Identification of small ubiquitin-related modifier (SUMO)-related genes-based biomarkers in Alzheimer's disease based on bioinformatics analysis

Datasets related to AD (GSE140831, GSE63060), a dementia dataset (GSE140830), and 189 SRGs were retrieved from public databases. Candidate genes were identified by intersecting differentially expressed genes (DEGs) with SRGs. A protein-protein interaction (PPI) network was constructed to select the top 15 core genes, and the support vector machine-recursive feature elimination (SVM-RFE) model identified feature genes. Validation was done using the GSE140831 and GSE63060 datasets, and the nomogram model was assessed by receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis (GSEA) and other analyses were performed. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was used for further validation. Overlapping 189 SRGs and 12,853 DEGs identified 107 candidate genes. Five feature genes were selected using the SVM-RFE algorithm. CREBBP, PIAS1, and TRIM28 were confirmed as AD biomarkers due to their increased expression in AD and strong ROC performance. GSEA highlighted their involvement in pathways such as olfactory transduction, lysosome, and spliceosome. Immune infiltration analysis suggested immune cell involvement in AD progression. Additionally, 21 potential drugs for AD therapy were predicted. RT-qPCR confirmed the over-expression of CREBBP and TRIM28 in AD samples, consistent with dataset trends.