PandaOmics, an AI-enabled platform for biological target discovery, can be used to identify dual-purpose therapeutic targets that are implicated in aging as well as glioblastoma multiplex.
Glioblastoma multiforme (GBM), the most aggressive primary malignant tumor, is also the most common. GBM is associated with a poor prognosis due to the age of patients. The average age at diagnosis is 62. The identification of new therapeutic targets associated with GBM and ageing as concurrent drivers is a promising way to prevent both. We present in this paper a multi-angle approach to identifying targets that takes into consideration not only disease-related genes, but also those important for aging. In order to achieve this, we developed three different strategies for identifying targets using correlation analysis, augmented by survival data, differences between expression levels, and previously published information on aging-related gene.
Source:
https://www.aging-us.com/article/204678/text
