Sun Manman, Zhang Pei, Yang Sheng, Qiao Yunyang, Shen Xuepo, Ji Jialing, Ding Ling
Department of Pediatrics, the Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Sci Rep. 2025 May 22;15(1):17799. doi: 10.1038/s41598-025-02663-y.
Currently, research has found a close correlation between childhood obesity (CO) and elevated levels of polyamines in the bloodstream. Thus, the identification of key genes associated with polyamines metabolism in CO could offer fresh insights for clinical management of CO. This study utilized two datasets from public databases (GSE205668 and GSE104815) and 59 polyamines metabolism-related genes (PMRGs) to screen for candidate genes. Subsequently, candidate key genes were selected using Mendelian randomization (MR) analysis, and machine learning algorithms were employed to obtain intersecting feature genes based on the MR results. Then key genes were identified through expression validation. Finally, we conducted research on the key genes including gene set enrichment analysis (GSEA), immune infiltration, and transcription factor(TF)-mRNA network. Differential analysis identified 432 candidate genes linked to childhood obesity and polyamine metabolism, with 4 key genes showing causal relationships. Specifically, WWC1, NPL, and LAPTM5 as risk factors [odd ratio (OR) > 1], while GPAT3 (OR < 1) was identified as a protective factor for CO. Machine learning algorithms pinpointed 3 feature genes (WWC1, NPL, and GPAT3) with significant differential expression and consistent trends. GSEA revealed ribosome and lysosome pathways linked to key genes. MITF regulated these genes in the TF-mRNA network. Twelve immune cell types, mostly correlating with key genes, were identified. We identified 3 key genes (WWC1, NPL, and GPAT3) related to polyamine metabolism in CO. Additionally, we investigated their potential biological functions and regulatory mechanisms, aiming to provide new theoretical basis for the treatment and diagnosis of CO.
目前，研究发现儿童肥胖（CO）与血液中多胺水平升高密切相关。因此，鉴定与CO中多胺代谢相关的关键基因可为CO的临床管理提供新的见解。本研究利用来自公共数据库的两个数据集（GSE205668和GSE104815）以及59个多胺代谢相关基因（PMRGs）来筛选候选基因。随后，使用孟德尔随机化（MR）分析选择候选关键基因，并采用机器学习算法根据MR结果获得相交特征基因。然后通过表达验证鉴定关键基因。最后，我们对关键基因进行了研究，包括基因集富集分析（GSEA）、免疫浸润和转录因子（TF）-mRNA网络。差异分析确定了432个与儿童肥胖和多胺代谢相关的候选基因，其中4个关键基因显示出因果关系。具体而言，WWC1、NPL和LAPTM5为危险因素[比值比（OR）>1]，而GPAT3（OR<1）被确定为CO的保护因素。机器学习算法确定了3个具有显著差异表达和一致趋势的特征基因（WWC1、NPL和GPAT3）。GSEA揭示了与关键基因相关的核糖体和溶酶体途径。MITF在TF-mRNA网络中调节这些基因。确定了12种免疫细胞类型，大多与关键基因相关。我们鉴定了3个与CO中多胺代谢相关的关键基因（WWC1、NPL和GPAT3）。此外，我们研究了它们潜在的生物学功能和调控机制，旨在为CO的治疗和诊断提供新的理论依据。
