Zhou Kaixia, Ma Xiaolu, Yan Tianqing, Zheng Hui, Xie Suhong, Guo Lin, Lu Renquan
Department of Clinical Laboratory, Fudan University Shanghai Cancer Center, Shanghai 200032, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
Stem Cells Int. 2025 Mar 27;2025:2505812. doi: 10.1155/sci/2505812. eCollection 2025.
Ovarian cancer (OC) stands as the leading cause of cancer-related deaths among women, globally, owing to metastasis and acquired chemoresistance. Cancer stem cells (CSCs) are accountable for tumor initiation and exhibit resistance to chemotherapy and radiotherapy. Identifying stemness-related biomarkers that can aid in the stratification of risk and the response to chemotherapy for OC is feasible and critical. Gene expression and clinical data of patients with OC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Four thousand three hundred seventeen stemness-related genes (SRGs) were acquired from the StemChecker database. TCGA was used as the training dataset, while GSE30161 served as validation dataset. Univariate Cox regression analysis was used to identify overall survival (OS)-related SRGs, and multivariate Cox regression analysis and random survival forest analysis were used for generating stemness-relevant prognostic model. Kaplan-Meier plots were used to visualize survival functions. Receiver operating characteristic (ROC) curves were used to assess the prognostic predictive ability of SRG-based features. Associations between signature score, tumor immune phenotype, and response to chemotherapy were analyzed via TIMER 2.0 and oncoPredict R package, respectively. A cohort of Shanghai Cancer Center was employed to verify the predictive robustness of the signature with respect to chemotherapy response. Seven SRGs (actin-binding Rho activating C-terminal like (ABRACL), growth factor receptor bound protein 7 (GRB7), Lin-28 homolog B (LIN28B), lipolysis stimulated lipoprotein receptor (LSR), neuromedin U (NMU), Solute Carrier Family 4 Member 11 (SLC4A11), and thymocyte selection associated family member 2 (THEMIS2)) were found to have excellent predictive potential for patient survival. Patients in the high stemness risk group presented a poorer prognosis ( < 0.0001), and patients with lower stemness scores were more likely to benefit from chemotherapy. Several tumorigenesis pathways, such as mitotic spindle and glycolysis, were enriched in the high stemness risk group. Tumor with high-risk scores tended to be in a status of relatively high tumor infiltration of CD4+ T cells, neutrophils, and macrophages, while tumor with low-risk scores tended to be in a status of relatively high tumor infiltration of CD8+ T cells. The stemness-relevant prognostic gene signature has the potential to serve as a clinically helpful biomarker for guiding the management of OC patients.
由于转移和获得性化疗耐药性,卵巢癌(OC)是全球女性癌症相关死亡的主要原因。癌症干细胞(CSCs)负责肿瘤的起始,并表现出对化疗和放疗的抗性。识别有助于OC风险分层和化疗反应的干性相关生物标志物是可行且至关重要的。从癌症基因组图谱(TCGA)和基因表达综合数据库(GEO)下载OC患者的基因表达和临床数据。从StemChecker数据库中获取了4317个干性相关基因(SRGs)。TCGA用作训练数据集,而GSE30161用作验证数据集。使用单变量Cox回归分析来识别与总生存期(OS)相关的SRGs,并使用多变量Cox回归分析和随机生存森林分析来生成与干性相关的预后模型。Kaplan-Meier图用于可视化生存函数。受试者工作特征(ROC)曲线用于评估基于SRG的特征的预后预测能力。分别通过TIMER 2.0和oncoPredict R包分析特征评分、肿瘤免疫表型与化疗反应之间的关联。采用上海癌症中心的队列来验证该特征对化疗反应的预测稳健性。发现7个SRGs(肌动蛋白结合Rho激活C末端样蛋白(ABRACL)、生长因子受体结合蛋白7(GRB7)、Lin-28同源物B(LIN28B)、脂解刺激脂蛋白受体(LSR)、神经介素U(NMU)、溶质载体家族4成员11(SLC4A11)和胸腺细胞选择相关家族成员2(THEMIS2))对患者生存具有出色的预测潜力。高干性风险组的患者预后较差(<0.0001),而干性评分较低的患者更有可能从化疗中获益。几个肿瘤发生途径,如有丝分裂纺锤体和糖酵解,在高干性风险组中富集。高风险评分的肿瘤往往处于CD4 + T细胞、中性粒细胞和巨噬细胞肿瘤浸润相对较高的状态,而低风险评分的肿瘤往往处于CD8 + T细胞肿瘤浸润相对较高的状态。与干性相关的预后基因特征有可能作为一种临床上有用的生物标志物,用于指导OC患者的管理。