Tumor-Derived Extracellular Vesicles as Liquid Biopsy for Diagnosis and Prognosis of Solid Tumors: Their Clinical Utility and Reliability as Tumor Biomarkers.

Simple Summary: The ongoing research of extracellular vehicles (EVs including exosomes, ectosomes, and apoptotic bodies) is gaining momentum to understand these vesicles' biology and clinical applications in cancer disease. The current limitations of using standard tumor biomarkers warrant the development of novel and reliable biomarkers to meet clinical needs. Exosomes are used as tumor biomarkers, for targeted therapy, for vaccine development, and as a vehicle for drug delivery. Here, we summarized the current approaches for different methods of EV isolation and EV cargo compositions, such as nucleic acids, proteins, and lipids. The unique cargo composition of exosomes makes it a potential candidate for liquid biopsies in the diagnosis and prognosis of cancer patients. Furthermore, the review highlights the use of machine learning algorithms to analyze complex EV datasets and create more robust models for biomarker discovery. Early cancer detection and accurate monitoring are crucial to ensure increased patient survival. Recent research has focused on developing non-invasive biomarkers to diagnose cancer early and monitor disease progression at low cost and risk. Extracellular vesicles (EVs), nanosized particles secreted into extracellular spaces by most cell types, are gaining immense popularity as novel biomarker candidates for liquid cancer biopsy, as they can transport bioactive cargo to distant sites and facilitate intercellular communications. A literature search was conducted to discuss the current approaches for EV isolation and the advances in using EV-associated proteins, miRNA, mRNA, DNA, and lipids as liquid biopsies. We discussed the advantages and challenges of using these vesicles in clinical applications. Moreover, recent advancements in machine learning as a novel tool for tumor marker discovery are also highlighted. [ABSTRACT FROM AUTHOR]