Wang Chao, Wei Xu-Pan, Zhou Chuan, Wang Jia, Zhang Yun-Feng, He Han, Zhang Wen-Bo, Lv Hao-Xuan, Wang Fang, Zhou Feng-Hai
The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China.
Department of Urology, Liaocheng People's Hospital, Liaocheng Hospital Affiliated to Shandong First Medical University, Liaocheng, 252000, China.
J Transl Med. 2025 Jun 18;23(1):677. doi: 10.1186/s12967-025-06691-0.
Bone metastasis of prostate cancer (PCa) is a challenging problem, leading to poor prognosis of patients. Existing biomarkers have limited sensitivity and specificity. Therefore, we urgently need a novel diagnostic tool to predict PCa bone metastasis.
Patient data and blood samples were collected according to the inclusion and exclusion criteria. logistic regression analysis was used to screen clinical indicators and miRNAs, and radiomics was used to construct a prediction model. Finally, the performance of the model was evaluated by internal verification, external verification and Delong test. Two nomograms were successfully established by analyzing clinical data, plasma miRNAs and imaging data. Nomogram 1 predicts the presence or absence of bone metastasis; Nomogram 2 predicts whether the number of bone metastases is ≥ 4.
Nomogram 1 constructed by tPSA, hsa-miR-548o-3p and radiomics had an AUC of 0.904. The AUC of the internal training set was 0.879, the internal test set was 0.956, and the AUC of the external data set was 0.877. The calibration curve and decision curve all performed well. Nomogram 2 constructed by ALP, hsa-miR-548o-3p and radiomics had an AUC of 0.849, with an AUC of 0.916 in the internal training set, 0.806 in the internal test set and 0.839 in the external data set. The calibration curve and decision curve showed good performance.
The combination of plasma exosomal miRNA and radiomics model has high reliability and accuracy in predicting the presence and number of bone metastases of PCa.
前列腺癌（PCa）骨转移是一个具有挑战性的问题，会导致患者预后不良。现有的生物标志物敏感性和特异性有限。因此，我们迫切需要一种新型诊断工具来预测PCa骨转移。
根据纳入和排除标准收集患者数据和血液样本。采用逻辑回归分析筛选临床指标和微小RNA（miRNAs），并运用放射组学构建预测模型。最后，通过内部验证、外部验证和德龙检验评估模型性能。通过分析临床数据、血浆miRNAs和影像数据成功建立了两个列线图。列线图1预测骨转移的有无；列线图2预测骨转移数量是否≥4。
由总前列腺特异抗原（tPSA）、hsa-miR-548o-3p和放射组学构建的列线图1的曲线下面积（AUC）为0.904。内部训练集的AUC为0.879，内部测试集为0.956，外部数据集的AUC为0.877。校准曲线和决策曲线表现均良好。由碱性磷酸酶（ALP）、hsa-miR-548o-3p和放射组学构建的列线图2的AUC为0.849，内部训练集的AUC为0.916，内部测试集为0.806，外部数据集为0.839。校准曲线和决策曲线表现良好。
血浆外泌体miRNA与放射组学模型相结合在预测PCa骨转移的有无及数量方面具有较高的可靠性和准确性。
