CCN3 Proteins as a diagnostic marker in osteosarcoma patients: A case control study

The role of CCNs in controlling cellular communication in the tumor microenvironment

The Cellular communication network (CCN) family of growth regulatory factors comprises six secreted matricellular proteins that promote signal transduction through cell-cell or cell-matrix interaction. The diversity of functionality between each protein is specific to the many aspects of healthy and cancer biology. For example, CCN family proteins modulate cell adhesion, proliferation, migration, invasiveness, apoptosis, and survival. In addition, the expression of each protein regulates many biological and pathobiological processes within its microenvironment to regulate angiogenesis, inflammatory response, chondrogenesis, fibrosis, and mitochondrial integrity. The collective range of CCN operation remains fully comprehended; however, understanding each protein's microenvironment may draw more conclusions about the abundance of interactions and signaling cascades occurring within such issues. This review observes and distinguishes the various roles a CCN protein may execute within distinct tumor microenvironments and the biological associations among them. Finally. We also review how CCN-family proteins can be used in nano-based therapeutic implications.

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.