Abstract：
Neisseria gonorrheae, the causative agent of genitourinary infections, has been associated with asymptomatic or recurrent infections and has the potential to form biofilms and induce inflammation and cell transformation. Herein, we aimed to use computational analysis to predict novel associations between chronic inflammation caused by gonorrhea infection and neoplastic transformation. Prioritization and gene enrichment strategies based on virulence and resistance genes utilizing essential genes from the DEG and PANTHER databases, respectively, were performed. Using the STRING database, protein‒protein interaction networks were constructed with 55 nodes of bacterial proteins and 72 nodes of proteins involved in the host immune response. MCODE and cytoHubba were used to identify 12 bacterial hub proteins (murA, murB, murC, murD, murE, purN, purL, thyA, uvrB, kdsB, lpxC, and ftsH) and 19 human hub proteins, of which TNF, STAT3 and AKT1 had high significance. The PPI networks are based on the connectivity degree (K), betweenness centrality (BC), and closeness centrality (CC) values. Hub genes are vital for cell survival and growth, and their significance as potential drug targets is discussed. This computational study provides a comprehensive understanding of inflammation and carcinogenesis pathways that are activated during gonorrhea infection.
摘要：
淋病奈瑟菌,泌尿生殖系统感染的病原体，与无症状或复发性感染有关，并有可能形成生物膜并诱导炎症和细胞转化。在这里，我们的目的是使用计算分析来预测由淋病感染引起的慢性炎症与肿瘤转化之间的新关联。利用DEG和PANTHER数据库中的必需基因，基于毒力和抗性基因的优先排序和基因富集策略，分别,被执行了。使用STRING数据库，蛋白质-蛋白质相互作用网络由55个细菌蛋白质节点和72个参与宿主免疫反应的蛋白质节点组成。MCODE和cytoHubba用于鉴定12种细菌hub蛋白(murA，murb,murc,Murd,mure,purN,purl,thya,uvrB,kdsB,lpxC,和FTSH）和19种人类枢纽蛋白，其中TNF，STAT3和AKT1有较高的意义。PPI网络基于连通性程度(K)，中间性中心性(BC)，和接近中心性(CC)值。Hub基因对细胞的存活和生长至关重要,并讨论了它们作为潜在药物靶点的意义。这项计算研究提供了对淋病感染期间激活的炎症和致癌途径的全面了解。
