Application of Podocan in predicting blood vessel plaque model and system and instrument thereof

Abstract

The invention relates to an application of Podocan in predicting a coronary blood vessel vulnerable plaque model. Plasma proteomics, machine learning and mechanism research are combined to discover and verify that Podocan is used as a biomarker and a regulatory factor of plaque stability. On the one hand, as a detection marker, the marker is beneficial to predicting and diagnosing the vulnerability of atherosclerotic plaques, performing more accurate risk stratification on patients, and taking more positive preventive measures or implementing early intervention on patients possibly suffering from acute coronary syndromes. On the other hand, as a plaque stability regulatory factor, the compound has the significance of targeted therapy.

Description

Technical Field

[0001] This invention relates to the application of a known substance in disease prediction, and more particularly to a protein for predicting the vulnerability and probability of atherosclerotic plaques, which is beneficial for the clinical prevention and early intervention of acute coronary syndrome. Background Technology

[0002] Atherosclerotic plaque rupture is a major pathological basis of acute coronary syndrome (ACS). In atherosclerotic lesions, vulnerable plaques, characterized by thin fibrous caps, large lipid cores, and increased inflammatory activity, are more prone to rupture and trigger acute events. Accurate identification of these high-risk plaques is crucial for early intervention and secondary prevention.

[0003] Currently, the detection of vulnerable plaques relies on intracoronary imaging techniques such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT). IVUS provides information on plaque burden and vascular remodeling, while OCT offers higher resolution, visualizing features such as fibrous cap thickness and macrophage accumulation. However, both methods are invasive, expensive, and limited to local assessment of accessible coronary artery segments. Furthermore, their predictive value for future cardiovascular events remains limited. These limitations highlight the clinical need for non-invasive, cost-effective, and widely applicable tools.

[0004] To address these limitations, recent studies have attempted to identify molecular markers or circulating biomarkers of plaque vulnerability. For example, spatial transcriptomic analysis of human carotid artery plaques has revealed region-specific gene expression patterns and highlighted matrix metalloproteinase-9 as a potential therapeutic target and an indicator of susceptibility to circulatory rupture. Other studies have focused on the mechanisms by which smooth muscle cell necrosis and apoptosis, and non-coding RNAs (such as circHIPK3 or miR-21), regulate plaque instability through oxidative stress or inflammatory signaling pathways. The comprehensive review also lists inflammatory mediators, proteomic markers, and epigenetic factors (such as microRNAs) that may have diagnostic or prognostic value in this context.

[0005] However, to date, no circulating biomarker has been validated to have sufficient sensitivity and specificity to meet the clinical need for non-invasive detection of vulnerable plaques. Summary of the Invention

[0006] One objective of this invention is to provide an application of Podocan in predicting vulnerable plaques in coronary arteries, predicting the vulnerability and probability of atherosclerotic plaques, which is beneficial for the clinical prevention and early intervention of acute coronary syndrome.

[0007] Another objective of this invention is to provide a system for predicting vulnerable vascular plaques, providing a basis and medical advice for the prevention and early intervention of acute coronary syndrome.

[0008] Another objective of this invention is to provide a medical device that loads a model for predicting vulnerable vascular plaques based on Podocan plasma concentration, periodically detects the Podocan concentration level in a patient's blood sample, and provides risk warnings after analyzing the data locally or remotely.

[0009] This invention categorizes recruited patients based on plaque stability and employs plasma proteomics analysis accordingly to identify candidate biomarkers associated with plaque vulnerability. Subsequent in vivo and in vitro studies further evaluate these candidates and explore their potential roles in the pathophysiology of plaque vulnerability.

[0010] By combining plasma proteomics, machine learning, and mechanistic studies, we discovered and validated podocyte protein (PODN) as a biomarker and regulator of plaque stability.

[0011] Ten differentially expressed proteins were identified through proteomics screening. These ten proteins were incorporated into the same model and subjected to ROC analysis, which showed statistical significance (AUROC = 0.98). Among them, PODN decreased in the serum of patients in the vulnerable plaque group.

[0012] Subsequent clinical validation showed that serum PODN levels were lower in the 64 patients with vulnerable plaques compared to those with stable plaques, and PODN was significantly correlated with plaque fibrous cap thickness, plaque burden, and other vulnerable plaque-related indicators.

[0013] A vulnerable plaque mouse model showed changes in PODN in serum and plaque smooth muscle cells consistent with those in human samples. Mechanistically, PODN reduces oxLDL uptake (by inhibiting LOX-1), enhances cholesterol efflux (via ABCA1), and inhibits vascular smooth muscle cell (VSMC) phenotypic transformation (maintaining ACTA2 and reducing CD68 / OPN). It also alleviates inflammation (IL-6, IL-1β, TNF-α) and apoptosis. PODN overexpression reduced lipid accumulation and VSMC-macrophage transdifferentiation, while knockdown exacerbated these effects.

[0014] These findings suggest that PODN participates in the regulation of plaque stability and can serve as a plasma biomarker for plaque vulnerability. On the one hand, it is beneficial for predicting the vulnerability and probability of atherosclerotic plaques, enabling more precise risk stratification and facilitating preventative measures or early intervention for acute coronary syndromes in clinical practice. On the other hand, as a regulatory factor, it has significance for targeted therapy.

[0015] An application of Podocan in a model for predicting vulnerable vascular plaques, wherein the model includes at least one of the following:

[0016] To determine whether Podocan plasma concentration is significantly reduced,

[0017] Establish a negative correlation between Podocan plasma concentration and plaque burden.

[0018] Establish a negative correlation between Podocan plasma concentration and lipid core specifications, and

[0019] Establish a positive correlation between Podocan plasma concentration and fibrous cap thickness.

[0020] This allows for the assessment of a patient's risk of vascular plaque damage.

[0021] The Podocan provided by this invention serves as a novel serum marker for predicting plaque vulnerability, enabling non-invasive identification of vulnerable plaques and ultimately leading to more personalized and preventative cardiovascular care strategies.

[0022] An application of Podocan in a model for predicting acute coronary syndrome, wherein the model includes at least one of the following:

[0023] To determine whether Podocan plasma concentration is significantly reduced,

[0024] Establish a negative correlation between Podocan plasma concentration and plaque burden.

[0025] Establish a negative correlation between Podocan plasma concentration and lipid core specifications, and

[0026] Establish a positive correlation between Podocan plasma concentration and fibrous cap thickness.

[0027] This allows for the assessment of a patient's risk of developing acute coronary syndrome.

[0028] A system for predicting vascular plaque vulnerability includes the following units:

[0029] The data storage unit stores the detection data of Podocan in each patient's sample;

[0030] The analysis unit uses the results from the data storage unit as input to analyze and establish the correlation between Podocan plasma concentration and plaque burden, lipid core size, and fibrous cap thickness; and

[0031] The evaluation unit outputs the corresponding detection data and its correlation results for the samples, as well as provides plaque vulnerability prediction results.

[0032] The system of the present invention also provides for the risk of developing acute coronary syndrome.

[0033] The system of the present invention also provides recommendations for personalized and preventative cardiovascular care.

[0034] The system of the present invention further includes a detection unit, the information of which is obtained by detection using a reagent kit or sequencing instrument.

[0035] The technical solution provided by this invention effectively addresses the current lack of non-invasive techniques for identifying high-risk, vulnerable plaques. It offers quantifiable and predictable molecular evidence for acute coronary events (primarily acute myocardial infarction) in patients with coronary heart disease. As a substitute or supplement to intravascular imaging (intravascular ultrasound (IVUS) or optical coronary computed tomography (OCT) during coronary angiography), it facilitates bedside monitoring and remote diagnosis, and provides timely risk alerts to patients. For example, wearable devices (medical devices) periodically detect (acquire) the podocan concentration level in a patient's blood sample, analyze the data locally or remotely, and then provide risk alerts. The device includes a photoelectric device, and the acquisition of podocan concentration data in the patient's blood sample is achieved through photoelectric signals. Attached Figure Description

[0036] Figure 1 This image shows the results of differential protein characterization analysis in vulnerable and stable coronary artery lesions identified in a data-independent acquisition (DIA)-based plasma proteomics study. A represents the workflow for collecting plasma and performing proteomics analysis on patients with vulnerable plaques and stable lesions, respectively; B represents the PCA analysis of each sample; C represents the differential expression analysis results between the two groups; and D represents a heatmap of hierarchical clustering analysis of quantitative protein expression levels.

[0037] Figure 2 The results of accurate prediction of vulnerable coronary artery plaques by a proteomics-guided machine learning model are shown in the figure. Among them, A is the model prediction performance analysis figure, B is the characteristic curve figure, C is the confusion matrix analysis result figure, and D is the statistical figure of differential protein expression levels between patients with vulnerable plaques (A) and patients with stable lesions (B).

[0038] Figure 3The following figures represent the validation results of Podocan expression in vulnerable plaques: A shows the statistical graph of Podocan content in the plasma of the two groups in the validation cohort; B shows the statistical graph of the correlation between Podocan expression and plaque burden; C shows the statistical graph of the correlation between Podocan expression and fibrous cap thickness; D shows the statistical graph of the correlation between Podocan expression and lipid core; E shows the statistical graph of plasma Podocan concentration in the AS and LC groups; F shows the immunohistochemical results of the AS and LC groups; and G shows the results of dual immunofluorescence staining of α-smooth muscle actin (αSMA) and Podocan in the AS and LC groups.

[0039] Figure 4 The results of the validation of the effect on podocan protein levels are shown in the figure. Among them, A is the statistical graph of PODN content detection in cells of the control group and oxLDL test group, B is the electrophoresis graph of PODN transcription and expression levels in samples of oxLDL test group and PDGF-BB test group at different time points, and C is the statistical graph of PODN transcription and expression levels in samples of oxLDL test group and PDGF-BB test group at different time points.

[0040] Figure 5 Figure 1 shows the results of the PODN overexpression and knockdown experiments; where A is the electrophoresis diagram and statistical graph of protein expression in each group of test animals, and B is the OilRed staining diagram and statistical graph of each group of test animals.

[0041] Figure 6 The results of the PODN overexpression and knockdown experiments on the effect of low-density lipoprotein are shown in the figure; where A is the fluorescence result of oxLDL uptake by cells in each group of test groups and its statistical graph, and B is the BODIPY staining result of cells in each group of test groups and its statistical graph.

[0042] Figure 7 The graph shows the results of the PODN overexpression and knockdown experiments on cholesterol; where A is the total cholesterol statistics for each test group and B is the cholesterol efflux rate statistics for each test group.

[0043] Figure 8The results of the validation of Podocan's inhibition of SMC transformation and atherosclerosis development induced by oxidized low-density lipoprotein are shown in the figure (confirmed by Tukey post-hoc test after one-way ANOVA: *p<0.05, **p<0.01, ***p<0.001); where A is the statistical graph of quantitative PCR analysis of ACTA2, Myh11, CNN1, CD68 and Lgals3 mRNA expression, B is the figure of Western blot analysis of MOVAS cells overexpressing or knocked down by Podocan, C is the statistical graph of Western blot analysis of ACTA2, CD68 and LOX1 protein expression in each experimental group, D is the immunofluorescence staining image of ACTA2 (red) in each experimental group, E is the immunofluorescence staining image of CD68 (green) in each experimental group, and F is the statistical graph of ELISA quantitative detection of pro-inflammatory cytokines TNF-α, IL-6 and IL-1β in the cell supernatant of each experimental group.

[0044] Figure 9 The images show the results of Podocan's inhibition of SMC migration and inflammation. Specifically, A is the Western blot result of Podocan protein expression in MOVAS cells after PDGF-BB stimulation; B is a statistical graph of the Western blot results; C is a statistical graph of quantitative PCR of Podocan mRNA levels in MOVAS cells after PDGF-BB stimulation; D is a statistical graph of ACTA2 mRNA expression determined by qPCR in MOVAS cells with PDGF-BB overexpression or Podocan knockdown; E is a statistical graph of qPCR determination of OPN mRNA expression; F is an image of SMC migration in the wound healing assay; G is a statistical graph of quantitative SMC migration in the wound healing assay; H is the cell proliferation result of PDGF-BB-treated PODOCAN-overexpressing cells measured by the BrdU incorporation method; and I is a statistical graph of cell proliferation in PDGF-BB-stimulated PODOCAN-overexpressing cells detected by the CCK-8 assay. Detailed Implementation

[0045] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings. The embodiments of the present invention are only used to illustrate the technical solution of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solution of the invention without departing from the spirit and scope of the technical solution of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

[0046] The specific experimental methods used in the following embodiments of the present invention are described below:

[0047] 1) Sample collection

[0048] In this embodiment, patients with coronary artery disease who underwent coronary angiography combined with optical coherence tomography (OCT) for angina pectoris (symptom-driven) were included. Exclusion criteria included: acute ST-segment elevation myocardial infarction (STEMI) or non-ST-segment elevation myocardial infarction (NSTEMI); life expectancy of less than 1 year; severe liver dysfunction (ALT greater than 3 times the upper limit of normal); renal insufficiency (eGFR less than 30 ml / min / 1.73 m² or requiring maintenance dialysis); inflammatory or autoimmune diseases; and pregnancy or planned pregnancy.

[0049] Plaque characteristics were assessed using OCT and intravascular ultrasound (IVUS). Patients were assigned to a stable plaque group (n=30) and a vulnerable plaque group (n=30) for proteomics analysis (Table 1). Patients were also assigned to a stable plaque group (n=36) and a vulnerable plaque group (n=64) for validation (Table 2). The stable plaque group was defined as having no thin-cap fibroadenomas (TCFA), macrophage infiltration, microchannels, or cholesterol crystals on OCT; or having a plaque burden of less than 70% and a lipid core of less than 20% on IVUS. The vulnerable plaque group required OCT evidence of TCFA (with or without macrophage infiltration, microchannels, or cholesterol crystals); or an IVUS result showing a plaque burden of greater than 70% and a lipid core of greater than 20%.

[0050] Table 1 shows the clinical characteristics of patients with proteomics.

[0051]

[0052] Table 2 Clinical characteristics of patients in the validation cohort

[0053]

[0054]

[0055] 2) Proteomic data analysis

[0056] All raw LC / MS data were converted to mzML files using MSConvert (version: 3.0.23260-5c6a147). A spectral library was created using the FragPipe (version: 20) computing platform, combined with MSFragger (version: 3.8) and EasyPQP (0.1.40). The human protein sequence database H. sapiens (20459 entries, UniProt2023-1 0-9) was used, with inverted protein sequences added as false positive controls. Precursor and fragment mass tolerances were set to ±20 ppm. Peak deisotopeing, mass calibration, and search parameter optimization were performed. Isotope error was set to 0 / 1 / 2. Enzyme specificity was set to "strict trypsin," allowing a maximum of two miscleavages. Peptide length range was set to 7 to 50 amino acids, and peptide mass range was set to 500 Da to 5000 Da. Methionine oxidation and N-terminal acetylation of proteins were set as variable modifications. Cysteine ​​carboxymethylation was set as a fixed modification. The maximum number of variable modifications for each peptide was set to 3. RT and MS / MS spectra were predicted using MSBooster and Percolator, and PSMs were re-scored. Reports were filtered at the protein, PSM, and ion levels using 1% FDR. The final filtered PSMs were used to generate a spectral library with EasyPQP for quantifying peptides and proteins with FragPipe's built-in DIA-NN (version 1.8.1).

[0057] 3) Machine learning models

[0058] To reduce dimensionality and identify the most informative proteins, this embodiment uses the SelectKBest feature selection function from the feature_selection module of scikit-learn (Python 3.8) for feature selection. This univariate statistical method evaluates the relevance of each protein individually. The ANOVA F-score is used as the scoring function (fclassif) to assess the linear dependence of each protein's expression level on the target variable. Proteins are ranked according to their F-scores, and the top 10 proteins with the highest scores are selected for model development. Optional parameters are determined using five-fold cross-validation to prevent overfitting. Multiple classification algorithms are evaluated using the 'sklearn' package in Python. Hyperparameters of each model are optimized on the training set using grid search combined with cross-validation. Five-fold cross-validation is used to evaluate model performance and generalization ability. The dataset is randomly divided into five equal subsets. In each iteration, four subsets are used for training, and the remaining subset is used for validation. Performance metrics for each fold are calculated, including the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. The average AUROC across all iterations is reported as the final performance metric.

[0059] 3) Detection of human serum PODN (Podocan) in animal models of ruptured plaques

[0060] In this study, an established protocol was used to develop an animal model of ruptured atherosclerotic plaques. Specifically, male mice underwent a one-week acclimatization period prior to the experimental phase, during which they were maintained on a high-fat diet (1.25% cholesterol and 21% fat; 42% of calories from fat). Subsequently, male ApoE- / - mice underwent partial ligation of the left common carotid artery (LCCA) and left renal artery (LRA). Eight weeks after the intervention, the mice were sacrificed, and the carotid artery was dissected for further analysis. All procedures were performed under a dissecting microscope.

[0061] 4) Detect the level of Podocan in mouse and human plasma

[0062] Plasma samples were collected from 64 patients with vulnerable coronary plaques and 36 patients with stable coronary plaques (Table 2). Plasma podocan levels were measured using a commercially available podocan enzyme-linked immunosorbent assay (ELISA) kit (SEG186Mu).

[0063] Each fraction was incubated overnight at 4°C with primary antibody against podocan (1:50; AF3104-SP; R&D Systems, UK). Subsequently, the fractions were treated with the corresponding biotin-labeled secondary antibody. Positive antibodies were conjugated using a 3,3′-diaminobenzidine (DAB) peroxidase substrate kit (SK4100; Vectorlabs, USA) for staining. Cell nuclei were stained with hematoxylin, and bright-field images were captured using an Olympus IX83 inverted microscope and analyzed using Image-Pro Plus 6.0 software (five microscopic fields per sample).

[0064] 5) Immunofluorescence staining

[0065] For immunofluorescence staining, frozen sections were fixed in methanol at -20°C for 10 minutes. After blocking with 5% normal goat serum for 1 hour, sections were incubated overnight at 4°C with primary antibodies against Podocan (1:50; AF3104-SP; R&D Systems, UK) and αSMA (1:400; 14395-1-AP; Proteintech, Wuhan, China). After washing three times with PBS, sections were incubated at 37°C in the dark with Alexa Fluor 488 / 555-labeled secondary antibody (1:200; Invitrogen, USA) for 1 hour. Finally, sections were mounted with 4′,6-diamino-2-phenylindole (DAPI) and observed under a fluorescence microscope.

[0066] 6) Cell Culture

[0067] Mouse aortic vascular smooth muscle cells (MOVAS) were obtained from the American Type Culture Collection (ATCC; Manassas, Virginia, USA). They were cultured in DuPont Modified Evans Medium (DMEM; Gibco, Gran Island, NY, USA) containing 10% (v / v) fetal bovine serum (FBS; Gibco) and 1% (w / v) penicillin / streptomycin (Beyotime Biotechnology, Shanghai, China) at 37°C and 5% CO2.

[0068] 7) Experimental Grouping

[0069] Control group; oxLDL stimulation group;

[0070] oePodocan: Podocan overexpression group; Podocan KD: Podocan knockdown group;

[0071] oxLDL+oePodocan: oxLDL stimulation + Podocan overexpression group;

[0072] oxLDL+Podocan KD: oxLDL stimulation + Podocan knockdown

[0073] 8) Statistical methods

[0074] Unless otherwise specified, SPSS 26.0 statistical software was used. Normally distributed continuous data are expressed as mean ± standard deviation, and independent samples t-tests were used for comparisons between groups; one-way ANOVA was used for comparisons among multiple groups, and LSD method was used for post-hoc pairwise comparisons. Non-normally distributed continuous data are expressed as median (interquartile range) [M(IQR)], and nonparametric tests (Mann-Whitney U test or Kruskal-Wallis H test) were used. Count data are expressed as number (percentage) [n(%)], and χ² tests were used for comparisons between groups. 2 Tests were performed. Pearson correlation analysis was used to assess the linear relationship between continuous variables. A p-value < 0.05 was considered statistically significant.

[0075] Example 1: Differential protein characterization analysis between patients with vulnerable plaques and those with stable lesions.

[0076] To analyze the proteomic characteristics associated with vulnerable plaques, a key lesion in the coronary arteries, we performed a comprehensive data-agnostic acquisition (DIA) proteomics analysis. We collected plasma samples from 30 patients with vulnerable plaques and 30 patients with stable lesions. Figure 1A). Quality control (QC) analyses were performed to evaluate the DIA proteomic results. Principal component analysis (PCA) was used to assess the overall variance and clustering patterns within the dataset. The PCA plots showed a clear separation between the QC samples and the experimental samples. Figure 1 B). However, in the PCA space, the two groups are not clearly separated.

[0077] To identify differentially expressed proteins between vulnerable and stable disease patients, we performed differential expression analysis between the two groups. We identified 51 upregulated proteins and 14 downregulated proteins in the vulnerable group. Figure 1 C). We also performed hierarchical clustering analysis based on quantitative protein expression levels. The generated heatmap ( Figure 1 D) visually represents the normalized expression levels of proteins across all samples, with each row corresponding to an individual sample and each column representing a protein. In the heatmap, samples are clustered along the vertical axis, while proteins are clustered along the horizontal axis. Notably, the samples are roughly divided into two clusters, corresponding to vulnerable and stable lesions, respectively.

[0078] Example 2: Machine Learning Predicts Lesion Vulnerability

[0079] Based on the results of proteomics analysis, this embodiment also developed a machine learning prediction model using protein quantification indicators to predict vulnerable plaques. To reduce the feature space and focus on the most informative proteins, we applied the SelectKBest feature selection method from scikit-learn's feature selection module to identify the top 10 proteins most strongly associated with lesion vulnerability. We tested the prediction performance using different numbers of features. We found that using all of the selected top 10 proteins yielded the best performance. Figure 2 A). Then, we built the prediction model using GradientBoostingClassifier. Model performance was evaluated using the receiver operating characteristic curve (AUROC). Figure 2 B). The model achieved a mean AUROC of 0.98 in five-fold cross-validation, indicating its high discriminative ability in differentiating patients with vulnerable plaque states. The confusion matrix also supports the predictive performance ( Figure 2 C). To explore the biological relevance of the selected proteins, this example also compared the expression levels between the two groups. Seven proteins were significantly expressed in patients with vulnerable plaques ( Figure 2 D). This finding suggests that these proteins are not only valuable for predictive modeling, but also play a crucial role in the pathophysiology of vulnerable plaques.

[0080] Example 3: Validation of differential PODN expression in samples

[0081] Podocan expression is upregulated during vulnerable plaque formation. Peripheral blood was collected from patients with clinically defined stable or vulnerable plaque atherosclerosis, and plasma podocan levels were measured. Results showed that podocan levels were significantly lower in patients with vulnerable plaques compared to controls. Figure 3 A). Correlation analysis showed that circulating podocan levels were negatively correlated with plaque burden and lipid core size. Figure 3 B and 3D), and are positively correlated with fibrous cap thickness. Figure 3 C).

[0082] To further investigate, an atherosclerotic plaque model was established using ApoE / mice (AS group), and partial ligation of the left common carotid artery and left renal artery was performed to induce vulnerable atherosclerotic plaques (AS+LCCA group). Consistent with human findings, plasma podocan concentrations were decreased in ApoE / mice during vulnerable plaque formation. Figure 3 E). Immunohistochemical analysis showed a significant increase in Podocan expression within atherosclerotic plaques. Figure 3 F). Furthermore, dual immunofluorescence staining for α-smooth muscle actin (αSMA) and Podocan showed high expression of Podocan in smooth muscle cells within vulnerable plaques. Figure 3 G).

[0083] Example 4: Podocan Overexpression and Knockdown Experiment

[0084] This study used ELISA to investigate the release level of podocanine (PODN) protein in cells after stimulation with oxidized low-density lipoprotein (oxLDL). After oxLDL treatment, the PODN level in the supernatant was significantly reduced. Figure 4 A). Conversely, upon stimulation with oxLDL and platelet-derived growth factor-BB (PDGF-BB), the transcriptional and expression levels of PODN in cell lysates increased in a time-dependent manner. Figure 4 B and Figure 4 C). This indicates that PODN plays a regulatory role at the intracellular level under oxidative stress conditions.

[0085] To further explore the functional role of PODN, this embodiment also included overexpression (oePODN) and knockdown (siPODN) experiments. Protein expression compared to the control group can be found in [link to relevant documentation]. Figure 5A. PODN overexpression significantly reduced the number of lipid droplets induced by oxLDL stimulation, while PODN knockdown did not produce a similar effect. Figure 5 B).

[0086] Furthermore, PODN overexpression inhibited the cellular uptake of DiI-labeled oxidized low-density lipoprotein (DiI-oxLDL). Figure 6 A), and BODIPY staining confirmed a decrease in intracellular lipid content (A ...). Figure 6 B).

[0087] It was also observed that PODN overexpression led to a decrease in intracellular total cholesterol levels, while simultaneously promoting cholesterol efflux. Figure 7 ).

[0088] Example 5: Podocan inhibits oxidized low-density lipoprotein-induced vascular smooth muscle cell transformation and the development of atherosclerosis.

[0089] In MOVAS cells overexpressing or silencing Podocan, stimulation with 100 μg / mL oxidized low-density lipoprotein (oxLDL) significantly reduced vasopressin levels, manifested as transcriptional downregulation of ACTA2, Myh11, and CNN1. Figure 8 A). At the same time, oxLDL can promote the transdifferentiation of vascular smooth muscle cells (VSMCs) into macrophage-like phenotypes, accompanied by a significant upregulation of CD68 and Lgals3 expression.

[0090] Functional experiments showed that overexpression of Podocan effectively inhibited this macrophage-like phenotypic transition in MOVAS cells, while knockdown of Podocan further promoted this transition. Western blot analysis of ACTA2, CD68, and LOX1 (…) Figure 8 B and 8C), and immunofluorescence staining for ACTA2 and CD68 ( Figure 8 These regulatory roles were confirmed at the protein level (D and 8E). Notably, overexpression of Podocan was also verified to reverse cholesterol-induced transdifferentiation of vascular smooth muscle cells into macrophage-like phenotypes.

[0091] Furthermore, knockdown of Podocan-induced macrophage-like phenotype transformation led to a significant increase in the secretion of pro-inflammatory cytokines (including TNF-α, IL-6, and IL-1β) by MOVAS cells. Figure 8 F) indicates an enhanced inflammatory response.

[0092] Example 6: Podocan inhibits PDGF-BB-induced proliferation and migration of vascular smooth muscle cells.

[0093] Stimulation with 20 nM platelet-derived growth factor-BB (PDGF-BB) can also induce the transcription and expression of Podocan in smooth muscle cells. Figure 9 A and Figure 9 B). Consistent with known biological effects, PDGF-BB triggers the conversion of vascular smooth muscle cells (VSMCs) to a synthetic phenotype, specifically manifested as decreased ACTA2 expression and upregulation of osteopontin (OPN) transcription. Figure 9 D and Figure 9 E). Notably, overexpression of Podocan significantly restored ACTA2 expression while suppressing OPN levels, suggesting that Podocan plays a protective role in antagonizing PDGF-BB-induced phenotypic regulation.

[0094] BrdU incorporation and CCK-8 assays confirmed that Podocan significantly inhibited PDGF-BB-stimulated vascular smooth muscle cell proliferation. Figure 9 H and Figure 9 I. Podocan overexpression can also significantly attenuate PDGF-BB-induced vascular smooth muscle cell migration—a key event promoting the development of atherosclerotic lesions. Figure 9 (F and 9G). In addition, Podocan knockdown promotes the adhesion of THP-1 monocytes to human umbilical vein endothelial cells (HUVECs), while Podocan overexpression has a protective effect against oxLDL-induced apoptosis in HUVECs.

[0095] These findings indicate that, under stimulation by PDGF-BB and oxLDL, Podocan is a key regulator of vascular smooth muscle cell motility and inflammatory response, highlighting its potential therapeutic value in vascular diseases such as atherosclerosis.

[0096] These results collectively indicate that PODN plays a crucial role in oxLDL-stimulated lipid metabolism, primarily by reducing lipid accumulation and enhancing cholesterol efflux. PODN is involved in plaque stability regulation and serves as a plasma biomarker of plaque vulnerability for disease risk stratification and targeted therapy.

Claims

1. Use of Podocan in a model for predicting vulnerable plaque in coronary artery.

2. Use according to claim 1, characterized in that The plaque is an atherosclerotic plaque.

3. Use according to claim 1, characterized in that The model predicts the risk of developing acute coronary syndrome.

4. Use according to claim 1, characterized in that The model comprises at least one of: determining whether the plasma concentration of Podocan is significantly reduced; establishing a negative correlation between the plasma concentration of Podocan and plaque burden, establishing a negative correlation between the plasma concentration of Podocan and lipid core size, and establishing a positive correlation between the plasma concentration of Podocan and fibrous cap thickness, to give a judgment on the risk of vulnerable plaque in the patient's blood vessels.

5. Use according to claim 4, characterized in that The model provides personalized and preventive cardiovascular care recommendations for the patient.

6. A system for predicting vascular plaque vulnerability, characterized in that... It comprises: a data storage unit that stores the detection data of Podocan in each sample of the patient; an analysis unit that analyzes the results of the data storage unit as input items to establish the correlation between the plasma concentration of Podocan and plaque burden, lipid core size and fibrous cap thickness; and an evaluation unit that outputs the corresponding detection data of the sample and the correlation results, and provides the prediction results of plaque vulnerability.

7. The system of claim 6, wherein It also comprises a detection unit, and the information of the detection unit is obtained by using a kit or a sequencing instrument.

8. A medical device, comprising: It comprises the prediction model of vulnerable plaque in blood vessels according to claim 1, and the concentration level of Podocan in the blood sample of the patient is obtained at a certain time to make a risk prompt.

9. The medical device of claim 8, wherein It is a wearable device.

10. The medical device of claim 8, wherein, It also comprises an optoelectronic device to obtain the concentration data of Podocan in the blood sample of the patient. It is a wearable device. It also comprises an optoelectronic device to obtain the concentration data of Podocan in the blood sample of the patient.

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