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Bioinformatics and experimental studies of atherosclerosis-related endothelial dysfunction genes in renal calculi and
Aina Li1, Chenjing Liu2, Xiangshen Liu2
1Department of Cardiology, the First Affiliated Hospital of Fujian Medical University, FuZhou, China.
Insights
This study identifies key genes, UCHL1 and NEK2, linked to kidney stones (KS) and atherosclerosis (AS) by analyzing gene expression. These findings offer insights into the molecular mechanisms of KS and potential therapeutic targets.
Area of Science:
- Biomedical Science
- Molecular Biology
- Genetics
Background:
- Patients with kidney stones (KS) exhibit an elevated risk of atherosclerosis (AS).
- Endothelial dysfunction (ED) is a known contributor to AS, but its specific role in KS pathogenesis is not well understood.
- Investigating AS-related ED genes in KS is crucial for understanding disease mechanisms.
Purpose of the Study:
- To identify and characterize the roles and regulatory mechanisms of AS-related endothelial dysfunction genes in kidney stone disease.
- To discover potential biomarkers for KS associated with AS.
- To explore therapeutic strategies targeting identified molecular pathways.
Main Methods:
- Analysis of three public gene expression datasets (GSE73680, GSE117518, GSE132651).
- Differential gene expression analysis, LASSO regression for biomarker selection, and RT-qPCR validation.
- Bioinformatic analyses including GeneMANIA, GSEA, pathway analysis, ceRNA network construction, and drug prediction.
Main Results:
- Identified 22 differentially expressed genes (DEGs) associated with both KS and AS.
- Selected UCHL1 and NEK2 as key biomarkers through LASSO regression and validation.
- Uncovered biomarker involvement in cell adhesion, focal adhesion, and lysosome pathways; NEK2 was found to be overexpressed in KS samples.
- Predicted 17 potential drugs targeting NEK2, including pazopanib and palbociclib.
Conclusions:
- UCHL1 and NEK2 are validated key biomarkers linking kidney stones and atherosclerosis.
- Comprehensive molecular and regulatory network analyses provide insights into KS pathogenesis.
- Findings support future personalized treatment strategies and drug development for kidney stone disease.
Abstract:
Patients with kidney stones (KS) often have an increased risk of atherosclerosis (AS). Because endothelial dysfunction (ED) is closely associated with AS, its role in KS remains unclear. This study aimed to examine the roles and mechanisms of AS-related ED genes in KS. Three datasets (GSE73680, GSE117518, and GSE132651) were analyzed. Differential expression analysis was conducted to identify differentially expressed genes (DEGs). To identify potential biomarkers, least absolute shrinkage and selection operator (LASSO) regression analysis and expression validation were conducted. Further analyses including GeneMANIA, gene set enrichment analysis (GSEA), examination of biomarkers within immune cells and subcellular localization analysis, molecular regulatory network analysis, tissue specificity analysis, and competing endogenous (ceRNA) network analysis were employed to comprehensively explore the functions and regulatory mechanisms of the identified biomarkers. Moreover, drug prediction analysis was conducted. Finally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was proceeded to verify the expression levels of the biomarkers. A total of 22 DEGs associated with KS and AS were identified. Lasso regression selected 4 candidate biomarkers (MMP10, UCHL1, NEK2, and HEY1), among which UCHL1 and NEK2 were validated as key biomarkers. GeneMANIA and GSEA analyses uncovered the potential involvement of these biomarkers in cell adhesion molecules, focal adhesion, and lysosome pathways. Analysis of immune cells and subcellular localization provided insight into the biological functions and intracellular distribution of the biomarkers. Transcription factor regulatory network and ceRNA network analyses elucidated potential upstream regulatory mechanisms. Drug prediction analysis identified 17 potential drugs, including pazopanib and palbociclib, that may target NEK2. RT-qPCR demonstrated that NEK2 was significantly overexpressed in KS samples. This study identified biomarkers associated with KS and AS and comprehensively analyzed their molecular regulatory networks. These findings provide novel understandings of the molecular mechanism underlying KS and lay the foundation for future personalized treatment and drug development.
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