Single-cell hdWGCNA reveals metastatic protective macrophages and development of deep learning model in uveal melanoma

作者：Yifang Sun, Jian Wu, Qian Zhang, Pengzhen Wang, Jinglin Zhang, Yonggang Yuan · 发表于：Journal of Translational Medicine · 年份：2024 · DOI：10.1186/s12967-024-05421-2 · 被引用次数：30 · 研究领域：Ocular Oncology and Treatments、Cancer Immunotherapy and Biomarkers、Immunotherapy and Immune Responses

BACKGROUND: 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. METHODS: 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. RESULTS: Single-cell RNA-seq analysis revealed a unique immune microenviron...