A 3-mRNA-based prognostic signature of survival in oral squamous cell carcinoma.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31396442", "kind": "science-record", "revision": 4, "content_hash": "sha256:5296320ea14c4c8e2fe29c8373b8e8be0555d0df7e862496afb868b5249c3fd5", "created_at": "2026-11-01T15:40:48Z", "updated_at": "2026-11-01T21:22:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A 3-mRNA-based prognostic signature of survival in oral squamous cell carcinoma.", "doi": "10.7717/peerj.7360", "pmid": "31396442", "authors": ["Ruoyan Cao", "Qiqi Wu", "Qiulan Li", "Mianfeng Yao", "Hongbo Zhou"], "journal": "PeerJ", "year": 2019, "abstract": "BACKGROUND: Oral squamous cell carcinoma (OSCC) is the most common type of head and neck squamous cell carcinoma with an unsatisfactory prognosis. The aim of this study was to identify potential prognostic mRNA biomarkers of OSCC based on analysis of The Cancer Genome Atlas (TCGA).\nMETHODS: Expression profiles and clinical data of OSCC patients were collected from TCGA database. Univariate Cox analysis and the least absolute shrinkage and selection operator Cox (LASSO Cox) regression were used to primarily screen prognostic biomarkers. Then multivariate Cox analysis was performed to build a prognostic model based on the selected prognostic mRNAs. Nomograms were generated to predict the individual's overall survival at 3 and 5 years. The model performance was assessed by the time-dependent receiver operating characteristic (ROC) curve and calibration plot in both training cohort and validation cohort (GSE41613 from NCBI GEO databases). In addition, machine learning was used to assess the importance of risk factors of OSCC. Finally, in order to explore the potential mechanisms of OSCC, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was completed.\nRESULTS: Three mRNAs (CLEC3B, C6 and CLCN1) were finally identified as a prognostic biomarker pattern. The risk score was imputed as: (-0.38602 × expression level of CLEC3B) + (-0.20632 × expression level of CLCN1) + (0.31541 × expression level of C6). In the TCGA training cohort, the area under the curve (AUC) was 0.705 and 0.711 for 3- and 5-year survival, respectively. In the validation cohort, AUC was 0.718 and 0.717 for 3- and 5-year survival. A satisfactory agreement between predictive values and observation values was demonstrated by the calibration curve in the probabilities of 3- and 5- year survival in both cohorts. Furthermore, machine learning identified the 3-mRNA signature as the most important risk factor to survival of OSCC. Neuroactive ligand-receptor interaction was most enriched mostly in KEGG pathway analysis.\nCONCLUSION: A 3-mRNA signature (CLEC3B, C6 and CLCN1) successfully predicted the survival of OSCC patients in both training and test cohort. In addition, this signature was an independent and the most important risk factor of OSCC.", "claims": [{"id": "claim-method", "statement": "Univariate Cox analysis and the least absolute shrinkage and selection operator Cox (LASSO Cox) regression were used to primarily screen prognostic biomarkers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31396442", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Univariate Cox analysis and the least absolute shrinkage and selection operator Cox (LASSO Cox) regression were used to primarily screen prognostic biomarkers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:The raw measurements are available in the Supplemental Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31396442", "locator": "sections[22] \"Data Availability\" paragraph 2", "quote": "The following information was supplied regarding data availability:The raw measurements are available in the Supplemental Files."}], "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:31396442"], "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. 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