Anti-TNF- αtreatment-related pathways and biomarkers revealed by transcriptome analysis in Chinese psoriasis patients.

Q1 Mathematics BMC Systems Biology Pub Date : 2019-04-05 DOI:10.1186/s12918-019-0698-7
Lunfei Liu, Wenting Liu, Yuxin Zheng, Jisu Chen, Jiong Zhou, Huatuo Dai, Suiqing Cai, Jianjun Liu, Min Zheng, Yunqing Ren
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引用次数: 8

Abstract

Background: Anti-tumor necrosis factor alpha (TNF- α) therapy has made a significant impact on treating psoriasis. Despite these agents being designed to block TNF- α activity, their mechanism of action in the remission of psoriasis is still not fully understood at the molecular level.

Results: To better understand the molecular mechanisms of Anti-TNF- α therapy, we analysed the global gene expression profile (using mRNA microarray) in peripheral blood mononuclear cells (PBMCs) that were collected from 6 psoriasis patients before and 12 weeks after the treatment of etanercept. First, we identified 176 differentially expressed genes (DEGs) before and after treatment by using paired t-test. Then, we constructed the gene co-expression modules by weighted correlation network analysis (WGCNA), and 22 co-expression modules were found to be significantly correlated with treatment response. Of these 176 DEGs, 79 DEGs (M_DEGs) were the members of these 22 co-expression modules. Of the 287 GO functional processes and pathways that were enriched for these 79 M_DEGs, we identified 30 pathways whose overall gene expression activities were significantly correlated with treatment response. Of the original 176 DEGs, 19 (GO_DEGs) were found to be the members of these 30 pathways, whose expression profiles showed clear discrimination before and after treatment. As expected, of the biological processes and functionalities implicated by these 30 treatment response-related pathways, the inflammation and immune response was the top pathway in response to etanercept treatment, and some known TNF- α related pathways, such as molting cycle process, hair cycle process, skin epidermis development, regulation of hair follicle development, were implicated. Furthermore, additional novel pathways were also suggested, such as heparan sulfate proteoglycan metabolic process, vascular endothelial growth factor production, whose transcriptional regulation may mediate the response to etanercept treatment.

Conclusion: Through global gene expression analysis in PBMC of psoriasis patient and subsequent co-expression module based pathway analyses, we have identified a group of functionally coherent and differentially expressed genes (DEGs) and related pathways, which has not only provided new biological insight about the molecular mechanism of anti-TNF- α treatment, but also identified several genes whose expression profiles can be used as potential biomarkers for anti-TNF- α treatment response in psoriasis.

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中国银屑病患者抗tnf - α治疗相关通路和生物标志物的转录组分析
背景:抗肿瘤坏死因子α (TNF- α)治疗银屑病疗效显著。尽管这些药物被设计为阻断TNF- α活性,但它们在银屑病缓解中的作用机制在分子水平上仍未完全了解。结果:为了更好地了解抗tnf - α治疗的分子机制,我们分析了6例银屑病患者在依那西普治疗前和治疗后12周外周血单个核细胞(PBMCs)的整体基因表达谱(采用mRNA微阵列技术)。首先,我们通过配对t检验鉴定了治疗前后176个差异表达基因(deg)。然后,我们通过加权相关网络分析(WGCNA)构建基因共表达模块,发现22个共表达模块与治疗反应显著相关。在这176个基因中,有79个基因(m_基因)是这22个共表达模块的成员。在这79个M_DEGs富集的287个氧化石墨烯功能过程和途径中,我们确定了30个总体基因表达活性与治疗反应显著相关的途径。在最初的176个DEGs中,发现19个(GO_DEGs)是这30条通路的成员,其表达谱在治疗前后表现出明显的差异。正如预期的那样,在这30个治疗反应相关途径所涉及的生物学过程和功能中,炎症和免疫反应是对依那西普治疗反应的主要途径,以及一些已知的TNF- α相关途径,如蜕皮周期过程、毛发周期过程、皮肤表皮发育、毛囊发育调节。此外,还提出了其他新的途径,如硫酸肝素蛋白多糖代谢过程,血管内皮生长因子的产生,其转录调节可能介导对依那西普治疗的反应。结论:通过对银屑病患者PBMC基因的全局表达分析以及随后基于共表达模块的通路分析,我们发现了一组功能一致和差异表达的基因(DEGs)及其相关通路,不仅为抗tnf - α治疗的分子机制提供了新的生物学见解,而且还发现了一些表达谱可作为银屑病抗tnf - α治疗反应的潜在生物标志物的基因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Systems Biology
BMC Systems Biology 生物-数学与计算生物学
CiteScore
6.30
自引率
0.00%
发文量
0
审稿时长
9 months
期刊介绍: Cessation. BMC Systems Biology is an open access journal publishing original peer-reviewed research articles in experimental and theoretical aspects of the function of biological systems at the molecular, cellular or organismal level, in particular those addressing the engineering of biological systems, network modelling, quantitative analyses, integration of different levels of information and synthetic biology.
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Correction to: A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice Identification of Hürthle cell cancers: solving a clinical challenge with genomic sequencing and a trio of machine learning algorithms. Anti-TNF- αtreatment-related pathways and biomarkers revealed by transcriptome analysis in Chinese psoriasis patients. Boolean network modeling of β-cell apoptosis and insulin resistance in type 2 diabetes mellitus. Ultrafast clustering of single-cell flow cytometry data using FlowGrid.
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