适体
生物标志物发现
生物标志物
计算生物学
癌症生物标志物
生物
癌症
蛋白质组学
人口
癌症研究
医学
分子生物学
生物化学
遗传学
环境卫生
基因
作者
Dihua Shangguan,Zehui Cao,Ling Meng,Prabodhika Mallikaratchy,Kwame Sefah,Hui Wang,Ying Li,Weihong Tan
摘要
Disease biomarkers play critical roles in the management of various pathological conditions of diseases. This involves diagnosing diseases, predicting disease progression and monitoring the efficacy of treatment modalities. While efforts to identify specific disease biomarkers using a variety of technologies has increased the number of biomarkers or augmented information about them, the effective use of disease-specific biomarkers is still scarce. Here, we report that a high expression of protein tyrosine kinase 7 (PTK7), a transmembrane receptor protein tyrosine kinase-like molecule, was discovered in a series of leukemia cell lines using whole cell aptamer selection. With the implementation of a two-step strategy (aptamer selection and biomarker discovery), combined with mass spectrometry, PTK7 was ultimately identified as a potential biomarker for T-cell acute lymphoblastic leukemia (T-ALL). Specifically, the aptamers for T-ALL cells were selected using the cell-SELEX process, without any prior knowledge of the cell biomarker population, conjugated with magnetic beads and then used to capture and purify their binding targets on the leukemia cell surface. This demonstrates that a panel of molecular aptamers can be easily generated for a specific type of diseased cells. It further demonstrates that this two-step strategy, that is, first selecting cancer cell-specific aptamers and then identifying their binding target proteins, has major clinical implications in that the technique promises to substantially improve the overall effectiveness of biomarker discovery. Specifically, our strategy will enable efficient discovery of new malignancy-related biomarkers, facilitate the development of diagnostic tools and therapeutic approaches to cancer, and markedly improve our understanding of cancer biology.
科研通智能强力驱动
Strongly Powered by AbleSci AI