Table_1_Tumor-Educated Platelets as a Promising Biomarker for Blood-Based Detection of Renal Cell Carcinoma.xlsx

PurposeTumor-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.
MethodsPlatelets were prospectively collected from 24 RCC patients and 25 controls. RNA-seq was 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 sets. 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.
ResultTwo hundred three DEGs, 64 (31.5%) upregulated and 139 (68.5%) downregulated, were detected in the platelets of RCC patients compared with 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.
ConclusionTEPs could function as a minimally invasive blood biomarker in the detection of RCC.

目的：肿瘤教育血小板（Tumor-educated platelets, TEPs）是多种癌症中极具前景的液体活检手段，但目前其在肾细胞癌（renal cell carcinoma, RCC）中的作用尚不明确。因此本研究探讨了TEPs在肾细胞癌患者中的诊断价值。
方法：本研究前瞻性收集了24例肾细胞癌患者与25例对照者的血小板样本，通过RNA-seq鉴定肾细胞癌患者与对照者之间的差异表达基因（differentially expressed genes, DEGs）。此外，下载泛癌TEPs的RNA-seq数据，并随机划分为训练集与验证集。利用支持向量机（support vector machine, SVM）在训练集中构建泛癌TEPs诊断模型，并在验证集与本研究的肾细胞癌数据集内进行验证。最终，通过SVM算法与递归特征消除（recursive feature elimination, RFE）方法构建并优化了肾细胞癌专属TEPs诊断模型。
结果：相较于对照者，肾细胞癌患者血小板中共检测到203个DEGs，其中64个（31.5%）上调，139个（68.5%）下调。泛癌TEPs诊断模型在内部验证中表现出优异的癌症检测性能：训练集准确率达98.8%，曲线下面积（AUC）为0.999；验证集准确率为95.4%，AUC为0.972；针对不同肿瘤亚型，准确率介于86.6%~96.1%之间，AUC为0.952~1.000。然而，泛癌TEPs诊断模型在针对肾细胞癌患者的外部验证中诊断价值有限，准确率仅为48.7%，AUC为0.615。因此，本研究通过RFE方法筛选出对模型贡献度最高的基因生物标志物，以构建肾细胞癌专属TEPs诊断模型。包含68个基因生物标志物的肾细胞癌TEPs诊断模型在训练集内诊断准确率达100%（AUC=1.000），验证集准确率为88.9%（AUC=0.963），全队列分析准确率为95.9%（AUC=0.988）。
结论：TEPs可作为一种微创血液生物标志物，用于肾细胞癌的检测。