Zhong Xingyu, Yang Yuxuan, He Haodong, Xiong Yifan, Zhong Mingliang, Wang Shaogang, Xia Qidong
Department and Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095 Jiefang Avenue, Wuhan, 430030, P.R. China.
Cancer Cell Int. 2025 Apr 22;25(1):158. doi: 10.1186/s12935-025-03788-w.
The global incidence of prostate cancer (PCa) has been rising annually, and early diagnosis and treatment remain pivotal for improving therapeutic outcomes and patient prognosis. Concurrently, advancements in liquid biopsy technology have facilitated disease diagnosis and monitoring, with its minimally invasive nature and low heterogeneity positioning it as a promising approach for predicting disease progression. However, current liquid biopsy strategies for PCa predominantly rely on prostate-specific antigen (PSA), which lacks specificity and compromises diagnostic accuracy. Thus, there is an urgent need to identify novel liquid biopsy biomarkers to enable early and precise PCa diagnosis.
We integrated 12 machine learning algorithms to construct 113 combinatorial models, screening and validating an optimal PCa diagnostic panel across five datasets from TCGA and GEO databases. Subsequently, the biological feasibility of the selected predictive model was verified in one prostate epithelial cell line and five PCa cell lines. Robust RNA diagnostic targets were further validated for their expression in plasma samples to establish an RNA-based liquid biopsy strategy for PCa. Finally, plasma samples from PCa and benign prostatic hyperplasia (BPH) patients at Wuhan Tongji Hospital were collected to evaluate the strategy's clinical significance.
Differential analysis identified 1,071 candidate mRNAs, which were input into the integrated machine learning framework. Among the 113 combinatorial models, the 9-gene diagnostic panel selected by the Stepglm[both] and Enet[alpha = 0.4] algorithms demonstrated the highest diagnostic efficacy (mean AUC = 0.91), including JPH4, RASL12, AOX1, SLC18A2, PDZRN4, P2RY2, B3GNT8, KCNQ5, and APOBEC3C. Cell line experiments further validated AOX1 and B3GNT8 as robust RNA biomarkers, both exhibiting consistent PCa-specific expression in human plasma samples. In liquid biopsy analyses, AOX1 and B3GNT8 outperformed PSA in diagnostic accuracy, achieving a combined AUC of 0.91. Notably, these biomarkers also demonstrated diagnostic utility in patients with ISUP ≤ 2.
Through an integrated machine learning approach and clinical validation, we developed an RNA-based diagnostic panel for PCa. Specifically, we identified AOX1 and B3GNT8 as novel liquid biopsy biomarkers with promising clinical diagnostic value. These findings provide new targets and insights for early and precise PCa diagnosis.
前列腺癌（PCa）的全球发病率逐年上升，早期诊断和治疗对于改善治疗效果和患者预后仍然至关重要。同时，液体活检技术的进步促进了疾病的诊断和监测，其微创性和低异质性使其成为预测疾病进展的一种有前景的方法。然而，目前用于PCa的液体活检策略主要依赖前列腺特异性抗原（PSA），其缺乏特异性，影响诊断准确性。因此，迫切需要识别新的液体活检生物标志物以实现PCa的早期精准诊断。
我们整合了12种机器学习算法来构建113个组合模型，在来自TCGA和GEO数据库的五个数据集中筛选并验证了一个最佳的PCa诊断面板。随后，在一种前列腺上皮细胞系和五种PCa细胞系中验证了所选预测模型的生物学可行性。进一步验证了稳健的RNA诊断靶点在血浆样本中的表达，以建立基于RNA的PCa液体活检策略。最后，收集了武汉同济医院PCa和良性前列腺增生（BPH）患者的血浆样本，以评估该策略的临床意义。
差异分析确定了1071个候选mRNA，将其输入到整合的机器学习框架中。在113个组合模型中，由Stepglm[both]和Enet[alpha = 0.4]算法选择的9基因诊断面板显示出最高的诊断效能（平均AUC = 0.91），包括JPH4、RASL12、AOX1、SLC18A2、PDZRN4、P2RY2、B3GNT8、KCNQ5和APOBEC3C。细胞系实验进一步验证了AOX1和B3GNT8作为稳健的RNA生物标志物，两者在人血浆样本中均表现出一致的PCa特异性表达。在液体活检分析中，AOX1和B3GNT8在诊断准确性方面优于PSA，联合AUC达到0.91。值得注意的是，这些生物标志物在ISUP≤2的患者中也显示出诊断效用。
通过整合机器学习方法和临床验证，我们开发了一种基于RNA的PCa诊断面板。具体而言，我们确定AOX1和B3GNT8为具有前景的临床诊断价值的新型液体活检生物标志物。这些发现为PCa的早期精准诊断提供了新的靶点和见解。
