Imagine Alzheimer’s disease as a complex puzzle waiting to be solved. In our study, we embarked on a journey to uncover the inner workings of this enigmatic condition. By analyzing data from three different genetics databases, we aimed to identify the crucial genes and pathways associated with Alzheimer’s. Like detectives gathering clues, we searched for specific keywords, selected the appropriate species, and ensured an adequate sample size. We then divided the datasets into training and test groups, allowing us to build a model and verify its accuracy. Through advanced techniques like weighted gene co-expression network analysis (WGCNA) and LASSO regression, we pinpointed the genes that play a pivotal role in Alzheimer’s. These genes were further analyzed using tools like Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genome Pathways (KEGG). As we uncovered the secrets locked within the genes, a clearer picture emerged. Among the 111 common differential AD genes we discovered, terms like cognition, learning, and memory stood out in the GO analysis. In addition, the KEGG analysis unveiled three key pathways: neuroactive ligand-receptor interaction, cAMP signaling pathway, and Calcium signaling pathway. Finally, we identified three feature genes – SST, MLIP, and HSPB3 – which could potentially serve as biomarkers for predicting disease onset and progression. With these exciting findings, we hope to inspire further exploration into Alzheimer’s disease.

While Alzheimer’s disease (AD) can cause a severe economic burden, the specific pathogenesis involved is yet to be elucidated. To identify feature genes associated with AD, we downloaded data from three GEO databases: GSE122063, GSE15222, and GSE138260. In the filtering, we used AD for search keywords, Homo sapiens for species selection, and established a sample size of > 20 for each data set, and each data set contains Including the normal group and AD group. The datasets GSE15222 and GSE138260 were combined as a training group to build a model, and GSE122063 was used as a test group to verify the model’s accuracy. The genes with differential expression found in the combined datasets were used for analysis through Gene Ontology (GO) and The Kyoto Encyclopedia of Genes and Genome Pathways (KEGG). Then, AD-related module genes were identified using the combined dataset through a weighted gene co-expression network analysis (WGCNA). Both the differential and AD-related module genes were intersected to obtain AD key genes. These genes were first filtered through LASSO regression and then AD-related feature genes were obtained for subsequent immune-related analysis. A comprehensive analysis of three AD-related datasets in the GEO database revealed 111 common differential AD genes. In the GO analysis, the more prominent terms were cognition and learning or memory. The KEGG analysis showed that these differential genes were enriched not only in In the KEGG analysis, but also in three other pathways: neuroactive ligand-receptor interaction, cAMP signaling pathway, and Calcium signaling pathway. Three AD-related feature genes (SST, MLIP, HSPB3) were finally identified. The area under the ROC curve of these AD-related feature genes was greater than 0.7 in both the training and the test groups. Finally, an immune-related analysis of these genes was performed. The finding of AD-related feature genes (SST, MLIP, HSPB3) could help predict the onset and progression of the disease. Overall, our study may provide significant guidance for further exploration of potential biomarkers for the diagnosis and prediction of AD.

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