Identification of potential extracellular vesicle protein markers altered in osteosarcoma from public databases.

Purpose: Extracellular vesicles (EVs) have become promising biomarkers for cancer management. Particularly, the molecular cargo such as proteins carried by EVs are similar to their cells of origin, providing important information that can be used for cancer diagnostics, prognosis, and treatment monitoring. However, to date, molecular analysis on EVs is still challenging, limited by the availability of efficient analytical technologies, largely due to the small size of EVs. In this work, we developed a computational workflow for in silico identification of potential EV protein markers from genomic and proteomic databases, and applied it for the discovery of osteosarcoma (OS) EV protein markers.

Experimental design: Both mRNA and protein data were computed and compared from publicly accessible databases, and top markers with high differential expression levels were selected.

Results: Thirty nine markers were identified overexpressed and seven found to be downregulated. These identified markers have been found to be associated with OS on different aspects in literature, demonstrating the usability of this workflow.

Conclusions and clinical relevance: This work provides a list of potential EV protein markers that are either overexpressed or downregulated in OS for further experimental validation for improved clinical management of OS.