ايجاد شبكه هم بياني ژني مرتبط با ديابت نوع دو و ريسك ابتلا به گليوما

Introduction: Gliomas, brain primary tumors, are known as the most common central nervous system tumors and the most lethal primary brain tumors. In gliomas, increasing morbidity and mortality rates have been reported, and the chance of patients’ survival is low, especially in glioblastoma. For these brain tumors, the existing diagnostic and treatment methods are not effective. A more accurate identification of the molecular profile of these tumors is needed to develop targeted treatments and personalized medicine related to glioma. One way to better understand the tumor molecular profile is to eva‎luate its relationship with risk factors. Type 2 diabetes (T2D), a complex metabolic disease, is a risk factor for various cancers through various mechanisms. Regarding the relationship between glioma and T2D, there are conflicting reports, none of which were based on a systematic and molecular approach. In this research, the relationship between the expression profile of glioma tumors and type 2 diabetes was eva‎luated in two bioinformatics and laboratory phases. Materials and methods: In the bioinformatics phase, single-cell and bulk RNA sequencing data were used and the transcriptomic relationship between the two diseases was eva‎luated by differential gene expression analysis and co-expression networks. The results were analyzed with downstream analyses including GSEA. In the laboratory phase, the expression of selected genes in peripheral blood mononuclear cells (PBMC) of people in healthy, grade II to IV glioma, and type two diabetes groups was investigated by realtime qPCR method, and its significance was examined with statistical methods. Results: In the analysis of single cell data, 34 genes were found which probably caused the formation of the relationship between T2D and glioma. The most significant ones were VCAN and PLBD1. The role of VCAN was confirmed in bulk data analysis. In the laboratory phase, the PLBD1 gene didn’t show a significant increase in expression in any of the groups. The VCAN gene showed a significant increase in glioma (p-value = 0.0039) and T2D (p-value = 0.001) groups. Conclusion: In this study, the existence of a transcriptomic relationship between T2D and glioma was proven. One of the most significant genes involved in this relationship is the VCAN gene. Our results suggest that this gene exerts its effect through the induction of glycolysis pathways in tumor cells and angiogenesis in the tumor microenvironment or by increasing the invasion of the immune system into the tumor. Therefore, T2D, in different people, can cause tumor malignancy or the immune system overcome the tumor. The present study, with a systems' biology approach, created a deeper understanding of the relationship between T2D and glioma at the level of gene co-expression networks. The results of this study can help to identify more precise treatment targets in glioma patients, especially glioma patients with diabetes.

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