Journal
FRONTIERS IN AGING NEUROSCIENCE
Volume 13, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnagi.2021.707165
Keywords
WGCNA (weighted gene co-expression network analyses); SOM (self-organization map); aging brain; random forest; machine learning
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Funding
- National Natural Science Foundation of China
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By using machine learning and WGCNA, this study explored the relationship between aging and gene expression in the human frontal cortex, identifying potential biomarkers and therapeutic targets for neurodegeneration and dementia associated with aging. Through analysis, specific genes such as MAPT, KLHDC3, RAP2A, RAP2B, ELAVL2, and SYN1 were found to be co-expressed and highly correlated with aging.
Aging is a major risk factor contributing to neurodegeneration and dementia. However, it remains unclarified how aging promotes these diseases. Here, we use machine learning and weighted gene co-expression network (WGCNA) to explore the relationship between aging and gene expression in the human frontal cortex and reveal potential biomarkers and therapeutic targets of neurodegeneration and dementia related to aging. The transcriptional profiling data of the human frontal cortex from individuals ranging from 26 to 106 years old was obtained from the GEO database in NCBI. Self-Organizing Feature Map (SOM) was conducted to find the clusters in which gene expressions downregulate with aging. For WGCNA analysis, first, co-expressed genes were clustered into different modules, and modules of interest were identified through calculating the correlation coefficient between the module and phenotypic trait (age). Next, the overlapping genes between differentially expressed genes (DEG, between young and aged group) and genes in the module of interest were discovered. Random Forest classifier was performed to obtain the most significant genes in the overlapping genes. The disclosed significant genes were further identified through network analysis. Through WGCNA analysis, the greenyellow module is found to be highly negatively correlated with age, and functions mainly in long-term potentiation and calcium signaling pathways. Through step-by-step filtering of the module genes by overlapping with downregulated DEGs in aged group and Random Forest classifier analysis, we found that MAPT, KLHDC3, RAP2A, RAP2B, ELAVL2, and SYN1 were co-expressed and highly correlated with aging.
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