title: Exploring targeted therapy of osteosarcoma using proteomics data.
abstract: Despite multimodal therapeutic treatments of osteosarcoma (OS), some patients develop resistance to currently available regimens and eventually end up with recurrent or metastatic outcomes. Many attempts have been made to discover effective drugs for improving outcome; however, due to the heterogeneity of the disease, new therapeutic options have not yet been identified. This study aims to explore potential targeted therapy related to protein profiles of OS. In this review of proteomics studies, we extracted data on differentially expressed proteins (DEPs) from archived literature in PubMed and our in-house repository. The data were divided into three experimental groups, DEPs in 1) OS/OB: OS vs osteoblastic (OB) cells, 2) metastasis: metastatic vs non-metastatic sublines plus fresh tissues from primary OS with and without pulmonary metastasis, and 3) chemoresistance: spheroid (higher chemoresistance) vs monolayer cells plus fresh tissues from biopsies from good and poor responders. All up-regulated protein entities in the list of DEPs were sorted and cross-referenced with identifiers of targets of US Food and Drug Administration (FDA)-approved agents and chemical inhibitors. We found that many targets of FDA-approved antineoplastic agents, mainly a group of epigenetic regulators, kinases, and proteasomes, were highly expressed in OS cells. Additionally, some overexpressed proteins were targets of FDA-approved non-cancer drugs, including immunosuppressive and antiarrhythmic drugs. The resulting list of chemical agents showed that some transferase enzyme inhibitors might have anticancer activity. We also explored common targets of OS/OB and metastasis groups, including amidophosphoribosyltransferase (PPAT), l-lactate dehydrogenase B chain (LDHB), and pyruvate kinase M2 (PKM2) as well as the common target of all categories, cathepsin D (CTSD). This study demonstrates the benefits of a text mining approach to exploring therapeutic targets related to protein expression patterns. These results suggest possible repurposing of some FDA-approved medicines for the treatment of OS and using chemical inhibitors in drug screening tests.
authors.affiliation: Orthopedic Laboratory and Research Netting Center, Department of Orthopedics.
authors.forename: Parunya
authors.initials: P
authors.lastname: Chaiyawat
authors.affiliation: Department of Pathology, Faculty of Medicine.
authors.forename: Jongkolnee
authors.initials: J
authors.lastname: Settakorn
authors.affiliation: Orthopedic Laboratory and Research Netting Center, Department of Orthopedics.
authors.forename: Apiruk
authors.initials: A
authors.lastname: Sangsin
authors.affiliation: Orthopedic Laboratory and Research Netting Center, Department of Orthopedics.
authors.forename: Pimpisa
authors.initials: P
authors.lastname: Teeyakasem
authors.affiliation: Orthopedic Laboratory and Research Netting Center, Department of Orthopedics.
authors.forename: Jeerawan
authors.initials: J
authors.lastname: Klangjorhor
authors.affiliation: Department of Pathology, Faculty of Medicine.
authors.forename: Aungsumalee
authors.initials: A
authors.lastname: Soongkhaw
authors.affiliation: Orthopedic Laboratory and Research Netting Center, Department of Orthopedics; Excellence Center in Osteology Research and Training Center, Chiang Mai University, Chiang Mai, Thailand.
authors.forename: Dumnoensun
authors.initials: D
authors.lastname: Pruksakorn
keywords: FDA-approved drugs; osteosarcoma; proteomics; targeted therapy; text mining