胶质纤维酸性蛋白
生物
蛋白质酪氨酸磷酸酶
神经科学
免疫失调
神经炎症
生物标志物
表型
疾病
炎症
免疫系统
免疫学
病理
细胞生物学
信号转导
基因
医学
遗传学
免疫组织化学
作者
Jingyue Huang,Xinping Pang,Hongmei Yang,Chonghao Gao,Dongxiao Wang,Yue Sun,Yezi Taishi,Chao-Yang Pang
标识
DOI:10.2174/0115672050333760241010061547
摘要
Introduction: Alzheimer's disease (AD) is a complex neurological disorder that progressively worsens. Although its exact causes are not fully understood, new research indicates that genes related to non-neuronal cells change significantly with age, playing key roles in AD's pathology. METHOD: This study focuses on a protein network centered on Glial Fibrillary Acidic Protein (GFAP) and Protein Tyrosine Phosphatase Receptor Type C (PTPRC). Method: This study focuses on a protein network centered on Glial Fibrillary Acidic Protein (GFAP) and Protein Tyrosine Phosphatase Receptor Type C (PTPRC). The Key Findings of this Study Include: 1. A significant correlation was observed between GFAP and PTPRC expression throughout AD progression, which links closely with clinical phenotypes and suggests their role in AD pathology. 2. A molecular network centered on GFAP and PTPRC, including Catenin Beta 1 (CTNNB1) and Integrin Beta 2 (ITGB2), showed distinct changes in interactions, highlighting its regulatory role in AD. 3. Analysis of GSE5281 data revealed a decline in the interaction strength within this network, pointing to potential desynchronization as a biomarker for AD. 4. SVM diagnostic models comparing GFAP expression and coupling values confirmed this desynchronization, suggesting it worsens with AD progression. Result: Based on these findings, it is hypothesized that as AD progresses, the GFAP- and PTPRCcentered molecular framework undergoes significant changes affecting key biological pathways. These changes disrupt immune regulation and cellular functions, increasing immune cell activation and inflammation in the brain. This may impair neuronal communication and synaptic functionality, exacerbating AD's pathology. Conclusion: To verify these findings, Support Vector Machine (SVM) diagnostic models and correlation analyses were used to examine changes in this network, indicating that its dysregulation significantly affects AD progression.
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