Abstract：
Prostate cancer (PCa) is a prevalent malignancy among men worldwide, and biochemical recurrence (BCR) after radical prostatectomy (RP) is a critical turning point commonly used to guide the development of treatment strategies for primary PCa. However, the clinical parameters currently in use are inadequate for precise risk stratification and informing treatment choice. To address this issue, we conducted a study that collected transcriptomic data and clinical information from 1662 primary PCa patients across 12 multicenter cohorts globally. We leveraged 101 algorithm combinations that consisted of 10 machine learning methods to develop and validate a 9-gene signature, named BCR SCR, for predicting the risk of BCR after RP. Our results demonstrated that BCR SCR generally outperformed 102 published prognostic signatures. We further established the clinical significance of these nine genes in PCa progression at the protein level through immunohistochemistry on Tissue Microarray (TMA). Moreover, our data showed that patients with higher BCR SCR tended to have higher rates of BCR and distant metastasis after radical radiotherapy. Through drug target prediction analysis, we identified nine potential therapeutic agents for patients with high BCR SCR. In conclusion, the newly developed BCR SCR has significant translational potential in accurately stratifying the risk of patients who undergo RP, monitoring treatment courses, and developing new therapies for the disease.
摘要：
前列腺癌（PCa）是全球男性中普遍存在的恶性肿瘤，前列腺癌根治术（RP）后生化复发（BCR）是指导原发性PCa治疗策略制定的重要转折点。然而,目前使用的临床参数不足以进行精确的风险分层和指导治疗选择.为了解决这个问题，我们进行了一项研究,收集了全球12个多中心队列中1662例原发性PCa患者的转录组数据和临床信息.我们利用了由10种机器学习方法组成的101种算法组合来开发和验证9基因签名，命名为BCRSCR，用于预测RP后BCR的风险。我们的结果表明，BCRSCR通常优于102个已发表的预后特征。我们通过组织微阵列（TMA）上的免疫组织化学，在蛋白质水平上进一步确定了这9个基因在PCa进展中的临床意义。此外，我们的数据显示,BCRSCR较高的患者在根治性放疗后BCR和远处转移率较高.通过药物靶标预测分析,我们为高BCRSCR患者确定了9种潜在的治疗药物.总之,新开发的BCRSCR在准确分层接受RP的患者的风险方面具有显著的翻译潜力，监测治疗过程，并开发这种疾病的新疗法。
