CN110577988B - Fetal growth restriction prediction model - Google Patents
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Abstract
The invention discloses a prediction model for fetal growth limitation. The research of the invention finds that the distribution condition of the peripheral blood free DNA in the gene transcription initiation site region can reflect the physiological states of pregnant women and fetuses, the serum free DNA abundance based on the gene transcription initiation site region has obvious difference in the pregnant women and healthy pregnant women with fetal growth limitation, and the morbidity with fetal growth limitation can be effectively predicted by using a machine learning algorithm and through the optimal combination of different differential genes after the homogenization correction of the free DNA abundance. Therefore, a screening and predicting model for fetal growth restriction based on peripheral blood free DNA prediction and an optimized target gene combination are constructed, the onset of fetal growth restriction can be predicted before clinical symptoms of fetal growth restriction appear, the method is a relatively noninvasive, economical and convenient method for early-stage fetal growth restriction prediction, and has a good application prospect in developing a fetal growth restriction prediction and screening product.
Description
Technical Field
The invention belongs to the technical field of disease detection products. And more particularly to a predictive model of fetal growth restriction.
Background
Fetal Growth Restriction (FGR) generally means that the fetal weight is below the 10 th percentile of fetuses of the same gestational age, the same sex, or less than 2.5kg of fetal birth weight after 37 weeks of gestation. Fetal growth restriction is a common complication of obstetrics and is often associated with adverse outcomes such as premature birth, intrauterine fetal death, neonatal death, and the like. The incidence of fetal growth restriction in China is 5-10%, which is the second cause of perinatal death, second only to premature delivery.
The diagnosis of fetal growth restriction during pregnancy is not easy, but early prediction of the occurrence of fetal growth restriction is more difficult, and known risk factors relate to three aspects of pregnant women (advanced age, hypertension, diabetes, hyperthyroidism, malnutrition, drug abuse, etc.), fetuses (intrauterine infection, congenital malformation, chromosomal abnormality, etc.), and placental umbilical cords (umbilical arteries, sailing placenta, placental chimeras, etc.). Although these high-risk physiological factors have certain guiding significance for the diagnosis and screening of fetal growth restriction, the occurrence of fetal growth restriction cannot be accurately predicted by only these factors.
In addition, foreign research groups have proposed methods for predicting fetal growth restriction by combining abundances of protein factors such as pregnancy-associated protein a (PAPP-a), placental growth factor (PLGF), and β -HCG (human chorionic gonadotropin) in serum, which can improve the predicted sensitivity to 67-75% under the condition of controlling the false positive rate to be not higher than 10%, but need further improvement and improvement as clinical application.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects and shortcomings of the existing screening and predicting technology for fetal growth restriction and provides a technology for predicting fetal growth restriction by peripheral blood free DNA high-throughput sequencing. According to the invention, a prediction model for predicting the fetal growth restriction based on the detection of the free DNA in the peripheral blood is firstly constructed, a group of target gene combinations suitable for predicting and screening the fetal growth restriction based on the free DNA in the peripheral blood is obtained, and the prediction model is optimized. The technology of the invention can predict the onset of fetal growth restriction before the clinical symptoms of fetal growth restriction appear, and is a relatively noninvasive, economic and convenient method for predicting fetal growth restriction earlier than the prior method.
The invention aims to provide a target gene combination suitable for predicting fetal growth limitation based on peripheral blood free DNA.
Another object of the present invention is to provide a model for predicting fetal growth limitation based on the detection of free DNA in peripheral blood.
The above purpose of the invention is realized by the following technical scheme:
the research of the invention finds that the distribution of the peripheral blood free DNA in the gene transcription initiation site region can reflect the physiological states of pregnant women and fetuses, namely, although the fetal growth of a patient with fetal growth limitation cannot be accurately measured before delivery, after 12 weeks of pregnancy, the distribution of the serum free DNA of the patient and a healthy pregnant woman on chromosomes is obviously different, and the abundance of the serum free DNA in a part of the gene transcription initiation site region is obviously different between the patient with fetal growth limitation and the healthy pregnant women. Meanwhile, after the abundance of the free DNA is subjected to homogenization correction, a machine learning algorithm is used, and the morbidity of the fetal growth limitation can be effectively predicted through the optimal combination of different differential genes. Based on the method, the fetal growth limitation can be predicted through the high-throughput sequencing of the free DNA of the peripheral blood of the pregnant woman, and an effective method can be provided for the early prediction of the fetal growth limitation.
The invention provides a screening and predicting model for fetal growth limitation based on peripheral blood free DNA detection based on research results, and optimizes related target gene combinations.
A target gene combination suitable for fetal growth restriction prediction based on peripheral blood free DNA detection is disclosed, and specifically comprises any of HPS5, PTDSS2, OR4P4, PNRC2, CD63, VPS35, VAV1, DNPEP, TCF7, DPP6, LANCL2, GPAT4 and FAM 214B.
The application of the target gene combination in screening the fetal growth restriction marker and preparing a fetal growth restriction prediction screening product also falls within the protection scope of the invention.
In addition, a fetus growth limitation prediction model based on peripheral blood free DNA detection (namely a method for predicting fetus growth limitation based on peripheral blood free DNA), high-throughput sequencing is carried out on the peripheral blood free DNA in a gestational period to be detected (the high-throughput sequencing method can be double-ended sequencing or single-ended sequencing), the sequencing result of the peripheral blood free DNA is compared with a chromosome group sequence map, then the number of DNA fragments from a transcription initiation site area of a gene to be detected in the same sample is calculated, then the total number of the DNA sequences is corrected according to formula 1 and formula 2, and the fetus growth limitation disease prediction result of the pregnant woman to be detected is calculated and output.
Wherein, the gene to be detected is a differential gene combination obtained by comparing a high-throughput sequencing result with a chromosome set sequence map. The combination of target genes is preferred.
Specifically, the prediction model of fetal growth limitation based on peripheral blood free DNA detection comprises three modules:
(1) A module for performing high-throughput sequencing and analysis on peripheral blood free DNA of a sample to be detected:
performing high-throughput sequencing on peripheral blood free DNA of a sample to be detected, comparing a sequencing result with a chromosome set sequence map, and calculating to obtain the number of DNA fragments from a transcription initiation site region of a gene to be detected in the same sample;
(2) Equation 1:
wherein, the total aligned sequence number refers to the total sequence number of human genome sequences aligned in the high-throughput sequencing data;
the formula 1 is used for correcting the number of the DNA fragments in the transcription initiation site region of the gene to be detected obtained in the step (1);
counting the number of serum free DNA sequences in a gene transcription initiation site region to estimate the chromosome openness;
(3) Equation 2:
in the formula, x i Corrected number of DNA fragments in the transcription initiation site region of the gene i,. Beta. i Is the coefficient beta of gene i; c is a constant;
and the formula 2 is used for calculating and outputting the prediction result of the limited fetal growth of the pregnant woman to be detected.
Further, the prediction criteria of the screening prediction model are as follows:
substituting the calculation result of equation 2 into equation 3 to calculate the Y value:
logic (Y) = ln (Y/(1-Y)) (equation 3)
Comparing the value Y with a fetal growth limited risk threshold value P, and when the sample value Y is greater than the threshold value P, judging the sample to be a fetal growth limited high risk; and when the sample value Y is smaller than the threshold value P, judging the sample as a low risk of fetal growth limitation.
Preferably, the fetal growth restriction risk threshold P is 0.190.
In addition, preferably, in formula 2, the c constant is-1.000.
Preferably, in formula 2, the corresponding coefficients β of the gene i are:
preferably, in formula 1, the transcription initiation site region of the gene has a size from 1000bp upstream to 1000bp downstream of the gene.
The invention has the following beneficial effects:
the research of the invention discovers that although the limited growth of the fetus cannot be accurately measured before delivery, after 12 weeks of pregnancy, the distribution of the serum free DNA on the chromosome of a patient is obviously different from that of a healthy pregnant woman, and the abundance of the serum free DNA in the region of the transcription starting site of a part of genes is obviously different between the patient with the limited growth of the fetus and the healthy pregnant woman. After the free DNA abundance is subjected to homogenization correction, a machine learning algorithm is used, and the onset of fetal growth limitation can be effectively predicted through the optimal combination of different differential genes. Therefore, a screening and predicting model of fetal growth limitation based on peripheral blood free DNA detection and an optimized target gene combination are constructed.
The technology of the invention can predict the onset of fetal growth restriction before delivery, is a relatively noninvasive, economic and convenient method for predicting fetal growth restriction earlier than the existing method, can provide an effective method for early prediction screening of fetal growth restriction, and has good application prospect in developing products related to screening and predicting fetal growth restriction.
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FIG. 1 shows that compared the coverage of free DNA in the gene transcription initiation site region of the pregnant women with fetal growth restriction and healthy pregnant women, the chromosome openness degree of part of the gene positions is significantly different, and the pregnant women with fetal growth restriction and healthy pregnant women with fetal growth restriction can be effectively distinguished.
FIG. 2 is a ROC curve for the present invention for the determination of fetal growth restriction in a training set and a validation set.
Detailed Description
The present invention is further illustrated by the following specific examples, which are not intended to limit the invention in any way. Reagents, methods and apparatus used in the present invention are conventional in the art unless otherwise indicated.
Unless otherwise indicated, reagents and materials used in the following examples are commercially available.
The terms are used herein to explain: paired-end sequencing refers to the separate testing of sequences at both ends of a sequence. Single-ended sequencing refers to testing sequences at one end of the sequence.
Example 1 model method for predicting fetal growth restriction based on peripheral blood free DNA
The method for predicting fetal growth limitation based on the peripheral blood free DNA comprises the following steps: comparing the sequencing result of the peripheral blood free DNA with a chromosome group sequence map, then calculating the quantity of DNA fragments from the transcription initiation site area of the gene to be detected in the same sample, correcting according to the total quantity of the DNA sequences, carrying out homogenization correction on the abundance of the free DNA, using a machine learning algorithm, calculating and outputting the fetal growth restriction disease prediction result of the pregnant woman to be detected through the optimal combination of different differential genes, and effectively predicting the onset of fetal growth restriction.
Specifically, the method comprises the following steps:
step 1: determination of the DNA fragment in plasma from a specific position on the chromosome
Control studies were performed on pre-morbid and healthy samples that had been diagnosed as having restricted fetal growth, and high-throughput paired-end sequencing (alternatively, single-end sequencing) was performed on peripheral blood free DNA from both samples.
After high-throughput paired-end sequencing of free peripheral blood DNA, the sequences at both ends are aligned with a human genome standard sequence 37.1 (http:// www.ncbi.nlm.nih.gov/subjects/genome/assembly/grc/human/data/.
The results show that although the patients with fetal growth restriction do not have clinical symptoms in 12 weeks of pregnancy, the distribution of serum free DNA on chromosome of the patients and healthy pregnant women is significantly different, and the abundance of the serum free DNA in the region of the transcription start site of part of genes is significantly different between the patients with fetal growth restriction and healthy pregnant women (as shown in FIG. 1).
Through a large number of research and exploration, an optimized gene combination to be tested is determined, and the following table 1 shows:
TABLE 1 genes to be tested
In addition, the differential gene combinations shown in table 1 in the present application represent only preferred combinations in a certain reagent and instrument platform, and the present invention does not limit the inventors to predict the preferred combinations in other instruments and reagents.
Step 2: determination of DNA fragment abundance in the region of the transcriptional start site of a test Gene
The total number of aligned sequences of the samples (in this example, the total number of aligned sequences of two samples, sample 1 and sample 2 are 75583 and 77406, respectively) is counted. And (3) calculating the number of the DNA fragments in the transcription start site region of the gene to be detected in the same sample, and correcting the abundance of the DNA fragments by using the formula 1.
Table 2 shows an example of the calculation of the abundance of DNA fragments in the transcription initiation site region of the genes to be tested in two samples:
TABLE 2
And 3, step 3: calculating the disease risk according to the expression condition of the gene to be detected
The risk of onset of fetal growth restriction is calculated using equation 2:
in the formula, x i Corrected number of DNA fragments, beta, of the transcription start region of the gene for gene i i Coefficient β for gene i; c is a constant and takes the value of 0.957.
The genes and their corresponding coefficients β are shown in table 3:
TABLE 3
And calculating the Y value according to the formula 3:
logic (Y) = ln (Y/(1-Y)) (equation 3)
The fetal growth limited risk threshold P is 0.190, and when the sample value Y is larger than the threshold P, the sample is judged to be the fetal growth limited high risk; and when the sample value Y is smaller than the threshold value P, judging the sample to be in low risk of fetal growth limitation.
In summary, the model (prediction method) for predicting fetal growth limitation based on peripheral blood free DNA of the present invention comprises three modules:
(1) Carrying out high-throughput sequencing and analysis on the peripheral blood free DNA of the sample to be detected:
performing high-throughput sequencing on peripheral blood free DNA of a sample to be detected, comparing a sequencing result with a chromosome set sequence map, and calculating to obtain the number of DNA fragments from a transcription initiation site region of a gene to be detected in the same sample;
(2) Correcting the quantity of the DNA fragments in the transcription initiation site region of the gene to be detected obtained in the step (1) according to a formula 1;
(3) And calculating and outputting a prediction result of the limited fetal growth disease of the pregnant woman to be detected according to a formula 2 and a formula 3.
Example 2 sample testing example
1. Experimental samples:
the training set contained 132 fetal growth-restricted samples, 378 healthy controls;
the validation group contained 103 fetal growth-restricted samples, 162 healthy controls.
The procedure was as in example 1. Accuracy, sensitivity and specificity of the statistical calculation method.
2. The results show that the method model of the invention is effective in judging fetal growth-restricted patients before early onset in both the training and validation groups (table 4 and fig. 2).
TABLE 4
Wherein, the calculation result is exemplified as follows:
sample 1 (pre-morbid sample with confirmed fetal growth restriction):
logit(Y)＝–1.000–0.797×HPS5–1.142×PTDSS2+0.846×OR4P4+0.636×PNRC2+1.208×CD63+0.310×VPS35–1.903×VAV1–1.313×DNPEP–1.370×TCF7+0.673×DPP6+0.574×LANCL2–1.288×GPAT4+1.238×FAM214B＝4.834
Y＝0.992
and (5) judging that the sample value is larger than the fetal growth limited threshold value P (0.190) and is a fetal growth limited high-risk sample. The result is accurate.
Sample 2 (healthy sample):
logit(Y)＝–1.000–0.797×HPS5–1.142×PTDSS2+0.846×OR4P4+0.636×PNRC2+1.208×CD63+0.310×VPS35–1.903×VAV1–1.313×DNPEP–1.370×TCF7+0.673×DPP6+0.574×LANCL2–1.288×GPAT4+1.238×FAM214B＝-6.374
Y＝0.017
and (5) judging the sample as a low-risk sample with fetal growth limitation when the sample value is less than the threshold value P (0.190). The result is accurate.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (3)
1. The application of a reagent for detecting the quantity of DNA fragments in the transcription initiation site region of target genes in free DNA of peripheral blood in preparing a fetal growth restriction prediction screening product, wherein the target genes are HPS5, PTDSS2, OR4P4, PNRC2, CD63, VPS35, VAV1, DNPEP, TCF7, DPP6, LANCL2, GPAT4 and FAM214B;
the transcription initiation sites of the target genes are respectively as follows:
the size of the transcription initiation site region of the target gene is a region from the upstream 1000bp to the downstream 1000bp of the gene.
2. A fetal growth limitation screening and predicting model based on peripheral blood free DNA detection is characterized by comprising three modules:
(1) A module for performing high-throughput sequencing and analysis on peripheral blood free DNA of a sample to be detected:
performing high-throughput sequencing on peripheral blood free DNA of a sample to be detected, comparing a sequencing result with a chromosome set sequence map, and calculating to obtain the number of DNA fragments from a transcription initiation site region of a gene to be detected in the same sample; the gene to be detected is a differential gene combination obtained by comparing a high-throughput sequencing result with a chromosome set sequence map;
(2) Equation 1:
wherein, the total aligned sequence number refers to the total sequence number of human genome sequences aligned in the high-throughput sequencing data;
the formula 1 is used for correcting the number of the DNA fragments in the transcription initiation site region of the gene to be detected obtained in the step (1);
the genes to be detected are HPS5, PTDSS2, OR4P4, PNRC2, CD63, VPS35, VAV1, DNPEP, TCF7, DPP6, LANCL2, GPAT4 and FAM214B;
the transcription initiation sites of the genes to be detected are respectively as follows:
the size of the transcription initiation site region of the gene to be detected is the region from upstream 1000bp to downstream 1000bp of the gene;
(3) Equation 2:
in the formula, x i Corrected number of DNA fragments in the transcription initiation site region of the gene i,. Beta. i Is the coefficient beta of gene i; c is constant-1.000;
the genes and their corresponding coefficients β are respectively: the coefficient β of HPS5 is-0.797, the coefficient β of PTDSS2 is-1.142, the coefficient β of OR4P4 is 0.846, the coefficient β of PNRC2 is 0.636, the coefficient β of CD63 is 1.208, the coefficient β of VPS35 is 0.310, the coefficient β of VAV1 is-1.903, the coefficient β of DNPEP is-1.313, the coefficient β of TCF7 is-1.370, the coefficient β of DPP6 is 0.673, the coefficient β of LANCL2 is 0.574, the coefficient β of GPAT4 is-1.288, and the coefficient β of FAM214B is 1.238;
the formula 2 is used for calculating and outputting a prediction result of the limited fetal growth of the sample to be detected; the prediction criteria are as follows:
substituting the calculation of equation 2 into equation 3: logic (Y) = ln (Y/(1-Y)), the Y value is calculated;
comparing the value Y with a fetal growth limited risk threshold P, wherein the fetal growth limited risk threshold P is 0.190; when the sample value Y is larger than the threshold value P, the sample is judged to be in high risk of fetal growth limitation; and when the sample value Y is smaller than the threshold value P, judging the sample to be in low risk of fetal growth limitation.
3. The screening prediction model of claim 2, wherein the sequencing is single-ended sequencing or double-ended sequencing.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2001271516A1 (en) * | 2000-06-27 | 2002-03-28 | Lexicon Pharmaceuticals, Inc. | Novel human gaba receptors and polynucleotides encoding the same |
| WO2016170348A2 (en) * | 2015-04-22 | 2016-10-27 | Mina Therapeutics Limited | Sarna compositions and methods of use |
| AR101088A1 (en) * | 2014-07-01 | 2016-11-23 | Teva Pharma | BIOLOGICAL CHARACTERIZATION OF A PHARMACOLOGICAL PRODUCT DERIVED FROM GLATIRAMER ACETATE BY HUMAN AND NON-HUMAN MAMMAL CELLS |
| CN107982518A (en) * | 2017-10-26 | 2018-05-04 | 南方医科大学 | A kind of Lgr5 positive liver stem cells abductive approach and its detection application |
| WO2018204764A1 (en) * | 2017-05-05 | 2018-11-08 | Camp4 Therapeutics Corporation | Identification and targeted modulation of gene signaling networks |
| CN109507426A (en) * | 2018-11-29 | 2019-03-22 | 上海晟燃生物科技有限公司 | Prostate cancer diagnosis, classification or prognostic marker, detection reagent or kit, system and its application |
| WO2019079647A2 (en) * | 2017-10-18 | 2019-04-25 | Wuxi Nextcode Genomics Usa, Inc. | Statistical ai for advanced deep learning and probabilistic programing in the biosciences |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002000720A2 (en) * | 2000-06-27 | 2002-01-03 | Lexicon Genetics Incorporated | Novel human gaba receptors and polynucleotides encoding the same |
-
2019
- 2019-07-19 CN CN201910655857.XA patent/CN110577988B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2001271516A1 (en) * | 2000-06-27 | 2002-03-28 | Lexicon Pharmaceuticals, Inc. | Novel human gaba receptors and polynucleotides encoding the same |
| AR101088A1 (en) * | 2014-07-01 | 2016-11-23 | Teva Pharma | BIOLOGICAL CHARACTERIZATION OF A PHARMACOLOGICAL PRODUCT DERIVED FROM GLATIRAMER ACETATE BY HUMAN AND NON-HUMAN MAMMAL CELLS |
| WO2016170348A2 (en) * | 2015-04-22 | 2016-10-27 | Mina Therapeutics Limited | Sarna compositions and methods of use |
| WO2018204764A1 (en) * | 2017-05-05 | 2018-11-08 | Camp4 Therapeutics Corporation | Identification and targeted modulation of gene signaling networks |
| WO2019079647A2 (en) * | 2017-10-18 | 2019-04-25 | Wuxi Nextcode Genomics Usa, Inc. | Statistical ai for advanced deep learning and probabilistic programing in the biosciences |
| CN107982518A (en) * | 2017-10-26 | 2018-05-04 | 南方医科大学 | A kind of Lgr5 positive liver stem cells abductive approach and its detection application |
| CN109507426A (en) * | 2018-11-29 | 2019-03-22 | 上海晟燃生物科技有限公司 | Prostate cancer diagnosis, classification or prognostic marker, detection reagent or kit, system and its application |
Non-Patent Citations (4)
| Title |
|---|
| "Neonatal and Long-Term Consequences of Fetal Growth Restriction ";Marina Colella;《Current Pediatric Reviews》;20181230;第212-218页 * |
| "The consequences of fetal growth restriction on brain structure and neurodevelopmental outcome";Suzanne L. Miller;《J Physiol 》;20161230;第807-823页 * |
| 孕早期血清PAPP-A、PP13水平对胎儿生长受限的预测价值;魏淑燕等;《中国妇幼保健》;20170201(第03期);全文 * |
| 预测胎儿体质量实用进展;刘咏丰等;《医学综述》;20080105(第01期);全文 * |
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