Jin Zhixin, Wang Xuegu, Li Xiang, Yang Shasha, Ding Biao, Fei Jiaojiao, Wang Xiaojing, Dou Chengli
Anhui Key Laboratory of Respiratory Tumors and Infectious Diseases, The First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
Department of Obstetrics and Gynecology (Center for Reproductive Medicine), The First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
PeerJ. 2025 Aug 11;13:e19764. doi: 10.7717/peerj.19764. eCollection 2025.
Ovarian cancer (OC) is a highly aggressive malignancy in the reproductive system of women, with a high recurrence rate. The present research was designed to establish a relapse-based RiskScore model to assess the drug sensitivity and prognosis for patients with OC.
Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were accessed to obtain relevant sample data. The single-cell atlas of primary and relapse OC was characterized using the "Seurat" package. Differentially expressed genes (DEGs) between primary and relapse samples were identified by FindMarkers function. Subsequently, univariate Cox, least absolute shrinkage and selection operator (LASSO) and stepwise regression analysis were employed to determine independent prognostic genes related to relapse in OC to establish a RiskScore model. Applying "timeROC" package, the predictive performance of RiskScore model was assessed. Drug sensitivity of different risk groups was evaluated using "pRRophetic" package. The effects of relapse-related prognostic genes on OC cells were detected with assays.
The single-cell atlas revealed that compared to primary OC, fibroblasts were reduced but epithelial cells were increased in relapse OC. Five prognostic genes (, , , , and ) independently linked to relapse in OC were identified to construct a RiskScore model, which showed high robustness in the prognostic prediction for OC patients. High-risk group tended to have worse outcomes in terms of different clinical features than the low-risk group. Further, six drugs (Vinorelbine, GW-2580, S-Trityl-L-cysteine, BI-2536, CP466722, NSC-87877) were found to be correlated with the RiskScore. While the high-risk group had higher IC values to these drugs, the low-risk group was more sensitive to the six drugs. In addition, silencing markedly inhibited the invasion and migration of OC cells.
This study established a relapse-related RiskScore model based on five prognostic genes (, , , , and ), offering novel insights into the recurrence mechanisms in OC and contributing to the development of individualized treatment strategies.
卵巢癌（OC）是女性生殖系统中一种侵袭性很强的恶性肿瘤，复发率很高。本研究旨在建立一种基于复发的风险评分模型，以评估OC患者的药物敏感性和预后。
访问基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）数据库以获取相关样本数据。使用“Seurat”软件包对原发性和复发性OC的单细胞图谱进行特征分析。通过FindMarkers函数鉴定原发性和复发性样本之间的差异表达基因（DEG）。随后，采用单变量Cox、最小绝对收缩和选择算子（LASSO）以及逐步回归分析来确定与OC复发相关的独立预后基因，以建立风险评分模型。应用“timeROC”软件包评估风险评分模型的预测性能。使用“pRRophetic”软件包评估不同风险组的药物敏感性。通过实验检测复发相关预后基因对OC细胞的影响。
单细胞图谱显示，与原发性OC相比，复发性OC中的成纤维细胞减少，但上皮细胞增加。鉴定出五个与OC复发独立相关的预后基因（、、、和），构建了一个风险评分模型，该模型在OC患者的预后预测中显示出高稳健性。在不同临床特征方面，高风险组的预后往往比低风险组更差。此外，发现六种药物（长春瑞滨、GW-2580、S-三苯甲基-L-半胱氨酸、BI-2536、CP466722、NSC-87877）与风险评分相关。虽然高风险组对这些药物的IC值较高，但低风险组对这六种药物更敏感。此外，沉默明显抑制了OC细胞的侵袭和迁移。
本研究基于五个预后基因（、、、和）建立了一个与复发相关的风险评分模型，为OC的复发机制提供了新的见解，并有助于制定个体化治疗策略。
