Method and system for predicting myocarditis by using circular RNAcirc0071542 and ribosomal protein RPL13A

Abstract

The invention provides a method for predicting myocarditis by using circular RNAcirc0071542 and ribosomal protein RPL13A. The method comprises the following steps: a) obtaining a biological sample of a subject; b) detecting the expression level of circACSL1 in the biological sample; c) comparing the expression level of the circACSL1 with a reference value from a healthy control; and d) when the expression level of the circACSL1 is higher than the reference value, judging that the subject has a myocarditis risk or is in a myocarditis state according to a difference value between the expression level of the circACSL1 and the reference value. The circACSL1 expression level is compared with the typical expression range of a dilated cardiomyopathy (DCM) subject to generate differential diagnosis data, and the expression difference of the marker between myocarditis and DCM can be utilized to effectively assist in clinically distinguishing the two diseases with different treatment strategies and prognosis but similar clinical manifestations.

Description

A method and system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A Technical Field

[0001] This application relates to techniques for predicting myocarditis, specifically to a method and system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A. Background Technology

[0002] Currently, the clinical diagnosis of myocarditis mainly relies on a comprehensive set of criteria, including clinical symptoms, abnormal electrocardiogram (ECG), elevated serum myocardial injury markers (such as troponin I / T and creatine kinase isoenzymes), abnormal wall motion or decreased cardiac function detected by echocardiography, and characteristic myocardial edema and late gadolinium enhancement shown on cardiac magnetic resonance imaging (MRI). However, these indicators are not specific to myocarditis. Serum myocardial injury markers can be elevated in various cardiac injury states, such as acute myocardial infarction, heart failure, and myocardial contusion. Although echocardiography and MRI can provide structural and functional information, they are not sensitive enough for early, focal inflammation, and MRI equipment is expensive and has limited availability. Endocardial biopsy is considered the "gold standard" for diagnosis, but it is an invasive procedure with risks of bleeding, perforation, and arrhythmias. Furthermore, due to the focal distribution of myocardial inflammation, biopsy sampling has a high false-negative rate. Therefore, its application in clinical practice, especially in pediatric subjects, is greatly limited. At the treatment level, current protocols mainly include rest, symptomatic treatment for heart failure and arrhythmias, and, in some cases, empirical use of immunomodulators (such as immunoglobulins and glucocorticoids). Because it is impossible to accurately assess the individual subject's immune inflammatory status and molecular pathway activity, this "one-size-fits-all" treatment approach lacks specificity and predictability, resulting in poor efficacy for some subjects and potential side effects such as infection due to unnecessary immunosuppression. Therefore, exploring novel biomarkers that can non-invasively, specifically, and objectively reflect myocarditis disease activity, aid in early diagnosis, guide individualized treatment, and predict prognosis has become a key scientific problem and clinical need urgently requiring breakthroughs in this field.

[0003] With the deepening of research on non-coding RNA, circular RNAs (circular RNAs), as an emerging regulatory molecule, offer a new perspective for solving the aforementioned challenges. Circular RNAs are a class of covalently closed circular non-coding RNA molecules formed by backsplicing of precursor mRNAs. Their unique circular structure enables them to resist degradation by exonucleases and exhibits higher stability than linear RNAs in body fluids such as peripheral blood, giving them an inherent advantage as ideal biomarkers for liquid biopsies. Functionally, the most widely studied mechanism is their role as competitive endogenous RNAs, acting as "molecular sponges" to adsorb microRNAs, thereby relieving the inhibitory effect of miRNAs on their target genes and finely regulating gene expression networks at the posttranscriptional level. In recent years, numerous studies have shown that circular RNAs play an important role in the physiological and pathological processes of the cardiovascular system, and changes in their expression profiles are closely related to various cardiovascular diseases such as atherosclerosis, myocardial infarction, heart failure, and arrhythmias. Some circular RNAs have been shown to serve as potential biomarkers for disease diagnosis or prognostic assessment.

[0004] However, research on circular RNAs in the specific disease area of ​​myocarditis is still in its early stages, with significant knowledge gaps. First, there is a lack of systematic, high-throughput screening and analysis of circular RNA expression profiles in peripheral blood or myocardial tissue of myocarditis subjects (especially children). Most studies focus only on the function of individual circular RNA molecules (such as circHIPK3 and circANKRD36) in animal or cell models, lacking comprehensive work on discovering and screening key differentially expressed circular RNAs at the whole-genome level. Second, no studies have clearly validated whether any single circular RNA can serve as a reliable biomarker for the clinical diagnosis, differential diagnosis (especially differentiation from dilated cardiomyopathy), and dynamic assessment of disease activity in myocarditis. Its clinical translational value urgently needs to be confirmed through rigorously designed case-control studies and longitudinal cohort studies. Finally, the specific downstream molecular mechanisms by which aberrantly expressed circular RNAs in myocarditis participate in regulating myocardial inflammatory responses and cell damage remain poorly understood. Elucidating the signaling pathways through which they function is a prerequisite for further developing them from biomarkers into potential therapeutic targets.

[0005] At the molecular level, mitogen-activated protein kinase 14 (MAPK14, or p38α) is a core member of the p38 MAPK signaling pathway, playing a pivotal role in cellular stress and inflammatory responses. Previous studies have confirmed that the MAPK14 pathway is activated in viral or autoimmune myocarditis models, and its inhibitors can alleviate myocardial inflammation and damage. However, the upstream regulatory mechanisms of MAPK14 in myocarditis, particularly how it is finely regulated by non-coding RNA networks, remain not fully elucidated. Whether a specific circular RNA regulates MAPK14 expression through a ceRNA mechanism, thereby driving myocarditis progression, is a scientific hypothesis worthy of further investigation.

[0006] In summary, the current clinical diagnosis and treatment of myocarditis faces the dual challenges of a lack of specific diagnostic tools and insufficient therapeutic targeting. Circular RNAs (RRNAs), due to their stability and regulatory functions, represent a highly promising source of novel biomarkers and intervention targets; however, research on them in the field of myocarditis is still unsystematic and superficial. Therefore, systematically mapping the expression profile of RRNAs in myocarditis, identifying key functional RRNA molecules, deeply elucidating their biological functions and molecular mechanisms in myocardial inflammation, and comprehensively evaluating their clinical diagnostic, prognostic, and therapeutic guidance value are of significant theoretical importance and clinical application prospects for overcoming current bottlenecks in myocarditis diagnosis and treatment and promoting precision medicine practices. Summary of the Invention

[0007] In view of this, the present invention provides a method and system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A to solve the above-mentioned technical problems.

[0008] A method for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A includes the following steps: a) obtaining a biological sample from a subject; b) detecting the expression level of circACSL1 in the biological sample; c) comparing the expression level of circACSL1 with a reference value from healthy controls; and d) when the expression level of circACSL1 is higher than the reference value, determining the subject's risk of myocarditis or state of myocarditis based on the difference between the two values.

[0009] Further, in step b), the expression level of at least one internal reference gene is detected, and the relative expression level of circACSL1 is calculated using a relative quantification method; wherein, the method for calculating the relative expression level of circACSL1 using a relative quantification method includes: step S100: obtaining the initial cycle threshold of the target gene from the subject's biological sample, detected by quantitative real-time polymerase chain reaction, wherein the target gene is the circular RNA circACSL1; simultaneously, obtaining the initial cycle threshold of at least one endogenous reference gene from the same biological sample, detected in the same reaction; step S200: for each subject sample, determining an in-sample correction value by subtracting the initial cycle threshold of the endogenous reference gene from the initial cycle threshold of the target gene; Step S300: Calculate the arithmetic mean of the in-sample corrected values ​​of all samples from the preset healthy control group, and establish this arithmetic mean as the baseline level for expression calculation; Step S400: For the experimental group samples, calculate the difference between its in-sample corrected value and the baseline level of the control group, thereby obtaining an intermediate value representing the logarithmic difference in gene expression between the experimental group and the control group; Step S500: Use the intermediate value representing the logarithmic difference as an exponent, and perform a power operation with base 2 to calculate the final linear expression fold; wherein, when the linear expression fold is greater than 1, it indicates that the target gene expression is upregulated in the experimental group; when the linear expression fold is less than 1, it indicates that the target gene expression is downregulated; when the linear expression fold is equal to 1, it indicates that the target gene expression has no change.

[0010] Furthermore, after a diagnosis of myocarditis, steps a) to c) were repeated at different time points to assess whether the disease had entered the recovery phase or whether the treatment was effective by monitoring the decreasing trend of circACSL1 expression levels.

[0011] Further, in step c), the expression level of circACSL1 is compared with a reference value from subjects with dilated cardiomyopathy. When the expression level is significantly higher than the myocarditis-dilated cardiomyopathy differentiation cutoff value, the diagnosis of myocarditis rather than dilated cardiomyopathy is supported.

[0012] The present invention also provides a system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A, comprising: a sample collection and processing module for acquiring and processing biological samples from a subject; a biomarker detection module connected to the sample collection and processing module for quantitatively detecting the expression level of circular RNA circACSL1 in the biological samples; a data analysis module connected to the biomarker detection module and configured to: receive the expression level data of circACSL1; compare the expression level of circACSL1 with a preset reference value or reference expression profile; and generate predictive data indicating the subject's myocarditis risk or disease status based on the comparison results; and a report generation module for outputting a report containing the predictive data.

[0013] Furthermore, the biomarker detection module is also configured to simultaneously detect the expression level of at least one endogenous reference gene, the endogenous reference gene including at least one of RPL13A, β-actin, or GAPDH.

[0014] Furthermore, the data analysis module includes: a database storing circACSL1 reference expression profiles from healthy control individuals and myocarditis subjects; and an algorithm unit configured to generate the predicted data by calculating the fold difference between the subject's circACSL1 expression level and the reference expression profile and / or applying a cutoff value determined based on the subject's operating characteristic curve.

[0015] Furthermore, the data analysis module is also configured to: receive circACSL1 expression level data in consecutive biological samples of the same subject at different time points; analyze the dynamic trend of circACSL1 expression level; and generate prognostic assessment data indicating disease progression or treatment response based on the dynamic trend.

[0016] Furthermore, the data analysis module includes a differential diagnosis unit, which is configured to: compare the expression level of circACSL1 in the subject with the typical expression range of dilated cardiomyopathy (DCM) subjects; and based on the comparison, generate differential diagnosis data to distinguish between myocarditis and dilated cardiomyopathy, wherein when the circACSL1 expression level is significantly higher than the typical range of DCM, the diagnosis of myocarditis is more likely.

[0017] Furthermore, the data analysis module also generates quantitative assessment data on the degree of myocardial cell inflammation or myocardial damage based on the expression level of circACSL1.

[0018] Furthermore, the biomarker detection module is also configured to simultaneously detect the expression level of microRNA miR-8055, and the data analysis module is further configured to: calculate the expression ratio or correlation between circACSL1 and miR-8055; and / or combine the expression levels of circACSL1 and miR-8055 to generate prediction data through a multivariate model.

[0019] Furthermore, the data analysis module stores or integrates a model of the circACSL1 / miR-8055 / MAPK14 regulatory axis. This model describes the interaction between circACSL1 and miR-8055, which inhibits miR-8055 through sponge adsorption, thereby upregulating MAPK14 expression. This model is used to help interpret the biological mechanisms of the predicted data.

[0020] By employing a standardized quantitative procedure for calculating relative expression levels using the 2^-ΔΔCt method, and an objective interpretation method for comparing expression levels with pre-defined reference values ​​or reference expression profiles, the expression level of circACSL1 can be transformed into a precise and comparable numerical indicator (RQ value). Furthermore, by comparing the expression level of circACSL1 with the typical expression range in dilated cardiomyopathy (DCM) subjects to generate differential diagnostic data, the expression differences of this biomarker between myocarditis and DCM can be effectively utilized to help clinicians differentiate between these two treatment strategies and diseases with vastly different prognoses but similar clinical manifestations. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only preferred embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 is a flowchart of the method of the system of the present invention; Figure 2 is a flowchart of the method of the present invention for calculating the relative expression level of circACSL1 using the relative quantification method. Detailed Implementation

[0023] To better understand the structure of the present invention and the functional features and advantages it can achieve, the preferred embodiments of the present invention will be described in detail below with reference to the drawings.

[0024] Referring to Figures 1 and 2, a workstation such as the Hamilton Microlab STAR series or Roche MagNAPure series was used, pre-installed with an optimized leukocyte separation and total RNA extraction program. For peripheral blood samples, the specific program parameters were: erythrocyte lysis buffer (ACKBuffer) incubation time 12 minutes (room temperature), followed by centrifugation at 1500g for 10 minutes (4°C). Total RNA extraction was performed using a magnetic bead method combined with online DNase I digestion of genomic DNA to ensure the specificity of subsequent circRNA detection. The extracted RNA was automatically aliquoted, and its concentration (ng / μL), purity (A260 / A280 and A260 / A230), and integrity (RIN value assessed using a microfluidic chip such as the Agilent 2100 Bioanalyzer, requiring >7.0) were recorded.

[0025] The high-throughput real-time quantitative PCR system is programmed with the following steps: pre-denaturation at 95°C for 30 seconds; PCR reaction for 45 cycles (95°C for 5 seconds, 60°C for 30 seconds, single-point fluorescence acquisition); melting curve analysis (95°C for 5 seconds, 60°C for 1 minute, then slowly ramping to 97°C, continuous fluorescence acquisition). The instrument is periodically validated for amplification efficiency using standard fluorescent dye plates and standards of known concentrations. The amplification efficiency of all detection channels is required to be between 90% and 110%, with R² > 0.99.

[0026] Design, synthesis and verification of primers and probes.

[0027] circACSL1 primer pair: Cross-bridging primers were designed targeting the circular adapter sequence of hsa_circ_0071542 (circACSL1). BLAST was used to verify their specificity for the human genome and transcriptome, ensuring that linear ACSL1 mRNA or other homologous sequences were not amplified.

[0028] Sequence: Forward primer (SEQ ID NO:1): 5'-AGGCTCAGCACTGATCCAGAA-3'; Reverse primer (SEQ ID NO:2): 5'-GAAATACACCAGGGCTCGTG-3'.

[0029] Validation: The primers were validated to produce a single expected band (135 bp) by conventional PCR and gel electrophoresis. The amplification efficiency was validated by qRT-PCR using serially diluted in vitro transcribed circACSL1 RNA standards, with a linear dynamic range spanning 6 orders of magnitude (10^2-10^7 copies / reaction).

[0030] Reference gene primer pair.

[0031] Selection and Validation: Among the candidate internal control genes (ACTB, GAPDH, RPL13A, U6), stability analysis of samples from 30 healthy individuals and 30 myocarditis subjects (using the geNorm and NormFinder algorithms) determined that the combination of ACTB and RPL13A was the optimal internal control (M value < 0.5). Using both simultaneously can further improve the standardization accuracy.

[0032] Sequence: ACTB primer (SEQ ID NO: 3 / 4), RPL13A primer (SEQ ID NO: 7 / 8).

[0033] miR-8055 detection reagent: Design: Stem-loop reverse transcription primers combined with TaqMan probe method are used to improve the specificity and sensitivity of short chain miRNA detection.

[0034] Sequences: stem-loop reverse transcription primer (SEQ ID NO: 5); forward PCR primer (SEQ ID NO: 6); TaqMan probe (SEQ ID NO: 9, 5' end labeled FAM, 3' end labeled BHQ1). The internal control miRNA used was miR-16-5p (which has been verified to be stably expressed under myocardial inflammation conditions).

[0035] qPCR premix: Contains HotStartTaq DNA polymerase, dNTPs, MgCl2, SYBR Green I dye (for circACSL1 and mRNA internal controls) or proprietary buffer (for TaqMan probe assays to detect miRNA). The premix is ​​RNase-free, DNase-free, and UNG enzyme is added to prevent product contamination.

[0036] After data acquisition, the following processing is performed: Standardized data streams are received from qRT-PCR instruments, digital PCR systems, and chemiluminescence immunoassay analyzers. Input data includes: 1) the cycle threshold (Ct value) or absolute copy number of circular RNA circ_000203; 2) the relative expression level of microRNA miR-let-7c; 3) the concentration or optical density value of ribosomal protein RPL13A; and 4) optional composite biomarker indices. All input data are automatically batch-corrected, standardized (for RNA data), and dimensionally normalized to generate a unified molecular feature vector.

[0037] The engine receives the molecular feature vector and performs the following calculations: First, it calculates the Dynamic CBI according to a predefined formula, D-CBI = (W1 * ΔCt_circ) + (W2 * ΔΔCt_miR) + (W3 *[RPL13A]), where W1, W2, and W3 are dynamic weight coefficients obtained through training on historical queues, which can be adaptively adjusted according to the disease stage (such as hyperacute phase, acute phase, and recovery phase); Second, the engine inputs the processed feature vector into the core prediction model.

[0038] A pre-trained machine learning model is set up, which is trained using a deep neural network (DNN). The training data comes from a longitudinal multi-omics database containing healthy controls, subjects diagnosed with myocarditis, and subjects with cardiomyopathy. The machine learning model receives the fused features and outputs a continuous myocarditis risk index (MRI) from 0 to 100, while simultaneously generating a risk confidence interval and an immediate risk level (low: <30, medium: 30-70, high: >70).

[0039] Based on MRI scores, risk levels, and specific combinations of abnormal molecular markers, tiered management recommendations are dynamically generated and pushed out. For example, for high-risk individuals with a sharp increase in circ_000203, immediate cardiac MRI and endocardial biopsy are recommended; for intermediate-risk individuals with only a slight decrease in RPL13A, enhanced outpatient follow-up and repeat molecular marker testing one week later are recommended. All recommendations are fed back to clinical workstations or mobile terminals in real time via a graphical interface or API.

[0040] The following steps were used for prediction: blood samples were obtained from the subjects; total RNA was extracted from the blood samples; the extracted RNA was converted into cDNA by reverse transcription; and circ_000203 was amplified by real-time quantitative PCR using specific primers designed to span the circular linker of circ_000203. The forward primer sequence is shown in SEQ ID NO:1 (5'-GTCCTGGAGGTGCTAAAGTC-3'), and the reverse primer sequence is shown in SEQ ID NO:2 (5'-CAGGTTCCAGGTCTTGTTCC-3').

[0041] The relative expression level of circ_000203 was calculated using the standard curve method or the ΔΔCt method, with the expression level of the endogenous reference gene GAPDH (forward primer: 5'-GGAGCGAGATCCCTCCAAAAT-3', reverse primer: 5'-GGCTGTTGTCATACTTCTCATGG-3') as a standardized reference. The calculated relative expression level of circ_000203 was compared with a preset threshold. If the expression level was higher than the threshold, it indicated that the subject had a high risk of myocarditis or already had myocarditis lesions. The preset threshold was determined by statistical analysis of the expression level distribution of circ_000203 in healthy controls and subjects diagnosed with myocarditis. The method further includes simultaneous detection of clinical indicators such as troponin T (cTnT) and N-terminal pro-brain natriuretic peptide (NT-proBNP) for comprehensive risk assessment. The increase in circ_000203 expression precedes significant changes in traditional myocardial injury markers, thus providing an earlier warning signal.

[0042] The expression level of RPL13A was combined with the expression level of circ_000203 to construct a composite biomarker index to improve predictive specificity. The detection of RPL13A was performed using specific antibodies via Western blotting or enzyme-linked immunosorbent assay (ELISA), and its expression level was expressed as a relative optical density or concentration value. The composite biomarker index (CBI) was calculated as follows: CBI = (a * circ_000203 expression level) + (b * RPL13A expression level), where a and b are weighting coefficients determined by a logistic regression model, and b is negative, reflecting the downregulated trend of RPL13A expression in myocarditis. The method also included determining the optimal cutoff value of the composite index through receiver operating characteristic (ROC) curve analysis to distinguish myocarditis subjects from healthy individuals or subjects with other myocardial diseases (such as dilated cardiomyopathy). Sample processing steps included using lysis buffers containing protease inhibitors and phosphatase inhibitors to maintain RNA and protein integrity, and employing standardized operating procedures to reduce batch-to-batch variation.

[0043] The miR-let-7c was predicted to have an interaction site with circ_000203 and participate in the regulation of RPL13A expression. The method includes: extracting small RNA from peripheral blood mononuclear cells or plasma of the subject; performing specific reverse transcription of miR-let-7c using stem-loop reverse transcription primers (sequence as shown in SEQ ID NO:3: 5'-GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACcaccac-3', where lowercase letters are sequences complementary to miR-let-7c); performing quantitative real-time PCR using TaqMan probe method or SYBR Green method, with U6 snRNA (forward primer: 5'-CTCGCTTCGGCAGCACA-3', reverse primer: 5'-AACGCTTCACGAATTTGCGT-3') as an internal reference gene; calculating the relative expression level of miR-let-7c and analyzing its negative correlation with the expression level of circ_000203. This method further includes verifying the binding ability of miR-let-7c to the 3'UTR of circ_000203 and RPL13A mRNA using a dual-luciferase reporter gene assay. This involves constructing a luciferase reporter vector containing wild-type or mutant binding sites and co-transfecting cells with a miR-let-7c mimic or inhibitor. Changes in luciferase activity are then detected to confirm the targeting-regulatory relationship. This method is used to assess the activity status of the ceRNA regulatory network during the pathological process of myocarditis.

[0044] A DNA probe (sequence as SEQ ID NO:6: 5'-Biotin-TGGAACAGGACCTGGAACCTG-3') complementary to the circ_000203 sequence and terminally biotin-labeled was designed. After co-incubation with cell lysate, circ_000203 and its binding molecules were captured using streptavidin magnetic beads. The enrichment of miR-let-7c in the capture complex was detected by qRT-PCR to confirm the direct binding between the two. Simultaneously, after knocking down circ_000203 with siRNA, the upregulation of miR-let-7c expression and the downregulation of RPL13A protein were observed to functionally verify the existence of this ceRNA axis. This experimental evidence serves as the mechanistic basis for the aforementioned predictive method and is used to optimize the weighting of the biomarker combination, particularly confirming the causal chain of increased circ_000203 expression leading to RPL13A protein translation inhibition through miR-let-7c adsorption.

[0045] The kit described above comprises: a separation column and lysis buffer for extracting total RNA and small RNA; specific primer pairs (SEQ ID NO:1 and SEQ ID NO:2) and premixed qPCR Master Mix for circ_000203 reverse transcription and qPCR; stem-loop RT primers (SEQ ID NO:3), qPCR forward primers (SEQ ID NO:4: 5'-CGCCGCTGAGGTAGTAGGTTG-3'), and TaqMan probes (SEQ ID NO:5: 5'-FAM-ATACGACCACTTG-MGB-3') for miR-let-7c detection; paired monoclonal antibodies for RPL13A protein detection, one of which is a biotin-labeled capture antibody and the other is a horseradish peroxidase-labeled detection antibody; and a standard curve containing a series of standards of known concentrations for quantitative analysis.

[0046] Based on the above description, this application provides a method for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A, comprising the following steps: a) obtaining a biological sample of a subject; b) detecting the expression level of circACSL1 in the biological sample; c) comparing the expression level of circACSL1 with a reference value from healthy controls; and d) when the expression level of circACSL1 is higher than the reference value, determining that the subject has a risk of myocarditis or is in a state of myocarditis based on the difference between the two.

[0047] Further, in step b), the expression level of at least one internal reference gene is detected, and the relative expression level of circACSL1 is calculated using a relative quantification method; wherein, the method for calculating the relative expression level of circACSL1 using a relative quantification method includes: step S100: obtaining the initial cycle threshold of the target gene from the subject's biological sample, detected by quantitative real-time polymerase chain reaction, wherein the target gene is the circular RNA circACSL1; simultaneously, obtaining the initial cycle threshold of at least one endogenous reference gene from the same biological sample, detected in the same reaction; step S200: for each subject sample, determining an in-sample correction value by subtracting the initial cycle threshold of the endogenous reference gene from the initial cycle threshold of the target gene; Step S300: Calculate the arithmetic mean of the in-sample corrected values ​​of all samples from the preset healthy control group, and establish this arithmetic mean as the baseline level for expression calculation; Step S400: For the experimental group samples, calculate the difference between its in-sample corrected value and the baseline level of the control group, thereby obtaining an intermediate value representing the logarithmic difference in gene expression between the experimental group and the control group; Step S500: Use the intermediate value representing the logarithmic difference as an exponent, and perform a power operation with base 2 to calculate the final linear expression fold; wherein, when the linear expression fold is greater than 1, it indicates that the target gene expression is upregulated in the experimental group; when the linear expression fold is less than 1, it indicates that the target gene expression is downregulated; when the linear expression fold is equal to 1, it indicates that the target gene expression has no change.

[0048] Furthermore, after a diagnosis of myocarditis, steps a) to c) were repeated at different time points to assess whether the disease had entered the recovery phase or whether the treatment was effective by monitoring the decreasing trend of circACSL1 expression levels.

[0049] Further, in step c), the expression level of circACSL1 is compared with a reference value from subjects with dilated cardiomyopathy. When the expression level is significantly higher than the myocarditis-dilated cardiomyopathy differentiation cutoff value, the diagnosis of myocarditis rather than dilated cardiomyopathy is supported.

[0050] The present invention also provides a system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A, comprising: a sample collection and processing module for acquiring and processing biological samples from a subject; a biomarker detection module connected to the sample collection and processing module for quantitatively detecting the expression level of circular RNA circACSL1 in the biological samples; a data analysis module connected to the biomarker detection module and configured to: receive the expression level data of circACSL1; compare the expression level of circACSL1 with a preset reference value or reference expression profile; and generate predictive data indicating the subject's myocarditis risk or disease status based on the comparison results; and a report generation module for outputting a report containing the predictive data.

[0051] Furthermore, the biomarker detection module is also configured to simultaneously detect the expression level of at least one endogenous reference gene, the endogenous reference gene including at least one of RPL13A, β-actin, or GAPDH.

[0052] Furthermore, the data analysis module includes: a database storing circACSL1 reference expression profiles from healthy control individuals and myocarditis subjects; and an algorithm unit configured to generate the predicted data by calculating the fold difference between the subject's circACSL1 expression level and the reference expression profile and / or applying a cutoff value determined based on the subject's operating characteristic curve.

[0053] Furthermore, the data analysis module is also configured to: receive circACSL1 expression level data in consecutive biological samples of the same subject at different time points; analyze the dynamic trend of circACSL1 expression level; and generate prognostic assessment data indicating disease progression or treatment response based on the dynamic trend.

[0054] Furthermore, the data analysis module includes a differential diagnosis unit, which is configured to: compare the expression level of circACSL1 in the subject with the typical expression range of dilated cardiomyopathy (DCM) subjects; and based on the comparison, generate differential diagnosis data to distinguish between myocarditis and dilated cardiomyopathy, wherein when the circACSL1 expression level is significantly higher than the typical range of DCM, the diagnosis of myocarditis is more likely.

[0055] Furthermore, the data analysis module also generates quantitative assessment data on the degree of myocardial cell inflammation or myocardial damage based on the expression level of circACSL1.

[0056] Furthermore, the biomarker detection module is also configured to simultaneously detect the expression level of microRNA miR-8055, and the data analysis module is further configured to: calculate the expression ratio or correlation between circACSL1 and miR-8055; and / or combine the expression levels of circACSL1 and miR-8055 to generate prediction data through a multivariate model.

[0057] Furthermore, the data analysis module stores or integrates a model of the circACSL1 / miR-8055 / MAPK14 regulatory axis. This model describes the interaction between circACSL1 and miR-8055, which inhibits miR-8055 through sponge adsorption, thereby upregulating MAPK14 expression. This model is used to help interpret the biological mechanisms of the predicted data.

[0058] This invention reveals the key molecular regulatory network in the pathophysiology of myocarditis, showing that the circular RNA circACSL1 (hsa_circ_0071542) plays a dual role as a core driving factor and an excellent biomarker in this disease. Based on this, a multi-level, closed-loop technical system from molecular event detection to clinical decision support is constructed.

[0059] The core pathological aspect of myocarditis is the excessive inflammatory response of myocardial tissue and the resulting cell damage. This invention reveals that, under stimulation by pathogens or damaging factors, the expression of circACSL1, generated by exon circularization of the ACSL1 gene in host cells, is specifically and significantly upregulated. Its sequence contains a conserved binding site that is completely complementary to the microRNA miR-8055. Under physiological conditions, miR-8055 maintains the balance of inflammatory signals by binding to the 3' untranslated region of its target gene MAPK14 (i.e., p38α MAP kinase) messenger RNA, inhibiting the translation of this key pro-inflammatory signaling protein. However, in myocarditis, highly expressed circACSL1 competitively adsorbs large amounts of miR-8055 from the cytoplasm, leading to a significant decrease in the concentration of free miR-8055, thereby relieving its inhibitory effect on MAPK14 translation. Upregulation of MAPK14 protein expression activates downstream classical pro-inflammatory signaling pathways such as NF-κB, driving the massive release of inflammatory cytokines such as interleukin-1β (IL-1β), IL-6, and tumor necrosis factor-α (TNF-α). Simultaneously, it exacerbates the expression of cardiomyocyte damage markers such as troponin T (cTnT), creatine kinase isoenzyme (CKMB), and brain natriuretic peptide (BNP), and promotes apoptosis. Therefore, the expression level of circACSL1 directly reflects the activation intensity of this pro-inflammatory pathway and is positively correlated with the severity and extent of myocardial inflammation. This principle lays the scientific foundation for using circACSL1 as a specific molecular probe for non-invasive, real-time monitoring of local myocardial inflammation in circulating blood. Meanwhile, the expression of its adsorbate miR-8055 is conversely inhibited and decreases; the ratio of their expression levels (circACSL1 / miR-8055) becomes a more sensitive indicator of imbalance in this regulatory axis. As the final effector molecule, the expression or activity of MAPK14 can serve as a supplement to mechanism validation.

[0060] A cross-circular primer was designed to target the unique back-splicing circular adapter sequence of circACSL1. The forward and reverse sequences of this primer are located in non-adjacent regions of the linear sequence that connect end-to-end after circularization. This ensures that PCR amplification can only occur on the successfully circularized circACSL1 template, completely avoiding the amplification of its parental linear ACSL1 mRNA or other homologous sequences, thus solving the most critical specificity problem in circular RNA detection.

[0061] Quantitative real-time PCR (qRT-PCR) technology is employed, relying on the fluorescence signal released by TaqMan probes or SYBR Green dye in each round of PCR amplification. The intensity of this signal is proportional to the amount of starting template. Absolute quantification of the starting template is achieved by monitoring the number of cycles (Ct value) required for fluorescence to reach a set threshold. To ensure comparability across samples, batches, and laboratories, a rigorous standardized procedure is implemented: a) Internal reference correction principle: Simultaneously detecting endogenous housekeeping genes stably expressed under myocardial inflammation conditions (such as the β-actin / RPL13A combination), ΔCt (Ct_target – Ct_reference) is calculated to correct for technical differences between samples in RNA extraction efficiency, reverse transcription efficiency, and sample loading volume. b) Relative quantification principle: The classic 2^-ΔΔCt algorithm is used. The ΔCt of the sample to be tested is compared with the average ΔCt (calibration baseline) of a pre-established large-scale healthy control population to obtain ΔΔCt. Since PCR theoretically doubles the product with each cycle (efficiency ~100%), 2^(-ΔΔCt) represents the relative fold change of the target molecule in the test sample relative to the average level of healthy controls. This method does not rely on an absolute standard curve and is more suitable for assessing differential expression in clinical samples.

[0062] From nucleic acid extraction to final data output, quality control points are embedded at every step. These include RNA integrity testing, genomic DNA contamination detection, PCR amplification efficiency verification (using a standard curve), and positive controls, negative controls, and template-free controls included in each batch of experiments. This combined approach ensures that the final relative expression level (RQ) of circACSL1 obtained from the subjects' blood samples is a highly standardized and reliable figure that accurately reflects the true in vivo level.

[0063] Based on a large-sample case-control study, receiver operating characteristic (ROC) curve analysis was used to determine the optimal circACSL1 RQ cutoff value (e.g., 2.05) for distinguishing between myocarditis patients and healthy individuals. This principle balances the risks of misdiagnosis and missed diagnosis by maximizing the Youden index (sensitivity + specificity - 1), providing a clear and objective binary decision boundary for primary screening.

[0064] Recognizing that certain diseases (such as dilated cardiomyopathy, DCM) may present with similar clinical manifestations to myocarditis but with different circACSL1 expression patterns, the system analyzes a cohort of DCM patients to establish a distribution model (mean and standard deviation) of their circACSL1 RQ. When the RQ value of a sample is significantly higher than the upper limit of the DCM distribution range (e.g., mean + 3 times the standard deviation), a diagnosis of myocarditis is favored. This principle utilizes the heterogeneity of biomarker expression across diseases to achieve differentiation.

[0065] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Any person skilled in the art can make many possible variations and modifications to the technical solutions of the present invention, or modify them into equivalent embodiments, without departing from the scope of the present invention. Therefore, any modifications, equivalent changes, and alterations made to the above embodiments based on the technology of the present invention without departing from the scope of the present invention are within the protection scope of the present invention.

Claims

1. A method for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A, characterized in that, The procedure includes the following steps: a) obtaining a biological sample from the subject; b) detecting the expression level of circACSL1 in the biological sample; c) comparing the expression level of circACSL1 with a reference value from healthy controls; and d) when the expression level of circACSL1 is higher than the reference value, determining that the subject is at risk of myocarditis or is in a state of myocarditis based on the difference between the two values.

2. The method for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A according to claim 1, characterized in that, In step b), the expression level of at least one internal reference gene is detected, and the relative expression level of circACSL1 is calculated using a relative quantification method; wherein, the method for calculating the relative expression level of circACSL1 using a relative quantification method includes: step S100: obtaining the initial cycle threshold of the target gene from the subject's biological sample, detected by quantitative real-time polymerase chain reaction, wherein the target gene is the circular RNA circACSL1; simultaneously, obtaining the initial cycle threshold of at least one endogenous reference gene from the same biological sample, detected in the same reaction; step S200: for each subject sample, determining an in-sample correction value by subtracting the initial cycle threshold of the endogenous reference gene from the initial cycle threshold of the target gene; step S... Step S400: Calculate the arithmetic mean of the in-sample corrected values ​​of all samples from the preset healthy control group, and establish this arithmetic mean as the baseline level for expression calculation; Step S500: For the experimental group samples, calculate the difference between their in-sample corrected values ​​and the baseline level of the control group, thereby obtaining an intermediate value representing the logarithmic difference in gene expression between the experimental group and the control group; Step S500: Use the intermediate value representing the logarithmic difference as an exponent, and perform a power operation with base 2 to calculate the final linear expression fold; wherein, when the linear expression fold is greater than 1, it indicates that the target gene is upregulated in the experimental group; when the linear expression fold is less than 1, it indicates that the target gene is downregulated; when the linear expression fold is equal to 1, it indicates that the target gene expression has no change.

3. The method for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A according to claim 1, characterized in that, After a diagnosis of myocarditis, steps a) to c) were repeated at different time points to assess whether the disease had entered the recovery phase or whether the treatment was effective by monitoring the decreasing trend of circACSL1 expression levels.

4. The method for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A according to claim 1, characterized in that, In step c), the expression level of circACSL1 is compared with a reference value from subjects with dilated cardiomyopathy. When the expression level is significantly higher than the myocarditis-dilated cardiomyopathy differentiation cutoff value, the diagnosis of myocarditis rather than dilated cardiomyopathy is supported.

5. A system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A, characterized in that, include: The sample collection and processing module is used to obtain and process biological samples from subjects. A biomarker detection module, connected to the sample collection and processing module, is used to quantitatively detect the expression level of circular RNA circACSL1 in the biological sample; a data analysis module, connected to the biomarker detection module, is configured to: receive the expression level data of circACSL1; and compare the expression level of circACSL1 with a preset reference value or reference expression profile. And based on the comparison results, predictive data indicating the subject's risk of myocarditis or disease status is generated; And a report generation module, used to output a report containing the predicted data.

6. The system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A according to claim 5, characterized in that, The biomarker detection module is also configured to simultaneously detect the expression level of at least one endogenous reference gene, including at least one of RPL13A, β-actin, or GAPDH.

7. The system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A according to claim 5, characterized in that, The data analysis module includes: a database storing circACSL1 reference expression profiles from healthy control individuals and myocarditis subjects; and an algorithm unit configured to generate the predicted data by calculating the fold difference between the subject's circACSL1 expression level and the reference expression profile and / or applying a cutoff value determined based on the subject's operating characteristic curve.

8. The system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A according to claim 5, characterized in that, The data analysis module is also configured to: receive circACSL1 expression level data in consecutive biological samples of the same subject at different time points; analyze the dynamic trend of circACSL1 expression level; and generate prognostic assessment data indicating disease progression or treatment response based on the dynamic trend.

9. The system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A according to claim 5, 7, or 8, characterized in that, The data analysis module includes a differential diagnosis unit, which is configured to: compare the expression level of circACSL1 in the subject with the typical expression range of the subject with dilated cardiomyopathy; and based on the comparison, generate differential diagnosis data to distinguish between myocarditis and dilated cardiomyopathy, wherein when the expression level of circACSL1 is significantly higher than the typical range of DCM, the diagnosis of myocarditis is more likely.

10. The system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A according to claim 5, 7, or 8, characterized in that, The data analysis module also generates quantitative assessment data on the degree of myocardial cell inflammation or myocardial damage based on the expression level of circACSL1.

11. The system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A according to claim 5, characterized in that, The biomarker detection module is also configured to simultaneously detect the expression level of microRNA miR-8055, and the data analysis module is further configured to: calculate the expression ratio or correlation between circACSL1 and miR-8055; and / or combine the expression levels of circACSL1 and miR-8055 to generate prediction data through a multivariate model.

12. The system for predicting myocarditis using circular RNA circ0071542 and ribosomal protein RPL13A according to claim 5, 7, or 8, characterized in that, The data analysis module stores or integrates a model of the circACSL1 / miR-8055 / MAPK14 regulatory axis. This model describes the interaction between circACSL1 and miR-8055, which inhibits miR-8055 through sponge adsorption, thereby upregulating MAPK14 expression. This model is used to help explain the biological mechanisms underlying the predicted data.

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