Biomarkers or combinations thereof for predicting the risk of preoperative pulmonary vein stenosis or prognosis in patients with TAPVC and their applications

Abstract

The present invention discloses a biomarker or a combination thereof for predicting the risk of preoperative pulmonary vein stenosis or prognosis in patients with TAPVC, a reagent for detecting the same, a kit comprising the biomarker or the combination thereof and / or the reagent, the application of the reagent and / or the kit in the preparation of a product for predicting preoperative pulmonary vein stenosis in patients with TAPVC, and also relates to a system and method for predicting the risk of preoperative pulmonary vein stenosis in patients with TAPVC, a computer-readable storage medium and a computer device comprising the same. Detecting the expression level of the biomarker or the combination thereof of the present invention in a patient can determine whether a patient with TAPVC has pulmonary vein stenosis before surgery. Taking NR3C2 as an example, the area under the receiver operating characteristic curve (AUC) of its training set is 0.89, and the sensitivity can reach 0.89. The AUC of the validation set is 1 and the sensitivity is 0.86.

Description

Technical Field

[0001] The present invention relates to the fields of genetic technology and biomedicine, and specifically to biomarkers or combinations thereof for predicting the risk of preoperative pulmonary vein stenosis in TAPVC patients, reagents for detecting the biomarkers or combinations thereof and / or reagents, and the use of the reagents and / or kits in the preparation of products for predicting preoperative pulmonary vein stenosis in TAPVC patients. The present invention also relates to systems and methods for predicting the risk or prognosis of preoperative pulmonary vein stenosis in TAPVC patients, and computer-readable storage media and computer devices containing the same. Background Art

[0002] Total anomalous pulmonary venous connection (TAPVC) is a rare congenital heart defect in which all four pulmonary veins fail to connect properly to the left atrium, instead connecting anomalously with the right atrium or the systemic venous system. Without any intervention, the mortality rate reaches 80%; however, after surgery, 5% to 20% of patients still experience postoperative obstruction. Preoperative pulmonary vein stenosis is a high-risk factor for postoperative obstruction. If patients with stenosis and non-stenosis can be classified preoperatively and different treatments can be selected, the incidence of postoperative obstruction can be reduced and patient outcomes improved.

[0003] Because the disease is relatively rare, there are currently no invention patents for the prediction of preoperative pulmonary vein stenosis. Only invention patents for congenital heart disease have been investigated, including TAPVC disease diagnosis, with patent number US20200268255A1.

[0004] Existing published articles show that machine learning has been widely applied to the diagnosis of preoperative pulmonary vein stenosis. For example, preoperative obstruction assessment is based on the imaging indicator maximum velocity; PVVI (maximum velocity minus minimum velocity / average velocity). Furthermore, preoperative obstruction assessment has been integrated with clinical indicators and CT imaging data. However, this assessment is not currently effective. Studies have shown that white blood cells can reflect cardiovascular pathology, such as Il-6, which is involved in atherosclerosis, and neutrophils, which are associated with impaired post-stroke vascular remodeling and remodeling. Furthermore, the blood transcriptome can reflect disease characteristics and has shown promising results in predicting eclampsia, lung cancer, and lupus erythematosus.

[0005] Currently, there are no invention patents related to markers for preoperative pulmonary vein stenosis judgment, and the published articles on preoperative pulmonary vein stenosis prediction have low accuracy.

[0006] Therefore, new and highly accurate detection methods and systems are urgently needed in technology. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to overcome the problem of low accuracy of preoperative pulmonary vein stenosis in the prior art, and to provide a biomarker or a combination thereof for predicting the risk or prognosis of preoperative pulmonary vein stenosis in TAPVC patients, a reagent for detecting the same, a kit containing the biomarker or the combination thereof and / or the reagent, and the use of the reagent and / or the kit in the preparation of a product for predicting the risk or prognosis of preoperative pulmonary vein stenosis in TAPVC patients. It also relates to a system and method for predicting the risk or prognosis of preoperative pulmonary vein stenosis in TAPVC patients, a computer-readable storage medium and a computer device containing the same.

[0008] The present invention creatively uses genes as markers and combines them with machine learning algorithms to build a model to predict the risk of pulmonary vein stenosis in TAPVC patients before surgery and classify patients with stenosis and non-stenosis before surgery.

[0009] The present invention mainly solves the above technical problems through the following technical solutions.

[0010] One of the technical solutions of the present invention is: a biomarker or a combination thereof for predicting the risk or prognosis of preoperative pulmonary vein stenosis in TAPVC patients, including the NR3C2 mRNA molecular marker and / or the MEG3 lncRNA molecular marker.

[0011] Preferably, the biomarker or combination thereof in the present invention further comprises at least one of the three mRNA molecular markers CNTNAP2, FAM3C and NETO1.

[0012] The combination of the present invention may be any of the following:

[0013] (1) NR3C2 mRNA molecular marker, and at least one of CNTNAP2, FAM3C, and NETO1 mRNA molecular markers;

[0014] (2) MEG3 lncRNA molecular marker, and at least one of CNTNAP2, FAM3C, and NETO1 mRNA molecular markers;

[0015] (3) NR3C2 mRNA molecular marker and MEG3 lncRNA molecular marker, and at least one of CNTNAP2, FAM3C and NETO1 mRNA molecular markers.

[0016] In a specific embodiment of the present invention, the combination includes two mRNA molecular markers: NR3C2 and CNTNAP2.

[0017] In another specific embodiment of the present invention, the combination includes two mRNA molecular markers: NR3C2 and FAM3C.

[0018] In another specific embodiment of the present invention, the combination includes two mRNA molecular markers: NR3C2 and NETO1.

[0019] In another specific embodiment of the present invention, the combination includes three mRNA molecular markers: NR3C2, CNTNAP2 and FAM3C.

[0020] In another specific embodiment of the present invention, the combination includes three mRNA molecular markers: NR3C2, FAM3C and NETO1.

[0021] In another specific embodiment of the present invention, the combination includes three mRNA molecular markers: NR3C2, CNTNAP2 and NETO1.

[0022] In another specific embodiment of the present invention, the combination includes four mRNA molecular markers: NR3C2, CNTNAP2, FAM3C and NETO1.

[0023] In another specific embodiment of the present invention, the combination comprises: a MEG3 lncRNA molecular marker and a CNTNAP2 mRNA molecular marker.

[0024] In another specific embodiment of the present invention, the combination comprises: MEG3 lncRNA molecular marker and FAM3C mRNA molecular marker.

[0025] In another specific embodiment of the present invention, the combination comprises: MEG3 lncRNA molecular marker and NETO1 mRNA molecular marker.

[0026] In another specific embodiment of the present invention, the combination includes: MEG3 lncRNA molecular marker and two mRNA molecular markers, NR3C2 and CNTNAP2.

[0027] In another specific embodiment of the present invention, the combination comprises: MEG3 lncRNA molecular marker and two mRNA molecular markers, NR3C2 and FAM3C.

[0028] In another specific embodiment of the present invention, the combination comprises: MEG3 lncRNA molecular marker and two mRNA molecular markers, NR3C2 and NETO1.

[0029] In another specific embodiment of the present invention, the combination comprises: MEG3 lncRNA molecular marker and two mRNA molecular markers, CNTNAP2 and FAM3C.

[0030] In another specific embodiment of the present invention, the combination comprises: MEG3 lncRNA molecular marker and two mRNA molecular markers, FAM3C and NETO1.

[0031] In another specific embodiment of the present invention, the combination comprises: MEG3 lncRNA molecular marker and two mRNA molecular markers, CNTNAP2 and NETO1.

[0032] In another specific embodiment of the present invention, the combination comprises: MEG3 lncRNA molecular marker and three mRNA molecular markers CNTNAP2, FAM3C and NETO1.

[0033] In another specific embodiment of the present invention, the combination includes: MEG3 lncRNA molecular marker and three mRNA molecular markers: NR3C2, CNTNAP2 and FAM3C.

[0034] In another specific embodiment of the present invention, the combination comprises: MEG3 lncRNA molecular marker and three mRNA molecular markers: NR3C2, FAM3C and NETO1.

[0035] In another specific embodiment of the present invention, the combination comprises: MEG3 lncRNA molecular marker and three mRNA molecular markers: NR3C2, CNTNAP2 and NETO1.

[0036] In another specific embodiment of the present invention, the combination comprises: MEG3 lncRNA molecular marker and four mRNA molecular markers: NR3C2, CNTNAP2, FAM3C and NETO1.

[0037] In the present invention, the NR3C2 mRNA molecular marker preferably contains the nucleic acid sequence shown in SEQ ID NO: 1.

[0038] In the present invention, the MEG3 lncRNA molecular marker preferably contains the nucleic acid sequence shown in SEQ ID NO: 5.

[0039] In the present invention, the CNTNAP2 mRNA molecular marker preferably contains the nucleic acid sequence shown in SEQ ID NO: 2.

[0040] In the present invention, the FAM3C mRNA molecular marker preferably contains the nucleic acid sequence shown in SEQ ID NO: 3.

[0041] In the present invention, the NETO1 mRNA molecular marker preferably contains the nucleic acid sequence shown in SEQ ID NO:4.

[0042] The second technical solution of the present invention is: a reagent for detecting the biomarker or combination thereof as described in one of the technical solutions, wherein the reagent includes a biological molecule that specifically hybridizes with the biomarker or combination thereof as described in one of the technical solutions.

[0043] The biomolecules can be conventional in the art; preferably, the biomolecules include one or more selected from primers, probes and antibodies.

[0044] The third technical solution of the present invention is: a kit comprising the biomarker or a combination thereof as described in one of the technical solutions, and / or the reagent as described in the second technical solution.

[0045] The fourth technical solution of the present invention is: the use of the biomarker or the combination thereof as described in the first technical solution or the reagent as described in the second technical solution in the preparation of a product for predicting the risk or prognosis of preoperative pulmonary vein stenosis in TAPVC patients.

[0046] A fifth technical solution of the present invention is a system for predicting the risk or prognosis of pulmonary vein stenosis in patients undergoing TAPVC surgery, the system comprising:

[0047] a data processing module, configured to calculate the data of the biomarkers or the combination thereof as described in one of the technical solutions of the TAPVC patient received or inputted, and obtain a calculation result; and

[0048] The judgment and output module is used to judge whether the calculation result meets the preset judgment conditions to predict the risk or prognosis of pulmonary vein stenosis before TAPVC surgery and output the prediction result.

[0049] Optionally, in the judgment and output module, when the calculation result meets the judgment condition, the output prediction result is "TAPVC patients have pulmonary vein stenosis before surgery"; when the calculation result does not meet the judgment condition, the output prediction result is "TAPVC patients do not have pulmonary vein stenosis before surgery".

[0050] Optionally, in the data processing module, the data is the expression level information of the biomarker or a combination thereof of the TAPVC patient as described in one of the technical solutions.

[0051] Optionally, in the judgment and output module, the judgment includes comparing the expression information with a reference data set or a reference value.

[0052] Optionally, the reference dataset includes expression level information of the biomarkers or a combination thereof according to one of the technical solutions in samples from multiple TAPVC patients with preoperative pulmonary vein stenosis and multiple TAPVC patients without preoperative pulmonary vein stenosis.

[0053] Optionally, the judgment and output module includes a machine learning model for performing the calculation.

[0054] Optionally, the machine learning model is preferably one or more of a generalized linear model (GLM), a random forest (RF) and a support vector machine (SVM).

[0055] Optionally, the judgment condition is that the calculated risk coefficient is greater than 0.5.

[0056] A sixth technical solution of the present invention is a method for predicting the risk or prognosis of pulmonary vein stenosis in patients undergoing TAPVC surgery, comprising the following steps:

[0057] (1) calculating the data of the biomarkers or the combination thereof received or inputted from the TAPVC patient as described in one of the technical solutions to obtain a calculation result;

[0058] (2) Determine whether the calculation result meets the preset judgment conditions to predict the risk or prognosis of pulmonary vein stenosis in TAPVC patients before surgery, and output the prediction result.

[0059] Optionally, in step (2), when the calculation result meets the judgment condition, the output prediction result is "TAPVC patients have pulmonary vein stenosis before surgery", and when the calculation result does not meet the judgment condition, the output prediction result is "TAPVC patients do not have pulmonary vein stenosis before surgery".

[0060] Optionally, the data in step (1) is the expression level information of the biomarker or a combination thereof of the TAPVC patient as described in one of the technical solutions.

[0061] Optionally, the determination in step (2) includes comparing the expression information with a reference data set or a reference value.

[0062] Optionally, the reference dataset includes expression level information of the biomarkers or a combination thereof according to one of the technical solutions in samples from multiple TAPVC patients with preoperative pulmonary vein stenosis and multiple TAPVC patients without preoperative pulmonary vein stenosis.

[0063] Optionally, in the step of comparing the expression information with a reference data set, the calculation is also performed using a machine learning model.

[0064] Optionally, the machine learning model is preferably one or more of a generalized linear model (GLM), a random forest (RF) and a support vector machine (SVM).

[0065] Optionally, the judgment condition is that the calculated risk coefficient is greater than 0.5.

[0066] Optionally, the expression level information of the biomarker in step (1) is obtained by sequencing, further comprising:

[0067] Obtaining a plasma leukocyte RNA sample from the sample, and performing PALM-seq sequencing based on the obtained RNA sample to obtain a sequencing result,

[0068] And based on the sequencing results, the sequencing results are compared with the reference gene set to determine the expression level information of the biomarker.

[0069] Optionally, the sample is a plasma sample.

[0070] Optionally, the sequencing method is based on library construction of the entire transcriptome using microplatelet-derived blood cells, followed by PE100 sequencing on Dipseq.

[0071] The prediction described in the sixth technical solution is preferably a computer-assisted prediction.

[0072] The seventh technical solution of the present invention is: a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can realize the functions of the system described in the fifth technical solution, or realize the method described in the sixth technical solution.

[0073] The eighth technical solution of the present invention is: a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is used to execute the computer program to implement the functions of the system described in the fifth technical solution or the steps of the method described in the sixth technical solution.

[0074] The ninth technical solution of the present invention is: the use of the biomarkers or combinations thereof as described in one of the technical solutions as targets in screening drugs that reduce or prevent the risk of preoperative pulmonary vein stenosis in TAPVC patients or in predicting the risk of preoperative pulmonary vein stenosis in TAPVC patients.

[0075] In a preferred embodiment of the present invention, the RNA expression profiles of leukocytes in the blood were compared to identify markers predicting preoperative pulmonary vein stenosis in TAPVC patients with and without pulmonary vein stenosis. Three risk prediction models, including a generalized linear model and a support vector machine random forest model, were constructed simultaneously. The model with the best prediction effect was selected as the optimal model. This allows for accurate preoperative prediction of pulmonary vein stenosis in TAPVC patients.

[0076] (1) Acquisition of blood leukocyte RNA

[0077] Peripheral blood was drawn from TAPVC patients, and leukocytes were separated. Leukocyte RNA was extracted using the TRIzol method.

[0078] (2) Sequencing of leukocyte RNA

[0079] Leukocyte RNA sequencing was performed using whole transcriptome sequencing (PALM-seq), a next-generation sequencing method that can sequence both mRNA and lncRNA simultaneously.

[0080] (3) Quantification of leukocyte RNA expression profile

[0081] The raw RNA sequencing data were quality-controlled, including adapter trimming, removal of low-quality reads, removal of reads <17 bp in length, and removal of rRNA, value RNA, and Y RNA sequences. The remaining reads were first aligned to the human transcriptome (in the order of miRNA, tRNA, and piRNA, followed by mRNA and lncRNA, and finally other RNAs). Expression levels of the samples were corrected based on the expression levels in the existing training set. RNA alignment was performed using Bowtie software, and quantification was performed using RSEM.

[0082] (4) Molecular markers in patients with TAPVC and preoperative pulmonary vein stenosis

[0083] We found that the mRNA molecular marker NR3C2 can accurately classify pulmonary vein stenosis samples in TAPVC patients.

[0084] (5) Construction of a preoperative pulmonary vein stenosis judgment model based on markers

[0085] Three machine learning models were constructed simultaneously, including a generalized linear model (GLM), a random forest (RF), and a support vector machine (SVM). These models, combined with the molecular marker NR3C2 identified in the training set, predicted the risk of pulmonary vein stenosis in patients undergoing TAPVC. A risk coefficient greater than 0.5 indicated the presence of preoperative pulmonary vein stenosis, while a coefficient less than 0.5 indicated the absence of preoperative pulmonary vein stenosis. This risk coefficient was automatically calculated by the model.

[0086] The sample used in the present invention is blood leukocytes, but similar judgment effects may be achieved in pulmonary vein endothelium, plasma, etc.

[0087] The present invention uses three model algorithms, but other models may be used instead to achieve the same effect.

[0088] On the basis of conforming to the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present invention.

[0089] The reagents and raw materials used in the present invention are commercially available.

[0090] The positive progress effect of the present invention is:

[0091] Detecting the expression of the biomarkers of the present invention, or their combination, in patients can preoperatively determine whether TAPVC patients have pulmonary vein stenosis. For example, using only the NR3C2 mRNA marker, the training set model achieved an AUC of 0.89, with a sensitivity of 0.89, and a validation set AUC of 1, with a sensitivity of 0.86. Based on the biomarkers of the present invention, or their combination, and their applications, there is hope that the development of pulmonary vein stenosis in TAPVC patients can be accurately predicted, thereby improving patient prognosis by altering treatment approaches. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 Selection process for molecular features.

[0093] Figure 2 To build a risk prediction model.

[0094] Figure 3 Build a risk prediction model.

[0095] Figures 4A to 4E Displays the difference in feature expression between the training set and the validation set. DETAILED DESCRIPTION

[0096] The present invention is further illustrated by way of examples below, but the present invention is not limited to the scope of the examples. Experimental methods in the following examples where specific conditions are not specified were performed according to conventional methods and conditions, or selected according to the product specifications.

[0097] Example 1

[0098] (1) Obtaining leukocyte RNA samples

[0099] Peripheral blood was obtained from 48 patients undergoing TAPVC (26 with pulmonary vein stenosis and 22 without pulmonary vein stenosis) before surgery at the Guangdong Cardiovascular Institute. White blood cells (leukocytes) were obtained by centrifugation and lysis of red blood cells, and RNA was extracted from these cells using the TRIzol method.

[0100] (2) Sequencing and expression quantification of leukocyte RNA

[0101] Leukocyte RNA libraries were constructed using whole-transcriptome sequencing, using next-generation sequencing (PALM-seq) to simultaneously sequence both mRNA and lncRNA. The raw cfRNA sequencing data were quality-controlled, including adapter trimming, removal of low-quality reads, removal of reads <17 bp in length, and removal of rRNA sequences, value RNA, and Y RNA sequences. The remaining reads were first aligned to the human transcriptome (in order of miRNA, tRNA, and piRNA, then mRNA and lncRNA, and finally other RNAs), and then aligned to the human genome. The expression levels of long RNAs (including mRNA and lncRNA) were normalized to TPM using the following formula.

[0102] TPM=(Ni / Li)*1000000 / (sum(N1 / L1+N2 / L2+N3 / L3+…+Nn / Ln))

[0103] Ni is the number of reads aligned to the i-th gene; Li is the length of the i-th gene; sum(N1 / L1+N2 / L2+...+Nn / Ln) is the sum of the values ​​of all (n) genes after normalization by length.

[0104] TotalMappingReads is the sum of the read lengths of all alignments.

[0105] (3) Molecular marker screening

[0106] First, samples from patients with preoperative pulmonary vein stenosis diagnosed by CT imaging were divided into a training set, and samples from patients who could not be diagnosed by CT imaging were divided into a validation set, achieving a ratio of 7:3. Patients without preoperative pulmonary vein stenosis were also randomly split into training and validation sets at a 7:3 ratio. The training set contained 35 samples, including 19 patients with pulmonary vein stenosis and 16 patients without pulmonary vein stenosis. The validation set contained 13 samples, including 7 patients with pulmonary vein stenosis and 6 patients without pulmonary vein stenosis. All molecular marker screening was completed on the training set, while the validation set was used to test the predictive performance of the molecular markers and model. Feature selection was then performed. First, the expression values ​​of the samples were scaled to have a mean of 0 and a variance of 1. A generalized linear model was then used to select features from the training set. Each selection was performed with 7-fold cross-validation, and the top 100 molecules were selected based on feature importance. A random forest algorithm was then used to perform 7-fold cross-validation to select the 10 to 20 most important molecules. This process was repeated 20 times. The genes with the highest importance among the molecules with a frequency greater than 50% were selected as the final molecular markers.

[0107] (4) Construction and validation of marker-based models

[0108] In the training set, based on the final screened molecular markers NR3C2 and MEG3, three machine learning algorithms (generalized linear model, random forest and support vector machine) were used to predict the risk of pulmonary vein stenosis in TAPVC patients (see Tables 1 and 2). Each algorithm used a 7-fold cross-validation method to select the optimal parameters for model construction. The final model will be verified in the validation set, and then the best model will be selected as the final model and the feature importance will be calculated. The molecular feature selection process and the process of building the risk prediction model are shown in Figure 1 and Table 2, respectively. Figure 1 and Figure 2 .

[0109] The prediction results of NR3C2 and MEG3 in stenotic and non-stenotic samples are shown in Tables 1 and 2, respectively.

[0110] Table 1

[0111] pred real T2004012130 No No T2004012132 No No T2004012133 No No T2004012137 Yes No T2004012139 No No T2004012140 No No T2004012144 No No T2004012150 Yes No T2004012155 No No T2004012158 No No T2004012162 No No T2004012167 No No T2004012170 No No T2004012181 No No T2004012182 No No T2004012186 No No T2004012127 Yes Yes T2004012128 Yes Yes T2004012131 Yes Yes T2004012134 Yes Yes T2004012145 Yes Yes T2004012149 No Yes T2004012151 No Yes T2004012153 Yes Yes T2004012154 Yes Yes T2004012156 Yes Yes T2004012157 Yes Yes T2004012159 Yes Yes T2004012163 Yes Yes T2004012164 Yes Yes T2004012168 Yes Yes T2004012169 Yes Yes T2004012174 Yes Yes T2004012179 Yes Yes T2004012185 Yes Yes T2004012126 No s T2004012135 No No T2004012146 No No T2004012147 No No T2004012166 No No T2004012176 No No T2004012129 No Yes T2004012143 Yes Yes T2004012160 Yes No T2004012161 Yes Yes T2004012165 Yes Yes T2004012180 Yes Yes T2004012183 Yes Yes

[0112] The results in Table 1 show that NR3C2 has a good discrimination effect on samples.

[0113] Table 2

[0114] Yes pred T2004012130 real No T2004012132 No No T2004012133 No No T2004012137 No No T2004012139 No Yes T2004012140 No No T2004012144 No No T2004012150 No No T2004012155 No No T2004012158 No Yes T2004012162 No No T2004012167 No No T2004012170 No Yes T2004012181 s No T2004012182 No No T2004012186 Yes No T2004012127 Yes No T2004012128 No Yes T2004012131 Yes Yes T2004012134 Yes Yes T2004012145 No Yes T2004012149 No Yes T2004012151 Yes Yes T2004012153 Yes Yes T2004012154 Yes Yes T2004012156 Yes Yes T2004012157 Yes Yes T2004012159 Yes Yes T2004012163 No Yes T2004012164 Yes Yes T2004012168 Yes Yes T2004012169 No Yes T2004012174 Yes Yes T2004012179 Yes Yes T2004012185 Yes Yes T2004012126 Yes Yes T2004012135 No No T2004012146 No No T2004012147 No No T2004012166 s No T2004012176 No No T2004012129 No No T2004012143 No Yes T2004012160 Yes Yes T2004012161 Yes Yes T2004012165 Yes Yes T2004012180 Yes No T2004012183 Yes Yes

[0115] The results in Table 2 show that MEG3 has a good discrimination effect on samples.

[0116] (5) Predicting the effect of molecular markers on preoperative obstruction risk

[0117] After feature screening based on mRNA and lncRNA, it was found that NR3C2 was repeatedly found and the most important, followed by MEG3. The AUC of the model trained with only NR3C2 was 0.89, with a sensitivity of 0.89, and the AUC of the validation set was 1, with a sensitivity of 0.86 (see Yes ). Then, the top 5 important genes were included in the features according to their importance (see Yes Yes Figure 3 Figures 4A to 4E ) to see if the prediction effect of the model can be further improved.

[0118] Table 3 Predicting the ability of NR3C2 and its gene combination to predict pulmonary vein stenosis

[0119]

[0120] Sensitivity was used as an evaluation indicator of the model. Incorporating MEG3, CNTNAP2, NETO1, and FAM3C into the features could improve the predictive ability of the model (see Table 3). SEQUENCE LISTING <110> Shenzhen BGI Life Sciences Institute <120> Biomarkers or their combination and their application for predicting the risk or prognosis of preoperative pulmonary vein stenosis in patients with TAPVC <130> P21018006C <160> 5 <170> PatentIn version 3.5 <210> 1 <211> 5909 <212> DNA <213> Artificial Sequence <220> <223> NR3C2 mRNA <400> 1 aaaagggcca gccctccagc tggcccactc cccctccggg ctttcgcccg ccctctctcc 60 ctcccttttt tgcccgcct ggccctgccc ctgcccctgc cccctcctct cagcccctcc 120 gcgcccgggg tgtcattggg cccgggagac gggagccaac ttcaggctgc tcagaggaag 180 cccgtgcagt cagtcacctg ggtgcaagag cgttgctgcc tcgggctctc ccgctgcagg 240 gagagcggca ctcgctggcc tggatgtggt tggatttagg ggggctccgc agcaggggtt 300 tcgtggcggt ggcaagcgct gcaacaggta gacggcgaga gacggaccccc ggccgaggca 360 gcgatggaga ccaaaggcta ccacagtctc cctgaaggtc tagatatgga aagacggtgg 420 ggtcaagttt ctcaggctgt ggagcgttct tccctgggac ctacagagag gaccgatgag 480 aataactaca tggagattgt caacgtaagc tgtgtttccg gtgctattcc aaacaacagt 540 actcaaggaa 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tggcacctct gctggatcca gtacattgcg ggatgtggtt 1440 cccagtccag acacgcagga gaaggtgct caagaggtcc cttttcctaa gactgaggaa 1500 gtagagagtg ccatctcaaa tggtgtgact ggccagctta atattgtcca gtacataaaa 1560 ccagaaccag atggagcttt tagcagctca tgtctaggag gaaatagcaa aataattcg 1620 gattcttcat tctcagtacc ataaagcaa gaatcaacca agcattcatg ttcaggcacc 1680 tcttttaaag ggaatccaac agtaaacccg tttccattta tggatggctc gtatttttcc 1740 tttatggatg ataaagacta ttatcccta tcaggaattt taggaccacc tgtgcccggc 1800 1860 gggagctatt acccagaggc cagcatccct tcctctgcta ttgttggggt gaattcaggt 1920 ggacagtcct tccactacag gattggtgct caaggtacaa tatctttatc acgatcggct 1980 agagaccaat ctttccaaca cctgagttcc tttcctcctg tcaatacttt agtggagtca 2040 tggaaatcac acggcgacct gtcgtctaga agaagtgatg ggtatccggt cttagaatac 2100 attccagaaa atgtatcaag ctctacttta cgaagtgttt ctactggatc ttcaagacct 2160 tcaaaaatat gtttggtgtg tggggatgag gcttcaggat gccattatgg ggtagtcacc 2220 tgtggcagct gcaaagtttt cttcaaaaga gcagtggaag gtaaatgttc atggcaacac 2280 aactatttat gtgctggaag aaatgattgc atcattgata agattcgacg aaagaattgt 2340 cctgcttgca gacttcagaa atgtcttcaa gctggaatga atttaggagc acgaaagtca 2400 aagaagttgg gaaagttaaa agggattcac gaggagcagc cacagcagca gcagccccca 2460 cccccacccc cacccccgca aagcccagag gaagggacaa cgtacatcgc tcctgcaaaa 2520 gaaccctcgg tcaacacagc actggttcct cagctctcca caatctcacg agcgctcaca 2580 ccttcccccg ttatggtcct tgaaaacatt gaacctgaaa ttgtatatgc aggctatgac 2640 agctcaaaac cagatacagc cgaaaatctg ctctccacgc tcaaccgctt agcaggcaaa 2700 cagatgatcc aagtcgtgaa gtgggcaaag gtacttccag gatttaaaaa cttgcctctt 2760 gaggaccaaa ttaccctaat ccagtattct tggatgtgtc tatcatcatt tgccttgagc 2820 tggagatcgt acaaacatac gaacagccaa tttctctatt ttgcaccaga cctagtcttt 2880 aatgaagaga agatgcatca gtctgccatg tatgaactat gccaggggat gcaccaaatc 2940 agccttcagt tcgttcgact gcagctcacc tttgaagaat acaccatcat gaaagttttg 3000 ctgctactaa gcacaattcc aaaggatggc ctcaaaagcc aggctgcatt tgaagaaatg 3060 aggacaaatt acatcaaaga actgaggaag atggtaacta agtgtcccaa caattctggg 3120 cagagctggc agaggttcta ccaactgacc aagctgctgg actccatgca tgacctggtg 3180 agcgacctgc tggaattctg cttctacacc ttccgagagt cccatgcgct gaaggtagag 3240 ttccccgcaa tgctggtgga gatcatcagc gaccagctgc ccaaggtgga gtcggggaac 3300 gccaagccgc tctacttcca ccggaagtga ctgcccgctg cccagaagaa ctttgcctta 3360 agtttccctg tgttgttcca caccagaag gacccaagaa aacctgtttt taacatgtga 3420 tggttgattc acacttgttc aacagtttct caagtttaaa gtcatgtcag aggtttggag 3480 ccgggaaagc tgtttttccg tggatttggc gagaccagag cagtctgaag gattccccac 3540 ctccaatccc ccagcgctta gaaacatgtt cctgttcctc gggatgaaaa gccatatcta 3600 gtcaataact ctgattttga tattttcaca gatggaagaa gttttaacta tgccgtgtag 3660 tttctggtat cgttcgcttg tttaaaaagg gttcaaggac taacgaacgt tttaaagctt 3720 acccttggtt tgcacataa acgtatagtc aatatggggc attaatattc ttttgttatt 3780 aaaaaaaac aaaaaataa taaaaaaata tatacagatt cctgttgtgt aataacagaa 3840 ctcgtggcgt ggggcagcag ctgcctctga gccctcgctc gtccacggtc ttctgcatca 3900 ctggtataca cactcgttag cgtccatttc ttatttaatt agaatggata agatgatgtt 3960 aaatgccttg gtttgatttc tagtatctat tgtgttggct ttacaaataa ttttttgcag 4020 tcttttgctg tgctgtacat tactgtatgt ataaaattatg aaggacctga ataaaggtat 4080 aaggatcttt tgtaatgag acacatacaa aaaaaatctt taatggtta taggat 4140 gggaaagtat tttgaaaga attctatttt gctggagact atttaagtac tatctttgtc 4200 taaacaggt aatttttt tgtaagtgc atgtcctgc atgcataatg aaccgtttac 4260 agtgtattta agaaagggaa agctgtgcct tttttagctt catatctaat ttaccattat 4320 ttcagtct ctgttgtaaa taaccact gaaacctt cggttgtctt gaaacctttc 4380 tactttttct gtacttttg tttgttctt gtctccccgc tggggcatt tgtgggactc 4440 cagcacgttt tctgcttct gctcatcct gctccatcgg ggatgacac actgcggtgt 4500 ctgcagctcc tggaggtgt catttgaca cacatgtggg agaggaggtc cttggagtgc 4560 tgcagctttg ggaaagctgc ctcgtttccc ttttccta gaagcagaac cagcttacg 4620 agagtgagac tgggaacttg atggctcaga gagcatcttt tccccatt ttagaaaatc 4680 agattttctc ctgtgggaaa aaaaaattcc atgcactc tctctgttaa agatcagcta 4740 ttcctctg atcttggaa gaggttctgc actcctggaa ccggtcacag gaacgcacag 4800 atcatggcag gatgcgctgg gacggcccat cttggcaagg ttcagtctga atggcatgga 4860 gaccgggaga tagaggggtt ttagattttt aaaaggtagg ttttaaaaat aagttttata 4920 cataaacagt tttggagaaa aattacagat catataagca agacagtggc actaaaatgt 4980 ttaattcatt aatctgtttg tttggcactg atgcaatgta tggcttttct cttgccccaa 5040 atcacaaaca tatgtatctt tggggaaact aacaatatga ttgcactaaa taaactactt 5100 tgaatagagg ccaaattaat cttttaaaaa tgatgataat catcaggttt actcagtgaa 5160 atkatataa ttattttcca aaatctaaaa gctgtagctg gagaagccca tggccacgag 5220 gaagcagcaa ttaattagat caacactttt ctccagggtt caccatgcag gcaacattac 5280 cttgtctttc aaaagacacc tgccttatgc aaggggaaac ctgtgaaagc tgcactcaga 5340 gggaggagtt tttctcat aatttgcaat ttcaggaatt taattatag gcagatcttt 5400 aaatacagtc aacttacggt gcacagtaat atgaaagcca cactttgaag gtaataaata 5460 cacagcatgc agactgggag ttgctagcaa acaaatggct tacttacaaa agcagctttt 5520 agttcagact tagtttttat aaaatggga ttctgactta cttaaccagg tttgggatgg agatggtctg catcagcttt ttgtattaac aaagttactg gctctttgtg tgtctccagg tgcttgct tgattaaca gcaaagccat attctaatt cactgttgaa tgcctgtccc 5760. gtccaaatt gtctgtctgc tcttattttt gtaccatatt gctcttaaaa atcttggttt ggtacagttc father aaaagttcat father gaacactaa attagtttaa aatgaagcaa ttatatctt tatgcaaaaa catatgtctg tctttgcaaa ggactgtaag caught up with actttaatc <210> 2 <211> 9896 <212> DNA <213> Artificial Sequence <220> <223> CNTNAP2 <400> 2 60. acacaagctc tccatgtgag ctgacaggcg agtggaaacc cctcgagtca cgctgcccgg cggcggaggg agcgctcgcc cgcagtggca acagctgcac caccgtcccc gtcgctctgc 120 cttcctcttc tgcagcctct gctcttctga ttacctccct cccccgtcct ttggtgattt 180 ttttttttca agaaggagag ggcggggtag gtgtccgttc cctcccctct tccccctcct 240 ttgccttctt ggtttgaatt tcctcccccg gcgttgcact ggcacacagt gcaagaggca 300 atacccgcac ggagggagaa cgaaggctga gactcccctg ccgctccaag cccggaagaa 360 ctggagcctg gaggggggtg aggggagaag aggaagcggg aggggcttgg cttcctcgcg 420 tatttgagga cagcccatct cccttcaaga accctacgga gagtcggact gcatctccgc 480 agcgagctct tggagcgccg ccggccggga ggcgaaggat gcaggcggct ccgcgcgccg 540 gctgcggggc agcgctcctg ctgtggattg tcagcagctg cctctgcaga gcctggacgg 600 ctccctccac gtcccaaaaa tgtgatgagc cacttgtctc tggactcccc catgtggctt 660 tcagcagctc ctcctccatc tctggtagct attctcccgg ctatgccaag ataaacaaga 720 gaggaggtgc tgggggatgg tctccatcag acagcgacca ttatcaatgg cttcaggttg 780 actttggcaa tcggaagcag atcagtgcca ttgcaaccca aggaaggtat agcagctcag 840 attgggtgac ccaataccgg atgctctaca gcgacacagg gagaaactgg aaaccctatc 900 atcaagatgg gaatatctgg gcatttcccg gaaacattaa ctctgacggt gtggtccggc 960 acgaattaca gcatccgatt attgcccgct atgtgcgcat agtgcctctg gattggaatg 1020 gagaaggtcg cattggactc agaattgaag tttatggctg ttcttactgg gctgatgtta 1080 tcaactttga tggccatgtt gtattaccat atagattcag aaacaagaag atgaaaacac 1140 tgaaagatgt cattgccttg aactttaaga cgtctgaaag tgaaggagta atcctgcacg 1200 gagaaggaca gcaaggagat tacattacct tggaactgaa aaaagccaag ctggtcctca 1260 gtttaaactt aggaagcaac cagcttggcc ccatatatgg ccacacatca gtgatgacag 1320 gaagtttgct ggatgaccac cactggcact ctgtggtcat tgagcgccag gggcggagca 1380 ttaacctcac tctggacagg agcatgcagc acttccgtac caatggagag tttgactacc 1440 tggacttgga ctatgagata acctttggag gcatcccttt ctctggcaag cccagctcca 1500 gcagtagaaa gaatttcaaa ggctgcatgg aaagcatcaa ctacaatggc gtcaacatta 1560 ctgatcttgc cagaaggaag aaattagagc cctcaaatgt gggaaatttg agcttttctt 1620 gtgtggaacc ctatacggtg cctgtctttt tcaacgctac aagttacctg gaggtgcccg 1680 gacggcttaa ccaggacctg ttctcagtca gtttccagtt taggacatgg aaccccaatg 1740 gtctcctggt cttcagtcac tttgcggata atttgggcaa tgtggagatt gacctcactg 1800 aaagcaaagt gggtgttcac atcaacatca cacagaccaa gatgagccaa atcgatattt 1860 cctcaggttc tgggttgaat gatggacagt ggcacgaggt tcgcttccta gccaaggaaa 1920 attttgctat tctcaccatc gatggagatg aagcatcagc agttcgaact aatagtcccc 1980 ttcaagttaa aactggcgag aagtactttt ttggaggttt tctgaaccag atgaataact 2040 caagtcactc tgtccttcag ccttcattcc aaggatgcat gcagctcatt caagtggacg 2100 atcaacttgt aaatttatac gaagtggcac aaaggaagcc gggaagtttc gcgaatgtca 2160 gcattgacat gtgtgcgatc atagacagat gtgtgcccaa tcactgtgag catggtggaa 2220 agtgctcgca aacatgggac agcttcaaat gcacttgtga tgagacagga tacagtgggg 2280 ccacctgcca caactctatc tacgagcctt cctgtgaagc ctacaaacac ctaggacaga 2340 catcaaatta ttactggata gatcctgatg gcagcggacc tctggggcct ctgaaagtttt 2400 actgcaacat gacagaggac aaagtgtgga catagtgtc tcatgacttg cagatgcaga 2460 cgcctgtggt cggctacaac ccagaaaaat actcagtgac acagctcgtt tacagcgcct 2520 ccatggacca gataagtgcc atcactgaca gtgccgagta ctgcgagcag tatgtctcct 2580 atttctgcaa gatgtcaaga ttgttgaaca ccccagatgg aagcccttac acttggtggg 2640 ttggcaaagc caacgagaag cactactact ggggaggctc tgggcctgga atccagaaat 2700 gtgcctgcgg catcgaacgc aactgcacag atcccaagta ctactgtaac tgcgacgcgg 2760 actacaagca atggaggaag gatgctggtt tcttatcata caaagatcac ctgccagtga 2820 gccaagtggt ggttggagat actgaccgtc aaggctcaga agccaaattg agcgtaggtc 2880 ctctgcgctg ccaaggagac aggaattatt ggaatgccgc ctctttccca aacccatcct 2940 cctacctgca cttctctact ttccaagggg aaactagcgc tgacatttct ttctacttca 3000 aaacattaac cccctgggga gtgtttcttg aaaatatggg aaaggaagat ttcatcaagc 3060 tggagctgaa gtctgccaca gaagtgtcct tttcatttga tgtgggaaat gggccagtag 3120 agattgtagt gaggtcacca acccctctca acgatgacca gtggcaccgg gtcactgcag 3180 agaggaatgt caagcaggcc agcctacagg tggaccggct accgcagcag atccgcaagg 3240 ccccaacaga aggccacacc cgcctggagc tctacagcca gttatttgtg ggtggtgctg 3300 ggggccagca gggcttcctg ggctgcatcc gctccttgag gatgaatggg gtgacacttg 3360 acctggagga aagagcaaag gtcacatctg ggttcatatc cggatgctcg ggccattgca 3420 ccagctatgg aacaaactgt gaaaatggag gcaaatgcct agagagatac cacggttact 3480 cctgcgattg ctctaatact gcatatgatg gaacattttg caacaaagat gttggtgcat 3540 tttttgaaga agggatgtgg ctacgatata actttcaggc accagcaaca aatgccagag 3600 actccagcag cagagtagac aacgctcccg accagcagaa ctcccacccg gacctggcac 3660 aggaggagat ccgcttcagc ttcagcacca ccaaggcgcc ctgcattctc ctctacatca 3720 gctccttcac cacagacttc ttggcagtcc tcgtcaaacc cactggaagc ttacagattc 3780 gatacaacct gggtggcacc cgagagccat aaatattga cgtagaccac aggaacatgg 3840 ccaatggaca gccccacagt gtcaacatca cccgccacaga gaagaccatc tttctcaagc 3900 tcgatcatta tccttctgtg agttaccatc tgccaagttc atccgacacc ctcttcaatt 3960 ctcccaagtc gctctttctg ggaaaagtta tagaaacagg gaaaattgac caagagattc 4020 aaaatacaa caccccagga ttcactggtt gcctctccag agtccagttc aaccagatcg 4080 cccctctcaa ggccgccttg aggcagacaa acgcctcggc tcacgtccac atccagggcg 4140 agctgggtgga gtccaactgc ggggcctcgc cgctgaccct ctcccccatg tcgtccgcca 4200 ccgacccctg gcacctggat cacctggatt cagccagtgc ggattttcca tataatccag 4260 gaaaggcca agctataaga aatggagtca aagaaactc ggctatcatt ggaggcgtca 4320 ttgctgtggt gattttcacc atcctgtgca ccctggtctt cctgatccgg tacatgttcc 4380 gccacaaggg cacctacat accaacgaag caaagggggc ggagtcggca gagagcgcgg 4440 acgccgccat catgaacaac gaccccaact tcacagagac cattgaatgaa agcaaaaagg 4500 aatggctcat ttgaggggtg gctacttggc tatgggatag ggaggaggga attactaggg 4560 aggagagaaa gggacaaaag caccctgctt catactcttg agcacatcct taaaatatca 4620 gcacaagttg ggggaggcag gcaatggaat ataatggaat attcttgaga ctgatcacaa 4680 aaaaaaaaac cttttaata tttctttata gctgagtttt cccttctgta tcaaaacaaa 4740 ataatacaaa aaatgctttt agagtttaag caatggttga aatttgtagg tactatctgt 4800 cttattttgt gtgtgtttag aggtgttcta aagacccgtg gtaacagggc aagttttcta 4860 cgtttttaag agcccttaga acgtgggtat ttttttctt gagaaaagct aatgcaccta 4920 cagatggccc ccaacattct cttccttttg cttctagtca accttaatgg gctgttacag 4980 aaactagttc gtgtttatat actatttcct ttgatgtcct ataagtcgga aaagaaaggg 5040 gcaaagagaa cctattattt gccagttttt aagcagagct caatctatgc cagctctctg 5100 gcatctgggg ttcctgactg ataccagcag ttgaaggaag agagtgcatg gcacctggtg 5160 tgtaacgaca caatcagcac aactggagag aggcattaaa gaaccaggga aggtagtttg 5220 attttcatt gaattctaca agctaatatt gttccacgta tgtagtctta gaccaatagc 5280 tgtaactatc agctgcaata ccatggtgac cagctgttac aaaagatttt ttcctgtttt 5340 atctgaaaca tactggattt atatatgtat aagcgcctca atggggaatt agagccagat 5400 gttatgattt gtttgctctt tttcttttat agtttagtta tagcaaaaat atggataatt 5460 tctagtgaat gcataaatta ggttgcgttt cttattttgc tttaaatctc tggtagttt 5520 tccacccctg tgacacaatc ctaatagaca gtgtcctgta aatggacaca acacaataaa 5580 gtcaagttat tattgctgtt actctggatg atatggaaaa cactgccata ttttaaatca 5640 actactccac gtgtttttcc atccaatcac actgctgtga ttcagggatc tttcttctaa 5700 gacggacaca tttgaacctc aggttcatca caaacctggt acctgttgct tcccagagga 5760 tggagaagtg tagttaatca cacctcttag tttaatctga aatcttgacc cagttatta 5820 acaaataaat acctcattga ttatatttaa aagtaataca cttcctgtaa acaaatgggg 5880 acaatgcatc caaaaaatct tttaaaacag attacacaaa aattatttcc agaaaggcta 5940 ccatttatca tcattatatt tcaagcctct tatacttaat aagcactttc taaaaagtct 6000 tgagatccca ccattctgag gattcaata tgatcacttt ttccttctt gcctgggaga 6060 ggttaagagg aggttcgaa ggtatagatg ctattgttct gatggcccgg ctgataaaa 6120 tggaaattct agtttgttag aattatgcat tcttttcaa gattctcagt gtgcctact 6180 tattggagca catcagtttc ttgggtaatg gaaaacatta cctagagttg ccagtggcac 6240 attackacaccag tacagagcac attchcaagg agacattgga ccagttaatt cccatacaag 6300 tcaagtaac agacaaag ggaatcctga tgccttta ccattgctgg tgagctcag 6360 gcactgtcat ggacacctt aattttaaaa ggttttaatc attctctat aaatacatt 6420 taaaatggaa aaatacttta tatcactaa tatcagaca atgtaacatt tacaatgac 6480 atattgaaag caaggctgt tttatttagc caatgatt accattagga gttactttat 6540 gtattgttga aagcaattt taaacatgat gttttagaag tgttctct ttttaaacct 6600 ggtttacagg tattactct gcacttacca ataatgcca gatggaattt tattattct 6660 tgcaattccc atgatagctc tgttctttat gcattgtctc aacactttcc cttttttccc 6720 aaaatgagta gagaattaaa gccacccaaa acagcttctg ctactaaaat gttctcatcc 6780 tttctcctcc ctctcctttt cctgccacaa aaggtgaaaa atgagatcca atcctctcac 6840 caaaatttca aacctaggac actggaatga ctgcagggat cagtggttct cccatatcac 6900 catcaattaa gacatatagg acactgtctt ccttcaagag ggttacaatg tggccatcag 6960 acaggaaacc aaacggtgga taaagtatta agtaactaag tgccaaataa atgctggaaa 7020 tcttgacctc tccttgggat tatgggtgta acaaaaatcc ctacatctgt ttatgaaggc 7080 catattcagt acattttaaa tggtaaataa tctgtttatg tgaagaaaaa gaattaagtc 7140 tttcttccaa ctctctcctt ggatagccta gcacagtgca gcctccataa ccatgacatt 7200 cccgcccaag ctctcagtgc ctaatcctgc tttgtcattc acatctcaca aaatcttgac 7260 atcttacatt ccaatacatt atcaagcaag cacaagtatg ctggtagtag cctctttaaa 7320 taatatgtat agacaacaac aacgacaaaa aatagactgt tttaaagttt cagggaaagt 7380 tggtggctga tttaaaagttg tgcaggaaac atcttctgtg tatgaagcaa atgtcgatgt 7440 tttgaaaaaa gctaggagat gactttgaat gaatgcaagg ttagtgagat cctaagctct 7500 caaaatagca tattccctag agctcaagaa agctggtcca ggaggttgaa aaagctattt 7560 tgttgttaaa ttatttctg gcccttctta atatttaaaa atgtatttcc ccttgtggct 7620 ttcaaccacc tgctcaaaaa aagagacttg ttacatgaaa gttttcatta aagagctgaa 7680 aacaagaatt tagagagcca ttcctagaaa atgtcctact gccctgcatt tgacaaacaa 7740 gcatccttta ctaacaagag caggaattca gaggcacaag aaaaagcatt ggcatgagcc 7800 aaagagtctg tcttaatgtt acttttgaaa atctgctgag cggccaccat atgcaggctg 7860 agagctggc acaggcgaag ccattggaag cacttcagga acaagcacac agctgtggga 7920 cttgaacatg caagtgttca ggttgtgtca agaagctttt ctttccttct atgatggaat 7980 ctgttcttt ctatcctact tttttctctc ttcctctcct caccacatta taccctgctc 8040 ttacgcagta aacgttttaa tggcccgttt atgtctcatg cctccaaaca acactgaatt 8100 tgaaaccccc cattttttct tttcaccacc ctgttgagca attttcccaa aaaaagggca 8160 gcaattatta aattgaattc aagtaagcca gccaaagata ggtcctaaat tgctagtccc 8220 agtagaacca cctgatccta aaccagtgcg aaacaaacag taacaatgtc cccagctgac 8280 ttcagctaag aaccaatggc tcctaccccc gccccgcttt tttttgttgt tttttgtttt 8340 gttttgagac ggagtcttgc tctgtccccc aggctggagt gcactggcgc aatctcgggc 8400 tcactgcaac ctcctcctcc tcccacattg aggcgattct cctgcctcag cctcccaagt 8460 agctgggatt acaggcaccc gccatcacac ccagctaatt tttttttttt tttgtattat 8520 tagtagaagc caggtttcac catgttggcc agggtggtct cgaactcctg acctcaagtg 8580 atccgtccac ctcggccttg caaattgctg ggattacagg tgtgagccac cgtgccgagc 8640 cagccccatt ttttaaatga tgttttggtt aagagtggac catgagaatt agctgacagc 8700 atcccctttc tctctccctg ccttggtggg accctccctg tgtgaccttg gtcaagtcct 8760 cgaacttttg tcccgtattt aagatggagc tgttttacct acttcataag acagttgcga 8820 ggtgccattg attcttgact gcaaaatacc ttgaaaccct third ctgaagtcaa cggagcctag tgaaagactt actttgtggc ttgtggttga aagtcacatc aaaagacaaa tgtggccacg ttcaggaatt ggagacttac tggcatggct ctacagctgc tcagttatta atcatgcaga ctaacctgtc aacactggga gatgcaacat agcaaagga cagagaatt agaattttt gtgcagaaag ccctaaattc ccacctgaat gtaacttaca gctcccttac ctactctcac acatgccctc aaacatgcta gattggctta tacataggcc aacacaaaat acaaacgtga cgtgttcatg tagcctagtg gctatatgcc tattctccat gtaccctgca tggtagtgct gcaaacttta aagtacattt ctttcacagc agtatttttt ttcataagtg gcatataaat gtcattcaat gaaatgggga aatcacgttg agaagttggt ctgtcatctc ccattgagca aagactggca ggagataata aaaataata tgggcacaca tgtattaata 9480. 9480. 9480. 9480. 9480. 9480. 9480. 9480. 9480 caagacagtc tgcaccattt ccaagtctca gttaatttac agcaactgct gctttcggag atggctgtga aaatatggaa gttcctctca agtaggccaa gaacagttc tagattttac 9600 taagttttat ttgtcaggt tttttaatt tttcagtga gcgtggtgac tgcagaggtt 9660 agtgctgtga aaagctgggc taaatattct ttctgtaag tcaacagga ttccatcccc 9720 tgtgaataa cacaaattt cactctctaa aaagcaacagc atgtaaacta gatgaaga 9780 aggaaattat gtacgtatgc ctaatattct tgtgaatgt ctttcattta actaaaatta 9840 tattagaaac cagattgata aaaaaaat tcaagtagt tttattatc ctaaaa 9896 <210> 3 <211> 2508 <212> DNA <213> Artificial Sequence <220> <223> FAM3C <400> 3 agcggcgagc tggctttc ctggccactt gccggggtga ggggcagccg gaggagtccg 60 agaggaagcg gaggcgcgag ctggaggcgg cggctcccgt cggcctccgg caggactgag 120 cgctgggagg ccggaggcg ggcgcgcgcg gcggagaggc gggcgggagg ccggagcata 180 ttaatgaaaa gtgccataaa ctgaaaaacc aaacatgagg gtagcaggtg ctgcaagtt 240 gtggtagct gtggcagtgt ttttactgac attttgtt atttctcaag tatttgaaat 300 aaaaatggat gcaagtttag gaatctatt tgcaatca gcattggaca cagctgcacg 360 ttctacaaag cctcccagat ataagtgtgg gatctcaaa gcttgccctg agaagcattt 420 tgcttttaaa atggcaagtg gagcagccaa cgtggtggga cccaaaatct gcctggaaga 480 taatgtttta atgagtggtg ttaagataa tgttgaaga gggatcaatg ttgccttggc 540 aaatggaaaaaaggagaag tattagacac taatatttt gatagtggg gaggagatgt 600 ggcaccattt attgagtttc tgaggccat acaatgga acatagtttt taatgggaac 660 atacgatgat ggagcaacca aactcaatga tgaggcacgg cggctcattg ctgatttggg 720 gagcacatct attactaatc ttgttttag agacaactgg gtctctgtg gtgggaaggg 780 help aaagccctt tgaaggca helpaagac aaaggataeaaaaaaa 840 tgaaggatgg cctgaagttg tagaatg aggatgcatc ccccagaagc agactaatg 900 gaatgtgga gagaattgaa gaagcgcac ttcactctt aatgggagag ctataaatgg 960 cagagctatg tgtaaatatt ttaagagcat gcagccatct tggtgtgtgc atgagtattg 1020 tctcttttga tatcaggatt atttattgct aacgtaaata gatagcattg taaataatca 1080 tcacaatgat caaatcactg aaccatgtct ccgcacattt ccctaaaagt acaatgttta 1140 gactgctatg gtaatacata ttttaaattc taaaagcata cacaatgtgt aactgaatgg 1200 tttgtgaaaa atatattgat atatatacta gttgctatga aaatatcatg gaataatagg 1260 gattttaggg tggatacttt attttctttt atgtttctat atgttgcgtt gtgatgacat 1320 tatcttttaa attaaaaaga gatttggcta gttgtgtgtg taatgttact ttacagtccg 1380 actctcctga tgtacctctt ttcatgatct ttttctttcc ttcccaagaa actgaggaat 1440 gtttaatatg aaaacataca tcggatatgt gaaaagcaca acaaaattct taatgtacac 1500 agtaaaaaag taaatatata aatgtagatg gcatttagga ccacagcttg ctggatttgt 1560 gttagctatg ggaataactt gattttgtat aagctattta gagtgaggct ggaggtggca 1620 gcttcacaga actggagaac caggccaagt cccctcccca acctaattag gtcattcagg 1680 acagctaagt agatattt agagcaatac tagcatacgt ttttcttaat tgttatcagc 1740 attgaccaag tggtttggaa ggaggcatgc tttaatatca caaatatttt gatttgtaaa 1800 ccaagaaatt aatcctgtgt ttatctaact tcatatatagc aattattgcc cgaagctata 1860 gtggcatatt tacaaaagtt cttatactg ggcggactga taacatttaa aaataattg 1920 tgtttgaccc caaatgactt tatacccaat tctacataaa aatatagaag atctatcttt 1980 ttttgttacc ttcagatgtt cactaaataa ctcagttttt aagcagaagt tttcagggca 2040 ttaaatat gttgtgtatg aagtatctca aactggaaca taaatttagt gatcaaactg 2100 ccattcacag tgtaaggcag cacttaaatt tcgaacctaa agtttagatg cattgtataa 2160 aaaaacctaa aagcagtatc tgttattag ctgtaaacca agttggaagc tattcggata 2220 atttcttaaa tattgatgaa ctttggagta ctgtttcttc cttcaaactg aatgtaatta 2280 attcatgaat aaatgcacct tatatgttta aacaatcttt gtatactttt gggatttttg 2340 gtgcttatat gctaaatcac attcagcatg tgtattttga catttaaaat acttccctca 2400 attctgtaaa ttaaaagaat agttatttta cagttccagg gattgtgaaa taaatgttgc 2460 agttttttaa aataatgaaa ataaatactc ttggttttgc tttgtgaa 2508 <210> 4 <211> 3058 <212> DNA <213> Artificial Sequence <220> <223> NET1 <400> 4 atgacggacc ccttcttcct gccttcaatg cctcagcgga agatccccaa gggctggagc 60 gaggagcgct gccgctggac atcctcccgg ggaggctgct ccgacctgct gcgcggcg 120 tctgagactg gggactgagc cacccgccg ccgccggcgc cgccgccgcc gccgctccg 180 tcgctgccgt cggtctggac tgcccccc ctcgctgcgc cctctcccg gcccggccc 240 cggctcgggg cgtcccgggg ctcgcctgc gaccgcgcc tcccgcgc cgcgtcctcc 300 cgaccccgcg gcggcgacga tgcccgggag gagggtcctg acggcggcgg cgcggatggt 360 ggcggccggc gcccgggtgt gatgcgagcg tcacggtggg gatgctgctg gctgcgcggc 420 gctgagggcc agcgagagcg agagcccgcc cggggcggag gacggactca tccggatctg 480 gctgcagcgt gggctcggag ctcccccttc ctctcggtct ccctctcggc ccccctttat 540 ttccttcttg ctttgcgtct ttaacacctc tcgaccctgt cctccccccg ccactggaag 600 tcttcccgtc tctaaatgga attagtggag cccggagcct ctggtgtaac gcacagacat 660 gatccatggg cgcagcgtgc ttcacattgt agcaagttta atcatcctcc atttgtctgg 720 ggcaaccaag aaaggaacag aaaagcaaac cacctcagaa acacagaagt cagtgcagtg 780 tggaacttgg acaaaacatg cagagggagg tatctttacc tctcccaact atcccagcaa 840 gtatccccct gaccgggaat gcatctacat catagaagcc gctccaagac agtgcattga 900 actttacttt gatgaaaagt actctattga accgtcttgg gagtgcaaat ttgatcatat 960 tgaagttcga gatggacctt ttggcttttc tccaataatt ggacgtttct gtggacaaca 1020 aaatccacct gtcataaaat ccagtggaag atttctatgg attaaatttt ttgctgatgg 1080 agagctggaa tctatgggat tttcagctcg atacaatttc acacctgatc ctgactttaa 1140 ggaccttgga gctttgaaac cattaccagc gtgtgagttt gagatgggcg gttccgaagg 1200 aattgtggag tctatacaaa ttatgaagga aggcaaagct actgctagcg aggctgttga 1260 ttgcaagtgg tacatccgag cacctccacg gtccaagatt tacttacgat tcttggacta 1320 tgagatgcag aattcaaatg agtgcaagag gaattttgtg gctgtgtatg atggaagcag 1380 ttccgtggag gatttgaaag ctaagttctg tagcactgtg gctaatgatg tcatgctacg 1440 cacgggtctt ggggtgatcc gcatgtgggc agatgagggc agtcgaaaca gccgatttca 1500 gatgctcttc acatcctttc aagaacctcc ttgtgaaggc aacacattct tctgccatag 1560 taacatgtgt attaataata ctttggtctg caatggactc cagaactgtg tgtatccttg 1620 ggatgaaaat cactgtaaag agaagaggaa aaccagcctg ctggaccagc tgaccaacac 1680 cagtgggact gtcattggcg tgacttcctg catcgtgatc atcctcatta tcatctctgt 1740 catcgtacag atcaaacagc ctcgtaaaaa gtatgtccaa aggaaatcag actttgacca 1800 gacagttttc caggaggtat ttgaacctcc tcattatgag ttatgcactc tcagagggac 1860 aggagctaca gctgactttg cagatgtggc agatgacttt gaaaattacc ataaactgcg 1920 gaggtcatct tccaaatgca ttcatgacca tcactgtgga tcacagctgt ccagcactaa 1980 2040 gccagggaaaa cccctcatcc cacccatgaa cagaagaat atccttgtca tgaaacag 2100 ctactcgcaa gatgctgcag atgcctgtga catagatgaa atcgaagagg tgccgaccac 2160 cagtcacagg ctgtccagac acgataaagc cgtccagcgg ttctgcctca ttgggtctct 2220 aagcaaacat gaatctgaat acaacacaac tagggtctag aaagaaaatt caagagaaga 2280 actatttata caaacatggg gactgtgaaa agaaaattct atagtgaatt gtgaaaagtg 2340 gacatatttc taaattcatt ccactgcctt tatccaaact taagaattac agacatttgt 2400 tattccttcg gcaagacatc cccgctgcac actgatatgt tcatttcgta atttggttgc 2460 2520 agaaattagt tcccgattaa gactatccca actttatttt tattgtcagt ttcacttttg 2580 tttctatgtt gttttatgtc tttgttatat aattgtacat tgtgtgatat gtgaaaaaaa 2640 aacacgaatt tggatgaacc ttgtgttaca tgtacattgt tttcattata tagacagctt 2700 gagaatagtg cgttcctgaa tgattttgaa catgctacag tgaaaagtga cagtgtggac 2760 catggaatca ccagctagag gttatggact atacacacct attattatat tctgattaaa 2820 tacaggaaaa agtaacaagt aaaattacta gattgcaata aaaaaattca ttttttggtc 2880 tttcctgtat acccctgatt tccttttgt tttatatcaa agaaagtact gagtttcaga 2940 aatcctttt gaagtcaggt atttaccat tcatactcta cagtagaaat gtttttgatt 3000 aagcactcag caatatgacc aaattgacag tttgagaaaa taccctgcat gtcagtac 3058 <210> 5 <211> 12990 <212> DNA <213> Artificial Sequence <220> <223> MEG3 <400> 5 cggagagcag agagggagcg cgccttggct cgctggcctt ggcggcggct cctcaggaga 60 gctggggcgc ccacgagagg atccctcacc cgggtctctc ctcagggatg acatcatccg 120 tccacctcct tgtcttcaag gaccacctcc tctccatgct gagctgctgc caaggggcct 180 gctgcccatc tacacctcac gagggcacta ggagcacggt ttcctggatc ccaccaacat 240 acaaagcagc cactcactga cccccaggac caggatggca aggatgaag aggaccggaa 300 ctgaccagcc agctgtccct cttacctaaa gacttaaacc aatgccctag tgaggggca 360 ttgggcatta agccctgacc tttgctatgc tcatactttg actctatgag tactttccta 420 taagtctttg cttgtgttca cctgctagca aactggagtg tttccctccc caagggggtg 480 tcagtctttg tcgactgact ctgtcatcac ccttatgatg tcctgaatgg aggatccct 540 ttgggaaatt ctcaggaggg ggacctgggc caagggcttg gccagcatcc tgctggcaac 600 tccaaggccc tgggtgggct tctggaatga gcatgctact gaatcaccaa aggcacgccc 660 gacctctctg aagatcttcc tatccttttc tgggggaatg gggtcgatga gagcaacctc 720 ctagggttgt tgtgagaatt aaatgagata aaagaggcct caggcaggat ctggcataga 780 ggaggtgatc agcaaatgtt tgttgaaaag gtttgacagg tcagtccctt cccacccctc 840 ttgcttgtct tacttgtctt atttattctc caacagcact ccaggcagcc cttgtccacg 900 ggctctcctt gcatcagcca agcttcttga aaggcctgtc tacacttgct gtcttccttc 960 ctcacctcca atttcctctt caacccactg cttcctgact cgctctactc cgtggaagca 1020 cgctcacaaa ggcacgtggg ccgtggcccg gctgggtcgg ctgaagaact gcggatggaa 1080 gctgcggaag aggccctgat ggggcccacc atcccggacc caagtcttct tcctggcggg 1140 cctctcgtct ccttcctggt ttgggcggaa gccatcacct ggatgcctac gtgggaaggg 1200 acctcgaatg tgggacccca gcccctctcc agctcgaaat cggcagacta ggatggaagt 1260 gccctgtgag ctggggggcc cttcaaaggg ccaaggagaa aacgcaggcc gagggaccag 1320 ccttccaaat gggcttcaag ctccaatgac ctccgctcgc cccctcgaaa tgtctggaaa 1380 acataatggg cagattttct gtcttcaaag tttccggcta aacctcttca agttctttat 1440 tgtttgggac tgagacactc agccatgtta atgggtagtt tcttttgtat ttgccttgaa 1500 aggccaaaat atttttatat tgccacagac aaagccacct atttaaaaat gaactccatg 1560 tccgtcgttt cccaccagga gactatgtac catgtgtgtg tctctatgta ttctggggtc 1620 ttgaaacagg tttctcatgg ggatggtcat tcaccacggt ccagaggggc agaacaggcg 1680 gcgcttgcct tgcccagggg gcctgggggaa cgtgggccct catctcagat ctgcccccag 1740 tatgtttagg acgcgagccc cagaaggatc tgggagtaaa cttaacattc actgtgtctc 1800 tgctctgcat ccgccatttg tgtgtgtttc tggactgtgg gctgtgtgta ccttggttgg 1860 tgactcagtg agaagaagca ggaatgccaa agatactgtg aatgttctga gttttgttgc 1920 tgttgttgtt gagaggttgt ttcactggta tctattgcat tgtataataa atgaccagat 1980 gatgaatga gtgaagcaag agaatgaa taacaagta aataggtaa gaagtaagca 2040 agccaggatg agagtgtgtg tacaagac catggttcat ccgctttgat ggctaggcaa 2100 tcaatatata aatagaaaaa aaccagtgaa tcactaagta atagggcaac acacaaagcg 2160 atatcaggtg attatggact aaggggtatg tgtaactcaa atatatgcct ctgacatttg 2220 2280 gagagatga ttttgaggcc cgtcaaaatg aaaagatgca agttagggaa caagtgatca 2340 aaagggaga gggaaaggtt ttttttaaaa aaccaaaaca acaaagaag gttaaaaaaa aaaacagact aggregate taatgagtaa ctctgtaagg aggregate cagactattg tagctagc attaggactg attack tatatgctcc tggcataga aataacca cagagacga gttcaaaga tagcaaaga agaagaga cccagtgggc gaaagatgag agtgtacttt taccaaaagt tatctaagcc tgagcacttg aagtctgcac father aaatgacaaa agaaagaaaa aaaggccaaa aagtctacat tgcgtgtgtg gatggatgaa tgagcagtgg gagtgcagcg ccaggtgaca agatgttgtg agggttttg agtcatccag tcctgggcac tgaggtctgt 2820. tcctgggcac tgaggtctgt gaatagga acaatgtaac aaatgttaag tacagaaata cattaatggg tggtaaata agatgtaaaa gaaggcaatg cgatcgatgg tggcaaaaga tcatcacaga tgggcta tggctggtcc acttctaga aaccacaggc tgtccatta father tctaagtga 3060. caagtcagtg agtacctaaa tagacaagga tgaggtgaat gagaagacat ggccccatgg gtcctcctga tgaggtgtt ggggtccccc ctgggcaccc cagctgcatg aaatgaagg 3120 acaggaggta tggaaagcta tgacagaga gagaaggaa cggtaaaag aaataacaac 3180 caaatggata aatgggtaga tccacgagaa gagttaggct aggacttgtc ataagggcac 3240 ctgactccac tatagagga ataatgcct aaaaaaga gagcagcag gagaagga 3300 tgctatgaat gcaggaagga agtaatgagt gagacgtgga accgcacggc caggatgga 3360 cgtttgcggg tggctttg atgcgtacag ccaagccact ccatggcaat gagctccgaa 3420 gaaagtgc aagagagaat gagtgagaga gtgagagaga gagaaacaat aaaaatggg 3480 aaaaaaaaaaaaaaaaaaaaaaaaaggaahd ggtatata taaggaata atacatgcat 3540 gcagatttaa gagagcca tgctagaaca ggaatgaaag gctgtgtgaa ccaagcagac 3600 cgcttaattg gcaccagtgc tgctggtag gtcaatcacc tactcacta aggaacggct 3660 caaagcatac acatgggagg gaggagtggg gccacagaga gaggcccat tagttgcaga 3720 ttacgatgta tccagttagg tgcacctgcc tcgagagt gtaaaaataa gtatttacat 3780 agaaagaaag actgaatgga tgcacggtga atgcatgaat gattgaacga cagaaaagat 3840 ttgcattgac cgatgaggag ggcattgtag acagggatga gggtcattga tcctgggtgc 3900 agatctccaa aagaatgaca gaaagaaaga gggagtggtg gaaagaaaca ataggatggg 3960 4020 tgatgaaagg ggactggttt agcacaagcc atccacatta attcaaacct gtggctctga 4080 agtttgtttt ttaaatgacc acaagtgtaa gactgaatga aagaataaat gcgtgcattc 4140 cataggatgc aagaaaagga gtgaggaatg ggaaaattgg aagaacgaga gagggagaga 4200 tgtaagaaaa gaaaggaaaa gtgaagtagg catatgaaag aaaaggcact tcttggacaa 4260 gcactgaaat ataatgagac agttttaccc attaaaatata ataaacagta aacgttgagg 4320 ttcatcaata aaagcacaga tacctgaata gaggagtgac ctgaatagaa ttcgttcagc 4380 cgaacgaatg agaatggatg attttcacta tcctgtgcac tcaaggccca aaagagaaag 4440 caagagagga gagaatatgg aaacgtatga caggatgtat ataagcaata caaacatatt 4500 gaatgaataa ataaagacat aaatatgtgg gagagtggac cacgcaagga caaaaagagg 4560 agaagaaggca gcaagaatta tgactaattc aaaactgggt tcctgagata gttaaataaa 4620 tcctgcacca aatccccagg gggagaaatt aacaaacaaa agacagcccc acacggacca 4680 gtgtgcagaa ggctccagga accgcagatt atggttaatc caattctgtg cacctgaggt 4740 ccataaataa aagaataagt attgaaatga aagaatgaca gaaagaatga atggacacat 4800 gaacgactga attagaaatg gaaatgcctg gcacagccag gaaggagctg cccatgggat 4860 tgtcattcat ctcactctgg gcacctgagg tccataagcg tgaaaagagg caggaagaga 4920 agtgtcaggg agtcaaagat agagctaagg aaaggcaaaa atgaaactaa atgaaagcga 4980 aagggaaaat aaagaaaaac caataaaaaa gagaacgaat acgtgggtgt atctgtaaga 5040 gtaggatctg ttaggattag tcataagact gtcagtaatc ctgaagatgg atgagataat 5100 ccaggcccag gttcccagg ggagggaaaa tggagaaaat ataaaaagat gtgaaaaagg 5160 aaaaaggaaa ggtaataaac aaacaaccaa agtgataaat ggatagttaa gggaggttgt 5220 ctgaacagggg attatatta gtttacatac atactcctta aacagataaa tacattacac 5280 ctttcaaaga aaatgaaa atagagaga catacctggc tccaaacaa ggctgtatctct 5340 tctgccactg taataaata gatgcaattg aggttcataa aataaagaat aaatactta 5400 acgtgaaagg tgactaatg cggggaaaa taaatacatg ggccaagat 5460 gtttggtttg cccatggagt tttattaaa aaaattaata aggaaaaaaaccaaaa 5520 taaggagac tgacaatga gtgagtggat gagagagtga atggtgcttg acgtaggagc 5580 agtagtgctt tagggaccag catgaggtg gtgaccggga gccctgattc atgggattct 5640 gtccacctga ctttataaga accaagaatg gctgggaatg gtggctcacg cctgtaatcc 5700 cagcactttg ggaggccgag gtgggcggat catgaggtca ggagttttg accagccagt 5760 ttgagatcag cctggccaac atggtgaat tccatctcta ctaaaaaaat acaaaaatta 5820 tggcgtggt ggcacctgtc tgtaatccta gctactcggg aggctgagag aggagaattg 5880 cttgaacccg ggaggtggag gttgtagtga gccaagattg caccactgca ctccagcctg 5940 ggctacagag cacacacta tcttagatca aaaaaaaaaaaaaagg agggaaag 6000 Aaccagagaa acataggaa gagtgagagg aaaaaaaaaaggcaattt gggaagaaat 6060 gaaaaaaaaaaaaaaaaaat gtaacggtca aaatagga cttgtgaatg 6120 gaggccttta ggccaaaggc tatgattaat ttcaagctat gttactgaag tccataaca 6180 aaggactcag atctaaatgg atgaacgaat gactgaaga aagggtggta ggaggtagg 6240 aagaaaggaa ggagggaaa agggaagaga ggaaggacc ttctttccag tcctgtgttc 6300 tagacagtgg aatgaagtgg tccccaggga gggtggctgt agcatgtca tgtgcttgtc 6360 acatgcactt gccctggcag ggaggagctg gctcaggaag accctgtct tggggtgctg 6420 ttgcctatc tggctgtgt gggccattc actgcatctg tctctcctc agttcccca 6480 tctgtaaacc tggagtggca ccagctgcct actagagttg atcttatgtg tctctgttga 6540 tggtacccca tctatggcct ggataggcag gaagggcttg gacctgagc cccgcagaag 6600 gttgcatgaa cgagtggtgt gaagcctgtt gggtagcttg gccactcccg cggcatgggt 6660 cacctgcaca ggaggttttg cccaccaggg ggcagcagag ggtcagggag caataggccc 6720 tgggtggagc atgggccccg cctgctgtgt gccaccctgg gtgtggcacc tactcacatc 6780 caggggttgg tgcagggaaa ggccagaagg tggccaggcg cacctgagaa gggggaccca 6840 gaagccccgg gacccaggag ccctgggcaa gccaccagaa accttgttct tgcaactctc 6900 tgcagtgtgc ccaggccacc ctctggcctg gtcttccatg gggcagggcg cccacccttc 6960 tcaactcagg tttccctggg cagcaggtgc acctcagcac ccctggggtt gcagaagtgg 7020 tccggggacc ctggcttcct tgacatgcca tccccagagc ctggttcaag gcctctctgt 7080 cttctcggct gtttcacgac gtgttttgta acttggcggg attgcgtttc gctgtgtcga 7140 ggttgtctct tctctgactc gccctccggg ggactgccgg ggtaaatctg gagagttgct 7200 cgtgctgaca gtcctccccc agggcctccc cggttctgtt gagtctcctt tctctgtagt 7260 ggaggaaatg tgtgtagttt tgtgttgtgt gcctgtgttt gtctgtaaaa gcaaggacca 7320 aagtctccct tgttgacctc tcaattccta tttgggacat ataaaaacac tggattctta 7380 acaagcgccc ggagcgtag ggcacgct tggatggact caggacttgt ggcagggagc 7440. acgtgggagg caggggagtg gggtggggcc aggccatctg gagtgggagg cgtcatgctc 7500 agagtgactc tgtagcgct gggtgggatg gggagtgcgg gcgcaggcat ggatggggct 7560 gttagctagt gtgatgcttg aggtctgagc tgatggcagc aaagtggggt gctcaggaat 7620 caaagctatg gggttataga caggatatga aggagggagg gaggcaagaa gaagggggtg gttcccacgc ttctagctcc ggccgagtgg atggcaacag catttggaag gcggaggaca 7740. tggaattcat gtgtcaggag ccaccttccg agcctccagt accacgtgtc agggccacat 7800 gagctgggcc tcgtgggcct gatgtggtgc tggggcctca ggggtctgct cttcttctct 7860 ttcagaatct ggggctccag gctatgcctt ggctggactg aggtctgggg gtgcacttat tatccctggg gacacctgct gaagcttctc cctgacaagc tgtgtcactg ttggatgagg 7980 atggggcggg aggggttcag ggcagaagaa gaccggggagg gtctttcaaa agaactcatg tacggctgtt aaaaaaagtc agcagaggct caggagact taaagtgtgc agaaggcggg 8100 gaagggaggg cccattgcat gcaccaagag gaaattggaa ggaacaagcg acgttggctg 8160 ctaggagagc ctgctcccaa catctagggg ctgtcctgac gggtcacagt gggtcgaact 8220 gagccaatga gagcagctct ggggagaccc actggtgccc tggaggctgg gtgggtttgg 8280 gttggatgaa ttctgtgtgt ccttttggaa atgtggaggc catgaggggg gatcagggct 8340 cttagggttt tgacccttaa gagttttgta tctgtaattc aaaggttctt tagttctggg 8400 atgctgagat tcgggatagg gttcctaatg gcacaaaagc cagagataaa acatccttca 8460 cgtgctccct acccggttct ttctgtacca gacccacaag gtccgagttg ggatcctagt 8520 gctcctgtct ggtcagggcc tatctttatg tgttcgttaa acttttaaca atgagaatta 8580 attctgtctc ttgacattgt catttgcatg ctccccacac acaaatcctt tcctggtgac 8640 accaggagct acaactctcc ttggcctcct cttgtgactc ccaactccct ccttgggaag 8700 cttggcctca ggacctctgg gatagacagg ccacgaatcc tgctgtgtcc cgttgtgttc 8760 ctaatataaa tggtgtggat ggcacttgac ctagagcagt gggaaatgca tgcaccactc 8820 aacattctga catgtcaccc attttacatt cttacaggca tacttttttt aaaaaaaagag 8880 tgtctattct ttaatgagca tcccttcttt aaaaaaaacct aattgccatt attcaccaca 8940 tacacttttt tttttttgta tcctgcctct tctatttaat tttctgtcat caacattttc 9000 ccttgttcca tgaatcttca taacctcact tgctgcgttg tgccttgttg agtggctatg 9060 gcatcattca cagaaccatt ctgttatct tatgtataac caccttttaa aaatattatg 9120 aataatgcca caactaactg cttaaaacac cctttttttc attcttaaga attatgttct 9180 tccacccaga aattatcatt gcttcactac agatcagttt cccctgctag actgtgagcc 9240 ccataagggc aaggagctta ttgaattggc ctttgtatct ctgatgccca acatgttgta 9300 gactataaat aaatgatgaa tgagtggatg gaagaatgga ggaaggagcg agtgagtgag 9360 tgtttggctg atggataaga gggtggaagg ataggcggaa ggatggattg gtgaatgaat 9420 gaatgaattt cctttggtta agtctcttga aagaaaggct atggatcttt gtatggatgt 9480 tgaataattt cagataagctt acagcatttt acaatgttca gcaatgtatg accacttaat 9540 taagatatgg ctagtttgtc tctgttataa agtacttttg cattacttta acttgcattg 9600 ctttaattac taatgatggg tgaacacttt gacctatgtt tgttaacaaa ttgtatttta 9660 tcttctgtga actgtttgtc caagtccttt gggtcatggc tttctgaaga gactggtccc 9720 agatgtcctt gggatgtagg gagcccatag ctcactggag gcattcaagg aaccagccag 9780 gcagccctca gagaaagtag tgtttaggag attctcatgg tgtggggttg gcctaggtgg 9840 cctttcaggt ctgtttgatg ttaggatttg cttctccctg ggaagtgggt gatgggggaaa 9900 aagacacctt ccattggcag gtgtagacac tgcaggctgg acctcctggg tgtgcttgtg 9960 gactccgatc ttgcccttga taaaacccct gtgggacagg aatagctctt tgaacctcca 10020 aggtccagac agccacatcc tagcaccctg tacaatcagt tagtggcctt cccaccagcg 10080 cagtcactca ttccttag atcccgatga agccaggccc tggggtttcc attttcccac 10140 ctcttagggg aattgggttc cccgcgtcct gtgatatgtc agcaaatgtc ctcagccctg 10200 gcctgcacat gtggcctcag tggtggtctt tggggtttaa ctgacgaatg gaacattttg 10260 gatcaggact gatgggagaa tctcctttca tttttcttca cctggggcaa ttacattcta 10320 aggagcggaa taaagggcat gttctgccca aagcatcagg gctcacaggt cagtcacagc 10380 catttaggga gggcatgtca cccaaggagg gctcgccctt ctttccagag catcctccgc 10440 tctcagcaga gctgcttctg cccacccatc cctctactat agcactgagc actgtttgcc 10500 cgtgtcagaa tccctcaccc acatgtttag cttggtatcc gagtttggga ggccggcaat 10560 gactttcaac atgaattgct ccatctaccc atccatgcat ttggcctact tatcttgacc 10620 ccgtgctttt ggccttttct tctcctgaaa gcaaaccctt tcattttggg tgggctgtgt 10680 agcgccatgg gctgtggtta tgaagcaaac accctttctt gtagctgcct cctccggggt 10740 tactgccctg agcacgtccc agctggatct cgtctgccac tgtcacccat agcttcttcc 10800 ccatggtgct ttccatgtgt cacacaccac gactgtgacc cagggtcggg gtcaagagta 10860 gcctggggcc aagccctccc acccatgagc ggagaagtcc tccccaggcc tcaccttgcc 10920 tggcgcatgg tccctcccat gagctttgct ttcagccttt cagcttcctc cacagggtgg 10980 cagtggttgt aactcatcca ttcatccctt catcccttca ttcattcact cacagccaac 11040 agacgttttt aaaaaattag ccagtgctat actagagctg gctcccaagg acccgctgcc 11100 gcattgcctt ttgaaacaaa acaatgaaca cgttggtaaa ggggccgtgc ttgtgtgtcg 11160 gtgacaaggc gagatccctg agtcaggtca ggcttgtaga ttcgagttct gttgcgagtt 11220 tgattgcccc tctgactttg tcccctgtac aactaggttg attaggaatc agccaactgt 11280 gttccctggg tgctcagaaa tcacagcca tatcctcgag aggccaaaat gagagccagg 11340 gggttccaag atgagtggct gcttctggcc gggagcaggt tttcaagtca ttagaacact 11400 ctggcctttc ctggaggtga tcttggagcc attcctgccc ctttcaagag gagttaatgc 11460 ccagctctgt ttagagaaaa ttgggggaga tgattgctca tgtgggtgat aagaatcacc 11520 tcccgtgcag gggtctgcat agaacactcc ataggcaaac ctgggtgtcc aaggcacgtg 11580 gcattttgca aactctgggt gcagctccga gctgtcctgc aggtcccaga ccaggtgaga 11640 actccctgag ttcctgctgc ctgggtcggg ggtgaggcat aggtcttggg ggttcaacct 11700 ggaattctga atgtcattca ttgcattgga gaggaaggag agtaggcaaa gccaagaccc 11760 tggaactgga caaactcgtg tggtttaaag tcactgtgag agctggagtt gagtctgcct 11820 acgggggagg actgcggcac ctacctcgca gggctgttgt gaggagcaat gtaaccgtga 11880 ttttgaactg tgattctgga agggcggtgt gcgtgtcccc gggggtgtgc caggggagtg 11940 aggagaaaag gccagggaga cagcctcact caggcagctg agtgggagag catttatctc 12000 taaacctgga ggggtatatg gtgggacagg aggaatttgg gcaggaactt tcatgctagg 12060 ggtttggggg actcgctgga caatgcccct ggaccccccg ggggtacgcg ttcacgctca 12120 cctctgagag gctggaaacg cctggctgtg ctttctgaat gctgtgtgct tcctgcctct 12180 gtgcctggcc tgtgtgcagc acctacttgt gtccgccttc aaaaggccct tctgggtggc 12240 gtccttttcc ccaaaatatt aggcaccagc catcaaagat actgcattgt tgcctccccc 12300 acccctcccc ccaactgaca acatttgggc tcaaatgcag caggctgggt gcccaacaca 12360 gtgcctggcg agtggtagcg cttacgtttc ttttctgttg aatggatgga tagctaatga 12420 aattgtaacc aatgacaagc cttgatgttt ataaccttta ctaagagatt attattttgc 12480 tcttcatgga cctgttaaca accaccatat tgtatcttac ggacgtttgt atgccacgtt 12540 tgaagagcag gagccttgtt tcggcgtcat gttgatggaa cttgagctgt ctgatgcgaa 12600 tctgtgtttt atgttagaaa gcgcgtagcc ttaggatctg gcagacccag gggccactta 12660 attaaccctt tgcctctttg accctcaatc tccttttctc taagccatag gtcacctgaa 12720 agcctacctc acagggctgt tgtgagggcc gagggtgggt gtgtttcaac agtgtgcaga 12780 tgctggcttt ccctgggaat gggcatatgt tgggatttgt cttgaaagca tgagtgatgg 12840 ctttactagt cctaagtgaa taaaaagtca gccctgacct tacgctggga ttgcatttcc 12900 cacagtcagt ggcatgtgca gaccactggc agagcagcct gcaggtgctt agcgatgtgg 12960 gcccagagta aatatttgtt tgattgatga 12990

Claims

1. Use of a reagent for detecting a biomarker or a combination thereof for predicting the risk of preoperative pulmonary vein stenosis in patients with TAPVC in the preparation of a product for predicting the risk of preoperative pulmonary vein stenosis in patients with TPAVC, wherein, the biomarker or the combination thereof includes an NR3C2 mRNA molecular biomarker and / or an MEG3 lncRNA molecular biomarker.

2. The use according to claim 1, wherein, the biomarker or the combination thereof further includes at least one of the CNTNAP2, FAM3C, and NETO1 mRNA molecular biomarkers.

3. The use according to claim 2, wherein, the combination is any one of the following combinations: (1) an NR3C2 mRNA molecular biomarker and at least one of the CNTNAP2, FAM3C, and NETO1 mRNA molecular biomarkers; (2) an MEG3 lncRNA molecular biomarker and at least one of the CNTNAP2, FAM3C, and NETO1 mRNA molecular biomarkers; (3) an NR3C2 mRNA molecular biomarker and an MEG3 lncRNA molecular biomarker, and at least one of the CNTNAP2, FAM3C, and NETO1 mRNA molecular biomarkers.

4. The use according to claim 3, wherein, the combination includes: a) two mRNA molecular biomarkers, NR3C2 and CNTNAP2, or b) two mRNA molecular biomarkers, NR3C2 and FAM3C, or c) two mRNA molecular biomarkers, NR3C2 and NETO1, or d) three mRNA molecular biomarkers, NR3C2, CNTNAP2, and FAM3C, or e) three mRNA molecular biomarkers, NR3C2, FAM3C, and NETO1, or f) three mRNA molecular biomarkers, NR3C2, CNTNAP2, and NETO1, or g) four mRNA molecular biomarkers, NR3C2, CNTNAP2, FAM3C, and NETO1; or includes: a) an MEG3 lncRNA molecular biomarker and a CNTNAP2 mRNA molecular biomarker, or b) an MEG3 lncRNA molecular biomarker and a FAM3C mRNA molecular biomarker, or c) an MEG3 lncRNA molecular biomarker and a NETO1 mRNA molecular biomarker, or d) an MEG3 lncRNA molecular biomarker and two mRNA molecular biomarkers, CNTNAP2 and FAM3C, or e) an MEG3 lncRNA molecular biomarker and two mRNA molecular biomarkers, FAM3C and NETO1, or f) an MEG3 lncRNA molecular biomarker and two mRNA molecular biomarkers, CNTNAP2 and NETO1, or g) an MEG3 lncRNA molecular biomarker and three mRNA molecular biomarkers, CNTNAP2, FAM3C, and NETO1; or includes: a) The MEG3 lncRNA molecular marker and two mRNA molecular markers, NR3C2 and CNTNAP2, or b) The MEG3 lncRNA molecular marker and two mRNA molecular markers, NR3C2 and FAM3C, or c) The MEG3 lncRNA molecular marker and two mRNA molecular markers, NR3C2 and NETO1, or d) The MEG3 lncRNA molecular marker and three mRNA molecular markers, NR3C2, CNTNAP2 and FAM3C, or e) The MEG3 lncRNA molecular marker and three mRNA molecular markers, NR3C2, FAM3C and NETO1, or f) The MEG3 lncRNA molecular marker and three mRNA molecular markers, NR3C2, CNTNAP2 and NETO1, or g) The MEG3 lncRNA molecular marker and four mRNA molecular markers, NR3C2, CNTNAP2, FAM3C and NETO1.

5. The application according to claim 4, characterized in that the NR3C2 mRNA molecular marker contains the nucleic acid sequence shown in SEQ ID NO: 1; the CNTNAP2 mRNA molecular marker contains the nucleic acid sequence shown in SEQ ID NO: 2; the FAM3C mRNA molecular marker contains the nucleic acid sequence shown in SEQ ID NO: 3; the NETO1 mRNA molecular marker contains the nucleic acid sequence shown in SEQ ID NO: 4; the MEG3 lncRNA molecular marker contains the nucleic acid sequence shown in SEQ ID NO:

5.

6. The application according to claim 1, characterized in that the reagent comprises a biomolecule that specifically hybridizes with the biomarker or its combination.

7. The application according to claim 6, characterized in that the biomolecule comprises a primer and / or a probe.

8. A system for predicting the risk of preoperative pulmonary vein stenosis in patients with TAPVC, characterized in that the system comprises: a data processing module for calculating the data of the biomarker or its combination of the TAPVC patient received or input to obtain a calculation result; the biomarker or its combination comprises the NR3C2 mRNA molecular marker and / or the MEG3 lncRNA molecular marker; and a judgment and output module for judging whether the calculation result meets a preset judgment condition to predict the risk of preoperative pulmonary vein stenosis in TAPVC patients and output a prediction result.

9. The system according to claim 8, characterized in that in the judgment and output module, when the calculation result meets the judgment condition, the prediction result output is "preoperative pulmonary vein stenosis in TAPVC patients", and when the calculation result does not meet the judgment condition, the prediction result output is "no preoperative pulmonary vein stenosis in TAPVC patients"; In the data processing module, the data is the expression level information of biomarkers or their combinations of the TAPVC patient; the biomarkers or their combinations include the NR3C2 mRNA molecular biomarker and / or the MEG3 lncRNA molecular biomarker.

10. The system according to claim 9, wherein, in the judgment and output module, the judgment includes comparing the expression level information with a reference data set or a reference value; the judgment condition is that the calculated risk coefficient is greater than 0.5; the reference data set includes the expression level information of biomarkers or their combinations in samples from multiple TAPVC patients with preoperative pulmonary vein stenosis and multiple TAPVC patients without preoperative pulmonary vein stenosis; the biomarkers or their combinations include the NR3C2 mRNA molecular biomarker and / or the MEG3 lncRNA molecular biomarker.

11. The system according to claim 10, wherein, the judgment and output module includes a machine learning model for performing the calculation.

12. The system according to claim 11, wherein, the machine learning model is selected from one or more of a generalized linear model, a random forest, and a support vector machine.

13. A computer-readable storage medium storing a computer program, wherein, when the computer program is executed by a processor, the functions of the system according to any one of claims 8-12 can be realized.

14. A computer device including a memory and a processor, the memory storing a computer program, wherein, the processor is used to execute the computer program to realize the functions of the system according to any one of claims 8-12.

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