Clinical Medical College, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
First Clinical Medical College, Liaoning University of Traditional Chinese Medicine, Shenyang, China.
Front Immunol. 2024 Feb 14;15:1323418. doi: 10.3389/fimmu.2024.1323418. eCollection 2024.
The incidence of pediatric Crohn's disease (PCD) is increasing worldwide every year. The challenges in early diagnosis and treatment of PCD persist due to its inherent heterogeneity. This study's objective was to discover novel diagnostic markers and molecular subtypes aimed at enhancing the prognosis for patients suffering from PCD.
Candidate genes were obtained from the GSE117993 dataset and the GSE93624 dataset by weighted gene co-expression network analysis (WGCNA) and differential analysis, followed by intersection with platelet-related genes. Based on this, diagnostic markers were screened by five machine learning algorithms. We constructed predictive models and molecular subtypes based on key markers. The models were evaluated using the GSE101794 dataset as the validation set, combined with receiver operating characteristic curves, decision curve analysis, clinical impact curves, and calibration curves. In addition, we performed pathway enrichment analysis and immune infiltration analysis for different molecular subtypes to assess their differences.
Through WGCNA and differential analysis, we successfully identified 44 candidate genes. Following this, employing five machine learning algorithms, we ultimately narrowed it down to five pivotal markers: GNA15, PIK3R3, PLEK, SERPINE1, and STAT1. Using these five key markers as a foundation, we developed a nomogram exhibiting exceptional performance. Furthermore, we distinguished two platelet-related subtypes of PCD through consensus clustering analysis. Subsequent analyses involving pathway enrichment and immune infiltration unveiled notable disparities in gene expression patterns, enrichment pathways, and immune infiltration landscapes between these subtypes.
In this study, we have successfully identified five promising diagnostic markers and developed a robust nomogram with high predictive efficacy. Furthermore, the recognition of distinct PCD subtypes enhances our comprehension of potential pathogenic mechanisms and paves the way for future prospects in early diagnosis and personalized treatment.
小儿克罗恩病（PCD）的发病率在全球范围内呈逐年上升趋势。由于其固有异质性，PCD 的早期诊断和治疗仍面临挑战。本研究旨在发现新的诊断标志物和分子亚型，以改善 PCD 患者的预后。
通过加权基因共表达网络分析（WGCNA）和差异分析从 GSE117993 数据集和 GSE93624 数据集获取候选基因，然后与血小板相关基因进行交集。在此基础上，通过五种机器学习算法筛选诊断标志物。基于关键标志物构建预测模型和分子亚型。使用 GSE101794 数据集作为验证集对模型进行评估，结合受试者工作特征曲线、决策曲线分析、临床影响曲线和校准曲线。此外，我们对不同的分子亚型进行了通路富集分析和免疫浸润分析，以评估它们的差异。
通过 WGCNA 和差异分析，我们成功鉴定出 44 个候选基因。然后，我们使用五种机器学习算法最终将其缩小到五个关键标记物：GNA15、PIK3R3、PLEK、SERPINE1 和 STAT1。使用这五个关键标记物作为基础，我们开发了一个具有出色性能的列线图。此外，我们通过共识聚类分析将 PCD 分为两个与血小板相关的亚型。随后的通路富集和免疫浸润分析揭示了这些亚型之间基因表达模式、富集通路和免疫浸润景观的显著差异。
在这项研究中，我们成功地鉴定了五个有前途的诊断标志物，并开发了一个具有高预测效果的强大列线图。此外，对不同 PCD 亚型的识别增强了我们对潜在发病机制的理解，并为早期诊断和个性化治疗的未来前景铺平了道路。
