Biomarker combination and application thereof in predicting Parkinson's disease

Abstract

The invention discloses a biomarker combination and application of the biomarker combination in prediction of Parkinson's disease. The biomarker combination is composed of ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG and GAPDH. The combination of 23 protein biomarkers in the invention can be used for risk estimation and detection of Parkinson's disease patients, has the advantages of high sensitivity and high specificity, and provides favorable technical support for early screening diagnosis, intervention treatment and the like of Parkinson's disease patients.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology and diagnosis, and specifically relates to a biomarker combination and its application in predicting Parkinson's disease. Background Art

[0002] Parkinson's disease (PD), also known as "tremor paralysis", is the second largest neurodegenerative disease after Alzheimer's disease, affecting millions of people worldwide, and the incidence rate increases significantly with age. Recent survey data show that the prevalence of PD in people over 65 years old in my country is as high as 1.7%. Medical research suggests that the clinical characteristics of PD are mainly manifested as α-synuclein aggregates and dopamine neuron loss. The symptoms of PD patients vary from person to person, but they are mostly manifested as motor symptoms (such as bradykinesia, tremor and muscle rigidity) and non-motor symptoms (such as constipation, depression, loss of smell and sleep disorders).

[0003] Although PD was first reported by British physician James Parkinson in 1817, the molecular events that trigger PD are not fully understood, which means that existing treatments do not slow down or reverse the progression of the disease. To date, the most accurate clinical auxiliary examination methods for PD mainly rely on molecular imaging (such as magnetic resonance diffusion tensor imaging, susceptibility weighted imaging, etc.) and related clinical scale scoring (such as UPDRS scale and Hoehn-Yahr scale score survey), but non-motor symptoms related to PD may appear more than ten years earlier than motor symptoms, and once discovered, the best time for rehabilitation treatment may be missed. Therefore, the mining of new biomarkers for early diagnosis of PD is particularly important.

[0004] Biomarkers are biochemical indicators that can mark changes or possible changes in the structure or function of systems, organs, tissues, cells and subcellular structures. There are many different biomarkers in the human body, and with the continuous advancement of molecular biology technology, the types of biomarkers are increasing, including basic physical conditions, imaging data (such as mammograms), specific molecules (such as prostate-specific antigen PSA), gene mutations (such as BRCA mutations), cell markers (such as circulating tumor cells), and biomarkers of biological molecules such as proteins or metabolites. At present, the major limitation of PD treatment is the inability to make a timely diagnosis. Therefore, it is particularly important to urgently need biomarkers with high sensitivity and specificity that can be used for early diagnosis and detection of disease progression. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a biomarker combination and its application in predicting Parkinson's disease.

[0006] A first aspect of the present invention provides an application of a biomarker combination in the preparation of a product for predicting or diagnosing Parkinson's disease, wherein the biomarker combination consists of ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG and GAPDH.

[0007] A second aspect of the present invention provides a reagent for detecting a biomarker combination, wherein the biomarker combination consists of ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG and GAPDH.

[0008] In a preferred embodiment, the reagent is used to detect the expression level of the biomarker combination.

[0009] In a preferred embodiment, the reagent is a reagent that specifically binds to the biomarker, or is a biomolecular reagent that specifically hybridizes with a nucleic acid encoding the biomarker.

[0010] In a preferred embodiment, the reagent is a reagent for genome, transcriptome and / or proteome sequencing.

[0011] In a preferred embodiment, the expression level is a protein expression level and / or an mRNA transcription level, and / or the biomolecule reagent is selected from one or more of a primer, a probe and an antibody.

[0012] A third aspect of the present invention provides a biomarker combination, which consists of ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG and GAPDH.

[0013] The fourth aspect of the present invention provides a kit, which comprises the reagents as described in the second aspect of the present invention and / or the biomarker combination as described in the third aspect of the present invention.

[0014] A fifth aspect of the present invention provides a method for constructing a Parkinson's disease prediction model, the method comprising: inputting protein expression data corresponding to a combination of biomarkers in a sample into a Caret R package containing a logistic regression model for machine learning to obtain a Parkinson's disease prediction model;

[0015] The biomarker combination consists of ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG and GAPDH.

[0016] Specifically, the code of the logistic regression model is:

[0017] The parameters of the generalized linear regression model are: using the backward regression method to screen markers, and using the train function of the R package Caret for model training and the predict function for prediction. Preferably, the R package of the generalized linear regression model includes: predict.model = train (formula, data = train_data, method = "glm", family = 'binomial') (formula: model formula, input molecular combination; train_data: training set); prediction code: predict (predict.model, test_data) (predict.model: prediction model obtained from the training set, test_data: test set or external validation set).

[0018] The samples were judged based on logistic regression analysis. If the probability of the expression level of the marker combination was greater than 0.5, it was determined to be a positive sample, otherwise it was a negative sample.

[0019] The code of the Caret R package described in the present invention is open source.

[0020] In a preferred embodiment, the sample includes but is not limited to exosomes from body fluids of Parkinson's disease patients, such as saliva, blood (serum or plasma), urine, and cerebrospinal fluid.

[0021] Preferably, the sample tested is cerebrospinal fluid from a Parkinson's disease patient.

[0022] In a preferred embodiment, before machine learning, the protein expression data of the sample is collected by DDA method, and peptide matching is performed by Firmiana software.

[0023] In a preferred embodiment, the sample includes Parkinson's disease patients and healthy people.

[0024] In a preferred embodiment, the protein expression data input into the logistic regression model satisfies: the protein expression level corresponding to the biomarker combination in the sample is 1.5 times or more than the corresponding protein expression level in a healthy person, and the t-test p value is less than 0.05.

[0025] In a preferred embodiment, the peptide segment matching utilizes the UniProt human protein database.

[0026] In a preferred embodiment, before performing machine learning, the samples are grouped to obtain modeling group samples and validation group samples.

[0027] The modeling group samples are used to construct a Parkinson's disease prediction model, and the validation group samples are used to validate the Parkinson's disease prediction model.

[0028] In a preferred embodiment, the protein expression data input into the logistic regression model is a protein frequency greater than or equal to 30%.

[0029] In a preferred embodiment, the step of using the validation group samples for validation includes: calculating the below-the-line area, sensitivity and specificity of the specificity curve of the protein expression data of the biomarker combination in the sample; and judging the accuracy of the prediction model based on the below-the-line area, sensitivity and specificity.

[0030] In a preferred embodiment, the method further includes determining whether the sample suffers from Parkinson's disease. When the probability is greater than or equal to 0.5, the sample is determined to suffer from Parkinson's disease; when the probability is less than 0.5, the sample is determined not to suffer from Parkinson's disease.

[0031] In a preferred embodiment of the present invention, the protein expression data is obtained by LC-MS technology and collected using DDA (data-dependent acquisition) detection method.

[0032] Preferably, the data collected by the DDA detection method are matched with peptides using Firmiana software. More preferably, the database for peptide matching is the UniProt human protein database.

[0033] Further preferably, the protein expression data after Firmiana processing is used: a label-free intensity-based absolute quantification (iBAQ) method is used for protein quantification, the FOT (Fraction of total, defined as the iBAQ (intensity-based absolute-protein-quantification) of the protein divided by the total iBAQ of all identified proteins in the sample) of each protein is calculated, and the FOT of each protein is input into the logistic regression model as the protein expression data.

[0034] A sixth aspect of the present invention provides a prediction model for Parkinson's disease, wherein the prediction model is constructed by the method described in the fifth aspect of the present invention.

[0035] A seventh aspect of the present invention provides a prediction system for Parkinson's disease, the prediction system comprising an analysis and judgment module, the analysis and judgment module containing the prediction model as described in the sixth aspect of the present invention, for outputting a prediction result of whether the sample suffers from Parkinson's disease;

[0036] Among them, the biomarker combination consists of ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG and GAPDH.

[0037] In a preferred embodiment, the detection module detects the protein expression level corresponding to the biomarker combination in the sample to be tested, and transmits the expression level data to the analysis and judgment module.

[0038] The eighth aspect of the present invention provides a method for predicting Parkinson's disease, wherein a sample is predicted using the reagent described in the second aspect of the present invention, the biomarker combination described in the third aspect of the present invention, the kit described in the fourth aspect of the present invention, the prediction model described in the sixth aspect of the present invention, or the prediction system described in the seventh aspect of the present invention.

[0039] The ninth aspect of the present invention provides an application of the reagent as described in the second aspect of the present invention, the biomarker combination as described in the third aspect of the present invention, the kit as described in the fourth aspect of the present invention, the prediction model as described in the sixth aspect of the present invention, or the prediction system as described in the seventh aspect of the present invention in the prediction of Parkinson's disease.

[0040] The tenth aspect of the present invention provides a use of the reagent as described in the second aspect of the present invention in the preparation of a kit for predicting or diagnosing Parkinson's disease; wherein the biomarker combination consists of ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG and GAPDH.

[0041] The eleventh aspect of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement the steps of the method described in the eighth aspect of the present invention, or implement the prediction model described in the sixth aspect of the present invention or the functions of the prediction system described in the seventh aspect of the present invention.

[0042] The twelfth aspect of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the steps of the method described in the eighth aspect of the present invention, or to implement the prediction model described in the sixth aspect of the present invention or the functions of the prediction system described in the seventh aspect of the present invention.

[0043] On the basis of being in accordance with the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0044] The reagents and raw materials used in the present invention are commercially available.

[0045] The positive and progressive effects of the present invention are:

[0046] Through experiments, it was found that the expression levels of the 23 protein biomarkers provided by the present invention in the cerebrospinal fluid of Parkinson's patients vary significantly. Therefore, these protein molecular markers provided by the present invention can be used as risk prediction and detection for Parkinson's patients, with the advantages of high sensitivity and high specificity, providing favorable technical support for early screening diagnosis, intervention treatment, etc. of Parkinson's patients. The development of corresponding auxiliary early diagnosis kits based on cerebrospinal fluid protein molecular markers of Parkinson's patients has extensive scientific research value and provides great convenience for early clinical diagnosis, intervention treatment, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is the ROC curve of 23 protein biomarkers combined in the training set.

[0048] Figure 2The confusion matrix results of 23 protein molecular biomarkers combined in the training set include prediction accuracy (positive accuracy is 100.00%, negative accuracy is 100.00%), sensitivity (100.00%) and specificity (100.00%) results.

[0049] Figure 3 This is the ROC curve of 23 protein biomarkers combined in the test set.

[0050] Figure 4 The confusion matrix results of 23 protein molecular biomarkers combined in the test set include prediction accuracy (positive accuracy is 85.71%, negative accuracy is 92.00%), sensitivity (75.00%) and specificity (85.71%) results.

[0051] Figure 5 This is the ROC curve of 23 protein biomarkers combined in the external validation set.

[0052] Figure 6 The confusion matrix results of 23 protein molecular biomarkers combined in the external validation set include prediction accuracy (positive accuracy is 100.00%, negative accuracy is 100.00%), sensitivity (100.00%) and specificity (100.00%) results.

[0053] Figure 7 Schematic diagram of the system for predicting Parkinson's disease risk.

[0054] Figure 8 A schematic diagram of the structure of an electronic device. DETAILED DESCRIPTION

[0055] The present invention is further described below by way of examples, but the present invention is not limited to the scope of the examples. The experimental methods in the following examples without specifying specific conditions are carried out according to conventional methods and conditions, or selected according to the product specifications.

[0056] The examples include 86 Parkinson's disease samples and 34 healthy control samples. The design and implementation of this study have been approved and supervised by ethical voting. Written informed consent has been obtained from all patients.

[0057] Example 1

[0058] 1. Pretreatment of cerebrospinal fluid samples

[0059] Add 100 μL of 50 mM ammonium bicarbonate to 30 μL of cerebrospinal fluid sample, vortex mix for 1 min, heat and incubate the sample at 95 °C for 4 min to denature the protein, cool to room temperature, add 2 μg of trypsin to the system, oscillate at 37 °C for 18 h, and then add 10 μL of ammonia water to the system to stop enzymatic hydrolysis. Desalt the peptide samples after enzymatic hydrolysis, drain, and freeze at -80 °C until mass spectrometry detection.

[0060] 2. Mass spectrometry of cerebrospinal fluid samples

[0061] The Orbitrap Fusion Lumos three-in-one high-resolution mass spectrometry system (Thermo Fisher Scientific, Rockford, USA) was used in series with a high-performance liquid chromatography system (EASY-nLC1200, Thermo Fisher) to obtain the mass spectrometry data of the whole protein corresponding to the peptide sample. The specific operation was as follows:

[0062] Nanoflow liquid chromatography was used, and the chromatographic column was a homemade C18 chromatographic column (150 μm ID × 8 cm, 1.9 μm / Filler). The column oven temperature was 60°C. The dry powder peptide was reconstituted with a loading buffer (0.1% formic acid in water), loaded and separated by a chromatographic column, eluted with a linear 6-30% mobile phase B (ACN and 0.1% formic acid) at 600nL / min, and a 150min liquid phase gradient was combined with a data-dependent acquisition (DDA) mass spectrometry detection method. The DDA mass spectrometry detection parameters were set as follows: the ion mode was positive ion; the primary mass spectrometry resolution was 120K, the maximum injection time was 80ms, the AGCTarget was 3e6, and the scanning range was 300-1400m / z; the secondary mass spectrometry resolution was 7.5K, the maximum injection time was 20ms, the AGCTarget was 5e4, and the collision energy was 27%; the liquid chromatography tandem mass spectrometry system was controlled by Xcalibur software for data acquisition.

[0063] 3. Data Analysis

[0064] All data were processed by searching the UniProt human protein database (updated on July 4, 2013, 32,015 entries) using Firmiana. Firmiana is a workflow based on the Galaxy system, consisting of multiple functional modules such as user login interface, raw data, identification and quantification, data analysis and knowledge mining. The mass difference of the parent ion is 20 ppm, and the mass difference of the daughter ion is 50 mmu. A maximum of two missed cleavage sites are allowed. The search engine sets cysteine ​​carbamidomethylation as a fixed modification and methionine N-acetylation and oxidation as variable modifications. The parent ion charge range is set to +2, +3 and +4. The false discovery rate (FDR) is set to 1%.

[0065] The quantification results of the identified peptides were recorded as the average of the peak areas of the chromatographic fragment ions in all reference spectral libraries. Protein quantification was performed using the label-free intensity-based absolute quantification (iBAQ) method. We calculated the peak area values ​​as a fraction of the corresponding protein. The total fraction (FOT) was used to represent the normalized abundance of a specific protein in the sample. FOT is defined as the iBAQ of a protein divided by the total iBAQ of all identified proteins in the sample. Proteins with at least one unique peptide and 1% FDR were selected for further analysis. The FOT of each protein was calculated and entered into the generalized linear regression model as protein expression data.

[0066] 4. Build a prediction model

[0067] 35 positive cases and 17 negative cases were used as training sets. First, 829 proteins with relatively wide distribution were screened out by using Frequency>30%. Then, molecules with significant expression differences were selected by comparing samples from Parkinson's disease patients with healthy samples (FOT difference multiple greater than 1.5 times, and t-test p value less than 0.05), and a total of 116 proteins were selected as candidate markers.

[0068] Based on the regression classifier and logistic regression analysis, the FOT values ​​of each candidate marker were input into the Caret R package to establish a prediction model (the FOT values ​​of the training set and the test set are shown in Tables 1 and 2, and the FOT values ​​of the external validation set are shown in Tables 3 and 4). According to the code of the Caret R package, the following 23 protein molecular markers were screened as the optimal marker combination for predicting Parkinson's disease: ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG and GAPDH.

[0069] The parameters of the generalized linear regression model are: using the backward regression method to screen markers, and using the train function of the R package Caret for model training and the predict function for prediction. Preferably, the R package of the generalized linear regression model includes: predict.model = train (formula, data = train_data, method = "glm", family = 'binomial') (formula: model formula, input molecular combination; train_data: training set); prediction code: predict (predict.model, test_data) (predict.model: prediction model obtained from the training set, test_data: test set or external validation set).

[0070] The samples were judged based on logistic regression analysis. If the probability of the expression level of the marker combination was greater than 0.5, it was determined to be a positive sample (positive case), otherwise it was a negative sample (negative case).

[0071] Table 1 FOT values ​​of 23 protein markers in the training and test sets

[0072]

[0073]

[0074] Table 2 FOT values ​​of 23 protein markers in the training and test sets

[0075]

[0076]

[0077]

[0078] Table 3 FOT values ​​of 23 protein markers in the external validation set

[0079]

[0080]

[0081] Table 4 FOT values ​​of 23 protein markers in the external validation set

[0082]

[0083] The cerebrospinal fluid samples of Parkinson's disease patients in Huashan Hospital of Fudan University were screened to obtain 23 protein molecular markers (ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG and GAPDH). The relative expression levels of these markers were plotted using ROC curves (Receiver Operating Curves), and a model for analyzing these markers was established. The ROC curves (Receiver Operating Curves) were plotted for the relative expression levels of the proteins of these 23 markers, and the AUC (Area Under the ROC Curve) was calculated. The training set included 35 positive cases and 17 negative cases. AUC = 1.000 (see Figure 1 ).Depend on Figure 2 It can be seen that the diagnostic sensitivity is 100.00%, the specificity is 100.00%, the positive accuracy is 100.00%, and the negative accuracy is 100.00%. The test set includes 25 positive cases and 7 negative cases. AUC = 0.894 (see Figure 3 ).Depend on Figure 4 It can be seen that the diagnostic sensitivity is 75.00%, the specificity is 85.71%, the positive accuracy is 85.71%, and the negative accuracy is 92.00%. The external validation set includes 26 positive cases and 10 negative cases. AUC = 1.000 (see Figure 5 ).Depend on Figure 6 It can be seen that the diagnostic sensitivity is 100.00%, the specificity is 100.00%, the positive accuracy is 100.00%, and the negative accuracy is 100.00%. For the analysis method, see Karimollah Hajian-Tilaki, Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation, Caspian J Intern Med 2013; 4(2): 627-635.

[0084] From the above results, it can be seen that the combination of 23 protein molecular markers (ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG and GAPDH) in the cerebrospinal fluid of patients with Parkinson's disease can be used for early screening and diagnosis of patients with Parkinson's disease.

[0085] To determine whether the sample to be tested is a patient with Parkinson's disease, the expression level (FOT) of the protein molecular biomarker collected by DDA as described above is input into the obtained prediction model to obtain an output result of determining whether the patient has Parkinson's disease.

[0086] Example 2 System for predicting Parkinson's disease risk

[0087] The system 61 for predicting the risk of Parkinson's disease includes a data processing module 52 and a judgment and output module 53, and also includes a data collection module 51 ( Figure 7 ).

[0088] The data collection module 51 is used to collect the expression level data of the biomarker combination in the patient's Parkinson's disease tissue sample and transmit it to the data processing module.

[0089] The data processing module 52 is used to analyze the received or inputted expression level data of the biomarker combination according to the data analysis method described in Example 1 to obtain a calculation result. The expression level data of the biomarker combination can be collected by the data collection module 51, or the expression level data of the biomarker combination can be obtained from other sources.

[0090] The judgment and output module 53 is used to judge whether the calculation result meets the preset judgment condition, that is, the risk probability of suffering from Parkinson's disease is greater than or equal to the predicted risk probability of not suffering from Parkinson's disease, so as to predict the risk of Parkinson's disease and output the prediction result; wherein, in the judgment and output module, when the expression level data meets the judgment condition that the risk probability of suffering from Parkinson's disease is greater than or equal to the predicted risk probability of not suffering from Parkinson's disease, the output prediction result is "having the risk of suffering from Parkinson's disease"; when the expression level data does not meet the judgment condition that the risk probability of suffering from Parkinson's disease is less than the predicted risk probability of not suffering from Parkinson's disease, the output prediction result is "not having the risk of Parkinson's disease".

[0091] Embodiment 3 Electronic device

[0092] This embodiment provides an electronic device, which can be expressed in the form of a computing device (for example, a server device), including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method for predicting the risk of Parkinson's disease in Embodiment 1 of the present invention can be implemented when the processor executes the computer program.

[0093] Figure 8 The hardware structure diagram of this embodiment is shown, and the electronic device 4 specifically includes:

[0094] At least one processor 91, at least one memory 92, and a bus 93 for connecting different system components (including the processor 91 and the memory 92), wherein:

[0095] The bus 93 includes a data bus, an address bus, and a control bus.

[0096] The memory 92 includes a volatile memory, such as a random access memory (RAM) 921 and / or a cache memory 922 , and may further include a read-only memory (ROM) 923 .

[0097] Memory 92 also includes a program / utility 925 having a set (at least one) of program modules 924, such program modules 924 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0098] The processor 91 executes various functional applications and data processing by running the computer programs stored in the memory 92, such as the data analysis method of embodiment 1 of the present invention.

[0099] The electronic device 9 can further communicate with one or more external devices 94 (e.g., keyboards, pointing devices, etc.). Such communication can be performed through an input / output (I / O) interface 95. In addition, the electronic device 9 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 through a bus 93. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 9, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.

[0100] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0101] Embodiment 4 Computer readable storage medium

[0102] The embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the method for predicting the risk of Parkinson's disease in Embodiment 1 of the present invention are implemented.

[0103] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0104] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps of the method for predicting Parkinson's disease risk in Example 1 of the present invention.

[0105] The program code for executing the present invention may be written in any combination of one or more programming languages, and may be executed entirely on a user device, partially on a user device, as an independent software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0106] Finally, the above specific implementation method is only used to illustrate the technical solution of the present invention, but not to limit it.

[0107] Full name of biomarker (refer to genecards database)

[0108] ALCAM: Activated Leukocyte Cell Adhesion Molecule

[0109] IGFALS: Insulin Like Growth Factor Binding Protein Acid Labile SubunitSELPLG: Selectin P Ligand

[0110] ITIH1:Inter-Alpha-Trypsin Inhibitor Heavy Chain 1

[0111] ELN:Elastin

[0112] ITIH2:Inter-Alpha-Trypsin Inhibitor Heavy Chain 2

[0113] LBP:Lipopolysaccharide Binding Protein

[0114] GPX3:Glutathione Peroxidase 3

[0115] LY6H:Lymphocyte Antigen 6Family Member H

[0116] SERPINC1:Serpin Family C Member 1

[0117] WSCD1:WSC Domain Containing 1

[0118] SERPINA6:Serpin Family A Member 6

[0119] GPLD1:Glycosylphosphatidylinositol Specific Phospholipase D1

[0120] PON1:Paraoxonase 1

[0121] GNS:Glucosamine(N-Acetyl)-6-Sulfatase

[0122] SPON2:Spondin 2

[0123] CTSL:Cathepsin L

[0124] CLEC3B:C-Type Lectin Domain Family 3Member B OLFM1:Olfactomedin 1

[0125] CD55:CD55 Molecule(Cromer Blood Group)

[0126] ORM1:Orosomucoid 1

[0127] BSG:Basigin(Ok Blood Group)

[0128] GAPDH:Glyceraldehyde-3-Phosphate Dehydrogenase。

Claims

1. A reagent for detecting a combination of biomarkers, characterized in that: The biomarker panel consists of ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG, and GAPDH; Preferably, the reagent is used to detect the expression level of the biomarker combination; and / or, the reagent is a reagent that specifically binds to the biomarker, or is a biomolecular reagent that specifically hybridizes with the nucleic acid encoding the biomarker; and / or, the reagent is a reagent for genome, transcriptome and / or proteome sequencing; More preferably, the expression level is a protein expression level and / or an mRNA transcription level, and / or the biomolecule reagent is selected from one or more of a primer, a probe and an antibody.

2. A biomarker combination, characterized in that: The biomarker combination consists of ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG and GAPDH.

3. A kit, characterized in that: The kit comprises the reagent according to claim 1 and / or the biomarker combination according to claim 2.

4. Use of the reagent according to claim 1, the biomarker combination according to claim 2, or the kit according to claim 3 in the preparation of a product for predicting or diagnosing Parkinson's disease.

5. A method for constructing a Parkinson's disease model prediction model, characterized in that: The method comprises: inputting protein expression data corresponding to the biomarker combination from the patient sample into the Caret R package containing the logistic regression model for machine learning to obtain a Parkinson's disease prediction model; The biomarker panel consists of ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG, and GAPDH; Preferably, the sample is from plasma, urine, saliva or cerebrospinal fluid; and / or, before machine learning, the sample is subjected to DDA to collect the protein expression data, and peptide matching is performed using Firmiana software; and / or, the sample includes Parkinson's disease patients and healthy people; and / or, the protein expression data input into the logistic regression model meet the following requirements: the protein expression corresponding to the biomarker combination in the sample is 1.5 or more than the corresponding protein expression of a healthy person, and the t-test p value is less than 0.05; More preferably, the peptide segment matching utilizes the UniProt human protein database; and / or, before machine learning, the samples are grouped to obtain training set samples, test set samples and external validation set samples, the training set samples are used to construct a Parkinson's disease prediction model, and the test set samples and the external validation set samples are used to verify the Parkinson's disease prediction model; and / or, the protein expression data input into the logistic regression model has a protein frequency greater than or equal to 30%; and / or, the step of using the external validation set samples for verification includes: calculating the below-the-line area, sensitivity and specificity of the specificity curve of the protein expression data of the biomarker combination in the sample; and judging the accuracy of the prediction model based on the below-the-line area, sensitivity and specificity; and / or, the method also includes judging whether the sample suffers from Parkinson's disease, when the probability is greater than or equal to 0.5, it is judged that the sample suffers from Parkinson's disease; when the probability is less than 0.5, it is judged that the sample does not suffer from Parkinson's disease.

6. A prediction model for Parkinson's disease, characterized in that: The prediction model is constructed by the method according to claim 5.

7. A prediction system for Parkinson's disease, characterized in that: The prediction system includes an analysis and judgment module; the analysis and judgment module contains the prediction model according to claim 6, which is used to output a prediction result of whether the sample suffers from Parkinson's disease; The biomarker panel consists of ALCAM, IGFALS, SELPLG, ITIH1, ELN, ITIH2, LBP, GPX3, LY6H, SERPINC1, WSCD1, SERPINA6, GPLD1, PON1, GNS, SPON2, CTSL, CLEC3B, OLFM1, CD55, ORM1, BSG, and GAPDH; Preferably, the prediction system further comprises a detection module, which detects the protein expression level corresponding to the biomarker combination in the sample to be tested and transmits the expression level data to the analysis and judgment module.

8. Use of the reagent according to claim 2, the biomarker combination according to claim 3, the kit according to claim 4, the prediction model according to claim 6 or the prediction system according to claim 7 in the prediction of Parkinson's disease.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the function of the prediction model according to claim 6 or the prediction system according to claim 7 can be realized.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: The processor is configured to execute the computer program to implement the functions of the prediction model according to claim 6 or the prediction system according to claim 7.

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