[1]曹俊杰,赵二川,王 瑞,等.基于GEO数据库采用生物信息学分析和机器学习筛选痛风中程序性细胞死亡相关关键基因及实验验证[J].现代检验医学杂志,2025,40(04):97-104.[doi:10.3969/j.issn.1671-7414.2025.04.017]
　CAO Junjie,ZHAO Erchuan,WANG Rui,et al.Identification and Experimental Validation of Programmed Cell Death- Related Key Genes in Gout Using Bioinformatics Analysis and Machine Learning Based on GEO Database[J].Journal of Modern Laboratory Medicine,2025,40(04):97-104.[doi:10.3969/j.issn.1671-7414.2025.04.017]
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基于GEO数据库采用生物信息学分析和机器学习筛选痛风中程序性细胞死亡相关关键基因及实验验证()

《现代检验医学杂志》[ISSN:/CN:]

卷:
第40卷
期数:
2025年04期
页码:
97-104
栏目:
论著
出版日期:
2025-07-15

文章信息/Info

Title:
Identification and Experimental Validation of Programmed Cell Death- Related Key Genes in Gout Using Bioinformatics Analysis and Machine Learning Based on GEO Database
文章编号:
1671-7414（2025）04-097-08
作者:
曹俊杰1a，赵二川2，王 瑞1b，刘娇娇1a
（1. 西安市第五医院 a．检验科；b．风湿免疫一病区，西安 710082；2. 陕西省人民医院放免中心，西安 710068）
Author(s):
CAO Junjie1a, ZHAO Erchuan2, WANG Rui1b, LIU Jiaojiao1a
（1a. Department of Laboratory Medicine；1b. Department of Rheumatology and Immunology, Xi’an Fifth Hospital, Xi’an 710082, China; 2. Radioimmunoassay Center, Shaanxi Provincial People’s Hospital, Xi’an 710068, China)
关键词:
痛风; 程序性细胞死亡; 免疫浸润; 生物信息学分析; 机器学习
分类号:
R589.7；R446
DOI:
10.3969/j.issn.1671-7414.2025.04.017
文献标志码:
A
摘要:
目的 通过生物信息学和机器学习筛选痛风程序性细胞死亡（PCD）关键基因以及免疫浸润分析。方法 从GEO 数据库中下载痛风相关数据集，其中包括人源痛风相关的GSE160170 数据集及鼠源痛风相关的GSE190138 数据集作为训练集。以差异倍数>2 倍，P ＜ 0.05 为标准，筛选出差异表达基因（DEGs）。将两个数据集的DEGs 取交集，获得共同的DEGs。对共同DEGs 进行基因本体功能注释（GO）和京都基因与基因组百科全书（KEGG）富集分析。将共同DEGs 与PCD 相关基因集取交集，获得PCD 相关DEGs。在STRING 数据库中构建PPI 网络。在Cytoscape中的MCODE 筛选关键模块基因，Cytohubba 插件内置的12 种算法(Degree, MCC, DMNC, MCN, EPC, BottleNeck,EcCentricity, closness, Radiality, Betweeness, Stress, Clusteringcoefficoent) 筛选枢纽基因，两者的共同基因作为候选基因。采用最小绝对收缩和选择算法（LASSO）回归模型分别筛选GSE160170 和GSE190138 数据集中关键基因，二者取交集，获得关键基因。采用受试者工作特征（ROC）曲线评价关键基因对痛风的诊断价值。采用单样本基因集富集分析（ssGSEA）免疫浸润分析，探讨痛风相关的免疫细胞的表达差异。收集2024 年2 ～ 4 月西安市第五医院风湿科收治的30 例痛风病人作为实验组，同时收集30 例健康体检者为对照组，提取外周血单个核细胞（PBMC）RNA，采用实时荧光定量聚合酶链反应（RT-qPCR）对关键基因表达进行验证。结果 获得GSE160170 和GSE190138 共同DEGs 53 个，其中基因表达上调43 个，表达下调10 个。GO 和KEGG 表明大多数基因参与了细胞死亡、凋亡、白细胞介素（interleukin，IL）-17 信号通路、肿瘤坏死因子（TNF）信号通路和核苷酸结合寡聚结构域（NOD) 样受体信号通路等。获得PCD 与痛风共同DEGs 12 个，MCODE 筛选关键模块和Cytohubba 内置的12 种算法共筛选7 个候选基因，LASSO 回归模型在两个数据集中分别筛选出5 个基因和4 个基因，二者取交集获得关键基因3 个，分别为IL-6，纤溶酶激活物尿激酶受体（PLAUR），NOD 受体热蛋白结构域相关蛋白3（NLRP3）。在训练集中进行验证，受试者工作特征（ROC）曲线结果表明这三个基因对痛风诊断的曲线下面积（AUC）均为1.00。免疫浸润分析显示活化的CD4+T 细胞、活化的CD8+T细胞和自然杀伤（NK）细胞等的改变与痛风的发生发展密切相关。在临床样本中进行验证，与对照组比较，PLAUR，NLRP3 和IL-6 在痛风患者中均为高表达，差异具有统计学意义（t=18.852, 9.633, 8.293, 均P<0.001）。结论 IL-6，PLAUR，NLRP3 为痛风的诊断和治疗提供了潜在的生物标志物和治疗靶点，为痛风的诊断和治疗提供了新的方向。
Abstract:
Objective To screening and validation of key genes for programmed cell death(PCD) in gout through bioinformatics and machine learning and immunoinfiltration analysis. Methods Gout-related datasets were obtained from the Gene Expression Omnibus (GEO) database, comprising the human gout dataset GSE160170 and murine gout dataset GSE190138, which served as the training cohort.Differentiall expression genes (DEGs) were screened with difference factor >2 and P < 0.05. The DEGs of two data sets were intersected to obtain the common DEGs (co-DEGs). The co-DEGs were enriched by GO function and KEGG pathway analysis. The combination of co-DEGs and PCD related gene set was used to obtain PCD related DEGs. A PPI network was built in the STRING database. The key module genes were screened in Cytoscape’s MCODE, the hub genes were screened in 12 algorithms built into the Cytohubba plugin, including Degree, MCC, DMNC, MCN, EPC, BottleNeck, EcCentricity, closness, Radiality, Betweeness, Stress and Clusteringcoeff icoent, the common genes of the two was as candidate genes. The regression model of least absolute shrinkage and selection operator (LASSO) was used to screen key genes in the GSE160170 and GSE190138 data sets respectively, and the intersection of the two was adopted to obtain key genes. The diagnostic value of key genes in gout was evaluated by receiver operating characteristic (ROC) curve. The expression difference of gout related immune cells was investigated by single sample gene set enrichmemt analysis (ssGSEA) immunoinfiltration analysis. Finally, blood samples from 30 gout patients admitted to the Department of Rheumatology, Xi’an Fifth Hospital from February to April 2024 were collected as the experimental group, while blood samples from 30 healthy subjects were collected as the control group. RNA was extracted from the Peripheral blood mononuclear cell (PBMC). Quantitative real time polymerase chain reaction (RT- qPCR) was used to validate the expression of key genes in clinical samples. Results 53 common DEGs of GSE160170 and GSE190138 were obtained, among which 43 genes were up-regulated and 10 were down-regulated. GO and KEGG indicated that most genes were involved in cell death, apoptosis, interlenkin(IL)-17 signaling pathway, tumor necrosis facter (TNF) signaling pathway and nucleotide-binding oligomerization domain- (NOD)-like receptor signaling pathway. 12 co- DEGs of programmed cell death and gout were obtained. A total of 7 candidate genes were screened. LASSO regression model screened 5 genes and 4 genes respectively in two datasets, and 3 key genes were abtained by the intersection of the two datasets, which were IL-6, plasminogen activator urokinase receptor (PLAUR) and NOD-like receptor thermal protein domain associated protein3 (NLRP3). Validation within the training set revealed that all three genes attained perfect diagnostic performance for gout, with area under the ROC (AUC-ROC) curve values of 1.00. Immunoinfiltration analysis showed that the changes of activated CD4+T cells, activated CD8+T cells and natural killer cells were closely related to the occurrence and development of gout. In the clinical samples, compared with the control group, PLAUR, NLRP3 and IL-6 were highly expressed in gout patients, and the differences were statistically significant(t=18.852, 9.633, 8.293, all P ＜ 0.05). Conclusion IL-6, PLAUR and NLRP3 provide potential biomarkers and therapeutic targets for the diagnosis and treatment of gout, offering new directions in this field.

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备注/Memo

备注/Memo:
基金项目：西安市科技局医学研究一般项目(24YXYJ0113)。
作者简介：曹俊杰（1989-），男，硕士研究生，主管技师，主要从事临床检验诊断学及生物信息学研究，E-mail:18629632721@163.com。
通讯作者：刘娇娇（1992-），女，硕士研究生，主管技师, 主要从事临床检验诊断学及分子生物学研究。
更新日期/Last Update: 2025-07-15