Seven LncRNA-mRNA based risk score predicts the survival of head and neck squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:28331188", "kind": "science-record", "revision": 8, "content_hash": "sha256:3253d089fe41c5a7e1e1fba507da3df5d748743c8be1e18745b7f2fb9ad8ad32", "created_at": "2026-11-01T15:40:24Z", "updated_at": "2026-11-01T21:59:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Seven LncRNA-mRNA based risk score predicts the survival of head and neck squamous cell carcinoma.", "doi": "10.1038/s41598-017-00252-2", "pmid": "28331188", "authors": ["Zhi-Li Zhang", "Li-Jing Zhao", "Liang Chai", "Shui-Hong Zhou", "Feng Wang", "Yan Wei", "Ya-Ping Xu", "Peng Zhao"], "journal": "Scientific reports", "year": 2017, "abstract": "Dysregulation of mRNAs and long non-coding RNAs (lncRNAs) is one of the most important features of carcinogenesis and cancer development. However, studies integrating the expression of mRNAs and lncRNAs to predict the survival of head and neck squamous cell carcinoma (HNSC) are still limited, hitherto. In current work, we identified survival related mRNAs and lncRNAs in three datasets (TCGA dataset, E-TABM-302, GSE41613). By random forest, seven gene signatures (six mRNAs and lncRNA) were further selected to develop the risk score model. The risk score was significantly associated with survival in both training and testing datasets (E-TABM-302, GSE41613, and E-MTAB-1324). Furthermore, correlation analyses showed that the risk score is independent from clinicopathological features. According to Cox multivariable hazard model and nomogram, the risk score contributes the most to survival than the other clinical information, including gender, age, histologic grade, and alcohol taking. The Gene Set Enrichment Analysis (GSEA) indicates that the risk score is associated with cancer related pathways. In summary, the lncRNA-mRNA based risk score model we developed successfully predicts the survival of 755 HNSC samples in five datasets and two platforms. 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