{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39587687", "kind": "science-record", "revision": 3, "content_hash": "sha256:d384a42051112f9d73d53fd9d35d52f9a840452bb5a487feaf6d211d05477d18", "created_at": "2026-11-01T20:29:51Z", "updated_at": "2026-11-01T22:13:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer.", "doi": "10.1186/s40246-024-00698-2", "pmid": "39587687", "authors": ["Qunfang Zhou", "Jingqiang Wu", "Jiaxin Bei", "Zixuan Zhai", "Xiuzhen Chen", "Wei Liang", "Jing Meng", "Mingyu Liu"], "journal": "Human genomics", "year": 2024, "abstract": "BACKGROUND: Liver cancer has a high global incidence, particularly in East Asia. Early detection difficulties lead to poor prognosis. Single-cell sequencing precisely identifies gene expression differences in specific cell types, making it valuable in tumor microenvironment research and immune drug development. However, the characteristics of tumor cells themselves are equally important for patient prognosis and treatment.\nMETHODS: We downloaded single-cell sequencing data from GSE189903, grouped cells by cluster markers, and classified epithelial cells into adjacent non-tumor, normal, and tumor cells. Differential gene and survival analyses identified significant differential genes. Using TCGA-LIHC data, we divided 370 patients into test and training sets. We constructed and validated a LASSO model based on these genes in both sets and two external datasets. Functional, immune infiltration, and mutation analyses were performed on high and low-risk groups. We also used RNA-seq and IC50 data of 15 liver cancer cell lines from GDSC, scoring them with our prognostic model to identify potential drugs for high-risk patients.\nRESULTS: Dimensionality reduction and clustering of 34 single-cell samples identified five subgroups, with epithelial cells further classified. Differential gene analysis identified 124 significant genes. An 11-gene prognostic model was constructed, effectively stratifying patient prognosis (p < 0.05) and achieving an AUC above 0.6 for 5 year survival prediction in multiple cohorts. Functional analysis revealed that upregulated genes in high-risk groups were enriched in cell adhesion pathways, while downregulated genes were enriched in metabolic pathways. Mutation analysis showed more TP53 mutations in the high-risk group and more CTNNB1 mutations in the low-risk group. Immune infiltration analysis indicated higher immune scores and less CD8 + naive T cell infiltration in the high-risk group. Drug sensitivity analysis identified 14 drugs with lower IC50 in the high-risk group, including clinically approved Sorafenib and Axitinib for treating unresectable HCC.\nCONCLUSION: We established an 11-gene prognostic model that effectively stratifies liver cancer patients based on differentially expressed genes between tumor and adjacent non-tumor cells clustered by scRNA-seq data. The two risk groups had significantly different molecular characteristics. We identified 14 drugs that might be effective for high-risk HCC patients. Our study provides novel insights into tumor cell characteristics, aiding in research on tumor development and treatment.", "claims": [{"id": "claim-method", "statement": "Using the glmnet R package, we constructed a LASSO-Cox regression model in the training group based on the candidate genes, and tenfold cross validation was used to determine the optimal regularization parameters.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39587687", "locator": "sections[6] \"Establishment and validation of a prognostic model\" paragraph 3", "quote": "Using the glmnet R package, we constructed a LASSO-Cox regression model in the training group based on the candidate genes, and tenfold cross validation was used to determine the optimal regularization parameters."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analysed during the current study are available in the GEO repository https://www.ncbi.nlm.nih.gov/, the TCGA repository https://portal.gdc.cancer.gov/, the NODE repository https://www.biosino.org/node.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39587687", "locator": "sections[22] \"notes\" paragraph 1", "quote": "The datasets generated and/or analysed during the current study are available in the GEO repository https://www.ncbi.nlm.nih.gov/, the TCGA repository https://portal.gdc.cancer.gov/, the NODE repository https://www.biosino.org/node."}], "state": "complete"}, {"id": "claim-target-result", "statement": "An 11-gene LASSO-Cox prognostic model was established and validated to stratify liver cancer patient prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39587687", "locator": "sections[12] \"Establishment and validation of a prognostic model in TCGA-LIHC\" paragraph 2", "quote": "A model containing eleven genes was established, including CCT3, CRYAB, ENO1, LDHA, MARCKSL1, MMP7, SAT2, SPP2, STOM, TMEM205, and TRAPPC9 (Fig. 2A)."}, {"asset": "source:pmid:39587687", "locator": "sections[12] \"Establishment and validation of a prognostic model in TCGA-LIHC\" paragraph 2", "quote": "The AUC for 1-year, 3-year, and 5-year survival were 0.77, 0.76, and 0.73, respectively (Fig. 2C), suggesting good predictive efficiency."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "An 11-gene LASSO-Cox prognostic model was established and validated to stratify liver cancer patient prognosis.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39587687"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE189903"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE189903"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39587687", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s40246-024-00698-2", "sha256": 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE189903", "GSE20140"], "resolved_accessions": ["GSE189903", "GSE20140"], "statements": ["The datasets generated and/or analysed during the current study are available in the GEO repository https://www.ncbi.nlm.nih.gov/, the TCGA repository https://portal.gdc.cancer.gov/, the NODE repository https://www.biosino.org/node."], "basis": "The paper names GSE189903, GSE20140 in GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:39587687", "data:geo:GSE189903"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE189903"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:39:27Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9623, "completion_tokens": 1731, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:39587687", "type": "paper", "label": "pmid:39587687"}, {"id": "source:pmid:39587687", "type": "manuscript", "label": "PMID 39587687 full text"}, {"id": "data:geo:GSE189903", "type": "dataset", "label": "GEO GSE189903"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39587687", "type": "paper", "label": "pmid:39587687"}, {"id": "source:pmid:39587687", "type": "manuscript", "label": "PMID 39587687 full text"}, {"id": "data:geo:GSE189903", "type": "dataset", "label": "GEO GSE189903"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39587687", "type": "extraction", "used": ["source:pmid:39587687"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE189903", "type": "accession_resolution", "used": ["data:geo:GSE189903"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:39587687", "parent_hash": "sha256:0feb8d60037eb09ee53e38f957afc0121e4555d98424369cd6204f67ee5ee21b", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE189903 licence not read", "asset:data:geo:GSE189903 not downloaded", "asset:data:geo:GSE20140 licence not read", "asset:data:geo:GSE20140 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ed on high and low-risk groups. We also used RNA-seq and IC50 data of 15 liver cancer cell lines"], "order_basis": "paper_text_order:1062", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["or and adjacent non-tumor cells clustered by scRNA-seq data. The two risk groups had significantly"], "order_basis": "paper_text_order:2391", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Using the glmnet R package, we constructed a LASSO-Cox regression model in the training group based on the candidate genes, and tenfold cross validation was used to determine the optimal regularization parameters."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]},"meta":{"api_version":"v1","automation_ladder_snapshot":"ba34b49490d9bfaf","automation_ladder_caveats":["Method extraction is rule-based and has no hand-judged accuracy sample. Evidence bands order textual support; method agreement intervals are null (n=0), never probabilities. Work-type validation does not validate methods.","Operation coverage describes only explicitly named methods. It does not prove a complete protocol, compatible parameters, successful compilation or re-execution. Unrecognised or ambiguous methods require review.","Data reachability is a textual accession or public-data statement. No dataset was downloaded or checked for access, completeness or licence.","Capabilities are planning states seeded from the dated equipment register. No bench ownership, completed quote or partner booking is inferred. Procurement counts are dependencies, not guaranteed replications or financial returns.","Tier and blocker counts are deterministic assessments of this corpus, not measured agreement. R3 is reserved for procurement; unresolved methods and data require human work (R4). The most restrictive axis wins, with capability, method, data as tie-breaks. Resolving one blocker can leave another at the same tier."],"engine":"corpus_engine","engine_provider":"rule_based_v1+lex99e1195c7d6f","run_id":"run-20261023T122449232913","source":"esearch","source_detail":"PubMed mirror biomed_pubmed (read-only)","run_status":"complete","imported_at":"2026-11-03T20:16:52+00:00","generated_at":"2026-11-04T01:35:24+00:00","total_papers":5529,"total_assays_tracked":29,"automatable_assay_families":6,"validated":true,"caveat_count":9,"blocking_caveats":5,"caveats":[{"code":"low_evidence_dominates","basis":["4,152 of 5,529 classifications (75.1%) matched no cue evidence and sit at the 0.05 floor.","34 of 230 data_only calls (14.8%) are in that group."],"claim":"Work type and confidence bands","gloss":"Most records were classified from their title and framing alone, with assay-cue coverage at zero.","detail":"Most classifications in this run rest on no matched evidence at all. At the evidence floor the work type comes from title framing and a fixed prior, not from anything in the paper, so the published work-type split should be read as a rough triage signal rather than a measured distribution. The impact is bounded but not removed: since 2026-10-23 the engine excludes floor-confidence records from the shortlist entirely and publishes them separately as held out, so the recommendation is unaffected even though the census is.","status":"unverified","severity":"high"},{"code":"runtime_is_modelled","basis":["Runtime is summed from published per-assay hour figures. Timing awaits the first executed replication.","4,407 of 5,529 records (79.7%) model to 0.00 hours because no assay was matched, which the ranking then reads as \"cheapest\".","0 of 10 shortlisted records carry a 0.00-hour estimate."],"claim":"Estimated replication runtime","gloss":"The hour figures are estimates built from assay mentions; timing awaits the first executed replication.","detail":"The hour figures are a cost model derived from the bench steps implied by each matched assay family. The first executed replication will supply measured timing. Records without an assay match currently receive a zero-hour model value.","status":"unverified","severity":"high"},{"code":"signal_score_without_citations","basis":["5,529 of 5,529 records (100.0%) have no citation count from the source metadata."],"claim":"Signal score","gloss":"The signal score uses publication-type weight because the mirror supplied zero citation counts.","detail":"The source supplied no citation counts for any record in this run, so the signal score is publication-type weight only. The citation term of the ranking is inert here, and the shortlist is ordered by publication type alone.","status":"unverified","severity":"high"},{"code":"abstract_absent","basis":["1,667 of 5,529 records (30.2%) carry no abstract at all."],"claim":"Evidence base for classification","gloss":"Some records contain a title and publication type only, defining the available classification evidence.","detail":"Where the mirror contains a title and publication types only, those fields define the classifier evidence. A methods section is required for fuller automated and human review.","status":"unverified","severity":"medium"},{"code":"classifier_agreement_measured","basis":["Published two-way decision agreement 77.7% (310 of 399 decidable records) from a stratified hand-check of 438 real records.","Three-way diagnostic agreement 73.9% (288 of 390 decidable records).","data_only agreement 75.2% (100 of 133 decidable).","48 record(s) could not be decided from title and abstract and were excluded from the denominator.","56 analysis-only records were published under another work type, so the data_only share is not an upper bound."],"claim":"Published automatable decision","gloss":"This figure has been checked against a human reading the same papers, so we know how often it is right.","detail":"The published automatable / not-automatable distribution comes from a rule-based classifier that was hand-checked against a fixed ground-truth definition. The agreement rates below are measured, not estimated. The three-way work type remains a diagnostic and is reported separately.","status":"measured","severity":"high"},{"code":"llm_comparison_not_measured","basis":["rule_based_v1+lex99e1195c7d6f produced 5,529 of 5,529 classifications.","Measured on 438 labelled records; these are RULE-BASED figures, not model results."],"claim":"Rule-based versus LLM agreement","gloss":"Every number here is the rule engine’s own. Model comparison begins when a working API credential is connected and measured.","detail":"The published two-way decision agreed with a human reading on 310 of 399 decidable records (77.7%). The diagnostic three-way label agreed on 288 of 390 decidable records (73.9%). Every percentage in this run belongs to the rule engine. Model comparison begins when a working API credential is connected and measured.","status":"not_measured","severity":"high"},{"code":"confidence_is_a_band","basis":["low band: 135 of 210 hand-judged records were correct, 64.3% [57.6, 70.5] (95% CI).","medium band: 80 of 104 hand-judged records were correct, 76.9% [68.0, 84.0] (95% CI).","high band: 73 of 76 hand-judged records were correct, 96.0% [89.0, 98.7] (95% CI).","A fitted calibrated probability was built, measured and withdrawn: it overstated held-out accuracy and reorders nothing."],"claim":"Confidence band and evidence score","gloss":"The confidence band carries a measured accuracy interval; 0.85 represents relative rank strength.","detail":"Confidence on this run is a BAND plus its measured accuracy interval. The number stored alongside each record is a monotone evidence score that orders records; 0.85 represents relative rank strength. A fitted probability was withdrawn after it overstated held-out accuracy. Threshold on the band.","status":"measured","severity":"medium"},{"code":"three_way_split_is_diagnostic","basis":["The corpus is published at the two-way decision: automatable or not-automatable. The three-way split (data_only / physical / mixed) is published as a diagnostic with its measured reliability attached, not as a claim, because the rule engine's `mixed` call cannot be defended.","The engine called 22 record(s) mixed; 10 matched the human judgement (45.5%, 95% CI 26.9-65.3).","The engine's `mixed` call is right on 10/22 hand-judged records (45.5%, 95% CI 26.9%-65.3%); a constant `physical` guess scores 36.4%.","The engine's `mixed` call does not reliably beat a constant `physical` guess on the hand-judged records it labels mixed.","The sign of that difference flips between two independent samples, so the three-way refinement is not demonstrably informative.","The mixed stratum contributes a disproportionate share of errors relative to its size.","At title-only depth the physical/mixed split collapses: a title says that work was done, not whether it was also analysed."],"claim":"The published decision is two-way","gloss":"The three-way split is shown for diagnosis only. The published split is two ways.","detail":"Count and filter the corpus on automatable versus not-automatable. The data_only / physical / mixed split remains an internal engine diagnostic because the mixed call has lower measured reliability.","status":"measured","severity":"medium"},{"code":"automatable_flag_unproven","basis":["6 of 29 assay families are marked automatable now: meta_analysis, machine_learning, survival_analysis, pca, molecular_docking, differential_expression.","The flag is a rule over assay family and work type; validation begins with the first executed replication.","It inherits the 75.2% data_only agreement rate, because the flag requires the record to be analysis-only."],"claim":"Automatable now","gloss":"This flag marks assay families the rules classify as data-only. The first executed replication will validate it.","detail":"This flag marks families the rules consider replicable from data alone. Its validity depends on the analysis-only classification and will be measured through an executed replication.","status":"unverified","severity":"medium"}],"confidence_policy":"band_with_measured_interval","confidence_contract":{"policy":"band_with_measured_interval","summary":"Confidence is reported as a BAND with a measured accuracy interval. The raw evidence score supplies monotone ordering. A fitted point estimate was withdrawn after it overstated held-out accuracy.","evidence_score":"A monotone evidence score in [0,1] built from cue mass, axis separation and text coverage. Use it to order records. Interpret 0.85 as relative rank strength.","confidence_band":"low | medium | high. Consumers threshold on this value.","band_accuracy":"Wilson 95% interval on the measured agreement of the hand-judged records in this band.","calibrated_confidence":"WITHDRAWN. Reintroduction requires held-out evidence demonstrating reliable estimates across every bin."},"band_accuracy":{"low":{"n":210,"low":0.576,"band":"low","high":0.7046,"agree":135,"accuracy":0.6429},"high":{"n":76,"low":0.8903,"band":"high","high":0.9865,"agree":73,"accuracy":0.9605},"medium":{"n":104,"low":0.6795,"band":"medium","high":0.8397,"agree":80,"accuracy":0.7692}},"provider":"rule_based_v1+lex99e1195c7d6f","lexicon_id":"99e1195c7d6f","contract_published_at":"2026-10-23T12:25:29.022232+00:00","confidence_is_band":true,"confidence_is_probability":false,"decision_taxonomy":"two_way","decidable_fraction_two_way":0.8854,"decidable_fraction_two_way_low":0.8565,"decidable_fraction_two_way_high":0.9143,"decidable_fraction_three_way":0.8586,"decidable_fraction_three_way_low":0.8287,"decidable_fraction_three_way_high":0.8884,"decidable_population":5529,"validation_sample_size":438,"three_way_cost_fraction":0.0268,"three_way_cost_records":148,"decision_counts":{"automatable":230,"not_automatable":5299},"decision_split":{"automatable":{"count":230,"share":0.04159884246699222},"not_automatable":{"count":5299,"share":0.9584011575330078}},"diagnostic_work_type_counts":{"mixed":24,"physical":5275,"data_only":230}}}