Application of CXCL1 in diagnosis of calcified aortic valve disease

Abstract

The invention discloses an application of CXCL1 in diagnosis of a calcified aortic valve disease. According to the invention, it is found for the first time that CXCL1 can be used in early diagnosis of calcified aortic valve disease (CAVD), and it is found through verification of a real clinical sample collected by the invention that CXCL1 has good diagnosis efficiency on diagnosis of CAVD, and the accuracy, sensitivity and specificity are all high. The invention provides a new thought and strategy for the technical field of CAVD diagnosis, and has important scientific significance and clinical application value.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biological medicine, and particularly relates to application of CXCL1 in diagnosis of calcific aortic valve disease. BACKGROUND

[0002] Calcific aortic valve disease (CAVD) is a general term for a series of pathological changes from hardening to calcification of the aortic valve caused by various reasons such as inflammation and aging, including early aortic valve calcification and late aortic valve stenosis. The clinical symptoms of CAVD vary depending on the severity of the disease. Patients may have no obvious symptoms in the early stage of the disease, and symptoms such as dyspnea, chest pain, palpitations, syncope, dizziness, fatigue, and edema may appear in the later stage of the disease. These symptoms may gradually worsen as the severity of CAVD increases and affect the quality of life. At present, the aging of the population has led to a sharp increase in the prevalence and mortality of CAVD. Therefore, early diagnosis and treatment are crucial for CAVD.

[0003] Clinically, the diagnosis of CAVD includes physical examination, echocardiography, and cardiac CT. Physical examination is the fastest and most direct bedside assessment method for doctors without any equipment. However, signs are not unique to CAVD, and many heart diseases may show similar signs, with extremely low sensitivity and almost no positive signs for mild and moderate lesions. Echocardiography is the cornerstone and preferred tool for diagnosing and evaluating CAVD, which is non-invasive, real-time, and repeatable, and can comprehensively evaluate valve morphology, blood flow dynamics, and secondary changes in the heart (such as ventricular hypertrophy and function). However, the image quality of echocardiography is affected by the patient's body type, and the measurement results of different operators may be biased. Cardiac CT is the gold standard for evaluating CAVD, with high spatial resolution and accurate quantification of calcification. However, both the patient and medical staff are exposed to radiation during the examination. For some patients, the use of contrast agents is contraindicated. For valves that only thicken and fibrosis but have not yet calcified, the disease may be underestimated. Therefore, finding a more convenient and effective diagnostic method is a technical problem that needs to be solved in the field of CAVD diagnosis and treatment. SUMMARY

[0004] To make up for the deficiencies of the prior art, the present application provides the following technical solutions:

[0005] The first aspect of the present application provides the use of a reagent for detecting the expression level of biomarker CXCL1 in a sample in the preparation of a product for diagnosing calcific aortic valve disease.

[0006] In this invention, diagnosis refers to the discovery, judgment, or recognition of an individual's health status or condition based on one or more symptoms, data, or other information related to the individual. An individual's health status may be diagnosed as healthy / normal (i.e., without disease or ailment), or as unhealthy / abnormal (i.e., with disease or ailment), or as a specific degree of severity or progression of disease. The term "diagnosis" includes the early detection of a disease / symptom associated with a specific disease or ailment; the characteristics or classification of the disease; the discovery of the progression, cure, or recurrence of the disease; and the discovery of the individual's response to the disease after treatment or intervention.

[0007] In this invention, the biomarker refers to a substance differentially expressed between subjects with a second phenotype (e.g., no disease) and subjects with a first phenotype (e.g., disease), including genes, proteins, metabolites, or microorganisms. Specifically, it is significantly present (i.e., increased or decreased) in biological samples from subjects with a first phenotype (e.g., disease) compared to those from subjects with a second phenotype (e.g., no disease); the biomarker can be differentially present at any level. In a specific embodiment of this invention, the biomarker refers to CXCL1.

[0008] Furthermore, the reagents include reagents for detecting the protein expression level of CXCL1 in the sample, reagents for detecting the DNA level of CXCL1 in the sample, reagents for detecting the RNA level of CXCL1 in the sample, or reagents for detecting the number of CXCL1-positive expression cells in the sample.

[0009] Furthermore, the reagents for detecting the DNA level of CXCL1 in the sample include reagents for detecting the DNA expression level, DNA methylation level, and / or DNA phosphorylation level of CXCL1 in the sample, such as reagents for detecting the DNA level by sequencing technology.

[0010] Furthermore, the reagents for detecting the RNA level of CXCL1 in the sample include reagents for detecting the expression levels of CXCL1 mRNA, lncRNA, and / or miRNA in the sample, such as primers that specifically amplify CXCL1 and / or probes that specifically recognize CXCL1.

[0011] Furthermore, the reagents for detecting the number of CXCL1-positive cells in the sample may include reagents for detecting the number of CXCL1-positive cells by means of immunohistochemistry, immunofluorescence, single-cell transcriptome sequencing, etc.

[0012] Furthermore, the reagent is used to detect the protein expression level of CXCL1 in the sample.

[0013] Furthermore, the data on the protein expression level of CXCL1 in the test samples include affinity proteins that specifically bind to the CXCL1 protein.

[0014] Furthermore, the affinity protein includes antibodies that specifically bind to the CXCL1 protein, antibody functional fragments, lectins, or receptors.

[0015] The antibodies and antibody functional fragments described in this invention can be any type of antibody or antibody functional fragment of any structure, size, immunoglobulin class, origin, etc., as long as they can bind to the target protein. The antibodies or antibody functional fragments described in this invention can be monoclonal or polyclonal. An antibody functional fragment refers to a portion of an antibody that retains its antigen-binding activity or a peptide containing a portion of an antibody. Antibody functional fragments may include F(ab')2, Fab', Fab, single-chain Fv (scFv), disulfide-bonded Fv (dsFv) or polymers thereof, dimerized V regions (biantibodies), or peptides containing CDRs. Antibodies can be obtained by methods known to those skilled in the art. For example, a mammalian cell expression vector containing a polypeptide that retains whole or part of the target protein or integrating polynucleotides encoding them can be prepared as an antigen. After immunizing an animal with the antigen, immune cells are obtained from the immunized animal and fused with cancer cells to obtain a hybridoma. Antibodies are then collected from the hybridoma culture. Finally, monoclonal antibodies against the marker protein can be obtained by antigen-specific purification of the obtained antibody using the marker protein used as the antigen or a portion thereof.

[0016] The term "expression level" as used in this invention generally refers to the amount of a biomarker in a biological sample. "Expression" generally refers to the process by which information (e.g., gene-encoded and / or epigenetic information) is transformed into structures present and functioning within the cell. Therefore, as used herein, "expression" can refer to transcription into a polynucleotide, translation into a polypeptide, polynucleotide, or polypeptide modification (e.g., post-translational modification of a polypeptide). Transcribed polynucleotide fragments, translated polypeptide fragments, polynucleotide fragments, or polypeptide modified fragments (e.g., post-translational modifications of a polypeptide) should also be considered as expressions, whether they originate from transcripts generated through alternative splicing or degraded transcripts, or from post-translational processing of polypeptides (e.g., through proteolysis).

[0017] In this invention, the subject refers to any animal, including both human and non-human animals. Non-human animals include all vertebrates, such as mammals like non-human primates (especially higher primates), sheep, dogs, rodents (such as mice or rats), guinea pigs, goats, pigs, cats, rabbits, cattle, any livestock or pets; and non-mammals such as chickens, amphibians, reptiles, etc. In a preferred embodiment, the subject is a human.

[0018] In some implementations, the samples include, but are not limited to: tissues, blood, cells, lymph, synovial fluid, exosomes, cell extracts, feces, urine, saliva, sputum, synovial fluid, pleural effusion, peritoneal effusion, serous cavity effusion, lymph, cerebrospinal fluid, uterine cavity fluid, digestive juices, bile, alveolar bronchial lavage fluid, organs, and any combination thereof from the subject.

[0019] Furthermore, the sample is blood from the subject.

[0020] Furthermore, the blood includes serum and plasma.

[0021] Furthermore, the sample is plasma derived from the subject.

[0022] In this invention, CXCL1 refers to CXC motif chemokine ligand 1, officially named CXC motifchemokine ligand 1 [Homo sapiens (human)], with a Gene ID of 2919 in NCBI. Detailed information about CXCL1 can be obtained from the NCBI database (https: / / www.ncbi.nlm.nih.gov / ) based on this Gene ID. In this invention, CXCL1 includes the CXCL1 gene and the CXCL1 protein.

[0023] In this invention, verification using real clinical samples collected by this invention revealed that CXCL1 has good diagnostic efficacy for CAVD. The diagnostic efficacy is verified using the receiver operating characteristic curve (ROC). The area under the curve (AUC) is the area under the ROC curve, which is well known to those skilled in the art. Measuring AUC helps to compare the accuracy of classifiers across the overall data range.

[0024] The ROC curve is a graph of the true positive rate (sensitivity) versus the false positive rate (100% - specificity) of an experiment. It is useful for depicting the performance of a specific characteristic when distinguishing between two populations. Typically, characteristic data are selected across the entire population in ascending order based on the values ​​of a single characteristic. Then, for each value of that characteristic, the true positive and false positive rates of the data are calculated. The true positive rate is determined by counting the number of cases with values ​​higher than that characteristic and dividing by the total number of cases. The false positive rate is determined by counting the number of controls with values ​​higher than that characteristic and dividing by the total number of controls. While this definition refers to cases where the characteristic is higher in cases compared to controls, it also applies to cases where the characteristic is lower in cases compared to controls (in which case samples with values ​​lower than that characteristic are counted). ROC curves can be generated with respect to individual characteristics and can also be generated with respect to other individual outputs. For example, combinations of two or more characteristics can be mathematically combined (e.g., addition, subtraction, multiplication, etc.) to provide individual sum values ​​that can be plotted on the ROC curve. Furthermore, any combination of multiple features derived from individual output values ​​can be plotted on a ROC curve.

[0025] A second aspect of the present invention provides a product for diagnosing or assisting in the diagnosis of calcific aortic valve disease, the product comprising the reagents described in the first aspect of the present invention.

[0026] Furthermore, the products include reagent kits, chips, test strips, systems, devices, and apparatuses.

[0027] In some embodiments, the kit further includes: a container, instructions for use, a positive control, a negative control, a buffer, and an adjuvant or solvent. The instructions describe how to use the kit for testing and how to use the test results to determine whether a subject has or is suspected of having calcific aortic valve disease.

[0028] In some embodiments, the chip includes a gene chip and a protein chip; the gene chip includes a solid support and oligonucleotide probes ordered immobilized on the solid support, the oligonucleotide probes specifically corresponding to some or all of the sequence of CXCL1. The protein chip includes a solid support and specific antibodies or ligands of the CXCL1-encoded protein immobilized on the solid support. The solid support can be made of various commonly used materials in the chip field, such as plastic products, microparticles, membrane carriers, etc.

[0029] The third aspect of this invention provides the application of CXCL1 in constructing a diagnostic model for calcific aortic valve disease.

[0030] The fourth aspect of the present invention provides a method for constructing a diagnostic model for calcific aortic valve disease, the method comprising the steps of acquiring CXCL1 expression level data in subject samples and inputting the data into a machine learning algorithm to construct a diagnostic model.

[0031] Furthermore, the diagnostic model obtains classification results using the following criteria: when the expression level of CXCL1 is higher than the optimal cutoff value, the subject is classified as having or suspected of having calcific aortic valve disease; if the expression level of CXCL1 is lower than the optimal cutoff value, the subject is classified as not having calcific aortic valve disease.

[0032] In this invention, the term "optimal cutoff value" refers to a value that is statistically relevant to a specific outcome when compared with the analysis results. In a preferred embodiment, the optimal cutoff value is determined based on the statistical conclusions of studies comparing CAVD patients and healthy subjects. The optimal cutoff value can also be determined by taking into account the patient's genetic background, clinical characteristics, work environment, and other relevant factors and outcomes.

[0033] In some embodiments of the present invention, the methods for constructing the diagnostic model are known to those skilled in the art and can be implemented and realized in different ways, linking biomarker expression levels with a certain probability or risk. Preferably, the measured concentrations of the biomarker and one or more other biomarkers are mathematically combined, and the combined value is associated with the fundamental question of whether or not one has or is suspected of having a disease. The measured biomarker values ​​can be combined using any suitable existing mathematical method, and the diagnostic model can be constructed using machine learning algorithms.

[0034] Furthermore, the machine learning algorithm includes algorithmic models developed using various development tools.

[0035] Furthermore, the development tools include, but are not limited to, TensorFlow, Scikit-Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell.

[0036] Furthermore, the algorithm models include, but are not limited to, linear regression models, logistic regression models, Lasso regression models, Ridge regression models, linear discriminant analysis models, nearest neighbor models, decision tree models, perceptron models, neural network models, support vector machine models, Naive Bayes models, AdaBoost models, GBDT models, XGBoost models, LightGBM models, CatBoost models, and random forest models.

[0037] A fifth aspect of the present invention provides a diagnostic system for calcific aortic valve disease, the system comprising:

[0038] Data acquisition unit: used to acquire CXCL1 expression level data in subject samples.

[0039] Data classification unit: used to input CXCL1 expression level data into the diagnostic model constructed by the method according to the fourth aspect of the present invention to obtain a classification result of whether the subject has or is suspected of having calcified aortic valve disease.

[0040] Output unit: Used to output classification results.

[0041] The sixth aspect of the present invention provides a diagnostic device or computer-readable storage medium for calcific aortic valve disease, the device comprising a memory and a processor; the memory for storing program instructions; the processor for executing the program instructions, which, when executed, perform the following operations: acquiring CXCL1 expression level data in a sample, inputting the CXCL1 expression level data into a diagnostic model constructed based on the method described in the fourth aspect of the present invention, and obtaining a classification result of whether the sample has or is suspected of having calcific aortic valve disease.

[0042] The computer-readable storage medium stores a computer program that, when executed by a processor, implements the following method: acquiring CXCL1 expression level data in a sample, inputting the CXCL1 expression level data into a diagnostic model constructed based on the method described in the fourth aspect of the present invention, and obtaining a classification result of whether the sample has or is suspected of having calcified aortic valve disease.

[0043] To provide interaction with the user, the device may be a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse) through which the user provides input to the computer. Other types of devices may also be used to provide interaction with the user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including voice input, speech input, or tactile input).

[0044] Any combination of one or more computer-readable media can be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. In this invention, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments, more specific examples of computer-readable storage media include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0045] Advantages and beneficial effects of the present invention:

[0046] This invention is the first to discover that CXCL1 can be used for the early diagnosis of CAVD. Validation using real clinical samples collected according to this invention revealed that CXCL1 has good diagnostic efficacy for CAVD, with high accuracy, sensitivity, and specificity. This invention provides new ideas and strategies for the technical field of CAVD diagnosis, and has significant scientific and clinical application value. Attached Figure Description

[0047] Figure 1 The image shows the results of CXCL1 expression level detection in clinical samples; where A is the discovery set and B is the validation set.

[0048] Figure 2 The ROC curve for CXCL1 diagnosis of CAVD in clinical samples is shown; where A is the discovery set and B is the validation set. Detailed Implementation

[0049] As used in this invention, the terms “having,” “comprising,” or “including,” or any grammatical variations thereof, are used in a non-exclusive manner. Thus, these terms can all refer to a situation where no other features exist in the entity described in this context besides the features introduced by these terms, and can also refer to a situation where one or more other features are present.

[0050] Furthermore, as used in this invention, the terms “preferred,” “more preferred,” “most preferred,” “particularly,” “more particularly,” “specifically,” “more specifically,” or similar terms are used in combination with optional features without limiting other possibilities.

[0051] Unless otherwise stated, all figures used in this specification and claims to represent volume, weight, temperature, time, density, parts by weight, technical effect, etc., should in any case be understood to be modified by the terms "about" or "approximately". Therefore, unless indicated to the contrary, the numerical parameters listed in the specification and appended claims are approximate values. Unless otherwise stated, the terminology used herein has its common meaning as understood by one of ordinary skill in the art, and each numerical parameter should be interpreted according to the number of significant figures and conventional rounding methods or in a manner understood by one of those skilled in the art.

[0052] Example 1: Study on the effectiveness of CXCL1 in diagnosing CAVD based on a discovery cohort

[0053] I. Experimental Methods

[0054] 1. Clinical Samples

[0055] The clinical plasma samples used in this invention were obtained from Fuwai Hospital, Chinese Academy of Medical Sciences. Control plasma samples were collected from healthy patients undergoing routine checkups (n=44), while disease group plasma samples were collected from CAVD patients (n=30). Both control and disease group patients were over 18 years of age. The diagnosis of CAVD is primarily based on echocardiography, characterized by aortic valve thickening and calcification, with a mean transvalvular aortic pressure gradient greater than or equal to 40 mmHg, and exclusion of patients with rheumatic heart disease.

[0056] 2. Analytical Methods

[0057] The expression level of CXCL1 protein in plasma was detected using a human CXC chemokine ligand 1 (CXCL1) ELISA kit (Nanjing Xinfan Biotechnology Co., Ltd., catalog number XFH12545). The specific steps are as follows:

[0058] (1) Sample pretreatment:

[0059] Plasma: Anticoagulate with EDTA or heparin, centrifuge at 1000 g for 15 minutes (2-8℃) within 30 minutes, and collect the supernatant.

[0060] (2) Reagent preparation:

[0061] Washing solution (1×): Dilute 1 part 20× washing solution with 19 parts distilled water.

[0062] All reagents and samples must be equilibrated at room temperature (20-25℃) before use.

[0063] (3) Operating steps:

[0064] Remove the required strips from the aluminum foil bag after equilibration at room temperature for 20 min. Seal the remaining strips in a resealable bag and return them to 4°C. Set up standard and sample wells. Add 50 μL of different concentrations of standard to each standard well; add 50 μL of the test sample to each sample well; leave blank wells empty. Except for the blank wells, add 100 μL of horseradish peroxidase (HRP)-labeled detection antibody to each standard and sample well. Seal the reaction wells with sealing film and incubate at 37°C in a water bath or incubator for 60 min. Discard the liquid, pat dry on absorbent paper, add 350 μL of washing buffer to each well, let stand for 1 min, discard the washing buffer, pat dry on absorbent paper, and repeat this washing process 5 times. Add 50 μL each of substrate A and B to each well and incubate at 37°C in the dark for 15 min. Add 50 μL of stop solution to each well. Within 15 min, measure the OD value of each well at a wavelength of 450 nm. The diagnostic efficacy of CXCL1 in CAVD was tested using statistical methods (ROC curve analysis), and the cutoff value that best distinguishes the two groups was determined.

[0065] II. Experimental Results

[0066] The results showed that the expression level of CXCL1 protein was significantly upregulated in the disease group. Figure 1 A) By calculating the ROC curve of the discovery set of CXCL1, patients with CAVD and healthy controls were distinguished. The results showed that CXCL1 had good diagnostic efficacy, with a corresponding AUC of 0.828 (AUC 0.828). Figure 2 A).

[0067] Example 2: Study on the effectiveness of CXCL1 in diagnosing CAVD based on independent validation cohorts

[0068] I. Experimental Methods

[0069] 1. Clinical Samples

[0070] Sample collection for the validation cohort was similar to that for the discovery cohort. Control plasma samples were collected from healthy patients undergoing routine checkups (n=44), while disease plasma samples were collected from patients with CAVD (n=30). Both control and disease group patients were over 18 years of age. The diagnosis of CAVD was primarily based on echocardiography, characterized by aortic valve thickening and calcification, with a mean transvalvular aortic pressure gradient greater than or equal to 40 mmHg, and exclusion of patients with rheumatic heart disease.

[0071] 2. Analytical methods (same as in Example 1)

[0072] II. Experimental Results

[0073] The results showed that the expression level of CXCL1 protein in the disease group of the validation set cohort was also significantly upregulated compared with the control group. Figure 1In this independent validation queue, CXCL1 also demonstrated high diagnostic performance, with an AUC of 0.810 (B). Figure 2 B).

[0074] Based on the above results, we believe that CXCL1 can serve as a reliable biomarker for the diagnosis of CAVD.

[0075] The above description of the embodiments is only for understanding the method and core ideas of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from the principles of the invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. Application of reagents for detecting the expression level of the biomarker CXCL1 in samples in the preparation of products for diagnosing calcific aortic valve disease.

2. The application according to claim 1, characterized in that, The reagents include reagents for detecting the protein expression level of CXCL1 in a sample, reagents for detecting the DNA level of CXCL1 in a sample, reagents for detecting the RNA level of CXCL1 in a sample, or reagents for detecting the number of CXCL1-positive cells in a sample. Preferably, the reagent is a reagent for detecting the expression level of CXCL1 protein in the sample; Preferably, the reagent used to detect the protein expression level of CXCL1 in the sample includes an affinity protein that specifically binds to the CXCL1 protein; Preferably, the affinity protein includes an antibody that specifically binds to the CXCL1 protein, an antibody functional fragment, a lectin, or a receptor.

3. The application according to claim 1, characterized in that, The sample was blood from the subject; Preferably, the blood includes serum and plasma; Preferably, the sample is plasma from the subject.

4. A product for diagnosing or assisting in the diagnosis of calcific aortic valve disease, characterized in that, The product comprises the reagent as described in claim 2.

5. The product according to claim 4, characterized in that, The products include reagent kits, chips, test strips, systems, equipment, and devices.

6. Application of CXCL1 in constructing a diagnostic model for calcific aortic valve disease.

7. A method for constructing a diagnostic model for calcific aortic valve disease, characterized in that, The method includes obtaining CXCL1 expression level data in subject samples and inputting the data into a machine learning algorithm to construct a diagnostic model. Preferably, the diagnostic model obtains classification results using the following criteria: when the expression level of CXCL1 is higher than the optimal cutoff value, the subject is classified as having or suspected of having calcific aortic valve disease; if the expression level of CXCL1 is lower than the optimal cutoff value, the subject is classified as not having calcific aortic valve disease.

8. The method according to claim 7, characterized in that, The machine learning algorithms include algorithm models developed using various development tools; Preferably, the development tools include TensorFlow, Scikit-Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell. Preferably, the algorithm model includes linear regression model, logistic regression model, Lasso regression model, Ridge regression model, linear discriminant analysis model, nearest neighbor model, decision tree model, perceptron model, neural network model, support vector machine model, Naive Bayes model, AdaBoost model, GBDT model, XGBoost model, LightGBM model, CatBoost model, and random forest model.

9. A diagnostic system for calcified aortic valve disease, characterized in that, The system includes: Data acquisition unit: used to acquire CXCL1 expression level data in subject samples; Data classification unit: used to input CXCL1 expression level data into the diagnostic model constructed according to the method of any one of claims 7 or 8 to obtain a classification result of whether the subject has or is suspected of having calcified aortic valve disease; Output unit: Used to output classification results.

10. A diagnostic device or computer-readable storage medium for calcified aortic valve disease, characterized in that, The device includes a memory and a processor; the memory is used to store program instructions; the processor is used to execute the program instructions, which, when executed, are used to perform the following operations: acquire CXCL1 expression level data in the sample, input the CXCL1 expression level data into a diagnostic model constructed based on the method of any one of claims 7 or 8, and obtain a classification result of whether the sample has or is suspected of having calcified aortic valve disease; The computer-readable storage medium stores a computer program that, when executed by a processor, implements the following method: acquiring CXCL1 expression level data in a sample, inputting the CXCL1 expression level data into a diagnostic model constructed based on the method described in any one of claims 7 or 8, and obtaining a classification result of whether the sample has or is suspected of having calcified aortic valve disease.

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