{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35117717", "kind": "science-record", "revision": 4, "content_hash": "sha256:6090d79d82c3617ae10c2eaf3b14fd7ffd46ebd0fe6d17666f4871fe2aed8432", "created_at": "2026-11-01T17:36:51Z", "updated_at": "2026-11-01T22:05:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a prognostic model for lung adenocarcinoma based on bioinformatics analysis of metabolic genes.", "doi": "10.21037/tcr-20-1571", "pmid": "35117717", "authors": ["Jie He", "Wentao Li", "Yu Li", "Guangnan Liu"], "journal": "Translational cancer research", "year": 2020, "abstract": "BACKGROUND: Long-term observations and studies have found that the occurrence and development of lung adenocarcinoma (LUAD) is associated with certain metabolic changes and that metabolic disorders are directly related to carcinogenic gene mutations. We attempted to establish a prognostic model for LUAD based on the expression profiles of metabolic genes.\nMETHOD: We analyzed the gene expression profiles of patients with LUAD obtained from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to assess the correlation between each metabolic gene and survival. The survival-related metabolic genes were fit into the least absolute shrinkage and selection operator (LASSO) to establish a prognostic model for LUAD. After 100,000 times of calculations and model construction, we successfully established a prognostic model consisting of 16 genes that can classify patients with LUAD into high-risk and low-risk groups. Further, the protein-protein interaction (PPI) network was built to determine the hub gene from16 metabolic genes. Finally, the top one hub gene was validated by real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry in our 50 paired LUAD and adjacent tissues, and the prognostic performance of 16 metabolic genes was validated in GEO LUAD cohorts.\nRESULTS: Univariate Cox regression analysis and LASSO regression analysis results showed that the prognostic model established based on 16 metabolic genes could differentiate patients with LUAD with significantly different overall survival (OS) and that the prognosis of the high-risk group was worse than that of the low-risk group. In addition, the model can independently predict the OS of patients in both the training cohort and the validation cohort (training cohort: HR =2.44, 95% CI: 1.58-3.74, P<0.05; validation cohort: HR =2.15, 95% CI: 2.52-2.70, P<0.05). The decision curve analysis further showed that the combination use of the prognostic model and clinical features could better predict the survival of patients and benefit patients. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed several basic signaling pathways and biological processes of metabolic genes in LUAD. Combined with the clinical features and metabolic gene characteristics of patients with LUAD, we also constructed a survival nomogram with a C-index of 0.701 to predict the survival probability of patients. The calibration curve confirmed that the nomogram predications were consistent with the actual observation results. The top one hub gene was TYMS, which was determined by PPI. TYMS levels in LUAD were detected by RT-qPCR and the expression of TYMS was significantly up-regulated in the LUAD tissue of all 50 pairs (t=11.079, P<0.0001). Simultaneously, the correct of the prognostic model was validated, based on the data in GSE37745.\nCONCLUSIONS: We constructed and validated a new prognostic model based on metabolic genes. This model could provide guidance for the personalized treatment of patients and improve the accuracy of individualized prognoses for patients with LUAD.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was performed to evaluate the relationship between the expression of various differentially expressed genes and the OS of patients with LUAD. Considering the number of selected metabolic genes and their relationship with prognosis, genes with P<0.05 were identified as candidate genes. The candidate genes were further subjected to LASSO regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35117717", "locator": "sections[5] \"Construction of a predictive model of metabolic genes\" paragraph 2", "quote": "Univariate Cox regression analysis was performed to evaluate the relationship between the expression of various differentially expressed genes and the OS of patients with LUAD. Considering the number of selected metabolic genes and their relationship with prognosis, genes with P<0.05 were identified as candidate genes. The candidate genes were further subjected to LASSO regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Metabolic gene expression profiles from 535 LUAD cases and 59 nontumor tissue samples as well as the clinical information of 522 patients with LUAD were downloaded from the official The Cancer Genome Atlas (TCGA) website (https://gdc-portal.nci.nih.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35117717", "locator": "sections[3] \"LUAD database and the clinical information of the patients\" paragraph 2", "quote": "Metabolic gene expression profiles from 535 LUAD cases and 59 nontumor tissue samples as well as the clinical information of 522 patients with LUAD were downloaded from the official The Cancer Genome Atlas (TCGA) website (https://gdc-portal.nci.nih.gov/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A 16-gene metabolic prognostic model classified patients with LUAD into groups with significantly different overall survival and independently predicted survival in both the training and validation cohorts.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Univariate Cox regression analysis and LASSO regression analysis results showed that the prognostic model established based on 16 metabolic genes could differentiate patients with LUAD with significantly different overall survival (OS) and that the prognosis of the high-risk group was worse than that of the low-risk group. In addition, the model can independently predict the OS of patients in both the training cohort and the validation cohort (training cohort: HR =2.44, 95% CI: 1.58–3.74, P<0.05; validation cohort: HR =2.15, 95% CI: 2.52–2.70, P<0.05)."}, "evidence": [{"asset": "source:pmid:35117717", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Univariate Cox regression analysis and LASSO regression analysis results showed that the prognostic model established based on 16 metabolic genes could differentiate patients with LUAD with significantly different overall survival (OS) and that the prognosis of the high-risk group was worse than that of the low-risk group. In addition, the model can independently predict the OS of patients in both the training cohort and the validation cohort (training cohort: HR =2.44, 95% CI: 1.58–3.74, P<0.05; validation cohort: HR =2.15, 95% CI: 2.52–2.70, P<0.05)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A 16-gene metabolic prognostic model classified patients with LUAD into groups with significantly different overall survival and independently predicted survival in both the training and validation cohorts.", "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:35117717"], "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:GSE37745"], "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:GSE37745"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35117717", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-20-1571", "sha256": 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Considering the number of selected metabolic genes and their relationship with prognosis, genes with P<0.05 were identified as candidate genes. The candidate genes were further subjected to LASSO regression analysis."}}, {"id": "method-2", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "Limma"}}, {"id": "method-3", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-4", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.8, "band": "high", "quote": "ROC curve"}}, {"id": "method-5", "family": "model_calibration", "specific": "decision_curve", "label": "Model calibration / clinical utility", "dsl_operations": ["calibration_bins", "net_benefit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictions_and_outcomes"], "evidence": {"score": 0.7, "band": "medium", "quote": "decision curve analysis"}}, {"id": "method-6", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "chi-square"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "build_graph", "expected_value": 95.0, "unit": "per cent", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:35117717", "data:geo:GSE37745"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE37745"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:32:34Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 11686, "completion_tokens": 1460, "cached_tokens": 256}}, "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:35117717", "type": "paper", "label": "pmid:35117717"}, {"id": "source:pmid:35117717", "type": "manuscript", "label": "PMID 35117717 full text"}, {"id": "data:geo:GSE37745", "type": "dataset", "label": "GEO GSE37745"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35117717", "type": "paper", "label": "pmid:35117717"}, {"id": "source:pmid:35117717", "type": "manuscript", "label": "PMID 35117717 full text"}, {"id": "data:geo:GSE37745", "type": "dataset", "label": "GEO GSE37745"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35117717", "type": "extraction", "used": ["source:pmid:35117717"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE37745", "type": "accession_resolution", "used": ["data:geo:GSE37745"], "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:35117717", "parent_hash": "sha256:8f1d0de47b0ed97b236e53d4f5c7e8240d9d0fd6355cef9c186ee7bb33ad508a", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE37745 licence not read", "asset:data:geo:GSE37745 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["tion quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry in our 50 paired L", "by PPI. TYMS levels in LUAD were detected by RT-qPCR and the expression of TYMS was significantly", "n quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry in our 50 paired L", "PPI. TYMS levels in LUAD were detected by RT-qPCR and the expression of TYMS was significantly"], "order_basis": "paper_text_order:1235", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["tive polymerase chain reaction (RT-qPCR) and immunohistochemistry in our 50 paired LUAD and adjacent tissues,", "g adenocarcinoma (magnification, ×100). IHC, immunohistochemistry.", "tal evidence of TYMS at the protein level by IHC The staining status of the TYMS antibody was", "tus of the TYMS antibody was evaluated by an IHC assay and the images of IHC are presented in"], "order_basis": "paper_text_order:1248", "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": ["Univariate Cox regression analysis was performed to evaluate the relationship between the expression of various differentially expressed genes and the OS of patients with LUAD. Considering the number of selected metabolic genes and their relationship with prognosis, genes with P<0.05 were identified as candidate genes. 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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}}}