Maddah Reza, Ghanbari Fahimeh, Veisi Maziyar, Koosehlar Eman, Shadpirouz Marzieh, Basharat Zarrin, Hejrati Alireza, Amiri Bahareh Shateri, Hejrati Lina
Department of Bioprocess Engineering, Institute of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
Applied Physiology Research Center, Isfahan University of Medical, Isfahan, Iran.
Adv Biomed Res. 2024 Jul 29;13:44. doi: 10.4103/abr.abr_355_23. eCollection 2024.
Urinary tract infections (UTIs) are a widespread health concern with high recurrence rates and substantial economic impact, and they can increase the prevalence of antibiotic resistance. This study employed an integrated bioinformatics approach to identify key genes associated with UTI development, offering potential targets for interventions.
For this study, the microarray dataset GSE124917 from the Gene Expression Omnibus (GEO) database was selected and reanalyzed. The differentially expressed genes (DEGs) between UTIs and healthy samples were identified using the LIMMA package in R software. In this section, Enrichr database was utilized to perform functional enrichment analysis of DEGs. Subsequently, the protein-protein interaction (PPI) network of the DEGs was constructed and visualized through Cytoscape, utilizing the STRING online database. The identification of hub genes was performed using Cytoscape's cytoHubba plug-in employing various methods. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic accuracy of hub genes.
Among the outcomes of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, the tumor necrosis factor (TNF) signaling pathway was identified as one of the notable pathways. The PPI network of the DEGs was successfully established and visualized in Cytoscape with the aid of the STRING online database. Using cytoHubba with different methods, we identified seven hub genes (STAT1, IL6, IFIT1, IFIT3, IFIH1, MX1, and IRF7). Based on the ROC analysis, all hub genes showed high diagnostic value.
These findings provide a valuable baseline for future research aimed at unraveling the intricate molecular mechanisms behind UTI.
尿路感染（UTIs）是一个广泛存在的健康问题，复发率高且具有重大经济影响，还会增加抗生素耐药性的发生率。本研究采用综合生物信息学方法来识别与尿路感染发展相关的关键基因，为干预措施提供潜在靶点。
在本研究中，从基因表达综合数据库（GEO）中选择并重新分析了微阵列数据集GSE124917。使用R软件中的LIMMA软件包识别尿路感染样本与健康样本之间的差异表达基因（DEGs）。在本节中，利用Enrichr数据库对DEGs进行功能富集分析。随后，利用STRING在线数据库构建DEGs的蛋白质-蛋白质相互作用（PPI）网络，并通过Cytoscape进行可视化。使用Cytoscape的cytoHubba插件采用多种方法进行枢纽基因的识别。进行受试者工作特征（ROC）分析以评估枢纽基因的诊断准确性。
在京都基因与基因组百科全书（KEGG）通路分析的结果中，肿瘤坏死因子（TNF）信号通路被确定为显著通路之一。借助STRING在线数据库，成功在Cytoscape中建立并可视化了DEGs的PPI网络。使用不同方法的cytoHubba，我们识别出七个枢纽基因（STAT1、IL6、IFIT1、IFIT3、IFI1H1、MX1和IRF7）。基于ROC分析，所有枢纽基因均显示出较高的诊断价值。
这些发现为未来旨在揭示尿路感染背后复杂分子机制的研究提供了有价值的基线。
