Gene biomarker discovery at different stages of Alzheimer using gene co-expression network approach

Abstract

Alzheimer's disease (AD) is a chronic neurodegenerative disorder. It is the most common type of dementia that has remained as an incurable disease in the world, which destroys the brain cells irreversibly. In this study, a systems biology approach was adopted to discover novel micro-RNA and gene-based biomarkers of the diagnosis of Alzheimer's disease. The gene expression data from three AD stages (Normal, Mild Cognitive Impairment, and Alzheimer) were used to reconstruct co-expression networks. After preprocessing and normalization, Weighted Gene Co-Expression Network Analysis (WGCNA) was used on a total of 329 samples, including 145 samples of Alzheimer stage, 80 samples of Mild Cognitive Impairment (MCI) stage, and 104 samples of the Normal stage. Next, three gene-miRNA bipartite networks were reconstructed by comparing the changes in module groups. Then, the functional enrichment analyses of extracted genes of three bipartite networks and miRNAs were done, respectively. Finally, a detailed analysis of the authentic studies was performed to discuss the obtained biomarkers. The outcomes addressed proposed novel genes, including MBOAT1, ARMC7, RABL2B, HNRNPUL1, LAMTOR1, PLAGL2, CREBRF, LCOR, and MRI1and novel miRNAs comprising miR-615-3p, miR-4722-5p, miR-4768-3p, miR-1827, miR-940 and miR-30b-3p which were related to AD. These biomarkers were proposed to be related to AD for the first time and should be examined in future clinical studies. Alzheimer is an incurable neurological disorder and is classified as an aging disease. It is one of the important neurological complications which can affect the whole society ranging from the patients themselves to the people who are around them. The aging population is growing in many countries, and the treatment costs of Alzheimer are dramatically high. These issues have drawn the attention of many researchers to the importance of the examination of this disease 1. There are many organizations all over the world which work in the field of early diagnosis and prevention of Alzheimer 2,3. National center for health statistics considers Alzheimer's disease as the sixth cause of death in the United States 4. As a result, Alzheimer's disease is among the costliest diseases for various socioeconomic classes. As the population of the world grows, the number of inflicted people increases. Therefore, the control of the affected population becomes more difficult 5. Significant advances in medical and neurological sciences have led to a longer life expectancy and have increased the number of Alzheimer's disease patients. Ultimately, the prevention of disease before its occurrence is regarded to be one of the most important pillars of treatment at different stages of this disease. Treatment or postponement of a disease depends on its discovery by identifying the biological pathways involved in the disease and adopting various drug-disease network approaches 6 to control these pathways. In recent decades, deep investigation of molecular mechanisms has become more prevalent as a researc...

Discovery and Validation of Key Biomarkers Based on Immune Infiltrates in Alzheimer’s Disease

“…Previous reports have pointed out that ABCA2 is a key regulator of endogenous APP expression and AD truncation. In research, CREBRF was proposed as a novel biomarker using weighted gene co-expression network analysis (WGCNA) based on a total of 329 samples ( Soleimani Zakeri et al, 2020 ). Moreover, it has been documented to block autophagy through the CREB3/ATG5 pathway in brain tumor ( Xue et al, 2016 ).…”

Identification of potential biomarkers for pathogenesis of Alzheimer’s disease

“…Among all 2215 genes in turquoise module, 42 genes were also differentially expressed in GSE63060 dataset. As far as we know, most of the previous studies only used WGCNA to find AD related genes [ 6 , 30 ], but we have integrated the results of DEGs and WGCNA in order to obtain a more reliable basis before further analysis. Based on the PPI network, 8 genes among all 42 genes were finally screened according to the degree and MCC algorithm, which included RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG.…”

Co-expression Network Analysis Reveals Novel Genes Underlying Alzheimer’s Disease Pathogenesis

“…Recently, the weighted gene co-expression network analysis (WGCNA) has been widely used to identify clusters of co-expressed genes with highly correlated expression patterns based on the genetic profile of many diseases (Zhao et al, 2010). WGCNA has also been adopted to screen crucial modules and genes that are associated with AD pathogenesis, and several genes have been identified and validated (Pandey et al, 2019;Shi et al, 2020;Soleimani Zakeri et al, 2020). However, there were several limitations in those studies, such as the small sample sizes of the datasets, the use of differentially expressed genes instead of the original genes, or the use of blood rather than nervous system tissues.…”