Wang Yuanzhao, Chen Nana, Zhang Bangqiu, Zhuang Pingping, Tan Bingtao, Cai Changlong, He Niancai, Nie Hao, Xiang Songtao, Chen Chiwei
The Seventh Clinical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong, China.
The Second Clinical Medical School of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Sci Rep. 2025 Jul 1;15(1):21650. doi: 10.1038/s41598-025-05087-w.
Renal stones (RS) are common urologic condition with unclear pathogenesis. Role of aging-related differentially expressed genes (ARDEGs) in RS remains poorly understood. This study aims to identify potential aging-related biomarkers for RS, explore the functions of aging-associated genes, and investigate the immunological microenvironment in RS. ARDEGs were collected from the GEO, GeneCards, and Molecular Signatures databases. The roles of ARDEGs were analyzed using Gene Ontology (GO) enrichment analysis. Key genes were identified using machine learning methods. Immune infiltration in RS was assessed using the CIBERSORT and ssGSEA algorithms. A total of 22 ARDEGs were identified through analysis, including 9 up-regulated and 13 down-regulated genes. GO enrichment analysis revealed that these genes were mainly involved in RS-related biological processes such as macrophage proliferation and neuroinflammatory response. GSEA analysis showed that RS-associated genes were predominantly involved in immune regulation-related pathways. Using logistic regression, SVM, and LASSO regression algorithms, a successful early-diagnosis model for RS was developed, yielding 7 key genes: CNR1, KIT, HTR2A, DES, IL33, UCP2, and PPT1. Immunocyte infiltration analysis of RS samples showed that CD8 + T cells had the strongest positive correlation with M1 macrophages, while resting NK cells had the strongest negative correlation with activated NK cells. The DES gene showed the strongest positive correlation with resting mast cells, and the IL33 gene displayed the highest negative correlation with regulatory T cells. Bioinformatics analysis screened out 7 new potential markers for RS and explored the possible mechanism of RS senescence. These findings provide novel insights into the relationship between RS and senescence, as well as the diagnosis and treatment of RS, and enhance our understanding of the disease's occurrence and development mechanisms.
肾结石（RS）是一种常见的泌尿系统疾病，其发病机制尚不清楚。衰老相关差异表达基因（ARDEGs）在RS中的作用仍知之甚少。本研究旨在识别RS潜在的衰老相关生物标志物，探索衰老相关基因的功能，并研究RS中的免疫微环境。从基因表达综合数据库（GEO）、基因卡片数据库和分子特征数据库中收集ARDEGs。使用基因本体论（GO）富集分析来分析ARDEGs的作用。通过机器学习方法识别关键基因。使用CIBERSORT和单样本基因集富集分析（ssGSEA）算法评估RS中的免疫浸润情况。通过分析共鉴定出22个ARDEGs，包括9个上调基因和13个下调基因。GO富集分析显示，这些基因主要参与与RS相关的生物学过程，如巨噬细胞增殖和神经炎症反应。基因集富集分析（GSEA）表明，RS相关基因主要参与免疫调节相关途径。使用逻辑回归、支持向量机（SVM）和套索回归算法，建立了一个成功的RS早期诊断模型，得出7个关键基因：大麻素受体1（CNR1）、原癌基因c-Kit（KIT）、5-羟色胺受体2A（HTR2A）、结蛋白（DES）、白细胞介素33（IL33）、解偶联蛋白2（UCP2）和棕榈酰蛋白硫酯酶1（PPT1）。对RS样本的免疫细胞浸润分析表明，CD8 + T细胞与M1巨噬细胞的正相关性最强，而静息自然杀伤（NK）细胞与活化NK细胞的负相关性最强。DES基因与静息肥大细胞的正相关性最强，IL33基因与调节性T细胞的负相关性最高。生物信息学分析筛选出7个新的RS潜在标志物，并探索了RS衰老的可能机制。这些发现为RS与衰老之间的关系以及RS的诊断和治疗提供了新的见解，并加深了我们对该疾病发生和发展机制的理解。
