山东大学耳鼻喉眼学报 ›› 2021, Vol. 35 ›› Issue (5): 75-84.doi: 10.6040/j.issn.1673-3770.0.2021.068

• • 上一篇    下一篇

复发性喉乳头状瘤中潜在生物学标志物的筛选及生物信息学分析

齐雯雯1,陈鲁秋2,贾涛1,陈雪梅1,张杰1,张皓1,金鹏1,张虎3   

  1. 1. 山东大学第二医院 耳鼻咽喉头颈外科, 山东 济南 250033;
    2. 山东大学齐鲁医院 小儿外科, 山东 济南 250012;
    3. 济南市章丘区人民医院 耳鼻喉科, 山东 济南 250200
  • 发布日期:2021-09-29
  • 通讯作者: 张虎. E-mail:Zq3250942@sina.com
  • 基金资助:
    山东省重点研发计划(2018GSF118091)

Potential biomarkers and bioinformatics analysis of differentially expressed genes in recurrent laryngeal papilloma

QI Wenwen1, CHEN Luqiu2, JIA Tao1, CHEN Xuemei1, ZHANG Jie1, ZHANG Hao1, JIN Peng1, ZHANG Hu3   

  1. 1. Department of Otorhinolaryngology & Head and Neck Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250033, Shandong, China;
    2. Department of Pediatric Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    3. Department of Otolaryngology, Jinan Zhangqiu District People's Hospital, Jinan 250200, Shandong, China
  • Published:2021-09-29

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摘要： 目的 通过生物信息学技术筛选复发性喉乳头状瘤中表达的差异基因,为复发性喉乳头状瘤的治疗提供新的思路和靶点。 方法 从GEO数据库下载复发性喉乳头状瘤的芯片数据集GSE10935,使用在线分析工具GEO2R筛选复发性喉乳头状瘤及其相邻正常喉黏膜组织间的差异表达基因,使用网络分析工具Metascape进行差异基因的GO分析及KEGG通路富集,通过STRING在线软件进行差异表达基因的蛋白-蛋白相互作用分析,并应用Cytoscape对蛋白-蛋白相互作用网络进行模块分析。最后,使用转录因子富集分析数据库ChEA3预测复发性喉乳头状瘤组织中差异表达基因的关键转录因子。 结果 在10对复发性喉乳头状瘤组织及其相邻的正常喉黏膜组织间共筛选出53个差异表达基因(DEGs)(|logFC|>1,adj P<0.05),其中上调的DEGs共30个,下调的DEGs共23个。GO分析和KEGG通路富集显示,这些差异表达的基因主要富集在多个代谢、免疫调节及PPAR信号通路等。通过蛋白-蛋白相互作用网络的构建及模块分析,筛选出SLC27A2、SCD、ECI2及FADS2四个枢纽差异表达基因。进一步的分析发现,TP63为复发性喉乳头状瘤中最为重要的转录因子。 结论 为进一步深入理解复发性喉乳头状瘤的生物学机制、探索其治疗的有效方案提供了新思路。

关键词: 喉乳头状瘤, 复发, 差异表达基因, 生物信息学分析

Abstract: Objective This study aimed to provide new ideas or targets for recurrent laryngeal papilloma using bioinformatics. Methods The chip dataset GSE10935 was downloaded from the Gene Expression Omnibus(GEO)database, and the online analysis tool GEO2R was used to screen differentially expressed genes(DEGs)between recurrent laryngeal papilloma tissues and their adjacent normal laryngeal mucosal tissues. Gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses of the DEGs were performed using the online software Metascape. The STRING online software was used to analyze the protein interactions of the DEGs. Module analysis of the hub genes was performed using the Cytoscape software. Finally, the online transcription factor enrichment analysis database CHEA3 was used to predict the key transcription factors in recurrent laryngeal papilloma tissues. Results A total of 53 DEGs(|logFC|>1, adj P<0.05)including 30 upregulated and 23 down-regulated genes were screened from 10 pairs of recurrent laryngeal papilloma tissues and their adjacent normal laryngeal mucosal tissues. GO and KEGG pathway enrichment analyses suggested that these DEGs were mainly enriched in multiple metabolic processes, including immunomodulation and PPAR signaling pathways. SLC27A2, SCD, ECI2 and FADS2 were screened as the hub genes through protein-protein interaction network construction and module analysis. Further analysis revealed that TP63 was the most important transcription factor in recurrent laryngeal papilloma. Conclusion This study provides new ideas for in-depth understanding of the biological mechanism of recurrent laryngeal papilloma and exploring effective treatment for recurrent laryngeal papilloma.

Key words: Laryngeal papilloma, Recurrent, Differentially expressed genes, Bioinformatics analysis

中图分类号: 

  • R767.1
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