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  • ISSN 1006-3080
  • CN 31-1691/TQ

基于非依赖数据采集的呼出气冷凝液蛋白质组加权基因共表达网络分析

马琳 孙东晓 镇华君 修光利

马琳, 孙东晓, 镇华君, 修光利. 基于非依赖数据采集的呼出气冷凝液蛋白质组加权基因共表达网络分析[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20210824001
引用本文: 马琳, 孙东晓, 镇华君, 修光利. 基于非依赖数据采集的呼出气冷凝液蛋白质组加权基因共表达网络分析[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20210824001
MA Lin, SUN Dongxiao, ZHEN Huajun, XIU Guangli. Weighted Gene Co-Expression Network Analysis on Proteomics of Exhaled Breath Condensate Based on Data-Independent Acquisition[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20210824001
Citation: MA Lin, SUN Dongxiao, ZHEN Huajun, XIU Guangli. Weighted Gene Co-Expression Network Analysis on Proteomics of Exhaled Breath Condensate Based on Data-Independent Acquisition[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20210824001

基于非依赖数据采集的呼出气冷凝液蛋白质组加权基因共表达网络分析

doi: 10.14135/j.cnki.1006-3080.20210824001
基金项目: 上海市浦江人才计划资助项目(20PJ1402700)
详细信息
    作者简介:

    马琳:马 琳(1991—),女,内蒙古人,博士生,主要从事环境健康的研究

    通讯作者:

    镇华君,E-mail:zhenhuajun@ecust.edu.cn

    修光利,E-mail:xiugl@ecust.edu.cn

  • 中图分类号: Q33

Weighted Gene Co-Expression Network Analysis on Proteomics of Exhaled Breath Condensate Based on Data-Independent Acquisition

  • 摘要: 呼出气冷凝液(Exhaled Breath Condensate, EBC)是一种呼吸道衬液,其收集过程无创、便捷,常常被作为肺部疾病研究的载体。建立了基于DIA(Data-Independent Acquisition)的EBC蛋白组学方法,共鉴定到2052个蛋白。通过加权基因共表达网络分析(Weighted Gene Co-expression Network Analysis, WGCNA)筛选出61个关键蛋白,分析发现这些关键蛋白活跃参与多个与人类疾病相关的代谢通路。结果表明,基于DIA的EBC蛋白组学方法,结合WGCNA分析,可以有效地挖掘出EBC中与疾病相关的生物标志物,未来可应用于大规模的临床研究。

     

  • 图  1  最佳软阈值筛选:(a)基于R2=0.9无尺度网络的软阈值筛选;(b)软阈值为5时网络的连通性

    Figure  1.  Screening of the best soft threshold: (a) The soft threshold of scale-free network based on R2=0.9; (b) The connectivity when the soft threshold was 5

    图  2  EBC蛋白共表达模块划分

    Figure  2.  Cluster dendrogram and module overview for EBC proteome

    图  3  基于TOM的拓扑网络热图

    Figure  3.  Heatmap of the topological network based on TOM

    图  4  模块与表型性状相关性热图

    Figure  4.  Heatmap of the module-trait correlations

    图  5  蛋白表达热图(a)及蓝色模块特征值分布图(b)

    Figure  5.  Heatmap of the eigenproteins expression (a) and module eigenvalue

    图  6  蛋白重要性与模块关系的散点分布图

    Figure  6.  Scatter plot of protein significance and module membership

    图  7  关键模块蛋白的GO分析: (a) GO term分类;(b) GO富集分析

    Figure  7.  GO analysis of proteins extracted from the core module: (a) GO terms classification; (b) Enriched GO terms

    图  8  关键模块蛋白的KEGG分析: (a) KEGG term分类;(b) KEGG富集分析

    Figure  8.  KEGG analysis of proteins extracted from the core module: (a) KEGG terms classification; (b) Enriched KEGG terms

    图  9  蛋白互作网络分析

    Figure  9.  Protein-protein interactions analysis

  • [1] FUMAGALLI M, FERRARI F, LUISETTI M, et al. Profiling the proteome of exhaled breath condensate in healthy smokers and COPD patients by LC-MS/MS[J]. International Journal of Molecular Sciences, 2012, 13(11): 13894-13910.
    [2] PATHAK A K, BHUTANI M, KUMAR S, et al. Circulating cell-free DNA in plasma/serum of lung cancer patients as a potential screening and prognostic tool[J]. Clinical Chemistry, 2006, 52(10): 1833-1842.
    [3] MUCCILLI V, SALETTI R, CUNSOLO V, et al. Protein profile of exhaled breath condensate determined by high resolutiosn mass spectrometry[J]. Journal of Pharmaceutical and Biomedical Analysis, 2015, 105: 134-149.
    [4] ZHONG L J, LI Y, TIAN H F, et al. Data-independent acquisition strategy for the serumproteomics of tuberculosis[J]. International Journal of Clinical and Experimental Pathology, 2017, 10(2): 1172-1185.
    [5] BRUDERER R, BERNHARDT O M, GANDHI T, et al. High-precision iRT prediction in the targeted analysis of data-independent acquisition and its impact on identification and quantitation[J]. Proteomics, 2016, 16(15/16): 2246-2256. doi: 10.1002/pmic.201500488
    [6] PEI G, CHEN L, ZHANG W. WGCNA application to proteomic and metabolomic data analysis[J]. Methods Enzymol, 2017, 585: 135-158.
    [7] TIAN Z L, HE W X, TANG J N, et al. Identification of important modules and biomarkers in breast cancer based on WGCNA[J]. Onco Targets and Therapy, 2020, 13: 6805-6817.
    [8] MA L, MUSCAT J E, SINHA R, et al. Proteomics of exhaled breath condensate in lung cancer and controls using data-independent acquisition (DIA): A pilot study[J]. Journal of Breath Research, 2020, 15: 026002.
    [9] LACOMBE M, MARIE-DESVERGNE C, COMBES F, et al. Proteomic characterization of human exhaled breath condensate[J]. Journal of Breath Research, 2018, 12(2): 021001. doi: 10.1088/1752-7163/aa9e71
    [10] LÓPEZ-SÁNCHEZ L M, JURADO-GÁMEZ B, FEU-COLLADO N, et al. Exhaled breath condensate biomarkers for the early diagnosis of lung cancer using proteomics[J]. American Journal of Physiology-Lung Cellular and Molecular Physiology, 2017, 313(4): L664-L676. doi: 10.1152/ajplung.00119.2017
    [11] SUN C, ZHOU T, XIE G, et al. Proteomics of exhaled breath condensate in stable COPD and non-COPD controls using tandem mass tags (TMTs) quantitative mass spectrometry: A pilot study[J]. Journal of Proteomics, 2019, 206: 103392.
    [12] REUBSAET L, SWEREDOSKI M J, MORADIAN A. Data-independent acquisition for the orbitrap Q exactive HF: A Tutorial[J]. Journal Proteome Research, 2019, 18(3): 803-813.
    [13] LIU J, JING L, TU X. Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease[J]. BMC Cardiovasc Disord, 2016, 16: 54.
    [14] LANGFELDER P, HORVATH S. WGCNA: An R package for weighted correlation network analysis[J]. BMC Bioinformatics, 2008, 9: 559.
    [15] LIAO Y, XIAO H, CHENG M Q, et al. Bioinformatics analysis reveals biomarkers with cancer stem cell characteristics in lung squamous cell carcinoma[J]. Front Genet, 2020, 11: 00427.
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出版历程
  • 收稿日期:  2021-08-24
  • 网络出版日期:  2022-04-12

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