Bioinformatic screening, expression validation and diagnostic value analysis of key genes in peripheral blood of childhood therapy-resistant asthma_West China Medical Journal

Authors：

ZHAO Tong ¹ , WANG Yiyun ² , GUAN Fei ³ , LI Gengxu ⁴ ,  LI Shifeng ²

1. Medical Record Department, Tangshan People’s Hospital, Tangshan, Hebei 063000, P. R. China;
2. School of Public Health, North China University of Science and Technology, Tangshan, Hebei 063210, P. R. China;
3. Emergency Department, Tangshan People’s Hospital, Tangshan, Hebei 063000, P. R. China;
4. Clinical Medicine College, North China University of Science and Technology, Tangshan, Hebei 063210, P. R. China;

Corresponding author：

LI Shifeng, Email: lishifeng@ncst.edu.cn

Keywords：

Pediatric asthma; therapy-resistant asthma; bioinformatics; G protein subunit alpha 15; receiver operating characteristic curve

DOI：

10.7507/1002-0179.202202014

Video：

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Objective To screen the key genes in childhood therapy-resistant asthma by bioinformatic method, and to verify its expression and diagnostic value in peripheral blood of children with therapy-resistant asthma. Methods The transcriptome dataset GSE27011 of peripheral blood mononuclear cells from healthy children (healthy control group), mild asthma (MA) children (MA group) and severe asthma (SA) children (SA group) was downloaded from the Gene Expression Omnibus of the National Center for Biotechnology Information of the United States. Key genes were obtained by using R software for gene differential expression analysis, weighted gene co-expression network analysis (WGCNA) and clinical phenotypic correlation analysis. The differential expression levels of key genes were verified in children with asthma and immune cell transcriptome datasets. Seventy-eight children with asthma and 30 healthy children who were diagnosed in the Department of Pediatrics of Tangshan People’s Hospital between September 2020 and September 2021 were selected and divided into control group, MA group and SA group. Peripheral blood samples from children with asthma and healthy children who underwent physical examination were collected to detect the expression levels of key genes and inflammatory factors interleukin (IL)-4 and IL-17 in peripheral blood of children. Receiver operating characteristic curve was used to evaluate the sensitivity, specificity and accuracy of key genes in predicting childhood therapy-resistant asthma. Results The key gene GNA15 was obtained by bioinformatic analysis. Analysis of asthma validation dataset showed that GNA15 was up-regulated in asthma groups, and was specifically expressed in eosinophils. Clinical results showed that the expression levels of IL-4, IL-17 and GNA15 among the three groups were significantly different (P<0.05). The expression levels of IL-4 and IL-17 in the MA group and the SA group were higher than those in the control group (P<0.05). Compared with the control group and the MA group, the expression level of GNA15 in the SA group was up-regulated (P<0.05). Neither the difference in the expression level of IL-4 or IL-17 between the MA group and the SA group, nor the difference in the expression level of GNA15 between the control group and the MA group was statistically significant (P>0.05). The specificity, sensitivity and accuracy of GNA15 in predicting SA were 92.90%, 80.00% and 86.10%, respectively. Conclusion GNA15 has a significant clinical value in predicting the childhood therapy-resistant asthma, and may become a potential diagnostic marker for predicting the severity of asthma in children.

Citation： ZHAO Tong, WANG Yiyun, GUAN Fei, LI Gengxu, LI Shifeng. Bioinformatic screening, expression validation and diagnostic value analysis of key genes in peripheral blood of childhood therapy-resistant asthma. West China Medical Journal, 2023, 38(2): 270-277. doi: 10.7507/1002-0179.202202014 Copy

1.	全国儿科哮喘协作组, 中国疾病预防控制中心环境与健康相关产品安全所. 第三次中国城市儿童哮喘流行病学调查. 中华儿科杂志, 2013, 51(10): 729-735.
2.	Li X, Song P, Zhu Y, et al. The disease burden of childhood asthma in China: a systematic review and meta-analysis. J Glob Health, 2020, 10(1): 010801.
3.	Lang A, Carlsen KH, Haaland G, et al. Severe asthma in childhood: assessed in 10 year olds in a birth cohort study. Allergy, 2008, 63(8): 1054-1060.
4.	Smith DH, Malone DC, Lawson KA, et al. A national estimate of the economic costs of asthma. Am J Respir Crit Care Med, 1997, 156 (3 Pt 1): 787-793.
5.	Pijnenburg MW, Fleming L. Advances in understanding and reducing the burden of severe asthma in children. Lancet Respir Med, 2020, 8(10): 1032-1044.
6.	Stefanowicz D, Hackett TL, Garmaroudi FS, et al. DNA methylation profiles of airway epithelial cells and PBMCs from healthy, atopic and asthmatic children. PLoS One, 2012, 7(9): e44213.
7.	Orsmark-Pietras C, James A, Konradsen JR, et al. Transcriptome analysis reveals upregulation of bitter taste receptors in severe asthmatics. Eur Respir J, 2013, 42(1): 65-78.
8.	Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res, 2013, 41(Database issue): D991-D995.
9.	Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res, 2015, 43(7): e47.
10.	Walter W, Sánchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics, 2015, 31(17): 2912-2914.
11.	Futschik ME, Carlisle B. Noise-robust soft clustering of gene expression time-course data. J Bioinform Comput Biol, 2005, 3(4): 965-988.
12.	Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9: 559.
13.	中华儿科杂志编辑委员会, 中华医学会儿科学分会呼吸学组, 中国医师协会儿科医师分会儿童呼吸专业委员会. 儿童支气管哮喘规范化诊治建议(2020 年版). 中华儿科杂志, 2020, 58(9): 708-717.
14.	李丹, 范宇杭, 李耕旭, 等. GNA15 对重型哮喘患儿预后的预测价值. 实用医学杂志, 2022, 38(6): 758-762.
15.	Offermanns S, Simon MI. G alpha 15 and G alpha 16 couple a wide variety of receptors to phospholipase C. J Biol Chem, 1995, 270(25): 15175-15180.
16.	Davignon I, Catalina MD, Smith D, et al. Normal hematopoiesis and inflammatory responses despite discrete signaling defects in Galpha15 knockout mice. Mol Cell Biol, 2000, 20(3): 797-804.
17.	van den Bos E, Ambrosy B, Horsthemke M, et al. Knockout mouse models reveal the contributions of G protein subunits to complement C5a receptor-mediated chemotaxis. J Biol Chem, 2020, 295(22): 7726-7742.
18.	Denlinger LC, Phillips BR, Ramratnam S, et al. Inflammatory and comorbid features of patients with severe asthma and frequent exacerbations. Am J Respir Crit Care Med, 2017, 195(3): 302-313.
19.	Levine SJ, Wenzel SE. Narrative review: the role of Th2 immune pathway modulation in the treatment of severe asthma and its phenotypes. Ann Intern Med, 2010, 16,152(4): 232-237.
20.	Khalfaoui L, Symon FA, Couillard S, et al. Airway remodelling rather than cellular infiltration characterizes both type2 cytokine biomarker-high and -low severe asthma. Allergy, 2022, 77(10): 2974-2986.
21.	Ross KR, Gupta R, DeBoer MD, et al. Severe asthma during childhood and adolescence: a longitudinal study. J Allergy Clin Immunol, 2020, 145(1): 140-146.e9.
22.	Benson VS, Hartl S, Barnes N, et al. Blood eosinophil counts in the general population and airways disease: a comprehensive review and meta-analysis. Eur Respir J, 2022, 59(1): 2004590.
23.	Yancey SW, Keene ON, Albers FC, et al. Biomarkers for severe eosinophilic asthma. J Allergy Clin Immunol, 2017, 140(6): 1509-1518.
24.	FitzGerald JM, Bleecker ER, Menzies-Gow A, et al. Predictors of enhanced response with benralizumab for patients with severe asthma: pooled analysis of the SIROCCO and CALIMA studies. Lancet Respir Med, 2018, 6(1): 51-64.
25.	Bacharier LB, Maspero JF, Katelaris CH, et al. Dupilumab in children with uncontrolled moderate-to-severe asthma. N Engl J Med, 2021, 385(24): 2230-2240.
26.	Wechsler ME, Ford LB, Maspero JF, et al. Long-term safety and efficacy of dupilumab in patients with moderate-to-severe asthma (TRAVERSE): an open-label extension study. Lancet Respir Med, 2022, 10(1): 11-25.

1. 全国儿科哮喘协作组, 中国疾病预防控制中心环境与健康相关产品安全所. 第三次中国城市儿童哮喘流行病学调查. 中华儿科杂志, 2013, 51(10): 729-735.
2. Li X, Song P, Zhu Y, et al. The disease burden of childhood asthma in China: a systematic review and meta-analysis. J Glob Health, 2020, 10(1): 010801.
3. Lang A, Carlsen KH, Haaland G, et al. Severe asthma in childhood: assessed in 10 year olds in a birth cohort study. Allergy, 2008, 63(8): 1054-1060.
4. Smith DH, Malone DC, Lawson KA, et al. A national estimate of the economic costs of asthma. Am J Respir Crit Care Med, 1997, 156 (3 Pt 1): 787-793.
5. Pijnenburg MW, Fleming L. Advances in understanding and reducing the burden of severe asthma in children. Lancet Respir Med, 2020, 8(10): 1032-1044.
6. Stefanowicz D, Hackett TL, Garmaroudi FS, et al. DNA methylation profiles of airway epithelial cells and PBMCs from healthy, atopic and asthmatic children. PLoS One, 2012, 7(9): e44213.
7. Orsmark-Pietras C, James A, Konradsen JR, et al. Transcriptome analysis reveals upregulation of bitter taste receptors in severe asthmatics. Eur Respir J, 2013, 42(1): 65-78.
8. Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Res, 2013, 41(Database issue): D991-D995.
9. Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res, 2015, 43(7): e47.
10. Walter W, Sánchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics, 2015, 31(17): 2912-2914.
11. Futschik ME, Carlisle B. Noise-robust soft clustering of gene expression time-course data. J Bioinform Comput Biol, 2005, 3(4): 965-988.
12. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 2008, 9: 559.
13. 中华儿科杂志编辑委员会, 中华医学会儿科学分会呼吸学组, 中国医师协会儿科医师分会儿童呼吸专业委员会. 儿童支气管哮喘规范化诊治建议(2020 年版). 中华儿科杂志, 2020, 58(9): 708-717.
14. 李丹, 范宇杭, 李耕旭, 等. GNA15 对重型哮喘患儿预后的预测价值. 实用医学杂志, 2022, 38(6): 758-762.
15. Offermanns S, Simon MI. G alpha 15 and G alpha 16 couple a wide variety of receptors to phospholipase C. J Biol Chem, 1995, 270(25): 15175-15180.
16. Davignon I, Catalina MD, Smith D, et al. Normal hematopoiesis and inflammatory responses despite discrete signaling defects in Galpha15 knockout mice. Mol Cell Biol, 2000, 20(3): 797-804.
17. van den Bos E, Ambrosy B, Horsthemke M, et al. Knockout mouse models reveal the contributions of G protein subunits to complement C5a receptor-mediated chemotaxis. J Biol Chem, 2020, 295(22): 7726-7742.
18. Denlinger LC, Phillips BR, Ramratnam S, et al. Inflammatory and comorbid features of patients with severe asthma and frequent exacerbations. Am J Respir Crit Care Med, 2017, 195(3): 302-313.
19. Levine SJ, Wenzel SE. Narrative review: the role of Th2 immune pathway modulation in the treatment of severe asthma and its phenotypes. Ann Intern Med, 2010, 16,152(4): 232-237.
20. Khalfaoui L, Symon FA, Couillard S, et al. Airway remodelling rather than cellular infiltration characterizes both type2 cytokine biomarker-high and -low severe asthma. Allergy, 2022, 77(10): 2974-2986.
21. Ross KR, Gupta R, DeBoer MD, et al. Severe asthma during childhood and adolescence: a longitudinal study. J Allergy Clin Immunol, 2020, 145(1): 140-146.e9.
22. Benson VS, Hartl S, Barnes N, et al. Blood eosinophil counts in the general population and airways disease: a comprehensive review and meta-analysis. Eur Respir J, 2022, 59(1): 2004590.
23. Yancey SW, Keene ON, Albers FC, et al. Biomarkers for severe eosinophilic asthma. J Allergy Clin Immunol, 2017, 140(6): 1509-1518.
24. FitzGerald JM, Bleecker ER, Menzies-Gow A, et al. Predictors of enhanced response with benralizumab for patients with severe asthma: pooled analysis of the SIROCCO and CALIMA studies. Lancet Respir Med, 2018, 6(1): 51-64.
25. Bacharier LB, Maspero JF, Katelaris CH, et al. Dupilumab in children with uncontrolled moderate-to-severe asthma. N Engl J Med, 2021, 385(24): 2230-2240.
26. Wechsler ME, Ford LB, Maspero JF, et al. Long-term safety and efficacy of dupilumab in patients with moderate-to-severe asthma (TRAVERSE): an open-label extension study. Lancet Respir Med, 2022, 10(1): 11-25.

West China Medical Journal

Bioinformatic screening, expression validation and diagnostic value analysis of key genes in peripheral blood of childhood therapy-resistant asthma

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