导言：口腔鳞状细胞癌（OSCC）在口腔癌中占有很高的比例，其特点是侵袭性强且发病率上升。赖氨酸乙酰化与癌症发生机制有关。赖氨酸乙酰化相关基因（LARGs）是各种肿瘤，包括口腔鳞状细胞癌的治疗靶点和潜在预后指标。然而，赖氨酸乙酰化相关基因在口腔鳞状细胞癌中的系统生物信息学分析尚未被探索。方法：我们分析了口腔鳞状细胞癌中33个赖氨酸乙酰化相关基因的表达以及它们的体细胞突变对口腔鳞状细胞癌预后的影响。一致聚类分析确定了两种赖氨酸乙酰化模式，并进一步评估了两种模式之间的差异。采用最小绝对收缩和选择算子（LASSO）回归分析，使用TCGA口腔鳞状细胞癌数据集发展了一个赖氨酸乙酰化相关预后模型，然后利用基因表达数据库GSE41613对模型进行验证。结果：低风险评分的患者预后较好，无论是整体队列还是亚群中。这些患者还具有“热”免疫微环境，并对免疫治疗更敏感。讨论：我们的发现为口腔鳞状细胞癌的分类和预后提供了一个新模型，并对口腔鳞状细胞癌的诊断和治疗提供了新的见解。版权所有 © 2023 Deng, Wu, Tang, Dai and Cheng.

Introduction: Oral squamous cell carcinoma (OSCC), which accounts for a high proportion of oral cancers, is characterized by high aggressiveness and rising incidence. Lysine acetylation is associated with cancer pathogenesis. Lysine acetylation-related genes (LARGs) are therapeutic targets and potential prognostic indicators in various tumors, including oral squamous cell carcinoma. However, systematic bioinformatics analysis of the Lysine acetylation-related genes in Oral squamous cell carcinoma is still unexplored. Methods: We analyzed the expression of 33 Lysine acetylation-related genes in oral squamous cell carcinoma and the effects of their somatic mutations on oral squamous cell carcinoma prognosis. Consistent clustering analysis identified two lysine acetylation patterns and the differences between the two patterns were further evaluated. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to develop a lysine acetylation-related prognostic model using TCGA oral squamous cell carcinoma datasets, which was then validated using gene expression omnibus (GEO) dataset GSE41613. Results: Patients with lower risk scores had better prognoses, in both the overall cohort and within the subgroups These patients also had "hot" immune microenvironments and were more sensitive to immunotherapy. Disscussion: Our findings offer a new model for classifying oral squamous cell carcinoma and determining its prognosis and offer novel insights into oral squamous cell carcinoma diagnosis and treatment.Copyright © 2023 Deng, Wu, Tang, Dai and Cheng.