Scholarlink

<p>Alzheimer’s disease (AD) and diabetic nephropathy (DN) share bidirectional pathological links through mechanisms such as metabolic dysregulation, vascular injury, and inflammation. Despite epidemiological evidence of their comorbidity, the molecular basis of their interaction remains unclear. This study aimed to identify shared diagnostic biomarkers and pathways underlying AD and DN using integrated bioinformatics approaches. Gene expression datasets were analyzed to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) networks were employed to pinpoint disease-associated modules. Functional enrichment, machine learning (LASSO, SVM-RFE, Random Forest), and receiver operating characteristic (ROC) analyses identified hub genes. Gene Set Enrichment Analysis (GSEA) and immune infiltration profiling via CIBERSORT elucidated pathway and immune correlations. A total of 57 shared genes were identified, enriched in extracellular matrix remodeling, cytoskeletal stability, and phagocytosis. Machine learning highlighted TUBB and VCAN as key diagnostic biomarkers. ROC analysis demonstrated high diagnostic accuracy for AD and DN. GSEA revealed their roles in mitochondrial dysfunction, immune activation, and metabolic pathways. Immune infiltration analysis identified overlapping immune dysregulation in both diseases. TUBB and VCAN correlated distinctly with immune cells: in AD, TUBB associated with CD8 + T cells, while VCAN linked to M1 macrophages; in DN, both genes positively correlated with mast cells and macrophages. This study identifies TUBB and VCAN as shared diagnostic biomarkers for AD and DN, implicating immune-metabolic-structural crosstalk in their comorbidity. These findings provide novel insights into therapeutic targeting of common pathways, offering potential strategies for dual disease management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning driven identification of shared biomarkers in alzheimer’s disease and diabetic nephropathy: toward dual-disease pathogenesis and diagnosis

  • 期刊论文

  • 发表日期：

    2026年3月14日

查看全文
  • Mengda Liang,
  • Fangfang Yin,
  • Jizhi Tong,
  • Wen Zong,
  • Ningkang Xie,
  • XuTong Shi,
  • Jingjun Yang,
  • Shaowu Lv,
  • Duo Xiao,
  • Juxin Yin

摘要

Alzheimer’s disease (AD) and diabetic nephropathy (DN) share bidirectional pathological links through mechanisms such as metabolic dysregulation, vascular injury, and inflammation. Despite epidemiological evidence of their comorbidity, the molecular basis of their interaction remains unclear. This study aimed to identify shared diagnostic biomarkers and pathways underlying AD and DN using integrated bioinformatics approaches. Gene expression datasets were analyzed to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) networks were employed to pinpoint disease-associated modules. Functional enrichment, machine learning (LASSO, SVM-RFE, Random Forest), and receiver operating characteristic (ROC) analyses identified hub genes. Gene Set Enrichment Analysis (GSEA) and immune infiltration profiling via CIBERSORT elucidated pathway and immune correlations. A total of 57 shared genes were identified, enriched in extracellular matrix remodeling, cytoskeletal stability, and phagocytosis. Machine learning highlighted TUBB and VCAN as key diagnostic biomarkers. ROC analysis demonstrated high diagnostic accuracy for AD and DN. GSEA revealed their roles in mitochondrial dysfunction, immune activation, and metabolic pathways. Immune infiltration analysis identified overlapping immune dysregulation in both diseases. TUBB and VCAN correlated distinctly with immune cells: in AD, TUBB associated with CD8 + T cells, while VCAN linked to M1 macrophages; in DN, both genes positively correlated with mast cells and macrophages. This study identifies TUBB and VCAN as shared diagnostic biomarkers for AD and DN, implicating immune-metabolic-structural crosstalk in their comorbidity. These findings provide novel insights into therapeutic targeting of common pathways, offering potential strategies for dual disease management.