A model of twenty-three metabolic-related genes predicting overall survival for lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33024640", "kind": "science-record", "revision": 6, "content_hash": "sha256:4b51ddb717c47fe1f3934258aa315255cb723701d30c82fed2cf166277ad64b9", "created_at": "2026-11-01T15:41:12Z", "updated_at": "2026-11-01T21:23:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A model of twenty-three metabolic-related genes predicting overall survival for lung adenocarcinoma.", "doi": "10.7717/peerj.10008", "pmid": "33024640", "authors": ["Zhenyu Zhao", "Boxue He", "Qidong Cai", "Pengfei Zhang", "Xiong Peng", "Yuqian Zhang", "Hui Xie", "Xiang Wang"], "journal": "PeerJ", "year": 2020, "abstract": "BACKGROUND: The highest rate of cancer-related deaths worldwide is from lung adenocarcinoma (LUAD) annually. Metabolism was associated with tumorigenesis and cancer development. Metabolic-related genes may be important biomarkers and metabolic therapeutic targets for LUAD.\nMATERIALS AND METHODS: In this study, the gleaned cohort included LUAD RNA-SEQ data from the Cancer Genome Atlas (TCGA) and corresponding clinical data (n = 445). The training cohort was utilized to model construction, and data from the Gene Expression Omnibus (GEO, GSE30219 cohort, n = 83; GEO, GSE72094, n = 393) were regarded as a testing cohort and utilized for validation. First, we used a lasso-penalized Cox regression analysis to build a new metabolic-related signature for predicting the prognosis of LUAD patients. Next, we verified the metabolic gene model by survival analysis, C-index, receiver operating characteristic (ROC) analysis. Univariate and multivariate Cox regression analyses were utilized to verify the gene signature as an independent prognostic factor. Finally, we constructed a nomogram and performed gene set enrichment analysis to facilitate subsequent clinical applications and molecular mechanism analysis.\nRESULT: Patients with higher risk scores showed significantly associated with poorer survival. We also verified the signature can work as an independent prognostic factor for LUAD survival. The nomogram showed better clinical application performance for LUAD patient prognostic prediction. Finally, KEGG and GO pathways enrichment analyses suggested several especially enriched pathways, which may be helpful for us investigative the underlying mechanisms.", "claims": [{"id": "claim-method", "statement": "Univariate and multivariate Cox regression analyses were utilized to verify the gene signature as an independent prognostic factor.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33024640", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Univariate and multivariate Cox regression analyses were utilized to verify the gene signature as an independent prognostic factor."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw measurements are available in the Supplemental Files. The training cohort data is available in at TCGA (https://portal.gdc.cancer.gov/projects/TCGA-LUAD”). The testing cohort data is available at NCBI GEO: GSE30219 and GSE72094.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33024640", "locator": "sections[22] \"Additional Information and Declarations\" paragraph 4", "quote": "The raw measurements are available in the Supplemental Files. The training cohort data is available in at TCGA (https://portal.gdc.cancer.gov/projects/TCGA-LUAD”). The testing cohort data is available at NCBI GEO: GSE30219 and GSE72094."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33024640"], "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:GSE30219"], "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:GSE30219"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33024640", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.10008", "sha256": "sha256:b105210dede49f33012ddd5743833406b7f879d949ebff4323ad2faeb383cca9", "bytes": 37905, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.10008", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7520091", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE30219", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30219", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE30219", "version": null, "note": "The raw measurements are available in the Supplemental Files. The training cohort data is available in at TCGA (https://portal.gdc.cancer.gov/projects/TCGA-LUAD”). The testing cohort data is available at NCBI GEO: GSE30219 and GSE72094.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The raw measurements are available in the Supplemental Files. The training cohort data is available in at TCGA (https://portal.gdc.cancer.gov/projects/TCGA-LUAD”). 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