Cross-disease drug discovery based on bioinformatics and virtual screening: Study of key genes in Alzheimer's disease and ovarian cancer

Abstract

Background: Alzheimer's disease (AD) and cancer, both age-related diseases, are characterized by abnormal cellular behavior. Epidemiological data indicate an inverse relationship between AD and various cancers. Accordingly, this study seeks to analyze the negatively correlated genes between AD and ovarian cancer and identify closely related compounds through virtual screening technology to explore potential therapeutic drugs.

Methods: Microarray data were downloaded from the Gene Expression Omnibus database, and negatively correlated genes between AD and ovarian cancer were identified using bioinformatics analysis. Clinical prognostic and survival analyses were performed to identify genes most negatively associated with these diseases. The top ten compounds with the strongest binding to the target genes were screened from the ChemDiv database using virtual screening technology, considering the blood-brain barrier. Molecular dynamics simulations were used to identify potential sites for the binding of these compounds to the target protein MX1. Additionally, point mutation analysis of the target protein was performed. Finally, the binding site was verified in vitro.

Results: The MX1 gene was most significantly negatively associated with AD and ovarian cancer. Molecular dynamics simulations revealed intersection sites at Glu-227 and Gly-188, where MX1 binds tightly to the head compound.

Conclusion: This study successfully identified MX1 as being negatively associated with AD and ovarian cancer and assessed the potential drug compounds that bind most closely to it. Our findings provide important rationale and candidate targets for the development of novel therapeutic strategies for AD and ovarian cancer.

Keywords: Alzheimer’s disease; Bioinformatics; Drug target; Molecular dynamics; Ovarian cancer; Virtual screening.