| Summary: | This study uncovered differential gene expression in blood to distinguish subjects with probable Alzheimer’s disease (AD) from normal elderly participants (non-demented controls, NDC). The participants were recruited via training (Phase 1) and validation cohorts (Phase 2). The changes of gene expression in blood samples from the training cohort (92 AD vs 92 NDC) were assessed using the microarray technology. The Partial Least Square Discrimination Analysis (PLSDA) was then used to develop a disease classifier algorithm (accuracy = 88.3%). Six differentially expressed genes were validated through RT-qPCR using blood samples from the validation cohort [(25 AD, 25 NDC, 12 mild cognitive impairment (MCI) and 12 vascular dementia (VaD) subjects] . The PLSDA model indicated a good separation between AD and NDC [area under the receiver operating characteristic curve (ROC AUC) = 0.88]. ABCA9, CNOT8, SESN1, UCP3, MAP2K1 and DDIT4 were found to be differentially expressed between the two groups. Validation of the panel of six genes gave an overall accuracy of 82.0% (AUC=0.86). The ABCA9 mRNA level, which was significantly (p < 0.05) lower in the AD group, correctly classified 90.9% of all subjects (AUC=0.94). This group of genes may be responsible for dysregulation of pathways related to inflammation, mitochondrial dysfunction, oxidative injury, DNA damage, apoptosis and lipid metabolism. The disease classifier algorithm discriminated probable AD from MCI and VaD at specificity of 83.3% and 75.0%, respectively. These findings warrant further validation of potential blood-based biomarkers in larger samples of clinical AD. © 2023 by Samsudin et al.
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