Research Paper Volume 15, Issue 8 pp 2863—2876

Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform

Andrea Olsen1, *, , Zachary Harpaz1,2, *, , Christopher Ren3, *, , Anastasia Shneyderman4, , Alexander Veviorskiy4, , Maria Dralkina4, , Simon Konnov4, , Olga Shcheglova4, , Frank W. Pun4, , Geoffrey Ho Duen Leung4, , Hoi Wing Leung4, , Ivan V. Ozerov4, , Alex Aliper4, , Mikhail Korzinkin4, , Alex Zhavoronkov4, ,

  • 1 The Youth Longevity Association, Sevenoaks, NA, United Kingdom
  • 2 Pine Crest School Science Research Department, Fort Lauderdale, Florida 33334, USA
  • 3 Shanghai High School International Division, Shanghai 200231, China
  • 4 Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
* Equal contribution

Received: February 7, 2023       Accepted: April 9, 2023       Published: April 26, 2023
How to Cite

Copyright: © 2023 Olsen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Glioblastoma Multiforme (GBM) is the most aggressive and most common primary malignant brain tumor. The age of GBM patients is considered as one of the disease's negative prognostic factors and the mean age of diagnosis is 62 years. A promising approach to preventing both GBM and aging is to identify new potential therapeutic targets that are associated with both conditions as concurrent drivers. In this work, we present a multi-angled approach of identifying targets, which takes into account not only the disease-related genes but also the ones important in aging. For this purpose, we developed three strategies of target identification using the results of correlation analysis augmented with survival data, differences in expression levels and previously published information of aging-related genes. Several studies have recently validated the robustness and applicability of AI-driven computational methods for target identification in both cancer and aging-related diseases. Therefore, we leveraged the AI predictive power of the PandaOmics TargetID engine in order to rank the resulting target hypotheses and prioritize the most promising therapeutic gene targets. We propose cyclic nucleotide gated channel subunit alpha 3 (CNGA3), glutamate dehydrogenase 1 (GLUD1) and sirtuin 1 (SIRT1) as potential novel dual-purpose therapeutic targets to treat aging and GBM.


AI: artificial intelligence; CNGA3: cyclic nucleotide gated channel subunit alpha 3; EGFR: epidermal growth factor receptor; Ivy GAP: Ivy Glioblastoma Atlas Project; IDH1: isocitrate dehydrogenase; KOLs: key opinion leaders; Log FC: logarithmic fold-changes; GBM: glioblastoma multiforme; GLUD1: glutamate dehydrogenase 1; SIRT1: sirtuin 1; TTF: tumor-treating fields.