Research progress on tumor-educated platelets in the diagnosis of common clinical tumors

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.