Department of Ophthalmology, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, Guangdong, 510220, China.
Department of Otorhinolaryngology-Head and Neck Surgery, Guangzhou Red Cross Hospital of Jinan University, Guangzhou, Guangdong, 510220, China.
J Transl Med. 2024 Jul 29;22(1):695. doi: 10.1186/s12967-024-05421-2.
Although there has been some progress in the treatment of primary uveal melanoma (UVM), distant metastasis remains the leading cause of death in patients. Monitoring, staging, and treatment of metastatic disease have not yet reached consensus. Although more than half of metastatic tumors (62%) are diagnosed within five years after primary tumor treatment, the remainder are only detected in the following 25 years. The mechanisms of UVM metastasis and its impact on prognosis are not yet fully understood.
scRNA-seq data of UVM samples were obtained and processed, followed by cell type identification and characterization of macrophage subpopulations. High-dimensional weighted gene co-expression network analysis (HdWGCNA) was performed to identify key gene modules associated with metastatic protective macrophages (MPMφ) in primary samples, and functional analyses were conducted. Non-negative matrix factorization (NMF) clustering and immune cell infiltration analyses were performed using the MPMφ gene signatures. Machine learning models were developed using the identified metastatic protective macrophages related genes (MPMRGs) to distinguish primary from metastatic patients. A deep learning convolutional neural network (CNN) model was constructed based on MPMRGs and cell type associations. Lastly, a prognostic model was established using the MPMRGs and validated in independent cohorts.
Single-cell RNA-seq analysis revealed a unique immune microenvironment landscape in primary samples compared to metastatic samples, with an enrichment of macrophage cells. Using HdWGCNA, MPMφ and marker genes were identified. Functional analysis showed an enrichment of genes related to antigen processing progress and immune response. Machine learning and deep learning models based on key genes showed significant effectiveness in distinguishing between primary and metastatic patients. The prognostic model based on key genes demonstrated substantial predictive value for the survival of UVM patients.
Our study identified key macrophage subpopulations related to metastatic samples, which have a profound impact on shaping the tumor immune microenvironment. A prognostic model based on macrophage cell genes can be used to predict the prognosis of UVM patients.
尽管原发性葡萄膜黑色素瘤（UVM）的治疗已经取得了一些进展，但远处转移仍然是患者死亡的主要原因。转移性疾病的监测、分期和治疗尚未达成共识。尽管超过一半的转移性肿瘤（62%）在原发性肿瘤治疗后五年内被诊断出来，但其余的肿瘤仅在接下来的 25 年内被检测到。UVM 转移的机制及其对预后的影响尚不完全清楚。
获取并处理 UVM 样本的 scRNA-seq 数据，然后对其进行细胞类型鉴定和巨噬细胞亚群特征分析。对原发性样本中与转移性保护型巨噬细胞（MPMφ）相关的关键基因模块进行高维加权基因共表达网络分析（HdWGCNA），并进行功能分析。使用 MPMφ 基因特征进行非负矩阵分解（NMF）聚类和免疫细胞浸润分析。使用鉴定出的与转移性保护型巨噬细胞相关的基因（MPMRGs）开发机器学习模型，以区分原发性和转移性患者。基于 MPMRGs 和细胞类型关联构建深度卷积神经网络（CNN）模型。最后，使用 MPMRGs 建立预后模型，并在独立队列中进行验证。
单细胞 RNA-seq 分析显示，与转移性样本相比，原发性样本具有独特的免疫微环境景观，其中巨噬细胞细胞丰富。使用 HdWGCNA 鉴定出 MPMφ 和标记基因。功能分析显示与抗原加工进展和免疫反应相关的基因富集。基于关键基因的机器学习和深度学习模型在区分原发性和转移性患者方面显示出显著的有效性。基于关键基因的预后模型对 UVM 患者的生存具有显著的预测价值。
本研究鉴定了与转移性样本相关的关键巨噬细胞亚群，这些亚群对塑造肿瘤免疫微环境具有深远影响。基于巨噬细胞基因的预后模型可用于预测 UVM 患者的预后。
