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基于生物信息学和机器学习的小儿脓毒症潜在诊断基因靶点的鉴定

Identification of Potential Diagnostic Gene Targets for Pediatric Sepsis Based on Bioinformatics and Machine Learning.

作者信息

Qiao Ying, Zhang Bo, Liu Ying

机构信息

Department of Pediatrics, Tianjin Union Medical Center, Tianjin, China.

Tianjin Key Laboratory of Cellular and Molecular Immunology, Department of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.

出版信息

Front Pediatr. 2021 Mar 4;9:576585. doi: 10.3389/fped.2021.576585. eCollection 2021.

DOI:10.3389/fped.2021.576585
PMID:33748037
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7969637/
Abstract

To develop a comprehensive differential expression gene profile as well as a prediction model based on the expression analysis of pediatric sepsis specimens. In this study, compared with control specimens, a total of 708 differentially expressed genes in pediatric sepsis (case-control at a ratio of 1:3) were identified, including 507 up-regulated and 201 down-regulated ones. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes indicated the close interaction between neutrophil activation, neutrophil degranulation, hematopoietic cell lineage, infection, and periodontitis. Meanwhile, the results also suggested a significant difference for 16 kinds of immune cell compositions between two sample sets. The two potential selected biomarkers (MMP and MPO) had been validated in septic children patients by the ELISA method. This study identified two potential hub gene biomarkers and established a differentially expressed genes-based prediction model for pediatric sepsis, which provided a valuable reference for future clinical research.

摘要

基于小儿脓毒症标本的表达分析，开发一个全面的差异表达基因谱以及一个预测模型。在本研究中，与对照标本相比，共鉴定出小儿脓毒症中708个差异表达基因（病例与对照比例为1:3），其中507个上调，201个下调。对差异表达基因的基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析表明，中性粒细胞活化、中性粒细胞脱颗粒、造血细胞谱系、感染和牙周炎之间存在密切相互作用。同时，结果还表明两个样本集之间16种免疫细胞组成存在显著差异。两种潜在的选定生物标志物（基质金属蛋白酶和髓过氧化物酶）已通过ELISA方法在脓毒症儿童患者中得到验证。本研究鉴定出两种潜在的核心基因生物标志物，并建立了基于差异表达基因的小儿脓毒症预测模型, 为未来的临床研究提供了有价值的参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e25/7969637/a5c1cd9eec5f/fped-09-576585-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e25/7969637/198dbc206f52/fped-09-576585-g0001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e25/7969637/a5c1cd9eec5f/fped-09-576585-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e25/7969637/198dbc206f52/fped-09-576585-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e25/7969637/38004ca01e07/fped-09-576585-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e25/7969637/52a0b19082f6/fped-09-576585-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e25/7969637/29db338b6967/fped-09-576585-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e25/7969637/a5c1cd9eec5f/fped-09-576585-g0005.jpg

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BMC Med Genomics. 2020 Aug 28;13(1):122. doi: 10.1186/s12920-020-00771-4.
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The SOFA score-development, utility and challenges of accurate assessment in clinical trials.SOFA 评分的发展、在临床试验中准确评估的效用和挑战。
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The Pathogenesis of Sepsis and Potential Therapeutic Targets.脓毒症的发病机制与潜在治疗靶点
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Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients.差异基因表达分析揭示了小儿感染性休克患者中的新基因和新途径。
Sci Rep. 2019 Aug 2;9(1):11270. doi: 10.1038/s41598-019-47703-6.
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Quantification of NETs formation in neutrophil and its correlation with the severity of sepsis and organ dysfunction.中性粒细胞 NETs 形成的定量及其与脓毒症严重程度和器官功能障碍的相关性。
Clin Chim Acta. 2019 Aug;495:606-610. doi: 10.1016/j.cca.2019.06.008. Epub 2019 Jun 8.
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