Zhou Qun, Song Bangli, He Yibo, Zhang Zhezhong, Chen Shiliang, Chen Wenjun, Li Xianbin, Jiang Jun
Department of gynecology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, China.
Department of Internal Medicine, Zhejiang University of Technology Hospital, Hangzhou, Zhejiang, China.
PLoS One. 2025 Apr 30;20(4):e0322387. doi: 10.1371/journal.pone.0322387. eCollection 2025.
Cervical cancer (CC) ranks as the fourth most common malignancy affecting women globally, with research highlighting a rising incidence among younger age groups. Disulfidptosis, a newly identified form of regulated cell death, has been implicated in the pathogenesis of numerous diseases. This study employs bioinformatics analyses to explore the expression profiles and functional roles of disulfidptosis-related genes (DRGs) in the context of cervical cancer.
Differential analysis of the gene expression matrix in CC was performed to identify differentially expressed genes. The overlap between these genes and disulfidptosis-related genes was then determined. Key hub genes were identified using multiple machine learning approaches, including LASSO regression, support vector machines (SVM), and random forest (RF). These hub genes were subsequently used to construct a predictive model, which was validated using external datasets to ensure robustness and reliability.
In this study, 11 overlapping genes were identified, among which four hub genes-BRK1, NDUFA11, RAC1, and NDUFS1-were extracted using machine learning techniques. The diagnostic performance of these hub genes was validated with external datasets, and a predictive model was constructed based on their expression. The model demonstrated an exceptionally high area under the curve (AUC) of 0.997. Moreover, AUC values exceeding 0.85 for two independent validation datasets further confirmed the model's accuracy and stability. Notably, NDUFA11 and BRK1 showed significant associations with patient survival, highlighting their prognostic importance in cervical squamous cell carcinoma. Using CMAP and DGIdb databases, Metformin and Coenzyme-I were identified as potential targeted therapies for NDUFS1 and NDUFA11, respectively, offering new therapeutic avenues for patients.
This study uncovered a strong association between disulfidptosis and CC and developed a predictive model to assess the risk in CC patients. These findings offer novel insights into identifying biomarkers and potential therapeutic targets for CC, paving the way for improved diagnostic and treatment strategies.
宫颈癌（CC）是全球影响女性的第四大常见恶性肿瘤，研究表明其在年轻年龄组中的发病率呈上升趋势。二硫化物诱导的细胞焦亡是一种新发现的程序性细胞死亡形式，已被证实与多种疾病的发病机制有关。本研究采用生物信息学分析方法，探讨二硫化物诱导的细胞焦亡相关基因（DRGs）在宫颈癌中的表达谱及其功能作用。
对宫颈癌基因表达矩阵进行差异分析，以识别差异表达基因。然后确定这些基因与二硫化物诱导的细胞焦亡相关基因的重叠情况。使用多种机器学习方法，包括套索回归、支持向量机（SVM）和随机森林（RF），确定关键枢纽基因。随后，利用这些枢纽基因构建预测模型，并使用外部数据集进行验证，以确保其稳健性和可靠性。
本研究共鉴定出11个重叠基因，其中利用机器学习技术提取了4个枢纽基因——BRK1、NDUFA11、RAC1和NDUFS1。通过外部数据集验证了这些枢纽基因的诊断性能，并基于它们的表达构建了预测模型。该模型的曲线下面积（AUC）高达0.997。此外，两个独立验证数据集的AUC值均超过0.85，进一步证实了该模型的准确性和稳定性。值得注意的是，NDUFA11和BRK1与患者生存率显著相关，凸显了它们在宫颈鳞状细胞癌中的预后重要性。利用CMAP和DGIdb数据库，分别确定二甲双胍和辅酶I为NDUFS1和NDUFA11的潜在靶向治疗药物，为患者提供了新的治疗途径。
本研究揭示了二硫化物诱导的细胞焦亡与宫颈癌之间的密切关联，并建立了一个预测模型来评估宫颈癌患者的风险。这些发现为识别宫颈癌的生物标志物和潜在治疗靶点提供了新的见解，为改进诊断和治疗策略铺平了道路。
