Application of MEIS3 as hypertrophic cardiomyopathy biomarker

Abstract

The invention belongs to the field of biomedicine, and particularly relates to application of MEIS3 as a hypertrophic cardiomyopathy biomarker. Aiming at the blank that the existing HCM diagnosis lacks a precise molecular marker, through batch RNA-seq, external myocardial scRNA-seq data sets and clinical sample ELISA detection multi-level verification, MEIS3 is remarkably up-regulated in hypertrophic cardiomyopathy patients, and the mRNA and protein expression level of MEIS3 shows high accuracy when being used for diagnosing HCM. The invention provides a brand new method for clinical early molecular diagnosis and prevention of hypertrophic cardiomyopathy, and has a wide clinical application prospect.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and in particular to the application of MEIS3 as a biomarker for hypertrophic cardiomyopathy. Background Art

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

[0003] MEIS3 (Meis homeobox 3) is a transcription factor not previously associated with HCM. It belongs to the TALE homeodomain transcription factor family, known for its role in embryonic development and cell differentiation. MEIS3 can directly regulate PDPK1 (PDK1), the master kinase in the PI3K / Akt signaling pathway, thereby promoting cell survival in other tissues. This is particularly interesting in the context of HCM, as PI3K–Akt signaling and its downstream hypertrophic pathways are dysregulated in HCM. 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-silent" phenotype characterized by low leukocyte infiltration, and perturbing the expression of MEIS family genes is thought to enhance response to immunotherapy. However, whether MEIS3 can serve as a marker 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 current field, the purpose of the present invention is to provide the use of MEIS3 as a biomarker for hypertrophic cardiomyopathy.

[0005] In order to achieve the above-mentioned purpose of the present invention, the present invention adopts the following technical solutions: The first aspect of the present invention is to provide a reagent for detecting the expression level of MEIS3 and its use in preparing a diagnostic product for hypertrophic cardiomyopathy.

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

[0007] In this application, the reagents used to detect the expression level of MEIS3 in a sample include: oligonucleotide probes targeting the MEIS3 sequence, such as those designed for specific fragments of MEIS3 mRNA, which can be used to detect mRNA through hybridization techniques such as Northern hybridization and in situ hybridization; PCR primers targeting MEIS3, which are 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, which are used to detect protein expression through immunological techniques such as Western Blot, immunohistochemistry, and immunofluorescence; MEIS3-specific nucleic acid aptamers, which utilize their specific binding ability to recognize MEIS3; and MEIS3-targeted molecular imprinting polymers, which are used for specific recognition in affinity chromatography or sensor construction.

[0008] In this application, the above-mentioned probes, primers, antibodies or nucleic acid aptamers and other reagents can be prepared or obtained by conventional methods in the art, such as chemical synthesis, genetic engineering preparation, hybridoma technology, etc. For example, oligonucleotide probes can be directly prepared by 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 buffers, reaction substrates, and standards, all of which fall within the scope of protection of the present invention.

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

[0011] In this application, the detection chip includes a protein chip and / or a gene chip, and the chip is provided 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 provides a system for diagnosing or assisting in the diagnosis of hypertrophic cardiomyopathy, and its use in the manufacture of products for diagnosing or assisting in the diagnosis of hypertrophic cardiomyopathy. The system comprises an input device for inputting the expression level of a subject's molecular marker; a calculation device for determining whether the subject has hypertrophic cardiomyopathy based on the expression level; and an output device for outputting the diagnosis result.

[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 the molecular marker; the detection device includes a device for running a PCR program to detect the nucleic acid level of the molecular marker through nucleic acid amplification technology; or a device used for immunoassay to detect the protein level of the molecular marker through antigen-antibody reaction, such as an enzyme-linked microplate reader, a chemiluminescence analysis device, or an immunoturbidimetric device.

[0015] A third aspect of the present invention is to provide a method for diagnosing or assisting in the diagnosis of hypertrophic cardiomyopathy, wherein whether a subject has hypertrophic cardiomyopathy is determined 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 for implementing 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 can readily understand how to use any currently known computer-readable media to create a storage medium containing the program and data for implementing the aforementioned diagnostic methods.

[0018] The significant advantages of the present invention are: The present 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 new method for the clinical early diagnosis and prevention of hypertrophic cardiomyopathy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 The volcano plot of differentially expressed genes (DEGs) between hypertrophic cardiomyopathy (HCM) and the healthy control group provided in Example 1 of the present invention; Figure 2 The module-trait association heat map of the weighted gene co-expression network analysis (WGCNA) according to Example 1 of the present invention; Figure 3 The intersection Venn diagram of the significantly differentially expressed genes (DEGs) and the WGCNA blue module genes provided in Example 1 of the present invention; Figure 4 The coefficient distribution diagram of the LASSO regression for screening HCM candidate genes provided in Example 1 of the present invention; Figure 5 A bar chart showing the predicted importance ranking of candidate genes for LASSO regression provided in Example 1 of the present invention; Figure 6 Provides an intersection graph of LASSO and random forest results for Example 1 of the present invention; Figure 7 This is a graph showing the expression level 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; Figure 8 The ROC curve diagram of MEIS3 gene diagnosis of hypertrophic cardiomyopathy (HCM) based on batch RNA-seq data provided in Example 2 of the present invention; Figure 9 This is a graph of 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 the present invention; Figure 10 The ROC curve diagram of MEIS3 gene diagnosis of hypertrophic cardiomyopathy (HCM) based on external myocardial single-cell RNA-seq (scRNA-seq) data provided in Example 3 of the present invention; Figure 11 This is a graph showing the expression levels of MEIS3 protein (ELISA assay) in the clinical sample hypertrophic cardiomyopathy (HCM) group and the healthy control group provided in Example 4 of the present invention; Figure 12 This is a ROC curve diagram for diagnosing hypertrophic cardiomyopathy (HCM) using ELISA-based detection of MEIS3 protein, as provided in Example 4 of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] The terms "first," "second," and "third" in this application are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features indicated. Therefore, features specified as "first," "second," or "third" may explicitly or implicitly include multiple such features. In the description of this application, "multiple" means at least two, for example, two, three, etc., unless otherwise specifically defined. All directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are intended only to illustrate the relative positional relationships and movement of components in a specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly. Furthermore, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device comprising a series of steps or modules is not limited to the listed steps or modules and may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to such process, method, product, or device.

[0023] References to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in multiple embodiments of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0024] Example 1: Screening for differentially expressed genes in hypertrophic cardiomyopathy 1) Study subjects: Peripheral blood samples were obtained from four patients with obstructive hypertrophic cardiomyopathy (HCM) (NYHA class III) undergoing septal myectomy and three age- and sex-matched healthy donors. All participants provided written informed consent, and the study protocol was approved by the institutional ethics committee.

[0025] 2) RNA Extraction and Bulk 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-AmRNA was enriched and used to construct cDNA libraries, which were sequenced on the Illumina NovaSeq platform (150 bp paired-end reads, approximately 50 million reads per sample).

[0026] 3) Sequencing data preprocessing and DEG screening: Read quality filtering was performed using fastp, and reads were aligned to the human genome (GRCh38) using STAR. Gene-level quantification was performed using featureCounts. Differential expression analysis between the HCM and control groups was performed using DESeq2, with an FDR < 0.05 and |log2FC| > 1 criteria applied.

[0027] 4) Weighted gene co-expression network analysis: The top 5000 genes by expression coefficient of variation were selected, and a scale-free co-expression network was constructed using the WGCNA package (v1.70-3) in R software. A dynamic pruning algorithm was used to partition the genes into multiple co-expression modules, each labeled with a different color. The Pearson correlation coefficient of the characteristic genes and key HCM clinical indicators of each module was calculated to identify the module with the strongest correlation with the HCM phenotype.

[0028] 5) Machine learning-based feature selection: Venn diagram analysis was performed to identify the intersection of significant DEGs and the most highly correlated modules. The overlapping genes identified were the core DEGs associated with HCM pathology. Two supervised machine learning algorithms, LASSO regression and random forest classification, were applied to screen candidate genes associated with HCM. These algorithms were trained on standardized RNA-seq data to identify features that best distinguish HCM from controls. LASSO logistic regression was implemented using the glmnet package, with L1 regularization applied to select a minimal gene set with the highest information value. Model tuning was performed using 10-fold cross-validation to determine the optimal penalty parameter. Genes with nonzero coefficients were retained as key predictors. Random forest analysis was performed using the randomForest package, with gene importance ranked based on mean reduction in precision and Gini impurity.

[0029] 6) Experimental Results See Figure 1 , which shows the results of DEGs screening 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 relative to the control group; See Figure 2 , which shows the relationship between each module and trait. The blue module in the figure shows the strongest and most significant positive correlation with the HCM phenotype (correlation coefficient = 0.81, p < 0.001). Figure 3 , which shows the overlapping genes between DEGs and WGCNA modules. Intersection analysis showed that 233 genes overlapped between significant DEGs and the blue WGCNA module; See Figure 4, which shows the coefficient distribution of HCM candidate genes screened by LASSO regression, among which MEIS3, CYP7A1, ANKRD20A1, TRAT1 and SYDE2 as candidate genes show stable and non-zero coefficients; see Figure 5 , which shows the prediction importance ranking plot of candidate genes from LASSO regression. The coefficients of genes selected from LASSO regression show that MEIS3 is the gene with the highest prediction importance; See Figure 6 The intersection analysis between the genes selected by LASSO and those selected by random forest revealed four robust significant genes: MEIS3, SYDE2, TRAT1, and ANKRD20A1, which were consistently selected by both algorithms, highlighting their potential biological relevance and reliability as diagnostic markers. The intersection analysis of multiple algorithms emphasized the reliability of MEIS3 as a diagnostic marker.

[0030] Example 2: Validation of the diagnostic efficacy of MEIS3 for hypertrophic cardiomyopathy 1) Experimental methods: Based on the batch RNA-seq in Example 1, receiver operating characteristic (ROC) analysis was performed on MEIS3, a gene significantly differentially expressed in hypertrophic cardiomyopathy identified in Example 1, to calculate the area under the curve (AUC). Using the presence or absence of hypertrophic cardiomyopathy as a binary classification result and the expression level of the MEIS3 gene as a predictor, an ROC curve was drawn to evaluate the accuracy of MEIS3 in diagnosing hypertrophic cardiomyopathy. 2) Experimental results, see Figure 7-Figure 8 , MEIS3 showed significantly increased expression in patients with hypertrophic cardiomyopathy (HCM), while SYDE2, TRAT1 and ANKRD20A1 showed decreased expression levels; the areas under the ROC curves of MEIS3, SYDE2, TRAT1 and ANKRD20A1 were all 1; the AUC values ​​ranged from 0 to 1, 0.7 was acceptable performance, and 0.9 was excellent performance; this indicated that using the expression level of the MEIS3 gene as a diagnostic indicator can better determine whether the subject has hypertrophic cardiomyopathy, and has good diagnostic efficacy.

[0031] Example 3: External Dataset Validation of MEIS3 for Diagnostic Efficacy of Hypertrophic Cardiomyopathy 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 protocols, and the dataset was processed using CellRanger and Seurat (v4.0). Differential gene expression analysis of MEIS3 was performed. Whether or not a patient had hypertrophic cardiomyopathy was used as a binary classification outcome, and MEIS3 expression level was used as a predictor. Receiver-operating characteristic (ROC) curves were drawn to assess the diagnostic accuracy of MEIS3 for hypertrophic cardiomyopathy. 2) Result analysis: Please refer to Figure 9 , in differential analysis of external datasets, MEIS3 was upregulated in HCM samples compared with controls, while SYDE2, TRAT1, and ANKRD20A1 were simultaneously downregulated, which was consistent with the trends observed in the aforementioned bulk RNA-seq; see Figure 10 In the external data set, the AUC value of MEIS3 for diagnosing hypertrophic cardiomyopathy was approximately 0.957, which was close to the findings in Example 2, indicating that measuring MEIS3 is helpful for the diagnosis of hypertrophic cardiomyopathy and that MEIS3 can be used as a diagnostic marker for hypertrophic cardiomyopathy.

[0032] Example 4: Clinical Dataset Verification of MEIS3 Diagnostic Efficacy 1) Sample collection Blood samples from 34 patients with hypertrophic cardiomyopathy (HCM) were collected as the HCM group, and blood samples from 34 healthy controls (CON) were collected as the healthy control group to validate the diagnostic efficacy of MEIS3.

[0033] All patients in the validation set of this application had no overlap with the patients in Example 1. All participants signed written informed consent, and the study protocol was approved by the institutional ethics committee; the inclusion and exclusion criteria for patients with hypertrophic cardiomyopathy and healthy controls were as follows: (A) HCM patient group - inclusion criteria Subjects must meet all of the following criteria to be included: Main diagnostic criteria: Meet the generally accepted diagnostic criteria for HCM, that is, the presence of left ventricular wall hypertrophy that cannot be explained by other cardiac or systemic diseases (such as hypertension, aortic valve stenosis, etc.) confirmed by imaging examination (usually echocardiography), with the maximum ventricular wall thickness ≥15mm.

[0034] For subjects with a clear family history of HCM (a confirmed patient among first-degree relatives), they can be included if their maximum ventricular wall thickness is ≥13 mm.

[0035] Genetic confirmation (if applicable): Patients carrying known pathogenic or likely pathogenic sarcomere protein gene mutations associated with HCM may be included after evaluation by the investigator even if the ventricular wall thickness does not fully meet the above criteria.

[0036] Clinical status: Patients with a clear diagnosis of HCM, whether obstructive (HOCM) or non-obstructive (nHCM), with or without clinical symptoms (such as dyspnea, chest pain, palpitations, syncope, etc.).

[0037] (B) HCM patient group - Exclusion criteria Subjects meeting any of the following criteria were excluded: 1. Secondary myocardial hypertrophy: Left ventricular hypertrophy due to other clear causes, including but not limited to: Severe, inadequately controlled hypertension (e.g., resting systolic blood pressure persisting ≥ 160 mmHg despite treatment).

[0038] Moderate to severe aortic stenosis or other valvular heart disease that can increase pressure overload.

[0039] Athlete's heart (differentiation requires combination of medical history, physical examination, and imaging evaluation).

[0040] Infiltrative or storage cardiomyopathy: Clear diagnosis or strong suspicion of other types of cardiomyopathy, such as cardiac amyloidosis, Fabry disease, Danon disease, etc.

[0041] 2. Serious complications: Acute myocardial infarction, unstable angina, or coronary artery revascularization (PCI or CABG) in the past 3 months.

[0042] The New York Heart Association (NYHA) cardiac function class was IV.

[0043] A history of life-threatening malignant arrhythmias (such as sustained ventricular tachycardia, ventricular fibrillation) that is not effectively controlled.

[0044] Severe hepatic insufficiency (e.g., ALT or AST > 3 times the upper limit of normal).

[0045] Severe renal insufficiency (e.g., estimated glomerular filtration rate eGFR < 30 mL / min / 1.73 m²).

[0046] 3. Other situations: Pregnant or breastfeeding women.

[0047] Suffering from malignant tumors, active infections, or other serious systemic diseases that may interfere with the study results or endanger the safety of the subjects.

[0048] Currently participating in another clinical interventional study.

[0049] 3. Healthy Volunteer Control Group (A) Healthy control group - inclusion criteria Subjects must meet all of the following criteria to be included: Health status: Self-reported good health, with no known history of cardiovascular disease or other major chronic diseases.

[0050] Matching: Age and sex were matched with the HCM patient group to ensure comparability between groups.

[0051] Objective examination: Physical examination revealed no clinically significant abnormalities.

[0052] A standard 12-lead electrocardiogram (ECG) is normal or shows only nonspecific, clinically insignificant changes.

[0053] Echocardiographic findings were within normal limits, with clear evidence of left ventricular hypertrophy (maximal left ventricular wall thickness <12 mm), and no structural heart disease, valvular disease, or abnormal cardiac function.

[0054] Resting blood pressure is normal (systolic blood pressure <140 mmHg and diastolic blood pressure <90 mmHg).

[0055] (B) Healthy control group - exclusion criteria Subjects meeting any of the following criteria were excluded: Cardiovascular related: Any known history of cardiovascular disease, including hypertension, coronary heart disease, cardiomyopathy, heart failure, valvular disease, clinically significant arrhythmias, etc.

[0056] A family history of HCM, hereditary arrhythmias, or unexplained sudden death in a first-degree relative (this is very important to exclude potential unaffected gene carriers).

[0057] Systemic diseases: Suffering from diabetes, chronic kidney disease, chronic liver disease, autoimmune disease, abnormal thyroid function (uncontrolled), malignant tumors, etc.

[0058] Drug use: Long-term use of drugs that may affect cardiovascular system function (such as beta-blockers, calcium channel antagonists, etc.), unless it is for the treatment of non-cardiac diseases and the researcher judges that it will not affect the study results.

[0059] Laboratory tests: Clinically significant abnormalities in routine hematological or biochemical test results.

[0060] Other situations: Pregnant or breastfeeding women.

[0061] Currently participating in another clinical study.

[0062] Professional athlete.

[0063] 2) Detect the expression level of MEIS3 in the samples using an enzyme-linked immunosorbent assay kit An enzyme-linked immunosorbent assay (ELISA) kit (Human Homeobox protein Meis3 (MEIS3) 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 centrifuged to separate serum and plasma. Samples were appropriately diluted with the accompanying diluent according to the kit instructions. The samples were then loaded, washed, added with detection antibodies, washed twice, added with enzyme conjugates, washed three times, reacted with substrates, and terminated. The relative optical density (OD) of the MEIS3 protein was measured at a specific wavelength using a microplate reader. Whether or not hypertrophic cardiomyopathy was present was used as a binary classification result, and the expression level of the MEIS3 protein was used as a predictor. By changing the threshold for judging the disease, the sensitivity and specificity at different thresholds were calculated, and the receiver operating characteristic (ROC) curve was plotted.

[0064] See the results Figure 11-12 Compared with the healthy control group, MEIS3 was significantly upregulated in the HCM patient group; the area under the receiver operating characteristic (ROC) curve (AUC) was 0.9645, with a sensitivity of 88.24% and a specificity of 97.06%. This indicates that MEIS3 protein detection corresponds closely to the MEIS3 gene expression (RNA) detection results in Examples 2 and 3, further validating MEIS3 as a predictor for hypertrophic cardiomyopathy.

[0065] In summary, the present invention verifies the application of MEIS3 as a biomarker in hypertrophic cardiomyopathy products through examples, and the MEIS3 marker has the characteristics of high sensitivity and good specificity, which can provide a basis for the clinical diagnosis of hypertrophic cardiomyopathy, and provide new biomarker support for early screening, accurate diagnosis or clinical condition assessment of hypertrophic cardiomyopathy, and has broad clinical application prospects.

[0066] 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 in the scope of protection of the present invention.

Claims

1. Application of reagents for detecting MEIS3 expression levels in the preparation of diagnostic products for hypertrophic cardiomyopathy.

2. The use according to claim 1, characterized in that The reagents include reagents for detecting the mRNA expression level and / or protein expression level of MEIS3.

3. The use according to claim 2, characterized in that The reagents are RNA-seq analysis reagents and / or enzyme-linked immunosorbent assay reagents.

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

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