Department of Urology, School of Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University, Shanghai, China.
Department of Urology, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China.
Front Immunol. 2022 Aug 18;13:901176. doi: 10.3389/fimmu.2022.901176. eCollection 2022.
To identify less invasive and easily applicable serum cytokine-derived biomarkers which contribute to the diagnostic utility and risk assessment ability of the prostate health index (PHI) based multivariable model in grey zone aggressive prostate cancer (AG PCa) early detection.
Serum 45 cytokines screening was performed in a small training cohort consisting of 10 sera by Luminex liquid array-based multiplexed immunoassays and identified TRAIL and IL-10 as new biomarkers for PHI diagnostic utility adjustment for further validation with a multivariable predictive model in a cohort including 79 aggressive prostate cancer patients and 209 benign prostatic hyperplasia or indolent PCa patients within the PSA grey zone.
TRAIL and IL-10 were identified as potential serum biomarkers for AG PCa detection by the result of multi-cytokines screening in the univariate analysis, while multivariable logistic regression confirmed the AUC of the full risk predictive model (0.915) including tPSA, fPSA, PHI, TRAIL, and IL-10 was higher than various diagnostic strategies. DCA suggested a superior net benefit and indicated a good discriminative ability of the full risk model consistently with the result of the nomogram.
We suggest a significant advantage for the PHI-based multivariate combinations of serum TRAIL and IL-10 comparing to PHI or other serum-derived biomarkers alone in the detection and risk stratification of grey zone AG PCa.
确定侵袭性较低且易于应用的血清细胞因子衍生生物标志物,以提高前列腺健康指数(PHI)多变量模型在灰色区域侵袭性前列腺癌(AG PCa)早期检测中的诊断效用和风险评估能力。
通过 Luminex 液体阵列基于多重免疫分析对由 10 例血清组成的小训练队列进行了 45 种血清细胞因子筛选,并鉴定 TRAIL 和 IL-10 作为 PHI 诊断效用调整的新生物标志物,以便在包括 79 例侵袭性前列腺癌患者和 209 例良性前列腺增生或惰性 PCa 患者的队列中,通过多变量预测模型进行进一步验证。
通过单变量分析,TRAIl 和 IL-10 被确定为 AG PCa 检测的潜在血清生物标志物,而多变量逻辑回归则证实了包括 tPSA、fPSA、PHI、TRAIL 和 IL-10 在内的全风险预测模型(0.915)的 AUC 高于各种诊断策略。DCA 表明全风险模型具有更高的净收益,并一致表明全风险模型具有良好的判别能力,这与列线图的结果一致。
与 PHI 或其他血清衍生生物标志物单独相比,基于 PHI 的 TRAIL 和 IL-10 的血清多变量组合在灰色区域 AG PCa 的检测和风险分层方面具有显著优势。