Application of MEIS3 as a biomarker for hypertrophic cardiomyopathy

Abstract

The application belongs to the field of biomedicine, and particularly relates to application of MEIS3 as a biomarker for hypertrophic cardiomyopathy. In view of the blank of lack of precise molecular markers in existing HCM diagnosis, the application verifies through batch RNA-seq, external myocardial scRNA-seq data set and clinical sample ELISA detection in multiple aspects, and finds that MEIS3 is significantly up-regulated in patients with hypertrophic cardiomyopathy, and the mRNA and protein expression levels of MEIS3 show high accuracy when used for diagnosing HCM. The application provides a new method for early molecular diagnosis and prevention of hypertrophic cardiomyopathy, and has a broad clinical application prospect.

Description

Technical Field

[0001] This invention relates to the field of biomedicine, and more specifically, to the application of MEIS3 as a biomarker for hypertrophic cardiomyopathy. Background Technology

[0002] Hypertrophic cardiomyopathy (HCM) is a familial myocardial disease defined as unexplained left ventricular hypertrophy that often leads to heart failure, arrhythmias, or sudden cardiac death in young adults. Pathogenic mutations in sarcomere proteins (such as MYH7 and MYBPC3) are recognized as the cause of HCM, but a significant proportion of patients still have undetectable sarcomere gene mutations, and the association between genotype and phenotype remains unpredictable. In fact, up to 50-68% of HCM patients have no known sarcomere mutations, and the regulatory network driving the pathology of HCM in these cases is not fully elucidated. This uncertainty has prompted researchers to conduct systematic transcriptome analyses to reveal novel molecular mechanisms and biomarkers that may improve the diagnosis and management of HCM.

[0003] MEIS3 (Meis Homeobox 3) is a transcription factor previously unassociated with hypertrophic cardiomyopathy (HCM), belonging to the TALE homeodomain transcription factor family, and is well-known for its roles in embryonic development and cell differentiation. MEIS3 can directly regulate the master kinase PDPK1 (PDK1) in the PI3K / Akt signaling pathway, thereby promoting cell survival in other tissues. This is particularly interesting in the context of HCM, where PI3K–Akt signaling and its downstream hypertrophic pathways are dysregulated. Furthermore, recent pan-cancer analyses have revealed that MEIS3 and its family members influence the immune microenvironment. High expression of MEIS3 in tumors is associated with an “immune silencing” phenotype characterized by low leukocyte infiltration, and interference with the expression of MEIS family genes is thought to enhance response to immunotherapy. However, whether MEIS3 can serve as a biomarker for hypertrophic cardiomyopathy remains unknown. Summary of the Invention

[0004] In view of this, in order to fill the above-mentioned technical gaps in the field, the purpose of this invention is to provide the application of MEIS3 as a biomarker for hypertrophic cardiomyopathy.

[0005] To achieve the above-mentioned objectives of the present invention, the present invention adopts the following technical solution: The first aspect of the present invention is to provide the application of a reagent for detecting MEIS3 expression level in the preparation of diagnostic products for hypertrophic cardiomyopathy.

[0006] In this application, the reagent can be used to perform quantitative or semi-quantitative analysis of MEIS3 mRNA transcripts using methods such as real-time quantitative qRT-PCR, RT-PCR, or gene chips; or to measure the concentration of MEIS3 protein using methods such as enzyme-linked immunosorbent assay (ELISA), chemiluminescent immunoassay (CLIA), or immunoturbidimetry.

[0007] In this application, reagents used to detect MEIS3 expression levels in samples include: oligonucleotide probes targeting the MEIS3 sequence, such as those designed for MEIS3 mRNA-specific fragments, which can detect mRNA using hybridization techniques such as Northern blotting and in situ hybridization; PCR primers targeting MEIS3, used for PCR amplification techniques such as conventional PCR, real-time quantitative PCR, and nested PCR to analyze mRNA levels; monoclonal or polyclonal antibodies targeting MEIS3, used for detecting protein expression using immunological techniques such as Western blotting, immunohistochemistry, and immunofluorescence; MEIS3-specific nucleic acid aptamers, which utilize their specific binding ability to recognize MEIS3; and MEIS3-targeting molecularly imprinted polymers, used for specific recognition in affinity chromatography or sensor construction.

[0008] In this application, the aforementioned reagents, such as probes, primers, antibodies, or nucleic acid aptamers, can be prepared or obtained using conventional methods in the art, such as chemical synthesis, genetic engineering, and hybridoma techniques. For example, oligonucleotide probes can be directly prepared through chemical synthesis based on the known MEIS3 mRNA sequence.

[0009] In this application, the reagent may contain not only detection components such as probes, primers, and antibodies, but also auxiliary components such as buffer solutions, reaction substrates, and standards, all of which fall within the scope of protection of this invention.

[0010] In this application, the products include detection tools such as detection chips, test strips, and reagent kits, which are implemented using high-throughput sequencing platforms such as Illumina sequencers, quantitative PCR instruments, and chip signal readers.

[0011] In this application, the detection chip includes a protein chip and / or a gene chip. The chip is equipped with a probe for detecting the expression level of MEIS3 and an internal reference probe. The internal reference probe can be selected from conventional internal reference genes or proteins such as GAPDH and β-Actin.

[0012] A second aspect of the present invention relates to providing a system for diagnosing or assisting in the diagnosis of hypertrophic cardiomyopathy, and the application of the system in the preparation of products for diagnosing or assisting in the diagnosis of hypertrophic cardiomyopathy. The system is configured to include an input device for inputting the expression levels of molecular markers of a subject; a calculation device for determining whether a subject has hypertrophic cardiomyopathy based on the expression levels; and an output device for outputting diagnostic results.

[0013] In this application, the molecular marker is MEIS3.

[0014] In this application, the system also includes a detection device for detecting the expression level of molecular markers; the detection device includes a device for running a PCR program to detect the nucleic acid level of molecular markers through nucleic acid amplification technology; or a device used for immunoassay to detect the protein level of molecular markers through antigen-antibody reaction, such as an enzyme-linked immunosorbent assay (ELISA) reader, chemiluminescence analyzer, or immunoturbidimetric analyzer.

[0015] A third aspect of the present invention is to provide a method for diagnosing or assisting in the diagnosis of hypertrophic cardiomyopathy, which determines whether a subject has hypertrophic cardiomyopathy based on the expression level of the molecular marker MEIS3.

[0016] A fourth aspect of the present invention is to provide a computer-readable medium on which the aforementioned method for diagnosing or assisting in the diagnosis of hypertrophic cardiomyopathy is recorded or executed.

[0017] In this application, computer-readable media include various media capable of storing, transmitting, or carrying computer programs and related data that implement the aforementioned diagnostic methods, such as hard disks, floppy disks, optical disks, USB flash drives, magnetic tapes, memory cards, solid-state drives, etc. Those skilled in the art will readily understand how to use any currently known computer-readable media to create a storage medium containing programs and data implementing the aforementioned diagnostic methods.

[0018] The significant advantages of this invention are:

[0019] This invention provides a new biomarker for the diagnosis of hypertrophic cardiomyopathy. By detecting the expression level of MEIS3 in subjects, it can assist existing clinical detection methods to achieve more sensitive and accurate identification of hypertrophic cardiomyopathy, providing a brand-new method for the early clinical diagnosis and prevention of hypertrophic cardiomyopathy. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 Volcano plot of differentially expressed genes (DEGs) between hypertrophic cardiomyopathy (HCM) and a healthy control group provided in Example 1 of this invention;

[0022] Figure 2This is a module-trait association heatmap based on the weighted gene co-expression network analysis (WGCNA) of Embodiment 1 of the present invention;

[0023] Figure 3 Venn diagram of the intersection of differentially expressed genes (DEGs) and WGCNA blue module genes provided in Example 1 of the present invention;

[0024] Figure 4 The coefficient distribution diagram of LASSO regression screening for HCM candidate genes provided in Embodiment 1 of the present invention;

[0025] Figure 5 This is a bar chart showing the predicted importance ranking of candidate genes in LASSO regression provided in Embodiment 1 of the present invention.

[0026] Figure 6 This provides an intersection plot of LASSO and random forest results for Embodiment 1 of the present invention;

[0027] Figure 7 This is a graph showing the expression levels of the MEIS3 gene (RNA-seq) in the hypertrophic cardiomyopathy (HCM) group and the healthy control group provided in Example 2 of the present invention.

[0028] Figure 8 ROC curve for diagnosing hypertrophic cardiomyopathy (HCM) using MEIS3 gene based on batch RNA-seq data, provided in Embodiment 2 of the present invention;

[0029] Figure 9 The graph shows the MEIS3 gene expression levels in the HCM group and the control group in the external myocardial single-cell RNA-seq (scRNA-seq) data provided in Example 3 of this invention.

[0030] Figure 10 ROC curve for diagnosing hypertrophic cardiomyopathy (HCM) using MEIS3 gene based on external myocardial single-cell RNA-seq (scRNA-seq) data, provided in Example 3 of this invention;

[0031] Figure 11 The graph shows the expression levels of MEIS3 protein (detected by ELISA) in the hypertrophic cardiomyopathy (HCM) group and the healthy control group of the clinical samples provided in Example 4 of this invention.

[0032] Figure 12 ROC curve for ELISA-based diagnosis of hypertrophic cardiomyopathy (HCM) using MEIS3 protein. This is provided in Example 4 of the present invention. Detailed Implementation

[0033] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] The terms "first," "second," and "third" used in this invention are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first," "second," or "third" may explicitly or implicitly include multiple such features. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationships and movements between components in a specific posture (as shown in the figures). If the specific posture changes, the directional indications also change accordingly. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to these processes, methods, products, or devices.

[0035] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in multiple embodiments of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0036] Example 1: Screening for differentially expressed genes in hypertrophic cardiomyopathy

[0037] 1) Study subjects: Peripheral blood samples were obtained from 4 patients with obstructive hypertrophic cardiomyopathy (HCM) (NYHA class III) who underwent ventricular septal myocardectomy and 3 age- and sex-matched healthy donors; all participants signed written informed consent forms, and the study protocol was approved by the institution's ethics committee.

[0038] 2) RNA extraction and batch transcriptome analysis (RNA-seq): Blood samples were collected in EDTA tubes, and RNA was extracted using TRIzol reagent. RNA quality was confirmed using an Agilent Bioanalyzer (RIN>7). Poly-Am RNA was enriched for cDNA library construction and sequenced on the Illumina NovaSeq platform (150bp paired-end reads, approximately 50 million reads per sample).

[0039] 3) Sequencing data preprocessing and DEGs screening: FastP was used for read quality filtering, and STAR was used to align reads to the human genome (GRCh38). FeatureCounts was used for gene-level quantification. DESeq2 was used for differential expression analysis between the HCM group and the control group, applying the criteria of FDR < 0.05 and |log2FC| > 1.

[0040] 4) Weighted gene co-expression network analysis: Genes with the top 5000 expression variation coefficients were selected, and scale-free co-expression networks were constructed using the WGCNA package (v1.70-3) in R software. The genes were divided into multiple co-expression modules using a dynamic pruning algorithm, and each module was marked with a different color. The Pearson correlation coefficients of the characteristic genes of each module with key clinical indicators of HCM were calculated, and the modules with the strongest correlation with the HCM phenotype were selected.

[0041] 5) Feature selection based on machine learning: The intersection of “significant DEGs” and “most relevant modules” is analyzed by Venn diagram. The overlapping genes obtained are the core DEGs related to HCM pathology. Two supervised machine learning algorithms, LASSO regression and random forest classification, are applied to screen candidate genes related to HCM. The algorithms are trained on standardized RNA-seq data to identify the features that best distinguish HCM from the control group.

[0042] LASSO logistic regression is implemented using the glmnet package, applying L1 regularization to select the minimum gene set with the richest information. Model tuning is performed using 10-fold cross-validation to determine the optimal penalty parameters. Genes with non-zero coefficients are retained as key predictors. Random forest analysis is performed using the randomForest package, ranking genes by importance based on mean precision reduction and Gini impurity.

[0043] 6) Experimental Results

[0044] Please see Figure 1The results of the DEGs screening were shown, using strict criteria (|log2 fold change|>1 and corrected p-value<0.05); a total of 692 significant DEGs were screened, of which 467 genes were upregulated and 225 genes were downregulated in the HCM group compared with the control group;

[0045] Please see Figure 2 The graph shows the module-trait relationships, with the blue module exhibiting the strongest and most significant positive correlation with the HCM phenotype (correlation coefficient = 0.81, p < 0.001). Please refer to [link to graph]. Figure 3 It shows the overlapping genes between DEGs and WGCNA modules. Intersection analysis showed that there were 233 overlapping genes between the significant DEGs and the blue WGCNA modules.

[0046] Please see Figure 4 The diagram shows the coefficient distribution of HCM candidate genes selected by LASSO regression, among which MEIS3, CYP7A1, ANKRD20A1, TRAT1, and SYDE2, as candidate genes, exhibit stable, non-zero coefficients; please refer to [link to documentation]. Figure 5 The diagram shows the ranking of predicted importance of candidate genes in LASSO regression. The gene coefficients selected from LASSO regression indicate that MEIS3 is the gene with the highest predicted importance.

[0047] Please see Figure 6 Intersection analysis between genes selected by LASSO and those selected by random forest revealed four robust salient genes: MEIS3, SYDE2, TRAT1, and ANKRD20A1. Both algorithms consistently selected these genes, highlighting their potential biological relevance and reliability as diagnostic biomarkers. Intersection analysis of multiple algorithms further emphasized the reliability of MEIS3 as a diagnostic biomarker.

[0048] Example 2: Validation of MEIS3's diagnostic efficacy for hypertrophic cardiomyopathy

[0049] 1) Experimental method: Based on the batch RNA-seq in Example 1, ROC analysis was performed on MEIS3, a gene that was significantly differentially expressed in hypertrophic cardiomyopathy and screened in Example 1. The area under the curve (AUC) was calculated. The presence or absence of hypertrophic cardiomyopathy was used as a binary classification result. The expression level of MEIS3 gene was used as a predictor to plot the ROC curve to evaluate the accuracy of MEIS3 in diagnosing hypertrophic cardiomyopathy.

[0050] 2) Experimental results, please refer to [link / reference]. Figures 7-8MEIS3 showed significantly increased expression in patients with hypertrophic cardiomyopathy (HCM), while SYDE2, TRAT1, and ANKRD20A1 showed decreased expression levels. The area under the ROC curve for MEIS3, SYDE2, TRAT1, and ANKRD20A1 was 1. The AUC values ​​ranged from 0 to 1, with 0.7 being acceptable performance and 0.9 being excellent performance. This indicates that using the expression level of the MEIS3 gene as a diagnostic indicator can effectively determine whether a subject has hypertrophic cardiomyopathy, demonstrating good diagnostic efficacy.

[0051] Example 3: Validating the diagnostic efficacy of MEIS3 for hypertrophic cardiomyopathy using an external dataset.

[0052] 1) The diagnostic efficacy of MEIS3 for hypertrophic cardiomyopathy was validated at the single-cell level (scRNA-seq) using publicly available myocardial scRNA-seq data from Figshare (https: / / doi.org / 10.6084 / m9.figshare.c.5777948.v2). Raw expression data were downloaded according to standard procedures, and the dataset was processed using CellRanger and Seurat (v4.0). Differential gene expression analysis of MEIS3 was performed. The presence or absence of hypertrophic cardiomyopathy was used as a binary classification result, and the expression level of MEIS3 was used as a predictor to plot ROC curves to evaluate the accuracy of MEIS3 in diagnosing hypertrophic cardiomyopathy.

[0053] 2) Results Analysis: Please refer to [link / reference needed]. Figure 9 In differential analysis of the external dataset, MEIS3 was upregulated in the HCM samples compared to the control group, while SYDE2, TRAT1, and ANKRD20A1 were downregulated, consistent with the trends observed in the aforementioned bulk RNA-seq; see [link to relevant documentation]. Figure 10 In external datasets, the AUC value of MEIS3 for diagnosing hypertrophic cardiomyopathy was approximately 0.957, which is close to the findings in Example 2, indicating that measuring MEIS3 is helpful for the diagnosis of hypertrophic cardiomyopathy and that MEIS3 can serve as a diagnostic biomarker for hypertrophic cardiomyopathy.

[0054] Example 4: Validating the diagnostic efficacy of MEIS3 using a clinical dataset.

[0055] 1) Sample collection

[0056] Blood samples were collected from 34 patients with hypertrophic cardiomyopathy (HCM) as the HCM group and blood samples were collected from 34 healthy controls (CON) as the healthy control group to verify the diagnostic efficacy of MEIS3.

[0057] All patients in this validation set had no overlap with those in Example 1. All participants signed written informed consent forms, and the study protocol was approved by the institution's ethics committee. The inclusion and exclusion criteria for patients with hypertrophic cardiomyopathy and healthy controls are as follows:

[0058] (A) HCM patient group - Inclusion criteria

[0059] Subjects must meet all of the following criteria to be eligible for enrollment:

[0060] Main diagnostic criteria:

[0061] Meets the recognized diagnostic criteria for HCM, namely, the presence of left ventricular wall thickening that cannot be explained by other cardiac or systemic diseases (such as hypertension, aortic stenosis, etc.) confirmed by imaging examination (usually echocardiography), with a maximum ventricular wall thickness ≥15mm.

[0062] For subjects with a clear family history of HCM (a first-degree relative with a confirmed case), a maximum ventricular wall thickness ≥13mm is sufficient for inclusion.

[0063] Genetic confirmation (if applicable): Individuals carrying known pathogenic or potentially pathogenic sarcomere protein gene mutations associated with HCM may be included after investigator evaluation, even if the ventricular wall thickness does not fully meet the above criteria.

[0064] Clinical status: Patients with a confirmed diagnosis of HCM, whether obstructive (HOCM) or non-obstructive (nHCM), regardless of whether they have clinical symptoms (such as dyspnea, chest pain, palpitations, syncope, etc.).

[0065] (B) HCM Patient Group - Exclusion Criteria

[0066] Subjects meeting any of the following criteria must be excluded:

[0067] 1. Secondary myocardial hypertrophy:

[0068] Left ventricular hypertrophy due to other identifiable causes, including but not limited to:

[0069] Uncontrolled severe hypertension (e.g., resting systolic blood pressure ≥160 mmHg despite treatment).

[0070] Moderate to severe aortic stenosis or other valvular heart diseases that can lead to increased pressure load.

[0071] The athlete's heart (this needs to be differentiated based on medical history, physical examination, and imaging evaluation).

[0072] Infiltrative or storage cardiomyopathy:

[0073] A definitive diagnosis or high suspicion of other types of cardiomyopathy, such as cardiac amyloidosis, Fabry disease, Danon disease, etc.

[0074] 2. Severe complications:

[0075] Having experienced an acute myocardial infarction, unstable angina, or undergone coronary revascularization (PCI or CABG) within the past 3 months.

[0076] The New York Heart Association (NYHA) classifies the patient as Class IV.

[0077] There is a history of life-threatening malignant arrhythmias (such as persistent ventricular tachycardia or ventricular fibrillation) that have not been effectively controlled.

[0078] Severe liver dysfunction (e.g., ALT or AST > 3 times the upper limit of normal).

[0079] Severe renal insufficiency (e.g., estimated glomerular filtration rate eGFR <30 mL / min / 1.73 m²).

[0080] 3. Other situations:

[0081] Pregnant or breastfeeding women.

[0082] Subjects may have malignant tumors, active infections, or other serious systemic diseases that may interfere with research results or endanger the safety of the subjects.

[0083] Currently participating in another clinical intervention study.

[0084] III. Control Group of Healthy Volunteers

[0085] (A) Healthy control group - Inclusion criteria

[0086] Subjects must meet all of the following criteria to be eligible for enrollment:

[0087] Health status: Self-reported as being in good health with no known history of cardiovascular disease or other major chronic illnesses.

[0088] Matching: Age and sex were matched to the HCM patient group to ensure comparability between groups.

[0089] Objective inspection:

[0090] Physical examination revealed no clinically significant abnormalities.

[0091] The standard 12-lead electrocardiogram (ECG) results are normal, or only nonspecific, clinically insignificant changes are present.

[0092] The echocardiogram results were within the normal range, with no evidence of left ventricular hypertrophy (maximum left ventricular wall thickness <12mm), and no structural heart disease, valvular disease, or abnormal cardiac function.

[0093] Normal resting blood pressure (systolic blood pressure <140 mmHg and diastolic blood pressure <90 mmHg).

[0094] (B) Healthy control group - Exclusion criteria

[0095] Subjects meeting any of the following criteria must be excluded:

[0096] Cardiovascular related:

[0097] Any known history of cardiovascular disease, including hypertension, coronary artery disease, cardiomyopathy, heart failure, valvular heart disease, clinically significant arrhythmias, etc.

[0098] A family history of HCM, hereditary arrhythmia, or unexplained sudden death in first-degree relatives (this is very important as it can rule out potential asymptomatic gene carriers).

[0099] Systemic diseases:

[0100] Patients with diabetes, chronic kidney disease, chronic liver disease, autoimmune diseases, uncontrolled thyroid dysfunction, malignant tumors, etc.

[0101] Medication use:

[0102] Long-term use of medications that may affect cardiovascular function (such as beta-blockers, calcium channel blockers, etc.) is discouraged unless it is for the treatment of non-cardiac diseases and the researchers determine that it will not affect the study results.

[0103] Laboratory tests:

[0104] Clinically significant abnormalities in routine hematological or biochemical tests.

[0105] Other situations:

[0106] Pregnant or breastfeeding women.

[0107] Currently participating in another clinical study.

[0108] Professional athlete.

[0109] 2) The expression level of MEIS3 in the samples was detected using an enzyme-linked immunosorbent assay (ELISA) kit.

[0110] An enzyme-linked immunosorbent assay (ELISA) kit (Human Homeobox protein Meis3 (MEIS3) ELISA Kit, catalog number KTE61660, purchased from Abbkine, https: / / www.abbkine.com / product / human-homeobox-protein-meis3-meis3-elisa-kit-kte61660 / ) was used. Blood samples were separated into serum and plasma by centrifugation. After appropriate dilution with the kit's diluent according to the kit requirements, the samples were subjected to the following steps: sample addition, washing, addition of detection antibody, second washing, addition of enzyme conjugate, third washing, substrate reaction, and termination of the reaction. The relative optical density (OD value) of MEIS3 protein was measured at a specific wavelength using an ELISA reader. The presence of hypertrophic cardiomyopathy was used as a binary classification result. The expression level of MEIS3 protein was used as a predictive factor. By changing the threshold for disease judgment, the sensitivity and specificity at different thresholds were calculated, and ROC curves were plotted.

[0111] Please see the results. Figures 11-12 Compared with the healthy control group, MEIS3 was significantly upregulated in the HCM patient group; the area under the ROC curve (AUC) was 0.9645, with a sensitivity of 88.24% and a specificity of 97.06%. This indicates that the MEIS3 protein detection results are basically consistent with the MEIS3 gene expression level (RNA) detection results in Examples 2 and 3, further verifying that MEIS3 can serve as a predictive indicator for hypertrophic cardiomyopathy.

[0112] In summary, this invention has verified the application of MEIS3 as a biomarker in products for hypertrophic cardiomyopathy through embodiments. Furthermore, the MEIS3 biomarker possesses both high sensitivity and good specificity, which can provide a basis for the clinical diagnosis of hypertrophic cardiomyopathy and offer new biomarker support for the early screening, accurate diagnosis, or clinical assessment of hypertrophic cardiomyopathy, thus showing broad prospects for clinical application.

[0113] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. Use of a reagent for detecting expression level of MEIS3 in the preparation of a diagnostic product for hypertrophic cardiomyopathy.

2. Use according to claim 1, characterized in that, The reagent comprises a reagent for detecting mRNA expression and / or protein expression of MEIS3.

3. Use according to claim 2, characterized in that, The reagent is an RNA-seq analysis reagent and / or an enzyme-linked immunoassay reagent.

4. Use according to claim 2, characterized in that, The reagent comprises at least one of an oligonucleotide probe targeting a coding sequence of MEIS3, a PCR primer targeting a coding sequence of MEIS3, a MEIS3-specific aptamer, and a MEIS3-targeting molecularly imprinted polymer.

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