Zeng Shaoying, Zeng Lijian, Xie Xiaoying, Peng Liang
Department of Gynecology and Obstetrics, The First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, Jiangxi, China.
Department of Gynecology, The Second People's Hospital of Jingdezhen, Jingdezhen, 333000, Jiangxi, China.
Discov Oncol. 2024 Dec 18;15(1):802. doi: 10.1007/s12672-024-01708-4.
Palmitoylation, a key post-translational modification, plays a significant role in ovarian cancer (OV) progression. However, the impact of palmitoylation-related genes on genomic instability, immune infiltration, and therapeutic response in OV remains poorly understood. This study aimed to investigate these factors to facilitate risk stratification and therapeutic intervention, providing insights into personalized treatment strategies.
Data from TCGA and GEO were utilized to develop a prognostic model based on palmitoylation-related genes. Differential expression, functional enrichment, and immune infiltration analyses were performed. Immune cell composition and pathway activities in different risk groups were assessed using CIBERSORT and ssGSEA algorithms. Immunotherapy response was predicted using TIDE and SubMap, while drug sensitivity differences were evaluated using the GDSC database.
Univariate, LASSO, and multivariate Cox regression analyses identified palmitoylation-related genes with significant prognostic value. The prognostic model effectively stratified patients into high- and low-risk groups, demonstrating significant survival differences. Immune infiltration analysis revealed distinct immune cell compositions and functions between risk groups. Low-risk patients exhibited higher immune scores and increased expression of immune checkpoints (PD-1, CD274, CTLA4), suggesting greater response to immunotherapy. Drug sensitivity analysis identified compounds with differential efficacy between risk groups, highlighting potential targeted treatment options.
Palmitoylation-related genomic features significantly influence OV progression and the immune landscape, offering potential for improved risk stratification and informing immunotherapy strategies to enhance patient outcomes.
棕榈酰化是一种关键的翻译后修饰，在卵巢癌（OV）进展中起重要作用。然而，棕榈酰化相关基因对OV基因组不稳定性、免疫浸润和治疗反应的影响仍知之甚少。本研究旨在调查这些因素，以促进风险分层和治疗干预，为个性化治疗策略提供见解。
利用来自TCGA和GEO的数据，基于棕榈酰化相关基因建立预后模型。进行差异表达、功能富集和免疫浸润分析。使用CIBERSORT和ssGSEA算法评估不同风险组中的免疫细胞组成和通路活性。使用TIDE和SubMap预测免疫治疗反应，同时使用GDSC数据库评估药物敏感性差异。
单因素、LASSO和多因素Cox回归分析确定了具有显著预后价值的棕榈酰化相关基因。预后模型有效地将患者分为高风险组和低风险组，显示出显著的生存差异。免疫浸润分析揭示了风险组之间不同的免疫细胞组成和功能。低风险患者表现出更高的免疫评分和免疫检查点（PD-1、CD274、CTLA4）表达增加，表明对免疫治疗的反应更大。药物敏感性分析确定了风险组之间疗效不同的化合物，突出了潜在的靶向治疗选择。
棕榈酰化相关的基因组特征显著影响OV进展和免疫格局，为改善风险分层和为免疫治疗策略提供信息以提高患者预后提供了潜力。
