CN116769900A - Biomarker combinations and their use in predicting ASD disease progression - Google Patents

Biomarker combinations and their use in predicting ASD disease progression Download PDF

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CN116769900A
CN116769900A CN202310647470.6A CN202310647470A CN116769900A CN 116769900 A CN116769900 A CN 116769900A CN 202310647470 A CN202310647470 A CN 202310647470A CN 116769900 A CN116769900 A CN 116769900A
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李明珠
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Shanghai Aipu Tikang Biotechnology Co ltd
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Abstract

The application discloses a biomarker combination and application thereof in prediction of ASD (autism spectrum disorder) disease progress. The biomarker combination consists of 44 biomarkers, and the disease process is a mild-moderate autism spectrum disorder disease or a severe autism spectrum disorder disease, and the disease process is specifically described in the specification of the application. The 44 biomarkers in the application can be used for risk prediction and detection of patients suffering from mild-moderate and severe autism spectrum disorders, have the advantages of high sensitivity and high specificity, and provide favorable technical support for predicting the disease course of the autism spectrum disorders, intervening treatment and the like.

Description

Biomarker combinations and their use in predicting ASD disease progression
Technical Field
The application belongs to the technical field of biological detection, and particularly relates to a biomarker combination and application thereof in prediction of autism spectrum disorder disease course.
Background
Autism spectrum disorder (Autism Spectrum Disorder, ASD) is a widespread disorder of development, most notably in children, whose symptoms include abnormal language ability, abnormal ability to communicate, narrow interests and patterns of executive behavior. Among autism spectrum disorders, childhood autism spectrum disorders are one of the most severe of childhood psychotic disorders. The etiology of ASD is still an unsolved problem in world medicine so far, and the incidence rate of the disease is significantly different between men and women, and is 6-9:1 in China.
ASD is a multifactorial disorder, possibly caused by genetic and environmental factors. It was found that 10% -20% of ASD patients have etiology in genetic factors, including gene defects and chromosomal abnormalities. Immune dysfunction may be associated with the occurrence and development of autism spectrum disorders. Furthermore, the risk factors during pregnancy may also indirectly lead to the onset of ASD.
Currently, the means of diagnosing ASD in common use include the following: manual for diagnosis and statistics of mental disorders (DMS-5), diagnosis and observation of Autism (ADOS), behavioral autism (Autism Behavior Checklist, ABC), rating scale for autism in children (ChildhoodAutism Rating Scale, CARS), table for language and intellectual screening, and other evaluation scales, electroencephalogram, mri, etc. Although diseases can be clinically screened from multiple angles, the main problems of the existing diagnosis means are that the diagnosis scale is high in subjectivity, misdiagnosis is easy to cause, the evaluation time is short, the actual condition of some patients cannot be reflected, and in addition, the influence of the mental state of the patients on the evaluation result is large.
Early detection and treatment can improve the prognosis of ASD patients, but there is currently a lack of specific tools required for personalized treatment. The blood test can provide the change condition of the expression level of protein molecules in blood, which not only can help to screen and diagnose the disease process condition of the patient suffering from the autism spectrum disorder, but also can provide a new solution idea for researching the occurrence and development of the autism spectrum disorder. Therefore, it is important to find a screening diagnostic marker of ASD disease progression by means of medical tests (e.g. blood tests).
Disclosure of Invention
In order to solve the technical problem of lack of an ASD early diagnosis method in the prior art, the application provides a biomarker combination and application thereof in prediction of the disease course of autism spectrum disorder. The 44 biomarkers can be used for risk prediction and detection of patients suffering from mild-moderate and severe autism spectrum disorder, have the advantages of high sensitivity and high specificity, and provide favorable technical support for predicting the disease course of the autism spectrum disorder, intervening treatment and the like.
The present application provides in a first aspect the use of a biomarker combination consisting of ACADVL, ACTA1, ACTA2, ACTC1, AP2A2, ARHGEF6, BLVRB, CALR, CFHR3, CFHR4, ESD, CLIP1, COPE, BAG2, CSN1S1, DBI, DDR1, EIF5B, FBN1, FTH1, GLUD2, GOSR2, H2AC21, H2AX, HRNR, KANK2, MAT2B, MCM2, PPIB, PSME1, QSOX1, MMRN2, RANBP2, SERPINB3, SPARC, SOD1, SUPT16H, SYNM, TUBB2A, TUBB2B, TUBB4A, USP, TXNRD1 and UTRN for the preparation of a product for the prediction of the course of an autism spectrum disorder, said course being a light or moderate autism spectrum disorder.
The judgment of the mild-moderate autism spectrum disorder disease and the severe autism spectrum disorder disease according to the present application can be judged by the standard common in the art.
In a preferred embodiment, the diagnostic criteria are:
items with a total score between 30 and 36 points and less than 3 points were rated as light to medium with less than 5 items.
Total score is greater than or equal to 36 and at least 5 scores are greater than 3 score for severity.
The following table can be found in particular for the Childhood Autism Rating Scale (CARS):
in another preferred embodiment, the judgment criteria are mainly represented by defects in three aspects of sociality and communication ability, language ability and ceremonies of the inscription behavior:
mild, manifested as social affective defects, speech ability, but speech impairment.
Moderately, there are obvious defects in individual linguistic, non-linguistic communication and communication abilities, and social interactions will not develop.
Severe, social interaction and communication difficulties, severe defects in individual language and non-language interaction skills, repetitive behavioral behaviors and abnormal interests, and no response to social willingness.
Autism spectrum disorders can be divided into three categories, mild, moderate and severe, depending on the severity of the clinical manifestations.
In a second aspect, the application provides a reagent combination for detecting a biomarker combination consisting of ACADVL, ACTA1, ACTA2, ACTC1, AP2A2, ARHGEF6, BLVRB, CALR, CFHR3, CFHR4, ESD, CLIP1, COPE, BAG2, CSN1S1, DBI, DDR1, EIF5B, FBN1, FTH1, glad 2, GOSR2, H2AC21, H2AX, HRNR, KANK2, MAT2B, MCM2, PPIB, PSME1, QSOX1, MMRN2, RANBP2, SERPINB3, SPARC, SOD1, SUPT16H, SYNM, TUBB2A, TUBB2B, TUBB4A, USP14, TXNRD1 and UTRN.
In a preferred embodiment, the combination of reagents is used to detect the expression level of the biomarker combination.
In a preferred embodiment, the combination of reagents comprises a reagent that specifically binds to the biomarker, or a biomolecular reagent that specifically hybridizes to a nucleic acid encoding the biomarker.
In a certain preferred embodiment, the combination of reagents comprises reagents for genomic, transcriptomic and/or proteomic sequencing.
In a preferred embodiment, the expression level is a protein expression level and/or an mRNA transcription level, and/or the biomolecular reagent is selected from one or more of a primer, a probe and an antibody.
Preferably, the protein expression level is detected by one or more of mass spectrometry, chips such as protein chips or microfluidic chips, digital single molecule immunoassays, ELISA, radioimmunoassays, immunonephelometry, immunohistochemistry, and Western blotting.
In a third aspect, the application provides a biomarker combination consisting of ACADVL, ACTA1, ACTA2, ACTC1, AP2A2, ARHGEF6, BLVRB, CALR, CFHR3, CFHR4, ESD, CLIP1, COPE, BAG2, CSN1S1, DBI, DDR1, EIF5B, FBN1, FTH1, glad 2, GOSR2, H2AC21, H2AX, HRNR, KANK2, MAT2B, MCM2, PPIB, PSME1, QSOX1, MMRN2, RANBP2, SERPINB3, SPARC, SOD1, SUPT16H, SYNM, TUBB2A, TUBB2B, TUBB A, USP, TXNRD1, and UTRN.
In a fourth aspect the application provides a kit comprising a combination of reagents as described in the second aspect of the application and/or a combination of biomarkers as described in the third aspect of the application.
In a fifth aspect, the present application provides a method of constructing a predictive model of disease progression in autism spectrum disorder, the method comprising: inputting protein expression data corresponding to biomarker combinations from patient samples into an R language Caret package containing a logistic regression model for machine learning to obtain an autism spectrum disorder disease process prediction model;
the biomarker combination consists of ACADVL, ACTA1, ACTA2, ACTC1, AP2A2, ARHGEF6, BLVRB, CALR, CFHR3, CFHR4, ESD, CLIP1, COPE, BAG2, CSN1S1, DBI, DDR1, EIF5B, FBN1, FTH1, glad 2, GOSR2, H2AC21, H2AX, HRNR, KANK2, MAT2B, MCM2, PPIB, PSME1, QSOX1, MMRN2, RANBP2, SERPINB3, SPARC, SOD1, SUPT16H, SYNM, TUBB2A, TUBB B, TUBB4A, USP14, TXNRD1, and UTRN; the course is a mild to moderate autism spectrum disorder or a severe autism spectrum disorder.
The Caret R package is the R language Caret package.
In a preferred embodiment, the sample is a body fluid exosome.
In a preferred embodiment, the sample is from blood, urine, saliva or cerebrospinal fluid.
Preferably, the blood is serum or plasma.
In a preferred embodiment, the samples are subjected to DIA-mode collection of the protein expression data and peptide fragment matching by Firmiana software prior to machine learning.
In a certain preferred embodiment, the patient sample is from a patient suffering from a disease comprising a mild to moderate autism spectrum disorder and a patient suffering from a severe autism spectrum disorder.
In a preferred embodiment, the FOT of the protein corresponding to the biomarker combination is input as protein expression data into the R language caret package of the logistic regression model for machine learning.
In a preferred embodiment, before machine learning, the samples are grouped to obtain a modeling group sample and a validation group sample, wherein the modeling group sample is used for constructing an autism spectrum disorder disease process prediction model, and the validation group sample is used for validating the autism spectrum disorder disease process prediction model.
In a preferred embodiment, the protein expression data entered into the logistic regression model satisfy: protein expression data input into the logistic regression model satisfy: the biomarker panel for the patient with severe autism spectrum disorder in the sample has a corresponding protein expression level that is 1.5 or more times greater than the corresponding protein expression level for the patient with mild-moderate autism spectrum disorder, and a t-test p-value of less than 0.05.
In a preferred embodiment, the peptide fragment matches utilize the UniProt human protein database.
In a preferred embodiment, the protein expression data entered into the logistic regression model is protein abundance greater than or equal to 30%.
In a preferred embodiment, the step of using the validation set sample for validation comprises: calculating the area under the line, the sensitivity and the specificity of a specificity curve of the protein expression data of the biomarker combinations in the sample; and judging the accuracy of the prediction model according to the offline area, sensitivity and specificity.
In a preferred embodiment, the method further comprises determining the progression of autism spectrum disorder in the sample, and determining that the patient is a severe autism spectrum disorder when the probability is greater than or equal to 0.5; when the probability is less than 0.5, it is judged that the disease is a mild-moderate autism spectrum disorder disease.
In a preferred embodiment, the method further comprises determining the autism spectrum disorder disease progression of the sample: when the probability of suffering from the mild-moderate autism spectrum disorder disease is greater than or equal to the probability of suffering from the severe autism spectrum disorder disease, judging that the sample suffers from the mild-moderate autism spectrum disorder disease; when the probability of suffering from severe autism spectrum disorder is less than the probability of suffering from mild moderate autism spectrum disorder, the sample is judged to suffer from severe autism spectrum disorder.
In a preferred embodiment of the application, the protein expression data are obtained by LC-MS technology and collected using DIA (data-dependent acquisition, data dependent) assay.
Preferably, the peptide fragment matching is carried out on the data acquired in the DIA detection mode through Firmiana software. More preferably, the database of peptide segment matches is the UniProt human protein database.
Further preferably, the protein expression data after the Firmiana treatment is used: protein quantification was performed using the unlabeled intensity-based absolute quantification (iBAQ) method, FOT (Fraction of total) was calculated for each protein, defined as the iBAQ (intensity-based absorption-protein-quantification) of that protein divided by the total iBAQ of all identified proteins in the sample, and FOT for each protein was entered as protein expression data into a logistic regression model.
A sixth aspect of the application provides a predictive model of the disease progression of autism spectrum disorder, the predictive model being constructed by a method as described in the fifth aspect of the application.
A seventh aspect of the present application provides a prediction system for an autism spectrum disorder disease process, where the prediction system includes an analysis and judgment module, where the analysis and judgment module includes a prediction model according to the fifth aspect of the present application, and is configured to judge a probability of suffering from a mild-moderate autism spectrum disorder disease or a severe autism spectrum disorder disease;
wherein the biomarker combination consists of ACADVL, ACTA1, ACTA2, ACTC1, AP2A2, ARHGEF6, BLVRB, CALR, CFHR3, CFHR4, ESD, CLIP1, COPE, BAG2, CSN1S1, DBI, DDR1, EIF5B, FBN1, FTH1, glad 2, GOSR2, H2AC21, H2AX, HRNR, KANK2, MAT2B, MCM2, PPIB, PSME1, QSOX1, MMRN2, RANBP2, SERPINB3, SPARC, SOD1, SUPT16H, SYNM, TUBB2A, TUBB B, TUBB4A, USP14, TXNRD1, and UTRN.
In a preferred embodiment, the prediction system further comprises an output module and/or a detection module; the output module outputs the judging result of the analysis judging module, and the detection module detects the protein expression level corresponding to the biomarker combination in the sample to be detected and transmits the expression level data to the analysis judging module.
In an eighth aspect the application provides a method of predicting the course of an autism spectrum disorder disease, whereby a sample is predicted using an agent according to the second aspect of the application, a biomarker combination according to the third aspect of the application, a kit according to the fourth aspect of the application, a prediction model according to the sixth aspect of the application or a prediction system according to the seventh aspect of the application.
A ninth aspect of the application provides the use of a combination of agents according to the second aspect of the application, a combination of biomarkers according to the third aspect of the application, a kit according to the fourth aspect of the application, a predictive model according to the sixth aspect of the application or a predictive system according to the seventh aspect of the application for the prediction of progression of autism spectrum disorder disease.
In a tenth aspect the application provides the use of a combination of agents according to the second aspect of the application for the preparation of a kit for the prediction or diagnosis of the course of an autism spectrum disorder disease; wherein the biomarker combination consists of ACADVL, ACTA1, ACTA2, ACTC1, AP2A2, ARHGEF6, BLVRB, CALR, CFHR3, CFHR4, ESD, CLIP1, COPE, BAG2, CSN1S1, DBI, DDR1, EIF5B, FBN1, FTH1, glad 2, GOSR2, H2AC21, H2AX, HRNR, KANK2, MAT2B, MCM2, PPIB, PSME1, QSOX1, MMRN2, RANBP2, SERPINB3, SPARC, SOD1, SUPT16H, SYNM, TUBB2A, TUBB B, TUBB4A, USP14, TXNRD1, and UTRN.
An eleventh aspect of the present application provides a computer readable storage medium storing a computer program which, when executed by a processor, performs the steps of the method according to the eighth aspect of the present application or performs the function of the predictive model according to the sixth aspect of the present application or the predictive system according to the seventh aspect of the present application.
A twelfth aspect of the application provides an electronic device comprising a memory storing a computer program for executing the computer program for carrying out the steps of the method according to the eighth aspect of the application or for carrying out the functions of the predictive model according to the sixth aspect of the application or the predictive system according to the seventh aspect of the application.
The inventor analyzes the plasma biomarker which can be applied to clinical prediction of the disease course of the autism spectrum disorder by researching the variation trend of the protein molecular expression level in the plasma samples of the patient with the light and moderate autism spectrum disorder and the patient with the severe autism spectrum disorder similar to the conditions of the patient with the light and moderate autism spectrum disorder, and provides possibility for the prediction of the disease course of the patient with the autism spectrum disorder and early intervention treatment.
On the basis of conforming to the common knowledge in the field, the above preferred conditions can be arbitrarily combined to obtain the preferred examples of the application.
The reagents and materials used in the present application are commercially available.
The application has the positive progress effects that:
experiments show that the expression level of the 44 protein biomarkers in autism spectrum disorder patients with different disease processes is obviously changed, so that the 44 protein biomarkers provided by the application can be used as risk prediction and detection of patients with mild-moderate and severe autism spectrum disorder, have the advantages of high sensitivity and high specificity, and provide favorable technical support for predicting the autism spectrum disorder disease process, intervening treatment and the like.
The corresponding prediction kit is developed based on the plasma protein biomarkers of autism spectrum disorder patients with different processes, has wide scientific research value and provides great convenience for early clinical diagnosis, intervention treatment and the like.
Drawings
Figure 1 is a ROC curve for a training set for 44 protein molecular biomarker combinations in the light moderate autism spectrum disorder disease group and the severe autism spectrum disorder disease group.
FIG. 2 is a ROC curve of a model constructed from a combination of 44 protein biomarkers for predicting an internal test set.
Fig. 3 is a schematic diagram of the architecture of a system for predicting the risk of progression of autism spectrum disorder disease.
Fig. 4 is a schematic structural diagram of an electronic device.
Detailed Description
The application is further illustrated by means of the following examples, which are not intended to limit the scope of the application. The experimental methods, in which specific conditions are not noted in the following examples, were selected according to conventional methods and conditions, or according to the commercial specifications.
Example 1
Plasma samples from patients with autism spectrum disorder at different disease courses required in the examples were all from third affiliated hospitals at the university of Zhengzhou, 40 of the light moderate autism spectrum disorder disease groups and 45 of the severe autism spectrum disorder disease groups. The design and implementation of this study was approved and supervised by the third affiliated hospital medical ethics committee of the university of zheng by ethical voting. Written informed consent was obtained for all patients.
1. Separation of plasma
Collecting whole blood sample, mixing in EDTA anticoagulant tube, centrifuging at 4deg.C for 10min with 1,600Xg, centrifuging, collecting supernatant (blood plasma) in new EP tube, centrifuging at 16,000Xg for 10min to remove cell debris, packaging blood plasma in centrifuge tube, and freezing at-80deg.C for use.
2. Plasma sample pretreatment
To 2. Mu.L of plasma sample was added ammonium bicarbonate at a concentration of 100. Mu.L of 50mM, vortexing was performed for 1min, the sample was incubated at 95℃for 4min to thermally denature the protein, after cooling to room temperature, 2. Mu.g of Trypsin (Trypsin) was added to the system, and shaking was performed at 37℃for 18h, and then 10. Mu.L of ammonia was added to the system to stop the enzymatic hydrolysis. Desalting the peptide sample after enzymolysis, pumping, and freezing at-80 ℃ until mass spectrum detection.
3. Mass spectrometric detection of ASD plasma samples
The peptide sample was detected by a Orbitrap Fusion Lumos three-in-one high resolution mass spectrometry system (Thermo Fisher Scientific, rockford, USA) in tandem with a high performance liquid chromatography system (EASY-nLC 1200,Thermo Fisher) and mass spectrometry data of the whole protein corresponding to the peptide sample was obtained. The specific operation is as follows:
adopts nano-flow liquid chromatography, the chromatographic column is a self-made C18 chromatographic column (150 μm ID multiplied by 8cm, and (3) filling). The temperature of the column temperature box is 60 ℃. The dry powder peptide is re-dissolved by using a loading buffer (0.1% formic acid aqueous solution), separated by a chromatographic column after loading, eluted by 600nL/min of linear 6-30% mobile phase B (ACN and 0.1% formic acid), and a mass spectrum detection means of Data Independent Acquisition (DIA) is utilized. The DIA mass spectrometry detection parameters were set as follows: the ion mode is positive ions; the resolution of the primary mass spectrum is 30K, the maximum injection time is 20ms, the AGC Target is 3e6, and the scanning range is 300-1400m/z; the secondary scanning resolution is 15K, 30 variable isolation windows are acquired, and the collision energy is 27%. The liquid chromatography tandem mass spectrometry system uses Xcalibur software control for data acquisition.
4. Data analysis
All data were searched using Firmiana. The Firmiana is a workflow based on Galaxy system, and consists of a plurality of functional modules such as a user login interface, raw data, identification and quantification, data analysis, knowledge mining and the like. DIA data were searched against the UniProt human protein database (updated at 2013.07.04, 32015 entries) using FragPipe (v 12.1) and MSFragger (2.2). The mass difference of the parent ion was 20ppm and the mass difference of the daughter ion was 50mmu. At most two leaky sites are allowed. The search engine sets cysteine carbamoyl methylation as the fixed modification and N-acetylation and oxidation of methionine as the variable modification. The parent ion charge range is set to +2, +3, and +4. The error discovery rate (FDR) was set to 1%.
The identified peptide fragment quantification results are recorded as the average of the peak areas of chromatographic fragment ions in all reference spectra libraries. Protein quantification was performed using the unlabeled intensity-based absolute quantification (iBAQ) method. We calculated the peak area values as part of the corresponding proteins. Total Fraction (FOT) is used to represent normalized abundance of a particular protein in a sample. FOT is defined as the iBAQ of the protein divided by the total iBAQ of all identified proteins in the sample. Proteins with at least one proprietary peptide fragment (unique peptide) and 1% fdr were selected for further analysis.
5. Establishing a predictive model
63 samples (75%) were randomly drawn from all samples as training sets (i.e., modeling) and the remaining 22 samples (25%) as internal test sets. Firstly, 48 kinds of more extensive and most existing proteins are screened out through abundance > 30%. And selecting molecules with obvious difference in expression (FOT difference multiple is more than 1.5 times and t-test p value is less than 0.05) between the light and medium autism spectrum disorder disease samples and the severe autism spectrum disorder disease samples by comparison, wherein 44 proteins are selected as candidate markers.
Based on the regression classifier, FOT values of the candidate markers are input into an R language Caret package to establish a prediction model according to logistic regression analysis.
The following biomarker combinations were analyzed and screened by the R language Caret package:
ACATVL, ACTA1, ACTA2, ACTC1, AP2A2, ARHGEF6, BLVRB, CALR, CFHR, CFHR4, ESD, CLIP1, COPE, BAG2, CSN1S1, DBI, DDR1, EIF5B, FBN1, FTH1, GLUD2, GOSR2, H2AC21, H2AX, HRNR, KANK2, MAT2B, MCM2, PPIB, PSME1, QSOX1, MMRN2, RANBP2, SERPINB3, SPARC, SOD1, SUPT16H, SYNM, TUBB2A, TUBB2B, TUBB4A, USP14, TXNRD1 and UTRN.
6. Early screening protein biomarkers for autism spectrum disorder disease progression
Classifier set-up for early screening of protein molecules for the progression of autism spectrum disorder disease includes two stages, discovery and testing.
In the examples, the blood samples of 85 patients with mild, moderate and severe autism spectrum disorders were randomly divided into a training set containing 63 samples (75%) randomly drawn and an internal test set, the remaining 22 samples (25%) being the internal test set. And constructing a classifier by adopting a logistic regression (Logistic Regression) algorithm on 63 samples in the training set. Logistic regression employs a 10-fold cross-validation method in estimating error rates, first dividing 63 samples randomly into 10 aliquots. The model was constructed with 9 aliquots of the samples, and the test was performed with the remaining 10% of the samples, and repeated 10 times, and the average value of the ROC curve (Receiver Operating Curve) was calculated for 10 times. Analytical methods are described in Karimollah Hajian-Tilaki, receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation, caspian J Intern Med2013;4 (2):627-635.
ROC curves were plotted (Receiver Operating Curve) for the relative expression levels of the 44 protein biomarkers obtained from the screening in the training set and models were created to analyze these markers. ROC curves were plotted against protein relative expression levels of these 44 markers and AUC was calculated, auc=1.000, diagnostic sensitivity 100.00% and specificity 100.00% (as shown in fig. 1).
The model of these 44 protein molecules was used to predict the internal test set, auc=0.888 for 22 internal test sets, diagnostic sensitivity 92.31%, specificity 77.78% (as shown in fig. 2).
From the above results, it can be seen that the use of 44 protein biomarkers (ACADVL, ACTA1, ACTA2, ACTC1, AP2A2, ARHGEF6, BLVRB, CALR, CFHR3, CFHR4, ESD, CLIP1, COPE, BAG2, CSN1S1, DBI, DDR1, EIF5B, FBN1, FTH1, glad 2, GOSR2, H2AC21, H2AX, HRNR, KANK2, MAT2B, MCM2, PPIB, PSME1, QSOX1, MMRN2, RANBP2, SERPINB3, SPARC, SOD1, SUPT16H, SYNM, TUBB2A, TUBB2B, TUBB 354A, USP14, TXNRD1, UTRN) in plasma of patients with autism spectrum disorders of different courses can be used to predict the course of autism spectrum disorder disease and to intervene early.
Inputting the FOT of the protein molecular biomarker collected by the DIA into the obtained prediction model for a sample to be tested to obtain an output result of predicting the disease course of the autism spectrum disorder, namely judging the autism spectrum disorder disease for the severity when the probability is more than or equal to 0.5; when the probability is less than 0.5, it is judged that the disease is a mild-moderate autism spectrum disorder disease.
Example 2 System for predicting the progression of autism spectrum disorder disease
System 61 for predicting the course of autism spectrum disorder disease: the data processing module 52 and the judging and outputting module 53 further include a data collecting module 51 (fig. 3).
The data collection module 51 is used to collect expression level data of the biomarker combinations in a patient's body fluid exosome sample (such as plasma, urine, saliva or cerebrospinal fluid) and transmit it to the data processing module.
The data processing module 52 is configured to analyze the expression level data of the received or input biomarker combinations according to the data analysis method described in example 1 to obtain a calculation result. Wherein the expression level data of the biomarker combinations can be collected by the data collection module 51, and the expression level data of the biomarker combinations can also be obtained from other sources.
The judging and outputting module 53 is configured to judge whether the calculated result meets a preset judging condition, that is, the probability of suffering from a disease with a mild-moderate autism spectrum disorder is greater than or equal to the probability of suffering from a disease with a severe autism spectrum disorder, so as to predict the course of the disease with the autism spectrum disorder, and output a prediction result; wherein, in the judging and outputting module, when the expression level data satisfies that the probability of suffering from the disease of the light-medium autism spectrum disorder is greater than or equal to the probability of suffering from the disease of the severe autism spectrum disorder, outputting a prediction result of "risk of suffering from the disease of the light-medium autism spectrum disorder"; when the expression level data does not meet the judgment condition, i.e., the probability of suffering from a disease of mild to moderate autism spectrum disorder is smaller than the probability of suffering from a disease of severe autism spectrum disorder, outputting a prediction result as "risk of suffering from a disease of severe autism spectrum disorder".
Example 3 electronic device
The present embodiment provides an electronic device, which may be expressed in the form of a computing device (e.g., may be a server device), including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor may implement the method for predicting the course of autism spectrum disorder disease in embodiment 1 of the present application when executing the computer program.
Fig. 4 shows a schematic diagram of the hardware structure of the present embodiment, and the electronic device 4 specifically includes:
at least one processor 91, at least one memory 92, and a bus 93 for connecting the different system components (including the processor 91 and the memory 92), wherein:
the bus 93 includes a data bus, an address bus, and a control bus.
The memory 92 includes volatile memory such as Random Access Memory (RAM) 921 and/or cache memory 922, and may further include Read Only Memory (ROM) 923.
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 or some combination of which may include an implementation of a network environment.
The processor 91 executes various functional applications and data processing, such as the data analysis method of embodiment 1 of the present application, by running a computer program stored in the memory 92.
The electronic device 9 may further communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication may occur through an input/output (I/O) interface 95. Also, the electronic device 9 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 via the bus 93. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection 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, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module in accordance with embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Embodiment 4 computer-readable storage Medium
An embodiment of the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of predicting the course of an autism spectrum disorder disease in embodiment 1 of the present application.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the application may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of implementing the method for predicting the course of autism spectrum disorder disease in embodiment 1 of the application, when said program product is run on the terminal device.
Wherein the program code for carrying out the application may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
Finally, the above embodiments are only for illustrating the technical solution of the present application, and are not limiting.
Biomarker holly (refer to genegards database)
ACADVL：Acyl-CoA Dehydrogenase Very Long Chain
ACTA1：Actin Alpha 1
ACTA2：Actin Alpha 2
ACTC1：Actin Alpha Cardiac Muscle 1
AP2A2：Adaptor Related Protein Complex 2Subunit Alpha 2
ARHGEF6：Rho Guanine Nucleotide Exchange Factor 6
BLVRB：Biliverdin Reductase B
CALR：CalreticulinCFHR3：Complement Factor H Related 3CFHR4：Complement Factor H Related 4ESD：Esterase DCLIP1：CAP-Gly Domain Containing Linker Protein 1COPE：COPI Coat Complex Subunit EpsilonBAG2：BAG Cochaperone 2
CSN1S1：Casein Alpha S1
DBI：Diazepam Binding InhibitorDDR1：Discoidin Domain Receptor Tyrosine Kinase 1EIF5B：Eukaryotic Translation Initiation Factor 5BFBN1：Fibrillin 1
FTH1：Ferritin Heavy Chain 1
GLUD2：Glutamate Dehydrogenase 2GOSR2：Golgi SNAP Receptor Complex Member 2
H2AC21：H2A Clustered Histone 21H2AX：H2A.X Variant Histone
HRNR：HornerinKANK2：KN Motif And Ankyrin Repeat Domains 2MAT2B：Methionine Adenosyltransferase 2BMCM2：Minichromosome Maintenance Complex Component 2PPIB：Peptidylprolyl Isomerase BPSME1：Proteasome Activator Subunit 1QSOX1：Quiescin Sulfhydryl Oxidase 1MMRN2：Multimerin 2
RANBP2：RAN Binding Protein 2
SERPINB3：Serpin Family B Member 3SPARC：Secreted Protein Acidic And Cysteine RichSOD1：Superoxide Dismutase 1
SUPT16H：SPT16 Homolog,Facilitates Chromatin Remodeling SubunitSYNM：Synemin
TUBB2A：Tubulin Beta 2A Class IIa
TUBB2B：Tubulin Beta 2B Class IIb
TUBB4A：Tubulin Beta 4A Class IVaUSP14：Ubiquitin Specific Peptidase 14
TXNRD1：Thioredoxin Reductase 1
UTRN：Utrophin。

Claims (16)

1. Use of a biomarker combination consisting of ACADVL, ACTA1, ACTA2, ACTC1, AP2A2, ARHGEF6, BLVRB, CALR, CFHR3, CFHR4, ESD, CLIP1, COPE, BAG2, CSN1S1, DBI, DDR1, EIF5B, FBN1, FTH1, glad 2, GOSR2, H2AC21, H2AX, HRNR, KANK2, MAT2B, MCM2, PPIB, PSME1, QSOX1, MMRN2, RANBP2, SERPINB3, SPARC, SOD1, SUPT16H, SYNM, TUBB2A, TUBB B, TUBB4A, USP14, TXNRD1 and UTRN for the preparation of a product for the prediction of an autism spectrum disorder disease progression, which progression is a light-moderate autism spectrum disorder disease or a severe autism spectrum disorder disease.
2. A reagent combination for detecting a biomarker combination, characterized in that the biomarker combination consists of ACADVL, ACTA1, ACTA2, ACTC1, AP2A2, ARHGEF6, BLVRB, CALR, CFHR3, CFHR4, ESD, CLIP1, COPE, BAG2, CSN1S1, DBI, DDR1, EIF5B, FBN1, FTH1, GLUD2, GOSR2, H2AC21, H2AX, HRNR, KANK2, MAT2B, MCM2, PPIB, PSME1, QSOX1, MMRN2, RANBP2, SERPINB3, SPARC, SOD1, SUPT16H, SYNM, TUBB2A, TUBB2B, TUBB4A, USP, TXNRD1 and UTRN.
3. The combination of reagents according to claim 2, wherein the combination of reagents is used to detect the expression level of the combination of biomarkers;
and/or the combination of reagents comprises a reagent that specifically binds to the biomarker, or a biomolecular reagent that specifically hybridizes to a nucleic acid encoding the biomarker;
and/or, the combination of reagents includes reagents for genomic, transcriptomic, and/or proteomic sequencing.
4. The combination of reagents according to claim 3, wherein the expression level is protein expression level and/or mRNA transcription level and/or the biomolecular reagent is selected from one or more of a primer, a probe and an antibody.
5. A biomarker combination, comprising ACADVL, ACTA1, ACTA2, ACTC1, AP2A2, ARHGEF6, BLVRB, CALR, CFHR3, CFHR4, ESD, CLIP1, COPE, BAG2, CSN1S1, DBI, DDR1, EIF5B, FBN1, FTH1, GLUD2, GOSR2, H2AC21, H2AX, HRNR, KANK2, MAT2B, MCM2, PPIB, PSME1, QSOX1, MMRN2, RANBP2, SERPINB3, SPARC, SOD1, SUPT16H, SYNM, TUBB2A, TUBB2B, TUBB4A, USP14, TXNRD1, and UTRN.
6. A kit comprising a combination of reagents according to any one of claims 2 to 4 and/or a biomarker combination according to claim 5.
7. A method of constructing a predictive model of the progression of an autism spectrum disorder, the method comprising: inputting protein expression data corresponding to biomarker combinations from patient samples into an R language Caret package containing a logistic regression model for machine learning to obtain an autism spectrum disorder disease process prediction model;
the biomarker combination consists of ACADVL, ACTA1, ACTA2, ACTC1, AP2A2, ARHGEF6, BLVRB, CALR, CFHR3, CFHR4, ESD, CLIP1, COPE, BAG2, CSN1S1, DBI, DDR1, EIF5B, FBN1, FTH1, glad 2, GOSR2, H2AC21, H2AX, HRNR, KANK2, MAT2B, MCM2, PPIB, PSME1, QSOX1, MMRN2, RANBP2, SERPINB3, SPARC, SOD1, SUPT16H, SYNM, TUBB2A, TUBB B, TUBB4A, USP14, TXNRD1, and UTRN; the course is a mild to moderate autism spectrum disorder or a severe autism spectrum disorder.
8. The method of claim 7, wherein the sample is from blood, urine, saliva, or cerebrospinal fluid;
and/or, before machine learning, the sample acquires the protein expression data in a DIA mode and carries out peptide segment matching through Firmiana software;
and/or, the patient samples are from patients with diseases including mild to moderate autism spectrum disorder and patients with severe autism spectrum disorder;
and/or, inputting the FOT of the protein corresponding to the biomarker combination as protein expression data into an R language caret package of a logistic regression model for machine learning;
and/or grouping the samples before machine learning to obtain a modeling group sample and a verification group sample, wherein the modeling group sample is used for constructing an autism spectrum disorder disease process prediction model, and the verification group sample is used for verifying the autism spectrum disorder disease process prediction model.
9. The method of claim 8, wherein the peptide fragment matching utilizes a UniProt human protein database;
and/or, the protein expression data input into the logistic regression model satisfies: the biomarker panels of patients with severe autism spectrum disorder in the sample correspond to protein expression levels that are 1.5 or more than 1.5 times the corresponding protein expression levels of patients with mild autism spectrum disorder, and the t-test p-value is less than 0.05;
and/or inputting protein expression data of the logistic regression model to the protein abundance of more than or equal to 30%;
and/or the step of employing the validation set sample for validation comprises: calculating the area under the line, the sensitivity and the specificity of a specificity curve of the protein expression data of the biomarker combinations in the sample; judging the accuracy of a prediction model according to the offline area, sensitivity and specificity;
and/or, the method further comprises judging the autism spectrum disorder disease course of the sample, and judging the autism spectrum disorder disease to be the severe autism spectrum disorder disease when the probability is greater than or equal to 0.5; when the probability is less than 0.5, it is judged that the disease is a mild-moderate autism spectrum disorder disease.
10. A predictive model of the course of an autism spectrum disorder disease, characterized in that it is constructed by a method according to any one of claims 7-9.
11. A prediction system for the course of autism spectrum disorder disease, comprising an analysis and judgment module comprising the prediction model of claim 10 for judging the probability of a sample suffering from a mild-moderate autism spectrum disorder disease or a severe autism spectrum disorder disease.
12. The prediction system of claim 11, wherein the prediction system further comprises an output module and/or a detection module; the output module outputs the judging result of the analysis judging module, and the detection module detects the protein expression level corresponding to the biomarker combination in the sample to be detected and transmits the expression level data to the analysis judging module.
13. Use of a predictive model according to claim 10 or a predictive system according to claim 11 or 12 for the prediction of the course of an autism spectrum disorder disease.
14. Use of a combination of agents according to any one of claims 2 to 4 for the preparation of a kit for the prediction or diagnosis of a disease course of autism spectrum disorder, either a mild-moderate autism spectrum disorder disease or a severe autism spectrum disorder disease.
15. A computer readable storage medium storing a computer program, which, when executed by a processor, performs the function of implementing the predictive model of claim 10 or the predictive system of claim 11 or 12.
16. An electronic device comprising a memory storing a computer program and a processor, wherein the processor is configured to execute the computer program to implement the functionality of the predictive model of claim 10 or the predictive system of claim 11 or 12.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117074679A (en) * 2023-09-20 2023-11-17 上海爱谱蒂康生物科技有限公司 Biomarker combination and application thereof in predicting effect of immunotherapy combined with chemotherapy in treating esophageal cancer

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117074679A (en) * 2023-09-20 2023-11-17 上海爱谱蒂康生物科技有限公司 Biomarker combination and application thereof in predicting effect of immunotherapy combined with chemotherapy in treating esophageal cancer
CN117074679B (en) * 2023-09-20 2024-06-11 上海爱谱蒂康生物科技有限公司 Biomarker combination and application thereof in predicting effect of immunotherapy combined with chemotherapy in treating esophageal cancer

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