Mapping the pathogenic nexus: Gene overlap and protein interaction networks in Alzheimer's and breast cancer as a precursor to protein structure prediction and analysis.

Abstract

This chapter focuses on how advanced computational techniques can reveal common pathways and interactions seemingly between Alzheimer's disease (AD) and breast cancer (BC). It also highlights their roles in bridging the gap between neurodegenerative and oncogenic processes by analyzing gene networks and identifying essential genes such as GAPDH, HSP90AA1, and HSPA8, which show differential regulation in AD and BC. These genes are upregulated in AD and downregulated in BC, illustrating their involvement in both disease contexts. A significant aspect of the analysis is the role of hub-bottleneck proteins within critical pathways. These hub-bottleneck proteins, including those involved in estrogen signaling, Alzheimer's disease pathways, neurodegeneration, and cancer pathways, serve as central nodes in the PPI networks. Their positioning underscores their crucial role in mediating disease mechanisms and influencing the progression of both AD and BC. The chapter emphasizes integrating gene expression data with PPI networks to uncover these critical nodes and interactions contributing to both diseases. Using network-based analysis and transcriptomics integration tools, it provides a detailed understanding of how shared genetic markers and their interactions influence disease mechanisms. This approach enables the identification of potential biomarkers and therapeutic targets by revealing underlying molecular connections and critical pathways involving hub-bottleneck proteins. The insights gained from gene overlap and PPI networks can serve as valuable input data for future studies focused on structural analysis. By laying the groundwork for understanding shared pathways and protein interactions, the research sets the stage for more detailed structural investigations and the development of precision medicine strategies tailored to the specific molecular features of Alzheimer's and breast cancer, inspiring the development of more effective treatments.