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《天津医科大学学报》[ISSN:1006-8147/CN:12-1259/R]

卷:

30卷

期数:

2024年06期

页码:

535-542

栏目:

基础医学

出版日期:

2024-11-20

文章信息/Info

Title:

Key genes and drug targets prediction of ankylosing spondylitis based on bioinformatics analysis

文章编号:

1006-8147(2024)06-0535-08

作者:

修占杰¹; 刘佳玙²; 王誉童³; 踪宇阳⁴; 郑子馨⁵; 施佳维⁶; 李津¹; 7; 孟歆怿⁷

（1.天津医科大学基础医学院生物信息学系，天津 300070；2.天津市实验中学滨海学校，天津 300459；3.天津市第二十中学，天津 300050；4.天津市南开中学，天津 300199；5.天津市耀华中学，天津 300040；6.天津市第四中学，天津 300211；7.天津医科大学基础医学院细胞生物学系，天津 300070）

Author(s):

XIU Zhanjie¹; LIU Jiayu²; WANG Yutong³; ZONG Yuyang⁴; ZHENG Zixin⁵; SHI Jiawei⁶; LI Jin¹; 7; MENG Xinyi⁷

（1.Department of Bioinformatics，School of Basic Medical Sciences，Tianjin Medical University，Tianjin 300070，China；2.Tianjin Experimental Binhai High School，Tianjin 300459，China；3.Tianjin No.20 High School，Tianjin 300050，China；4.Tianjin Nankai High School，Tianjin 300199，China；5.Tianjin Yaohua High School，Tianjin 300040，China；6.Tianjin No.4 High School，Tianjin 300211，China；7.Department of Cell Biology，School of Basic Medical Sciences，Tianjin Medical University，Tianjin 300070，China）

关键词:

强直性脊柱炎; 差异表达基因; 加权基因共表达网络分析; CIBERSORT;  免疫细胞; 药物靶点

Keywords:

ankylosing spondylitis;  differentially expressed gene;  weighted gene co-expression network analysis;  CIBERSORT;  immune cells;  drug target

分类号:

R593.23

DOI:

10.20135/j.issn.1006-8147.2024.06.0535

文献标志码:

A

摘要:

目的：基于强直性脊柱炎（AS）基因芯片数据，应用生物信息学分析方法识别免疫细胞亚型相关的关键基因及药物靶点预测。方法：GEO数据库下载AS基因表达数据，筛选差异表达基因（DEGs）；运用R软件包进行基因本体论（GO）和京都基因和基因组百科全书（KEGG）富集分析；应用CIBERSORT 反卷积算法和加权基因共表达网络分析（WGCNA）建立免疫细胞亚型与基因表达的关联性，结合蛋白质互作（PPI）网络筛选的枢纽基因进而确定AS关键基因，药物优先指数（Pi）数据库预测关键基因药物靶点指标。结果：共筛选出 126 个DEGs，通路富集分析显示DEGs主要富集于淋巴及自然杀伤（NK）细胞免疫调节和毒性信号通路。WGCNA确定了3个模块与AS的中性粒细胞、CD8+T细胞和激活型NK细胞亚型相关性较强：棕色模块与中性粒细胞、绿色模块与中性粒细胞、蓝绿色模块与CD8+T细胞、蓝绿色模块与激活型NK细胞（cor=0.83、0.68、0.52、 0.28，均P<0.05）。PPI与模块基因结合筛选出 5 个关键基因，分别为CXCR1、IKZF1、RUNX3、ID2和ITGB3，基因Pi分析IKZF1和 ITGB3在AS中排名较高。结论： CXCR1、IKZF1、RUNX3、ID2和ITGB3在AS中发挥重要作用，IKZF1和ITGB3可作为潜在药物治疗靶点。

Abstract:

Objective：Based on ankylosing spondylitis（AS） microarray data，bioinformatics analysis was used to identify key genes and drug targets related to immune cell subtypes. Methods： AS gene expression data were downloaded from GEO database，and differentially expressed genes （DEGs） were screened. Enrichment analysis of gene ontology（GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） were performed using R software package； CIBERSORT deconvolution algorithm and weighted gene co-expression network analysis （WGCNA） were used to establish the association between immune cell subtypes and gene expression，and key AS genes were identified by combining hub genes screened by protein-protein interaction （PPI） network. Key genes were predicted as drug targets in priority index （Pi） database. Results： A total of 126 DEGs were screened. Pathway enrichment analysis showed that they were mainly enriched in immune regulation and toxicity signaling pathways of lymphocytes and NK cells. WGCNA identified 3 modules were strongly correlated with neutrophils，CD8+T cells and activated NK cells of AS： brown module with neutrophils，green module with neutrophils，turquoise module with CD8+T cells，turquoise module with activated NK cells（cor=0.83，0.68，0.52，0.28，all P<0.05）. Five key genes were screened by combing PPI with module genes，namely CXCR1，IKZF1，RUNX3，ID2 and ITGB3. IKZF1 and ITGB3 rank top in AS by Priority index analysis. Conclusion： CXCR1，IKZF1，RUNX3，ID2 and ITGB3 play important roles in AS； IKZF1 and ITGB3 can be used as potential drug therapeutic targets.

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备注/Memo

备注/Memo:

基金项目 天津市教委科研计划项目（2020KJ196） 作者简介 修占杰（1995-），男，硕士在读，研究方向：生物信息学与计算生物学；
通信作者：李津，E-mail：jli01@tmu.edu.cn；孟歆怿，E-mail：mengxy@tmu.edu.cn。

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更新日期/Last Update: 2024-11-25