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<p>Despite recent advancements, the development of an efficient and non-invasive early detection approach for Alzheimer’s disease (AD) remains unresolved. The specificity of a diagnostic biomarker is contingent upon its foundation in the molecular basis of the diseases. Immune system dysfunction has a significant role in the genesis and progression of AِِD; thus, it should be included into the formulation of novel treatment and diagnostic strategies. A screening step was conducted through the analysis of a microarray dataset to identify differentially expressed genes (DEGs) and co-expression patterns using weighted gene co-expression network analysis. Subsequently, common genes were discovered and subjected to functional enrichment analysis. Subsequently, during the validation phase, the expression and diagnostic capabilities of candidate genes were evaluated in a group of 50 AD&#xa0;patients. Initially, 269 DEGs&#xa0;were found in the blood of AD&#xa0;patients. Analyzing the co-expression patterns revealed 18 distinct topological modules, with the module exhibiting the highest correlation (blue) selected for further study. A compilation of immune-related genes was extracted from the Immunology Database and Analysis Portal (ImmPort) and cross-referenced with DEGs&#xa0;and genes inside the blue module, as the blue module was found to primarily govern immune response. The anomalous expression of three potential genes—specifically IL17C, TEK, and CCL4—was confirmed in the blood of AD&#xa0;patients by RT-PCR. A biomarker panel consisting of these genes attained an accuracy of 80.2%. The proposed biomarker in this study is based on the immunological response observed in AD&#xa0;and demonstrates high precision in identifying patients.</p>

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Revolutionizing Alzheimer’s Detection: Immune-Related Gene Biomarkers as Non-Invasive Predictors

  • 期刊论文

  • 发表日期：

    2025年4月28日

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  • Samin Abed,
  • Amir Ebrahimi,
  • Fatemeh Fattahi,
  • Mahmoud Shekari-Khaniani,
  • Sima Mansoori Derakhshan

摘要

Despite recent advancements, the development of an efficient and non-invasive early detection approach for Alzheimer’s disease (AD) remains unresolved. The specificity of a diagnostic biomarker is contingent upon its foundation in the molecular basis of the diseases. Immune system dysfunction has a significant role in the genesis and progression of AِِD; thus, it should be included into the formulation of novel treatment and diagnostic strategies. A screening step was conducted through the analysis of a microarray dataset to identify differentially expressed genes (DEGs) and co-expression patterns using weighted gene co-expression network analysis. Subsequently, common genes were discovered and subjected to functional enrichment analysis. Subsequently, during the validation phase, the expression and diagnostic capabilities of candidate genes were evaluated in a group of 50 AD patients. Initially, 269 DEGs were found in the blood of AD patients. Analyzing the co-expression patterns revealed 18 distinct topological modules, with the module exhibiting the highest correlation (blue) selected for further study. A compilation of immune-related genes was extracted from the Immunology Database and Analysis Portal (ImmPort) and cross-referenced with DEGs and genes inside the blue module, as the blue module was found to primarily govern immune response. The anomalous expression of three potential genes—specifically IL17C, TEK, and CCL4—was confirmed in the blood of AD patients by RT-PCR. A biomarker panel consisting of these genes attained an accuracy of 80.2%. The proposed biomarker in this study is based on the immunological response observed in AD and demonstrates high precision in identifying patients.