Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36566175", "kind": "science-record", "revision": 4, "content_hash": "sha256:a8028050e9c83b9b1d527e4316fea8f44c16f2e100074bfd4aac314751a5822c", "created_at": "2026-11-01T19:01:06Z", "updated_at": "2026-11-01T22:50:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics.", "doi": "10.1186/s12920-022-01417-3", "pmid": "36566175", "authors": ["Jingfei Zhang", "Chenxi Ma", "Han Qin", "Zhi Wang", "Chao Zhu", "Xiujuan Liu", "Xiuyan Hao", "Jinghua Liu", "Ling Li", "Zhen Cai"], "journal": "BMC medical genomics", "year": 2022, "abstract": "BACKGROUND: Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome.\nMETHODS: This work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database.\nRESULTS: The present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan-Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P < 0.001). According to subsequent univariate and multivariate Cox regression, risk score served as the factor to predict prognosis relative to additional clinical features (P < 0.001). Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy. Then, we validated this clinical prognostic model using GSE41613. To enhance our model prediction accuracy, age, gender, risk score together with TNM stage were incorporated in a nomogram. As indicated by results of ROC curve and calibration curve analyses, the as-constructed nomogram had enhanced prediction accuracy compared with clinicopathological features alone, besides, combining clinicopathological characteristics with risk score contributed to predicting patient prognosis and guiding clinical decision-making.\nCONCLUSION: In this study, 11 MRGs prognostic models based on TCGA database showed superior predictive performance and had a certain clinical application prospect in guiding individualized.", "claims": [{"id": "claim-method", "statement": "This work employed R software (Version 4.1.2) limma R package for screening metabolic DEGs within cancer compared with non-carcinoma samples upon |log2FC|> 0.5 and FDR < 0.05 thresholds. Then, we used the glmnet R package to integrate survival status, survival time, as well as DEGs expression profiles.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36566175", "locator": "sections[4] \"Screening differentially expressed genes (DEGs)\" paragraph 2", "quote": "This work employed R software (Version 4.1.2) limma R package for screening metabolic DEGs within cancer compared with non-carcinoma samples upon |log2FC|> 0.5 and FDR < 0.05 thresholds. Then, we used the glmnet R package to integrate survival status, survival time, as well as DEGs expression profiles."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analysed during the current study are available from NCBI Gene Expression Omnibus (GEO: GSE41613) https://www.ncbi.nlm.nih.gov/geo/ and the cancer genome database (TCGA: OSCC) https://portal.gdc.cancer.gov/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36566175", "locator": "sections[25] \"notes\" paragraph 1", "quote": "The datasets used and/or analysed during the current study are available from NCBI Gene Expression Omnibus (GEO: GSE41613) https://www.ncbi.nlm.nih.gov/geo/ and the cancer genome database (TCGA: OSCC) https://portal.gdc.cancer.gov/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36566175", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy.", "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:36566175"], "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:GSE172577"], "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:GSE172577"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36566175", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-022-01417-3", "sha256": 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