Identification of necroptosis-related genes in Parkinson's disease by integrated bioinformatics analysis and experimental validation

Cheng Lei; Zhou Zhongyan; Shi Wenting; Zhang Jing; Qin Liyun; Hu Hongyi; Yan Juntao; Ye Qing

doi:10.3389/fnins.2023.1097293

Identification of necroptosis-related genes in Parkinson's disease by integrated bioinformatics analysis and experimental validation

Front Neurosci. 2023 May 22:17:1097293. doi: 10.3389/fnins.2023.1097293. eCollection 2023.

Authors

Cheng Lei¹, Zhou Zhongyan², Shi Wenting², Zhang Jing², Qin Liyun³, Hu Hongyi², Yan Juntao⁴, Ye Qing³

Affiliations

¹ Department of Tuina, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
² Cardiovascular Research Laboratory, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
³ Department of Neurology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
⁴ Department of Tuina, Yueyang Hospital of Integrated Traditional Chinese Medicine and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

Abstract

Background: Parkinson's disease (PD) is the second most common neurodegeneration disease worldwide. Necroptosis, which is a new form of programmed cell death with high relationship with inflammation, plays a vital role in the progression of PD. However, the key necroptosis related genes in PD are not fully elucidated.

Purpose: Identification of key necroptosis-related genes in PD.

Method: The PD associated datasets and necroptosis related genes were downloaded from the GEO Database and GeneCards platform, respectively. The DEGs associated with necroptosis in PD were obtained by gap analysis, and followed by cluster analysis, enrichment analysis and WGCNA analysis. Moreover, the key necroptosis related genes were generated by PPI network analysis and their relationship by spearman correlation analysis. Immune infiltration analysis was used for explore the immune state of PD brain accompanied with the expression levels of these genes in various types of immune cells. Finally, the gene expression levels of these key necroptosis related genes were validated by an external dataset, blood samples from PD patients and toxin-induced PD cell model using real-time PCR analysis.

Result: Twelve key necroptosis-related genes including ASGR2, CCNA1, FGF10, FGF19, HJURP, NTF3, OIP5, RRM2, SLC22A1, SLC28A3, WNT1 and WNT10B were identified by integrated bioinformatics analysis of PD related dataset GSE7621. According to the correlation analysis of these genes, RRM2 and WNT1 were positively and negatively correlated with SLC22A1 respectively, while WNT10B was positively correlated with both OIF5 and FGF19. As the results from immune infiltration analysis, M2 macrophage was the highest population of immune cell in analyzed PD brain samples. Moreover, we found that 3 genes (CCNA1, OIP5 and WNT10B) and 9 genes (ASGR2, FGF10, FGF19, HJURP, NTF3, RRM2, SLC22A1, SLC28A3 and WNT1) were down- and up- regulated in an external dataset GSE20141, respectively. All the mRNA expression levels of these 12 genes were obviously upregulated in 6-OHDA-induced SH-SY5Y cell PD model while CCNA1 and OIP5 were up- and down- regulated, respectively, in peripheral blood lymphocytes of PD patients.

Conclusion: Necroptosis and its associated inflammation play fundamental roles in the progression of PD and these identified 12 key genes might be served as new diagnostic markers and therapeutic targets for PD.

Keywords: death; immune infiltration; inflammation; integrated bioinformatics analysis; macrophage; necroptosis; parkinson’s disease; programmed cell.