Ding Chenchun, Liao Quan, Zuo Renjie, Zhang Shichao, Guo Zhenzhen, He Junjie, Ye Ziwei, Chen Weibin, Ke Sunkui
Department of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
Front Genet. 2025 Jan 22;15:1464471. doi: 10.3389/fgene.2024.1464471. eCollection 2024.
Idiopathic pulmonary fibrosis (IPF) is a severe chronic respiratory disease characterized by treatment challenges and poor prognosis. Identifying relevant biomarkers for effective early-stage risk prediction is therefore of critical importance.
In this study, we obtained gene expression profiles and corresponding clinical data of IPF patients from the GEO database. GO enrichment and KEGG pathway analyses were performed using R software. To construct an IPF risk prediction model, we employed LASSO-Cox regression analysis and the SVM-RFE algorithm. PODNL1 and PIGA were identified as potential biomarkers associated with IPF onset, and their predictive accuracy was confirmed using ROC curve analysis in the test set. Furthermore, GSEA revealed enrichment in multiple pathways, while immune function analysis demonstrated a significant correlation between IPF onset and immune cell infiltration. Finally, the roles of PODNL1 and PIGA as biomarkers were validated through and experiments using qRT-PCR, Western blotting, and immunohistochemistry.
These findings suggest that PODNL1 and PIGA may serve as critical biomarkers for IPF onset and contribute to its pathogenesis.
This study highlights their potential for early biomarker discovery and risk prediction in IPF, offering insights into disease mechanisms and diagnostic strategies.
特发性肺纤维化（IPF）是一种严重的慢性呼吸系统疾病，其特征在于治疗挑战和预后不良。因此，识别相关生物标志物以进行有效的早期风险预测至关重要。
在本研究中，我们从GEO数据库中获取了IPF患者的基因表达谱和相应的临床数据。使用R软件进行基因本体（GO）富集分析和京都基因与基因组百科全书（KEGG）通路分析。为构建IPF风险预测模型，我们采用了套索-考克斯回归分析和支持向量机-递归特征消除（SVM-RFE）算法。PODNL1和PIGA被确定为与IPF发病相关的潜在生物标志物，并在测试集中使用受试者工作特征（ROC）曲线分析证实了它们的预测准确性。此外，基因集富集分析（GSEA）显示多个通路富集，而免疫功能分析表明IPF发病与免疫细胞浸润之间存在显著相关性。最后，通过定量逆转录聚合酶链反应（qRT-PCR）、蛋白质免疫印迹法和免疫组织化学实验验证了PODNL1和PIGA作为生物标志物的作用。
这些发现表明，PODNL1和PIGA可能是IPF发病的关键生物标志物，并有助于其发病机制。
本研究强调了它们在IPF早期生物标志物发现和风险预测方面的潜力，为疾病机制和诊断策略提供了见解。
