The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Chongqing Key Lab of Ophthalmology, Chongqing Eye Institute, Chongqing, China.
Cancer Biomark. 2023;36(2):161-175. doi: 10.3233/CBM-210427.
Uveal melanoma (UM) is a rare but deadly cancer. The main cause of death from UM is liver metastasis. Though the metastasis mechanism remains unclear, it is closely related to the immune microenvironment and gene expression.
This study aimed to identify the prognostic genes in primary and metastatic UM and their relationship with the immune microenvironment.
Primary and metastatic UM data from the GEO database included GSE22138 and GSE44295 datasets. Kaplan-Meier analysis, Cox regression models, and ROC analysis were applied to screen genes in GSE22138. TIMER2.0 was employed to analyze the immune microenvironment from gene expression. Prognostic immune gene correlation was tested by Spearman. The results were validated in the independent dataset of cohort GSE44295.
Metastasis and primary differential gene analysis showed 107 significantly different genes associated with prognosis, and 11 of them were immune-related. ROC analysis demonstrated that our signature was predictive for UM prognosis (AUC > 0.8). Neutrophil and myeloid dendritic cells were closely associated with metastasis with scores that significantly divided patients into high-risk and low-risk groups (log-rank p< 0.05). Of these 11 genes, FABP5 and SHC4 were significantly associated with neutrophils in metastatic tumors, while ROBO1 expression was significantly correlated with myeloid dendritic cells in the primary tumors.
The present study constructed an 11-gene signature and established a model for risk stratification and prediction of overall survival in metastatic UM. Since FABP5 and SHC4 are related to neutrophil infiltration in metastatic UM, FABP5 and neutrophil regulation might be crucial in metastatic UM.
葡萄膜黑色素瘤(UM)是一种罕见但致命的癌症。UM 患者死亡的主要原因是肝转移。尽管转移机制尚不清楚,但它与免疫微环境和基因表达密切相关。
本研究旨在鉴定原发性和转移性 UM 的预后基因及其与免疫微环境的关系。
从 GEO 数据库中获取原发性和转移性 UM 数据,包括 GSE22138 和 GSE44295 数据集。Kaplan-Meier 分析、Cox 回归模型和 ROC 分析用于筛选 GSE22138 中的基因。TIMER2.0 用于分析基因表达的免疫微环境。采用 Spearman 检验对预后免疫基因相关性进行检验。在独立数据集 GSE44295 中进行验证。
转移和原发性差异基因分析显示,有 107 个与预后相关的差异显著基因,其中 11 个与免疫相关。ROC 分析表明,我们的特征对 UM 预后具有预测能力(AUC > 0.8)。中性粒细胞和髓样树突状细胞与转移密切相关,评分显著将患者分为高风险和低风险组(对数秩检验 p<0.05)。在这 11 个基因中,FABP5 和 SHC4 与转移性肿瘤中的中性粒细胞显著相关,而 ROBO1 表达与原发性肿瘤中的髓样树突状细胞显著相关。
本研究构建了一个 11 基因特征,并建立了转移性 UM 风险分层和总生存预测模型。由于 FABP5 和 SHC4 与转移性 UM 中的中性粒细胞浸润有关,因此 FABP5 和中性粒细胞调节可能在转移性 UM 中至关重要。