Tumor-Educated Platelets as a Promising Biomarker for Blood-Based Detection of Renal Cell Carcinoma - Xiao, Ruotao; Liu, Cheng; Zhang, Bo; Ma, Lulin (Frontiers in Oncology, 2022)
Purpose: Tumor-educated platelets (TEPs) are a promising liquid biopsy in many cancers. However, their role in renal cell carcinoma (RCC) is unknown. Thus, this study explored the diagnostic value of TEPs in RCC patients. Methods: Platelets were prospectively collected from 24 RCC patients and 25 controls. RNA-seq were performed to identify the differentially expressed genes (DEGs) between RCC patients and controls. Besides, RNA-seq data of pan-cancer TEPs were downloaded and randomly divided into training and validation set. A pan-cancer TEP model was developed in the training set using the support vector machine (SVM), and validated in the validation set and our RCC dataset. Finally, an RCC-based TEP model was developed and optimized through the SVM algorithms and recursive feature elimination (RFE) method. Result: Two hundred three DEGs, 64 (31.5%) up-regulated and 139 (68.5%) down-regulated, were detected in the platelets of RCC patients compared to controls. The pan-cancer TEP model had a high accuracy in detecting cancer in the internal validation (training set, accuracy 98.8%, AUC:0.999; validation set, accuracy 95.4%, AUC:0.972; different tumor subtypes, accuracy 86.6%-96.1%, AUC: 0.952-1.000). However, the pan-cancer TEP model in the external validation had a scarce diagnostic value in RCC patients (accuracy 48.7%, AUC: 0.615). Therefore, to develop the RCC-based TEP model, the gene biomarkers mostly contributing to the model were selected using the RFE method. The RCC-based TEP model containing 68 gene biomarkers reached a diagnostic accuracy of 100% (AUC:1.000) in the training set, 88.9% (AUC: 0.963) in the validation set, and 95.9% (AUC: 0.988) in the overall cohort. Conclusion: TEPs could function as a minimally invasive blood biomarker in the detection of RCC.