Li Chao, Abdurehim Aliya, Zhao Shuang, Sun Qing, Xu Jiawen, Xie Junbo, Zhang Yanqing
School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China; Tianjin Key Laboratory of Therapeutic Substance of Traditional Chinese Medicine, Tianjin, China.
School of Chinese Materia Medica, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
Eur J Pharmacol. 2024 Jul 5;974:176603. doi: 10.1016/j.ejphar.2024.176603. Epub 2024 Apr 26.
Pulmonary fibrosis (PF) is a group of respiratory diseases that are extremely complex and challenging to treat. Due to its high mortality rate and short survival, it's often referred to as a "tumor-like disease" that poses a serious threat to human health.
We aimed validate the potential of Deapioplatycodin D (DPD) to against PF and clarify the underlying mechanism of action of DPD for the treatment of PF based on bioinformatics and experimental verification. This finding provides a basis for the development of safe and effective therapeutic PF drugs based on DPD.
We used LPS-induced early PF rats as a PF model to test the overall efficacy of DPD in vivo. Then, A variety of bioinformatics methods, such as WGCNA, LASSO algorithm and immune cell infiltration (ICI), were applied to analyze the gene microarray related to PF obtained from Gene Expression Omnibus (GEO) to obtained key targets of PF. Finally, an in vitro PF model was constructed based on BEAS-2B cells while incorporating rat lung tissues to validate the regulatory effects of DPD on critical genes.
DPD can effectively alleviate inflammatory and fibrotic markers in rat lungs. WGCNA analysis resulted in a total of six expression modules, with the brown module having the highest correlation with PF. Subsequently, seven genes were acquired by intersecting the genes in the brown module with DEGs. Five key genes were identified as potential biomarkers of PF by LASSO algorithm and validation dataset verification analysis. In the ICI analysis, infiltration of activated B cell, immature B cell and natural killer cells were found to be more crucial in PF. Ultimately, it was observed that DPD could modulate key genes to achieve anti-PF effects.
In short, these comprehensive analysis methods were employed to identify critical biomarkers closely related to PF, which helps to elucidate the pathogenesis and potential immunotherapy targets of PF. It also provides essential support for the potential of DPD against PF.
肺纤维化(PF)是一组极其复杂且治疗具有挑战性的呼吸系统疾病。由于其高死亡率和短生存期,它常被称为对人类健康构成严重威胁的“肿瘤样疾病”。
我们旨在验证去甲桔梗皂苷D(DPD)抗PF的潜力,并基于生物信息学和实验验证阐明DPD治疗PF的潜在作用机制。这一发现为开发基于DPD的安全有效的PF治疗药物提供了依据。
我们使用脂多糖诱导的早期PF大鼠作为PF模型来测试DPD在体内的整体疗效。然后,应用多种生物信息学方法,如加权基因共表达网络分析(WGCNA)、套索算法和免疫细胞浸润(ICI),分析从基因表达综合数据库(GEO)获得的与PF相关的基因微阵列,以获得PF的关键靶点。最后,基于BEAS-2B细胞构建体外PF模型,并结合大鼠肺组织来验证DPD对关键基因的调节作用。
DPD可有效减轻大鼠肺部的炎症和纤维化标志物。WGCNA分析共产生六个表达模块,其中棕色模块与PF的相关性最高。随后,通过将棕色模块中的基因与差异表达基因(DEGs)相交获得了七个基因。通过套索算法和验证数据集验证分析确定了五个关键基因作为PF的潜在生物标志物。在ICI分析中,发现活化B细胞、未成熟B细胞和自然杀伤细胞的浸润在PF中更为关键。最终,观察到DPD可调节关键基因以实现抗PF作用。
简而言之,采用这些综合分析方法来鉴定与PF密切相关的关键生物标志物,这有助于阐明PF的发病机制和潜在的免疫治疗靶点。它也为DPD抗PF的潜力提供了重要支持。