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pmid:39934551

Machine learning model reveals the risk, prognosis, and drug response of histamine-related signatures in pancreatic cancer.

Discover oncology · 2025 doi:10.1007/s12672-025-01910-y

Paper

partial

Experiment

partial

Data

partial

Analysis

absent

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
sci_version
1.0
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pmid:39934551
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science-record
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2026-11-01T10:25:49Z
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paper object · 10 fields
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partial
title
Machine learning model reveals the risk, prognosis, and drug response of histamine-related signatures in pancreatic cancer.
doi
10.1007/s12672-025-01910-y
pmid
39934551
authors
null
journal
Discover oncology
year
2025
abstract
null
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claim-1
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the headline numerical result the paper states for its main finding
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complete
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null
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chars 1639-1793 of source sha256 d0ecdce69f921d40abf145628391f63f63e575703776f3edb7e887751978e619
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The RSF model emerged as the optimal choice, and a 9-HRGs signature was selected to construct a prognostic model that boasted an average C-index of 0.777.
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the headline numerical result the paper states for its main finding
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Read the published estimate and its interval from the source
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The number a replication must land on, and the sentence it came from.
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the published estimate, its unit and its interval
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Clinicopathological differences between high- and low-HRG score groups in TCGA
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High HRG score was associated with poor prognosis and clinicopathologic variables
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Table 1
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Clinicopathological differences between high- and low-HRG score groups in TCGA
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[0] array · 5 items
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Characteristics
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High expression group
[2]
Low expression group
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P value
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n
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86
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63 (36.6%)
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58 (33.7%)
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[3]
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44 (25.6%)
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[3]
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Fisher.test
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[4]
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[4]
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[3]
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[0]
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[4]
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71 (41.3%)
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[4]
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Univariable Logistics regression analysis of HRG score
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PMC11813851
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High HRG score was associated with poor prognosis and clinicopathologic variables
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[0]
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[0]
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0.673
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[0]
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[1]
[2]
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0.381
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[0]
AJCC stage
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[2]
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[0]
High(vs. Low)
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OS event
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section[15]/table-wrap[2]#Tab2
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[0]
asset:source:pmid:39934551 not verified
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xml
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source:pmid:39934551
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blocked
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minimax
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{"parent_value":0.777,"unit":"C-index","evidence":"The RSF model emerged as the optimal choice, and a 9-HRGs signature was selected to construct a prognostic model that boasted an average C-index of 0.777.","target_result":"The 9-HRG prognostic model had an average C-index of 0.777.","number_stated":true}
reading object · 6 fields
parent_value
0.777
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The RSF model emerged as the optimal choice, and a 9-HRGs signature was selected to construct a prognostic model that boasted an average C-index of 0.777.
target_result
The 9-HRG prognostic model had an average C-index of 0.777.
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raw_response
<think>We need answer JSON only. Need identify single numerical result replication registered target headline main finding. Source explicitly: "9-HRGs signature ... average C-index of 0.777." Headline number likely 0.777 C-index. Need unit paper prints. Could unit be "C-index" perhaps dimensionless. parent_value 0.777, unit "C-index". Evidence exact contiguous quotation containing number, likely full sentence/clause. target_result textual statement. Need ensure only one numerical result. Quote exact: "The RSF model emerged as the optimal choice, and a 9-HRGs signature was selected to construct a prognostic model that boasted an average C-index of 0.777." This includes 9, potentially another numerical result, but requested exact quote containing number; whole clause needed attribution. They say headline number only, evidence may include 9. Could quote clause beginning "a prognostic model that boasted an average C-index of 0.777." Is that exact contiguous and attaches number directly to model. Exact source yes. It doesn't contain another number. Evidence exact quote can be substring. Unit as printed: "average C-index" maybe unit "C-index". The value paper itself states for registered target. target_result maybe "The prognostic model boasted an average C-index of 0.777." Number stated true. Need think if headline result might 9 HRGs? "main finding" likely headline numerical result = average C-index 0.777. User asks registered target "the headline numerical result the paper states for its main finding." Need choose likely paper's headline result. Abstract says model boasted average C-index 0.777. Yes. Output valid JSON. Ensure parent_value numeric 0.777, unit "C-index", evidence exact contiguous quote. Could unit be empty since C-index is statistic not physical unit, but requirement unit must paper prints. "C-index" is printed. target_result string. Need no markdown.</think> {"parent_value":0.777,"unit":"C-index","evidence":"a prognostic model that boasted an average C-index of 0.777.","target_result":"The prognostic model achieved an average C-index of 0.777.","number_stated":true}
reading object · 6 fields
parent_value
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a prognostic model that boasted an average C-index of 0.777.
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agreement
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disagreements object · 3 fields
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minimax
The 9-HRGs prognostic model achieved an average C-index of 0.777, with 1-, 2-, and 3-year prediction accuracies of 0.898, 0.932, and 0.922 in the training set and 0.909, 0.974, and 0.962 in the validation set, respectively.
minimax_alt
The RSF model emerged as the optimal choice, and a 9-HRGs signature was selected to construct a prognostic model that boasted an average C-index of 0.777.
decisive
false
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[0]
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[4]
inverse variance weighted (IVW)
[5]
MR-Egger
[6]
weighted median
[7]
Kaplan-Meier analysis
[8]
Pearson chi-square test
[9]
Wilcoxon rank-sum test
[10]
Student’s t-test
[11]
decision curve analysis (DCA)
[12]
calibration graph analysis
[13]
nomogram analysis
[14]
immune infiltration analysis
[15]
somatic mutation analysis
[16]
targeted drug sensitivity analysis
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random survival forest (RSF)
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random survival forest (RSF) model
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pmid:39934551
parent_hash
sha256:c86974788b65944ff55ecab35338390820dbde40fe047b4132283e0123f6365d
fork_of
null
generation
2
gaps array · 13 items
[0]
abstract
[1]
asset:source:pmid:39934551 not verified
[2]
authors
[3]
capability
[4]
corpus_record_absent
[5]
execution.not_run
[6]
method
[7]
review.human_critique
[8]
review.machine_critique.blocked
[9]
step:obtain_source_data blocked on data:not_downloaded
[10]
step:run_analysis blocked on no owned computational capability is bound to this experiment
[11]
step:run_analysis input 'materials:the source data behind the published result in pmid:39934551' does not resolve to a declared asset
[12]
targets
Complete file 
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["Gender, n (%)", "", "", "0.82", "Chisq.test"], ["Male", "49 (28.5%)", "48 (27.9%)", "", ""], ["Female", "37 (21.5%)", "38 (22.1%)", "", ""], ["T, n (%)", "", "", "", ""], ["T1", "2 (1.2%)", "5 (2.9%)", "0.13", "Fisher.test"], ["T2", "8 (4.7%)", "14 (8.1%)", "", ""], ["T3", "72 (41.9%)", "63 (36.6%)", "", ""], ["T4", "4 (2.3%)", "4 (2.3%)", "", ""], ["N, n (%)", "", "", "0.011", "Chisq.test"], ["N0", "17 (9.9%)", "28 (16.3%)", "", ""], ["N1", "69 (40.1%)", "58 (33.7%)", "", ""], ["M, n (%)", "", "", "0.84", "Chisq.test"], ["M0", "36 (20.9%)", "44 (25.6%)", "", ""], ["M1", "50 (29.1%)", "42 (24.4%)", "", ""], ["AJCC pathologic, n (%)", "", "", "0.056", "Fisher.test"], ["I", "5 (2.9%)", "13 (7.6%)", "", ""], ["II", "75 (43.6%)", "67 (39.0%)", "", ""], ["III", "1 (0.58%)", "3 (1.7%)", "", ""], ["IV", "2 (1.2%)", "3 (1.7%)", "", ""], ["AJCC stage, n (%)", "", "", "0.39", "Chisq.test"], ["Low", "12 (7.0%)", "15 (8.7%)", "", ""], ["High", "74 (43.0%)", "71 (41.3%)", "", ""], ["OS event, n (%)", "", "", "< 0.0001", "Chisq.test"], ["Alive", "14 (8.1%)", "63 (36.6%)", "", ""], ["Dead", "72 (41.9%)", "23 (13.4%)", "", ""]], "row_count": 30, "column_count": 5, "numeric_cells": 52, "header_rows": 1, "locator": "section[15]/table-wrap[1]#Tab1"}}, {"id": "asset:table:pmid:39934551:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:31791cce52f77c86bf820ee428a30432e7328374bff029062e24a6f949a94019", "bytes": 950, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[15]/table-wrap[2]#Tab2", "version": null, "note": "Univariable Logistics regression analysis of HRG score", "structure": {"pmcid": "PMC11813851", "section_index": 15, "section_title": "High HRG score was associated with poor prognosis and clinicopathologic variables", "row_count": 21, "column_count": 3}, "table": {"label": "Table 2", "caption": "Univariable Logistics regression analysis of HRG score", "rows": [["Characteristics", "Odds ratio in HRGs score", "P value"], ["Age", "", ""], ["> = 65(vs. < 65)", "0.94 (0.52–1.72)", "0.853"], ["Gender", "", ""], ["Male (vs. Female)", "1.14 (0.62–2.08)", "0.673"], ["T", "", ""], ["T2(vs. T1)", "1.43 (0.22–9.14)", "0.706"], ["T3(vs. T1)", "3.10 (0.58–16.55)", "0.185"], ["T4(vs. T1)", "0.83 (0.05–13.63)", "0.898"], ["N", "", ""], ["N1(vs. N0)", "2.09 (1.04–4.19)", "0.038"], ["M", "", ""], ["M1(vs. M0)", "0.68 (0.37–1.25)", "0.214"], ["AJCC pathologic", "", ""], ["II(vs. I)", "1.87 (0.73–4.79)", "0.192"], ["III(vs. I)", "0.54 (0.05–6.14)", "0.621"], ["IV(vs. I)", "2.44 (0.33–17.91)", "0.381"], ["AJCC stage", "", ""], ["High(vs. Low)", "0.97 (0.43–2.20)", "0.938"], ["OS event", "", ""], ["Dead(vs. Alive)", "19.80 (8.84–44.33)", "< 0.0001"]], "row_count": 21, "column_count": 3, "numeric_cells": 25, "header_rows": 1, "locator": "section[15]/table-wrap[2]#Tab2"}}], "gaps": ["asset:source:pmid:39934551 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:39934551", "asset:table:pmid:39934551:1", "asset:table:pmid:39934551:2"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:39934551"]}, "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": "0b79f0f3ca6e956e2af06979df887fdbd8ecc25faf486b9c1e5ec6b251707f57", "source_hash": "d0ecdce69f921d40abf145628391f63f63e575703776f3edb7e887751978e619", "raw_response_sha256": "sha256:e47cc839e160cd1575bdadb3583e14ce74df9535653dd905104a892b5501510e", "raw_response_chars": 4482}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "e8a44673cc08b239c4d677518c5d3d4e86d99784ba8828d9e01385b374201a73", "source_hash": "d0ecdce69f921d40abf145628391f63f63e575703776f3edb7e887751978e619", "raw_response_sha256": "sha256:327bec7efc6bdb679b9038bef809dfb520921af1262afc82e43efa058b86e0fc", "raw_response_chars": 39690}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "dbbc42c36846ca2cde74c5aa5cd0e3dd5b227a066bb2c5be28d7d17ec04b7776", "source_hash": "d0ecdce69f921d40abf145628391f63f63e575703776f3edb7e887751978e619", "raw_response": "{\"parent_value\":0.777,\"unit\":\"C-index\",\"evidence\":\"The RSF model emerged as the optimal choice, and a 9-HRGs signature was selected to construct a prognostic model that boasted an average C-index of 0.777.\",\"target_result\":\"The 9-HRG prognostic model had an average C-index of 0.777.\",\"number_stated\":true}", "reading": {"parent_value": 0.777, "unit": "C-index", "evidence": "The RSF model emerged as the optimal choice, and a 9-HRGs signature was selected to construct a prognostic model that boasted an average C-index of 0.777.", "target_result": "The 9-HRG prognostic model had an average C-index of 0.777.", "number_stated": true, "source_locator": "chars 1639-1793 of source sha256 d0ecdce69f921d40abf145628391f63f63e575703776f3edb7e887751978e619"}, "recorded_at": "2026-10-31T15:23:35.060899+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "1cab60bacf655fd1ea9879a34ad4653dc74038d5fe3d86e068b75467d340add0", "source_hash": "d0ecdce69f921d40abf145628391f63f63e575703776f3edb7e887751978e619", "raw_response": "<think>We need answer JSON only. Need identify single numerical result replication registered target headline main finding. Source explicitly: \"9-HRGs signature ... average C-index of 0.777.\" Headline number likely 0.777 C-index. Need unit paper prints. Could unit be \"C-index\" perhaps dimensionless. parent_value 0.777, unit \"C-index\". Evidence exact contiguous quotation containing number, likely full sentence/clause. target_result textual statement. Need ensure only one numerical result. Quote exact: \"The RSF model emerged as the optimal choice, and a 9-HRGs signature was selected to construct a prognostic model that boasted an average C-index of 0.777.\" This includes 9, potentially another numerical result, but requested exact quote containing number; whole clause needed attribution. They say headline number only, evidence may include 9. Could quote clause beginning \"a prognostic model that boasted an average C-index of 0.777.\" Is that exact contiguous and attaches number directly to model. Exact source yes. It doesn't contain another number. Evidence exact quote can be substring. Unit as printed: \"average C-index\" maybe unit \"C-index\". The value paper itself states for registered target. target_result maybe \"The prognostic model boasted an average C-index of 0.777.\" Number stated true.\n\nNeed think if headline result might 9 HRGs? \"main finding\" likely headline numerical result = average C-index 0.777. User asks registered target \"the headline numerical result the paper states for its main finding.\" Need choose likely paper's headline result. Abstract says model boasted average C-index 0.777. Yes.\n\nOutput valid JSON. Ensure parent_value numeric 0.777, unit \"C-index\", evidence exact contiguous quote. Could unit be empty since C-index is statistic not physical unit, but requirement unit must paper prints. \"C-index\" is printed. target_result string.\n\nNeed no markdown.</think>\n\n{\"parent_value\":0.777,\"unit\":\"C-index\",\"evidence\":\"a prognostic model that boasted an average C-index of 0.777.\",\"target_result\":\"The prognostic model achieved an average C-index of 0.777.\",\"number_stated\":true}", "reading": {"parent_value": 0.777, "unit": "C-index", "evidence": "a prognostic model that boasted an average C-index of 0.777.", "target_result": "The prognostic model achieved an average C-index of 0.777.", "number_stated": true, "source_locator": "chars 1733-1793 of source sha256 d0ecdce69f921d40abf145628391f63f63e575703776f3edb7e887751978e619"}, "recorded_at": "2026-10-31T15:23:36.154133+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "The 9-HRGs prognostic model achieved an average C-index of 0.777, with 1-, 2-, and 3-year prediction accuracies of 0.898, 0.932, and 0.922 in the training set and 0.909, 0.974, and 0.962 in the validation set, respectively.", "minimax_alt": "The RSF model emerged as the optimal choice, and a 9-HRGs signature was selected to construct a prognostic model that boasted an average C-index of 0.777.", "decisive": false}, "analysis_methods": {"minimax": ["random survival forest (RSF)", "drug-targeted Mendelian randomization (MR)", "two-sample MR", "Wald ratio", "inverse variance weighted (IVW)", "MR-Egger", "weighted median", "Kaplan-Meier analysis", "Pearson chi-square test", "Wilcoxon rank-sum test", "Student’s t-test", "decision curve analysis (DCA)", "calibration graph analysis", "nomogram analysis", "immune infiltration analysis", "somatic mutation analysis", "targeted drug sensitivity analysis"], "minimax_alt": ["random survival forest (RSF) model", "two-sample drug-targeted Mendelian randomization (MR)"], "decisive": false}, "statistical_method": {"minimax": "random survival forest (RSF)", "minimax_alt": "random survival forest (RSF) model", "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": []}}, {"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:12b96c81be068051f3dad711b1539a96eea9d288db23101441916e339d963422", "type": "sep_package", "label": "SEP v1 package for pmid:39934551"}], "activities": [{"id": "activity:conversion:pmid:39934551", "type": "conversion", "used": ["package:12b96c81be068051f3dad711b1539a96eea9d288db23101441916e339d963422"], "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:39934551", "parent_hash": "sha256:c86974788b65944ff55ecab35338390820dbde40fe047b4132283e0123f6365d", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:39934551 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:39934551' does not resolve to a declared asset", "targets"]}