Abstract

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 [33]GSE118553, [34]GSE132903, [35]GSE33000, and
   [36]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.

   Keywords: Alzheimer’s disease, ferroptosis, diagnosis biomarkers,
   immune infiltration, machine learning

Introduction

   As the primary cause of dementia, AD is a progressive neurodegenerative
   disease associated with aging ([37]Ambrogio et al., 2019). Its core
   pathological features include the accumulation of amyloid-β protein and
   hyperphosphorylated tau ([38]Surguchov et al., 2023). According to the
   standards of the National Institute on Aging, NIH and the Alzheimer’s
   Association, the appearance of AD pathology may occur without any
   symptoms ([39]Knopman et al., 2019). It then progresses to mild
   cognitive impairment, with a decline in learning and memory abilities,
   and ultimately to significant dementia and loss of the ability to live
   independently. This span can last for 15–25 years, but not every
   patient will follow this continuous process ([40]Scheltens et al.,
   2021). An assessment report on survival after dementia diagnosis in the
   United States indicated a survival time of only 3–4 years ([41]Jack et
   al., 2019). Similarly, a study of another European cohort showed a
   median survival time of 6 years after diagnosis ([42]Vermunt et al.,
   2019). The sharp increase in AD patients has brought heavy pressure to
   families and society. Based on a highly complex process that integrates
   behavioral, genetic, and environmental factors, extensive research has
   been conducted on the pathogenesis of AD.

   Interestingly, in addition to the deposition of amyloid-β protein and
   tau, the brains of AD patients also exhibit progressive neuronal loss
   and oxidative stress caused by metal homeostasis imbalance
   ([43]Plascencia-Villa and Perry, 2021). With the discovery of
   ferroptosis - a form of programmed cell death mediated by lipid
   peroxidation that is distinct from autophagy, apoptosis, and necrosis
   ([44]Dixon et al., 2012; [45]Jiang et al., 2021) – the hypothesis of
   transitional metal imbalance has gained strong support, and researchers
   believe that ferroptosis may be an important cause of neuronal loss in
   AD patients. Iron metal is involved in critical processes such as
   myelin formation, neuronal activity, neurotransmitter synthesis, and
   energy metabolism in the brain ([46]Crichton et al., 2011). Its
   storage, distribution, and efflux are precisely regulated by
   transcriptional levels of iron response elements and iron regulatory
   proteins ([47]Ward et al., 2014). Studies have shown that iron
   homeostasis imbalance can block the activity of the mitochondrial
   electron transport chain, causing oxidative stress ([48]Onukwufor et
   al., 2022). In summary, ferroptosis plays an important role in AD
   pathology. Moreover, recent reports have also pointed to the
   pathogenesis of AD to lysosomal autophagy and neuroinflammation
   ([49]Calsolaro and Edison, 2016; [50]Lee et al., 2022). Peripheral
   immune cells, such as bone marrow-derived monocytes, are recruited into
   the brain to efficiently clear amyloid deposits ([51]Simard et al.,
   2006). On the other hand, researchers found CD8 + T cells in the
   hippocampus of AD patients after death, which may be one of the direct
   causes of neuronal dysfunction ([52]Unger et al., 2020). Therefore,
   this study will also focus on the immune penetration in AD, to analyze
   the composition of immune cells.

   MicroRNAs are small RNA molecules which associated with many
   neurodegenerative diseases that regulate gene expression by binding to
   target mRNAs ([53]Juźwik et al., 2019). Previous studies have reported
   the abnormal expression of hsa-miR-4286 in AD ([54]Henriques et al.,
   2020) and its association with Parkinson’s disease neurodegeneration
   ([55]Su et al., 2018). MicroRNAs (miRNAs) and their target genes are
   intensely studied as candidates for diagnostic and prognostic
   biomarkers. For instance, in the cerebrospinal fluid of AD patients,
   exosomal miR-193b was negatively correlated with amyloid-β ([56]Liu et
   al., 2014). In this study, we aimed to screen abnormally expressed
   ferroptosis-related genes in AD and identify miRNAs with diagnostic or
   therapeutic potential by targeting ferroptosis-related genes.
   Diagnostic models were constructed through hub genes to judge the
   potential of these genes to identify AD, while assessing the
   correlation of these genes with immune infiltration in AD patients.
   Finally, by screening the MicroRNAs and drugs targeting hub genes, we
   can provide valuable data support for the clinical treatment and drug
   development of AD.

Materials and methods

Dataset download

   The microarray datasets [57]GSE118553, [58]GSE132903, [59]GSE33000 and
   [60]GSE157239 on AD were downloaded from the Gene Expression Omnibus
   (GEO) database[61]^1 ([62]Barrett et al., 2013). The annotation
   information of the chip probes of the corresponding platforms was
   obtained from the GEO database, respectively. We collected clinical
   features from various datasets, including age, gender and the number of
   individuals in each group ([63]Table 1).

Table 1.

   Microarrays datasets clinical characteristics.
   Dataset [64]GSE118553 [65]GSE132903 [66]GSE33000 [67]GSE157239
   Groups Control AsymAD AD Control AD Control AD Control AD
   Number 100 134 167 98 97 157 310 8 8
   Age 70.44 ± 15.79 86.28 ± 8.59 82.92 ± 10.20 84.98 ± 6.90 85.02 ± 6.75
   63.52 ± 9.91 80.60 ± 8.99 79.88 ± 7.32 79.75 ± 9.66
   Gender
   Male 55 40 69 50 49 123 135 3 2
   Female 45 94 98 48 48 34 175 5 6
   [68]Open in a new tab

Differential expression and pathways enrich analysis

   The R software package limma ([69]Ritchie et al., 2015) was used to
   achieved differential analysis on control and AD samples. Genes with
   p_adj < 0.05 and abs(logFC) > 0.585 were considered as DEGs. Heat maps
   and bot plots of DEGs were created using the “pheatmap” and “ggplot2”
   packages. Kyoto encyclopedia of gene and genomes (KEGG) pathway
   enrichment analysis were performed on DEGs to explore the biological
   functions and related pathways of DEGs.

Ferroptosis-related genes acquisition

   The list of ferroptosis-related genes was extracted from the FerrDb
   website ([70]Zhou et al., 2023). By intersecting the
   ferroptosis-related genes list with DEGs, finally 18
   ferroptosis-related hub genes were identified. An interaction network
   for the ferroptosis-related genes was generated by the STRING database
   ([71]Szklarczyk et al., 2011).

Diagnostic model construction

   The 18 ferroptosis-related hub genes were used to constrct the random
   forest model. To further obtain the error-stable model, appropriate
   parameters were selected by varying the number of decision trees, and
   the 1,000 trees were finally set as the optimal parameters of the
   model. Two hundred and sixty-seven samples from [72]GSE118553 were
   randomly divided into a training set and a testing set using a ratio of
   4:1. The importance of features by calculating the purity of nodes
   through Gini coefficient method were computed.

Gene set enrichment analysis

   Gene set enrichment analysis (GSEA) was used for pathway enrichment
   analysis ([73]Subramanian et al., 2005). The Molecular Signatures
   Database (MSigDB) of hallmark gene sets (H), curated gene sets (H2),
   and GO gene sets (C5) were used for enrichment analysis. An FDR value
   of 0.05 was used as a cutoff.

Immune infiltration analysis

   CIBERSORTx is an analytical tool to assess the abundance of immune cell
   subsets in tissue samples by using a deconvolution algorithm
   ([74]Newman et al., 2019). The LM 22 signature matrix file, which
   contains 22 immune cell components, was used as a reference for cell
   quantification. Correlation between immune cells and genes was analyzed
   by Pearson correlation analysis.

Exploration microRNAs targeting the hub genes

   Differential analysis of the [75]GSE157239 expression matrix was
   performed using the Limma R package to obtain differentially expressed
   miRNAs. MiRNAs with p.value <0.05 and abs(logFC) > 0.263 were
   considered as DEmiRNAs. Potential miRNAs of hub genes were retrieved by
   miRNet database ([76]Chang et al., 2020). The association between
   microRNAs and AD was investigated through the HMDD database ([77]Cui et
   al., 2023).

Drugs and genes interaction scores

   Drugs targeting hub genes were retrieved from the Drug-Gene Interaction
   Database (DGIdb). The DGIdb is a web resource that provides information
   on drug-gene interactions and druggable genes from publications,
   databases, and other web-based sources ([78]Freshour et al., 2021). The
   bar plots of interaction scores were created using “ggplot2” package.

Statistical analysis

   R software was used for statistical analysis. Principal Component
   Analysis (PCA) was performed using an R package “factoextra.” The
   association between continuous variables was assessed using Pearson’s
   correlation coefficient. *p < 0.05; **p < 0.01; and ***p < 0.001 are
   considered significant.

Results

Identification of DEGs associated with ferroptosis in AD

   To screen out the genes associated with ferroptosis that are robustly
   expressed in AD, two datasets ([79]GSE118553 and [80]GSE132903) were
   used for the analysis. The expression of amyloid precursor protein
   (APP) was downregulated in both two datasets ([81]Figure 1A). Firstly,
   we identified differentially expressed genes (DEGs) between Alzheimer’s
   disease (AD) and healthy control groups in [82]GSE118553
   ([83]Supplementary Figure S1A). We found that upregulated genes in AD
   were mainly associated with apoptosis and ferroptosis, while
   downregulated genes were mainly involved in neuroactive ligand-receptor
   interaction ([84]Figure 1B). To further determine hub genes associated
   with ferroptosis in AD, we used FerrDB to identify ferroptosis-related
   genes that were aberrantly expressed in AD ([85]Figure 1C). According
   to FerrDB, we classify ferroptosis regulators into three categories:
   Driver, Suppressor, and Unclassified genes. Furthermore, we observed
   consistent expression trends with [86]GSE118553 of these genes in AD in
   [87]GSE132903 ([88]Figure 1D) ([89]Supplementary Figures S1B,C) and
   found significant downregulation of Driver regulators such as KLF2 and
   YAP1 in AD, as well as significant upregulation of multiple Suppressor
   regulators ([90]Figure 1E). Correlation analysis revealed that the hub
   genes exhibit strong associations in both the [91]GSE132903 and
   [92]GSE118553, additionally, protein–protein interaction network also
   indicates the interrelated interactions among the hub genes ([93]Figure
   1F). The results indicate that we identified a robustly differentially
   expressed gene list associated with ferroptosis in AD by screening
   through [94]GSE132903 and [95]GSE118553.

Figure 1.

   [96]Figure 1
   [97]Open in a new tab

   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 [98]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 [99]GSE118553 and the lower
   triangle represents [100]GSE132903. Red dots means negative
   correlation, blue dots means positive correlation. The graph on the
   right shows part of the interactions between genes.

Machine-learning-based for constructing diagnostic model of AD

   To assess the potential of the above gene list for diagnosing AD, we
   constructed a RandomForest model. We performed model construction using
   18 genes associated with ferroptosis and selected 1,000 as the number
   of decision trees in the Random Forest model ([101]Figure 2A). The Gini
   coefficient indicated that these genes have high importance for
   predicting AD ([102]Figure 2B). Firstly, we divided the 267 samples
   from [103]GSE118553 into a training set and a testing set using a ratio
   of 4:1 ([104]Figure 2C). The AUC value was 0.824 (95% confidence
   interval [CI] = 0.759–0.889) and corresponding matrix demonstrated that
   the training model could correctly classify AD and healthy samples with
   high accuracy. Then, we performed model testing using [105]GSE132903
   ([106]Figure 2D). The AUC value and corresponding matrix demonstrated
   that the model achieved a high diagnostic accuracy in [107]GSE132903 as
   well. Furthermore, we found that the model can also be used for the
   classification of asymptomatic AD (AsymAD). Compared to diagnosing
   healthy controls and AsymAD (AUC: 0.689), the model exhibited higher
   accuracy in diagnosing AsymAD and AD (AUC: 0.705) ([108]Figure 2E).

Figure 2.

   [109]Figure 2
   [110]Open in a new tab

   Machine-learning-based for constructing diagnostic model of AD. (A) The
   line chart shows the change in the number of decision trees and the
   error value of the model. When the number of decision trees is greater
   than 1,000, the error value tends to be stable. (B) The barplot shows
   importance of variables through MeanDecreaseGini coefficients. (C) The
   flow chart of random forest model construction through [111]GSE118553.
   Receiver operating characteristic (ROC) curve for the testing model and
   confusion matrix of the hub genes combination in [112]GSE118553. (D)
   The diagnostic model verification model in [113]GSE132903. ROC curve
   for the testing model and confusion matrix in validation cohort. (E)
   ROC curve for the classification model between three cohorts.
   Calculation of AUC values in the cohorts by 5-fold cross-validation.
   (F) Boxplot shows 18 ferroptosis related genes expression in control,
   AsymAD and AD groups. *p < 0.05, **p < 0.01, ***p < 0.001,
   ****p < 0.0001.

   We also discovered that these genes exhibit consistent expression
   trends in AsymAD and AD compare to healthy controls ([114]Figure 2F).
   This may suggest that the hub genes related to ferroptosis may play an
   important role in the progression stages of the disease.

Clinical and molecular characteristics of AsymAD and AD

   We analyzed the demographic differences including age and gender
   between AsymAD and AD. The results indicated that there were
   significant differences in age among the control, AsymAD, and AD groups
   ([115]Figure 3A). Studies have shown that there may be a gender
   difference in the prevalence and risk of AD ([116]Masters et al.,
   2015). Women are more likely to develop AD than men and our results
   showed that there was a higher proportion of females than male
   ([117]Figure 3B). We also observed that the expression level of APP AD
   was significantly lower than that of AsymAD, while there was no
   significant difference between AsymAD and control ([118]Figure 3C). In
   contrast, in order to comprehend the potential biological functions
   difference between AsymAD and AD, enrichment analyses were conducted.
   The GSVA results revealed that the expression of oxidative stress
   response pathways was elevated in both AsymAD and AD
   ([119]Supplementary Table 1), and the expression of Neuron-to-Neuron
   synapse and Nerve impulse pathways were decreased in AD compared to
   control and AsymAD, suggesting abnormalities in neurological related
   pathways in AD ([120]Figure 3D). GSEA of the DEGs in AsymAD and AD has
   shown that they are associated with neurological related pathways
   include Neurotransmitter secretion and learning or memory ([121]Figures
   3E,[122]F) and iron ion-related biological processes include Response
   to iron ion ([123]Figures 3G,[124]H). The results reveal significant
   demographic disparities, particularly in age and gender, between AsymAD
   and AD groups, and also underscore the complex interplay between
   demographic factors and molecular mechanisms in the progression of
   Alzheimer’s disease.

Figure 3.

   [125]Figure 3
   [126]Open in a new tab

   Clinical and molecular characteristics of AsymAD and AD. (A) The age
   difference between controls, AsymAD and AD patients. Data were analyzed
   by Kruskal−Wallis test. ***p < 0.001, ****p < 0.0001. (B) The
   difference of gender ratio in different groups. Data were analyzed by
   chi-squared test. (C) The boxplot shows APP expression in controls,
   AsymAD and AD groups. ns, not significant; *p < 0.05, **p < 0.01. (D)
   The heatmap plot shows pathway mean expression levels in different
   groups. The expression level of each pathway was determined by GSVA
   score. (E–G) The enrichscores and adjust pvalue of Neurotransmitter
   secretion, Learning or memory and Response to iron ion pathways. The
   results were calculated by GSEA analysis between AsymAD and AD groups.
   (H) The network plot shows the major down-regulated genes onvolved in
   pathway between AsymAD and AD groups.

Distinct brain regional molecular characteristics and gene expression
patterns in AsymAD and AD

   To further investigate the connection between different brain regions
   and AD, we examined four regions in the brain, namely the Cerebellum,
   Entorhinal Cortex, Frontal Cortex, and Temporal Cortex, in both AD and
   AsymAD ([127]Figure 4A). We observed that the Temporal Cortex and
   Entorhinal Cortex exhibited the most significant changes in both AD and
   AsymAD, suggesting that these two regions may play a crucial role in
   disease progression ([128]Figure 4B). Further, we examined the
   expression of ferroptosis-related hub genes across different brain
   regions ([129]Figure 4C). We found that MUC1, MT1G, SREBF1, AKR1C3,
   YAP1, PRDX6, RARRES2, and CD44 were specifically high-expressed in
   Entorhinal Cortex, and this trend was more pronounced in AD. Similarly,
   DDIT4, AKR1C2, DUSP1, and KLF2 were highly expressed in the Frontal
   Cortex of both AD and AsymAD. CIRBP, ATP6V1G2, BEX1, FBXW7, NGB, and
   RGS4 were highly expressed in the Temporal Cortex of AsymAD, but were
   highly expressed in the Frontal Cortex of AD. These findings suggest
   region-specific roles of these genes in the pathogenesis of AD and
   AsymAD.

Figure 4.

   [130]Figure 4
   [131]Open in a new tab

   Molecular characteristics in different brain regions of AD and AsymAD.
   (A) The number of four brain regions (cerebellum, entorhinal, frontal
   and temporal cortex) in AsymAD and AD groups. (B) Barplot shows the
   contribution of DEGs (AsymAD and AD) from different brain regions. (C)
   Heatmap indicates ferroptosis-related genes specifically highly
   expressed in different brain regions. (D) Principal component analysis
   (PCA) showing two principal components of different regions in AsymAD
   and AD samples. (E) Barplot shows the score of genes with principal
   component 1 (PC1). Red bar represents AD, blue bar represents AsymAD.
   (F) Heatmap of specifically highly expressed genes in each brain region
   and the pathways in which they are involved. (G,H) The abundance of APP
   gene in four regions of AsymAD and AD. ns, not significant; *p < 0.05,
   **p < 0.01, ***p < 0.001, ****p < 0.0001.

   The results of principal component analysis (PCA) revealed distinct
   differences between the Cerebellum and other regions in patients AsymAD
   ([132]Figure 4D). On the other hand, the distribution of the Entorhinal
   Cortex, Frontal Cortex, and Temporal Cortex appeared to be more
   similar, indicating a similarity in molecular characteristics among
   these three regions in AsymAD. In contrast, the molecular
   characteristics of the four regions showed significant changes in AD.
   By observing the score of gene with principal component 1 (PC1), we
   found that genes highly expressed in entorhinal cortex (SREBF1, PRDX6,
   CD44, MTIG and AKR1C3) had high scores with PC1 in both AsymAD and AD
   ([133]Figure 4E). Furthermore, we conducted further analysis on
   region-specific genes present in both AsymAD and AD ([134]Figure 4E).
   The results demonstrated that both the Cerebellum and Entorhinal Cortex
   exhibited distinct molecular features compared to other regions
   ([135]Figure 4F). In the Entorhinal Cortex, region-specific genes are
   primarily involved in Myeloid leukocyte activation, Ensheathment of
   neurons, MAPK cascade, and Oligodendrocyte differentiation. In the
   Cerebellum, region-specific genes are mainly associated with
   Telencephalon development, Chemical synaptic transmission, Excitatory
   postsynaptic potential, and Postsynaptic membrane potential.
   Additionally, we identified a group of genes that are highly expressed
   in regions other than the Cerebellum. These genes are primarily
   involved in Forebrain development, Neuron projection development, and
   Trans-synaptic signaling. Additionally, we observed consistent
   downregulation of APP in both AsymAD and AD cerebellum ([136]Figures
   4G,[137]H). Our findings shed light on the regional heterogeneity and
   molecular signatures associated with AD and AsymAD, providing valuable
   insights for further research and understanding of the disease.

Immune microenvironment alterations in AD

   The clinical therapeutic sensitivity and disease diagnosis are
   significantly influenced by the microenvironment, which comprises
   immune cells, extracellular matrix, inflammatory factors, and a diverse
   range of growth factors. To investigate the alterations in the immune
   microenvironment of AD patients, we employed the CIBERSORT algorithm to
   assess the proportions of immune cells in AD and healthy control groups
   in [138]GSE33000. Firstly, we compared the expression of APP and
   ferroptosis-related genes in the [139]GSE33000 dataset between control
   and AD groups. We found that APP was significantly downregulated in AD
   group ([140]Figure 5A). Additionally, the expression patterns of
   ferroptosis-related hub genes were consistent with our previous
   findings ([141]Figure 5B). The CIBERSORT algorithm results showed that
   CD4+ T cells naive, CD4+ T cells resting memory, macrophages M2 and
   neutrophils were significantly higher in AD. In contrast, plasma cells,
   CD8+ T cells, T cells follicular helper and NK cells activated were
   significantly lower in AD ([142]Figures 5C,[143]D). We subsequently
   investigated the correlation between hub genes and immune infiltration
   ([144]Figure 5E). The hub genes were significantly negatively
   associated with CD4+ T cells resting memory, CD8+ T cells, macrophages
   M2, NK cells activated and neutrophils, which suggests that hub genes
   have a significant impact on the immune microenvironment. DDIT4 showed
   strong positive correlation with CD4 T cells memory resting and AKR1C2
   had positive correlation with Macrophages M2 ([145]Figure 5F). These
   results suggest that ferroptosis-related hub genes may play an
   important role in the immune microenvironment of AD patients.

Figure 5.

   [146]Figure 5
   [147]Open in a new tab

   Immune infiltration and immune-related factors. (A) The boxplot shows
   the expression of APP in control and AD groups. ****p < 0.0001. (B) The
   boxplot shows expression of ferroptosis related genes in [148]GSE33000
   including healthy controls and AD groups. Ns, not significant;
   *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. (C) Boxplots
   showed the differences in immune infiltration between control, HD and
   AD. (D) The relative abundances of 22 infiltrated immune cells among
   control, HD and AD. (E) Correlation dotplot shows correlation between
   genes and immune cell infiltration. Red dot represents positive
   correlation while blue dot represents negative. The size of the points
   are determined by the value of Spearman’s coefficient. (F) Scatter
   plots showed the correlation between AKR1C2 and M2 macrophages, DDIT4
   and T cells CD4 memory resting.

Potential therapeutic drug search based on ferroptosis-related hub genes

   In order to identify potential small molecule drugs for the treatment
   of AD, we analyzed 55 differentially expressed miRNAs (DEmiRNAs)
   including 19 up-regulated miRNAs and 34 up-regulated miRNAs between AD
   and healthy control groups using [149]GSE157239 ([150]Figures
   6A,[151]B). Subsequently, to further investigate whether these DEmiRNAs
   are associated with ferroptosis-related hub genes, we utilized the
   miRnet database and finally identified 14 miRNAs that target
   ferroptosis-related hub genes as potential therapeutic targets
   ([152]Figures 6C,[153]D; [154]Supplementary Table 2). Furthermore, we
   further confirmed the close association between AD and 7 miRNAs
   (hsa-miR-125a, hsa-miR-18b-5p, hsa-miR-193b, hsa-miR-373, hsa-miR-4286,
   hsa-miR-483, and hsa-miR-664b-3p) through the Human microRNA Disease
   Database (HMDD) ([155]Table 2).

Figure 6.

   [156]Figure 6
   [157]Open in a new tab

   Potential therapeutic drug search based on ferroptosis-related hub
   genes. (A) The volcano map shows differentially expressed miRNAs
   between control and AD. (B) The abundances of 55 (Up:19, Down:34)
   differentially expressed miRNAs in control and AD patients. (C) A total
   of 633 miRNAs targeting ferroptosis related genes were obtained from
   miRNANnet. Fourteen kinds of miRNAs are obtained by intersected with
   differentially expressed miRNAs. (D) The Sankey plot shows 14 miRNAs
   and genes they target. (E) The interaction scores of genes and drugs
   were obtained by DGIdb.

Table 2.

   Associations between microRNAs and AD.
   miRNA name Evidence code Disease name PMID Description Causality
   hsa-mir-125a genetics_knock down_promote Alzheimer disease 32626959
   Rescue experiments were conducted by transfecting lnc-ANRIL knockdown
   plasmid and lnc-ANRIL knockdown plasmid and miR-125a inhibitor in the
   PC12 cellular AD model as the KD-ANRIL group or KD-ANRIL KD-miR-125a
   group, respectively. Lnc-ANRIL knockdown suppressed cell apoptosis and
   inflammation while promoting neurite outgrowth via binding of miR-125a
   in AD. Unknown
   hsa-mir-18b other Alzheimer disease 36723144 Taking miR-18b-5p and
   miR3p which are associated with Alzheimer’s disease as an example, a
   FRET sensing system was fabricated for the simultaneous analysis of two
   miRNAs within 1 h at picomolar concentration. Unknown
   hsa-mir-193b circulation_biomarker_diagnosis_ns Alzheimer disease
   25119742 MicroRNA-193b is a regulator of amyloid precursor protein in
   the blood and cerebrospinal fluid derived exosomal microRNA-193b is a
   biomarker of Alzheimer’s disease. NO
   hsa-mir-373 circulation_biomarker_diagnosis_up Alzheimer disease
   36422510 Therefore, we suggest that miR-204 and miR-373 are potential
   biomarkers for AD. Unknown
   hsa-mir-4286 tissue_expression_down Alzheimer disease 32920076 The
   analyses yielded 6 miRNAs differentially expressed Unknown
   hsa-mir-483 circulation_biomarker_diagnosis_ns Alzheimer disease
   24577456 Genome-wide serum microRNA expression profiling identifies
   serum biomarkers for Alzheimer’s disease. NO
   hsa-mir-664b Other Alzheimer disease 36153426 Moreover, two miRNAs
   (miR-3651, miR-664b-3p) showed significant differential expression in
   AD brains versus controls, in accordance with the change direction of
   lead exposure. Unknown
   [158]Open in a new tab

   To further refine our search, we employed the Drug-Gene Interaction
   Database (DGIdb) to identify compounds with high interaction scores
   with the hub genes ([159]Figure 6E; [160]Supplementary Table 3). These
   findings suggest that these miRNAs and drugs may hold promise as
   potential therapeutic agents for AD, and further investigation is
   warranted to determine their efficacy and safety.

Discussion

   Ferroptosis is a form of regulated cell death characterized by the
   accumulation of iron and lipid peroxidation, leading to cell membrane
   damage and ultimately cell death. In the context of AD, dysregulation
   of iron metabolism and increased oxidative stress contribute to the
   accumulation of toxic protein aggregates, such as amyloid-β plaques and
   tau tangles. This exacerbates neurodegeneration and cognitive decline
   in Alzheimer’s patients. In addition, a substantial proportion from 20
   to 30%, of cognitively intact elderly individuals exhibit amyloid-β
   protein accumulation. Compared to those without amyloid-β, these
   individuals have a higher risk of progressing to AD and are commonly
   referred to as AsymAD individuals ([161]Driscoll and Troncoso, 2011).

   In this study, we identified differentially expressed genes associated
   with ferroptosis in AD patients using the [162]GSE118553. We further
   validated the expression trends of these hub genes using the
   [163]GSE132903, demonstrating consistency in their expression patterns.
   Among the ferroptosis-related hub genes we identified, four genes were
   involved in driving ferroptosis: KLF2, YAP1, CIRBP, and FBXW7. KLF2 has
   been found to have a negative regulatory relationship with APP and
   overexpression of KLF2 attenuated Aβ-induced oxidative stress ([164]Wu
   et al., 2013; [165]Fang et al., 2017). YAP1 is a transcriptional
   co-activator that has been linked to neurodegenerative diseases,
   including AD ([166]Xu et al., 2021). CIRBP demonstrated neuroprotective
   effects against amyloid-induced neuronal toxicity through antioxidative
   and antiapoptotic pathways ([167]Su et al., 2020). FBXW7 is a ubiquitin
   ligase that plays a role in protein degradation and has been reported
   that plays a crucial role in neurological functions, particularly in
   neurodevelopment and the pathogenesis of neurodegeneration ([168]Yang
   et al., 2021). Furthermore, we identified that 9 DEGs are involved in
   suppressing ferroptosis process. AKR1C2 and AKR1C3 belong to the
   aldo-keto reductase family, which are involved in the metabolism of
   steroid hormones and other endogenous compounds. Dysregulation of
   AKR1C2 and AKR1C3 has been observed in Alzheimer’s disease
   ([169]Luchetti et al., 2011; [170]Chik et al., 2022), suggesting their
   potential role in disease progression. PRDX6 has already been reported
   to be involved in the oxidative stress and antioxidant defense process
   in AD ([171]Viejo et al., 2022). CD44 is a cell surface glycoprotein
   and involve in the neuroinflammatory processes mediated by microglial
   cells ([172]Rangaraju et al., 2018; [173]Smajić et al., 2022). SREBF1
   has also been confirmed as a disease-associated transcription factor
   ([174]Morabito et al., 2021). BEX1 has been reported to potentially be
   associated with the gender disparity in AD ([175]Garcia et al., 2023).
   However, the relationship between MUC1, RARRES2 and MT1G with the
   disease is not extensively studied, and the specific role of them in AD
   pathogenesis is still being elucidated. In addition, we have also
   identified 5 unclassified ferroptosis-related genes that are associated
   with AD: DUSP1, DDIT4, NGB, ATP6V1G2, RGS4. There have been reports
   indicating a significant negative correlation between DUSP1 and
   cognitive abilities in AD patients ([176]Qi et al., 2022). In the
   context of AD, DDIT4 responds to extracellular amyloid-β and regulates
   the cytotoxic effects of amyloid-β in vitro ([177]Morel et al., 2009).
   NGB is a neuroglobin protein, belonging to the globin family. The
   overexpression of NGB can protect neurons from mitochondrial
   dysfunction and neurodegenerative diseases such as AD ([178]Ascenzi et
   al., 2016). ATP6V1G2, a subunit of the vacuolar ATPase (V-ATPase)
   complex. Studies have shown that ATP6V1G2 as significantly regulated by
   DNA methylation at hub CpGs in AD ([179]Kim et al., 2023). RGS4, a
   regulator of G-protein signaling, has been identified as a potential
   biomarker for AD in multiple studies ([180]Zou et al., 2019; [181]Chen
   et al., 2022), and this finding has been confirmed in our result as
   well. Although these genes exhibit consistent strong correlations in
   both datasets, there are some genes whose correlations show completely
   opposite patterns between the two datasets. Therefore, further
   investigation into the stability of interactions between
   ferroptosis-related hub genes may be necessary to provide additional
   evidence. This could be an important aspect to consider in future
   research.

   To investigate the potential of these genes as biomarkers for AD, we
   constructed a random forest classifier model. Our results showed that
   this model was able to accurately distinguish between healthy controls
   and AD patients in both the training sets ([182]GSE118553, AUC = 0.824)
   and validation sets ([183]GSE132903, AUC = 0.734). Furthermore, we
   found that the model was also able to differentiate between AsymAD and
   control groups (AUC = 0.689), and also can distinguish between AsymAD
   and AD (AUC = 0.705), indicating that these genes may play an important
   role in the progression of AD. Demographic characteristics also
   indicated that both AsymAD and AD patients had significantly higher age
   compared to the healthy control group. Additionally, a higher
   proportion of females was observed in both AsymAD and AD patients,
   which is consistent with previous research findings. Furthermore, we
   discovered significant downregulation of pathways related to learning
   or memory, neurotransmitter secretion, and response to iron ion in AD
   patients compared to AsymAD patients. Besides, in our analysis of genes
   involved in the learning or memory pathways, we discovered that APOE
   and AGER exhibit dysregulated expression in AD. APOE is involved in
   lipid metabolism and is a major genetic risk factor for late-onset
   Alzheimer’s disease, with the APOE ε4 allele being particularly
   associated with an increased risk ([184]Serrano-Pozo et al., 2021;
   [185]Martens et al., 2022). AGER is involved in the regulation of
   inflammation and oxidative stress. In Alzheimer’s disease, AGER
   contributes to the accumulation of amyloid-beta plaques and the
   activation of microglia, which can exacerbate neuroinflammation and
   neuronal damage ([186]Ding et al., 2020).

   Further, we observed the transcriptional expression profiles of
   different brain regions in AsymAD and AD, respectively. By assessing
   the contribution of different brain regions in DEGs of AsymAD and AD,
   we found that the DEGs of AsymAD and AD were mostly from temporal
   cortex and frontal cortex, which was consistent with previous studies
   ([187]Patel et al., 2019). In different regions of AsymAD and AD, we
   found MUC1, MT1G, SREBF1, AKR1C3, YAP1, PRDX6, RARRES2 and CD44 were
   upregulated in both AsymAD and AD entorhinal cortex region. DDIT4,
   AKR1C2, DUSP1 and KLF2 were all upregulated in frontal cortex region.
   Notably, CIRBP, ATP6V1G2, BEX1, FBXW7, NGB, and RGS4 were upregulated
   in AsymAD temporal cortex region but upregulated in AD frontal cortex
   region. These results indicate that ferroptosis-related hub genes can
   reflect the transcriptional changes of different brain regions in
   pathological states. It is worth noting that the changes of
   ferroptosis-related hub genes in other brain regions, such as the
   hippocampus, which have a significant impact on AsymAD or AD, have not
   been elucidated in our study. This is a crucial aspect that requires
   further research and validation in the future. We also identified
   specific high-expression genes in different brain regions. Entorhinal
   cortex specific high expression genes are mainly related to myeloid
   leukocyte activation, ensheathment of neurons, MAPK cascade and
   oligodendrocyte differentiation. The highly expressed genes of
   cerebellum are mainly involved in telencephalon development, chemical
   synaptic transmission, excitatory postsynaptic potential and
   postsynaptic membrane potential. We also identified a group of genes
   that were highly expressed in regions other than the cerebellum, which
   primarily associated with forebrain development, neuron projection
   development, and trans−synaptic signaling.

   Studies have shown that both microglia and astrocytes, as well as
   peripheral immune cells, are involved in neuroinflammation associated
   with AD ([188]Leng and Edison, 2021). Therefore, we investigated immune
   infiltration in AD, we found that M2 macrophages’ infiltration was
   significantly increased in AD, which is consistent with previous study
   ([189]Lin et al., 2022). In addition, neutrophils and monocytes were
   also upregulated in AD. In contrast, CD4 T cells memory resting was
   downregulated. Further, we found ferroptosis-related hub genes was
   highly correlated with immune cell infiltrations. DDIT4 showed strong
   positive correlation with CD4 T cells memory resting and AKR1C2 had
   positive correlation with Macrophages M2. It has been shown that
   downregulation of neuronal DDIT4 can restore the proliferative
   characteristics of glial cells and the abnormal expression of key
   proteins of inflammasome ([190]Pérez-Sisqués et al., 2021). Therefore,
   studying the role of immune and inflammatory cells in AD may provide
   anti-inflammatory and immunomodulatory targets for AD treatment.

   Here, miRNAs targeting ferroptosis related hub genes were identified by
   miRNAnet. We identified 14 miRNAs which targeting 10 of ferroptosis
   related genes. miR-125a showed promise in regulating cell functions and
   inflammation in diseases associated with neuronal dysfunction
   ([191]Potenza and Russo, 2013). In addition, study also reported that
   in a rat model of ethanol-induced neurotoxicity, the SVCT2 mitigated
   oxidative injury by modulating the JNK/p38 MAPK, NF-κB, and miR-125a-5p
   pathways ([192]Tian et al., 2016). Moreover, it has been observed that
   miR-483-5p plays a role in repressing the activity of ERK1/2, leading
   to a decrease in the phosphorylation of TAU protein at epitopes
   associated with TAU neurofibrillary pathology in AD ([193]Nagaraj et
   al., 2021). Then, 19 drugs targeting the above genes were retrieved
   from the DGIdb database. Albuterol also known as Salbutamol, is a
   short-acting β 2-adrenoceptor agonist. Increased amyloid β production
   after β 2-adrenoreceptor activation was reported by [194]Ni et al.
   (2006), animal models of memory disruption have shown improved
   performance after Albuterol treatment ([195]Ciprés-Flores et al.,
   2019). In our results, Albuterol showed the highest interaction score
   with DUSP1, indicating that DUSP1 may be a promising drug target.
   Insulin plays an important role in the regulation of glucose metabolism
   and can influences cerebral bioenergetics, turnover of
   neurotransmitters in AD ([196]Arnold et al., 2018; [197]Kellar and
   Craft, 2020). A study has reported Hydroxyurea provides neuroprotection
   in vitro against neurotoxins, which increase oxidative stress and
   excitotoxicity and reduce mitochondrial function ([198]Brose et al.,
   2018).

Conclusion

   In conclusion, we acquired 18 ferroptosis-related hub genes in AD. We
   explored the potential of these genes as diagnostic markers and their
   role in the disease process which will help in understanding the
   development of AD. However, further experimental validation is needed
   to verify their functions. In addition, these hub genes have high
   correlation with infiltration of immune cells. Currently, a few drugs
   targeting these hub genes are predicted to relieve AD, provides new
   research clues for preventing the development of AD. Our study also has
   limitations, we used data from public databases for our analysis, which
   were from different platforms. Due to different sequencing technologies
   and platforms, the inclusion criteria of patients are different.
   Furthermore, our study is limited to the transcriptome level, the
   significance of these findings requires further validation through
   prospective clinical and basic experiments.

Data availability statement

   The original contributions presented in the study are included in the
   article/[199]Supplementary material, further inquiries can be directed
   to the corresponding authors.

Author contributions

   HZ: Writing – original draft. JW: Writing – original draft. ZL: Formal
   analysis, Writing – review & editing. SW: Visualization, Writing –
   review & editing. GY: Methodology, Writing – original draft, Writing –
   review & editing. LW: Writing – original draft.

Funding Statement

   The author(s) declare financial support was received for the research,
   authorship, and/or publication of this article. The work was supported
   by the 111 Project “State Innovation Base for Pulmonary Fibrosis,” and
   Henan Province Science and Technology Project, 232102521025.

Footnotes

   ^1 [200]https://www.ncbi.nlm.nih.gov/geo/

Conflict of interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Publisher’s note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

Supplementary material

   The Supplementary material for this article can be found online at:
   [201]https://www.frontiersin.org/articles/10.3389/fnmol.2023.1280639/fu
   ll#supplementary-material
   SUPPLEMENTARY FIGURE 1

   (A) Volcano plot of differentially expressed genes differing
   significantly between healthy and AD samples in [202]GSE118553. (B)
   Volcano plot of differentially expressed genes differing significantly
   between healthy and AD samples in [203]GSE132903. (C) The boxplot shows
   expression of ferroptosis related genes in [204]GSE132903 including
   healthy controls and AD groups. *p < 0.05, **p < 0.01, ***p < 0.001,
   ****p < 0.0001.
   [205]Click here for additional data file.^ (394.9KB, tif)
   [206]Click here for additional data file.^ (11.6KB, ZIP)
   [207]Click here for additional data file.^ (69.5KB, XLSX)
   [208]Click here for additional data file.^ (12.3KB, XLSX)
   [209]Click here for additional data file.^ (11.2KB, XLSX)

References