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

Abstract

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.

Keywords: GBM; PandaOmics; aging; glioblastoma; target discovery.

Figure 1

Overall workflow of the study. Current pipeline is designed to prioritize dual-purpose therapeutic targets by combining several data modalities following three distinct strategies of target identification. Potential target hypotheses are ranked using AI-driven scores obtained via PandaOmics TargetID engine and information regarding the combined expression, druggability, safety, novelty and accessibility by small molecules.

Figure 2

Correlation analysis. UpSet plots [30] representing the overlap of positively (A) and negatively (B) correlated with age genes across 12 transcriptomic datasets. The combination matrix identifies the intersections, while the bars on top represent the size of each intersection divided into significantly and not significantly (NS) correlated genes. Bars on the left depict the overall amount of correlated genes in each dataset.

Figure 3

Survival analysis results. Each of the genes that is significantly correlated with age (n = 38) was tested for significant or not significant difference in survival rates with respect to the high and low expression levels in young, middle-aged and senior patient cohorts from TCGA-GBM dataset.

Figure 4

PandaOmics TargetID scoring approach for potential targets selected according to strategy 1 (A), strategy 2 (B) and strategy 3 (C). Target hypotheses are ranked according to the scores obtained from different AI-powered predictive models: omics-, text-, key opinion leaders (KOLs) and funding- based. For each target additional information on tissue specific expression, accessibility by small molecules and antibodies, safety, novelty, structure availability, development level and protein family are provided. For a detailed description of all scores and filters see Materials and Methods section, as well as the user manual at https://insilico.com/pandaomics/help.