Systematic RNA-SEQ based identification of Sarcoma Biomarkers and their miRNA regulatory circuits

Author: Anne E. Sarver - University of Minnesota, Surgery

Co-Authors: Aaron L. Sarver - University of Minnesota, Masonic Cancer Center; Venugopal Thayanithy - University of Minnesota, Surgery; Subbaya Subramanian - University of Minnesota, Surgery

Abstract: Human sarcomas are a heterogenous group of over 50 different subtypes that are broadly classified into bone and soft tissue sarcomas. Their heterogeneity and relative rarity have made them challenging targets for classification, biomarker identification, and development of improved treatment strategies. Thirty-five tissue samples comprising of 14 different types of sarcoma along with normal bone and muscle tissues, were analyzed by RNA Sequencing. Unique mRNA expression signatures were detected for each sarcoma subtype and the signatures were further subjected to bioinformatic Functional Analysis, Upstream Regulator Analysis, and microRNA Targeting Analysis. Significantly upregulated genes and their deduced upstream regulators included both "known players" and, interestingly, one or more "novel candidates" that had not been previously reported to be associated with the sarcoma subtype. We integrated our expression profiles with an extensive Sarcoma microRNA Expression Database (SMED), and were able to deduce potential key microRNA-gene regulator relationships for each sarcoma subtype. In addition, we are identifying transcribed fusion events and cataloging the expression of long non-coding RNAs in each of these sarcoma tissues. In conclusion, despite the small number of samples per sarcoma subtype, we were able to identify key known players and concurrently discover novel genes that may prove to be important players in the molecular classification and development of novel treatments for sarcoma.