介绍：泛素化参与了许多生物过程，其对宫颈癌预后的预测价值仍不清楚。方法：为了进一步探索泛素化相关基因的预测价值，我们从泛素和泛素样共轭数据库中获取URGs，并分析来自TCGA和GEO数据库的数据集，然后选择正常和癌组织之间差异表达的泛素化相关基因。然后，通过单变量Cox回归选择与总生存率有显著关联的DURGs。进一步使用机器学习选择DURGs。然后，我们通过多元分析构建并验证了一个可靠的预后基因签名。此外，我们预测了签名基因的底物蛋白质，并进行功能分析以进一步了解分子生物学机制。该研究为评估宫颈癌预后提供了新的指导，并提出了药物开发的新方向。结果：通过分析TCGA和GEO数据库中的1,390个URGs，我们获得了175个DURGs。结果显示，有19个DURGs与预后有关。最终，通过机器学习确定了8个DURGs，以构建第一个泛素化预后基因签名。患者被分为高危和低危两组，并且高危组的预后更差。此外，这些基因蛋白质水平与其转录水平大多数是一致的。根据底物蛋白质的功能性分析，这些签名基因可能通过转录因子活性和经典P53通路的泛素化相关信号通路参与癌症的发展。此外，还确定了71种小分子化合物作为潜在药物。结论：我们系统地研究了泛素化相关基因对宫颈癌预后的影响，通过机器学习算法建立了预后模型，并验证了它。此外，我们的研究为宫颈癌提供了新的治疗策略。版权所有©2023 Hao，Guy，Liu，Li，Mao，Jiang，Wang，Cui和Zhang。

Introduction: Ubiquitination is involved in many biological processes and its predictive value for prognosis in cervical cancer is still unclear. Methods: To further explore the predictive value of the ubiquitination-related genes we obtained URGs from the Ubiquitin and Ubiquitin-like Conjugation Database, analyzed datasets from The Cancer Genome Atlas and Gene Expression Omnibus databases, and then selected differentially expressed ubiquitination-related genes between normal and cancer tissues. Then, DURGs significantly associated with overall survival were selected through univariate Cox regression. Machine learning was further used to select the DURGs. Then, we constructed and validated a reliable prognostic gene signature by multivariate analysis. In addition, we predicted the substrate proteins of the signature genes and did a functional analysis to further understand the molecular biology mechanisms. The study provided new guidelines for evaluating cervical cancer prognosis and also suggested new directions for drug development. Results: By analyzing 1,390 URGs in GEO and TCGA databases, we obtained 175 DURGs. Our results showed 19 DURGs were related to prognosis. Finally, eight DURGs were identified via machine learning to construct the first ubiquitination prognostic gene signature. Patients were stratified into high-risk and low-risk groups and the prognosis was worse in the high-risk group. In addition, these gene protein levels were mostly consistent with their transcript level. According to the functional analysis of substrate proteins, the signature genes may be involved in cancer development through the transcription factor activity and the classical P53 pathway ubiquitination-related signaling pathways. Additionally, 71 small molecular compounds were identified as potential drugs. Conclusion: We systematically studied the influence of ubiquitination-related genes on prognosis in cervical cancer, established a prognostic model through a machine learning algorithm, and verified it. Also, our study provides a new treatment strategy for cervical cancer.Copyright © 2023 Hao, Guy, Liu, Li, Mao, Jiang, Wang, Cui and Zhang.