Machine learning and single-cell RNA sequencing reveal relationship between intratumor CD8+ T cells and uveal melanoma metastasis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39478575", "kind": "science-record", "revision": 2, "content_hash": "sha256:1edb4824c1baa78aad86712580f3cd496b4e524a63f11caab765751a7195449b", "created_at": "2026-11-01T20:29:44Z", "updated_at": "2026-11-01T21:34:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning and single-cell RNA sequencing reveal relationship between intratumor CD8+ T cells and uveal melanoma metastasis.", "doi": "10.1186/s12935-024-03539-3", "pmid": "39478575", "authors": ["Shuming Chen", "Zichun Tang", "Qiaoqian Wan", "Weidi Huang", "Xie Li", "Xixuan Huang", "Shuyan Zheng", "Caiyang Lu", "Jinzheng Wu", "Zhuo Li", "Xiao Liu"], "journal": "Cancer cell international", "year": 2024, "abstract": "PURPOSE: Uveal melanoma (UM) is adults' most common primary intraocular malignant tumor. It has been observed that 40% of patients experience distant metastasis during subsequent treatment. While there exist multigene models developed using machine learning methods to assess metastasis and prognosis, the immune microenvironment's specific mechanisms influencing the tumor microenvironment have not been clarified. Single-cell transcriptome sequencing can accurately identify different types of cells in a tissue for precise analysis. This study aims to develop a model with fewer genes to evaluate metastasis risk in UM patients and provide a theoretical basis for UM immunotherapy.\nMETHODS: RNA-seq data and clinical information from 79 μm patients from TCGA were used to construct prognostic models. Mechanisms were probed using two single-cell datasets derived from the GEO database. After screening for metastasis-related genes, enrichment analysis was performed using GO and KEGG. Prognostic genes were screened using log-rank test and one-way Cox regression, and prognostic models were established using LASSO regression analysis and multifactor Cox regression analysis. The TCGA-UVM dataset was used as internal validation and dataset GSE22138 as external validation data. A time-dependent subject work characteristic curve (time-ROC) was established to assess the predictive ability of the model. Subsequently, dimensionality reduction, clustering, pseudo-temporal analysis and cellular communication analysis were performed on GSE138665 and GSE139829 to explore the underlying mechanisms involved. Cellular experiments were also used to validate the relevant findings.\nRESULTS: Based on clinical characteristics and RNA-seq transcriptomic data from 79 samples in the TCGA-UVM cohort, 247 metastasis-related genes were identified. Survival models for three genes (SLC25A38, EDNRB, and LURAP1) were then constructed using lasso regression and multifactorial cox regression. Kaplan-Meier survival analysis showed that the high-risk group was associated with poorer overall survival (OS) and metastasis-free survival (MFS) in UM patients. Time-dependent ROC curves demonstrated high predictive performance in 6 m, 18 m, and 30 m prognostic models. Cell scratch assay showed that the 24 h and 48 h migration rates of cells with reduced expression of the three genes were significantly higher than those of the si-NC group. CD8 + T cells may play an important role in tumour metastasis as revealed by immune infiltration analysis. An increase in the percentage of cytotoxic CD8 + T cells in the metastatic high-risk group was found in the exploration of single-cell transcriptome data. The communication intensity of cytotoxic CD8 was significantly enhanced. It was also found that the CD8 + T cells in the two groups were in different states, although the number of CD8 + T cells in the high-risk group increased, they were mostly in the exhausted and undifferentiated state, while in the low-risk group, the CD8 + T cells were mostly in the functional state.\nCONCLUSIONS: We developed a precise and stable 3-gene model to predict the metastatic risk and prognosis of patients. CD8 + T cells exhaustion in the tumor microenvironment play a crucial role in UM metastasis.", "claims": [{"id": "claim-method", "statement": "Prognostic genes were screened using log-rank test and one-way Cox regression, and prognostic models were established using LASSO regression analysis and multifactor Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39478575", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Prognostic genes were screened using log-rank test and one-way Cox regression, and prognostic models were established using LASSO regression analysis and multifactor Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "No datasets were generated or analysed during the current study.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39478575", "locator": "sections[31] \"Data availability\" paragraph 2", "quote": "No datasets were generated or analysed during the current study."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We developed a precise and stable 3-gene model to predict the metastatic risk and prognosis of patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39478575", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We developed a precise and stable 3-gene model to predict the metastatic risk and prognosis of patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We developed a precise and stable 3-gene model to predict the metastatic risk and prognosis of patients.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39478575"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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