Identification of diagnostic biomarkers in Alzheimer’s disease using metabolic-pathway related genes. (P10-6.011)

Abstract

<h3>Objective:</h3> In this study, we aim to study the role of metabolic pathways in AD and to conduct a bioinformatics analysis and gene ontology to understand the candidate metabolic pathway genes in the pathogenesis of AD, and to construct a machine learning diagnostic model using those genes. <h3>Background:</h3> Alzheimer’s disease (AD) is the most common form of dementia. It is known that the metabolic pathways are altered in AD. So, we investigated their mechanisms to aid in finding a therapeutic targets for patients with AD. <h3>Design/Methods:</h3> We used (GSE159541, GSE104704, GSE15222, GSE5281) datasets from Gene Expression Omnibus database to find the differentially expressed genes (DEGs). Then we analyzed the metabolic pathway-related genes from the Molecular Signature database (MSigDB) and intersected them with the DEGs. We then constructed a machine learning diagnostic model using Support vector machine (SVM) and recursive feature elimination (RFE) to select the top 5 ranking genes. Then we fitted them in a random forest classification (RFC) to assess their performance. <h3>Results:</h3> We identified 2054 DEGs and intersected them with metabolic pathway-related genes resulting in 72 metabolic genes. Then we fitted them into a SVM and used RFE to choose the top 5 ranked genes which were (ENOPH1, PSMA2, PSMB2, TXN2, UROD). Among those genes, UROD was the highest contributing gene. Those genes were fitted into an RFC which was trained to predict the diagnosis of patients in a dataset of 343 total patients. The performance of the RFC model on the testing set was measured by a mean bootstrap estimate of 0.711 with 95% CI: [0.58–0.83], 10-fold cross-validation of 0.72, and AUC of 0.70. <h3>Conclusions:</h3> We identified 5 metabolic pathway-related genes and constructed a novel diagnostic model. Those results may help in understanding the pathogenesis in AD, which may aid in the creation of therapeutic targets and more widely available biomarkers. <b>Disclosure:</b> Miss Al-Bzour has nothing to disclose. Mr. Ahmed has nothing to disclose. Mr. Al-Khalili has nothing to disclose. Mr. Hamza has nothing to disclose. Ms. Ibrahim has nothing to disclose. Miss Al-Bzour has nothing to disclose.