A prediction method, system, electronic device and storage medium for congenital heart disease combined with intellectual disability

Abstract

The present invention discloses a prediction method, system, electronic device and storage medium for congenital heart disease combined with intellectual retardation, and relates to the field of intelligent medical technology. The method comprises: obtaining gene expression data of a sample to be tested; extracting expression data of a target gene in the gene expression data, wherein the target gene comprises any one or more combinations of the following: RAB3IL1, XKR3, H2BC17; performing classification prediction based on the expression data of the target gene to obtain a classification result of whether the sample to be tested is a sample of congenital heart disease combined with intellectual retardation; the present invention provides information for early intervention treatment of congenital heart disease combined with intellectual retardation, and has broad application prospects.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent medical technology, and specifically relates to a prediction method, system, electronic device and storage medium for congenital heart disease combined with intellectual disability. Background Art

[0002] Congenital heart disease (CHD) is the most common type of congenital malformation, accounting for about 28% of all congenital malformations. It is referred to as CHD. Congenital heart disease refers to anatomical abnormalities caused by formation disorders or abnormal development of the heart and large blood vessels during embryonic development, or the failure of channels that should close automatically after birth to close (normal in the fetus). Epidemiological survey data show that the incidence of congenital heart disease in live births is 0.8%-1.2%. Some children with CHD have intellectual disabilities, which has seriously affected the health of children under 5 years old.

[0003] Therefore, developing a non-invasive prediction method that can effectively predict the combination of congenital heart disease and intellectual developmental delay has important clinical significance for the early diagnosis and early intervention treatment of congenital heart disease and intellectual developmental delay. In addition to the early diagnosis of congenital heart disease and intellectual developmental delay by imaging, understanding the molecular genetic mechanism of congenital heart disease and intellectual developmental delay, and clarifying the key genes and related signal pathway regulation of congenital heart disease and intellectual developmental delay are of great significance for the early diagnosis and early intervention treatment of congenital heart disease and intellectual developmental delay. Therefore, this field urgently needs to develop an efficient prediction method for congenital heart disease and intellectual developmental delay. Summary of the invention

[0004] In view of this, the object of the present invention is to provide a prediction method, system, electronic device and storage medium for congenital heart disease combined with intellectual disability.

[0005] The present invention aims to solve the technical problem in the prior art of the lack of an efficient prediction method for congenital heart disease combined with intellectual developmental delay. To this end, the present invention provides a prediction method, system, electronic device and storage medium for congenital heart disease combined with intellectual developmental delay. The present invention detects the expression level of the target gene in the sample to be tested to achieve the classification result of predicting whether the subject from the sample to be tested suffers from congenital heart disease combined with intellectual developmental delay, provides information for early intervention treatment of congenital heart disease combined with intellectual developmental delay, and explains the complex genetic factors related to congenital heart disease combined with intellectual developmental delay.

[0006] The above-mentioned object of the present invention is achieved through the following technical solutions:

[0007] A first aspect of the present invention provides a method for predicting congenital heart disease combined with intellectual disability, the method comprising:

[0008] Obtaining gene expression data of the sample to be tested;

[0009] Extracting expression data of target genes from the gene expression data, wherein the target genes include any one or more combinations of the following: RAB3IL1, XKR3, H2BC17;

[0010] Based on the expression data of the target gene, classification prediction is performed to obtain a classification result of whether the sample to be tested is a sample of congenital heart disease combined with intellectual disability;

[0011] The classification result is obtained based on a prediction model, and the method for constructing the prediction model includes: obtaining the target gene expression data of the training set samples and the clinical characteristics corresponding to the samples, the clinical characteristics including patients with congenital heart disease combined with intellectual disability and patients with congenital heart disease combined with normal intellectual development, extracting the target gene expression data in the training set and inputting it into a machine learning model to construct a prediction model, thereby obtaining a constructed prediction model.

[0012] Further, if the expression level of the target gene RAB3IL1 is lower than the threshold, and / or the expression levels of any one or more of XKR3 and H2BC17 are higher than the threshold, the classification result of the sample to be tested is obtained as a sample of congenital heart disease combined with intellectual developmental retardation; if the expression level of the target gene RAB3IL1 is higher than the threshold, and / or the expression levels of any one or more of XKR3 and H2BC17 are lower than the threshold, the classification result of the sample to be tested is obtained as a sample of non-congenital heart disease combined with intellectual developmental retardation.

[0013] Furthermore, the machine learning model includes a linear regression model, a logistic regression model, a random forest model, a Lasso regression model, a Ridge regression model, a linear discriminant analysis model, a nearest neighbor model, a decision tree model, a perceptron model, a neural network model, a support vector machine model, a naive Bayes model, an AdaBoost model, a GBDT model, an XGBoost model, a LightGBM model and / or a CatBoost model.

[0014] Further, the expression data includes mRNA expression data of the target gene or protein expression data of the target gene;

[0015] The mRNA expression data is obtained by high-throughput sequencing technology, RT-PCR, qRT-PCR or in situ hybridization technology;

[0016] The protein expression data are obtained by mass spectrometry, immunoblotting, immunohistochemistry or immunoprecipitation.

[0017] A second aspect of the present invention provides a prediction system for congenital heart disease combined with intellectual disability, the system comprising:

[0018] A data acquisition unit is used to acquire gene expression data of the sample to be tested;

[0019] A data extraction unit, used to extract expression data of target genes in the gene expression data, wherein the target genes include any one or more combinations of the following: RAB3IL1, XKR3, H2BC17;

[0020] A prediction result unit, used for performing classification prediction based on the expression data of the target gene to obtain a classification result of whether the sample to be tested is a sample of congenital heart disease combined with intellectual disability;

[0021] The classification result is obtained based on a prediction model, and the method for constructing the prediction model includes: obtaining the target gene expression data of the training set samples and the clinical characteristics corresponding to the samples, the clinical characteristics including patients with congenital heart disease combined with intellectual disability and patients with congenital heart disease combined with normal intellectual development, extracting the target gene expression data in the training set and inputting it into a machine learning model to construct a prediction model, thereby obtaining a constructed prediction model.

[0022] Further, if the expression level of the target gene RAB3IL1 is lower than the threshold, and / or the expression levels of any one or more of XKR3 and H2BC17 are higher than the threshold, the classification result of the sample to be tested is obtained as a sample of congenital heart disease combined with intellectual developmental retardation; if the expression level of the target gene RAB3IL1 is higher than the threshold, and / or the expression levels of any one or more of XKR3 and H2BC17 are lower than the threshold, the classification result of the sample to be tested is obtained as a sample of non-congenital heart disease combined with intellectual developmental retardation.

[0023] Furthermore, the machine learning model includes a linear regression model, a logistic regression model, a random forest model, a Lasso regression model, a Ridge regression model, a linear discriminant analysis model, a nearest neighbor model, a decision tree model, a perceptron model, a neural network model, a support vector machine model, a naive Bayes model, an AdaBoost model, a GBDT model, an XGBoost model, a LightGBM model and / or a CatBoost model.

[0024] Further, the expression data includes mRNA expression data of the target gene or protein expression data of the target gene;

[0025] The mRNA expression data is obtained by high-throughput sequencing technology, RT-PCR, qRT-PCR or in situ hybridization technology;

[0026] The protein expression data are obtained by mass spectrometry, immunoblotting, immunohistochemistry or immunoprecipitation.

[0027] The third aspect of the present invention provides a computer device, comprising: a memory and a processor, the memory being used to store program instructions; the processor being used to call program instructions, and when the program instructions are executed, the prediction method for congenital heart disease combined with intellectual disability described in the first aspect of the present invention is implemented.

[0028] The fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the prediction method for congenital heart disease combined with intellectual disability described in the first aspect of the present invention.

[0029] The present invention also provides the use of a reagent for detecting the expression amount of RAB3IL1, XKR3 and / or H2BC17 in the above-mentioned test sample in the preparation of a diagnostic product for diagnosing and / or assisting in the diagnosis of congenital heart disease combined with intellectual retardation, or for diagnosing and distinguishing between subjects with congenital heart disease combined with intellectual retardation and subjects with congenital heart disease combined with normal intellectual development.

[0030] In some embodiments, the sample to be tested is derived from the subject.

[0031] The beneficial effects of the present invention are as follows:

[0032] The present invention discovers for the first time that RAB3IL1, XKR3, and H2BC17 are significantly differentially expressed in patients with congenital heart disease and intellectual disability, and has good diagnostic efficacy for the diagnosis of congenital heart disease and intellectual disability, with high accuracy, sensitivity, and specificity. Based on this, the present invention innovatively develops an effective prediction method for congenital heart disease and intellectual disability, and provides relevant prediction systems, computer devices, and computer-readable storage media, which provide information for early intervention treatment of congenital heart disease and intellectual disability, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 A schematic flow chart of a method for predicting congenital heart disease combined with intellectual disability provided by an embodiment of the present invention;

[0035] Figure 2 A schematic diagram of a prediction system for congenital heart disease combined with intellectual disability provided by an embodiment of the present invention;

[0036] Figure 3 A schematic diagram of a computer device provided in an embodiment of the present invention;

[0037] Figure 4 This is an example diagram of the FASTQ data format for read segments;

[0038] Figure 5 is the sequencing quality histogram;

[0039] Figure 6 is the average quality score of the sequence;

[0040] Figure 7 This is a correlation analysis diagram between mRNA samples;

[0041] Figure 8 The volcano plot and heat map (top100) corresponding to the differentially expressed genes;

[0042] Fig. 9 The enrichment results of differentially expressed genes (GO enrichment on the left and KEGG enrichment on the right);

[0043] Fig.10 The results of differential expression of RAB3IL1, XKR3, and H2BC17 are shown in Figure 1, where A is a sample from a congenital heart disease patient with normal nutrition and normal intellectual development, and B is a sample from a congenital heart disease patient with normal nutrition and intellectual retardation;

[0044] Fig.11 The results of the diagnostic efficacy validation of RAB3IL1, XKR3, and H2BC17 for predicting congenital heart disease combined with intellectual disability were presented;

[0045] Fig.12 The results of the diagnostic efficacy validation of any two of RAB3IL1, XKR3, and H2BC17 in predicting congenital heart disease with intellectual disability;

[0046] Fig.13 This is the result of validating the diagnostic efficacy of the combination of RAB3IL1, XKR3 and H2BC17 in predicting congenital heart disease combined with intellectual disability. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0048] In some of the processes described in the specification and claims of the present invention and the above-mentioned drawings, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as S101, S102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0050] Figure 1 The present invention provides a method for predicting congenital heart disease combined with intellectual disability, and the method comprises the following steps:

[0051] S101: Obtaining gene expression data of the sample to be tested;

[0052] In one embodiment, the sample to be tested is derived from a subject.

[0053] In one embodiment, the subject includes mammals and non-mammals. Examples of mammals include, but are not limited to, any member of the class Mammalia: humans, non-human primates such as chimpanzees and other apes and monkeys; farm animals such as cattle, horses, sheep, goats, pigs; domestic animals such as rabbits, dogs and cats; laboratory animals, including rodents such as rats, mice and guinea pigs, etc. Examples of non-mammals include, but are not limited to, birds, fish or other non-mammals, etc.

[0054] In one embodiment, the subject is a human.

[0055] In one embodiment, the sample to be tested refers to a composition obtained from or derived from a target subject, which contains cell entities and / or other molecular entities to be characterized and / or identified, for example, based on physical, biochemical, chemical and / or physiological characteristics. The sample can be obtained from the subject's blood and other fluid samples and tissue samples of biological origin, such as biopsy tissue samples or tissue cultures or cells derived therefrom. The source of the tissue sample can be solid tissue, such as from fresh, frozen and / or preserved organ or tissue samples, biopsy tissues or aspirates; blood or any blood component; body fluids; cells from any time of individual pregnancy or development; or plasma.

[0056] In one embodiment, the sample to be tested includes a blood sample, a tissue sample, a blood-derived cell sample, a serum sample, a plasma sample, a lymph fluid sample, a synovial fluid sample, a cell extract sample and / or any combination thereof, derived from a subject.

[0057] In one embodiment, the sample to be tested is a blood sample from a subject.

[0058] In one embodiment, the gene expression data includes gene mRNA expression data or gene protein expression data.

[0059] In one embodiment, the mRNA expression data is obtained by high-throughput sequencing technology, RT-PCR, qRT-PCR or in situ hybridization technology. Any method or technology that can be used to detect the mRNA expression level corresponding to a gene can be used in the present invention.

[0060] In one embodiment, the protein expression data is obtained by mass spectrometry, immunoblotting, immunohistochemistry or immunoprecipitation. Any method or technique that can be used to detect the protein expression corresponding to a gene can be used in the present invention.

[0061] In one embodiment, the gene expression data is the mRNA expression data of the gene, and the mRNA expression data of the gene can be obtained by high-throughput sequencing technology. In a specific embodiment of the present invention, the high-throughput sequencing technology is the Illumina high-throughput sequencing platform.

[0062] S102: extracting expression data of target genes from the gene expression data, wherein the target genes include any one or more combinations of the following: RAB3IL1, XKR3, H2BC17;

[0063] In one embodiment, the target genes include RAB3IL1, XKR3, H2BC17, a combination of RAB3IL1 and XKR3, a combination of RAB3IL1 and H2BC17, and a combination of RAB3IL1, XKR3 and H2BC17.

[0064] In one embodiment, the inventors collected blood samples from patients with congenital heart disease combined with intellectual disability and patients with congenital heart disease combined with normal intellectual development for sequencing analysis to verify the expression of the above target genes and the diagnostic efficacy of the disease of congenital heart disease combined with intellectual disability. The clinical information of the patients with congenital heart disease combined with intellectual disability and patients with congenital heart disease combined with normal intellectual development is shown in Table 1 below.

[0065] Table 1 Clinical information of patients with congenital heart disease and intellectual disability and patients with congenital heart disease and normal intellectual development

[0066]

[0067]

[0068] In one embodiment, based on the sequencing by synthesis (SBS) technology, the Illumina high-throughput sequencing platform is used to sequence the cDNA library, which can produce a large number of high-quality reads. These reads or bases produced by the sequencing platform are called raw data. To facilitate the analysis of sequencing data, the raw image data obtained by Illumina sequencing is converted into sequence data, i.e., FASTQ format, through Base Calling to obtain the most original sequencing data file. The FASTQ format file can record the bases of the measured reads and their quality scores. Figure 4 As shown, the FASTQ format is stored in units of sequencing reads, and each read occupies 4 lines, where the first and third lines consist of file identification marks (sequence identifiers) and read names (IDs) (the first line starts with "@" and the third line starts with "+"; the ID in the third line can be omitted, but the "+" cannot be omitted), the second line is the base sequence, and the fourth line is the sequencing quality score of the base at the corresponding position.

[0069] An Illumina sequencer has two flow cells in one run. A flow cell contains eight lanes. One lane contains two columns. Each column contains 60 tiles. Each tile is seeded with a different cluster. The detailed information in the generated sequencing file identification mark (Sequence Identifiers) is shown in Table 2.

[0070] Table 2 Interpretation of sequencing file identification marks

[0071]

[0072] The quality score of the read is represented by different characters, where the ASCII value of each character minus 33 is the corresponding sequencing quality value. Generally, the base quality ranges from 0-40, that is, the corresponding ASCII code is from "!" (0+33) to "I" (40+33). If the sequencing error rate is represented by E and the base quality value of Illunima HiSeq / Miseq is represented by Q, then the following relationship exists: Formula 1: Q = -10log 10(E) The error rate of sequencing reads increases as sequencing progresses (see Table 3), which is caused by the consumption of chemical reagents during the sequencing process and is a common feature of the Illumina high-throughput sequencing platform.

[0073] Table 3. Concise correspondence between sequencing error rate and sequencing quality value

[0074]

[0075] In one embodiment, the present application performs data volume statistics on the sequence of the original data, and the results are shown in Table 4. In one embodiment, the present application performs quality control on the sequencing data. Since the original sequencing data may contain sequencing adapter sequences, low-quality reads and other types of redundant sequences, this will seriously affect the quality of subsequent assembly. In order to ensure the accuracy of subsequent bioinformatics analysis, the original sequencing data is first filtered to obtain high-quality sequencing data (clean data). Cutadapt can find and delete redundant sequences such as adapters, primers, polyA tails and other types in reads. Cutadapt (version, 3.5, default parameters) is used to clean the sequencing data. In one embodiment, the present application performs statistics on the data after quality control, and MultiQC supports the integration of quality control results, and performs data volume statistics on the sequence after data quality control. In one embodiment, the present application performs data quality assessment after quality control. Illumina sequencing belongs to the second generation sequencing technology, and a single run can generate billions of reads. Such a large amount of data cannot show the quality of each read one by one; using statistical methods, all sequencing reads are quality assessed, which can intuitively reflect the sequencing quality and library construction quality of the sample from a macro perspective. We perform sequencing-related quality assessment on the sequencing data of each sample. The average quality value of each base position in the reads is as follows: Figure 5 The statistics of the average sequence quality score and the number of reads are shown in Figure 6 As shown. In one embodiment, the present application performs gene quantification, HISAT2 (version, 2.1.0) aligns the genome, samtools (version, 1.9) sorts and indexes bam files, QualiMap (version, 2.2.2) quality controls bam files, and featureCounts (version, 1.6.4) performs gene quantification. The reference genome version is GRCh38.primary_assembly.genome.fa.

[0076] Table 4 Sequencing data statistics

[0077]

[0078]

[0079] The correlation of gene expression levels between samples is an important indicator for testing the reliability of the experiment and whether the sample selection is reasonable. Biological repetition is necessary for biological experiments. There are two main purposes for setting up biological repetition: one is to prove that the biological experimental operations involved are not accidental, but repeatable; the other is to ensure that the subsequent differential gene analysis obtains more reliable results. The correlation of gene expression levels between samples is an important indicator for testing the reliability of the experiment and whether the sample selection is reasonable. The closer the correlation coefficient is to 1, the higher the similarity of the expression patterns between samples. If there are biological repetitions in the sample, the correlation coefficient between biological repetitions is usually required to be higher. The Encode plan recommends that the square of the Pearson correlation coefficient (R2) be greater than 0.92 (under ideal sampling and experimental conditions). In a specific embodiment, we require that R2 between biological replicate samples be at least greater than 0.8. The correlation coefficients within and between groups are calculated based on the count values ​​of all genes / transcripts in the sample, and drawn into a heat map, which can intuitively display the sample differences between groups and the sample repetitions within the group. The sample correlation heat map involved in this embodiment is shown in the figure. Figure 7 shown.

[0080] In one embodiment, the present application performs differential expression analysis. In the transcriptome, determining whether the expression level of a gene in different samples is different is one of the core contents of the analysis. After obtaining the gene expression level, the expression data can be statistically analyzed to screen genes with significant differences between different samples. The differential analysis is mainly divided into two steps: (1) First, the original read count is normalized, mainly to correct the sequencing depth. (2) The hypothesis test probability (P-value) is calculated through a statistical model. The differential expression analysis uses DEseq2, and the screening parameters are pvalue<0.05 and |log2foldchange|>1. Using the above standards, 362 differentially expressed genes were screened between patients with congenital heart disease and intellectual development retardation and patients with congenital heart disease and normal intellectual development, of which 145 genes were upregulated and 217 genes were downregulated. The volcano plot and heat map are shown in the figure. Figure 8 shown.

[0081] In one embodiment, the present application further performs functional enrichment analysis on the differentially expressed genes between the above-mentioned patients with congenital heart disease and intellectual disability and patients with congenital heart disease and normal intellectual development, and uses the David database (https: / / david.ncifcrf.gov / tools.jsp) to perform GO functional enrichment and KEGG functional enrichment analysis on the differentially expressed genes. The screening criteria are: pvalue<0.05, and the enrichment results are as follows: Fig. 9 And as shown in Table 5.

[0082] Table 5 KEGG enrichment results

[0083]

[0084]

[0085] In one embodiment, the differentially expressed genes screened in this application between patients with congenital heart disease combined with mental retardation and patients with congenital heart disease combined with normal mental development include RAB3IL1, XKR3, and H2BC17. After verification, it is found that there are significant differences in the expression of the said RAB3IL1, XKR3, and H2BC17 between patients with congenital heart disease combined with mental retardation and patients with congenital heart disease combined with normal mental development (see Fig.10 ).

[0086] In one embodiment, this application further verified the diagnostic efficacy of the screened differentially expressed genes RAB3IL1, XKR3, and H2BC17 in predicting the disease of congenital heart disease combined with mental retardation. The receiver operating characteristic (ROC) analysis was performed using the R package "pROC" (version 1.15.0), and the area under the receiver operating characteristic curve (AUC) was calculated to evaluate the accuracy of the above single differentially expressed gene, the combination of any two differentially expressed genes, and the combination of three differentially expressed genes in predicting the disease of congenital heart disease combined with mental retardation. Among them, the range of the AUC value is 0-1. Among them, when judging the diagnostic efficacy of a single differentially expressed gene in predicting the disease of congenital heart disease combined with mental retardation, the expression level of the single differentially expressed gene was directly used for analysis, and the level corresponding to the point with the largest Youden index was selected as its cutoff value. Genes with differential expression with 0.5 < AUC < 0.8 were used for combined analysis; among them, when judging the diagnostic efficacy of the combination of any two differentially expressed genes and the combination of three differentially expressed genes in predicting the disease of congenital heart disease combined with mental retardation, logistic regression analysis was performed on the expression levels of each differentially expressed gene, and the probability of each patient having congenital heart disease combined with mental retardation was calculated through the fitted regression curve. Different probability division thresholds were determined, and based on the determined probability division thresholds, the accuracy, specificity, and sensitivity of the combination of any two differentially expressed genes and the combination of three differentially expressed genes in predicting the disease of congenital heart disease combined with mental retardation were calculated, etc.

[0087] The verification results are as shown in Figure 11-13As shown, the results showed that single genes RAB3IL1, XKR3, and H2BC17 all had high diagnostic efficacy for predicting congenital heart disease combined with intellectual disability. Among them, the AUC value corresponding to RAB3IL1 was 0.871, the sensitivity was 80%, the specificity was 85.7%, and the cutoff value was 20.000; the AUC value corresponding to XKR3 was 0.814, the sensitivity was 60%, the specificity was 100%, and the cutoff value was 8.500; the AUC value corresponding to H2BC17 was 0.736, the sensitivity was 60%, the specificity was 92.9%, and the cutoff value was 15.000. The combination of any two genes among RAB3IL1, XKR3, and H2BC17 also has a high diagnostic efficacy for predicting congenital heart disease combined with intellectual disability. Among them, the AUC value corresponding to RAB3IL1+XKR3 is 0.886, the sensitivity is 80%, the specificity is 85.7%, and the cutoff value is 0.345; the AUC value corresponding to RAB3IL1+H2BC17 is 0.871, the sensitivity is 80%, the specificity is 100%, and the cutoff value is 0.488; the AUC value corresponding to XKR3+H2BC17 is 0.814, the sensitivity is 80%, the specificity is 85.7%, and the cutoff value is 0.195. The combination of RAB3IL1, XKR3, and H2BC17 also has a high diagnostic efficacy for predicting congenital heart disease combined with intellectual developmental retardation. The AUC value corresponding to RAB3IL1+XKR3+H2BC17 is 0.871, the sensitivity is 80%, the specificity is 100%, and the cutoff value is 0.464. The above verification results prove that RAB3IL1, XKR3 and / or H2BC17 have a high diagnostic efficacy for predicting congenital heart disease combined with intellectual developmental retardation, which also proves the effectiveness of the prediction method provided in this application (with high diagnostic efficacy, accuracy, sensitivity and specificity), and can be used for the effective prediction of congenital heart disease combined with intellectual developmental retardation.

[0088] S103: Classification prediction is performed based on the expression data of the target gene to obtain a classification result of whether the sample to be tested is a sample of congenital heart disease combined with intellectual development retardation. If the expression level of the target gene RAB3IL1 is lower than the threshold, and / or the expression levels of any one or more of XKR3 and H2BC17 are higher than the threshold, the classification result of the sample to be tested is obtained as a sample of congenital heart disease combined with intellectual development retardation; if the expression level of the target gene RAB3IL1 is higher than the threshold, and / or the expression levels of any one or more of XKR3 and H2BC17 are lower than the threshold, the classification result of the sample to be tested is obtained as a sample of non-congenital heart disease combined with intellectual development retardation;

[0089] In one embodiment, the target gene is the target gene as described above, that is, the target gene includes any one or more combinations of the following: RAB3IL1, XKR3, H2BC17.

[0090] In one embodiment, the classification result is obtained based on a prediction model, and the method for constructing the prediction model includes: obtaining the target gene expression data of the training set samples and the clinical characteristics corresponding to the samples, the clinical characteristics including patients with congenital heart disease combined with intellectual disability and patients with congenital heart disease combined with normal intellectual development, extracting the target gene expression data in the training set and inputting it into a machine learning model to construct a prediction model, thereby obtaining a constructed prediction model.

[0091] In one embodiment, as described above, the present application collects blood samples of patients with congenital heart disease combined with intellectual retardation and patients with congenital heart disease combined with normal intellectual development for sequencing analysis, and obtains the target gene expression data corresponding to the two clinical characteristics of patients with congenital heart disease combined with intellectual retardation and patients with congenital heart disease combined with normal intellectual development. The clinical information of the patients with congenital heart disease combined with intellectual retardation and patients with congenital heart disease combined with normal intellectual development is shown in Table 1. The target gene expression data is extracted and input into the machine learning model to construct a prediction model, and a constructed prediction model is obtained.

[0092] In one embodiment, the machine learning model includes a linear regression model, a logistic regression model, a random forest model, a Lasso regression model, a Ridge regression model, a linear discriminant analysis model, a nearest neighbor model, a decision tree model, a perceptron model, a neural network model, a support vector machine model, a naive Bayes model, an AdaBoost model, a GBDT model, an XGBoost model, a LightGBM model and / or a CatBoost model.

[0093] In one embodiment, when the construction method of the above-mentioned prediction model is determined, the prediction model contains the threshold value, that is, when the prediction model is determined, the threshold value is also determined. Based on the determined threshold value, the classification result of whether the sample to be tested is a sample of congenital heart disease combined with intellectual retardation can be predicted. The specific judgment result based on the prediction model is: if the expression level of the target gene RAB3IL1 is lower than the threshold value, and / or the expression level of any one or more of XKR3 and H2BC17 is higher than the threshold value, the classification result of the sample to be tested is a sample of congenital heart disease combined with intellectual retardation; if the expression level of the target gene RAB3IL1 is higher than the threshold value, and / or the expression level of any one or more of XKR3 and H2BC17 is lower than the threshold value, the classification result of the sample to be tested is a sample of non-congenital heart disease combined with intellectual retardation.

[0094] In one embodiment, the effectiveness of the constructed prediction model can also be predicted, that is, another data set containing the target gene expression data corresponding to patients with congenital heart disease and intellectual disability and patients with congenital heart disease and normal intellectual development is taken, and the effectiveness of the constructed prediction model is verified in the data set.

[0095] In one embodiment, the gene expression data includes gene mRNA expression data or gene protein expression data.

[0096] In one embodiment, the mRNA expression data is obtained by high-throughput sequencing technology, RT-PCR, qRT-PCR or in situ hybridization technology. Any method or technology that can be used to detect the mRNA expression level corresponding to a gene can be used in the present invention.

[0097] In one embodiment, the protein expression data is obtained by mass spectrometry, immunoblotting, immunohistochemistry or immunoprecipitation. Any method or technique that can be used to detect the protein expression corresponding to a gene can be used in the present invention.

[0098] In one embodiment, the gene expression data is the mRNA expression data of the gene, and the mRNA expression data of the gene can be obtained by high-throughput sequencing technology. In a specific embodiment of the present invention, the high-throughput sequencing technology is the Illumina high-throughput sequencing platform.

[0099] In one embodiment, the sample to be tested is derived from a subject.

[0100] In one embodiment, the subject includes mammals and non-mammals. Examples of mammals include, but are not limited to, any member of the class Mammalia: humans, non-human primates such as chimpanzees and other apes and monkeys; farm animals such as cattle, horses, sheep, goats, pigs; domestic animals such as rabbits, dogs and cats; laboratory animals, including rodents such as rats, mice and guinea pigs, etc. Examples of non-mammals include, but are not limited to, birds, fish or other non-mammals, etc.

[0101] In one embodiment, the subject is a human.

[0102] In one embodiment, the sample to be tested refers to a composition obtained from or derived from a target subject, which contains cell entities and / or other molecular entities to be characterized and / or identified, for example, based on physical, biochemical, chemical and / or physiological characteristics. The sample can be obtained from the subject's blood and other fluid samples and tissue samples of biological origin, such as biopsy tissue samples or tissue cultures or cells derived therefrom. The source of the tissue sample can be solid tissue, such as from fresh, frozen and / or preserved organ or tissue samples, biopsy tissues or aspirates; blood or any blood component; body fluids; cells from any time of individual pregnancy or development; or plasma.

[0103] In one embodiment, the sample to be tested includes a blood sample, a tissue sample, a blood-derived cell sample, a serum sample, a plasma sample, a lymph fluid sample, a synovial fluid sample, a cell extract sample and / or any combination thereof, derived from a subject.

[0104] In one embodiment, the sample to be tested is a blood sample from a subject.

[0105] Figure 2 1 is a schematic diagram of a prediction system for congenital heart disease combined with intellectual disability provided by an embodiment of the present invention. Specifically, the system includes:

[0106] A data acquisition unit is used to acquire gene expression data of the sample to be tested;

[0107] A data extraction unit, used to extract expression data of target genes in the gene expression data, wherein the target genes include any one or more combinations of the following: RAB3IL1, XKR3, H2BC17;

[0108] A prediction result unit, used for performing classification prediction based on the expression data of the target gene to obtain a classification result of whether the sample to be tested is a sample of congenital heart disease combined with intellectual disability;

[0109] The classification result is obtained based on a prediction model, and the method for constructing the prediction model includes: obtaining the target gene expression data of the training set samples and the clinical characteristics corresponding to the samples, the clinical characteristics including patients with congenital heart disease combined with intellectual disability and patients with congenital heart disease combined with normal intellectual development, extracting the target gene expression data in the training set and inputting it into a machine learning model to construct a prediction model, thereby obtaining a constructed prediction model.

[0110] In one embodiment, the judgment criterion for predicting the classification result based on the prediction model is: if the expression level of the target gene RAB3IL1 is lower than the threshold, and / or the expression level of any one or more of XKR3 and H2BC17 is higher than the threshold, the classification result of the sample to be tested is obtained as a sample with congenital heart disease and intellectual development retardation; if the expression level of the target gene RAB3IL1 is higher than the threshold, and / or the expression level of any one or more of XKR3 and H2BC17 is lower than the threshold, the classification result of the sample to be tested is obtained as a sample without congenital heart disease and intellectual development retardation.

[0111] In one embodiment, the machine learning model includes a linear regression model, a logistic regression model, a random forest model, a Lasso regression model, a Ridge regression model, a linear discriminant analysis model, a nearest neighbor model, a decision tree model, a perceptron model, a neural network model, a support vector machine model, a naive Bayes model, an AdaBoost model, a GBDT model, an XGBoost model, a LightGBM model and / or a CatBoost model.

[0112] In one embodiment, the expression data includes mRNA expression data of the target gene or protein expression data of the target gene;

[0113] The mRNA expression data is obtained by high-throughput sequencing technology, RT-PCR, qRT-PCR or in situ hybridization technology;

[0114] The protein expression data are obtained by mass spectrometry, immunoblotting, immunohistochemistry or immunoprecipitation.

[0115] Figure 3 It is a schematic diagram of a computer device provided in an embodiment of the present invention. Specifically, the computer device includes: a memory and a processor, the memory is used to store program instructions; the processor is used to call program instructions, and when the program instructions are executed, the prediction method for congenital heart disease combined with intellectual disability as described above is implemented.

[0116] An embodiment of the present invention further provides a computer-readable storage medium. Specifically, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the prediction method for congenital heart disease combined with intellectual disability as described above is implemented.

[0117] The validation results of this validation example show that assigning intrinsic weights to indications can moderately improve the performance of the method relative to the default setting.

[0118] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0119] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0120] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0122] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0123] A person skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned storage medium can be a read-only memory, a disk or an optical disk, etc.

[0124] The above is a detailed introduction to a computer device provided by the present invention. For a person skilled in the art, according to the concept of the embodiments of the present invention, there may be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for predicting congenital heart disease combined with intellectual disability, characterized in that: The method comprises: Obtaining gene expression data of the sample to be tested; Extracting expression data of target genes from the gene expression data, wherein the target genes include any one or more combinations of the following: RAB3IL1, XKR3, H2BC17; Based on the expression data of the target gene, classification prediction is performed to obtain a classification result of whether the sample to be tested is a sample of congenital heart disease combined with intellectual disability; The classification result is obtained based on a prediction model, and the method for constructing the prediction model includes: obtaining the target gene expression data of the training set samples and the clinical characteristics corresponding to the samples, the clinical characteristics including patients with congenital heart disease combined with intellectual disability and patients with congenital heart disease combined with normal intellectual development, extracting the target gene expression data in the training set and inputting it into a machine learning model to construct a prediction model, thereby obtaining a constructed prediction model.

2. The method for predicting congenital heart disease combined with intellectual disability according to claim 1, characterized in that: If the expression level of the target gene RAB3IL1 is lower than the threshold, and / or the expression levels of any one or more of XKR3 and H2BC17 are higher than the threshold, the classification result of the sample to be tested is obtained as a sample of congenital heart disease combined with intellectual developmental retardation; if the expression level of the target gene RAB3IL1 is higher than the threshold, and / or the expression levels of any one or more of XKR3 and H2BC17 are lower than the threshold, the classification result of the sample to be tested is obtained as a sample of non-congenital heart disease combined with intellectual developmental retardation.

3. The method for predicting congenital heart disease combined with intellectual disability according to claim 1, characterized in that: The machine learning models include linear regression models, logistic regression models, random forest models, Lasso regression models, Ridge regression models, linear discriminant analysis models, nearest neighbor models, decision tree models, perceptron models, neural network models, support vector machine models, naive Bayes models, AdaBoost models, GBDT models, XGBoost models, LightGBM models and / or CatBoost models.

4. The method for predicting congenital heart disease combined with intellectual disability according to claim 1, characterized in that: The expression data includes mRNA expression data of the target gene or protein expression data of the target gene; The mRNA expression data is obtained by high-throughput sequencing technology, RT-PCR, qRT-PCR or in situ hybridization technology; The protein expression data are obtained by mass spectrometry, immunoblotting, immunohistochemistry or immunoprecipitation.

5. A prediction system for congenital heart disease combined with intellectual disability, characterized in that: The system comprises: A data acquisition unit is used to acquire gene expression data of the sample to be tested; A data extraction unit, used to extract expression data of target genes in the gene expression data, wherein the target genes include any one or more combinations of the following: RAB3IL1, XKR3, H2BC17; A prediction result unit, used for performing classification prediction based on the expression data of the target gene to obtain a classification result of whether the sample to be tested is a sample of congenital heart disease combined with intellectual disability; The classification result is obtained based on a prediction model, and the method for constructing the prediction model includes: obtaining the target gene expression data of the training set samples and the clinical characteristics corresponding to the samples, the clinical characteristics including patients with congenital heart disease combined with intellectual disability and patients with congenital heart disease combined with normal intellectual development, extracting the target gene expression data in the training set and inputting it into a machine learning model to construct a prediction model, thereby obtaining a constructed prediction model.

6. The prediction system for congenital heart disease combined with intellectual disability according to claim 5, characterized in that: If the expression level of the target gene RAB3IL1 is lower than the threshold, and / or the expression levels of any one or more of XKR3 and H2BC17 are higher than the threshold, the classification result of the sample to be tested is obtained as a sample of congenital heart disease combined with intellectual developmental retardation; if the expression level of the target gene RAB3IL1 is higher than the threshold, and / or the expression levels of any one or more of XKR3 and H2BC17 are lower than the threshold, the classification result of the sample to be tested is obtained as a sample of non-congenital heart disease combined with intellectual developmental retardation.

7. The prediction system for congenital heart disease combined with intellectual disability according to claim 5, characterized in that: The machine learning models include linear regression models, logistic regression models, random forest models, Lasso regression models, Ridge regression models, linear discriminant analysis models, nearest neighbor models, decision tree models, perceptron models, neural network models, support vector machine models, naive Bayes models, AdaBoost models, GBDT models, XGBoost models, LightGBM models and / or CatBoost models.

8. The prediction system for congenital heart disease combined with intellectual disability according to claim 5, characterized in that: The expression data includes mRNA expression data of the target gene or protein expression data of the target gene; The mRNA expression data is obtained by high-throughput sequencing technology, RT-PCR, qRT-PCR or in situ hybridization technology; The protein expression data are obtained by mass spectrometry, immunoblotting, immunohistochemistry or immunoprecipitation.

9. A computer device, characterized in that: The computer device comprises: a memory and a processor, wherein the memory is used to store program instructions; the processor is used to call the program instructions, and when the program instructions are executed, the prediction method for congenital heart disease combined with intellectual disability described in any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting congenital heart disease combined with intellectual disability according to any one of claims 1 to 4 is implemented.

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