Objective: To provide new diagnostic markers and immune infiltration pattern for Parkinson’s disease (PD) by identifying key genes of PD using bioinformatics analysis. Methods: The microarray expression data were downloaded from GSE20163 and GSE20164 in the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between the healthy control samples and PD samples were identified and the biological functions of DEGs were predicted using gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) databases. Meanwhile,key module genes were screened via weighted gene co-expression network analysis (WGCNA), and the intersection of key module genes and DEGs was taken. Least absolute shrinkage and selection operator (LASSO) analysis was used to further screen candidate genes for PD. Finally, the immune infiltration was analyzed by a single-sample gene set enrichment analysis (ssGSEA) algorithm, and the above four genes were validated in GSE49036. Results: Thirty-four DEGs were identified, which were mainly related to biological processes such as neurotransmitter transport, learning and memory, and cognition. A unique gene module related to PD was identified and 19 PD-related genes were obtained by intersection of key module genes and DEGs. Four candidate genes (SLC18A2, SV2C, CUX2, and CALB1) were further identified through LASSO analysis. The receiver operating characteristic (ROC) curve indicated that the 4 candidate genes had a good performance in distinguishing the PD samples from healthy control samples. Comparing with the control group, the proportion of immature dendritic cells and γδ T cells in PD was relatively lower, and the proportion of neutrophils cells was higher. It was validated in GSE49036 that SLC18A2, SV2C, and CUX2 had significant differences in gene expression between the PD group and the control group. The ROC curve indicated 3 genes have a high degree of accuracy in diagnosing the PD, while the difference in CALB1 gene expression was not significant. Conclusion: Four candidate genes (SLC18A2, SV2C, CUX2, and CALB1) were identified by bioinformatics analysis and the correlation between the CUX2 gene and PD was reported for the first time, which provides a reference for exploring the diagnosis and development mechanism of PD.
YANG Xinrui
,
LIU Yanhua
,
YANG Yanyan
,
LI Qianqian
,
ZHANG Yue
,
HE Fang
. Weighted gene co-expression network analysis to identify four hub genes of Parkinson’s disease and immune infiltration analysis[J]. Journal of Baotou Medical College, 2024
, 40(1)
: 30
-35
.
DOI: 10.16833/j.cnki.jbmc.2024.01.005
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