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Development and Validation of a Prognostic Signature Based on the Lysine Crotonylation Regulators in Head and Neck Squamous Cell Carcinoma

BACKGROUND: Lysine crotonylation (Kcr) is a newly identified posttranslational modification type regulated by various enzymes and coenzymes, including lysine crotonyltransferase, lysine decrotonylase, and binding proteins. However, the role of Kcr regulators in head and neck squamous cell carcinoma...

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Detalles Bibliográficos
Autores principales: Jiang, Linlin, Yin, Xiteng, Zhang, Hongbo, Zhang, Xinyu, Cao, Zichen, Zhou, Meng, Xu, Wenguang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9940974/
https://www.ncbi.nlm.nih.gov/pubmed/36814797
http://dx.doi.org/10.1155/2023/4444869
Descripción
Sumario:BACKGROUND: Lysine crotonylation (Kcr) is a newly identified posttranslational modification type regulated by various enzymes and coenzymes, including lysine crotonyltransferase, lysine decrotonylase, and binding proteins. However, the role of Kcr regulators in head and neck squamous cell carcinoma (HNSCC) remains unknown. The aim of this study was to establish and validate a Kcr-related prognostic signature of HNSCC and to assess the clinical predictive value of this signature. METHODS: The mRNA expression profiles and clinicopathological data from The Cancer Genome Atlas (TCGA) database were downloaded to explore the clinical significance and prognostic value of these regulators in HNSCC. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to generate the Kcr-related prognostic signature for HNSCC. Subsequently, the GSE65858 dataset from the Gene Expression Omnibus (GEO) database was used to validate the signature. The prognostic value of the signature was evaluated using the Kaplan-Meier survival, receiver operating characteristic (ROC) curve, and univariate and multivariate Cox regression analyses. RESULTS: We established a nine-gene risk signature associated with the prognosis of HNSCC based on Kcr regulators. High-risk patients demonstrated significantly poorer overall survival (OS) than low-risk patients in the training (TCGA) and validation (GEO) datasets. Then, the time-dependent receiver operating characteristic (ROC) curve analysis showed that the nine-gene risk signature was more accurate for predicting the 5-year OS than other clinical parameters, including age, gender, T stage, N stage, and histologic grade in the TCGA and GEO datasets. Moreover, the Cox regression analysis showed that the constructed risk signature was an independent risk factor for HNSCC. CONCLUSION: Our study identified and validated a nine-gene signature for HNSCC based on Kcr regulators. These results might contribute to prognosis stratification and treatment escalation for HNSCC patients.