Exploring the Molecular Interactions Between Nephrolithiasis and Carotid Atherosclerosis: Asporin as a Potential Biomarker

Exploring the Molecular Interactions Between Nephrolithiasis and Carotid Atherosclerosis: Asporin as a Potential Biomarker | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the Molecular Interactions Between Nephrolithiasis and Carotid Atherosclerosis: Asporin as a Potential Biomarker Yibo Hua, Zijian Zhou, Suyu Miao, Zijie Wang, Shangqian Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5059612/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Nov, 2024 Read the published version in Urolithiasis → Version 1 posted 8 You are reading this latest preprint version Abstract Background Increasing evidence has suggested nephrolithiasis has a close linkage with carotid atherosclerosis (CAS). Randall’s plaque (RP) is considered the precursor lesion of nephrolithiasis. This study aimed to examine the crosstalk genes and potential molecular mechanisms between RP and CAS. Methods We downloaded microarray data for calcium oxalate (CaOx) RP and CAS from the Gene Expression Omnibus (GEO) repository. To pinpoint common genes associated with RP and CAS, researchers employed weighted gene co-expression network analysis (WGCNA) alongside differentially expressed gene (DEG) analysis. Enrichment analyses using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) were conducted on the common genes. A central gene was discovered, and a receiver operating characteristic (ROC) curve was created to assess its diagnostic effectiveness. The hub gene was also analyzed using Gene Set Enrichment Analysis (GSEA). Additionally, the xCell algorithm evaluated immune cell infiltration levels, and the relationship between each immune cell type and the central gene was analyzed. Subsequently, the hub gene's expression in human RP and CAS tissues was assessed using quantitative reverse-transcription PCR (qRT-PCR) and immunohistochemistry (IHC) staining. Finally, we established a CaOx nephrolithiasis rat model by administering 1% ethylene glycol. The expression patterns of ASPN in rat kidney tissues were confirmed using IHC and qRT-PCR. Results WGCNA was used to select highly correlated modules and resulted in 225 intersection genes in GSE73680 and GSE100927. On the other hand, 23 overlapping DEGs were identified in GSE117518 and GSE43292. Asporin (ASPN) emerged as the central gene linking RP and CAS by intersecting the highly correlated module genes from WGCNA with the differentially expressed genes (DEGs). The findings on immune infiltration indicated a notable correlation between ASPN and various immune cells in both RP and CAS. IHC and qRT-PCR verified that ASPN expression was lower in human RP and CAS plaque tissues than in normal tissues. Furthermore, the expression pattern of ASPN in CaOx nephrolithiasis model rats was consistent with the results in human tissues. Conclusion We identified ASPN as an important crosstalk gene in RP and CAS. Further study of the immune response and osteoblast differentiation may reveal the shared pathogenesis between RP and CAS. Randall’s plaque Carotid atherosclerosis Bioinformatics Immune infiltration Osteoblast differentiation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Nephrolithiasis, or kidney stone disease, is a widespread condition that affects men and women of all ages. Globally, its occurrence varies from 7–13% in North America, 5–9% in Europe, and 1–5% in Asia. The primary substance in kidney stones is calcium oxalate (CaOx) [ 1 ]. Nephrolithiasis may lead to ureter obstruction, blood in the urine, recurrent urinary tract infections, discomfort during urination, and vomiting. If not properly treated, these conditions can result in long-term damage to the kidneys. Randall’s plaque (RP), first proposed by Alexander Randall in 1937 [ 2 ], is a region of subepithelial mineralized tissue located at the tip of the renal papillae. He proposed that kidney stones form from two initial lesions in the renal papillae. Type I, or Randall’s plaques, involve calcium phosphate (CaP) and calcium carbonate deposits in the subendothelial space. Randall’s plugs, also known as Type II, are made up of crystallized salts, cellular debris, and dead cells caused by high levels of urinary supersaturation [ 3 ]. Recently, Dr. Khan proposed a unified theory for the formation of plaques and plugs. Renal epithelial cells as stone formers undergo stress resulting from altered urinary chemistry, leading to their de-differentiation into osteoblast-like cells. The transformation from a RP to a kidney stone may be explained by oxidative stress and osteogenic calcification [ 4 ]. Atherosclerosis is a long-term condition characterized by the gradual accumulation of oxidized fats, dying cells, and extracellular matrices within the inner walls of arteries. It stems from metabolic irregularities that modify the composition of body fluids and arteries [ 5 ]. Carotid atherosclerosis (CAS) is a type of atherosclerotic disease that occurs in the cervical arteries. It is a major contributor to strokes and is linked to an increased likelihood of cardiovascular disease (CVD) [ 6 , 7 ]. Studies suggest that a correlation exists between RP and CAS [ 8 ]. Metabolic syndrome, including hypertension, diabetes mellitus, obesity, and dyslipidemia, is considered a common risk factor for nephrolithiasis and atherosclerotic CVD [ 9 ]. The generation of reactive oxygen species (ROS) is also crucial in the development of kidney stones and atherosclerosis [ 10 ]. ROS have the potential to unsettle atherosclerotic plaques by triggering inflammation, resulting in vascular smooth muscle cell demise and plaque rupture [ 11 ]. This concept of atherogenesis shares similarities with the vascular theory of Randall plaque formation. Moreover, hypercalcemia is a contributing factor to stone formation as increased serum calcium levels can transform muscle cells into osteoblast-like cells, which further leads to vascular calcification [ 12 ]. Through this research, we discovered potential biomarkers and mechanisms related to the progression of RP and CAS by examining datasets from the GEO database. WGCNA and DEG analysis were used to identify the hub gene ASPN, which was significantly associated with RP and CAS. In addition, an immune infiltration analysis revealed a role for immune cells in these diseases and provided insight into their pathophysiological processes. Furthermore, we successfully established a CaOx nephrolithiasis rat model. Quantitative RT-PCR and IHC were conducted to validate the expression patterns of ASPN in human and rat tissues. Materials and methods Data source Expression profiles for RP and CAS were retrieved from the GEO database ( http://www.ncbi.nlm.nih.gov/geo/ ). The research approach for this investigation included: 1) acquiring nephrolithiasis dataset samples from CaOx RP tissue and CAS dataset samples from carotid plaques; 2) ensuring datasets had control groups; 3) sourcing samples from human subjects; and 4) downloading GEO datasets GSE73680, GSE100927, GSE117518, and GSE43292. Weighted gene co-expression network analysis The Weighted Gene Co-expression Network Analysis (WGCNA) is a biological algorithm designed to detect important co-expressed gene clusters and assess the relationship between gene networks and various diseases [ 13 ]. We used WGCNA to analyze the GSE73680 and GSE100927 datasets, which revealed modules associated with RP and CAS. Following the application of a 25% variance threshold for all genes, we utilize the R programming language to remove outlier samples from the hierarchical clustering process. To ensure a scale-free network, we calculated the scale-free fit index and mean connectivity. A β value of 19 was selected for soft thresholding of GSE73680, and 9 for GSE100927. Afterwards, the adjacency matrix was converted into the topological overlap matrix (TOM) and the dissimilarity matrix derived from TOM (1-TOM). Genes with similar expression profiles were further merged into different modules by generating a hierarchical clustering dendrogram. Ultimately, the relationship between each module and each disease was assessed, and genes within the modules showing strong correlations were chosen for additional examination. Differential gene expression analysis We performed an analysis of differential expression comparing disease and control groups on supplementary RP and CAS datasets (GSE117518 and GSE43292) utilizing the LIMMA package in R (version 4.3.2). Genes were identified as DEGs in both the RP and CAS datasets if they had a P-value less than 0.05 and an absolute log2 fold change greater than 0.8. R software was utilized to generate heatmaps for differential gene clustering and create volcano plots. Identification of shared genes and enrichment analysis We selected modules with a high relevance to RP and CAS. The shared genes in the positively or negatively related modules of RP and CAS were evaluated using a Venn diagram. They were further examined by GO and KEGG pathway enrichment analyses. Statistics were considered significant when the P-value was less than 0.05. Similarly, we extracted overlapping DEGs in two additional RP and CAS databases (GSE117518 and GSE43292) and performed functional enrichment analyses. The shared genes from WGCNA and the DEGs were overlapped using a Venn diagram. The overlapping gene that was ultimately identified was considered the hub gene. Hub gene expression levels and diagnostic value We analyzed the variations in hub gene expression between the disease and control groups across the datasets GSE73680, GSE100927, GSE117518, and GSE43292. We assessed the diagnostic significance of the hub gene by computing the area under the ROC curve across four datasets. GSEA for the hub gene We categorized each disease cohort into two subgroups according to the hub gene expression and conducted a differential gene expression analysis. The 'clusterProfiler' package was utilized to perform GSEA. The MSigDB dataset (c2.cp.kegg.v7.0.symbols.gmt) was utilized to compare the biological signaling pathways between groups with high and low expression levels. The top 10 up- and downregulated pathways were displayed using enrichplot. Immune infiltration analysis The xCell algorithm ( https://xcell.ucsf.edu/ ) is a computational method based on gene signatures that calculates the relative infiltration scores for 64 types of immune and stromal cells. By categorizing the diseased tissues of RP and CAS and analyzing hub gene expression levels, we employed the xCell algorithm to evaluate immune infiltration between groups with high and low expression. Immune and stromal infiltrated cells showing statistical significance (P < 0.05) were chosen to assess their correlation with the central gene using Spearman's rank correlation analysis. Human tissues collection Human RP tissues were biopsied from patients with kidney stones during percutaneous nephrolithotomy. The stone composition of all patients was diagnosed post-surgery as CaOx using Fourier transform infrared spectroscopy. We obtained normal renal papillary tissues from individuals who had nephrectomies due to kidney tumors. The tissues were obtained from the papillary region, which was devoid of any tumor invasion. Plaque tissues from human carotid arteries were obtained from patients who had carotid endarterectomy due to artery narrowing. The disease samples were carotid intima attached with atheroma plaque, whereas the control samples were adjacent smooth intima without atheroma plaque. This research was approved by the Ethics Committee at the First Affiliated Hospital of Nanjing Medical University in China. CaOx nephrolithiasis rat models A CaOx nephrolithiasis model was developed using 6-week-old male Sprague-Dawley (SD) rats. This study received approval from the Nanjing Medical University Laboratory Animal Ethics Board. At the start, we split 12 SD rats into two groups of equal size: a control group and a model group. For the model group, the rats were administered 1% ethylene glycol in their drinking water. Sterile tap water was given to the rats in the control group. Following a 4-week period, the rats were euthanized, and kidney tissues were harvested for Von Kossa (VK), Alizarin Red (AR), and Periodic Acid-Schiff (PAS) staining to identify calcium oxalate crystal deposits and evaluate renal damage. Dihydroethidium (DHE) staining was used with microfluorimetry to measure ROS production. The expression profile of the key gene in rat kidney tissues was identified using qRT-PCR and IHC. RNA extraction and qRT-PCR RNA was isolated from these human samples with TRIzol reagent (Invitrogen, Carlsbad, USA) and converted into cDNA using HiScript II RT SuperMix for qPCR (Vazyme, Nanjing, China). Subsequently, ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China) was employed to quantify ASPN expression in the two conditions via qRT-PCR. The ASPN primer sequences utilized were: Forward, 5′‑ AACAAGCTAACGAAGATTCACCC ‑3′ and Reverse, 5′‑ CCCCTGGCTCTATCCCATTATT ‑3′, sourced from Realgene Biotech in Nanjing, China. The β-actin primer sequence used as an internal control was forward 5′‑TGACGGGGTCACCCACACTGTGCCCATCTA‑3′ and reverse 5′‑CTAGAAGCATTTGCGGTGGACGATGGAGGG‑3′, sourced from Realgene Biotech in Nanjing, China. A Student's t-test was used to compare differences between disease and control groups. Statistical significance was defined as a P value less than 0.05. IHC staining Tissue sections around 4–5 µm thick were deparaffinized using xylene and then rehydrated through a series of ethanol solutions. The tissue samples underwent antigen retrieval by being heated in 10 mL of citrate buffer (pH 6.0) inside a pressure cooker at 121°C for 20 minutes. To inhibit the natural peroxidase activity, a 3% hydrogen peroxide solution in methanol was applied for 15 minutes at ambient temperature. A blocking mixture containing 5% normal goat serum in PBS was applied to the slides for one hour at room temperature. Then, the specimens were left to incubate with primary antibodies targeting ASPN at a 1:100 dilution overnight at 4˚C (AV42487; Sigma, St. Louis, USA). The specimens were rinsed in PBS for 10 minutes and then exposed to goat anti-rabbit IgG secondary antibody (1:1,000 dilution; ZB-2301; ZSGB-BIO, Beijing, China) for one hour at ambient temperature. Following three additional washes with PBS, peroxidase activity was visualized using a fresh substrate solution containing 3,3′diaminobenzidine. The samples were stained with hematoxylin at ambient temperature for two minutes. The slides were dehydrated and mounted with a coverslip. IHC quantitation was done using ImageJ software based on the positive area and intensity of the staining. Results GEO information The diagram in Fig. 1 illustrates the process for this research. We selected four datasets for analysis based on our selection criteria, including GSE 73680, GSE117518, GSE100927, and GSE43292. Detailed information on these four datasets is listed in Table 1 . WGCNA analysis was done using the paired datasets, GSE73680 and GSE100927, and a DEG analysis was performed using GSE117518 and GSE43292. Table 1 Detailed information of GEO datasets GSE number Platform Samples Disease GSE73680 GPL17077 24 patients and 6 controls CaOx Kidney stones GSE117518 GPL21827 3 patients and 3 controls CaOx Kidney stones GSE100927 GPL17077 29 patients and 12 controls Atherosclerotic carotid artery GSE43292 GPL6244 32 patients and 32 controls Atherosclerotic carotid artery Screening for co-expression modules in RP and CAS To examine the relationship between the diseases and key genes, WGCNA analysis was conducted with GSE73680 and GSE100927. After selecting a 25% variance cutoff for all genes, a cluster analysis was performed to identify outliers; however, no samples were excluded in either dataset. The scale-free fit index and mean connectivity were calculated to ensure scale-free networks (Fig. 2 A, D). RP's soft-threshold power was configured to β = 19, achieving a scale-free R2 of 0.85. For CAS, the soft-thresholding power was adjusted to β = 9 (scale-free R2 = 0.80). A total of 15 and 12 modules were identified in GSE73680 and GSE100927, respectively (Fig. 2 C, F). The MEdarkgreen module had the strongest positive correlation with RP (r = 0.39, p = 0.03), whereas the MEred module exhibited the strongest negative correlation to RP (r = − 0.45, p = 0.01). In the case of CAS, the MEpink module exhibited the highest positive correlation (r = 0.91, P < 0.001), while the MEblue module showed the most significant negative correlation (r = − 0.82, P < 0.001). Finally, we overlapped the hub modules between RP and CAS using a Venn diagram, which resulted in the identification of 125 intersecting positive genes and 100 intersecting negative genes (Fig. 3 A). Identification of overlapping DEGs of RP and CAS To further identify the key genes common to the two diseases, we performed a DEG analysis on an additional RP dataset, GSE117518, and an additional CAS dataset, GSE43292.For GSE117518, we identified a total of 1151 DEGs, comprising 690 genes that were upregulated and 461 that were downregulated. (Fig. 4 A, B). Among the 456 DEGs identified for GSE43292, 301 were upregulated and 155 were downregulated (Fig. 4 C, D). Using Venn diagrams, we identified 10 overlapping upregulated DEGs and 13 overlapping downregulated DEGs between RP and CAS (Fig. 5 A). Functional enrichment analysis of intersecting modules genes and overlapping DEGs We conducted GO and KEGG enrichment analyses on the 225 intersecting genes identified by WGCNA and the 23 overlapping DEGs to explore the possible co-pathogenesis of RP and CAS. GO analysis indicated that the overlapping genes within the common trend modules were mainly linked to collagen-containing extracellular matrix, contractile fiber, and extracellular matrix structural constituent (Fig. 3 B). In contrast, the common DEGs were mainly associated with heart process, oligosaccharide binding, and regulation of cardiac muscle contraction by regulation of the release of sequestered calcium ion (Fig. 5 B). KEGG analysis indicated that the overlapping genes in the common trend modules were mainly linked to protein digestion and absorption, regulation of actin cytoskeleton, and hypertrophic cardiomyopathy (Fig. 3 C), while the shared DEGs were mainly associated with cardiac muscle contraction, AGE-RAGE signaling pathway in diabetic complications, and dilated cardiomyopathy (Fig. 5 C). Overall, the findings suggest that these shared genes were mainly associated with extracellular matrix, actin function, calcium ion signaling, cardiac muscle contraction, and cardiomyopathy. Identification and validation of the hub gene in RP and CAS We overlapped the shared genes obtained by WGCNA and DEG analysis to identify potential core genes between both diseases. No gene was identified after crossing the 125 intersecting positive genes and the 10 overlapping upregulated DEGs. Interestingly, we identified ASPN after crossing the 100 intersecting negative genes and the 13 overlapping downregulated DEGs (Fig. 6 ). The findings indicate that ASPN is a central gene crucial to the development of these two conditions. To confirm the expression levels and diagnostic significance of ASPN in both conditions, we examined the expression profiles and created ROC curves for the four datasets. The expression of ASPN was significantly decreased in both RP and CAS when contrasted with the control groups (Fig. 7 A–D). With respect to RP, ASPN exhibited good diagnostic values (Fig. 7 E, G) in GSE73680 (AUC = 0.743) and GSE117518 (AUC = 1). With respect to CAS, ASPN exhibited perfect diagnostic accuracy (Fig. 7 F, H) in GSE100927 (AUC = 0.931) and GSE43292 (AUC = 0.817). GSEA of ASPN We continued our examination to determine the role of ASPN in these two diseases. Initially, the disease specimens were categorized into groups with high and low ASPN expression levels. GSEA was used to compare the signaling pathways between the high-expression groups and the low-expression groups. In the RP dataset GSE73680, GSEA indicated that the group with elevated expression levels was linked to axon guidance, basal cell carcinoma, cell cycle, ECM receptor interaction, focal adhesion, spliceosome, ubiquitin-mediated proteolysis, and vascular smooth muscle contraction (Fig. 8 A). Conversely, the group with lower expression levels showed an enrichment of leishmania infection and olfactory signaling. Within the CAS dataset GSE100927, the group exhibiting elevated expression levels was linked to arrhythmogenic right ventricular cardiomyopathy, dilated cardiomyopathy, drug metabolism cytochrome P450, focal adhesion, hypertrophic cardiomyopathy, and the nod-like receptor signaling pathway (Fig. 8 B). In contrast, the low-expression group showed enrichment in the chemokine signaling pathway, cytokine receptor interaction, lysosome, and transforming growth factor-beta (TGF-β) signaling pathway. Immune and stroma infiltration analysis We determined the correlation of ASPN expression with the immune microenvironment in RP and CAS using the xCell algorithm. The results indicated the abundance of 64 immune cells and stroma cells in different groups.In the RP dataset GSE73680, there was a notable rise in the infiltration of immune cells like aDC and M1 macrophages in the low-expression group, while the infiltration of chondrocytes, or stroma cells, was reduced in the high-expression group (Fig. 9 A).In the CAS dataset GSE100927, a notable rise in immune cell infiltration, including B cells and natural killer T (NKT) cells, was observed in the low-expression group. Conversely, the infiltration of stromal cells like adipocytes, chondrocytes, fibroblasts, keratinocytes, myocytes, neurons, and smooth muscle cells, was reduced in the same group (Fig. 10 A). In addition, we also determined the correlation between ASPN and different cells. In RP samples, M1 macrophages were significantly negatively correlated with ASPN.In contrast, chondrocytes showed a positive correlation (Fig. 9 B). In the CAS samples, ASPN was significantly negatively correlated with B cells and NKT cells, whereas stroma cells, such as adipocytes, chondrocytes, fibroblasts, keratinocytes, myocytes, neurons, and smooth muscle cells exhibited significantly positive correlations with ASPN (Fig. 10 B). As a result of the findings, ASPN may be crucial in the infiltration of immune and stromal cells during RP and CAS progression. Experimental validation of ASPN expression in human tissues To validate ASPN expression in human tissues, we gathered RP samples from five individuals with CaOx kidney stones and also acquired normal renal papillary tissues from five other patients who had nephrectomies for renal tumors. We also collected human CAS plaque tissues and adjacent smooth intima from five patients who underwent carotid endarterectomy because of carotid artery stenosis. The relative mRNA levels of ASPN in human tissues were quantified using RT-PCR. As the results show, ASPN mRNA expression was significantly reduced in RP tissues and CAS plaque tissues compared with normal renal papillary tissues and normal intima (Fig. 11 C, F). Moreover, IHC consistently showed that the relative expression of the ASPN protein in the RP tissues (Fig. 11 A, B) and CAS plaque tissues (Fig. 11 D, E) was markedly lower compared with that in normal tissues. Establishment of a rat model and validation of ASPN expression We used VK, AR, PAS, and DHE staining to evaluate the effectiveness of an EG-induced CaOx nephrolithiasis rat model (Fig. 12 A). VK and AR staining revealed crystal accumulation within the renal tubule lumens of CaOx rats, along with kidney structural issues like tubular and glomerular atrophy, interstitial inflammation, and brush border loss. On the other hand, PAS staining revealed renal damage and interstitial fibrosis in CaOx rats. We used DHE staining to measure cytoplasmic ROS production in the renal tissues of CaOx rats. Next, qRT-PCR (Fig. 12 D) and IHC (Fig. 12 B, C) were carried out to measure the expression of ASPN in rat renal tissues. The expression of ASPN mRNA and protein was downregulated in the CaOx rat models. These findings are consistent with the results observed in human renal tissues. Discussion Nephrolithiasis and CVDs are common disorders and the global prevalence of both conditions is on the rise. Recent research has shown that kidney stone development and arteriosclerosis are linked by similar pathological mechanisms and numerous risk factors, including dyslipidemia, diabetes and hypertension [ 14 – 16 ]. They are both chronic progressive diseases and involve the accumulation of lipids and oxidized proteins in the kidney tissue and arterial wall [ 17 ]. However, there have been no studies focused on the molecular mechanism of nephrolithiasis and arteriosclerosis. Therefore, we conducted an analysis using transcriptomic data for RP and CAS to identify a key crosstalk gene (ASPN) and elucidated the common mechanisms of these two diseases from a novel perspective. For this research, we obtained transcriptome data for RP and CAS from the GEO repository. By overlapping the shared genes screened by WGCNA and DEGs, ASPN was identified as the sole key crosstalk gene between RP and CAS. ASPN was downregulated in RP tissues and CAS plaque tissues and exhibited a strong negative correlation with both diseases. In addition, qRT-PCR and IHC assays confirmed that ASPN mRNA and protein expression was lower compared with that of normal tissues from humans and a nephrolithiasis rat model. To assess the diagnostic significance of ASPN, we conducted a ROC curve analysis. The results indicated that ASPN had high AUC values in RP and CAS. These findings indicate that ASPN may represent a valuable biomarker for distinguishing nephrolithiasis and CAS from normal controls. ASPN, alternatively referred to as periodontal ligament-associated protein-1 (PLAP-1), is an important component of the Class I Small Leucine-Rich Proteoglycan family. It is an important component of the extracellular matrix. Additionally, it acts as a negative regulator in maintaining the balance of periodontal tissues, safeguarding the periodontal ligament from excessive bone growth by preventing osteoblast differentiation and bone formation [ 18 , 19 ]. ASPN is linked to the progression of multiple conditions, including bone degeneration [ 20 , 21 ], malignancies [ 22 , 23 ], and endometriosis [ 24 ]. To date, there have been no studies on ASPN in the context of nephrolithiasis and atherosclerosis. Earlier researches indicate that the development of RP and its conversion into a stone nucleus are strongly linked to oxidative stress, causing renal epithelial cells to transform into osteoblast-like cells [ 4 , 25 ]. Similarly, the conversion of vascular smooth muscle cells into osteoblast-like cells occurs during the pathological process of atherosclerosis [ 12 ]. Thus, we proposed that ASPN's capacity to suppress osteoblast differentiation might be a shared pathway contributing to the onset and progression of kidney stones and atherosclerosis. This research has, for the first time, verified that reduced ASPN expression is strongly linked to the onset of kidney stones and arterial plaque buildup. In order to uncover additional molecular mechanisms linked to ASPN in both conditions, patients were categorized into groups with high and low ASPN expression, followed by enrichment and immune cell infiltration analyses. Using GSEA, the expression differences of ASPN in RP and CAS were associated with various signaling pathways, whereas focal adhesion was a common biological process between the two. Further studies are needed to confirm the underlying mechanisms of these pathways and the role of ASPN in nephrolithiasis and atherosclerosis. Experimental and clinical data have substantiated the involvement of immune response and inflammation in the development of kidney stones and atherosclerosis [ 26 , 27 ]. By analyzing immune cell infiltration, we discovered notable differences in immune cell profiles between the high and low ASPN subgroups in both RP and CAS. In the low ASPN subgroups, there was a notable rise in immune cells, while stroma cells showed a reduction. In the RP disease groups, we found that ASPN was highly negatively correlated with M1 macrophages, which suggests that downregulated expression of ASPN promotes the infiltration of M1 macrophages. Other studies have explored the role of macrophages in the crystal inflammatory and immune response in nephrolithiasis. Recent research indicates that M1 macrophages promote crystal accumulation [ 28 ], and CaOx might trigger the transformation of monocytes into M1 macrophages during their polarization [ 29 ]. Thus, we hypothesize that downregulated expression of ASPN promotes stone formation by driving M1 macrophage polarization. Within the CAS samples, we noticed that ASPN showed a strong inverse relationship with immune cells, including B cells and NKT cells. Although some studies suggest that B cells prevent atherosclerosis by producing protective antibodies [ 30 ], subsequent studies indicate that exacerbation of atherosclerosis by B-2 cells reveal the subset-dependent impact of B cells [ 31 , 32 ]. NKT cells exhibit various effector functions, such as activating neighboring immune cells and exerting powerful cytotoxic potential [ 33 , 34 ]. Various research findings have shown that NKT cells significantly contribute to the development of atherosclerosis [ 35 , 36 ]. Taken together, these results suggest that ASPN is a key factor associated with the immune response in nephrolithiasis and atherosclerosis. Our study for the first time used a comprehensive bioinformatics analysis to identify ASPN as the key crosstalk gene in RP and CAS. Additionally, we performed tests to confirm the expression patterns of ASPN mRNA and protein in human tissues and a rat model of kidney stones. However, there are some limitations to the present study. Initially, the RP dataset GSE117518 includes just 3 patients and 3 controls, so the AUC value for ASPN in this dataset (AUC = 1) might be unreliable. In addition, RP samples and normal renal papillary samples were not obtained from the same individual. Moreover, we validated the expression of ASPN in a nephrolithiasis rat model, but did not construct an animal model for CAS. Ultimately, the fundamental molecular process of ASPN in RP and CAS needs additional confirmation through both in vitro and in vivo studies. Conclusion We explored the intimate genetic relationship between nephrolithiasis and CAS. We identified ASPN as a key crosstalk gene that may be involved in osteoblast differentiation and the immune response in nephrolithiasis and CAS. This research improves our comprehension of the processes leading to the formation of kidney stones and CAS. Declarations Acknowledgements We deeply appreciate the efforts of everyone and every organization that played a role in bringing this research study to fruition. Author contributions YH analyzed the data and drafted the manuscript. ZZ conducted the experiments. SM recruited the patient samples. ZW and SW reviewed and edited the manuscript. LX conducted pathological analysis. RS and XM designed the study and provided funding. Every writer reviewed the completed manuscript and gave their consent for its publication. Funding Funding for this research was provided by the National Natural Science Foundation of China under grant number 81801438. Access to information and resources The public datasets were downloaded and analyzed in this study are available in the GEO repository, including GSE73680, GSE100927, GSE117518 and GSE43292. Declarations Approval of ethics and agreement to join The Ethics Committee of the First Affiliated Hospital of Nanjing Medical University granted ethical approval for this research, under reference number 2022-SRFA-429. This study's employment of animal subjects was evaluated and sanctioned by the Institutional Animal Care and Use Committee at Nanjing Medical University. Consent for publication All participants provided their written consent after being informed. Competing interests The writers state that they possess no conflicting interests. 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Sci Rep 6:35167 Dominguez-Gutierrez PR et al (2018) Calcium Oxalate Differentiates Human Monocytes Into Inflammatory M1 Macrophages. Front Immunol 9:1863 Kyaw T et al (2011) B1a B lymphocytes are atheroprotective by secreting natural IgM that increases IgM deposits and reduces necrotic cores in atherosclerotic lesions. Circ Res 109(8):830–840 Kyaw T et al (2013) BAFF receptor mAb treatment ameliorates development and progression of atherosclerosis in hyperlipidemic ApoE(-/-) mice. PLoS ONE 8(4):e60430 Kyaw T et al (2010) Conventional B2 B cell depletion ameliorates whereas its adoptive transfer aggravates atherosclerosis. J Immunol 185(7):4410–4419 Brennan PJ, Brigl M, Brenner MB (2013) Invariant natural killer T cells: an innate activation scheme linked to diverse effector functions. Nat Rev Immunol 13(2):101–117 Metelitsa LS et al (2001) Human NKT cells mediate antitumor cytotoxicity directly by recognizing target cell CD1d with bound ligand or indirectly by producing IL-2 to activate NK cells. J Immunol 167(6):3114–3122 Nakai Y et al (2004) Natural killer T cells accelerate atherogenesis in mice. Blood 104(7):2051–2059 Tupin E et al (2004) CD1d-dependent activation of NKT cells aggravates atherosclerosis. J Exp Med 199(3):417–422 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2024 Read the published version in Urolithiasis → Version 1 posted Editorial decision: Revision requested 04 Nov, 2024 Reviews received at journal 01 Nov, 2024 Reviewers agreed at journal 29 Oct, 2024 Reviewers agreed at journal 04 Oct, 2024 Reviewers invited by journal 15 Sep, 2024 Editor assigned by journal 12 Sep, 2024 Submission checks completed at journal 12 Sep, 2024 First submitted to journal 09 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5059612","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373708853,"identity":"989d6f20-6401-4fda-8ded-2cd7600b80f2","order_by":0,"name":"Yibo Hua","email":"","orcid":"","institution":"Jiangsu Province Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yibo","middleName":"","lastName":"Hua","suffix":""},{"id":373708854,"identity":"140d7304-b8c5-4eed-8952-4b0835050cfa","order_by":1,"name":"Zijian Zhou","email":"","orcid":"","institution":"Huashan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zijian","middleName":"","lastName":"Zhou","suffix":""},{"id":373708857,"identity":"c662db51-b1d6-4398-8b29-027732d3f1af","order_by":2,"name":"Suyu Miao","email":"","orcid":"","institution":"Jiangsu Province Hospital","correspondingAuthor":false,"prefix":"","firstName":"Suyu","middleName":"","lastName":"Miao","suffix":""},{"id":373708858,"identity":"2d04c615-3a15-4c71-9cae-097dea22ea22","order_by":3,"name":"Zijie Wang","email":"","orcid":"","institution":"Jiangsu Province Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zijie","middleName":"","lastName":"Wang","suffix":""},{"id":373708859,"identity":"7dc9fb8e-4487-4167-bda1-d5517b1b5ad5","order_by":4,"name":"Shangqian Wang","email":"","orcid":"","institution":"Jiangsu Province Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shangqian","middleName":"","lastName":"Wang","suffix":""},{"id":373708860,"identity":"18c07f2b-b0b7-499a-91c9-f94a63e3e696","order_by":5,"name":"Lei Xi","email":"","orcid":"","institution":"Jiangsu Province Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Xi","suffix":""},{"id":373708862,"identity":"f9411948-8f68-4b3d-8e41-d2cf3a02bbc1","order_by":6,"name":"Rijin Song","email":"","orcid":"","institution":"Jiangsu Province Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rijin","middleName":"","lastName":"Song","suffix":""},{"id":373708864,"identity":"d28ac043-d87e-4990-930c-d93821de8f20","order_by":7,"name":"Xianghu Meng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCTBpw8DYAKLZiNeSRrqWw1AeMVoMbncnPi74dT6PedoZA4YPZYcZ+Gc34NciOefsZuOZfbeLGWfnGDDOOHeYQeLOAfxa+CVyt0nz9txObARqYeZtO8xgIJGAXwubRO7237w95yBa/hKjBWQLM8+PAxAtjMRokZyRu1matyEZqCWt4GDPuXQeiRsEtBjcyN34meePXeLG2ckbH/wos5bjn0FACxgwtjEwGDYwMBwAsnmIUA8CfxgY5IlUOgpGwSgYBSMQAABJcETYwO3dCgAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangsu Province Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xianghu","middleName":"","lastName":"Meng","suffix":""}],"badges":[],"createdAt":"2024-09-09 17:01:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5059612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5059612/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00240-024-01665-1","type":"published","date":"2024-11-26T15:58:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68743406,"identity":"26195dff-2381-422c-94e5-0da655f65686","added_by":"auto","created_at":"2024-11-11 14:41:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2410389,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart for the research design. GSE, Gene Expression Omnibus Series; WGCNA, Weighted Gene Co-expression Network Analysis; DEG, Differentially Expressed Gene; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, Gene Set Enrichment Analysis; qRT-PCR, quantitative reverse transcription polymerase chain reaction.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/b8c374fbd71d44b7e0877514.png"},{"id":68742189,"identity":"2d5d00e9-2118-4156-a181-62204bed4a1c","added_by":"auto","created_at":"2024-11-11 14:33:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3267562,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA of GSE73680 and GSE100927 datasets. (A) Soft threshold evaluation in RP. (B) Cluster dendrogram of RP highly connected genes in key modules. (C) Heatmap depicting the relationships between modules and traits in RP. Each cell contains correlations and P-values. (D) Soft threshold evaluation within CAS. (E) Cluster dendrogram of CAS modules with highly connected genes. (F) Connections between modules and traits in CAS. Each cell contains correlations and P-values. WGCNA, weighted genes correlation network analysis.RP, Randall’s plaque; CAS, carotid atherosclerosis.\u003c/p\u003e","description":"","filename":"Fig2300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/01570c80c4c903508b15749b.png"},{"id":68742187,"identity":"7d4907cf-6da1-48ce-a578-0ea07e68cb6b","added_by":"auto","created_at":"2024-11-11 14:33:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":623014,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analyses of the intersecting genes obtained by WGCNA. (A) Venn diagram shows 225 overlapping genes in the RP and CAS modules. (B) GO analysis of the common genes. (C) Analysis of KEGG pathway enrichment for the common genes. WGCNA, weighted genes correlation network analysis. RP, Randall’s plaque; CAS, carotid atherosclerosis; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"Fig3300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/86f512d9ea58e83f3ae4ae00.png"},{"id":68743663,"identity":"06bf466d-b0da-4f97-9b31-ded41a23800e","added_by":"auto","created_at":"2024-11-11 14:49:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4088818,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of differentially expressed genes in RP and CAS. (A) Heatmap of DEGs in GSE117518. (B) Volcano plot of DEGs in GSE117518. (C) Heatmap of DEGs in GSE43292. (D) Volcano plot of DEGs in GSE43292. RP, Randall’s plaque; CAS, carotid atherosclerosis; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes.\u003c/p\u003e","description":"","filename":"Fig4300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/5b667c47dfa16ed35dad5590.png"},{"id":68742192,"identity":"aa793761-8225-4f15-a762-b8b3197117dc","added_by":"auto","created_at":"2024-11-11 14:33:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":728143,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analyses of the overlapping DEGs. (A) The Venn diagram illustrates that 23 DEGs are shared between the RP and CAS. (B) GO analysis of the common genes. (C) Analysis of KEGG pathway enrichment for the common genes. RP, Randall’s plaque; CAS, carotid atherosclerosis. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes. DEGs, differentially expressed genes.\u003c/p\u003e","description":"","filename":"Fig5300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/4336cfb64e5ac5988034d108.png"},{"id":68742190,"identity":"b6ddbb69-54a0-416e-9deb-c13c03009263","added_by":"auto","created_at":"2024-11-11 14:33:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":433253,"visible":true,"origin":"","legend":"\u003cp\u003eThe Venn diagram reveals that the ASPN is the core gene identified by both WGCNA and DEG analysis. WGCNA, weighted genes correlation network analysis; DEG, differentially expressed gene.\u003c/p\u003e","description":"","filename":"Fig6300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/8d851642102bd2c7c1779ab9.png"},{"id":68743407,"identity":"f7f05eb4-748a-43f0-a950-aa2e42490d15","added_by":"auto","created_at":"2024-11-11 14:41:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1123214,"visible":true,"origin":"","legend":"\u003cp\u003eExpression level and diagnostic value of ASPN. (A) Expression of ASPN in GSE73680. (B) Expression of ASPN in GSE100927. (C) Expression of ASPN in GSE117518. (D) Expression of ASPN in GSE43292. (E) ROC curve of ASPN in GSE73680. (F) ROC curve of ASPN in GSE100927. (G) ROC curve of ASPN in GSE117518. (H) ROC curve of ASPN in GSE43292. RP, Randall’s plaque; CAS, carotid atherosclerosis. ROC, receiver operating characteristic; *P \u0026lt; 0.05; **P \u0026lt; 0.01; ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig7300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/913c0a43dc7442e3f4d8045b.png"},{"id":68744604,"identity":"42a7c238-48e7-43ef-9df9-f9f9d4177fa6","added_by":"auto","created_at":"2024-11-11 14:57:47","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3761665,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA for the core gene. (A) GSEA analysis for ASPN in RP group. (B) GSEA analysis for ASPN in CAS group. GSEA, Gene Set Enrichment Analysis.\u003c/p\u003e","description":"","filename":"Fig8300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/252c32fd4fc043b742f0c67e.png"},{"id":68743408,"identity":"a965195e-6b17-4be1-8201-ff7e2fbf8fcc","added_by":"auto","created_at":"2024-11-11 14:41:47","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2591047,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of immune infiltration associated with RP. (A) The infiltrated abundance of immune and stroma cells in the high-expression group and low-expression group of ASPN. (B) Correlation analysis of ASPN and immune cell infiltration. RP, Randall’s plaque.\u003c/p\u003e","description":"","filename":"Fig9300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/fcb27153389dc5f4d1439896.png"},{"id":68742196,"identity":"58df4490-7a28-4659-83e0-1cd86b82101c","added_by":"auto","created_at":"2024-11-11 14:33:47","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":3719620,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of immune infiltration associated with CAS. (A) The infiltrated abundance of immune and stroma cells in the high-expression group and low-expression group of ASPN. (B) Correlation analysis of ASPN and immune cell infiltration. CAS, carotid atherosclerosis.\u003c/p\u003e","description":"","filename":"Fig10300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/c58500d84f5c509b8639ec0d.png"},{"id":68742197,"identity":"a3fdb54c-276d-4381-a95c-245ccfdbf2fc","added_by":"auto","created_at":"2024-11-11 14:33:47","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":23789173,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression level of ASPN was decreased in human RP tissues and CAS plaque tissues. (A) IHC for ASPN and (B) its relative staining intensity in human RP tissues compared to normal renal papillary tissues (magnification × 40; scale bar, 200 μm). (C) Quantitative RT-PCR was utilized to measure ASPN mRNA levels in human RP tissues compared to normal renal papillary tissues (n = 5, mean ± SD, *P \u0026lt; 0.05, ***P \u0026lt; 0.001). (D) IHC for ASPN and (E) its relative staining level in human CAS plaque and normal intima tissues (magnifcation × 40; scale bar, 200 μm). (F) Quantitative RT-PCR was utilized to measure the mRNA levels of ASPN in human CAS plaques and normal intimal tissues (n = 5, mean ± SD, *P \u0026lt; 0.05, **P \u0026lt; 0.01). IHC, immunohistochemistry; RP, Randall’s plaque; CAS, carotid atherosclerosis.\u003c/p\u003e","description":"","filename":"Fig11300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/d221fde9720b195983cf57e3.png"},{"id":68742198,"identity":"dc19dfc7-0e5c-4107-82d3-3e6426ee182d","added_by":"auto","created_at":"2024-11-11 14:33:47","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":37116217,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation on an EG-induced CaOx rat model and IHC for ASPN. (A) Histological slides stained with VK, AR, and PAS revealed the presence of CaOx crystals and the extent of renal damage (magnification × 40; scale bar, 200 μm; arrows in VK indicate calcium deposits; arrows in AR point to CaOx crystals; arrows in PAS highlight kidney damage). DHE staining combined with microfluorimetry was conducted to assess ROS generation. (B) IHC was utilized to detect ASPN protein levels (C) and its comparative staining intensity in kidney tissues from rats with EG-induced CaOx nephrolithiasis and control rats (magnification × 40; scale bar, 200 μm). (D) Quantitative RT-PCR was utilized to measure ASPN mRNA levels in renal tissues of CaOx and control rats (n = 3, mean ± SD, *P \u0026lt; 0.05, **P \u0026lt; 0.01). EG, ethylene glycol; CaOx, calcium oxalate; IHC, immunohistochemistry; VK, Von Kossa; AR, Alizarin Red; PAS, Periodic Acid-Schiff; DHE, Dihydroethidium; ROS, reactive oxygen species;\u003c/p\u003e","description":"","filename":"FIg12300dpi.png","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/ee5139bff9e7595a7b39dec7.png"},{"id":70390616,"identity":"5ff59cb0-37f1-4220-b812-9f2fcbd6cbec","added_by":"auto","created_at":"2024-12-02 17:30:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":77760264,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5059612/v1/0f3566e9-b01a-4c8d-9e75-af4a09fdc6cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Molecular Interactions Between Nephrolithiasis and Carotid Atherosclerosis: Asporin as a Potential Biomarker","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNephrolithiasis, or kidney stone disease, is a widespread condition that affects men and women of all ages. Globally, its occurrence varies from 7\u0026ndash;13% in North America, 5\u0026ndash;9% in Europe, and 1\u0026ndash;5% in Asia. The primary substance in kidney stones is calcium oxalate (CaOx) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Nephrolithiasis may lead to ureter obstruction, blood in the urine, recurrent urinary tract infections, discomfort during urination, and vomiting. If not properly treated, these conditions can result in long-term damage to the kidneys. Randall\u0026rsquo;s plaque (RP), first proposed by Alexander Randall in 1937 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], is a region of subepithelial mineralized tissue located at the tip of the renal papillae. He proposed that kidney stones form from two initial lesions in the renal papillae. Type I, or Randall\u0026rsquo;s plaques, involve calcium phosphate (CaP) and calcium carbonate deposits in the subendothelial space. Randall\u0026rsquo;s plugs, also known as Type II, are made up of crystallized salts, cellular debris, and dead cells caused by high levels of urinary supersaturation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recently, Dr. Khan proposed a unified theory for the formation of plaques and plugs. Renal epithelial cells as stone formers undergo stress resulting from altered urinary chemistry, leading to their de-differentiation into osteoblast-like cells. The transformation from a RP to a kidney stone may be explained by oxidative stress and osteogenic calcification [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAtherosclerosis is a long-term condition characterized by the gradual accumulation of oxidized fats, dying cells, and extracellular matrices within the inner walls of arteries. It stems from metabolic irregularities that modify the composition of body fluids and arteries [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Carotid atherosclerosis (CAS) is a type of atherosclerotic disease that occurs in the cervical arteries. It is a major contributor to strokes and is linked to an increased likelihood of cardiovascular disease (CVD) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Studies suggest that a correlation exists between RP and CAS [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Metabolic syndrome, including hypertension, diabetes mellitus, obesity, and dyslipidemia, is considered a common risk factor for nephrolithiasis and atherosclerotic CVD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The generation of reactive oxygen species (ROS) is also crucial in the development of kidney stones and atherosclerosis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. ROS have the potential to unsettle atherosclerotic plaques by triggering inflammation, resulting in vascular smooth muscle cell demise and plaque rupture [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This concept of atherogenesis shares similarities with the vascular theory of Randall plaque formation. Moreover, hypercalcemia is a contributing factor to stone formation as increased serum calcium levels can transform muscle cells into osteoblast-like cells, which further leads to vascular calcification [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThrough this research, we discovered potential biomarkers and mechanisms related to the progression of RP and CAS by examining datasets from the GEO database. WGCNA and DEG analysis were used to identify the hub gene ASPN, which was significantly associated with RP and CAS. In addition, an immune infiltration analysis revealed a role for immune cells in these diseases and provided insight into their pathophysiological processes. Furthermore, we successfully established a CaOx nephrolithiasis rat model. Quantitative RT-PCR and IHC were conducted to validate the expression patterns of ASPN in human and rat tissues.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eExpression profiles for RP and CAS were retrieved from the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The research approach for this investigation included: 1) acquiring nephrolithiasis dataset samples from CaOx RP tissue and CAS dataset samples from carotid plaques; 2) ensuring datasets had control groups; 3) sourcing samples from human subjects; and 4) downloading GEO datasets GSE73680, GSE100927, GSE117518, and GSE43292.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eWeighted gene co-expression network analysis\u003c/h2\u003e \u003cp\u003eThe Weighted Gene Co-expression Network Analysis (WGCNA) is a biological algorithm designed to detect important co-expressed gene clusters and assess the relationship between gene networks and various diseases [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. We used WGCNA to analyze the GSE73680 and GSE100927 datasets, which revealed modules associated with RP and CAS. Following the application of a 25% variance threshold for all genes, we utilize the R programming language to remove outlier samples from the hierarchical clustering process. To ensure a scale-free network, we calculated the scale-free fit index and mean connectivity. A β value of 19 was selected for soft thresholding of GSE73680, and 9 for GSE100927. Afterwards, the adjacency matrix was converted into the topological overlap matrix (TOM) and the dissimilarity matrix derived from TOM (1-TOM). Genes with similar expression profiles were further merged into different modules by generating a hierarchical clustering dendrogram. Ultimately, the relationship between each module and each disease was assessed, and genes within the modules showing strong correlations were chosen for additional examination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene expression analysis\u003c/h2\u003e \u003cp\u003eWe performed an analysis of differential expression comparing disease and control groups on supplementary RP and CAS datasets (GSE117518 and GSE43292) utilizing the LIMMA package in R (version 4.3.2). Genes were identified as DEGs in both the RP and CAS datasets if they had a P-value less than 0.05 and an absolute log2 fold change greater than 0.8. R software was utilized to generate heatmaps for differential gene clustering and create volcano plots.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of shared genes and enrichment analysis\u003c/h2\u003e \u003cp\u003eWe selected modules with a high relevance to RP and CAS. The shared genes in the positively or negatively related modules of RP and CAS were evaluated using a Venn diagram. They were further examined by GO and KEGG pathway enrichment analyses. Statistics were considered significant when the P-value was less than 0.05.\u003c/p\u003e \u003cp\u003eSimilarly, we extracted overlapping DEGs in two additional RP and CAS databases (GSE117518 and GSE43292) and performed functional enrichment analyses. The shared genes from WGCNA and the DEGs were overlapped using a Venn diagram. The overlapping gene that was ultimately identified was considered the hub gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eHub gene expression levels and diagnostic value\u003c/h2\u003e \u003cp\u003eWe analyzed the variations in hub gene expression between the disease and control groups across the datasets GSE73680, GSE100927, GSE117518, and GSE43292. We assessed the diagnostic significance of the hub gene by computing the area under the ROC curve across four datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGSEA for the hub gene\u003c/h2\u003e \u003cp\u003e We categorized each disease cohort into two subgroups according to the hub gene expression and conducted a differential gene expression analysis. The 'clusterProfiler' package was utilized to perform GSEA. The MSigDB dataset (c2.cp.kegg.v7.0.symbols.gmt) was utilized to compare the biological signaling pathways between groups with high and low expression levels. The top 10 up- and downregulated pathways were displayed using enrichplot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eImmune infiltration analysis\u003c/h2\u003e \u003cp\u003eThe xCell algorithm (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xcell.ucsf.edu/\u003c/span\u003e\u003cspan address=\"https://xcell.ucsf.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a computational method based on gene signatures that calculates the relative infiltration scores for 64 types of immune and stromal cells. By categorizing the diseased tissues of RP and CAS and analyzing hub gene expression levels, we employed the xCell algorithm to evaluate immune infiltration between groups with high and low expression. Immune and stromal infiltrated cells showing statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were chosen to assess their correlation with the central gene using Spearman's rank correlation analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHuman tissues collection\u003c/h2\u003e \u003cp\u003eHuman RP tissues were biopsied from patients with kidney stones during percutaneous nephrolithotomy. The stone composition of all patients was diagnosed post-surgery as CaOx using Fourier transform infrared spectroscopy. We obtained normal renal papillary tissues from individuals who had nephrectomies due to kidney tumors. The tissues were obtained from the papillary region, which was devoid of any tumor invasion. Plaque tissues from human carotid arteries were obtained from patients who had carotid endarterectomy due to artery narrowing. The disease samples were carotid intima attached with atheroma plaque, whereas the control samples were adjacent smooth intima without atheroma plaque. This research was approved by the Ethics Committee at the First Affiliated Hospital of Nanjing Medical University in China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCaOx nephrolithiasis rat models\u003c/h2\u003e \u003cp\u003eA CaOx nephrolithiasis model was developed using 6-week-old male Sprague-Dawley (SD) rats. This study received approval from the Nanjing Medical University Laboratory Animal Ethics Board. At the start, we split 12 SD rats into two groups of equal size: a control group and a model group. For the model group, the rats were administered 1% ethylene glycol in their drinking water. Sterile tap water was given to the rats in the control group. Following a 4-week period, the rats were euthanized, and kidney tissues were harvested for Von Kossa (VK), Alizarin Red (AR), and Periodic Acid-Schiff (PAS) staining to identify calcium oxalate crystal deposits and evaluate renal damage. Dihydroethidium (DHE) staining was used with microfluorimetry to measure ROS production. The expression profile of the key gene in rat kidney tissues was identified using qRT-PCR and IHC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and qRT-PCR\u003c/h2\u003e \u003cp\u003eRNA was isolated from these human samples with TRIzol reagent (Invitrogen, Carlsbad, USA) and converted into cDNA using HiScript II RT SuperMix for qPCR (Vazyme, Nanjing, China). Subsequently, ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China) was employed to quantify ASPN expression in the two conditions via qRT-PCR. The ASPN primer sequences utilized were: Forward, 5\u0026prime;‑ AACAAGCTAACGAAGATTCACCC ‑3\u0026prime; and Reverse, 5\u0026prime;‑ CCCCTGGCTCTATCCCATTATT ‑3\u0026prime;, sourced from Realgene Biotech in Nanjing, China. The β-actin primer sequence used as an internal control was forward 5\u0026prime;‑TGACGGGGTCACCCACACTGTGCCCATCTA‑3\u0026prime; and reverse 5\u0026prime;‑CTAGAAGCATTTGCGGTGGACGATGGAGGG‑3\u0026prime;, sourced from Realgene Biotech in Nanjing, China. A Student's t-test was used to compare differences between disease and control groups. Statistical significance was defined as a P value less than 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIHC staining\u003c/h2\u003e \u003cp\u003eTissue sections around 4\u0026ndash;5 \u0026micro;m thick were deparaffinized using xylene and then rehydrated through a series of ethanol solutions. The tissue samples underwent antigen retrieval by being heated in 10 mL of citrate buffer (pH 6.0) inside a pressure cooker at 121\u0026deg;C for 20 minutes. To inhibit the natural peroxidase activity, a 3% hydrogen peroxide solution in methanol was applied for 15 minutes at ambient temperature. A blocking mixture containing 5% normal goat serum in PBS was applied to the slides for one hour at room temperature. Then, the specimens were left to incubate with primary antibodies targeting ASPN at a 1:100 dilution overnight at 4˚C (AV42487; Sigma, St. Louis, USA). The specimens were rinsed in PBS for 10 minutes and then exposed to goat anti-rabbit IgG secondary antibody (1:1,000 dilution; ZB-2301; ZSGB-BIO, Beijing, China) for one hour at ambient temperature. Following three additional washes with PBS, peroxidase activity was visualized using a fresh substrate solution containing 3,3\u0026prime;diaminobenzidine. The samples were stained with hematoxylin at ambient temperature for two minutes. The slides were dehydrated and mounted with a coverslip. IHC quantitation was done using ImageJ software based on the positive area and intensity of the staining.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGEO information\u003c/h2\u003e \u003cp\u003eThe diagram in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the process for this research. We selected four datasets for analysis based on our selection criteria, including GSE 73680, GSE117518, GSE100927, and GSE43292. Detailed information on these four datasets is listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. WGCNA analysis was done using the paired datasets, GSE73680 and GSE100927, and a DEG analysis was performed using GSE117518 and GSE43292.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetailed information of GEO datasets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE73680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL17077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 patients and 6 controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCaOx Kidney stones\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE117518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL21827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 patients and 3 controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCaOx Kidney stones\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE100927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL17077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 patients and 12 controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAtherosclerotic carotid artery\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE43292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPL6244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 patients and 32 controls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAtherosclerotic carotid artery\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eScreening for co-expression modules in RP and CAS\u003c/h2\u003e \u003cp\u003eTo examine the relationship between the diseases and key genes, WGCNA analysis was conducted with GSE73680 and GSE100927. After selecting a 25% variance cutoff for all genes, a cluster analysis was performed to identify outliers; however, no samples were excluded in either dataset. The scale-free fit index and mean connectivity were calculated to ensure scale-free networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, D). RP's soft-threshold power was configured to β\u0026thinsp;=\u0026thinsp;19, achieving a scale-free R2 of 0.85. For CAS, the soft-thresholding power was adjusted to β\u0026thinsp;=\u0026thinsp;9 (scale-free R2\u0026thinsp;=\u0026thinsp;0.80). A total of 15 and 12 modules were identified in GSE73680 and GSE100927, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, F). The MEdarkgreen module had the strongest positive correlation with RP (r\u0026thinsp;=\u0026thinsp;0.39, p\u0026thinsp;=\u0026thinsp;0.03), whereas the MEred module exhibited the strongest negative correlation to RP (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.45, p\u0026thinsp;=\u0026thinsp;0.01). In the case of CAS, the MEpink module exhibited the highest positive correlation (r\u0026thinsp;=\u0026thinsp;0.91, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the MEblue module showed the most significant negative correlation (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.82, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Finally, we overlapped the hub modules between RP and CAS using a Venn diagram, which resulted in the identification of 125 intersecting positive genes and 100 intersecting negative genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of overlapping DEGs of RP and CAS\u003c/h2\u003e \u003cp\u003eTo further identify the key genes common to the two diseases, we performed a DEG analysis on an additional RP dataset, GSE117518, and an additional CAS dataset, GSE43292.For GSE117518, we identified a total of 1151 DEGs, comprising 690 genes that were upregulated and 461 that were downregulated. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). Among the 456 DEGs identified for GSE43292, 301 were upregulated and 155 were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). Using Venn diagrams, we identified 10 overlapping upregulated DEGs and 13 overlapping downregulated DEGs between RP and CAS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of intersecting modules genes and overlapping DEGs\u003c/h2\u003e \u003cp\u003eWe conducted GO and KEGG enrichment analyses on the 225 intersecting genes identified by WGCNA and the 23 overlapping DEGs to explore the possible co-pathogenesis of RP and CAS. GO analysis indicated that the overlapping genes within the common trend modules were mainly linked to collagen-containing extracellular matrix, contractile fiber, and extracellular matrix structural constituent (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In contrast, the common DEGs were mainly associated with heart process, oligosaccharide binding, and regulation of cardiac muscle contraction by regulation of the release of sequestered calcium ion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). KEGG analysis indicated that the overlapping genes in the common trend modules were mainly linked to protein digestion and absorption, regulation of actin cytoskeleton, and hypertrophic cardiomyopathy (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), while the shared DEGs were mainly associated with cardiac muscle contraction, AGE-RAGE signaling pathway in diabetic complications, and dilated cardiomyopathy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Overall, the findings suggest that these shared genes were mainly associated with extracellular matrix, actin function, calcium ion signaling, cardiac muscle contraction, and cardiomyopathy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and validation of the hub gene in RP and CAS\u003c/h2\u003e \u003cp\u003eWe overlapped the shared genes obtained by WGCNA and DEG analysis to identify potential core genes between both diseases. No gene was identified after crossing the 125 intersecting positive genes and the 10 overlapping upregulated DEGs. Interestingly, we identified ASPN after crossing the 100 intersecting negative genes and the 13 overlapping downregulated DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The findings indicate that ASPN is a central gene crucial to the development of these two conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo confirm the expression levels and diagnostic significance of ASPN in both conditions, we examined the expression profiles and created ROC curves for the four datasets. The expression of ASPN was significantly decreased in both RP and CAS when contrasted with the control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA\u0026ndash;D). With respect to RP, ASPN exhibited good diagnostic values (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE, G) in GSE73680 (AUC\u0026thinsp;=\u0026thinsp;0.743) and GSE117518 (AUC\u0026thinsp;=\u0026thinsp;1). With respect to CAS, ASPN exhibited perfect diagnostic accuracy (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF, H) in GSE100927 (AUC\u0026thinsp;=\u0026thinsp;0.931) and GSE43292 (AUC\u0026thinsp;=\u0026thinsp;0.817).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eGSEA of ASPN\u003c/h2\u003e \u003cp\u003eWe continued our examination to determine the role of ASPN in these two diseases. Initially, the disease specimens were categorized into groups with high and low ASPN expression levels. GSEA was used to compare the signaling pathways between the high-expression groups and the low-expression groups. In the RP dataset GSE73680, GSEA indicated that the group with elevated expression levels was linked to axon guidance, basal cell carcinoma, cell cycle, ECM receptor interaction, focal adhesion, spliceosome, ubiquitin-mediated proteolysis, and vascular smooth muscle contraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Conversely, the group with lower expression levels showed an enrichment of leishmania infection and olfactory signaling. Within the CAS dataset GSE100927, the group exhibiting elevated expression levels was linked to arrhythmogenic right ventricular cardiomyopathy, dilated cardiomyopathy, drug metabolism cytochrome P450, focal adhesion, hypertrophic cardiomyopathy, and the nod-like receptor signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). In contrast, the low-expression group showed enrichment in the chemokine signaling pathway, cytokine receptor interaction, lysosome, and transforming growth factor-beta (TGF-β) signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eImmune and stroma infiltration analysis\u003c/h2\u003e \u003cp\u003eWe determined the correlation of ASPN expression with the immune microenvironment in RP and CAS using the xCell algorithm. The results indicated the abundance of 64 immune cells and stroma cells in different groups.In the RP dataset GSE73680, there was a notable rise in the infiltration of immune cells like aDC and M1 macrophages in the low-expression group, while the infiltration of chondrocytes, or stroma cells, was reduced in the high-expression group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA).In the CAS dataset GSE100927, a notable rise in immune cell infiltration, including B cells and natural killer T (NKT) cells, was observed in the low-expression group. Conversely, the infiltration of stromal cells like adipocytes, chondrocytes, fibroblasts, keratinocytes, myocytes, neurons, and smooth muscle cells, was reduced in the same group (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, we also determined the correlation between ASPN and different cells. In RP samples, M1 macrophages were significantly negatively correlated with ASPN.In contrast, chondrocytes showed a positive correlation (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). In the CAS samples, ASPN was significantly negatively correlated with B cells and NKT cells, whereas stroma cells, such as adipocytes, chondrocytes, fibroblasts, keratinocytes, myocytes, neurons, and smooth muscle cells exhibited significantly positive correlations with ASPN (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). As a result of the findings, ASPN may be crucial in the infiltration of immune and stromal cells during RP and CAS progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eExperimental validation of ASPN expression in human tissues\u003c/h2\u003e \u003cp\u003eTo validate ASPN expression in human tissues, we gathered RP samples from five individuals with CaOx kidney stones and also acquired normal renal papillary tissues from five other patients who had nephrectomies for renal tumors. We also collected human CAS plaque tissues and adjacent smooth intima from five patients who underwent carotid endarterectomy because of carotid artery stenosis. The relative mRNA levels of ASPN in human tissues were quantified using RT-PCR. As the results show, ASPN mRNA expression was significantly reduced in RP tissues and CAS plaque tissues compared with normal renal papillary tissues and normal intima (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC, F). Moreover, IHC consistently showed that the relative expression of the ASPN protein in the RP tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA, B) and CAS plaque tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD, E) was markedly lower compared with that in normal tissues.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eEstablishment of a rat model and validation of ASPN expression\u003c/h2\u003e \u003cp\u003eWe used VK, AR, PAS, and DHE staining to evaluate the effectiveness of an EG-induced CaOx nephrolithiasis rat model (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA). VK and AR staining revealed crystal accumulation within the renal tubule lumens of CaOx rats, along with kidney structural issues like tubular and glomerular atrophy, interstitial inflammation, and brush border loss. On the other hand, PAS staining revealed renal damage and interstitial fibrosis in CaOx rats. We used DHE staining to measure cytoplasmic ROS production in the renal tissues of CaOx rats. Next, qRT-PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eD) and IHC (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB, C) were carried out to measure the expression of ASPN in rat renal tissues. The expression of ASPN mRNA and protein was downregulated in the CaOx rat models. These findings are consistent with the results observed in human renal tissues.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eNephrolithiasis and CVDs are common disorders and the global prevalence of both conditions is on the rise. Recent research has shown that kidney stone development and arteriosclerosis are linked by similar pathological mechanisms and numerous risk factors, including dyslipidemia, diabetes and hypertension [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. They are both chronic progressive diseases and involve the accumulation of lipids and oxidized proteins in the kidney tissue and arterial wall [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, there have been no studies focused on the molecular mechanism of nephrolithiasis and arteriosclerosis. Therefore, we conducted an analysis using transcriptomic data for RP and CAS to identify a key crosstalk gene (ASPN) and elucidated the common mechanisms of these two diseases from a novel perspective.\u003c/p\u003e \u003cp\u003eFor this research, we obtained transcriptome data for RP and CAS from the GEO repository. By overlapping the shared genes screened by WGCNA and DEGs, ASPN was identified as the sole key crosstalk gene between RP and CAS. ASPN was downregulated in RP tissues and CAS plaque tissues and exhibited a strong negative correlation with both diseases. In addition, qRT-PCR and IHC assays confirmed that ASPN mRNA and protein expression was lower compared with that of normal tissues from humans and a nephrolithiasis rat model. To assess the diagnostic significance of ASPN, we conducted a ROC curve analysis. The results indicated that ASPN had high AUC values in RP and CAS. These findings indicate that ASPN may represent a valuable biomarker for distinguishing nephrolithiasis and CAS from normal controls.\u003c/p\u003e \u003cp\u003eASPN, alternatively referred to as periodontal ligament-associated protein-1 (PLAP-1), is an important component of the Class I Small Leucine-Rich Proteoglycan family. It is an important component of the extracellular matrix. Additionally, it acts as a negative regulator in maintaining the balance of periodontal tissues, safeguarding the periodontal ligament from excessive bone growth by preventing osteoblast differentiation and bone formation [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. ASPN is linked to the progression of multiple conditions, including bone degeneration [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], malignancies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and endometriosis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. To date, there have been no studies on ASPN in the context of nephrolithiasis and atherosclerosis. Earlier researches indicate that the development of RP and its conversion into a stone nucleus are strongly linked to oxidative stress, causing renal epithelial cells to transform into osteoblast-like cells [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, the conversion of vascular smooth muscle cells into osteoblast-like cells occurs during the pathological process of atherosclerosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thus, we proposed that ASPN's capacity to suppress osteoblast differentiation might be a shared pathway contributing to the onset and progression of kidney stones and atherosclerosis.\u003c/p\u003e \u003cp\u003eThis research has, for the first time, verified that reduced ASPN expression is strongly linked to the onset of kidney stones and arterial plaque buildup. In order to uncover additional molecular mechanisms linked to ASPN in both conditions, patients were categorized into groups with high and low ASPN expression, followed by enrichment and immune cell infiltration analyses. Using GSEA, the expression differences of ASPN in RP and CAS were associated with various signaling pathways, whereas focal adhesion was a common biological process between the two. Further studies are needed to confirm the underlying mechanisms of these pathways and the role of ASPN in nephrolithiasis and atherosclerosis.\u003c/p\u003e \u003cp\u003eExperimental and clinical data have substantiated the involvement of immune response and inflammation in the development of kidney stones and atherosclerosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. By analyzing immune cell infiltration, we discovered notable differences in immune cell profiles between the high and low ASPN subgroups in both RP and CAS. In the low ASPN subgroups, there was a notable rise in immune cells, while stroma cells showed a reduction. In the RP disease groups, we found that ASPN was highly negatively correlated with M1 macrophages, which suggests that downregulated expression of ASPN promotes the infiltration of M1 macrophages. Other studies have explored the role of macrophages in the crystal inflammatory and immune response in nephrolithiasis. Recent research indicates that M1 macrophages promote crystal accumulation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and CaOx might trigger the transformation of monocytes into M1 macrophages during their polarization [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Thus, we hypothesize that downregulated expression of ASPN promotes stone formation by driving M1 macrophage polarization. Within the CAS samples, we noticed that ASPN showed a strong inverse relationship with immune cells, including B cells and NKT cells. Although some studies suggest that B cells prevent atherosclerosis by producing protective antibodies [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], subsequent studies indicate that exacerbation of atherosclerosis by B-2 cells reveal the subset-dependent impact of B cells [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. NKT cells exhibit various effector functions, such as activating neighboring immune cells and exerting powerful cytotoxic potential [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Various research findings have shown that NKT cells significantly contribute to the development of atherosclerosis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Taken together, these results suggest that ASPN is a key factor associated with the immune response in nephrolithiasis and atherosclerosis.\u003c/p\u003e \u003cp\u003eOur study for the first time used a comprehensive bioinformatics analysis to identify ASPN as the key crosstalk gene in RP and CAS. Additionally, we performed tests to confirm the expression patterns of ASPN mRNA and protein in human tissues and a rat model of kidney stones. However, there are some limitations to the present study. Initially, the RP dataset GSE117518 includes just 3 patients and 3 controls, so the AUC value for ASPN in this dataset (AUC\u0026thinsp;=\u0026thinsp;1) might be unreliable. In addition, RP samples and normal renal papillary samples were not obtained from the same individual. Moreover, we validated the expression of ASPN in a nephrolithiasis rat model, but did not construct an animal model for CAS. Ultimately, the fundamental molecular process of ASPN in RP and CAS needs additional confirmation through both in vitro and in vivo studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe explored the intimate genetic relationship between nephrolithiasis and CAS. We identified ASPN as a key crosstalk gene that may be involved in osteoblast differentiation and the immune response in nephrolithiasis and CAS. This research improves our comprehension of the processes leading to the formation of kidney stones and CAS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe deeply appreciate the efforts of everyone and every organization that played a role in bringing this research study to fruition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYH analyzed the data and drafted the manuscript. ZZ conducted the experiments. SM recruited the patient samples. ZW and SW reviewed and edited the manuscript. LX conducted pathological analysis. RS and XM designed the study and provided funding. Every writer reviewed the completed manuscript and gave \u0026nbsp;their consent for its publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this research was provided by the National Natural Science Foundation of China under grant number 81801438.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccess to information and resources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe public datasets were downloaded and analyzed in this study are available in the GEO repository, including GSE73680, GSE100927, GSE117518 and GSE43292.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval of ethics and agreement to join The Ethics Committee of the First Affiliated Hospital of Nanjing Medical University granted ethical approval for this research, under reference number 2022-SRFA-429. This \u0026nbsp;study\u0026apos;s employment of animal subjects was evaluated and sanctioned by the Institutional Animal Care and Use Committee at Nanjing Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided their written consent after being informed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe writers state that they possess no conflicting interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSorokin I et al (2017) Epidemiology of stone disease across the world. World J Urol 35(9):1301\u0026ndash;1320\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRandall A, THE ORIGIN AND GROWTH OF RENAL CALCULI (1937) Ann Surg 105(6):1009\u0026ndash;1027\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaudon M, Bazin D, Letavernier E (2015) Randall's plaque as the origin of calcium oxalate kidney stones. Urolithiasis 43(Suppl 1):5\u0026ndash;11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan SR, Canales BK (2015) Unified theory on the pathogenesis of Randall's plaques and plugs. Urolithiasis 43(0 1):109\u0026ndash;123\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLusis AJ (2000) Atherosclerosis Nat 407(6801):233\u0026ndash;241\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHollander M et al (2002) Carotid plaques increase the risk of stroke and subtypes of cerebral infarction in asymptomatic elderly: the Rotterdam study. 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Adv Urol, 2018: p. 3068365\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEwence AE et al (2008) Calcium phosphate crystals induce cell death in human vascular smooth muscle cells: a potential mechanism in atherosclerotic plaque destabilization. Circ Res 103(5):e28\u0026ndash;34\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu KC et al Vascular calcification and renal bone disorders. ScientificWorldJournal, 2014. 2014: p. 637065\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLangfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRule AD et al (2010) Kidney stones associate with increased risk for myocardial infarction. J Am Soc Nephrol 21(10):1641\u0026ndash;1644\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObligado SH, Goldfarb DS (2008) The association of nephrolithiasis with hypertension and obesity: a review. Am J Hypertens 21(3):257\u0026ndash;264\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor EN, Stampfer MJ, Curhan GC (2005) Diabetes mellitus and the risk of nephrolithiasis. Kidney Int 68(3):1230\u0026ndash;1235\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDevarajan A (2018) Cross-talk between renal lithogenesis and atherosclerosis: an unveiled link between kidney stone formation and cardiovascular diseases. Clin Sci (Lond) 132(6):615\u0026ndash;626\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun J et al (2014) Overexpression of the PLAP-1 gene inhibits the differentiation of BMSCs into osteoblast-like cells. J Mol Histol 45(5):599\u0026ndash;608\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamada S et al (2007) PLAP-1/asporin, a novel negative regulator of periodontal ligament mineralization. J Biol Chem 282(32):23070\u0026ndash;23080\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEge B et al (2021) Asporin levels in patients with temporomandibular joint disorders. J Oral Rehabil 48(10):1109\u0026ndash;1117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra A et al (2019) Identifying the role of ASPN and COMP genes in knee osteoarthritis development. J Orthop Surg Res 14(1):337\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastellana B et al (2012) ASPN and GJB2 Are Implicated in the Mechanisms of Invasion of Ductal Breast Carcinomas. J Cancer 3:175\u0026ndash;183\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRochette A et al (2017) Asporin is a stromally expressed marker associated with prostate cancer progression. Br J Cancer 116(6):775\u0026ndash;784\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Sun J (2022) ASPN Is a Potential Biomarker and Associated with Immune Infiltration in Endometriosis. Genes (Basel), 13(8)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGambaro G et al (2004) Crystals, Randall's plaques and renal stones: do bone and atherosclerosis teach us something? J Nephrol 17(6):774\u0026ndash;777\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Water R et al (1999) Calcium oxalate nephrolithiasis: effect of renal crystal deposition on the cellular composition of the renal interstitium. Am J Kidney Dis 33(4):761\u0026ndash;771\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLey K (2021) Inflammation and Atherosclerosis. Cells, 10(5)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaguchi K et al (2016) M1/M2-macrophage phenotypes regulate renal calcium oxalate crystal development. Sci Rep 6:35167\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDominguez-Gutierrez PR et al (2018) Calcium Oxalate Differentiates Human Monocytes Into Inflammatory M1 Macrophages. Front Immunol 9:1863\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyaw T et al (2011) B1a B lymphocytes are atheroprotective by secreting natural IgM that increases IgM deposits and reduces necrotic cores in atherosclerotic lesions. Circ Res 109(8):830\u0026ndash;840\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyaw T et al (2013) BAFF receptor mAb treatment ameliorates development and progression of atherosclerosis in hyperlipidemic ApoE(-/-) mice. PLoS ONE 8(4):e60430\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKyaw T et al (2010) Conventional B2 B cell depletion ameliorates whereas its adoptive transfer aggravates atherosclerosis. J Immunol 185(7):4410\u0026ndash;4419\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrennan PJ, Brigl M, Brenner MB (2013) Invariant natural killer T cells: an innate activation scheme linked to diverse effector functions. Nat Rev Immunol 13(2):101\u0026ndash;117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMetelitsa LS et al (2001) Human NKT cells mediate antitumor cytotoxicity directly by recognizing target cell CD1d with bound ligand or indirectly by producing IL-2 to activate NK cells. J Immunol 167(6):3114\u0026ndash;3122\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakai Y et al (2004) Natural killer T cells accelerate atherogenesis in mice. Blood 104(7):2051\u0026ndash;2059\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTupin E et al (2004) CD1d-dependent activation of NKT cells aggravates atherosclerosis. J Exp Med 199(3):417\u0026ndash;422\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"urolithiasis","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ures","sideBox":"Learn more about [Urolithiasis](http://link.springer.com/journal/240)","snPcode":"240","submissionUrl":"https://submission.nature.com/new-submission/240/3","title":"Urolithiasis","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Randall’s plaque, Carotid atherosclerosis, Bioinformatics, Immune infiltration, Osteoblast differentiation","lastPublishedDoi":"10.21203/rs.3.rs-5059612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5059612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIncreasing evidence has suggested nephrolithiasis has a close linkage with carotid atherosclerosis (CAS). Randall\u0026rsquo;s plaque (RP) is considered the precursor lesion of nephrolithiasis. This study aimed to examine the crosstalk genes and potential molecular mechanisms between RP and CAS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe downloaded microarray data for calcium oxalate (CaOx) RP and CAS from the Gene Expression Omnibus (GEO) repository. To pinpoint common genes associated with RP and CAS, researchers employed weighted gene co-expression network analysis (WGCNA) alongside differentially expressed gene (DEG) analysis. Enrichment analyses using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) were conducted on the common genes. A central gene was discovered, and a receiver operating characteristic (ROC) curve was created to assess its diagnostic effectiveness. The hub gene was also analyzed using Gene Set Enrichment Analysis (GSEA). Additionally, the xCell algorithm evaluated immune cell infiltration levels, and the relationship between each immune cell type and the central gene was analyzed. Subsequently, the hub gene's expression in human RP and CAS tissues was assessed using quantitative reverse-transcription PCR (qRT-PCR) and immunohistochemistry (IHC) staining. Finally, we established a CaOx nephrolithiasis rat model by administering 1% ethylene glycol. The expression patterns of ASPN in rat kidney tissues were confirmed using IHC and qRT-PCR.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWGCNA was used to select highly correlated modules and resulted in 225 intersection genes in GSE73680 and GSE100927. On the other hand, 23 overlapping DEGs were identified in GSE117518 and GSE43292. Asporin (ASPN) emerged as the central gene linking RP and CAS by intersecting the highly correlated module genes from WGCNA with the differentially expressed genes (DEGs). The findings on immune infiltration indicated a notable correlation between ASPN and various immune cells in both RP and CAS. IHC and qRT-PCR verified that ASPN expression was lower in human RP and CAS plaque tissues than in normal tissues. Furthermore, the expression pattern of ASPN in CaOx nephrolithiasis model rats was consistent with the results in human tissues.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe identified ASPN as an important crosstalk gene in RP and CAS. Further study of the immune response and osteoblast differentiation may reveal the shared pathogenesis between RP and CAS.\u003c/p\u003e","manuscriptTitle":"Exploring the Molecular Interactions Between Nephrolithiasis and Carotid Atherosclerosis: Asporin as a Potential Biomarker","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-11 14:33:42","doi":"10.21203/rs.3.rs-5059612/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-04T08:08:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-01T15:43:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257437889460898415733874300000764214544","date":"2024-10-29T21:22:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284927717794323010173182786383240175215","date":"2024-10-04T20:05:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-15T08:00:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-12T05:41:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-12T05:39:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Urolithiasis","date":"2024-09-09T17:00:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"urolithiasis","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ures","sideBox":"Learn more about [Urolithiasis](http://link.springer.com/journal/240)","snPcode":"240","submissionUrl":"https://submission.nature.com/new-submission/240/3","title":"Urolithiasis","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"256044f0-18fa-4fb8-8f60-354a7a6132d4","owner":[],"postedDate":"November 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T17:25:40+00:00","versionOfRecord":{"articleIdentity":"rs-5059612","link":"https://doi.org/10.1007/s00240-024-01665-1","journal":{"identity":"urolithiasis","isVorOnly":false,"title":"Urolithiasis"},"publishedOn":"2024-11-26 15:58:18","publishedOnDateReadable":"November 26th, 2024"},"versionCreatedAt":"2024-11-11 14:33:42","video":"","vorDoi":"10.1007/s00240-024-01665-1","vorDoiUrl":"https://doi.org/10.1007/s00240-024-01665-1","workflowStages":[]},"version":"v1","identity":"rs-5059612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5059612","identity":"rs-5059612","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}