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

• Published in: Cancer cell international 2024-10, Vol.24 (1), p.359-17, Article 359
• Main Authors: Chen, Shuming, Tang, Zichun, Wan, Qiaoqian, Huang, Weidi, Li, Xie, Huang, Xixuan, Zheng, Shuyan, Lu, Caiyang, Wu, Jinzheng, Li, Zhuo, Liu, Xiao
• Format: Article
• Language: English
• Subjects: Immune microenvironment; Prognostic models; Tumour metastasis; Uveal melanoma
• ISSN: 1475-2867; 1475-2867
• DOI: 10.1186/s12935-024-03539-3

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. 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. 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 a