scholarly journals Comprehensive Analyses of Type 1 Diabetes Ketosis- or Ketoacidosis-Related Genes in Activated CD56+CD16+ NK Cells

2021 ◽  
Vol 12 ◽  
Author(s):  
Ruifeng Shi ◽  
Fang Dai ◽  
Yong He ◽  
Li Sun ◽  
Min Xu ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Nk Cells ◽  
Nk Cell ◽  
Mirna Expression Profile ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Specific Gene ◽  
Hub Genes ◽  
Microarray Datasets

ObjectivesAlterations in natural killer (NK) cells activity cause damage to pancreatic islets in type 1 diabetes mellitus (T1DM). The aim of this study is to identify T1DM ketosis- or ketoacidosis-related genes in activated CD56+CD16+ NK cells.MethodsMicroarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were analyzed using the GEO2R tool. Enrichment analyses were performed using Metascape online database and GSEA software. Cell-specific gene co-expression network was built using NetworkAnalyst tools. Cytoscape software was used to identify hub genes and construct co-expressed networks. Target miRNAs were predicted based on the DIANA-micro T, miRDB, and miRWalk online databases.ResultsA total of 70 DEGs were identified between T1DM patients recovered from ketosis or ketoacidosis and healthy control blood samples in GSE44314. Among the DEGs, 10 hub genes were screened out. The mature NK cell-specific gene co-expression network for DEGs in T1DM was built using NetworkAnalyst tools. DEGs between activated CD56+CD16+ NK cells and CD56brightCD16- NK cells were identified from GSE1511. After intersection, 13 overlapping genes between GSE44314 and GSE1511 microarray datasets were screened out, in which 7 hub genes were identified. Additionally, 59 target miRNAs were predicted according to the 7 hub genes. After validating with the exosome miRNA expression profile dataset of GSE97123, seven differentially expressed miRNAs (DEmiRNAs) in plasma-derived exosome were selected. Finally, a mRNA–miRNA network was constructed, which was involved in the T1DM ketosis or ketoacidosis process.ConclusionThis work identified seven hub genes in activated CD56+CD16+ NK cells and seven miRNAs in plasma-derived exosome as potential predictors of T1DM ketoacidosis, which provided a novel insight for the pathogenesis at the transcriptome level.

scholarly journals Analysis of immune cell components and immune-related gene expression profiles in peripheral blood of patients with type 1 diabetes mellitus

2021 ◽  
Author(s):  
Jian Lin ◽  
Yuanhua Lu ◽  
Bizhou Wang ◽  
Ping Jiao ◽  
Jie Ma
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Nk Cells ◽  
Immune Cells ◽  
Support Vector ◽  
Hub Genes ◽  
Immune Related Genes

Abstract Background Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease caused by severe loss of pancreatic β cells. Immune cells are key mediators of β cell destruction. This study attempted to investigate the role of immune cells and immune-related genes in the occurrence and development of T1DM. Methods The raw gene expression profile of the samples from 12 T1DM patients and 10 normal controls was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by Limma package in R. The least absolute shrinkage and selection operator (LASSO) - support vector machines (SVM) were used to screen the hub genes. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. Correlation of the hub genes and immune cells was analyzed by Spearman, and gene-GO-BP and gene-pathway interaction networks were constructed by Cytoscape plug-in ClueGO. Receiver operating characteristic (ROC) curves were used to assess diagnostic value of genes in T1DM. Results The 50 immune-related DEGs were obtained between the T1DM and normal samples. Then, the 50 immune-related DEGs were further screened to obtain the 5 hub genes. CIBERSORT analysis revealed that the distribution of plasma cells, resting mast cells, resting NK cells and neutrophils had significant difference between T1DM and normal samples. Natural cytotoxicity triggering receptor 3 (NCR3) was significantly related to the activated NK cells, M0 macrophages, monocytes, resting NK cells, and resting memory CD4+ T cells. Moreover, tumor necrosis factor (TNF) was significantly associated with naive B cell and naive CD4+ T cell. NCR3 [Area under curve (AUC) = 0.918] possessed a higher accuracy than TNF (AUC = 0.763) in diagnosis of T1DM. Conclusions The immune-related genes (NCR3 and TNF) and immune cells (NK cells) may play a vital regulatory role in the occurrence and development of T1DM, which possibly provide new ideas and potential targets for the immunotherapy of diabetes mellitus (DM).

scholarly journals NK Cell Subsets Changes in Partial Remission and Early Stages of Pediatric Type 1 Diabetes

2021 ◽  
Vol 11 ◽  
Author(s):  
Laia Gomez-Muñoz ◽  
David Perna-Barrull ◽  
Adrian Villalba ◽  
Silvia Rodriguez-Fernandez ◽  
Rosa-Maria Ampudia ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Nk Cells ◽  
Partial Remission ◽  
Immune Cell ◽  
Nk Cell ◽  
Disease Onset ◽  
Potential Candidate ◽  
Early Stages ◽  
Cd16 Expression

Type 1 diabetes (T1D) is a chronic metabolic disease characterized by the autoimmune destruction of β-cells in the pancreatic islets. T1D is preceded by islet-specific inflammation led by several immune cells. Among them, natural killer (NK) cells are emerging as important players in T1D development. Human NK cells are characterized by CD56 and CD16 expression, which allows classifying NK cells into four subsets: 1) CD56dimCD16+ or effector NK cells (NKeff); 2) CD56brightCD16− or regulatory NK cells (NKreg); 3) intermediate CD56brightCD16+ NK cells; and 4) CD56dimCD16− NK cells, whose function is not well determined. Since many studies have shown that T1D progression is associated with changes in various immune cell types, we hypothesize that the kinetics of NK cell subsets in the blood could correlate with different stages of T1D. To that aim, pediatric patients newly diagnosed with T1D were recruited, and peripheral NK cell subsets were analyzed by flow cytometry at several disease checkpoints: disease onset, partial remission (PR), 8 months (for non-remitters), and 12 months of progression. Our results showed that total NK cells and their four subsets are altered at the early stages of T1D. A decrease in the counts and percentage of total NK cells and NKeff cells at the different disease stages was found when compared to controls. These results suggest the extravasation of these cells into the islets at disease onset, which is maintained throughout the follow-up. By contrast, NKreg cells increased during the early stages after T1D onset, and both intermediate NK cells and CD56dimCD16- NK cells diminished at the PR stage, which might reflect the immunoregulatory attempts and could be candidate biomarkers for this stage. Also, CD56dimCD16- NK cells increased during T1D progression. Finally, changes in CD16 expression were identified in the different T1D stages, highlighting a CD16 expression reduction in total NK cells and NKeff cells 1 year after diagnosis. That may reflect a state of exhaustion after multiple cell-to-cell interactions. Altogether, our preliminary data provide a longitudinal picture of peripheral NK cell subpopulations during the different T1D stages, which could be potential candidate biomarkers indicators of disease progression.

scholarly journals Analysis of immune cell components and immune-related gene expression profiles in peripheral blood of patients with type 1 diabetes mellitus

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jian Lin ◽  
Yuanhua Lu ◽  
Bizhou Wang ◽  
Ping Jiao ◽  
Jie Ma
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Nk Cells ◽  
Immune Cells ◽  
Support Vector ◽  
Hub Genes ◽  
Immune Related Genes

Abstract Background Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease caused by severe loss of pancreatic β cells. Immune cells are key mediators of β cell destruction. This study attempted to investigate the role of immune cells and immune-related genes in the occurrence and development of T1DM. Methods The raw gene expression profile of the samples from 12 T1DM patients and 10 normal controls was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by Limma package in R. The least absolute shrinkage and selection operator (LASSO)—support vector machines (SVM) were used to screen the hub genes. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. Correlation of the hub genes and immune cells was analyzed by Spearman, and gene-GO-BP and gene-pathway interaction networks were constructed by Cytoscape plug-in ClueGO. Receiver operating characteristic (ROC) curves were used to assess diagnostic value of genes in T1DM. Results The 50 immune-related DEGs were obtained between the T1DM and normal samples. Then, the 50 immune-related DEGs were further screened to obtain the 5 hub genes. CIBERSORT analysis revealed that the distribution of plasma cells, resting mast cells, resting NK cells and neutrophils had significant difference between T1DM and normal samples. Natural cytotoxicity triggering receptor 3 (NCR3) was significantly related to the activated NK cells, M0 macrophages, monocytes, resting NK cells, and resting memory CD4+ T cells. Moreover, tumor necrosis factor (TNF) was significantly associated with naive B cell and naive CD4+ T cell. NCR3 [Area under curve (AUC) = 0.918] possessed a higher accuracy than TNF (AUC = 0.763) in diagnosis of T1DM. Conclusions The immune-related genes (NCR3 and TNF) and immune cells (NK cells) may play a vital regulatory role in the occurrence and development of T1DM, which possibly provide new ideas and potential targets for the immunotherapy of diabetes mellitus (DM).

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

2020 ◽  
Vol 26 (29) ◽  
pp. 3619-3630
Author(s):  
Saumya Choudhary ◽  
Dibyabhaba Pradhan ◽  
Noor S. Khan ◽  
Harpreet Singh ◽  
George Thomas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Gene ◽  
Therapeutic Targets ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Keratinocyte Differentiation ◽  
Hub Genes ◽  
Lesional Skin ◽  
Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

Abstract WP319: miRNA Screening of Natural Killer Cell Identifies miR-1224 as a Post-Transcriptional Regulator of NK Cell Function After Ischemic Stroke

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Yan Feng ◽  
Hui Zhao ◽  
Fu-Dong Shi ◽  
Weina Jin
Keyword(s):  
Ischemic Stroke ◽  
Cerebral Ischemia ◽  
Nk Cells ◽  
Expression Profile ◽  
Mirna Expression ◽  
Cell Function ◽  
Nk Cell ◽  
Killer Cell ◽  
Mirna Expression Profile ◽  
Control Group

Objectives: To screen miRNA profile of peripheral NK cells in ischemic stroke mouse model and investigate a most promising candidate (miR-1224) for post-transcriptional regulation of NK cell function after ischemic stroke. Methods: Mice were subjected to a 60 min focal cerebral ischemia produced by transient intraluminal occlusion of MCAO. For NK cell isolation, cell suspensions from the spleens after reperfusion were enriched for NK cells using magnetic-bead sorting system after staining with anti-NK1.1 microbeads. The nCounter Mouse miRNA array was used to analyze miRNA expression profile in splenic NK cells over the time course of experimental ischemic stroke. Based on the miRNA data, we further in vitro modulated miR-1224 in NK cells using mimics or inhibitor, then injected i.v into Rag2-/-γc-/- recipient mice. Neurological function score was compared and spontaneous infection was assessed by pulmonary bacteria colony culture, and changes in potential signaling pathway (SP1/TNF-α) were verified by rt-PCR and western blot. Results: Through miRNA expression profile analysis, we have identified significant changes at each time point in peripheral NK cells after cerebral ischemia. Among all screened miRNA, miR-1224 remarkably increased in MCAO group, which was verified by PCR. Then isolated NK cells treated with mimics or inhibitors, were transferred to Rag2-/-γc-/- recipient mice. Compared with WT mice, Rag2-/-γc-/- mice with miR-1224 inhibitor exhibited increased NK cell number, enhanced NK cell activation/cytotoxicity feature, as well as better neurological behaviors and reduced pulmonary infection after MCAO. Moreover, compared with the control group, NK cells with miR-1224 inhibitor showed significantly increased SP1 gene and protein phosphorylation. As SP1 gene is one of the potential targets of miR-1224, this study suggests that miR-1224 may regulate NK cell function after MCAO, which is associated with SP1 pathway. Conclusion: The miRNA profiling of splenic NK cells provided insight into the functional mechanism and signaling pathways underlying the distinct organ-specific NK cell properties, which will contribute to the better understanding of NK cell mediated immune-response in relation to different stages of stroke.

scholarly journals A comprehensive integrated post-GWAS analysis of Type 1 diabetes reveals enhancer-based immune dysregulation

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257265
Author(s):  
Seung-Soo Kim ◽  
Adam D. Hudgins ◽  
Jiping Yang ◽  
Yizhou Zhu ◽  
Zhidong Tu ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Target Genes ◽  
Association Studies ◽  
Regulatory Elements ◽  
Immune Dysregulation ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Gwas Analysis ◽  
Regulatory Variants

Type 1 diabetes (T1D) is an organ-specific autoimmune disease, whereby immune cell-mediated killing leads to loss of the insulin-producing β cells in the pancreas. Genome-wide association studies (GWAS) have identified over 200 genetic variants associated with risk for T1D. The majority of the GWAS risk variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes substantially contribute to T1D. However, identification of causal regulatory variants associated with T1D risk and their affected genes is challenging due to incomplete knowledge of non-coding regulatory elements and the cellular states and processes in which they function. Here, we performed a comprehensive integrated post-GWAS analysis of T1D to identify functional regulatory variants in enhancers and their cognate target genes. Starting with 1,817 candidate T1D SNPs defined from the GWAS catalog and LDlink databases, we conducted functional annotation analysis using genomic data from various public databases. These include 1) Roadmap Epigenomics, ENCODE, and RegulomeDB for epigenome data; 2) GTEx for tissue-specific gene expression and expression quantitative trait loci data; and 3) lncRNASNP2 for long non-coding RNA data. Our results indicated a prevalent enhancer-based immune dysregulation in T1D pathogenesis. We identified 26 high-probability causal enhancer SNPs associated with T1D, and 64 predicted target genes. The majority of the target genes play major roles in antigen presentation and immune response and are regulated through complex transcriptional regulatory circuits, including those in HLA (6p21) and non-HLA (16p11.2) loci. These candidate causal enhancer SNPs are supported by strong evidence and warrant functional follow-up studies.

scholarly journals Identification of Hub Genes and Immune Infiltration in Psoriasis by Bioinformatics Method

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenxing Su ◽  
Yuqian Wei ◽  
Biao Huang ◽  
Jiang Ji
Keyword(s):  
T Cells ◽  
B Cells ◽  
Nk Cells ◽  
Signaling Pathway ◽  
Control Sample ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Chemokine Signaling

BackgroundPsoriasis is a chronic, prolonged, and recurrent skin inflammatory disease. However, the pathogenesis of psoriasis is not completely clear, thus we aimed to explore potential molecular basis of it.MethodsTwo datasets were downloaded from the Gene Expression Omnibus database. After identifying the differentially expressed genes of psoriasis skin lesion samples and healthy controls, three kinds of analyses, namely functional annotation, protein-protein interaction (PPI) network, and immune infiltration analyses, were performed.ResultsA total of 152 up-regulated genes and 38 down-regulated genes were selected for subsequent analyses. Evaluation of the PPI network identified the most important module containing 13 hub genes. Gene ontology analysis showed that the hub genes have a significant enrichment effect on positive regulation of cell migration, defense response to the other organism and epithelial cell differentiation. KEGG signaling pathway analysis showed that the hub genes were significantly enriched in chemokine signaling, Toll-like receptor signaling pathway, and IL-17 signaling pathway. Compared with the normal control sample, naive B cells, CD8+ T cells, activated memory CD4+ T cells, follicular helper T cells, gamma delta T cells, resting NK cells, monocytes, M0 macrophages, M1 macrophages, activated dendritic cells and neutrophils infiltrated more, while memory B cells, naive CD4+ T cells, regulatory T cells (Tregs), activated NK cells, resting mast cells, and eosinophils infiltrated less.ConclusionTo conclude, the hub genes and pathways identified from psoriasis lesions and normal controls along with the immune infiltration profile may provide new insights into the study of psoriasis.

Comprehensive Analysis of Differentially Expressed Genes and Key Pathways in Neuropathic Pain by Spinal Nerve Ligation

2020 ◽  
Author(s):  
Chao Xu ◽  
HuiFang Li ◽  
YunPeng Zhang ◽  
TianYu Liu ◽  
Yi Feng
Keyword(s):  
Functional Analysis ◽  
Neuropathic Pain ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Spinal Nerve ◽  
Spinal Nerve Ligation ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Long Term Treatment

Abstract Background: Neuropathic pain can cause significant physical and economic burden to people, and there are no effective long-term treatment methods for this condition. We conducted a bioinformatics analysis of microarray data to identify related mechanisms to determine strategies for more effective treatments of neuropathic pain.Methods: GSE24982 and GSE63442 microarray datasets were extracted from the Gene Expression Omnibus (GEO) database to analyze transcriptome differences of neuropathic pain in the dorsal root ganglions caused by spinal nerve ligation. We filtered the differentially expressed genes (DEGs) in the two datasets and Webgestalt was applied to conduct GeneOntology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the shared DEGs. String Database and Cytoscape software were used to construct the Protein-Protein Interaction (PPI) network to determine the hub genes, which were subsequently verified in the GSE30691 dataset. Finally, miRDB and miRWalk Databases were used to predict potential miRNA of the selected DEGs.Results: A total of 182 overlapped DEGs were found between GSE24982 and GSE63442 datasets. The GO functional analysis and KEGG enrichment analysis showed that the selected DEGs were mainly enriched in infection, transmembrane transport of ion channels, and synaptic transmission. Combining the results of PPI analysis and the verification of the GSE30691 dataset, we identified seven hub genes related to neuropathic pain (Atf3, Aif1, Ctss, Gfap, Scg2, Jun, and Vgf). Predicted miRNA targeting each selected hub genes were identified.Conclusion: Seven hub genes related to the pathogenesis of neuropathic pain and potential targeting miRNA were identified, expanding understanding of the mechanism of neuropathic pain and facilitating treatment development.

scholarly journals Identification of key genes, pathways and potential therapeutic agents for IgA nephropathy using an integrated bioinformatics analysis

2020 ◽  
Vol 21 (2) ◽  
pp. 147032032091963
Author(s):  
Xiaoxue Chen ◽  
Mindan Sun
Keyword(s):  
Differentially Expressed Genes ◽  
Immunoglobulin A ◽  
Therapeutic Targets ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Signal Pathways ◽  
Immunoglobulin A Nephropathy ◽  
Hub Genes ◽  
Limma Package

Purpose: This study aims to identify immunoglobulin-A-nephropathy-related genes based on microarray data and to investigate novel potential gene targets for immunoglobulin-A-nephropathy treatment. Methods: Immunoglobulin-A-nephropathy chip data was obtained from the Gene Expression Omnibus database, which included 10 immunoglobulin-A-nephropathy and 22 normal samples. We used the limma package of R software to screen differentially expressed genes in immunoglobulin-A-nephropathy and normal glomerular compartment tissues. Functional enrichment (including cellular components, molecular functions, biological processes) and signal pathways were performed for the differentially expressed genes. The online analysis database (STRING) was used to construct the protein-protein interaction networks of differentially expressed genes, and Cytoscape software was used to identify the hub genes of the signal pathway. In addition, we used the Connectivity Map database to predict possible drugs for the treatment of immunoglobulin-A-nephropathy. Results: A total of 348 differentially expressed genes were screened including 107 up-regulated and 241 down-regulated genes. Functional analysis showed that up-regulated differentially expressed genes were mainly concentrated on leukocyte migration, and the down-regulated differentially expressed genes were significantly enriched in alpha-amino acid metabolic process. A total of six hub genes were obtained: JUN, C3AR1, FN1, AGT, FOS, and SUCNR1. The small-molecule drugs thapsigargin, ciclopirox and ikarugamycin were predicted therapeutic targets against immunoglobulin-A-nephropathy. Conclusion: Differentially expressed genes and hub genes can contribute to understanding the molecular mechanism of immunoglobulin-A-nephropathy and providing potential therapeutic targets and drugs for the diagnosis and treatment of immunoglobulin-A-nephropathy.

Immune-Related Hub Genes and the Competitive Endogenous RNA Network in Alzheimer’s Disease

2020 ◽  
Vol 77 (3) ◽  
pp. 1255-1265
Author(s):  
Hui Xu ◽  
Jianping Jia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Data Set ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Immune Phenomena ◽  
Competitive Endogenous Rna

Background: The pathogenesis of Alzheimer’s disease (AD) involves various immune-related phenomena; however, the mechanisms underlying these immune phenomena and the potential hub genes involved therein are unclear. An understanding of AD-related immune hub genes and regulatory mechanisms would help develop new immunotherapeutic targets. Objective: The aim of this study was to explore the hub genes and the mechanisms underlying the regulation of competitive endogenous RNA (ceRNA) in immune-related phenomena in AD pathogenesis. Methods: We used the GSE48350 data set from the Gene Expression Omnibus database and identified AD immune-related differentially expressed RNAs (DERNAs). We constructed protein–protein interaction (PPI) networks for differentially expressed mRNAs and determined the degree for screening hub genes. By determining Pearson’s correlation coefficient and using StarBase, DIANA-LncBase, and Human MicroRNA Disease Database (HMDD), the AD immune-related ceRNA network was generated. Furthermore, we assessed the upregulated and downregulated ceRNA subnetworks to identify key lncRNAs. Results: In total, 552 AD immune-related DERNAs were obtained. Twenty hub genes, including PIK3R1, B2M, HLA-DPB1, HLA-DQB1, PIK3CA, APP, CDC42, PPBP, C3AR1, HRAS, PTAFR, RAB37, FYN, PSMD1, ACTR10, HLA-E, ARRB2, GGH, ALDOA, and VAMP2 were identified on PPI network analysis. Furthermore, upon microRNAs (miRNAs) inhibition, we identified LINC00836 and DCTN1-AS1 as key lncRNAs regulating the aforementioned hub genes. Conclusion: AD-related immune hub genes include B2M, FYN, PIK3R1, and PIK3CA, and lncRNAs LINC00836 and DCTN1-AS1 potentially contribute to AD immune-related phenomena by regulating AD-related hub genes.

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