{"title":"Exploring the impact of deubiquitination on melanoma prognosis through single-cell RNA sequencing.","authors":"Su Peng, Jiaheng Xie, Xiaohu He","doi":"10.3389/fgene.2024.1509049","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Cutaneous melanoma, characterized by the malignant proliferation of melanocytes, exhibits high invasiveness and metastatic potential. Thus, identifying novel prognostic biomarkers and therapeutic targets is essential.</p><p><strong>Methods: </strong>We utilized single-cell RNA sequencing data (GSE215120) from the Gene Expression Omnibus (GEO) database, preprocessing it with the Seurat package. Dimensionality reduction and clustering were executed through Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). Cell types were annotated based on known marker genes, and the AUCell algorithm assessed the enrichment of deubiquitination-related genes. Cells were categorized into DUB_high and DUB_low groups based on AUCell scores, followed by differential expression analysis. Importantly, we constructed a robust prognostic model utilizing various genes, which was evaluated in the TCGA cohort and an external validation cohort.</p><p><strong>Results: </strong>Our prognostic model, developed using Random Survival Forest (RSF) and Ridge Regression methods, demonstrated excellent predictive performance, evidenced by high C-index and AUC values across multiple cohorts. Furthermore, analyses of immune cell infiltration and tumor microenvironment scores revealed significant differences in immune cell distribution and microenvironment characteristics between high-risk and low-risk groups. Functional experiments indicated that TBC1D16 significantly impacts the migration and proliferation of melanoma cells.</p><p><strong>Conclusion: </strong>This study highlights the critical role of deubiquitination in melanoma and presents a novel prognostic model that effectively stratifies patient risk. The model's strong predictive ability enhances clinical decision-making and provides a framework for future studies on the therapeutic potential of deubiquitination mechanisms in melanoma progression. Further validation and exploration of this model's applicability in clinical settings are warranted.</p>","PeriodicalId":12750,"journal":{"name":"Frontiers in Genetics","volume":"15 ","pages":"1509049"},"PeriodicalIF":2.8000,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11659643/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Genetics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.3389/fgene.2024.1509049","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Cutaneous melanoma, characterized by the malignant proliferation of melanocytes, exhibits high invasiveness and metastatic potential. Thus, identifying novel prognostic biomarkers and therapeutic targets is essential.
Methods: We utilized single-cell RNA sequencing data (GSE215120) from the Gene Expression Omnibus (GEO) database, preprocessing it with the Seurat package. Dimensionality reduction and clustering were executed through Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP). Cell types were annotated based on known marker genes, and the AUCell algorithm assessed the enrichment of deubiquitination-related genes. Cells were categorized into DUB_high and DUB_low groups based on AUCell scores, followed by differential expression analysis. Importantly, we constructed a robust prognostic model utilizing various genes, which was evaluated in the TCGA cohort and an external validation cohort.
Results: Our prognostic model, developed using Random Survival Forest (RSF) and Ridge Regression methods, demonstrated excellent predictive performance, evidenced by high C-index and AUC values across multiple cohorts. Furthermore, analyses of immune cell infiltration and tumor microenvironment scores revealed significant differences in immune cell distribution and microenvironment characteristics between high-risk and low-risk groups. Functional experiments indicated that TBC1D16 significantly impacts the migration and proliferation of melanoma cells.
Conclusion: This study highlights the critical role of deubiquitination in melanoma and presents a novel prognostic model that effectively stratifies patient risk. The model's strong predictive ability enhances clinical decision-making and provides a framework for future studies on the therapeutic potential of deubiquitination mechanisms in melanoma progression. Further validation and exploration of this model's applicability in clinical settings are warranted.
背景:皮肤黑色素瘤以黑色素细胞恶性增殖为特征,具有高侵袭性和转移潜力。因此,确定新的预后生物标志物和治疗靶点是必不可少的。方法:利用Gene Expression Omnibus (GEO)数据库中的单细胞RNA测序数据(GSE215120),用Seurat软件包进行预处理。通过主成分分析(PCA)和均匀流形逼近与投影(UMAP)进行降维和聚类。根据已知的标记基因对细胞类型进行标注,AUCell算法评估去泛素化相关基因的富集程度。根据AUCell评分将细胞分为DUB_high组和DUB_low组,并进行差异表达分析。重要的是,我们利用各种基因构建了一个强大的预后模型,并在TCGA队列和外部验证队列中进行了评估。结果:我们使用随机生存森林(RSF)和Ridge回归方法建立的预后模型显示出出色的预测性能,在多个队列中显示出较高的c指数和AUC值。此外,免疫细胞浸润和肿瘤微环境评分分析显示,免疫细胞分布和微环境特征在高危组和低危组之间存在显著差异。功能实验表明,TBC1D16显著影响黑色素瘤细胞的迁移和增殖。结论:本研究强调了去泛素化在黑色素瘤中的关键作用,并提出了一种新的预后模型,可以有效地分层患者的风险。该模型强大的预测能力增强了临床决策,并为黑色素瘤进展中去泛素化机制的治疗潜力的未来研究提供了框架。进一步验证和探索该模型在临床环境中的适用性是必要的。
Frontiers in GeneticsBiochemistry, Genetics and Molecular Biology-Molecular Medicine
CiteScore
5.50
自引率
8.10%
发文量
3491
审稿时长
14 weeks
期刊介绍:
Frontiers in Genetics publishes rigorously peer-reviewed research on genes and genomes relating to all the domains of life, from humans to plants to livestock and other model organisms. Led by an outstanding Editorial Board of the world’s leading experts, this multidisciplinary, open-access journal is at the forefront of communicating cutting-edge research to researchers, academics, clinicians, policy makers and the public.
The study of inheritance and the impact of the genome on various biological processes is well documented. However, the majority of discoveries are still to come. A new era is seeing major developments in the function and variability of the genome, the use of genetic and genomic tools and the analysis of the genetic basis of various biological phenomena.