Construction of prognostic risk prediction model of oral squamous cell carcinoma based on co-methylated genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31198983", "kind": "science-record", "revision": 6, "content_hash": "sha256:02047f6ab92d9b002f1bfc71ca3588b7bc56c552ede1a08687df764352ff20fe", "created_at": "2026-11-01T15:40:46Z", "updated_at": "2026-11-01T21:22:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of prognostic risk prediction model of oral squamous cell carcinoma based on co-methylated genes.", "doi": "10.3892/ijmm.2019.4243", "pmid": "31198983", "authors": ["Qiang Zhu", "Gang Tian", "Jianyong Gao"], "journal": "International journal of molecular medicine", "year": 2019, "abstract": "This study aimed to identify DNA methylation markers in oral squamous cell carcinoma (OSCC) and to construct a prognostic prediction model of OSCC. For this purpose, the methylation data of patients with OSCC downloaded from The Cancer Genome Atlas were considered as a training dataset. The methylation profiles of GSE37745 for OSCC samples were downloaded from Gene Expression Omnibus and considered as validation dataset. Differentially methylated genes (DMGs) were screened from the TCGA training dataset, followed by co‑methylation analysis using weighted correlation network analysis (WGCNA). Subsequently, the methylation and gene expression levels of DMGs involved in key modules were extracted for correlation analysis. Prognosis‑related methylated genes were screened using the univariate Cox regression analysis. Finally, the risk prediction model was constructed and validated through GSE52793. The results revealed that a total of 948 DMGs with CpGs were screened out. Co‑methylation gene analysis obtained 2 (brown and turquoise) modules involving 380 DMGs. Correlation analysis revealed that the methylation levels of 132 genes negatively correlated with the gene expression levels. By combining with the clinical survival prognosis of samples, 5 optimized prognostic genes [centromere protein V (CENPV), Tubby bipartite transcription factor (TUB), synaptotagmin like 2 (SYTL2), occludin (OCLN) and CAS1 domain containing 1 (CASD1)] were selected for constructing a risk prediction model. It was consistent in the training dataset and GSE52793 that low‑risk samples had a better survival prognosis. On the whole, this study indicates that the constructed risk prediction model based on CENPV, SYTL2, OCLN, CASD1, and TUB may have the potential to be used for predicting the survival prognosis of patients with OSCC.", "claims": [{"id": "claim-method", "statement": "The association between risk model and prognosis was assessed by the Kaplan-Meier (KM) survival curve (25) in survival package (version 2.41-1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31198983", "locator": "sections[8] \"Establishment of prognostic prediction model based on methylated genes\" paragraph 4", "quote": "The association between risk model and prognosis was assessed by the Kaplan-Meier (KM) survival curve (25) in survival package (version 2.41-1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31198983", "locator": "sections[18] \"Availability of data and materials\" paragraph 2", "quote": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "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:31198983"], "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:31198983", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3892/ijmm.2019.4243", "sha256": 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