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. 2024 Nov 13:15:1440774.
doi: 10.3389/fgene.2024.1440774. eCollection 2024.

Comprehensive analysis of molecular mechanisms underlying kidney stones: gene expression profiles and potential diagnostic markers

Kaisaier Aji #  1 , Aierken Aikebaier #  2 , Asimujiang Abula  1 , Guang Lu Song  1
Affiliations

Comprehensive analysis of molecular mechanisms underlying kidney stones: gene expression profiles and potential diagnostic markers

Kaisaier Aji et al. Front Genet. .

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: gene expression; gene expression profiles; kidney stones; machine learning; molecular mechanism.

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Conflict of interest statement

The authors declare that the study was carried out without any commercial or financial relationships that may be served as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Identification of DEGs and gene set enrichment analysis in the kidney stone dataset.
FIGURE 2
FIGURE 2
Selection of WGCNA modules and identification of hub genes.
FIGURE 3
FIGURE 3
Functional enrichment analysis of overlapping differentially expressed genes (DEGs).
FIGURE 4
FIGURE 4
Identification of optimal feature genes through integration of multiple machine learning algorithms.
FIGURE 5
FIGURE 5
Venn diagram showing KLK1 and MMP10 as key feature genes identified across seven machine learning algorithms.
FIGURE 6
FIGURE 6
Evaluation of expression and diagnostic significance of optimal feature genes.
FIGURE 7
FIGURE 7
Functional identification of 2 feature genes.
FIGURE 8
FIGURE 8
Signature gene sets and immune cell infiltration.
See this image and copyright information in PMC

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