BACKGROUND Predicting the clinical course of prostate cancer is challenging due to the wide biological spectrum of the disease. The objective of our study was to identify prostate cancer prognostic markers in patients 'sera using a multi-omics discovery platform. METHODS Pre-surgical serum samples collected from a longitudinal, racially diverse, prostate cancer patient cohort (N = 382) were examined. Linear Regression and Bayesian computational approaches integrated with multi-omics, were used to select markers to predict biochemical recurrence (BCR). BCR-free survival was modeled using unadjusted Kaplan-Meier estimation curves and multivariable Cox proportional hazards analysis, adjusted for key pathologic variables. Receiver operating characteristic (ROC) curve statistics were used to examine the predictive value of markers in discriminating BCR events from non-events. The findings were further validated by creating a training set (N = 267) and testing set (N = 115) from the cohort. RESULTS Among 382 patients, 72 (19%) experienced a BCR event in a median follow-up time of 6.9 years. Two proteins-Tenascin C (TNC) and Apolipoprotein A1V (Apo-AIV), one metabolite-1-Methyladenosine (1-MA) and one phospholipid molecular species phosphatidic acid (PA) 18:0-22:0 showed a cumulative predictive performance of AUC = 0.78 [OR (95% CI) = 6.56 (2.98-14.40), P < 0.05], in differentiating patients with and without BCR event. In the validation set all four metabolites consistently reproduced an equivalent performance with high negative predictive value (NPV; > 80%) for BCR. The combination of pTstage and Gleason score with the analytes, further increased the sensitivity [AUC = 0.89, 95% (CI) = 4.45-32.05, P < 0.05], with an increased NPV (0.96) and OR (12.4) for BCR. The panel of markers combined with the pathological parameters demonstrated a more accurate prediction of BCR than the pathological parameters alone in prostate cancer. CONCLUSIONS In this study, a panel of serum analytes were identified that complemented pathologic patient features in predicting prostate cancer progression. This panel offers a new opportunity to complement current prognostic markers and to monitor the potential impact of primary treatment versus surveillance on patient oncological outcome.

中文翻译：

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多组份血清生物标志物，用于前列腺癌疾病进展的预后。

背景技术由于前列腺癌的广泛生物学谱，预测前列腺癌的临床病历具有挑战性。我们研究的目的是使用多组学发现平台鉴定患者血清中的前列腺癌预后标志物。方法检查从纵向，种族不同的前列腺癌患者队列（N = 382）收集的手术前血清样本。线性回归和贝叶斯计算方法与多组学集成在一起，用于选择标记来预测生化复发（BCR）。使用未经调整的Kaplan-Meier估计曲线和多变量Cox比例风险分析对无BCR生存进行建模，并针对关键病理变量进行调整。接收者操作特征（ROC）曲线统计数据用于检查标志物在区分BCR事件与非事件中的预测价值。通过创建队列的训练集（N = 267）和测试集（N = 115）进一步验证了发现。结果在382名患者中，有72名（19％）经历了BCR事件，中位随访时间为6.9年。两种蛋白质-Tenascin C（TNC）和载脂蛋白A1V（Apo-AIV），一种代谢物-1-甲基腺苷（1-MA）和一种磷脂分子种类磷脂酸（PA）18：0-22：0表现出累积的预测性能区分有无BCR事件的患者的AUC = 0.78 [OR（95％CI）= 6.56（2.98-14.40），P <0.05]。在验证组中，所有四种代谢物始终具有相同的性能，具有很高的阴性预测值（NPV;> 80％）用于BCR。pTstage和Gleason得分与分析物的组合，进一步提高了灵敏度[AUC = 0.89，95％（CI）= 4.45-32.05，P <0.05]，同时BCR的NPV（0.96）和OR（12.4）增加。与前列腺癌中单独的病理参数相比，与病理参数组合的一组标志物证明了对BCR的更准确的预测。结论在这项研究中，确定了一组血清分析物，它们在预测前列腺癌的进展中补充了病理患者的特征。该小组提供了一个新的机会来补充当前的预后指标，并监测初级治疗与监视对患者肿瘤学结局的潜在影响。45-32.05，P <0.05]，BCR的NPV（0.96）和OR（12.4）增加。与前列腺癌中单独的病理参数相比，与病理参数组合的一组标志物证明了对BCR的更准确的预测。结论在这项研究中，确定了一组血清分析物，它们在预测前列腺癌的进展中补充了病理患者的特征。该小组提供了一个新的机会来补充当前的预后指标，并监测初级治疗与监视对患者肿瘤学结局的潜在影响。45-32.05，P <0.05]，BCR的NPV（0.96）和OR（12.4）增加。与前列腺癌中单独的病理参数相比，与病理参数组合的一组标志物证明了对BCR的更准确的预测。结论在这项研究中，确定了一组血清分析物，它们在预测前列腺癌的进展中补充了病理患者的特征。该小组提供了一个新的机会来补充当前的预后指标，并监测初级治疗与监视对患者肿瘤学结局的潜在影响。鉴定了一组血清分析物，它们在预测前列腺癌进展中补充了病理患者的特征。该小组提供了一个新的机会来补充当前的预后指标，并监测初级治疗与监视对患者肿瘤学结局的潜在影响。鉴定了一组血清分析物，它们在预测前列腺癌进展中补充了病理患者的特征。该小组提供了一个新的机会来补充当前的预后指标，并监测初级治疗与监视对患者肿瘤学结局的潜在影响。