Bioinformatics and experimental studies of atherosclerosis-related endothelial dysfunction genes in renal calculi and exploration of their molecular mechanisms

Aina Li; Chenjing Liu; Xiangshen Liu; Lei Chen; Dong Wang; Changyan Zhu

doi:10.1371/journal.pone.0354595

. 2026 Aug 4;21(8):e0354595. doi: 10.1371/journal.pone.0354595

Bioinformatics and experimental studies of atherosclerosis-related endothelial dysfunction genes in renal calculi and exploration of their molecular mechanisms

Aina Li ^1,^#, Chenjing Liu ^2,^#, Xiangshen Liu ^2,^#, Lei Chen ^2,^*, Dong Wang ^2,^*, Changyan Zhu ^2,^*

Editor: Patrick Goymer³

PMCID: PMC13436723 PMID: 42550868

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.

Introduction

Kidney stones (KS) are hard crystalline deposits formed by the aggregation of minerals and salts (e.g., calcium oxalate, calcium phosphate, and uric acid) in the kidneys [1]. As a global public health problem, the prevalence of KS is rising, particularly in industrialized nations, where dietary factors are recognized as major contributors to the rising incidence of urolithiasis [2]. In China, the prevalence of KS among adult is approximately 7.54% [3]. Common stone components include calcium oxalate, calcium phosphate, and uric acid. The pathogenic mechanisms underlying KS include urinary supersaturation, a deficiency of crystallization inhibitors, and urinary tract infections [4]. Clinical manifestations may include flank pain, hematuria, and urinary frequency. Treatment strategies include medical clearance, extracorporeal shock wave lithotripsy, and surgical interventions [5]. The clinical management of KS faces challenges such as complex stone morphology, technical limitations of therapeutic approaches, postoperative complications, and high recurrence rates [6]. Addressing these challenges requires technological innovation, personalized treatment strategies, and enhanced patient education [7].

Emerging evidence suggests a potential association between KS and atherosclerosis (AS), mediated by shared pathophysiological mechanisms, including dysregulated calcium metabolism, chronic inflammation, and oxidative stress [8]. Epidemiological studies have demonstrated that patients with KS have a significantly elevated risk of AS compared with individuals without KS. For instance, a cohort study of 1,027 patients with KS revealed a twofold increase in the prevalence of abdominal aortic calcification (AAC) compared to controls [9]. Elucidating the precise relationship between KS and AS is critical for understanding the pathogenesis of KS and may provide novel therapeutic avenues to improve clinical outcomes and reduce recurrence [10].

Endothelial dysfunction (ED), characterized by impaired vasodilation, angiogenesis, and barrier function, is an early pathogenic hallmark of cardiovascular disease. ED is strongly associated with metabolic disorders such as diabetes, obesity, and metabolic syndrome conditions that are also linked to cardiovascular morbidity [11]. The pivotal roles of oxidative stress and inflammation in ED pathogenesis are well established, and ED is recognized as an early stage of vascular disease and atherogenesis, predisposing individuals to clinical complications during the preclinical phase [12]. In AS, ED contributes to endothelial injury, lipid deposition, inflammatory plaque formation, smooth muscle cell proliferation, fibrous cap development, and plaque instability leading to thrombosis [13]. Epidemiological data indicate that KS patients have an increased risk of ED and cardiovascular disease, suggesting a shared pathological basis [14]. KS-associated local inflammation releases cytokines (e.g., IL-6, TNF-α) that activate endothelial inflammatory pathways (e.g., NF-κB), upregulate adhesion molecules (e.g., VCAM-1 and ICAM-1), and promote leukocyte infiltration. ED further exacerbates renal inflammation and promotes stone formation [15]. Despite the established interplay between ED and KS, the molecular mechanisms by which AS-related ED genes in KS development remain incompletely understood. Deciphering these mechanisms is essential for the development of novel preventive and therapeutic strategies to improve KS prognosis.

In this study, we performed differential gene expression analysis between KS and normal tissues and applied least absolute shrinkage and selection operator (LASSO) regression-based machine learning to identify and validate key AS-related ED biomarkers. We further delineated upstream regulatory networks and predicted potential therapeutic agents, thereby providing novel insights into the molecular mechanisms, biomarker identification, and personalized treatment of KS.

Materials and methods

Data collection

The datasets, GSE73680-GPL17077, GSE117518-GPL21827, and GSE132651-GPL96, were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/). The training cohort (GSE73680) comprised 29 renal papillary (RP) tissue samples from patients with KS and 6 RP tissue samples from controls [16], whereas the validation cohort (GSE117518) included 3 RP tissue samples from patients with KS and 3 RP tissue samples from controls. Both datasets consisted of RP tissues from patients and normal controls, which closely matched the specific lesion site investigated in this study. The GSE132651 dataset comprised blood endothelial cell samples from 6 patients with AS exhibiting normal endothelial function and 13 patients with AS and ED, focusing on genes associated with AS-related ED [17].

Difference expression analysis

In GSE73680, differential expression analysis between KS and control samples was performed using the limma package (v3.54.0) to identify differentially expressed genes (DEGs) (P < 0.05 and |log₂FC| > 1) [18].Subsequently, the limma package (v3.54.0) was also used to obtain DEGs in GSE132651 between AS and control groups (P < 0.05 and |log₂FC| > 0.5). Volcano plots and heat maps were generated using the ggplot2 (v3.4.1) and ComplexHeatmap packages (v2.15.1), respectively, to visualize the results [19,20].

Identification and functional annotation of key genes

To identify genes associated with AS-related ED in KS, the intersection of DEGs was determined using the ggVenn package (v1.1.0), and the overlapping genes were defined as key genes [21]. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed, with an adjusted P value < 0.05 considered statistically significant.

Identification of biomarkers

To identify potential biomarkers, the glmnet package (v4.1.8) was utilized to perform LASSO regression analysis on the key genes, and the selected genes were defined as candidate biomarkers [22]. Subsequently, in GSE73680 and GSE117518, the expression levels of the candidate biomarkers in KS and control samples were compared using the Wilcoxon test. Candidate biomarkers that exhibited consistent and statistically significant expression pattern in both datasets were defined as validated biomarkers.

Functional enrichment of biomarkers

To identify genes functionally associated with the biomarkers, GeneMANIA (http://www.genemania.org/) was used to predict interacting genes and their related biological functions. Following this, expression levels of the biomarkers in GSE73680 were then dichotomized into high- and low-expression groups based on the median value. Subsequently, differential expression analysis was performed between the high- and low-expression groups in the training cohort to calculate logFC values for all genes. The genes were ranked in descending order according to their logFC values to generate a gene-ranked list for each biomarker. The GSVA package (v1.32.0) was used to conduct gene set enrichment analysis (GSEA) for the biomarkers, based on the c2.cp.v2023.1.Hs.symbols.gmt gene set from the Molecular Signatures Database (MSigDB) (http://www.gsea-msigdb.org/) [23].

Analysis of biomarkers in immune cells and their subcellular localization

The expression of the biomarkers in immune cells was analyzed using the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/) to determine their expression patterns across immune cell types. Additionally, the subcellular localization of the biomarkers was analyzed using the Universal Protein Resource (UniProt) (https://www.uniprot.org/) and the HPA database.

Molecular regulatory networks and tissue-specific analysis

To identify the transcription factors (TFs) that regulate the biomarkers in KS, the NetworkAnalyst platform (https://www.networkanalyst.ca/) was used to predict TF-biomarkers interaction. The TF-biomarker regulatory network was constructed using the Cytoscape software (v3.8.2) [24]. To further characterize the tissue-specific expression of the biomarkers, the Genotype-Tissue Expression (GTEx) portal (https://gtexportal.org/home/) was used to analyze their expression profiles across various tissues.

Competing endogenous RNA (ceRNA) network and drug prediction

To investigate the molecular regulatory mechanisms of the biomarkers, the Encyclopedia of RNA Interactomes (ENCORI) (http://starbase.sysu.edu.cn/), miRcode (https://www.broadinstitute.org/ccle/miRcode), and miRwalk (http://mirwalk.umm.uni-heidelberg.de/) databases were used to predict upstream miRNAs targeting these biomarkers. The predicted results from the three databases were intersected to identify shared miRNAs. Furthermore, ENCORI was used to predict long noncoding RNAs (lncRNAs) that regulate the identified miRNAs, with inclusion limited to interactions supported by at least five experimental validations. Cytoscape software (v3.8.2) was then applied to construct the lncRNA-miRNA-biomarker regulatory network. The limma package (v3.54.0) was applied to perform differential expression analysis of the lncRNA expression matrix in GSE117518 between the KS and control groups, with P < 0.05 and |log₂FC| > 0.5 set as the screening criteria. The intersection between the predicted lncRNAs and the differentially expressed lncRNAs was analyzed to validate the reliability of the lncRNA predictions.

To explore potential therapeutic agents for KS, candidate drugs targeting the biomarkers were identified using the Drug-Gene Interaction Database (DGIdb) (http://www.dgidb.org/) and the Comparative Toxicogenomics Database (CTD) (http://ctdbase.org), both available through the NetworkAnalyst platform (https://www.networkanalyst.ca/). The gene-drug network was visualized using the Cytoscape software (v3.8.2).

Reverse transcription quantitative polymerase chain reaction (RT-qPCR)

The expression levels of the biomarkers were validated by RT-qPCR. A total of 5 pairs of KS and control tissue samples collected between January 2023 and April 2025, were obtained from the 900th Hospital of PLA Joint Logistic Support Force. This study was approved by the Ethics Committee of the 900th Hospital of PLA Joint Logistic Support Force and was conducted in accordance with the Declaration of Helsinki (approval number: 2025–092). Written informed consent was obtained from all participants. Patient data were accessed on August 11, 2025. No authors had access to information that could identify individual participants during or after data collection. The samples were processed for RNA isolation on August 23, 2025. Total RNA was extracted from the ten samples using TRIzol reagent (Ambion, USA) according to the manufacturer'ss instructions. RNA concentrations were measured using a NanoPhotometer N50. Complementary DNA (cDNA) was synthesized using the SureScript First Strand cDNA Synthesis Kit (Servicebio, China). Quantitative PCR was performed using a CFX Connect Thermal Cycler (Bio-Rad, USA). GAPDH was used as the internal reference gene for RT-qPCR. Relative mRNA expression levels were calculated using the 2^-ΔΔCT method. The primer sequences are provided in S1 Table.

Statistical analysis

All data were analyzed using the R software. The Wilcoxon test was used to compare differences between groups, with P < 0.05 considered statistically significant.

Results

A total of 22 key genes were obtained

A total of 2,298 DEGs (S2 Table) was identified between KS and control samples, of which 569 were upregulated and 1,729 were downregulated (Fig 1A and 1B). A total of 159 DEGs (S3 Table) were identified between AS and control groups, including 81 upregulated and 78 downregulated genes (Fig 1C and 1D). Moreover, 22 key genes were obtained by intersecting the two DEG lists (Fig 1E). Functional enrichment analysis revealed that these key genes were primarily associated with GO terms related to renal system development, kidney growth, the external wrapping tissue, extracellular matrix organization, basement membrane components, microtubule structure, collagen-containing ECM, endoplasmic reticulum lumen, condensed chromatin, chromosomes with intense staining, basement membrane, extracellular matrix structural components and myosin heavy chain binding etc. (Fig 1F). In addition, KEGG pathway analysis indicated enrichment in pathways such as amebiasis, cell cycle, lesion adhesion, glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate, and type 1 diabetes mellitus (Fig 1G).

UCHL1 and NEK2 were identified as biomarkers

LASSO regression analysis of the 22 key genes yielded 4 candidate biomarkers, namely MMP10, UCHL1, NEK2, and HEY1 (Fig 2A). Concurrently, among these 4 candidate biomarkers, UCHL1 and NEK2 exhibited consistent and statistically significant differential expression in both datasets. Therefore, UCHL1 and NEK2 were selected as the final biomarkers (Fig 2B and 2C).

High functional similarity between the biomarkers

To identify additional genes associated with UCHL1 and NEK2, GeneMANIA was used to construct an interactive network diagram comprising 20 predicted genes. Within this network diagram, most interactions were attributed to physical interactions, accounting for 77.64% of the total interactions. Co-expression interactions represented 8.01%, predicted interactions accounted for 5.37%, co-localization interactions comprised 3.63%, genetic interactions accounted for 2.87%, pathway-related interactions represented 1.88%, and shared protein domains accounted for a mere 0.6%. The associated functional pathways encompassed a variety of cellular processes such as interaction between spindle microtubules and kinetochores, regulation of the centrosome cycle, ubiquitin hydrolases activity, omega peptidases activity, chromosome region organization, and separation (Fig 2D).

Furthermore, GSEA enrichment analysis demonstrated that NEK2 was primarily enriched in positively correlated pathways, including Huntington's disease, neuroactive ligand-receptor interactions, and negatively correlated pathways including ribosome and spliceosome pathways (S4 Table). UCHL1 was positively associated with pathways involving cell adhesion molecules, complement and coagulation cascades, ECM receptors interactions, focal adhesion, and the lysosomal pathway (Fig 2E, S5 Table).

Strong correlation between immune cells and biomarkers

Biomarker expression across immune cell types were analyzed. The results showed that NEK2 exhibited moderate-to-high expression levels across multiple immune cell lineages and was highly expressed in regulatory T cells, natural killer cells, and basophils (Fig 3A). UCHL1 expression, on the other hand, was detected only in memory B-cells and naive B-cells (Fig 3B).

Fig 3 — (A) Expression level of NEK2 in immune cells. (B) Expression level of UCHL1 in immune cells. The expression levels of NEK2 and UCHL1 in immune cells were standardized based on data from the HPA database. NEK2 exhibited moderate-to-high expression across multiple immune cell types. (C, D) Subcellular localization of NEK2. (E, F) Subcellular localization of UCHL1.

Furthermore, analysis of the subcellular localization of these biomarkers demonstrated that NEK2 protein was predominantly localized to the nuclear matrix and centrosomes, which is consistent with its established role in cell cycle regulation (Fig 3C and 3D). Conversely, UCHL1 protein was primarily localized to the nuclear matrix and cytoplasm (Fig 3E and 3F).

Potential regulatory mechanisms of biomarkers

To further elucidate the regulatory roles of the biomarkers, TF prediction analysis was performed. The results identified 17 TFs predicted to regulate NEK2 expression, potentially influencing the formation and progression of AS (Fig 4A). In addition, 66 miRNAs were identified as potential regulators of NEK2, which were further regulated by 39 upstream lncRNAs. For UCHL1, 37 miRNAs were predicted to regulation its expression, and these miRNAs were controlled by 31 upstream lncRNAs. The lncRNA-miRNA-biomarker regulatory network is shown in Fig 4B and highlights several regulatory axes, including EBLN3P-hsa-miR-186-5p-NEK2, XIST-hsa-miR-199b-5p-UCHL1, AC010980.2-hsa-miR-361-5p-NEK2, and LINC01089-hsa-miR-3184-5p-UCHL1. Differential expression analysis of the lncRNA expression matrix from GSE117518 was conducted between the KS and control groups, yielding 1,625 differentially expressed lncRNAs, including 1,025 upregulated and 600 downregulated lncRNAs (Fig 4C and 4D; S6 Table). Among the 39 predicted lncRNAs involved in the NEK2 regulatory network, intersection analysis with the 1,625 differentially expressed lncRNAs identified three overlapping lncRNAs: AC108134.2, SNHG15, and MIR4458HG (Fig 4E).

Fig 4 — (A) TF-biomarker network. (B) lncRNA-miRNA-biomarker network. (C) Volcano plot of differentially expressed lncRNAs between the KS and control groups. (D) Heatmap of differentially expressed lncRNAs between the KS and control groups. (E) Venn diagram of intersecting lncRNAs.

Tissue-specific analysis and drug prediction

Analysis of the GTEx database was performed to further characterize the tissue-specific expression of the biomarkers. NEK2 exhibited higher expression levels in the testes, lymphocytes, and fibroblasts (Fig 5A). Similarly, UCHL1 showed higher expression in brain tissue, kidney tissue, and fibroblasts (Fig 5B). Subsequently, a total of 15 drugs were predicted to target and regulate NEK2, among which 2 drugs had been approved (pazopanib and palbociclib) (Fig 5C).

Fig 5 — (A) Tissue-specific expression of NEK2. (B) Tissue-specific expression of UCHL1. (C) Drug prediction for the biomarkers.

NEK2 was over-expressed in KS samples

RT-qPCR analysis demonstrated that NEK2 was significantly upregulated in KS samples (P = 0.0355) (Fig 6A). In contrast, UCHL1 expression showed no statistically significant difference between the two groups (P > 0.05), although a trend toward lower expression was observed in KS samples (Fig 6B).

Discussion

Globally, both KS and AS have high incidence rates and exhibit substantial population heterogeneity. Pronounced differences in genetic backgrounds, living environments, and metabolic traits exist among populations, including those in Western countries and Asia [25]. International transcriptomic and genome-wide association studies have consistently identified dysregulated calcium metabolism, inflammation, oxidative stress, and aberrant cell adhesion as core pathogenic pathways in kidney stone disease (KSD). The strong concordance between these global findings and our bioinformatic results reflects the cross-population conservation of these molecular mechanisms. Currently, KS is recognized as a systemic disorder and is closely associated with cardiovascular diseases, including AS [26,27]. The generation of reactive oxygen species and the subsequent development of oxidative stress are common pathological features shared by both AS and KS. However, the precise relationship between AS and KS remains to be fully elucidated. In the present study, we identified NEK2 and UCHL1 as shared biomarkers of KS and AS.

NEK2, a serine/threonine kinase that regulates centrosome duplication and mitotic progression, was significantly overexpressed in KS tissues. This finding is consistent with studies demonstrating that oxidative stress and calcium oxalate crystals induce centrosome amplification in renal tubular cells, thereby promoting aberrant cell proliferation and crystal adhesion [28]. The enrichment of NEK in pathways such as Huntington'ss disease further suggests shared mechanisms involving oxidative damage and mitochondrial dysfunction in KSD. Conversely, UCHL1, a deubiquitinating enzyme involved in proteostasis and apoptosis, may mitigate renal injury by stabilizing anti-apoptotic proteins under conditions of metabolic stress. Although UCHL1 has not previously been linked to KSD, its upregulation in diabetic nephropathy suggests a conserved role in cellular stress adaptation, potentially counteracting tubular cell death triggered by crystal-induced inflammation [29]. In this study, based on the epidemiological association between KSD and ED, bioinformatic analysis of GEO datasets identified NEK2 and UCHL1 as key biomarkers, which were validated by RT-qPCR. Functional enrichment analysis indicated that both genes are involved in processes critical to renal tubular injury and fibrosis. Regulatory network analysis further identified their upstream transcriptional regulators and potential drug candidates. These findings suggest that NEK2 and UCHL1 may serve as dual biomarkers of disease progression and that targeting NEK2 or modulating UCHL1 activity may represent potential therapeutic strategies. This study provides a foundation for precision therapies targeting ED-associated pathways in KSD, with the potential to reduce stone recurrence and cardiovascular risk.

UCHL1, a deubiquitinating enzyme primarily involved in protein degradation and cellular signaling, has been extensively studied in neurological disorders and cancer. Recent studies have demonstrated a crucial role of UCHL1 in maintaining endothelial homeostasis. It regulates lung endothelial cell permeability [30], promotes the angiogenesis of brain microvascular endothelial cells by stabilizing SOX17 [31], and contributes to pulmonary hypertension through the deubiquitination of AKT [32]. In the kidney, UCHL1 deficiency can lead to proteinuria, urinary retention, and hypotension, suggesting an indispensable role in renal homeostasis [33]. NEK2, a member of the NIMA-related serine/threonine kinase family located on chromosome 1q32.2-1q41, contains an N-terminal kinase domain and a C-terminal regulatory domain. NEK2 localizes to both the cytoplasm and nucleus and plays a critical role in mitosis [34]. Several studies have reported the involvement of NEK2 in lipopolysaccharide-induced endothelial barrier dysfunction [35,36]. Inhibition of NEK2 reduces endothelial permeability, reactive oxygen species generation, and phosphorylation of key inflammatory proteins in lipopolysaccharide-treated cells [36]. In clear cell renal cell carcinoma, NEK2 overexpression is associated with worse clinical outcomes, higher mutation burden, and increased infiltration of various immune cells. Nevertheless, the involvement and functions of NEK2 and UCHL1 in AS and KS remain unclear.

Although no direct studies have investigated UCHL1 or NEK2 in the pathogenesis of KSD, their established roles in oxidative stress, inflammation, and renal tubular repair suggest potential involvement. UCHL1 may modulate oxidative stress and inflammation during stone formation, whereas NEK2 may influence cell cycle regulation in tubular epithelial cells or fibrosis-related pathways [37]. Given that aberrant proliferation and impaired repair of renal tubular epithelial cells are pivotal processes in KS formation, and that ED and abnormal proliferation contribute to AS progression, our findings suggest that NEK2 may act as a molecular link between these two diseases by modulating cell cycle dynamics. Of the 17 predicted therapeutic agents, most have no direct clinical application in KSD. However, mechanistic plausibility supports potential drug repurposing. CYC-116, an Aurora A/B and VEGFR2 inhibitor used in cancer therapy, could theoretically target tubular cell proliferation if such proliferation contributes to stone formation [38]. SP-600125, a JNK/c-Jun inhibitor, may attenuate stone-related injury by suppressing apoptosis, inflammation, and oxidative stress [39]. Pazopanib, a multitarget tyrosine kinase inhibitor (VEGFR/PDGFR), may inhibit angiogenesis or inflammatory pathways if these mechanisms are implicated in KSD. RT-qPCR analysis revealed upregulated NEK2 expression in stone formers, identifying this ED-related gene as a potential novel biomarker. TF regulatory network analysis identified FOSL1, a TF involved in cell proliferation and inflammatory responses, as a potential upstream regulator of NEK2 [40]. These findings suggest that NEK2 dysregulation may be driven by conserved pro-inflammatory and proliferative transcriptional programs. ceRNA network analysis further indicated that NEK2 is regulated by specific lncRNAs and miRNAs. Notably, NEAT1 has been implicated in renal inflammation, fibrosis, and vascular lesions [41,42]. Tissue expression analysis showed high NEK2 expression in fibroblasts, suggesting its involvement in fibroblast proliferation, activation, and subsequent fibrotic processes. Excessive fibroblast activation and fibrosis are hallmark features of maladaptive repair in KS and progression of atherosclerotic plaques in AS. Further mechanistic studies and clinical validation are essential to translate these findings into therapeutic strategies.

This study identified UCHL1 and NEK2 as novel biomarkers for KS through integrated bioinformatics analysis and experimental validation. Comprehensive analyses revealed their roles in regulating centrosome dynamics (NEK2) and proteostasis (UCHL1), processes that are critical to renal tubular injury and calcium crystal adhesion. The PI3K-Akt pathway and ECM remodeling emerged as key mechanisms linking ED to KS progression. Furthermore, drug prediction analysis, including the identification of pazopanib as a potential NEK2-targeting agent, provides a theoretical foundation for repurposing existing therapies to mitigate stone recurrence and fibrosis [43]. These findings contribute to a deeper understanding of the molecular mechanisms underlying related diseases, as well as to biomarker identification and therapeutic development.

This study has several limitations that warrant consideration. First, UCHL1 did not reach statistical significance in our cohort, which may be attributable to the small sample size and tissue heterogeneity. Second, NEK2 and UCHL1 were preliminary biomarkers identified through bioinformatics analysis, and their expression was evaluated only at the transcriptional level in a relatively small cohort. Western blotting and immunohistochemistry should be performed to assess their protein expression levels in an independent clinical cohort with a larger sample size. Third, although their roles in ED and KS were investigated through bioinformatics analysis, cellular functional assays are required to elucidate their precise biological functions and molecular mechanisms in disease progression.

Conclusions

Through integrated analysis of three gene expression datasets related to KS, this study identified a total of 2 biomarkers commonly associated with both KS and AS. In-depth functional analyses indicated that these biomarkers may participate in the pathogenesis of KS by regulating mechanisms such as cell adhesion molecules, focal adhesion, and lysosome pathways. These findings deepen our understanding of the molecular mechanisms underlying KS, particularly uncovering the shared pathological pathways between KS and AS, and provides a theoretical foundation for the development of individualized treatment strategies. Future studies should focus on validating the clinical utility of these biomarkers and exploring the translational potential of the predicted drugs in kidney stone management.

Supporting information

S1 Table. The primer sequences of NEK2, UCHL1, and GAPDH.

(DOCX)

pone.0354595.s001.docx^{(13.8KB, docx)}

S2 Table. Differentially expressed genes in the GSE73680 dataset.

(XLSX)

pone.0354595.s002.xlsx^{(162.1KB, xlsx)}

S3 Table. Differentially expressed genes in the GSE132651 dataset.

(XLSX)

pone.0354595.s003.xlsx^{(21.7KB, xlsx)}

S4 Table. GSEA enrichment pathways for NEK6.

(XLSX)

pone.0354595.s004.xlsx^{(25.3KB, xlsx)}

S5 Table. GSEA enrichment pathways for UCHL1.

(XLSX)

pone.0354595.s005.xlsx^{(28.8KB, xlsx)}

S6 Table. Differentially expressed lncRNAs in the GSE117518 dataset.

(XLSX)

pone.0354595.s006.xlsx^{(160.5KB, xlsx)}

Abbreviations

KS: Kidney stones
ED: Endothelial dysfunction
AS: Atherosclerosis
LASSO: Least absolute shrinkage and selection operator
DEGs: Differentially expressed genes
GSEA: Gene set enrichment analysis
RT-qPCR: Reverse transcription quantitative polymerase chain reaction
AAC: Abdominal aortic calcification
RP: Renal papillary
GSVA: Gene set variation analysis
GO: Gene Ontology
KEGG: Kyoto Encyclopedia of Genes and Genomes
HPA: Human Protein Atlas
UniProt: Universal Protein Resource
GTEx: Genotype-Tissue Expression
ENCORI: ENcyclopedia of RNA InteractiONs
DGIdb: Drug-Gene Interaction Database
TFs: Transcription factors
CTD: Comparative Toxicogenomics Database
ECM: Extracellular matrix
T-reg cells: T-regulatory cells
NK cells: Natural killer cells

Data Availability

The datasets, including GSE73680, GSE117518, and GSE132651, analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo/).

Funding Statement

The research reported in this project was generously supported by the Fujian provincial key Clinical Specialty Construction Project under grant agreement number [Grant Number:2017ZDZKMN]. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip.

References

1.Tamborino F, Cicchetti R, Mascitti M, Litterio G, Orsini A, Ferretti S, et al. Pathophysiology and Main Molecular Mechanisms of Urinary Stone Formation and Recurrence. Int J Mol Sci. 2024;25(5):3075. doi: 10.3390/ijms25053075 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Thornton SN. Re: Charles D. Scales Jr., Alexandria C. Smith, Janet M. Hanley, Christopher S. Saigal, Urologic Diseases in America Project. Prevalence of kidney stones in the United States. Eur Urol. 2012;62:160–5. European urology. 2012;62(3):e67. [DOI] [PubMed] [Google Scholar]
3.Wang W, Fan J, Huang G, Li J, Zhu X, Tian Y, et al. Prevalence of kidney stones in mainland China: A systematic review. Sci Rep. 2017;7:41630. doi: 10.1038/srep41630 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Bargagli M, Scoglio M, Howles SA, Fuster DG. Kidney stone disease: risk factors, pathophysiology and management. Nat Rev Nephrol. 2025;21(11):794–808. doi: 10.1038/s41581-025-00990-x [DOI] [PubMed] [Google Scholar]
5.Türk C, Petřík A, Sarica K, Seitz C, Skolarikos A, Straub M, et al. EAU Guidelines on Diagnosis and Conservative Management of Urolithiasis. European urology. 2016;69(3):468–74. [DOI] [PubMed] [Google Scholar]
6.Alelign T, Petros B. Kidney Stone Disease: An Update on Current Concepts. Adv Urol. 2018:3068365. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Rule AD, Bergstralh EJ, Melton LJ 3rd, Li X, Weaver AL, Lieske JC. Kidney stones and the risk for chronic kidney disease. Clin J Am Soc Nephrol. 2009;4(4):804–11. doi: 10.2215/CJN.05811108 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Devarajan A. Cross-talk between renal lithogenesis and atherosclerosis: an unveiled link between kidney stone formation and cardiovascular diseases. Clin Sci (Lond). 2018;132(6):615–26. doi: 10.1042/CS20171574 [DOI] [PubMed] [Google Scholar]
9.Li B, Tang Y, Zhou L, Jin X, Liu Y, Li H, et al. Association between aortic calcification and the presence of kidney stones: calcium oxalate calculi in focus. Int Urol Nephrol. 2022;54(8):1915–23. doi: 10.1007/s11255-021-03058-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Muschialli L, Mannath A, Moochhala SH, Shroff R, Ferraro PM. Epidemiological and biological associations between cardiovascular disease and kidney stone formation: A systematic review and meta-analysis. Nutr Metab Cardiovasc Dis. 2024;34(3):559–68. doi: 10.1016/j.numecd.2023.09.011 [DOI] [PubMed] [Google Scholar]
11.Gimbrone MA Jr, García-Cardeña G. Endothelial Cell Dysfunction and the Pathobiology of Atherosclerosis. Circ Res. 2016;118(4):620–36. doi: 10.1161/CIRCRESAHA.115.306301 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Libby P, Ridker PM, Hansson GK, Leducq Transatlantic Network on Atherothrombosis. Inflammation in atherosclerosis: from pathophysiology to practice. J Am Coll Cardiol. 2009;54(23):2129–38. doi: 10.1016/j.jacc.2009.09.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Wang X, He B. Endothelial dysfunction: molecular mechanisms and clinical implications. MedComm (2020). 2024;5(8):e651. doi: 10.1002/mco2.651 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Liu Y, Li S, Zeng Z, Wang J, Xie L, Li T, et al. Kidney stones and cardiovascular risk: a meta-analysis of cohort studies. Am J Kidney Dis. 2014;64(3):402–10. doi: 10.1053/j.ajkd.2014.03.017 [DOI] [PubMed] [Google Scholar]
15.Saenz-Medina J, Muñoz M, Rodriguez C, Sanchez A, Contreras C, Carballido-Rodríguez J, et al. Endothelial Dysfunction: An Intermediate Clinical Feature between Urolithiasis and Cardiovascular Diseases. Int J Mol Sci. 2022;23(2):912. doi: 10.3390/ijms23020912 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Taguchi K, Hamamoto S, Okada A, Unno R, Kamisawa H, Naiki T, et al. Genome-Wide Gene Expression Profiling of Randall’s Plaques in Calcium Oxalate Stone Formers. J Am Soc Nephrol. 2017;28(1):333–47. doi: 10.1681/ASN.2015111271 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Hebbel RP, Wei P, Milbauer L, Corban MT, Solovey A, Kiley J, et al. Abnormal Endothelial Gene Expression Associated With Early Coronary Atherosclerosis. J Am Heart Assoc. 2020;9(14):e016134. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi: 10.1093/nar/gkv007 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Gustavsson EK, Zhang D, Reynolds RH, Garcia-Ruiz S, Ryten M. ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2. Bioinformatics. 2022;38(15):3844–6. doi: 10.1093/bioinformatics/btac409 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32(18):2847–9. doi: 10.1093/bioinformatics/btw313 [DOI] [PubMed] [Google Scholar]
21.Pelikan A, Herzel H, Kramer A, Ananthasubramaniam B. Venn diagram analysis overestimates the extent of circadian rhythm reprogramming. FEBS J. 2022;289(21):6605–21. doi: 10.1111/febs.16095 [DOI] [PubMed] [Google Scholar]
22.Li Y, Lu F, Yin Y. Applying logistic LASSO regression for the diagnosis of atypical Crohn’s disease. Sci Rep. 2022;12(1):11340. doi: 10.1038/s41598-022-15609-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7. doi: 10.1186/1471-2105-14-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. doi: 10.1101/gr.1239303 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Romero V, Akpinar H, Assimos DG. Kidney stones: a global picture of prevalence, incidence, and associated risk factors. Rev Urol. 2010;12(2–3):e86-96. [PMC free article] [PubMed] [Google Scholar]
26.Hsi RS, Spieker AJ, Stoller ML, Jacobs DR Jr, Reiner AP, McClelland RL, et al. Coronary Artery Calcium Score and Association with Recurrent Nephrolithiasis: The Multi-Ethnic Study of Atherosclerosis. J Urol. 2016;195(4 Pt 1):971–6. doi: 10.1016/j.juro.2015.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Reiner AP, Kahn A, Eisner BH, Pletcher MJ, Sadetsky N, Williams OD, et al. Kidney stones and subclinical atherosclerosis in young adults: the CARDIA study. J Urol. 2011;185(3):920–5. doi: 10.1016/j.juro.2010.10.086 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Na H-J, Sung M-J, Park J-S. Age- and oxidative stress-induced centrosome amplification and renal stones in Drosophila Malpighian tubules. Biol Open. 2024;13(12):bio061743. doi: 10.1242/bio.061743 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Zhang H, Luo W, Sun Y, Qiao Y, Zhang L, Zhao Z, et al. Wnt/β-Catenin Signaling Mediated-UCH-L1 Expression in Podocytes of Diabetic Nephropathy. Int J Mol Sci. 2016;17(9):1404. doi: 10.3390/ijms17091404 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Mitra S, Epshtein Y, Sammani S, Quijada H, Chen W, Bandela M, et al. UCHL1, a deubiquitinating enzyme, regulates lung endothelial cell permeability in vitro and in vivo. Am J Physiol Lung Cell Mol Physiol. 2021;320(4):L497–507. doi: 10.1152/ajplung.00492.2020 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Wang J, Ji C, Ye W, Rong Y, Ge X, Wang Z, et al. Deubiquitinase UCHL1 promotes angiogenesis and blood-spinal cord barrier function recovery after spinal cord injury by stabilizing Sox17. Cell Mol Life Sci. 2024;81(1):137. doi: 10.1007/s00018-024-05186-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Tang H, Gupta A, Morrisroe SA, Bao C, Schwantes-An T-H, Gupta G, et al. Deficiency of the Deubiquitinase UCHL1 Attenuates Pulmonary Arterial Hypertension. Circulation. 2024;150(4):302–16. doi: 10.1161/CIRCULATIONAHA.123.065304 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Radón V, Czesla M, Reichelt J, Fehlert J, Hammel A, Rosendahl A, et al. Ubiquitin C-Terminal Hydrolase L1 is required for regulated protein degradation through the ubiquitin proteasome system in kidney. Kidney Int. 2018;93(1):110–27. [DOI] [PubMed] [Google Scholar]
34.Xia J, Zhao H, Edmondson JL, Koss B, Zhan F. Role of NEK2 in tumorigenesis and tumor progression. Trends Mol Med. 2025;31(1):79–93. doi: 10.1016/j.molmed.2024.07.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Barabutis N, Akhter MS. Involvement of NEK2 and NEK9 in LPS - induced endothelial barrier dysfunction. Microvasc Res. 2024;152:104651. doi: 10.1016/j.mvr.2023.104651 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Fakir S, Sarker MMR, Sigdel M, Barabutis N. NEK2 inhibition alleviates lipopolysaccharide-induced endothelial injury. ASPET Discov. 2025;1:100002. doi: 10.1016/j.aspetd.2025.100002 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Khan SR, Canales BK, Dominguez-Gutierrez PR. Randall’s plaque and calcium oxalate stone formation: role for immunity and inflammation. Nat Rev Nephrol. 2021;17(6):417–33. doi: 10.1038/s41581-020-00392-1 [DOI] [PubMed] [Google Scholar]
38.Bavetsias V, Linardopoulos S. Aurora Kinase Inhibitors: Current Status and Outlook. Frontiers in oncology. 2015;5:278. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Bogoyevitch MA, Boehm I, Oakley A, Ketterman AJ, Barr RK. Targeting the JNK MAPK cascade for inhibition: basic science and therapeutic potential. Biochim Biophys Acta. 2004;1697(1–2):89–101. doi: 10.1016/j.bbapap.2003.11.016 [DOI] [PubMed] [Google Scholar]
40.Niidome K, Taniguchi R, Yamazaki T, Tsuji M, Itoh K, Ishihara Y. FosL1 Is a Novel Target of Levetiracetam for Suppressing the Microglial Inflammatory Reaction. Int J Mol Sci. 2021;22(20):10962. doi: 10.3390/ijms222010962 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Ma T, Li H, Liu H, Peng Y, Lin T, Deng Z, et al. Neat1 promotes acute kidney injury to chronic kidney disease by facilitating tubular epithelial cells apoptosis via sequestering miR-129-5p. Mol Ther. 2022;30(10):3313–32. doi: 10.1016/j.ymthe.2022.05.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Yang Q, Chen S, Wang X, Yang X, Chen L, Huang T, et al. Exercise Mitigates Endothelial Pyroptosis and Atherosclerosis by Downregulating NEAT1 Through N6-Methyladenosine Modifications. Arterioscler Thromb Vasc Biol. 2023;43(6):910–26. [DOI] [PubMed] [Google Scholar]
43.Chen T, Liu L, Zou Y, Hu X, Zhang W, Zhou T, et al. Nobiletin downregulates the SKP2-p21/p27-CDK2 axis to inhibit tumor progression and shows synergistic effects with palbociclib on renal cell carcinoma. Cancer Biol Med. 2021;18(1):227–44. doi: 10.20892/j.issn.2095-3941.2020.0186 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0354595.r001

Decision Letter 0

Pramodkumar Gupta

16 Feb 2026

-->PONE-D-25-68234-->-->Bioinformatics and experimental studies of atherosclerosis-related endothelial dysfunction genes in renal calculi and exploration of their molecular mechanisms-->-->PLOS One

Dear Dr. Zhu,

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Additional Editor Comments (if provided):

Over-interpretation of in-silico outcomes, the identified genes needs to be validated by experimental techniques.

The training cohort (GSE73680) contained 29 KS patient renal papillary (RP) tissue samples and 9 control RP tissue samples, and validation cohort (GSE117518) included 3 KS patient RP tissue samples and 3 control RP tissue samples. The authors should signify the rationale in doing so and selection criteria

Results for UCHL1 need strong statistical significance

Reference should be more strong to the manuscript

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Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: No

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

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Reviewer #1: In this manuscript, Li et al. describe a bioinformatic analysis aimed at identifying differentially regulated ED-related genes associated with AS in KS. Using in silico approaches, the authors identify four candidate biomarkers (MMP10, UCHL1, NEK2, and HEY1) and further attempt experimental validation of UCHL1 and NEK2 using qRT-PCR. While the bioinformatic workflow is well described and systematically executed, the experimental validation and interpretation substantially weaken the overall conclusions.

Major comments:

1. Overinterpretation of bioinformatic predictions

The manuscript frequently draws conclusions related to molecular mechanisms and functional roles of the identified genes. However, no experimental data are presented to directly support these mechanistic claims. In the absence of functional or mechanistic validation, the findings derived from bioinformatic analyses should be framed strictly as predictions or associations. I strongly recommend extensive rephrasing throughout the manuscript to clearly distinguish predictive bioinformatic insights from experimentally validated molecular mechanisms.

2. Rationale and scope of experimental validation

The rationale for selecting only UCHL1 and NEK2 for validation is unclear. Given that four candidate biomarkers were identified, it is important to justify why MMP10 and HEY1 were excluded from experimental follow-up. Additionally, it is not clear whether these biomarkers are intended to be relevant only at the transcript level or whether protein-level validation was considered. Validation using immunohistochemistry (IHC) in patient samples would likely be more clinically relevant and informative, particularly for biomarker studies.

3. Lack of convincing validation for UCHL1

The qRT-PCR results for UCHL1 do not reach statistical significance. It is therefore difficult to consider this as successful validation of the bioinformatic prediction. The authors should either provide additional supporting data, expand the sample size, or reconsider the strength of the claim regarding UCHL1 as a validated biomarker.

Reviewer #2: The manuscript explains how authors have identified two potential biomarkers that are associated with Kidney Stones and Atherosclerosis in Chinese people.

My suggestion will be to provide a global scenario also with a comparative analysis to understand how the molecular mechanisms differ/not differ (A paragraph highlighting the global scenario would be sufficient).

Also it will be a good idea to take help of a professional for shaping the English language of the manuscript and also take care of the Capital letters in between the sentences. There’s no uniformity. Once done the manuscript can be accepted for publication.

Reviewer #3: Summary

The authors present a joint analysis of three publicly available gene expression microarray studies of either kidney stone formers or patients with coronary endothelial dysfunction (atherosclerosis). The authors identify differentially expressed genes from these datasets, comparing the affected tissues to control samples. They find that 22 DEGs intersect between the kidney stone and atherosclerosis datasets, and, using a LASSO regression model and a validation dataset, narrow their focus to two candidate biomarker genes. The authors then use a series of bioinformatics tools to characterize these two genes and infer a possible connection between kidney stone formation and atherosclerosis. However, the manuscript lacks a convincing interpretation of these findings and has technical problems with the analysis. Furthermore, there appear to be issues with the references (detailed below) that call into question the rigor of the subsequent analysis and conclusions. In the current state, the manuscript is not fit for publication.

Major

1. The references in the introduction are either incorrect or inappropriately used to support the text. Although I found the introduction to be well written and believe it could be factually correct, the misuse of references forces me to question the results and conclusions of the study. The authors must examine the references carefully and fix any mistakes. I reviewed the first 10 references (in the introduction) and found the following issues. Presumably there are more incorrect references, but I stopped after the first 10.

- Reference 2 has the wrong DOI listed – currently it links to a letter to the editor in reference to the intended reference (doi: 10.1016/j.eururo.2012.03.052)

- Reference 3 is used to justify the statistic that kidney stone prevalence is 7.54% in China; this statistic is actually from a different reference (doi: 10.1038/srep41630)

- Reference 4 is used to support a sentence describing the pathogenic mechanisms underlying KS; however, this reference does not discuss these mechanisms

- Reference 5 is used to support a discussion of the challenges in managing KS; however, reference 5 does not discuss kidney stones

- Reference 7 is used to support a discussion of the potential links between KS and AS; however reference 7 does not discuss AS

- Reference 8 is used to support a finding that KS patients have twofold higher aortic calcification; this statistic actually from a different reference (doi: 10.1007/s11255-021-03058-4)

- Reference 9 does not discuss kidney stones

2. The authors describe the data from GSE73680 as the training cohort and the data from GSE117518 as the validation cohort for this study. The authors should explain what steps, if any, they took to perform batch correction between these datasets. A brief review of these data revealed that a previous study also analyzed these two datasets together and found systematic differences in expression levels due to batch effect (doi: 10.1038/s41598-025-05087-w). The authors should perform a similar analysis and consider citing this paper.

3. The authors should comment on whether they expect any gene expression differences to exist between the N=23 CaOx kidney stone formers and the N=6 CaP stone formers from GSE73680. These differences in KS presentation should be annotated where appropriate in Fig. 1A-B. Furthermore, how does the present data analysis differ from the analysis that was performed for each original dataset?

4. The authors should elaborate on how the gene set enrichment analysis was performed. The input to GSEA should be a ranked list of all genes, but Fig. 2D appears to show enrichment of a single gene (either NEK2 or UCHL1). Furthermore, the text describing this figure should distinguish between enrichment and depletion in the pathways, as some have positive enrichment scores while others have negative enrichment scores. Also, the text mentions pathways (i.e., insulin and squamous cell functions) that are not supported by the figure.

5. The authors should attempt to interpret the findings from the bioinformatics tools that were employed. For example, what relevance, if any, do the following analyses have on identifying biomarkers shared between atherosclerosis and kidney stone formation? i.e., subcellular localization, TF interaction predictions, miRNA/lncRNA predictions, tissue expression, drug prediction. For example, without more information on the mechanism of either biomarker gene in the development or progression of disease, how are the drug interaction predictions useful for the current study?

6. Similarly, the discussion of unrelated disorders that either NEK2 or UCHL1 may be involved in (paragraph 2 of the discussion) does not appear relevant to the current study. I would suggest rewriting this part of the discussion to focus on synthesizing relevant literature on atherosclerosis and kidney stone formation in light of the findings from this study.

7. On line 339 the authors say that "PCR analysis revealed upregulated expression of UCHL1 and NEK2 in stone formers." However, Fig. 6 shows that median UCHL1 expression is actually lower in stone formers and that the difference is not significant. If the expression of UCHL1 could not be validated by RT-qPCR in a third cohort, what utility does this gene have as a biomarker?

Minor

1. On line 89 in the introduction, the authors say that “Conversely, ED exacerbates renal inflammation…” However, this statement is not the converse of the preceding sentence. I suggest rephrasing, e.g., “ED further exacerbates renal inflammation…”

2. On line 104 in the data collection section of the methods, the authors say they accessed 29 KS samples and 9 control samples from GSE73680. However, on GEO this study is described as containing only 7 control samples.

3. Why were different effect sizes thresholds used for identifying differentially expressed genes in GSE73680 (abs. LFC >1) vs. GSE132651 (abs. LFC >0.5)? Does this affect the number of overlapping DEGs found between KS and AS conditions?

4. The readability of the methods is poor due to grammatical mistakes

5. Is Fig. 1B missing a control sample? Only 6 controls are shown in the heatmap but the GSE73680 dataset has 7 controls.

6. The y axis in Fig. 3A is too large. Please select better limits for the y axis. Furthermore, the authors should comment on whether these expression levels are biologically significant. For example, show a comparison to other cell types that are not immune cells.

7. Instead of using lncRNA prediction algorithms, why not use the non-coding RNA microarray data from GSE117518? How do these data compare with the predictions?

8. Readability of Fig. 5 and Fig. 6 is poor

9. The authors should provide the full list of DEGs (p values, effect sizes, etc.) as well as the complete results of the GSEA analysis as supplemental data files

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Reviewer #1: No

Reviewer #2: Yes: Dr. Gauri Awasthi

Reviewer #3: No

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PLoS One. 2026 Aug 4;21(8):e0354595. doi: 10.1371/journal.pone.0354595.r002

Author response to Decision Letter 1

1 Apr 2026

Dear Editors and Reviews,

We would like to express our sincere gratitude for the time and effort that you have invested in reviewing our manuscript. We found the comments to be highly constructive and helpful in improving the quality and rigor of our work. We have carefully considered every comment and have made corresponding revisions to the manuscript. A point-by-point response to the editor’s and reviewers’ comments is presented below.

Response to the editor,

Dear editor,

Thank you very much for the opportunity to revise our manuscript and for your invaluable editorial feedback. The highly insightful comments provided by you have been instrumental in enhancing the clarity and depth of our research. We have addressed all your concerns point-by-point and hope the revised version now meets the high standards of your journal.

Comment 1: Over-interpretation of in-silico outcomes, the identified genes need to be validated by experimental techniques.

Response:

We sincerely appreciate your valuable and professional suggestions. We fully agree with your concern regarding the over-interpretation of our bioinformatics results and the lack of experimental validation for the key candidate genes. Following your advice, we have carefully revised the manuscript to tone down the functional inferences made solely from bioinformatics predictions. We ensured that all conclusions are presented objectively and are strictly supported by the existing data. Additionally, we have added a new paragraph in the Discussion section (Lines 404-413). We explicitly state that NEK2 and UCHL1 are preliminary biomarkers screened via bioinformatics, and our study is limited to expression-level analysis. We acknowledge that further in vitro and in vivo experiments, such as Western blot, immunohistochemistry, and cellular functional assays, are indispensable to elucidate their exact biological functions and molecular mechanisms in disease progression.

Thank you again for your rigorous evaluation, which has greatly strengthened the scientific rigor of our study.

Comment 2: The training cohort (GSE73680) contained 29 KS patient renal papillary (RP) tissue samples and 9 control RP tissue samples, and validation cohort (GSE117518) included 3 KS patient RP tissue samples and 3 control RP tissue samples. The authors should signify the rationale in doing so and selection criteria

Response:

Thank you very much for your valuable comments on the dataset selection and grouping scheme of our study. Regarding your concerns about the rationale for choosing the training and validation cohorts and the sample inclusion criteria, we have supplemented the relevant explanations in the Method and Material section.

The selection of GSE73680 and GSE117518 datasets was primarily based on two criteria. First, there is a high consistency in tissue origins. Both datasets consist of renal papillary (RP) tissues from kidney stone patients and normal controls, which highly matches the specific lesion site focused on in our study, maximizing the homogeneity of the subjects. Second, these datasets have strong literature support and reliability. Both have been applied and validated by previous studies in the field of kidney stones (PMID: 39759764; PMID: 40664942), confirming their data quality and clinical representativeness.

Thanks again for your rigorous review and professional guidance. We have carefully revised and refined the manuscript according to your comments.

Comment 3: Results for UCHL1 need strong statistical significance

Response:

We sincerely appreciate your rigorous comments regarding the statistical significance of the UCHL1 results. We chose to retain UCHL1 as a biomarker based on the following three robust scientific justifications. Firstly, UCHL1 was stably selected by the LASSO screening model, making it a pivotal gene in our model construction. Secondly, the expression trend of UCHL1 is highly consistent between the training set (GSE73680) and the validation set (GSE117518), showing excellent reproducibility. Furthermore, functional annotations indicate that UCHL1 is closely related to cell adhesion, proteasome pathways, and inflammatory responses. Given that these pathways are well-supported by existing literature regarding kidney stone pathogenesis, the inclusion of UCHL1 holds strong biological plausibility.

We completely agree with your statistical observation. Therefore, we have explicitly noted this in the Discussion section (Lines 404-413) of the manuscript. We explain that the failure of UCHL1 to reach statistical significance may be influenced by the small sample size and tissue heterogeneity. Despite this, UCHL1 remains of significant biological importance. We emphasize that expanding the clinical sample size and further validating its expression and function at the protein level are essential next steps. Thank you again for your professional scrutiny and constructive advice.

Comment 4: Reference should be stronger to the manuscript

Response:

Thank you very much for your insightful comments concerning the authority and relevance of the references cited in our study. Following the advice of you and Review 3, we have systematically reviewed, supplemented, and updated the references across the entire manuscript to bolster our theoretical foundation and academic support.

Specifically, we first conducted a rigorous, sentence-by-sentence verification of the Introduction, Results, and Discussion sections to ensure that every core argument is firmly backed by high-quality and highly relevant literature. Secondly, we prioritized citing high-quality research directly pertaining to kidney stones, atherosclerosis, and renal tubular pathology, thereby significantly improving the timeliness and persuasiveness of our theoretical basis.

We are confident that these modifications have greatly enhanced the theoretical depth and scientific rigor of our manuscript. We sincerely appreciate your professional guidance, and we have fully updated the references as required.

Response to Reviewer 1:

Dear Reviewer,

Thank you very much for your careful review and invaluable suggestions. Your professional feedback regarding our bioinformatics interpretations, biomarker screening rationale, and statistical analyses have significantly enhanced the scientific rigor and objectivity of our study. We have thoroughly revised the manuscript according to your guidance, particularly by toning down absolute statements and expanding the discussion on study limitations. Please find our detailed, point-by-point responses below.

Comment 1: Overinterpretation of bioinformatic predictions. The manuscript frequently draws conclusions related to molecular mechanisms and functional roles of the identified genes. However, no experimental data are presented to directly support these mechanistic claims. In the absence of functional or mechanistic validation, the findings derived from bioinformatic analyses should be framed strictly as predictions or associations. I strongly recommend extensive rephrasing throughout the manuscript to clearly distinguish predictive bioinformatic insights from experimentally validated molecular mechanisms.

Response:

Thank you very much for your rigorous and professional comments. We fully agree with your criticism regarding the over-interpretation of our bioinformatics predictions and the lack of strict distinction between computational inferences and experimental conclusions.

According to your suggestions, we have toned down absolute statements throughout the text to strictly separate bioinformatics predictions from experimental validations. Specifically, we systematically replaced overly definitive mechanistic terms (e.g., reveal, elucidate, confirm, prove) with more cautious alternatives (e.g., suggest, predict, imply, provide clues for). Similarly, assertive regulatory phrases (e.g., regulate, mediate, participate in) were modified to associative descriptions (e.g., may be associated with, potentially involved in, might act through). Furthermore, the expression differences validated by RT-qPCR are now described purely objectively. For instance, the phrase "GSEA analysis indicates that NEK2 regulates the insulin signaling pathway" has been carefully revised to "GSEA analysis suggests that NEK2 may be associated with the insulin signaling pathway."

By strictly differentiating between bioinformatics predictions and experimental validations throughout the text, the scientific rigor of our manuscript has been substantially improved. We sincerely appreciate your invaluable guidance.

Comment 2: Rationale and scope of experimental validation. The rationale for selecting only UCHL1 and NEK2 for validation is unclear. Given that four candidate biomarkers were identified, it is important to justify why MMP10 and HEY1 were excluded from experimental follow-up. Additionally, it is not clear whether these biomarkers are intended to be relevant only at the transcript level or whether protein-level validation was considered. Validation using immunohistochemistry in patient samples would likely be more clinically relevant and informative, particularly for biomarker studies.

Response:

Thank you sincerely for your insightful comments regarding the rationale and scope of the candidate biomarker validation in our study.

Regarding the rationale for our validation targets, although four candidate biomarkers (NEK2, UCHL1, MMP10, and HEY1) were initially screened via bioinformatics analysis, we ultimately prioritized only NEK2 and UCHL1 for experimental validation. This decision was primarily driven by the fact that NEK2 and UCHL1 maintained highly consistent expression trends and significant differential expression (P < 0.05) across two independent datasets, demonstrating strong reproducibility and stability. Conversely, MMP10 and HEY1 showed inconsistent trends: while differentially expressed in the training set, they failed to reach statistical significance in the validation set. To ensure the reliability of our experimental validation based on the principle of cross-cohort reproducibility, we temporarily excluded MMP10 and HEY1. Their roles may be further evaluated in an independent cohort with larger sample size. Furthermore, we completely agree with your comment regarding the unclear validation dimensions. Accordingly, we have supplemented the manuscript with an explicit limitation statement concerning protein-level validation in the Discussion section (Line 404-413). We clarify that our current work is limited to preliminary validation at the transcriptional level. Future studies are warranted to verify the protein expression of these target genes through techniques such as immunohistochemistry in expanded clinical cohorts, thereby evaluating their true clinical translational value.

We greatly appreciate your professional guidance, which has strengthened our manuscript.

Comment 3: Lack of convincing validation for UCHL1. The qRT-PCR results for UCHL1 do not reach statistical significance. It is therefore difficult to consider this as successful validation of the bioinformatic prediction. The authors should either provide additional supporting data, expand the sample size, or reconsider the strength of the claim regarding UCHL1 as a validated biomarker.

Response:

We sincerely appreciate your rigorous comments regarding the statistical significance of the UCHL1 results. After careful consideration, we chose to retain UCHL1 as a biomarker based on several scientific justifications.

First, UCHL1 was stably selected by the LASSO screening model, making it a pivotal gene in our model construction. Second, its expression trend is completely consistent between the training set and the validation set, indicating excellent reproducibility. Third, functional annotations show that UCHL1 is closely related to cell adhesion, proteasome pathways, and inflammatory responses. These mechanisms that are well-supported by existing literature on kidney stone pathogenesis, thus providing strong biological plausibility.

Additionally, we have transparently acknowledged your valid statistical concern in the Discussion section (Line 404-413). We state that the failure of UCHL1 to reach strict statistical significance may be influenced by the small sample size and tissue heterogeneity. Despite this, UCHL1 remains of significant biological importance. We emphasize that expanding the clinical sample size and further validating its expression and function at the protein level are essential next steps.

Thank you again for your professional scrutiny and constructive advice. We have thoroughly refined the manuscript as requested.

Response to Reviewer 2:

Dear Reviewer,

We sincerely thank you for your time, positive evaluation, and constructive feedback. We are highly encouraged by your recommendation that the manuscript can be accepted for publication upon revision. We deeply appreciate your insightful suggestions regarding the inclusion of a global scenario for comparative analysis of the molecular mechanisms, as well as the need for professional English editing and capitalization standardization. We have carefully considered and fully implemented all your valuable advice in the revised manuscript. Our detailed point-by-point responses are provided below.

Comment 1: My suggestion will be to provide a global scenario also with a comparative analysis to understand how the molecular mechanisms differ/not differ (A paragraph highlighting the global scenario would be sufficient).

Response:

We sincerely appreciate your insightful and valuable suggestion. As requested, we have introduced a new paragraph to the manuscript that provides a global context and population comparison. This revision places our research within an international framework, clarifying both the commonalities and the population-specific features of the underlying molecular mechanisms.

Specifically, we highlight that globally, kidney stones and atherosclerosis share high incidence rates and notable population heterogeneity, driven by significant differences in genetic backgrounds, lifestyles, and metabolic profiles across different populations (e.g., Western vs. Asian). Furthermore, we note that international transcriptomic and GWAS studies have consistently identified calcium metabolism disorders, inflammation, oxidative stress, and abnormal cell adhesion as core pathogenic pathways in kidney stones. The high concordance between these global findings and our bioinformatic results strongly reflects the cross-population conservation of these molecular mechanisms. Importantly, we emphasize that while global studies have largely concentrated on classic susceptibility genes (e.g., the SLC family and CLDN14), our study is the first to report NEK2 and UCHL1 as shared molecular biomarkers for both kidney stones and atherosclerosis.

We have incorporated this into the first paragraph of the Discussion section (Line 311-324) in the revised manuscript. We are incredibly grateful for your professional guidance, which has greatly broadened the perspective of our study.

Comment 2: it will be a good idea to take help of a professional for shaping the English language of the manuscript and also take care of the Capital letters in between the sentences. There’s no uniformity. Once done the manuscript can be accepted for publication.

Response:

Thank you very much for your valuable feedback regarding the English language quality and capitalization formatting. The manuscript has undergone comprehensive language polishing by professional editors at Editage to improve fluency and correct any grammatical, structural, or lexical inaccuracies. We have reviewed the entire text to standardize capitalization. Strict rules have been applied so that only sentence initials and proper nouns are capitalized, thoroughly resolving the previous inconsistencies.

We believe these revisions have substantially elevated the manuscript's readability and formatting to meet the journal's standards. Below is the certificate of English editing.

Response to Reviewer 3:

Dear Reviewer,

We would like to express our profound gratitude for the exceptional time and effort you invested in reviewing our manuscript. Your highly rigorous, systematic, and insightful comments have been instrumental in e

Attachment

Submitted filename: Response letter to the editor and reviewers.docx

pone.0354595.s008.docx^{(470.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0354595.r003

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<p>Bioinformatics and experimental studies of atherosclerosis-related endothelial dysfunction genes in renal calculi and exploration of their molecular mechanisms

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PLoS One. doi: 10.1371/journal.pone.0354595.r004

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. The primer sequences of NEK2, UCHL1, and GAPDH.

(DOCX)

pone.0354595.s001.docx^{(13.8KB, docx)}

S2 Table. Differentially expressed genes in the GSE73680 dataset.

(XLSX)

pone.0354595.s002.xlsx^{(162.1KB, xlsx)}

S3 Table. Differentially expressed genes in the GSE132651 dataset.

(XLSX)

pone.0354595.s003.xlsx^{(21.7KB, xlsx)}

S4 Table. GSEA enrichment pathways for NEK6.

(XLSX)

pone.0354595.s004.xlsx^{(25.3KB, xlsx)}

S5 Table. GSEA enrichment pathways for UCHL1.

(XLSX)

pone.0354595.s005.xlsx^{(28.8KB, xlsx)}

S6 Table. Differentially expressed lncRNAs in the GSE117518 dataset.

(XLSX)

pone.0354595.s006.xlsx^{(160.5KB, xlsx)}

Attachment

Submitted filename: Response letter to the editor and reviewers.docx

pone.0354595.s008.docx^{(470.4KB, docx)}

Data Availability Statement

The datasets, including GSE73680, GSE117518, and GSE132651, analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo/).

[pone.0354595.ref001] 1.Tamborino F, Cicchetti R, Mascitti M, Litterio G, Orsini A, Ferretti S, et al. Pathophysiology and Main Molecular Mechanisms of Urinary Stone Formation and Recurrence. Int J Mol Sci. 2024;25(5):3075. doi: 10.3390/ijms25053075 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref002] 2.Thornton SN. Re: Charles D. Scales Jr., Alexandria C. Smith, Janet M. Hanley, Christopher S. Saigal, Urologic Diseases in America Project. Prevalence of kidney stones in the United States. Eur Urol. 2012;62:160–5. European urology. 2012;62(3):e67. [DOI] [PubMed] [Google Scholar]

[pone.0354595.ref003] 3.Wang W, Fan J, Huang G, Li J, Zhu X, Tian Y, et al. Prevalence of kidney stones in mainland China: A systematic review. Sci Rep. 2017;7:41630. doi: 10.1038/srep41630 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref004] 4.Bargagli M, Scoglio M, Howles SA, Fuster DG. Kidney stone disease: risk factors, pathophysiology and management. Nat Rev Nephrol. 2025;21(11):794–808. doi: 10.1038/s41581-025-00990-x [DOI] [PubMed] [Google Scholar]

[pone.0354595.ref005] 5.Türk C, Petřík A, Sarica K, Seitz C, Skolarikos A, Straub M, et al. EAU Guidelines on Diagnosis and Conservative Management of Urolithiasis. European urology. 2016;69(3):468–74. [DOI] [PubMed] [Google Scholar]

[pone.0354595.ref006] 6.Alelign T, Petros B. Kidney Stone Disease: An Update on Current Concepts. Adv Urol. 2018:3068365. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref007] 7.Rule AD, Bergstralh EJ, Melton LJ 3rd, Li X, Weaver AL, Lieske JC. Kidney stones and the risk for chronic kidney disease. Clin J Am Soc Nephrol. 2009;4(4):804–11. doi: 10.2215/CJN.05811108 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref008] 8.Devarajan A. Cross-talk between renal lithogenesis and atherosclerosis: an unveiled link between kidney stone formation and cardiovascular diseases. Clin Sci (Lond). 2018;132(6):615–26. doi: 10.1042/CS20171574 [DOI] [PubMed] [Google Scholar]

[pone.0354595.ref009] 9.Li B, Tang Y, Zhou L, Jin X, Liu Y, Li H, et al. Association between aortic calcification and the presence of kidney stones: calcium oxalate calculi in focus. Int Urol Nephrol. 2022;54(8):1915–23. doi: 10.1007/s11255-021-03058-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref010] 10.Muschialli L, Mannath A, Moochhala SH, Shroff R, Ferraro PM. Epidemiological and biological associations between cardiovascular disease and kidney stone formation: A systematic review and meta-analysis. Nutr Metab Cardiovasc Dis. 2024;34(3):559–68. doi: 10.1016/j.numecd.2023.09.011 [DOI] [PubMed] [Google Scholar]

[pone.0354595.ref011] 11.Gimbrone MA Jr, García-Cardeña G. Endothelial Cell Dysfunction and the Pathobiology of Atherosclerosis. Circ Res. 2016;118(4):620–36. doi: 10.1161/CIRCRESAHA.115.306301 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref012] 12.Libby P, Ridker PM, Hansson GK, Leducq Transatlantic Network on Atherothrombosis. Inflammation in atherosclerosis: from pathophysiology to practice. J Am Coll Cardiol. 2009;54(23):2129–38. doi: 10.1016/j.jacc.2009.09.009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref013] 13.Wang X, He B. Endothelial dysfunction: molecular mechanisms and clinical implications. MedComm (2020). 2024;5(8):e651. doi: 10.1002/mco2.651 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref014] 14.Liu Y, Li S, Zeng Z, Wang J, Xie L, Li T, et al. Kidney stones and cardiovascular risk: a meta-analysis of cohort studies. Am J Kidney Dis. 2014;64(3):402–10. doi: 10.1053/j.ajkd.2014.03.017 [DOI] [PubMed] [Google Scholar]

[pone.0354595.ref015] 15.Saenz-Medina J, Muñoz M, Rodriguez C, Sanchez A, Contreras C, Carballido-Rodríguez J, et al. Endothelial Dysfunction: An Intermediate Clinical Feature between Urolithiasis and Cardiovascular Diseases. Int J Mol Sci. 2022;23(2):912. doi: 10.3390/ijms23020912 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref016] 16.Taguchi K, Hamamoto S, Okada A, Unno R, Kamisawa H, Naiki T, et al. Genome-Wide Gene Expression Profiling of Randall’s Plaques in Calcium Oxalate Stone Formers. J Am Soc Nephrol. 2017;28(1):333–47. doi: 10.1681/ASN.2015111271 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref017] 17.Hebbel RP, Wei P, Milbauer L, Corban MT, Solovey A, Kiley J, et al. Abnormal Endothelial Gene Expression Associated With Early Coronary Atherosclerosis. J Am Heart Assoc. 2020;9(14):e016134. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref018] 18.Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi: 10.1093/nar/gkv007 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref019] 19.Gustavsson EK, Zhang D, Reynolds RH, Garcia-Ruiz S, Ryten M. ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2. Bioinformatics. 2022;38(15):3844–6. doi: 10.1093/bioinformatics/btac409 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref020] 20.Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32(18):2847–9. doi: 10.1093/bioinformatics/btw313 [DOI] [PubMed] [Google Scholar]

[pone.0354595.ref021] 21.Pelikan A, Herzel H, Kramer A, Ananthasubramaniam B. Venn diagram analysis overestimates the extent of circadian rhythm reprogramming. FEBS J. 2022;289(21):6605–21. doi: 10.1111/febs.16095 [DOI] [PubMed] [Google Scholar]

[pone.0354595.ref022] 22.Li Y, Lu F, Yin Y. Applying logistic LASSO regression for the diagnosis of atypical Crohn’s disease. Sci Rep. 2022;12(1):11340. doi: 10.1038/s41598-022-15609-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref023] 23.Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14:7. doi: 10.1186/1471-2105-14-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref024] 24.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. doi: 10.1101/gr.1239303 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref025] 25.Romero V, Akpinar H, Assimos DG. Kidney stones: a global picture of prevalence, incidence, and associated risk factors. Rev Urol. 2010;12(2–3):e86-96. [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref026] 26.Hsi RS, Spieker AJ, Stoller ML, Jacobs DR Jr, Reiner AP, McClelland RL, et al. Coronary Artery Calcium Score and Association with Recurrent Nephrolithiasis: The Multi-Ethnic Study of Atherosclerosis. J Urol. 2016;195(4 Pt 1):971–6. doi: 10.1016/j.juro.2015.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref027] 27.Reiner AP, Kahn A, Eisner BH, Pletcher MJ, Sadetsky N, Williams OD, et al. Kidney stones and subclinical atherosclerosis in young adults: the CARDIA study. J Urol. 2011;185(3):920–5. doi: 10.1016/j.juro.2010.10.086 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref028] 28.Na H-J, Sung M-J, Park J-S. Age- and oxidative stress-induced centrosome amplification and renal stones in Drosophila Malpighian tubules. Biol Open. 2024;13(12):bio061743. doi: 10.1242/bio.061743 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref029] 29.Zhang H, Luo W, Sun Y, Qiao Y, Zhang L, Zhao Z, et al. Wnt/β-Catenin Signaling Mediated-UCH-L1 Expression in Podocytes of Diabetic Nephropathy. Int J Mol Sci. 2016;17(9):1404. doi: 10.3390/ijms17091404 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref030] 30.Mitra S, Epshtein Y, Sammani S, Quijada H, Chen W, Bandela M, et al. UCHL1, a deubiquitinating enzyme, regulates lung endothelial cell permeability in vitro and in vivo. Am J Physiol Lung Cell Mol Physiol. 2021;320(4):L497–507. doi: 10.1152/ajplung.00492.2020 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref031] 31.Wang J, Ji C, Ye W, Rong Y, Ge X, Wang Z, et al. Deubiquitinase UCHL1 promotes angiogenesis and blood-spinal cord barrier function recovery after spinal cord injury by stabilizing Sox17. Cell Mol Life Sci. 2024;81(1):137. doi: 10.1007/s00018-024-05186-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref032] 32.Tang H, Gupta A, Morrisroe SA, Bao C, Schwantes-An T-H, Gupta G, et al. Deficiency of the Deubiquitinase UCHL1 Attenuates Pulmonary Arterial Hypertension. Circulation. 2024;150(4):302–16. doi: 10.1161/CIRCULATIONAHA.123.065304 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref033] 33.Radón V, Czesla M, Reichelt J, Fehlert J, Hammel A, Rosendahl A, et al. Ubiquitin C-Terminal Hydrolase L1 is required for regulated protein degradation through the ubiquitin proteasome system in kidney. Kidney Int. 2018;93(1):110–27. [DOI] [PubMed] [Google Scholar]

[pone.0354595.ref034] 34.Xia J, Zhao H, Edmondson JL, Koss B, Zhan F. Role of NEK2 in tumorigenesis and tumor progression. Trends Mol Med. 2025;31(1):79–93. doi: 10.1016/j.molmed.2024.07.013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref035] 35.Barabutis N, Akhter MS. Involvement of NEK2 and NEK9 in LPS - induced endothelial barrier dysfunction. Microvasc Res. 2024;152:104651. doi: 10.1016/j.mvr.2023.104651 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref036] 36.Fakir S, Sarker MMR, Sigdel M, Barabutis N. NEK2 inhibition alleviates lipopolysaccharide-induced endothelial injury. ASPET Discov. 2025;1:100002. doi: 10.1016/j.aspetd.2025.100002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref037] 37.Khan SR, Canales BK, Dominguez-Gutierrez PR. Randall’s plaque and calcium oxalate stone formation: role for immunity and inflammation. Nat Rev Nephrol. 2021;17(6):417–33. doi: 10.1038/s41581-020-00392-1 [DOI] [PubMed] [Google Scholar]

[pone.0354595.ref038] 38.Bavetsias V, Linardopoulos S. Aurora Kinase Inhibitors: Current Status and Outlook. Frontiers in oncology. 2015;5:278. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref039] 39.Bogoyevitch MA, Boehm I, Oakley A, Ketterman AJ, Barr RK. Targeting the JNK MAPK cascade for inhibition: basic science and therapeutic potential. Biochim Biophys Acta. 2004;1697(1–2):89–101. doi: 10.1016/j.bbapap.2003.11.016 [DOI] [PubMed] [Google Scholar]

[pone.0354595.ref040] 40.Niidome K, Taniguchi R, Yamazaki T, Tsuji M, Itoh K, Ishihara Y. FosL1 Is a Novel Target of Levetiracetam for Suppressing the Microglial Inflammatory Reaction. Int J Mol Sci. 2021;22(20):10962. doi: 10.3390/ijms222010962 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref041] 41.Ma T, Li H, Liu H, Peng Y, Lin T, Deng Z, et al. Neat1 promotes acute kidney injury to chronic kidney disease by facilitating tubular epithelial cells apoptosis via sequestering miR-129-5p. Mol Ther. 2022;30(10):3313–32. doi: 10.1016/j.ymthe.2022.05.019 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0354595.ref042] 42.Yang Q, Chen S, Wang X, Yang X, Chen L, Huang T, et al. Exercise Mitigates Endothelial Pyroptosis and Atherosclerosis by Downregulating NEAT1 Through N6-Methyladenosine Modifications. Arterioscler Thromb Vasc Biol. 2023;43(6):910–26. [DOI] [PubMed] [Google Scholar]

[pone.0354595.ref043] 43.Chen T, Liu L, Zou Y, Hu X, Zhang W, Zhou T, et al. Nobiletin downregulates the SKP2-p21/p27-CDK2 axis to inhibit tumor progression and shows synergistic effects with palbociclib on renal cell carcinoma. Cancer Biol Med. 2021;18(1):227–44. doi: 10.20892/j.issn.2095-3941.2020.0186 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Bioinformatics and experimental studies of atherosclerosis-related endothelial dysfunction genes in renal calculi and exploration of their molecular mechanisms

Aina Li

Chenjing Liu

Xiangshen Liu

Lei Chen

Dong Wang

Changyan Zhu

Roles

Abstract

Introduction

Materials and methods

Data collection

Difference expression analysis

Identification and functional annotation of key genes

Identification of biomarkers

Functional enrichment of biomarkers

Analysis of biomarkers in immune cells and their subcellular localization

Molecular regulatory networks and tissue-specific analysis

Competing endogenous RNA (ceRNA) network and drug prediction

Reverse transcription quantitative polymerase chain reaction (RT-qPCR)

Statistical analysis

Results

A total of 22 key genes were obtained

Fig 1. Differentially expressed genes between the KS group and the control group.

UCHL1 and NEK2 were identified as biomarkers

Fig 2. UCHL1 and NEK2 identified as diagnostic genes.

High functional similarity between the biomarkers

Strong correlation between immune cells and biomarkers

Fig 3. Analysis of NEK2 and UCHL1 expression in immune cells and their subcellular localization.

Potential regulatory mechanisms of biomarkers

Fig 4. Molecular regulatory networks.

Tissue-specific analysis and drug prediction

Fig 5. Tissue specific expression and drug prediction analysis.

NEK2 was over-expressed in KS samples

Fig 6. Validation of NEK2 and UCHL1 expression levels.

Discussion

Conclusions

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Pramodkumar Gupta

Roles

Author response to Decision Letter 1

Decision Letter 1

Patrick Goymer

Roles

Acceptance letter

Patrick Goymer

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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