scholarly journals Identification of genes and functional coexpression modules closely related to ulcerative colitis by gene datasets analysis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8061 ◽  
Author(s):  
Jie Zhu ◽  
Zheng Wang ◽  
Fengzhe Chen ◽  
Changhong Liu
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Tyrosine Kinases ◽  
Gene Expression Signature ◽  
Enrichment Analysis ◽  
Rank Aggregation ◽  
Integrated Analysis ◽  
Pathway Enrichment Analysis ◽  
Functional Modules ◽  
The Core

Background Ulcerative colitis is a type of inflammatory bowel disease posing a great threat to the public health worldwide. Previously, gene expression studies of mucosal colonic biopsies have provided some insight into the pathophysiological mechanisms in ulcerative colitis; however, the exact pathogenesis is unclear. The purpose of this study is to identify the most related genes and pathways of UC by bioinformatics, so as to reveal the core of the pathogenesis. Methods Genome-wide gene expression datasets involving ulcerative colitis patients were collected from gene expression omnibus database. To identify most close genes, an integrated analysis of gene expression signature was performed by employing robust rank aggregation method. We used weighted gene co-expression network analysis to explore the functional modules involved in ulcerative colitis pathogenesis. Besides, biological process and pathways analysis of co-expression modules were figured out by gene ontology enrichment analysis using Metascape. Results A total of 328 ulcerative colitis patients and 138 healthy controls were from 14 datasets. The 150 most significant differentially expressed genes are likely to include causative genes of disease, and further studies are needed to demonstrate this. Seven main functional modules were identified, which pathway enrichment analysis indicated were associated with many biological processes. Pathways such as ‘extracellular matrix, immune inflammatory response, cell cycle, material metabolism’ are consistent with the core mechanism of ulcerative colitis. However, ‘defense response to virus’ and ‘herpes simplex infection’ suggest that viral infection is one of the aetiological agents. Besides, ‘Signaling by Receptor Tyrosine Kinases’ and ‘pathway in cancer’ provide new clues for the study of the risk and process of ulcerative colitis cancerization.

scholarly journals Identification of Four Oxidative Stress-Responsive MicroRNAs, miR-34a-5p, miR-1915-3p, miR-638, and miR-150-3p, in Hepatocellular Carcinoma

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yong Wan ◽  
Ruixia Cui ◽  
Jingxian Gu ◽  
Xing Zhang ◽  
Xiaohong Xiang ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Hepatocellular Carcinoma ◽  
Stress Response ◽  
Clinical Investigation ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Oxidative Stress Response ◽  
Rank Aggregation ◽  
Integrated Analysis ◽  
Pathway Enrichment Analysis

Increasing evidence suggests that oxidative stress plays an essential role during carcinogenesis. However, the underlying mechanism between oxidative stress and carcinogenesis remains unknown. Recently, microRNAs (miRNAs) are revealed to be involved in oxidative stress response and carcinogenesis. This study aims to identify miRNAs in hepatocellular carcinoma (HCC) cells which might involve in oxidative stress response. An integrated analysis of miRNA expression signature was performed by employing robust rank aggregation (RRA) method, and four miRNAs (miR-34a-5p, miR-1915-3p, miR-638, and miR-150-3p) were identified as the oxidative stress-responsive miRNAs. Pathway enrichment analysis suggested that these four miRNAs played an important role in antiapoptosis process. Our data also revealed miR-34a-5p and miR-1915-3p, but not miR-150-3p and miR-638, were regulated by p53 in HCC cell lines under oxidative stress. In addition, clinical investigation revealed that these four miRNAs might be involved in oxidative stress response by targeting oxidative stress-related genes in HCC tissues. Kaplan-Meier analysis showed that these four miRNAs were associated with patients’ overall survival. In conclusion, we identified four oxidative stress-responsive miRNAs, which were regulated by p53-dependent (miR-34a-5p and miR-1915-3p) and p53-independent pathway (miR-150-3p and miR-638). These four miRNAs may offer new strategy for HCC diagnosis and prognosis.

scholarly journals Identification of candidate genes and proteins in aging skeletal muscle (sarcopenia) using gene expression and structural analysis

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5239 ◽  
Author(s):  
Gita Shafiee ◽  
Yazdan Asgari ◽  
Akbar Soltani ◽  
Bagher Larijani ◽  
Ramin Heshmat
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Ribosome Biogenesis ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Lipid Storage ◽  
Integrated Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Men And Women

Sarcopenia is an age-related disease characterized by the loss of muscle mass and muscle function. A proper understanding of its pathogenesis and mechanisms may lead to new strategies for diagnosis and treatment of the disease. This study aims to discover the underlying genes, proteins, and pathways associated with sarcopenia in both genders. Integrated analysis of microarray datasets has been performed to identify differentially expressed genes (DEGs) between old and young skeletal muscles. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were then performed to uncover the functions of the DEGs. Moreover, a protein–protein interaction (PPI) network was constructed based on the DEGs. We have identified 41,715 DEGs, including 19 downregulated and 41,696 upregulated ones, in men. Among women, 3,015 DEGs have been found, with 2,874 of them being upregulated and 141 downregulated genes. Among the top up-regulated and downregulated genes, the ribosome biogenesis genes and genes involved in lipid storage may be closely related to aging muscles in men and women respectively. Also, the DEGs were enriched in the pathways including those of ribosome and Peroxisome proliferator-activated receptor (PPAR) in men and women, respectively. In the PPI network, Neurotrophic Receptor Tyrosine Kinase 1 (NTRK1), Cullin 3 (CUL3) and P53 have been identified as significant hub proteins in both genders. Using the integrated analysis of multiple gene expression profiles, we propose that the ribosome biogenesis genes and those involved in lipid storage would be promising markers for sarcopenia in men and women, respectively. In the reconstructed PPI network, neurotrophic factors expressed in skeletal muscle are essential for motoneuron survival and muscle fiber innervation during development. Cullin E3 ubiquitin ligase (Cul3) is an important component of the ubiquitin–proteasome system—it regulates the proteolysis. P53 is recognized as a central regulator of the cell cycle and apoptosis. These proteins, which have been identified as the most significant hubs, may be involved in aging muscle and sarcopenia.

scholarly journals Meta-analysis of gene expression disease signatures in colonic biopsy tissue from patients with ulcerative colitis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bryan Linggi ◽  
Vipul Jairath ◽  
Guangyong Zou ◽  
Lisa M. Shackelton ◽  
Dermot P. B. McGovern ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ulcerative Colitis ◽  
Chemokine Receptors ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Interleukin 10 ◽  
Pathway Enrichment Analysis ◽  
Tissue Samples ◽  
Biopsy Tissue

AbstractPublicly available ulcerative colitis (UC) gene expression datasets from observational studies and clinical trials include inherently heterogeneous disease characteristics and methodology. We used meta-analysis to identify a robust UC gene signature from inflamed biopsies. Eight gene expression datasets derived from biopsy tissue samples from noninflammatory bowel disease (IBD) controls and areas of active inflammation from patients with UC were publicly available. Expression- and meta-data were downloaded with GEOquery. Differentially expressed genes (DEG) in individual datasets were defined as those with fold change > 1.5 and a Benjamini–Hochberg adjusted P value < .05. Meta-analysis of all DEG used a random effects model. Reactome pathway enrichment analysis was conducted. Meta-analysis identified 946 up- and 543 down-regulated genes in patients with UC compared to non-IBD controls (1.2 and 1.7 times fewer up- and down-regulated genes than the median of the individual datasets). Top-ranked up- and down-regulated DEG were LCN2 and AQP8. Multiple immune-related pathways (e.g., ‘Chemokine receptors bind chemokine’ and ‘Interleukin-10 signaling’) were significantly up-regulated in UC, while ‘Biological oxidations’ and ‘Fatty acid metabolism’ were downregulated. A web-based data-mining tool with the meta-analysis results was made available (https://premedibd.com/genes.html). A UC inflamed biopsy disease gene signature was derived. This signature may be an unbiased reference for comparison and improve the efficiency of UC biomarker studies by increasing confidence for identification of disease-related genes and pathways.

scholarly journals Identification of pivotal lncRNAs in papillary thyroid cancer using lncRNA–mRNA–miRNA ceRNA network analysis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7441 ◽  
Author(s):  
Weiwei Liang ◽  
Fangfang Sun
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Network Analysis ◽  
Papillary Thyroid Cancer ◽  
Enrichment Analysis ◽  
Integrated Analysis ◽  
Pathway Enrichment Analysis ◽  
Papillary Thyroid ◽  
Cerna Network ◽  
Go Terms

Background To identify pivotal lncRNAs in papillary thyroid cancer (PTC) using lncRNA–mRNA–miRNA ceRNA network analysis. Methods We obtained gene expression profiles from the gene expression omnibus database. Cancer specific lncRNA, cancer specific miRNA and cancer specific mRNA were identified. An integrated analysis was conducted to detect potential lncRNA–miRNA–mRNA ceRNA in regulating disease transformation. The lncRNA regulated gene ontology (GO) terms and regulated pathways were performed by function analysis. Survival analysis was performed for the pivotal lncRNAs. Results A total of four lncRNAs, 15 miRNAs and 375 mRNAs are identified as the key mediators in the pathophysiological processes of PTC. GO annotation enrichment analysis showed the most relevant GO terms are signal transduction, integral component of membrane and calcium ion binding. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed different changed genes mainly enriched in pathways in cancer, PI3K-Akt signaling pathway and focal adhesion. Among four lncRNAs, only SLC26A4-AS1 was significantly associated with PTC patient disease free survival. Conclusion This study has constructed lncRNA–mRNA–miRNA ceRNA networks in PTC. The study provides a set of pivotal lncRNAs for future investigation into the molecular mechanisms.

scholarly journals Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Ulcerative Colitis

2021 ◽  
Vol 12 ◽  
Author(s):  
Zi-An Chen ◽  
Yu-Feng Sun ◽  
Quan-Xu Wang ◽  
Hui-Hui Ma ◽  
Zhi-Zhao Ma ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Mouse Model ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Rank Aggregation ◽  
Integrated Analysis ◽  
Control Group ◽  
Functional Modules ◽  
Ppi Network ◽  
Hub Genes

Background: Ulcerative colitis (UC) is a chronic, complicated, inflammatory disease with an increasing incidence and prevalence worldwide. However, the intrinsic molecular mechanisms underlying the pathogenesis of UC have not yet been fully elucidated.Methods: All UC datasets published in the GEO database were analyzed and summarized. Subsequently, the robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between UC patients and controls. Gene functional annotation and PPI network analysis were performed to illustrate the potential functions of the DEGs. Some important functional modules from the protein-protein interaction (PPI) network were identified by molecular complex detection (MCODE), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG), and analyses were performed. The results of CytoHubba, a plug for integrated algorithm for biomolecular interaction networks combined with RRA analysis, were used to identify the hub genes. Finally, a mouse model of UC was established by dextran sulfate sodium salt (DSS) solution to verify the expression of hub genes.Results: A total of 6 datasets met the inclusion criteria (GSE38713, GSE59071, GSE73661, GSE75214, GSE87466, GSE92415). The RRA integrated analysis revealed 208 significant DEGs (132 upregulated genes and 76 downregulated genes). After constructing the PPI network by MCODE plug, modules with the top three scores were listed. The CytoHubba app and RRA identified six hub genes: LCN2, CXCL1, MMP3, IDO1, MMP1, and S100A8. We found through enrichment analysis that these functional modules and hub genes were mainly related to cytokine secretion, immune response, and cancer progression. With the mouse model, we found that the expression of all six hub genes in the UC group was higher than that in the control group (P &lt; 0.05).Conclusion: The hub genes analyzed by the RRA method are highly reliable. These findings improve the understanding of the molecular mechanisms in UC pathogenesis.

A Method of Pathway Enrichment Analysis Based Gene Expression Variability

2013 ◽  
Vol 40 (12) ◽  
pp. 1256
Author(s):  
XiaoDong JIA ◽  
XiuJie CHEN ◽  
Xin WU ◽  
JianKai XU ◽  
FuJian TAN ◽  
...  
Keyword(s):  
Gene Expression ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Expression Variability ◽  
Pathway Enrichment

scholarly journals AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1037.2-1038
Author(s):  
X. Sun ◽  
S. X. Zhang ◽  
S. Song ◽  
T. Kong ◽  
C. Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Shanxi Province ◽  
Receptor Interaction ◽  
Term Therapy ◽  
Pathway Enrichment Analysis ◽  
Key Genes

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

Identification of CCNB2 expression in triple-negative breast cancer based on bioinformatics results

2021 ◽  
Author(s):  
jintao cao ◽  
SHUAI SUN ◽  
RAN LI ◽  
RUI MIN ◽  
XINGYU FAN ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Protein Complex ◽  
Triple Negative ◽  
Expression Profiles ◽  
Pathway Enrichment Analysis ◽  
Analysis Tool ◽  
The Core ◽  
Core Genes

Abstract Background The current epidemiology shows that the incidence of breast cancer is increasing year by year and tends to be younger. Triple-negative breast cancer is the most malignant of breast cancer subtypes. The application of bioinformatics in tumor research is becoming more and more extensive. This study provided research ideas and basis for exploring the potential targets of gene therapy for triple-negative breast cancer (TNBC). Methods We analyzed three gene expression profiles (GSE64790、GSE62931、GSE38959) selected from the Gene Expression Omnibus (GEO) database. The GEO2R online analysis tool was used to screen for differentially expressed genes (DEGs) between TNBC and normal tissues. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied to identify the pathways and functional annotation of DEGs. Protein–protein interaction network of these DEGs were visualized by the Metascape gene-list analysis tool so that we could find the protein complex containing the core genes. Subsequently, we investigated the transcriptional data of the core genes in patients with breast cancer from the Oncomine database. Moreover, the online Kaplan–Meier plotter survival analysis tool was used to evaluate the prognostic value of core genes expression in TNBC patients. Finally, immunohistochemistry (IHC) was used to evaluated the expression level and subcellular localization of CCNB2 on TNBC tissues. Results A total of 66 DEGs were identified, including 33 up-regulated genes and 33 down-regulated genes. Among them, a potential protein complex containing five core genes was screened out. The high expression of these core genes was correlated to the poor prognosis of patients suffering breast cancer, especially the overexpression of CCNB2. CCNB2 protein positively expressed in the cytoplasm, and its expression in triple-negative breast cancer tissues was significantly higher than that in adjacent tissues. Conclusions CCNB2 may play a crucial role in the development of TNBC and has the potential as a prognostic biomarker of TNBC.

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