m 6 A-related lncRNA signature for predicting prognosis and immune response in head and neck squamous cell carcinoma.

Autor:	Yin J; The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University Luzhou 646000, Sichuan, China., He X; The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University Luzhou 646000, Sichuan, China., Qin F; The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University Luzhou 646000, Sichuan, China., Zheng S; The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University Luzhou 646000, Sichuan, China., Huang Y; The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University Luzhou 646000, Sichuan, China., Hu L; The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University Luzhou 646000, Sichuan, China., Chen Y; The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University Luzhou 646000, Sichuan, China., Zhong L; The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University Luzhou 646000, Sichuan, China., Hu W; The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University Luzhou 646000, Sichuan, China., Li S; The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University Luzhou 646000, Sichuan, China.
Jazyk:	angličtina
Zdroj:	American journal of translational research [Am J Transl Res] 2022 Nov 15; Vol. 14 (11), pp. 7653-7669. Date of Electronic Publication: 2022 Nov 15 (Print Publication: 2022).
Abstrakt:	Objectives: N6-methyladenosine (m 6 A) and long non-coding RNAs (lncRNAs) significantly impact the prognosis and the response to immunotherapy in head and neck squamous cell carcinoma (HNSCC). Therefore, this study aimed to develop an m 6 A-related lncRNA (m 6 AlncRNA) model for predicting the prognosis and the immunotherapeutic response in HNSCC. Methods: We identified the m 6 AlncRNAs and constructed a risk assessment signature by using univariable Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses. The Kaplan-Meier analysis, receiver-operating characteristic (ROC) curves, principal component analysis (PCA), decision curve analysis (DCA), consistency index (C-index), and nomogram were applied to assess the risk model. Finally, we investigated the predictability of this model in prognosis and response to immunotherapy and evaluated various novel compounds for the clinical treatment of HNSCC. Results: HNSCC patients were assigned to high- and low-risk groups based on the median risk scores, and the high- and low-risk groups had different clinical features, tumor immune infiltration status, tumor immune dysfunction and exclusion (TIDE), tumor mutational burden (TMB), sensitivity to novel potential compounds, and immunotherapeutic response. Conclusions: The model we developed was accurate and efficient in predicting the prognosis of patients with HNSCC. It was also sensitive in stratifying HNSCC patients with good response to immunotherapy. Therefore, our study provided insight into elucidating the processes and mechanisms of m 6 AlncRNAs. Competing Interests: None. (AJTR Copyright © 2022.)
Databáze:	MEDLINE
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