A Six-Gene Signature Related to Liquid-Liquid Phase Separation for Diagnosis of Alzheimer's Disease.

Abstract

Background: Liquid-liquid phase separation (LLPS) has been increasingly recognized as a crucial mechanism in the pathogenesis of various neurodegenerative disorders, including Alzheimer's disease (AD). There remains a paucity of effective diagnostic biomarkers for this condition. This study aims to develop and validate a novel LLPS-related molecular signature to enhance the diagnostic accuracy and early detection of AD.

Methods: LLPS-related genes were identified from online databases and subjected to bioinformatic analyses, including protein-protein interaction (PPI) network analysis and least absolute shrinkage and selection operator (LASSO) regression. Based on the optimal LLPS-related genes, a diagnosis risk model was constructed, and the diagnostic ability was evaluated using a receiver operator characteristic (ROC) curve. To elucidate the biological functions of the identified LLPS-related genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted.

Results: A total of 149 LLPS-related genes were screened, which were found to be involved in functions related to oxidative stress, apoptosis, and cancer progression. The 149 genes were refined to six optimal candidates through PPI network analysis and LASSO regression: Activator of HSP90 ATPase Activity 1 (AHSA1), Eukaryotic Translation Initiation Factor 2 Alpha Kinase 2 (EIF2AK2), Heat Shock Protein Family A (Hsp70) Member 4 (HSPA4), Notch Receptor 1 (NOTCH1), Superoxide Dismutase 1 (SOD1), and Thioredoxin (TXN). Based on the six optimal genes, a diagnostic risk model was constructed, and the diagnostic ability was verified to be promising in AD both in training, internal validation, and two external validation datasets, with area under ROC curve (AUC) above 0.8. Furthermore, significant correlations were observed between the expression of these genes and tumor immune cell infiltration.

Conclusions: A six-gene diagnosis model was constructed and verified to exhibit robust diagnostic ability in AD.