In silico functional analyses and discovery of survivalassociated microRNA signatures in pediatric osteosarcoma
Patricia C. Sanchez-Diaz1,6, Tzu-Hung Hsiao1, Yi Zou1, Aaron J. Sugalski2, Josefine Heim-Hall3,4, Yidong Chen1,4,5, Anne-Marie Langevin2,4, and Jaclyn Y. Hung1,2,4
1 Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
2 Division of Hematology and Oncology, Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
3 Department of Pathology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
4 Cancer Therapy and Research Center, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
5 Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
6 Current address: Rosenberg School of Optometry, University of the Incarnate Word, San Antonio, Texas, USA
Jaclyn Y. Hung, email:
Yidong Cheng, email:
Keywords: osteosarcoma, microRNA expression, prognosis, pathways, pediatric cancers
Received: August 20, 2014 Accepted: September 17, 2014 Published: September 23, 2014
Purpose: Osteosarcoma is the most common bone tumor in children, adolescents, and young adults. In contrast to other childhood malignancies, no biomarkers have been consistently identified as predictors of outcome. This study was conducted to assess the microRNAs(miRs) expression signatures in pre-treatment osteosarcoma specimens and correlate with outcome to identify biomarkers for disease relapse.
Results: A 42-miRs signature whose expression levels were associated with overall and relapse-free survival was identified. There were 8 common miRs between the two sets of survival-associated miRs. Computational analyses of these survival-associated miRs suggested that they might regulate genes involved in ubiquitin proteasome system, TGFb, IGF, PTEN/AKT/mTOR, MAPK, PDGFR/RAF/MEK/ERK, and ErbB/HER pathways.
Methods: The cohort consisted of 27 patients of 70% Mexican-American ethnicity. High-throughput RT-qPCR approach was used to generate quantitative datasets of expression of 754 miRs in the human genome. We examined tumor recurrence status, survival time and their association with miR expression levels by Cox proportional hazard regression analysis. TargetScan was used to predict miR/genes interactions and computational analyses: KEGG, BioCarta, Gene Ontology were applied to these potential targets to predict deregulated pathways.
Conclusions: Our findings suggested that these miRs might be potentially useful as prognostic biomarkers and therapeutic targets in pediatric osteosarcoma.