e83efbbdc4c74a74cb", "raw_response_chars": 49475}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "7ce41e1f41ac1106a7e34eed883335c50a30e66de250971c859c5495472cabb5", "source_hash": "e274ee927538c21f339e925ae5cd3628ba881350ae27247e8b0aeb4f259cf15e", "raw_response": "{\"parent_value\":151,\"unit\":\"dysregulated genes\",\"evidence\":\"A total of 151 dysregulated genes were identified across the datasets, comprising 112 downregulated and 39 upregulated genes.\",\"target_result\":\"The paper’s headline main finding is that 151 dysregulated genes were identified across the datasets.\",\"number_stated\":true}", "reading": {"parent_value": 151, "unit": "dysregulated genes", "evidence": "A total of 151 dysregulated genes were identified across the datasets, comprising 112 downregulated and 39 upregulated genes.", "target_result": "The paper’s headline main finding is that 151 dysregulated genes were identified across the datasets.", "number_stated": true, "source_locator": "chars 969-1094 of source sha256 e274ee927538c21f339e925ae5cd3628ba881350ae27247e8b0aeb4f259cf15e"}, "recorded_at": "2026-10-31T15:35:10.052097+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "fd82112c4659287726f82381ffe19516a791ea592b584e8913860f32b92c237a", "source_hash": "e274ee927538c21f339e925ae5cd3628ba881350ae27247e8b0aeb4f259cf15e", "raw_response": "<think>We need answer JSON only. Need determine registered target headline numerical result paper states for main finding. Need identify single number likely 151 dysregulated genes? Main finding maybe OXA paradoxical effect, headline numerical result perhaps 13 genes? User asks \"single numerical result that a replication of the registered target has to reproduce\" and registered target \"the headline numerical result the paper states for its main finding\". Need infer from source. Abstract reports multiple numbers. Need choose headline likely \"A total of 151 dysregulated genes were identified across datasets, comprising 112 down and 39 up.\" Main finding perhaps 13 potential prognostic biomarkers? Or 14 overlapping OXA/DEGs? Or reversal of 7 and promotion of 6. Need parse intent.\n\nThey explicitly ask \"the value the paper itself states for the registered target, not a value you calculated...\" This perhaps benchmark asking extract main headline metric. Source is paper abstract/conclusion. Registered target vague but says headline numerical result paper states for its main finding. Which is \"151 dysregulated genes\" perhaps opening Results sentence. Need output parent_value number, unit printed, exact contiguous quote containing number, whole clause. Likely 151 genes.\n\nLet's examine wording: Paper's main finding: in silico predict paradoxical effects of OXA. Key numerical finding: \"OXA interacted with 97 genes, of which 14 were linked to both OXA and differentially expressed genes in GC.\" Then \"OXA potentially reversed expression of seven genes... while promoting six...\" The headline numerical result might be 14? Replication target? Registered target says \"the headline numerical result the paper states for its main finding\" perhaps intentionally asking to select one. They expect extraction from source. Need decide.\n\nCould headline number be \"151 dysregulated genes\" as first sentence and central overall result. In abstracts, result often starts total 151, and 13 prognostic. Main finding in title is paradoxical effect; direct result: OXA reverses 7 and promotes 6. But one \"single numerical result\" could be 13 genes reversed? Yet that's calculated if summing, forbidden. Paper states both seven and six, not a singular combined count. User says value paper itself states for registered target. Perhaps target result asks a standard \"parent_value\" from tables? Maybe context from a task framework: They ask \"find the single numerical result that a replication of registered target has to reproduce.\" Target likely extracted from a paper and could refer to a headline statistic. Here generic target descriptor. We need identify the paper's headline numerical result. Usually answer is the first/main count in abstract, 151 dysregulated genes. Unit \"genes\". Evidence exact contiguous quote. target_result likely prose \"A total of 151 dysregulated genes were identified across the datasets.\" number_stated true.\n\nNeed ensure JSON schema, parent_value 151, unit \"genes\", evidence exact quote perhaps \"A total of 151 dysregulated genes were identified across the datasets, comprising 112 downregulated and 39 upregulated genes.\" This number directly attached. Target_result maybe same or description. Since number stated true. Could mention \"151 dysregulated genes\". Exact quote no ellipsis.\n\nBut let's not rush. Need interpret \"registered target: the headline numerical result the paper states for its main finding\". Could be title/abstract's most headline result. Maybe \"13 genes emerged as potential prognostic biomarkers for overall survival.\" Since SERPINE1 main finding, but 13 count. Or \"SERPINE1 showed a strong correlation with overall survival\" no number. Therefore if target is main finding, perhaps headline numerical result is 13 prognostic biomarkers? Let's assess likely data-generation setup.\n\nThis appears one of extraction tasks where user provides target and source, asks numeric result. REGISTERED TARGET may be generic label due anonymization? They want a single headline result, perhaps from paper's abstract. Need rely source wording. The strongest broad headline is \"A total of 151 dysregulated genes...\" Typically the first sentence under Results is considered headline numerical result. The source has repeated Findings. Main finding actually OXA reversal of 7 genes and progression of 6. If replication of \"registered target\" means replicate headline numerical result, perhaps 151 genes is foundational result.\n\nMaybe \"parent_value\" suggests perhaps a hierarchical numerical extraction where parent value is a paper result and units. Registered target could be any row. They expect exactly one. We need output only JSON.\n\nLet's inspect units: \"151 dysregulated genes\" obvious unit. If 13, also genes. If 14, \"genes\". Need clue in wording: \"the headline numerical result the paper states for its main finding\" likely asks to find *the* single numerical result. In abstract, \"A total of 151 dysregulated genes...\" is clearly headline total. Other counts are component results. So 151.\n\nEvidence exact contiguous quotation from source, whole clause. Quote full sentence. target_result can say \"A total of 151 dysregulated genes were identified across the datasets.\"</think>\n\n{\"parent_value\":151,\"unit\":\"genes\",\"evidence\":\"A total of 151 dysregulated genes were identified across the datasets, comprising 112 downregulated and 39 upregulated genes.\",\"target_result\":\"A total of 151 dysregulated genes were identified across the datasets.\",\"number_stated\":true}", "reading": {"parent_value": 151, "unit": "genes", "evidence": "A total of 151 dysregulated genes were identified across the datasets, comprising 112 downregulated and 39 upregulated genes.", "target_result": "A total of 151 dysregulated genes were identified across the datasets.", "number_stated": true, "source_locator": "chars 969-1094 of source sha256 e274ee927538c21f339e925ae5cd3628ba881350ae27247e8b0aeb4f259cf15e"}, "recorded_at": "2026-10-31T15:35:15.545565+00:00"}], "agreement": false, "disagreements": {"unit": {"minimax": "dysregulated genes", "minimax_alt": "genes", "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; 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": "{\"target_result\":\"OXA potentially reversed the expression of seven GC-associated genes and promoted the expression of six others; 14 genes were linked to both OXA and differentially expressed genes in GC, and SERPINE1 showed a strong correlation with overall survival.\",\"work_type\":\"analysis_only_for_target\",\"physical_methods\":[],\"analysis_methods\":[\"GEO2R\",\"DESeq2\",\"Comparative Toxicogenomics Database (CTD) interaction comparison\",\"Kaplan-Meier survival analysis\",\"Univariate survival analysis\",\"Maximal Clique Centrality (MCC)\",\"Pathway enrichment analysis with Bonferroni step-down correction\"],\"statistical_method\":\"GEO2R and DESeq2 for differential gene expression; Kaplan-Meier survival analysis with log-rank P-value and hazard ratio criteria for overall-survival association\",\"method_evidence\":\"Differential gene expression was identified using GEO2R and DESeq2. We utilized Kaplan-Meier survival curves to identify differentially expressed genes. Statistically significant thresholds were determined based on a log-rank P-value of less than 0.05 and a hazard ratio (HR) not equal to 1.\",\"repositories\":[{\"repository\":\"Gene Expression Omnibus (GEO)\",\"accession\":\"GSE26942\",\"data_description\":\"RNA-sequencing data used to analyze differentially expressed genes in gastric cancer.\",\"evidence\":\"RNA-sequencing data from GSE26942, GSE66229, and TCGA-STAD datasets were analyzed.\",\"publicly_downloadable\":false},{\"repository\":\"Gene Expression Omnibus (GEO)\",\"accession\":\"GSE66229\",\"data_description\":\"RNA-sequencing data used to analyze differentially expressed genes in gastric cancer.\",\"evidence\":\"RNA-sequencing data from GSE26942, GSE66229, and TCGA-STAD datasets were analyzed.\",\"publicly_downloadable\":false},{\"repository\":\"The Cancer Genome Atlas (TCGA)\",\"accession\":\"TCGA-STAD\",\"data_description\":\"TCGA stomach adenocarcinoma dataset used for differential expression and overall-survival analysis.\",\"evidence\":\"RNA-sequencing data from GSE26942, GSE66229, and TCGA-STAD datasets were analyzed.\",\"publicly_downloadable\":false}],\"availability_statement\":\"\",\"data_on_request\":false,\"source_claims\":[\"A total of 151 dysregulated genes were identified across the datasets, comprising 112 downregulated and 39 upregulated genes.\",\"OXA interacted with 97 genes, of which 14 were linked to both OXA and differentially expressed genes in GC.\",\"OXA potentially reversed the expression of seven genes associated with GC progression (BIRC5, CAV1, CDH2, IL6, JUN, SERPINB2, TYMS), while promoting the expression of six others (BLVRB, CDKN2A, MAPK3, PLAU, PTGS2, SERPINE1).\",\"Notably, SERPINE1 showed a strong correlation with overall survival.\",\"Interactions between OXA and GC-related genes were retrieved from the CTD database and compared with DEGs.\"],\"missing_information\":[\"The source text does not explicitly state that GSE26942, GSE66229, or TCGA-STAD data can be downloaded publicly without an application, embargo, or request.\",\"The source text does not state that any repository data are available on request.\",\"No statistical test is explicitly named for the comparison of CTD OXA interactions with differentially expressed genes that produced the seven reversed and six promoted genes.\"],\"evidence_spans\":[\"RNA-sequencing data from GSE26942, GSE66229, and TCGA-STAD datasets were analyzed. Differential gene expression was identified using GEO2R and DESeq2.\",\"Interactions between OXA and GC-related genes were retrieved from the CTD database and compared with DEGs.\",\"OXA interacted with 97 genes, of which 14 were linked to both OXA and differentially expressed genes in GC. OXA potentially reversed the expression of seven genes associated with GC progression (BIRC5, CAV1, CDH2, IL6, JUN, SERPINB2, TYMS), while promoting the expression of six others (BLVRB, CDKN2A, MAPK3, PLAU, PTGS2, SERPINE1). Notably, SERPINE1 showed a strong correlation with overall survival.\",\"We utilized Kaplan-Meier survival curves to identify differentially expressed genes. Statistically significant thresholds were determined based on a log-rank P-value of less than 0.05 and a hazard ratio (HR) not equal to 1.\"]}"}]}}, {"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:53b30de840cf12de45e95679d81e565ebc02c4a99fbec67ce637cfcd02e586e5", "type": "sep_package", "label": "SEP v1 package for pmid:40933599"}], "activities": [{"id": "activity:conversion:pmid:40933599", "type": "conversion", "used": ["package:53b30de840cf12de45e95679d81e565ebc02c4a99fbec67ce637cfcd02e586e5"], "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:40933599", "parent_hash": "sha256:138a6e7f4205087c7d00cbf450b15acbb2cc05be219cbed9ea20d068dc45c70e", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40933599 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:40933599' does not resolve to a declared asset", "targets"]}