A diagnostic kit for autism spectrum disorder based on pan-apoptosis-related genes

Abstract

The present invention discloses a diagnostic kit for autism spectrum disorder based on pan-apoptosis-related genes, which belongs to the field of biomedicine. The diagnostic kit for autism spectrum disorder based on the pan-apoptosis-related genes of the present invention includes a detection kit. <h2 style=";text-align:left;direction:ltr">CD36 <h2 style=";text-align:left;direction:ltr"> LMNB1, DNAJA1, MSH2, JAK2, PDCD4, DEK This study identified and screened key pan-apoptosis genes that were differentially expressed between children with autism spectrum disorder and healthy children. Using primer sequences for these key pan-apoptosis genes and internal reference genes, a diagnostic kit was constructed to calculate autism risk based on the expression levels of these key genes. External sample validation of the kit and its diagnostic efficacy was conducted, demonstrating positive results.

Description

Technical Field

[0001] The present invention belongs to the field of biomedical technology, and in particular relates to an autism spectrum disorder diagnostic kit based on pan-apoptosis-related genes. Background Art

[0002] Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social communication impairments and repetitive, stereotyped behaviors or interests. More than 30% of individuals with autism exhibit self-injurious, aggressive, or destructive behaviors. Symptoms persist into adulthood in most individuals with ASD, and more than half require lifelong care. A recent systematic review estimates that 1% of children worldwide are diagnosed with ASD. Monitoring by the US Centers for Disease Control and Prevention shows that the prevalence of ASD continues to rise rapidly, increasing from 0.67% in 2000 to 2.76% in 2020. Currently, no medication has been proven to cure ASD or improve its core symptoms. Although early behavioral intervention (beginning in early childhood) can significantly improve the life skills of individuals with ASD, the lack of objective diagnostic biomarkers has prevented early screening before behavioral disorders in infants and young children fully manifest. Therefore, identifying objective biomarkers is crucial for early screening and intervention for ASD.

[0003] The diagnostic difficulties of autism are as follows. (1) Reliance on behavioral observation: Currently, diagnosis is mainly based on clinical behavioral assessment (such as DSM-5 criteria and ADOS scale), lacking objective biomarkers such as blood tests and imaging. It takes more than 2 hours to complete a behavioral assessment scale, and it is difficult to make a conclusion based on the results of a single assessment. (2) The diagnostic method of the behavioral scale is highly subjective: different doctors may have different interpretations of the same behavior, especially in mild cases. (3) Symptoms in infancy and childhood are vague: early signs (such as eye avoidance, lack of response to calls, and lack of joint attention) may be mistaken for "shyness" or "slightly slow development." Therefore, there is an urgent need for objective and sensitive diagnostic tools.

[0004] Pan-apoptosis is a newly discovered form of programmed cell death that integrates extensive signaling crosstalk between pyroptosis, apoptosis, and necroptosis, encompassing hallmark features of neurological disorders such as abnormal neuronal death, neuroinflammation, and oxidative stress. Therefore, dysregulation of the pan-apoptotic pathway holds promise as an objective diagnostic biomarker for these diseases. Recent studies have demonstrated excellent diagnostic efficacy for Alzheimer's disease (AD) based on three pan-apoptosis-associated genes (PAGs). Bioinformatics analysis has also revealed differential expression patterns of pan-apoptosis genes between epilepsy patients and controls. However, strategies and methods for diagnosing ASD using pan-apoptosis genes remain elusive. Therefore, we systematically screened ASD-associated pan-apoptosis genes, identified effective diagnostic markers, and developed a diagnostic kit. External validation across multiple populations demonstrated that the kit performed well in both domestic and international populations, with a particularly high accuracy rate of 91% in the Chinese population. Summary of the Invention

[0005] The purpose of the present invention is to provide an autism spectrum disorder diagnostic kit based on pan-apoptosis-related genes.

[0006] The object of the present invention is achieved in that the diagnostic kit includes a detection CD36, LMNB1, DNAJA1, MSH2, JAK2, PDCD4, DEK, GADPH Primer set for the (internal reference) gene.

[0007] Furthermore, the primer sequence of the detection gene is:

[0008] CD36 -Forward:ACCCAGGACGCTGAGGACAAC (SEQ ID NO: 1);

[0009] CD36 -Reverse: TGCCACAGCCAGATTGAGAACTG (SEQ ID NO: 2);

[0010] LMNB1 -Forward: GAGGAAAGCGGAAGAGGGTTGATG (SEQ ID NO: 3);

[0011] LMNB1 -Reverse: AGTGGCTGAGGCGGAATGAGAG (SEQ ID NO: 4);

[0012] DNAJA1 -Forward:ACCCGAGAGGAAGGAAGTGGAAG (SEQ ID NO: 5);

[0013] DNAJA1-Reverse: GTCTGACACTGAACACCACCTCTG (SEQ ID NO: 6);

[0014] MSH2 -Forward: GCAGCAGTCAGGCCCTTAACC (SEQ ID NO: 7);

[0015] MSH2 -Reverse: ACTTATTCAGCAAGGCAGCCAGAG (SEQ ID NO: 8);

[0016] JAK2 -Forward: GCAACAGAGCCTATCGGCATGG (SEQ ID NO: 9);

[0017] JAK2 -Reverse: CCGCCACTGAGCAAAGAGGTAAG (SEQ ID NO: 10);

[0018] PDCD4 -Forward: CAAAGGGAAGGTTGCTGGATAGGC (SEQ ID NO: 11);

[0019] PDCD4 -Reverse:TCCACCTCCTCCACATCATACACC (SEQ ID NO: 12);

[0020] DEK -Forward:GAGGACGAGGACGACGAGGAG (SEQ ID NO: 13);

[0021] DEK -Reverse: AGTGCCTGGCCTGTTGTAAAGC (SEQ ID NO: 14).

[0022] Furthermore, the internal reference gene of the detection kit is GAPDH , the primer sequence of the internal reference gene is:

[0023] GADPH -Forward:ACACCCACTCCTCCACCTTTG (SEQ ID NO: 15);

[0024] GADPH -Reverse:TCCACCACCCTGTTGCTGTAG (SEQ ID NO: 16).

[0025] Preferably, the detection kit further includes RNA extraction reagent, RNA reverse transcription reagent, TaKaRa PCR amplification Kit, and RNase-free water.

[0026] A method for calculating the results of the above-mentioned kit uses the following diagnostic model to calculate the risk of autism:

[0027] , P is the risk of autism, ranging from 0 to 1. >-0.119 is a positive diagnosis.

[0028] A method for constructing an autism spectrum disorder diagnostic model based on pan-apoptosis-related genes comprises the following steps:

[0029] (1) Identification of differentially expressed pan-apoptotic genes;

[0030] (2) Functional enrichment and PPI network analysis of differentially expressed pan-apoptotic genes;

[0031] (3) Screening of key pan-apoptotic genes based on machine learning algorithms;

[0032] (4) WGCNA analysis and identification of key genes;

[0033] (5) Diagnostic model construction and external validation.

[0034] A diagnostic kit for autism spectrum disorder based on pan-apoptosis-related genes, the method of using the kit comprising the following steps:

[0035] (1) Collect 2 mL of fasting peripheral venous blood using an EDTA anticoagulant tube;

[0036] (2) Pretreatment: Peripheral blood mononuclear cells (PBMCs) were isolated using Ficoll density gradient centrifugation;

[0037] (3) Detection: Using the above kit, add TRIzol reagent to lyse PBMC and extract total RNA. Determine the RNA quality, purity, and integrity. Use RT-qPCR to detect the Ct value of target gene mRNA.

[0038] (4) Results: Calculate the relative expression of target gene mRNA, substitute it into the formula of the diagnostic model, calculate the risk of autism, and obtain a negative / positive diagnosis result.

[0039] Compared with the prior art, the present invention has the following technical effects:

[0040] 1. The present invention systematically screens ASD-related pan-apoptosis genes and constructs a multi-gene diagnostic kit, ensuring the reliability and comprehensiveness of diagnostic biomarkers.

[0041] 2. Key genes (such as DNAJA1 and JAK2) are involved in pan-apoptotic pathways such as neuronal apoptosis and inflammatory response, and are closely related to ASD pathology.

[0042] 3. The diagnostic effect is better than traditional screening tools, such as the Autism Behavior Checklist (ABC Scale), and it has been validated through domestic and international population samples, which can improve the accuracy of ASD risk calculation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Graphs of experimental data from step (1) in Example 1 of the present invention, wherein AB are principal component analysis results of GSE18123 before and after removing the batch effect; C is a volcano plot of differentially expressed genes between the ASD group and the control group; and D is a Venn diagram of the intersection of differentially expressed genes and pan-apoptotic genes.

[0044] Figure 2 Graphs of experimental data for step (2) in Example 1 of the present invention, wherein A is the GO function enrichment analysis result of pan-apoptotic differentially expressed genes; B is the KEGG pathway enrichment analysis result of pan-apoptotic differentially expressed genes; and C is the protein-protein interaction network of pan-apoptotic differentially expressed genes.

[0045] Figure 3 Graphs of experimental data from step (3) in Example 1 of the present invention, wherein A is the residual inverse cumulative distribution graph of the six machine learning models; B is the residual box plot of the six machine learning models; C is the receiver operating characteristic curve of the six machine learning models; and D is the importance of each feature (gene) in the six machine learning models.

[0046] Figure 4 The experimental data diagram of step (4) in Example 1 of the present invention, wherein A is the determination of the soft threshold of the weighted gene co-expression network analysis; B is the clustering dendrogram of the co-expression modules; C is the clustering heat map of the module characteristic genes; D is the association between each module and ASD; E is the intersection of the top ten genes ranked by the XGBoost model and the WGCNA hub genes;

[0047] Figure 5 Graphs of experimental data for steps 1-3 in Example 2 of the present invention, wherein A is a nomogram diagnostic model for ASD risk; B is a decision curve for evaluating the diagnostic efficacy of the nomogram; C is a calibration curve for evaluating the diagnostic efficacy of the nomogram; D is a receiver operating characteristic curve externally validated based on the GSE42133 dataset; and E is a receiver operating characteristic curve externally validated based on a local clinical dataset. DETAILED DESCRIPTION

[0048] The present invention is further described below with reference to the embodiments, but the present invention is not limited in any way. Any changes or substitutions made based on the teachings of the present invention fall within the scope of protection of the present invention.

[0049] Example 1 - Kit Development Method:

[0050] 1. Identification of differentially expressed pan-apoptotic genes (DEPAGs)

[0051] (1) The microarray dataset GSE18123 in the NCBI Gene Expression Omnibus (GEO) was used as a training set to construct the gene differential expression profiles of ASD patients and healthy controls. Since the GSE18123 dataset used two platforms (GPL570 and GPL6244) to generate gene expression profiles of peripheral blood mononuclear cells (PMBCs), it was first merged and the batch effect was corrected using the R software "SVA" package. A total of 170 ASD children and 115 healthy controls ( Figure 1 AB).

[0052] (2) Using |log2(differential expression fold)| ≥ 0.3 and P <0.05, and the results showed that there were 200 differentially expressed genes (DEGs) between the ASD group and the control group, including 37 down-regulated genes and 163 up-regulated genes ( Figure 1 C).

[0053] (3) In the GeneCards database, a pan-apoptosis-associated gene (PAG) library was searched and constructed. A total of 1442 PAGs were included, including 1368 apoptosis-related genes, 17 necrosis-related genes, and 58 pyroptosis-related genes.

[0054] (4) The above 200 DEGs were intersected with 1442 PAGs to obtain 15 pan-apoptotic differentially expressed genes (DEPAGs) ( Figure 1 D).

[0055] 2. Functional enrichment and protein-protein interaction (PPI) analysis were performed. (1) Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to perform functional enrichment analysis on DEPAGs. GO analysis showed that 15 DEPAGs were significantly enriched in biological processes such as intracellular protein trafficking regulation, endogenous apoptosis signaling pathway, protein nuclear localization, defense response positive regulation, and nucleocytoplasmic transport regulation ( Figure 2 A). KEGG analysis showed that DEPAGs were enriched in lipid metabolism and other pathways ( Figure 2 B).

[0056] (2) Using the PPI analysis tool in the STRING online database, PPIs with a confidence threshold > 0.4 were screened. Based on the cytoHubba maximum cluster centrality algorithm, the constructed PPI network contains 11 nodes and 10 links ( Figure 2 C).

[0057] 3. Screening of pan-apoptotic key genes based on machine learning algorithms. 15 DEPAGs were used to distinguish the ASD group from the control group. A total of 6 machine learning models were established, including least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), extreme gradient boosting (XGBoost), generalized linear model (GLM) and gradient boosting model (GBM). The results showed that the XGBoost model had the smallest residual error ( Figure 3 AB), and the area under the receiver operating characteristic curve (AUC) was the largest (0.810), and the model performed best ( Figure 3 C). Display the feature importance of DEPAGs in each model. According to the importance ranking results of each feature in the XGBoost model, the top 10 genes (UCP2, MSH2, TXN, DNAJA1, PDCD10, CD36, LMNB1, S100A8, JAK2, PDCD4) were selected for subsequent analysis ( Figure 3 D).

[0058] 4 Weighted gene co-expression network analysis (WGCNA) and identification of key genes. (1) The co-expression network of 1442 PAGs in the ASD group and the control group was constructed based on the GSE18123 dataset using the R software "WGCNA" package. The optimal soft threshold was determined using the "pickSoftThreshold" function based on the weighted adjacency matrix and topological overlap matrix (TOM). Among the modules with the strongest correlation with the disease phenotype, the core module genes were screened based on the criteria of gene significance (GS) > 0.2 and module membership (MM) > 0.5. Among them, GS is defined as the correlation of each gene with the module characteristic genes, and MM is defined as the correlation of each gene with the disease phenotype.

[0059] (2) WGCNA analysis was performed on the expression of 1442 PAGs in GSE18123, and the model fit R²=0.85 ( Figure 4 A). Four co-expression modules were constructed based on the characteristic gene clustering dendrogram ( Figure 4 B), module distribution is represented by TOM heat map ( Figure 4 C). Among them, the blue-green module contains 332 hub genes and has the strongest correlation with ASD (GS>0.2, MM>0.5) ( Figure 4 D).

[0060] (3) The intersection of the 332 hub genes and the top ten genes in the XGBoost model was taken, and 7 key genes were finally determined ( CD36 、 LMNB1 、 MSH2 、 DEK 、 DNAJA1 、 PDCD4 、 JAK2 )( Figure 4 E).

[0061] 5. Construction of an autism spectrum disorder diagnostic kit based on pan-apoptosis-related genes. (1) Obtain the full-length mRNA sequences of the above 7 target genes from the NCBI Gene database and design specific primers using Primer Premier 6.0 software. The primer sequences are:

[0062] CD36 -Forward:ACCCAGGACGCTGAGGACAAC (SEQ ID NO: 1);

[0063] CD36 -Reverse: TGCCACAGCCAGATTGAGAACTG (SEQ ID NO: 2);

[0064] LMNB1 -Forward: GAGGAAAGCGGAAGAGGGTTGATG (SEQ ID NO: 3);

[0065] LMNB1 -Reverse: AGTGGCTGAGGCGGAATGAGAG (SEQ ID NO: 4);

[0066] DNAJA1 -Forward:ACCCGAGAGGAAGGAAGTGGAAG (SEQ ID NO: 5);

[0067] DNAJA1 -Reverse: GTCTGACACTGAACACCACCTCTG (SEQ ID NO: 6);

[0068] MSH2 -Forward: GCAGCAGTCAGGCCCTTAACC (SEQ ID NO: 7);

[0069] MSH2 -Reverse: ACTTATTCAGCAAGGCAGCCAGAG (SEQ ID NO: 8);

[0070] JAK2 -Forward: GCAACAGAGCCTATCGGCATGG (SEQ ID NO: 9);

[0071] JAK2 -Reverse: CCGCCACTGAGCAAAGAGGTAAG (SEQ ID NO: 10);

[0072] PDCD4 -Forward: CAAAGGGAAGGTTGCTGGATAGGC (SEQ ID NO: 11);

[0073] PDCD4 -Reverse:TCCACCTCCTCCACATCATACACC (SEQ ID NO: 12);

[0074] DEK -Forward:GAGGACGAGGACGACGAGGAG (SEQ ID NO: 13);

[0075] DEK -Reverse: AGTGCCTGGCCTGTTGTAAAGC (SEQ ID NO: 14).

[0076] (2) The internal reference gene is GAPDH , the primer sequence of the internal reference gene is:

[0077] GADPH -Forward:ACACCCACTCCTCCACCTTTG (SEQ ID NO: 15);

[0078] GADPH -Reverse:TCCACCACCCTGTTGCTGTAG (SEQ ID NO: 16).

[0079] (3) The detection kit also includes RNA extraction reagent, RNA reverse transcription reagent, TaKaRa PCR amplification Kit, and RNase-free water.

[0080] Example 2 - Development of a diagnostic kit and verification of diagnostic efficacy:

[0081] 1. Quantify the risk of ASD based on the test kit values. Use the mRNA expression levels of the above 7 key genes to construct a nomogram diagnostic model ( Figure 5 A), thereby quantifying the risk of ASD. The calculation formula is as follows:

[0082] ， Among them, each gene name represents the expression level of the corresponding gene; P is the risk of ASD, ranging from 0 to 1. >-0.119 is a positive diagnosis.

[0083] 2. The effectiveness of the diagnostic model was evaluated using decision curves and calibration curves. Decision curve analysis showed that when the probability threshold was 0.18-0.73, the model was more effective than the full intervention or no intervention strategy ( Figure 5 B). The calibration curve shows that there is no significant deviation between the model probability and the actual probability ( Figure 5 C). The above results show that the diagnostic model has high accuracy and practical value.

[0084] 3. External validation was used to evaluate the diagnostic efficacy of the model. The external data included two parts: ① GSE42133 dataset, derived from the GEO database, including 91 children with ASD and 56 healthy controls, a total of 147 American children; ② local clinical samples, including 15 children with ASD and 15 healthy controls, a total of 30 Chinese children. The external validation results showed that the model AUC was 0.75 (95% CI: 0.67-0.83) and 0.91 (0.81-1.00) in the GSE42133 dataset and local clinical samples, respectively ( Figure 5 DE). External validation results indicate that the diagnostic results of this kit have good universality, especially in Chinese children.

[0085] Example 3 - How to use the kit:

[0086] 1. Collect whole blood samples

[0087] 2 mL of peripheral venous blood was collected from children in the early morning in a fasting state. The blood was collected using an EDTA anticoagulant tube and gently mixed by inversion. The sample was then processed within 2 hours or stored at 4°C for no more than 6 hours.

[0088] 2. Sample preprocessing methods

[0089] Isolation of PMBCs: Gently mix the collected whole blood sample with an equal volume of PBS (1:1) in a 15 mL centrifuge tube. Add 2 mL of Ficoll density gradient solution to the bottom of the centrifuge tube. Centrifuge at 400 × g for 30 minutes. Collect the middle PBMC layer and place it in a new tube. Wash twice with PBS.

[0090] 3. Detection Methods

[0091] (1) Extract total RNA. Add 1 mL of TRIzol to PBMCs to fully lyse the cells. Add chloroform to extract total RNA. After centrifugation and separation, collect the aqueous phase and precipitate the RNA with isopropanol. After washing, dissolve the RNA in DEPC water and measure the RNA concentration and purity. Use a spectrophotometer to measure the RNA concentration and purity, ensuring that the A260 / A280 ratio is between 1.8 and 2.0.

[0092] (2) Reverse transcription. Prepare a 10 µL reverse transcription reaction mixture on ice: 2 µL 5× reverse transcription buffer, 0.5 µL reverse transcriptase, 0.5 µL 50 µM Oligo dT primer, 0.5 µL 100 µM random primers, and 1 µL total RNA. Make up to 10 µL with RNase-free water. Reaction conditions: Incubate at 37°C for 15 minutes to synthesize cDNA, then heat at 85°C for 5 seconds to terminate the reaction.

[0093] (3) qPCR reaction. Using the SYBR Green dye method, each reaction system (20 µL) included: 10 µL of 2× SYBR Green Master Mix, 0.4 µL of upstream and downstream primers (10 µM), 1 µL of cDNA template, and 8.2 µL of RNase-free water. The qPCR reaction conditions were: pre-denaturation at 95°C for 3 minutes; then 40 cycles, each cycle consisting of denaturation at 95°C for 15 seconds and annealing / extension at 60°C for 30 seconds. After the reaction, melting curve analysis was performed to verify the specificity of the amplified product, and the Ct value of each target gene mRNA was read.

[0094] 4. Data Analysis and Diagnosis Results

[0095] The ΔΔCt method was used to calculate the relative mRNA expression of the target gene. Three technical replicates were set for each sample and each gene, and the average of the three results was taken as the relative expression level of each target gene. Substitute the following formula:

[0096] ,

[0097] Calculated A value greater than -0.119 indicates a positive diagnosis, and a value ≤ -0.119 indicates a negative diagnosis.

Claims

1. A diagnostic kit for autism spectrum disorder based on pan-apoptosis-related genes, characterized in that The diagnostic kit includes a detection CD36, LMNB1, DNAJA1, MSH2, JAK2, PDCD4 and DEC primer sets for genes; Detection CD36 The primer set sequences are shown in SEQ ID NO: 1 and SEQ ID NO: 2, and the detection LMNB1 The primer set sequences are shown in SEQ ID NO: 3 and SEQ ID NO: 4, and the detection DNAJA1 The primer set sequences are shown in SEQ ID NO: 5 and SEQ ID NO: 6, and the detection MSH2 The primer set sequences are shown in SEQ ID NO: 7 and SEQ ID NO: 8, and the detection JAK2 The primer set sequences are shown in SEQ ID NO: 9 and SEQ ID NO: 10, and the detection PDCD4 The primer set sequences are shown in SEQ ID NO: 11 and SEQ ID NO: 12, and the detection DEC The primer set sequences are shown in SEQ ID NO: 13 and SEQ ID NO: 14, and are as follows: SEQ ID NO:1:ACCCAGGACGCTGAGGACAAC; SEQ ID NO:2:TGCCACAGCCAGATTGAGAACTG; SEQ ID NO:3:GAGGAAAGCGGAAGAGGGTTGATG; SEQ ID NO:4:AGTGGCTGAGGCGGAATGAGAG; SEQ ID NO:5:ACCCGAGAGGAAGGAAGTGGAAG; SEQ ID NO:6:GTCTGACACTGAACACCACCTCTG. SEQ ID NO:7:GCAGCAGTCAGAGCCCTTAACC; SEQ ID NO:8:ACTTATTCAGCAAGGCAGCCAGAG SEQ ID NO:9:GCAACAGAGCCTATCGGCATGG; SEQ ID NO:10:CCGCCACTGAGCAAAGAGGTAAG; SEQ ID NO:11:CAAAGGGAAGGTTGCTGGATAGGC; SEQ ID NO:12:TCCACCTCCTCCACATCATACACC; SEQ ID NO:13:GAGGACGAGGACGAGGAG. SEQ ID NO:14:AGTGCCTGGCCTGTTGTAAAGC.

2. The autism spectrum disorder diagnostic kit based on pan-apoptosis-related genes according to claim 1, characterized in that The diagnostic kit also includes a detection reference gene GAPDH The primer set is as shown in SEQ ID NO: 15 and SEQ ID NO: 16, specifically: SEQ ID NO:15:ACACCCACTCCTCCACCTTTG; SEQ ID NO:16:TCCACCACCCTGTTGCTGTAG.

3. The autism spectrum disorder diagnostic kit based on pan-apoptosis-related genes according to claim 1, characterized in that Manufacturer of RNA-free, RNA-processing kit, TaKaRa PCR AmplificationKit, RNase-free.

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