Protein biomarkers for identifying right ventricular dysfunction in idiopathic dilated cardiomyopathy and their applications

Abstract

This application relates to protein biomarkers for identifying right ventricular dysfunction in idiopathic dilated cardiomyopathy and their applications. RARRES1, MVB12B, and GSK3A proteins have been found in the urine of patients with idiopathic dilated cardiomyopathy to be significantly associated with right ventricular dysfunction. These three proteins exhibit good diagnostic or prognostic predictive performance in diagnosis and prognostic evaluation, providing an economical, non-invasive, and accurate tool for the diagnosis and prognosis of right ventricular dysfunction.

Description

Technical Field

[0001] This application belongs to the field of medical diagnostic reagent technology, specifically relating to protein biomarkers for identifying right ventricular dysfunction in idiopathic dilated cardiomyopathy and their applications. Background Technology

[0002] Idiopathic dilated cardiomyopathy (iDCM) is a group of cardiomyopathy characterized by decreased left ventricular systolic function and enlargement of the heart chambers in the absence of a clear etiology. It is a significant cause of heart failure and sudden cardiac death, especially common in younger patients. In recent years, advances in molecular characterization and imaging phenotyping have led to increasingly precise subtype identification of iDCM. However, due to the lack of targeted treatments, the overall prognosis remains poor, with a 5-year mortality rate of approximately 20% even with standard medical therapy.

[0003] Right ventricular dysfunction (RVD) is a common and underestimated complication in patients with incomplete heart disease (iDCM), with an incidence rate as high as 36-75%. RVD is independently associated with endpoint events such as hospitalization for heart failure, cardiac death, implantation of left ventricular assist devices, and heart transplantation, and is a significant determinant of poor prognosis. However, due to the complex structure and high load dependence of the right ventricle, current imaging methods such as cardiac magnetic resonance imaging (MRI), although considered the gold standard for assessing right ventricular ejection fraction, are difficult to widely apply in clinical practice due to high cost, equipment limitations, and operator constraints. Echocardiography has low sensitivity in detecting RVD, resulting in insufficient diagnostic rates. Therefore, there is an urgent need to develop a simple, accurate, and non-invasive method for early identification of RVD patients to guide individualized treatment and risk assessment.

[0004] In recent years, urinary proteomics has attracted widespread attention as an emerging non-invasive detection method. Urine is not only widely available and easy to collect, but it also enriches proteins that are low in abundance in plasma, reflecting the pathophysiological state of the cardiovascular system. Compared with serum or tissue, urine is more suitable for high-throughput screening and disease monitoring. However, current research on the application of urinary proteins in right ventricular dysfunction in patients with iDCM is still very limited, and there are no validated urinary biomarkers available for the diagnosis and prognostic assessment of RVD. Summary of the Invention

[0005] Based on this, one embodiment of this application provides a protein biomarker for diagnosing right ventricular dysfunction in patients with idiopathic dilated cardiomyopathy and assessing their prognosis, and proposes its application in the preparation of diagnostic or prognostic assessment products for identifying right ventricular dysfunction in patients with dilated cardiomyopathy.

[0006] The technical solutions include the following:

[0007] In a first aspect, this application provides protein biomarkers for the differential diagnosis of right ventricular dysfunction in patients with idiopathic dilated cardiomyopathy or for predicting the prognostic risk of patients with idiopathic dilated cardiomyopathy, said protein biomarkers including one or more of the following: Retinoic Acid Receptor Responder Protein 1 (RARRES1), Multivesicular Body Subunit 12B (MVB12B), and Glycogen Synthase Kinase-3 Alpha (GSK3A).

[0008] In one embodiment, compared with patients without right ventricular dysfunction, the expression levels of RARRES1 and MVB12B were significantly increased and the expression level of GSK3A was significantly decreased in the samples of patients with right ventricular dysfunction.

[0009] In one embodiment, the sample includes urine.

[0010] Secondly, this application provides the application of the detection reagent for the expression level of the above-mentioned protein markers in the preparation of diagnostic products for identifying or recognizing right ventricular dysfunction in patients with dilated cardiomyopathy.

[0011] Thirdly, this application provides the application of the detection reagent for the expression level of the above-mentioned protein biomarkers in the preparation of products for predicting the prognostic risk of patients with idiopathic dilated cardiomyopathy.

[0012] In one embodiment, the product is used to predict the risk of a composite endpoint event, namely cardiovascular death, readmission from heart failure, implantation of a left ventricular assist device, or heart transplantation.

[0013] In one embodiment, the detection reagent is selected from one or more of the following methods: liquid chromatography, mass spectrometry, and immunoassay.

[0014] In one embodiment, the immunoassay includes one or more of Western blot analysis, radioimmunoassay, immunofluorescence assay, immunoprecipitation, immunodiffusion, electrochemiluminescence immunoassay, ELISA assay, and immunopolymerase chain reaction.

[0015] In one embodiment, the detection reagent is a reagent suitable for liquid chromatography-tandem mass spectrometry (LC-MS / MS) or double antibody sandwich ELISA.

[0016] In one embodiment, mass spectrometry based on protein characteristic tag peptides is employed.

[0017] In one embodiment, the detection reagent comprises an antibody or a functional fragment thereof.

[0018] In one embodiment, the product includes a reagent kit, a chip, a test strip, a system, or a device.

[0019] In one embodiment, the kit is a mass spectrometry identification tag peptide kit or an enzyme-linked immunosorbent assay (ELISA) kit.

[0020] In one embodiment, mass spectrometry identification of the tagged peptide refers to its use in a data-independent mass spectrometry acquisition mode.

[0021] Fourthly, this application provides a product for the differential diagnosis of right ventricular functional status in patients with idiopathic dilated cardiomyopathy, the product comprising a detection reagent for the expression level of the protein biomarkers defined above.

[0022] Fifthly, this application provides a diagnostic model for identifying right ventricular dysfunction in patients with idiopathic dilated cardiomyopathy. The diagnostic model includes RARRES1, MVB12B, and GSK3A protein markers, and the model includes the following calculation formula:

[0023] logit(p)=1.5205+0.4729×RARRES1+0.5582×MVB12B-0.5875×GSK3A

[0024] Where logit(p) represents the probability value, RARRES1 represents the expression level of RARRES1 protein, MVB12B represents the expression level of MVB12B protein, and GSK3A represents the expression level of GSK3A protein.

[0025] In one embodiment, when p ≥ 0.5, patients with idiopathic dilated cardiomyopathy are determined to have right ventricular dysfunction; when p < 0.5, patients with idiopathic dilated cardiomyopathy are determined to not have right ventricular dysfunction.

[0026] Sixthly, this application provides a diagnostic system for identifying the right ventricular functional status in patients with idiopathic dilated cardiomyopathy, the diagnostic system comprising a data acquisition module, an analysis module, and a result output module:

[0027] (1) Data acquisition module, used to acquire the expression level of protein markers in the sample of the subject to be tested, wherein the protein markers include one or more of RARRES1, MVB12B and GSK3A.

[0028] (2) Analysis module, used to analyze the expression level of protein markers obtained by the data acquisition module, and to analyze whether the subject has right ventricular dysfunction.

[0029] In one embodiment, the analysis module is used to construct a diagnostic model based on the expression levels of protein biomarkers obtained by the data acquisition module, thereby enabling the diagnosis and prognostic risk assessment of right ventricular dysfunction.

[0030] In one embodiment, the analysis module is used to provide the expression level of the protein marker obtained by the data acquisition module as input data to the constructed diagnostic model to analyze whether the result is combined with or without right ventricular dysfunction.

[0031] In one embodiment, the diagnostic model is constructed using logistic regression or other learning algorithms based on the expression levels of the protein biomarkers in patients with idiopathic dilated cardiomyopathy. Optionally, the diagnostic model is the diagnostic model described in the fifth aspect above.

[0032] It should be noted that, in addition to logistic regression, other machine learning algorithms can also be used for modeling, such as linear regression, support vector machine, nearest neighbor / k-nearest neighbor, decision tree, k-means, random forest, naive Bayes, dimensionality reduction, and gradient enhancement. Any system that uses the expression level or content detected by the protein biomarker of this application to build a model is within the scope of protection of this application.

[0033] (3) Result output module, used to output the analysis results of the analysis module to obtain the diagnosis result of whether the patient has right ventricular dysfunction.

[0034] Seventhly, this application provides a risk scoring model for predicting the prognosis of idiopathic dilated cardiomyopathy. The risk scoring model includes RARRES1, MVB12B, and GSK3A protein biomarkers, and the risk scoring model calculates the risk score using the following formula:

[0035] Risk Score=0.594×RARRES1+0.738×MVB12B-0.637×GSK3A

[0036] Here, Risk Score represents the risk score, RARRES1 represents the expression level of RARRES1 protein, MVB12B represents the expression level of MVB12B protein, and GSK3A represents the expression level of GSK3A protein.

[0037] The risk scoring model constructed based on the protein biomarkers calculates the risk score of patients to experience multiple adverse events, which can effectively distinguish between high-risk and low-risk patients. Patients in the high-risk group have a significantly increased risk of cardiovascular death, heart failure rehospitalization, left ventricular assist device implantation, or heart transplantation.

[0038] In one embodiment, a patient is identified as high-risk when the Risk Score is ≥ 0.86, and as low-risk when the Risk Score is < 0.86.

[0039] Eighthly, this application provides a device or system for predicting the prognostic risk of idiopathic dilated cardiomyopathy, the device or system comprising:

[0040] (1) Data acquisition module, used to acquire the expression level of protein markers in the sample of the subject to be tested, wherein the protein markers include one or more of RARRES1, MVB12B and GSK3A.

[0041] (2) Analysis module, used to provide the expression level of urinary protein markers obtained by the data acquisition module as input data to the constructed risk scoring model.

[0042] Optionally, the risk scoring model is a Cox regression model.

[0043] Optionally, the risk scoring model is the risk scoring model described in the seventh aspect above.

[0044] (3) Output module, used to output the prognostic risk stratification results of the risk scoring model.

[0045] Ninthly, this application provides a computer-readable storage medium including a computer program, which is executed by a processor and, when executed by the processor, implements the functions of different modules in the system.

[0046] In a tenth aspect, this application provides an electronic device including a memory and a processor, the memory storing a computer program and the processor executing the computer program to implement the functions of the diagnostic system.

[0047] It should be understood that the terms "system," "apparatus," and / or "device" as used herein are used to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions. Those skilled in the art will recognize that this application can be implemented as a method or a computer program product. Therefore, the disclosure of this application can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. Furthermore, in some specific embodiments, this application can also be implemented as a computer program product contained in one or more computer-readable media, which contains computer-readable program code.

[0048] Based on a quantitative proteomics method using mass spectrometry, this application found that the above three proteins in the urine of patients with idiopathic dilated cardiomyopathy were significantly associated with right ventricular dysfunction. A diagnostic model was constructed and a prognostic evaluation was conducted, providing an economical, non-invasive, and accurate tool for the diagnosis and prognosis of right ventricular dysfunction.

[0049] Eleventhly, this application provides a method for differential diagnosis of right ventricular dysfunction in patients with idiopathic dilated cardiomyopathy or for prognostic risk assessment or diagnosis of patients with idiopathic dilated cardiomyopathy, comprising:

[0050] (1) Collect urine samples from the subjects;

[0051] (2) Detect the expression levels of RARRES1, MVB12B and GSK3A proteins in urine samples;

[0052] (3) Based on the protein expression level, a pre-established risk scoring model or diagnostic model is applied for analysis to determine whether the subject has right ventricular dysfunction, or

[0053] Patients are stratified clinically based on risk scores calculated using a risk scoring model.

[0054] The above method is a non-invasive diagnostic and / or prognostic stratification method for right ventricular dysfunction in patients with idiopathic dilated cardiomyopathy, developed based on urinary proteomics and machine learning.

[0055] Compared with traditional technologies, this application has the following advantages:

[0056] This application employs a mass spectrometry method based on protein characteristic tag peptides to detect the expression levels of RARRES1, MVB12B, and GSK3A in urine. The expression levels of these three biomarkers demonstrate strong ability to differentiate between idiopathic dilated cardiomyopathy and right ventricular dysfunction, and possess significant prognostic value, thus providing a novel non-invasive diagnostic and risk assessment method for identifying this disease state.

[0057] This application provides a non-invasive, stable urine testing method suitable for clinical translation, offering a new approach for the accurate identification and individualized management of right ventricular dysfunction in patients with idiopathic dilated cardiomyopathy. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application and to more completely understand this application and its beneficial effects, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 This is a volcano plot showing differentially expressed proteins in urine between the right ventricular dysfunction (RVD) group and the non-RVD group. Red indicates proteins upregulated in RVD, and blue indicates proteins downregulated. Dashed lines represent the fold change threshold (1.5-fold) and the statistical significance threshold (adjusted P-value = 0.05), respectively. Gene names are used instead of full protein names in the plot.

[0060] Figure 2 The performance of the three-protein diagnostic model on the training and validation sets is shown, along with comparisons with NT-proBNP, TAPSE, and their combinations. The three-protein model (GSK3A, MVB12B, and RARRES1) demonstrated superior diagnostic efficacy compared to single proteins, NT-proBNP, TAPSE, and their combinations in both the training (A) and validation (B) sets, exhibiting higher AUC, sensitivity, and specificity. GSK3A: Glycogen synthase kinase 3α; MVB12B: Multivesicular body sorting protein 12B; RARRES1: Retinoic acid regulation-associated protein 1; NT-proBNP: N-terminal pro-brain natriuretic peptide; TAPSE: Tricuspid annular systolic displacement.

[0061] Figure 3Kaplan-Meier survival curves are used to stratify patient survival using a Cox risk score model based on three urinary protein markers (GSK3A, MVB12B, and RARRES1). Patients were divided into high-risk and low-risk groups based on median risk score (cut-off = 0.86). Patients in the high-risk group had a significantly increased risk of cardiovascular death or hospitalization for heart failure (HR = 3.24, 95% CI: 1.56–6.71, P = 0.001). Detailed Implementation

[0062] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, a detailed description of specific embodiments of this application is provided below. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0064] In this document, the term "and / or" includes any and all combinations of one or more of the related listed items.

[0065] In this article, the term "prognosis" refers to the prediction of disease progression and outcome based on clinical data, including two types: natural prognosis (the outcome without treatment) and interventional prognosis (the outcome after medical intervention).

[0066] In this article, the term "NT-proBNP" refers to N-terminal pro-B-type natriuretic peptide, a biomarker secreted by ventricular myocytes when cardiac pressure load increases. It reflects cardiac function and is widely used to assess the severity and prognostic risk of heart failure.

[0067] In this article, the term "TAPSE" refers to Tricuspid Annular Plane Systolic Excursion, an important echocardiographic indicator for assessing right ventricular longitudinal systolic function. The lower the value, the more likely it is that right ventricular systolic function is impaired.

[0068] This application relates to three biomarkers in urine: retinoic acid regulation-associated protein 1 (RARRES1), multivesicular body sorting protein 12B (MVB12B), and glycogen synthase kinase 3α (GSK3A). RARRES1 (Retinoic Acid Receptor Responder Protein 1, UniProt ID: P49788) is a protein associated with cell differentiation and tumor suppression; MVB12B (Multivesicular Body Subunit 12B, UniProt ID: Q9H7P6) participates in the formation of intracellular multivesicular bodies and regulates the transport and degradation of membrane proteins; GSK3A (Glycogen Synthase Kinase-3 Alpha, UniProt ID: P49840) is a serine / threonine protein kinase that is widely involved in the regulation of multiple signaling pathways and affects cell metabolism and proliferation.

[0069] Currently, there are no publicly available reports on the expression of RARRES1, MVB12B, and GSK3A in urine and their combined use in the diagnosis and prognostic evaluation of right ventricular dysfunction in dilated cardiomyopathy.

[0070] This application utilizes data-independent acquisition (DIA) mass spectrometry analysis and multivariate statistical modeling to screen and establish three urinary proteins—retinoic acid regulation-related protein 1 (RARRES1), multivesicular body sorting protein 12B (MVB12B), and glycogen synthase kinase 3α (GSK3A)—as core biomarkers. The logistic regression model constructed based on these three proteins demonstrated excellent diagnostic efficacy (AUC = 0.946) on both the training and validation sets, significantly outperforming traditional indicators such as NT-proBNP and TAPSE. Furthermore, the protein risk score constructed using a Cox regression model effectively differentiated patient prognostic risks and predicted composite endpoint events such as cardiovascular death and heart failure readmission, exhibiting good independent prognostic predictive ability.

[0071] The embodiments of this application will be described in detail below with reference to examples. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of this application. For experimental methods in the following embodiments where specific conditions are not specified, please refer to the guidelines given in this application, or follow experimental manuals or conventional conditions in the art, or follow the conditions recommended by the manufacturer, or refer to experimental methods known in the art.

[0072] In the specific embodiments described below, the measurement parameters involving raw material components may have slight deviations within the weighing accuracy range unless otherwise specified. Temperature and time parameters are subject to acceptable deviations due to instrument testing accuracy or operational precision.

[0073] The following examples

[0074] Example 1: Mass spectrometry detection of right ventricular dysfunction-related proteins in urine of patients with dilated cardiomyopathy

[0075] Materials and reagents:

[0076] 1) Instrument: Orbitrap Exploris 480 mass spectrometer (Thermo Scientific).

[0077] 2) Main reagents: trypsin (Promega); C18 solid phase extraction column (3CC, 60mg, Waters); C18 reversed phase chromatography column (4.6mm×250mm, C18, 3μm, Waters).

[0078] 3) Samples: Urine samples were collected from patients with idiopathic dilated cardiomyopathy at the time of initial diagnosis, including a group with normal right ventricular function (n=83) and a group with right ventricular dysfunction (n=64), from Peking Union Medical College Hospital.

[0079] We used a data-independent acquisition (DIA) method to screen for proteins in urine that are associated with RVD in dilated cardiomyopathy.

[0080] 1. Collection of human urine samples and enrichment of urinary protein

[0081] Collect urine from the first medical visit, centrifuge at 1000g for 20 minutes to remove sediment. Use the supernatant for subsequent testing.

[0082] 2. Proteolytic enzyme digestion

[0083] Protein digestion was performed using an on-membrane enzymatic digestion method. Protein samples were first reduced with 20 mM DTT at 95°C for 5 min, followed by alkylation of exposed thiol groups with 50 mM MIAAA at room temperature in the dark for 45 min. The reductively alkylated protein solution was transferred to a 30 kDa ultrafiltration tube, centrifuged, and the lower layer was discarded. The on-membrane protein samples were washed twice with UA solution (containing 8 M urea) and twice with 25 mM ammonium bicarbonate solution. The on-membrane protein samples were then digested with 1:50 trypsin overnight at 37°C, and the resulting peptides were collected by centrifugation. The digested peptide solution was purified using a C18 extraction column, and the purified product was dried under vacuum. After reconstitution, the peptide concentration was determined using a BCA kit.

[0084] 3. Building a document library

[0085] To construct the spectral library, all urine samples were mixed in equal volumes and separated by offline high-pH high-performance liquid chromatography (HPLC). The collected eluent was dried under vacuum and then reconstituted in 1‰ formic acid for LC-MS / MS analysis. Data for each fraction was acquired using a data-independent acquisition (DIA) method. The raw data were retrieved using Proteome Discoverer (Thermo Scientific, Germany) software with the following search criteria: enzyme digestion: trypsin; maximum of two missed cleavage sites allowed; fixed modification: cysteine ​​alkylation; variable modification: aspartate and glutamine deamination, methionine oxidation; lysine and N-terminal carbamylation of peptides; precursor ion mass error: 10 ppm; daughter ion mass error: 0.02 Da. Protein level FDR <1%, and each protein contained at least one unique peptide. The search results were imported into Spectronaut Pulsar (Biognosys, Switzerland) software to generate the database.

[0086] 4. DIA analysis of experimental data:

[0087] 147 samples were analyzed using 1D-LC-MS / MS. Data was acquired for each sample via DIA. Data acquired via DIA were processed using Spectronaut software. The constructed spectral library was searched using the same search parameters. The results were then exported.

[0088] Differential expression analysis (Fold change > 1.5 or < 0.68, FDR-adjusted P value < 0.05) identified a total of 46 differentially expressed proteins (23 upregulated and 23 downregulated). Figure 1 Subsequently, multivariate logistic regression models were constructed for each of the 46 differentially expressed proteins, with age, sex, body mass index, left ventricular ejection fraction, N-terminal pro-brain natriuretic peptide (PPB), and systolic blood pressure as covariates for adjustment. After multivariate adjustment, 15 proteins were found to be independently associated with RVD.

[0089] After initial protein screening based on the above criteria, all patients (64 with right ventricular dysfunction and 83 with normal right ventricular function) were randomly assigned in a 3:1 ratio (stratified by age and sex) to a training set (48 with right ventricular dysfunction and 61 with normal right ventricular function) and a validation set (16 with right ventricular dysfunction and 22 with normal right ventricular function). In the training set, LASSO regression was used for feature selection, identifying 11 variables with predictive value. Based on the LASSO coefficient, ROC analysis results, and the biological mechanisms of these proteins in cardiovascular disease, RARRES1, MVB12B, and GSK3A were ultimately identified as the core biomarkers.

[0090] The three differentially expressed proteins were jointly modeled, and a diagnostic model was constructed using logistic regression to generate a diagnostic score logit(p) for determining the disease type of the urine sample. The logistic regression equation of the constructed joint diagnostic model in the training set is as follows:

[0091] logit(p)=1.5205+0.4729×RARRES1+0.5582×MVB12B-0.5875×GSK3A

[0092] Wherein, RARRES1, MVB12B, and GSK3A represent the expression levels of the corresponding proteins. In this embodiment, the probability value output by the model is p. When p ≥ 0.5, the patient is determined to have right ventricular dysfunction (RVD); when p < 0.5, the patient is determined to not have RVD.

[0093] In the training set, the AUCs of the individual diagnostic performance of RARRES1, GSK3A, and MVB12B proteins were 0.797 (95% CI: 0.712–0.882), 0.801 (95% CI: 0.705–0.896), and 0.747 (95% CI: 0.652–0.841), respectively. The diagnostic model constructed by combining the three proteins significantly outperformed the single protein, NT-proBNP, and TAPSE models, with an AUC as high as 0.946 (95% CI: 0.908–0.983), sensitivity of 0.854, specificity of 0.918, positive predictive value (PPV) of 0.891, and negative predictive value (NPV) of 0.889. On the validation set, the joint model also demonstrated excellent discriminative ability, with an AUC of 0.946 (95% CI: 0.878–1.000), significantly higher than NT-proBNP (AUC = 0.662), TAPSE (AUC = 0.636), and their combined model (AUC = 0.668). Figure 2 ).

[0094] Example 2: Prognostic evaluation of patients with dilated cardiomyopathy using a combined model of RARRES1, MVB12B, and GSK3A in urine.

[0095] 1. Materials, reagents, and sample sources

[0096] The sample source was the same as in Example 1, consisting of 147 patients diagnosed with idiopathic dilated cardiomyopathy.

[0097] 2. Prognostic follow-up and data collection

[0098] All patients were followed up for a median period of 19.3 months (interquartile range 10.9–28.1 months), and composite endpoint events including cardiovascular death, rehospitalization for heart failure, implantation of left ventricular assist device or heart transplantation were collected. A total of 37 prognostic events were recorded.

[0099] 3. Risk Score Calculation

[0100] Based on the expression levels of three proteins (GSK3A, MVB12B, and RARRES1), and combined with a multivariate Cox regression model (adjusted for age, sex, and NT-proBNP), a protein risk score was calculated for each patient.

[0101] Risk score calculation formula:

[0102] Risk Score=0.594×RARRES1+0.738×MVB12B-0.637×GSK3A

[0103] Here, Risk Score represents the risk score, RARRES1 represents the expression level of RARRES1 protein, MVB12B represents the expression level of MVB12B protein, and GSK3A represents the expression level of GSK3A protein.

[0104] Patients were divided into high-risk and low-risk groups based on the median risk score (cut-off = 0.86).

[0105] 4. Statistical Analysis

[0106] The Cox proportional hazards model was used to assess the correlation between protein risk scores and prognostic events. Kaplan-Meier survival analysis results showed ( Figure 3 Patients in the high-risk group had a significantly increased risk of the composite endpoint event compared to those in the low-risk group (HR = 3.24, 95% CI: 1.56–6.71, P = 0.001). The predictive power of the model was assessed using Harrell's C index, which yielded a value of 0.78 (95% CI: 0.72–0.85), indicating that the model has good discriminative power.

[0107] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0108] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims, and the specification can be used to interpret the content of the claims.

Claims

1. The application of a reagent for detecting the expression level of protein biomarkers in the preparation of a diagnostic product for identifying right ventricular dysfunction in patients with idiopathic dilated cardiomyopathy, wherein the protein biomarkers include RARRES1, MVB12B, and GSK3A.

2. The application according to claim 1, characterized in that, Compared with patients with normal right ventricular function in idiopathic dilated cardiomyopathy, the expression levels of RARRES1 and MVB12B were increased in the samples of patients with right ventricular dysfunction in idiopathic dilated cardiomyopathy, while the expression level of GSK3A was decreased in the samples of patients with right ventricular dysfunction in idiopathic dilated cardiomyopathy.

3. The application according to claim 2, characterized in that, The samples tested included urine.

4. Application of reagents for detecting the expression levels of protein biomarkers in the preparation of products for predicting the prognostic risk of patients with idiopathic dilated cardiomyopathy, wherein the protein biomarkers include RARRES1, MVB12B and GSK3A.

5. The application according to claim 4, characterized in that, The product is used to predict the risk of a composite endpoint event, including cardiovascular death, readmission from heart failure, implantation of a left ventricular assist device, or heart transplantation.

6. The application according to any one of claims 1 to 5, characterized in that, The detection reagents are selected from one or more of the following methods: liquid chromatography, mass spectrometry, and immunoassay.

7. The application according to claim 6, characterized in that, Immunoassays include one or more of the following: Western blot analysis, radioimmunoassay, immunofluorescence assay, immunoprecipitation, immunodiffusion, electrochemiluminescence immunoassay, ELISA assay, and immunopolymerase chain reaction.

8. The application according to claim 6, characterized in that, The detection reagents are those applicable to liquid chromatography-tandem mass spectrometry or double antibody sandwich ELISA.

9. The application according to claim 6, characterized in that, The detection reagent contains specific antibodies or functional fragments thereof against RARRES1, MVB12B and GSK3A.

10. The application according to any one of claims 1 to 5, characterized in that, The products mentioned are reagent kits, chips, test strips, detection systems, or devices.

11. The application according to claim 10, characterized in that, The kit is a mass spectrometry labeled peptide detection kit or an enzyme-linked immunosorbent assay kit.

12. A diagnostic model for identifying right ventricular dysfunction in patients with idiopathic dilated cardiomyopathy, characterized in that, The diagnostic model includes RARRES1, MVB12B, and GSK3A protein markers, and the diagnostic model includes the following calculation formula: logit(p) = 1.5205 + 0.4729 × RARRES1 + 0.5582 × MVB12B - 0.5875 ×GSK3A Where logit(p) represents the probability value, RARRES1 represents the expression level of RARRES1 protein, MVB12B represents the expression level of MVB12B protein, and GSK3A represents the expression level of GSK3A protein.

13. The diagnostic model according to claim 12, characterized in that, When p ≥ 0.5, patients with idiopathic dilated cardiomyopathy are considered to have right ventricular dysfunction; when p < 0.5, patients with idiopathic dilated cardiomyopathy are considered to have no right ventricular dysfunction.

14. A diagnostic device or system for identifying the right ventricular functional status in patients with idiopathic dilated cardiomyopathy, characterized in that, The diagnostic device or system includes: The data acquisition module is used to acquire the expression levels of protein biomarkers in the samples of the subjects to be tested, wherein the protein biomarkers include one or more of RARRES1, MVB12B and GSK3A; The analysis module is used to analyze the expression levels of protein biomarkers obtained by the data acquisition module to analyze whether the subject has right ventricular dysfunction; The result output module is used to output the analysis results of the analysis module to obtain the diagnostic result of whether the patient has right ventricular dysfunction.

15. The diagnostic device or system according to claim 14, characterized in that, The analysis module is used to provide the expression levels of protein markers obtained by the data acquisition module as input data to the constructed diagnostic model, and to analyze whether the result is combined with or without right ventricular dysfunction.

16. The diagnostic device or system according to claim 15, characterized in that, The diagnostic model was constructed using logistic regression based on the expression levels of the protein biomarkers in patients with idiopathic dilated cardiomyopathy.

17. The diagnostic device or system according to claim 16, characterized in that, The diagnostic model includes the diagnostic model as described in claim 12 or 13.

18. The diagnostic device or system according to claim 14, characterized in that, The subjects included patients with idiopathic dilated cardiomyopathy.

19. A risk scoring model for predicting the prognosis of idiopathic dilated cardiomyopathy, characterized in that, The risk scoring model includes RARRES1, MVB12B, and GSK3A protein biomarkers, and the risk score is calculated using the following formula: Risk Score = 0.594 × RARRES1 + 0.738 × MVB12B-0.637 × GSK3A Here, Risk Score represents the risk score, RARRES1 represents the expression level of RARRES1 protein, MVB12B represents the expression level of MVB12B protein, and GSK3A represents the expression level of GSK3A protein.

20. The risk scoring model according to claim 19, characterized in that, Patients with a Risk Score ≥ 0.86 are classified as high-risk, while those with a Risk Score < 0.86 are classified as low-risk. High-risk patients have a significantly increased risk of cardiovascular death, heart failure readmission, left ventricular assist device implantation, or heart transplantation.

21. A device or system for predicting the prognostic risk of idiopathic dilated cardiomyopathy, characterized in that, The prediction device or system includes: The data acquisition module is used to acquire the expression levels of protein biomarkers in the samples of the subjects to be tested, wherein the protein biomarkers include one or more of RARRES1, MVB12B and GSK3A; The analysis module is used to provide the expression levels of urinary protein biomarkers obtained by the data acquisition module as input data to the constructed risk scoring model; The output module is used to output the prognostic risk stratification results of the risk scoring model.

22. The prediction device or system according to claim 21, characterized in that, The analysis module uses Cox regression to model the risk scoring model.

23. The prediction device or system according to claim 22, characterized in that, The risk scoring model is the risk scoring model described in claim 19 or 20.

24. A computer-readable storage medium, characterized in that, It stores a computer program for performing the system functions as described in claim 14 or 21.

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