Rapid eye movement sleep behavior disorder gene screening method based on genetic polymorphism of Parkinson's disease and application
Screening specific SNPs through high-throughput gene analysis and logistic regression solves the complexity of iRBD diagnosis, realizes early identification of PD risks, improves diagnostic efficiency and accuracy, reduces patient burden, and has important clinical value.
Patent Information
- Application Number
- CN202510289668.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-12
- Publication Date
- 2025-07-29
AI Technical Summary
The prior art is difficult to effectively use genetic polymorphism analysis to identify rapid eye movement sleep behavior disorder (iRBD) in Chinese populations, and the diagnosis process is complicated and it is impossible to identify the risk of Parkinson's disease early, resulting in the inability of patients to take timely preventive measures.
By collecting subject information, extracting genomic DNA, querying SNPs using PPMI database, performing high-throughput gene analysis and clustering, combining logistic regression analysis, specific SNPs related to PD were screened out, such as SCN3A/SCN2A rs353116, MCCC1 rs12637471, SH3GL2 rs13294100, COMT rs165599, and drawing ROC curves to evaluate diagnostic effects.
It has achieved rapid and accurate identification of iRBD patients, reduced the burden of traditional polysomnography, improved diagnostic efficiency, early identification of PD risks, reduced medical resource consumption, and provided convenience and clinical significance.
Smart Images
Figure CN120388606A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of disease diagnosis, and particularly relates to a method for screening genes for rapid eye movement sleep behavior disorder based on genetic polymorphisms of Parkinson's disease and its application. Background Art
[0002] Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a sleep disorder characterized by abnormal behaviors during rapid eye movement (REM) sleep, including vivid dreams and muscle activity, which may cause patients to shout, move their limbs, etc. during sleep, and sometimes even lead to self-harm or harm to bed partners. iRBD is closely related to neurodegenerative diseases such as Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). These diseases are classified as synucleinopathies because their pathogenic proteins are all α-synuclein. A large number of studies have shown that iRBD is an early stage of synucleinopathy, and thus plays a crucial role in the research of such diseases. During a 14-year follow-up period, up to 91% of iRBD patients showed phenotypic conversion to neurodegenerative diseases, most of which were synucleinopathies. Since iRBD may be a prodromal stage of Parkinson's disease, several years or even decades before the onset of motor symptoms, iRBD patients are expected to be ideal candidates for early detection of Parkinson's disease and the application of disease-modifying drugs.
[0003] Considering that iRBD patients are more likely to have a family history of dream behaviors than patients without RBD, genetic factors may play a crucial role in iRBD. So far, almost all gene variants found in gene polymorphism analysis related to iRBD are closely related to PD. As for whether PD and RBD are related to the same gene, the present application believes that they may share some gene components. Gene variants are also used as prognostic biomarkers to evaluate the risk of phenotypic conversion of iRBD.
[0004] Although multiple genes and loci related to Parkinson's disease (PD) have been identified globally, differences in allele frequencies among different ethnic groups mean that these research results cannot be directly generalized to the Chinese population. In addition, due to the limited number of iRBD patients and the complex diagnostic process, gene analysis for the Chinese population is still insufficient. Considering that PD is the most common synucleinopathy and longitudinal studies have shown that more than half of iRBD patients will eventually develop into PD, it is of great significance to study iRBD genes in the context of PD genetic polymorphisms for the Chinese population. Summary of the Invention
[0005] To solve the above technical problems in the prior art, the first aspect of the present application proposes a method for screening genes for rapid eye movement sleep behavior disorder based on genetic polymorphisms of Parkinson's disease, which includes:
[0006] Step S1: Collect the demographic information, scores of clinical assessment scales, and peripheral blood of a number of subjects; the subjects include a number of patients with rapid eye movement sleep behavior disorder and a number of healthy controls.
[0007] Step S2: Extract genomic DNA from peripheral blood mononuclear cells of all subjects.
[0008] Step S3: Query genes and single nucleotide polymorphisms (SNPs) through the PPMI database, create a target gene panel, and perform clustering analysis on all target genes using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases.
[0009] Step S4: Perform high-throughput analysis on SNPs using the target gene panel.
[0010] Step S5: Genotype SNPs using the SNaPshot technique.
[0011] Step S6: Estimate the risk of each SNP using logistic regression analysis, and screen out a number of specific SNPs most relevant to Parkinson's disease and rapid eye movement sleep behavior disorder.
[0012] Step S7: Plot the ROC curve to analyze the diagnostic efficacy of SNPs in Parkinson's disease and rapid eye movement sleep behavior disorder.
[0013] Further, in Step S1, the clinical assessment scales include the Rapid Eye Movement Sleep Behavior Disorder Screening Questionnaire, the Hoehn-Yahr staging scale, and the Unified Parkinson's Disease Rating Scale.
[0014] Further, in Step S2, genomic DNA is extracted from peripheral blood mononuclear cells using the standard phenol-chloroform method.
[0015] Further, in Step S6, the risk of each SNP is estimated using logistic regression analysis based on the codominant genetic model, dominant genetic model, recessive genetic model, overdominant genetic model, and additive genetic model, respectively.
[0016] Further, the rapid eye movement sleep behavior disorder is idiopathic rapid eye movement sleep behavior disorder.
[0017] Further, in Step S6, the number of specific SNPs most relevant to Parkinson's disease and rapid eye movement sleep behavior disorder screened out are SCN3A / SCN2A rs353116, MCCC1 rs12637471, SH3GL2 rs13294100, and COMT rs165599.
[0018] Further, the sequence of MCCC1 rs12637471 is as shown in SEQ ID NO.1; the sequence of SH3GL2 rs13294100 is as shown in SEQ ID NO.2; the sequence of COMT rs165599 is as shown in SEQ ID NO.3; the sequence of SCN3A / SCN2A rs353116 is as shown in SEQ ID NO.4.
[0019] The second aspect of the present application provides the use of the gene screened by the above method as a biomarker in products for diagnosing rapid eye movement sleep behavior disorder.
[0020] Further, the product is a kit, and / or, a prediction system, and / or, a computer-readable storage medium; the prediction system is an artificial intelligence prediction model constructed after training with a machine learning algorithm based on the biomarker; a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the rapid eye movement sleep behavior disorder prediction method based on Parkinson's disease genetic polymorphisms, wherein the prediction method is an artificial intelligence prediction model constructed after training with a machine learning algorithm based on the biomarker.
[0021] The machine learning algorithm includes, but is not limited to, linear regression algorithm, support vector machine algorithm, nearest neighbor / k-nearest neighbor algorithm, logistic regression algorithm, decision tree algorithm, k-means algorithm, random forest algorithm, naive Bayes algorithm, dimensionality reduction algorithm, gradient boosting algorithm, etc.
[0022] The computer-readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. The computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, a mechanically encoded device, such as a punched card or raised structures in grooves storing instructions thereon, and any suitable combination of the foregoing. The computer-readable program instructions described herein can be downloaded from the computer-readable storage medium to various computing / processing devices, or downloaded to an external computer or external storage device through a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include a copper transmission cable, an optical fiber transmission, a wireless transmission, a router, a firewall, a switch, a gateway computer, and / or an edge server. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.
[0023] After the present application adopts the above technical solutions, it has the following excellent technical effects:
[0024] The present invention provides a method for predicting genes of rapid eye movement sleep behavior disorder (RBD) based on the genetic polymorphism of Parkinson's disease (PD). This method evaluates the risk of an individual developing RBD by analyzing genetic markers related to PD. The present invention adopts high-throughput gene analysis technology, which can rapidly and accurately analyze a large number of genes and single nucleotide polymorphisms (SNPs), and this is very important for quickly identifying genetic markers related to the risk of Parkinson's disease (PD). In the present invention, by using high-throughput gene analysis technology, the present application can quickly identify specific SNPs related to the risk of PD, such as SCN3A / SCN2A rs353116, MCCC1 rs12637471, SH3GL2 rs13294100, COMT rs165599. The identification of these SNPs, combined with the evaluation of the diagnostic effect of the ROC curve, provides a brand-new strategy for the identification of iRBD. Compared with the traditional polysomnography (PSG) method, this method is more convenient and efficient, and does not require the patient to stay overnight in the hospital, greatly reducing the burden on the patient. Through this method, the present application can identify iRBD patients who are likely to develop PD earlier, so as to take preventive measures in a timely manner and delay the progression of the disease. This gene-based diagnostic method not only improves the accuracy of diagnosis, but also provides more convenience for patients, reduces the consumption of medical resources, and has important clinical significance and social value. Description of the Drawings
[0025] Figure 1 It is a flowchart of a method for screening genes of rapid eye movement sleep behavior disorder based on the genetic polymorphism of Parkinson's disease in an embodiment of the present application.
[0026] Figure 2 It is an ROC curve for evaluating the discrimination effect of SNP loci related to iRBD. Detailed Embodiments
[0027] The advantages of the present invention are further elaborated below in conjunction with the drawings and specific embodiments. Those skilled in the art should understand that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the protection scope of the present invention.
[0028] As Figure 1 shown, this embodiment provides a method for screening genes of rapid eye movement sleep behavior disorder based on the genetic polymorphism of Parkinson's disease. This method mainly includes the following steps:
[0029] 1. Research design and subject recruitment
[0030] This study was carried out by the Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, aiming to deeply explore the genetic relationship between Parkinson's disease (PD) and rapid eye movement sleep behavior disorder (RBD). A total of 246 subjects were recruited, including 57 RBD patients and 189 healthy controls (HC). The diagnosis of RBD patients was based on the results of video polysomnography (vPSG) and strictly followed the International Classification of Sleep Disorders (ISCD)-II criteria to ensure the objectivity and standardization of the diagnosis.
[0031] During the recruitment process, the research team detailedly recorded the demographic information of the subjects, including age, gender, race, etc., as well as the scores of a series of clinical assessment scales. These scales included the Rapid Eye Movement Sleep Behavior Disorder Screening Questionnaire (RBDSQ) to evaluate the severity of RBD symptoms, and the revised Unified Parkinson's Disease Rating Scale (MDS-UPDRS) initiated by the Movement Disorder Society to comprehensively evaluate the symptoms and functional status of RBD patients.
[0032] This study has obtained the approval of the Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, ensuring that all research procedures comply with ethical standards. All participants provided written informed consent before participating in the study to ensure the protection of their rights and interests.
[0033] 2. DNA Extraction and SNPs Selection
[0034] The standard phenol-chloroform method used in the study is a widely recognized DNA extraction method that can efficiently extract genomic DNA from peripheral blood mononuclear cells. Genes and single nucleotide polymorphisms (SNPs) were queried through the PPMI database and made into a target gene Panel. This step is crucial for the study because it involves screening a large number of genes and SNPs to determine the genetic markers most relevant to PD and RBD.
[0035] All candidate genes were subjected to cluster analysis according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases to identify genes that may play a key role in the pathogenesis of PD and RBD. SNPs were detected using a custom-designed Panel (TWIST Bioscience, South San Francisco, CA, United States), which allows high-throughput analysis of a large number of SNPs, improving the efficiency and accuracy of the study. The present invention uses high-throughput gene analysis technology, which can rapidly and accurately analyze a large number of genes and single nucleotide polymorphisms (SNPs), which is very important for quickly identifying genetic markers associated with RBD risk. High-throughput sequencing technology (NGS) is characterized by high throughput, high efficiency, and high sensitivity, making it possible to analyze thousands of SNPs in a short time. The application of this technology, especially in targeted sequencing (tNGS), improves the detection efficiency of specific genetic variations by enriching target microbial sequences. In the present invention, by using high-throughput gene analysis technology, specific SNPs associated with PD risk can be rapidly identified. The identification of these SNPs, combined with the evaluation of the diagnostic effect of the ROC curve, provides a new strategy for the differential diagnosis of iRBD. The SNaPshot technique (Applied Biosystems, Foster City, CA, United States) was used for SNP genotyping, which is an accurate technique capable of accurately detecting single nucleotide polymorphisms.
[0036] 3. Statistical analysis
[0037] All statistical analyses were performed using the SPSS 25.0 software package (SPSS Inc.) to ensure the professionalism and reliability of data analysis. The t-test was used to analyze the age difference between the two groups, which is a commonly used statistical method for comparing whether there are significant differences in the means of two groups of data. The chi-square test was used for differences in gender, allele and genotype frequencies, and Hardy-Weinberg equilibrium (HWE) of the entire cohort, which is a statistical method for comparing the differences between observed and expected frequencies.
[0038] After adjusting for age and sex, logistic regression analysis was used to estimate the risk of each SNP. Logistic regression is a method widely used in epidemiological studies to evaluate the impact of one or more predictor variables on an outcome. In this study, five genetic models were adopted: the co-dominant model, the dominant model, the recessive model, the over-dominant model, and the additive model, to comprehensively evaluate the associations between SNPs and PD and RBD under different genetic patterns. Several specific SNPs screened in this example that are most relevant to Parkinson's disease and rapid eye movement sleep behavior disorder are: SCN3A / SCN2A rs353116, MCCC1 rs12637471, SH3GL2 rs13294100, COMT rs165599, see Table 1.
[0039] Table 1 SNPs related to iRBD
[0040]
[0041]
[0042] The sequences of several specific SNPs screened in this example that are most relevant to Parkinson's disease and rapid eye movement sleep behavior disorder are shown in Table 2.
[0043] Table 2 Sequences of SNPs
[0044] A P-value < 0.05 was considered statistically significant, which is a commonly used criterion in research to determine whether the results are statistically significant. The heritability of each SNP was calculated using Power and Sample Size software (version 3.1.6), which is a software specifically for calculating statistical power and sample size and helps to evaluate the reliability and validity of the study. An ROC curve was plotted to analyze the diagnostic performance of SNPs in different diseases. The ROC curve is a graphical tool for evaluating the performance of a diagnostic test, including its sensitivity and specificity. As Figure 2As shown, through drawing the ROC curve, the present invention can evaluate the diagnostic effects of single nucleotide polymorphisms (SNPs) in different diseases, providing an intuitive understanding of the diagnostic performance. The ROC curve is an important tool for evaluating the performance of diagnostic tests, which demonstrates the efficacy of the test by comparing the true positive rate (sensitivity) and the false positive rate (1 - specificity). In the present invention, the application of the ROC curve not only helps to determine the potential of SNPs as biomarkers, but also provides a quantitative method to evaluate the effectiveness of these biomarkers in actual clinical applications. By calculating the area under the ROC curve (AUC), the present application can quantify the diagnostic or predictive effects of the model. The closer the AUC value is to 1, the higher the accuracy of the model. In addition, the ROC curve can also help to determine the optimal cut-off point, that is, the point where the sensitivity and specificity reach the best balance, which is crucial for clinical decision-making. This method of the present invention provides a more precise and efficient tool to evaluate and optimize the application of SNPs in disease diagnosis.
[0045] It should be noted that the embodiments of the present invention have better implementability and do not impose any form of limitation on the present invention. Any person skilled in the art may use the technical content disclosed above to change or modify it into equivalent effective embodiments. However, as long as it does not depart from the content of the technical solution of the present invention, any modification, equivalent change or modification made to the above embodiments according to the technical essence of the present invention still falls within the scope of the technical solution of the present invention.
Claims
1. A method for screening genes of rapid eye movement sleep behavior disorder based on genetic polymorphisms of Parkinson's disease, characterized in that, Comprising: Step S1: Collect demographic information, scores of clinical assessment scales, and peripheral blood of a number of subjects; the subjects include a number of patients with rapid eye movement sleep behavior disorder and a number of healthy controls; Step S2: Extract genomic DNA from peripheral blood mononuclear cells of all subjects; Step S3: Query genes and single nucleotide polymorphisms (SNPs) through the PPMI database, and prepare a target gene Panel, and perform cluster analysis on all target genes using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases; Step S4: Perform high-throughput analysis of SNPs using the target gene Panel; Step S5: Perform genotyping of SNPs using the SNaPshot technique; Step S6: Use logistic regression analysis to estimate the disease risk correlation of each SNP, and screen out a number of specific SNPs most relevant to rapid eye movement sleep behavior disorder; Step S7: Plot an ROC curve to analyze the diagnostic effect of SNPs in rapid eye movement sleep behavior disorder.
2. The rapid eye movement sleep behavior disorder gene screening method based on the genetic polymorphism of Parkinson's disease according to claim 1, wherein In Step S1, the clinical assessment scale includes the rapid eye movement sleep behavior disorder screening questionnaire and the Unified Parkinson's Disease Rating Scale.
3. The rapid eye movement sleep behavior disorder gene screening method based on the genetic polymorphism of Parkinson's disease according to claim 1, characterized in that In Step S2, genomic DNA is extracted from peripheral blood mononuclear cells using the standard phenol-chloroform method.
4. The rapid eye movement sleep behavior disorder gene screening method based on Parkinson's disease genetic polymorphism according to claim 1, wherein In Step S6, based on the co-dominant genetic model, dominant genetic model, recessive genetic model, over-dominant genetic model, and additive genetic model, logistic regression analysis is used to estimate the risk of each SNP respectively.
5. The rapid eye movement sleep behavior disorder gene screening method based on Parkinson's disease genetic polymorphism according to claim 1, wherein The rapid eye movement sleep behavior disorder is idiopathic rapid eye movement sleep behavior disorder.
6. The rapid eye movement sleep behavior disorder gene screening method based on Parkinson's disease genetic polymorphism according to any one of claims 1 to 5, characterized in that, In Step S6, the number of specific SNPs most relevant to Parkinson's disease and rapid eye movement sleep behavior disorder screened out are SCN3A / SCN2A rs353116, MCCC1 rs12637471, SH3GL2 rs13294100, and COMT rs165599.
7. The rapid eye movement sleep behavior disorder gene screening method based on Parkinson's disease genetic polymorphism according to claim 6, wherein The sequence of MCCC1 rs12637471 is as shown in SEQ ID NO.1; the sequence of SH3GL2 rs13294100 is as shown in SEQ ID NO.2; the sequence of COMT rs165599 is as shown in SEQ ID NO.3; the sequence of SCN3A / SCN2A rs353116 is as shown in SEQ ID NO.
4.
8. Application of the gene screened by the method according to claims 1 to 7 as a biomarker in a product for diagnosing rapid eye movement sleep behavior disorder.
9. The application according to claim 8, characterized in that, The product is a kit, and / or, a prediction system, and / or, a computer-readable storage medium; the prediction system is an artificial intelligence prediction model constructed after training with a machine learning algorithm based on the biomarker; the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it can implement the steps of the rapid eye movement sleep behavior disorder prediction method based on Parkinson's disease genetic polymorphisms, wherein the prediction method is an artificial intelligence prediction model constructed after training with a machine learning algorithm based on the biomarker.