Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease - Chai, Keping; Zhang, Xiaolin; Chen, Shufang; Gu, Huaqian; Tang, Huitao; Cao, Panlong; Wang, Gangqiang; Ye, Weiping; Wan, Feng; Liang, Jiawei; Shen, Daojiang (Frontiers in Aging Neuroscience, 2022)
Aberrant deposits of neurofibrillary tangles (NFT), the mainly character of Alzheimer's disease (AD), is highly related to cognitive impairment. However, the pathological mechanism of NFT formation is still unclear. Here, we explored the differences of gene expression patterns in multiple brain regions (entorhinal, temporal and frontal cortex (EC, TC, FC)) with distinct Braak stages (0- VI), and identified the hub genes via weighted gene co-expression network analysis (WGCNA) and machine learning. For WGCNA, consensus modules were detected and correlated with the single sample gene set enrichment analysis (ssGSEA) scores. Next, overlapping the differentially expressed genes (DEGs, Braak stages 0 VS I-VI) with that in interest module. Metascape analysis and Random Forest were conducted to explore the function of overlapping genes and obtain the most significant genes. We found that the three brain regions have high similarities in the gene expression pattern and the oxidative damage plays a vital role in NFT formation via machine learning. Through further filtering of genes from interested modules by Random Forest, we screened out key genes, such as LYN, LAPTM5 and IFI30. These key genes, including LYN, LAPTM5 and ARHGDIB, may play an important role in the development of AD through the inflammatory response pathway mediated by microglia.