Drug Development Candidates Identification in Grade II Astrocytoma: A Systems Biology Approach

Drug Development Candidates Identification in Grade II Astrocytoma: A Systems Biology Approach
Hamid Dehghani,¹ Shirin Farivar,^2,*
1. Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University
2. Department of Cell and Molecular Biology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University
Introduction: Grade II astrocytomas are a subgroup of low-grade glioma that have slow growth but have a high probability of developing more aggressive forms of tumors. Even with breakthroughs in neuro-oncology, the existing therapeutic approaches are still limited by the frequent tumor relapse, underlying and emergent drug resistance, and strong molecular heterogeneity of the tumor microenvironment. The mentioned challenges emphasize the exigency of finding critical molecular targets that may serve as a basis for deriving precisely directed therapeutic approaches. Systems biology offers an effective paradigm to tackle these various complications via the combination of high-throughput transcriptomic data and protein-protein interaction PPI networks to potentially identify central regulatory genes and signaling pathways of tumorigenesis. Network-based data analysis tools are used to identify the hub genes, molecular nodes with numerous connections and regulatory powers, which have good measures to get to drug development and targeted therapy applications.
Methods: In order to identify differentially expressed genes (DEGs), the GEO database was searched, and the GSE108474 dataset (array type: HG-U133Plus2), which consists of 28 normal brain samples and 65 astrocytoma grade II samples, was chosen. The cutoff for the DEGs was set to the log FC more than 1 and an adjusted p-value less than 0.05. The robust multi-array analysis (RMA) was used to preprocess and normalize the data, and the limma package (version 3.62.2) in R (version 4.4.3) was used to compute the differential expression analysis. The DEGs protein-protein interaction (PPI) network was built with the help of the STRING database (12.0; https://string-db.org/), and the network visualization was done in Cytoscape (version 3.10.3). The cytoHubba plugin (version 0.1) within Cytoscape was then used in order to conduct hub genes. The top 10 hub genes on degree and the top 10 hub genes on betweenness centrality were determined. The intersection of the best genes according to the two indices was considered to be the core ones with a high probability of being intensively developed into medicines. To investigate the possible biological functions and pathways, gene Ontology (GO) functional enrichment analysis was carried out on the overlapping top-ranked genes with the clusterProfiler package (version 4.14.6) in R (version 4.4.3). Three GO categories that were used during the analysis and reported based on the lowest adjusted p-values were Biological Process (BP), Molecular Function (MF), and Cellular Component (CC).
Results: This research identified 1,787 differentially expressive genes (DEGs), 641 genes were up-regulated, and 1,114 genes was down-regulated. After the protein-protein interaction network and centrality parameters of DEGs were analyzed, MYC, SRC, SMAD3, STAT1, EP300, TP53, CTNNB1, and CD44 were selected as major candidates. GO enrichment analysis revealed that the processes that were the most significant in terms of the GO biological process were regulation of the proliferation of the epithelial cells, cell fate commitment, and gliogenesis. DNA-binding transcription factor binding, SMAD binding, and RNA polymerase II-specific DNA-binding transcription factor binding were the top results in the GO molecular function. The terms that appeared the most important in the GO cellular component were the RNA polymerase II transcription regulator complex, MHC protein complex, and MHC class II protein complex.
Conclusion: In the current study, there were eight high-potential targets in the therapeutic development of grade II astrocytoma discovered by a network-based systems-biology analysis. The presented results offer a list of the molecular targets, creating a solid foundation that cannot only instill the viability of the subsequent experimental validation presentation but also provide a global array of the targeted therapeutic interventions that potentially leads to favorable clinical outcomes.
Keywords: Astrocytoma Grade II, Systems Biology, DEGs, PPI, Hub Genes