Docetaxel-based chemotherapy, as the first-line treatment for metastatic castration-resistant prostate cancer (mCRPC), has succeeded in helping quite a number of patients to improve quality of life and prolong survival time. However, almost half of mCRPC patients are not sensitive to docetaxel chemotherapy initially. This study aimed to establish models to predict sensitivity to docetaxel chemotherapy in patients with mCRPC by using serum surface-enhanced Raman spectroscopy (SERS). A total of 32 mCPRC patients who underwent docetaxel chemotherapy at our center from July 2016 to March 2018 were included in this study. Patients were dichotomized in prostate-specific antigen (PSA) response group (n=17) versus PSA failure group (n=15) according to the response to docetaxel. In total 64 matched spectra from 32 mCRPC patients were obtained by using SERS of serum at baseline (q0) and after 1 cycle of docetaxel chemotherapy (q1). Comparing Raman peaks of serum samples at baseline (q0) between two groups, significant differences revealed at the peaks of 638, 810, 890 (p<0.05) and 1136cm−1 (p<0.01). The prediction models of peak 1363cm−1 and principal component analysis and linear discriminant analysis (PCA–LDA) based on Raman data were established, respectively. The sensitivity and specificity of the prediction models were 71%, 80% and 69%, 78% through the way of leave-one-out cross-validation. According to the results of five-cross-validation, the PCA–LDA model revealed an accuracy of 0.73 and AUC of 0.83.

中文翻译：

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血清表面增强拉曼光谱预测转移性去势抵抗性前列腺癌患者对多西紫杉醇化疗的敏感性

以多西他赛为基础的化疗作为转移性去势抵抗性前列腺癌（mCRPC）的一线治疗方法，已成功帮助不少患者提高生活质量，延长生存时间。然而，几乎一半的 mCRPC 患者最初对多西他赛化疗不敏感。本研究旨在通过使用血清表面增强拉曼光谱 (SERS) 建立模型来预测 mCRPC 患者对多西他赛化疗的敏感性。本研究共纳入 2016 年 7 月至 2018 年 3 月在我中心接受多西他赛化疗的 32 例 mCPRC 患者。患者被分为前列腺特异性抗原（PSA）反应组（n=17) 与 PSA 失败组 (n=15) 根据对多西紫杉醇的反应。通过使用基线血清的 SERS，从 32 名 mCRPC 患者中获得了总共 64 个匹配的光谱。q0) 和 1 个多西他赛化疗周期后 (q1）。比较基线时血清样品的拉曼峰 (q0) 两组间，在 638, 810, 890 (p<0.05) 和 1136厘米-1(p<0.01）。1363峰的预测模型厘米-1并分别建立了基于拉曼数据的主成分分析和线性判别分析（PCA-LDA）。通过留一法交叉验证，预测模型的敏感性和特异性分别为71%、80%和69%、78%。根据五次交叉验证的结果，PCA-LDA 模型的准确度为 0.73，AUC 为 0.83。