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Prioritizing potential diagnostic biomarkers of Alzheimer’s disease by investigating gene expression data: A network‐based approach
Author(s) -
Chand Yamini,
Sao Prachi,
Singh Shivani,
Chandra Niharika,
Das Surojeet,
Singh Sachidanand
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.044322
Subject(s) - biology , entorhinal cortex , subnetwork , gene , computational biology , betweenness centrality , genetics , neuroscience , hippocampus , computer science , combinatorics , centrality , computer security , mathematics
Background Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder. The identification of differentially expressed genes (DEGs) across affected brain regions can provide new insights into the mechanisms of AD. Method Our study aims to identify potential biomarkers of AD across brain regions using gene expression and network‐based approaches. The gene expression data were downloaded from Gene Expression Omnibus (GEO) for the series GSE5281. The series comprises expression data from 6 brain regions Entorhinal Cortex (EC), Hippocampus (HIP), Middle temporal gyrus (MTG), Posterior cingulate cortex (PC), Superior frontal gyrus (SFG) and visual cortex (VCX). Differentially expressed genes (DEGs) were identified using GeneSpring GX 12.6.1 software. A protein‐protein interaction network (PPIN) was constructed from high throughput experiments using Biosogenet. A subnetwork comprising common DEGs and their first neighbors was extracted from the complex PPIN, and the network centralities, including degree and betweenness, were calculated using NetworkAnalyzer plugin in Cytoscape 3.7.1. Result Our results identified 4748 non‐redundant DEGs, of which 1493 and 3255 constitute the up and down‐regulated genes, respectively. A total of 124 common DEGs were identified across more than four brain regions. The subnetwork comprised 5723 and 140625 nodes and edges, respectively. Topological analysis of the subnetwork identified 474/148 Hub/Bottleneck genes and 146 Hub‐Bottleneck genes. Two HB genes EGFR and FYN and two H genes NOTCH2NL and SRRM2 were identified as up‐regulated across four brain regions. Six HB genes CUL3, COPS5, HSP90AB1, YWHAZ, YWHAB, and CDC42, and two H genes SNCA, TUBA4A were identified down‐regulated across five brain regions. Furthermore, four HB genes UBC, CUL1, C1QBP, and UBQLN1 and 10 H genes TUBB, GAPDH, SSX2IP, AP2M1, PSMA1, SKP1, TERF2IP, ATP5A1, CCT7, and NDUFA4 are found to be down‐regulated across four brain‐region. Of these, SNCA, GAPDH, UBQLN1 were known to be associated with AD. Conclusion The identification of AD biomarkers across different brain regions integrating differential gene expression study and network‐based approach may provide new insights into the mechanisms of AD.