Hub Genes, Diagnostic Model, and Predicted Drugs Related to Iron Metabolism in Alzheimer's Disease

Identification of hub fatty acid metabolism-related genes and immune infiltration in IgA nephropathy

AIMS

To investigate the potential mechanisms of fatty acid metabolism (FAM)-related genes in IgA nephropathy (IgAN) and to explore its immune cell infiltration characteristic.

METHODS

Datasets for IgAN and FAM-related genes were obtained from GEO and MSigDB database, respectively. We employed differential expression analysis and WGCNA to identify common genes. GO and KEGG analyses were performed to compare the differences between IgAN and control groups. Furthermore, LASSO logistic regression was applied to develop a predictive model based on FAM-related genes. The efficacy of this prognostic model was evaluated using ROC analysis. The infiltration of immune cells and immune-related functions were assessed with CIBERSORT tool. Finally, the identified key genes were validated in blood samples from IgAN and control patients, as well as in human mesangial cells (HMCs) following Gd-IgA stimulation using Real-time PCR.

RESULTS

A total of 12 hub genes linked to FAM were identified in patients with IgAN. A predictive model consisting of four genes was conducted through COX and LASSO regression analysis, revealing AUC values that indicate a relatively strong diagnostic capability. Immune infiltration analysis indicated that various immune cells have significant associations with IgAN. Additionally, Real-time PCR assays confirmed that the expression levels of hub genes were markedly reduced in IgAN patients and in Gd-IgA treated HMCs compared to controls.

CONCLUSION

This study employed bioinformatics methods to unveiled the immune cell infiltration associated with IgAN and to explore the potential genetic connection between FAM and IgAN. This could aid in predicting the risk of IgAN and enhance both diagnosis and prognosis of this condition.

Identification of oxidative stress-related biomarkers in uterine leiomyoma: a transcriptome-combined Mendelian randomization analysis

Background

Oxidative stress has been implicated in the pathogenesis of uterine leiomyoma (ULM) with an increasing incidence. This study aimed to identify potential oxidative stress-related biomarkers in ULM using transcriptome data integrated with Mendelian randomization (MR) analysis.

Methods

Data from GSE64763 and GSE31699 in the Gene Expression Omnibus (GEO) were included in the analysis. Oxidative stress-related genes (OSRGs) were identified, and the intersection of differentially expressed genes (DEGs), Weighted Gene Co-expression Network Analysis (WGCNA) genes, and OSRGs was used to derive differentially expressed oxidative stress-related genes (DE-OSRGs). Biomarkers were subsequently identified via MR analysis, followed by Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis. Nomograms, regulatory networks, and gene-drug interaction networks were constructed based on the identified biomarkers.

Results

A total of 883 DEGs were identified between ULM and control samples, from which 42 DE-OSRGs were screened. MR analysis revealed four biomarkers: ANXA1, CD36, MICB, and PRDX6. Predictive nomograms were generated based on these biomarkers. ANXA1, CD36, and MICB were significantly enriched in chemokine signaling and other pathways. Notably, ANXA1 showed strong associations with follicular helper T cells, resting mast cells, and M0 macrophages. CD36 was positively correlated with resting mast cells, while MICB was negatively correlated with macrophages. Additionally, ANXA1 displayed strong binding energy with amcinonide, and MICB with ribavirin.

Conclusion

This study identified oxidative stress-related biomarkers (ANXA1, CD36, MICB, and PRDX6) in ULM through transcriptomic and MR analysis, providing valuable insights for ULM therapeutic research.

Investigating the effects of Ginkgo biloba leaf extract on cognitive function in Alzheimer's disease

AIMS

Alzheimer's disease (AD) is a neurodegenerative disorder with limited treatment options. This study aimed to investigate the therapeutic effects of Ginkgo biloba leaf extract (GBE) on AD and explore its potential mechanisms of action.

METHODS

Key chemical components of GBE, including quercetin, luteolin, and kaempferol, were identified using network pharmacology methods. Bioinformatics analysis revealed their potential roles in AD through modulation of the PI3K/AKT/NF-κB signaling pathway.

RESULTS

Mouse experiments demonstrated that GBE improved cognitive function, enhanced neuronal morphology, and reduced serum inflammatory factors. Additionally, GBE modulated the expression of relevant proteins and mRNA.

CONCLUSION

GBE shows promise as a potential treatment for AD. Its beneficial effects on cognitive function, neuronal morphology, and inflammation may be attributed to its modulation of the PI3K/AKT/NF-κB signaling pathway. These findings provide experimental evidence for the application of Ginkgo biloba leaf in AD treatment and highlight its potential mechanisms of action.

Identification of Autophagy-Related Biomarkers and Diagnostic Model in Alzheimer's Disease

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. Its accurate pathogenic mechanisms are incompletely clarified, and effective therapeutic treatments are still inadequate. Autophagy is closely associated with AD and plays multiple roles in eliminating harmful aggregated proteins and maintaining cell homeostasis. This study identified 1191 differentially expressed genes (DEGs) based on the GSE5281 dataset from the GEO database, intersected them with 325 autophagy-related genes from GeneCards, and screened 26 differentially expressed autophagy-related genes (DEAGs). Subsequently, GO and KEGG enrichment analysis was performed and indicated that these DEAGs were primarily involved in autophagy-lysosomal biological process. Further, eight hub genes were determined by PPI construction, and experimental validation was performed by qRT-PCR on a SH-SY5Y cell model. Finally, three hub genes (TFEB, TOMM20, GABARAPL1) were confirmed to have potential application for biomarkers. A multigenic prediction model with good predictability (AUC = 0.871) was constructed in GSE5281 and validated in the GSE132903 dataset. Hub gene-targeted miRNAs closely associated with AD were also retrieved through the miRDB and HDMM database, predicting potential therapeutic agents for AD. This study provides new insights into autophagy-related genes in brain tissues of AD patients and offers more candidate biomarkers for AD mechanistic research as well as clinical diagnosis.

Discovery and Validation of Ferroptosis-Associated Genes of Ulcerative Colitis

Background

Ulcerative colitis (UC) is a long-lasting idiopathic condition, but its precise mechanisms remain unclear. Meanwhile, evidence has demonstrated that ferroptosis seems to interlock with the progress of UC. This research sought to identify hub genes of UC related to ferroptosis.

Methods

First, the relevant profiles for this article were obtained from GEO database. From the FerrDb, 479 genes linked to ferroptosis were retrieved. Using analysis of the difference and WGCNA on colonic samples from GSE73661, the remaining six hub genes linked to ferroptosis and UC were discovered. Through logistic regression analyses, the diagnostic model was constructed and was then evaluated by external validation using dataset GSE92415. Afterwards, the correlation between immune cell filtration in UC and hub genes was examined. Finally, a mice model of colitis was established, and the results were verified using qRT-PCR.

Results

We acquired six hub genes linked to ferroptosis and UC. In order to create a diagnostic model for UC, we used logistic regression analysis to screen three of the six ferroptosis related genes (HIF1A, SLC7A11, and LPIN1). The ROC curve showed that the three hub genes had outstanding potential for disease diagnosis (AUC = 0.976), which was subsequently validated in samples from GSE92415 (AUC = 0.962) and blood samples from GSE3365 (AUC = 0.847) and GSE94648 (AUC = 0.769). These genes might be crucial for UC immunity based upon the results on the immune system. Furthermore, mouse samples examined using qRT-PCR also verified our findings.

Conclusion

In conclusion, the findings have important implications for ferroptosis and UC, and these hub genes may also offer fresh perspectives on the aetiology and therapeutic approaches of UC.

SCG5 and MITF may be novel markers of copper metabolism immunorelevance in Alzheimer's disease

The slow-developing neurological disorder Alzheimer's disease (AD) has no recognized etiology. A bioinformatics investigation verified copper metabolism indicators for AD development. GEO contributed AD-related datasets GSE1297 and GSE5281. Differential expression analysis and WGCNA confirmed biomarker candidate genes. Each immune cell type in AD and control samples was scored using single sample gene set enrichment analysis. Receiver Operating Characteristic (ROC) analysis, short Time-series Expression Miner (STEM) grouping, and expression analysis between control and AD samples discovered copper metabolism indicators that impacted AD progression. We test clinical samples and cellular function to ensure study correctness. Biomarker-targeting miRNAs and lncRNAs were predicted by starBase. Trust website anticipated biomarker-targeting transcription factors. In the end, Cytoscape constructed the TF/miRNA-mRNA and lncRNA-miRNA networks. The DGIdb database predicted biomarker-targeted drugs. We identified 57 differentially expressed copper metabolism-related genes (DE-CMRGs). Next, fourteen copper metabolism indicators impacting AD progression were identified: CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38. A TF/miRNA-mRNA regulation network was then established with two miRNAs (hsa-miR-34a-5p and 34c-5p), six TFs (NFKB1, RELA, MYC, HIF1A, JUN, and SP1), and four biomarkers. The DGIdb database contained 171 drugs targeting ten copper metabolism-relevant biomarkers (BRCA1, MITF, NFKBIA, CD38, CCK2, HPRT1, SPHK1, LDHA, SCG5, and SYT1). Copper metabolism biomarkers CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38 alter AD progression, laying the groundwork for disease pathophysiology and novel AD diagnostic and treatment.

Transcriptomic Analysis of Lipid Metabolism Genes in Alzheimer's Disease: Highlighting Pathological Outcomes and Compartmentalized Immune Status

The dysregulation of lipid metabolism has been strongly associated with Alzheimer's disease (AD) and has intricate connections with various aspects of disease progression, such as amyloidogenesis, bioenergetic deficit, oxidative stress, neuroinflammation, and myelin degeneration. Here, a comprehensive bioinformatic assessment was conducted on lipid metabolism genes in the brains and peripheral blood of AD-derived transcriptome datasets, characterizing the correlation between differentially expressed genes (DEGs) of lipid metabolism and disease pathologies, as well as immune cell preferences. Through the application of weighted gene co-expression network analysis (WGCNA), modules eigengenes related to lipid metabolism were pinpointed, and the examination of their molecular functions within biological processes, molecular pathways, and their associations with pathological phenotypes and molecular networks has been characterized. Analysis of biological networks indicates notable discrepancies in the expression patterns of the DEGs between neuronal and immune cells, as well as variations in cell type enrichments within both brain tissue and peripheral blood. Additionally, drugs targeting the DEGs from central and peripheral and a diagnostic model for hub genes from the blood were retrieved and assessed, some of which were shown to be useful for therapeutic and diagnostic. These results revealed the distinctive pattern of transcriptionally abnormal lipid metabolism in central, peripheral, and immune cell activation, providing valuable insight into lipid metabolism for diagnosing and guiding more effective treatment for AD.

Bulk RNA-sequencing, single-cell RNA-sequencing analysis, and experimental validation reveal iron metabolism-related genes CISD2 and CYP17A1 are potential diagnostic markers for recurrent pregnancy loss

BACKGROUND

Recurrent pregnancy loss (RPL) is associated with variable causes. Its etiology remains unexplained in about half of the cases, with no effective treatment available. Individuals with RPL have an irregular iron metabolism. In the present study, we identified key genes impacting iron metabolism that could be used for diagnosing and treating RPL.

METHODS

We obtained gene expression profiles from the Gene Expression Omnibus (GEO) database. The Molecular Signatures Database was used to identify 14 gene sets related to iron metabolism, comprising 520 iron metabolism genes. Differential analysis and a weighted gene co-expression network analysis (WGCNA) of gene expression revealed two iron metabolism-related hub genes. Reverse transcriptase-polymerase chain reaction (RT-PCR) and immunohistochemistry were used on clinical samples to confirm our results. The receiver operating characteristic (ROC) analysis and immune infiltration analysis were conducted. In addition, we analyzed the distribution of genes and performed CellChat analysis by single-cell RNA sequencing.

RESULTS

The expression of two hub genes, namely, CDGSH iron sulfur domain 2 (CISD2)and Cytochrome P450 family 17 subfamily A member 1 (CYP17A1), were reduced in RPL, as verified by both qPCR and immunohistochemistry. The Gene Ontology (GO) analysis revealed the genes predominantly engaged in autophagy and iron metabolism. The area under the curve (AUC) demonstrated better diagnostic performance for RPL using CISD2 and CYP17A1. The single-cell transcriptomic analysis of RPL demonstrated that CISD2 is expressed in the majority of cell subpopulations, whereas CYP17A1 is not. The cell cycle analysis revealed highly active natural killer (NK) cells that displayed the highest communications with other cells, including the strongest interaction with macrophages through the migratory inhibitory factor (MIF) pathway.

CONCLUSIONS

Our study suggested that CISD2 and CYP17A1 genes are involved in abnormal iron metabolism, thereby contributing to RPL. These genes could be used as potential diagnostic and therapeutic markers for RPL.

Iron metabolism disorder and multiple sclerosis: a comprehensive analysis

Background

Multiple sclerosis (MS) is the most common chronic inflammatory disease of the central nervous system. Currently, the pathological mechanisms of MS are not fully understood, but research has suggested that iron metabolism disorder may be associated with the onset and clinical manifestations of MS.

Methods and materials

The study utilized publicly available databases and bioinformatics techniques for gene expression data analysis, including differential expression analysis, weighted correlation network analysis, gene enrichment analysis, and construction of logistic regression models. Subsequently, Mendelian randomization was used to assess the causal relationship between different iron metabolism markers and MS.

Results

This study identified IREB2, LAMP2, ISCU, ATP6V1G1, ATP13A2, and SKP1 as genes associated with multiple sclerosis (MS) and iron metabolism, establishing their multi-gene diagnostic value for MS with an AUC of 0.83. Additionally, Mendelian randomization analysis revealed a potential causal relationship between transferrin saturation and MS (p=2.22E-02; OR 95%CI=0.86 (0.75, 0.98)), as well as serum transferrin and MS (p=2.18E-04; OR 95%CI=1.22 (1.10, 1.36)).

Conclusion

This study comprehensively explored the relationship between iron metabolism and MS through integrated bioinformatics analysis and Mendelian randomization methods. The findings provide important insights for further research into the role of iron metabolism disorder in the pathogenesis of MS and offer crucial theoretical support for the treatment of MS.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

Identification of Immune Infiltration and Iron Metabolism-Related Subgroups in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with a broad spectrum of symptoms and prognoses. Effective therapy requires understanding this variability. ASD children's cognitive and immunological development may depend on iron homoeostasis. This study employs a machine learning model that focuses on iron metabolism hub genes to identify ASD subgroups and describe immune infiltration patterns. A total of 97 control and 148 ASD samples were obtained from the GEO database. Differentially expressed genes (DEGs) and an iron metabolism gene collection achieved the intersection of 25 genes. Unsupervised cluster analysis determined molecular subgroups in individuals with ASD based on 25 genes related to iron metabolism. We assessed gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, gene set variation analysis (GSVA), and immune infiltration analysis to compare iron metabolism subtype effects. We employed machine learning to identify subtype-predicting hub genes and utilized both training and validation sets to assess gene subtype prediction accuracy. ASD can be classified into two iron-metabolizing molecular clusters. Metabolic enrichment pathways differed between clusters. Immune infiltration showed that clusters differed immunologically. Cluster 2 had better immunological scores and more immune cells, indicating a stronger immune response. Machine learning screening identified SELENBP1 and CAND1 as important genes in ASD's iron metabolism signaling pathway. These genes express in the brain and have AUC values over 0.8, implying significant predictive power. The present study introduces iron metabolism signaling pathway indicators to predict ASD subtypes. ASD is linked to immune cell infiltration and iron metabolism disorders.

Mitochondria-Related Candidate Genes and Diagnostic Model to Predict Late-Onset Alzheimer's Disease and Mild Cognitive Impairment

Background

Late-onset Alzheimer's disease (LOAD) is the most common type of dementia, but its pathogenesis remains unclear, and there is a lack of simple and convenient early diagnostic markers to predict the occurrence.

Objective

Our study aimed to identify diagnostic candidate genes to predict LOAD by machine learning methods.

Methods

Three publicly available datasets from the Gene Expression Omnibus (GEO) database containing peripheral blood gene expression data for LOAD, mild cognitive impairment (MCI), and controls (CN) were downloaded. Differential expression analysis, the least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature elimination (SVM-RFE) were used to identify LOAD diagnostic candidate genes. These candidate genes were then validated in the validation group and clinical samples, and a LOAD prediction model was established.

Results

LASSO and SVM-RFE analyses identified 3 mitochondria-related genes (MRGs) as candidate genes, including NDUFA1, NDUFS5, and NDUFB3. In the verification of 3 MRGs, the AUC values showed that NDUFA1, NDUFS5 had better predictability. We also verified the candidate MRGs in MCI groups, the AUC values showed good performance. We then used NDUFA1, NDUFS5 and age to build a LOAD diagnostic model and AUC was 0.723. Results of qRT-PCR experiments with clinical blood samples showed that the three candidate genes were expressed significantly lower in the LOAD and MCI groups when compared to CN.

Conclusion

Two mitochondrial-related candidate genes, NDUFA1 and NDUFS5, were identified as diagnostic markers for LOAD and MCI. Combining these two candidate genes with age, a LOAD diagnostic prediction model was successfully constructed.

Deciphering the Hypoxia-immune interface in esophageal squamous carcinoma: a prognostic network model

Introduction

The rapid progress and poor prognosis of the exercise of esophageal squamous cell carcinoma (ESCA) bring great challenges to the treatment. Hypoxia in the tumor microenvironment has become a key factor in the pathogenesis of tumors. However, due to the lack of clear therapeutic targets, hypoxia targeted therapy of ESCA is still in the exploratory stage.

Methods

To bridge this critical gap, we mined a large number of gene expression profiles and clinical data on ESCA from public databases. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed. We next delved into the relationship between hypoxia and apoptotic cell interactions. Meanwhile, using LASAS-Cox regression, we designed a robust prognostic risk score, which was subsequently validated in the GSE53625 cohort. In addition, we performed a comprehensive analysis of immune cell infiltration and tumor microenvironment using cutting-edge computational tools.

Results

Hypoxia-related genes were identified and classified by WGCNA. Functional enrichment analysis further elucidated the mechanism by which hypoxia affected the ESCA landscape. The results of the interaction analysis of hypoxia and apoptotic cells revealed their important roles in driving tumor progression. The validation results of the prognostic risk score model in the GSE53625 cohort obtained a good area under the receiver operating characteristic (ROC) curve, and the risk score was independently verified as a significant predictor of ESCA outcome. The results of immune cell infiltration and tumor microenvironment analysis reveal the profound impact of immune cell dynamics on tumor evolution.

Conclusion

Overall, our study presents a pioneering hypoxiacentered gene signature for prognostication in ESCA, providing valuable prognostic insights that could potentially revolutionize patient stratification and therapeutic management in clinical practice.

Identification of mitochondrial-related genes as potential biomarkers for the subtyping and prediction of Alzheimer's disease

Introduction

Alzheimer's disease (AD) is a progressive and debilitating neurodegenerative disorder prevalent among older adults. Although AD symptoms can be managed through certain treatments, advancing the understanding of underlying disease mechanisms and developing effective therapies is critical.

Methods

In this study, we systematically analyzed transcriptome data from temporal lobes of healthy individuals and patients with AD to investigate the relationship between AD and mitochondrial autophagy. Machine learning algorithms were used to identify six genes-FUNDC1, MAP1LC3A, CSNK2A1, VDAC1, CSNK2B, and ATG5-for the construction of an AD prediction model. Furthermore, AD was categorized into three subtypes through consensus clustering analysis.

Results

The identified genes are closely linked to the onset and progression of AD and can serve as reliable biomarkers. The differences in gene expression, clinical features, immune infiltration, and pathway enrichment were examined among the three AD subtypes. Potential drugs for the treatment of each subtype were also identified.

Discussion

The findings observed in the present study can help to deepen the understanding of the underlying disease mechanisms of AD and enable the development of precision medicine and personalized treatment approaches.

A novel Alzheimer's disease prognostic signature: identification and analysis of glutamine metabolism genes in immunogenicity and immunotherapy efficacy

Alzheimer's disease (AD) is characterized as a distinct onset and progression of cognitive and functional decline associated with age, as well as a specific neuropathology. It has been discovered that glutamine (Gln) metabolism plays a crucial role in cancer. However, a full investigation of its role in Alzheimer's disease is still missing. This study intended to find and confirm potential Gln-related genes associated with AD using bioinformatics analysis. The discovery of GlnMgs was made possible by the intersection of the WGCNA test and 26 Gln-metabolism genes (GlnMgs). GlnMgs' putative biological functions and pathways were identified using GSVA. The LASSO method was then used to identify the hub genes as well as the diagnostic efficiency of the four GlnMgs in identifying AD. The association between hub GlnMgs and clinical characteristics was also studied. Finally, the GSE63060 was utilized to confirm the levels of expression of the four GlnMgs. Four GlnMgs were discovered (ATP5H, NDUFAB1, PFN2, and SPHKAP). For biological function analysis, cell fate specification, atrioventricular canal development, and neuron fate specification were emphasized. The diagnostic ability of the four GlnMgs in differentiating AD exhibited a good value. This study discovered four GlnMgs that are linked to AD. They shed light on potential new biomarkers for AD and tracking its progression.

Identification of oxidative stress-related genes differentially expressed in Alzheimer's disease and construction of a hub gene-based diagnostic model

Alzheimer's disease (AD) is the most prevalent dementia disorder globally, and there are still no effective interventions for slowing or stopping the underlying pathogenic mechanisms. There is strong evidence implicating neural oxidative stress (OS) and ensuing neuroinflammation in the progressive neurodegeneration observed in the AD brain both during and prior to symptom emergence. Thus, OS-related biomarkers may be valuable for prognosis and provide clues to therapeutic targets during the early presymptomatic phase. In the current study, we gathered brain RNA-seq data of AD patients and matched controls from the Gene Expression Omnibus (GEO) to identify differentially expressed OS-related genes (OSRGs). These OSRGs were analyzed for cellular functions using the Gene Ontology (GO) database and used to construct a weighted gene co-expression network (WGCN) and protein-protein interaction (PPI) network. Receiver operating characteristic (ROC) curves were then constructed to identify network hub genes. A diagnostic model was established based on these hub genes using Least Absolute Shrinkage and Selection Operator (LASSO) and ROC analyses. Immune-related functions were examined by assessing correlations between hub gene expression and immune cell brain infiltration scores. Further, target drugs were predicted using the Drug-Gene Interaction database, while regulatory miRNAs and transcription factors were predicted using miRNet. In total, 156 candidate genes were identified among 11046 differentially expressed genes, 7098 genes in WGCN modules, and 446 OSRGs, and 5 hub genes (MAPK9, FOXO1, BCL2, ETS1, and SP1) were identified by ROC curve analyses. These hub genes were enriched in GO annotations "Alzheimer's disease pathway," "Parkinson's Disease," "Ribosome," and "Chronic myeloid leukemia." In addition, 78 drugs were predicted to target FOXO1, SP1, MAPK9, and BCL2, including fluorouracil, cyclophosphamide, and epirubicin. A hub gene-miRNA regulatory network with 43 miRNAs and hub gene-transcription factor (TF) network with 36 TFs were also generated. These hub genes may serve as biomarkers for AD diagnosis and provide clues to novel potential treatment targets.