Shannon entropy approach reveals relevant genes in Alzheimer's disease

• Published in: PloS one 2019-12, Vol.14 (12), p.e0226190-e0226190
• Main Authors: Monaco, Alfonso, Amoroso, Nicola, Bellantuono, Loredana, Lella, Eufemia, Lombardi, Angela, Monda, Anna, Tateo, Andrea, Bellotti, Roberto, Tangaro, Sabina
• Format: Article
• Language: English
• Subjects: Advertising executives; Algorithms; Alzheimer Disease - genetics; Alzheimer's disease; Analysis; Biology and Life Sciences; Case-Control Studies; Cluster Analysis; Complexity; Computational Biology - methods; Computer and Information Sciences; Correlation; Correlation analysis; Correlation coefficient; Correlation coefficients; Data analysis; Datasets; Dementia; Dementia disorders; Disease; DNA microarrays; Entropy; Entropy (Information theory); Gene expression; Gene Expression Profiling; Gene Expression Regulation; Gene Regulatory Networks; Genes; Genetic aspects; Genetic Predisposition to Disease; Hippocampus; Hippocampus - chemistry; Human subjects; Humans; Information theory; Medical research; Medicine and Health Sciences; Methods; Network analysis; Neurodegenerative diseases; Phenotypes; Physical Sciences; Physics; Research and Analysis Methods; Standard deviation
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0226190

Alzheimer's disease (AD) is the most common type of dementia and affects millions of people worldwide. Since complex diseases are often the result of combinations of gene interactions, microarray data and gene co-expression analysis can provide tools for addressing complexity. Our study aimed to find groups of interacting genes that are relevant in the development of AD. In this perspective, we implemented a method proposed in a previous work to detect gene communities linked to AD. Our strategy combined co-expression network analysis with the study of Shannon entropy of the betweenness. We analyzed the publicly available GSE1297 dataset, achieved from the GEO database in NCBI, containing hippocampal gene expression of 9 control and 22 AD human subjects. Co-expressed genes were clustered into different communities. Two communities of interest (composed by 72 and 39 genes) were found by calculating the correlation coefficient between communities and clinical features. The detected communities resulted stable, replicated on two independent datasets and mostly enriched in pathways closely associated with neuro-degenative diseases. A comparison between our findings and other module detection techniques showed that the detected communities were more related to AD phenotype. Lastly, the hub genes within the two communities of interest were identified by means of a centrality analysis and a bootstrap procedure. The communities of the hub genes presented even stronger correlation with clinical features. These findings and further explorations on the detected genes could shed light on the genetic aspects related with physiological aspects of Alzheimer's disease.