Introduction

Neurodegenerative diseases (NDDs) such as Alzheimer's Disease (AD) and Parkinson's Disease (PD) present significant challenges, often progressing stealthily and causing irreversible neuronal damage before symptoms appear. Despite being classified under NDDs, AD and PD exhibit distinct molecular pathologies. However, there is limited research investigating the concordance and discordance between AD and PD on a global omics scale. Conducting a meta-analysis of proteomic and transcriptomic data to identify commonalities between these diseases can provide insights into their pathobiology and uncover markers for classification, offering potential therapeutic insights.

Methods

The methodology involved data mining of proteomics and transcriptomics datasets from public repositories such as ProteomeXchange Consortium, PRIDE, ArrayExpress, Gene Expression Omnibus (GEO), and Omics Discovery Index (OmicsDI). A standardized approach was used for data preprocessing, quality assessment, and statistical analysis across all omics datasets. Significance annotation was performed using parametric T-tests to calculate p-values, and differential expression analysis was conducted comparing Disease versus Control groups. Common markers and unique features were identified to investigate the interplay between AD and PD.

Results

Seven transcriptomics datasets for Alzheimer's and fourteen for Parkinson's, as well as seven proteomics datasets for Alzheimer's and three for Parkinson's, were analyzed, chosen for their balanced sample distribution. Among the top 1000 markers identified from each transcriptomics and proteomics analysis, 231 were common across diseases in transcriptomics, and 181 proteins were common in the proteomics workflow, highlighting shared molecular signatures. Through feature selection techniques, key features were identified across both diseases. Remarkably, 13 common markers emerged, underscoring both shared and distinctive aspects of these NDDs. The selection process confirmed the relevance of well-known markers, corroborating with BrainProt's BDMC score, validating their importance in disease classification and pathobiology.

Conclusion

This study has shed light on the molecular landscape of Alzheimer's and Parkinson's diseases, revealing both shared and distinct features across transcriptomic and proteomic datasets. Through meticulous analysis and feature selection, a subset of common markers has emerged, providing insights into the pathobiology of these neurodegenerative disorders. The identification of shared molecular signatures suggests potential converging pathways and therapeutic targets, while the recognition of disease-specific markers underscores the heterogeneity within these conditions. These findings hold promise for guiding future research aimed at better understanding, diagnosing, and treating Alzheimer's and Parkinson's diseases.