Diagnostic value of a lactylation-related gene signature in hepatocellular carcinoma.

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selection", "row_count": 5, "column_count": 6}, "table": {"label": "Table 1", "caption": "Gene signature characteristics from TCGA", "rows": [["Variables", "β", "S.E.", "Wald", "OR", "P"], ["BSG", "0.079", "0.021", "13.535", "1.082", "0.001"], ["KAT2A", "0.730", "0.170", "18.370", "2.075", "0.001"], ["ZEB1", "1.190", "0.539", "4.882", "3.289", "0.03"], ["Constant", "−9.469", "1.760", "28.957", "0.000", "0.001"]], "row_count": 5, "column_count": 6, "numeric_cells": 20, "header_rows": 1, "locator": "section[15]/table-wrap[1]#t1"}}, {"id": "asset:table:pmid:39974411:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:cfb1fbdbae5d4e923e1e4c0dafb1aa25e3b3a8fc5039ec277f9d31a2276cc443", "bytes": 339, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[17]/table-wrap[1]#t2", "version": null, "note": "The classification of TCGA and GEO data", "structure": 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"section_title": "Prognostic analysis", "row_count": 29, "column_count": 2}, "table": {"label": "Table 3", "caption": "Details of TCGA data", "rows": [["Parameters", "TCGA-LIHC (n=363)"], ["Age (years)", ""], ["≤65", "136"], [">65", "227"], ["Survival status", ""], ["Death", "129"], ["Alive", "234"], ["Gender", ""], ["Male", "245"], ["Female", "118"], ["Stage", ""], ["I", "170"], ["II", "84"], ["III", "81"], ["IV", "4"], ["Pathologic_M", ""], ["M0", "262"], ["M1", "3"], ["MX", "98"], ["Pathologic_N", ""], ["N0", "246"], ["N1", "4"], ["NX", "113"], ["Pathologic_T", ""], ["T1", "180"], ["T2", "91"], ["T3", "76"], ["T4", "13"], ["TX", "1"]], "row_count": 29, "column_count": 2, "numeric_cells": 24, "header_rows": 1, "locator": "section[17]/table-wrap[2]#t3"}}], "gaps": ["asset:source:pmid:39974411 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:39974411", "asset:table:pmid:39974411:1", "asset:table:pmid:39974411:2", "asset:table:pmid:39974411:3"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:39974411"]}, "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": "643be5fbdb2672b962b01feb5817e565143f69c173c267cb9871104995858ba1", "source_hash": "14d97a641367d09e24b341bcd5537281c42c6ebfa0d92c1cc7083e0c85b7c65e", "raw_response_sha256": "sha256:d11ff3fbb1038a4dc659bf873169c86c69c400019052af2363029fc9ec069504", "raw_response_chars": 69694}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "8880c3cf85785a5e51bdad6aa25ba10d14dea807de2ff94f6b52e39bec05764c", "source_hash": "14d97a641367d09e24b341bcd5537281c42c6ebfa0d92c1cc7083e0c85b7c65e", "raw_response": "{\"parent_value\":3,\"unit\":\"gene signatures\",\"evidence\":\"• Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\",\"target_result\":\"The paper states that three lactylation-related diagnostic gene signatures predicted prognosis and response to immunotherapy in HCC patients.\",\"number_stated\":true}", "reading": {"parent_value": 3, "unit": "gene signatures", "evidence": "• Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.", "target_result": "The paper states that three lactylation-related diagnostic gene signatures predicted prognosis and response to immunotherapy in HCC patients.", "number_stated": true, "source_locator": "chars 3683-3835 of source sha256 14d97a641367d09e24b341bcd5537281c42c6ebfa0d92c1cc7083e0c85b7c65e"}, "recorded_at": "2026-10-31T15:31:04.811683+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "ff5e69504143e366699dab54bfe76bbfda9f193c2b29b8da6c76375b7f8063fa", "source_hash": "14d97a641367d09e24b341bcd5537281c42c6ebfa0d92c1cc7083e0c85b7c65e", "raw_response": "<think>We need answer JSON only. Need identify single numerical result replication of registered target headline numerical result paper states main finding. Source appears abstract/results but no obvious headline performance metric like AUC. Need inspect all numbers and infer target. Registered target generic \"headline numerical result the paper states for its main finding\". The paper key finding: \"Three lactylation-related diagnostic gene signatures predicted...\" Number three. Maybe headline numerical result? Could be three signatures. Need parse wording instruction: \"find the single numerical result that a replication of the registered target has to reproduce. Report the headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed. The unit must be the unit the paper prints. The evidence must be an exact, contiguous quotation from SOURCE TEXT that contains the number; do not shorten it, do not add an ellipsis and do not paraphrase it. Attribution may be carried by the text's own wording: a number may be attached to the target by a parenthetical list, by the word 'respectively', or by words that name the target directly. When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves. If the text states no number for the registered target, set number_stated to false, leave parent_value at 0, leave unit and evidence empty, and say in target_result what the text does state instead.\"\n\nCould registered target correspond to hidden target concept likely \"headline numerical result paper states for its main finding\". Main finding perhaps three diagnostic gene signatures. Number = 3, unit perhaps \"lactylation-related diagnostic gene signatures\" or \"signatures\". Evidence exact contiguous quote entire bullet. Target result explain. But need determine likely intended numerical result. They may expect a number that is directly stated for main finding, not P values etc. Text has \"[Key findings] • Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\" This clearly has headline numerical result: Three. A replication of registered target likely reproduce three gene signatures. Unit as printed? \"Three lactylation-related diagnostic gene signatures\" — unit likely \"diagnostic gene signatures\" or \"lactylation-related diagnostic gene signatures\". parent_value 3.\n\nBut perhaps main finding's headline result is no AUC due truncated source, and instruction asks if no number set false. Could be target \"headline numerical result the paper states for its main finding\" and source only says \"good predictive capability\" without an AUC. Need identify registered target perhaps dynamically phrased in benchmark. We need understand task format. It asks target is \"the headline numerical result the paper states for its main finding\". Could refer to a specific numerical metric like AUC in abstract normally, but source doesn't include actual AUC. Maybe \"Three lactylation-related diagnostic gene signatures...\" is key finding, number 3. Is that a numerical result replication has to reproduce? Yes number of signatures. But \"headline number only\" perhaps asks for \"the value paper itself states for registered target\". Unit printed. Evidence exact quote.\n\nLet's inspect source details. Title. Abstract methods/results no quantitative result except datasets and versions, etc. Key findings has \"Three lactylation-related diagnostic gene signatures\". This is explicit count and main finding. Elsewhere risk signature consists BSG, KAT2A, ZEB1 and risk score (three genes plus score). Number \"22 varied immune cells\", \"11 types\". Main model perhaps 4 genes? The key finding explicitly says three signatures. Which is likely \"headline numerical result\"? The registered target wording isn't specific like \"number of diagnostic gene signatures\"; it literally says headline numerical result for main finding. Maybe benchmark generated target from paper's table? Need reason.\n\nThis user prompt likely part of extraction dataset where registered target often e.g. \"the AUC reported for X\". Here target is generic perhaps intentionally asks \"headline numerical result\". We should only extract direct number in headline/key finding. Number 3.\n\nUnit: \"lactylation-related diagnostic gene signatures\" perhaps unit must be unit paper prints. The paper prints \"Three lactylation-related diagnostic gene signatures\". JSON:\n{\n \"parent_value\":3,\n \"unit\":\"lactylation-related diagnostic gene signatures\",\n \"evidence\":\"Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\",\n \"target_result\":\"The paper states that three lactylation-related diagnostic gene signatures predicted prognosis and response to immunotherapy in HCC patients.\",\n \"number_stated\":true\n}\nBut instruction says report headline number only. target_result perhaps should not duplicate? Required target_result string. Could say \"Three lactylation-related diagnostic gene signatures...\" or \"The headline result is three...\" Evidence exact. Ensure evidence contains number exact. \"Three\" isn't numerical digit but number. parent value 3.\n\nYet perhaps unit should be \"signatures\" since count unit. They say unit must be unit paper prints. \"lactylation-related diagnostic gene signatures\" is noun phrase and paper prints. Could use \"gene signatures\". Is there concern \"Three\" is spelled out, and number exact contiguous? Yes.\n\nCould there be a more headline numeric result: P&lt;0.001 in prognostic analysis, perhaps higher risk score at T3 vs T1 and stage III vs I; list of 7 subgroup P values. But no singular headline result. Key findings bullet definitely headline.\n\nLet's not rush. Need provide valid JSON, no markdown. target_result likely concise. The source wording exact for evidence only; target can paraphrase perhaps. They explicitly ask evidence exact quote. Use whole clause. Could include bullet? No need. Evidence contiguous exact sentence.\n\nMaybe number_stated false because registered target = \"headline numerical result the paper states for its main finding\" but no actual quantitative performance metric is given. They emphasize \"If the text states no number for the registered target, set number_stated to false ... and say in target_result what the text does state instead.\" Why include this fallback? Perhaps source deliberately has a key finding with \"Three...\" so straightforward. But perhaps target refers to something like \"headline numerical result\" and they expect false due no AUC. Let's parse semantics closely.\n\n\"Read the supplied paper text and find the single numerical result that a replication of the registered target has to reproduce. Report the headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed.\"\n\nRegistered target is not a metric; it's meta: \"the headline numerical result the paper states for its main finding\". So determine main finding and its headline numerical result. In paper source, [Key findings] bullet is explicitly headline and starts \"Three\". That likely is intended answer.\n\n\"The unit must be the unit the paper prints.\" Count of signatures unit. \"The evidence must be an exact, contiguous quotation from SOURCE TEXT that contains the number; do not shorten it, do not add an ellipsis and do not paraphrase it. Attribution may be carried by the text's own wording: a number may be attached to the target by a parenthetical list, by the word 'respectively', or by words that name the target directly. When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves.\"\n\nHere number directly attached: \"Three lactylation-related diagnostic gene signatures...\" Quote whole clause/sentence. So yes.\n\nCould \"Three\" refer to key findings bullet but maybe not a result to replicate? Definitely a model count. A replication would reproduce the identification of three signatures. It's main finding. Use unit perhaps \"lactylation-related diagnostic gene signatures\". Parent_value number 3.\n\nNeed maybe target_result = exact phrase \"Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\" But then evidence same. They ask \"headline number only\" probably parent_value and unit, while target_result can state target. Fine.\n\nLet's ensure JSON valid, no extra properties. Could set unit \"lactylation-related diagnostic gene signatures\" and target_result \"The main finding is that three lactylation-related diagnostic gene signatures predicted prognosis and immunotherapy response in HCC patients.\" The \"headline number only\" perhaps don't include extraneous numbers in target_result (HCC no number). Fine.\n\nLet's answer.</think>\n\n{\"parent_value\":3,\"unit\":\"lactylation-related diagnostic gene signatures\",\"evidence\":\"Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\",\"target_result\":\"The main finding is that three lactylation-related diagnostic gene signatures predicted prognosis and response to immunotherapy in HCC patients.\",\"number_stated\":true}", "reading": {"parent_value": 3, "unit": "lactylation-related diagnostic gene signatures", "evidence": "Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.", "target_result": "The main finding is that three lactylation-related diagnostic gene signatures predicted prognosis and response to immunotherapy in HCC patients.", "number_stated": true, "source_locator": "chars 3685-3835 of source sha256 14d97a641367d09e24b341bcd5537281c42c6ebfa0d92c1cc7083e0c85b7c65e"}, "recorded_at": "2026-10-31T15:31:17.484438+00:00"}], "agreement": false, "disagreements": {"unit": {"minimax": "gene signatures", "minimax_alt": "lactylation-related diagnostic gene signatures", "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 repository quotation is absent from the supplied text", "raw_response": "{\n  \"target_result\": \"Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\",\n  \"work_type\": \"analysis_plus_physical_validation\",\n  \"physical_methods\": [\n    \"real-time quantitative polymerase chain reaction (RT-PCR)\"\n  ],\n  \"analysis_methods\": [\n    \"Differential analysis\",\n    \"random forest analysis\",\n    \"Receiver operating characteristic (ROC) curve analysis\",\n    \"logistic regression\",\n    \"ConsensusClusterPlus\",\n    \"single-sample gene set enrichment analysis (ssGSEA)\",\n    \"Kaplan-Meier (KM) analysis\",\n    \"log-rank test\",\n    \"oncoPredict\",\n    \"t-tests\"\n  ],\n  \"statistical_method\": \"Differential analysis and random forest analysis were used to derive the gene signatures, while logistic regression was used to obtain the risk-score equation and ROC curves were used to evaluate diagnostic efficiency.\",\n  \"method_evidence\": \"Differential analysis and random forest analysis were performed in hepatocellular carcinoma (HCC). Receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficiency, and logistic regression was established to obtain the risk score equation.\",\n  \"repositories\": [\n    {\n      \"repository\": \"The Cancer Genome Atlas (TCGA)\",\n      \"accession\": \"TCGA\",\n      \"data_description\": \"The TCGA cohort was used for identification and analysis of the lactylation-related signatures, including risk-score stratification and survival analysis.\",\n      \"evidence\": \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets. Differential analysis and random forest analysis were performed in hepatocellular carcinoma (HCC). Receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficiency, and logistic regression was established to obtain the risk score equation. Besides, the expression of genes in the signature was identified by TCGA database and verified by real-time quantitative polymerase chain reaction (RT-PCR).\",\n      \"publicly_downloadable\": false\n    },\n    {\n      \"repository\": \"Gene Expression Omnibus (GEO)\",\n      \"accession\": \"GSE14520\",\n      \"data_description\": \"HCC gene-expression dataset downloaded for analysis and used as part of the GEO validation resources for the diagnostic model.\",\n      \"evidence\": \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets. The prognostic performance of the diagnostic model was validated in the Gene Expression Omnibus (GEO) dataset.\",\n      \"publicly_downloadable\": false\n    },\n    {\n      \"repository\": \"Gene Expression Omnibus (GEO)\",\n      \"accession\": \"GSE84402\",\n      \"data_description\": \"HCC gene-expression dataset downloaded for analysis and used as part of the GEO validation resources for the diagnostic model.\",\n      \"evidence\": \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets. The prognostic performance of the diagnostic model was validated in the Gene Expression Omnibus (GEO) dataset.\",\n      \"publicly_downloadable\": false\n    },\n    {\n      \"repository\": \"Gene Expression Omnibus (GEO)\",\n      \"accession\": \"GSE62232\",\n      \"data_description\": \"HCC gene-expression dataset downloaded for analysis and used as part of the GEO validation resources for the diagnostic model.\",\n      \"evidence\": \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets. The prognostic performance of the diagnostic model was validated in the Gene Expression Omnibus (GEO) dataset.\",\n      \"publicly_downloadable\": false\n    }\n  ],\n  \"availability_statement\": \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets.\",\n  \"data_on_request\": false,\n  \"source_claims\": [\n    \"Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\",\n    \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets.\",\n    \"Differential analysis and random forest analysis were performed in hepatocellular carcinoma (HCC).\",\n    \"Receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficiency, and logistic regression was established to obtain the risk score equation.\",\n    \"Besides, the expression of genes in the signature was identified by TCGA database and verified by real-time quantitative polymerase chain reaction (RT-PCR).\",\n    \"The prognostic performance of the diagnostic model was validated in the Gene Expression Omnibus (GEO) dataset.\",\n    \"The patients with high risk score had worse outcomes than those with low risk score in the TCGA database.\",\n    \"ROC curves showed the characteristics of lactylation-related gene signature with a good predictive capability.\",\n    \"Finally, in terms of assessing the potency of immunotherapy in patients, the low risk score group had a lower Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating a good response to immunotherapy.\"\n  ],\n  \"missing_information\": [\n    \"The supplied text does not report a numerical diagnostic performance value such as AUC, sensitivity, or specificity for the three-signature target result.\",\n    \"The supplied text does not explicitly state whether the repository data can be downloaded without an application, embargo, or request.\",\n    \"The supplied text does not explicitly identify a request-based or restricted access procedure.\",\n    \"The supplied text does not specify which individual GEO accession was used for each separate validation analysis.\",\n    \"The supplied text does not provide the physical RT-PCR samples or a repository accession for the experimentally generated expression data.\"\n  ],\n  \"evidence_spans\": [\n    \"Three lactylation-related diagnostic gene signatures predicted the prognosis and response to immunotherapy in hepatocellular carcinoma (HCC) patients.\",\n    \"Data were downloaded from The Cancer Genome Atlas (TCGA), GSE14520, GSE84402, and GSE62232 datasets.\",\n    \"Differential analysis and random forest analysis were performed in hepatocellular carcinoma (HCC). Receiver operating characteristic (ROC) curve was used to evaluate diagnostic efficiency, and logistic regression was established to obtain the risk score equation.\",\n    \"Besides, the expression of genes in the signature was identified by TCGA database and verified by real-time quantitative polymerase chain reaction (RT-PCR).\",\n    \"The prognostic performance of the diagnostic model was validated in the Gene Expression Omnibus (GEO) dataset.\",\n    \"The patients with high risk score had worse outcomes than those with low risk score in the TCGA database.\",\n    \"ROC curves showed the characteristics of lactylation-related gene signature with a good predictive capability.\",\n    \"Finally, in terms of assessing the potency of immunotherapy in patients, the low risk score group had a lower Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating a good response to immunotherapy.\"\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:a2412edc10e95cb059c8d0229b73ec9416a88dcb07c4428b1463d3deb50f2eed", "type": "sep_package", "label": "SEP v1 package for pmid:39974411"}], "activities": [{"id": "activity:conversion:pmid:39974411", "type": "conversion", "used": ["package:a2412edc10e95cb059c8d0229b73ec9416a88dcb07c4428b1463d3deb50f2eed"], "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:39974411", "parent_hash": "sha256:dfa2646ad1a5b74652b1d94fc64eaab0264fe70e0add88622fe55470dcc0b70b", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:39974411 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:39974411' does not resolve to a declared asset", "targets"]}