Zou Daoyang, Wu Xiuhong, Xin Xi, Xu Tianwen
The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China.
Ganzhou Cancer Hospital, Ganzhou, 341000, China.
Reprod Sci. 2025 Sep 4. doi: 10.1007/s43032-025-01973-w.
Cervical cancer (CC) is the fourth most frequently diagnosed cancer and the fourth leading cause of cancer-related deaths in women worldwide, however, the treatment options for advanced CC are limited. Therefore, there is an urgent need in the clinic for reliable prognostic models to guide clinical decision-making.
We conducted differential gene expression analysis on cervical cancer samples and normal samples to obtain differentially expressed genes (DEGs). We used WGCNA analysis to identify the most relevant module associated with cervical cancer and intersected with DEGs to obtain cervical cancer-related genes. We then constructed a protein-protein interaction (PPI) network using these genes and identified core genes using the Hubba plugin in Cytoscape software. Subsequently, we built a prognostic model using the identified cervical cancer-related genes in combination with the TCGA database. GSE44001 was used to verify the accuracy of the model. We performed a single-gene survival analysis on the genes involved in model construction.
We obtained 52 cervical cancer-related genes and 22 core genes (DNA2, CEP55, GINS1, RFC4, KIF14, GINS2, MYBL2, KIF4A, RAD54L, KNTC1, SPAG5, MELK, CENPE, MCM2, NCAPH, MCM5, ASPM, HELLS, DTL, FOXM1, TOP2A, CDC45). We successfully constructed a prognostic model using cervical cancer-related genes. The comprehensive analysis showed that the constructed prognostic model could effectively predict the prognosis of cervical cancer patients, with AUC values of 0.858, 0.802, and 0.797 for 1, 3, and 5 years in the training group, respectively. The results were consistent in the validation using the GSE44001 dataset. Single-gene survival analysis showed that APOD was an independent prognostic biomarker for cervical cancer.
APOD is a prognostic biomarker for cervical cancer, and the prognostic model constructed by identified cervical cancer-related genes can successfully distinguish the prognosis of patients with cervical cancer.
宫颈癌（CC）是全球女性中第四大最常被诊断出的癌症，也是癌症相关死亡的第四大主要原因，然而，晚期CC的治疗选择有限。因此，临床上迫切需要可靠的预后模型来指导临床决策。
我们对宫颈癌样本和正常样本进行差异基因表达分析以获得差异表达基因（DEG）。我们使用加权基因共表达网络分析（WGCNA）来识别与宫颈癌最相关的模块，并与DEG进行交集以获得宫颈癌相关基因。然后我们使用这些基因构建蛋白质-蛋白质相互作用（PPI）网络，并使用Cytoscape软件中的Hubba插件识别核心基因。随后，我们结合TCGA数据库，使用鉴定出的宫颈癌相关基因构建了一个预后模型。GSE44001用于验证该模型的准确性。我们对参与模型构建的基因进行了单基因生存分析。
我们获得了52个宫颈癌相关基因和22个核心基因（DNA2、CEP55、GINS1、RFC4、KIF14、GINS2、MYBL2、KIF4A、RAD54L、KNTC1、SPAG5、MELK、CENPE、MCM2、NCAPH、MCM5、ASPM、HELLS、DTL、FOXM1、TOP2A、CDC45）。我们使用宫颈癌相关基因成功构建了一个预后模型。综合分析表明，构建的预后模型可以有效预测宫颈癌患者的预后，训练组中1年、3年和5年的AUC值分别为0.858、0.802和0.797。在使用GSE44001数据集进行验证时结果一致。单基因生存分析表明，载脂蛋白D（APOD）是宫颈癌的独立预后生物标志物。
APOD是宫颈癌的预后生物标志物，通过鉴定出的宫颈癌相关基因构建的预后模型可以成功区分宫颈癌患者的预后。
