AUTHOR=Sun Hongyu , Yang Jin , Li Xiaohui , Lyu Yi , Xu Zhaomeng , He Hui , Tong Xiaomin , Ji Tingyu , Ding Shihan , Zhou Chaoli , Han Pengyong , Zheng Jinping 

TITLE=Identification of feature genes and pathways for Alzheimer's disease via WGCNA and LASSO regression

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2022.1001546

DOI=10.3389/fncom.2022.1001546

ISSN=1662-5188

ABSTRACT=Alzheimer's disease (AD) can cause a severe economic burden, and the specific pathogenesis of AD needs to be supplemented by more research. To identify feature genes associated with AD, We downloaded three data from the GEO database, GSE122063, GSE15222 and GSE138260. The search keywords are Alzheimer's disease, the species selection is Homo sapiens, the sample size of each data set is more than 20, and each data set contains Including the normal group and AD group. The datasets GSE15222 and GSE138260 were combined as a Training group to build a model, and GSE122063 was used as a Test group to verify the model's accuracy. The differential genes of the combined datasets were used for GO and KEGG analysis. Then, AD-related module genes were identified using the combined dataset through a weighted gene co-expression network analysis (WGCNA). The differential genes and AD-related module genes were intersected to obtain the key genes of AD. These genes were filtered through LASSO regression, and finally, AD-related feature genes were obtained for subsequent immune-related analysis.
A comprehensive analysis of three AD-related datasets in the GEO database revealed 111 common differential genes in AD. The more prominent ones in The GO analysis are cognition and learning or memory. The KEGG analysis showed that these differential genes were enriched not only in In the KEGG analysis, but three pathways also stand out---Neuroactive ligand-receptor interaction, cAMP signalling pathway, and Calcium signalling pathway. AD-related feature genes(SST, MLIP, HSPB3)were finally identified, which could predict the formation and progression of Alzheimer's disease. The area under the ROC curve of these AD-related feature genes is greater than 0.7 in both the Training and Test groups. Finally, an immune-related analysis of these genes was performed. Our study may provide guiding significance for further exploration of potential biomarkers for diagnosing and predicting AD patients.