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Exosomes are stable, lipid bilayer-enclosed vesicles capable of crossing biological barriers. They can carry a wide range of molecules required for intercellular communication. Once exosomes are released from the cell where they originated, they enter a recipient cell through various pathways such as fusion, receptor-mediated endocytosis, macropinocytosis, and phagocytosis.
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  1. Home
  3. Tumor-derived Extracellular Vesicles As Liquid Biopsy For Diagnosis And Prognosis Of Solid Tumors: Their Clinical Utility And Reliability As Tumor Biomarkers.
  1. Home
  3. Tumor-derived Extracellular Vesicles As Liquid Biopsy For Diagnosis And Prognosis Of Solid Tumors: Their Clinical Utility And Reliability As Tumor Biomarkers.

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

Prerna Dabral1, Nobel Bhasin2, Manish Ranjan3

  • 1Vitalant Research Institute, University of California San Francisco, San Francisco, CA 94105, USA.

Cancers
|July 13, 2024

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
cargo moleculesextracellular vesiclesisolationliquid biopsytumor biomarkers

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Early cancer detection relies on non-invasive biomarkers like extracellular vesicles (EVs). Research explores EV cargo for liquid biopsies, improving patient survival through early diagnosis and monitoring.

Area of Science:

  • Biomarker Discovery
  • Cancer Diagnostics
  • Nanotechnology

Background:

  • Early cancer detection and monitoring are vital for patient survival.
  • Non-invasive biomarkers are sought for cost-effective and low-risk cancer diagnosis and progression tracking.
  • Extracellular vesicles (EVs) are emerging as promising biomarker candidates for liquid biopsies.

Purpose of the Study:

  • To review current methods for isolating extracellular vesicles (EVs).
  • To discuss the use of EV-associated molecules (proteins, miRNA, mRNA, DNA, lipids) as liquid biopsy markers.
  • To explore the clinical applications, advantages, and challenges of using EVs in cancer diagnostics.

Main Methods:

  • Literature search on extracellular vesicle isolation and analysis.
  • Review of studies utilizing EV cargo for liquid biopsy applications.
  • Examination of machine learning applications in tumor marker discovery.

    • Main Results:

    • Extracellular vesicles (EVs) can be isolated using various techniques.
    • EVs contain proteins, nucleic acids, and lipids suitable for biomarker analysis.
    • Machine learning shows potential for identifying novel tumor markers from EV data.

    Conclusions:

    • Extracellular vesicles (EVs) show significant promise as non-invasive biomarkers for early cancer detection and monitoring.
    • Further research is needed to overcome challenges in EV isolation and standardization for clinical use.
    • Machine learning integration can enhance the discovery of EV-based cancer biomarkers.