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The present study aimed to examine potential crucial genes associated with Parkinson's disease (PD) in addition to the interactions and regulators of these genes. The chip data (GSE7621) were obtained from the Gene Expression Omnibus and standardized using the robust multi‑array average in the Affy package of R software. The differentially expressed genes (DEGs) were then screened using the Samr package with a false discovery rate (FDR) &lt;0.05 and |log2 fold change (FC)|&gt;1. Crucial PD‑associated genes were predicted using the Genetic Association Database in the Database for Annotation, Visualization and Integrated Discovery and sequence alignment. Furthermore, transcription factors (TFs) of the crucial PD‑associated genes were predicted, and protein‑protein interactions (PPIs) between the crucial PD‑associated genes were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins. Additionally, another dataset of PD was used to validate the expression of crucial PD‑associated genes. A total of 670 DEGs (398 upregulated and 272 downregulated genes) were identified in the PD samples. Of these, 10 DEGs enriched in pathways associated with the nervous system were predicted to be crucial in PD, including C‑X‑C chemokine receptor type 4 (CXCR4), deleted in colorectal cancer (DCC) and NCL adaptor protein 2 (NCK2). All 10 genes were associated with neuron development and differentiation. They were simultaneously modulated by multiple TFs, including GATA, E2F and E4 promoter‑binding protein 4. The PPI networks showed that DCC and CXCR4 were hub proteins. The DCC‑netrin 1‑roundabout guidance receptor 2‑slit guidance ligand 1 interaction pathway, and several genes, including TOX high mobility group box family member 4, kinase insert domain receptor and zymogen granule protein 16B, which interacted with CXCR4, were novel findings. Additionally, CXCR4 and NCK2 were upregulated in another dataset (GSE8397) of PD. These genes, interactions of proteins and TFs may be important in the progression of PD.

Identification of crucial genes associated with Parkinson's disease using microarray data