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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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 (MPM\u03c6) in primary samples, and functional analyses were conducted. Non-negative matrix factorization (NMF) clustering and immune cell infiltration analyses were performed using the MPM\u03c6 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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, MPM\u03c6 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12967-024-05421-2","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T10:03:52Z","timestamp":1722247432000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Single-cell hdWGCNA reveals metastatic protective macrophages and development of deep learning model in uveal melanoma"],"prefix":"10.1186","volume":"22","author":[{"given":"Yifang","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengzhen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinglin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonggang","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"key":"5421_CR1","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1111\/j.1755-3768.1983.tb04350.x","volume":"61","author":"M Abrahamsson","year":"1983","unstructured":"Abrahamsson M. 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