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中华肺部疾病杂志(电子版) ›› 2022, Vol. 15 ›› Issue (03) : 306 -310. doi: 10.3877/cma.j.issn.1674-6902.2022.03.004

论著

非特异性间质性肺炎相关基因筛选和生物信息学分析
李德峰1, 毛杨1, 付万垒2,()   
  1. 1. 重庆 400037,陆军(第三)军医大学第二附属医院临床医学研究中心
    2. 重庆 400037,陆军(第三)军医大学第二附属医院病理科
  • 收稿日期:2021-10-05 出版日期:2022-06-25
  • 通信作者: 付万垒
  • 基金资助:
    国家自然科学基金资助项目(82002446)

Screening and bioinformatics analysis of nonspecific interstitial pneumonia related genes

Defeng Li1, Yang Mao1, Wanlei Fu2,()   

  1. 1. Clinical Medical Research Center, Second Affiliated Hospital, Army Medical University, Chongqing 400037, China
    2. Department of Pathology, Second Affiliated Hospital, Army Medical University, Chongqing 400037, China
  • Received:2021-10-05 Published:2022-06-25
  • Corresponding author: Wanlei Fu
引用本文:

李德峰, 毛杨, 付万垒. 非特异性间质性肺炎相关基因筛选和生物信息学分析[J]. 中华肺部疾病杂志(电子版), 2022, 15(03): 306-310.

Defeng Li, Yang Mao, Wanlei Fu. Screening and bioinformatics analysis of nonspecific interstitial pneumonia related genes[J]. Chinese Journal of Lung Diseases(Electronic Edition), 2022, 15(03): 306-310.

目的

通过生物信息学的方法筛选非特异性间质性肺炎(nonspecific interstitial pneumonia, NSIP)的致病基因,为进一步研究提供靶点。

方法

从GEO数据库下载基因芯片数据集GSE110147、GSE21369、GSE40839,使用limma包分析工具筛选正常组织与NSIP的差异表达基因。通过clusterProfiler包对差异表达基因进行GO分析和KEGG通路富集分析,找到NSIP发病过程中差异表达基因主要参与的生物功能及其集中的信号通路。利用STRING数据库和CYTOSCAPE软件构建蛋白相互作用网络,使用MCODE软件提取蛋白相互作用网络中的子网络模块。

结果

3个数据集的差异表达基因做韦恩图得到3个共同差异表达基因。GO富集分析表明NSIP中上调的差异表达基因主要影响RNA剪接、抗病毒感染、对肽的细胞反应等相关的生物过程,富集的分子主要参与细胞组分的囊腔合成分泌、剪接复合体,富集的分子功能主要参与ATP酶活性,受体配体活性及DNA结合转录激活因子活性。NSIP中下调的蛋白主要涉及转移酶活性调节的生物过程。KEGG通路分析表明NSIP中上调的差异表达基因主要参与PI3K-Akt、人类乳头瘤病毒感染及MAPK等信号通路。

结论

利用生物信息学筛选出差异表达基因,可能是NSIP发病机制的新靶点,对诊断治疗NSIP具有临床意义。

Objective

Screening the causative genes of nonspecific interstitial pneumonia (NSIP) by bioinformatics and provide targets for further research.

Method

By downloading the gene chip datasets GSE110147, GSE21369, GSE40839 from the GEO database, and using the limma package analysis tool to screen out the differentially expressed genes between normal tissues and NSIP. The clusterProfiler package was used to perform GO analysis and KEGG pathway enrichment analysis on the differentially expressed genes to find the biological functions of the differentially expressed genes and their concentrated signaling pathways in the pathogenesis of NSIP. To study the relationship between differentially expressed genes and proteins, the STRING database and CYTOSCAPE software were used to construct the protein-protein interaction (PPI) network, and the MCODE software was used to extract the sub-network modules in the protein interaction network.

Result

Veen plots result suggested three common significantly differentially expressed genes were found. GO enrichment analysis showed that up-regulated differentially expressed genes in NSIP mainly affected biological processes related to RNA splicing, antiviral infection, and cellular responses to peptides. The enriched molecules are mainly involved in the synthesis, secretion, and splicing complexes of cellular components, and the enriched molecular function are mainly involved in ATPase activity, receptor ligand activity and DNA-binding transcription activator activity. The down-regulated proteins in NSIP are mainly involved in biological processes regulated by transferase activity. KEGG pathway analysis showed that the up-regulated differentially expressed genes in NSIP were mainly involved in signaling pathways such as PI3K-Akt pathways, human papilloma virus infection pathways and MAPK pathways.

Conclusion

The differentially expressed genes screened by bioinformatics may be new targets for the pathogenesis of NSIP, which is significant for the future clinical diagnosis and treatment of NSIP.

图1 GSE110147、GSE21369、GSE40839中分组数据的PCA分析。注:主成分分析(PCA),基于每个样品中全部基因的表达信息,图中每个点代表了一个样本。两点之间在横、纵坐标上的距离,代表了样品受主成分(Normals或NSIP)影响下的相似性距离。样本数量越多,该分析意义越大,反之样本数量过少,会产生个体差异,导致PCA分析成图后形成较大距离的分开
图2 GSE110147、GSE21369、GSE40839中差异表达基因的筛选结果韦恩图。注:GSE110147、GSE21369、GSE40839指这三个研究项目的系列数据,包括实验设计、描述、组别、样本等信息以及检测数据文件
图3 GSE110147、GSE21369、GSE40839数据集差异基因的GO富集分析结果。注:cellular response to peptide(肽的细胞应答),fibroblast apoptotic process(成纤维细胞凋亡过程),response to virus(病毒应答),type Ⅰ interferon signaling pathway(Ⅰ型干扰素信号通路),RNA splicing(RNA剪接),ribonucleoprotein complex biogenesis(核糖核蛋白复合物的生物生成)
图4 GSE110147、GSE21369、GSE40839数据集差异基因的KEGG富集分析结果。注:PI3K-Akt signaling pathway(PI3K-Akt信号通路),MAPK signaling pathway(MAPK信号通路),Human papillomavirus infection(人类乳头瘤病毒感染),Calcium signaling pathway(钙信号通路),Salmonella infection(沙门氏菌感染)
图5 GSE110147差异表达蛋白的子模块互相作用图。注:DNTTIP2(末端脱氧核苷酸转移酶作用因子2),NOC3L(核仁复合体关联3同源物),LYAR(Ly1抗体反应克隆基因),CEBPZ(CCAAT增强子结合蛋白),RBM34(RNA结合基元蛋白34),UTP3(小亚基加工体体成分),ESF1(核仁rRNA前体加工蛋白),RPF2(核糖体产生因子2),LTV1(核糖体生成因子),WDR36(WD重复蛋白36),MPHOSPH10(M期磷蛋白10),DHX15(DEAH盒解旋酶15),BMS1(核糖体组装蛋白),NMD3(核糖体输出接头蛋白),DDX18(DEAD盒解旋酶18),UTP20(小亚基加工体体成分),DDX21(DEAD盒解旋酶21),GTPBP4(GTP结合蛋白4)
1
Fischer A, Swigris JJ, Groshong SD, et al. Clinically significant interstitial lung disease in limited sclero- derma: histopathology, clinical features, and survival[J]. Chest, 2008, 134(3): 601-605.
2
Kim DS, Yoo B, Lee JS, et al. The major histopathologic pattern of pulmonary fibrosis in scleroderma is nonspecific interstitial pneumonia[J]. Sarcoidosis Vasc Diffuse Lung Dis, 2002, 19(2): 121-127.
3
Okamoto M, Fujimoto K, Sadohara J, et al. A retrospective cohort study of outcome in sys-temic sclerosis-associated interstitial lung disease[J]. Respir Investig, 2016, 54(6): 445-453.
4
吴 晓,王庆文. 系统性硬化病相关肺部疾病的诊治进展[J/CD]. 中华肺部疾病杂志(电子版), 2020, 13(2): 282-285.
5
Lynch DA. Nonspecific interstitial pneumonia: evolving concepts[J].Radiology, 2001, 221(3): 583-584.
6
Flaherty KR, Martinez FJ. Nonspecific interstitial pneumonia[J]. Semin Respir Crit Care Med, 2006, 27(6): 652-658.
7
Myers JL. Nonspecific interstitial pneumonia: pathologic features and clinical implications[J]. Semin Diagn Pathol, 2007, 24(3): 183-187.
8
Alan KY, Tamera JC. Nonspecific interstitial pneumonia[J]. Semin Respir Crit Care Med, 2020, 41(2): 184-201.
9
Jongmin L, Yong HK, So YP, et al. Korean Guidelines for diagnosis and management of interstitial lung diseases: Part3. Idiopathic nonspecific interstitial pneumonia[J]. Tuberc Respir Dis (Seoul), 2019, 82(4): 277-284.
10
Tomassetti S, Ryu JH, Poletti V, et al. Nonspecific interstitial pneumonia:What is the optimal approach to management?[J]. Semin Respir Crit Care Med, 2016, 37(3): 378-394.
11
Nagai S, Handa T, Izumi T, et al. Nonspecific interstitial pneumonia:a real clinical entity?[J]. Clin Chest Med, 2004, 25(4): 705-715.
12
Hansell DM, Bankier AA, Remy J, et al. Fleischner Society: glossary of terms for thoracic imaging[J]. Radiology, 2008, 246(3): 697-722.
13
Flieder DB, Koss MN. Nonspecific interstitial pneumonia: a provisional category of idiopathic interstitial pneumonia[J]. Curr Opin Pulm Med, 2004, 10(5): 441-446.
14
Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets-update[J]. Nucleic Acids Res, 2013, 41 (Database issue): D991-D995.
15
Cecchini MJ, Hosein K, Joseph M, et al. Comprehensive gene expression profiling identifies distinct and overlapping transcriptional profiles in non-specific interstitial pneumonia and idiopathic pulmonary fibrosis[J]. Respir Res, 2018, 19(1): 153.
16
Cho JH, Gelinas R, Etheridge A, et al. Systems biology of interstitial lung diseases: integration of mRNA and microRNA expression changes[J]. BMC Med Genomics, 2011, 4: 8.
17
Lindahl GE, Stock CJ, Leoni P, et al.Microarray profiling reveals suppressed interferon stimulated gene program in fibroblasts from scleroderma-associated interstitial lung disease[J]. Respir Res, 2013, 14(1): 80.
18
Yu GC, Wang LG, He QY, et al. clusterProfiler: an R package for comparing biological themes among gene clusters[J]. OMICS, 2012, 16(5): 284-287.
19
Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks[J]. Genome Res, 2003, 13(11): 2498-2504.
20
Snider J, Kotlyar M, Saraon P, et al. Fundamentals of protein interaction network mapping[J]. Mol Syst Biol, 2015, 11(12): 848-856.
21
李 敏,孟祥茂. 动态蛋白质网络的构建、分析及应用研究进展[J]. 计算机研究与发展2017, 54(6): 1281-1299.
22
Perelas A, Silver RM, Highland KB, et al. Systemic sclerosis-associated interstitial lung disease[J]. Lancet Respir Med, 2020, 8(3): 304-320.
23
Kono M, Nakamura Y, Yoshimura K, et al. Nonspecific interstitial pneumonia preceding diagnosis of collagen vascular disease[J]. Respir Med, 2016, 117: 40-47.
24
晏学德.糖皮质激素辅助治疗儿童肺炎支原体肺炎的临床疗效分析[J]. 中外女性健康研究2020, (10): 30,51.
25
黄 慧,徐作军.非特异性间质性肺炎[J]. 国际呼吸杂志2007, (4): 281-286.
26
杨亚琴,杜敏娟,徐晓光.普通型间质性肺炎的临床病理特征及其与特发性非特异性间质性肺炎的鉴别诊断分析[J]. 中外医疗2015, 34(17): 11-12,15.
27
易祥华.非特异性间质性肺炎病理诊断中国专家共识(草案)[J]. 中华结核和呼吸杂志2018, 41(11): 833-839.
28
黎剑宇,邓 宇,林晓锋,等.特发性非特异性间质性肺炎与结缔组织病相关性非特异性间质性肺炎的临床及HRCT比较[J]. 实用医学杂志2017, 33(15): 2496-2500.
29
王贵良.糖皮质激素治疗非特异性间质性肺炎的临床疗效分析[J]. 中国医药指南2019, 17(25): 67.
30
李晓珊,沈玉倩.HRCT诊断特发性非特异性间质性肺炎与结缔组织病相关性非特异性间质性肺炎的价值比较[J]. 中国现代药物应用2019, 13(12): 44-45.
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