scholarly journals Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis

2019 ◽  
Author(s):  
Yu‑Jing Zhang ◽  
Yu‑Zhe Sun ◽  
Xing‑Hua Gao ◽  
Rui‑Qun Qi
Keyword(s):  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Plaque Psoriasis ◽  
Bioinformatic Analysis ◽  
Differentially Expressed

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

scholarly journals Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?

Biomolecules ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 850 ◽  
Author(s):  
Mehran Piran ◽  
Reza Karbalaei ◽  
Mehrdad Piran ◽  
Jehad Aldahdooh ◽  
Mehdi Mirzaie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Expression Profile ◽  
Signaling Network ◽  
Differentially Expressed ◽  
Network Activity ◽  
Gene Products ◽  
Expression Data ◽  
Gene Pairs

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.

scholarly journals Assessment of Embryo-Induced Transcriptomic Changes in Hamster Uterus Using RNA-Seq

2018 ◽  
Vol 46 (5) ◽  
pp. 1868-1878 ◽  
Author(s):  
Ming-Yu Huang ◽  
Wen-Qian Zhang ◽  
Miao Zhao ◽  
Can Zhu ◽  
Jia-Peng He ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Embryo Implantation ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Implantation Site ◽  
Rna Seq ◽  
Promoter Regions ◽  
Functional Clustering ◽  
Gene Network Analysis ◽  
Transcriptomic Changes

Background/Aims: The mouse is widely used as an animal model for studying human embryo implantation. However, the mouse is unique in that both ovarian progesterone and estrogen are critical to implantation, whereas in the majority of species (e.g. human and hamster) implantation can occur in the presence of progesterone alone. Methods: In this study, we analyzed embryo-induced transcriptomic changes in the hamster uterus during embryo implantation by using RNA-seq. Differentially expressed genes were characterized by bioinformatic analysis. Results: We identified a total of 781 differentially expressed genes, of which 367 genes were up-regulated and 414 genes were down-regulated at the implantation site compared to the inter-implantation site. Functional clustering and gene network analysis highlighted the cell cycle process in uterus upon embryo implantation. By examining of the promoter regions of differentially expressed genes, we identified 7 causal transcription factors. Additionally, through connectivity map (CMap) analysis, multiple compounds were identified to have potential anti-implantation effects due to their ability to reverse embryo-induced transcriptomic changes. Conclusion: Our study provides a valuable resource for in-depth understanding of the mechanism underlying embryo implantation.

Bioinformatic Analysis of Differentially Expressed Genes and Screening of Hub Genes in Uveal Melanoma Cells with BRCA1-Associated Protein 1 Related Protein 1 Depletion

2020 ◽  
Vol 16 (8) ◽  
pp. 1205-1218
Author(s):  
Wei Li ◽  
Aiqin Nie ◽  
Qiang Li ◽  
He Cao ◽  
Yinwei Song ◽  
...  
Keyword(s):  
Cell Adhesion ◽  
Differentially Expressed Genes ◽  
Uveal Melanoma ◽  
Wnt Signaling Pathway ◽  
Tumor Development ◽  
Bioinformatic Analysis ◽  
Melanoma Cells ◽  
Differentially Expressed ◽  
Data Sets ◽  
And Migration

Recent studies have found that chromosome 3 is frequently mutated in metastatic uveal melanoma (UVM), which leads to the loss of BAP1 expression or the weakening of BRCA1-associated protein 1 (BAP1) function and promotes metastasis of uveal melanoma cells. However, the specific signaling pathways that are affected by BAP1 depletion in uveal melanoma remain unclear. Our aim in this study was to verify the effect and regulatory mechanism of BAP1 on uveal melanoma. RT-qPCR and western blotting results showed that BAP1 was significantly down-regulated in OCM-1A cells treated with a BAP1 shRNA vector. MTT, cell scratch and transwell migration assays showed that low expression of BAP1 significantly promoted the proliferation and migration of UVM cells. A total of 269 up-regulated and 807 down-regulated genes were identified from the combined GSE110193 and GSE48863 data sets. These differentially expressed genes are mainly involved in the composition of extracellular matrix and the regulation of the Wnt signaling pathway and are closely related to the cell adhesion pathway. CXCL8, COL5A3, COL11A1, and COL12A1 were among the differentially expressed genes and are closely related to the prognosis of UVM. Therefore, the deletion of BAP1 is closely related to poor prognosis of UVM and is a risk factor for UVM metastasis. The potential targets of BAP1 include CXCL8, COL5A3, COL11A1, and COL12A1. It is believed that BAP1 regulates UVM cell adhesion through these four genes and ultimately regulates tumor development and migration.

scholarly journals Differentially expressed genes and interacting pathways in bladder cancer revealed by bioinformatic analysis

2014 ◽  
Vol 10 (4) ◽  
pp. 1746-1752 ◽  
Author(s):  
YINZHOU SHEN ◽  
XUELEI WANG ◽  
YONGCHAO JIN ◽  
JIASUN LU ◽  
GUANGMING QIU ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Differentially Expressed Genes ◽  
Bioinformatic Analysis ◽  
Differentially Expressed

scholarly journals Transcriptomic analysis of PPARα-dependent alterations during cardiac hypertrophy

2008 ◽  
Vol 36 (1) ◽  
pp. 15-23 ◽  
Author(s):  
Pascal J. H. Smeets ◽  
Heleen M. de Vogel-van den Bosch ◽  
Peter H. M. Willemsen ◽  
Alphons P. Stassen ◽  
Torik Ayoubi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Lipid Metabolism ◽  
Cardiac Hypertrophy ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Left Ventricular ◽  
Transcriptomic Analysis ◽  
Differentially Expressed ◽  
Wild Type

Peroxisome proliferator-activated receptor (PPAR)α regulates lipid metabolism at the transcriptional level and modulates the expression of genes involved in inflammation, cell proliferation, and differentiation. Although PPARα has been shown to mitigate cardiac hypertrophy, knowledge about underlying mechanisms and the nature of signaling pathways involved is fragmentary and incomplete. The aim of this study was to identify the processes and signaling pathways regulated by PPARα in hearts challenged by a chronic pressure overload by means of whole genome transcriptomic analysis. PPARα−/− and wild-type mice were subjected to transverse aortic constriction (TAC) for 28 days, and left ventricular gene expression profile was determined with Affymetrix GeneChip Mouse Genome 430 2.0 arrays containing >45,000 probe sets. In unchallenged hearts, the mere lack of PPARα resulted in 821 differentially expressed genes, many of which are related to lipid metabolism and immune response. TAC resulted in a more pronounced cardiac hypertrophy and more extensive changes in gene expression (1,910 and 312 differentially expressed genes, respectively) in PPARα−/− mice than in wild-type mice. Many of the hypertrophy-related genes were related to development, signal transduction, actin filament organization, and collagen synthesis. Compared with wild-type hypertrophied hearts, PPARα−/− hypertrophied hearts revealed enrichment of gene clusters related to extracellular matrix remodeling, immune response, oxidative stress, and inflammatory signaling pathways. The present study therefore demonstrates that, in addition to lipid metabolism, PPARα is an important modulator of immune and inflammatory response in cardiac muscle.

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