MCI diagnostic markers, MCI diagnostic kits, and methods for detecting the same.

Abstract

The present invention relates to a diagnostic marker for mild cognitive impairment (MCI) in the prodromal stage of Alzheimer's disease. The marker is a plasma long non-coding RNA (lncRNA), and the plasma lncRNA includes one or more of ENST00000549762, NR_024049, T324988, and ENST00000567919. The present invention also relates to applications of the marker and kits using the marker. The beneficial effect of the present invention is the discovery of a biomarker with high diagnostic value for MCI for the first time, providing an effective marker for minimally damaging the diagnosis of MCI using peripheral plasma, which is advantageous for early diagnosis and early intervention of Alzheimer's disease.

Description

Technical Field

[0001] This patent application claims the benefit of the filing date of Chinese Patent Provisional Application No. 202010760263.8, filed on July 31, 2020, the disclosure of which is incorporated herein by reference.

[0002] The present invention relates to the technical field of the prodromal stage of Alzheimer's disease, particularly to diagnostic markers, aiming at mild cognitive impairment diagnostic markers and their applications.

Background Art

[0003] Alzheimer's disease (AD) is a chronic neurodegenerative disease and the most common type of dementia. Clinically, deterioration of cognitive and memory functions, progressive decline in activities of daily living, various neuropsychiatric symptoms and behavioral abnormalities appear. The main pathological features include senile plaques formed by the accumulation of amyloid proteins outside nerve cells and neurofibrillary tangles formed by the hyperphosphorylation of Tau proteins related to microtubules inside nerve cells, resulting in nerve cell death in the cerebral cortex. With the aging of the population, the incidence of Alzheimer's disease has been increasing year by year. According to the World Alzheimer Report, a new dementia patient is added every three seconds, and the number of patients has already reached 50 million and is predicted to reach 152 million in 2050. Currently, the number of Alzheimer's disease patients in China has reached more than 8 million. Without an effective treatment method that can prevent or reverse it, only drugs are used to improve symptoms, imposing a heavy burden on medical care and nursing. Taking the example of the United States, the average annual medical cost per patient with mid- to late-stage AD is as high as $50,000. As the aging of China accelerates, the problem of the medical and economic burden caused by AD is becoming increasingly serious. Therefore, it is necessary to further explore the etiology of AD, diagnose and intervene in the onset at an early stage, find an effective treatment method, and reduce the medical burden, which is a medical health and social economic issue that must be solved in an aging society.

[0004] Mild cognitive impairment (MCI) is a transitional state between normal cognitive decline and dementia. MCI is classified into two subtypes: amnesia-type MCI and non-amnesia-type MCI. Among these, amnesia-type MCI is increasingly considered a precursor to Alzheimer's disease (AD). Studies indicate that approximately 10-15% of patients with amnesia-type MCI may develop AD each year. MCI is a high-risk case requiring intervention and represents an optimal time for early preventive intervention. Research on the diagnosis of AD and MCI has made significant progress over the past 20 years. Currently, diagnostic methods for AD and MCI involve assessment based on medical history taken by specialists and scores on criterion scales, mainly using the Montreal Cognitive Assessment scale, neuropsychological test series, simple mental status scales, and clinical dementia scales. However, evaluation using these scales is time-consuming and requires significant personnel, and is not always reliable. While auxiliary tests, primarily brain structural imaging, reveal macroscopic changes in a patient's brain structure, they often fail to detect characteristic changes in MCI early on. Amyloid plaque imaging, currently used as an absolute criterion for AD diagnosis due to its pathological changes, is costly and not clinically applied in Japan. Therefore, lacking a widely adopted and practically applied biomarker for early clinical diagnosis in Japan, exploring peripheral blood biomarkers to screen for potential MCI patients is beneficial for the early diagnosis and treatment of AD.

[0005] Long non-coding RNAs (lncRNAs) are RNA molecules whose length exceeds 200 times the length of a nucleotide. They are typically considered transcriptional "noise" because they cannot encode proteins, often as a byproduct of RNA polymerase II transcription. Recent research has revealed that lncRNAs exhibit specificity in different tissues of the human body and perform various biological functions, particularly playing crucial roles in processes such as nervous system development, neuronal differentiation, and synaptic plasticity. LncRNAs are differentially expressed in the brains of Alzheimer's disease patients and have been shown to regulate Aβ production during the onset of Alzheimer's disease, playing a vital role in synaptic damage, neurotrophic factor depletion, inflammatory responses, granular body damage, and neuronal stress responses, thus influencing the development of Alzheimer's disease. LncRNAs are not only abundant in brain tissue but are also stably present in cerebrospinal fluid, plasma, and serum samples. Peripheral blood samples are among the easiest, simplest, and least traumatic human biological samples to obtain, and are among the least frightening and painful for patients. Blood is considered the most suitable biological sample for testing high-risk cases of AD and is ideal for early detection, diagnosis, and therapeutic intervention of AD. The structural characteristics of LncRNAs give them the ability to serve as stable plasma biomarkers for MCI and AD diagnosis without being affected by endogenous RNase enzyme activity. Our provided lncRNA research results suggest differences in the expression of multiple genetic marker lncRNAs in peripheral blood between MCI and control groups. Therefore, detection of plasma lncRNA expression levels can be used as a standard test for early diagnosis of AD and may optimize clinical diagnostic strategies for AD. [Overview of the project] [Problems that the invention aims to solve]

[0006] The present invention aims to overcome the shortcomings of the prior art described above and to provide a mild cognitive impairment diagnostic marker and its applications that enable early diagnosis and effective testing of Alzheimer's disease (AD), and optimize clinical diagnostic strategies for AD. [Means for solving the problem]

[0007] To achieve the above objective, on the one hand, the mild cognitive impairment (MCI) diagnostic marker provided by the present invention, The marker is a plasma lncRNA, and the plasma lncRNA is characterized by containing one or more of the following: ENST00000549762, NR_024049, T324988, and ENST00000567919. Specifically, the MCI diagnostic marker is a plasma lncRNA, and the plasma lncRNA consists of one or more of ENST00000549762, NR_024049, T324988, and ENST00000567919, and is an MCI diagnostic marker that includes at least ENST00000549762. More specifically, the marker is an MCI diagnostic marker consisting of ENST00000549762 and NR_024049. More specifically, the marker is an MCI diagnostic marker consisting of ENST00000549762 and T324988. More specifically, the marker is an MCI diagnostic marker consisting of ENST00000549762, NR_024049, T324988, and ENST00000567919.

[0008] Further provided by the present invention are applications of mild cognitive impairment diagnostic markers for manufacturing mild cognitive impairment diagnostic kits.

[0009] A mild cognitive impairment (MCI) diagnostic kit further provided by the present invention, characterized in that the diagnostic kit measures the content of one or more of ENST00000549762, NR_024049, T324988, and ENST00000567919 in plasma. Specifically, the MCI diagnostic kit measures the content of at least ENST00000549762 among the MCI diagnostic markers consisting of one or more of ENST00000549762, NR_024049, T324988, and ENST00000567919 in plasma.

[0010] Preferably, the MCI diagnostic kit includes one or more primers and probes from among ENST00000549762, NR_024049, T324988, and ENST00000567919. Specifically, the diagnostic kit is an MCI diagnostic kit that includes at least the primer and probe of ENST00000549762, among the primers and probes of MCI diagnostic markers consisting of one or more of ENST00000549762, NR_024049, T324988, and ENST00000567919.

[0011] Preferably, the MCI diagnostic kit is a reference gene Includes 18S.

[0012] Preferably, measured by the diagnostic kit. ru The plasma content of one or more of the following was introduced: ENST00000549762, NR_024049, T324988, and ENST00000567919. ru The formula is: (1)-4.749+7972.194×ENST00000549762+5609.072×NR_024049+5.073×T324988+961.747×ENST00000567919, (2) -4.628 + 8552.604 × ENST00000549762 + 5885.819 × NR_024049 + 6.017 × T324988, (3)-4.549+9376.777×ENST00000549762+6908.534×NR_024049+2195.991×ENST00000567919 and (4)-4.220+6823.570×NR_024049+5.849×T324988+1213.731×ENST00000567919, (5) -4.129 + 6.122 × T324988 + 1128.888 × ENST00000567919 + 9678.308 × ENST00000549762, (6) ENST00000549762 and, (7) NR_024049 and, (8) T324988 and, (9) It is one of the ENST00000567919.

[0013] A method for detecting MCI diagnostic markers based on the mild cognitive impairment diagnostic kit, further provided by the present invention, wherein the method (1) Perform a reverse transcription reaction on the total RNA to be detected using an lncRNA reverse transcription kit to obtain the corresponding cDNA; (2) Perform real-time fluorescence quantitative PCR on the obtained cDNA and refer to 18S. gene The detection result should be expressed as ΔCt, which is the same as CtlncRNA-Ct18S; (3) The obtained ΔCt result is You can either substitute -4.749+7972.194×ENST00000549762+5609.072×NR_024049+5.073×T324988+961.747×ENST00000567919 and compare the calculated value to -0.8392, or Introduce it into -4.628 + 8552.604×ENST00000549762 + 5885.819×NR_024049 + 6.017×T324988 and compare the calculated value with -0.6984, or Introduce it into -4.549 + 9376.777×ENST00000549762 + 6908.534×NR_024049 + 2195.991×ENST00000567919 and compare the calculated value with -0.5859, or Introduce it into -4.220 + 6823.570×NR_024049 + 5.849×T324988 + 1213.731×ENST00000567919 and compare the calculated value with -0.3367, or Introduce it into -4.129 + 6.122×T324988 + 1128.888×ENST00000567919 + 9678.308×ENST00000549762 and compare the calculated value with -0.2550, or Introduce it into ENST00000549762 and compare the calculated value with 0.000160542039, or Introduce it into NR_024049 and compare the calculated value with 0.000235892192, or Introduce it into T324988 and compare the calculated value with 0.243255710000, or A detection method characterized by including introducing it into ENST00000567919 and comparing the calculated value with 0.000502313314 That is 。

Advantages of the Invention

[0014] The beneficial effects of the present invention are as follows. Through rigorous experiments and statistical analysis, we have for the first time discovered four nucleic acid molecules, namely ENST00000549762, NR_024049, T324988, and ENST00000567919, which have high diagnostic value for memory-loss type mild cognitive impairment. By developing and applying lncRNA markers and diagnostic kits, we have broken through the dilemma of having no convenient peripheral plasma diagnostic markers for memory-loss type mild cognitive impairment. This is beneficial for the early diagnosis and early intervention of Alzheimer's dementia, and may await a great progress in providing scientific basis and clinical support for discovering targets for new anti-AD therapeutic drugs with potential therapeutic value.

Brief Description of Drawings

[0015] [Figure 1] Figure 1 is a flowchart of the experimental design for screening, culturing, and verifying an lncRNA combination chip for identifying plasma targets of MCI patients in the present invention. [Figure 2] Figure 2 is a main stage diagram for determining an lncRNA combination for diagnosing plasma of MCI patients in the present invention. [Figure 3] Figure 3 is a ROC curve for diagnosing MCI patients with four types of lncRNA combinations. [Figure 4] Figure 4 is a ROC curve for diagnosing MCI patients with three types of lncRNA combinations. [Figure 5] Figure 5 is a ROC curve for diagnosing MCI patients with one type of lncRNA combination.

Modes for Carrying Out the Invention

[0016] To more clearly explain the technical content of the present invention, the following further explanations will be made in combination with specific examples.

[0017] In combination with Figures 1 to 5, specific explanations will be given regarding the screening and verification of the MCI diagnostic markers provided by the present invention.

[0018] 1. Research Subjects The study group consisted of 50 elderly individuals with mild cognitive impairment (MCI) collected from the Xuhui District area of ​​Shanghai, while the control group consisted of healthy elderly individuals whose age, sex, and educational attainment were consistent with the study group.

[0019] 2. Research method 1. Chip sorting (1) RNA is extracted using the TRIzol method and purified using the RNasey Mini Kit (GIAGEN). The RNA concentration after purification is measured using the Alexgent ND-1000, and RNA integrity is detected by electrophoresis.

[0020] (2) After the extracted RNA passes quality control, the lncRNA is marked with the Arraystar RNA Flash Labeling Kit. After marking, a hybrid of the sample and the Array human lncRNA chip (v4.0) is prepared in Agilent SureHyb, and the total reaction volume is 50 μl.

[0021] (3) After washing the chip, scan it with an Agilent DNA Microarray Scanner. Collect the chip signal values ​​with Agilent Feature Extraction software. Standardize the chip with Agilent GeneSpring GX v12.1 software and select differentially expressed lncRNAs.

[0022] 2. Verification of real-time quantitative PCR After selecting the chips, we will select lncRNAs that were clearly elevated in the control group and perform real-time quantitative PCR verification. The specific procedure is as follows:

[0023] (1) RNA is extracted using the TRIzol method, purified using the RNasey Mini Kit (GIAGEN), and the concentration of the purified RNA is measured using NanoDrop ND-1000.

[0024] After the extracted RNA passes quality control, the reverse transcription reaction is performed. The total reaction volume is 20 μl (300 ng of total RNA, 1 μl of reverse transcription-specific primer, 1.6 μl of dNTPs, 13.5 μl of water without nuclease, 0.5 μl of RNA inhibitory enzyme, 1 μl of reverse transcriptase, 4 μl of buffer, and 1 μl of 0.1 M DTT), and the reaction is carried out at various temperatures (50°C, 70°C) and for different reaction times (60 minutes, 15 minutes).

[0025] (2) Real-time quantitative PCR is performed with a total amplification system of 10 μl at 95°C for 10 minutes, and the PCR reaction is carried out over 40 cycles (95°C, 10 seconds; 60°C, 60 seconds). See 18S. gene The detection result is expressed as 2-ΔΔCt compared to the amount of the reference sample, and the smaller 2-ΔΔCt, the lower the expression level. The primer information used in PCR is shown in Table 1 below.

[0026] [Table 1]

[0027] 3. Research results During the chip selection phase, the expression levels of ENST00000567919, T264003, T286616, T324988, ENST00000549762, NR_024049, and NR_040772 in the MCI test group were health The results are significantly lower than the control group. See the following table for specific data. 2 This will be shown.

[0028] [Table 2]

[0029] In the validation phase of real-time quantitative PCR, the expression levels of ENST00000567919, ENST00000549762, NR_024049, and T324988 in the MCI test group were health The results are significantly lower than those of the control group. See the following table for specific data. 3This will be shown.

[0030] [Table 3]

[0031] The ROC curve analysis results show that four lncRNAs—NST00000567919, ENST00000549762, NR_024049, and T324988—have high diagnostic value as biomarkers for MCI.

[0032] Logistic binomial regression fitting is performed using the four lncRNAs mentioned above as independent variables, and their individual or combined predicted values ​​as dependent variables, to calculate predicted probability values. After performing ROC curve analysis again on the values ​​obtained from these calculation models (Calculation Model 1, Calculation Model 2, Calculation Model 3, Calculation Model 4, Calculation Model 5, Calculation Model 6, Calculation Model 7, Calculation Model 8, Calculation Model 9), the analysis results show that the values ​​obtained from these calculation models still have high diagnostic values.

[0033] In computational model 1, COMPUTE combined diagnosis = -4.749 + 7972.194 × ENST00000549762 + 5609.072 × NR_024049 + 5.073 × T324988 + 961.747 × ENST00000567919, The AUC is 0.941, the critical value is -0.8392, the sensitivity is 92%, and the specificity is 84%.

[0034] In computational model 2, COMPUTE combined diagnosis 123 = -4.628 + 8552.604 × ENST00000549762 + 5885.819 × NR_024049 + 6.017 × T324988, The AUC is 0.935, the critical value is -0.6984, the sensitivity is 88%, and the specificity is 84%.

[0035] In computational model 3, COMPUTE combined diagnosis 124 = -4.549 + 9376.777 × ENST00000549762 + 6908.534 × NR_024049 + 2195.991 × ENST00000567919, The AUC is 0.926, the critical value is -0.5859, the sensitivity is 90%, and the specificity is 84%.

[0036] In computational model 4, COMPUTE combined diagnosis 234 = -4.220 + 6823.570 × NR_024049 + 5.849 × T324988 + 1213.731 × ENST00000567919, The AUC is 0.920, the limit is -0.3367, the sensitivity is 88%, and the specificity is 84%.

[0037] In computational model 5, COMPUTE combined diagnosis 134 = -4.129 + 6.122 × T324988 + 1128.888 × ENST00000567919 + 9678.308 × ENST00000549762, The AUC is 0.930, the limit is -0.2550, the sensitivity is 90%, and the specificity is 90%.

[0038] In computational model 6, COMPUTE standalone diagnosis = ENST00000549762, The AUC is 0.846, the critical value is 0.000160542039, the sensitivity is 80%, and the specificity is 80%.

[0039] In computational model 7, COMPUTE standalone diagnosis = NR_024049, The AUC is 0.877, the limit is 0.000235892192, the sensitivity is 82%, and the specificity is 86%.

[0040] In computational model 8, COMPUTE standalone diagnosis = T324988, The AUC is 0.900, the limit is 0.243255710000, the sensitivity is 92%, and the specificity is 82%.

[0041] In computational model 9, COMPUTE standalone diagnosis = ENST00000567919, The AUC is 0.856, the limit value is 0.000502313314, the sensitivity is 82%, and the specificity is 80%.

[0042] Explanation of the analysis method: When comparing data between two groups using the SPSS 24.0 software package, a test for equal variances is performed first. Then, a comparative analysis using a t-test is performed on the data from the two groups with equal variances. If the p-value is < 0.05, it is considered to have statistical significance. For values ​​with statistical differences, a two-way logistic regression is performed to obtain predicted probability values, which are then used in the subsequent ROC curve analysis. The ROC curve is used to evaluate the value of lncRNA in MCI diagnosis, and the closer the area under the curve is to 1, the higher the diagnostic value of the index.

[0043] Currently, the biological diagnosis of MCI relies on expensive PET-CT scans for senile plaques and invasive, impractical lumbar punctures, without convenient and minimally invasive peripheral plasma testing for early diagnosis. [Industrial applicability]

[0044] The beneficial effects of this invention are as follows: Through rigorous experimentation and statistical analysis, we have discovered for the first time four nucleic acid molecules—ENST00000549762, NR_024049, T324988, and ENST00000567919—which possess high diagnostic value for memory loss-type mild cognitive impairment. By developing and applying lncRNA markers and diagnostic kits, we have overcome the plight of the lack of convenient peripheral plasma diagnostic markers for memory loss-type mild cognitive impairment. This is advantageous for the early diagnosis and intervention of Alzheimer's disease dementia and may lead to significant progress in providing scientific evidence and clinical support for discovering new targets for anti-AD drugs with potential therapeutic value.

[0045] In this specification, the present invention will be described with reference to specific embodiments thereof, but it will be apparent that various modifications and transformations will be made without departing from the spirit and scope of the invention. Furthermore, the specification and drawings should be considered illustrative but not restrictive.

Claims

1. An MCI diagnostic kit, characterized in that the diagnostic kit measures the content of at least ENST00000549762 among one or more MCI diagnostic markers consisting of ENST00000549762, NR_024049, T324988, and ENST00000567919 in plasma.

2. The MCI diagnostic kit according to claim 1, characterized in that the diagnostic kit includes at least the primer and probe of ENST00000549762 among the primers and probes of MCI diagnostic markers consisting of one or more of ENST00000549762, NR_024049, T324988, and ENST00000567919.

3. The MCI diagnostic kit according to claim 1, characterized in that the diagnostic kit includes reference 18S.

4. The formula into which the content of one or more of ENST00000549762, NR_024049, T324988, and ENST00000567919 in plasma measured by the diagnostic kit is: (1)-4.749+7972.194×ENST00000549762+5609.072×NR_024049+5.073×T324988+961.747×ENST00000567919, (2) -4.628 + 8552.604 × ENST00000549762 + 5885.819 × NR_024049 + 6.017 × T324988, (3)-4.549+9376.777×ENST00000549762+6908.534×NR_024049+2195.991×ENST00000567919 and (4)-4.220+6823.570×NR_024049+5.849×T324988+1213.731×ENST00000567919, (5) -4.129 + 6.122 × T324988 + 1128.888 × ENST00000567919 + 9678.308 × ENST00000549762, (6) ENST00000549762 and, (7) NR_024049 and, (8) T324988 and, (9) One of the following: ENST00000567919 The MCI diagnostic kit according to feature 1.

5. A method for detecting an MCI diagnostic marker based on the MCI diagnostic kit according to any one of claims 1 to 4, wherein the method (1) Perform a reverse transcription reaction on the total RNA to be detected using an lncRNA reverse transcription kit to obtain the corresponding cDNA; (2) Perform real-time fluorescence quantitative PCR on the obtained cDNA, using 18S as a reference, and express the detection result as ΔCt, which is the same as CtlncRNA-Ct18S; (3) The obtained ΔCt results You can either substitute -4.749+7972.194×ENST00000549762+5609.072×NR_024049+5.073×T324988+961.747×ENST00000567919 and compare the calculated value to -0.8392, or You can either input -4.628+8552.604×ENST00000549762+5885.819×NR_024049+6.017×T324988 and compare the calculated value to -0.6984, or You can either substitute -4.549+9376.777×ENST00000549762+6908.534×NR_024049+2195.991×ENST00000567919 and compare the calculated value to -0.5859, or Either input -4.220 + 6823.570 × NR_024049 + 5.849 × T324988 + 1213.731 × ENST00000567919 and compare the calculated value to -0.3367, or Either substitute -4.129 + 6.122 × T324988 + 1128.888 × ENST00000567919 + 9678.308 × ENST00000549762 and compare the calculated value to -0.2550, or Enter it into ENST00000549762 and compare the calculated value with 0.000160542039, or Enter it into NR_024049 and compare the calculated value with 0.000235892192, or Enter it into T324988 and compare the calculated value to 0.243255710000, This includes introducing it into ENST00000567919 and comparing the calculated value with 0.000502313314. A detection method characterized by the above.

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