Thrombosis causes recurrent miscarriage risk gene detection primer and evaluation model

Abstract

The present application relates to a gene detection primer and evaluation model for recurrent miscarriage risk caused by thrombosis, and belongs to the field of molecular biology and bioinformatics, in particular: based on multiple PCR amplification and capillary electrophoresis detection of multiple SNPs, and through the construction of a regression prediction model, different weights are given to the included SNP sites, scoring, and finally classifying and evaluating the thrombosis risk of the sample to be tested. The present application discloses a gene for recurrent miscarriage risk caused by thrombosis, including 11 gene names and 12 corresponding sites set, and primers corresponding to the 12 sites. The present application also discloses an evaluation model for recurrent miscarriage risk caused by thrombosis and a corresponding evaluation model.

Description

Technical Field

[0001] This invention belongs to the fields of molecular biology and bioinformatics, and in particular relates to a method for detecting multiple SNPs based on multiplex PCR amplification and capillary electrophoresis, and for assigning different weights to the included SNP sites through the construction of a regression prediction model, scoring them, and finally classifying and assessing the thrombosis risk of the test sample. Background Technology

[0002] Recurrent spontaneous abortion (RSA) refers to two or more consecutive spontaneous abortions. Its causes are complex, with relatively well-defined factors including genetics, endocrine factors, immune factors, prothrombotic state (PTS), and anatomical factors. However, due to the lack of specific manifestations, accurate clinical diagnosis is difficult, and certain related factors are easily overlooked, affecting live birth rates. Unexplained habitual abortion not only causes severe psychological and physical trauma to patients but also brings immense suffering to their families.

[0003] Research on the immunogenetic causes of recurrent miscarriage of unknown origin is increasing, and scholars from various countries are dedicated to identifying susceptibility genes for recurrent miscarriage. Screening for these genes primarily focuses on genes associated with risk factors for recurrent miscarriage. Studies have shown that recurrent miscarriage is caused by abnormalities in maternal endocrine, metabolic, and immune factors.

[0004] Homozygous MTHFR gene mutations are a common cause of hyperhomocysteinemia. Hyperhomocysteinemia causes endothelial damage and dysfunction, stimulates vascular smooth muscle cell proliferation, disrupts the balance of the body's coagulation and fibrinolytic systems, affects lipid metabolism, and puts the body in a hypercoagulable state, making it prone to thrombosis. Hyperhomocysteinemia is associated with placental abruption, fetal growth restriction, low birth weight infants, and preeclampsia. MTHFR plays a very important role in homocysteine ​​metabolism. The most common variant of the MTHFR gene is MTHFR677T. Carriers of this variant often have mildly elevated homocysteine ​​levels. The MTHFR677T gene is quite common in Caucasian populations, with homozygous carriers accounting for 10% of the total population, but their homocysteine ​​levels are only slightly elevated. Therefore, it is difficult to estimate the impact of MTHFR gene mutations on thrombotic risk. A case-control study of 10 multicenter clinical data by Nelen et al. found that patients with hyperhomocysteinemia had a 3-4 times increased probability of recurrent miscarriage. Homozygous MTHFR C677T and A1298C are polymorphic, with prevalences of 10%–16% and 4%–6% respectively in Europeans. However, no definitive research results have shown that MTHFR gene mutations increase the risk of venous thrombosis in either pregnant or non-pregnant women. Although hyperhomocysteinemia has been reported as a moderate risk factor for venous thrombosis, recent studies suggest that elevated homocysteine ​​levels are only a minor risk factor.

[0005] Plasminogen activator inhibitor-1 (PAI-1) is a member of the serine protease inhibitor superfamily and is a major physiological regulator of urokinase-type and tissue-type plasminogen activators. Elevated PAI-1 levels are associated with arterial thrombosis. Increased plasma PAI-1 concentration reduces the conversion of plasminogen to plasmin, leading to the accumulation of fibrin and fibrinogen in the bloodstream, resulting in hypercoagulability and thrombus formation. Studies have shown a correlation between PAI-1 levels and 4G / 5G gene polymorphisms.

[0006] Prothrombin (coagulation factor II) is a vitamin K-dependent protein with a molecular weight of 72 kDa, composed of 579 amino acid residues. The prothrombin gene is located at 11p11–q12, with a total length of 21 kb, containing 14 exons and 13 introns. The G20210A mutation in the prothrombin gene is a relatively weaker risk factor for venous thromboembolism (VTE) than the Leiden mutation in the coagulation factor V gene. In 1996, Poort et al. discovered a mutation at position 20210 of the 3′ untranscribed region of the prothrombin gene, where guanine is replaced by adenine (GA). This mutation increases plasma prothrombin levels and simultaneously increases the risk of venous thrombosis. The incidence of this mutation is 1%–3% in women with normal pregnancies and approximately 10% in women with adverse pregnancy outcomes. It is associated with fetal growth restriction, preeclampsia, recurrent miscarriage, preterm birth, and placental abruption.

[0007] Different causes of hereditary thrombosis require different treatments and interventions. For example, patients with hyperhomocysteinemia caused by abnormal expression of the methylenetetrahydrofolate reductase (MTHFR) gene may receive folic acid, vitamin B6, and B12 supplementation. In cases of factor V Leiden gene mutations, protein S deficiency, or protein C deficiency, heparin anticoagulation therapy may be considered during pregnancy. Therefore, for pregnant women with unexplained recurrent miscarriages or those with pathologically confirmed thrombosis, hereditary thrombosis risk assessment plays a crucial role in treatment and future fertility.

[0008] Traditional studies typically employ univariate analysis, but the accuracy and positive rate of predicting or retrospectively analyzing recurrent miscarriage based on a single factor are not high. Therefore, improvements to existing techniques are necessary.

[0009] Patent CN201610940238.1 only detected three polymorphic sites in two genes related to key folic acid metabolism enzymes to predict thrombosis; patent CN201310461481.1 only detected one gene, MTHFR, to predict thrombosis; and patent CN109055368B detected two genes, PAI-1 and F5, to predict thrombosis. These existing technologies only provide a typing result but cannot inform the risk, thus lacking standardized judgment criteria and being prone to human error.

[0010] Patent CN201710309444.7, while testing a wide range of genes, including all exons of 18 genes and specific polymorphisms of three genes, only provides a list of mutations within that large gene pool, without offering any clinical reference values. Any ordinary person testing so many genes and regions would likely find several mutation sites. If finding even one mutation constitutes high risk, then everyone would be considered high-risk. This method ignores the weight of individual genes and leads to differing interpretations of thrombosis caused by different mutations among doctors, resulting in variations in intervention and treatment. Summary of the Invention

[0011] The technical problem to be solved by the present invention is to provide a primer and assessment model for detecting the risk of recurrent miscarriage caused by thrombosis.

[0012] To address the aforementioned issues, this invention provides a gene for the risk of recurrent miscarriage due to thrombosis. Based on gene weights, the gene name, corresponding locus, and rs number are as follows:

[0013] Gene name site rs number F2 G20210A rs1799963 F5 G1691A rs6025 IL10 c.-149+2211A>G rs1800871 PAI-1 -675 4G / 5G rs1799768 MTHFR A1298C rs1801131 MTHFR C677T rs1801133 MTRR A66G rs1801394 PROC C169T rs757583846 VEGFA -1154G / A rs1570360 PROS A586G rs121918474 SERPINC1 C218T rs121909551 THBD G1209T rs398122807 .

[0014] As an improvement to the genes for the risk of recurrent miscarriage caused by thrombosis in this invention, the primers corresponding to 12 sites of 11 genes are as follows:

[0015]

[0016]

[0017] wt represents wild type, and mt represents mutant type.

[0018] This invention also provides an assessment model for genes that contribute to the risk of recurrent miscarriage due to thrombosis, using the genes described above, with the following specific assignment table:

[0019]

[0020]

[0021] The formula for calculating the RSA risk value is:

[0022] 2 × {2.8 rs1801394 )+2×rs757583846}×3 rs1800871;

[0023] The judgment threshold is 60-65.

[0024] As an improvement to the assessment model of genes for risk of recurrent miscarriage caused by thrombosis in this invention:

[0025] When the threshold is 65, the corresponding result judgment criteria are:

[0026] An RSA risk value of 65 or higher indicates a high risk of thrombosis.

[0027] An RSA risk value of 55 or less indicates a low risk of thrombosis.

[0028] An RSA risk value within the range of [55, 65] indicates a risk of thrombosis.

[0029] This invention also provides a method for using the above-mentioned assessment model of genes that contribute to the risk of recurrent miscarriage due to thrombosis, including the following steps:

[0030] The sample to be tested was amplified by PCR to obtain the types corresponding to 12 sites of 11 genes in the sample;

[0031] The RSA risk value is obtained according to the assignment table and the calculation formula of the RSA risk value;

[0032] Finally, a corresponding risk assessment is conducted.

[0033] An improvement to the method of using the thrombosis-induced recurrent miscarriage risk gene assessment model of the present invention:

[0034] Genomic DNA was extracted from the sample to be tested using a DNA extraction kit, and then multiplex PCR amplification was performed using PCR primers.

[0035] Reaction system:

[0036] The wild-type amplification system consisted of: 12.5 μL of 2×PCR amplification premix, 2 μL of wild-type upstream primer mixture, 2 μL of downstream primer mixture, 100 ng-500 ng of DNA template, and nuclease-free water to a final volume of 25 μL.

[0037] The mutant amplification system is as follows: 12.5 μL of 2×PCR amplification premix, 2 μL of mutant upstream primer mixture, 2 μL of downstream primer mixture, 100 ng-500 ng of DNA template, and nuclease-free water to make up to 25 μL.

[0038] The 2×PCR amplification premix consisted of 25 mM Tris-HCl (pH 8.0), 125 mM KCl, and 6.5 mM MgCl₂. 2+ 0.6 mM dNTP; the remainder is deionized water;

[0039] PCR amplification program: 95℃, 2 minutes -> (94℃, 30 seconds; 60℃, 60 seconds; 72℃, 60 seconds; 30 cycles) -> 72℃, 10 minutes -> store at 4℃.

[0040] This invention also provides a method for obtaining potential disease-associated genes based on multi-site information fusion, specifically for genes associated with the risk of recurrent miscarriage due to thrombosis, comprising the following steps:

[0041] Step 1: Based on knowledge from unrelated literature, search for gene loci reported domestically and internationally that are associated with thrombophilia-related recurrent miscarriages, and whose frequency exceeds one in a thousand in the Chinese population;

[0042] Thus, the corresponding gene loci are determined;

[0043] Step 2: Design and synthesize primers for the specified gene loci and perform detection;

[0044] Step 3: Through clinical trials, the primers from Step 2 were used to detect recurrent miscarriages associated with known thrombophilia, as well as individuals clinically diagnosed with non-recurrent miscarriages.

[0045] Step 4: Normalize the results of Step 3, then use computer tools such as bioinformatics to construct a decision tree and obtain the computational model, i.e., the corresponding computational formula.

[0046] Step 5: Perform a test on the sample and set a threshold, then evaluate the sensitivity, specificity, accuracy, etc. at that threshold.

[0047] The present invention obtains potential disease-associated genes based on multi-site information fusion. First, it searches for gene loci related to thrombophilia and recurrent miscarriage based on unrelated literature. Then, it uses gene chip hybridization technology to detect these gene loci simultaneously. Next, it uses a regression prediction model algorithm to determine the included gene loci, assigns corresponding weights to these loci, scores them, and finally classifies and assesses the thrombosis risk of the test samples.

[0048] This invention uses a medium-throughput method to detect 12 SNP risk sites of 11 genes that are prevalent in the Chinese population, and then forms a thrombophilia assessment model associated with recurrent miscarriage to assess the risk of thrombosis, thereby predicting recurrent miscarriage and guiding the treatment of high-risk groups.

[0049] Note: Thrombosis is a significant cause of recurrent miscarriage. It is treatable. For example, for MTHFR-related thrombosis, the risk can be reduced through folic acid intake, while PAI-1-related thrombosis can be prevented by injecting small molecule heparin sodium. However, these treatments are not recommended for low-risk individuals. Natural pregnancy should be the preferred option. In other words, the risk of thrombosis should be assessed first, including whether the individual has thrombophilia. Thrombophilia can lead to many problems (such as myocardial infarction, stroke, and recurrent miscarriage). Individuals with a high risk of thrombophilia have a higher risk of recurrent miscarriage, and different genes require different treatment methods.

[0050] The beneficial effects of this invention are mainly reflected in the following aspects: by detecting 12 thrombotic risk factors, assigning values ​​according to different risks, and using an algorithm to obtain a quantified risk value from the final result, it facilitates standardization across different regions and laboratories. The invention's judgment method, based on evaluation thresholds, is intuitive.

[0051] This invention uses a dichotomy method to assign values ​​and perform calculations on the results of 12 sites from 100 samples, i.e., a 100×12 data matrix, to obtain a calculation formula that can distinguish between 50 known low-risk individuals and 50 known recurrent miscarriage individuals. Attached Figure Description

[0052] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0053] Figure 1 Capillary electrophoresis image of QFPCR primer amplification standards;

[0054] Figure 1 The upper figure shows capillary electrophoresis images of 12 homozygous wild-type individuals detected using the primers of this invention, while the lower figure shows capillary electrophoresis images of 12 homozygous mutant individuals detected using the primers of this invention.

[0055] Figure 2 This is a QFPCR capillary electrophoresis image of 12 sites from a real sample. Detailed Implementation

[0056] The present invention will be further described below with reference to specific embodiments, but the scope of protection of the present invention is not limited thereto:

[0057] Example 1: Obtaining a gene that reduces the risk of recurrent miscarriage due to thrombosis:

[0058] Step 1: Based on knowledge from unrelated literature, search for gene loci reported domestically and internationally related to thrombophilia-related recurrent miscarriage. The search scope includes expert consensus, medical guidelines, etc. Chinese keywords: thrombophilia, recurrent miscarriage, SNP; English keywords: thrombophilia, RSA, SNP

[0059] And by filtering through common frequencies in China, the following 12 sites were found (as shown in Table 1):

[0060] Table 1, 12 loci

[0061]

[0062]

[0063] Step 2: For the 12 loci from Step 1, design QF-PCR primers for wild-type and mutant strains respectively, following these design rules: primer length 20-22 bp, TM value 40-60, GC content 35%-65%. The downstream primer is a universal primer with a FAM fluorescent label at the 5' end. Two upstream primers are used, one for wild-type and one for mutant strains. The mutant upstream primer has two more bp at the 5' end than the wild-type upstream primer (mainly for product size differentiation), and the last base is different. The wild-type upstream primer's 3' end is complementary to the wild-type sequence, and the mutant upstream primer's 3' end is complementary to the mutant sequence. The two products must differ in length by at least 1 bp. The primers for the 12 loci are shown in Table 2 below.

[0064] Table 2

[0065]

[0066]

[0067] -wt indicates wild-type, -mt indicates mutant. All primers can be synthesized at Shanghai Sangon Biotech Co., Ltd. Downstream primers are universal, and all are 5' end labeled with FAM fluorescence. All primers are prepared to a working concentration of 10 pmol / ul using nuclease-free water. Mix all wild-type upstream primers in equal proportions to form a wild-type upstream primer mixture. Mix all mutant upstream primers in equal proportions to form a mutant upstream primer mixture. Mix all fluorescently modified downstream primers in equal proportions to form a downstream primer mixture.

[0068] Step 3:

[0069] 3.1) Genomic DNA was extracted from the samples (including the samples to be tested) using a DNA extraction kit, and then multiplex PCR amplification was performed using the PCR primers from step 2.

[0070] PCR reaction system:

[0071] The wild-type amplification system is as follows: 12.5 μL of 2×PCR amplification premix, 2 μL of wild-type upstream primer mixture, 2 μL of wild-type downstream primer mixture, and 100-500 ng of DNA template. If necessary, add nuclease-free water to bring the total to 25 μL.

[0072] The mutant amplification system is as follows: 12.5 μL of 2×PCR amplification premix, 2 μL of mutant upstream primer mixture, 2 μL of downstream primer mixture, and 100-500 ng of DNA template. If necessary, add nuclease-free water to bring the total to 25 μL.

[0073] The final concentrations of the wild-type upstream primer mixture and the mutant upstream primer mixture in the system were each 40 nmole / L, and the final concentration of the downstream primer mixture in the system was 80 nmole / L.

[0074] The 2×PCR amplification premix consisted of 25 mM Tris-HCl (pH 8.0), 125 mM KCl, and 6.5 mM Mg. 2+ 0.6 mM dNTP; the remainder is deionized water;

[0075] PCR amplification program: 95℃, 2 minutes -> (94℃, 30 seconds; 60℃, 60 seconds; 72℃, 60 seconds; 30 cycles) -> 72℃, 10 minutes -> store at 4℃.

[0076] 3.2) Amplification products were subjected to capillary electrophoresis using an ABI 3100 or ABI 3730XL to obtain an electrophoretic image of 24 amplification products from 12 loci in a sample. The 12 types corresponding to the 11 genes were ultimately confirmed based on the amplification results. Capillary electrophoresis of QF-PCR to determine the amplification products is a standard technique. Figure 1 As shown.

[0077] Figure 1 The results were obtained using standard plasmids as templates. The top image shows the electrophoresis results of 12 plasmids, all of which were homozygous wild-type, while the bottom image shows the electrophoresis results of 12 plasmids, all of which were homozygous mutant. Therefore... Figure 1 The baseline peak consists of 24 sites from 12 samples, representing both wild-type and mutant types.

[0078] Figure 1 In the diagram: the vertical axis represents the fluorescence signal value, and the horizontal axis represents the peak elution time. Figure 1 The above figure shows the peak times of the wild-type peaks at 12 loci. Figure 1 The figure below shows the peak times for the 12 mutant sites. It indicates no extraneous peaks, no cross-contamination between products, and high specificity. Additionally, this invention includes an ALDH2 gene locus as an internal control for quality control; it is not used in the analysis.

[0079] Step 4: Interpretation of clinical sample results:

[0080] Obtain the DNA from a clinical sample and perform testing according to step 3 above to obtain the following results: Figure 2 The test results shown, combined with Figure 1 The peak times of wild-type and mutant PCR can be used to identify the clinical sample. One site of F2, PROC, PAI, THBD, SERPINC1, IL10, PROS, and MTHFR shows a single peak, and the peak time is consistent with the standard wild-type peak time, therefore it is identified as homozygous wild-type. Another site of VEGFA and MTHFR indicates a homozygous mutant, while F5 and MTRR indicate a heterozygous mutant. The interpretation of QFPCR results is a standard technique.

[0081] Step 5: The SNP detection results obtained in Step 3 are weighted and scored based on the risk OR values ​​of different loci. Homozygous wild-types are standardized to 0, and the scores for heterozygous and homozygous mutants are shown in Table 3 below (refer to https: / / www.reddit.com / r / SNPedia).

[0082] Table 3, Assignment Table

[0083] locus number Gene name rs number Hybrid homozygous mutant Pure wild type 1 F2 rs1799963 2.8 7 0 2 F5 rs6025 4.1 11.4 0 3 IL10 rs1800871 -0.5 -0.5 0 4 PAI-1 rs1799768 4 6.4 0 5 MTHFR rs1801131 2.2 2.8 0 6 MTHFR rs1801133 2.1 2.5 0 7 MTRR rs1801394 0 1.4 0 8 PROC rs757583846 5 9 0 9 VEGFA rs1570360 3 3 0 10 PROS rs121918474 5 5 0 11 SERPINC1 rs121909551 3.5 5 0 12 THBD rs398122807 2.1 4 0

[0084] Step 6: with Figure 2 Taking the 12 loci of the sample as an example, rs1570360 of VEGFA is a homozygous mutant, rs1801131 of MTHFR, rs6025 of F5, and rs1801394 of MTRR are heterozygous, and the others are wild-type. According to the settings in Table 3, the values ​​assigned to this sample are shown in Table 4:

[0085] Table 4

[0086] locus number Gene name rs number Score 1 F2 rs1799963 0 2 F5 rs6025 4.1 3 IL10 rs1800871 0 4 PAI-1 rs1799768 0 5 MTHFR rs1801131 2.2 6 MTHFR rs1801133 0 7 MTRR rs1801394 0 8 PROC rs757583846 0 9 VEGFA rs1570360 3 10 PROS rs121918474 0 11 SERPINC1 rs121909551 0 12 THBD rs398122807 0

[0087] Step 7: Calculate the RSA risk value of the sample using the following formula:

[0088] The calculation formula is:

[0089] =2×{2.8×rs1799963+3×rs6025+(3+0.4×rs757583846+0.5×rs1570360+0.3×rs12191847+0. 35×rs12190955+0.5×rs398122807)×(1+rs1799768)+[(1+rs1801131)×(1+rs1801133)+1]^2 rs1801394}×3 rs1800871

[0090] Taking the sample values ​​in Table 4 as an example, substituting them into the formula yields:

[0091] 2×{2.8×0+3×4.1+(3+0.4×0+0.5×3+0.3×0+0.35×0+0.5×0)×(1+0)+[(1+2.2)×(1+0)+1]^2 0}×3 0 =42.

[0092] Result judgment criteria:

[0093] An RSA risk value of 65 or higher indicates a high risk of thrombosis.

[0094] An RSA risk value of 55 or less indicates a low risk of thrombosis.

[0095] When the RSA risk value is in the range of [55, 65], it indicates a risk of thrombosis.

[0096] Taking the sample 42 obtained from the calculation formula in Table 4 as an example, according to the judgment criteria, this sample is of low risk of thrombosis.

[0097] Clinicians need to consider factors such as the high risk of thrombosis, gestational age, and current medications to determine the risk of recurrent miscarriage in this case.

[0098] To verify the effectiveness of the present invention, the following verification experiments were conducted:

[0099] Experimental Case 1:

[0100] The applicant's cooperating hospital was responsible for collecting and providing peripheral blood from 50 clinically and pathologically confirmed pregnant women with recurrent miscarriages related to thrombophilia, and 50 women with a proven history of no recurrent miscarriages and normal childbirth. After DNA extraction, the primers designed in step 2 were used, and the operation was performed according to step 4 above. The detection results of 12 SNP loci from the following 100 samples were obtained, and the detection results are shown in Table 5 below:

[0101] Table 5: Detection results of 12 SNPs in 50 patients and 50 normal controls.

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] Note: "4G / 5G" represents heterozygous type, "4G / 4G" represents homozygous mutant type, and "5G / 5G" represents homozygous wild type.

[0110] Substitute the results from Table 5 into the assignment table in Table 3 to obtain the assigned values ​​for the patient's 12 genes. Then, substitute these values ​​into the calculation formula in step 7 to calculate the RSA score. The risk is then assessed using the scoring criteria, as shown in Table 6-1 below:

[0111] Table 6-1

[0112]

[0113]

[0114] Based on Table 6-1 and the gold standard, the statistical results are shown in Table 6-2 below;

[0115] Table 6-2

[0116] High risk Medium risk Low risk Gold standard (pathological) positive 50 43 2 5 Gold standard negative 50 5 5 40 total 100 48 7 45

[0117] Based on the statistical results obtained above, the statistical results in Table 6-3 are further presented. Calculations show that, according to the dichotomy principle, if medium risk is classified into the risky range, the specificity is 88.8%, the sensitivity is 81.8%, and the accuracy is 85%; if medium risk is classified into the risk-free range, the specificity is 86.5%, the sensitivity is 89.6%, and the accuracy is 88%.

[0118] Table 6-3

[0119]

[0120]

[0121] Comparison Case 1

[0122] All other steps are the same as in Experimental Case 1, but the criteria for judging risk are different. If we consider hybrid (4G / 5G) and pure 4G / 4G of PAI-1 as medium to high risk in this comparative case 1, we will get the following consistent results.

[0123] That is, the traditional PAI-1 is judged as high risk when "PAI-1" in Table 5 is 4G / 4G, medium risk when it is 4G / 5G, and low risk when it is 5G / 5G.

[0124] The results are shown in Table 7-1 below.

[0125] Table 7-1

[0126]

[0127]

[0128] Based on Table 7-1, the risk statistics results are shown in Table 7-2 below;

[0129] Table 7-2

[0130] High risk Medium risk Low risk Gold standard (pathological) positive 50 21 21 8 Gold standard negative (normal person) 50 12 32 6 total 100 33 53 14

[0131] Therefore, based on the statistical results obtained above, further statistical analysis yielded the results in Table 7-3. The results are as follows: if medium risk is classified as high risk, the specificity is 42.8%, the sensitivity is 48.8%, and the accuracy is 48%. If medium risk is classified as low risk, the specificity is 56.7%, the sensitivity is 63.6%, and the accuracy is 59%.

[0132] 7-3

[0133]

[0134]

[0135] The results show that if only the traditional PAI-1 hybrid (4G / 5G) and homozygous 4G / 4G are considered as high-risk, the specificity, sensitivity, and accuracy are all worse than the algorithm of this invention.

[0136] Comparison Example 2

[0137] The other experimental steps are the same as in Experiment Case 1. The subsequent judgment criteria are based on the C677T site of MTHFR, which is currently commonly used in the market. Homozygous wild type is considered low risk, heterozygous type is considered medium risk, and homozygous mutant type is considered high risk. Medium risk is also included in high risk.

[0138] That is, for “MTHFR C677T”, when “rs1801133” in Table 5 is a homozygous mutant, it is considered high risk; when “rs1801133” is a heterozygous mutant, it is considered medium risk; and when “rs1801133” is a homozygous wild type, it is considered low risk.

[0139] The results are shown in Table 8-1 below.

[0140] Table 8-1

[0141]

[0142]

[0143] Based on Table 8-1, the results were further statistically analyzed to generate Table 8-2 below;

[0144] Table 8-2

[0145] High risk Medium risk Low risk Gold standard (pathological) positive 50 8 24 18 Gold standard negative 50 12 25 13 total 100 20 49 31

[0146] Further statistical analysis based on the results above, resulting in Table 8-3, yielded the following results: If medium risk is classified as high risk, the specificity is 41.9%, the sensitivity is 42.4%, and the accuracy is 45%. If medium risk is classified as low risk, the specificity is 47.5%, the sensitivity is 40%, and the accuracy is 46%.

[0147] Table 8-3

[0148]

[0149]

[0150] The comparison table of the three implementation cases is as follows:

[0151] Table 9

[0152]

[0153] The results show that the present invention, through the joint detection of multiple indicators and the regression prediction risk calculation formula, is more accurate than the traditional single-factor prediction method in terms of specificity, sensitivity, and accuracy.

[0154] Comparison Example 3

[0155] The calculation formula and threshold judgment of this invention are both machine learning-based, and various test simulations are performed between different thresholds to achieve a relatively balanced state of specificity and sensitivity. Other steps are the same as in Implementation Case 1. If the judgment threshold in Implementation Case 1 is adjusted to 60, the medium risk is [55, 60], and other factors remain unchanged, then the results of Implementation Case 1 are shown in Table 10-1 below:

[0156] Table 10-1

[0157] High risk Medium risk Low risk Gold standard (pathological) positive 50 45 0 5 Gold standard negative 50 9 1 40 total 100 54 1 45

[0158] The comparison results are shown in Table 10-2 below:

[0159] Table 10-2:

[0160]

[0161] The results showed differences in specificity, sensitivity, and accuracy, but still maintained a high accuracy rate.

[0162] Finally, it should be noted that the above examples are merely some specific embodiments of the present invention. Obviously, the present invention is not limited to the above embodiments and many variations are possible. All variations that can be directly derived or conceived by those skilled in the art from the disclosure of the present invention should be considered within the scope of protection of the present invention. sequence list <110> Yixi Micro Medical Technology (Shanghai) Co., Ltd. <120> Primers and assessment model for gene detection of risk factors for recurrent miscarriage caused by thrombosis <160> 36 <170> SIPOSequenceListing 1.0 <210> 1 <211> twenty three <212> DNA <213> Artificial Sequence <400> 1 cccaataaaa gtgactctca gcg 23 <210> 2 <211> 26 <212> DNA <213> Artificial Sequence <400> 2 gttcccaata aaagtgactc tcagca 26 <210> 3 <211> twenty two <212> DNA <213> Artificial Sequence <400> 3 agctgcccat gaatagcact gg 22 <210> 4 <211> 27 <212> DNA <213> Artificial Sequence <400> 4 ttcaaggaca aaatacctgt attcctc 27 <210> 5 <211> 29 <212> DNA <213> Artificial Sequence <400> 5 acttcaagga caaaatacct gtattcctt 29 <210> 6 <211> 27 <212> DNA <213> Artificial Sequence <400> 6 ctgggctaat aggactactt ctaatct 27 <210> 7 <211> 27 <212> DNA <213> Artificial Sequence <400> 7 tagtgagcaa actgaggcac agagata 27 <210> 8 <211> 25 <212> DNA <213> Artificial Sequence <400> 8 gtgagcaaac tgaggcacag agatg 25 <210> 9 <211> 27 <212> DNA <213> Artificial Sequence <400> 9 ggtttcattc tatgtgctgg agatggt 27 <210> 10 <211> twenty three <212> DNA <213> Artificial Sequence <400> 10 ggcacagaga gagtctggac acg 23 <210> 11 <211> twenty three <212> DNA <213> Artificial Sequence <400> 11 ggcacagaga gagtctggac acg 23 <210> 12 <211> twenty four <212> DNA <213> Artificial Sequence <400> 12 actcttggtc tttccctcat ccct 24 <210> 13 <211> 29 <212> DNA <213> Artificial Sequence <400> 13 gtaaagaacg aagacttcaa agacacttt 29 <210> 14 <211> 27 <212> DNA <213> Artificial Sequence <400> 14 aaagaacgaa gacttcaaag acacttg 27 <210> 15 <211> 20 <212> DNA <213> Artificial Sequence <400> 15 acctctgggc acccctctgc 20 <210> 16 <211> twenty one <212> DNA <213> Artificial Sequence <400> 16 gctgcgtgat gatgaaatcg g 21 <210> 17 <211> twenty three <212> DNA <213> Artificial Sequence <400> 17 aagctgcgtg atgatgaaat cga 23 <210> 18 <211> twenty one <212> DNA <213> Artificial Sequence <400> 18 ctcgccttga acaggtggag g 21 <210> 19 <211> twenty four <212> DNA <213> Artificial Sequence <400> 19 gcaaaggcca tcgcagaaga aata 24 <210> 20 <211> twenty two <212> DNA <213> Artificial Sequence <400> 20 aaaggccatc gcagaagaaa tg 22 <210> twenty one <211> 27 <212> DNA <213> Artificial Sequence <400> twenty one cacttcccaa ccaaaattct tcaaagc 27 <210> twenty two <211> 20 <212> DNA <213> Artificial Sequence <400> twenty two ccgtcacagc agcctggagc 20 <210> twenty three <211> twenty two <212> DNA <213> Artificial Sequence <400> twenty three ctccgtcaca gcagcctgga gt 22 <210> twenty four <211> 27 <212> DNA <213> Artificial Sequence <400> twenty four gtcatccaca ttttggaaaa tttcctt 27 <210> 25 <211> twenty one <212> DNA <213> Artificial Sequence <400> 25 ggacaggcga gcctcagccc t 21 <210> 26 <211> 19 <212> DNA <213> Artificial Sequence <400> 26 acaggcgagc ctcagcccc 19 <210> 27 <211> twenty two <212> DNA <213> Artificial Sequence <400> 27 gcttcactga gcgtccgcag ag 22 <210> 28 <211> 28 <212> DNA <213> Artificial Sequence <400> 28 tcctgctctt acctttacaa tctttctt 28 <210> 29 <211> 25 <212> DNA <213> Artificial Sequence <400> 29 tccctcaaat ataaatggag gttgc 25 <210> 30 <211> 25 <212> DNA <213> Artificial Sequence <400> 30 tccctcaaat ataaatggag gttgc 25 <210> 31 <211> twenty three <212> DNA <213> Artificial Sequence <400> 31 gagggctcag aacagaagat ccc 23 <210> 32 <211> 25 <212> DNA <213> Artificial Sequence <400> 32 atgagggctc agaacagaag atcct 25 <210> 33 <211> twenty four <212> DNA <213> Artificial Sequence <400> 33 tctgccaggt gctgatagaa agtg 24 <210> 34 <211> twenty four <212> DNA <213> Artificial Sequence <400> 34 gtctggttgc aaaacatctg gcac 24 <210> 35 <211> 26 <212> DNA <213> Artificial Sequence <400> 35 cagtctggtt gcaaaacatc tggcaa 26 <210> 36 <211> 25 <212> DNA <213> Artificial Sequence <400> 36 agcccctgaa ccaaactagc tacct 25

Claims

1. A gene combination that leads to the risk of recurrent miscarriage due to thrombosis, characterized by: Based on gene weights, the gene names, corresponding loci, and rs numbers are set as follows: 。 2. The gene combination for risking recurrent miscarriage due to thrombosis according to claim 1, characterized in that: The primers corresponding to the 12 sites of the 11 genes are as follows: ； wt represents wild type, and mt represents mutant type.

3. An assessment model for gene combinations that contribute to the risk of recurrent miscarriage due to thrombosis, characterized by: Using the gene combination as described in claim 1 or 2, the specific assignment table is as follows: ； The formula for calculating the RSA risk value is: 2×{2.8×rs1799963+3×rs6025+(3+rs1570360+rs121918474+rs121909551+rs398122807)×(1+rs1799768)+[(1+rs1801131)×(1+rs1801133)+1]^(2 rs1801394 )+2×rs757583846}×3 rs1800871 ; The judgment threshold is 60~65.

4. The assessment model for the risk combination of thrombosis leading to recurrent miscarriage according to claim 3, characterized in that: When the threshold is 65, the corresponding result judgment criteria are: An RSA risk value of 65 or higher indicates a high risk of thrombosis. An RSA risk value of 55 or less indicates a low risk of thrombosis. An RSA risk value within the range of [55, 65] indicates a risk of thrombosis.

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