Application of seven interactive genes in preparation of kit for predicting transformation of iRBD to synucleinopathies

Abstract

The invention relates to the field of medical diagnosis, in particular to seven interactive genes, a prediction system for predicting transformation of primary fast-moving eye sleep disorder (iRBD) to synucleinopathies and application of the seven interactive genes to preparation of a kit for predicting transformation of the iRBD to the synucleinopathies. According to the prediction system, the median of the model value is innovatively adopted as a critical point to distinguish the transformation risk, so that the prediction accuracy and reliability are remarkably improved. Compared with an existing prediction method, the method shows higher specificity and sensitivity, so that the system can accurately identify high-risk patient groups in the early stage of the disease course. The method has convenience and high efficiency in clinical application. By applying the prediction system to preparation of the kit, the clinical detection process is simplified, so that a doctor can quickly perform detection and obtain a result, and more effective management and treatment on an iRBD patient are realized.

Description

Technical Field

[0001] The present invention relates to the field of medical diagnosis, in particular to the application of seven interactive genes in preparing a kit for predicting the transformation of iRBD into synucleinopathy. Background Art

[0002] Primary rapid eye movement sleep behavior disorder (iRBD) is a sleep behavior disorder characterized by vivid dreams and the absence of normal muscle atonia during REM sleep. Patients do not show any symptoms during the day, but yell, punch, and kick at night, resulting in injuries to others and themselves. iRBD is closely related to Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). These diseases are classified as synucleinopathies because the pathogenic proteins are all α-synuclein. A large number of studies have shown that iRBD is an early stage of synucleinopathies, so it plays a vital role in the research of this type of disease.

[0003] Predictive models for the transformation of iRBD to synucleinopathies still face significant limitations and challenges. Current prediction models are limited by the varying degrees of progression between individuals, a poor understanding of how markers change over time, and heterogeneous expression of markers. Furthermore, accurate prediction of the onset of synucleinopathies remains a huge challenge due to the long prodromal phase of synucleinopathies and the complex interactions between genetic, biological, and environmental factors. The lack of large, diverse cohort studies and the need for external validation of these markers further limit their reliable application in clinical settings. These limitations highlight the need for further research to improve the accuracy of predictions and establish a universally applicable prognostic model for the progression of iRBD to overt synucleinopathies.

[0004] Genetic studies have played an important role in elucidating the genetic architecture of the risk of transitioning from iRBD to synucleinopathies. A key genetic locus associated with this risk is the SNCA gene, which is well known for encoding α-synuclein, a hallmark protein that is closely associated with the pathogenesis of various synucleinopathies. The aggregation tendency of α-synuclein is believed to be the main cause of the neurodegenerative process in these diseases. In addition, variants in genes such as LRRK2 and GBA have also been implicated as influential genetic factors. These genetic insights not only provide avenues for therapeutic intervention, but also serve as important prognostic markers that may one day provide personalized treatment plans for patients in the prodromal stage of these diseases. However, to date, no gene has been reported to have a clear value in predicting the transition from iRBD to synucleinopathies. Summary of the invention

[0005] The purpose of the present invention is to provide a new use of seven interactive genes in predicting the transformation of primary rapid eye movement sleep behavior disorder (iRBD) to synucleinopathy.

[0006] The first aspect of the present invention provides seven interactive genes for predicting the transformation of primary rapid eye movement sleep behavior disorder (iRBD) to synucleinopathy, wherein the seven interactive genes are CACNA1D (rs312480_C), ITPR3 (rs2296342_A), NOS1 (rs3741475_G), SYNJ2 (rs146694394_C), PLCE1 (rs7084339_A), INPP5F (rs117896735_G), and PLCG1 (rs2228246_A).

[0007] Furthermore, the synuclein diseases include Parkinson's disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy (MSA).

[0008] The second aspect of the present invention provides a detection product for seven interactive genes that predict the transformation of iRBD to synucleinopathy, including a reagent for detecting the expression level of the seven interactive genes mentioned above in a biological sample.

[0009] Furthermore, the biological sample is peripheral blood DNA from an iRBD patient.

[0010] Furthermore, the expression level is the number of gene bases.

[0011] The third aspect of the present invention provides the use of the seven interactive genes as described above and the detection products of the seven interactive genes in establishing a prediction and evaluation system for the transformation of iRBD to synucleinopathy.

[0012] The fourth aspect of the present invention provides the use of the seven interactive genes and detection products of the seven interactive genes as described above in the preparation of a kit for predicting the transformation of iRBD to synucleinopathy.

[0013] Furthermore, the kit comprises a reagent combination for detecting the expression levels of seven interactive genes PLCG1, SYNJ2, INPP5F, CACNA1D, NOS1, PLCE1 and ITPR3 in a biological sample.

[0014] In a fifth aspect of the present invention, a kit for predicting the transformation of iRBD to synucleinopathy is provided, the kit comprising reagents for detecting the expression levels of seven interactive genes PLCG1, SYNJ2, INPP5F, CACNA1D, NOS1, PLCE1 and ITPR3 in a biological sample.

[0015] The present invention also provides a method for predicting the transformation of iRBD to synucleinopathy using the above-mentioned kit, which specifically comprises the following steps:

[0016] (a) using the reagents in the kit to detect the expression levels of the seven interactive genes in the biological sample;

[0017] (b) The prediction score for iRBD to synucleinopathy conversion was calculated according to the following formula:

[0018] Seven-gene model score =

[0019] (-3.16E-04)*[rs146694394_C] count +(-1.14E-03)*[rs2228246_A] count +(-3.57E-04)*[rs7084339_A] count +3.62E-03*[rs312480_C] count +(-2.63E-04)*[rs3741475_G] count +(-1.06E-03)*[rs117896735_G] count +2.13E-04*[rs2296342_A] count

[0020] When the prediction score is higher than -3.5, the risk of iRBD transforming into synucleinopathy is high, and when it is lower than -3.5, the risk of iRBD transforming into synucleinopathy is low.

[0021] A sixth aspect of the present invention provides a prediction system for the transformation of iRBD to synucleinopathy, the system comprising:

[0022] A processor and a memory, the memory being coupled to the processor, the memory storing instructions which, when executed by the processor, use the following steps:

[0023] The expression levels of the seven interactive genes in the biological samples described above are input into the following calculation formula to obtain the prediction score; the calculation formula is as follows:

[0024] Seven-gene model score =

[0025] (-3.16E-04)*[rs146694394_C] count +(-1.14E-03)*[rs2228246_A] count +(-3.57E-04)*[rs7084339_A] count +3.62E-03*[rs312480_C]count +(-2.63E-04)*[rs3741475_G] count +(-1.06E-03)*[rs117896735_G] count +2.13E-04*[rs2296342_A] count

[0026] When the prediction score is higher than -3.5, the risk of iRBD transforming into synucleinopathy is high, and when it is lower than -3.5, the risk of iRBD transforming into synucleinopathy is low.

[0027] A seventh aspect of the present invention provides an application of the prediction system as described above in the preparation of a product for predicting the transformation of iRBD to synucleinopathy.

[0028] Furthermore, in the product for predicting the transformation of iRBD to synucleinopathy, the expression levels of the seven interactive genes are input into a calculation formula to obtain a prediction score.

[0029] According to an eighth aspect of the present invention, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are performed:

[0030] The expression levels of the seven interactive genes described above were input into the following calculation formula to obtain the prediction score; the calculation formula is as follows:

[0031] Seven-gene model score =

[0032] (-3.16E-04)*[rs146694394_C] count +(-1.14E-03)*[rs2228246_A] count +(-3.57E-04)*[rs7084339_A] count +3.62E-03*[rs312480_C] count +(-2.63E-04)*[rs3741475_G] count +(-1.06E-03)*[rs117896735_G] count +2.13E-04*[rs2296342_A] count .

[0033] The ninth aspect of the present invention provides a product for predicting the transformation of iRBD to synucleinopathy, comprising:

[0034] The expression level measurement products of the seven interactive genes;

[0035] and the prediction system for the transformation of iRBD into synucleinopathy, or the computer-readable storage medium.

[0036] The beneficial effects of the seven-gene multivariate Cox model (MultiModel) of the present invention and the advantages over the prior art include:

[0037] 1. Comprehensive analysis capability: MultiModel can comprehensively consider the interactions of seven genes through multivariate Cox regression analysis to provide a more comprehensive disease risk assessment.

[0038] 2. Improve prediction accuracy: Compared with single gene models and other traditional models, MultiModel shows higher accuracy in predicting the transformation of iRBD to PD or MSA.

[0039] 3. Superior statistical performance: MultiModel outperformed other models in key indicators such as the P value of the Kaplan-Meier curve, the AUC value of the ROC curve, and the percentage of successful prediction of PD or MSA.

[0040] 4. Potential for early diagnosis: The high predictive efficiency of MultiModel helps to identify high-risk patients at an early stage, providing the possibility for early diagnosis and intervention.

[0041] 5. Clinical decision support: The model provides clinicians with a powerful tool to support more accurate clinical decision making and patient management.

[0042] 6. Improve research efficiency: Through its application in the iRBD discovery cohort and validation cohort, MultiModel has demonstrated its validity and reliability in different patient populations.

[0043] 7. The prediction system of the present invention calculates the model value by in-depth research and comprehensive analysis of the expression data of specific interactive genes, using advanced bioinformatics algorithms. The system innovatively uses the median of the model value as the critical point to distinguish the risk of transformation, thereby significantly improving the accuracy and reliability of the prediction. Compared with existing prediction methods, the present invention exhibits higher specificity and sensitivity, which enables the system to accurately identify those high-risk patient groups early in the course of the disease. In addition, another significant advantage of the present invention is its convenience and efficiency in clinical applications. By applying the prediction system to the preparation of the test kit, the clinical testing process is simplified, allowing doctors to quickly perform tests and obtain results, thereby achieving more effective management and treatment of iRBD patients. This rapid and accurate detection method not only helps to improve the patient's treatment prognosis, but also enables preventive measures to be taken in the early stages to reduce potential medical costs.

[0044] In summary, MultiModel provides a more accurate and reliable tool for disease risk prediction in iRBD patients through its advanced statistical methods and superior performance in key indicators, which has significant advantages over existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 .Establishment of a seven-gene prediction model.

[0046] Figure 2 .The combined diagnostic value of seven interactive genes as markers.

[0047] Figure 3 .ROC curve analysis verified the diagnostic value.

[0048] Figure 4 . Clinical cohort validation. DETAILED DESCRIPTION

[0049] The specific implementation methods provided by the present invention are described in detail below in conjunction with examples.

[0050] The reagents and raw materials used in the present invention are all commercially available or can be prepared according to literature methods. The experimental methods in the following examples without specifying specific conditions are usually carried out under conventional conditions or under conditions recommended by the manufacturers.

[0051] In the following examples, the DNA samples of the research subjects were all from Shanghai Ruijin Hospital.

[0052] Example 1: Peripheral blood gene WES sequencing and longitudinal cohort follow-up of IRBD patients

[0053] Patients were enrolled from a longitudinal iRBD cohort of the Department of Neurology at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. Baseline assessments were performed from 2017 to 2021 and included nocturnal video polysomnography (PSG), biospecimen collection, imaging examinations, and interviews covering demographic information and assessment scales. The diagnosis of iRBD was based on the third edition of the International Classification of Sleep Disorders. Patients underwent clinical evaluation once a year until evidence of Parkinson's disease or cognitive impairment developed. Depending on whether RBD patients converted or not, they were divided into RBD conversion ("RBDc") group and RBD non-conversion ("RBDnc") group). 68 iRBD patients from Ruijin Hospital were enrolled, and peripheral blood was collected and DNA extracted. (The specimens were obtained from Shanghai Ruijin Hospital and passed the ethics approval of the unit).

[0054] The specific steps are:

[0055] DNA was extracted from peripheral blood samples donated by iRBD patients. WES libraries were constructed and indexed according to the SeqCap EZ HyperCap User Guide v2.3. Each DNA sample was quantified into 180-220 bp fragments with a total fragment amount of 100 ng using Covais E220. Subsequently, the sheared DNA was end-repaired, A-tailed, and adapter-ligated using the KAPA DNA HyperPrep Kit. Next, 250 ng of synthesized DNA was extracted into a 15 ul volume using SeqCap EZ Human Exome v3.0 Probes. The extracted library was then amplified using index primers. The library was quality controlled using an Agilent 4200 Bioanalyzer equipped with a DNA chip. After quantification using a Qubit 3.0 fluorometer, the library was sequenced on the Illumina NovaSeq 6000 platform.

[0056] Based on the WES sequencing data, we used BWA to map the sequences to the human reference genome, igvtools to extract base data, and Samtools to store the resulting data. The single nucleotide polymorphism_base (SNP_base) in the resulting data was analyzed using the limma package in R language to identify SNP sites that showed significant differences between the two groups of RBD conversion and non-conversion. These candidate sites were sequenced a second time with a greater sequencing depth and the above analysis was repeated, including BWA mapping of reference genome data, igvtools to extract base data, Samtools to store the resulting data, and the limma package to perform a comparative analysis of SNP_base between the two groups, and finally obtain the P value. The SNP base sites and their corresponding P values ​​were entered into OpenXGR, and the KEGG network was used to further screen out genes with mutual association.

[0057] Example 2: Correlation between the seven-gene model and iRBD transformation

[0058] To identify specific genes associated with iRBD phenotypic conversion, we first analyzed the differences in SNP count values ​​between the RBDc group and the RBDnc group to identify several key SNPs that may be associated with iRBD phenotypic conversion, thereby generating a SNP count matrix.

[0059] Based on this matrix, we constructed a Cox proportional hazards model to predict the risk of iRBD phenotypic conversion. Multiple SNPs / genes were significantly associated with the occurrence of phenotypic conversion and showed consistent effect directions in multiple analyses. Given the interactions between genes and their impact on neurodegenerative diseases, we further expanded the existing pathway cross-pathway approach by combining gene interaction information from KEGG pathways with Cox regression p-values ​​of genes significantly associated with phenotypic conversion.

[0060] Using this approach, we identified a pathway intersection involving seven genes: PLCG1, SYNJ2, INPP5F, CACNA1D, NOS1, PLCE1, and ITPR3. Figure 1 As shown, these genes are visualized in an interaction network, with each gene displayed in a Kaplan-Meier plot based on univariate Cox proportional hazards regression. The identified interactions represent genes as nodes, with the size of the nodes determined by the -log10(p-value) from the Cox regression, and the edges represent pathway-based interactions.

[0061] Example 3: Application of KM curve to analyze the combined diagnostic value of seven interactive genes as markers

[0062] We continued to apply multivariate Cox regression models to evaluate the joint effects of the above seven interactive genes as markers. CACNA1D (rs312480_C), ITPR3 (rs2296342_A), NOS1 (rs3741475_G), SYNJ2 (rs146694394_C), PLCE1 (rs7084339_A), INPP5F (rs117896735_G) and PLCG1 (rs2228246_A) were 3.62×10 -3 , 2.13×10 -4 , -2.63×10 -4 , -3.16×10 -4 , -3.57×10 -4 , -1.06×10 -3 , and-1.14×10 -3 .

[0063] Seven-gene model score =

[0064] (-3.16E-04)*[rs146694394_C] count +(-1.14E-03)*[rs2228246_A] count +(-3.57E-04)*[rs7084339_A] count+3.62E-03*[rs312480_C] count +(-2.63E-04)*[rs3741475_G] count +(-1.06E-03)*[rs117896735_G] count +2.13E-04*[rs2296342_A] count

[0065] Based on these coefficients, we used the above seven-gene multivariate Cox model (MultiModel) score to divide iRBD patients into two groups, using -3.5 as the cutoff point to form a "high-risk group" and a "low-risk group". Kaplan-Meier analysis of the multivariate Cox regression model score (also known as the seven-gene model score) showed a significant risk difference between the two score groups, with patients in the low-risk group showing a lower risk of transformation (P = 1.17 × 10 -5 , HR=9.3,95%CI=2.77-31.32), such as Figure 2 .

[0066] Example 4: Using ROC curve to analyze the value of the model in predicting conversion

[0067] Multivariate analysis further confirmed this finding, with the model obtaining a HR of 9.4 (p < 0.001) in predicting conversion. The model showed high specificity and sensitivity, with an area under the curve (AUC) of 0.817 at 2 years, 0.880 at 5 years, and 0.924 at 6 years. Figure 3 .

[0068] Embodiment 5:

[0069] The diagnostic steps of the seven-gene model in clinical practice:

[0070] 1) Peripheral blood was collected from iRBD patients and DNA was extracted, and seven genes were sequenced using the panel of the present invention.

[0071] 2) Igvtools tool was used to extract the base data of seven genes.

[0072] 3) Calculate the prediction score by the regression equation of the seven-gene model;

[0073] Seven-gene model score =

[0074] (-3.16E-04)*[rs146694394_C] count +(-1.14E-03)*[rs2228246_A] count +(-3.57E-0

[0075] 4)*[rs7084339_A] count +3.62E-03*[rs312480_C] count +(-2.63E-04)*[rs3741475_G] coun t +(-1.06E-03)*[rs117896735_G] count +2.13E-04*[rs2296342_A] count .

[0076] 4) When the prediction score is higher than -3.5, the risk of iRBD transforming into synucleinopathy is high, and when it is lower than -3.5, the risk of iRBD transforming into synucleinopathy is low.

[0077] Example 6: Clinical Validation

[0078] We validated this seven-gene model in 85 iRBD patients who subsequently transformed to multiple system atrophy (MSA) and Parkinson's disease (PD). All DNA extraction, genetic testing, and processing steps were performed simultaneously and identically to the original iRBD cohort. We incorporated data from this transformed iRBD patient population into the model and obtained model scores. We found that 82 patients (97.3%, 39 PD patients and 44 MSA patients) had a seven-gene model score above the threshold of -3.5, indicating a higher likelihood of pathological transformation, such as Figure 4 This suggests that, based on the seven-gene model, the intrinsic genetic information of these patients can predict RBD onset transition.

[0079] The preferred embodiments of the present invention have been specifically described above, but the present invention is not limited to the embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A method for predicting the transformation of iRBD to synucleinopathy by seven interacting genes, characterized in that: The seven interacting genes are CACNA1D (rs312480_C), ITPR3 (rs2296342_A), NOS1 (rs3741475_G), SYNJ2 (rs146694394_C), PLCE1 (rs7084339_A), INPP5F (rs117896735_G), and PLCG1 (rs2228246_A).

2. The seven interactive genes according to claim 1, characterized in that: The synucleinopathies include Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy.

3. A detection product for seven interacting genes that predict the transformation of iRBD to synucleinopathy, characterized by: The invention comprises reagents for detecting the expression levels of the seven interactive genes as claimed in claim 1 in a biological sample.

4. Use of a detection product of the seven interactive genes according to claim 1 and the seven interactive genes according to claim 2 in establishing a prediction and evaluation system for the transformation of iRBD to synucleinopathy.

5. Use of a detection product of the seven interactive genes according to claim 1 and the seven interactive genes according to claim 2 in the preparation of a kit for predicting the transformation of iRBD to synucleinopathy.

6. A kit for predicting the transformation of iRBD to synucleinopathy, characterized in that: The kit comprises reagents for detecting the expression levels of seven interactive genes, PLCG1, SYNJ2, INPP5F, CACNA1D, NOS1, PLCE1 and ITPR3, in a biological sample.

7. A prediction system for the transformation of iRBD to synucleinopathy, characterized in that: The system comprises: A processor and a memory, the memory being coupled to the processor, the memory storing instructions that, when executed by the processor, perform the following steps: The expression levels of the seven interactive genes described in claim 1 in the biological sample are input into the following calculation formula to obtain a prediction score; the calculation formula is as follows: Seven-gene model score = (-3.16E-04)*[rs146694394_C] count +(-1.14E-03)*[rs2228246_A] count +(-3.57E-04)*[rs7084339_A] count +3.62E-03*[rs312480_C] count +(-2.63E-04)*[rs3741475_G] count +(-1.06E-03)*[rs117896735_G] count +2.13E-04*[rs2296342_A] count When the prediction score is higher than -3.5, the risk of iRBD transforming into synucleinopathy is high, and when it is lower than -3.5, the risk of iRBD transforming into synucleinopathy is low.

8. Use of the prediction system according to claim 7 in preparing a product for predicting the transformation of iRBD to synucleinopathy.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the following steps are performed: The expression levels of the seven interactive genes described in claim 1 are input into the following calculation formula to obtain a prediction score; the calculation formula is as follows: Seven-gene model score = (-3.16E-04)*[rs146694394_C] count +(-1.14E-03)*[rs2228246_A] count +(-3.57E-04)*[rs7084339_A] count +3.62E-03*[rs312480_C] count +(-2.63E-04)*[rs3741475_G] count +(-1.06E-03)*[rs117896735_G] count +2.13E-04*[rs2296342_A] count 。 10. A product for predicting the transformation of iRBD to synucleinopathy, characterized in that: include: The product for measuring the expression levels of the seven interactive genes according to claim 1; and the prediction system for the transformation of iRBD to synucleinopathy according to claim 7, or the computer-readable storage medium according to claim 9.

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