呼吸系统疾病（RD）是全世界重大的公共卫生负担和恶性疾病。然而，与RD相关的生物信息和互连仍然需要更好地理解。因此，本研究旨在检测常见的差异基因和潜在的枢纽基因（HubG），强调它们的作用、信号通路、诊断 RD 的调节生物标志物和治疗 RD 的候选药物。在本文中，我们使用综合生物信息学方法（例如基因本体（GO）和KEGG通路富集分析、分子对接、分子动力学模拟和基于网络的分子相互作用分析）。我们发现了 73 个常见 DEG（CDEG）和 10 个 HubG（ATAD2B、PPP1CB、FOXO1、AKT3、BCR、PDE4D、ITGB1、PCBP2、CD44 和 SMARCA2）。一些重要的功能和信号通路与 RD 密切相关。我们识别了六种转录因子 (TF) 蛋白（FOXC1、GATA2、FOXL1、YY1、POU2F2 和 HINFP）和五种 microRNA（hsa-mir-218-5p、hsa-mir-335-5p、hsa-mir-16-5p、 hsa-mir-106b-5p 和 hsa-mir-15b-5p) 作为 RD 的重要转录和转录后调节因子。十种 HubG 和六种主要 TF 蛋白被认为是药物特异性受体。他们对通过网络分析检测到的 63 种药物进行了结合能分析研究。最后，基于其强结合亲和力评分，选择了五种复合物（PDE4D-苯并[a]芘、SMARCA2-苯并[a]芘、HINFP-苯并[a]芘、CD44-酮替芬和ATAD2B-帕纳替尼）进行RD作为最有可能重新利用的蛋白质-药物复合物，其性能稳定。我们相信我们的研究结果将使读者、湿实验室科学家和制药公司全面了解 RD 背后的生物学原理。© 2023。作者。

Respiratory diseases (RD) are significant public health burdens and malignant diseases worldwide. However, the RD-related biological information and interconnection still need to be better understood. Thus, this study aims to detect common differential genes and potential hub genes (HubGs), emphasizing their actions, signaling pathways, regulatory biomarkers for diagnosing RD and candidate drugs for treating RD. In this paper we used integrated bioinformatics approaches (such as, gene ontology (GO) and KEGG pathway enrichment analysis, molecular docking, molecular dynamic simulation and network-based molecular interaction analysis). We discovered 73 common DEGs (CDEGs) and ten HubGs (ATAD2B, PPP1CB, FOXO1, AKT3, BCR, PDE4D, ITGB1, PCBP2, CD44 and SMARCA2). Several significant functions and signaling pathways were strongly related to RD. We recognized six transcription factor (TF) proteins (FOXC1, GATA2, FOXL1, YY1, POU2F2 and HINFP) and five microRNAs (hsa-mir-218-5p, hsa-mir-335-5p, hsa-mir-16-5p, hsa-mir-106b-5p and hsa-mir-15b-5p) as the important transcription and post-transcription regulators of RD. Ten HubGs and six major TF proteins were considered drug-specific receptors. Their binding energy analysis study was carried out with the 63 drug agents detected from network analysis. Finally, the five complexes (the PDE4D-benzo[a]pyrene, SMARCA2-benzo[a]pyrene, HINFP-benzo[a]pyrene, CD44-ketotifen and ATAD2B-ponatinib) were selected for RD based on their strong binding affinity scores and stable performance as the most probable repurposable protein-drug complexes. We believe our findings will give readers, wet-lab scientists, and pharmaceuticals a thorough grasp of the biology behind RD.© 2023. The Author(s).