آناليز داده‌هاي single-cell RNA-seq مربوط به بافت تومور و خون محيطي بيماران مبتلا به GBM، بازسازي شبكه هم بيان ژني و تاييد آزمايشگاهي بيوماركرهاي كليدي

Analysis of Single-Cell RNA-seq Data from Tumor Tissue an‎d Peripheral Blood of GBM Patients، Reconstruction of Gene Co-expression Network an‎d Laboratory Validation of Key Biomarkers

زيست شناسي سلولي مولكولي و ميكروبيولوژي

Introduction: Glioma is a group of central nervous system tumors that originate from glial cells. Among these, glioblastoma multiforme (GBM) is the most malignant an‎d aggressive type, typically presenting as a rapidly growing mass. This tumor infiltrates healthy brain tissue, causing inflammation an‎d edema around itself, leading to symptoms such as headaches, neurological disorders, an‎d cognitive impairment. Despite stan‎dard treatments including surgery, radiotherapy, an‎d chemotherapy with temozolomide (TMZ), the average survival time of patients is less than 15 months. In this study, to identify effective biomarkers for predicting disease progression an‎d therapeutic response, gene expression changes related to immune cells in the blood an‎d tissue of GBM patients were examined using bioinformatics an‎d laboratory approaches. Materials an‎d Methods: In the bioinformatics phase, RNA sequencing data at both single-cell an‎d bulk levels were utilized. Through differential gene expression analysis an‎d co-expression network analysis, transcriptomic patterns between blood an‎d tissue samples of GBM patients were investigated. In the laboratory phase, the expression levels of selec‎ted genes in peripheral blood mononuclear cells (PBMCs) an‎d brain tissue samples from healthy individuals an‎d GBM patients were measured using Real-Time qPCR. The statistical significance of gene expression changes was eva‎luated using appropriate statistical tests. Results: In the analysis of RNA sequencing data at the single-cell level, 14 genes were identified that could represent the relationship between blood an‎d tissue in GBM patients. Among these genes, two, G0S2 an‎d BASP1, played a significant role. The importance of these two genes was also confirmed in the bulk RNA sequencing data analysis. In the laboratory phase, examining the expression of these two genes in blood an‎d tissue samples of GBM patients compared to healthy individuals showed a significant increase in their expression levels. Conclusion: The findings of this study showed that gene expression patterns in blood an‎d tissue of GBM overlap. Two key genes, G0S2 an‎d BASP1, were identified as biomarkers of this transcriptomic correlation between blood an‎d tissue in GBM. This study, utilizing a systems biology approach, provided deeper insights into the interaction between blood an‎d tissue at the gene co-expression network level.

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