Abstract：
High-grade serous ovarian cancer (HGSOC) is one of the most common histologic types of ovarian cancer. The purpose of this study was to identify potential prognostic biomarkers in urine specimens from patients with HGSOC. First, 56 urine samples with information on relapse-free survival (RFS) months were collected and classified into good prognosis (RFS ≥ 12 months) and poor prognosis (RFS < 12 months) groups. Next, data-independent acquisition (DIA)-based mass spectrometry (MS) analysis was combined with MSFragger-DIA workflow to identify potential prognostic biomarkers in a discovery set (n = 31). With the aid of parallel reaction monitoring (PRM) analysis, four candidate biomarkers (ANXA1, G6PI, SPB3, and SPRR3) were finally validated in both the discovery set and an independent validation set (n = 25). Subsequent RFS and Cox regression analyses confirmed the utility of these candidate biomarkers as independent prognostic factors affecting RFS in patients with HGSOC. Regression models were constructed to predict the 12-month RFS rate, with area under the receiver operating characteristic curve (AUC) values ranging from 0.847 to 0.905. Overall, candidate prognostic biomarkers were identified in urine specimens from patients with HGSOC and prediction models for the 12-month RFS rate constructed. SIGNIFICANCE: OC is one of the leading causes of death due to gynecological malignancies. HGSOC constitutes one of the most common histologic types of OC with aggressive characteristics, accounting for the majority of advanced cases. In cases where patients with advanced HGSOC potentially face high risk of unfavorable prognosis or disease advancement within a 12-month period, intensive medical monitoring is necessary. In the era of precision cancer medicine, accurate prediction of prognosis or 12-month RFS rate is critical for distinguishing patient groups requiring heightened surveillance. Patients could significantly benefit from timely modifications to treatment regimens based on the outcomes of clinical monitoring. Urine is an ideal resource for disease surveillance purposes due to its easy accessibility. Furthermore, molecules excreted in urine are less complex and more stable than those in other liquid samples. In the current study, we identified candidate prognostic biomarkers in urine specimens from patients with HGSOC and constructed prediction models for the 12-month RFS rate.
摘要：
高级别浆液性卵巢癌（HGSOC）是卵巢癌最常见的组织学类型之一。这项研究的目的是确定HGSOC患者尿液标本中潜在的预后生物标志物。首先,收集56例含无复发生存期（RFS）月信息的尿液样本，分为预后良好（RFS≥12个月）和预后不良（RFS<12个月）组。接下来,基于数据独立采集(DIA)的质谱(MS)分析与MSFragger-DIA工作流程相结合,在发现集中鉴定潜在的预后生物标志物(n=31).借助平行反应监测（PRM）分析，四种候选生物标志物(ANXA1、G6PI、SPB3和SPRR3）最终在发现集和独立验证集（n=25）中进行了验证。随后的RFS和Cox回归分析证实了这些候选生物标志物作为影响HGSOC患者RFS的独立预后因素的效用。构建回归模型来预测12个月的RFS率，接收器工作特性曲线下面积（AUC）值范围为0.847至0.905。总的来说,在HGSOC患者的尿液标本中鉴定了候选预后生物标志物,并构建了12个月RFS率的预测模型.意义：OC是妇科恶性肿瘤死亡的主要原因之一。HGSOC是最常见的OC组织学类型之一，具有侵袭性特征，占先进案例的大多数。如果晚期HGSOC患者在12个月内可能面临不良预后或疾病进展的高风险，加强医疗监测是必要的。在精准癌症医学时代，准确预测预后或12个月RFS率对于区分需要加强监测的患者群体至关重要.根据临床监测结果，患者可以从及时修改治疗方案中获益。由于尿液易于获取，因此尿液是用于疾病监测目的的理想资源。此外，尿液中排泄的分子比其他液体样品中的分子更不复杂，更稳定。在目前的研究中,我们在HGSOC患者的尿液标本中鉴定了候选预后生物标志物,并构建了12个月RFS率的预测模型.
