Shannon entropy approach reveals relevant genes in Alzheimer’s disease


Autoři: Alfonso Monaco aff001;  Nicola Amoroso aff001;  Loredana Bellantuono aff002;  Eufemia Lella aff002;  Angela Lombardi aff001;  Anna Monda aff002;  Andrea Tateo aff002;  Roberto Bellotti aff001;  Sabina Tangaro aff001
Působiště autorů: Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy aff001;  Department of Physics ‘Michelangelo Merlin’, University of Bari ‘Aldo Moro’, Bari, Italy aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pone.0226190

Souhrn

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.

Klíčová slova:

Algorithms – Alzheimer's disease – Clustering algorithms – Gene expression – Gene regulatory networks – Genetic networks – Hierarchical clustering – Network analysis


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