A biomarker and use thereof

Abstract

The present application relates to a kind of biomarkers, which is selected from one or more of the following: UBR2, NPY4R, KLRF2, HABP2, GLP1R, DR1, RUNX2, ARHGAP5, LINC02135, DDHD1, MIR4523, TC2N.The biomarker is especially used for diagnosing stroke and differential diagnosis of cerebral hemorrhage.The present application screens 12 characteristic biomarkers to construct model, adopts receiver operating characteristic (ROC) analysis to evaluate the performance of model, uses sklearn to calculate area under curve (AUC), the sensitivity and specificity of model reach 0.898, 0.818 respectively, AUC value is 0.913;While differential diagnosis of cerebral hemorrhage, sensitivity and specificity reach 0.864, 0-854 respectively, AUC value is 0.909.The present application combines 5hmC modification spectrum of plasma evDNA and machine learning algorithm for the first time, the characteristic biomarker and / or model screened has the advantages of strong specificity and high sensitivity, overcome the problem of low efficiency and poor accuracy in the process of identifying stroke and cerebral hemorrhage in prior art.

Description

Technical Field

[0001] This invention relates to the field of molecular biology, and more particularly to a biomarker and its application. Background Technology

[0002] Stroke is the second leading cause of non-communicable disease death worldwide, and its classification and treatment heavily rely on early and accurate identification. In acute ischemic stroke (AIS), patients with large vessel occlusion (LVO-AIS) require emergency endovascular thrombectomy, while hemorrhagic stroke (such as intracranial hemorrhage, ICH) requires avoiding anticoagulation and controlling blood pressure. However, current clinical triage techniques face significant limitations. While CT / MRI is the "gold standard" for diagnosis, its low accessibility and time-consuming transport in pre-hospital settings severely compress the treatment time window (e.g., the 4.5-hour intravenous thrombolysis window for AIS).

[0003] CN119470922A discloses biomarkers for detecting traumatic brain injury, including NSE, GFAP, and S100B. However, these biomarkers can only predict traumatic brain injury and cannot distinguish between acute ischemic stroke and hemorrhagic stroke. CN110257525A discloses biomarkers with significant diagnostic value for tumors and their uses, utilizing methylation-modified tumor markers as a diagnostic approach. However, this method relies on the identification of expression levels, making it difficult to meet the need for rapid identification.

[0004] Recent studies have shown that extracellular vesicles (EVs) possess unique advantages in early disease diagnosis due to their carrying of mother cell-specific biomolecules (such as DNA and RNA). In particular, EV DNA (evDNA) not only retains the epigenetic characteristics of the parent cell (such as 5-hydroxymethylcytosine, 5hmC), but also exhibits significantly better stability than cell-free DNA (EV-DNA). 5hmC, as a product of the oxidation of 5-methylcytosine (5mC) by the TET enzyme, is closely related to gene transcription activation, and its dynamic changes have been confirmed to be highly correlated with the pathological process of ischemic brain injury. However, how to utilize the 5hmC epigenetic characteristics of evDNA for non-invasive and rapid identification of stroke types remains a gap in research.

[0005] Furthermore, on the one hand, there are differences in understanding among those skilled in the art; on the other hand, the applicant studied a large number of documents and patents when making this invention, but due to space limitations, not all details and contents were listed in detail. However, this does not mean that the present invention does not possess the features of these prior art. On the contrary, the present invention already possesses all the features of the prior art, and the applicant reserves the right to add relevant prior art to the background art. Summary of the Invention

[0006] In view of the shortcomings of the prior art, the first aspect of the present invention provides a biomarker selected from one or more of the following: UBR2, NPY4R, KLRF2, HABP2, GLP1R, DR1, RUNX2, ARHGAP5, LINC02135, DDHD1, MIR4523, TC2N.

[0007] According to a preferred embodiment, the biomarker is the presence and / or content of a biomarker in an individual's body fluid sample.

[0008] According to a preferred embodiment, the body fluid sample can be any one of blood, plasma, urine, saliva, and sweat. Preferably, the body fluid sample of the individual being tested is peripheral blood. Preferably, the body fluid sample of the individual being tested is plasma.

[0009] The second aspect of the present invention provides the application of the biomarkers provided in the first aspect of the present invention in the preparation of products and / or models for diagnosing stroke and for differential diagnosis of cerebral hemorrhage.

[0010] A third aspect of this invention provides a kit for diagnosing stroke and differentially diagnosing cerebral hemorrhage, the kit comprising a detection reagent for detecting the biomarkers provided in the first aspect of this invention. The biomarkers are selected from one or more of the following: UBR2, NPY4R, KLRF2, HABP2, GLP1R, DR1, RUNX2, ARHGAP5, LINC02135, DDHD1, MIR4523, and TC2N.

[0011] The fourth aspect of the present invention provides a model for diagnosing stroke and differentiating cerebral hemorrhage, wherein the input variable of the model is the content of the biomarkers provided in the first aspect of the present invention.

[0012] According to a preferred embodiment, the method for determining the content of biomarkers is 5hmC high-throughput detection. Specifically, the method for determining the content of biomarkers is 5hmC-Seal.

[0013] According to a preferred embodiment, a model for diagnosing stroke and differentiating cerebral hemorrhage is obtained through the following construction method, which includes the following steps:

[0014] Plasma samples from multiple stroke and cerebral hemorrhage patients were analyzed to obtain 5hmC sequencing data of extracellular vesicle DNA in the plasma, and the two sets of data were intersected.

[0015] The obtained sequencing data were sequentially filtered, screened, and filtered again to obtain biomarkers for diagnosing stroke and differentiating cerebral hemorrhage.

[0016] The first filter includes: removing peak information that appears only in 10 or fewer samples.

[0017] The screening process included: comparing sequencing data from different samples using DEseq2 software, retaining 5hmC peak regions with more than 50 reads, and identifying differentially regulated biomarkers for 5hmC upregulation or downregulation based on |log2FoldChange| >=0.5 and p value <0.01.

[0018] The second filtering includes: filtering the differential biomarkers obtained through the screening using the recursive feature elimination algorithm (RFECV) in Scikit-Learn to obtain biomarkers for diagnosing stroke and differentiating cerebral hemorrhage: UBR2, MIR4523, NPY4R, KLRF2, GLP1R, RUNX2, LINC02135, ARHGAP5, TC2N, DR1, HABP2, and DDHD1;

[0019] The obtained biomarker data are input into a machine learning model, the model is trained, and the trained model is stored to obtain a model for diagnosing stroke and differentiating cerebral hemorrhage.

[0020] According to a preferred embodiment, the parameters used in the second filtering step are: (estimator = LogisticRegressionCV(class_weight = 'balanced', cv = 2, max_iter = 1000), scoring = 'accuracy').

[0021] According to a preferred embodiment, the machine learning model includes: training a logistic regression (LR) model.

[0022] The fifth aspect of this invention provides a method for diagnosing stroke and differentiating cerebral hemorrhage, the method being implemented based on the model provided in the fourth aspect of this invention, and comprising the following steps:

[0023] Collect test samples from the subjects;

[0024] The 5hmC sequencing data of extracellular vesicle DNA in the sample to be tested were detected to obtain the content of the biomarker provided in the first aspect of the present invention;

[0025] The obtained biomarker content is input into the model provided in the fourth aspect of the present invention.

[0026] According to a preferred embodiment, the sample to be tested is peripheral blood or plasma.

[0027] The present invention also provides the use of reagents for detecting the following proteins in the preparation of products for the diagnosis of stroke and differential diagnosis of cerebral hemorrhage, said proteins being selected from one or more of the following: UBR2, NPY4R, KLRF2, HABP2, GLP1R, DR1, RUNX2, ARHGAP5, LINC02135, DDHD1, MIR4523, TC2N.

[0028] The technical effects of this invention are as follows: This invention screened 12 characteristic biomarkers for model construction, used receiver operating characteristic (ROC) analysis to evaluate the model's performance, and used sklearn to calculate the area under the curve (AUC). The model's sensitivity and specificity reached 0.898 and 0.818, respectively, with an AUC value of 0.913. Simultaneously, in the differential diagnosis of cerebral hemorrhage, the sensitivity and specificity reached 0.864 and 0.854, respectively, with an AUC value of 0.909. This invention is the first to combine the 5hmC modification profile of plasma evDNA with machine learning algorithms. The screened characteristic biomarkers and / or models have the advantages of high specificity and high sensitivity, providing an accurate molecular-level non-invasive diagnostic method for stroke and cerebral hemorrhage. The diagnostic model is reliable, and the diagnostic results are highly accurate. Attached Figure Description

[0029] Figure 1 The experimental design process provided by this invention involves randomly dividing clinical samples into a training group, a validation group, and a differential group.

[0030] Figure 2 The relevant parameters of 12 5hmC characteristic biomarkers in the model for diagnosing stroke and differentiating cerebral hemorrhage screened in this invention;

[0031] Figure 3 Receiver operating characteristic (ROC) curves (Figure AC) for the diagnostic models constructed using 12 5hmC characteristic biomarkers in the training, validation, and identification groups;

[0032] Figure 4 The diagnostic accuracy results for a single biomarker among the 12 5hmC biomarkers in the training group;

[0033] Figure 5 To verify the diagnostic accuracy of individual biomarkers among the 12 5hmC characteristic biomarkers in the group;

[0034] Figure 6 To determine the diagnostic accuracy of individual biomarkers among the 12 5hmC characteristic biomarkers in the identification group;

[0035] Figure 7 It has the DBCO-PEG4-Biotin structural formula. Detailed Implementation

[0036] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should be understood that these descriptions are exemplary only and are not intended to limit the scope of this disclosure. In the following detailed description, numerous specific details are set forth to provide a comprehensive understanding of the embodiments of this disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this disclosure. To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer are followed. All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having meanings consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0037] Stroke includes ischemic stroke and hemorrhagic stroke. Cerebral hemorrhage is a specific type of hemorrhagic stroke, which is caused by the rupture of blood vessels within the brain parenchyma.

[0038] The present invention provides the following technical methods.

[0039] Step 1: Separation of extracellular vesicles from plasma samples

[0040] 1. Collect 8 mL of whole blood into a 10 mL Kangwei Company (CW2815M) cfDTM free nucleic acid collection tube (operate at room temperature, store at room temperature (18-25℃) after blood collection, and subsequent plasma extraction must be performed within a maximum of 72 hours);

[0041] 2. First centrifugation: 1350g, 4℃ for 12 minutes. Carefully remove the pale yellow supernatant (to remove blood cells and platelets) and transfer it to a 2mL DNase-free sterile centrifuge tube.

[0042] 3. Second centrifugation: centrifuge at 1350g, 4℃ for 12min, carefully remove the supernatant (to remove white blood cells and cell debris), and transfer it to 2-3 2mL DNase-free sterile centrifuge tubes;

[0043] 4. Centrifuge for the third time at 13500g, 4℃ for 5 minutes. Carefully remove the supernatant and transfer it to a 2mL DNase-free sterile centrifuge tube. Store at -80℃.

[0044] Step 2: Isolation of extracellular vesicles (EVs) from plasma

[0045] EVs were isolated from plasma samples using the Wayen Exosome Isolation Kit (EIQ3-02001).

[0046] Step 3: Extraction of EV-DNA from Plasma

[0047] 1. Using the Quick-DNA Miniprep Plus Kit (ZYMO, D4069), extracellular vesicle DNA (EV-DNA) was extracted from the extracellular vesicle (EV) samples prepared in step 1.

[0048] 2. EV-DNA concentration was determined using Qubit3.0.

[0049] Step 4: End-complete the EV-DNA and ligate it to the sequencing adapter.

[0050] Perform the following procedures according to the KAPA HyperPlus Library Preparation Kit (KK8514) instructions:

[0051] 1. Prepare a reaction mixture containing 20 μL of 10 ng EV-DNA (obtained in step 2 above), 2-8 μL of End Repair & A-Tailing Buffer, and 1.2 μL of End Repair & A-Tailing Enzymemix (total volume 24 μL), as shown in Table 1.

[0052] Table 1

[0053] EV-DNA 20 End Repair & A-Tailing Buffer 2.8 End Repair&A-Tailing Enzyme Mix 1.2

[0054] The reaction mixture prepared according to Table 1 was incubated at 20°C for 30 minutes, and then at 65°C for 30 minutes, as shown in Table 2.

[0055] Table 2

[0056]

[0057] 2. Next, the following ligation reaction mixture was prepared in a 1.5 mL low-adsorption EP tube: 2 μL Nuclease freewater, 12 μL Ligation Buffer and 4 μL DNA Ligase, as shown in Table 3;

[0058] Table 3

[0059] Nuclease free water 2 Ligation Buffer 12 DNA Ligase 4

[0060] Add 2 μL of sequencing index (PKR2015, PKR2016 and PKR2017) to 18 μL of ligation reaction mixture, mix, add to 24 μL of reaction sample, and heat at 20 °C for 4 hours, as shown in Table 4.

[0061] Table 4

[0062] Adaptor Ligation 20 4 hours HOLD 4 ∞

[0063] 3. The reaction product was purified using the DNA Clean & Concentrator 5 (ZYMO, D4014) purification kit, and eluted with 20 μL of elution buffer to obtain the final DNA ligation sample.

[0064] Step 5, 5hmC labeling

[0065] Prepare a 4 μL labeling reaction mixture: 1 μL 50 μM UDP-6-N3-Glu (Liaoning Cook Biotechnology Co., Ltd., catalog number: 537039-67-1), 2-5 μL βGT enzyme (NEB), and 2.5 μL HEPES buffer (pH 8-0, final concentration 50 mM), as shown in Table 5. Add 6 μL of the labeling reaction mixture to 20 μL of DNA ligation sample. Incubate the mixture at 37°C for 1 hour.

[0066] Table 5

[0067] hmC-labeled substrate 1 1 βGT enzyme 2.5 HEPES buffer 2.5

[0068] Remove the mixture and purify the reaction product using the DNA Clean & Concentrator 5 (ZYMO, D4014) purification kit to obtain 30 μL of purified DNA;

[0069] Then, 1 μL of 45 μM DBCO-PEG4-Biotin (Click Chemistry Tools) was added to the purified 30 μL DNA and incubated in a water bath at 37°C for 1 hour.

[0070] The DBCO-PEG4-Biotin structural formula is as follows: Figure 7 As shown.

[0071] The reaction product was then purified using the DNA Clean & Concentrator 5 (ZYMO, D4014) purification kit to obtain 30 μL of purified labeled product.

[0072] Step 6: 5hmC enrichment

[0073] Equilibrate the bound magnetic beads using the following steps: Take 2.5 μL of Dynabeads (Invitrogen, 65306) and add 100 μL of washing buffer (5 mM Tris (pH 7.5), 1 M NaCl and 0.02% Tween 20), mix by pipetting, place on a 1.5 mL magnetic rack, wash the bound magnetic beads three times with 100 μL of washing buffer, and finally add 100 μL of washing buffer, mix the magnetic beads, and vortex for 30 min;

[0074] After 30 min, wash the magnetic beads three times with 100 μL of washing buffer, and finally add 32 μL of binding buffer (10 mM Tris (pH 7.5), 2 M NaCl and 0.04% Tween 20) and mix well.

[0075] Next, the purified labeling product obtained in step four above is added to the magnetic bead mixture, and the mixture is stirred in a rotary mixer for 30 minutes to ensure complete binding.

[0076] Finally, wash the magnetic beads five times with 100 μL of washing buffer and add 23.8 μL of RNase-Freewater.

[0077] Step 7: PCR Amplification

[0078] 1. Add 25 μL of 2×PCR mastermix and 1.25 μL of PCR primers (total volume 50 μL) to the purified labeled product obtained in the final system of step 5 above, and amplify according to the temperature and conditions of the PCR reaction cycle shown in Table 6.

[0079] Table 6

[0080]

[0081] 2. The amplification product was purified using AmpureXP beads (KK8001), and finally eluted with 20 μL of elution buffer to obtain the final 5 hmC library. The library concentration was then determined using Qubit 3.0.

[0082] Step 8: Perform high-throughput sequencing on the 5hmC library after quality control.

[0083] 1. The obtained 5hmC library was subjected to quality control using the Fragment Analyzer™ fully automated capillary electrophoresis system (reagent: DNF-900; software used: Fragment Analyzer instrument control software, PROSize data analysis software) to determine the size of DNA fragments in the library and whether there are any impurities (the library size is about 300bp).

[0084] 2. Next, qPCR concentration determination (kit: KAPA SYBR FAST Universal qPCR Kit (KK4601)) is performed to determine whether the sample library meets the sequencing standards; the specific steps provided by this invention are as follows.

[0085] Sample preparation:

[0086] Prepare five 1.5 mL EP tubes, and mix four samples to be sequenced into one EP tube, for a total of 20 samples in five tubes (index cannot be repeated); for each 5 hmC library sample, aspirate 5 ng, and the total volume of each EP tube is 20 μL, with a final concentration of 1 ng / μL;

[0087] Sample reaction system (20 μL): 4 μL of the Chinese library was aspirated from each EP tube for qPCR quantification, as shown in Table 7.

[0088] Table 7

[0089]

[0090]

[0091] The qPCR program settings are shown in Table 8.

[0092] Table 8

[0093]

[0094] Results analysis: The results were analyzed using qPCR software to determine whether the 5hmC library had degraded and whether it met sequencing requirements.

[0095] 3. The quality-tested library (16 μL) was sequenced using an Illumina NovaSeq6000. The sequencing throughput of each sample was 4-6 G, and the sequencing band size was 150 bp at both ends.

[0096] Step 9: Comparison of raw sequencing data

[0097] 1. Each raw FASTQ sequencing dataset was first trimmed for low-quality data using Trimmomatic software, and then aligned to the human genome hg19 using Bowtie2 software;

[0098] 2. Use MACS software to identify read peaks containing 5hmC with the following parameters: effective genomesize = 2.72e+09; tag size = 38; band width = 100; model fold = 10; P value cutoff = 1.00e-05. Then, call peaks and generate a counts file.

[0099] 3. Using DEseq2 software, compare the counts files from different samples and, according to |log2FoldChange|>=0.5 and pvalue<0.05, screen out the differentially expressed biomarkers of 5hmC up- and down-regulation.

[0100] This invention provides a biomarker, particularly useful for diagnosing stroke and differentiating cerebral hemorrhage. This embodiment included 279 samples: 131 stroke patients, 48 ​​cerebral hemorrhage patients, and 100 healthy individuals. Peripheral blood samples (3-4 mL) were collected from these 279 patients, and extracellular vesicle DNA was extracted from the plasma for 5hmC-Seal high-throughput sequencing (5hmC library construction and high-throughput sequencing are described in the "Technical Methods" section of the specific embodiment). All participants' records included complete medical history, clinical, and biochemical parameter information. Simultaneously, the samples were randomly divided into a training cohort, a validation cohort, and a differential cohort according to a certain ratio, such as... Figure 1 As shown.

[0101] Example 1

[0102] In the training group, the DEseq2 package was used to perform differential analysis between stroke patients and healthy controls, and 5hmC-specific biomarkers were screened according to |log2FoldChange|>=0.5, p value<0.05. Further, machine learning methods were used to screen for characteristic biomarkers (using parameters: estimator=LogisticRegressionCV(class_weight='balanced', cv=2, max_iter=1000), scoring='accuracy'), resulting in 12 biomarkers, including: UBR2, NPY4R, KLRF2, HABP2, GLP1R, DR1, RUNX2, ARHGAP5, LINC02135, DDHD1, MIR4523, and TC2N, as shown below. Figure 2 As shown.

[0103] Figure 3Receiver operating characteristic (ROC) curves for the diagnostic models constructed using 12 5hmC characteristic biomarkers in the training, validation, and identification groups. This embodiment utilizes 12 selected feature biomarkers to construct a stroke diagnostic model and employs Receiver Operating Characteristic (ROC) analysis to evaluate the model's performance. The model's diagnostic AUC value is 0.975, as shown in Figure A. Simultaneously, in the training group, this embodiment analyzes the diagnostic performance of individual biomarkers among the 12 biomarkers, including ARHGAP5 (AUC = 0.58), DDHD1 (AUC = 0.60), DR1 (AUC = 0.73), GLP1R (AUC = 0.69), HABP2 (AUC = 0.65), LINC02135 (AUC = 0.59), MIR4523 (AUC = 0.58), NPY4R (AUC = 0.76), RUNX2 (AUC = 0.50), TC2N (AUC = 0.56), UBR2 (AUC = 0.79), and KLRF2 (AUC = 0.79). Figure 4 As shown in the results, UBR2 (AUC = 0.79) and KLRF2 (AUC = 0.79) exhibited the highest diagnostic accuracy.

[0104] Example 2

[0105] To verify the diagnostic capability of the stroke diagnostic model, this embodiment uses a validation group to test the model's diagnostic efficacy. The constructed diagnostic model was used to diagnose stroke patients in the validation group. This embodiment found that the model achieved an AUC value as high as 0.913 in the validation group. Figure 3 As shown in Figure B; simultaneously, in the validation group, the diagnostic accuracy of individual biomarkers among the 12 characteristic 5hmC biomarkers was assessed, with NPY4R (AUC = 0.83) and KLRF2 (AUC = 0.73) exhibiting the highest diagnostic accuracy, as shown in Figure B; Figure 5 As shown. The diagnostic accuracy of individual biomarkers among the 12 5hmC biomarkers in the validation group was as follows: ARHGAP5 (AUC = 0.53); DDHD1 (AUC = 0.57); DR1 (AUC = 0.67); GLP1R (AUC = 0.54); HABP2 (AUC = 0.53); LINC02135 (AUC = 0.59); MIR4523 (AUC = 0.54); RUNX2 (AUC = 0.50); ​​TC2N (AUC = 0.65); UBR2 (AUC = 0.68).

[0106] Example 3

[0107] To test the model's diagnostic ability, this embodiment performs a differential diagnosis test on stroke patients in the differential group. The constructed diagnostic model is used to diagnose stroke patients in the differential group. This embodiment found that the model's AUC value in the differential group is as high as 0.909. Figure 3 As shown in Figure C.

[0108] The diagnostic accuracy of individual biomarkers among the 12 characteristic 5hmC biomarkers in the differential diagnosis group was evaluated. ARHGAP5 (AUC = 0.66), LINC02135 (AUC = 0.66), and MIR4523 (AUC = 0.66) showed the highest diagnostic accuracy. The diagnostic accuracies of the other nine biomarkers were as follows: DDHD1 (AUC = 0.63); DR1 (AUC = 0.65); GLP1R (AUC = 0.63); HABP2 (AUC = 0.60); KLRF2 (AUC = 0.64); NPY4R (AUC = 0.63); RUNX2 (AUC = 0.63); TC2N (AUC = 0.63); UBR2 (AUC = 0.61). Figure 6 As shown.

[0109] Therefore, the diagnostic model constructed based on the above 12 5hmC characteristic biomarkers provided in this embodiment can effectively diagnose stroke patients.

[0110] It should be noted that the specific embodiments described above are exemplary. Those skilled in the art can devise various solutions inspired by the disclosure of this invention, and these solutions all fall within the scope of this invention and its protection. Those skilled in the art should understand that this specification and its accompanying drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of this invention is defined by the claims and their equivalents. This specification contains multiple inventive concepts; for example, "according to a preferred embodiment" indicates that the corresponding paragraph discloses an independent concept. The applicant reserves the right to file divisional applications based on each inventive concept. Throughout the text, the features introduced by "according to a preferred embodiment" are merely optional and should not be construed as mandatory. Therefore, the applicant reserves the right to abandon or delete relevant preferred features at any time.

Claims

1. Application of reagents for detecting the 5hmC modification level of UBR2 in the preparation of products for the diagnosis of stroke and differential diagnosis of cerebral hemorrhage.

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