CN110387414B - Model for predicting gestational diabetes by using peripheral blood free DNA - Google Patents

Model for predicting gestational diabetes by using peripheral blood free DNA Download PDF

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CN110387414B
CN110387414B CN201910655858.4A CN201910655858A CN110387414B CN 110387414 B CN110387414 B CN 110387414B CN 201910655858 A CN201910655858 A CN 201910655858A CN 110387414 B CN110387414 B CN 110387414B
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韩博炜
郭智伟
李明
吴英松
梁志坤
欧阳国军
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Abstract

The invention discloses a model for predicting gestational diabetes by using peripheral blood free DNA. 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 the pregnant women and fetuses, the serum free DNA abundance based on the gene transcription initiation site region has obvious difference between the gestational diabetes patients and healthy pregnant women, and the onset of the gestational diabetes 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 gestational diabetes based on peripheral blood free DNA prediction and an optimized target gene combination are constructed, the onset of gestational diabetes can be predicted before clinical symptoms of gestational diabetes appear, the method is a relatively noninvasive, economical and convenient method for predicting early gestational diabetes, and has a good application prospect in developing a screening and predicting product for gestational diabetes.

Description

Model for predicting gestational diabetes by using peripheral blood free DNA
Technical Field
The invention belongs to the technical field of disease detection products. And more particularly, to a model for predicting gestational diabetes using peripheral blood free DNA.
Background
Gestational Diabetes (GDM) is diabetes occurring during pregnancy, with an incidence rate of over 5%, of which 80% of patients are pregnant women with no history of diabetes. Gestational diabetes brings great physiological burden to pregnant women, and long-term hyperglycemia is easy to cause ketoacidosis, excessive amniotic fluid, premature rupture of fetal membranes and premature birth of the pregnant women, and can also cause abnormal development and even death of embryos, and the abortion incidence rate reaches 15% -30%. In addition, gestational diabetes also has great harm to the fetus, and the incidence rates of fetal deformity, fetal growth limitation and giant fetus are all obviously improved; after the newborn comes out, the incidence rate of respiratory distress syndrome and hypoglycemia of the newborn is also obviously increased. Based on the adverse effects of gestational diabetes on pregnant women and fetuses, effective diagnosis and intervention means aiming at gestational diabetes are developed, and the method has important significance and value for the better prenatal and postnatal care and the improvement of the life quality of pregnant women.
The classic diagnosis method of gestational diabetes is screening gestational diabetes, wherein a pregnant woman orally takes a glucose solution between 24 and 28 weeks of pregnancy, draws blood one hour later and detects the blood glucose level, thereby carrying out screening. However, since the late pregnancy is already near 24-28 weeks, the development of the fetus approaches maturity, and partial symptoms caused by gestational diabetes cannot be prevented and intervened in time, a screening means capable of discovering gestational diabetes earlier is needed.
The gene-level-based high-sensitivity detection technology is widely applied to early detection and screening of diseases. Aiming at screening of gestational diabetes, research shows that the level of fetal free DNA-SRY in the peripheral blood of a pregnant woman with gestational diabetes is obviously higher than that of a control group (P < 0.05); therefore, the abnormal increase of the DNA level of the free fetus in the peripheral blood of the pregnant woman with the gestational diabetes is expected to be an effective index for predicting and diagnosing the disease incidence and the disease degree of the GDM (China journal of eugenics and genetics, 2010, 12 th year, Tang Dong Ling). However, since the SRY gene is located on the Y chromosome, it can only be used for disease prediction in males and females, and prediction models based on only a single gene have high variability, and it is difficult to propose a stable and reliable prediction model.
In addition, aiming at different early disease predictions, related abnormally expressed genes are obtained, and the relationship between related genes and diseases, especially in a complex system with multiple genes and multiple factors participating together, the determination of detection targets and combinations thereof is a key factor for the accuracy and stability of early disease prediction, and has important influence.
Disclosure of Invention
The invention aims to solve the technical problems of overcoming the defects and shortcomings of the existing gestational diabetes screening and predicting technology and providing a technology for predicting gestational diabetes by peripheral blood free DNA high-throughput sequencing. The invention firstly constructs a prediction model for predicting gestational diabetes based on peripheral blood free DNA detection, obtains a group of target gene combinations suitable for predicting and screening gestational diabetes based on peripheral blood free DNA, and optimizes the prediction model. The technology of the invention can predict the onset of the gestational diabetes before the clinical symptoms of the gestational diabetes appear, and is a relatively noninvasive, economic and convenient method for predicting the gestational diabetes earlier than the prior method.
The invention aims to provide a target gene combination suitable for predicting gestational diabetes based on free DNA of peripheral blood.
Another objective of the invention is to provide a model for predicting gestational diabetes 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 gestational diabetes patients have no clinical symptoms in 12 weeks of pregnancy, the distribution of the serum free DNA of the patients and healthy pregnant women 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 gestational diabetes patients and the healthy pregnant women. Meanwhile, after the abundance of the free DNA is subjected to homogenization correction, the onset of gestational diabetes can be effectively predicted by using a machine learning algorithm and through the optimal combination of different differential genes. Based on the method, the gestational diabetes can be predicted by high-throughput sequencing of free DNA of the peripheral blood of the pregnant woman, and an effective method can be provided for early prediction of the gestational diabetes.
The invention provides a screening and predicting model of gestational diabetes based on peripheral blood free DNA detection based on research results, and optimizes related target gene combinations.
A target gene combination suitable for gestational diabetes prediction based on peripheral blood free DNA detection is disclosed, which is specifically any of CC2D2B, NAT10, SIPA1, ZNF565, ZNF552, WDR35, MICALL1, CTNNB1, CLOCK, BCKDHB and TGIF2 LY.
The application of the target gene combination in the aspect of screening the gestational diabetes mellitus as a marker and the application in preparing a gestational diabetes mellitus prediction screening product also belong to the protection scope of the invention.
In addition, a gestational diabetes prediction model based on peripheral blood free DNA detection (namely a method for predicting gestational diabetes based on peripheral blood free DNA), high-throughput sequencing (the high-throughput sequencing method can be double-ended sequencing or single-ended sequencing) is carried out on peripheral blood free DNA to be detected during pregnancy, the sequencing result of the peripheral blood free DNA is compared with a chromosome set 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, and then the total number of the DNA sequences is corrected according to a formula 1 and a formula 2, and then the gestational diabetes 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 gestational diabetes based on peripheral blood free DNA detection comprises three modules:
(1) the module is used for carrying out high-throughput sequencing and analysis on the peripheral blood free DNA of a sample to be detected:
performing high-throughput sequencing on the peripheral blood free DNA of a sample to be tested, comparing a sequencing result with a chromosome set sequence map, and calculating to obtain the quantity of DNA fragments from a transcription initiation site region of the gene to be tested in the same sample;
(2) equation 1:
Figure BDA0002136852300000031
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 quantity 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:
Figure BDA0002136852300000032
in the formula, x i Corrected number of DNA fragments, beta, of the transcription start region of the gene for gene i i Is the coefficient beta of gene i; c is a constant;
the formula 2 is used for calculating and outputting the prediction result of the gestational diabetes 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)) (formula 3)
Comparing the value Y with a gestational diabetes risk threshold value P, and judging the sample as the gestational diabetes high-risk when the sample value Y is greater than the threshold value P; and when the sample value Y is smaller than the threshold value P, judging the sample to be low-risk of the gestational diabetes.
Preferably, the gestational diabetes risk threshold P is 0.259.
In addition, in formula 2, the constant c is preferably 0.957.
Preferably, in formula 2, the corresponding coefficients β of the gene i are:
Figure BDA0002136852300000041
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 finds that although the gestational diabetes patients do not have clinical symptoms in 12 weeks of pregnancy, the distribution of serum free DNA on chromosomes of the patients and healthy pregnant women is obviously different, and the abundance of the serum free DNA in a part of gene transcription starting site regions is obviously different between the gestational diabetes patients and the healthy pregnant women. After the abundance of the free DNA is subjected to homogenization correction, a machine learning algorithm is used, and the onset of gestational diabetes can be effectively predicted through the optimal combination of different differential genes. Therefore, a screening and predicting model of gestational diabetes 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 gestational diabetes before the clinical symptoms of gestational diabetes appear, is a relatively noninvasive, economic and convenient method for predicting gestational diabetes earlier than the existing method, can provide an effective method for early prediction and screening of gestational diabetes, and has good application prospect in developing related products for screening and predicting gestational diabetes.
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FIG. 1 shows that when the coverage of free DNA of serum of pregnant women with non-onset diabetes and healthy pregnant women at the gene transcription initiation site is compared, the chromosome openness degree of partial gene positions is significantly different, and the pregnant women with non-onset diabetes and the healthy pregnant women can be effectively distinguished.
FIG. 2 is a ROC curve for the present invention in the training and validation groups for the determination of gestational diabetes patients.
Detailed Description
The present invention is further illustrated by the following specific examples, which are not intended to limit the invention in any way. The reagents, methods and apparatus employed in the present invention are conventional in the art, except as 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 gestational diabetes mellitus based on peripheral blood free DNA
The method for predicting gestational diabetes based on the free DNA of the peripheral blood 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 a transcription initiation site region of a 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 a prediction result of gestational diabetes of the pregnant woman to be detected through the optimal combination of different differential genes, and effectively predicting the onset of gestational diabetes.
Specifically, the method comprises the following steps:
step 1: determination of the DNA fragment in plasma from a specific location on a chromosome
Control studies were performed on pre-morbid and healthy samples confirmed to be gestational diabetes, and peripheral blood free DNA from both samples was subjected to high throughput paired-end sequencing (alternatively, single-end sequencing).
After high-throughput paired-end sequencing of free DNA in peripheral blood, the sequences at both ends are aligned with a standard human genome sequence 37.1 (http:// www.ncbi.nlm.nih.gov/projects/genome/assembly/grc/human/data/.
The results show that although the gestational diabetic patients do not have clinical symptoms at 12 weeks of pregnancy, the distribution of serum free DNA on chromosome of the patients and healthy pregnant women is obviously different, and the abundance of the serum free DNA in the partial gene transcription initiation site region is obviously different between the gestational diabetic patients and the healthy pregnant women (as shown in figure 1).
Through a large number of research and exploration, an optimized gene combination to be detected is determined, and is shown in table 1:
TABLE 1 genes to be tested
Figure BDA0002136852300000061
In addition, the differential gene combinations shown in table 1 in the present application represent only preferred combinations under a certain reagent and instrument platform, and the present invention does not limit the inventors to predict the preferred combinations using other differential gene combinations under other instruments and reagent conditions.
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 in the sample (69479 and 57037 for the two samples, sample 1 and sample 2, 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.
Figure BDA0002136852300000062
Table 2 is an example of the calculation of abundance of DNA fragments in the transcription initiation site region of the genes to be tested in two samples:
TABLE 2
Figure BDA0002136852300000071
And step 3: calculating the disease risk according to the expression condition of the gene to be detected
The risk of onset of gestational diabetes is calculated using equation 2:
Figure BDA0002136852300000072
in the formula, x i Corrected number of DNA fragments, beta, of the transcription start region of the gene for gene i i Is the coefficient beta of 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
Figure BDA0002136852300000073
Figure BDA0002136852300000081
And calculating the Y value according to the formula 3:
logic (Y) ═ ln (Y/(1-Y)) (formula 3)
The risk threshold value P of the gestational diabetes is 0.259, and when the sample value Y is greater than the threshold value P, the sample is judged to be high-risk of the gestational diabetes; and when the sample value Y is smaller than the threshold value P, judging the sample as the low risk of gestational diabetes.
In summary, the model (prediction method) for predicting gestational diabetes based on peripheral blood free DNA of the present invention comprises three modules:
(1) carrying out high-throughput sequencing and analysis on peripheral blood free DNA of a sample to be detected:
performing high-throughput sequencing on the peripheral blood free DNA of the sample to be tested, comparing a sequencing result with a chromosome set sequence map, and calculating to obtain the quantity of DNA fragments from the transcription initiation site region of the gene to be tested 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 the prediction result of the gestational diabetes mellitus 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 group contained 126 gestational diabetes samples, 378 healthy controls;
the validation group contained 54 gestational diabetes samples and 162 healthy controls.
The procedure is as in example 1. Accuracy, sensitivity and specificity of the statistical calculation method.
2. The results show that the method model of the invention can effectively judge the gestational diabetes patients before early onset in both the training group and the verification group (Table 4 and figure 2).
TABLE 4
Figure BDA0002136852300000082
Figure BDA0002136852300000091
Wherein, the calculation result is exemplified as follows:
sample 1 (pre-onset sample with confirmed gestational diabetes):
logit(Y)＝0.957+0.565×CC2D2B–1.060×NAT10–1.070×SIPA1–0.620×ZNF565–0.805×ZNF552–0.367×WDR35+0.559×MICALL1–0.653×CTNNB1–0.529×CLOCK–0.674×BCKDHB–0.693×TGIF2LY＝2.835
Y＝0.944
the sample value is greater than the gestational diabetes threshold value P (0.259), and the sample is judged to be a gestational diabetes high-risk sample. The result is accurate.
Sample 2 (healthy sample):
logit(Y)＝0.957+0.565×CC2D2B–1.060×NAT10–1.070×SIPA1–0.620×ZNF565–0.805×ZNF552–0.367×WDR35+0.559×MICALL1–0.653×CTNNB1–0.529×CLOCK–0.674×BCKDHB–0.693×TGIF2LY＝-4.040
Y＝0.017
and (5) judging the sample as a low-risk sample of the gestational diabetes when the sample value is less than the threshold value P (0.259). 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 (6)

1. A target gene combination suitable for predicting gestational diabetes based on peripheral blood free DNA, wherein the target gene combination is CC2D2B, NAT10, SIPA1, ZNF565, ZNF552, WDR35, MICALL1, CTNNB1, CLOCK, BCKDHB, and TGIF2 LY;
the criteria for predicting gestational diabetes using the target gene combination are as follows:
(1) performing high-throughput sequencing on the peripheral blood free DNA of a sample to be tested, comparing a sequencing result with a chromosome set sequence map, and calculating to obtain the quantity of DNA fragments from a transcription initiation site region of the gene to be tested in the same sample; the gene to be detected is the target gene combination;
(2) equation 1:
Figure FDA0003803961100000011
wherein the total aligned sequence number refers to the total sequence number of sequences aligned to human genomic sequences 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);
(3) equation 2:
Figure FDA0003803961100000012
in the formula, x i The number of DNA fragments in the transcription start site region of the gene corrected for the gene i;
β i is a baseBecause of the coefficient beta of i, the specific genes and the corresponding coefficients beta thereof are respectively: the coefficient beta 0 of CC2D2B is 0.565, the coefficient beta 1 of NAT10 is-1.060, the coefficient beta 2 of SIPA1 is-1.070, the coefficient beta of ZNF565 is-0.620, the coefficient beta of ZNF552 is-0.805, the coefficient beta of WDR35 is-0.367, the coefficient beta of MICALL1 is 0.559, the coefficient beta of CTNNB1 is-0.653, the coefficient beta of CLOCK is-0.529, the coefficient beta of BCKDHB is-0.674, and the coefficient beta of TGIF2LY is-0.693;
c is a constant, and the value is 0.957;
the formula 2 is used for calculating and outputting a prediction result of the gestational diabetes mellitus of the pregnant woman to be detected; the prediction criteria are as follows:
substituting the calculation result of equation 2 into the following equation 3 to calculate the Y value:
equation 3: logic (Y) ═ ln (Y/(1-Y))
The risk threshold value P of the gestational diabetes is 0.259, and when the sample value Y is greater than the threshold value P, the sample is judged to be high-risk of the gestational diabetes; and when the sample value Y is smaller than the threshold value P, judging the sample to be low-risk of the gestational diabetes.
2. The use of the target gene combination of claim 1 as a marker for screening gestational diabetes.
3. The use of the target gene combination or the detection reagent thereof in the preparation of a gestational diabetes prediction screening product according to claim 1.
4. The model for screening and predicting the gestational diabetes based on the peripheral blood free DNA detection is characterized by comprising three modules:
(1) the module is used for carrying out high-throughput sequencing and analysis on the peripheral blood free DNA of a sample to be detected:
performing high-throughput sequencing on the peripheral blood free DNA of a sample to be tested, comparing a sequencing result with a chromosome set sequence map, and calculating to obtain the quantity of DNA fragments from a transcription initiation site region of the gene to be tested in the same sample; the gene to be tested is the target gene combination of claim 1;
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:
Figure FDA0003803961100000021
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);
(3) equation 2:
Figure FDA0003803961100000022
in the formula, x i The number of DNA fragments in the transcription start site region of the gene corrected for the gene i;
β i the coefficient beta of the gene i is shown as follows, and the specific genes and the corresponding coefficients beta are respectively: the coefficient beta 0 of CC2D2B is 0.565, the coefficient beta 1 of NAT10 is-1.060, the coefficient beta 2 of SIPA1 is-1.070, the coefficient beta of ZNF565 is-0.620, the coefficient beta of ZNF552 is-0.805, the coefficient beta of WDR35 is-0.367, the coefficient beta of MICALL1 is 0.559, the coefficient beta of CTNNB1 is-0.653, the coefficient beta of CLOCK is-0.529, the coefficient beta of BCKDHB is-0.674, and the coefficient beta of TGIF2LY is-0.693;
c is a constant, and the value is 0.957;
the formula 2 is used for calculating and outputting the prediction result of the gestational diabetes of the pregnant woman to be detected; the prediction criteria are as follows:
substituting the calculation result of equation 2 into the following equation 3 to calculate the Y value:
equation 3: logic (Y) ═ ln (Y/(1-Y))
The risk threshold value P of the gestational diabetes is 0.259, and when the sample value Y is greater than the threshold value P, the sample is judged to be at high risk of the gestational diabetes; and when the sample value Y is smaller than the threshold value P, judging the sample to be low-risk of the gestational diabetes.
5. The screening and predicting model of claim 4, wherein the transcription initiation site region of the gene in formula 1 is from 1000bp upstream to 1000bp downstream of the gene.
6. The screening prediction model of claim 4, wherein the sequencing is single-ended sequencing or double-ended sequencing.
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