Integrating multimodal data to identify single nucleotide polymorphism-related biomarkers and regulatory mechanisms...

Alzheimer's disease (AD) is the most common neurodegenerative disease with unclear regulatory mechanisms at the cell-type level. A multi-omics model called single nucleotide polymorphisms (SNPs)-transcriptomic-single-nucleus ribonucleic acid sequencing (snRNA-seq) integration (STSNI) is proposed to identify SNPs-related biomarkers and regulatory mechanisms in AD. Differential expression analysis identified differentially expressed genes (DEGs) between AD patients and healthy controls (HCs) in the GSE118553 dataset. Cell-type annotation in the GSE138852 dataset revealed several cell subclusters, and DEGs were identified within these subclusters. Intersection analysis among DEGs from the GSE118553 dataset, cell-subcluster-specific DEGs from the GSE138852 dataset, and SNP-associated genes from the ADNI2 dataset yielded 14 overlapping genes. Using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms, six biomarkers were identified. Functional enrichment and gene set enrichment analysis (GSEA) linked these biomarkers to pathways such as carboxylic acid catabolic process, exocytic vesicle membrane, and carbon metabolism. Meanwhile, six cell types were identified, including astrocytes, endothelial cells, oligodendrocytes, oligodendrocyte progenitor cells (OPCs), microglia, and neurons. The biomarker-transcription factor (TF) network indicated that Cispb M4676 regulates IQGAP2, NRXN1, GRIA3 and FGF14. Overall, our study identified six SNP-related biomarkers (IQGAP2, HHAT, FGF14, CTNNA3, GRIA3, and NRXN1) associated with AD. The STSNI framework provided novel insights into the cellular and molecular mechanisms underlying AD. Significance Statement: As the global population continues to age, Alzheimer's disease (AD) has emerged as a major public health concern. The pathological changes associated with AD include the formation of extracellular amyloid plaques, intracellular neurofibrillary tangles, and neuronal loss with gliosis proliferation. Bioinformatics methods are used to explore the immune infiltration characteristics, biological pathways and regulatory mechanisms of single nucleotide polymorphisms (SNPs) related key genes in AD. The pathogenesis of AD from the overall level and single-cell level is explored based on SNPs-related genes, combined with snRNA-seq data and transcriptome data. This study provides an opportunity for the discovery of novel diagnostic molecular markers and potential treatment targets to serve as the foundation for the development of more effective management techniques for AD.