Eight-gene signature predicts recurrence in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32508318", "kind": "science-record", "revision": 4, "content_hash": "sha256:26e7cb093689dfe9e1391f3e18e20d215b1b8ec51da09ad264246aff45f7c495", "created_at": "2026-11-01T17:33:44Z", "updated_at": "2026-11-01T22:01:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Eight-gene signature predicts recurrence in lung adenocarcinoma.", "doi": "10.3233/CBM-190329", "pmid": "32508318", "authors": ["Yongjian Zhang", "Qiang Fan", "Yingying Guo", "Koujun Zhu"], "journal": "Cancer biomarkers : section A of Disease markers", "year": 2020, "abstract": "BACKGROUND: Recurrence significantly influences the survival in patients with lung adenocarcinoma (LUAD). However, there are less gene signatures that predict recurrence risk of LUAD.\nOBJECTIVE: We performed this study to construct a model to predict risk of recurrence in LUAD.\nMETHODS: RNA-seq data from 426 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and were randomly assigned into the training (n= 213) and validation set (n= 213). Differentially expressed genes (DEGs) between recurrent and non-recurrent tumors in the training set were identified. Recurrence-associated DEGs were selected using multivariate Cox regression analysis. The recurrence risk model that identifies patients at low and high risk for recurrence was constructed, followed by the validation of its performance in the validation set and a microarray dataset.\nRESULTS: In total, 378 DEGs, including 20 recurrence-associated DEGs, were identified between the recurrent and non-recurrent tumors in the training set. The signatures of 8 genes (including AZGP1, INPP5J, MYBPH, SPIB, GUCA2A, HTR1B, SLC15A1 and TNFSF11) were used to construct the prognostic model to assess the risk of recurrence. This model indicated that patients with high risk scores had shorter recurrence-free survival time compared with patients with low risk scores. ROC curve analysis of this model showed it had high predictive accuracy (AUC > 0.8) to predict LUAD recurrence in the TCGA cohort (the training and validation sets) and GSE50081 dataset. This prognostic model showed high predictive power and performance in predicting recurrence in LUAD.\nCONCLUSION: We concluded that this model might be of great value for evaluating the risk of recurrence of LUAD in clinics.", "claims": [{"id": "claim-method", "statement": "Recurrence-associated DEGs were selected using multivariate Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32508318", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Recurrence-associated DEGs were selected using multivariate Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "RNA-seq data from 426 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and were randomly assigned into the training (n= 213) and validation set (n= 213).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32508318", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "RNA-seq data from 426 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and were randomly assigned into the training (n= 213) and validation set (n= 213)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The recurrence risk model had high predictive accuracy (AUC > 0.8) for predicting LUAD recurrence in the TCGA training and validation sets and the GSE50081 dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:32508318", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "ROC curve analysis of this model showed it had high predictive accuracy (AUC > 0.8) to predict LUAD recurrence in the TCGA cohort (the training and validation sets) and GSE50081 dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The recurrence risk model had high predictive accuracy (AUC > 0.8) for predicting LUAD recurrence in the TCGA training and validation sets and the GSE50081 dataset.", "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:32508318"], "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:GSE50081"], "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:GSE50081"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32508318", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3233/CBM-190329", "sha256": 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METHODS: RNA-seq data from 426 patients with LUAD were downlo"], "order_basis": "paper_text_order:308", "id": "assay:1"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["its performance in the validation set and a microarray dataset. RESULTS: In total, 378 DEGs, includ"], "order_basis": "paper_text_order:866", "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": ["Recurrence-associated DEGs were selected using multivariate Cox regression analysis."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}