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

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.