AUTHOR=Yang Yajun , Wang Yi , Wang Ce , Xu Xinjuan , Liu Cai , Huang Xintao 

TITLE=Identification of hub genes of Parkinson's disease through bioinformatics analysis

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 16 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.974838

DOI=10.3389/fnins.2022.974838

ISSN=1662-453X

ABSTRACT=Parkinson's disease(PD) is one of the common neurodegenerative diseases, and there is still a lack of effective diagnostic and treatment methods. Therefore, the purpose of this study is to search for hub genes that might serve as biomarkers or therapeutic targets  in PD. All the analysis was performed in R software. The raw data of PD (number:GSE7621) was acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) associated with PD were screened by the Limma package of the R software. Key genes associated with PD were screened by the WGCNA  package of the R software. Target genes are screened out by merging the results of Limma and WGCNA. Enrichment analysis of target genes was performed by Gene Ontology(GO), Disease Ontology(DO), and Kyoto Enrichment of Genes and Genomes(KEEG).The protein-protein interaction (PPI) network and machine learning algorithms were employed to screen for hub genes. Nomogram was constructed using the rms package. And the receiver operating characteristic curve(ROC) were plotted for detecting and validating our prediction model sensitivity and specificity. GSEA was used to determine the biological functions of the hub genes. Additional expression profile data of PD (number:GSE22491) was acquired from the GEO database to validate the hub genes. Finally， RPL3L, PLEK2, CD99P1,and LOC100133130 have higher value. They can provide a new direction for the diagnosis and treatment of PD.