Yang Bo, Wan Yu, Wang Jieqiong, Liu Yun, Wang Shaohua
Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, PR China.
Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, PR China.
Comput Biol Chem. 2024 Dec;113:108258. doi: 10.1016/j.compbiolchem.2024.108258. Epub 2024 Oct 19.
Oral squamous cell carcinoma (OSCC), a significant type of head and neck cancer, has witnessed increasing incidence and mortality rates. Immune-related genes (IRGs) and metabolic-related genes (MRGs) play essential roles in the pathogenesis, metastasis, and progression of OSCC. This study exploited data from The Cancer Genome Atlas (TCGA) to identify IRGs and MRGs related to OSCC through differential analysis. Univariate Cox analysis was utilized to determine immune-metabolic-related genes (IMRGs) associated with patient prognosis. A prognostic model for OSCC was constructed using Lasso-Cox regression and subsequently validated with datasets from the Gene Expression Omnibus (GEO). Non-Negative Matrix Factorization (NMF) clustering identified three molecular subtypes of OSCC, among which the C2 subtype showed better overall survival (OS) and progression-free survival (PFS). A prognostic model based on nine IMRGs was developed to categorize OSCC patients into high- and low-risk groups, with the low-risk group demonstrating significantly longer OS in both training and testing cohorts. The model showed strong predictive capabilities, and the risk score served as an independent prognostic factor. Additionally, expression levels of programmed death 1 (PD1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) differed between the risk groups. Gene Set Enrichment Analysis (GSEA) indicated distinct enriched pathways between high-risk and low-risk groups, highlighting the crucial roles of immune and metabolic processes in OSCC. The nine IMRGs prognostic model presented excellent predictive performance and has potential for clinical application.
口腔鳞状细胞癌（OSCC）是头颈癌的一种重要类型，其发病率和死亡率呈上升趋势。免疫相关基因（IRGs）和代谢相关基因（MRGs）在OSCC的发病机制、转移和进展中起着至关重要的作用。本研究利用来自癌症基因组图谱（TCGA）的数据，通过差异分析确定与OSCC相关的IRGs和MRGs。采用单因素Cox分析来确定与患者预后相关的免疫代谢相关基因（IMRGs）。使用Lasso-Cox回归构建OSCC的预后模型，随后用来自基因表达综合数据库（GEO）的数据集进行验证。非负矩阵分解（NMF）聚类确定了OSCC的三种分子亚型，其中C2亚型显示出更好的总生存期（OS）和无进展生存期（PFS）。基于九个IMRGs开发了一个预后模型，将OSCC患者分为高风险和低风险组，低风险组在训练和测试队列中的OS均显著更长。该模型显示出强大的预测能力，风险评分是一个独立的预后因素。此外，程序性死亡1（PD1）和细胞毒性T淋巴细胞相关抗原4（CTLA4）的表达水平在风险组之间存在差异。基因集富集分析（GSEA）表明高风险和低风险组之间有不同的富集途径，突出了免疫和代谢过程在OSCC中的关键作用。九个IMRGs预后模型表现出优异的预测性能，具有临床应用潜力。
