Personalized Identification of Differentially Expressed Modules in Osteosarcoma

BACKGROUND: Osteosarcoma (OS), an aggressive malignant neoplasm, is the most common primary bone cancer mainly in adolescents and young adults. Differentially expressed modules tend to distinguish differences integrally. Identifying modules individually has been crucial for understanding OS mechanisms and applications of custom therapeutic decisions in the future.
MATERIAL AND METHODS: Samples came from individuals were used from control group (n=15) and OS group (n=84). Based on clique-merging, module-identification algorithm was used to identify modules from OS PPI networks. A novel approach – the individualized module aberrance score (iMAS) was performed to distinguish differences, making special use of accumulated normal samples (ANS). We performed biological process ontology to classify functionally modules. Then Support Vector Machine (SVM) was used to test distribution results of normal and OS group with screened modules.
RESULTS: We identified 83 modules containing 2084 genes from PPI network in which 61 modules were significantly different. Cluster analysis of OS using the iMAS method identified 5 modules clusters. Specificity=1.00 and Sensitivity=1.00 proved the distribution outcomes of screened modules were mainly consistent with that of total data, which suggested the efficiency of 61 modules.
CONCLUSIONS: We conclude that a novel pipeline that identified the dysregulated modules in individuals of OS. The constructed process is expected to aid in personalized health care, which may present fruitful strategies for medical therapy.

Keywords: Gene Regulatory Networks, support vector machines