血小板不仅参与了人体正常的止凝血过程，其与恶性肿瘤的发生、发展及转移也关系密切。血小板中含有丰富的RNA，其可将RNA翻译成蛋白质来发挥多种功能。肿瘤细胞可以通过多种方式影响血小板RNA的表达谱，这种因肿瘤影响而改变了RNA表达谱的血小板，被称为肿瘤诱导的血小板（tumor-educated platelets, TEP）。TEP在肿瘤诊断中的研究主要聚焦在以下两大方向。一是对TEP RNA进行转录组测序，运用生物信息学方法对测序结果进行分析，结合深度机器学习，开发新型算法、构建诊断模型，从而区分癌与非癌；二是聚焦TEP的某些mRNA、microRNA、snRNA、snoRNA、lncRNA的表达水平，与非癌组对照比较，评估其诊断效能。目前，TEP在肺癌、肝癌、乳腺癌、卵巢癌、神经胶质母细胞瘤等临床常见肿瘤的诊断中表现出极高的价值。依据TEP mRNA谱改变，采用生物信息分析，诊断肺癌的准确率88%~91%；以血小板ITGA2B的mRNA表达升高诊断肺癌的受试者操作特征曲线下面积（area under curve，AUC）为0.922，临界值为0.001 759。肝癌患者TEPmiRNA-122升高，当最佳截断值为4.46时，其诊断肝癌的灵敏度和特异度高达100.0%和93.3%，miRNA-21也有不俗表现；血小板RNA谱进行生物信息学分析，发现TEP mRNA表达谱用于区分乳腺癌患者与非癌症患者的AUC为0.72，灵敏度为91%；由102个血小板RNA组成的卵巢癌诊断模型TEPOC用于诊断卵巢癌时的AUC为0.93，高于CA125；TEP mRNA表达谱用于区分胶质母细胞瘤（Glioblastoma，GBM）与其他肿瘤脑转移、多发性硬化症患者及健康个体的AUC分别为0.84、0.94、0.97。血小板的数量优势、易于分离及极高的诊断效能，有望使其成为理想的肿瘤液体活检标志物，走向临床应用。

Platelets not only play a crucial role in normal hemostasis and coagulation, but they are also closely associated with the occurrence, development, and metastasis of malignant tumors. Platelets are rich in RNA, which can be translated into proteins to perform various functions. Tumor cells can influence the RNA expression profiles of platelets in multiple ways. Platelets with altered RNA expression profiles due to tumor influence are referred to as tumor-educated platelets (TEPs). Research on TEPs in tumor diagnosis primarily focuses on two key areas. The first is transcriptomic sequencing of TEP RNA, followed by the analysis of sequencing results using bioinformatics methods. By integrating deep machine learning, novel algorithms and diagnostic models are developed to differentiate between cancer and non-cancer cases. The second area focuses on the expression levels of specific mRNA, microRNA, snRNA, snoRNA, and lncRNA in TEPs. The expression levels are analyzed and compared with non-cancer groups to evaluate their diagnostic efficacy. Currently, TEPs demonstrate significant diagnostic value in common clinical tumors such as lung cancer, liver cancer, breast cancer, ovarian cancer, and glioblastoma. Based on changes in the TEP mRNA profile, bioinformatics analysis shows an accuracy of 88%-91% for lung cancer diagnosis. Elevated mRNA expression of platelet ITGA2B in lung cancer patients results in an area under the curve (AUC) of 0.922, with a threshold value of 0.001759. TEP miRNA-122 levels are significantly elevated in liver cancer patients. At an optimal cutoff value of 4.46, its diagnostic performance achieves a sensitivity of 100.0% and a specificity of 93.3%. miRNA-21 also shows promising diagnostic performance. Bioinformatics analysis of platelet RNA profiles reveals that the TEP mRNA expression profile has an AUC of 0.72 and a sensitivity of 91% in distinguishing breast cancer patients from non-cancer patients. The ovarian cancer diagnostic model, TEPOC, based on 102 platelet RNAs, achieves an AUC of 0.93, outperforming CA125. The TEP mRNA expression profile distinguishes glioblastoma (GBM) from other brain metastases, multiple sclerosis patients, and healthy individuals, with AUCs of 0.84, 0.94, and 0.97, respectively. Due to platelets’ large amount, their ease of isolation, and their high diagnostic efficacy, TEPs show great promise as ideal biomarkers for tumor liquid biopsy, paving the way for clinical application.