Deng Zhengrong, Xu Yicong, Zhang Peidong, Peng Yixiang, Tan Jiaxing, Chen Zihang, Ma Yimei
Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University) Ministry of Education, West China Second University Hospital of Sichuan University, Chengdu, China.
Department of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University, Chengdu, China.
Front Immunol. 2025 Jun 4;16:1577781. doi: 10.3389/fimmu.2025.1577781. eCollection 2025.
Ovarian cancer (OC) exhibits high mortality and chemoresistance rates, underscoring the urgent need for precise prognostic biomarkers and novel therapeutic targets. SUMOylation, crucial in cellular stress responses, is frequently dysregulated in various cancers. This study aims to characterize SUMOylation and its regulators in OC and identify potential biomarkers and therapeutic targets.
In this study, using multi-omics data, we characterized the unique features of SUMOylation in OC and revealed the association between SUMOylation-related genes (SRGs) and OC malignancy. We conducted integrated machine learning and single-cell RNA sequencing data analysis to identify key SRGs and explored their functional characteristics. The prognostic potential of these SRGs was confirmed in ID8 mouse models and in samples from 213 OC patients at West China Second Hospital.
An integrated machine learning framework identified 22 prognostic-related SRGs from the TCGA-OV cohort. Further single-cell analysis refined these findings, pinpointing five SRGs as biomarkers closely associated with OC cell function, metabolism and the tumor microenvironment. In cancer cells, the expression of four SRGs ( and ) is closely associated with epigenetic regulation and epithelial-mesenchymal signaling. Notably, we found that overexpression may contribute to chemoresistance in OC. In the tumor microenvironment, CD8 cytotoxic T cell with high (another SRG) expression exhibit inhibited cytotoxicity activity.
Overall, five SRGs were identified and further evaluated as potential prognostic and therapeutic targets, offering deeper insights into precision oncology for OC.
卵巢癌（OC）具有较高的死亡率和化疗耐药率，这凸显了对精确预后生物标志物和新型治疗靶点的迫切需求。SUMO化在细胞应激反应中至关重要，在各种癌症中经常失调。本研究旨在表征OC中的SUMO化及其调节因子，并确定潜在的生物标志物和治疗靶点。
在本研究中，我们使用多组学数据表征了OC中SUMO化的独特特征，并揭示了SUMO化相关基因（SRGs）与OC恶性肿瘤之间的关联。我们进行了综合机器学习和单细胞RNA测序数据分析，以识别关键的SRGs并探索其功能特征。这些SRGs的预后潜力在ID8小鼠模型和来自华西第二医院的213例OC患者的样本中得到了证实。
一个综合机器学习框架从TCGA-OV队列中识别出22个与预后相关的SRGs。进一步的单细胞分析细化了这些发现，确定了五个SRGs作为与OC细胞功能、代谢和肿瘤微环境密切相关的生物标志物。在癌细胞中，四个SRGs（和）的表达与表观遗传调控和上皮-间质信号密切相关。值得注意的是，我们发现过表达可能导致OC的化疗耐药。在肿瘤微环境中，高表达（另一个SRG）的CD8细胞毒性T细胞表现出抑制的细胞毒性活性。
总体而言，我们鉴定了五个SRGs，并将其进一步评估为潜在的预后和治疗靶点，为OC的精准肿瘤学提供了更深入的见解。
