Aji Kaisaier, Aikebaier Aierken, Abula Asimujiang, Song Guang Lu
Urology Department, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Department of Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Front Genet. 2024 Nov 13;15:1440774. doi: 10.3389/fgene.2024.1440774. eCollection 2024.
The study aimed to investigate the molecular mechanisms underlying kidney stones by analyzing gene expression profiles. They focused on identifying differentially expressed genes (DEGs), performing gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and screening optimal feature genes using various machine learning algorithms.
Data from the GSE73680 dataset, comprising normal renal papillary tissues and Randall's Plaque (RP) tissues, were downloaded from the GEO database. DEGs were identified using the limma R package, followed by GSEA and WGCNA to explore functional modules. Functional enrichment analysis was conducted using KEGG and Disease Ontology. Various machine learning algorithms were used for screening the most suitable feature genes, which were then assessed for their expression and diagnostic significance through Wilcoxon rank-sum tests and ROC curves. GSEA and correlation analysis were performed on optimal feature genes, and immune cell infiltration was assessed using the CIBERSORT algorithm.
412 DEGs were identified, with 194 downregulated and 218 upregulated genes in kidney stone samples. GSEA revealed enriched pathways related to metabolic processes, immune response, and disease states. WGCNA identified modules correlated with kidney stones, particularly the yellow module. Functional enrichment analysis highlighted pathways involved in metabolism, immune response, and disease pathology. Through machine learning algorithms, KLK1 and MMP10 were identified as optimal feature genes, significantly upregulated in kidney stone samples, with high diagnostic value. GSEA further elucidated their biological functions and pathway associations.
The study comprehensively analyzed gene expression profiles to uncover molecular mechanisms underlying kidney stones. KLK1 and MMP10 were identified as potential diagnostic markers and key players in kidney stone progression. Functional enrichment analysis provided insights into their roles in metabolic processes, immune response, and disease pathology. These results contribute significantly to a better understanding of kidney stone pathogenesis and may inform future diagnostic and therapeutic strategies.
本研究旨在通过分析基因表达谱来探究肾结石潜在的分子机制。他们着重于识别差异表达基因（DEG）、进行基因集富集分析（GSEA）、加权基因共表达网络分析（WGCNA）、功能富集分析，并使用各种机器学习算法筛选最佳特征基因。
从GEO数据库下载了GSE73680数据集的数据，该数据集包含正常肾乳头组织和兰德尔斑（RP）组织。使用limma R包识别DEG，随后进行GSEA和WGCNA以探索功能模块。使用KEGG和疾病本体进行功能富集分析。使用各种机器学习算法筛选最合适的特征基因，然后通过Wilcoxon秩和检验和ROC曲线评估其表达和诊断意义。对最佳特征基因进行GSEA和相关性分析，并使用CIBERSORT算法评估免疫细胞浸润情况。
共识别出412个DEG，肾结石样本中有194个基因下调，218个基因上调。GSEA显示与代谢过程、免疫反应和疾病状态相关的富集通路。WGCNA识别出与肾结石相关的模块，特别是黄色模块。功能富集分析突出了参与代谢、免疫反应和疾病病理的通路。通过机器学习算法，KLK1和MMP10被确定为最佳特征基因，在肾结石样本中显著上调，具有较高的诊断价值。GSEA进一步阐明了它们的生物学功能和通路关联。
本研究全面分析了基因表达谱以揭示肾结石潜在的分子机制。KLK1和MMP10被确定为潜在的诊断标志物以及肾结石进展中的关键因素。功能富集分析揭示了它们在代谢过程、免疫反应和疾病病理中的作用。这些结果有助于更好地理解肾结石的发病机制，并可能为未来的诊断和治疗策略提供依据。
