Identification of Copper Metabolism Related Biomarkers,Polygenic Prediction Model,and Potential Therapeutic Agents in Alzheimer’s Disease

Abstract

Background:

The underlying pathogenic genes and effective therapeutic agents of Alzheimer’s disease (AD) are still elusive. Meanwhile, abnormal copper metabolism is observed in AD brains of both human and mouse models.

Objective:

To investigate copper metabolism-related gene biomarkers for AD diagnosis and therapy.

Methods:

The AD datasets and copper metabolism-related genes (CMGs) were downloaded from GEO and GeneCards database, respectively. Differentially expressed CMGs (DE-CMGs) performed through Limma, functional enrichment analysis and the protein-protein interaction were used to identify candidate key genes by using CytoHubba. And these candidate key genes were utilized to construct a prediction model by logistic regression analysis for AD early diagnosis. Furthermore, ROC analysis was conducted to identify a single gene with AUC values greater than 0.7 by GSE5281. Finally, the single gene biomarker was validated by quantitative real-time polymerase chain reaction (qRT-PCR) in AD clinical samples. Additionally, immune cell infiltration in AD samples and potential therapeutic drugs targeting the identified biomarkers were further explored.

Results:

A polygenic prediction model for AD based on copper metabolism was established by the top 10 genes, which demonstrated good diagnostic performance (AUC values). COX11, LDHA, ATOX1, SCO1, and SOD1 were identified as blood biomarkers for AD early diagnosis. 20 agents targeting biomarkers were retrieved from DrugBank database, some of which have been proven effective for the treatment of AD.

Conclusions:

The five blood biomarkers and copper metabolism-associated model can differentiate AD patients from non-demented individuals and aid in the development of new therapeutic strategies.

Keywords

Agents Alzheimer’s disease biomarkers copper metabolism polygenic prediction model

INTRODUCTION

Alzheimer’s disease (AD), the most prevalent form of dementia in the elderly [1], is a progressive neurodegenerative disease. Early diagnosis of AD is essential for disease therapy [2]. The pathophysiology of AD involves amyloid-β cascade, tau protein, neuroinflammation, metal ions, and oxidative stress hypotheses [3]. These several hypotheses serve as the basis for diagnosing and treating AD, while oxidative stress serves as the key “bridge” connecting the distinct pathways in AD [4]. Multiple studies have indicated that anomalies in copper metabolism can significantly trigger Fenton-like reactions to produce reactive hydroxyl radicals, which further cause a global oxidative stress response [5, 6]. Thus, copper metabolism may improve our understanding of the disease and facilitate the development of AD.

In recent years, there has been an increasing interest in exploring the intricate interplay between abnormal copper metabolism and gene expression regulation [7–9]. Specifically, certain genes involved in copper metabolism, such as ATP7B and ceruloplasmin, have been identified to increase the risk of AD [10]. In addition, excessive copper accumulation can induce oxidative stress and cell death by disrupting the mitochondrial respiration chain [11]. Moreover, La Mendola et al. [12] discovered that dysregulation of copper metabolism is an early event in AD pathology. These findings highlight the significance of exploring the potential consequences of genes related to copper metabolism in the onset and progression of AD.

Previous studies have identified several genes implicated in the etiology of AD, including APOE, PSEN1, PSEN2, and APP [13]. APOE is associated with late-onset familial AD [14], and rare mutations in PSEN1, PSEN2, and APP genes can cause early-onset AD in a minority of cases [15–17]. Although these genes are strongly associated with AD, they are mainly used for genetic screening of familial AD. In fact, the non-familial form of AD accounts for the large majority of AD cases [18]. Besides, single-gene screening lacks the sensitivity and specificity required for clinical diagnosis [19]. For non-familial AD, copper metabolism-related genes could facilitate early diagnosis and preventive strategies [20]. Integrating multiple biomarker approaches can provide comprehensive information to improve the diagnostic accuracy and specificity of AD [21]. Therefore, the identification of specific genes associated with copper metabolism would help discover AD biomarkers and develop potential treatment targets.

In this study, four AD-related datasets were downloaded from the Gene Expression Omnibus (GEO), and copper metabolism-related genes (CMGs) were obtained from the GeneCards database. The differentially expressed CMGs (DE-CMGs) were visualized using a Venn diagram. Then, enrichment analyses of DE-CMGs were performed. Based on a protein-protein interaction (PPI) network constructed, the top 10 hub genes were selected. Logistic regression modeling and receiver operating characteristic (ROC) curve analysis were performed, and the results were further validated using an external dataset. Genes with an AUC value greater than 0.7 were considered as potential diagnostic genes. Neuroinflammation is considered an important etiology and pathogenesis of AD, and copper accumulation in the brain has been shown to trigger inflammation [22]. Therefore, the immune infiltration level was further explored using CIBERSORTx. In addition, agents targeting biomarkers were obtained from the DrugBank database, which could have implications for pharmacological treatment in the future. Finally, biomarkers were identified by quantitative real-time polymerase chain reaction (qRT-PCR) in AD clinical samples. This study could provide potential blood biomarker candidates for clinical early diagnosis and treatment.

MATERIALS AND METHODS

Collection of AD-associated microarray dataset

The GEO database, created by the US National Center for Biotechnology Information, is a publicly accessible repository for microarray and high-throughput sequencing datasets [23]. Inclusion criteria were as follows: 1) organism: Homo sapiens; 2) study type: expression profiling by array; and 3) samples: AD-related samples and non-demented control samples, including the largest possible sample size. Finally, based on the inclusion criteria, 4 microarray datasets were obtained from the GEO database to analyze the differential mRNA expressions. The GSE5281 used to identify DEGs is composed of 87 AD (age 68–97 years) and 74 age-matched healthy controls (age 63–102 years). GSE36980 dataset was used for model validation in subsequent analyses. Gene expression profiles of peripheral blood were obtained from GSE63060 and GSE97760. The data sample collection is illustrated in Table 1.

Table 1

Dataset detailed characteristics

Dataset	Samples	Source	Platform/Technology
GSE5281	161 (87 AD, 74 ND)	Brain tissue	GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array
GSE36980	80 (34 AD, 46 ND)	Brain tissue	GPL6244 [HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version]
GSE63060	249 (145 AD, 104 ND)	Peripheral blood	GPL6947 Illumina HumanHT-12 V3.0 expression bead chip
GSE97760	19 (9 AD, 10 ND)	Peripheral blood	GPL16699 Agilent-039494 SurePrint G3 Human GE v2 8x60K Microarray 039381 (Feature Number version)

AD, Alzheimer’s disease; ND, non-demented.

Copper metabolism-related genes

GeneCards integrates automatically from over 125 data sources where comprehensive useful and annotative information of known and predicted human genes can be acquired [24]. A relevance score was assigned to assess the strength of the association between genes and copper metabolism, ranging from 0 to 100. Higher scores represent the stronger associations. Genes with association score > 10 were taken into copper metabolism-related genes to facilitate subsequent difference analysis.

Identification of DE-CMGs in AD

Based on the predetermined statistical threshold of log (fold-change) values > 0.5 and adjusted p-values < 0.05, DEGs were screened out using the limma package in R. Volcano plots and heatmaps were performed in R software to describe the expression of DEGs. Additionally, to find the DE-CMGs, Venn diagram was made for results visualization.

Functional and pathway enrichment analysis on DE-CMGs

In order to comprehensively verify gene ontology (GO) and biological pathways of DE-CMGs, we applied three enrichment analysis tools which are based on different algorithms to perform mutual detection. Specifically, GO is a knowledge base that is a commonly used bioinformatics method to approach gene function or individual genomic products, which includes three essential aspects: biological processes (BP), molecular functions (MF), and cellular components (CC) [26]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) refers to use genome sequencing and other high-throughput to understand advanced biological functions [27]. Metascape is a publicly available database that provides comprehensive gene list annotation and analysis. In this study, Cytoscape (V3.9.1) along with its plugin ClueGO was then adopted to re-analyze the biological process (p < 0.05).

Protein-protein interaction networks and identification of hub genes

For predicting the interaction among the DE-CMGs, STRING, a web-based database which was used to identify a PPI network. The PPI pairs were screened by confidence score (>0.40) and visualized using the Cytoscape software. Furthermore, on the premise of obtaining the PPI network, the hub genes were extracted using the plugin cytoHubba application and the MCC algorithm.

Construction and validation of the logistic regression

To effectively distinguish the AD patients from controls, the logistic regression (LR) was also constructed. LR is a generalized linear regression analysis model, which is often used in fields related to data analysis, such as automatic diagnosis of diseases [28]. For this study, a response variable was assigned 1 for AD and 0 for ND. Stepwise regression analysis was initially employed to eliminate no-significant factors and account for inter-variable association to simplify the model. Afterward, logistic regression models were fitted to identify all candidate variables. Finally, the diagnostic efficacy of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) using\enlargethispage 2pt the pROC package in R.

Identify blood biomarkers and enrichment analysis

To find potential genes for AD diagnosis based on blood levels, candidate biomarkers were intersected with DEGs in the blood datasets. Subsequently, the biomarkers then conducted biological pathways enrichment analysis using the Metascape tool, a free online platform.

Immune landscape

Assessed immune cell infiltration by using CIBERSORTx, which contains 547 biomarkers and 22 human immune cells, including myeloid subpopulations, natural killer (NK) cells, plasma cells, naive and memory B cells and seven T cell types. The tool for deconvolution analysis of the expression matrix of immune cells is based on the linear support vector regression principle. This study used expression data from GSE5281 and quantified the relative proportions of the 22 immune cells in each sample. The results were displayed as heatmaps.

Biomarkers potential agents prediction using the DrugBank

The DrugBank database is a bioinformatics and cheminformatics repository containing detailed drug data with comprehensive drug target information, which was used to explore drugs targeting hub genes. The database consists of more than 7,800 drugs, including FDA-approved small-molecule drugs, FDA-approved biotech drugs, nutraceuticals, and experimental drugs. In this research, the DrugBank was used to predict the potential therapeutic agents for AD.

Validation of biomarkers expression by qRT-PCR

This study was approved by the Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine (ZE2022-321-01). Blood samples of eleven healthy controls and ten patients with AD were acquired to verify the predictive analysis results. The peripheral blood mononuclear cells (PBMCs) were isolated from blood samples and total RNA was extracted using TRIzol^® reagent (Invitrogen; Thermo Fisher Scientific, Inc.) following standard protocols. RNA samples’ concentration and purity (NanoDrop2000, Thermo) were assessed by an ultraviolet spectrophotometer and reverse transcribed to cDNA. The mRNA was assessed by qRT-PCR using a SYBR-Green PCR kit (Takara Bio, Inc.) on a Roche Z480 Real-Time PCR system (Roche Molecular Diagnostics). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) acted as the internal control for mRNA expression. Comparative quantification was determined using the 2 ^-ΔΔCq method [29]. All samples were prepared in triplicate, and the results are expressed as the mean±SD for comparative analysis. Sequences of primers used in the study are shown in Table 2.

Table 2

Primer sequences used for gene expression studies

Gene Name	Forward (5’-3’)	Reverse (5’-3’)
GAPDH	GCACCGTCAAGGCTGAGAAC	TGGTGAAGACGCCAGTGGA
SOD1	GGTGTGGCCGATGTGTCTAT	CAAGCCAAACGACTTCCAGC
LDHA	CAGGTGGTTGAGAGGGTCTTT	CTTCAAACGGGCCTCTTCCT
COMMD1	GCTGGAGAGTTGATGGCAAGTC	GACCTCATCAAATTCCAAACACAG
SCO1	GGCCTGTTTCCTGGAAGTCT	TTGTGAGGGAAAACGGTCCC
COX11	CTGCCGTACCCCTTTATCGG	TCTGAGGCATGACCTGCAAC
ATOX1	CATGCCGAAGCACGAGTT	CTTCAGGGTTGCAAGCAGAG

Statistical analysis

All statistical analyses were conducted using R software (version 4.1.0), and GraphPad Prism 8.0 software (GraphPad Software, Inc., La Jolla, CA, USA). Univariate logistic proportional regression analysis was performed to analyze the effect of hub genes on AD. An unpaired t-test was used to compare significant differences in two independent groups with experimental results. A probability (p) value of less than 0.05 was considered statistically significant.

RESULTS

Identification of hub genes associated with DE-CMGs

The flowchart of this study is shown in Fig. 1. To identify genes related to AD, we first distinguished 4,812 DEGs from the GSE5281 dataset. These DEGs were presented in a volcano plot (Fig. 2A). A heatmap of the top 20 differentially expressed genes was plotted (Fig. 2B). When comparing the DEGs with the 271 CMGs collected from the GeneCards, 66 identical DE-CMGs were identified (Fig. 2C).

Fig. 1

The workflow of the analyses.

Fig. 2

A) Red genes indicate significantly high expression, blue genes indicate significantly low expression, and gray genes indicate non-significantly differentially expressed genes from the GSE5281 dataset in the volcano map. B) The heatmap shows the top 20 differentially genes expressed in AD and ND samples. C) Venn diagram showing the 66 DE-CMGs (the intersection of the DEGs and copper metabolism-related genes).

GO and KEGG terms enrichment analysis of DE-CMGs

The DE-CMGs were subjected to functional enrichment analysis using ClueGo, which included GO with significant pathways for 4 MF highlighted “telomerase activity”, “peroxidase activity”, “oxidoreductase activity”, and “hydro-lyase activity”. For BP analysis, which is displayed in Fig. 3A. Most pathways involved metabolic processes, such as “transition metal ion homeostasis”, “electron transport chain”, “glycosyl compound metabolic process”, and “catecholamine metabolic process” and so on. Furthermore, the results from Metascape revealed that these DE-CMGs were also mainly enriched in metabolic processes (Fig. 3B), such as “transition metal ion homeostasis”, “generation of precursor metabolites and energy”, “amino acid metabolism”, and “copper homeostasis”. For KEGG, mainly enriching in “Ferroptosis”, “Chemical carcinogenesis”, “Tyrosine metabolism”, “Tryptophan metabolism”, and “Central carbon metabolism in cancer” (Fig. 3C).

Fig. 3

Functional annotation for DE-CMGs. A) GO biological processes enrichment analysis of DE-CMGs. B) Metascape enrichment analysis of DE-CMGs. C) KEGG enrichment analysis of DE-CMGs.

PPI network construction

The PPI network of the DE-CMGs was constructed using STRING and Cytoscape (Fig. 4A). The top ten highest-scored genes, as determined by MCC scores in CytoHubba, were selected as the hub genes (Fig. 4B). The gene list with gene names, abbreviations, and functions calculated by CytoHubba was shown in Table 3.

Fig. 4

PPI network construction and identification of hub genes. A) PPI network of the DE-CMGs. The edge shows the interaction between two genes. B) The top 10 genes were screened from the PPI network using the MCC algorithm of CytoHubba plugin.

Table 3

Top 10 Hub Genes Ranked with Degrees

No	Gene	Full Name	Function
1	SOD1	Superoxide Dismutase 1	Destroys radicals which are normally produced within the cells and toxic to biological systems
2	SCO1	Synthesis Of Cytochrome C Oxidase 1	Copper metallochaperone is essential for the maturation of cytochrome c oxidase subunit II (MTCO2/COX2)
3	CP	Ceruloplasmin	Ceruloplasmin is a blue, copper-binding (6-7 atoms per molecule) glycoprotein
4	COX11	Cytochrome C Oxidase Copper Chaperone COX11	Exerts its effect at some terminal stages of cytochrome c oxidase synthesis
5	ATOX1	Antioxidant 1 Copper Chaperone	Binds and delivers cytosolic copper to the copper ATPase proteins
6	SLC31A1	Solute Carrier Family 31 Member 1	High-affinity, saturable copper transporter involved in dietary copper uptake
7	COMMD1	Copper Metabolism Domain Containing 1	Regulating ubiquitination of specific cellular proteins
8	TP53	Tumor Protein P53	Involved in cell cycle regulation as a trans-activator that acts to negatively regulate cell division
9	LDHA	Lactate Dehydrogenase A	Interconverts simultaneously and stereo-specifically pyruvate and lactate with concomitant interconversion of NADH and NAD (+)
10	FH	Fumarate Hydratase	Catalyzes the reversible stereospecific interconversion of fumarate to L-malate

Immune infiltration

The components of the microenvironment, which includes immune cells, extracellular matrix, inflammatory factors, and a variety of growths that have an important impact on the sensitivity of disease diagnosis and treatment. In this part of the study, the CIBERSORTx algorithm was used to estimate the composition of the 22 kinds of immune cells in 87 AD samples and 74 ND samples, as illustrated in the histogram (Fig. 5A). The expression of T cells was the highest, and accounting for about one-third of all cells. The level of NK cells was relatively low. The immune cell infiltration levels of AD and ND samples were compared in a boxplot (Fig. 5B). The results showed that the AD group had significantly higher proportions of naive B cells (p = 0.0349), M1 macrophages (p = 0.0044), and M2 macrophages (p = 0.0063) than the ND group.

Fig. 5

Immune cell infiltration between AD and ND samples. A) The relative percentages of 22 immune cell subpopulations in each sample. B) The difference levels of immune infiltration between AD (red) and ND (blue).

Construction and validation of a diagnostic model

Logistic regression algorithm was used to construct a multigene prediction model based on GSE5281. After that, backward stepwise regression analysis was performed, and nine of these ten genes, including SOD1, SCO1, LDHA, COMMD1, FH, TP53, SLC31A1, COX11, and ATOX1were selected to obtain the best model. The results showed that the predictive model constructed from these nine genes had good diagnostic performance and the area under the ROC curve was 0.927 (Fig. 6A). To further validate the results of our analysis, GSE36980 dataset was used, with an AUC of 0.808 (Fig. 6B).

Fig. 6

ROC curves (AUC) for evaluating the diagnostic value of the logistic regression model in the [(A), GSE5281] training set, and [(B), GSE36980] test set.

Identification of blood biomarkers

TOP 10 DE-CMGs were acquired from the CytoHubba. Furthermore, 6 DE-CMGs (SOD1, SCO1, LDHA, COX11, ATOX1, and COMMD1) were screened as candidate key genes according to AUC > 0.7 (Fig. 7). To identify peripheral blood genes related to AD, we distinguished 3,693 DEGs from the GSE63060, and 6,697 DEGs from GSE97760. Six candidate hub genes also showed differences in the blood datasets (Fig. 8A). Besides, biological processes enrichment analysis showed that the 6 biomarkers putatively participated in the “copper homeostasis”, “the citric acid (TCA) cycle and respiratory electron transport”, which are visualized in Fig. 8B. Deficiencies of TCA cycle-related metabolites and mitochondria functions in AD models were highlighted in many researches [30–32].

Fig. 7

A-F) Candidate key genes with AUC values > 0.7.

Fig. 8

A) Venn diagram showing the 6 candidate hub genes (the intersection of the blood datasets DEGs and candidate hub genes). B) Biological processes enrichment analysis of 6 candidate hub genes.

The experiment of biomarkers

To confirm the reliability of the prediction results, qRT-PCR was used to detect the expression of the above 6 candidate biomarkers based on clinical specimens. The research data demonstrated that the mRNA expression level of COX11, LDHA and ATOX1 in AD decreases compared to the controls (p < 0.05 COX11, LDHA, p < 0.01, ATOX1). On the contrary, the expression changes of SCO1 and SOD1 were observed (p < 0.05, SCO1; p < 0.001, SOD1). Besides, there was no significant difference in the levels of COMMD1 between the AD group and ND group (Fig. 9). These identified five genes may serve as the potential diagnostic and prognostic blood biomarkers.

Fig. 9

qRT-PCR validation of the hub gene between AD and normal controls. A-F) data were presented as mean±SEM of triplicate experiments (*p < 0.05, **p < 0.01, ***p < 0.001, ns, nonsignificant).

Identification of small molecular therapeutic agents

By utilizing drug and target information from the DrugBank database, 20 drugs targeting the hub genes were retrieved, including 13 approved drugs, 5 experimental drugs, and 2 investigational drugs approved by the FDA (Table 4). However, in the present study, potential therapeutic targets for the COMMD1 are not yet available. Indeed, it was demonstrated that zinc levels markedly decrease with both mouse and human aging and even decline in AD [33]. Copper (DB09130) is a transition metal found in a variety of supplements and vitamins. Arimoclomol (DB05025) is an experimental drug used to treat amyotrophic lateral sclerosis [34]. Aspirin (DB00945) is a commonly used drug for relieving pain and reducing fever caused by various conditions. Cannabidiol (DB09061) and medical cannabis (DB14009) are active cannabinoids that can relieve moderate to severe neuropathic pain. Dopamine (DB00988) is a catecholamine neurotransmitter used to treat hemodynamic imbalances, poor perfusion of vital organs, low cardiac output, and hypotension [35]. Isoprenaline (DB01064) is a catecholamine non-selective beta-adrenergic agonist typically used to treat bradycardia and heart block. Cisplatin (DB00515) is a platinum-based chemotherapy agent for the treatment of various tumors, such as sarcomas, carcinomas, lymphomas, and germ cell tumors. Benzamidine (DB03127) is a drug to treat painful and inflammatory conditions in the oral cavity, such as infections and gingivitis [36]. Stiripentol (DB09118) is an antiepileptic agent that is commonly prescribed in combination with other anticonvulsants to treat seizures associated with Dravet syndrome [37]. Nicotinamide (DB02701) is an important compound functioning as a component of the coenzyme NAD [38].

Table 4

Potential drugs targeting these hub genes obtained from

Gene symbol	DrugBank ID	Name	Drug group	Actions	UniProt ID
SOD1	DB01593	Zinc	Approved, Investigational		P00441
	DB14487	Zinc acetate	Approved, Investigational		P00441
	DB14533	Zinc chloride	Approved, Investigational	Cofactor	P00441
	DB09061	Cannabidiol	Approved, Investigational	Inhibitor	P00441
	DB00988	Dopamine	Approved		P00441
	DB01064	Isoprenaline	Approved, Investigational	Stabilization	P00441
	DB01629	5-fluorouridine	Experimental		P00441
	DB14009	Medical Cannabis	Experimental, Investigational	Inhibitor	P00441
	DB05025	Arimoclomol	Investigational		P00441
	DB14011	Nabiximols	Investigational		P00441
SCO1	DB09130	Copper	Approved, Investigational		O75880
ATOX1	DB00515	Cisplatin	Approved		O00244
	DB02772	Sucrose	Approved, Experimental, Investigational		O00244
	DB03127	Benzamidine	Experimental		O00244
LDHA	DB09130	Copper	Approved, Investigational		P00338
	DB09118	Stiripentol	Approved	Inhibitor	P00338
	DB02701	Nicotinamide	Approved, Investigational		P00338
	DB11638	Artenimol	Approved, Experimental, Investigational	Ligand	P00338
	DB02483	Etheno-NAD	Experimental		P00338
	DB03940	Oxamic Acid	Experimental		P00338

DISCUSSION

Copper is an essential element for brain cells, serving as a cofactor and structural component of various enzymes involved in physiological processes, including the respiratory chain, antioxidative defense, iron metabolism, and neurotransmitter synthesis [39, 40]. Copper overload toxicity as well as copper deficiency may be accompanied by neurological diseases [41, 42]. Recent studies have also indicated that abnormalities in copper metabolism can induce toxic amyloid-β deposition, concurrently with reduced intracellular copper bioavailability [43, 44].

Currently, AD models tend to focus on a small subset of genes identified in familial AD, which remains challenging to identify optimal gene candidates for modeling and treating AD [45]. In the study, the 4,812 DEGs obtained from the GSE5281 dataset in the GEO database were intersected with 271 CMGs selected by GeneCards, and then screened 66 DE-CMGs. Comprehensive GO and KEGG analyses revealed that the DE-CMGs were primarily involved in metabolism-related and metal ion homeostasis biological processes, which are associated with neurodegenerative diseases, particularly AD. Top ten DE-CMGs were further screened by CytoHubba. Nine of the ten genes, including SOD1, SCO1, LDHA, COMMD1, FH, TP53, SLC31A1, COX11, and ATOX1, were screened to construct a diagnostic model in clinical applications, which can be a more effective approach compared to a single biomarker and validated in another dataset.

Given that certain cells can cross the blood-brain barrier, it is crucial to identify pathological differences in brain tissue that can be discerned in the blood for AD, as this holds great potential for broad applications. Hub genes with an AUC > 0.7 were identified as candidate diagnostic biomarkers, including SOD1, SCO1, LDHA, COX11, ATOX1, and COMMD1. These genes were also identified as DEGs in blood datasets (GSE63060 and GSE97760). The qRT-PCR experiment shows that there were five genes with significant differences in the samples of AD patients. Due to the limited number of samples, COMMD1 might be associated with AD pathology but no significant difference in expression in the blood. Further studies utilizing different analyses of both blood and tissue samples are required to confirm our results. Furthermore, Metascape analysis revealed that the six biomarkers may be involved in copper homeostasis, TCA cycle and respiratory electron transport, thus confirming the prediction accuracies to some extent. SOD1 is a risk gene for amyotrophic lateral sclerosis [46] and found more abundant in AD CSF compared to non-AD CSF [47]. SCO1 and COX11 are oxidase assembly proteins, with both being inner membrane proteins tethered by a single transmembrane helix. They are involved in the assembly of the CuA and CuB centers, respectively [48, 49]. LDHA competes with the mitochondrial NADH/NAD+ shuttle systems to regenerate NAD+ [50], by decreasing mitochondrial membrane potentials with or without exposure to toxins, which likely contributed to their resistance through the associated decrease in mitochondrial reactive oxygen species [51]. COMMD1, a regulator of the Cu pathway, exists in ischemic injury [52]. FH is involved in mitochondrial dysfunction [53]. ATOX1 belongs to divalent metal transporter proteins family and may be contributory factors to provoking protein misfolding disease [54]. Many studies demonstrated oxidative stress and mitochondrial dysfunction as key pathogenic mechanisms in aging-related metabolic and neurodegenerative diseases, such as AD [55, 56]. Moreover, the distribution of candidate hub genes on chromosomes may provide valuable insights for future investigations into potential pathogenic mechanisms [57].

Studies have indicated that neuroinflammation in AD is not only associated with resident cells (microglia, astrocytes, and neurons) but also, by peripheral immune cells that can enter the brain [58, 59]. Here, we also explore immune cell infiltration and immune factors. The box plot results suggested significant differences in multiple immune cells between the AD and the ND groups. These results suggest that M1 macrophages lead to higher levels in AD consistent with a previous study [60]. Microglia, resident immune cells of the CNS can assume a pro-inflammatory M1 phenotype [61]. Conversely, upon stimulation with the anti-inflammatory, macrophages are converted to the M2 phenotype [62].

Gene therapy has rapidly advanced and is now considered a potential therapeutic option for treating AD [63]. Finally, 20 drugs targeting the above genes were retrieved from the DrugBank database. Increasing evidence suggests that the disruption of zinc homeostasis is involved in various neurodegenerative diseases including AD [64]. Zinc (DB01593), zinc acetate (DB14487), and zinc chloride (DB14533) are known due to commonly and dietary supplements, and nutrient “zinc” is a relevant micronutrient involved in integrity of preserving body homeostatic mechanisms, including immune efficiency [65]. Copper (DB09130) imbalance is associated with the pathogenesis of AD, which is an avenue for new therapeutic strategies [66]. The influence of aspirin (DB00945), due to its anti-thrombotic and anti-inflammatory, on the formation of amyloid conglomerates was verified [67]. Acting as inhibitors for SOD1 target gene, cannabidiol (DB09061) and medical cannabis (DB14009) can reduce oxidative stress and excitotoxicity, amyloid plaques, and neurofibrillary tangles formation [68–70], and nabiximols (DB14011) is a whole plant extract from the cannabis species. Isoproterenol (DB01064) could be potentially beneficial as a drug for the treatment of AD through inhibiting the aggregation of tau protein [71]. The small molecule 5-fluorouridine (DB01629) can attenuate protein aggregation [72]. Inhibition of the copper-catalyzed oxidation of amyloid-β, cisplatin (DB00515) may lead to better designing of platinum complexes to treat oxidative stress in AD [73]. It is known that sucrose (DB02772) acts as a carbon source, which plays a very important role in affecting the immune response [74]. Currently, there are few drugs targeting these genes that are predicted to relieve AD, suggesting that needed to explore the more possibility of additional drugs.

Footnotes

ACKNOWLEDGMENTS

We thank the researchers of the public datasets for sharing the data used in this study and the participants that included in the public datasets.

FUNDING

We greatly appreciate the financial support from the National Natural Science Foundation of China (82102504), “Young Talents Program” of Guangdong Academy of Traditional Chinese Medicine (SZ2022QN09), the Natural Science Foundation of Guangdong Province (2021A1515220099, 2021A1515220149), Guangzhou Science and Technology Plan Projects (202201011506, 2023A03J0755), the Specific Research Fund for TCM Science and Technology of Guangdong Provincial Hospital of Chinese Medicine (SZ2022KF27, YN2022QN16), Research Fund for Bajian Talents of Guangdong Provincial Hospital of Chinese Medicine (No. BJ2022KY04).

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

Data in the article could be obtained through access to the GEO and GeneCards database.

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