A miRNA marker combination, kit and application for distinguishing central nervous system lymphoma and glioblastoma

Abstract

The present invention discloses a miRNA marker combination, a kit and an application for distinguishing central nervous system lymphoma and glioblastoma in the fields of biomedicine and molecular biology. The miRNA marker combination includes one or more combinations of the following miRNAs detectable in human serum: miR-371a-3p, miR-3202, miR-6757-3p, miR-4763-3p, miR-1915-3p, and miR-3679-5. The present invention uses bioinformatics and molecular biology techniques to perform differential analysis based on blood miRNA chip detection data of PCNSL and GBM in the NCBI-GEO database, establishes candidate serum miRNA markers for PCNSL and GBM, and constructs a risk assessment model, thereby improving detection efficiency and accuracy, reducing the probability of misdiagnosis, and improving patient prognosis.

Description

Technical Field

[0001] The present invention belongs to the fields of biomedicine and molecular biology, and specifically relates to a miRNA marker combination, a kit and an application for distinguishing central nervous system lymphoma and glioblastoma. Background Art

[0002] Primary central nervous system lymphoma (PCNSL) is a lymphoma with a high incidence in the central nervous system. PCNSL can occur anywhere in the skull, with a probability of over 45% in the frontal lobe. It grows infiltratively from the perivascular space and approaches the ventricles, with headache, drowsiness, and fatigue as the main symptoms. Glioblastoma (GBM) is a primary nervous system tumor of neuroepithelial origin with the highest malignancy and the worst prognosis.

[0003] The onset and clinical symptoms of PCNSL and GBM are very similar. Both have similar biological characteristics such as high cell density and invasive growth of lesions, which makes it difficult to distinguish between the two in clinical diagnosis, and misdiagnosis and missed diagnosis are very likely to occur. However, it is worth noting that the treatment options for these two tumors are very different. GBM is mainly treated by surgical resection and supplemented by radiotherapy combined with temozolomide and synchronous chemotherapy, while PCNSL is ineffective due to surgical treatment and is mainly treated with radiotherapy. Based on this, accurate preoperative differentiation between PCNSL and GBM is extremely important for patient treatment and prognosis.

[0004] At present, the clinical diagnosis of PCNSL mainly relies on symptoms and imaging examinations. Given the lack of specificity in symptoms between PCNSL and GBM, CT and MRI are commonly used to diagnose and differentiate the two. CT has the advantages of simplicity, non-invasiveness, and safety, but the imaging parameters it provides are relatively single and are only used for screening intracranial masses. MRI has the advantages of multi-directional and multi-sequence imaging and high spatial resolution, and can observe the growth pattern, morphology, and diameter of the tumor. It is currently the main means of distinguishing PCNSL from GBM, but there are still important issues regarding the MRI imaging characteristics for distinguishing PCNSL from GBM. There is overlap between GBM and PCNSL. Both can be manifested as: low signal or equal or low signal on T1WI; high signal or equal or high signal on T2WI; high signal on DWI; enhanced T1, etc. Due to the lack of highly specific MRI sequences between the two, it is difficult to accurately differentiate between the two by MRI-based imaging tests. If GBM patients are misdiagnosed as PCNSL and receive radiotherapy, surgical treatment will be delayed, affecting the patient's prognosis. In addition, some PCNSL patients are diagnosed only after undergoing surgical treatment and pathological examination, which causes them to suffer unnecessary craniotomy trauma and increases the risk of treatment.

[0005] miRNAs are a type of non-coding RNA of approximately 22 nucleotides that play an important role in a variety of biological and pathological processes, including cell proliferation, differentiation, apoptosis, and carcinogenesis. miRNAs are involved in all stages of tumor development. Studies have shown that abnormal expression of miRNAs plays an important regulatory role in the expression of known oncogenes or tumor suppressor genes during tumor development. More importantly, miRNAs are extremely abundant in the blood and are easy to detect. Compared with protein detection, which is restricted by antibody technology, miRNA detection is simpler and more accurate, and therefore has received great attention in clinical practice. Currently, more and more miRNAs have been discovered and become diagnostic or therapeutic markers for a variety of diseases, used to assist in the diagnosis and prognosis of tumor patients or as therapeutic targets.

[0006] Based on the above reasons, the research of the present invention aims to screen accurate and specific blood miRNA marker combinations, distinguish PCNSL from GBM through non-invasive diagnostic methods, reduce the occurrence of misdiagnosis, and improve the treatment effect of the two nervous system tumors. Summary of the Invention

[0007] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a miRNA marker combination, kit and application for distinguishing central nervous system lymphoma and glioblastoma. The present invention uses bioinformatics and molecular biology techniques to perform differential analysis based on the blood miRNA chip detection data of PCNSL and GBM in the NCBI-GEO database, establishes candidate serum miRNA markers for PCNSL and GBM and constructs a risk assessment model. At the same time, the present invention develops a miRNA diagnostic kit for preoperative differentiation of PCNSL and GBM, and determines the diagnostic results through the constructed risk assessment model, thereby improving the efficiency and accuracy of the detection. The kit prepared by the miRNA marker combination provided by the present invention has the characteristics of non-invasive, rapid and accurate identification of central nervous system lymphoma and glioblastoma categories, reduces the probability of misdiagnosis, and improves patient prognosis.

[0008] To achieve the above objectives, the present invention provides a miRNA marker combination for identifying central nervous system lymphoma and glioblastoma, wherein the miRNA marker combination includes one or more combinations of the following miRNAs that can be detected in human serum: miR-371a-3p, miR-3202, miR-6757-3p, miR-1915-3p, miR-4763-3p, and miR-3679-5.

[0009] Preferably, the miRNA marker combination is a combination of miR-371a-3p, miR-3202, miR-6757-3p, miR-1915-3p, miR-4763-3p, and miR-3679-5.

[0010] Preferably, the miRNA marker combination includes a miRNA marker combination for detecting central nervous system lymphoma and a miRNA marker combination for detecting glioblastoma, the miRNA marker combination for detecting central nervous system lymphoma is miR-371a-3p, miR-3202, and miR-6757-3p, and the miRNA marker combination for detecting glioblastoma is miR-1915-3p, miR-4763-3p, and miR-3679-5p.

[0011] In addition, the present invention also provides a kit for identifying miRNA marker combinations for central nervous system lymphoma and glioblastoma, the kit comprising a miRNA reverse transcription forward primer, a detection primer for the miRNA marker combination, and basic reagents.

[0012] Preferably, the miRNA reverse transcription forward primer is one or more combinations of SEQ ID NO. 1, SEQ ID NO. 2 and SEQ ID NO. 3.

[0013] Preferably, the detection primers of the miRNA marker combination include a forward primer for miR-371a-3p as shown in SEQ ID NO. 4; a forward primer for miR-3202 as shown in SEQ ID NO. 5; a forward primer for miR-6757-3p as shown in SEQ ID NO. 6; a forward primer for miR-1915-3p as shown in SEQ ID NO. 7; a forward primer for miR-4763-3p as shown in SEQ ID NO. 8; a forward primer for miR-3679-5 as shown in SEQ ID NO. 9; and reverse primers for miR-371a-3p, miR-3202, miR-6757-3p, miR-1915-3p, miR-4763-3p and miR-3679-5 as shown in SEQ ID NO. 10.

[0014] Preferably, the basic reagents include at least one of a positive quality control, a negative quality control, a poly-A tailing enzyme, a reverse transcriptase, dNTPs, a reverse transcription buffer, RNase-free water, a qPCR buffer, magnesium chloride, a DNA polymerase, and a SYBR Green fluorescent dye.

[0015] In addition, the present invention also provides the use of a kit for identifying a combination of miRNA markers for central nervous system lymphoma and glioblastoma, wherein the kit can identify the types of central nervous system lymphoma and glioblastoma by detecting the expression level of at least one of miR-371a-3p, miR-3202, miR-6757-3p, miR-1915-3p, miR-4763-3p, and miR-3679-5 in the blood.

[0016] Preferably, the kit is used by the following method:

[0017] (1) Collect peripheral blood samples from clinical subjects and detect the presence of miRNA in extracellular vesicles of peripheral blood;

[0018] (2) measuring the expression value of the miRNA marker combination in the peripheral blood extracellular vesicles;

[0019] (3) A risk assessment model based on a combination of miRNA markers can be used to identify the types of central nervous system lymphoma and glioblastoma.

[0020] Preferably, the risk assessment model is:

[0021] Y(PCNSL)=(0.16639×A miR-371a-3p )+(0.24691×B miR-3202 )+(0.18779×C miR-6757-3p )-2.6631;

[0022] Y(GBM)=(1.448667×D miR-1915-3p )+(2.202190×E miR-4763-3p )+(-0.421747×F miR-3679-5p )-29.16927;

[0023] Among them, A miR-371a-3p is the expression value of miR-371a-3p; B miR-3202 is the expression value of miR-3202; C miR-6757-3p is the expression value of miR-6757-3p; D miR-1915-3p is the expression value of miR-1915-3p; E miR-4763-3p is the expression value of miR-4763-3p; F miR-3679-5p is the expression value of miR-3679-5p.

[0024] Preferably, the risk assessment model also includes a result output method, which is: when the Y(PCNSL) output value is greater than or equal to 0.5 and the Y(GBM) output value is less than 0.5, it is judged as positive and the patient suffers from central nervous system lymphoma; when the Y(GBM) output value is greater than or equal to 0.5 and the Y(PCNSL) output value is less than 0.5, it is judged as positive and the patient suffers from glioblastoma; when the Y(PCNSL) output value is less than 0.5 and the Y(GBM) output value is less than 0.5, it is judged as negative and the patient does not suffer from either central nervous system lymphoma or glioblastoma; when the Y(PCNSL) output value is greater than or equal to 0.5 and the Y(GBM) output value is greater than or equal to 0.5, it is judged as positive and the patient suffers from glioblastoma.

[0025] The beneficial effects achieved by the present invention using the above structure are as follows:

[0026] The present invention provides a miRNA marker combination, kit, and application for distinguishing central nervous system lymphoma and glioblastoma. The present invention utilizes bioinformatics and existing molecular biology techniques to perform differential analysis based on blood miRNA chip detection data of PCNSL and GBM in the NCBI-GEO database, establishes candidate serum miRNA markers for PCNSL and GBM, and constructs a risk assessment model:

[0027] Y(PCNSL)=(0.16639×A miR-371a-3p )+(0.24691×B miR-3202 )+(0.18779×C miR-6757-3p )-2.6631;

[0028] Y(GBM)=(1.448667×D miR-1915-3p )+(2.202190×E miR-4763-3p )+(-0.421747×F miR-3679-5p )-29.16927;

[0029] Among them, A miR-371a-3p is the expression value of miR-371a-3p; B miR-3202 is the expression value of miR-3202; C miR-6757-3p is the expression value of miR-6757-3p; D miR-1915-3p is the expression value of miR-1915-3p; E miR-4763-3p is the expression value of miR-4763-3p; F miR-3679-5p is the expression value of miR-3679-5p;

[0030] In the present invention, miR-4763-3p, miR-1915-3p, miR-3679-5, miR-371a-3p, miR-3202, and miR-6757-3p are used to distinguish PCNSL from GBM, which is the core technical point. Among them, miR-371a-3p, miR-3202, and miR-6757-3p are used as blood diagnostic molecular markers for PCNSL, and miR-1915-3p, miR-4763-3p, and miR-3679-5p are used as blood diagnostic molecular markers for GBM.

[0031] On this basis, the present invention provides the use of the combined detection of miR-4763-3p, miR-1915-3p, miR-3679-5, miR-371a-3p, miR-3202, and miR-6757-3p expression levels in the preparation of products for the differential diagnosis of PCNSL and GBM, as well as related detection kits. The diagnostic results are determined by the constructed risk assessment model, which improves the efficiency and accuracy of the test. Clinical validation results show that the identification accuracy rate is over 94%, and the category misdiagnosis rate is 0-1.37%.

[0032] Since the present invention uses the Poly-A tailing method to reverse transcribe miRNA, the reverse transcription primer is a universal primer. At the same time, in the qPCR reaction, only the qPCR forward primer is used to identify the six miRNA markers. The qPCR reverse primer is also a universal primer, which avoids the problem of complex reaction primers when the number of miRNAs in the miRNA marker combination is large;

[0033] The main treatment option for PCNSL patients is radiotherapy, while GBM requires craniotomy and surgical resection. Due to the similar imaging characteristics of the two, traditional MRI technology is difficult to accurately distinguish between the two in some cases. This application does not require craniotomy, and the peripheral blood test kit can be used to accurately differentiate PCNSL and GBM patients before surgery;

[0034] The kit provided by the present invention is prepared based on the combination of miRNA markers and has the characteristics of non-invasive, rapid and accurate identification of central nervous system lymphoma and glioblastoma, reducing the probability of misdiagnosis and improving patient prognosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Cluster analysis of differential expression of GBM-related miRNA markers was performed;

[0036] Figure 2 Cluster analysis of differential expression of PCNSL-related miRNA markers was performed;

[0037] Figure 3Sample distribution diagram for establishing GBM risk scoring model;

[0038] Figure 4 Sample distribution diagram for establishing PCNSL risk scoring model;

[0039] Figure 5 This is the ROC curve diagram of training group 1 in Example 2;

[0040] Figure 6 This is the ROC curve diagram of training group 2 in Example 2;

[0041] Figure 7 This is the ROC curve diagram of the verification group 1 in Example 2;

[0042] Figure 8 This is the ROC curve diagram of the second verification group in Example 2;

[0043] Figure 9 Schematic diagram of the output of the risk assessment model for distinguishing PCNSL and GBM.

[0044] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those familiar to those skilled in the art. Furthermore, any methods and materials similar or equivalent to those described herein can be applied to the present invention. The preferred embodiments and materials described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0047] The experimental methods in the following examples, unless otherwise specified, are conventional methods; the test materials and test strains used in the following examples, unless otherwise specified, are purchased from commercial channels.

[0048] Example 1

[0049] Screening of miRNA marker combinations provided by the present invention

[0050] The present invention uses bioinformatics technology to perform expression difference analysis on serum miRNA chip detection data of 42 PCNSL patients, 170 GBM patients and 314 healthy volunteers in the NCBI-GEO database (accession number: GSE139031), establish PCNSL and GBM serum miRNA candidate markers, and then use collinearity analysis to eliminate collinear miRNAs and retain non-collinear miRNA candidate markers. As a result, miR-371a-3p, miR-3202, and miR-6757-3p are obtained as blood diagnostic molecular markers for detecting PCNSL, and miR-1915-3p, miR-4763-3p, and miR-3679-5p are obtained as blood diagnostic molecular markers for detecting GBM.

[0051] As shown in Table 1, all miRNAs involved in the present invention are mature miRNAs that have been annotated and published in the miRBase database (website: http: / / www.mirbase.org / ).

[0052] like Figure 1 and Figure 2 The cluster analysis results of miRNA expression differences of miR-371a-3p, miR-3202, miR-6757-3p and miR-1915-3p, miR-4763-3p, and miR-3679-5p showed that GBM patients and healthy volunteers were clustered in a single family distribution, indicating that the data effect was good, and there were significant expression differences of miR-1915-3p, miR-4763-3p, and miR-3679-5p between GBM patients and healthy volunteers; there were significant expression differences of miR-371a-3p, miR-3202, and miR-6757-3p between PCNSL patients and GBM patients, indicating that the selected miRNA markers can be used to distinguish and detect GBM and PCNSL types.

[0053] Table 1 Public database information of the six miRNA markers provided by the present invention

[0054]

[0055] Example 2

[0056] Establishment and verification of miRNA marker risk scoring model in the kit prepared based on miRNA marker combination provided by the present invention

[0057] like Figure 3 and Figure 4As shown, in Example 1, 170 GBM patients and 314 healthy volunteers were divided into training group 1 (100 GBM patients, 200 healthy volunteers) and validation group 1 (70 GBM patients, 114 healthy volunteers) to construct a GBM diagnostic formula, and 170 GBM patients and 42 PCNSL patients were divided into training group 2 (136 GBM patients, 34 PCNSL patients) and validation group 2 (34 GBM patients, 8 PCNSL patients).

[0058] (1) Establishment of PCNSL and GBM risk assessment model

[0059] The miRNA expression data in training group 1 and training group 2 were used to establish PCNSL and GBM risk assessment models, with miR-371a-3p, miR-3202, miR-6757-3p and miR-1915-3p, miR-4763-3p, and miR-3679-5p as variables, respectively.

[0060] Logistic regression model and k-fold cross-validation (k=5) were used to obtain the PCNSL risk assessment model coefficients of miRNA biomarkers. Among them, miR-371a-3p, miR-3202, and miR-6757-3p were 0.16639, 0.24691, and 0.18779, respectively, with a correction parameter of -2.66316. Fisher linear discriminant analysis and leave-one-out cross-validation were used to obtain the GBM risk assessment model coefficients of miRNA biomarkers. Among them, miR-1915-3p, miR-4763-3p, and miR-3679-5 were 1.448667, 2.202190, and -0.421747, respectively, with a correction parameter of -29.16927.

[0061] The PCNSL and GBM risk assessment model is:

[0062] Y(PCNSL)=(0.16639×A miR-371a-3p )+(0.24691×B miR-3202 )+(0.18779×C miR-6757-3p )-2.6631;

[0063] Y(GBM)=(1.448667×D miR-1915-3p )+(2.202190×E miR-4763-3p )+(-0.421747×F miR-3679-5p )-29.16927;

[0064] According to the risk value results of each patient in training group 1 and training group 2, ROC curves were drawn respectively, and the reference value was calculated based on the conditions that the specificity value in the ROC curve was greater than 0.9 and the sensitivity value was greater than 0.5. The reference value in this embodiment was 0.5.

[0065] like Figure 5 and Figure 6 The AUC values ​​in the ROC curves of the training group 1 and the training group 2 were 0.998 and 0.71, respectively, indicating that the kit provided by the present invention has good diagnostic efficacy for distinguishing and predicting GBM and PCNSL.

[0066] (2) Validation of PCNSL and GBM risk assessment models

[0067] Using the miRNA expression data in validation group 1 and validation group 2, miR-371a-3p, miR-3202, miR-6757-3p and miR-1915-3p, miR-4763-3p, miR-3679-5p were used as variables, and the PCNSL and GBM risk assessment models established in step (1) of Example 2 were used to calculate the risk value results and draw ROC curves, as shown in Figure 2. Figure 7 and Figure 8 As shown, the AUC values ​​in the ROC curves of validation group 1 and validation group 2 were 0.989 and 0.92, respectively, thereby validating the risk assessment model for PCNSL and GBM, and indicating that the kit based on the miRNA marker combination provided by the present invention has good diagnostic efficacy for distinguishing GBM and PCNSL.

[0068] Experimental Example 3

[0069] Detection of PCNSL and GBM using the kit based on the miRNA marker combination and the risk assessment model provided by the present invention

[0070] The specific operation methods and procedures for sample collection and preparation are as follows:

[0071] (1) Sample collection standards

[0072] Serum samples from GBM patients, PCNSL patients and normal physical examination population were collected from January 1, 2022 to July 30, 2022. After sorting the data, 326 samples were selected, including 292 GBM patients, 34 PCNSL patients, and 24 healthy people. The peripheral samples of the selected GBM and PCNSL patients were all first-time patients with no history of surgery or medication, and were pathologically confirmed as GBM and PCNSL patients after hospitalization. The healthy population was healthy people who passed the physical examination at the physical examination center. The demographic and clinical data of these samples were systematically collected.

[0073] (2) Serum sample preparation

[0074] Collect venous whole blood using a 5 mL blood collection tube with inert separation gel. Immediately transfer blood to a centrifuge and centrifuge at 3000 g for 10 minutes at 4°C. Pipette 1 mL of the supernatant and transfer it to an RNase-free EP tube and store in a -80°C refrigerator.

[0075] When using, take out the serum from the -80℃ refrigerator, place it in an ice bath at 0-4℃ until it is completely melted, centrifuge it at 10000g at 4℃ for 15 minutes, take 200μL of the supernatant and place it in a new RNase-free EP tube.

[0076] (3) Serum miRNA extraction

[0077] The sample in step (2) of Example 3 was placed in a fully automatic nucleic acid extraction instrument (QIAGEN, model QIAcube). The specific operation method was carried out according to the instructions. The RNA volume was prepared to 25 μL and the concentration was greater than 120 ng / μL.

[0078] (4) RNA reverse transcription

[0079] The miRNA was reverse transcribed using a reverse transcription kit with the tailing method (preparation of qPCR assay templates for multiple miRNAs after one transcription). The forward primers for reverse transcription were the specific primers "5'-gctgtcaacgatacgctacgtacgggcatgacagtgttttttttttttttttttttttttttttttttttt-3' (as shown in SEQ ID NO. 1)", "5'-gctgtcaacgatacgctacgtacgggcatgacagtgtt ... 3) were prepared in a 1:1:1 mixture. The reverse primer was the universal primer of the kit. The reverse transcription system was shown in Table 2. The reaction conditions were: 37°C, 60 minutes; 85°C, 5 seconds. For specific RNA reverse transcription experimental procedures, refer to the TransScript miRNA First-Strand cDNA Synthesis SuperMix instruction manual.

[0080] Table 2 miRNA reverse transcription system

[0081]

[0082] (5) Real-time quantitative PCR (qPCR)

[0083] The kit used for the amplification reaction was the TransStart Tip Green qPCR SuperMix kit commercially available from Quanshijin. The qPCR primer sequences for the miRNA markers provided by the present invention are shown in Table 3. The composition of the qPCR reaction system is shown in Table 4. The qPCR reaction conditions were: 94°C, 30 seconds, one cycle; 94°C, 5 seconds, 60°C, 15 seconds, 72°C, 10 seconds, 45 cycles. The instrument used for the amplification reaction was the Thermo Fisher Scientific QuantStudio5 real-time fluorescence quantitative PCR instrument. Other settings were the system default values, and the instrument's own software, QuantStudioTM Design & Analysis, was used. The results were analyzed using RT-PCR software to obtain the expression values ​​(CT values) of miR-4763-3p, miR-1915-3p, miR-3679-5, miR-371a-3p, miR-3202, and miR-6757-3p. The expression values ​​were normalized according to the internal reference gene U6 using the ΔCt method. The calculation method was: ΔCt = CT(miRNA) - CT(U6). The miRNA expression values ​​were obtained using the ΔΔCt method. The calculation method was: ΔΔCt = ΔCt patient group - ΔCt healthy population mean.

[0084] Table 3 qPCR primer sequences for miRNA markers

[0085]

[0086] Table 4 Real-time fluorescence quantitative PCR amplification reaction system

[0087]

[0088] (6) Result determination

[0089] Calculate the risk value based on the miRNA expression value calculated in step (5) of Example 3 and determine the test result;

[0090] Y(PCNSL)=(0.16639×A miR-371a-3p )+(0.24691×B miR-3202 )+(0.18779×C miR-6757-3p )-2.6631;

[0091] When the Y value ≥ 0.5, the patient was judged to be PCNSL;

[0092] Y(GBM)=(1.448667×D miR-1915-3p )+(2.202190×E miR-4763-3p )+(-0.421747×FmiR-3679-5p )-29.16927; when Y ≥ 0.5, the patient was diagnosed with GBM;

[0093] Among them, A miR-371a-3p is the expression value of miR-371a-3p; B miR-3202 is the expression value of miR-3202; C miR-6757-3p is the expression value of miR-6757-3p; D miR-1915-3p is the expression value of miR-1915-3p; E miR-4763-3p is the expression value of miR-4763-3p; F miR-3679-5p is the expression value of miR-3679-5p.

[0094] According to Figure 9 The result judgment method shown determines the patient category: when the Y(PCNSL) output value is greater than or equal to 0.5 (yes) and the Y(GBM) output value is less than 0.5 (no), it is judged as positive and the patient has central nervous system lymphoma; when the Y(GBM) output value is greater than or equal to 0.5 (yes) and the Y(PCNSL) output value is less than 0.5 (no), it is judged as positive and the patient has glioblastoma; when the Y(PCNSL) output value is less than 0.5 (no) and the Y(GBM) output value is less than 0.5 (no), it is judged as negative and the patient does not have either central nervous system lymphoma or glioblastoma; when the Y(PCNSL) output value is greater than or equal to 0.5 (yes) and the Y(GBM) output value is greater than or equal to 0.5 (yes), it is judged as positive and the patient has glioblastoma.

[0095] As shown in Table 5, the results showed that the class misdiagnosis rate between PCNSL and GBM was low.

[0096] Table 5 Detection of PCNSL and GBM using the miRNA markers provided by the present invention and based on the risk assessment model

[0097]

[0098] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0099] The present invention and its embodiments are described above. Such description is not restrictive. The drawings show only one embodiment of the present invention, and actual applications are not limited thereto. In short, if a person skilled in the art is inspired by the above, and does not deviate from the purpose of the present invention, any method and embodiment similar to the technical solution without creative design shall fall within the scope of protection of the present invention.

Claims

1. Use of a reagent for detecting a combination of miRNA markers in the preparation of a kit for distinguishing central nervous system lymphoma and glioblastoma, characterized in that: The miRNA marker combination is a combination of miR-371a-3p, miR-3202, miR-6757-3p, miR-1915-3p, miR-4763-3p and miR-3679-5; The miRNA marker combination includes a miRNA marker combination for detecting central nervous system lymphoma and a miRNA marker combination for detecting glioblastoma, wherein the miRNA marker combination for detecting central nervous system lymphoma is miR-371a-3p, miR-3202 and miR-6757-3p, and the miRNA marker combination for detecting glioblastoma is miR-1915-3p, miR-4763-3p and miR-3679-5p; The kit includes a miRNA reverse transcription forward primer, a detection primer for a miRNA marker combination, and basic reagents; The miRNA reverse transcription forward primers are shown in SEQ ID NO. 1, SEQ ID NO. 2, and SEQ ID NO.

3. The detection primers for the miRNA marker combination include: a forward primer for miR-371a-3p is shown in SEQ ID NO. 4; a forward primer for miR-3202 is shown in SEQ ID NO. 5; a forward primer for miR-6757-3p is shown in SEQ ID NO. 6; a forward primer for miR-1915-3p is shown in SEQ ID NO. 7; a forward primer for miR-4763-3p is shown in SEQ ID NO. 8; and a forward primer for miR-3679-5 is shown in SEQ ID NO. 9; the reverse primers of miR-371a-3p, miR-3202, miR-6757-3p, miR-1915-3p, miR-4763-3p and miR-3679-5 are shown in SEQ ID NO. 10; The kit can identify the types of central nervous system lymphoma and glioblastoma by detecting the expression levels of miR-371a-3p, miR-3202, miR-6757-3p, miR-1915-3p, miR-4763-3p and miR-3679-5 in the blood.

2. Use of the reagent for detecting the miRNA marker combination according to claim 1 in preparing a kit for distinguishing central nervous system lymphoma and glioblastoma, characterized in that: A risk assessment model based on a combination of miRNA markers can differentiate between central nervous system lymphoma and glioblastoma types; The risk assessment model is: Y(PCNSL)=(0.16639×A miR-371a-3p )+(0.24691×B miR-3202 )+(0.18779×C miR-6757-3p )-2.6631; Y(GBM)=(1.448667×D miR-1915-3p )+(2.202190×E miR-4763-3p )+(-0.421747×F miR-3679-5p )-29.16927; Among them, A miR-371a-3p is the expression value of miR-371a-3p; B miR-3202 is the expression value of miR-3202; C miR-6757-3p is the expression value of miR-6757-3p; D miR-1915-3p is the expression value of miR-1915-3p; E miR-4763-3p is the expression value of miR-4763-3p; F miR-3679-5p is the expression value of miR-3679-5p.

3. Use of the reagent for detecting the miRNA marker combination according to claim 1 in preparing a kit for distinguishing central nervous system lymphoma and glioblastoma, characterized in that: The basic reagents include at least one of a positive quality control product, a negative quality control product, a poly-A tailing enzyme, a reverse transcriptase, dNTPs, a reverse transcription buffer, RNase-free water, a qPCR buffer, magnesium chloride, a DNA polymerase, and a SYBR Green fluorescent dye.

4. Use of the reagent for detecting the miRNA marker combination according to claim 2 in the preparation of a kit for distinguishing central nervous system lymphoma and glioblastoma, characterized in that: The risk assessment model also includes a result output method, which is: when the Y(PCNSL) output value is greater than or equal to 0.5 and the Y(GBM) output value is less than 0.5, it is judged as positive and the patient suffers from central nervous system lymphoma; when the Y(GBM) output value is greater than or equal to 0.5 and the Y(PCNSL) output value is less than 0.5, it is judged as positive and the patient suffers from glioblastoma; when the Y(PCNSL) output value is less than 0.5 and the Y(GBM) output value is less than 0.5, it is judged as negative and the patient does not suffer from either central nervous system lymphoma or glioblastoma; when the Y(PCNSL) output value is greater than or equal to 0.5 and the Y(GBM) output value is greater than or equal to 0.5, it is judged as positive and the patient suffers from glioblastoma.

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