Department of Urology, RenJi Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Shanghai Institute for Advanced Communication and Data Science, Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, Shanghai, People's Republic of China.
Urol Oncol. 2020 Jun;38(6):601.e1-601.e9. doi: 10.1016/j.urolonc.2020.02.009. Epub 2020 Mar 30.
To predict Gleason grade group (GG) upgrade in biopsy Gleason grade group 1 (GG1) prostate cancer (CaP) patients using surface-enhanced Raman spectroscopy (SERS).
Preoperative serum samples of patients with biopsy GG1 and subsequent radical prostatectomy were analyzed using SERS. The role of clinical variables and distinctive SERS spectra in the prediction of GG upgrade were evaluated. Principal component analysis and linear discriminant analysis (PCA-LDA) were used to manage spectral data and develop diagnostic algorithms.
A total of 342 preoperative serum SERS spectra from 114 patients were obtained. SERS detected a higher level of circulating free nucleic acid bases and a lower level of lipids in patients with GG upgrade to GG3 and higher, presenting as SERS spectral peaks of 728 cm and 1,655 cm, respectively. Both spectral peaks were independent predictors of GG upgrade and their addition to clinical predictors of PSA and positive core percent significantly improved predictive power of the logistic regression model with area under curve improved from 0.65 to 0.80 (P = 0.0045). Meanwhile, PCA-LDA diagnostic model based on serum SERS spectra showed a high accuracy of 91.2% in predicted groups and remained stable with a sensitivity, specificity, and accuracy of 65%, 97.3%, 86.0%, respectively when validated by leave-one-out cross-validation method.
By analyzing preoperative serum samples, SERS combined with PCA-LDA model could be a promising tool for prediction of Gleason GG upgrade in biopsy GG1 CaP and assist in treatment decision-making in clinical practice.
利用表面增强拉曼光谱（SERS）预测活检 Gleason 分级组 1（GG1）前列腺癌（CaP）患者的 Gleason 分级组升级。
对活检 GG1 且随后接受根治性前列腺切除术的患者的术前血清样本进行 SERS 分析。评估临床变量和独特 SERS 谱在预测 GG 升级中的作用。主成分分析和线性判别分析（PCA-LDA）用于管理光谱数据和开发诊断算法。
共获得 114 例患者的 342 例术前血清 SERS 光谱。SERS 检测到 GG 升级至 GG3 及更高的患者循环游离核酸碱基水平更高，脂质水平更低，表现为 728cm 和 1655cm 的 SERS 光谱峰。这两个光谱峰都是 GG 升级的独立预测因子，将其与 PSA 和阳性核心百分比的临床预测因子相结合，显著提高了逻辑回归模型的预测能力，曲线下面积从 0.65 提高到 0.80（P=0.0045）。同时，基于血清 SERS 光谱的 PCA-LDA 诊断模型在预测组中表现出 91.2%的高准确率，当通过留一法交叉验证方法验证时，其敏感性、特异性和准确率分别为 65%、97.3%和 86.0%，稳定性良好。
通过分析术前血清样本，SERS 结合 PCA-LDA 模型可能成为预测活检 GG1 CaP 中 Gleason GG 升级的有前途的工具，并有助于指导临床实践中的治疗决策。
