What question did this study set out to answer?

This research aims to uncover molecular mechanisms underlying region-specific vulnerability in Alzheimer’s disease through transcriptomic analysis.

June 3, 2026Open Access

Region-specific Transcriptomic Signatures in Alzheimer’s Disease: A Meta-analysis of Vulnerable Brain Regions Reveals MicroRNA–hub Gene Regulatory Networks

Key Points

This research aims to uncover molecular mechanisms underlying region-specific vulnerability in Alzheimer’s disease through transcriptomic analysis.
Conducted a meta-analysis of transcriptomic datasets from affected brain regions in early-to-moderate Alzheimer’s disease.
Analyzed seven mRNA and one microRNA microarray studies with preprocessing techniques including normalization and batch correction.
Defined differentially expressed genes as those with false discovery rate <0.05 and |logFC| ≥ 1.23 (genes) or ≥ 2 (miRNAs).
Identified 172 differentially expressed genes (122 upregulated, 50 downregulated) and 82 significant miRNAs.
Hub genes included ITPKB, DTNA, and RGS4, with functional enrichment indicating involvement in calcium signaling and neuroinflammation.
RGS4 showed moderate predictive ability with AUC = 0.70, while ITPKB had AUC = 0.40, highlighting the limitations of single-gene classifiers.

Abstract

Abstract Background: Alzheimer’s disease (AD) is characterized by progressive neurodegeneration in regionally vulnerable brain areas, yet molecular insights into early pathogenic mechanisms remain limited. Methods: We conducted a meta-analysis of transcriptomic datasets from brain regions affected in early-to-moderate AD – including entorhinal cortex, CA1 hippocampus, angular gyrus, and frontal cortex synaptoneurosomes – using data from seven mRNA and one microRNA (miRNA) microarray studies (GSE16759, GSE110226, GSE37264, GSE26972, GSE36980, GSE37263, GSE39420, and GSE157239). Preprocessing included background correction, log 2 transformation, quantile normalization, and batch correction via ComBat. Differentially expressed features were defined as false discovery rate <0.05 and | logFC| ≥ 1.23 (genes) or ≥ 2 (miRNAs). Results: We identified 172 differentially expressed genes (122 upregulated and 50 downregulated) and 82 significant miRNAs. Hub genes included Inositol-trisphosphate 3-kinase B ( ITPKB ), Synaptotagmin 1, Dystrobrevin alpha ( DTNA ), X Inactive Specific Transcript, and Regulator of G protein signaling 4 ( RGS4 ). Functional enrichment highlighted calcium signaling, synaptic failure, and neuroinflammation. Notably, hsa-miR-30d-5p was predicted to target both ITPKB and DTNA , suggesting a regulatory axis linking miRNA dysregulation to calcium dyshomeostasis. Receiver operating characteristic analysis revealed that only RGS4 showed moderate discriminative capacity (area under the curve AUC =0.70), while other hub genes (e.g., ITPKB , AUC = 0.40) exhibited below-chance performance, underscoring the limitations of single-gene classifiers in postmortem tissue. Conclusion: This study provides mechanistic hypotheses – rather than diagnostic biomarkers – by uncovering region-specific, miRNA-mediated regulatory networks in AD-affected brain tissues. Future validation in accessible biofluids is essential before clinical translation.

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