Chen Yu-Ning, Xiu Jing-Ying, Zhao Han-Qing, Luo Jing-Ting, Yang Qiong, Li Yang, Wei Wen-Bin
Beijing Tongren Eye Center, Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology & Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Key Laboratory of Intelligent Diagnosis, Treatment and Prevention of Blinding Eye Diseases, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing 100005, China.
Int J Ophthalmol. 2025 May 18;18(5):765-778. doi: 10.18240/ijo.2025.05.02. eCollection 2025.
To explore the relationship between matrix metalloproteinases (MMPs) expression levels in the tumor and the prognosis of uveal melanoma (UM) and to construct prognostic prediction models.
Transcriptome sequencing data from 17 normal choroid tissues and 53 UM tumor tissues were collected. Based on the differential gene expression levels and their function, MMPs family was selected for establishing risk-score system and prognostic prediction model with machine learning. Tumor microenvironment (TME) analysis was also applied for the impact of immune cell infiltration on prognosis of the disease.
Eight MMPs were significantly different expression levels between normal and the tumor tissues. MMP-2 and MMP-28 were selected to construct a risk-score system and divided patients accordingly into high- and low-risk groups. The prediction model based on the risk-score achieved an accuracy of approximately 80% at 1-, 3-, and 5-year after diagnosis. Besides, a Nomogram prognostic prediction model which based on risk-score and pathological type (independent prognostic factors after Cox regression analysis) demonstrated good consistency between the predicted outcomes at 1-, 3-, and 5-year after diagnosis and the actual prognosis of patients. TME analysis revealed that the high-risk group exhibited higher immune and stromal scores and increased infiltration of tumor-associated macrophages (TAMs) and regulatory T cells compared to the low-risk group.
Based on MMP-2 and MMP-28 expression levels, our prediction model demonstrates accurate long-term prognosis prediction for UM patients. The aggregation of TAMs and regulatory T cells in the TME of UM may be associated with an unfavorable prognosis.
探讨葡萄膜黑色素瘤（UM）肿瘤中基质金属蛋白酶（MMPs）表达水平与预后的关系，并构建预后预测模型。
收集17例正常脉络膜组织和53例UM肿瘤组织的转录组测序数据。基于差异基因表达水平及其功能，选择MMPs家族，通过机器学习建立风险评分系统和预后预测模型。还应用肿瘤微环境（TME）分析免疫细胞浸润对疾病预后的影响。
8种MMPs在正常组织和肿瘤组织中的表达水平存在显著差异。选择MMP-2和MMP-28构建风险评分系统，并据此将患者分为高风险组和低风险组。基于风险评分的预测模型在诊断后1年、3年和5年的准确率约为80%。此外，基于风险评分和病理类型（Cox回归分析后的独立预后因素）的列线图预后预测模型显示，诊断后1年、3年和5年的预测结果与患者的实际预后之间具有良好的一致性。TME分析显示，与低风险组相比，高风险组表现出更高的免疫和基质评分，以及肿瘤相关巨噬细胞（TAM）和调节性T细胞浸润增加。
基于MMP-2和MMP-28的表达水平，我们的预测模型对UM患者的长期预后具有准确的预测能力。UM的TME中TAM和调节性T细胞的聚集可能与不良预后相关。
