Multiple miRNA markers and kits for diagnosis of Alzheimer's disease

Abstract

The present invention provides a multiple miRNA marker and a kit for diagnosing Alzheimer's disease. The multiple miRNA markers include: hsa-miR-29c-3p, hsa-miR-92a-3p and hsa-miR-206. The present invention has high sensitivity and specificity for diagnosing Alzheimer's disease. The kit uses a multiple real-time fluorescence quantitative method to conveniently and effectively distinguish AD patients from healthy people, thereby performing clinical auxiliary diagnosis, and has high clinical application value.

Description

Technical Field

[0001] The present invention belongs to the technical field of Alzheimer's disease (AD) diagnosis, and relates to multiple miRNA markers and a kit for diagnosing Alzheimer's disease. Background Art

[0002] Alzheimer's disease (AD) is a neurodegenerative disease: AD is the most common form of dementia, and its disease is characterized by a range of symptoms, including memory loss and cognitive impairment (i.e., difficulties with language, thinking, and behavior). Damaged brain cells (i.e., neurons) in the AD brain first start in the hippocampus, the part of the brain responsible for cognitive function; however, studies suggest that it may take 20 years or more for disease symptoms to appear, and these symptoms worsen over time. Severe AD may interfere with most daily activities, and patients may require around-the-clock care. The exact pathogenesis of AD is still unclear, and it is generally believed to be the result of multiple factors including aging, genetics, and the environment. There are currently multiple theories, among which the most influential is the amyloid-β cascade hypothesis.

[0003] AD has an insidious onset, and its main clinical manifestations are cognitive impairment, mental and behavioral abnormalities, and decreased social function. It is a progressive neurodegenerative disease. According to the patient's condition, AD is usually divided into preclinical, mild cognitive impairment (MCI), mild, moderate, and severe AD. When clinically diagnosed, patients are usually in the latter three stages. In the preclinical and MCI stages, although the patient's symptoms are not obvious, the biomarker indicators are already abnormal.

[0004] Currently, there is no sufficiently accurate method for early screening and identification of Alzheimer's disease, and the main diagnostic method is combined diagnosis. For patients who are to be diagnosed with dementia, cognitive function assessment is the first choice, and imaging examinations such as CT and MRI are routinely performed. When conventional methods are unclear, consider using PET or cerebrospinal fluid / blood biomarker testing. For those with a family history of dementia or rapidly progressive dementia, genetic testing should be considered.

[0005] At present, there are still certain difficulties in the early diagnosis of AD, such as the lack of certain norms and standards for the detection of markers, and the lack of awareness and acceptance of early AD among patients. However, biomarkers are of great significance in the early diagnosis of AD, and they create an effective time window for early identification of the disease and timely intervention. They are currently the only effective measure to delay the progression of AD. Summary of the invention

[0006] Based on the above, the purpose of the present invention is to provide multiple miRNA markers and kits for the diagnosis of Alzheimer's disease, using serum or plasma samples to distinguish the differences in the expression levels of related miRNAs between AD patients and healthy people, so as to diagnose AD quickly, accurately, non-invasively and at low cost.

[0007] The technical solution adopted by the present invention to achieve the technical purpose is:

[0008] The present invention provides multiple miRNA markers for diagnosing Alzheimer's disease, including: hsa-miR-29c-3p, hsa-miR-92a-3p and hsa-miR-206.

[0009] Preferably, an internal reference U6 is also included.

[0010] The present invention also provides a kit for diagnosing Alzheimer's disease, comprising the above-mentioned multiple miRNA markers.

[0011] Preferably, the kit constructs an AD patient diagnostic model by fitting each miRNA index through a Logit regression equation, and the diagnostic model formula 1 is:

[0012] Logit=-4.215-1.245*ΔCt1-0.183*ΔCt2+1.035*ΔCt3

[0013] According to the Ct values ​​of the target and the internal reference, the Ct value of the internal reference was subtracted from the Ct value of the three targets to obtain the ΔCt of the three targets relative to the internal reference, and then substituted into the diagnostic model formula 1 to calculate the Logit value; ΔCt1 is the difference between the Ct values ​​of hsa-miR-29c-3p and the internal reference, ΔCt2 is the difference between the Ct values ​​of hsa-miR-92a-3p and the internal reference, and ΔCt3 is the difference between the Ct values ​​of hsa-miR-206 and the internal reference.

[0014] Preferably, the kit constructs an AD patient diagnostic model by fitting each miRNA index through a Logit regression equation, and the diagnostic model formula 2 is:

[0015] Logit=-4.394-1.045*ΔCt1-0.266*ΔCt2+0.954*ΔCt3

[0016] According to the Ct values ​​of the target and the internal reference, the Ct value of the internal reference was subtracted from the Ct value of the three targets to obtain the ΔCt of the three targets relative to the internal reference, and then substituted into the diagnostic model formula 2 to calculate the Logit value; ΔCt1 is the difference between the Ct values ​​of hsa-miR-29c-3p and the internal reference, ΔCt2 is the difference between the Ct values ​​of hsa-miR-92a-3p and the internal reference, and ΔCt3 is the difference between the Ct values ​​of hsa-miR-206 and the internal reference.

[0017] Preferably, the kit constructs an AD patient diagnostic model by fitting each miRNA index through a Logit regression equation, and the diagnostic model formula 3 is:

[0018] Logit=-4.303-1.309*ΔCt1-0.137*ΔCt2+1.032*ΔCt3

[0019] According to the Ct values ​​of the target and the internal reference, the Ct value of the internal reference was subtracted from the Ct value of the three targets to obtain the ΔCt of the three targets relative to the internal reference, and then substituted into the diagnostic model formula 3 to calculate the Logit value; ΔCt1 is the difference between the Ct values ​​of hsa-miR-29c-3p and the internal reference, ΔCt2 is the difference between the Ct values ​​of hsa-miR-92a-3p and the internal reference, and ΔCt3 is the difference between the Ct values ​​of hsa-miR-206 and the internal reference.

[0020] More preferably, U6 is used as an internal reference.

[0021] More preferably, if the three targets are NoCt, the Ct value should be assigned to 40 before calculation.

[0022] More preferably, the cutoff value is: 0.00, if logit(P)≥0.00, the patient is diagnosed as an AD patient; if logit(P)<0.00, the patient is diagnosed as a healthy person.

[0023] The beneficial effects of the present invention are

[0024] 1. The present invention selects hsa-miR-29c-3p, hsa-miR-92a-3p, hsa-miR-206, and U6 as miRNA molecular markers, which have high sensitivity and specificity through clinical sample verification experiments. The AD serum miRNA detection kit uses multiple real-time fluorescence quantitative methods to conveniently and effectively distinguish AD patients from healthy people, thereby performing clinical auxiliary diagnosis, and has high clinical application value.

[0025] 2. The multiplex detection kit for assisting in the diagnosis of AD of the present invention can detect AD simply, effectively and non-invasively. It has obvious advantages in sampling difficulty compared with conventional cerebrospinal fluid samples, has great cost advantages compared with protein detection, and has the advantages of being simpler and cheaper than ordinary PCR. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 These are the performance test results of different miRNA marker combinations in Example 1.

[0027] Figure 2 The figure is a flow chart of the operation method of the present invention.

[0028] Figure 3 This is the ROC curve drawn based on formula 1.

[0029] Figure 4 This is the ROC curve drawn based on formula 2.

[0030] Figure 5 This is the ROC curve drawn based on formula 3.

[0031] Figure 6 It is a comprehensive indicator of different model formulas. DETAILED DESCRIPTION

[0032] In order to explain the present invention more clearly, the present invention is further described in detail below in conjunction with embodiments and with reference to the accompanying drawings. It should be understood by those skilled in the art that the content described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0033] Example 1

[0034] 1. Single-plex optimization test of different miRNA site combinations

[0035] 1. Extraction of miRNA

[0036] The human serum / plasma miRNA nucleic acid extraction and purification kit (column extraction) (Merrick, Suzhou, China, catalog number: MI00101) was used to extract miRNA from serum samples. The serum samples used were obtained through legal channels and met the basic requirements for miRNA extraction.

[0037] (1) Add 100 μL of serum sample to a 1.5 mL DNase / RNase-free centrifuge tube.

[0038] (2) Pipette 200 μL of lysis buffer (vortex for 5s-10s before use) and add it to the 1.5 mL centrifuge tube in step (1). Then add 10 μL of proteinase K to the above centrifuge tube (proteinase K can be flicked 10 times with fingers before use. Do not vortex).

[0039] (3) Place the 1.5 mL centrifuge tube in step (2) at 65°C for 10 min and then cool to room temperature.

[0040] (4) While waiting in the warm bath in step (3), place the elution column into the collection tube and set aside.

[0041] (5) Aspirate 600 μL of anhydrous ethanol and add it to a 1.5 mL centrifuge tube cooled to room temperature. Vortex and mix for 5-10 seconds and then briefly centrifuge. Aspirate 650 μL of the mixture and add it to the elution column prepared above. Centrifuge at 12,000 rpm for 1 minute. After centrifugation, discard the liquid in the collection tube.

[0042] (6) Place the elution column back into the collection tube, add all the remaining mixed solution in the 1.5 mL centrifuge tube to the elution column, centrifuge at 12,000 rpm for 1 min, and discard the liquid in the collection tube after centrifugation.

[0043] (7) Place the elution column back into the collection tube, pipette 500 μL of wash solution 1 (with anhydrous ethanol added) into the elution column, and centrifuge at 12,000 rpm for 30 seconds. After centrifugation, discard the liquid in the collection tube.

[0044] (8) Place the elution column back into the collection tube, pipette 500 μL of wash solution 2 (with anhydrous ethanol added) into the elution column, and centrifuge at 12,000 rpm for 30 seconds. After centrifugation, discard the liquid in the collection tube.

[0045] (9) Repeat step (8) once.

[0046] Note: When adding reagents in the above steps (5) to (9), the pipette tip should be inserted at least 5 mm away from the core tube opening, and the pipette tip should be tilted close to the tube wall, with the pipette tip and the tube wall at an angle of 30°, but should not touch the bottom of the elution column.

[0047] (10) Place the elution column back into the collection tube and centrifuge at 12,000 rpm for 2 min to remove any remaining liquid. After centrifugation, discard the collection tube and place the elution column into a new 1.5 mL DNase / RNase-free centrifuge tube.

[0048] (11) Aspirate 60 µL of elution buffer and add it vertically to the center of the elution column. Let it stand at room temperature for 2 min.

[0049] (12) After standing, centrifuge the centrifuge tube containing the elution column at 12,000 rpm for 1 min. After centrifugation, discard the elution column and collect the filtrate in the centrifuge tube, which is the extracted miRNA. For long-term storage, it should be placed at -80°C.

[0050]

[0051] 2. Reverse transcription reaction

[0052]

[0053]

[0054]

[0055] The reverse transcription products were immediately subjected to qPCR reaction or stored at -20°C.

[0056] 3. qPCR reaction

[0057] qPCR reagents: 2×miRNA stem-loop qPCR master mix (2×miRNA stem-loop qPCR MasterMix, Micro-Research, Suzhou, MR01501)

[0058]

[0059]

[0060]

[0061]

[0062] Fluorescence signal acquisition was performed at 60°C.

[0063] 4. Construction of AD patient diagnostic model

[0064] The AD patient diagnostic model was constructed by fitting each miRNA index through the Logit regression equation. Through data analysis, the diagnostic model formula and regression equation were obtained:

[0065] Combination 1: Logit1 = 5.19490019221151 + -0.235779942276096 * ΔCt1 + -0.995989157561543 * ΔCt2 + 0.391515665770919 * ΔCt3

[0066] ΔCt1 is the difference in Ct values ​​between hsa-miR-146a-5p and U6, ΔCt2 is the difference in Ct values ​​between hsa-let-7i-5p and U6, and ΔCt3 is the difference in Ct values ​​between hsa-miR-21-5p and U6;

[0067] Combination 2: Logit2 = -7.50919555092704 + -0.765900177270035 * ΔCt1 + -0.26723528737786 * ΔCt2 + 1.18633897122198 * ΔCt3

[0068] ΔCt1 is the difference in Ct values ​​between hsa-miR-146a-5p and U6, ΔCt2 is the difference in Ct values ​​between hsa-miR-29c-3p and U6, and ΔCt3 is the difference in Ct values ​​between hsa-miR-206 and U6;

[0069] Combination 3: Logit3 = 3.65101474588154 + 1.11821994239351 * ΔCt1 + -1.28387550176088 * ΔCt2 + -0.763072566857382 * ΔCt3

[0070] ΔCt1 is the difference in Ct values ​​between hsa-miR-21-5p and U6, ΔCt2 is the difference in Ct values ​​between hsa-miR-29c-3p and U6, and ΔCt3 is the difference in Ct values ​​between hsa-miR-92a-3p and U6;

[0071] Combination 4: Logit4 = -24.2277105883235 + 0.550291445526092 * ΔCt1 + -2.03069844853161 * ΔCt2 + 1.85861872662244 * ΔCt3

[0072] ΔCt1 is the difference in Ct values ​​between hsa-miR-29c-3p and U6, ΔCt2 is the difference in Ct values ​​between hsa-miR-92a-3p and U6, and ΔCt3 is the difference in Ct values ​​between hsa-miR-206 and U6;

[0073] If the three targets have NoCt, the Ct value should be assigned to 40 (maximum number of cycles) before calculation.

[0074] Cutoff value: 0.00, if logit(P)≥0.00, the patient is diagnosed as AD; if logit(P)<0.00, the patient is diagnosed as healthy.

[0075] Different combinations of miRNA markers obtained from the screening were used to test sensitivity and specificity. Based on AUC, sensitivity and specificity, the combination of hsa-miR-29c-3p, hsa-miR-92a-3p, hsa-miR-206 and internal reference U6 (i.e., combination 4) had the best results. Specific data are shown in Tables 9 and Figure 1 shown.

[0076]

[0077] 2. Optimal marker combinations for multiplex testing

[0078] Combination Figure 2 , serum and plasma samples were subjected to miRNA extraction, multiplex reverse transcription, and multiplex qPCR experiments using the above markers.

[0079] 1. Multiplex reverse transcription reaction

[0080] The reverse transcription primers of hsa-miR-29c-3p, hsa-miR-92a-3p, hsa-miR-206 and internal reference U6 are shown in Table 2, the reaction system is shown in Table 3, the final concentration of RT primer mixture is shown in Table 10, and the reaction conditions are shown in Table 4.

[0081]

[0082] The reverse transcription products were immediately subjected to qPCR reaction or stored at -20°C.

[0083] 2. Multiplex qPCR reaction

[0084] The names and sequences of specific upstream primers and universal downstream primers are shown in Table 5, the names and sequences of specific probes are shown in Table 6, the reaction system is shown in Table 11, the reaction conditions are the same as Table 8, and the fluorescence signal is collected at 60°C.

[0085]

[0086] Multiplex amplification reaction solution: 2× miRNA stem-loop qPCR premix (MiRick, Suzhou) qPCR reagents and all upstream and downstream primers and probes required for amplification (final concentrations see the table below) were combined into a multiplex amplification reaction solution.

[0087]

[0088] 3. Result determination

[0089] The AD patient diagnostic model was constructed by fitting each miRNA index through the Logit regression equation. Through data analysis, the diagnostic model formula and regression equation were obtained as follows:

[0090] Logit=-4.215-1.245*ΔCt1-0.183*ΔCt2+1.035*ΔCt3

[0091] Note: ΔCt is the difference between target Ct and internal reference Ct. ΔCt1 is the difference between the Ct values ​​of hsa-miR-29c-3p and U6, ΔCt2 is the difference between the Ct values ​​of hsa-miR-92a-3p and U6, and ΔCt3 is the difference between the Ct values ​​of hsa-miR-206 and U6.

[0092] According to the Ct values ​​of the target and the internal reference (U6), the Ct value of the internal reference (U6) was subtracted from the Ct value of the three targets to obtain the ΔCt of the three targets relative to the internal reference, and then substituted into the following formula to calculate the Logit value.

[0093] If the three targets have NoCt, the Ct value should be assigned to 40 (maximum number of cycles) before calculation.

[0094] Cutoff value: 0.00, if Logit(P)≥0.00, the patient is diagnosed as AD; if Logit(P)<0.00, the patient is diagnosed as healthy.

[0095] The test results were statistically analyzed using MedCalc 20.015. The ROC curve of the model constructed by combining three loci and one internal reference is shown in Figure 2. Figure 3 As shown in the figure. Using these three miRNA markers and one internal reference combination to detect sensitivity and specificity can well distinguish AD patients from healthy controls. Compared with single-plex detection, multiplex detection is simpler and more practical.

[0096] Example 2

[0097] Similar to Example 1, the same markers and the same operation steps were used in this example to perform miRNA extraction, multiplex reverse transcription and multiplex qPCR experiments on serum and plasma samples.

[0098] The only difference from Example 1 is that the diagnostic model formula and regression equation used in this embodiment are:

[0099] Formula 2 Logit=-4.394-1.045*ΔCt1-0.266*ΔCt2+0.954*ΔCt3

[0100] Note: ΔCt is the difference between target Ct and internal reference Ct. ΔCt1 is the difference between the Ct values ​​of hsa-miR-29c-3p and U6, ΔCt2 is the difference between the Ct values ​​of hsa-miR-92a-3p and U6, and ΔCt3 is the difference between the Ct values ​​of hsa-miR-206 and U6.

[0101] According to the Ct values ​​of the target and the internal reference (U6), the Ct value of the internal reference (U6) was subtracted from the Ct value of the three targets to obtain the ΔCt of the three targets relative to the internal reference, and then substituted into the following formula to calculate the Logit value.

[0102] If the three targets have NoCt, the Ct value should be assigned to 40 (maximum number of cycles) before calculation.

[0103] Cutoff value: 0.00, if Logit(P)≥0.00, the patient is diagnosed as AD; if Logit(P)<0.00, the patient is diagnosed as healthy.

[0104] The test results were statistically analyzed using MedCalc 20.015. The ROC curve of the model constructed by combining three loci and one internal reference is shown in Figure 2. Figure 4 The sensitivity and specificity of the three miRNA markers and one internal reference combination can well distinguish AD patients from healthy controls.

[0105] Example 3

[0106] Similar to Example 1, the same markers and the same operation steps were used in this example to perform miRNA extraction, multiplex reverse transcription and multiplex qPCR experiments on serum and plasma samples.

[0107] The only difference from Example 1 is that the diagnostic model formula and regression equation used in this embodiment are:

[0108] Formula 3 Logit=-4.303-1.309*ΔCt1-0.137*ΔCt2+1.032*ΔCt3

[0109] Note: ΔCt is the difference between target Ct and internal reference Ct. ΔCt1 is the difference between the Ct values ​​of hsa-miR-29c-3p and U6, ΔCt2 is the difference between the Ct values ​​of hsa-miR-92a-3p and U6, and ΔCt3 is the difference between the Ct values ​​of hsa-miR-206 and U6.

[0110] According to the Ct values ​​of the target and the internal reference (U6), the Ct value of the internal reference (U6) was subtracted from the Ct value of the three targets to obtain the ΔCt of the three targets relative to the internal reference, and then substituted into the following formula to calculate the Logit value.

[0111] If the three targets have NoCt, the Ct value should be assigned to 40 (maximum number of cycles) before calculation.

[0112] Cutoff value: 0.00, if Logit(P)≥0.00, the patient is diagnosed as AD; if Logit(P)<0.00, the patient is diagnosed as healthy.

[0113] The test results were statistically analyzed using MedCalc 20.015. The ROC curve of the model constructed by combining three loci and one internal reference is shown in Figure 2. Figure 5 The sensitivity and specificity of the three miRNA markers and one internal reference combination can well distinguish AD patients from healthy controls.

[0114] The comprehensive indicators of different model formulas are as follows: Figure 6 It can be seen that the three model formulas established by the above biomarker combinations all have good performance in distinguishing healthy people from AD.

[0115] Obviously, the above embodiments of the present invention are merely examples to more clearly illustrate the present invention, and are not limitations on the implementation methods of the present invention. For ordinary technicians in the relevant field, other different forms of changes or modifications can be made on the basis of the above description. It is impossible to list all the implementation methods here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. Use of a reagent for detecting multiple miRNA markers in the preparation of a kit for diagnosing Alzheimer's disease, characterized in that: The multiple miRNA markers consist of hsa-miR-29c-3p, hsa-miR-92a-3p and hsa-miR-206.

2. The use according to claim 1, characterized in that: The kit also contains an internal reference U6.

3. The use according to claim 2, characterized in that: The kit constructs an AD patient diagnostic model by fitting each miRNA index through a Logit regression equation, and the diagnostic model formula 1 is: Logit=-4.215-1.245*ΔCt1-0.183*ΔCt2+1.035*ΔCt3 According to the Ct values ​​of the target and the internal reference, the Ct value of the internal reference was subtracted from the Ct value of the three targets to obtain the ΔCt of the three targets relative to the internal reference, and then substituted into the diagnostic model formula 1 to calculate the Logit value; ΔCt1 is the difference between the Ct values ​​of hsa-miR-29c-3p and the internal reference, ΔCt2 is the difference between the Ct values ​​of hsa-miR-92a-3p and the internal reference, and ΔCt3 is the difference between the Ct values ​​of hsa-miR-206 and the internal reference.

4. The use according to claim 2, characterized in that: The kit constructs an AD patient diagnostic model by fitting each miRNA index through a Logit regression equation, and the diagnostic model formula 2 is: Logit=-4.394-1.045*ΔCt1-0.266*ΔCt2+0.954*ΔCt3 According to the Ct values ​​of the target and the internal reference, the Ct value of the internal reference was subtracted from the Ct value of the three targets to obtain the ΔCt of the three targets relative to the internal reference, and then substituted into the diagnostic model formula 2 to calculate the Logit value; ΔCt1 is the difference between the Ct values ​​of hsa-miR-29c-3p and the internal reference, ΔCt2 is the difference between the Ct values ​​of hsa-miR-92a-3p and the internal reference, and ΔCt3 is the difference between the Ct values ​​of hsa-miR-206 and the internal reference.

5. The use according to claim 2, characterized in that: The kit constructs an AD patient diagnostic model by fitting each miRNA index through a Logit regression equation, and the diagnostic model formula 3 is: Logit=-4.303-1.309*ΔCt1-0.137*ΔCt2+1.032*ΔCt3 According to the Ct values ​​of the target and the internal reference, the Ct value of the internal reference was subtracted from the Ct value of the three targets to obtain the ΔCt of the three targets relative to the internal reference, and then substituted into the diagnostic model formula 3 to calculate the Logit value; ΔCt1 is the difference between the Ct values ​​of hsa-miR-29c-3p and the internal reference, ΔCt2 is the difference between the Ct values ​​of hsa-miR-92a-3p and the internal reference, and ΔCt3 is the difference between the Ct values ​​of hsa-miR-206 and the internal reference.

6. The use according to any one of claims 3 to 5, characterized in that: If the three targets have NoCt, the Ct value should be assigned to 40 before calculation.

7. The use according to any one of claims 3 to 5, characterized in that: Cutoff value: 0.00, if logit(P)≥0.00, the patient is diagnosed as AD; if logit(P)<0.00, the patient is diagnosed as healthy.

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