Hu Suwan, Wang Mengting
Department of Obstetrics and Gynecology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China.
Discov Oncol. 2025 Apr 10;16(1):510. doi: 10.1007/s12672-025-02267-y.
Ovarian carcinoma represents an aggressive malignancy with poor prognosis and limited therapeutic efficacy. While deubiquitinating (DUB) genes are known to regulate crucial cellular processes and cancer progression, their specific roles in ovarian carcinoma remain poorly understood.
We conducted an integrated analysis of single-cell RNA sequencing and bulk transcriptome data from public databases. DUB genes were identified through Genecard database. Using the Seurat package, we performed cell clustering and differential expression analysis. Cell-cell communications were analyzed using CellChat. A DUB-related risk signature (DRS) was developed using machine learning approaches through integration of GEO and TCGA datasets. The prognostic value and immune characteristics of the signature were systematically evaluated.
Our analysis revealed eight distinct cell subtypes in the tumor microenvironment, including epithelial, fibroblast, myeloid, and Treg cells. DUB-high cells were predominantly found in Treg and myeloid populations, exhibiting elevated expression of tumor-related pathways and enhanced cell-cell communication networks, particularly between fibroblasts and myeloid cells. Conversely, DUB-low cells were enriched in epithelial populations with reduced immune activity. The DRS model demonstrated robust prognostic value across multiple independent cohorts. High-risk patients, as classified by the DRS, showed significantly poorer survival outcomes and distinct immune infiltration patterns compared to low-risk patients.
This study provides comprehensive insights into DUB gene expression patterns across different cell populations in ovarian carcinoma. The established DRS model offers a promising tool for risk stratification and may guide personalized therapeutic strategies. Our findings highlight the potential role of DUB genes in modulating the tumor immune microenvironment and patient outcomes in ovarian carcinoma.
卵巢癌是一种侵袭性恶性肿瘤，预后较差且治疗效果有限。虽然已知去泛素化（DUB）基因可调节关键的细胞过程和癌症进展，但其在卵巢癌中的具体作用仍知之甚少。
我们对来自公共数据库的单细胞RNA测序和批量转录组数据进行了综合分析。通过Genecard数据库鉴定DUB基因。使用Seurat软件包进行细胞聚类和差异表达分析。使用CellChat分析细胞间通讯。通过整合GEO和TCGA数据集，采用机器学习方法开发了一个与DUB相关的风险特征（DRS）。系统评估了该特征的预后价值和免疫特征。
我们的分析揭示了肿瘤微环境中的八种不同细胞亚型，包括上皮细胞、成纤维细胞、髓样细胞和调节性T细胞（Treg）。DUB高表达细胞主要存在于Treg和髓样细胞群体中，表现出肿瘤相关通路的表达升高以及细胞间通讯网络增强，尤其是在成纤维细胞和髓样细胞之间。相反，DUB低表达细胞在上皮细胞群体中富集，免疫活性降低。DRS模型在多个独立队列中显示出强大的预后价值。根据DRS分类，高危患者与低危患者相比，生存结果明显更差，免疫浸润模式也不同。
本研究全面深入地了解了卵巢癌不同细胞群体中DUB基因的表达模式。所建立的DRS模型为风险分层提供了一个有前景的工具，并可能指导个性化治疗策略。我们的发现突出了DUB基因在调节卵巢癌肿瘤免疫微环境和患者预后方面的潜在作用。
