Integrative analysis of somatic mutations and differential expression profiles in glioblastoma based on aging acceleration.

BACKGROUND Glioblastoma (GBM) is an aggressive brain tumor and the mechanisms of progression are very complex. Accelerated aging is a driving factor of GBM. However, there has not been thorough research about the mechanisms of GBM progression based on aging acceleration. METHODS The aging predictor was modeled based on normal brain samples. Then an aging acceleration background network was constructed to explore GBM mechanisms. RESULTS The accelerated aging-related mechanisms provided an innovative way to study GBM, wherein integrative analysis of somatic mutations and differential expression revealed key pathologic characteristics. Moreover, the influence of the immune system, the nervous system and other critical factors on GBM were identified. The survival analysis also disclosed crucial GBM markers. CONCLUSION An integrative analysis of multi-omics data based on aging acceleration identified new driving factors for GBM.