Cisplatin has been widely applied in the clinical treatment of various cancers, whereas liver injury induced by its hepatotoxicity is still a severe issue. Reliable identification of early-stage cisplatin-induced liver injury (CILI) can improve clinical care and help to streamline drug development. Traditional methods, however, cannot achieve enough information at the subcellular level due to the requirement of the labeling process and low sensitivity. To overcome these, we designed an Au-coated Si nanocone array (Au/SiNCA) to fabricate the microporous chip as the surface-enhanced Raman scattering (SERS) analysis platform for the early diagnosis of CILI. A CILI rat model was established, and the exosome spectra were obtained. The principal component analysis (PCA)-representation coefficient-based k-nearest centroid neighbor (RCKNCN) classification algorithm was proposed as the multivariate analysis method to build the diagnosis and staging model. The PCA-RCKNCN model has been validated to achieve a satisfactory result, with accuracy and AUC of over 97.5%, and sensitivity and specificity of over 95%, indicating that SERS combined with the PCA-RCKNCN analysis platform can be a promising tool for clinical applications.

顺铂已广泛应用于各种癌症的临床治疗，但其肝毒性引起的肝损伤仍然是一个严重的问题。早期顺铂诱导的肝损伤 (CILI) 的可靠鉴定可以改善临床护理并有助于简化药物开发。然而，由于标记过程的要求和低灵敏度，传统方法无法在亚细胞水平上获得足够的信息。为了克服这些问题，我们设计了一种镀金硅纳米锥阵列 (Au/SiNCA) 来制造微孔芯片作为表面增强拉曼散射 (SERS) 分析平台，用于 CILI 的早期诊断。建立CILI大鼠模型，获得外泌体谱图。提出了基于主成分分析 (PCA)-表示系数的 k 最近质心邻域 (RCKNCN) 分类算法作为构建诊断和分期模型的多变量分析方法。PCA-RCKNCN模型经验证取得了令人满意的结果，准确率和AUC均超过97.5%，灵敏度和特异度均超过95%，表明SERS结合PCA-RCKNCN分析平台可成为临床应用前景广阔的工具应用程序。