Comorbidity-Guided Text Mining and Omics Pipeline to Identify Candidate Genes and Drugs for Alzheimer's Disease

Area of Science:

• Neuroscience
• Genetics
• Pharmacology

Background:

• Alzheimer's disease (AD) is a prevalent neurodegenerative disorder in the elderly, characterized by complex genetic factors.
• Current AD treatments are limited, highlighting the need for novel therapeutic strategies.
• Elderly patients often have multiple comorbid conditions, complicating treatment plans.

Purpose of the Study:

• To identify diseases comorbid with Alzheimer's disease using text mining.
• To pinpoint common genes shared between AD and five major comorbid diseases: dementia, type 2 diabetes, hypertension, Parkinson's disease, and Down syndrome.
• To discover potential therapeutic drugs targeting these common genes for enhanced treatment strategies.

Main Methods:

• A hybrid pipeline combining text mining and omics approaches was developed.
• Text mining identified comorbid diseases associated with AD.
• Omics analysis pinpointed common genes and pathways between AD and its comorbidities, followed by gene-drug interaction analysis using LINCS perturbation.

Main Results:

• Seven common genes (PSEN1, PSEN2, MAPT, APP, APOE, NOTCH, HFE) were identified between AD and the five comorbid diseases.
• Analysis revealed potential drug candidates, including MG-132 and Masitinib, targeting these common genes.
• These identified drugs show promise for treating both AD and its comorbid conditions.

Conclusions:

• The study successfully identified shared genetic factors and potential therapeutic targets for AD and its common comorbidities.
• The proposed hybrid pipeline offers a novel approach for drug discovery in complex, multifactorial diseases.
• The findings suggest that targeting common genes could lead to more effective, integrated treatment strategies for elderly patients with multiple conditions.