Identification of key genes and pathways in castrate-resistant prostate cancer by integrated bioinformatics analysis

ObjectiveTo identify hub genes and pathways involved in castrate-resistant prostate cancer (CRPC).MethodsThe gene expression profiles of GSE70768 were downloaded from Gene Expression Omnibus (GEO) datasets. A total of 13 CRPC samples and 110 tumor samples were identified. The differentially expressed genes (DEGs) were identified, and the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was performed. Protein-protein interaction (PPI) network module analysis was constructed and performed in Cytoscape software. Weighted correlation network analysis (WGCNA) was conducted to determine hub genes involved in the development and progression of CRPC. The gene expression profiles of GSE80609 were used for validation.ResultsA total of 1738 DEGs were identified, consisting of 962 significantly down-regulated DEGs and 776 significantly upregulated DEGs for the subsequent analysis. GO term enrichment analysis suggested that DEGs were mainly enriched in the extracellular matrix organization, extracellular exosome, extracellular matrix, and extracellular space. KEGG pathway analysis found DEGs significantly enriched in the focal adhesion pathway. PPI network demonstrated that the top 10 hub genes were ALB, ACACB, KLK3, CDH1, IL10, ALDH1A3, KLK2, ALDH3B2, HBA1, COL1A1. Also, WGCNA identified the top 5 hub genes in the turquoise module, including MBD4, BLZF1, PIP5K2B, ZNF486, LRRC37B2. Plus, the Venn diagram demonstrated that HBA1 was the key gene in both GSE70768 and GSE80609 datasets.ConclusionsThese newly identified genes and pathways could help urologists understand the differences in the mechanism between CRPC and PCa. Besides, it might be promising targets for the treatment of CRPC.     

Identification of core genes and potential molecular mechanisms in breast cancer using bioinformatics analysis

BackgroundBreast cancer is the most frequently diagnosed cancer in women worldwide. This study aimed to elucidate the potential key candidate genes and pathways in breast cancer.MethodsThe gene expression profile dataset GSE65212 was downloaded from GEO database. Differentially expressed genes (DEGs) were obtained by the R Bioconductor packages. The Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using DAVID database. The protein–protein interaction (PPI) network was then established by STRING and visualized by Cytoscape software. Module analysis of the PPI network was performed by the plug-in Molecular Complex Detection (MCODE). Then, the identified genes were verified by Kaplan–Meier plotter online database and quantitative real-time PCR (qPCR) in breast cancer tissue samples.ResultsA total of 857 differential expressed genes were identified, of which, the upregulated genes were mainly enriched in the cell cycle, while the downregulated genes were mainly enriched in PPAR signaling pathway. Moreover, six hub genes with high degree were identified, including TOP2A, PCNA, CCNB1, CDC20, BIRC5 and CCNA2. Lastly, the Kaplan–Meier plotter online database confirmed that higher expression levels of these hub genes were related to lower overall survival. Experimental validation showed that all six hub genes had the same expression trend as predicted.ConclusionThese results identified key genes, which could be used as a new biomarker for breast cancer diagnosis and treatment.     

基于生物信息学对扩张型心肌病与慢性心力衰竭共同生物标志物的预测*

目的：通过生物信息学的方法预测扩张型心肌病（DCM）与慢性心力衰竭（CHF）发病的共同生物标志物, 为临床上2 种疾病的发病及相关性奠定理论基础。方法：从Gene Expression Omnibus（GEO）数据库下载芯片数 据GSE3585,此为DCM和正常对照组原始数据,同时下载芯片数据GSE76701,此为CHF 和对照组原始数据。 通过R软件分析获得DCM和CHF 发病的差异表达基因,并获得2 种疾病发病的共同差异表达基因,进一步对共 同差异表达基因进行GO 和KEGG富集分析,构建差异表达基因的PPI 相互作用网络图,获得扩张型心肌病和心 衰发病的共同关键基因。结果：DCM的差异表达基因有240 个,其中141 个上调基因,99 个下调基因,CHF 的 差异表达基因有654 个,其中355 个上调基因,299 个下调基因。DCM和CHF 共同的差异表达基因有36 个,其中 19 个上调基因,17 个下调基因。GO 分析显示,差异表达基因主要集中在12 种不同的生理、病理过程中,KEGG 分析获得差异表达基因参与的主要信号通路为5 条,预测7 个关键差异表达基因,分别为：CD163、KYVE1、 MRC1、VSIG4、FCER1G、S100A9、F13A1。结论：该研究初步探讨了DCM与CHF 两种疾病发病分子机制, 获得了两种疾病发病的共同差异表达基因,仍需进一步的实验研究对基因的表达和临床病理特征的相关性进行验 证。     

Identification of cancer-related gene network in hepatocellular carcinoma by combined bioinformatic approach and experimental validation

HCC (hepatocellular carcinoma) is a highly aggressive malignancy that cause a mass of deaths world widely. We chose gene expression datasets of GSE27635 and GSE28248 from GEO database to find out key genes and their interaction network during the progression and metastasis of HCC. GEO2R online tool was used to screen differentially expressed genes (DEGs) between tumor and peri-tumor tissues based on these two datasets. The identified differentially expressed genes were prepared for further analysis such as GO function, KEGG pathway, PPI network analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID) and Retrieval of Interacting Genes (STRING). Two modules were constructed by MOCDE plugin in Cytoscape and 21 genes were selected as hub genes during this analysis. The expression heatmap and GO function of hub genes were performed using R pheatmap package and BiNGO plugin in Cytoscape respectively. Six hub genes including CDC25 A, CDK1, HMMR, MYBL2, TOP2A were recollected for survival analysis and their expression was validated using Kaplan Meier-plotter and GEPIA website. We also investigated the DEGs between metastasis and non-metastasis tissues and two genes (NQO1 and PTHLH) are highly associated with the metastasis in HCC. Further verification using woundhealing and transwell assay confirmed their ability to mediate cell migration and invasion. In summary, our results obtained by bioinformatic analysis and experimental validation revealed the dominant genes and their interaction networks that are associated with the progression and metastasis of HCC and might serve as potential targets for HCC therapy and diagnosis.     

Bioinformatics analysis of gene expression profiles of Inclusion body myositis

Inclusion body myositis (IBM) is a disease with a poor prognosis and limited treatment options. This study aimed at exploring gene expression profile alterations, investigating the underlying mechanisms and identifying novel targets for IBM. We analysed two microarray datasets (GSE39454 and GSE128470) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) between IBM and normal samples. Gene Ontology（GO）function and Kyoto Encyclopedia of Genes and Genomes（KEGG）pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery to identify the pathways and functional annotation of DEGs. Finally, protein-protein interaction (PPI) networks were constructed using STRING and Cytoscape, in order to identify hub genes. A total of 144 upregulated DEGs and one downregulated DEG were identified. The GO enrichment analysis revealed that the immune response was the most significantly enriched term within the DEGs. The KEGG pathway analysis identified 22 significant pathways, the majority of which could be divided into the immune and infectious diseases. Following the construction of PPI networks, ten hub genes with high degrees of connectivity were picked out, namely PTPRC, IRF8, CCR5, VCAM1, HLA-DRA, TYROBP, C1QB, HLA-DRB1, CD74 and CXCL9. Our research hypothesizes that autoimmunity plays an irreplaceable role in the pathogenesis of IBM. The novel DEGs and pathways identified in this study may provide new insight into the underlying mechanisms of IBM at the molecular level.     

Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gastric cancer

Background and objective

The underlying molecular mechanisms of gastric cancer (GC) have yet not been investigated clearly. In this study, we aimed to identify hub genes involved in the pathogenesis and prognosis of GC.

Identification of four novel prognosis biomarkers and potential therapeutic drugs for human colorectal cancer by bioinformatics analysis

Colorectal cancer(CRC)is one of the most deadly cancers in the world with few reliable biomarkers that have been selected into clinical guidelines for prognosis of CRC patients.In this study,mRNA microarray datasets GSE113513,GSE21510,GSE44076,and GSE32323 were obtained from the Gene Expression Omnibus(GEO)and analyzed with bioinformatics to identify hub genes in CRC development.Differentially expressed genes(DEGs)were analyzed using the GEO2 R tool.Gene ontology(GO)and KEGG analyses were performed through the DAVID database.STRING database and Cytoscape software were used to construct a protein-protein interaction(PPI)network and identify key modules and hub genes.Survival analyses of the DEGs were performed on GEPIA database.The Connectivity Map database was used to screen potential drugs.A total of 865 DEGs were identified,including 374 upregulated and 491 downregulated genes.These DEGs were mainly associated with metabolic pathways,pathways in cancer,cell cycle and so on.The PPI network was identified with 863 nodes and 5817 edges.Survival analysis revealed that HMMR,PAICS,ETFDH,and SCG2 were significantly associated with overall survival of CRC patients.And blebbistatin and sulconazole were identified as candidate drugs.In conclusion,our study found four hub genes involved in CRC,which may provide novel potential biomarkers for CRC prognosis,and two potential candidate drugs for CRC.     

Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis.Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively.Results: We downloaded the expression profiles of {"type":"entrez-geo","attrs":{"text":"GSE103236","term_id":"103236"}}GSE103236 and {"type":"entrez-geo","attrs":{"text":"GSE118897","term_id":"118897"}}GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines.Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.     

Identification of collaboration patterns of dysfunctional pathways in breast cancer

Breast cancer (BC) is the most common malignancy among women. We aimed to illuminate the molecular dysfunctional mechanisms of BC progression. The mRNA expression profile of BC {"type":"entrez-geo","attrs":{"text":"GSE15852","term_id":"15852"}}GSE15852 was downloaded from Gene Expression Omnibus database, including 43 normal samples and 43 cancer samples. Differentially expressed genes (DEGs) in BC were screened using the t-test by Benjamin and Hochberg method. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the selected DEGs were enriched using Hypergeomeric distribution model. In addition, functional similarity network among the enriched pathways was constructed to further analyze the collaboration of these pathways. We found 848 down-regulated DEGs were associated with 16 significant dysfunctional pathways, including PPAR signaling fatty acid metabolism, and 1584 up-regulated DEGs were related to 6 significant dysfunctional pathways, like cell cycle, protein export, and antigen processing and presentation in BC samples. Crosstalk network analysis of pathways indicated that pyruvate metabolism, propanoate metabolism, and glycolysis gluconeogenesis were the pathways with closest connections with other pathways in BC. In addition, other antigen processing and presentation, including 19 DEGs; PPAR signaling pathway, including 18 DEGs; and pyruvate metabolism pathway, including 13 DEGs were further analyzed. Our results suggested that dysfunctional of significant pathways can greatly affect the progression of BC. Several significant disorder pathways were enriched in our comprehensive study. They may provide guidelines to explore the dysfunctional mechanism of BC progression.     

Comparison of the gene expression profiles between gallstones and gallbladder polyps

Background: Gallstones and gallbladder polyps (GPs) are two major types of gallbladder diseases that share multiple common symptoms. However, their pathological mechanism remains largely unknown. The aim of our study is to identify gallstones and GPs related-genes and gain an insight into the underlying genetic basis of these diseases. Methods: We enrolled 7 patients with gallstones and 2 patients with GP for RNA-Seq and we conducted functional enrichment analysis and protein-protein interaction (PPI) networks analysis for identified differentially expressed genes (DEGs). Results: RNA-Seq produced 41.7 million in gallstones and 32.1 million pairs in GPs. A total of 147 DEGs was identified between gallstones and GPs. We found GO terms for molecular functions significantly enriched in antigen binding (GO:0003823, P=5.9E-11), while for biological processes, the enriched GO terms were immune response (GO:0006955, P=2.6E-15), and for cellular component, the enriched GO terms were extracellular region (GO:0005576, P=2.7E-15). To further evaluate the biological significance for the DEGs, we also performed the KEGG pathway enrichment analysis. The most significant pathway in our KEGG analysis was Cytokine-cytokine receptor interaction (P=7.5E-06). PPI network analysis indicated that the significant hub proteins containing S100A9 (S100 calcium binding protein A9, Degree=94) and CR2 (complement component receptor 2, Degree=8). Conclusion: This present study suggests some promising genes and may provide a clue to the role of these genes playing in the development of gallstones and GPs.     

Identification ACTA2 and KDR as key proteins for prognosis of PD‐1/PD‐L1 blockade therapy in melanoma

Programmed cell death protein 1 (PD‐1) /programmed cell death ligand 1 (PD‐L1) blockade is an important therapeutic strategy for melanoma, despite its low clinical response. It is important to identify genes and pathways that may reflect the clinical outcomes of this therapy in patients. We analyzed clinical dataset {"type":"entrez-geo","attrs":{"text":"GSE96619","term_id":"96619"}}GSE96619, which contains clinical information from five melanoma patients before and after anti‐PD‐1 therapy (five pairs of data). We identified 704 DEGs using these five pairs of data, and then the number of DEGs was narrowed down to 286 in patients who responded to treatment. Next, we performed KEGG pathway enrichment and constructed a DEG‐associated protein‐protein interaction network. Smooth muscle actin 2 (ACTA2) and tyrosine kinase growth factor receptor (KDR) were identified as the hub genes, which were significantly downregulated in the tumor tissue of the two patients who responded to treatment. To confirm our analysis, we demonstrated similar expression tendency to the clinical data for the two hub genes in a B16F10 subcutaneous xenograft model. This study demonstrates that ACTA2 and KDR are valuable responsive markers for PD‐1/PD‐L1 blockade therapy.     

良恶性乳腺肿瘤外周血基因差异表达研究

目的:利用基因表达谱数据,探讨良恶性乳腺肿瘤患者外周血基因表达变化。方法:从GEO 数据库中获取良性和恶性乳腺肿瘤患者外周血单个核细胞(PBMCs)表达谱。GEO2R 在线工具筛选差异表达基因, DAVID 工具富集基因功能和通路。STRING 数据库构建差异表达基因蛋白产物相互作用的网络,筛选核心基因。结果:良恶性乳腺肿瘤分别筛选到563和237 个差异基因,乳腺癌差异基因涉及白细胞激活、血管生成等生物学过程以及白细胞跨内皮迁移信号通路。IL8、RHOB、ITGB1 等为关键基因。结论:良恶性乳腺肿瘤患者外周血基因表达模式存在明显差异,为将外周血作为替代材料应用于乳腺肿瘤的诊断及监测研究开辟了新思路。     

Identification of DNA methylation-regulated differentially-expressed genes and related pathways using Illumina 450K BeadChip and bioinformatic analysis in gastric cancer

In the current research, we aimed to identify and analyze methylation-regulated differentially-expressed genes (MeDEGs) and related pathways using bioinformatic methods. We downloaded RNA-seq, Illumina Human Methylation 450 K BeadChip and clinical information of gastric cancer (GC) from The Cancer Genome Atlas (TCGA) project. Differentially-expressed genes (DEGs) were identified using the edgeR package. Then, we performed Spearman’s correlation analysis between DEG expression levels and methylation levels. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in the DAVID database. We then conducted Kaplan–Meier survival analysis to explore the relationship between methylation, expression and prognosis. The protein–protein interaction networks were further analyzed using the STRING database. A total of 204 down-regulated DEGs and 164 up-regulated DEGs were identified as MeDEGs. GO and KEGG pathway analyses showed that MeDEGs were enriched in multiple cancer-related terms. Kaplan–Meier survival analysis showed that eight up-regulated MeDEGs (CAMKV, COMP, FGF3, FGF19, FOXL2, IGF2BP1, IGFBP1 and NPPB) and five down-regulated MeDEGs (ALDH3B2, CALML3, FLRT1, G6PC and HRASLS2) were associated with prognosis of GC patients. In addition, PPI networks and KEGG pathway analyses further confirmed the critical role of prognosis-related MeDEGs. In conclusion, methylation plays a critical role in GC progression. Multiple MeDEGs are related to prognosis, suggesting that they may be potential targets in tumor treatment.     

唾液腺腺样囊性癌中相关微小RNAs及其靶基因的生物信息学分析

目的 探讨唾液腺腺样囊性癌(SACC)潜在的微小RNAs(miRNAs)分子标志物,构建miRNA-mRNA调控网络,并阐明其潜在的分子机制.方法 从Gene Expression Omnibus(GEO)数据库下载2个SACC的微阵列芯片数据,通过R语言进行分析差异的miRNAs与mRNA.应用FunRich 3.1...     

Identification of potential drugs for diffuse large b-cell lymphoma based on bioinformatics and Connectivity Map database

Diffuse large B-cell lymphoma (DLBCL) is the most main subtype in non-Hodgkin lymphoma. After chemotherapy, about 30% of patients with DLBCL develop resistance and relapse. This study was to identify potential therapeutic drugs for DLBCL using the bioinformatics method. The differentially expressed genes (DEGs) between DLBCL and non-cancer samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were analyzed using the Database for Annotation, Visualization, and Integrated Discovery. The R software package (SubpathwayMiner) was used to perform pathway analysis on DEGs affected by drugs found in the Connectivity Map (CMap) database. Protein–protein interaction (PPI) networks of DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes online database and Cytoscape software. In order to identify potential novel drugs for DLBCL, the DLBCL-related pathways and drug-affected pathways were integrated. The results showed that 1927 DEGs were identified from TCGA and GEO. We found 54 significant pathways of DLBCL using KEGG pathway analysis. By integrating pathways, we identified five overlapping pathways and 47 drugs that affected these pathways. The PPI network analysis results showed that the CDK2 is closely associated with three overlapping pathways (cell cycle, p53 signaling pathway, and small cell lung cancer). The further literature verification results showed that etoposide, rinotecan, methotrexate, resveratrol, and irinotecan have been used as classic clinical drugs for DLBCL. Anisomycin, naproxen, gossypol, vorinostat, emetine, mycophenolic acid and daunorubicin also act on DLBCL. It was found through bioinformatics analysis that paclitaxel in the drug-pathway network can be used as a potential novel drug for DLBCL.     

去分化脂肪肉瘤潜在核心基因的生物信息学预测及分析

目的 探讨去分化脂肪肉瘤的潜在核心基因在其恶性生物学行为中的作用.方法 获取基因表达数据库(gene expres-sion omnibus,GEO)数据库中GSE21122和GSE52390的芯片数据,通过GEO2R筛选差异表达基因,对差异表达基因进行GO功能、KEGG通路富集分析和蛋白互作分析,并用Cytoscap...     

利什曼原虫感染树突状细胞早期基因表达差异分析及关键基因筛选

目的:分析利什曼原虫感染树突状细胞(DCs)早期的基因表达与信号通路变化,探究DCs感染后应答,寻找利什曼原虫感染后基于DCs的免疫治疗方法。方法:GEO数据库下载利什曼原虫感染前后DCs基因芯片数据,RStudio软件筛选差异表达基因(DEGs),STRING构建DEGs蛋白质相互作用网络(PPI),Cytoscape筛选差异表达蛋白质的核心模块,RStudio软件对DEGs进行GO和KEGG富集分析。结果:共筛选出DEGs 129个,其中IL12B与CXCL10差异最为显著,GO分析共富集23个过程,主要涉及病毒感染过程相关细胞反应及Ⅰ-IFN相关免疫反应;KEGG分析共富集3条信号通路,分别为甲型流感、麻疹及DNA复制信号通路。结论:利什曼原虫感染DCs前后Ⅰ-IFN信号通路和TLR4/NF-κB信号通路激活,影响IL12表达,提示Ⅰ-IFN/IL12信号通路与TLR4/NF-κB/IL12信号通路可作为利什曼原虫感染治疗的靶点,CXCL10也有望成为潜在的治疗靶点;利什曼原虫感染后,出现类似病毒感染现象,推测抗病毒免疫疗法可能在对抗利什曼原虫感染中具有一定疗效。     

小鼠哮喘模型颈静脉-结状神经节内参基因的筛选

目的　观察12个常用看家基因在正常小鼠和哮喘小鼠颈静脉-结状神经节的表达变化,选择合适的内参基因为进一步基因表达分析研究得到可信数据提供依据。 方法　雌性BALB/c小鼠随机分为哮喘模型组和对照组,制备哮喘模型,用实时荧光定量PCR技术,经geNorm及Normfinder程序分析,评价了12个常用看家基因CYC1、UBC、RPL13、YWHAZ、18srRNA、B2M、SDHA、ATP5B、CANX、　ACTB、EIF4A2和GAPDH在小鼠颈-结状神经节的表达稳定性。 结果 12个候选看家基因在小鼠神经节的表达差异较大,其中GAPDH、18srRNA、CANX、ACTB和EIF4A2表达相对稳定,而CYC1、UBC和RPL13的表达在两组小鼠神经节变化幅度较大,结合两种分析证实GAPDH是其中最稳定的内参基因。 结论 本研究结果提示GAPDH是研究哮喘小鼠和正常小鼠颈静脉-结状神经节基因表达的最佳内参基因,同时也为进一步认识在不同实验模型确定适宜内参基因的重要性提供依据。     

Deciphering crucial genes in coeliac disease by bioinformatics analysis

Abstract

Coeliac disease (CD) is a chronic autoimmune disease that is characterized by malabsorption in sensitive individuals. CD is triggered by the ingestion of grains containing gluten. CD is concomitant with several other disorders, including dermatitis herpetiformis, selective IgA deficiency, thyroid disorders, diabetes mellitus, various connective tissue disorders, inflammatory bowel disease, and rheumatoid arthritis. The advent of high throughput technologies has provided a massive wealth of data which are processed in various omics scale fields. These approaches have revolutionized the medical research and monitoring of the biological systems. In this regard, omics scaled analyses of CD by Comparative Toxicogenomics Database (CTD), DISEASES, and GeneCards databases have retrieved 2656?CD associated genes. Amongst, 54 genes were assigned by Venn Diagram of the intersection to be shared by these 3 databases for CD. These common genes were subjected to further analysis and screening. The Enrich database, GeneMANIA, Cytoscape, and WebGestalt (WEB-based GEne SeT AnaLysis Toolkit) were employed for functional analysis. These analyses indicated that the obtained genes are mainly involved in the immune system and signalling pathways related to autoimmune diseases. The STAT1, ALB, IL10, IL2, IL4, IL17A, TGFB1, IL1B, IL6, TNF, IFNG hub genes were particularly indicated to have significant roles in CD. Functional analyses of these hub genes by GeneMANIA indicated that they are involved in immune systems regulation. Moreover, 25 out of 54 genes were identified to be seed genes by the WebGestalt database. Gene set analysis with GEO2R tool from Gene Expression Omnibus (GEO) showed that there were 15 significant genes in GSE76168, 29 significant genes in GSE87460, 12 significant genes in GSE87458, 9 significant genes in GSE87457, 3753 significant genes in GSE112102 and 1043 significant genes in GSE102991 with differential expression in coeliac patients compared to controls. The IRF1and STAT1 genes were common between the significant genes from GEO and the 54?CD related genes from three public databases. In the light these results, nine key genes, including IRF1, STAT1, IL17A, TGFB1, ALB, IL10, IL2, IL4, and IL1B, were identified to be associated with CD. These findings could be used to find novel diagnostic biomarkers, understand the pathology of disease, and devise more efficient treatments.