Extracellular vesicles, the cornerstone of next-generation cancer diagnosis?

Cancer has risen up to be a major cause of mortality worldwide over the past decades. Despite advancements in cancer screening and diagnostics, a significant number of cancers are still diagnosed at a late stage with poor prognosis. Hence, the discovery of reliable and accurate methods to diagnose cancer early would be of great help in reducing cancer mortality. Extracellular vesicles (EVs) are phospholipid vesicles found in many biofluids and are released by almost all types of cells. In recent years, using EVs as cancer biomarkers has garnered attention as a novel technique of cancer diagnosis. Compared with traditional tissue biopsy, there are many advantages that this novel diagnostic tool presents - it is less invasive, detects early-stage asymptomatic cancers, and allows for monitoring of tumour progression. As such, EV biomarkers have great potential in improving the diagnostic accuracy of cancers and guiding subsequent therapeutic decisions. Efficient isolation and accurate characterization of EVs are essential for reliable outcomes of clinical application. However, these are complicated by the size and biomolecular diversity of EVs. In this review, we present an analysis and evaluation of the current techniques of EV isolation and characterization, as well as discuss the potential EV biomarkers for specific types of cancer. Taken together, EV biomarkers have a lot of potential as a novel method in cancer diagnostics and diagnosis. However, more work is still needed to streamline the purification, characterization and biomarker identification process to ensure optimal outcomes for patients.

Automated summary

Cancer has become a major global cause of death, and the early diagnosis of cancer using extracellular vesicles as biomarkers has potential in improving diagnostic accuracy and guiding therapeutic decisions. However, more research is needed to optimize the process of isolation, characterization, and identification of EV biomarkers for optimal outcomes.