Oxytosis/Ferroptosis: Unraveling the Mechanisms and Its Multifaceted Role in Neurodegenerative Diseases

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Identification of DEGs associated with ferroptosis in AD. (A) The boxplot shows the expression difference of APP between the Control group and AD group. Data were analyzed by Kruskal−Wallis test. **p < 0.01, ****p < 0.0001. (B) The heatmap plot shows DEGs between frequent exacerbators and non-frequent exacerbators. Dotplot displays KEGG enrichment analysis of over-represented genes in control and AD groups. (C) The expression patterns of ferroptosis-related genes were presented in the boxplot in GSE118553. Data were analyzed by Kruskal−Wallis test. *p < 0.05, **p < 0.01, ***p < 0.001. (D) The collection diagram shows driving, suppressing, and unclassified ferroptosis related genes overlapping in the two datasets. (E) Ferroptosis related genes with the same expression trend were found in both datasets. Barplot and dotplot show their log2FC and-log10 adjust pvalue. (F) Correlation dotplot shows correlations between genes in two datasets. The upper triangle represents GSE118553 and the lower triangle represents GSE132903. Red dots means negative correlation, blue dots means positive correlation. The graph on the right shows part of the interactions between genes.
Original Research
30 October 2023
Identification ferroptosis-related hub genes and diagnostic model in Alzheimer’s disease
Huabin Zhao
4 more and 
Lan Wang

Background: Ferroptosis is a newly defined form of programmed cell death and plays an important role in Alzheimer’s disease (AD) pathology. This study aimed to integrate bioinformatics techniques to explore biomarkers to support the correlation between ferroptosis and AD. In addition, further investigation of ferroptosis-related biomarkers was conducted on the transcriptome characteristics in the asymptomatic AD (AsymAD).

Methods: The microarray datasets GSE118553, GSE132903, GSE33000, and GSE157239 on AD were downloaded from the GEO database. The list of ferroptosis-related genes was extracted from the FerrDb website. Differentially expressed genes (DEGs) were identified by R “limma” package and used to screen ferroptosis-related hub genes. The random forest algorithm was used to construct the diagnostic model through hub genes. The immune cell infiltration was also analyzed by CIBERSORTx. The miRNet and DGIdb database were used to identify microRNAs (miRNAs) and drugs which targeting hub genes.

Results: We identified 18 ferroptosis-related hub genes anomalously expressed in AD, and consistent expression trends had been observed in both AsymAD The random forest diagnosis model had good prediction results in both training set (AUC = 0.824) and validation set (AUC = 0.734). Immune cell infiltration was analyzed and the results showed that CD4+ T cells resting memory, macrophages M2 and neutrophils were significantly higher in AD. A significant correlation of hub genes with immune infiltration was observed, such as DDIT4 showed strong positive correlation with CD4+ T cells memory resting and AKR1C2 had positive correlation with Macrophages M2. Additionally, the microRNAs (miRNAs) and drugs which targeting hub genes were screened.

Conclusion: These results suggest that ferroptosis-related hub genes we screened played a part in the pathological progression of AD. We explored the potential of these genes as diagnostic markers and their relevance to immune cells which will help in understanding the development of AD. Targeting miRNAs and drugs provides new research clues for preventing the development of AD.

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