Yan Li, Li Jiang-Han, Zhang Ai-Li, Li He, Pang Bo, Meng De-Yang, Fu Qian, Du Li-Juan, Su Yan
Department of Pulmonary and Critical Care Medicine, Hebei General Hospital, No. 348 of Heping West Road, Xinhua District, Shijiazhuang, 050051, Hebei Province, China.
Department of Graduate College, Hebei Medical University, Shijiazhuang, 050000, Hebei Province, China.
Hereditas. 2025 Jun 7;162(1):98. doi: 10.1186/s41065-025-00464-x.
This study aims to identify and investigate biomarkers associated with mitochondrial-related genes (MRGs) and programmed cell death-related genes (PCDRGs) that concurrently influence the progression of idiopathic pulmonary fibrosis (IPF) and to explore the underlying biological mechanisms involved.
The GSE28042 and GSE27957 datasets, comprising 1,136 MRGs and 1,548 PCDRGs, were utilized in this study. Differentially expressed genes (DEGs) between the IPF and control groups were initially identified through differential expression analysis. Subsequently, key module genes closely associated with IPF samples were selected using Weighted Gene Co-expression Network Analysis (WGCNA). Intersection genes 1 and 2 were then identified by overlapping DEGs with key module genes, MRGs, and PCDRGs. Candidate genes were further selected through Spearman correlation analysis involving intersection genes 1 and 2. Additionally, biomarkers were identified, and a risk model was developed using Cox regression analysis, proportional hazards (PH) assumption testing, and machine learning methods. Patients with IPF were stratified into high- and low-risk cohorts. Finally, functional enrichment analysis, immune infiltration analysis, regulatory network construction, and reverse transcription quantitative PCR (RT-qPCR) were conducted separately to validate the findings.
CD28 and PF4 were identified as biomarkers, and a risk model was established. The distinct risk cohorts exhibited differences in pathways related to hemostasis, prion diseases, and other biological processes. A significant positive correlation with was observed between CD28 and native CD4 T cells, while PF4 showed a negative correlation with activated NK cells. Based on these two biomarkers, 30 miRNAs and 532 lncRNAs were predicted, resulting in the construction of a lncRNA-miRNA-biomarker network. Additionally, 11 chemicals associated with these biomarkers were identified. RT-qPCR analysis further confirmed that expression levels of CD28 and PF4 were significantly reduced in IPF samples (P < 0.05).
The results of this study suggested that the biomarkers CD28 and PF4 might play a potential role in the pathogenesis of IPF and might have an impact on the prognosis of the disease. These findings might offer valuable insights for future treatment strategies and prognostic evaluation for patients with IPF.
本研究旨在识别和研究与线粒体相关基因(MRGs)和程序性细胞死亡相关基因(PCDRGs)相关的生物标志物,这些基因共同影响特发性肺纤维化(IPF)的进展,并探索其中潜在的生物学机制。
本研究使用了包含1136个MRGs和1548个PCDRGs的GSE28042和GSE27957数据集。首先通过差异表达分析确定IPF组和对照组之间的差异表达基因(DEGs)。随后,使用加权基因共表达网络分析(WGCNA)选择与IPF样本密切相关的关键模块基因。然后通过将DEGs与关键模块基因、MRGs和PCDRGs重叠来确定交集基因1和2。通过涉及交集基因1和2的Spearman相关性分析进一步选择候选基因。此外,鉴定生物标志物,并使用Cox回归分析、比例风险(PH)假设检验和机器学习方法建立风险模型。将IPF患者分层为高风险和低风险队列。最后,分别进行功能富集分析、免疫浸润分析、调控网络构建和逆转录定量PCR(RT-qPCR)以验证研究结果。
鉴定出CD28和PF4作为生物标志物,并建立了风险模型。不同的风险队列在与止血、朊病毒疾病和其他生物过程相关的途径上存在差异。观察到CD28与天然CD4 T细胞之间存在显著正相关,而PF4与活化的NK细胞呈负相关。基于这两种生物标志物,预测了30个miRNA和532个lncRNA,构建了lncRNA-miRNA-生物标志物网络。此外,鉴定出11种与这些生物标志物相关的化学物质。RT-qPCR分析进一步证实,IPF样本中CD28和PF4的表达水平显著降低(P < 0.05)。
本研究结果表明,生物标志物CD28和PF4可能在IPF的发病机制中发挥潜在作用,并可能对疾病的预后产生影响。这些发现可能为IPF患者未来的治疗策略和预后评估提供有价值的见解。