scholarly journals Weighted gene co-expression network analysis to identify key modules and hub genes associated with paucigranulocytic asthma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Min Li ◽  
Wenye Zhu ◽  
Chu Wang ◽  
Yuanyuan Zheng ◽  
Shibo Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Immune Status ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes

Abstract Background Asthma is a heterogeneous disease that can be divided into four inflammatory phenotypes: eosinophilic asthma (EA), neutrophilic asthma (NA), mixed granulocytic asthma (MGA), and paucigranulocytic asthma (PGA). While research has mainly focused on EA and NA, the understanding of PGA is limited. In this study, we aimed to identify underlying mechanisms and hub genes of PGA. Methods Based on the dataset from Gene Expression Omnibus(GEO), weighted gene coexpression network analysis (WGCNA), differentially expressed genes (DEGs) analysis and protein–protein interaction (PPI) network analysis were conducted to construct a gene network and to identify key gene modules and hub genes. Functional enrichment analyses were performed to investigate the biological process, pathways and immune status of PGA. The hub genes were validated in a separate dataset. Results Compared to non-PGA, PGA had a different gene expression pattern, in which 449 genes were differentially expressed. One gene module significantly associated with PGA was identified. Intersection between the differentially expressed genes (DEGs) and the genes from the module that were most relevant to PGA were mainly enriched in inflammation and immune response regulation. The single sample Gene Set Enrichment Analysis (ssGSEA) suggested a decreased immune infiltration and function in PGA. Finally six hub genes of PGA were identified, including ADCY2, CXCL1, FPRL1, GPR109B, GPR109A and ADCY3, which were validated in a separate dataset of GSE137268. Conclusions Our study characterized distinct gene expression patterns, biological processes and immune status of PGA and identified hub genes, which may improve the understanding of underlying mechanism and provide potential therapeutic targets for PGA.

scholarly journals Identification of differentially expressed genes between the colon and ileum of patients with inflammatory bowel disease by gene co-expression analysis

2019 ◽  
Vol 48 (5) ◽  
pp. 030006051988726
Author(s):  
Yuting Zhang ◽  
Bo Shen ◽  
Liya Zhuge ◽  
Yong Xie
Keyword(s):  
Inflammatory Bowel Disease ◽  
Differentially Expressed Genes ◽  
Bowel Disease ◽  
Gene Expression Pattern ◽  
Functional Enrichment ◽  
Wilcoxon Test ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Inflammatory Bowel

Objective We aimed to identify differentially expressed genes (DEG) in patients with inflammatory bowel disease (IBD). Methods RNA-seq data were obtained from the Array Express database. DEG were identified using the edgeR package. A co-expression network was constructed and key modules with the highest correlation with IBD inflammatory sites were identified for analysis. The Cytoscape MCODE plugin was used to identify key sub-modules of the protein–protein interaction (PPI) network. The genes in the sub-modules were considered hub genes, and functional enrichment analysis was performed. Furthermore, we constructed a drug–gene interaction network. Finally, we visualized the hub gene expression pattern between the colon and ileum of IBD using the ggpubr package and analyzed it using the Wilcoxon test. Results DEG were identified between the colon and ileum of IBD patients. Based on the co-expression network, the green module had the highest correlation with IBD inflammatory sites. In total, 379 DEG in the green module were identified for the PPI network. Nineteen hub genes were differentially expressed between the colon and ileum. The drug–gene network identified these hub genes as potential drug targets. Conclusion Nineteen DEG were identified between the colon and ileum of IBD patients.

scholarly journals Comprehensive analysis of DNA methylation and gene expression profiles in cholangiocarcinoma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Cheng Zhang ◽  
Bingye Zhang ◽  
Di Meng ◽  
Chunlin Ge
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks ◽  
Differentially Methylated Genes ◽  
Tumorigenesis And Progression

Abstract Background The incidence of cholangiocarcinoma (CCA) has risen in recent years, and it has become a significant health burden worldwide. However, the mechanisms underlying tumorigenesis and progression of this disease remain largely unknown. An increasing number of studies have demonstrated crucial biological functions of epigenetic modifications, especially DNA methylation, in CCA. The present study aimed to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) involved in CCA tumorigenesis and progression by bioinformatics analysis. Methods The gene expression profiling dataset (GSE119336) and gene methylation profiling dataset (GSE38860) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified using the limma packages of R and GEO2R, respectively. The MeDEGs were obtained by overlapping the DEGs and DMGs. Functional enrichment analyses of these genes were then carried out. Protein–protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape to determine hub genes. Finally, the results were verified based on The Cancer Genome Atlas (TCGA) database. Results We identified 98 hypermethylated, downregulated genes and 93 hypomethylated, upregulated genes after overlapping the DEGs and DMGs. These genes were mainly enriched in the biological processes of the cell cycle, nuclear division, xenobiotic metabolism, drug catabolism, and negative regulation of proteolysis. The top nine hub genes of the PPI network were F2, AHSG, RRM2, AURKB, CCNA2, TOP2A, BIRC5, PLK1, and ASPM. Moreover, the expression and methylation status of the hub genes were significantly altered in TCGA. Conclusions Our study identified novel methylation-regulated differentially expressed genes (MeDEGs) and explored their related pathways and functions in CCA, which may provide novel insights into a further understanding of methylation-mediated regulatory mechanisms in CCA.

scholarly journals Exploring the Key Genes and Pathways in the Formation of Corneal Scar Using Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Binfeng Liu ◽  
Ang Li ◽  
Hongbo Wang ◽  
Jialin Wang ◽  
Gongwei Zhai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Signal Pathways ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Corneal Scar

The Corneal wound healing results in the formation of opaque corneal scar. In fact, millions of people around the world suffer from corneal scars, leading to loss of vision. This study aimed to identify the key changes of gene expression in the formation of opaque corneal scar and provided potential biomarker candidates for clinical treatment and drug target discovery. We downloaded Gene expression dataset GSE6676 from NCBI-GEO, and analyzed the Differentially Expressed Genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses, and protein-protein interaction (PPI) network. A total of 1377 differentially expressed genes were identified and the result of Functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) identification and protein-protein interaction (PPI) networks were performed. In total, 7 hub genes IL6 (interleukin-6), MMP9 (matrix metallopeptidase 9), CXCL10 (C-X-C motif chemokine ligand 10), MAPK8 (mitogen-activated protein kinase 8), TLR4 (toll-like receptor 4), HGF (hepatocyte growth factor), EDN1 (endothelin 1) were selected. In conclusion, the DEGS, Hub genes and signal pathways identified in this study can help us understand the molecular mechanism of corneal scar formation and provide candidate targets for the diagnosis and treatment of corneal scar.

Integrated computational analysis revealed hub genes in lung adenocarcinoma

2021 ◽  
Author(s):  
Muhammad Jamal ◽  
Abdul Saboor Khan ◽  
Hina Iqbal Bangash ◽  
Tian Xie ◽  
Tianbao Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Survival Analysis ◽  
Differentially Expressed Genes ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Normal Lung ◽  
Hub Genes ◽  
Altered Expression ◽  
Study Gene Expression

Abstract Background Lung cancer (LUCA) is the leading cause of cancer-related morbidities and mortalities globally. Despite the recent advancements in lung cancer research, understanding of the molecular mechanism underlying LUCA tumorigenesis and prognosis remains suboptimal. This study aims to identify the candidate biomarkers and therapeutic genes in lung cancer. Methods In this study, gene expression profiles of GSE30219, GSE33532, GSE32863 and GSE43458 were downloaded from GEO. The differentially expressed genes (DEGs) in LUAD tissue and normal lung tissue with a p-value < 0.05 and a |log fold change (FC)| >1.0 were identified by GEO2R. For functional enrichment analysis of these DEGs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed with KOBAS and DAVID tools. Next, the candidate hub genes were filtered out with Cytoscape using CytoHubba plugin. These hub genes were validated by (the Cancer Genome Atlas) TCGA-based gene expression analysis, protein-protein network interaction (PPI) analysis, survival analysis. Moreover, the expression of these genes in cancer and normal tissue was assessed in the Human Protein Atlas (HPA) database. In addition, miRNA network of the hub genes was constructed. Finally, DGIdb database was used to check the drug-targeting potentials of the hub genes. Results a total of 332 overlapping differentially expressed genes (DEGs) including 73 upregulated and 259 downregulated, respectively were identified. GO analysis revealed that the DEGs were principally regulating various cancer-associated functions and pathways. The module analysis revealed 55 hub genes in 4 modules. The survival analysis through Kaplan-Meier (KM) plotter indicated that the altered expression of these genes resulted in the poor overall survival (OS) of LUCA patients. Moreover, these genes show a differential expression on both protein and mRNA level in cancer patient compared to the normal. In addition, in addition, 6 potential microRNAs (miRNAs) interacting with hub genes were identified. Finally, a list of 117 therapeutic small molecules was tabulated that could facilitate LUCA treatment. Conclusions the findings of this study may help in the development of novel and reliable biomarkers for diagnosis, prognosis and therapeutic intervention for LUAD.

scholarly journals Identification of crucial aberrantly methylated and differentially expressed genes related to cervical cancer using an integrated bioinformatics analysis

2020 ◽  
Vol 40 (5) ◽  
Author(s):  
Xiaoling Ma ◽  
Jinhui Liu ◽  
Hui Wang ◽  
Yi Jiang ◽  
Yicong Wan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Functional Analysis ◽  
Estrogen Receptor ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Venn Diagram ◽  
Hub Genes

Abstract Methylation functions in the pathogenesis of cervical cancer. In the present study, we applied an integrated bioinformatics analysis to identify the aberrantly methylated and differentially expressed genes (DEGS), and their related pathways in cervical cancer. Data of gene expression microarrays (GSE9750) and gene methylation microarrays (GSE46306) were gained from Gene Expression Omnibus (GEO) databases. Hub genes were identified by ‘limma’ packages and Venn diagram tool. Functional analysis was conducted by FunRich. Search Tool for the Retrieval of Interacting Genes Database (STRING) was used to analyze protein–protein interaction (PPI) information. Gene Expression Profiling Interactive Analysis (GEPIA), immunohistochemistry staining, and ROC curve analysis were conducted for validation. Gene Set Enrichment Analysis (GSEA) was also performed to identify potential functions.We retrieved two upregulated-hypomethylated oncogenes and eight downregulated-hypermethylated tumor suppressor genes (TSGs) for functional analysis. Hypomethylated and highly expressed genes (Hypo-HGs) were significantly enriched in cell cycle and autophagy, and hypermethylated and lowly expressed genes (Hyper-LGs) in estrogen receptor pathway and Wnt/β-catenin signaling pathway. Estrogen receptor 1 (ESR1), Erythrocyte membrane protein band 4.1 like 3 (EPB41L3), Endothelin receptor B (EDNRB), Inhibitor of DNA binding 4 (ID4) and placenta-specific 8 (PLAC8) were hub genes. Kaplan–Meier method was used to evaluate survival data of each identified gene. Lower expression levels of ESR1 and EPB41L3 were correlated with a shorter survival time. GSEA results showed that ‘cell adhesion molecules’ was the most enriched item. This research inferred the candidate genes and pathways that might be used in the diagnosis, treatment, and prognosis of cervical cancer.

scholarly journals Comprehensive Analysis to Identify Key Genes Involved in Advanced Atherosclerosis

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Tian-ming Huo ◽  
Zhi-wei Wang
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Lupus Erythematosus ◽  
Inflammatory Responses ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Key Genes

Background. The study was aimed at finding accurate and effective therapeutic targets and deepening our understanding of the mechanisms of advanced atherosclerosis (AA). Methods. We downloaded the gene expression datasets GSE28829, GSE120521, and GSE43292 from Gene Expression Omnibus. Weighted gene coexpression network analysis (WGCNA) was performed for GSE28829, and functional enrichment analysis and protein–protein interaction network analysis were conducted on the key module. Significant genes in the key module were analyzed by molecular complex detection, and genes in the most important subnetwork were defined as hub genes. Multiple dataset analyses for hub genes were conducted. Genes that overlapped between hub genes and differentially expressed genes (DEGs) of GSE28829 and GSE120521 were defined as key genes. Further validation for key genes was performed using GSE28829 and GSE43292. Gene set enrichment analysis (GSEA) was applied to key genes. Results. A total of 77 significant genes in the key module of GSE28829 were screened out that were mainly associated with inflammation and immunity. The subnetwork was obtained from significant genes, and 18 genes in this module were defined as hub genes, which were related to immunity and expressed in multiple diseases, particularly systemic lupus erythematosus. Some hub genes were regulated by SPI1 and associated with the blood, spleen, and lung. After overlapping with DEGs of GSE28829 and GSE120521, a total of 10 genes (HCK, ITGAM, CTSS, TYROBP, LAPTM5, FCER1G, ITGB2, NCF2, AIF1, and CD86) were identified as key genes. All key genes were validated and evaluated successfully and were related to immune response pathways. Conclusion. Our study suggests that the key genes related to immune and inflammatory responses are involved in the development of AA. This may deepen our understanding of the mechanisms of and provide valuable therapeutic targets for AA.

Gene Expression Profiling Distinguishes Essential Thrombocythemia from Polycythemia Vera Patients and Identifies a Common Expressed Set of Genes in Relation to JAK2V617F Status

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 2788-2788
Author(s):  
Eulalia Puigdecanet ◽  
Blanca Espinet ◽  
Juan Jose Lozano ◽  
Lauro Sumoy ◽  
Beatriz Bellosillo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Polycythemia Vera ◽  
Gene Expression Profile ◽  
Differentially Expressed Genes ◽  
Expression Profile ◽  
Essential Thrombocythemia ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Differentially Expressed ◽  
Molecular Abnormalities

Abstract Introduction. The existence of the JAK2V617F mutation in a high proportion of Myeloproliferative Disorders (MPD) BCR-ABL-negative has provided important insight into the pathogenesis of these diseases. However, much of the molecular abnormalities associated to BCR-ABL-negative MPD remain unknown, specially in those which do not display JAK2V617F. In a previous study, we performed gene expression analysis by using the microarray technique in 20 essential thrombocythemia (ET) patients (44K whole human genome oligo microarrays, Agilent Technologies) and the results were confirmed in 40 ET patients by using TaqMan® Low Density Arrays Arrays (LDA, Applied Biosystems). In our previous experience the results showed different gene expression patterns in ET and a supervised clustering of the data identified genes differentially expressed between JAK2V617F-negative and JAK2V617F-positive ET patients, and a characteristic gene expression profile for JAK2V617F-negative patients (Puigdecanet et al.,2008). Aim. Our aim was to confirm the ET gene expression profile in an extended group of patients and to explore the differences and similarities in polycythemia vera (PV) and reactive thrombocytosis (RT) patients by real-time quantitative RT-PCR (RQ-PCR) technique using the LDA platform. In addition, we wanted to analyze the relationship between gene expression data and JAK2V617F status. Patients and Methods. The following patients were included in the study: 58 ET (23 JAK2V617F-negative, 34 JAK2V617F in heterozygosity and one JAK2V617F in homozygosity) and 41 PV (7 JAK2V617F-negative, 25 JAK2V617F in heterozygosity and 9 JAK2V617F in homozygosity) patients, diagnosed according to the WHO criteria (2001) and who had never received cytoreductive treatment, and six patients with RT. Based on the previous results, we designed a new LDA platform containing 96 assays in duplicate, which included the most expressed genes in ET in relation to healthy controls and the most differentially expressed genes between JAK2V617F-negative and JAK2V617F-positive ET patients. The RQ-PCR analysis was performed in RNA from peripheral blood granulocytes and the relative gene expression quantification was achieved using GAPDH as the endogenous control and a pool of 10 healthy individuals as the calibrator. Results. ET vs PV: The majority of the genes studied presented significant higher expression in ET than in PV patients. Interestingly, FOSB was one of the most differentially expressed gene (FC= 8.3), and CISH and C13orf18 did not distinguish between the two groups. ET: We confirmed the differentially expression of the majority of the genes previously detected between JAK2V617F-negative and JAK2V617F-positive ET patients and we extended the set of genes. Among them, we highlight the differential expression of CISH, FOSB and C13orf18 genes (p&lt;0.01). PV: Supervised analysis showed that CD44, BATF and CISH clearly distinguish JAK2V617F-negative PV patients from the JAK2V617F-positive. ET and PV vs RT: Some differentially expressed genes between ET and RT patients were detected, but the most significant gene was TNF (p&lt;0.001), which presented a higher expression in RT (FC=5.9). The same difference was observed between PV and RT. Conclusions. We have detected a different gene expression pattern in ET and in PV patients. However, we also identified a set of genes which expression was related to JAK2V617F status, both in ET and PV patients. These findings would be interesting to identify other signal transduction pathways besides JAK-STAT involved in the pathogenesis of ET and PV.

scholarly journals Screening and identification of MSX1 for predicting progestin resistance in endometrial cancer

2020 ◽  
Author(s):  
Linlin Yang ◽  
Yunxia Cui ◽  
Ting Huang ◽  
Xiao Sun ◽  
Yudong Wang
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Endometrial Cancer ◽  
Endometrial Carcinoma ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Hub Genes

Abstract Background: Progestin resistance is a critical obstacle for endometrial conservative therapy. Therefore, the studies to acquire a more comprehensive understanding of the mechanisms and specific biomarkers to predict progestin resistance are very important. However, the pivotal roles of essential molecules of progestin resistance are still unexplored. Methods: We downloaded GSE121367 with gene expression profiles of medroxyprogesterone acetate (MPA) resistant and sensitive cell lines from the GEO database. The “limma” R language package was applied to identify differentially expressed genes (DEGs). Gene ontology and pathway enrichment analysis was performed through the database of DAVID. Meanwhile, we conducted GSEA analysis to identify pathway enrichments. Protein–protein interaction construction of top genes was conducted to screen hub genes by STRING and visualized in Cytoscape. A high connectivity degree of hub genes were picked out to perform the differential expression, methylation validation and overall survival analysis in the Gene Expression Profiling Interactive Analysis database, Human Protein Atlas database and Kaplan–Meier plotter online tool, respectively. In addition, microRNAs and upstream transcription factors of hub genes were predicted by miRTarBase and Network Analyst database. Results: A total number of 3282 differentially expressed genes were identified. Functional enrichment analysis demonstrated that these genes were mostly enriched in negative regulation of DNA binding, chronic inflammatory response and cell adhesion molecules pathway. We screened out ten hub genes including CDH1, JAG1, PTGES, EPCAM, CNTNAP2, TBX1, MSX1, KRT19, OAS1 and DAB2 among different groups. The genomic alteration rates of hub genes were low based on the current uterine corpus endometrial carcinoma sample sets. Their relevant microRNA and transcription factor were detected and has-miR-335-5p, has-miR-124-3p, MAZ and TFDP1 were the most prominent. The methylation status of CDH1, JAG1, EPCAM and MSX1 were decreased, corresponding to their high protein expression in endometrial cancers, which also indicated better overall survival. The homeobox protein of MSX1 showed significantly tissue specificity. Conclusions: Our study identified ten hub genes associated with progestin resistance of endometrial cancer and screened out the gene of MSX1 which promised to be the specific indicator. This would shed new light on the underlying biological marker to overcome the progestin resistance of endometrial cancer. Keywords : Bioinformatic analysis, Progestin resistance, Endometrial carcinoma, MSX1

scholarly journals A bioinformatics analysis of the contribution of m6A methylation to the occurrence of diabetes mellitus

2021 ◽  
Author(s):  
Lei Lei ◽  
Yi-Hua Bai ◽  
Hong-Ying Jiang ◽  
Ting He ◽  
Meng Li ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Differentially Expressed Genes ◽  
Disease Process ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Hub Genes

N6-methyladenosine (m6A) methylation has been reported to play a role in type 2 diabetes (T2D). However, the key component of m6A methylation has not been well explored in T2D. This study investigates the biological role and underlying mechanism of m6A methylation genes in T2D. The Gene Expression Omnibus (GEO) database combined with the m6A methylation and transcriptome data of T2D patients were used to identify m6A methylation differentially expressed genes (mMDEGs). Ingenuity Pathway Analysis was used to predict T2D-related differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to determine the biological functions of mMDEGs. Gene Set Enrichment Analysis (GSEA) was performed to further confirm the functional enrichment of mMDEGs and determine candidate hub genes. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was carried out to screen for the best predictors of T2D, and real-time polymerase chain reaction and western blot were used to verify the expression of the predictors. A total of 194 overlapping mMDEGs were detected. GO, KEGG, and GSEA analysis showed that mMDEGs were enriched in T2D and insulin signaling pathways, where the insulin gene (INS), the type 2 membranal glycoprotein gene MAFA, and hexokinase 2 (HK2) gene were found. The LASSO regression analysis of candidate hub genes showed the INS gene could be invoked as a predictive hub gene for T2D. INS, MAFA, and HK2 genes participate in the T2D disease process, but INS can better predict the occurrence of T2D.

scholarly journals Differential gene expression analysis of ankylosing spondylitis shows deregulation of the HLA-DRB, HLA-DQB, ITM2A, and CTLA4 genes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rowan AlEjielat ◽  
Anas Khaleel ◽  
Amneh H. Tarkhan
Keyword(s):  
Gene Expression ◽  
Ankylosing Spondylitis ◽  
Differentially Expressed Genes ◽  
Gene Network ◽  
Disease Diagnosis ◽  
Receptor Activation ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Inflammatory Disorder ◽  
Data Set

Abstract Background Ankylosing spondylitis (AS) is a rare inflammatory disorder affecting the spinal joints. Although we know some of the genetic factors that are associated with the disease, the molecular basis of this illness has not yet been fully elucidated, and the genes involved in AS pathogenesis have not been entirely identified. The current study aimed at constructing a gene network that may serve as an AS gene signature and biomarker, both of which will help in disease diagnosis and the identification of therapeutic targets. Previously published gene expression profiles of 16 AS patients and 16 gender- and age-matched controls that were profiled on the Illumina HumanHT-12 V3.0 Expression BeadChip platform were mined. Patients were Portuguese, 21 to 64 years old, were diagnosed based on the modified New York criteria, and had Bath Ankylosing Spondylitis Disease Activity Index scores > 4 and Bath Ankylosing Spondylitis Functional Index scores > 4. All patients were receiving only NSAIDs and/or sulphasalazine. Functional enrichment and pathway analysis were performed to create an interaction network of differentially expressed genes. Results ITM2A, ICOS, VSIG10L, CD59, TRAC, and CTLA-4 were among the significantly differentially expressed genes in AS, but the most significantly downregulated genes were the HLA-DRB6, HLA-DRB5, HLA-DRB4, HLA-DRB3, HLA-DRB1, HLA-DQB1, ITM2A, and CTLA-4 genes. The genes in this study were mostly associated with the regulation of the immune system processes, parts of cell membrane, and signaling related to T cell receptor and antigen receptor, in addition to some overlaps related to the IL2 STAT signaling, as well as the androgen response. The most significantly over-represented pathways in the data set were associated with the “RUNX1 and FOXP3 which control the development of regulatory T lymphocytes (Tregs)” and the “GABA receptor activation” pathways. Conclusions Comprehensive gene analysis of differentially expressed genes in AS reveals a significant gene network that is involved in a multitude of important immune and inflammatory pathways. These pathways and networks might serve as biomarkers for AS and can potentially help in diagnosing the disease and identifying future targets for treatment.

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