WO2023102840A1 - Utilisation d'un marqueur génétique pour prédire le risque de prééclampsie chez la femme enceinte - Google Patents

Utilisation d'un marqueur génétique pour prédire le risque de prééclampsie chez la femme enceinte Download PDF

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WO2023102840A1
WO2023102840A1 PCT/CN2021/136842 CN2021136842W WO2023102840A1 WO 2023102840 A1 WO2023102840 A1 WO 2023102840A1 CN 2021136842 W CN2021136842 W CN 2021136842W WO 2023102840 A1 WO2023102840 A1 WO 2023102840A1
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preeclampsia
related diseases
risk
pregnant women
pregnant woman
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PCT/CN2021/136842
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English (en)
Chinese (zh)
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徐晨明
王琳
陈松长
王文婧
黄荷凤
徐讯
孙井花
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复旦大学附属妇产科医院
深圳华大基因股份有限公司
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Priority to CN202180102282.4A priority Critical patent/CN117940583A/zh
Priority to PCT/CN2021/136842 priority patent/WO2023102840A1/fr
Publication of WO2023102840A1 publication Critical patent/WO2023102840A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis

Definitions

  • the present invention relates to the field of preeclampsia diseases, in particular to the application of gene markers in predicting the risk of preeclampsia or related diseases in pregnant women.
  • Preeclampsia is a pregnancy disorder associated with new-onset hypertension in pregnancy, affecting 3–5% of pregnancies [1] .
  • preeclampsia is defined as systolic blood pressure ⁇ 140mmHg and/or diastolic blood pressure ⁇ 90mmHg in pregnant women after 20 weeks of gestation, accompanied by any one of the following: urine protein quantity ⁇ 0.3g/24h, or urine protein/ Creatinine ratio ⁇ 0.3, or random urine protein ⁇ (+) (examination method when protein quantification is unconditional); no proteinuria but accompanied by any one of the following organs or system involvement: heart, lung, liver, kidney and other important organs, Or abnormal changes in the blood system, digestive system, nervous system, placenta-fetus is affected, etc.
  • preeclampsia is divided into four types according to the time of diagnosis and delivery: early-onset, late-onset preeclampsia, premature, and
  • preeclampsia it is known that some risk factors for preeclampsia are used, such as advanced age; family history and history of preeclampsia; pregnancy interval; assisted reproductive technology; The lack of known risk factors does not mean that preeclampsia will not occur, and it is not accurate to predict the high-risk population of preeclampsia through maternal risk factors.
  • researchers have conducted some transcriptomic studies on the mechanism of placenta [8] , plasma [9] , decidua [10] , exosomes [11] , amniotic fluid [12] preeclampsia, and reported some Potential biomarkers associated with early prediction of preeclampsia.
  • Placental cell dysfunction can lead to serious pregnancy complications, but invasive placental tissue sampling will cause certain unsafety for pregnant women and fetuses, and analysis of free circulating RNA in maternal blood can be performed non-invasively
  • the discovery of abnormal function of extravillous trophoblast cells in the preeclamptic placenta may help in the early prediction of preeclampsia in pregnant women before the onset of preeclampsia symptoms.
  • the patent "circulating mRNA as a diagnostic marker for pregnancy-related diseases" (authorization number: 100379882) of Lo Yuk-ming of the Chinese University of Hong Kong authorized in 2008 proposes to diagnose, A method and a kit for monitoring or predicting the disease state of preeclampsia, fetal chromosomal aneuploidy and premature birth in pregnant women, and detecting pregnancy in women, said mRNA species comprising encoding human chorionic gonadotropin beta subunit (hCG-beta ), human corticotropin-releasing hormone (hCRH), human placental lactogen (hPL), KiSS-1 transfer suppressor (KISS1), tissue factor pathway inhibitor 2 (TPFI2), placenta-specific 1 (PLAC1), or glycerol Aldehyde-3-phosphate dehydrogenase (GAPDH) mRNA.
  • hCG-beta human corticotropin-releasing hormone
  • hPL human placental lactogen
  • the first step of the method is to quantify the level of one or more specific mRNA species in the blood of said pregnant woman.
  • the mRNA may encode hCG- ⁇ , hCRH, hPL, KISS1, TPFI2, PLAC1 or GAPDH.
  • the second step is to compare the mRNA level obtained in the first step with a standard control representing the level of mRNA encoding the same protein in the blood of normal non-preeclampsia women. An increase or decrease in said mRNA level is indicative of the presence of preeclampsia or an increased risk of developing said disease state.
  • peripheral blood samples of pregnant women were collected, RNA was extracted, real-time quantitative RT-PCR was performed, and statistical analysis was performed using Sigma Stat 2.03 software (SPSS).
  • SPSS Sigma Stat 2.03 software
  • This technology is based on the ratio of sFlt-1/P1GF or endoglin/P1GF to predict preeclampsia. This method can only predict whether there is a risk of preeclampsia in a short period of time, and has certain limitations.
  • the patent for circulating RNA markers specific to preeclampsia submitted by ILLUMINA INC in 2019 (application number: 201980002993.7) relates to a method for detecting preeclampsia and/or determining an increased risk of preeclampsia in pregnant women.
  • the method includes identifying a plurality of circulating RNA (C-RNA) molecules in a biological sample obtained from said pregnant woman.
  • C-RNA circulating RNA
  • the patent is based on the analysis of the confirmed population, and these C-RNAs are used to build models for classification, not prediction.
  • the markers selected by this technology come from the research on cases diagnosed with preeclampsia, which cannot accurately predict preeclampsia before the onset of symptoms.
  • the main purpose of the present invention is to provide the application of gene markers in predicting the risk of preeclampsia or related diseases in pregnant women, so as to provide a high specificity and high sensitivity prediction scheme for the risk of preeclampsia or related diseases in pregnant women.
  • a gene marker for detecting whether a pregnant woman suffers from preeclampsia or related diseases or predicting the risk or prognostic effect of pregnant women suffering from preeclampsia or related diseases Or a combination thereof including one or more of the following genes: EVI2B, MARCH7, MED21, NEMF, PAAF1, SNX14, SRSF7, TMEM245, TRIB2, ZNF224.
  • gene markers or their combination also include one or more of the following genes: ATF6, ATP6AP2, FOS, RASA2.
  • the reagents include the gene markers of the first aspect or their combinations or their expression products Biomolecules that specifically hybridize;
  • the biomolecules include one or more selected from primers, probes and antibodies;
  • the reagents also include related reagents for preparing high-throughput sequencing libraries from the gene markers or combinations thereof in the first aspect above.
  • a method for detecting whether a pregnant woman suffers from preeclampsia or a related disease or predicting the risk or prognosis of a pregnant woman suffering from preeclampsia or a related disease comprising:
  • Step S1 providing biological samples from pregnant women
  • Step S2 determining the expression profile of the above-mentioned gene markers or combinations thereof in the first aspect in the biological sample
  • Step S3 Based on the expression profiles of gene markers or combinations thereof, identifying whether the pregnant woman is suffering from preeclampsia or related diseases or the risk of suffering from preeclampsia or related diseases or the prognostic effect of the preeclampsia or related diseases in said pregnant women .
  • step S3 identifying whether the pregnant woman suffers from preeclampsia or related diseases or the risk of suffering from preeclampsia or related diseases or the prognostic effect of preeclampsia or related diseases in pregnant women is by using pregnant women's preeclampsia or The related disease risk prediction model is implemented, and the preeclampsia or related disease risk prediction model of pregnant women is implemented by using the genetic markers in biological samples from pregnant women who have been diagnosed with preeclampsia or related diseases and healthy control pregnant women Expression profiles of substances or combinations thereof are trained to generate a computer.
  • training computer is implemented through machine learning methods
  • the machine learning method comprises one or more of the following: generalized linear model, gradient boosting machine, random forest, support vector machine;
  • the machine learning method automatically calculates the risk score
  • a risk score greater than a threshold indicates that the pregnant woman suffers from preeclampsia or a related disease or has a risk of suffering from preeclampsia or a related disease or has a poor prognosis;
  • the threshold is 0.5;
  • the biological sample is one or more of the following: plasma, serum, whole blood, urine, amniotic fluid; preferably, the biological sample is collected at the 11th to 25th week of pregnancy of the pregnant woman.
  • step S2 the expression profile of the gene markers or their combination is determined by quantitatively analyzing the extracellular free RNA in the biological sample;
  • the extracellular free RNA in the biological sample is quantitatively analyzed by high-throughput sequencing or RT-PCR;
  • a high-throughput sequencing method is used to quantitatively analyze the extracellular free RNA in the biological sample.
  • a kit comprising the gene markers of the above-mentioned first aspect of the present invention or a combination thereof, and/or the reagents of the above-mentioned second aspect of the present invention.
  • the gene marker of the above-mentioned first aspect of the present invention or its combination and/or the reagent of the second aspect in the preparation of a kit for detecting whether a pregnant woman suffers from eclampsia Preeclampsia or related diseases or predicting the risk or prognosis of pregnant women with preeclampsia or related diseases.
  • a device for detecting whether a pregnant woman suffers from preeclampsia or related diseases or predicting the risk or prognostic effect of pregnant women suffering from preeclampsia or related diseases is provided.
  • Preeclampsia or related disease risk prediction model said prediction model is by using the expression of the gene markers or the combination thereof in the above-mentioned first aspect of the present invention in biological samples derived from pregnant women diagnosed with preeclampsia or related diseases The spectrum is generated by training the computer.
  • a method for constructing a model for detecting whether a pregnant woman suffers from preeclampsia or related diseases or predicting the risk or prognostic effect of a pregnant woman suffering from preeclampsia or related diseases includes the step of detecting a differentially expressed substance between biological samples derived from a group of pregnant women with preeclampsia or related diseases and a group of pregnant women without preeclampsia or related diseases, wherein the differentially expressed substance includes the above-mentioned first aspect of the present invention Gene markers or combinations thereof.
  • the biological sample is one or more of the following: plasma, serum, whole blood, urine, amniotic fluid; preferably, the biological sample is collected during the 11th to 25th week of gestation of a pregnant woman.
  • a computer-readable storage medium includes a stored program, wherein, when the program is running, the device where the storage medium is located is controlled to execute the method for detecting pregnant women in the fourth aspect of the present invention Whether suffering from preeclampsia or related diseases or predicting the risk or prognostic effect of pregnant women suffering from preeclampsia or related diseases or the seventh aspect of the present invention for detecting whether pregnant women have preeclampsia or related diseases or predicting pregnant women Methods for modeling the risk or prognostic effect of having preeclampsia or related disorders.
  • a processor the processor is used to run a program, wherein, when the program is running, the fourth aspect of the present invention is executed for detecting whether a pregnant woman suffers from preeclampsia or related diseases or The method for predicting the risk or prognosis of a pregnant woman suffering from preeclampsia or related diseases, or the method for detecting whether a pregnant woman has preeclampsia or related diseases or predicting the risk or prognosis of pregnant women suffering from preeclampsia or related diseases according to the seventh aspect of the present invention How to model the effect.
  • the use of genetic markers as targets for screening drugs for the treatment or prevention of preeclampsia or related diseases in pregnant women wherein the genetic markers include the genetic markers of the first aspect of the present invention substances or combinations thereof.
  • the eleventh aspect of the present invention there is provided a use of a gene marker in detecting whether a pregnant woman suffers from preeclampsia or related diseases or predicting the risk or prognostic effect of a pregnant woman suffering from preeclampsia or related diseases, wherein the gene
  • the markers include the gene markers of the first aspect of the present invention or a combination thereof.
  • a drug for treating or preventing preeclampsia or related diseases in pregnant women characterized in that the drug can increase the expression of PAAF1 in the pregnant woman; or the drug It can reduce the expression of one or more genes among ATF6, ATP6AP2, EVI2B, FOS, MARCH7, MED21, NEMF, RASA2, SNX14, SRSF7, TMEM245, TRIB2 and ZNF224 in the pregnant woman.
  • the present invention aims at the problem of low prediction accuracy of the risk of preeclampsia or related diseases in pregnant women in the prior art, and proposes to use the gene markers of the application as detection targets.
  • the association of related diseases has achieved high specificity and high sensitivity risk prediction for preeclampsia or related diseases in pregnant women.
  • Fig. 1 shows the AUC curve graph predicted by the model in a preferred embodiment of the present invention.
  • this application compares the gene expression differences between the preeclampsia or related disease group and the non-preeclampsia or related disease group in the first and second trimesters (before the diagnosis of the disease), and combines machine learning algorithms to screen Gene markers for predicting the risk of preeclampsia or related diseases have been developed, and the high accuracy prediction of the risk of preeclampsia or related diseases during pregnancy has been realized by constructing a model.
  • the gene markers and prediction model of the present invention have high specificity and sensitivity for the prediction of the risk of preeclampsia or related diseases, and can detect the risk of preeclampsia or related diseases in pregnant women with high accuracy in the first trimester, realizing Intervene early.
  • preeclampsia-related diseases include systolic blood pressure ⁇ 140 mmHg and/or diastolic blood pressure ⁇ 90 mmHg in pregnant women after 20 weeks of gestation, accompanied by any one of the following: urine protein quantity ⁇ 0.3 g/24h, or Urinary protein/creatinine ratio ⁇ 0.3, or random urinary protein ⁇ (+) (examination method when protein quantification is unconditional); no proteinuria but with involvement of any of the following organs or systems: heart, lung, liver, kidney, etc. Vital organs, or abnormal changes in the blood system, digestive system, nervous system, placenta-fetus are affected, etc.
  • the FIGO guidelines divide preeclampsia into four types according to the time of diagnosis and delivery: early-onset, late-onset preeclampsia, premature, and term preeclampsia.
  • a gene marker or a combination thereof for detecting whether a pregnant woman suffers from preeclampsia or related diseases or predicting the risk or prognosis effect of pregnant women suffering from preeclampsia or related diseases is provided, which includes One or more of the following genes: EVI2B, MARCH7, MED21, NEMF, PAAF1, SNX14, SRSF7, TMEM245, TRIB2, ZNF224.
  • This application is the first to discover that gene markers in biological samples of pregnant women (including one or more of EVI2B, MARCH7, MED21, NEMF, PAAF1, SNX14, SRSF7, TMEM245, TRIB2, ZNF224) are associated with preeclampsia or related diseases in pregnant women
  • Significant correlation therefore, can be used as a marker for detecting whether a pregnant woman suffers from preeclampsia or related diseases or predicting pregnant women with preeclampsia or related diseases.
  • medical means can be used to intervene the pregnant woman with the disease, and the prognostic effect of preeclampsia or related diseases can be monitored through the expression profile of these gene markers or their combination in the present invention.
  • the above-mentioned gene markers or combinations thereof further include one or more of ATF6, ATP6AP2, FOS, and RASA2.
  • each of the genes listed above can be used alone or in combination.
  • each genetic marker can be used alone, or any two or more of them can be used in combination.
  • the combination of all the following genes can also be used as gene markers: EVI2B, MARCH7, MED21, NEMF, PAAF1, SNX14, SRSF7, TMEM245, TRIB2, ZNF224, ATF6, ATP6AP2, FOS, RASA2, so as to achieve preeclampsia or Associated disease risk prediction.
  • the present invention provides a reagent for detecting the above-mentioned gene markers or their combinations
  • the reagents include specific hybridization with the above-mentioned gene markers or their combinations or their expression products Biomolecules; preferably, the biomolecules include one or more selected from primers, probes and antibodies; more preferably, the reagents also include preparing the RNA of the above-mentioned gene markers or a combination thereof into a high-throughput Related reagents for sequencing libraries.
  • the detection reagents for gene markers or combinations thereof preferably include probes and/or primers for detecting gene markers or combinations thereof, specifically one or more probes and/or primers that specifically bind (hybridize) to gene markers Or one or more primers that specifically amplify gene markers.
  • the detection reagent of the present invention preferably includes a reagent for converting RNA in a biological sample into a library of cDNA fragments.
  • a method for detecting whether a pregnant woman suffers from preeclampsia or related diseases or predicting the risk or prognosis of pregnant women with preeclampsia or related diseases comprising:
  • Step S1 providing biological samples from pregnant women
  • Step S2 determining the expression profile of the above-mentioned gene markers or combinations thereof of the present invention in the biological sample
  • Step S3 Based on the expression profiles of gene markers or combinations thereof, identifying whether the pregnant woman is suffering from preeclampsia or related diseases or the risk of suffering from preeclampsia or related diseases or the prognostic effect of the preeclampsia or related diseases in said pregnant women .
  • the identification in step S3 can be carried out in a conventional way of comparing with healthy controls, or can be carried out by using a computer prediction model.
  • the identification process of the above step S3 may include the step of comparing the expression profile with a reference data set or reference value; preferably, the reference data set or reference value includes biological samples derived from healthy control pregnant women.
  • the reference data set or reference value in the present invention refers to the expression profile of each gene marker obtained by operating the samples of healthy control individuals, which is used as a reference or control for the expression profile of the above gene marker.
  • the reference data set or reference value in the present invention refers to the reference value or normal value of healthy controls. Those skilled in the art know that when the sample volume is large enough, detection and calculation methods known in the art can be used to obtain the normal value (absolute value) range of each gene marker in the sample.
  • the absolute value of the gene marker level in the sample can be directly compared with the reference value, so as to assess the risk of disease and diagnose or early diagnose preeclampsia or related diseases.
  • a reduction in PAAF1 when compared to a reference data set or reference value indicates that said pregnant woman has preeclampsia or a related disorder or is at risk of having preeclampsia or a related disorder or has a poor prognosis
  • the increase of ATF6, ATP6AP2, EVI2B, FOS, MARCH7, MED21, NEMF, RASA2, SNX14, SRSF7, TMEM245, TRIB2, ZNF224 indicates that the pregnant woman suffers from preeclampsia or related diseases or has preeclampsia or related diseases risk or poor prognosis.
  • step S3 it is identified whether the pregnant woman is suffering from preeclampsia or related diseases or is at risk of suffering from preeclampsia or related diseases or the pregnant woman is
  • the prognostic effect of preeclampsia or related diseases is implemented by using a risk prediction model for preeclampsia or related diseases in pregnant women, which is obtained by using The expression profiles of the gene markers or combinations thereof in the biological samples of pregnant women with related diseases and healthy control pregnant women are generated by training a computer.
  • a training set and a verification set need to be used.
  • the training set and validation set have meanings known in the art.
  • the training set refers to a data set comprising a certain number of samples of gene marker expression profiles in diagnosed patients with preeclampsia or related diseases and healthy control samples.
  • the verification set is an independent data set used to test the performance of the training set and the effect of the model.
  • Machine learning generally refers to algorithms that give computers the ability to learn without being explicitly programmed, including algorithms that learn from data and make predictions about that data.
  • the machine learning methods used in the present invention may include random forest, least absolute shrinkage and selection operator logistic regression, regularized logistic regression, XGBoost, decision tree learning, artificial neural network, deep neural network, support vector machine, rule-based machine Learning, Generalized Linear Models, Gradient Boosting Machines, etc.
  • Preferred machine learning methods include one or more of the following: generalized linear models, gradient boosting machines, random forests, and support vector machines.
  • the risk score automatically calculated by the model can be used to evaluate and predict the risk or prognosis of preeclampsia or related diseases.
  • a risk score greater than a threshold indicates that the pregnant woman has or is at risk of having preeclampsia or a related disorder or has a poor prognosis.
  • the threshold is 0.5.
  • the risk score is greater than 0.5, it is considered that the pregnant woman suffers from preeclampsia or related diseases or is at risk of suffering from preeclampsia or related diseases (or the risk of preeclampsia or related diseases is high) or the prognosis is poor, if the risk A score of less than 0.5 indicates that the pregnant woman does not have preeclampsia or a related disorder or is not at risk of developing preeclampsia or a related disorder (or is at low risk of preeclampsia or a related disorder) or has a good prognosis.
  • the biological sample derived from a pregnant woman can be one or more of the following: plasma, serum, whole blood, urine, amniotic fluid. Plasma, serum or whole blood derived from pregnant women are preferably used for the detection and identification steps of the present invention.
  • the biological sample is most preferably plasma, for example, peripheral blood can be obtained from a pregnant woman and subjected to plasma separation to obtain a plasma biological sample to be used.
  • plasma, serum or whole blood other bodily fluid samples such as urine, amniotic fluid, etc. can also be used.
  • Biological samples can be obtained by conventional methods in the art.
  • the collection of biological samples can be carried out at the 11th to 25th gestational week (preferably 13 to 25th gestational week) of the pregnant woman.
  • the application population of the present invention does not need to distinguish whether pregnant women are high-risk or not, and can be applied to asymptomatic general pregnant populations.
  • the present invention can realize the prediction of preeclampsia or related diseases in the first trimester.
  • the present invention can realize prediction before symptoms appear (up to 18 weeks in advance). Therefore, the method of the present invention is applicable to a wider population and has more clinical applicability.
  • the expression profile of the gene markers or their combination is determined by quantitatively analyzing the free extracellular RNA (cfRNA) in the biological sample; preferably, high-throughput sequencing or RT Quantitative analysis of free extracellular RNA in biological samples by PCR; more preferably, quantitative analysis of free extracellular RNA in biological samples by high-throughput sequencing (such as next-generation sequencing).
  • cfRNA free extracellular RNA
  • the free extracellular RNA in the biological sample can be extracted by a method or a kit commonly used in the art or a combination of the two.
  • cell-free extracellular RNA can be isolated from plasma biological samples using TRIzol LS standard RNA extraction procedures.
  • the quantitative analysis of extracellular free RNA includes the method of building a library of extracellular free RNA, which can simultaneously capture long and short fragments of RNA in plasma, providing more information for prediction. Characteristics. Sequencing of cell-free extracellular RNA can be performed using whole-transcriptome sequencing [13] , using next-generation sequencing to sequence cell-free extracellular RNA in biological samples (preferably plasma) from pregnant women. RT-PCR method can also be used for analysis. The expression profile of extracellular free RNA can also be quantitatively analyzed by other methods known in the art such as qPCR.
  • the quantitative analysis of extracellular free RNA also includes the step of quality control of the original extracellular free RNA sequencing data, preferably including cutting adapters, removing low-quality reads, removing ⁇ 17bp reads, and removing rRNA sequence, value RNA and Y RNA sequence, the remaining reads are first compared to the human transcriptome (the order is miRNA, tRNA and piRNA, mRNA and lncRNA, and finally other RNAs), and then the remaining reads are compared to the human genome.
  • the human transcriptome the order is miRNA, tRNA and piRNA, mRNA and lncRNA, and finally other RNAs
  • a prediction kit for the gene markers of the present invention or a combination thereof can be prepared according to the existing kit preparation principles. Detection probes, chips, etc. for predicting the risk of preeclampsia or related diseases in pregnant women can also be prepared for these gene markers.
  • the present invention achieves high specificity and high sensitivity for preeclampsia or related diseases in pregnant women by using specific gene markers as detection targets, based on the correlation between the expression profile of gene markers and pregnant women's preeclampsia or related diseases risk prediction.
  • the present invention provides a kit, which may include the above-mentioned gene markers of the present invention or a combination thereof.
  • the present invention provides a kit for predicting the risk or prognosis of preeclampsia or related diseases in pregnant women.
  • the kit includes detection reagents for genetic markers, and the genetic markers include the following: One or more genes: EVI2B, MARCH7, MED21, NEMF, PAAF1, SNX14, SRSF7, TMEM245, TRIB2, ZNF224.
  • the kit is used for prediction, which makes the prediction more convenient, simple and fast.
  • the above gene markers also include one or more of the following genes: ATF6, ATP6AP2, FOS, RASA2.
  • the detection reagents for gene markers may include probes and/or primers for detecting gene markers, specifically one or more probes and/or primers that specifically bind (hybridize) to gene markers One or more primers that specifically amplify a genetic marker.
  • RNA sequencing generally includes a reverse transcription step to generate cDNA molecules for sequencing, when RNA sequencing is used, the kit of the present invention may also include reagents for converting RNA in a biological sample into a library of cDNA fragments.
  • the application of the above-mentioned gene markers or combinations thereof and/or the above-mentioned detection reagents in the preparation of a kit for detecting whether a pregnant woman suffers from preeclampsia or related diseases or To predict the risk or prognostic effect of pregnant women with preeclampsia or related disorders.
  • the present invention provides a device for detecting whether a pregnant woman suffers from preeclampsia or related diseases or predicting the risk or prognosis of pregnant women suffering from preeclampsia or related diseases, the device There is a built-in risk prediction model for pregnant women with preeclampsia or related diseases, which is developed by using the expression profiles of the above gene markers or their combinations in biological samples from pregnant women who have been diagnosed with preeclampsia or related diseases. produce.
  • the prediction model is a generalized linear model, a gradient boosting machine, a random forest or a support vector machine model.
  • a method for constructing a model for detecting whether a pregnant woman suffers from preeclampsia or related diseases or predicting the risk or prognosis of a pregnant woman suffering from preeclampsia or related diseases includes the step of detecting the differentially expressed substances between the biological samples derived from the population of pregnant women with preeclampsia or related diseases and the population of pregnant women without preeclampsia or related diseases, wherein the differentially expressed substances include the above-mentioned gene markers of the present invention substances or combinations thereof.
  • the construction method includes: detecting the differential expression of gene markers in biological samples derived from a group of pregnant women with preeclampsia or related diseases and a group of pregnant women without preeclampsia or related diseases; Part of the group of pregnant women with preeclampsia or related diseases and part of the group of pregnant women without preeclampsia or related diseases are used as the training set, and the best gene markers are screened out using the training set; in the training set, the best gene markers are used to train the computer,
  • the risk prediction model for pregnant women with preeclampsia or related diseases is obtained; the remaining part of the group of pregnant women with preeclampsia or related diseases and the remaining group of pregnant women without preeclampsia or related diseases are used as a verification set, and the verification set is used to verify the risk of pregnant women.
  • the best genetic markers include one or more of the following genes: EVI2B, MARCH7, MED21, NEMF, PAAF1, SNX14, SRSF7, TMEM245, TRIB2, ZNF224.
  • the above optimal gene markers also include one or more of the following genes: ATF6, ATP6AP2, FOS, RASA2.
  • the biological sample used in the model construction method of the present invention is preferably one or more of the following: plasma, serum, whole blood, urine, amniotic fluid; particularly preferably plasma, serum, whole blood; most preferably plasma. Also, biological samples can be collected from the 11th to 25th week of pregnancy.
  • a machine learning method can be used, preferably the machine learning method includes one or more of the following: generalized linear model, gradient boosting machine, random forest and support vector machine.
  • the training set and the verification set can be split according to a certain ratio according to needs, preferably, all pregnant women with preeclampsia or related diseases are randomly split into the training set according to the ratio of 2:1
  • the verification set all pregnant women without preeclampsia or related diseases were randomly split into a training set and a verification set according to the ratio of 2:1.
  • the screening of the best gene markers is done in the training set, and the validation set is used to test the prediction effect of the best gene markers and models.
  • the candidate gene markers are preliminarily screened by comparing the difference in gene expression profile between a group of pregnant women with preeclampsia or related diseases and a group of pregnant women without preeclampsia or related diseases.
  • This step can use the DESeq2 package (R package) implementation.
  • R package DESeq2 package
  • the difference and stability of the average expression level in the two populations will be considered in this step (preferably the average expression level difference is greater than 1, and the p value is less than 0.001), and finally the genes that pass the screening become candidate gene markers things.
  • the two models can be used to screen according to the importance of features, and the gene markers with higher frequency can be selected as the best gene markers.
  • two models of generalized linear and random forest are used to carry out 7-fold cross-validation, and a plurality (for example, 30) of the most important molecules are screened out. This step can be iterated 100 times, and then the molecules with a frequency higher than 50% are selected The gene marker with the highest ranking of importance among them was regarded as the best gene marker.
  • each method uses 7-fold cross-validation to select the optimal parameters for prediction model construction.
  • the resulting model can be validated against the validation set.
  • the model with the best effect can be selected and the feature importance can be calculated through the effect verification of the verification set.
  • the prediction model constructed by the method of the present invention can be used in the first trimester and up to 18 weeks in advance, and only need to collect peripheral blood from pregnant women to use non-invasive methods to predict the risk of preeclampsia or related diseases, predict The effect (sensitivity, specificity) is higher than the state of the art.
  • the present application can be realized by means of software plus necessary detection instruments and other hardware devices.
  • the data processing part in the technical solution of the present application can be embodied in the form of software products, and the computer software products can be stored in storage media, such as ROM/RAM, magnetic disks, optical disks, etc., including several instructions.
  • a computer device which may be a personal computer, a server, or a network device, etc. executes the methods of various embodiments or some parts of the embodiments of the present application.
  • the application can be used in numerous general purpose or special purpose computing system environments or configurations. Examples: personal computers, server computers, handheld or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, including A distributed computing environment for any of the above systems or devices, etc.
  • modules or steps of the above-mentioned application can be implemented on general-purpose computing devices, and they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices , alternatively, they can be implemented with executable program codes of the computing device, thus, they can be stored in the storage device and executed by the computing device, or they can be made into individual integrated circuit modules respectively, or the Multiple modules or steps are implemented as a single integrated circuit module.
  • the present application is not limited to any specific combination of hardware and software.
  • a computer-readable storage medium includes a stored program, wherein, when the program is running, the device where the storage medium is located is controlled to execute the above-mentioned method for detecting whether a pregnant woman suffers from preeclampsia. or related diseases or methods for predicting the risk or prognosis of pregnant women suffering from preeclampsia or related diseases or performing the above methods for detecting whether pregnant women have preeclampsia or related diseases or predicting the risk of pregnant women suffering from preeclampsia or related diseases Or the construction method of the model of prognostic effect.
  • a processor is provided, and the processor is used to run a program, wherein, when the program is running, the above-mentioned method for detecting whether a pregnant woman suffers from preeclampsia or related diseases or predicting that a pregnant woman has preeclampsia is performed.
  • the present invention also provides the use of genetic markers as targets for screening drugs for the treatment or prevention of preeclampsia or related diseases in pregnant women, wherein the genetic markers include the genetic markers described above in the present invention substances or combinations thereof.
  • the present invention also provides the use of genetic markers in detecting whether pregnant women suffer from preeclampsia or related diseases or predicting the risk or prognosis of pregnant women suffering from preeclampsia or related diseases, wherein the genetic markers include the above The gene markers or a combination thereof.
  • the present invention also provides a drug for treating or preventing preeclampsia or related diseases in pregnant women, characterized in that the drug can increase the expression of PAAF1 in the pregnant woman; or the drug can increase the expression of PAAF1 in the pregnant woman.
  • the peripheral blood of 64 cases of singleton pregnant women was obtained from the hospital, and the blood was collected from 13 to 25 weeks of gestation. There were 31 and 33 pregnant women with preeclampsia and non-preeclampsia respectively (see Table 1 for relevant data of pregnant women). In the preeclampsia group, the gestational age difference from blood collection to preeclampsia diagnosis was 18 weeks. All blood samples were immediately stored at 4°C and plasma separation was performed within 8 hours. Plasma was separated by a two-step centrifugation method, centrifuged at 1,600g for 10 minutes at 4°C, and then centrifuged at 12,000g for 10 minutes. Immediately after separation, the plasma was stored at -80°C pending further processing.
  • Trizol LS Add Trizol LS to the plasma and vortex immediately to mix.
  • the subsequent cfRNA extraction steps are performed using the standard RNA extraction method of TRIzol LS.
  • Sequencing of cfRNA utilized whole-transcriptome sequencing of preeclampsia and non-preeclampsia plasma samples using next-generation sequencing.
  • RNA and Y RNA sequences Quality control was performed on the original cfRNA sequencing data, including cutting adapters, removing low-quality reads, removing reads ⁇ 17bp in length, removing rRNA sequences, value RNA and Y RNA sequences. Align the remaining reads to the human transcriptome (in the order of miRNA, tRNA and piRNA, mRNA and lncRNA, and finally other RNAs), and then align the remaining reads to the human genome.
  • the expression level of long RNA is corrected to TPM, the formula is as follows:
  • TPM (Ni/Li)*1000000/(sum(N1/L1+N2/L2+N3/L3+...+Nn/Ln))
  • 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 length of all (n) genes The sum of values after normalization.
  • the pregnant women with preeclampsia and the pregnant women without preeclampsia were randomly split into a training set and a validation set at a ratio of 2:1.
  • the training set contained 21 samples of preeclampsia and 23 samples of non-preeclampsia.
  • the validation set Contains 10 preeclamptic samples and 10 non-preeclamptic samples.
  • the screening of gene markers is completed in the training set, and the verification set is used to test the prediction effect of gene markers and models. Please refer to Table 1 for the relevant data of the pregnant women group.
  • Table 1 Relevant data of the preeclampsia pregnant women group (case) and non-preeclampsia pregnant women group (control) in embodiment 1
  • Candidate gene markers were initially screened by comparing the expression profile differences between the preeclampsia and non-preeclampsia groups, and this step was implemented using the DESeq2 package (R package). For each gene, the difference and stability of the average expression level in the two groups are considered in this step (the average expression level difference is greater than 1, and the p value is less than 0.001), and finally the genes that pass the screening become candidate gene markers.
  • the generalized linear model and random forest were used to screen according to the importance of features, and the gene markers with higher frequency were selected as the best gene markers.
  • the specific sequence information of the above gene markers can be obtained according to the sequence numbers in Genbank.
  • “Up” indicates that the expression level of the corresponding gene in pregnant women with preeclampsia or related diseases is increased compared with healthy controls
  • “Down” indicates that the expression level of the corresponding gene in pregnant women with preeclampsia or related diseases is similar. decreased compared to healthy controls.
  • Example 2 Based on the 14 best gene markers finally screened out in Example 1, they were randomly combined and single gene markers EVI2B, MARCH7, MED21, NEMF, PAAF1, SNX14, SRSF7, TMEM245, TRIB2, ZNF224, The verification of the prediction effect is carried out in the above verification set. See Table 3 for gene markers or their combinations and their corresponding predictive effects.
  • Table 3 Model results of genetic markers or their combinations in the prediction of preeclampsia
  • the above-mentioned embodiment of the present invention has achieved the following technical effects: using the combination of multiple mRNA gene markers of the present invention in plasma, combined with a machine learning model, can predict preeclampsia up to 18 weeks earlier.
  • the present invention can predict the risk of preeclampsia in a non-invasive way only by taking peripheral blood from pregnant women.
  • the gene markers of the present invention can be used alone or in combination. When used alone, the predictive sensitivity and specificity of the gene markers of the present invention can reach at least 40% and up to 80%, respectively, which is higher than the predictive effect of preeclampsia using the gene markers alone in the prior art.
  • the gene markers of the present invention can achieve more than 70% prediction sensitivity and prediction specificity, the area under the receiver operating characteristic curve (AUC) reaches more than 0.92 in the training set, and more than 0.82 in the verification set, are higher than the state of the art.
  • the sensitivity of prediction can reach 80%, and specificity can reach 100%, and the area under the receiver operating characteristic curve (AUC) reaches more than 0.98 in the training set,
  • the validation set reaches above 0.93, which is much higher than the state of the art.
  • the method of the present invention can be applied to asymptomatic general pregnant women groups, regardless of whether high-risk or not, and can be predicted before symptoms appear, and the applicable population is wider, and it has more clinical applicability.
  • the prediction model of the present invention has relatively high accuracy, and is suitable for early prediction of preeclampsia in pregnant women, so as to achieve early intervention.

Abstract

La présente invention concerne l'utilisation d'un marqueur génétique pour prédire le risque de prééclampsie chez une femme enceinte. La présente invention fournit un marqueur génétique ou une combinaison de ceux-ci pour détecter si une femme enceinte souffre de prééclampsie ou de maladies apparentées ou pour prédire le risque qu'une femme enceinte souffre de prééclampsie ou de maladies apparentées. La présente invention concerne également un réactif permettant de détecter un marqueur génétique ou une combinaison de ceux-ci, ainsi qu'un procédé, un kit et un dispositif permettant de détecter si une femme enceinte souffre de prééclampsie ou de maladies apparentées ou de prédire le risque qu'une femme enceinte souffre de prééclampsie ou de maladies apparentées, et fournit en outre un procédé de construction d'un modèle utilisé pour détecter si une femme enceinte souffre de prééclampsie ou de maladies apparentées ou pour prédire le risque qu'une femme enceinte souffre de prééclampsie ou de maladies apparentées. Selon la présente invention, la corrélation entre un profil d'expression du marqueur génique et la prééclampsie ou les maladies apparentées chez la femme enceinte permet d'obtenir une prédiction de risque très spécifique et très sensible pour la prééclampsie ou les maladies apparentées chez la femme enceinte.
PCT/CN2021/136842 2021-12-09 2021-12-09 Utilisation d'un marqueur génétique pour prédire le risque de prééclampsie chez la femme enceinte WO2023102840A1 (fr)

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