An energy metabolism-based eight-gene signature correlates with the clinical outcome of esophagus carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33794814", "kind": "science-record", "revision": 7, "content_hash": "sha256:d6290d0fe82ea2d08d7a2fa3809f1b7bf502848024ddabab833bcb50da5a878f", "created_at": "2026-11-01T15:41:31Z", "updated_at": "2026-11-01T22:03:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An energy metabolism-based eight-gene signature correlates with the clinical outcome of esophagus carcinoma.", "doi": "10.1186/s12885-021-08030-0", "pmid": "33794814", "authors": ["Weifeng Zheng", "Chaoying Chen", "Jianghao Yu", "Chengfeng Jin", "Tiemei Han"], "journal": "BMC cancer", "year": 2021, "abstract": "BACKGROUND: The essence of energy metabolism has spread to the field of esophageal cancer (ESC) cells. Herein, we tried to develop a prognostic prediction model for patients with ESC based on the expression profiles of energy metabolism associated genes.\nMATERIALS AND METHODS: The overall survival (OS) predictive gene signature was developed, internally and externally validated based on ESC datasets including The Cancer Genome Atlas (TCGA), GSE54993 and GSE19417 datasets. Hub genes were identified in each energy metabolism related molecular subtypes by weighted gene correlation network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analysis were applied to assess prognostic genes and build the prognostic gene signature. Kaplan-Meier curve, time-dependent receiver operating characteristic (ROC) curve, nomogram, decision curve analysis (DCA), and restricted mean survival time (EMST) were used to assess the performance of the gene signature.\nRESULTS: A novel energy metabolism based eight-gene signature (including UBE2Z, AMTN, AK1, CDCA4, TLE1, FXN, ZBTB6 and APLN) was established, which could dichotomize patients with significantly different OS in ESC. The eight-gene signature demonstrated independent prognostication potential in patient with ESC. The prognostic nomogram constructed based on the gene signature showed excellent predictive performance, whose robustness and clinical usability were higher than three previous reported prognostic gene signatures.\nCONCLUSIONS: Our study established a novel energy metabolism based eight-gene signature and nomogram to predict the OS of ESC, which may help in precise clinical management.", "claims": [{"id": "claim-method", "statement": "To narrow the gene range and maximize the accuracy, LASSO Cox regression analysis [18], a method screening signatures with generally effective prognostication performance by performing automatic feature selection, was performed by using the R package glmnet, and optimal genes were evaluated by 3-fold cross validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33794814", "locator": "sections[7] \"Screening of robust prognostic feature genes and construction of gene signature\" paragraph 2", "quote": "To narrow the gene range and maximize the accuracy, LASSO Cox regression analysis [18], a method screening signatures with generally effective prognostication performance by performing automatic feature selection, was performed by using the R package glmnet, and optimal genes were evaluated by 3-fold cross validation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analyzed during the current study are available in the TCGA repository (https://portal.gdc.cancer.gov/) with the accession number TCGA-ESCA, and the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) the accession number were GSE54993 and GSE19417.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33794814", "locator": "sections[27] \"notes\" paragraph 1", "quote": "The datasets generated and analyzed during the current study are available in the TCGA repository (https://portal.gdc.cancer.gov/) with the accession number TCGA-ESCA, and the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) the accession number were GSE54993 and GSE19417."}], "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:33794814"], "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:GSE19417"], "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:GSE19417"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33794814", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-021-08030-0", "sha256": 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