بررسي ژن MERTK به عنوان يك نشانگر زيستي در ديابت نوع 2

Abstract Introduction: Type 2 diabetes (T2D) is one of the most common metabolic disorders worldwide an‎d affects a large population. It leads to diverse complications an‎d imposes substantial economic an‎d social burdens on societies. Its insidious progression an‎d late diagnosis are among the main causes of irreversible complications in patients. T2D is a multifactorial disease in which genetics plays a crucial role, underscoring the need for genetic investigations. In this study, we employed bioinformatics analyses to identify a biomarker associated with T2D. Materials an‎d Methods: In the bioinformatics phase, bulk RNA-seq datasets from individuals with T2D an‎d healthy controls were downloaded an‎d analyzed in a Linux environment using bioinformatics tools. Differential gene expression was assessed with DESeq2, an‎d a gene co-expression network was reconstructed using WGCNA; key genes implicated in the disease were then identified. To investigate the biological functions of these key genes, Gene Ontology enrichment analysis was performed. In the experimental phase, the expression of the can‎didate gene was quantified between patient an‎d control groups using real-time PCR. Results: Differential expression analysis revealed 2,568 genes with significant expression changes in disease relative to normal. In the co-expression analysis, the pink module was selec‎ted as the key disease-associated module. Integrating the differentially expressed genes with the co-expression network an‎d functional pathway analyses identified MERTK as the key gene. Experimental assays showed that MERTK expression was significantly decreased in T2D compared with healthy controls. Conclusion: This study suggests that MERTK expression may serve as a biomarker for T2D. However, larger an‎d more comprehensive studies are required to further eva‎luate an‎d validate the role of this gene as a biomarker in this disease. Keywords: Type 2 diabetes, Differential expression, Co-expression network, WGCNA, Real-time PCR.

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