• Identification of Potential Prognostic Biomarkers 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 astrocytoma is a subtype of diffuse and infiltrative glioma that has uncertain clinical behavior and prognosis for the patient. Surgery and radiotherapy are the most frequently used forms of treatment. Nevertheless, patient survival rates vary. The current prognostic assessment is on the integrated use of molecular biomarkers, particularly IDH mutants, and 1p/19q deletions, along with histopathological grading. However, all these molecular changes do not necessarily correlate with the evolution of the disease or the effectiveness of treatment. The multidimensional nature of glioma pathogenesis supports the need to apply interdisciplinary measures that do not rely exclusively on measures of one gene or one protein. Systems biology is one of such frameworks that consider tumors to be dynamic molecular networks, as opposed to an isolated structure. With the assistance of massive omics sources, such as transcriptomics and proteomics, we can reconstitute biological networks and provide a deeper understanding of disease. A network-based approach, in the case of Grade II astrocytoma, would be useful in identifying important regulatory hub genes that regulate tumor growth, invasion, and treatment resistance. Because of their central locations in molecular networks, such hubs genes have increased chances of revealing the real biological features of the tumor.
  • Methods: To provide a list of differentially expressed genes (DEGs), the GEO database was searched, and the dataset GSE108474 (array type: HG-U133Plus2), which included 28 normal and 65 tumor (astrocytoma grade II) samples, was downloaded. A threshold of |logFC| increased above 1 and adjusted p-values set to less than 0.05 were used to identify DEGs. The robust multi-array analysis (RMA) method was used to normalize data, and the limma package (version 3.62.2) was used on R (version 4.4.3) to conduct differential expression analysis. The clusterProfiler package (version v4.14.6) provided in R (version v4.4.3) applied the three GO terms: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) to functional enrichment analysis of DEGs. On the basis of the minimum adjusted p-value, the three most substantially enriched terms in each group were chosen. STRING was used to reconstruct a protein-protein interaction (PPI) network of DEGs based on the STRING (version 12.0) web server at https://string-db.org/ and network visualization done in Cytoscape (version 3.10.3). According to the cytoHubba (version 0.1) plug-in on Cytoscape, the top 10 MCC score hub genes were chosen as possible prognostic biomarkers. Lastly, the prognostic value of hub genes was assessed on the GEPIA web server, which is accessible at http://gepia.cancerpku.cn, to analyze overall survival (OS) and disease-free survival (DFS) of low-grade glioma (LGG) patients, and their association with patient prognosis was confirmed when the log-rank p-value was less than 0.05.
  • Results: In our study, 1787 DEGs were identified, including 641 up-regulated genes and 1146 down-regulated genes. GO term enrichment analysis indicated that in the GO biological process, DEGs were enriched in vesicle-mediated transport in synapse, synaptic vesicle cycle, and regulation of synapse structure or activity. In the GO molecular function, the top terms were neuron-to-neuron synapse, gated channel activity, and voltage-gated monoatomic ion channel activity. In the GO cellular component, the most significant terms included calmodulin binding, synaptic membrane, and postsynaptic specialization. The 10 hub genes were then picked following analysis of the protein-protein interaction network of DEGs. As a result, SYN1, SNAP25, and VAMP2 were the final proposed biomarkers after being evaluated in GEPIA.
  • Conclusion: This study identified three promising biomarkers with high prognostic potential in grade II astrocytoma through a comprehensive network-based systems biology analysis of microarray expression data. Survival analysis highlighted the relevance of these candidates as reliable prognostic indicators and provided a basis for future development of targeted therapies and advancing the foundation for more personalized treatment approaches.
  • Keywords: Astrocytoma Grade II, Prognostic Biomarkers, Systems Biology, DEGs, PPI