Uncovering potential lncRNAs and nearby mRNAs in systemic lupus erythematosus from the Gene Expression Omnibus dataset

Epigenomics ◽  
2019 ◽  
Vol 11 (16) ◽  
pp. 1795-1809 ◽  
Author(s):  
Haiyu Cao ◽  
Dong Li ◽  
Huixiu Lu ◽  
Jing Sun ◽  
Haibin Li

Aim: The aim of this study was to find potential differentially expressed long noncoding RNAs (lncRNAs) and mRNAs in systemic lupus erythematosus. Materials & methods: Differentially expressed lncRNAs and mRNAs were obtained in the Gene Expression Omnibus dataset. Functional annotation of differentially expressed mRNAs was performed, followed by protein–protein interaction network analysis. Then, the interaction network of lncRNA-nearby targeted mRNA was built. Results: Several interaction pairs of lncRNA-nearby targeted mRNA including NRIR-RSAD2, RP11-153M7.5-TLR2, RP4-758J18.2-CCNL2, RP11-69E11.4-PABPC4 and RP11-496I9.1-IRF7/ HRAS/ PHRF1 were identified. Measles and MAPK were significantly enriched signaling pathways of differentially expressed mRNAs. Conclusion: Our study identified several differentially expressed lncRNAs and mRNAs. And their interactions may play a crucial role in the process of systemic lupus erythematosus.

Epigenomics ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 973-988
Author(s):  
Gangqiang Guo ◽  
Aqiong Chen ◽  
Lele Ye ◽  
Huijing Wang ◽  
Zhiyuan Chen ◽  
...  

Aim: We aimed to identify differentially expressed Long noncoding RNAs (lncRNAs) and explore their functional roles in systemic lupus erythematosus (SLE). Materials & methods: We identified dysregulated lncRNAs and investigated their prognostic values and potential functions using MiRTarget2, catRAPID omics and Bedtools/blast/Pearson analyses. Results: Among the 143 differentially expressed lncRNAs, TCONS_00483150 could be used to distinguish patients with SLE from healthy controls and those with rheumatoid arthritis and patients with active/stable SLE from healthy controls. TCONS_00483150 was significantly correlated with anti-Rib-P antibody positivity and low C3 levels; TCONS_00483150 dysregulation might contribute to the metabolism of RNA and proteins in SLE patients. Conclusion: Overall, our findings offer a transcriptome-wide overview of aberrantly expressed lncRNAs in patients with SLE and highlight TCONS_00483150 as a potential novel diagnostic biomarker.


2021 ◽  
Vol 20 ◽  
pp. 153303382199036
Author(s):  
Kai Cui ◽  
Jin-hui Chen ◽  
Yang-fan Zou ◽  
Shu-yuan Zhang ◽  
Bing Wu ◽  
...  

Background: Glioblastoma (GBM) is the most common clinical intracranial malignancy worldwide, and the most common supratentorial tumor in adults. GBM mainly causes damage to the brain tissue, which can be fatal. This research explored potential gene targets for the diagnosis and treatment of GBM using bioinformatic technology. Methods: Public data from patients with GBM and controls were downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were identified by Gene Expression Profiling Interactive Analysis (GEPIA) and Gene Expression Omnibus 2R (GEO2R). Construction of the protein–protein interaction network and the identification of a significant module were performed. Subsequently, hub genes were identified, and their expression was examined and compared by real-time quantitative (RT-q)PCR between patients with GBM and controls. Results: GSE122498 (GPL570 platform), GSE104291 (GPL570 platform), GSE78703_DMSO (GPL15207 platform), and GSE78703_LXR (GPL15207 platform) datasets were obtained from the GEO. A total of 130 DEGs and 10 hub genes were identified by GEPIA and GEO2R between patients with GBM and controls. Of these, strong connections were identified in correlation analysis between CCNB1, CDC6, KIF23, and KIF20A. RT-qPCR showed that all 4 of these genes were expressed at significantly higher levels in patients with GBM compared with controls. Conclusions: The hub genes CCNB1, CDC6, KIF23, and KIF20A are potential biomarkers for the diagnosis and treatment of GBM.


2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 896-897
Author(s):  
W. Liu ◽  
X. Zhang

Background:Myositis, including dermatomyositis and polymyositis, is autoimmune disorders that is characterized by muscle degeneration in the proximal extremities, with the complications of weakness of muscles, interstitial lung disease and vascular lesions, even leading to death in an acute progressive process[1,2]. However, the molecular mechanisms of myositis are rarely understood.Objectives:Identify the candidate genes in myositis.Methods:Microarray datasets GSE128470, GSE48280 and GSE39454 were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and function enrichment analyses were conducted. The protein-protein interaction network and the analyses of hub genes were performed with STRING and Cytoscape.Results:There were 98 DEGs, of which the function and pathways enrichment analyses showed defense response, immune response, response to virus, inflammatory response, response to wounding, cell adhesion, cell proliferation, cell death and macromolecule metabolic process. 20 hub genes were identified, of which 7 including IRF9 TRIM22 MX2 IFITM1 IFI6 IFI44 IFI44L had not been reported in the literature, related to the response to virus, immune response, transcription from RNA polymerase II promoter, cell apoptosis, cell death. The verification analysis about the 7 genes in GSE128314 showed significant differences in myositis.Conclusion:In conclusion, DEGs and hub genes identified in our study showed the potential molecular mechanisms in myositis, providing the helpful targets for diagnosis and clinical strategy of myositis.References:[1] Wu H, Geng D, Xu J. An approach to the development of interstitial lung disease in dermatomyositis: a study of 230 cases in China[J]. Journal of International Medical Research. 2013;41(2):493–501.[2] Fathi M, Dastmalchi M, Rasmussen E, Lundberg IE, Tornling G. Interstitial lung disease, a common manifestation of newly diagnosed polymyositis and dermatomyositis[J]. Annals of the Rheumatic Diseases. 2004;63(3):297–301.Figure 1.The protein-protein interaction network of 20 hub genesFigure 2.7 genes in GSE128314 showed significant differences in myositisAcknowledgments:The authors acknowledge the efforts of the Gene Expression Omnibus (GEO) database. The interpretation and reporting of these data are the sole responsibility of the authors.Disclosure of Interests:None declared


2020 ◽  
Author(s):  
Jian-Ruei Ciou ◽  
Pu-Wei Ho ◽  
Po-Chang Wu ◽  
Shu-I Chen ◽  
Ching-Mao Chang ◽  
...  

Abstract Objectives: Malar rash is one of clinical phenotypes seen in systemic lupus erythematosus (SLE). However, the pathogenesis of malar rash is not clear for each case of SLE patients. In this paper we endeavored to investigate the linking of clinical phenotype from the gene expression profiles between both patients with malar rash and without malar rash. Therefore we might perform better evaluation of the possible prognosis for different SLE patients in the future.Methods: This study utilizes transcriptome sequencing (RNA-Seq) technologies to discover underlying gene expression profile for systemic lupus erythematosus patients. We performed transcriptome sequencing experiments and analyzed differentially expressed genes (DEGs) and associated pathways.Results: From the analysis of gene expression profiling, we identified the gene DAAM2 is the most differentially expressed gene for patients with malar rash. Using a gene set enrichment analysis, we discuss the linkage between DAAM2 and the possible pathways for systemic lupus erythematosus with malar rash. Conclusions: We identified DAAM2 as a candidate biomarker for the clinical phenotype of malar rash for systemic lupus erythematosus.


PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6425 ◽  
Author(s):  
Yang Fang ◽  
Pingping Wang ◽  
Lin Xia ◽  
Suwen Bai ◽  
Yonggang Shen ◽  
...  

Background The elderly population is at risk of osteoarthritis (OA), a common, multifactorial, degenerative joint disease. Environmental, genetic, and epigenetic (such as DNA hydroxymethylation) factors may be involved in the etiology, development, and pathogenesis of OA. Here, comprehensive bioinformatic analyses were used to identify aberrantly hydroxymethylated differentially expressed genes and pathways in osteoarthritis to determine the underlying molecular mechanisms of osteoarthritis and susceptibility-related genes for osteoarthritis inheritance. Methods Gene expression microarray data, mRNA expression profile data, and a whole genome 5hmC dataset were obtained from the Gene Expression Omnibus repository. Differentially expressed genes with abnormal hydroxymethylation were identified by MATCH function. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the genes differentially expressed in OA were performed using Metascape and the KOBAS online tool, respectively. The protein–protein interaction network was built using STRING and visualized in Cytoscape, and the modular analysis of the network was performed using the Molecular Complex Detection app. Results In total, 104 hyperhydroxymethylated highly expressed genes and 14 hypohydroxymethylated genes with low expression were identified. Gene ontology analyses indicated that the biological functions of hyperhydroxymethylated highly expressed genes included skeletal system development, ossification, and bone development; KEGG pathway analysis showed enrichment in protein digestion and absorption, extracellular matrix–receptor interaction, and focal adhesion. The top 10 hub genes in the protein–protein interaction network were COL1A1, COL1A2, COL2A1, COL3A1, COL5A1, COL5A2, COL6A1, COL8A1, COL11A1, and COL24A1. All the aforementioned results are consistent with changes observed in OA. Conclusion After comprehensive bioinformatics analysis, we found aberrantly hydroxymethylated differentially expressed genes and pathways in OA. The top 10 hub genes may be useful hydroxymethylation analysis biomarkers to provide more accurate OA diagnoses and target genes for treatment of OA.


2020 ◽  
Author(s):  
Jingdi Yang ◽  
Bo Peng ◽  
Xianzheng Qin ◽  
Tian Zhou

Abstract Background: Although the morbidity and mortality of gastric cancer are declining, gastric cancer is still one of the most common causes of death. Early detection of gastric cancer is of great help to improve the survival rate, but the existing biomarkers are not sensitive to diagnose early gastric cancer. The aim of this study is to identify the novel biomarkers for gastric cancer.Methods: Three gene expression profiles (GSE27342, GSE63089, GSE33335) were downloaded from Gene Expression Omnibus database to select differentially expressed genes. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were performed to explore the biological functions of differentially expressed genes. Cytoscape was utilized to construct protein-protein interaction network and hub genes were analyzed by plugin cytoHubba of Cytoscape. Furthermore, Gene Expression Profiling Interactive Analysis and Kaplan-Meier plotter were used to verify the identified hub genes.Results: 35 overlapping differentially expressed genes were screened from gene expression datasets, which consisted of 11 up-regulated genes and 24 down-regulated genes. Gene Ontology functional enrichment analysis revealed that differentially expressed genes were significantly enriched in digestion, regulation of biological quality, response to hormone and steroid hormone, and homeostatic process. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed differentially expressed genes were enriched in the secretion of gastric acid and collecting duct acid, leukocyte transendothelial migration and ECM-receptor interaction. According to protein-protein interaction network, 10 hub genes were identified by Maximal Clique Centrality method.Conclusion: By using bioinformatics analysis, COL1A1, BGN, THY1, TFF2 and SST were identified as the potential biomarkers for early detection of gastric cancer.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhengqing Zhu ◽  
Lei Zhong ◽  
Ronghang Li ◽  
Yuzhe Liu ◽  
Xiangrun Chen ◽  
...  

Osteoarthritis (OA) is a common cause of morbidity and disability worldwide. However, the pathogenesis of OA is unclear. Therefore, this study was conducted to characterize the pathogenesis and implicated genes of OA. The gene expression profiles of GSE82107 and GSE55235 were downloaded from the Gene Expression Omnibus database. Altogether, 173 differentially expressed genes including 68 upregulated genes and 105 downregulated genes in patients with OA were selected based on the criteria of ∣log fold‐change∣>1 and an adjusted p value < 0.05. Protein-protein interaction network analysis showed that FN1, COL1A1, IGF1, SPP1, TIMP1, BGN, COL5A1, MMP13, CLU, and SDC1 are the top ten genes most closely related to OA. Quantitative reverse transcription-polymerase chain reaction showed that the expression levels of COL1A1, COL5A1, TIMP1, MMP13, and SDC1 were significantly increased in OA. This study provides clues for the molecular mechanism and specific biomarkers of OA.


2020 ◽  
Vol 26 (29) ◽  
pp. 3619-3630
Author(s):  
Saumya Choudhary ◽  
Dibyabhaba Pradhan ◽  
Noor S. Khan ◽  
Harpreet Singh ◽  
George Thomas ◽  
...  

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.


Rheumatology ◽  
2009 ◽  
Vol 48 (12) ◽  
pp. 1491-1497 ◽  
Author(s):  
B. C.-H. Kwan ◽  
L.-S. Tam ◽  
K.-B. Lai ◽  
F. M.-M. Lai ◽  
E. K.-M. Li ◽  
...  

2021 ◽  
Author(s):  
Victoria Oberreiter ◽  
Tobias Goellner ◽  
David L. Morris ◽  
Helmut Schaschl

Abstract Background: Systemic lupus erythematosus (SLE) shows marked population-specific disparities in disease prevalence, including substantial variation in manifestations and complications according to genetic ancestry. Several recent studies suggest that a substantial proportion of variation of gene expression shows genetic ancestry-associated differences in gene regulation on immune responses. Positive selection may act in a population-specific manner on expression quantitative trait loci (eQTLs) and thereby contributes to the difference in the differences of SLE prevalence and manifestation in human populations. We tested the hypothesises that some of the identified SLE risk polymorphisms display pleiotropic effects or polygenicity driven by positive selection. We performed a genome-wide scan for recent positive selection by using integrated Haplotype Score (iHS) statistics in different human populations. In addition, we estimated the timing of beneficial mutations to understand what possible selective pressures drive positive selection at SLE-associated loci. Results: We identified several SLE risk loci that are population-specifically under positive selection. Almost all SNPs that are under positive selection function as cis-eQTLs in different tissue types. We determined that adaptive eQTLs affect the expression of fewer genes than non-adaptive eQTLs, suggesting a limited range of effect of an eQTL at SLE risk sites that show signatures of positive selection. Furthermore, some positively selected SNPs are located in transcription factor binding sequences. The timing of positive selection for the studied loci suggests that both environmental and recent lifestyle changes during as well as after the Neolithic Transition may have become selectively effective. We propose a novel link between positively selected eQTLs at a certain SLE risk locus in Europeans and a physiological pathway not previously considered in SLE.Conclusions: We conclude that population-specific adaptive eQTLs contribute to the observed variation in specific manifestations and complications of SLE in different ethnicities. Our results suggest also that human populations adapt more rapidly to environmental and lifestyle stimuli via modification of gene expression without having to alter the genetic code.


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