RNA compositions, kits, and their applications for diagnosing idiopathic inflammatory myopathy or evaluating its treatment.

Abstract

This invention provides an RNA composition, kit, and application for diagnosing idiopathic inflammatory myopathy (IIM) or evaluating its treatment. The RNA composition includes hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448|9.2. In patients with IIM, hsa-miR-590, hsa-miR-181a, and RP3-448|9.2 are significantly downregulated, while DEFA1 is significantly upregulated. Therefore, this RNA composition can serve as a biomarker for diagnosing IIM or evaluating the treatment efficacy of IIM, exhibiting advantages of high sensitivity, high specificity, and high accuracy. It can provide reliable scientific evidence and personalized treatment strategies for the treatment and clinical diagnosis of IIM.

Description

Technical Field

[0001] This invention relates to the field of molecular biology, and more particularly to an RNA composition, kit, and application thereof for diagnosing idiopathic inflammatory myopathy or evaluating its treatment. Background Technology

[0002] Idiopathic inflammatory myopathies (IIM) are a group of inflammatory autoimmune diseases with unclear etiologies, primarily affecting skeletal muscle. Interstitial lung disease (ILD) is the most common organ complication of IIM and a major factor determining its prognosis. Among these, anti-aminoacyl-tRNA synthetase-positive idiopathic inflammatory myopathy-associated interstitial lung disease (ARS) is a specific type of IIM. + Patients with IIM-ILD are not uncommon in clinical practice, and their condition progresses rapidly and severely, with an extremely poor prognosis. Therefore, there is an urgent clinical need to screen for and manage IIM-ILD. + IIM-ILD subtype classification-related novel diagnostic biomarkers with excellent sensitivity and specificity, enabling the exploration of new therapeutic targets, thereby achieving ARS + Early diagnosis and effective treatment of IIM-ILD subtypes have significant clinical and health economic value. Summary of the Invention

[0003] This invention provides an RNA composition and kit for diagnosing idiopathic inflammatory myopathy or evaluating the treatment effect of idiopathic inflammatory myopathy. It has the advantages of high sensitivity, high specificity and high accuracy, and can provide reliable scientific basis and personalized treatment ideas for the treatment and clinical diagnosis of idiopathic inflammatory myopathy.

[0004] The present invention provides the use of the above-described RNA composition or the above-described kit in the preparation of products for diagnosing idiopathic inflammatory myopathy or evaluating the therapeutic effect of idiopathic inflammatory myopathy.

[0005] This invention provides a diagnostic or assessment system for idiopathic inflammatory myopathy (EV) and a computer-readable storage medium that enables the system's functions. By combining reverse transcription with next-generation sequencing technology, it can comprehensively and objectively reflect circulating RNA in body fluids. This overcomes the low sensitivity of high-throughput sequencing technology in detecting sncRNA and mRNA in EVs, and also solves the problem of poor sequencing data quality caused by numerous byproducts generated during EV RNA library construction, which makes it difficult to obtain sufficient useful information for identifying specific biomarkers. The detection results obtained using the above system or computer-readable storage medium are more accurate, comprehensive, and reliable.

[0006] The present invention provides an RNA composition for diagnosing idiopathic inflammatory myopathy or evaluating the treatment effect of idiopathic inflammatory myopathy, wherein the RNA composition comprises hsa-miR-590, hsa-miR-181a, DEFA1 and RP3-448|9.2.

[0007] The present invention provides a kit for diagnosing idiopathic inflammatory myopathy or evaluating the treatment effect of idiopathic inflammatory myopathy, wherein the kit includes reagents for detecting the above-mentioned RNA composition.

[0008] The kit described above includes reagents for extracting extracellular vesicle RNA from plasma and reagents for determining RNA expression.

[0009] The kit described above includes at least one of the following reagents for extracting extracellular vesicle RNA from plasma: ethylenediaminetetraacetic acid, exosome RNA extraction reagent, DNA lysis reagent, deoxyribonuclease I, ethanol, phosphate buffer solution, nucleic acid fluorescent dye, protease inhibitor, and RNase inhibitor.

[0010] The kit described above includes at least one of the following reagents for determining RNA expression: T4 polynucleotide kinase, Escherichia coli Poly(A) polymerase, reverse transcriptase, dithiothreitol, dNTPs, PEG 8000, template-converting oligonucleotides, UMI molecular tags, DNA polymerase, Cas9 nuclease, sgRNA, and DNA quantification reagents.

[0011] The present invention provides the use of the above-described RNA composition or the above-described kit in the preparation of products for diagnosing idiopathic inflammatory myopathy or evaluating the therapeutic effect of idiopathic inflammatory myopathy.

[0012] As described above, idiopathic inflammatory myopathy is idiopathic inflammatory myopathy with interstitial lung disease that is positive for anti-aminoacyl-tRNA synthetase antibody;

[0013] Optionally, in diagnosis or evaluation, the concentration of each RNA in the plasma RNA composition is detected to determine its expression level;

[0014] Optionally, each RNA is derived from extracellular vesicles in the blood plasma.

[0015] This invention provides a diagnostic or assessment system for idiopathic inflammatory myopathy, comprising:

[0016] The sample acquisition module is used to provide plasma samples from the individual to be tested;

[0017] The sample detection module is used to determine the expression level of RNA composition in plasma samples;

[0018] The analysis and evaluation module is used to diagnose or assess the prognosis of the individual being tested based on the expression level of the RNA composition being measured.

[0019] Among them, diagnosing the individual to be tested means determining whether the individual to be tested is a patient with idiopathic inflammatory myopathy with interstitial lung disease who is positive for anti-aminoacyl-tRNA synthetase antibody;

[0020] Among them, prognostic assessment of the individuals to be tested refers to assessing the prognosis of patients with idiopathic inflammatory myopathy and interstitial lung disease who are positive for anti-aminoacyl-tRNA synthetase antibodies.

[0021] The RNA composition includes hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448|9.2.

[0022] The present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is a computer program that implements the functions of the system described above.

[0023] This invention provides a computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements a computer-aided diagnostic or evaluation method based on the aforementioned RNA composition or the aforementioned kit, optionally wherein the diagnostic or evaluation method includes:

[0024] i) Receive test data from plasma samples from the individuals to be tested;

[0025] ii) The detection data is compared with the reserved reference data using a pre-set machine learning model;

[0026] iii) Output diagnostic or assessment results based on the comparison results.

[0027] This invention provides an RNA composition for diagnosing idiopathic inflammatory myopathy (IIM) or evaluating the treatment efficacy of IIM. The RNA composition comprises hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448|9.2. In patients with IIM, hsa-miR-590, hsa-miR-181a, and RP3-448|9.2 are significantly downregulated, while DEFA1 is significantly upregulated. Therefore, this RNA composition can serve as a biomarker for diagnosing IIM or evaluating the treatment efficacy of IIM, exhibiting advantages of high sensitivity, high specificity, and high accuracy. It can provide reliable scientific evidence and personalized treatment strategies for the treatment and clinical diagnosis of IIM.

[0028] This invention was supported by the Clinical Research Project of Peking Union Medical College Hospital, a key project of the Central High-level Hospital Clinical Research Program, entitled "Exploratory Study of Bortezomib in the Treatment of Refractory Antisynthetic Syndrome" (Grant No.: 2022-PUMCH-C-020). Attached Figure Description

[0029] Figure 1 This is a flowchart of the EV RNA sequencing process in Example 3;

[0030] Figure 2 This is a heatmap of differentially expressed RNA in the ARS group and the HC group in Example 4;

[0031] Figure 3 This is a volcano diagram of differentially expressed RNA in the ARS and HC groups in Example 4;

[0032] Figure 4 This is a graph showing the RNA marker expression levels in the ARS and HC groups in Example 4.

[0033] Figure 5 This is a diagram showing the confusion matrix of the training set and the confusion matrix of the validation set in Example 4;

[0034] Figure 6 The support vector machine classifier in Example 4 in ARS + Performance results graph in IIM-ILD diagnostics;

[0035] Figure 7 This is a graph showing the AUC values ​​of the ROC curve of the support vector machine classifier in Example 4. Detailed Implementation

[0036] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below. The specific embodiments listed below are merely descriptions of the principles and features of the present invention, and the examples are only for explaining the present invention and are not intended to limit the scope of the present invention. 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.

[0037] Idiopathic inflammatory myopathies (IIM) are a heterogeneous group of autoimmune diseases with varying clinical presentations, treatment responses, and prognoses. They are typically characterized by muscle weakness, but other organs may also be affected, such as the skin, joints, lungs, heart, or gastrointestinal tract. Based on clinical, histopathological, and serological characteristics, IIM can be divided into different subtypes, each with distinct organ manifestations, treatment responses, and prognoses, indicating a different pathophysiological mechanism within each subtype.

[0038] Currently, the main diagnostic methods for IIM subtypes include clinical symptoms, imaging examinations, and antibody testing. However, these methods all have certain limitations: clinical symptom diagnosis results may vary depending on the doctor and the time of day; imaging examinations may not detect obvious abnormalities or provide an accurate diagnosis; immunoprecipitation (IP), although considered the gold standard for antibody testing, is complex to perform, requires the use of radioactive isotopes, and carries associated risks, limiting its clinical application; enzyme-linked immunosorbent assay (ELISA) for antibody testing has relatively low sensitivity, and ARS... + The sensitivity of subtype-based assays is only 55%; and the dot immunoassay (DIA) in antibody testing is primarily a qualitative method, its diagnostic value limited by a false positive rate as high as 70%. Therefore, there is currently a lack of reliable, non-invasive, highly sensitive, and specific subtype diagnostic molecular biomarkers for IIM to accurately understand the disease's pathogenesis, classify phenotypes, and identify therapeutic targets. There is an urgent need to develop minimally invasive diagnostic tests to classify IIM patients, thereby improving their quality of life and reducing the social burden, which is crucial for drug development and personalized treatment.

[0039] To address the aforementioned problems, the first aspect of the present invention provides an RNA composition for diagnosing idiopathic inflammatory myopathy or evaluating the therapeutic effect of idiopathic inflammatory myopathy, the RNA composition comprising hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448|9.2.

[0040] The RNA markers hsa-miR-590 and hsa-miR-181a in the RNA composition belong to microRNAs (miRNAs). miRNAs are mature miRNAs formed from single-stranded RNA precursors (pre-miRNAs) with a hairpin structure of approximately 70 base pairs, which are then processed by the Dicer enzyme. The reference sequence for hsa-miR-590 can be found at NCBI NR_030321.1, and the reference sequence for hsa-miR-181a can be found at NCBI NR_029626.1.

[0041] Furthermore, the 5' and 3' arms of some pre-miRNAs can each produce a mature miRNA, hence they are named "-5p" and "-3p" respectively;

[0042] The nucleotide sequence of hsa-miR-590-5p is as follows:

[0043] 5'-GAGCUUAUUCAUAAAAGUGGCAG-3' (SEQ ID NO: 11);

[0044] The nucleotide sequence of hsa-miR-590-3p is as follows:

[0045] 5'-UAAUUUUAUGUAUAAGCUAGU-3' (SEQ ID NO: 12);

[0046] The nucleotide sequence of hsa-miR-181a-5p is as follows:

[0047] 5'-AACAUUCAACGCUGUCGGUGAGU-3' (SEQ ID NO: 13);

[0048] The nucleotide sequence of hsa-miR-181a-3p is as follows:

[0049] 5'-ACCAUCGACCGUUGAUUGUACC-3' (SEQ ID NO: 14).

[0050] In addition, DEFA1 (Defensin Alpha 1) is a gene encoding human α-defensin 1, belonging to the defensin family. It is mainly expressed in neutrophils and has antibacterial activity. In this invention, the RNA marker DEFA1 is a messenger RNA (mRNA) encoding human α-defensin 1. Its reference sequence can be found in NCBI NM_004084.3, and its nucleotide sequence is shown in SEQ ID NO:15.

[0051] RP3-448|9.2 belongs to long non-coding RNA (lncRNA). lncRNA is a non-coding RNA with a length greater than 200 nucleotides. It plays an important role in many life activities such as dose compensation effect, epigenetic regulation, cell cycle regulation and cell differentiation regulation. Its reference sequence can be found in the lncHUB2 database, and its nucleotide sequence is shown in SEQ ID NO:16.

[0052] The aforementioned RNA composition can serve as a biomarker for diagnosing idiopathic inflammatory myopathy (IIM) or evaluating the treatment efficacy of IIM, thereby providing a reliable scientific basis and personalized treatment approach for the treatment and clinical diagnosis of IIM. It is particularly suitable for anti-aminoacyl-tRNA synthetase-positive idiopathic inflammatory myopathy-associated interstitial lung disease (ARS). + IIM-ILD). Specifically, in ARS + In IIM-ILD patients, hsa-miR-590, hsa-miR-181a, and RP3-448|9.2 were significantly downregulated, while DEFA1 was significantly upregulated. Experimental validation showed that, in the training set, the above RNA composition served as an ARS (Advanced Response System). + The biomarker for IIM-ILD showed a sensitivity of 90.32%, specificity of 100%, accuracy of 95%, and an AUC of 0.973. In the validation set, its sensitivity was 84.21%, specificity 85.71%, accuracy 85%, and AUC 0.973. Furthermore, this invention utilizes a large sample size and well-designed controls, resulting in highly reliable results that fully demonstrate the identified biomarker is ARS. + Biomarkers for IIM-ILD. The above RNA composition can be used to detect potential ARS at an early stage. + Targeting IIM-ILD subtypes and providing personalized interventions and treatments can ultimately improve ARS. + Cure rate for the IIM-ILD subtype.

[0053] Based on the above research results, a second aspect of the present invention provides a kit for diagnosing idiopathic inflammatory myopathy or evaluating the treatment effect of idiopathic inflammatory myopathy, the kit comprising reagents for detecting the above-mentioned RNA composition.

[0054] It is understood that, given the above-mentioned RNA combination, those skilled in the art can also design primers based on the sequence of the above-mentioned RNA combination and use polymerase chain reaction (PCR) combined with electrophoresis or real-time quantitative PCR (qPCR) to detect the above-mentioned RNA combination in order to determine the expression status of each RNA molecule in the RNA combination, and then use it to diagnose idiopathic inflammatory myopathy or evaluate the treatment effect of idiopathic inflammatory myopathy.

[0055] Furthermore, the above reagents include: reagents for extracting extracellular vesicular RNA from plasma and reagents for determining RNA expression.

[0056] Specifically, reagents used to extract extracellular vesicle RNA from plasma may include at least one of the following: ethylenediaminetetraacetic acid, exosome RNA extraction reagent, DNA lysis reagent, deoxyribonuclease I, ethanol, phosphate buffer solution, nucleic acid fluorescent dye, protease inhibitor, and RNase inhibitor.

[0057] Reagents used to determine RNA expression may include at least one of the following: T4 polynucleotide kinase, Escherichia coli Poly(A) polymerase, reverse transcriptase, dithiothreitol, dNTPs, PEG 8000, template-converting oligonucleotides, UMI molecular tags, DNA polymerase, Cas9 nuclease, sgRNA, and DNA quantitative reagents.

[0058] A third aspect of the present invention provides the use of the above-described RNA composition or the above-described kit in the preparation of products for diagnosing idiopathic inflammatory myopathy or evaluating the therapeutic effect of idiopathic inflammatory myopathy.

[0059] In detail, the aforementioned RNA composition or kit can be used to prepare products for diagnosing idiopathic inflammatory myopathy or evaluating the therapeutic effects of idiopathic inflammatory myopathy, including but not limited to therapeutic drugs. Specifically, the aforementioned idiopathic inflammatory myopathy can be anti-aminoacyl-tRNA synthetase antibody-positive idiopathic inflammatory myopathy with interstitial lung disease (ARS). + IIM-ILD). Experimental verification has shown that this invention is particularly suitable for diagnosing ARS. + IIM-ILD or assessment of ARS + The therapeutic efficacy of IIM-ILD. In diagnosis or evaluation, the concentration of individual RNAs in the plasma RNA composition can be measured to determine their expression levels. These RNAs can originate from extracellular vesicles in the plasma.

[0060] Specifically, extracellular vesicles (EVs) exist in bodily fluids such as plasma, serum, urine, saliva, cerebrospinal fluid, and breast milk. These are heterogeneous membrane structures secreted by cells, enclosed in a lipid bilayer, and unable to self-replicate (lacking a functional nucleus). Numerous studies have shown that EVs can carry various proteins, lipids, DNA, mRNA, lncRNA, and non-coding small RNAs (sncRNAs), possessing functions in substance transport and information exchange. They participate in processes such as cell communication, cell migration, repair of damaged tissue, angiogenesis, antigen presentation, and promotion of tumor cell growth and migration. As a source of self-antigens and immune complexes, EVs play a crucial role in the pathogenesis of autoimmune diseases. EVs have become potential biomarkers for various autoimmune diseases, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), primary Sjögren's syndrome (pSS), systemic sclerosis (SSc), chronic inflammatory demyelinating polyneuropathy (CIDP), myasthenia gravis (MG), and intraepithelial neoplasia (IIM). Because EVs are widely distributed in body fluids and possess a lipid bilayer that effectively protects the encapsulated RNA from degradation by ribonucleases, they exhibit high stability and tissue specificity in body fluids. Therefore, molecular loading of EVs holds potential for disease subtype classification and elucidating biological mechanisms, potentially improving diagnostic accuracy, predicting prognosis, and monitoring treatment efficacy. However, currently, using plasma EV RNA as an ARS (Adverse Reactions Syndrome) is not widely accepted. + Research on IIM-ILD-related biomarkers still suffers from low sensitivity, low specificity, and low accuracy, which urgently need to be addressed. Therefore, this invention aims to solve this problem by extracting EVs from plasma, further extracting EV RNA, and then using the aforementioned kit to detect the concentration of each RNA in the above-mentioned RNA composition in plasma to determine its expression level, ultimately achieving the diagnostic or monitoring function of ARS. + The IIM-ILD has the advantages of high sensitivity, high specificity and high accuracy.

[0061] In addition, those skilled in the art can determine the expression level by detecting the concentration of each RNA in the above RNA composition using PCR technology (e.g., real-time quantitative PCR technology), or by using sequencing methods to detect the concentration of each RNA in the above RNA composition.

[0062] A fourth aspect of the present invention provides a diagnostic or assessment system for idiopathic inflammatory myopathy, comprising:

[0063] The sample acquisition module is used to provide plasma samples from the individual to be tested;

[0064] The sample detection module is used to determine the expression level of RNA composition in plasma samples;

[0065] The analysis and evaluation module is used to diagnose or assess the prognosis of the individual being tested based on the expression level of the RNA composition being measured.

[0066] Among them, diagnosing the individual to be tested means determining whether the individual to be tested is a patient with idiopathic inflammatory myopathy with interstitial lung disease who is positive for anti-aminoacyl-tRNA synthetase antibody;

[0067] Among them, prognostic assessment of the individuals to be tested refers to assessing the prognosis of patients with idiopathic inflammatory myopathy and interstitial lung disease who are positive for anti-aminoacyl-tRNA synthetase antibodies.

[0068] The RNA composition includes hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448|9.2.

[0069] First, the sample acquisition module of the present invention is used to provide a plasma sample from the individual to be tested.

[0070] In one specific embodiment, peripheral venous blood can be collected from the individual to be tested. The peripheral venous blood is placed in a blood collection tube and stored vertically at room temperature (25°C). When needed, the blood collection tube is opened, and the peripheral blood is drawn into a centrifuge tube. After balancing, the tube is centrifuged at 1300×g for 10 min at room temperature (25°C). The supernatant is then transferred to a new centrifuge tube, balanced, and centrifuged at 2500×g for 15 min at room temperature (25°C). The supernatant is then collected to obtain a plasma sample. This plasma sample is poorplatelet plasma (PPP) and can be stored for a long time at -80°C for future use.

[0071] Secondly, the sample detection module of the present invention is used to determine the expression level of RNA composition in plasma samples.

[0072] Specifically, plasma samples can be removed from a -80°C freezer and immediately thawed at 37°C. They are then centrifuged at 2500×g for 15 min at 4°C to remove precipitated proteins. The supernatant is used to separate plasma EVs and extract EV RNA using the Qiagen exoRNeasy Midi kit, yielding EV RNA samples. The EV RNA samples are washed and eluted, then incubated with DNase I at 37°C for 20 min to remove residual DNA. The RNA is then purified and concentrated using the RNA Clean and Concentrator-5 kit to obtain purified EV RNA samples. The purified EV RNA samples are dissolved in RNase-free ultrapure water for quality control. Quantification of EV RNA samples is performed using the Quant-iT RiboGreen RNA High Sensitivity Kit, and integrity is checked using an Agilent 2200 Bioanalyzer. Only EV RNA samples that pass both quantitative and qualitative quality control are suitable for subsequent EV RNA library construction and sequencing analysis. End repair and tailing of qualified EV RNA samples were performed using T4 polynucleotide kinase and E. coli Poly(A) polymerase, followed by incubation at 37°C for 30 min to obtain a reaction mixture. The reaction mixture was then purified and concentrated using the RNA Clean and Concentrator-5 kit to obtain concentrated EV RNA samples.

[0073] Subsequently, the concentrated EV RNA sample was captured using primer Oligo(dT)-UMI-N6-barcode-primer, followed by reverse transcription. The reverse transcription reaction system included: SMARTScribe reverse transcriptase, SMARTScrib reverse transcriptase reaction buffer, dithiothreitol, dNTPs, RiboLock RNase inhibitor, template-converting oligonucleotides, PEG 8000, primer Oligo(dT)-UMI-N6-barcode-primer, and the concentrated EV RNA sample. The reaction consisted of incubation at 42°C for 90 min followed by incubation at 70°C for 10 min. The resulting cDNA sample was stored at 4°C. The obtained cDNA sample, 2×SeqAmp CB PCR Buffer, PCR1 primers, and SeqAmp DNA polymerase were mixed and subjected to thermal cycling amplification: denaturation at 98°C for 15 s, annealing at 55°C for 15 s, and extension at 68°C for 30 s. The thermal cycling amplification products were purified and size-selected twice using Hieff NGS DNA selection beads. The CRISPR-Cas9 reaction system was prepared and thoroughly mixed, then incubated in a 25°C metal bath for 10 min. The CRISPR-Cas9 reaction system was added to the selection beads to completely resuspend them, and the beads were incubated on a magnetic rack at room temperature (25°C) for 5 min to completely elute the DNA from the beads. The CRISPR-Cas9 reaction system containing DNA was then placed in a PCR instrument and incubated at 37°C for 60 min, followed by incubation at 65°C for 5 min to remove high-abundance ribosomal RNA and mitochondrial RNA, yielding the amplified and purified cDNA sample.

[0074] Next, the amplification reaction system for the sequencing library included: amplified and purified cDNA sample, 2×SeqAmp CB PCR Buffer, PCR2 primers, and SeqAmp DNA polymerase. The amplification program for the sequencing library included: initial denaturation at 94℃ for 1 min; denaturation at 98℃ for 15 s, annealing at 55℃ for 30 s, extension at 68℃ for 30 s, for 17 cycles; and final extension at 68℃ for 2 min. The amplified products were purified and size-selected twice using Hieff NGS DNA selection beads, and the supernatant was collected to obtain the sequencing library. The concentration of the sequencing library was determined using the Qubit® dsDNA HS Assay Kit, and the concentration needed to be >1 ng / μL. The distribution of nucleic acid fragments in the sequencing library was evaluated using an Agilent 2100 bioanalyzer and a high-sensitivity DNA analysis kit.

[0075] Finally, the analysis and evaluation module of the present invention is used to diagnose or assess the prognosis of the individual to be tested based on the expression level of the measured RNA composition.

[0076] Sequencing libraries can be placed on the Illumina NovaSeq 6000 platform for sequencing to detect the expression levels of hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448|9.2. Using machine learning, 60% of the total samples are defined as the training set, and the remaining 40% as the validation set. Boruta feature selection is used on both the training and validation sets to find all relevant machine learning variables. This process is repeated to identify variables that can distinguish ARS. + Support Vector Machine (SVM) classifiers were used for IIM-ILD patients and healthy individuals. The classifier consisted of an RNA combination of four RNA markers: hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448|9.2. This SVM classifier model could diagnose IIM-ILD patients with a threshold of 0.5; values ​​above 0.5 were considered ARS. + For IIM-ILD patients, a value below 0.5 indicates non-ARS. + IIM-ILD patients.

[0077] In this context, diagnosing the individual being tested refers to determining whether the individual is a patient with idiopathic inflammatory myopathy and interstitial lung disease who is positive for anti-aminoacyl-tRNA synthetase antibodies.

[0078] For example, in one embodiment, a support vector machine model can be used to diagnose or assess the prognosis of the individual to be tested, with 0.5 as the diagnostic threshold; values ​​above 0.5 are considered ARS (Adverse Respiratory Syndrome). + For IIM-ILD patients, a value below 0.5 indicates they are not ARS. + IIM-ILD patients.

[0079] In addition, prognostic assessment of the individuals to be tested refers to evaluating the prognosis of patients with idiopathic inflammatory myopathy and interstitial lung disease who are positive for anti-aminoacyl-tRNA synthetase antibodies.

[0080] For example, assessing ARS using the expression levels of RNA compositions. + The prognosis of IIM-ILD patients is determined by whether their expression levels are similar to those of healthy individuals, with no statistically significant difference, thus proving ARS. + Patients with IIM-ILD have a good prognosis; if the expression level is similar to that before treatment and there is no statistically significant difference, it proves that ARS... + Patients with IIM-ILD have a poor prognosis, and the corresponding treatment plan is not suitable for them.

[0081] This invention overcomes the limitation of most studies that rely solely on reverse transcription combined with next-generation sequencing (NGS) technology to assess specific dysregulated miRNAs or other RNA types in body fluids as candidate markers, failing to comprehensively and objectively reflect circulating RNA in body fluids. Furthermore, by using NGS, this invention also overcomes the low sensitivity of high-throughput sequencing technologies for detecting small non-coding RNAs (sncRNAs) and messenger RNAs (mRNAs) in EVs. Moreover, the CRISPR-Cas9 reaction system addresses the issue of poor sequencing data quality caused by numerous byproducts generated during EV RNA library construction, which hinders the acquisition of sufficient useful information for identifying specific biomarkers.

[0082] Based on the above-described diagnostic or assessment system for idiopathic inflammatory myopathy, the present invention also provides a method for diagnosing or assessing idiopathic inflammatory myopathy, comprising:

[0083] Obtain a plasma sample from the individual to be tested;

[0084] Determine the expression level of RNA composition in plasma samples;

[0085] The expression level of the RNA composition measured is used to diagnose or assess the prognosis of the individual being tested.

[0086] Among them, diagnosing the individual to be tested means determining whether the individual to be tested is a patient with idiopathic inflammatory myopathy with interstitial lung disease who is positive for anti-aminoacyl-tRNA synthetase antibody;

[0087] Among them, prognostic assessment of the individuals to be tested refers to assessing the prognosis of patients with idiopathic inflammatory myopathy and interstitial lung disease who are positive for anti-aminoacyl-tRNA synthetase antibodies.

[0088] The RNA composition includes hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448|9.2.

[0089] A fifth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is a computer program that implements the functions of the system as described above.

[0090] A sixth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a computer-aided diagnostic or evaluation method based on the aforementioned RNA composition or the aforementioned kit; optionally, the diagnostic or evaluation method includes:

[0091] i) Receive test data from plasma samples from the individuals to be tested;

[0092] ii) The detection data is compared with the reserved reference data using a pre-set machine learning model;

[0093] iii) Output diagnostic or assessment results based on the comparison results.

[0094] The technical solution of this application will be further explained below with reference to specific embodiments. Experimental methods in the following embodiments that do not specify specific conditions are generally performed under conventional conditions or as recommended by the manufacturer. Unless otherwise specified, all reagents used are commercially available or obtained through public channels.

[0095] Example 1: Collection and preparation of plasma samples

[0096] Idiopathic inflammatory myopathies (IIM) are a group of heterogeneous, autoimmune-mediated systemic connective tissue diseases characterized by inflammatory cell infiltration and myofiber necrosis, degeneration, and regeneration in skeletal muscle. Clinically, they mainly manifest as symmetrical weakness of proximal muscles of the limbs, limb girdle muscles, neck muscles, and pharyngeal muscles. They often involve multiple organs, and when the alveoli and pulmonary interstitium are involved, they can cause interstitial lung disease (ILD). IIM mainly includes polymyositis (PM), dermatomyositis (DM), and inclusion body myositis (IBM).

[0097] Patients with idiopathic inflammatory myopathy and interstitial lung disease who are positive for anti-aminoacyl-tRNA synthetase (ARS) antibodies (ARS) + IIM-ILD) is used as the object to be detected. Among them, ARS + The inclusion criteria for IIM-ILD patients are as follows: (1) PM or DM are diagnosed according to the Bohan and Peter criteria, and positive anti-ARS antibody is diagnosed according to the Connor criteria; (2) ILD is diagnosed according to respiratory symptoms, high-resolution computed tomography (HRCT) results, and pulmonary function test results; Based on the confirmed diagnosis, ILD patients are divided into mild (>60%), moderate (40%-60%), and severe (<40%) according to the degree of decrease in carbon monoxide diffusion capacity (DLCO, % predicted value). Healthy individuals are used as the control group. Healthy individuals and enrolled patients (MDA5) +The general clinical features of IIM-ILD are shown in Table 1.

[0098] Table 1

[0099]

[0100] Peripheral venous blood samples were collected from 50 subjects and 51 control subjects. 2 ml of blood was collected from each sample and placed in an EDTA-K2 blood collection tube. The tubes were then stored vertically at room temperature (25°C). The blood collection tubes were opened, and 500 μl of peripheral blood was drawn into a 2 ml centrifuge tube. After balancing, the tubes were centrifuged at 1300×g for 10 min at room temperature (25°C). The supernatant was transferred to a new 2 ml centrifuge tube, balanced, and centrifuged at 2500×g for 15 min at room temperature (25°C). The supernatant was then collected to obtain the plasma sample. This plasma sample was poor platelet plasma (PPP) and can be stored at -80°C for extended periods.

[0101] Example 2: Preparation and quality control of extracellular vesicle RNA samples from plasma

[0102] Plasma samples were removed from a -80°C freezer and immediately thawed at 37°C for 2 min. They were then centrifuged at 2500×g for 15 min at 4°C to remove precipitated proteins. One ml of the supernatant was collected and used to separate plasma-derived extracellular vesicles (EVs) and extract EV RNA using the Qiagen exoRNeasy Midi kit, yielding EV RNA samples. The EV RNA samples were washed and eluted, then incubated at 37°C for 20 min with DNase I (TaKaRa, catalog number 2270A) to remove residual DNA. The purified EV RNA samples were then purified and concentrated using the Zymo RNA Clean and Concentrator-5 kit (catalog number R1016).

[0103] The purified EV RNA sample was dissolved in 10 μl of RNase-free ultrapure water, and 2 μl was used for quality control. Quantification of the EV RNA sample was performed using the Quant-iT RiboGreen RNA High Sensitivity Kit, and integrity was checked using an Agilent 2200 Bioanalyzer. 1.2–5.6 ng of EV RNA can be extracted per mL of plasma, and the distribution of EV RNA must conform to a standard length peak pattern. Only EV RNA samples that pass both quantitative and qualitative quality control can be used for subsequent EV RNA library construction and sequencing analysis.

[0104] Example 3: EV RNA sequencing

[0105] This embodiment establishes a simple, standardized, and highly sensitive cell-free RNA sequencing (cfRNA-seq) technique for EV RNA to construct EV RNA sequencing libraries and perform sequencing. The involved process can be found in [link to documentation]. Figure 1 The primers involved are shown in Table 2. In Table 2, the 5' end of the nucleotide sequence of primer TSO (SEQ ID NO:1) is marked with biotin, and rG at position 19-21 bp of this nucleotide sequence represents riboguanine (RNA-G); the 5' end of the nucleotide sequence of primer Oligo(dT)-UMI-N6-barcode-primer (SEQ ID NO:2) is marked with biotin, and 8N at position 21-28 bp of this nucleotide sequence represents the UMI sequence, and 4N at position 29-32 bp represents the barcode. Additionally, in this nucleotide sequence, N indicates that the base of the deoxyribonucleotide at this position can be any of adenine (A), guanine (G), cytosine (C), or thymine (T), and V indicates that the base of the deoxyribonucleotide at this position can be any of A, G, or C; the 5' end of the nucleotide sequence of primer PCR1 (SEQ ID NO:3) is marked with biotin, and rG at position 20-27 bp of this nucleotide sequence represents riboguanine (RNA-G). The 8N at bp represents the tag index, and in this nucleotide sequence, N indicates that the base of the deoxyribonucleotide at that position can be any of A, G, C, or T. Additionally, * indicates phosphorothioate. The nucleotide sequence of primer 3'PCR1 (SEQ ID NO:4) is labeled with Biotin at the 5' end. The 8N at bp 20-27 of this nucleotide sequence represents the tag index, and in this nucleotide sequence, N indicates that the base of the deoxyribonucleotide at that position can be any of A, G, C, or T. Additionally, * indicates phosphorothioate. In the nucleotide sequence of primer 5'PCR2 (SEQ ID NO:5), * indicates phosphorothioate. In the nucleotide sequence of primer 3'PCR2 (SEQ ID NO:6), * indicates phosphorothioate. The nucleotide sequence of the forward primer for the sgRNA Pool library of the sgRNA DNA template (SEQ ID NO:7) at bp 20-39... The 20N at the bp position represents the specific target sequence in the sgRNA, and in this nucleotide sequence, N indicates that the deoxyribonucleotide base at that position can be any of A, G, C, or T. The specific procedure is as follows:

[0106] (1) Pretreatment of EV RNA samples: Qualified EV RNA samples were end-repaired and tailed using T4 polynucleotide kinase (NEB, catalog number M0201) and E. coli Poly(A) polymerase (NEB, catalog number M0276). The reaction volume was 20 μL, and the mixture was incubated at 37°C for 30 min to obtain the reaction mixture. Subsequently, the reaction mixture was purified and concentrated using the RNA Clean and Concentrator-5 kit (Zymo, catalog number R1016) to obtain a 6 μL concentrated EV RNA sample.

[0107] (2) Reverse transcription: The concentrated EV RNA sample was captured using primer Oligo(dT)-UMI-N6-barcode-primer and then reverse transcribed. The reverse transcription reaction system included: 2 μL SMARTScribe reverse transcriptase, 4 μL SMARTScrib reverse transcriptase reaction buffer, 2 μL 20 mM dithiothreitol, 2 μL dNTPs, 0.5 μL RiboLock RNase inhibitor, 0.5 μL template-switching oligos (TSO), 2 μL PEG 8000, 1 μL 1 μM Oligo(dT)-UMI-N6-barcode-primer, and 6 μL concentrated EV RNA sample. The reverse transcription reaction system included incubation at 42℃ for 90 min and incubation at 70℃ for 10 min. Finally, 20 μL of cDNA sample was obtained and stored at 4℃.

[0108] (3) Pre-amplification and purification of sequencing library: 20 μL of cDNA sample, 25 μL of 2×SeqAmp CB PCR Buffer, 1 μL of 5' PCR1 and 1 μL of 3' PCR1, and 1 μL of SeqAmp DNA polymerase were mixed and subjected to thermal cycling amplification at 98℃ for 15 s, 55℃ for 15 s, and 68℃ for 30 s (cycle number 6). The thermally cycled amplification products were purified and size-selected twice using Hieff NGS DNA selection beads (Yeasen, catalog number 12601ES56) at a volume ratio of 1.8:1 of selection beads to DNA in the thermally cycled amplification products. Prepare the CRISPR-Cas9 reaction system and mix thoroughly. Incubate at 25°C in a metal bath for 10 min. The CRISPR-Cas9 reaction system includes: 12 μL NEBuffer 3.1, 1 μL (300 ng) rRNA sgRNA, 1 μL (40 ng) mtRNA sgRNA, 8.3 μL nuclease-free water, and 10 μL (1 μM) Cas9 nuclease. The cDNA sample is amplified in one step using primer pairs (forward primer and reverse primer for the sgRNA library of the sgRNA DNA template) and primer pairs (5' universal primer and 3' universal primer for the sgRNA DNA template) to obtain the sgRNA DNA template. Then, in vitro transcription is performed using T7 RNA polymerase (NEB, catalog number E2050) to obtain RNA sgRNA and mtRNA sgRNA. Add 21.5 μL of CRISPR-Cas9 reaction system to the selected beads to completely resuspend the selected beads, and incubate the selected beads at room temperature (25℃) for 5 min on a magnetic rack to completely wash the DNA off the magnetic beads. Then place the CRISPR-Cas9 reaction system containing DNA in a PCR instrument and incubate at 37℃ for 60 min, then at 65℃ for 5 min, and store at 4℃ to remove high-abundance ribosomal RNA and mitochondrial RNA, and obtain the amplified and purified cDNA sample.

[0109] (5) Amplification and purification of sequencing libraries: The amplification reaction system for sequencing libraries included: 20 μL of amplified and purified cDNA sample, 50 μL of 2×SeqAmp CB PCR Buffer, 2.5 μL of 5' PCR2 and 2.5 μL of 3' PCR2, 2 μL of SeqAmp DNA polymerase, and 23 μL of nuclease-free water. The amplification program for sequencing libraries included: initial denaturation at 94℃ for 1 min; denaturation at 98℃ for 15 s, annealing at 55℃ for 30 s, extension at 68℃ for 30 s, for 17 cycles; and final extension at 68℃ for 2 min. Using Hieff NGS DNA selection beads, the amplification products were purified and size-selected twice at a 1:1 volume ratio of selection beads to DNA in the amplification products. 16 μL of supernatant was then collected to obtain the sequencing library.

[0110] (6) Sequencing: The concentration of the sequencing library was determined using the Qubit® dsDNA HS Assay Kit. The concentration of the sequencing library should be >1 ng / μL. The distribution of nucleic acid fragments in the sequencing library was evaluated using an Agilent 2100 bioanalyzer and a high-sensitivity DNA analysis kit (catalog number 5067-4626) manufactured by Agilent Technologies. Sequencing was performed on the Illumina NovaSeq6000 platform.

[0111] Table 2

[0112]

[0113] Example 4: Plasma EV RNA Combination in ARS + Effectiveness of IIM-ILD Diagnosis

[0114] (1) Differentially expressed RNAs between the subjects (ARS group) and the control group (HC group) were identified using limma47 software (P value < 0.05, fold change > 1.0), and heatmaps and volcano plots were generated. Figure 2 Heatmap of differentially expressed RNAs in the ARS and HC groups; Figure 3 This is a volcano plot of differentially expressed RNA in the ARS and HC groups, with red representing ARS. + RNA upregulated (UP) in IIM-ILD patients, blue represents ARS5. + Downregulated RNA in IIM-ILD patients; gray represents ARS. + RNA was not expressed differently in IIM-ILD patients and healthy individuals (Not).

[0115] (2) According to Figure 2 and Figure 3The results were used to perform unsupervised hierarchical clustering and principal component analysis (PCA) on these differentially expressed RNAs. Machine learning was employed, defining 60% of the total samples as the training set and the remaining 40% as the validation set. Boruta selection was used on both the training and validation sets to identify all relevant machine learning variables. This process was repeated to find the optimal set of features capable of distinguishing ARS. + Support vector machine (SVM) classifiers were used for IIM-ILD patients and healthy individuals, comprising an RNA combination of four RNA markers: hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448|9.2. Among these, hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448|9.2 markers showed significant differences compared to the HC group, and were also observed in ARS. + In IIM-ILD patients, hsa-miR-590, hsa-miR-181a, and RP3-448|9.2 were significantly downregulated, while DEFA1 was significantly upregulated, as detailed in [link to relevant documentation]. Figure 4 , Figure 4 * indicates a significant difference (p < 0.05); ** indicates a significant difference (p < 0.01); *** indicates a significant difference (p < 0.001). Figure 4 The results showed that the above four RNA markers had good ARS (Advanced Response Time) properties. + IIM-ILD diagnostic efficacy.

[0116] Secondly, the Wilcoxon rank sum test was used to analyze the Log2 fold difference and P-value of the four RNA markers in the subjects to be tested (ARS group) and the control group (HC group), as detailed in Table 3.

[0117] Table 3

[0118]

[0119] As shown in Table 3, the fold differences of hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448|9.2 were large, with very small p-values, indicating that these four RNA markers have good ARS (Advanced Reactivity System) performance. + IIM-ILD diagnostic efficacy.

[0120] (3) The five-fold cross-validation algorithm was used to calculate the classifier of the RNA combination consisting of 4 RNA markers. The results are shown in the figure. Figure 5 and Figure 6 The bootstrap method was used for 100 iterations, and the area under the curve (AUC) of the four RNA marker classifiers was evaluated using the receiver operating characteristic (ROC) curve. The results are shown below. Figure 7 .

[0121] in, Figure 5 The confusion matrix for the training set and the confusion matrix for the validation set are shown in the diagram. Figure 6 For support vector machine classifiers in ARS + The performance (metrics) results for IIM-ILD diagnosis are shown in the figure. Sensitivity, specificity, and accuracy are labeled on the figure. Calculations show that the classifier containing the above combination of four RNA markers has a sensitivity of 90.32%, a specificity of 100%, and an accuracy of 95% for distinguishing between healthy individuals and ARS+ IIM-ILD patients on the training set, and a sensitivity of 84.21%, a specificity of 85.71%, and an accuracy of 85% for distinguishing between healthy individuals and ARS+ IIM-ILD patients on the validation set. These results indicate that the diagnostic performance of the combination of four RNA markers is good on both the training and validation sets.

[0122] Figure 7 This is a graph showing the AUC values ​​of the ROC curve for the Support Vector Machine (SVM) classifier, where ROC represents the Receiver Operating Characteristic Curve (ROC), and AUC represents the area under the curve. The results show that the AUC value for the combination of four RNA markers is 0.973 in both the training and validation sets. Since the AUC value ranges between 0.5 and 1, and the closer the AUC is to 1.0, the higher the realism of the detection method, while an AUC of 0.5 indicates the lowest realism, these results demonstrate that the diagnostic performance of the combination of five RNA markers is good in both the training and validation sets.

[0123] In the training set, the AUC value of the combination of four RNA markers was 0.973 (95% CI, 0.971–0.974); in the validation set, the AUC value was also 0.973 (95% CI, 0.97–0.975). Since the AUC value ranges between 0.5 and 1, and the closer the AUC is to 1.0, the higher the reliability of the detection method, while an AUC of 0.5 indicates the lowest reliability, these results demonstrate that the diagnostic performance of the combination of four RNA markers is good in both the training and validation sets.

[0124] (4) Using the methods described in Examples 1-4 above, a diagnostic or assessment system for idiopathic inflammatory myopathy (ARS) is developed, comprising: a sample acquisition module for providing a plasma sample from the individual to be tested; a sample detection module for determining the expression level of an RNA composition in the plasma sample; and an analysis and assessment module for diagnosing or assessing the prognosis of the individual to be tested based on the measured expression level of the RNA composition. The analysis and assessment module can distinguish ARS as described in Example 4. + Support vector machine classifier models were implemented for IIM-ILD patients and healthy individuals, with a diagnostic threshold of 0.5; values ​​above 0.5 were classified as ARS. + For IIM-ILD patients, a value below 0.5 indicates non-ARS. + IIM-ILD patients.

[0125] In summary, this invention selects ARS + A total of 101 plasma samples were collected from IIM-ILD patients and healthy individuals, including 51 healthy individuals and 50 ARS patients. + In patients with IIM-ILD, endoblastic vesicles (EVs) were isolated and purified from plasma, and RNA was extracted from EVs and subjected to high-throughput RNA sequencing to detect RNA expression levels in plasma EVs. Bioinformatics analysis yielded 217 differentially expressed RNAs. Finally, feature selection and machine learning were used to select four RNAs as differentially expressed RNAs (ARS). + The biomarkers for IIM-ILD showed a sensitivity of 90.32%, specificity of 100%, accuracy of 95%, and an AUC of 0.973 in the training set, and a sensitivity of 84.21%, specificity of 85.71%, accuracy of 85%, and an AUC of 0.973 in the validation set. However, the role of EVs in IIM is not yet fully characterized, and comprehensive research on EV-related RNAs in IIM remains insufficient, especially regarding ARS. +The EV-related RNA lineage in the plasma of IIM-ILD patients requires further exploration. Therefore, this invention establishes a more minimal, stable, and standardized EVRNA sequencing technology to identify high-quality RNA compositions that can diagnose clinical subtypes of idiopathic inflammatory myopathy as biomarkers. The aforementioned EVRNA sequencing technology and RNA compositions lay the foundation for further research and identification of new therapeutic or diagnostic targets, as well as elucidating pathophysiological mechanisms and developing targeted treatment strategies, facilitating timely and appropriate prevention and treatment measures by clinicians.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. The use of a reagent for detecting an RNA composition in the preparation of a product for diagnosing idiopathic inflammatory myopathy with interstitial lung disease positive for anti-aminoacyl-tRNA synthetase antibody, characterized in that, The RNA composition comprises hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448I9.2; In the diagnosis, the concentration of the RNA composition in the plasma is detected to determine its expression level.

2. The application according to claim 1, characterized in that, The reagents include: reagents for extracting extracellular vesicular RNA from plasma and reagents for determining RNA expression.

3. The application according to claim 2, characterized in that, The reagents used for extracting extracellular vesicle RNA from plasma include at least one of the following: ethylenediaminetetraacetic acid, ethanol, phosphate buffer solution, protease inhibitor, and RNase inhibitor.

4. The application according to claim 2 or 3, characterized in that, The reagents used to determine RNA expression include at least one of the following: dithiothreitol, dNTPs, PEG 8000, and UMI molecular tags.

5. The application according to claim 1, characterized in that, The RNA is derived from extracellular vesicles in the plasma.

6. A diagnostic system for idiopathic inflammatory myopathy, characterized in that, include: The sample acquisition module is used to provide plasma samples from the individual to be tested; A sample detection module for determining the expression level of the RNA composition in the plasma sample; An analysis and evaluation module is used to diagnose the individual to be tested based on the expression level of the measured RNA composition; The diagnosis of the individual to be tested refers to determining whether the individual to be tested is a patient with idiopathic inflammatory myopathy with interstitial lung disease who is positive for anti-aminoacyl-tRNA synthetase antibody. The RNA composition includes hsa-miR-590, hsa-miR-181a, DEFA1, and RP3-448I9.

2.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is a computer program that implements the functions of the system as described in claim 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements a computer-aided diagnostic method based on the reagent for detecting the RNA composition as described in claim 1, wherein the diagnostic method includes: i) Receive test data from plasma samples from the individuals to be tested; ii) The detected data is compared with the reserved reference data using a preset machine learning model; iii) Output the diagnostic results based on the comparison results; The output diagnostic result refers to determining whether the individual to be tested is a patient with idiopathic inflammatory myopathy with interstitial lung disease who is positive for anti-aminoacyl-tRNA synthetase antibody. In the diagnosis, the concentration of the RNA composition in the plasma is detected to determine its expression level.

Images