Li Jiankui, Chen Xi, Li Juan
Department of Gynecology, The 960th Hospital of the Joint Logistics Support Force, Jinan City, 250031, Shandong Province, China.
Shaanxi Eye Hospital, Xi'an People's Hospital (Xi'an Fourth Hospital), Affiliated People's Hospital of Northwest University, Xi 'an city, 710004, Shaanxi Province, China.
Sci Rep. 2025 Jul 10;15(1):24950. doi: 10.1038/s41598-025-09310-6.
The mechanisms underlying mitophagy and mitochondrial dynamics (MD) in cervical cancer (CC), a disease with a high mortality rate, remain poorly understood. This study aimed to assess the prognostic significance of these processes in CC. Mendelian randomization (MR) and 101 machine learning models were employed to identify mitophagy- and MD-associated prognostic genes in CC. A subsequent risk model was developed to stratify patients by risk. Further analyses included functional pathway enrichment, immune infiltration, and single-cell RNA sequencing (scRNA-seq) analysis. The results identified PLOD3, SBK1, and SLC39A10 as prognostic genes for CC. Among these, PLOD3 and SLC39A10 were associated with poor prognosis, while SBK1 was protective. The risk model demonstrated high accuracy, with area under the curve (AUC) values exceeding 0.6. Following this, a prognostic nomogram was constructed incorporating risk score and pathological T stage, achieving high predictive accuracy. Gene Set Enrichment Analysis (GSEA) revealed significant enrichment in pathways such as ECM receptor interaction and olfactory transduction in high-risk groups. Additionally, SBK1 showed the strongest correlation with neutrophil infiltration. Expression pattern alterations of prognostic genes were observed in endothelial cells, T cells, and epithelial cells. In conclusion, a risk model based on mitophagy- and MD-related prognostic genes was established, offering a promising approach for the personalized management of patients with CC.
宫颈癌（CC）是一种死亡率很高的疾病，其线粒体自噬和线粒体动力学（MD）的潜在机制仍知之甚少。本研究旨在评估这些过程在CC中的预后意义。采用孟德尔随机化（MR）和101种机器学习模型来识别CC中与线粒体自噬和MD相关的预后基因。随后建立了一个风险模型，根据风险对患者进行分层。进一步的分析包括功能通路富集、免疫浸润和单细胞RNA测序（scRNA-seq）分析。结果确定PLOD3、SBK1和SLC39A10为CC的预后基因。其中，PLOD3和SLC39A10与预后不良相关，而SBK1具有保护作用。风险模型显示出高准确性，曲线下面积（AUC）值超过0.6。在此基础上，构建了一个结合风险评分和病理T分期的预后列线图，并具有较高的预测准确性。基因集富集分析（GSEA）显示，在高危组中，如细胞外基质受体相互作用和嗅觉转导等通路显著富集。此外，SBK1与中性粒细胞浸润的相关性最强。在内皮细胞、T细胞和上皮细胞中观察到预后基因的表达模式改变。总之，建立了一个基于线粒体自噬和MD相关预后基因的风险模型，为CC患者的个性化管理提供了一种有前景的方法。
