WO2020215902A1 - 一种判断子宫内膜容受性的方法及其应用 - Google Patents

一种判断子宫内膜容受性的方法及其应用 Download PDF

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WO2020215902A1
WO2020215902A1 PCT/CN2020/078074 CN2020078074W WO2020215902A1 WO 2020215902 A1 WO2020215902 A1 WO 2020215902A1 CN 2020078074 W CN2020078074 W CN 2020078074W WO 2020215902 A1 WO2020215902 A1 WO 2020215902A1
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endometrial receptivity
genes
endometrial
receptivity
present
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PCT/CN2020/078074
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English (en)
French (fr)
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胡春旭
李艳萍
董鑫
陆思嘉
胡旻涛
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苏州亿康医学检验有限公司
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Priority to JP2021563381A priority Critical patent/JP2022530135A/ja
Priority to US17/594,567 priority patent/US20230313295A1/en
Priority to EP20794034.7A priority patent/EP3960873A4/en
Publication of WO2020215902A1 publication Critical patent/WO2020215902A1/zh

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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
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • 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
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • 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
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/36—Gynecology or obstetrics
    • G01N2800/367—Infertility, e.g. sperm disorder, ovulatory dysfunction

Definitions

  • the invention relates to the field of biomedicine, in particular to a method for judging the receptivity of the endometrium and its application.
  • the fertilized egg locates, adheres, and implants in the mother's uterus, and finally develops into a mature fetus.
  • the implantation process has an important influence on a successful pregnancy.
  • a successful clinical pregnancy requires good quality in addition to high-quality embryos.
  • Endometrial receptivity refers to the ability of the endometrium to accept the embryo.
  • the endometrium allows the embryo to implant only in a short specific period of time. This period is called the "implantation window period", as far as adult women are concerned. , Equivalent to the 20th-24th day of the menstrual cycle or 6-8 days after ovulation.
  • IVF-ET in vitro fertilization-embryo transfer
  • the purpose of the present invention is to provide a stable, non-invasive, and accurate endometrial receptivity evaluation marker and evaluation method, which can help medical personnel to determine the endometrial receptivity state and accurately help patients find
  • the bed window period is of great significance for improving the success rate of in vitro fertilization-embryo transfer.
  • a method for judging endometrial receptivity which includes the steps:
  • step (c) Comparing the expression levels of endometrial receptivity-related genes obtained in step (b) with a predetermined value to determine the endometrial receptivity.
  • the sample is selected from the following group: endometrial tissue, uterine cavity fluid, uterine cavity lavage fluid, vaginal exfoliated cells, vaginal secretions, endometrial biopsy products, serum, plasma, or Its combination.
  • step (b) when the expression level of the endometrial receptivity-related gene obtained in step (b) is higher than a predetermined value, it indicates that there is endometrial receptivity.
  • the expression level of the endometrial receptivity-related gene includes the expression level of the endometrial receptivity-related gene cDNA.
  • the sample is a sample of the following periods: LH+n, LH+n+2, LH+n+4, where n is 3-7, preferably, n is 4-6.
  • the sample is a sample of the following period: the nth day after ovulation, where n is 3-7, preferably, n is 4-6.
  • the endometrial receptivity-related genes include at least 70%, preferably at least 80%, more preferably at least 90%, and more preferably at least 95% selected from Table A
  • EFCAB14 570 PRMT2 571 FLAD1 572 SLMAP 573 TKT 574 SLFN5 575 CSNK1G1 576 EXOSC10 577 NADSYN1 578 KDM2A 579 KPNA4 580 TMEM120A 581 COX19 582 ARPIN 583 SYNRG 584 LYPLA2 585 TOLLIP 586 CDC37 587 H2AFY 588 RBCK1 589 RAF1 590 GPS2 591 NMT1 592 FLOT1 593 FBXW5 594 SQSTM1 595 DTX3L 596 PPIA 597 SMG5 598 EGLN2 599 ROCK1 600 PXN 601 RANGAP1 602 PSMA7 603 MBD4 604 ADRM1 605 ARF3 606 SMIM12 607 PPP1CA 608 SMIM29 609 WDR5
  • the endometrial receptivity-related genes include at least 40 genes selected from Table A.
  • the endometrial receptivity-related genes include at least 147 genes selected from Table A.
  • the endometrial receptivity-related genes include at least 259 genes selected from Table A.
  • the genes related to endometrial receptivity further include additional 5-200 genes.
  • genes related to endometrial receptivity further include one or more genes selected from Table B:
  • ENSG00000163406 SLC15A2; ENSG00000172137 CALB2; ENSG00000106483 SFRP4; ENSG00000066032 CTNNA2; ENSG00000153234 NR4A2; ENSG00000137857 DUOX1; ENSG00000112984 KIF20A; ENSG00000181195 PENK; ENSG00000133110 POSTN; ENSG00000134569 LRP4; ENSG00000108932 SLC16A6; ENSG00000173698 GPR64; ENSG00000204764 RANBP17; ENSG00000124205 EDN3; ENSG00000138180 C10orf3; ENSG00000138778 CENPE; ENSG00000198780 KIAA0888; ENSG00000084636 COL16A1; ENSG0000000119514 GALNT12; ENSG00000151150 ANK3; ENSG
  • the genes related to endometrial receptivity further include additional genes, so that the total number of genes reaches 10,000.
  • the second aspect of the present invention provides a biomarker set, the set includes at least 70% selected from Table A, preferably at least 80%, more preferably at least 90%, more preferably at least 95% % Of genes.
  • the biomarker set includes at least 40 genes selected from Table A.
  • the biomarker set includes at least 147 genes selected from Table A.
  • the biomarker set includes at least 259 genes selected from Table A.
  • the biomarker set further includes additional 5-200 genes.
  • the biomarker set further includes additional genes, so that the total number of genes reaches 10,000.
  • the biomarker set is used to judge the endometrial receptivity, or used to prepare a kit or reagent, and the kit or reagent is used to evaluate the intrauterine test object Membrane receptivity state or diagnosis (including early diagnosis and/or auxiliary diagnosis) of the endometrial receptivity state of the test subject.
  • the biomarkers or biomarker collections are derived from endometrial tissue, uterine cavity fluid, uterine lavage fluid, vaginal exfoliated cells, vaginal secretions, endometrial biopsy products, serum , Plasma samples.
  • one or more biomarkers selected from Table A increase, indicating that the test subject has endometrial receptivity.
  • each biomarker is identified by a method selected from the following group: RT-qPCR, RT-qPCR chip, second-generation sequencing, expression profile chip, methylation chip, third-generation sequencing, or a combination thereof .
  • the set is used to evaluate the receptivity state of the endometrium of the subject to be tested.
  • the third aspect of the present invention provides a reagent combination for judging the receptivity state of the endometrium.
  • the reagent combination includes reagents for detecting each biomarker in the set according to the second aspect of the present invention.
  • the reagent includes a method selected from the group consisting of: RT-qPCR, RT-qPCR chip, next-generation sequencing, and detection of each biomarker substance in the collection according to the second aspect of the present invention.
  • the fourth aspect of the present invention provides a kit, which includes the set according to the second aspect of the present invention and/or the reagent combination according to the third aspect of the present invention.
  • the fifth aspect of the present invention provides the use of a biomarker collection for preparing a kit for evaluating the receptivity state of the endometrium of a subject to be tested, wherein the biomarker
  • the collection of species includes at least 70%, preferably at least 80%, more preferably at least 90%, and more preferably at least 95% of genes selected from Table A.
  • the evaluation or diagnosis includes the steps:
  • the sample is selected from the following group: endometrial tissue, uterine cavity fluid, uterine cavity lavage fluid, vaginal exfoliated cells, vaginal secretions, endometrial biopsy products, serum, plasma, Or a combination.
  • one or more biomarkers selected from Table A increase, indicating that the test subject has endometrial receptivity.
  • the method before step (1), further includes a step of processing the sample.
  • the sixth aspect of the present invention provides a method for evaluating the endometrial receptivity state of a subject to be tested, including the steps:
  • the collection includes at least 70%, preferably at least 80%, and more preferably selected from Table A Ground, at least 90%, more preferably at least 95% of genes;
  • the seventh aspect of the present invention provides a system for evaluating the endometrial receptivity state of a subject to be tested, the system comprising:
  • a feature input module of the endometrial receptivity state the input module is used to input the feature of the endometrial receptivity state of the test subject;
  • the characteristics of the endometrial receptivity state include at least 70%, preferably, at least 80%, more preferably, at least 90%, and more preferably, at least 95% of genes selected from Table A;
  • a judgment processing module for the endometrial receptivity state the processing module performs scoring processing on the input characteristics of the endometrial receptivity state according to a predetermined judgment standard, thereby obtaining the endometrial receptivity state Score; and the endometrial receptivity state score is compared with a predetermined value to obtain an auxiliary diagnosis result, wherein, when the endometrial receptivity state score is higher than the predetermined value, it is prompted The subject has endometrial receptivity; and
  • Auxiliary diagnosis result output module the output module is used to output the auxiliary diagnosis result.
  • the endometrial receptivity-related genes include at least 40 genes selected from Table A.
  • the endometrial receptivity-related genes include at least 147 genes selected from Table A.
  • the endometrial receptivity-related genes include at least 259 genes selected from Table A.
  • the genes related to endometrial receptivity further include additional 5-200 genes. In another preferred embodiment, the genes related to endometrial receptivity further include additional genes, so that the total number of genes reaches 10,000.
  • the subject is a human.
  • the score includes (a) the score of a single feature; and/or (b) the sum of the scores of multiple features.
  • the feature input module is selected from the following group: sample collector, sample preservation tube, cell lysis and nucleic acid sample extraction kit, RNA nucleic acid reverse transcription and amplification kit, next-generation sequencing library construction A kit, a library quantification kit, a sequencing reaction kit, or a combination thereof.
  • the said endometrial receptivity state discrimination processing module includes a processor and a storage, wherein the storage stores the characteristics based on the endometrial receptivity state Scoring data of endometrial receptivity status.
  • the output module includes a reporting system.
  • Figure 1 is a distribution diagram of sample cDNA amplification products in Example 4 of the present invention.
  • Figure 2 is an overview of the flow of the supervised learning method used in embodiment 7 of the present invention.
  • Embodiment 7 of the present invention is a flowchart of data processing in Embodiment 7 of the present invention.
  • FIG. 4 is a schematic diagram of the result of patient detection in Example 7 of the present invention.
  • Fig. 5 is a schematic diagram of subject detection in Example 7 of the invention.
  • the present invention discovers a set of biomarkers, the set includes the endometrial receptivity state or diagnosis (including early diagnosis and/or auxiliary diagnosis) of the test subject that can be used to evaluate the test subject
  • the receptive state of the endometrium can greatly reduce the error rate and has important application value.
  • the inventor completed the present invention.
  • the term “endometrial receptivity” refers to the ability of the endometrium to accept embryos, and the endometrium allows embryo implantation only in a short specific period of time.
  • biomarker set refers to one biomarker, or a combination of two or more biomarkers.
  • the level of biomarkers is identified by methods such as RT-qPCR, RT-qPCR chip, second-generation sequencing, expression profile chip, methylation chip, third-generation sequencing and the like.
  • biomarker also known as “biological marker” refers to a measurable indicator of the biological state of an individual.
  • biomarkers can be any substances in the individual, as long as they are related to the specific biological state (for example, disease) of the subject, for example, nucleic acid markers (for example, DNA), protein markers, cytokine markers , Chemokine markers, carbohydrate markers, antigen markers, antibody markers, species markers (species/genus markers) and functional markers (KO/OG markers), etc.
  • Biomarkers are measured and evaluated, often used to check normal biological processes, pathogenic processes, or pharmacological responses to therapeutic interventions, and are useful in many scientific fields.
  • the term "individual” refers to animals, especially mammals, such as primates, and preferably humans.
  • plasma refers to the liquid component of whole blood.
  • plasma may contain no cellular components at all, or it may contain varying amounts of platelets and/or small amounts of other cellular components.
  • the substance of each biomarker in the collection of the present invention is detected by a method selected from the following group: RT-qPCR, RT-qPCR chip, second-generation sequencing, expression profile chip, methylation chip, third-generation sequencing, Or a combination.
  • the kit of the present invention includes the set according to the second aspect of the present invention and/or the reagent combination according to the third aspect of the present invention.
  • the predetermined value refers to the use of artificial intelligence or decision tree C4.5 algorithm (Decision Tree), hidden Markov model (HMM), neural network back propagation (BP), support vector machine (SVM), and Various clustering analysis algorithms (including simple clustering, hierarchical clustering, K-means clustering, self-organizing map neural network, fuzzy clustering, Bayesian classification, K nearest neighbor method, neural network method, decision tree method, Voting classification method, principal component analysis method, etc.) scoring the endometrial receptivity period (that is, the period during which it has been clinically proven that the endometrium allows the embryo to be positioned, adhered, and implanted on it).
  • Decision Tree Decision Tree
  • HMM hidden Markov model
  • BP neural network back propagation
  • SVM support vector machine
  • clustering analysis algorithms including simple clustering, hierarchical clustering, K-means clustering, self-organizing map neural network, fuzzy clustering, Bayesian classification, K nearest neighbor method, neural network method, decision tree method, Voting classification method
  • W1, W2...Wn are weights
  • S1, S2...Sn is the score of each marker.
  • the weight may be based on the analysis value in Table 9.
  • any weight (such as W1) can be the analytical value of the corresponding marker in Table 9.
  • the experimental results of the present invention show that the marker of the present invention can greatly reduce the error rate and significantly improve the accuracy of the judgment or diagnosis of the endometrial receptivity state.
  • the method for constructing an analytical model for judging endometrial receptivity includes the following steps:
  • RNA reverse transcription and amplification from cDNA After high-sensitivity RNA reverse transcription and amplification from cDNA, the cDNA is then subjected to second-generation sequencing before the library is built, sequenced on the computer, and the expression profile information of the sample is constructed from the off-computer data, and through the analysis and classification of bioinformatics, Determine the receptivity status of the endometrium, accurately determine the window period of endometrial implantation, and realize individualized and accurate judgment.
  • the present invention provides a method for constructing an analytical model for judging endometrial receptivity.
  • the method includes the following steps:
  • the present invention is based on the highly sensitive RNA reverse transcription and cDNA amplification process, based on the RNA-seq sequencing method, and obtains the expression profile of a large number of patients’ endometrium, uterine fluid or other reproductive endocrine-related body fluids or sheddings information. And for these samples, according to the different sampling period, sampling method, and expression profile characteristics, the ultra-high-dimensional classification and typing are carried out by means of bioinformatics, statistics and machine learning. According to different types, judge the receptivity state of the endometrium.
  • the task of supervised learning is to learn a model so that the model can map a prediction result to any given input, and can realize high-dimensional predictive analysis.
  • the “multiple samples” of "learning” in step (3) of the present invention refers to samples from the same sample source and different individuals, such as 102 cases of endometrial tissue in the receptive period, and 205 cases of endometrial tissue in the receptive period. 300 cases tolerate the later endometrial tissue.
  • the different menstrual cycles in step (1) are three periods.
  • the middle period of the three periods is LH+7, or the 5th day after ovulation.
  • test sample used in the present invention is a female subject in a natural menstrual cycle. Endometrial biopsy is performed on the seventh day (LH+7) after the peak of luteinizing hormone (LH) appears, or in hormone replacement (HRT) ) Cycle female subjects, undergo endometrial biopsy on day 5 (P+5) after ovulation.
  • LH+7 the seventh day
  • HRT hormone replacement
  • the three periods also include a period of 1-3 days before and after the intermediate period, preferably a period of 2 days.
  • the intermediate period is the tolerance period, 1-3 days before the intermediate period is the early tolerance period, and 1-3 days after the intermediate period is the late tolerance period.
  • the entry criteria of the "training data” or “training set” used in the model are: different healthy Chinese women in the natural cycle, no previous medical history, no primary infertility, and a body mass index of 19-25kg/ m 2 between.
  • the inventors have mastered the expression profiles of the endometrial tissue, uterine fluid, or other reproductive endocrine-related body fluids or exfoliated substances in these cases.
  • this period (After tracking the clinical outcome, if the embryo implantation at this period can effectively implant and develop, then this period is defined as the "acceptance period”), and we also consider the "acceptance window period” for these cases Sampling was taken two days before and two days after the “acceptance window period” to obtain the corresponding RNA-seq data, and their class labels were respectively defined as “pre-acceptance period” and “end-acceptance period”.
  • the differentially expressed genes can be better guided and the probability of false positives can be reduced.
  • the sample in step (1) includes any one or a combination of at least two of the fundus endometrial tissue, uterine cavity fluid, or vaginal shedding.
  • the sample can be a biopsy product of the endometrium, or a patient’s uterine fluid obtained by non-invasive means, even uterine lavage fluid, vaginal exfoliated cells and vaginal secretions.
  • the sample source is wide and the sampling is convenient and quick. , Increase the compliance of women, while verifying samples from different sources of the same individual can improve the accuracy of the gene expression profile; the sample volume of uterine cavity lavage, vaginal shedding or vaginal secretions is small, and a large number of samples are required in conventional detection methods Therefore, only the biopsy product of the endometrium can be selected, and the test needs can be met by a small amount of samples in this application, thus expanding the types of samples and reducing the pain and discomfort of the subjects.
  • the sampling amount of the fundus endometrial tissue is greater than 5 mg, preferably 5-10 mg, for example, 5 mg, 6 mg, 7 mg, 8 mg, 9 mg or 10 mg.
  • the sample volume of the uterine cavity fluid is greater than 10 ⁇ L, preferably 10-15 ⁇ L, for example, it may be 10 ⁇ L, 11 ⁇ L, 12 ⁇ L, 13 ⁇ L, 14 ⁇ L or 15 ⁇ L.
  • the sampling amount of the vaginal shedding substance is greater than 5 mg, preferably 5-10 mg, for example, 5 mg, 6 mg, 7 mg, 8 mg, 9 mg or 10 mg.
  • the sample size is small, the accuracy rate is high, and the receptivity state of the sample can be accurately predicted without a large number of samples.
  • the concentration of cDNA in the library in step (2) is not less than 5ng/ ⁇ L.
  • the sequencing in step (2) includes RNA-Seq sequencing and/or qPCR sequencing.
  • the RNA-Seq sequencing method has more advantages than chip sequencing.
  • the number of differentially expressed genes that can be detected is 2-8 times that of chip sequencing.
  • RNA-Seq qPCR verification The rate is 5 times that of the chip.
  • the correlation between RNA-Seq and qRCR is 14% higher than that of the chip.
  • the RNA-Seq or qPCR sequencing method used in the present invention is a common technical means by those skilled in the art.
  • the read length of the sequencing in step (2) is greater than 45 nt.
  • the number of reads sequenced in step (2) is not less than 2.5M reads.
  • the read length for sequencing is greater than 45 bases, and the number of reads for sequencing is not less than 2.5 megabytes to meet the sequencing requirements.
  • the read length and the number of reads for on-machine sequencing are specifically selected, which reduces the experimental period and cost while ensuring accuracy.
  • a data preprocessing step is further included before step (3).
  • the data preprocessing step is to standardize gene length and sequencing depth.
  • the standardized method includes any one or a combination of at least two of RPKM, TPM or FPKM, preferably FPKM.
  • FPKM Frragments Per Kilobase Million
  • RPKM Reads Per Kilobase Million
  • TPM Trans Per Million
  • FPKM is suitable for paired-end sequencing libraries or single-end sequencing libraries, so it is more flexible and easy to commercialize; while RPKM is only suitable for single-end sequencing libraries; the TPM value can reflect the ratio of reads of a certain gene on the comparison, making this value You can directly compare samples, but the process is cumbersome, the calculation is slow, and the efficiency of batch analysis is not high.
  • the model is constructed by using training data with tolerance pre-period, tolerance window period, and late tolerance category, and the tolerance of the model obtained through training to unknown data (referring to new samples) State is predicted. For example, input a new sample of uterine fluid expression profile, and use the machine learning model to determine its receptivity state.
  • the class label is the expression profile characteristics of endometrial tissue, uterine cavity fluid or vaginal shedding under different tolerance states.
  • samples obtained from different sources for the same individual including any one of endometrial tissue, uterine fluid, or vaginal shedding, or a combination of at least two of them, can significantly improve the expression profile under different tolerance states.
  • the reliability of the forecast results can significantly improve the expression profile under different tolerance states.
  • the analysis method of the differentially expressed genes is: find all genes with FPKM>0 in each sample, and then screen for the tolerance period and tolerance period, tolerance period and tolerance period, tolerance period and tolerance period.
  • the intersection of differentially expressed genes satisfies p_value ⁇ 0.05, and satisfies Fold_change>2 or Fold_change ⁇ 0.5.
  • the influence of genes that are always highly expressed or always low expressed in different class markers on the analysis model is excluded, in order to obtain a good fitting effect for the subsequent analysis model while ensuring that overfitting is eliminated.
  • the supervised learning method in step (3) includes any one or a combination of at least two of naive Bayes, decision tree, logistic regression, KNN, or support vector machine, preferably a support vector machine.
  • the support vector machine does not have many restrictions on the distribution of the original data and does not require prior information. However, the amount of data obtained by RNA-Seq sequencing is extremely large, and different genes have different expression levels. The support vector machine can maintain a super multidimensional (ultra-high dimension). ) Analysis makes the model more accurate.
  • the script of the support vector machine is:
  • shdata2 log2(shdata[,-length(shdata[1,])]+1)
  • the method for constructing an analysis model for judging endometrial receptivity specifically includes the following steps:
  • step (3) The expression profile characteristics of endometrial tissue, uterine fluid, or vaginal shedding obtained in step (2) under different tolerance states are used as class markers.
  • the method of supervised learning is adopted, and the data with class markers are used for model training , To compare the expression levels of different genes in multiple samples of different categories. After obtaining the RNA-Seq sequencing data of different categories, use the FPKM method to standardize the gene length and sequencing depth. Through the standardization process, Compare the expression levels of different genes in the sample, exclude the influence of consistently high or low expression genes in different categories on the model, and analyze the differentially expressed genes;
  • the learning model of support vector machine is adopted, and the analysis model is constructed by using the training data with the pre-acceptance, the tolerant period and the post-acceptance category to predict the unknown data receptivity state, in order to improve the accuracy of judgment
  • the sampling time can be adjusted repeatedly in multiple cycles, and then the tolerance test can be performed.
  • the offline data is obtained through RNAseq, and the expression level of FPKM is standardized. Comparing the difference of gene expression in different periods of the same individual, the marked difference is the expression difference gene. Find the genes with the most significant differences in expression in the same sample that tolerate the early, tolerable, and tolerant later stages. Then use the different periods of multiple individuals to optimize the "expressed differential genes", and build a feature library of three periods through machine learning. For a new sample to be tested, the expression level is standardized, and the machine is used to determine the features. Automatic classification.
  • the model construction method of the present invention uses a huge training set. The machine is fully trained through specific and significant expression difference genes in conjunction with a support vector machine, and the model is adjusted by long-term tracking of clinical outcomes to improve the accuracy of the model.
  • the sampling time can be adjusted repeatedly in multiple cycles. If the first test is LH+5, the next cycle test needs to be postponed for 2 days to obtain LH+7; If the first test is LH+9, the next cycle of testing needs to be 2 days in advance to obtain LH+7.
  • the biomarker of the present invention can accurately determine the state of endometrial receptivity, greatly reduces the error rate, and has important application value.
  • the present invention relies on highly sensitive RNA reverse transcription and cDNA amplification processes, and is based on RNA-seq sequencing methods to obtain a large number of patients’ endometrium, uterine fluid or other reproductive endocrine-related body fluids or shedding Expression profile information. And for these samples, according to the different sampling period, sampling method, and expression profile characteristics, the ultra-high-dimensional classification and typing are carried out by means of bioinformatics, statistics and machine learning. According to different types, judge the receptivity state of the endometrium.
  • the present invention provides a model construction method that stably uses gene expression profile characteristics to determine endometrial receptivity, and optimizes and adjusts the overall RNA extraction, reverse transcription, cDNA purification library building, computer sequencing and performing Corresponding data processing, analysis and modeling can significantly improve the accuracy of judging endometrial receptivity.
  • the method of the present invention has the characteristics of low damage caused by non-invasive uterine cavity fluid biopsy, and the simplicity of the process and short cycle.
  • the MALBAC platinum trace RNA amplification kit produced by Yikang Gene, catalog number KT110700724;
  • the samples include more than 5mg of fundus and endometrial tissue, more than 10ul of uterine fluid or more than 10ul of other reproductive endocrine-related body fluids, and more than 5mg of vaginal shedding.
  • the samples After the biopsy is completed, completely infiltrate the tissue, shed material or liquid in the RNA preservation solution (about 20uL RNA Later) as soon as possible.
  • the sample Before the sample is shipped, the sample should be stored in a refrigerator at -20°C or -80°C.
  • step 6 Take 10uL DNase I solution (obtained in step 5) into the RNase-free EP tube, add 70uLbufferRDD, pipette to mix, and place on ice for later use (labeled as DNase I mixture). Each withdrawal needs to be allocated for immediate use.
  • step 3 Add 500uL of Buffer RPE containing absolute ethanol (obtained in step 3) to the adsorption column, centrifuge at 14000xg for 30s, and discard the waste liquid in the collection sleeve.
  • step 16 Add 500uL 80% ethanol (obtained in step 2) to the adsorption column, centrifuge at 14000xg for 30s, and discard the waste liquid in the collection sleeve.
  • RNA samples and Lysis Buffer are thawed on ice, vortexed after thawing, centrifuged briefly and placed on ice for later use.
  • RNA sample Take 2uL RNA from each sample and put it in a 1.5mL EP tube. According to the measured RNA concentration of the sample, dilute the RNA sample to about 5ng/uL with RNase Free water.
  • RT-NC reverse transcription negative control
  • RT Enzyme Mix to the RT Buffer (step 6) incubated at 72°C. Mix gently by pipetting and place on ice for later use. The mixture is labeled "RT mix”.
  • PCR Mix (provided with the kit) from the refrigerator at -20°C. Thaw on ice, mix upside down, centrifuge briefly, and place on ice for later use.
  • PCR-NC PCR negative control
  • test preparation A different test area must be used with the steps of reverse transcription and amplification. Use nucleic acid detergent to wipe hands, pipettes, and the benchtop.
  • step 7 Repeat step 7 and discard the waste liquid in the collection sleeve.
  • RNA and use Qubit DNA HS kit for quantification Take 1uL RNA and use Qubit DNA HS kit for quantification.
  • the negative control RT-NC set during the reverse transcription process should be less than 2ng/uL after amplification, and the negative control PCR-NC set during the PCR process should be less than 0.4ng/uL after amplification.
  • the cDNA amplification product concentration of the sample should be greater than 40ng/uL. RT-NC and PCR-NC do not require subsequent sequencing steps.
  • the cDNA amplification products of the sample are distributed between 400-10000 bp, and the main peak is around 2000 bp.
  • the cDNA composite quality requirements used in this application are required.
  • Component volume Fragmentation buffer 8.5uL ⁇ (N+1) Fragmentase 1uL ⁇ (N+1) total capacity 9.5uL ⁇ (N+1)
  • Component volume Amplification buffer 11.5uL ⁇ (N+1)
  • Amplification enzyme 0.5uL ⁇ (N+1) total capacity 12uL ⁇ (N+1)
  • the purified library can be quantified separately using Qubit dsDNA HS Assay Kit, and the concentration is generally above 5ng/ul (in order to obtain high-quality sequencing results, real-time fluorescent quantitative PCR method can be used for quantification).
  • training data is marked with a category, which is marked as endometrial tissue, uterine fluid or other reproductive endocrine-related body fluids or shedding under different tolerance states Expression profile characteristics;
  • the criteria for entering the "training data" or “training set” in the judgment model are: different healthy Chinese women in the natural cycle, no previous medical history, no primary infertility, and a body mass index of 19-25kg/m 2.
  • use FPKM to standardize the gene length and sequencing depth to eliminate the influence of the sequencing depth. Through the standardization process, compare the differences among the (multiple) samples of different categories.
  • the expression level of genes, the analysis of gene differential expression specifically: find all genes with FPKM>0 in each sample, and then look for "early tolerance” vs. all genes with FPKM>0 that enter the training set sample. "Tolerance period”, "tolerance period” vs.
  • the model is constructed by using training data with pre-acceptance, tolerant window period, and post-acceptance categories.
  • the model obtained by training can be used to analyze unknown data (referring to new cases).
  • the receptivity state is predicted. For example, input the expression profile of uterine fluid of a new case, and use the machine learning model to judge its receptivity state (as shown in Figure 3);
  • shdata2 log2(shdata[,-length(shdata[1,])]+1)
  • Sample period, sample type, past medical history, and infertility type are the clinical information of the sample, RNA concentration, cDNA concentration, sequencing data volume, Unique Mapping Ratio, exon ratio are sequencing quality control information, and support vector machines are classified as machines
  • the tolerance state of the learning judgment, the implantation method is the implantation period adjustment based on the machine learning analysis result. It can be seen from Tables 8-1, 8-2 and 8-3 that, after clinical verification, 19 cases of infertility women were successfully pregnant with the timing of embryo implantation guided by the model of the present invention, indicating that the model of the present invention is extremely accurate. High helps promote medical progress.
  • markers can be useful auxiliary judgment or diagnostic information for endometrial receptivity, so it is especially useful for early and/ Or auxiliary diagnosis.
  • the error rate can be further significantly reduced (the error rate is only 17.5%) and the accuracy of the evaluation can be improved.
  • the error rate can be as low as 16.5%.
  • the calculation method of error rate is: clinical outcome is the gold standard, and the date of successful embryo implantation is the tolerance period.
  • the marker of the present invention has high predictive value, especially when used in combination, it can further reduce the error rate of the judgment of the endometrial receptivity state and improve the accuracy of the judgment.
  • the present invention further screened the markers in Table 9 and obtained 40 markers, 147 markers, and 259 markers with very low error rate and high accuracy effects. Among them, blank Refers to analytical value ⁇ 4.
  • the genes in Table 9 of the present invention are core genes.
  • the newly added genes will increase the accuracy rate, but the weight of the newly added genes is very low.
  • the present invention relies on highly sensitive RNA reverse transcription and cDNA amplification processes, and is based on the RNA-seq sequencing method, to obtain a large number of patients’ endometrium, uterine fluid or other reproductive endocrine-related body fluids or Expression profile information of the shed material. And for these samples, the sampling period and sampling method are specifically selected to obtain different expression profile characteristics, and ultra-high-dimensional classification and typing are carried out by means of bioinformatics, statistics and machine learning. According to the different types, the receptivity state of the endometrium is judged; the present invention uses the expression profile characteristics to judge the model of the preimplantation endometrial receptivity of the embryo, and accurately judges the window period of endometrial implantation.

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Abstract

本发明提供了一种判断子宫内膜容受性的方法及其应用。具体地，本发明提供了一种判断子宫内膜容受性的方法以及判断子宫内膜容受性状态的标志物。

Description

一种判断子宫内膜容受性的方法及其应用 技术领域
本发明涉及生物医药领域，具体地涉及一种判断子宫内膜容受性的方法及其应用。
背景技术
人类生殖过程中，受精卵在母体子宫内定位、黏附、着床，最终发育成一个成熟的胎儿，着床过程对成功妊娠具有重要影响，成功的临床妊娠除需要优质的胚胎之外还需要良好的子宫内膜容受性(endometrial receptivity，ER)、子宫内膜与胚胎的同步发育。子宫内膜容受性是指子宫内膜对胚胎的接受能力，只有在短暂的特定时期内子宫内膜才允许胚胎着床，这一时期称为“着床窗口期”，就成年女性而言，相当于月经周期第20-24日或排卵后6-8日。
在体外受精-胚胎移植(IVF-ET)领域中，反复移植失败的奴性，或者罹患其他继发性不孕症的女性，着床窗口期的时间节点并不准确，如果按月经周期或者排卵日推算着床窗口期，会有很大移植失败的风险。虽然子宫内膜容受性的具体机制目前尚不清楚，但可以明确的是，子宫内膜容受性差是导致体外受精-胚胎移植(IVF-ET)中胚胎着床失败的重要原因之一。
因此，本领域迫切需要找到一种稳定的、无创的，且准确的子宫内膜容受性评估标志物和评估方法，就可以帮助医疗人员明确子宫内膜容受性状态，准确地帮助患者找到着床窗口期，对于提升体外受精-胚胎移植的成功率意义重大。
发明内容
本发明的目的在于提供一种稳定的、无创的，且准确的子宫内膜容受性评估标志物和评估方法，就可以帮助医疗人员明确子宫内膜容受性状态，准确地帮助患者找到着床窗口期，对于提升体外受精-胚胎移植的成功率意义重大。
在本发明的第一方面，提供了一种判断子宫内膜容受性的方法，包括步骤：
(a)提供一样本；
(b)测定所述样本中的子宫内膜容受性相关基因的表达量；
(c)将步骤(b)获得的子宫内膜容受性相关基因的表达量与预定值进行比较，从而判断子宫内膜容受性。
在另一优选例中，所述样本选自下组：子宫内膜组织、宫腔液、宫腔灌洗液、阴道脱落细胞、阴道分泌物、子宫内膜的活检产物、血清、血浆、或其组合。
在另一优选例中，步骤(b)获得的子宫内膜容受性相关基因的表达量高于预定值时，则表明存在子宫内膜容受性。
在另一优选例中，所述子宫内膜容受性相关基因的表达量包括子宫内膜容受性相关基因cDNA的表达量。
在另一优选例中，所述样本为以下时期的样本：LH+n，LH+n+2，LH+n+4，其中，n为3-7，较佳地，n为4-6。
在另一优选例中，所述样本为以下时期的样本：排卵后的第n天，其中，n为3-7，较佳地，n为4-6。
在另一优选例中，所述子宫内膜容受性相关基因包括选自表A的至少70％、较佳地，至少80％，更佳地，至少90％，更佳地，至少95％的基因：
表A
  名称
1 KRIT1
2 RBM5
3 PAF1
4 RFC1
5 TPR
6 ACAA1
7 SFSWAP
8 SPEN
9 CPSF1
10 XRCC1
11 KDM5A
12 SART3
13 PIK3C3
14 YTHDC1
15 PPIE
16 NFX1
17 CDC5L
18 SF3A1
19 TXN2
20 EIF3D
21 EP300
22 CHD8
23 PNN
24 CSTF1
25 PRPF6
26 PQBP1
27 CIAO3
28 HMOX2
29 PIH1D1
30 AKAP8
31 BUD31
32 EIF3A
33 CASC3
34 CDK5RAP3
35 SUPT6H
36 CNOT2
37 SUDS3
38 TBCCD1
39 EIF2B4
40 ORC2
41 SRSF4
42 SFPQ
43 SRSF11
44 PRRC2C
45 FBXW2
46 SNX19
47 EPC1
48 TBCC
49 CNOT1
50 GTF2F1
51 KDM5C
52 NSRP1
53 UXT
54 ATG14
55 AKAP9
56 PRKRIP1
57 CCNT1
58 LSM4
59 RLIM
60 ERAL1
61 PTPRA
62 NASP
63 SRRM1
64 PRPF38A
65 CDK5RAP2
66 PRPF4
67 PPIG
68 SMARCC2
69 TCF25
70 CSNK1D
71 ENSA
72 TEX261
73 FIP1L1
74 CENPC
75 ZMAT2
76 CELF1
77 CPSF7
78 UPF2
79 MMS19
80 SON
81 ADAR
82 MAGOH
83 ELP6
84 NIPBL
85 SLU7
86 PCF11
87 NSD1
88 YWHAB
89 DDB1
90 SF1
91 ATG4B
92 FEM1B
93 SIN3A
94 LUZP1
95 GPS1
96 SF3B5
97 HNRNPA3
98 PYM1
99 RBM4
100 PRPF8
101 ZBTB4
102 CKAP5
103 SMAD2
104 POLR2A
105 RNF135
106 RNF41
107 MRPS11
108 CEP63
109 EIF3C
110 SF3B3
111 SIAH1
112 SND1
113 UBL5
114 NELFE
115 EIF3CL
116 FIS1
117 TRIM26
118 MRPL20
119 KMT2E
120 AFF4
121 GTF3C1
122 ANAPC5
123 MAEA
124 TOX4
125 GID8
126 ARFGAP1
127 ARHGEF7
128 H2AFV
129 ZNHIT1
130 COA1
131 GBF1
132 GOSR1
133 IFT20
134 ANAPC15
135 IK
136 KANSL3
137 GTF3C2
138 CHMP3
139 FAM20B
140 CHCHD5
141 RPAIN
142 UBE4B
143 C19orf12
144 ANKRD17
145 MED6
146 TMEM258
147 ERCC5
148 ATP5MC2
149 SMPD4
150 ECPAS
151 DMAC1
152 SEC24B
153 NCOR1
154 PI4KB
155 C1orf43
156 ASXL2
157 VTI1A
158 PPP1R15B
159 SNF8
160 GATD3A
161 MED11
162 RAD21
163 SPIDR
164 ANAPC16
165 VPS39
166 ATP5PD
167 FIBP
168 CORO1B
169 RAB1B
170 RMDN1
171 BET1L
172 ASB8
173 EXOC7
174 UQCR10
175 TOP1
176 SPOUT1
177 ARMCX6
178 PPP1R10
179 LIN52
180 SMIM7
181 TOMM6
182 PDCD6
183 GGNBP2
184 GATD3B
185 KIAA0100
186 ELOA
187 AQR
188 FBXO42
189 LSG1
190 FAM120A
191 THRAP3
192 ARID4B
193 POLR3E
194 GPBP1
195 RFXANK
196 TAF11
197 BUD23
198 PDCD2
199 BCS1L
200 ZNF638
201 ZNF37A
202 EXOSC7
203 TOP2B
204 DELE1
205 GCN1
206 DDX24
207 DHPS
208 WAC
209 HPS4
210 PPP6R2
211 PACSIN2
212 HMGXB4
213 POLR3H
214 RBM23
215 ZC3H14
216 DCAF11
217 NDRG3
218 GYS1
219 CCDC130
220 DNAJC2
221 CHCHD2
222 TMEM248
223 NUFIP2
224 UBTF
225 MTMR4
226 RSRC2
227 KRR1
228 CHD4
229 ZNF451
230 SENP6
231 PRPF4B
232 PRKAR2A
233 FXR1
234 HDLBP
235 PPP1R7
236 ASH1L
237 GON4L
238 TSNAX
239 HMGCL
240 MED28
241 NEK9
242 PANK3
243 SPOP
244 MTIF3
245 ZC3H13
246 SMUG1
247 RAB22A
248 STAU1
249 DDX27
250 SERPINB6
251 MEA1
252 COX6B1
253 TIMM17B
254 XPO7
255 SAFB2
256 EIF2S3
257 UBA1
258 RBM39
259 ACLY
260 DHX30
261 SCO1
262 LARS
263 PPHLN1
264 LPIN1
265 TIMM10
266 ARGLU1
267 TFCP2
268 C2orf49
269 SLTM
270 CIR1
271 TMOD3
272 SBNO1
273 DCAF5
274 ANP32A
275 COMMD4
276 ARHGAP17
277 RHOT2
278 SERBP1
279 STRIP1
280 UFC1
281 MRPL9
282 UBAP2L
283 SDE2
284 SNRNP200
285 C7orf50
286 MDH2
287 NDUFB11
288 TAF1
289 EIF4EBP2
290 MTG1
291 NUDT22
292 VIPAS39
293 KIN
294 ATP5F1A
295 PELO
296 SAR1B
297 HNRNPDL
298 CCDC174
299 LARP1
300 SCAF4
301 APPL1
302 GPBP1L1
303 PSKH1
304 SSU72
305 CCDC12
306 ZYG11B
307 PMVK
308 KIAA1143
309 UBXN7
310 GAPVD1
311 NEMF
312 HIF1AN
313 MARF1
314 NDUFV1
315 HARS
316 ATF7
317 AKAP13
318 QARS
319 ZNF24
320 FAM192A
321 MRPL57
322 CHD2
323 TOMM20
324 MGA
325 IP6K1
326 DNAJC30
327 IMP3
328 NDUFAF3
329 SPTY2D1
330 CLK3
331 MRPS23
332 TTC3
333 GPATCH8
334 USP7
335 LAMTOR4
336 TBC1D9B
337 GSTK1
338 QRICH1
339 DDX39B
340 GIGYF2
341 BRD2
342 GPANK1
343 PRRC2A
344 DHX16
345 NAP1L4
346 SELENOH
347 RBMXL1
348 ACBD6
349 FAM133B
350 CDKN2AIPNL
351 CDK11B
352 PRKDC
353 MYO19
354 LAS1L
355 PPP1R12A
356 CCAR1
357 SMC1A
358 ARAF
359 HSP90AA1
360 CHERP
361 SRRT
362 SF3B2
363 HNRNPC
364 HNRNPM
365 RBX1
366 TELO2
367 UBE2I
368 TIMM50
369 PRPF31
370 TCERG1
371 TUSC2
372 EIF4G1
373 NCL
374 PRPF3
375 SNRPB
376 PRKCSH
377 TUBGCP2
378 EIF3G
379 SYNCRIP
380 HUS1
381 ACTR1A
382 MBD1
383 HDGF
384 PARP1
385 RPL7L1
386 RPUSD3
387 ACOX1
388 U2SURP
389 CPSF2
390 TSR1
391 RFWD3
392 CD2BP2
393 PCBP1
394 PA2G4
395 PPID
396 HCFC1
397 FKBP2
398 BRMS1
399 EIF3K
400 PUF60
401 NOC2L
402 PRPF40A
403 RNPS1
404 DCP1A
405 CWC25
406 MED24
407 PHF20
408 EIPR1
409 KAT6A
410 PSMD8
411 NOP56
412 COPE
413 SSR3
414 COPA
415 THOC6
416 WDR74
417 PSMB7
418 HAX1
419 SURF6
420 VPS28
421 VKORC1
422 PSMD13
423 TMEM222
424 C6orf106
425 MRPL38
426 CSNK2B
427 PSMB3
428 CCDC124
429 RANBP3
430 NOP58
431 ZFR
432 IDH3G
433 HSD17B10
434 MRPL28
435 PSMC5
436 HSP90AB1
437 L3MBTL2
438 CINP
439 NAA10
440 SGTA
441 EDF1
442 NDUFS8
443 TPI1
444 MFN2
445 DNPEP
446 CLPP
447 RBM42
448 PNKD
449 ILF3
450 COX4I1
451 RBSN
452 ILKAP
453 NIP7
454 THUMPD3
455 CCT7
456 TBRG4
457 DDX56
458 DCAF7
459 YME1L1
460 MAN2C1
461 SCYL1
462 GPN2
463 GMPPA
464 DDX46
465 SRFBP1
466 CXXC1
467 EIF5B
468 GPATCH4
469 EIF4A1
470 UBXN1
471 IWS1
472 PSMC3
473 CIAO2B
474 ZNF592
475 DNAJC7
476 DTYMK
477 RNF181
478 SLC25A6
479 TRMT112
480 EIF1AD
481 AURKAIP1
482 ACSF3
483 TALDO1
484 COX5A
485 TUFM
486 FARSA
487 MRPL14
488 ARL6IP4
489 EWSR1
490 DDX41
491 CDK10
492 FAAP100
493 RPS19BP1
494 PTMA
495 MRPL21
496 MRPS18B
497 ABCF1
498 MCRIP1
499 CNPY2
500 MRPL12
501 BAZ2A
502 USP4
503 SMG7
504 ARPP19
505 NR1H2
506 NPEPPS
507 BIN3
508 UBE3B
509 WASF2
510 TAGLN2
511 IRF2
512 RELA
513 DCTN2
514 CIB1
515 SPTAN1
516 WWP2
517 MSRB1
518 DCTN1
519 EIF6
520 CUX1
521 WDR1
522 PDRG1
523 SH3GLB1
524 SNAP29
525 KLHDC3
526 CHMP1A
527 LGALS3
528 GLYR1
529 NOSIP
530 HERC4
531 UBE2J2
532 CHTOP
533 PEF1
534 ZDHHC3
535 ATP5MD
536 SETD3
537 MCRS1
538 AP1G2
539 CHMP1B
540 ARF5
541 RNF10
542 SNX1
543 HAGH
544 FAM50A
545 MYL6
546 NANS
547 LPIN2
548 UBL4A
549 TBCB
550 PRKD2
551 DMAC2
552 RNF7
553 WRAP73
554 PEX16
555 ANXA11
556 CYREN
557 DYNLRB1
558 HECTD3
559 PGLS
560 COX5B
561 CDK9
562 ARPC5L
563 RTCA
564 UNC45A
565 NARF
566 GUK1
567 CAST
568 NIT1
569 EFCAB14
570 PRMT2
571 FLAD1
572 SLMAP
573 TKT
574 SLFN5
575 CSNK1G1
576 EXOSC10
577 NADSYN1
578 KDM2A
579 KPNA4
580 TMEM120A
581 COX19
582 ARPIN
583 SYNRG
584 LYPLA2
585 TOLLIP
586 CDC37
587 H2AFY
588 RBCK1
589 RAF1
590 GPS2
591 NMT1
592 FLOT1
593 FBXW5
594 SQSTM1
595 DTX3L
596 PPIA
597 SMG5
598 EGLN2
599 ROCK1
600 PXN
601 RANGAP1
602 PSMA7
603 MBD4
604 ADRM1
605 ARF3
606 SMIM12
607 PPP1CA
608 SMIM29
609 WDR5
610 GRIPAP1
611 CWF19L1
612 MED15
613 TSPO
614 MYH9
615 ITPK1
616 TPD52L2
617 GSDMD
618 PSMD9
619 ADPRHL2
620 CCDC32
621 NSUN5
622 EIF4E2
623 MGST3
624 PCYT1A
625 SAP30BP
626 RNASEK-C17orf49
627 SHISA5
628 BLCAP
629 DDX23
630 FLII
631 GAK
632 PAK2
633 HGS
634 AATF。
在另一优选例中，所述子宫内膜容受性相关基因至少包括选自表A的40个基因。
在另一优选例中，所述子宫内膜容受性相关基因至少包括选自表A的147个基因。
在另一优选例中，所述子宫内膜容受性相关基因至少包括选自表A的259个基因。
在另一优选例中，所述子宫内膜容受性相关基因还包括额外的5-200个基因。
在另一优选例中，所述子宫内膜容受性相关基因还包括选自表B的一个或多个基因：
表B
ENSG00000170893 TRH；
ENSG00000241106 HLA-DOB；
ENSG00000171130 ATP6V0E2；
ENSG00000175183 CSRP2；
ENSG00000102837 OLFM4；
ENSG00000163406 SLC15A2；
ENSG00000172137 CALB2；
ENSG00000106483 SFRP4；
ENSG00000066032 CTNNA2；
ENSG00000153234 NR4A2；
ENSG00000137857 DUOX1；
ENSG00000112984 KIF20A；
ENSG00000181195 PENK；
ENSG00000133110 POSTN；
ENSG00000134569 LRP4；
ENSG00000108932 SLC16A6；
ENSG00000173698 GPR64；
ENSG00000204764 RANBP17；
ENSG00000124205 EDN3；
ENSG00000138180 C10orf3；
ENSG00000138778 CENPE；
ENSG00000198780 KIAA0888；
ENSG00000084636 COL16A1；
ENSG00000119514 GALNT12；
ENSG00000151150 ANK3；
ENSG00000164120 HPGD；
ENSG00000139514 SLC7A1；
ENSG00000167346 MMP26；
ENSG00000128606 LRRC17；
ENSG00000026559 KCNG1；
ENSG00000134716 CYP2J2；
ENSG00000117122 MFAP2；
ENSG00000162551 ALPL；
ENSG00000117399 CDC20；
ENSG00000180875 GREM2；
ENSG00000164736 SOX17；
ENSG00000013810 TACC3；
ENSG00000135547 HEY2；
ENSG00000162073 PAQR4；
ENSG00000167183 MGC11242；
ENSG00000176387 HSD11B2；
ENSG00000138160 KIF11；
ENSG00000158458 NRG2；
ENSG00000130558 OLFM1；
ENSG00000106078 COBL；
ENSG00000131747 TOP2A；
ENSG00000080986 KNTC2；
ENSG00000168502 KIAA0802；
ENSG00000188488 SERPINA5；
ENSG00000126787 DLG7；
ENSG00000066279 ASPM；
ENSG00000124664 SPDEF；
ENSG00000117009 KMO；
ENSG00000157613 CREB3L1；
ENSG00000143153 ATP1B1；
ENSG00000198721 PECI；
ENSG00000138413 IDH1；
ENSG00000159231 CBR3；
ENSG00000066382 C11orf8；
ENSG00000166165 CKB；
ENSG00000134917 ADAMTS8；
ENSG00000023445 BIRC3；
ENSG00000095397 DFNB31；
ENSG00000131773 KHDRBS3；
ENSG00000257594 GALNT4；
ENSG00000124225 TMEPAI；
ENSG00000090889 KIF4A；
ENSG00000123700 KCNJ2；
ENSG00000138376 BARD1；
ENSG00000108984 MAP2K6；
ENSG00000046651 OFD1；
ENSG00000144837 PLA1A；
ENSG00000197275 RAD54B；
ENSG00000168078 PBK；
ENSG00000165795 NDRG2；
ENSG00000143369 ECM1；
ENSG00000198901 PRC1；
ENSG00000163132 MSX1；
ENSG00000157456 CCNB2；
ENSG00000137269 LRRC1；
ENSG00000140263 SORD；
ENSG00000182580 EPHB3；
ENSG00000158164 TMSL8；
ENSG00000101265 RASSF2；
ENSG00000123607 TTC21B；
ENSG00000082556 OPRK1；
ENSG00000131620 TMEM16A；
ENSG00000143320 CRABP2；
ENSG00000140525 FLJ10719；
ENSG00000065675 PRKCQ；
ENSG00000062822 CDC2；
ENSG00000156970 BUB1B；
ENSG00000127954 STEAP4；
ENSG00000164683 HEY1；
ENSG00000115380 EFEMP1；
ENSG00000100385 IL2RB；
ENSG00000113916 BCL6；
ENSG00000072840 EVC；
ENSG00000130707 ASS1；
ENSG00000164136 IL15；
ENSG00000198848 CES1；
ENSG00000114573 ATP6V1A；
ENSG00000138792 ENPEP；
ENSG00000071967 CYBRD1；
ENSG00000137731 FXYD2；
ENSG00000258818 RNASE4；
ENSG00000109819 PPARGC1A；
ENSG00000205358 MT1H；
ENSG00000139112 GABARAPL1；
ENSG00000111348 ARHGDIB；
ENSG00000173621 LRFN4；
ENSG00000162645 GBP2；
ENSG00000173762 CD7；
ENSG00000175482 POLD4；
ENSG00000180448 HMHA1；
ENSG00000176485 HRASLS3；
ENSG00000160678 S100A1；
ENSG00000139211 AMIGO2；
ENSG00000205220 PSMB10；
ENSG00000196177 ACADSB；
ENSG00000075426 FOSL2；
ENSG00000164035 EMCN；
ENSG00000184500 PROS1；
ENSG00000180176 TH；
ENSG00000154734 ADAMTS1；
ENSG00000090530 LEPREL1；
ENSG00000084110 HAL；
ENSG00000278053 DDX52；
ENSG00000153233 PTPRR；
ENSG00000164022 SCYE1；
ENSG00000167244 IGF2；
ENSG00000125144 MT1G；
ENSG00000162692 VCAM1；
ENSG00000189143 CLDN4；
ENSG00000197614 MFAP5；
ENSG00000130513 GDF15；
ENSG00000135480 KRT7；
ENSG00000122140 MRPS2；
ENSG00000173338 KCNK7；
ENSG00000054654 SYNE2；
ENSG00000180447 GAS1；
ENSG00000171564 FGB；
ENSG00000071282 LMCD1；
ENSG00000141441 FAM59A；
ENSG00000172201 ID4；
ENSG00000103187 COTL1；
ENSG00000107796 ACTA2；
ENSG00000007062 PROM1；
ENSG00000106541 AGR2；
ENSG00000134873 CLDN10；
ENSG00000126458 RRAS；
ENSG00000100234 TIMP3；
ENSG00000125148 MT2A；
ENSG00000143185 XCL2；
ENSG00000133321 RARRES3；
ENSG00000155792 DEPDC6；
ENSG00000126746 NP；
ENSG00000156234 CXCL13；
ENSG00000164107 HAND2；
ENSG00000141401 IMPA2；
ENSG00000163431 LMOD1；
ENSG00000147465 STAR；
ENSG00000154153 FLJ20152；
ENSG00000111371 SLC38A1；
ENSG00000165507 C10orf10；
ENSG00000158825 CDA；
ENSG00000145649 GZMA；
ENSG00000095383 TBC1D2；
ENSG00000148702 HABP2；
ENSG00000107984 DKK1；
ENSG00000118785 SPP1；
ENSG00000164825 DEFB1；
ENSG00000150347 ARID5B；
ENSG00000196975 ANXA4；
ENSG00000081181 ARG2；
ENSG00000124107 SLPI；
ENSG00000102879 CORO1A；
ENSG00000105374 NKG7；
ENSG00000131203 INDO；
ENSG00000115523 GNLY；
ENSG00000196154 S100A4；
ENSG00000165272 AQP3；
ENSG00000125730 C3；
ENSG00000137331 IER3；
ENSG00000170412 GPRC5C；
ENSG00000120885 CLU；
ENSG00000162496 DHRS3；
ENSG00000101335 MYL9；
ENSG00000172543 CTSW；
ENSG00000138356 AOX1；
ENSG00000106258 CYP3A5；
ENSG00000139278 GLIPR1；
ENSG00000118849 RARRES1；
ENSG00000173210 ABLIM3；
ENSG00000136160 EDNRB；
ENSG00000184502 GAST；
ENSG00000177519 RPRM；
ENSG00000133962 C14orf161；
ENSG00000096006 CRISP3；
ENSG00000197766 CFD；
ENSG00000149131 SERPING1；
ENSG00000186340 THBS2；
ENSG00000173083 HPSE；
ENSG00000125384 PTGER2；
ENSG00000146678 IGFBP1；
ENSG00000088386 SLC15A1；
ENSG00000149591 TAGLN；
ENSG00000134545 KLRC1；
ENSG00000169242 EFNA1；
ENSG00000150594 ADRA2A；
ENSG00000143184 XCL1；
ENSG00000214274 ANG；
ENSG00000108846 ABCC3；
ENSG00000124466 C4.4A；
ENSG00000123689 G0S2；
ENSG00000047457 CP；
ENSG00000086300 SNX10；
ENSG00000163993 S100P；
ENSG00000110484 SCGB2A2；
ENSG00000197635 DPP4；
ENSG00000166741 NNMT；
ENSG00000178726 THBD；
ENSG00000181143 MUC16；
ENSG00000116717 GADD45A；
ENSG00000112096 SOD2；
ENSG00000189221 MAOA；
ENSG00000196878 LAMB3；
ENSG00000127324 TSPAN8；
ENSG00000123838 C4BPA；
ENSG00000145824 CXCL14；
ENSG00000134827 TCN1；
ENSG00000128342 LIF；
ENSG00000106688 SLC1A1；
ENSG00000105664 COMP；
ENSG00000122133 PAEP；
ENSG00000211445 GPX3。
在另一优选例中，所述子宫内膜容受性相关基因还包括额外的基因，使得总基因数量达到10000个。
本发明第二方面提供了一种生物标志物集合，所述的集合包括选自表A的至少70％、较佳地，至少80％，更佳地，至少90％，更佳地，至少95％的基因。
在另一优选例中，所述生物标志物集合至少包括选自表A的40个基因。
在另一优选例中，所述生物标志物集合至少包括选自表A的147个基因。
在另一优选例中，所述生物标志物集合至少包括选自表A的259个基因。
在另一优选例中，所述生物标志物集合还包括额外的5-200个基因。
在另一优选例中，所述生物标志物集合还包括额外的基因，使得总基因数量达到10000个。
在另一优选例中，所述生物标志物集合用于判断子宫内膜容受性，或用于制备一试剂盒或试剂，所述的试剂盒或试剂用于评估待测对象的的子宫内膜容受性状态或诊断(包括早期诊断和/或辅助诊断)待测对象的子宫内膜容受性的状态。
在另一优选例中，所述的生物标志物或生物标志物集合来源子宫内膜组织、宫腔液、宫腔灌洗液、阴道脱落细胞、阴道分泌物、子宫内膜的活检产物、血清、血浆样本。
在另一优选例中，与预定值进行比较，一个或多个选自表A的生物标志物增加，表明待测对象存在子宫内膜容受性。
在另一优选例中，通过选自下组的方法对各个生物标志物进行鉴定：RT-qPCR，RT-qPCR芯片，二代测序，表达谱芯片，甲基化芯片，三代测序、或其组合。
在另一优选例中，所述的集合用于评估待测对象的子宫内膜容受性的状态。
本发明第三方面提供了一种用于子宫内膜容受性状态判断的试剂组合，所述试剂组合包括用于检测本发明第二方面所述的集合中各个生物标志物的试剂。
在另一优选例中，所述的试剂包括用选自下组的方法检测本发明第二方面所述的集合中各个生物标志物的物质：RT-qPCR，RT-qPCR芯片，二代测序，表达谱芯片，甲基化芯片，三代测序、或其组合。
本发明第四方面提供了一种试剂盒，所述的试剂盒包括本发明第二方面所述的集合和/或本发明第三方面所述的试剂组合。
本发明第五方面提供了一种生物标志物集合的用途，用于制备一试剂盒，所述的试剂盒用于评估待测对象的子宫内膜容受性的状态，其中，所述生物标志物集合包括选自表A的至少70％、较佳地，至少80％，更佳地，至少90％，更佳地，至少95％的基因。
在另一优选例中，所述的评估或诊断包括步骤：
(1)提供一来源于待测对象的样品，对样品中所述集合中各个生物标记物的水平进行检测；
(2)将步骤(1)测得的水平与一预定值进行比较。
在另一优选例中，所述的样品选自下组：子宫内膜组织、宫腔液、宫腔灌洗液、阴道脱落细胞、阴道分泌物、子宫内膜的活检产物、血清、血浆、或其组合。
在另一优选例中，与预定值进行比较，一个或多个选自表A的生物标志物增加，表明待测对象存在子宫内膜容受性。
在另一优选例中，在步骤(1)之前，所述的方法还包括对样品进行处理的步骤。
本发明第六方面提供了一种评估待测对象的子宫内膜容受性状态的方法，包括步骤：
(1)提供一来源于待测对象的样品，对样品中集合中各个生物标记物的水平进行检测，所述集合包括选自表A的至少70％、较佳地，至少80％，更佳地，至少90％，更佳地，至少95％的基因；
(2)将步骤(1)测得的水平与一预定值进行比较。
本发明第七方面提供了一种评估待测对象的子宫内膜容受性状态的系统，所述系统包括：
(a)子宫内膜容受性状态的特征输入模块，所述输入模块用于输入待测对象的子宫内膜容受性状态的特征；
其中所述的子宫内膜容受性状态的特征包括选自表A的至少70％、较佳地，至少80％，更佳地，至少90％，更佳地，至少95％的基因；
(b)子宫内膜容受性状态的判别处理模块，所述处理模块对于输入的子宫内膜容受性状态的特征，按预定的判断标准进行评分处理，从而获得子宫内膜容受性状态评分；并且将所述子宫内膜容受性状态评分与预定值进行比较，从而得出辅助诊断结果，其中，当所述子宫内膜容受性状态评分高于所述预定值时，则提示该对象具有子宫内膜容受性；和
(c)辅助诊断结果输出模块，所述输出模块用于输出所述的辅助诊断结果。
在另一优选例中，所述子宫内膜容受性相关基因至少包括选自表A的40个基因。
在另一优选例中，所述子宫内膜容受性相关基因至少包括选自表A的147个基因。
在另一优选例中，所述子宫内膜容受性相关基因至少包括选自表A的259个基因。
在另一优选例中，所述子宫内膜容受性相关基因还包括额外的5-200个基因。在另一优选例中，所述子宫内膜容受性相关基因还包括额外的基因，使得总基因数量达到10000个。
在另一优选例中，所述的对象是人。
在另一优选例中，所述的评分包括(a)单个特征的评分；和/或(b)多个特征的评分之和。
在另一优选例中，所述的特征输入模块选自下组：样本采集器、样本保存管、细胞裂解与核酸样本提取试剂盒、RNA核酸逆转录与扩增试剂盒、二代测序文库构建试剂盒、文库定量试剂盒、测序反应试剂盒、或其组合。
在另一优选例中，所述的子宫内膜容受性状态的判别处理模块包括一处理器，以及一储存器，其中所述的储存器中存储有基于子宫内膜容受性状态特征的子宫内膜容受性状态的评分数据。
在另一优选例中，所述的输出模块包括报告系统。
应理解，在本发明范围内中，本发明的上述各技术特征和在下文(如实施例)中具体描述的各技术特征之间都可以互相组合，从而构成新的或优选的技术方案。限于篇幅，在此不再一一累述。
附图说明
图1为本发明实施例4中样本cDNA扩增产物分布图；
图2为本发明实施例7所用监督学习方法的流程概述；
图3为本发明实施例7数据处理的流程图；
图4为本发明实施例7中患者检测后的结果示意图；
图5为本发明实施例7受试者检测模式图。
具体实施方式
本发明人经过广泛而深入地研究，首次意外地发现了判断子宫内膜容受性的生物标志物及其组合。具体地，本发明发现了一种生物标志物集合，所述的集合包括可以用于评估待测对象的的子宫内膜容受性状态或诊断(包括早期诊断和/或辅助诊断)待测对象的子宫内膜容受性的状态，可极大减少错误率，具有重要的应用价值。在此基础上，发明人完成了本发明。
术语
本发明所用术语具有相关领域普通技术人员通常理解的含义。然而，为了更好地理解本发明，对一些定义和相关术语的解释如下：
根据本发明，术语“子宫内膜容受性”，是指子宫内膜对胚胎的接受能力,只有在短暂的特定时期内子宫内膜才允许胚胎着床。
根据本发明，术语“生物标志物集合”是指一种生物标志物、或两种及两种以上生物标志物的组合。
根据本发明，生物标志物质的水平通过RT-qPCR，RT-qPCR芯片，二代测序，表达谱芯片，甲基化芯片，三代测序等方法进行鉴定。
根据本发明，术语“生物标志物”，也称为“生物学标志物”，是指个体的生物状态的可测量指标。这样的生物标记物可以是在个体中的任何物质，只要它们与被检个体的特定生物状态(例如，疾病)有关系，例如，核酸标志物(例如DNA)，蛋白质标志物，细胞因子标记物，趋化因子标记物，碳水化合物标志物，抗原标志物，抗体标志物，物种标志物(种/属的标记)和功能标志物(KO/OG标记)等。生物标记物经过测量和评估，经常用以检查正常生物过程，致病过程，或治疗干预药理响应，而且在许多科学领域都是有用的。
根据本发明，术语“个体”指动物，特别是哺乳动物，如灵长类动物，最好是人。
根据本发明，术语“血浆”指的是全血的液体成分。根据所使用的分离方法，血浆可能完全不含细胞成分，也可能含有不同量的血小板和/或少量其它细胞成分。
根据本发明，术语如“一”、“一个”和“这”不仅指单数的个体，而是包括可以用来说明特定实施方式的通常的一类。
需要说明的是，在此提供术语的解释仅为了使本领域技术人员更好地理解本发明，并非对本发明限制。
检测方法
在本发明中，通过选自下组的方法检测本发明的集合中各个生物标志物的物质：RT-qPCR，RT-qPCR芯片，二代测序，表达谱芯片，甲基化芯片，三代测序、或其组合。
试剂盒
在本发明中，本发明的试剂盒包括本发明第二方面所述的集合和/或本发明第三方面所述的试剂组合。
预定值
在本发明中，预定值是指通过人工智能或决策树C4.5算法(Decision Tree)、隐马尔可夫模型(HMM)、神经网络反向传播(BP)、支持向量机(SVM)、和各种聚类分析算法(包括简单聚类、层次聚类、K平均聚类、自组织映射神经网络、模糊聚类、贝叶斯分类法、K最邻近法、神经网络法、决策树法、投票分类法、主成分分析法等)对子宫内膜容受期(即临床上已经证明该内膜允许胚胎定位、黏附、着床于其上的时期)进行评分所获得的评分分值。
评估方法
在另一优选例中，本发明方法可通过公式S＝W1S1+W2S2+WiSi+……WnSn计算加权综合评分；
其中，W1、W2……Wn为权重；
S1、S2……Sn为每个标志物的评分。
优选地，所述权重可以基于表9中的分析价值。例如对于子宫内膜容受性的评估而言，任一权重(如W1)可以为表9中相应标志物的分析价值。
在一优选实施方式中，
待测人群的S subject＝W1S1+W2S2+WiS3+……WnSn。
当待测人群的S subject＞预定值时，则表明该对象存在子宫内膜容受性状态。
本发明的实验结果表明，本发明的标志物可极大降低错误率，显著提高子宫内膜容受性状态判断或诊断的准确性。
判断子宫内膜容受性的分析模型的构建方法
在本发明中，判断子宫内膜容受性的分析模型的构建方法包括如下步骤：
经过高灵敏性RNA逆转录和从cDNA扩增，而后将cDNA进行二代测序前建库，上机测序，通过下机数据构建样本的表达谱信息，通过与生物信息学的分析和归类，确定子宫内膜容受性状态，准确的判断子宫内膜着床的窗口期，实现个体化精准判断。
在一优选实施方式中，本发明提供一种判断子宫内膜容受性的分析模型的构建方法，所述方法包括如下步骤：
(1)获取健康女性不同月经周期的样本，提取RNA、逆转录RNA并扩增cDNA；
(2)构建cDNA文库进行高通量测序；
(3)通过强化学习方法、无监督学习方法或监督学习方法，在不同类标的多个样本中比较不同基因的表达量，得到差异表达的基因，构建分析模型。
本发明以高灵敏的RNA逆转录与cDNA扩增流程为依托，以RNA-seq测序方法 为基础，获得了大量患者的子宫内膜、宫腔液或其他生殖内分泌相关体液或脱落物的表达谱信息。并针对这些样本，以取样时期、取样方式、表达谱特征的不同，以生物信息学、统计学以及机器学习的方式进行超高维度的分类和分型。依据分型的不同，判断子宫内膜的容受性状态。
监督学习的任务是学习一个模型，使模型对给定的任意的一个输入，对其都可以映射出一个预测结果，可以实现高维度预测分析。
本发明步骤(3)所述“学习”的“多个样本”指的是同一样本来源、不同个体的样本，比如102例容受期子宫内膜组织，205例容受前期子宫内膜组织，300例容受后期子宫内膜组织等。
优选地，步骤(1)所述不同月经周期为三个时期。
优选地，所述三个时期的中间时期为LH+7，或排卵后的第5天。
本发明使用的检验样本为处于自然月经周期的女性受试者，在促黄体生成素(LH)峰值出现后的第七天(LH+7)进行子宫内膜活检，或者，处于激素替代(HRT)周期的女性受试者，在排卵后第5天(P+5)进行子宫内膜活检。不适用于有子宫内膜病变者(包括宫腔粘连内膜息肉内膜结核等)、有输卵管积水且未行输卵管近端结扎者、有粘膜下子宫肌瘤或突向宫腔的肌壁间子宫肌瘤或腺肌瘤患者以及子宫内膜异位症(III–IV期)患者。
优选地，所述三个时期还包括中间时期前后各1-3天的时期，优选为2天的时期。
优选地，所述中间时期为容受期，中间时期前1-3天为容受前期，中间时期后1-3天为容受后期。
本发明中，模型所用“训练数据”或称“训练集”的入组标准为：自然周期中不同的健康中国女性，没有既往病史，没有原发性不孕证，体重指数在19-25kg/m 2之间。随着大量样本的入组、测试和入组病例临床结局的积累，发明人掌握了这些病例的子宫内膜组织、宫腔液或其他生殖内分泌相关体液或脱落物“容受期”表达谱，(经过对临床结局的追踪，如该时期进行胚胎植入，均能有效着床并发育，那么就将该时期定义为“容受期”)，同时我们也对这些病例的“容受窗口期”前两天、“容受窗口期”后两天进行取样，获得相应的RNA-seq数据，将其类标分别定义为“容受前期”、“容受后期”。
本发明中，通过分别获取相同个体的不同月经周期的样本，并测序比较不同周期样本基因表达谱特征，可以更好地指导差异表达基因，降低假阳性概率。
优选地，步骤(1)所述样本包括宫底子宫内膜组织、宫腔液或阴道脱落物中的任一种或至少两种的组合。
本发明中，样本可以是子宫内膜的活检产物，也可以是通过无创手段取得的患者宫腔液，甚至是宫腔灌洗液、阴道脱落细胞和阴道分泌物，样本来源广泛，取样方便快捷，增加女性的依从度，同时验证同一个体的不同来源样本可提高基因表达谱特征的准确性；宫腔灌洗液、阴道脱落物或阴道分泌物样本量小，常规检测方法中需要大量的样本，因此只能选择子宫内膜的活检产物，而本申请通过微量的样本即可满足测试需要，因此扩大了样本种类，降低受试者的痛苦与不适。
优选地，所述宫底子宫内膜组织的取样量大于5mg，优选为5-10mg，例如可以是5mg、6mg、7mg、8mg、9mg或10mg。
优选地，所述宫腔液的取样量大于10μL，优选为10-15μL，例如可以是10μL、11μL、12μL、13μL、14μL或15μL。
优选地，所述阴道脱落物的取样量大于5mg，优选为5-10mg，例如可以是5mg、6mg、7mg、8mg、9mg或10mg。
本发明中，样本取样量小，准确率高，无需大量样本即可准确预测样本的容受性状态。
优选地，步骤(2)所述文库中cDNA的浓度为不小于5ng/μL。
优选地，步骤(2)所述测序包括RNA-Seq测序和/或qPCR测序。
本发明中，RNA-Seq测序方法较芯片测序更具优势，所能检测到的差异表达基因数量是芯片测序的2-8倍，针对低丰度基因检测的准确性，RNA-Seq的qPCR验证率是芯片的5倍，差异表达倍数准确性方面，RNA-Seq与qRCR相关性比芯片高14％，本发明所用RNA-Seq或qPCR测序方法为本领域技术人员常用技术手段。
优选地，步骤(2)所述测序的读长大于45nt。
优选地，步骤(2)所述测序的reads数不少于2.5兆reads。
本发明中，测序的读长大于45个碱基，测序的读段数不少于2.5兆以满足测序需要。
本发明中，特异性选择上机测序的读长与reads数，在保证准确性的同时降低实验周期和成本。
优选地，步骤(3)之前还包括数据预处理步骤。
优选地，所述数据预处理步骤为对基因长度和测序深度进行标准化。
优选地，所述标准化的方法包括RPKM、TPM或FPKM中的任一种或至少两种的组合，优选为FPKM。
本发明中，在获取不同类标的RNA-Seq数据后，使用FPKM(Fragments Per Kilobase Million)对基因长度和测序深度进行标准化，以排除测序深度的影响，同类的标准化方法还有RPKM(Reads Per Kilobase Million)和TPM(Trans Per Million)，本发明优选FPKM进行标准化。
FPKM适用于双端测序文库或单端测序文库，因而比较灵活，易于产品化；而RPKM只适用于单端测序文库；TPM数值能体现出比对上某个基因的reads的比例，使得该数值可以直接进行样本间的比较，但过程比较繁琐，运算缓慢，批量分析的效率不高。
在本发明的监督学习中，通过使用带有容受前期、容受窗口期、容受后期类标的训练数据构建模型，可以通过训练得到的模型对未知的数据(指新样本)的容受性状态进行预测。比如，新输入一例样本的宫腔液表达谱，使用该机器学习模型的判断其容受性状态。
优选地，所述类标为子宫内膜组织、宫腔液或阴道脱落物在不同容受状态下的表达谱特征。
本发明中，针对同一个体获取不同来源的样本，包括子宫内膜组织、宫腔液或阴道脱落物中的任一种或至少两种的组合在不同容受状态下的表达特征谱，显著提高预测结果可信度。
优选地，所述差异表达基因的分析方法为：找到每个样本FPKM>0的所有基因，再筛选容受前期与容受期、容受前期与容受后期、容受期与容受后期的差异表达基因的交集，满足p_value<0.05，且满足Fold_change>2或Fold_change<0.5。
本发明中，通过对样本的筛选，排除在不同类标中始终高表达或始终低表达的基因对分析模型的影响，为了后续分析模型取得良好的拟合效果同时确保排除过拟合的情况。
优选地，步骤(3)所述监督学习方法包括朴素贝叶斯、决策树、逻辑回归、KNN或支持向量机中的任一种或至少两种的组合，优选为支持向量机。
支持向量机对原始数据的分布没有很多限制，不需要先验信息，而RNA-Seq测序获得的数据量极大，不同基因有着不同的表达量，支持向量机可以保持一个超多维(超高维度)的分析，使模型更加精准。
优选地，所述支持向量机的脚本为：
library(e1071)
svm.model<-svm(data.class～.,data4,kernel＝'linear')
summary(svm.model)
table(data$class,predict(svm.model,data4,type＝"data.class"))
mydata＝read.table(file.choose(),header＝T,row.names＝1)
mydata2＝log2(mydata+1)
predict(svm.model,mydata2)
table(shdata3$shdata.class,predict(svm.model,shdata3,type＝"shdata.class"))
##
table(testdata$data.class,predict(svm.model,testdata,type＝"data.class"))
shdata＝read.table(file.choose(),header＝T,row.names＝1)
shdata2＝log2(shdata[,-length(shdata[1,])]+1)
shdata3＝data.frame(shdata2,shdata$class)
predict(svm.model,shdata3)
table(shdata3$shdata.class,predict(svm.model,shdata3,type＝"shdata.class")).
优选地，所述判断子宫内膜容受性的分析模型的构建方法具体包括如下步骤：
(1)获取健康女性容受前期、容受期和容受后期的样本，分别提取RNA、逆转录RNA并扩增cDNA；
(2)构建cDNA文库，富集并纯化文库，文库浓度不低于5ng/μL，进行高通量上机测序，读长大于45nt，不少于2.5兆reads；
(3)由步骤(2)得到的子宫内膜组织、宫腔液或阴道脱落物在不同容受状态下的表达谱特征作为类标，采用监督学习的方法，使用带类标的数据进行模型训练，在不同类标的多个样本中比较不同基因的表达量，在获取不同类标的RNA-Seq测序数据后，使用FPKM法对基因长度和测序深度进行标准化，通过标准化过程，在不同类标的多个样本中比较不同基因的表达量，排除在不同类标中始终高表达或低表达基因对模型的影响，分析得到差异表达的基因；
(4)采用支持向量机的学习模型，通过使用带有容受前期、容受期和容受后期类标的训练数据构建得到分析模型，对未知的数据容受性状态进行预测，为了提高判断准确率可在多个周期内反复调整取样时间，然后进行容受性测试。
本发明中，通过RNAseq得到下机数据，FPKM进行表达量标准化。比较同一个体不同时期基因表达量差异，差异显著的标记为表达差异基因。找到同一样本容 受前期，容受期，容受后期的表达差异最显著的基因。然后用多个个体的不同时期来优化“表达差异基因”，通过机器学习构建三个时期的特征库，对于新来某个待测样本，进行表达量标准化后，根据机器对特征的判别，进行自动归类。本发明的模型构建方法所用的训练集庞大，通过特异性显著的表达差异基因配合支持向量机，充分训练机器，并通过长期跟踪临床结局调整模型，提高模型的准确度。
本发明中，为获得不同个体的个体性容受期可在多个周期内反复调整取样时间，如第一次检测为LH+5，则下一周期测试需推迟2日以获得LH+7；若第一次检测为LH+9，则下一周期测试需提前2日以获得LH+7。
本发明的主要优点包括：
(a)本发明的生物标志物可准确判断子宫内膜容受性的状态，极大降低了错误率，具有重要的应用价值。
(b)本发明以高灵敏的RNA逆转录与cDNA扩增流程为依托，以RNA-seq测序方法为基础，获得了大量患者的子宫内膜、宫腔液或其他生殖内分泌相关体液或脱落物的表达谱信息。并针对这些样本，以取样时期、取样方式、表达谱特征的不同，以生物信息学、统计学以及机器学习的方式进行超高维度的分类和分型。依据分型的不同，判断子宫内膜的容受性状态。
(c)本发明提供了一种稳定地将基因表达谱特征用于判断子宫内膜容受性的模型构建方法，整体优化并调整RNA提取、逆转录、cDNA纯化建库、上机测序并进行相应的数据处理分析建模，显著提高判断子宫内膜容受性的准确度。
(d)本发明的方法具有无创宫腔液活检带来的低伤害性，以及流程快周期短的带来的简便性等特点。
下面结合具体实施例，进一步阐述本发明。应理解，这些实施例仅用于说明本发明而不用于限制本发明的范围。下列实施例中未注明具体条件的实验方法，通常按照常规条件，或按照制造厂商所建议的条件。除非另外说明，否则百分比和份数按重量计算。
如无特别说明，本发明实施例中所用的试剂和材料均为市售产品。
材料
Qiagen生产的Qiagen RNeasy Micro Kit进行，货号74004；
亿康基因生产的MALBAC白金微量RNA扩增试剂盒进行，货号KT110700724；
Zymo生产的DNA Clean&Concentrator-5，货号D4014；
亿康基因生产的基因测序文库试剂盒(Illumina转座酶法)货号：KT100801924；
Agilent 2100生物分析仪；
高灵敏度DNA芯片；
以下实施例中采用的材料不限于上述列举，可用其他同类材料替代，仪器未注明具体条件的，按照常规条件，或按照制造厂商所建议的条件，本领域技 术人员应当掌握使用常规材料及仪器的相关知识。
实施例1样本的预处理及RNA提取
妇产科医生或有活检样本获取资质的专业人员取样，样本包括大于5mg的宫底子宫内膜组织、大于10ul的宫腔液或大于10ul其他生殖内分泌相关体液、大于5mg阴道脱落物。活检完成后，尽快将组织、脱落物或液体完全浸润于RNA保存液(约20uL RNA Later)中。样品运输之前，样品保存于-20℃或-80℃冰箱。
使用Qiagen生产的Qiagen RNeasy Micro Kit提取子宫内膜组织的RNA，具体方法如下：
1.实验准备：使用RNA酶清除剂和核酸去污剂擦拭双手、移液器以及实验台面(请注意，所有的提取步骤都应于无RNA酶污染区进行)。
2.用去离子水和无水乙醇配制70％乙醇溶液(每样品350uL)以及80％乙醇溶液(每样品500uL)备用。
3.向Buffer RPE(随试剂盒提供)中加入4倍体积无水乙醇，混匀后备用。
4.在通风橱中，取10uLβ-巯基乙醇加入到990uLbufferRLT中(每样品将会使用350uL混合液)，混匀备用(请注意，每次抽提需现配现用)。
5.向DNase I冻干粉(随试剂盒提供)中加入550uLRNasefree水，上下颠倒5次混匀，室温静置2min。将所得液体分装(标记为DNase I solution)，并储存于-20℃冰箱备用，反复冻融不宜超过3次。
6.取10uL DNase I solution(步骤5获得)于无RNA酶的EP管中，再加入70uLbufferRDD，吹打混匀，置于冰上备用(标记为DNase I混合液)。每次抽提需现配现用。
7.从-80℃冰箱取出样品，置于冰上解冻后，将样本连同保存液转移至无RNA酶的1.5mL EP管中。
8.向EP管中加入350uL含有β-巯基乙醇的Buffer RLT(步骤4获得)，涡旋振荡30s，并短暂离心。
9.向EP管内继续加入350uL 70％乙醇，涡旋振荡30s，并短暂离心。
10.小心吸取上清650uL(注意不要吸取到未裂解的组织碎片)，转移至吸附柱中(随试剂盒提供)。
11.14000xg离心30s，倒掉收集套管中的废液。
12.向吸附柱中加入350uL Buffer RW1(随试剂盒提供)，14000xg离心30s，倒掉收集套管中的废液。
13.小心向吸附柱的中心处加入80uL DNase I混合液，室温孵育20min。
14.向吸附柱中加入350uL Buffer RW1(随试剂盒提供)，14000xg离心30s，倒掉收集套管中的废液。
15.向吸附柱中加入500uL含有无水乙醇的Buffer RPE(步骤3获得)，14000xg离心30s，倒掉收集套管中的废液。
16.向吸附柱中加入500uL80％乙醇(步骤2获得)，14000xg离心30s，倒掉收集套管中的废液。
17.将空吸附柱插入收集套管14000xg离心2min。丢弃收集套管。
18.将吸附柱插入新的1.5mL无RNA酶的EP管中，开盖晾干1min。
19.小心向吸附柱中心处加入21uL RNase Free水，室温孵育1min，17000xg离心2min，收集到的液体既为RNA。
20.取1uLRNA使用Qubit RNA HS kit进行定量，定量后的RNA存储于-80℃冰箱备用。
实施例2RNA逆转录与cDNA扩增反应
1.实验准备：使用RNA酶清除剂和核酸去污剂擦拭双手、移液器以及超净台台面。在超净中准备无RNA酶及核酸污染的枪头、1.5mL EP管以及0.2mL PCR管，打开超净台紫外照射30min(请注意，步骤2至11都应于无RNA酶及核酸污染的超净台中进行)。
2.RNA样品及Lysis Buffer(随试剂盒提供)置于冰上解冻，解冻后涡旋振荡，短暂离心后置于冰上备用。
3.每个样品取2uL RNA置于1.5mL EP管中。根据样品已测定的RNA浓度，用RNase Free水将RNA样品稀释至约5ng/uL。
4.向0.2mL PCR管中依次加入1.5uL Lysis Buffer、3.5uL稀释后的RNA样品。
5.设置逆转录阴性对照(RT-NC)：用3.5uL RNase Free水代替步骤4中的RNA样品。
6.取13.3uL*(反应份数+移液损耗)RT buffer于新的0.2mL PCR中。(每个PCR管中的体积不宜超过50uL)。
7.预热PCR仪后，将步骤4-6中的PCR管置入PCR仪72℃孵育3min。
8.立即将PCR管置于冰上孵育至少2min后，短暂离心。
9.从-20℃冰箱中取出RT Enzyme Mix(随试剂盒提供)，短暂离心，切勿震荡，置于冰上备用。
10.向72℃孵育后的RT Buffer(步骤6)中加入1.7uL*(反应份数+移液损耗)RT Enzyme Mix。轻轻吹打混匀后置于冰上备用。混合物标记为“RT mix”。
11.吸取15uL RT mix(步骤10)加入含有RNA样品或RT-NC的PCR管(步骤4-5)中，轻轻吹打混匀并短暂离心后置于冰上。
12.在预热的PCR仪上孵育样本，条件见表1。
表1 PCR条件设置
Figure PCTCN2020078074-appb-000001
13.从-20℃冰箱中取出PCR Mix(随试剂盒提供)。置于冰上解冻，上下颠倒混匀后，短暂离心，置于冰上备用。
14.向每个逆转录反应产物中加入30uL PCR Mix，轻轻吹打混匀并短暂离心后置于冰上备用。
15.设置PCR阴性对照(PCR-NC)：用20uL RNase Free水代替步骤3中的逆转录反应产物。
16.在预热的PCR仪上孵育样本，条件见表2。
表2 PCR反应条件
Figure PCTCN2020078074-appb-000002
备注：可根据样品酌情增减扩增循环数，适应性调整建议如表3所示。
表3不同样本量对应的参考循环数
总RNA 参考循环数
10-20ng 7-8
1ng 11-12
100pg 14-15
10pg 17-18
实施例3扩增产物的纯化
1.实验准备：必须与步骤逆转录和扩增步骤使用不同的试验区。使用核酸去污剂擦拭双手、移液器以及实验台面。
2.向Wash Buffer加入4倍体积无水乙醇，上下颠倒混匀后备用。
3.PCR产物于冰上静置2min后，短暂离心备用。
4.向1.5mL EP管中依次加入250uL DNA Binding Buffer以及50uL PCR产物。涡旋振荡混匀后，短暂离心。
5.将吸附柱插入收集套管中，并将步骤4中的300uL混合液移入吸附柱中。
6. 14000xg离心30s。
7.向吸附柱中加入200uL含有乙醇的Wash buffe，14000xg离心30s。
8.重复步骤7，并倒掉收集套管中的废液。
9.将吸附柱插回收集套管，14000xg离心2min。丢弃收集套管。
10.将吸附柱插入新的1.5mL EP管中，开盖晾干1min。
11.小心向吸附柱中心处加入30uL Elution buffer(DNA Clean&Concentrator-5提供)，室温孵育1min，17000xg离心2min，收集到的液体既为cDNA扩增产物。
12.取1uL RNA使用Qubit DNA HS kit进行定量。逆转录过程中设置的阴性对照RT-NC，扩增后浓度应小于2ng/uL，PCR过程中设置的阴性对照PCR-NC，扩增后浓度应小于0.4ng/uL。样品的cDNA扩增产物浓度应大于40ng/uL。RT-NC和PCR-NC无需进行后续测序步骤。
实施例4 cDNA产物的质检
取1μl纯化后的cDNA扩增产物合理稀释后进行检测，操作说明见High Sensitivity DNA Chip操作手册结果见图1。
一般情况下，样品cDNA扩增产物分布于400-10000bp，主峰位于2000bp左右，由图1可知，本申请所用cDNA复合质量要求。
实施例5转座建库
1.DNA片段化
(1)从-20℃拿出片段化缓冲液，室温解冻，震荡混匀，瞬时离心，备用。根据样本的数目(N)，反应体系见表4.
表4 PCR反应体系配制
组分 体积
片段化缓冲液 8.5uL×(N+1)
片段化酶 1uL×(N+1)
总体积 9.5uL×(N+1)
(2)将上述配制的片断化混合液各取9.5uL分装至0.2mL PCR管内，瞬时离心。取0.5uL(约10ng)MALBAC扩增产物，分别加入上步装有9.5uL片断化混合液的PCR管中。涡旋震荡，充分混匀，瞬时离心。
(3)将配制好的反应体系放置于PCR仪中，“热盖”选择“On,105℃”，拧紧热盖，反应程序见表5。
表5 PCR反应程序
Figure PCTCN2020078074-appb-000003
2.文库富集
(1)从-20℃拿出扩增缓冲液，室温解冻，震荡混匀，瞬时离心，备用。根据样本的数目(N)，反应体系见表6.
表6 PCR反应体系
组分 体积
扩增缓冲液 11.5uL×(N+1)
扩增酶 0.5uL×(N+1)
总体积 12uL×(N+1)
(2)涡旋震荡，充分混匀，瞬时离心。
(3)将上述配制的扩增反应混合液各取12uL加入“步骤1.1”的片断化产物中。向上述反应体系中各加入3uL标签引物，涡旋震荡，充分混匀，瞬时离心。
(4)记录每个样本对应的标签引物编号(注意：此处标签引物共24种，每个反应加入一种即可，同一批上机的样品标签引物不可重复)。
(5)将配制好的反应体系放置于PCR仪中，“热盖”选择“On,105℃”，拧紧热盖，反应程序如表7所示。
表7 PCR反应程序
Figure PCTCN2020078074-appb-000004
3.文库纯化
(1)从4℃取出磁珠，室温放置，平衡至室温。涡旋振荡磁珠20秒，使其彻底混匀为均一溶液。
(2)取构建的文库各20uL置于新的1.5mL离心管中，分别加入0.6×重悬的磁珠(例如：初始文库体积为20uL，加入12uL磁珠)，涡旋震荡混匀，瞬时离心，室温静置5分钟。
(3)瞬时离心，将离心管放于磁力架上使磁珠与上清液分离，约5分钟后，溶液澄清，保持离心管在磁力架上，小心打开管盖，防止液体溅出，小心将上清液转移至一新的1.5mL离心管中(注意：不要吸到磁珠)，并弃去磁珠(注意：不要弃上清)。
(4)向上清中加入起始文库体积0.15×的重悬磁珠(例如：初始文库体积为20uL，加入3uL磁珠)，涡旋振荡，混合均匀，瞬时离心，室温孵育5分钟。
(5)将离心管置于磁力架上使磁珠与上清液分离，约5分钟后，液体变得澄清，小心吸取上清并弃除(注意：不要弃磁珠，不要吹打磁珠)。
(6)继续保持离心管固定于磁力架上，向离心管中加入200uL左右新鲜配制的80％乙醇(注意：小心沿着管壁加入乙醇，不要将磁珠冲散，保证乙醇能够淹没磁珠)，室温放置30秒，小心弃除上清。
(7)重复上一步。
(8)保持离心管固定于磁力架上，室温静置10分钟左右，使乙醇完全挥发。
(9)将离心管从磁力架上取下，加入17.5uL洗脱液，涡旋振荡，使磁珠完全重悬，瞬时离心，室温放置5分钟，将离心管置于磁力架中使磁珠和液体分离，约5分钟后，溶液澄清，小心吸取15uL上清至新的离心管中(注意：不要吸到磁珠)，于-20℃保存。
4.文库质控
纯化后的文库可使用Qubit dsDNA HS Assay Kit分别进行定量，浓度一般在5ng/ul以上(为了得到高质量的测序结果，可采用实时荧光定量PCR方法进行定量)。
实施例6上机测序
实验步骤参考Illumina测试试剂盒说明书。上机策略：单端或双端均可，读长大于45nt，需保证2.5兆reads。
实施例7数据处理步骤
利用有明确临床结局的志愿者样本的表达谱当作训练数据，利用机器学习建立分析模型，而后输入位置数据，利用分析模型进行预判，具体的为：
1.采用监督学习的方法(如图2)，利用“训练数据”是带类标的，该类标为子宫内膜组织、宫腔液或其他生殖内分泌相关体液或脱落物在不同容受状态下的表达谱特征；
2.判断模型中“训练数据”或称“训练集”的入组标准为：自然周期中不同的健康中国女性，没有既往病史，没有原发性不孕证，体重指数在19-25kg/m 2之间，在获取三个类标的RNA-seq数据后，使用FPKM对基因长度和测序深度进行标准化，以排除测序深度的影响，通过标准化过程，在不同类标的(多个)样本中比较不同基因的表达量，进行基因差异表达的分析，具体的为：找到每个样本FPKM>0的所有基因，然后在所有进入训练集样本的FPKM>0的所有基因中寻找“容受前期”vs.“容受期”，“容受前期”vs.“容受后期”，“容受期”vs.“容受后期”差异基因的交集(满足p_value<0.05,Fold_change>2或<0.5的均满足选择的标准)，得到12734个差异表达基因，其中包括ENSG00000000003、ENSG00000104881、ENSG00000128928、ENSG00000151116、ENSG00000171222、ENSG00000198961、ENSG00000261732、ENSG00000000419、 ENSG00000104883、ENSG00000128944、ENSG00000151117、ENSG00000171223、ENSG00000198963、ENSG00000261740、ENSG00000000457、ENSG00000104886、ENSG00000128951、ENSG00000151131、ENSG00000171224、ENSG00000198964、ENSG00000261760等，上述基因编号均唯一确定地与NCBI网站数据库中的基因相对应(https://www.ncbi.nlm.nih.gov/)，因所得差异表达基因数量众多，限于篇幅因素不再赘述；
3.在本发明的监督学习中，通过使用带有容受前期、容受窗口期、容受后期类标的训练数据构建模型，我们可以通过训练得到的模型对未知的数据(指新病例)的容受性状态进行预测。比如，新输入一例病例的宫腔液表达谱，使用该机器学习模型的判断其容受性状态(如图3所述)；
4.采用支持向量机，将3中类标对应的样本作为输入变量，进行训练集构建，脚本入下：
library(e1071)
svm.model<-svm(data.class～.,data4,kernel＝'linear')
summary(svm.model)
table(data$class,predict(svm.model,data4,type＝"data.class"))
mydata＝read.table(file.choose(),header＝T,row.names＝1)
mydata2＝log2(mydata+1)
predict(svm.model,mydata2)
table(shdata3$shdata.class,predict(svm.model,shdata3,type＝"shdata.class"))
##
table(testdata$data.class,predict(svm.model,testdata,type＝"data.class"))
shdata＝read.table(file.choose(),header＝T,row.names＝1)
shdata2＝log2(shdata[,-length(shdata[1,])]+1)
shdata3＝data.frame(shdata2,shdata$class)
predict(svm.model,shdata3)
table(shdata3$shdata.class,predict(svm.model,shdata3,type＝"shdata.class"))；
5.根据支持向量机的结果，为未知样品容受状态做定义，判断该样品的子宫内膜容受性，根据容受性状态指导胚胎植入(如图4)；为获得良好的妊娠结局，可在多个周期内反复调整取样时间，然后进行容受性测试(如图5)。
实施例8临床验证
选择23-39岁不同不孕个体做对象，采用本发明构建的分析模型做预测，依据支持向量机的结果，为未知样品容受状态做定义，判断该样品的子宫内膜容受性，根据容受性状态指导胚胎植入，记录妊娠结局，结果见表8-1、8-2和8-3.
表8-1模型预测及妊娠结果
Figure PCTCN2020078074-appb-000005
表8-2模型预测及妊娠结果
Figure PCTCN2020078074-appb-000006
表8-3模型预测及妊娠结果
Figure PCTCN2020078074-appb-000007
样本时期、样本类型、既往病史、不孕症类型为样本的临床信息，RNA浓度、cDNA浓度、测序数据量、Unique Mapping Ratio、外显子占比为测序质控信息，支持向量机分类为机器学习判定的容受状态，植入方法为根据机器学习分析结果进行的植入时期调整。由表8-1、8-2和8-3可知，经过临床验证，19例不孕症妇女经本发明所述模型指导胚胎植入时机，均成功妊娠，说明本发明所述模型准确率极高有助于推动医学进步。
本发明的标志物的判断结果如表9所示。
表9
Figure PCTCN2020078074-appb-000008
Figure PCTCN2020078074-appb-000009
Figure PCTCN2020078074-appb-000010
Figure PCTCN2020078074-appb-000011
Figure PCTCN2020078074-appb-000012
Figure PCTCN2020078074-appb-000013
Figure PCTCN2020078074-appb-000014
Figure PCTCN2020078074-appb-000015
Figure PCTCN2020078074-appb-000016
Figure PCTCN2020078074-appb-000017
Figure PCTCN2020078074-appb-000018
Figure PCTCN2020078074-appb-000019
Figure PCTCN2020078074-appb-000020
Figure PCTCN2020078074-appb-000021
Figure PCTCN2020078074-appb-000022
Figure PCTCN2020078074-appb-000023
Figure PCTCN2020078074-appb-000024
Figure PCTCN2020078074-appb-000025
Figure PCTCN2020078074-appb-000026
Figure PCTCN2020078074-appb-000027
Figure PCTCN2020078074-appb-000028
Figure PCTCN2020078074-appb-000029
Figure PCTCN2020078074-appb-000030
Figure PCTCN2020078074-appb-000031
Figure PCTCN2020078074-appb-000032
Figure PCTCN2020078074-appb-000033
Figure PCTCN2020078074-appb-000034
Figure PCTCN2020078074-appb-000035
Figure PCTCN2020078074-appb-000036
Figure PCTCN2020078074-appb-000037
Figure PCTCN2020078074-appb-000038
Figure PCTCN2020078074-appb-000039
Figure PCTCN2020078074-appb-000040
Figure PCTCN2020078074-appb-000041
Figure PCTCN2020078074-appb-000042
Figure PCTCN2020078074-appb-000043
Figure PCTCN2020078074-appb-000044
Figure PCTCN2020078074-appb-000045
Figure PCTCN2020078074-appb-000046
Figure PCTCN2020078074-appb-000047
Figure PCTCN2020078074-appb-000048
Figure PCTCN2020078074-appb-000049
Figure PCTCN2020078074-appb-000050
Figure PCTCN2020078074-appb-000051
Figure PCTCN2020078074-appb-000052
Figure PCTCN2020078074-appb-000053
Figure PCTCN2020078074-appb-000054
Figure PCTCN2020078074-appb-000055
Figure PCTCN2020078074-appb-000056
Figure PCTCN2020078074-appb-000057
Figure PCTCN2020078074-appb-000058
Figure PCTCN2020078074-appb-000059
Figure PCTCN2020078074-appb-000060
Figure PCTCN2020078074-appb-000061
Figure PCTCN2020078074-appb-000062
Figure PCTCN2020078074-appb-000063
Figure PCTCN2020078074-appb-000064
Figure PCTCN2020078074-appb-000065
Figure PCTCN2020078074-appb-000066
Figure PCTCN2020078074-appb-000067
Figure PCTCN2020078074-appb-000068
Figure PCTCN2020078074-appb-000069
Figure PCTCN2020078074-appb-000070
Figure PCTCN2020078074-appb-000071
Figure PCTCN2020078074-appb-000072
Figure PCTCN2020078074-appb-000073
Figure PCTCN2020078074-appb-000074
Figure PCTCN2020078074-appb-000075
Figure PCTCN2020078074-appb-000076
Figure PCTCN2020078074-appb-000077
Figure PCTCN2020078074-appb-000078
Figure PCTCN2020078074-appb-000079
Figure PCTCN2020078074-appb-000080
Figure PCTCN2020078074-appb-000081
Figure PCTCN2020078074-appb-000082
Figure PCTCN2020078074-appb-000083
Figure PCTCN2020078074-appb-000084
Figure PCTCN2020078074-appb-000085
Figure PCTCN2020078074-appb-000086
Figure PCTCN2020078074-appb-000087
Figure PCTCN2020078074-appb-000088
Figure PCTCN2020078074-appb-000089
如表9所示，在本发明中，即使仅用单个标志物，只要高于预定值，这些标志物可子宫内膜容受性的有用的辅助判断或诊断信息，从而尤其用于早期和/或辅助诊断。
此外，当采用表9所述的多个标志物进行综合评判时，可以进一步地显著降低错误率(错误率仅为17.5％)，提高评判的准确性。
当采用多个标志物(含有额外的5-200个基因)时，错误率可低至16.5％。
当采用的标志物数量达到10000时，错误率仅为6.7％。
其中，错误率的计算方式为：以临床结局为金标准，进行胚胎植入成功的日期为容受期。准确率＝本方法判断为容受期的案例数/胚胎植入成功的总案例数；错误率＝本方法判断为非容受期的案例数/胚胎植入成功的案例数。
因此，从上述可知，本发明的标志物具有很高的预测价值，尤其是组合使用，可进一步降低子宫内膜容受性状态判断的错误率，提高评判的准确性。
此外，根据表11，本发明对表9的标志物进行进一步筛选，并获得了具有非常低错误率、高准确性效果的40个标志物、147个标志物、259个标志物，其中，空白指分析价值＜4。
表11
Figure PCTCN2020078074-appb-000090
此外，从表10可以看出，本发明的表9中的基因是核心基因，在此基础上，新增加的基因会提高正确率，但是新增加的基因的权重很低。
表10
Figure PCTCN2020078074-appb-000091
综上所述，本发明以高灵敏的RNA逆转录与cDNA扩增流程为依托，以RNA-seq测序方法为基础，获得了大量患者的子宫内膜、宫腔液或其他生殖内分泌相关体液或脱落物的表达谱信息。并针对这些样本，特异性地选择取样时期、取样方式获得不同的表达谱特征，以生物信息学、统计学以及机器学习的方式进 行超高维度的分类和分型。依据分型的不同，判断子宫内膜的容受性状态；本发明将表达谱特征用于判断胚胎植入前子宫内膜容受性的模型，准确地判断子宫内膜着床的窗口期。
申请人声明，本发明通过上述实施例来说明本发明的详细方法，但本发明并不局限于上述详细方法，即不意味着本发明必须依赖上述详细方法才能实施。所属技术领域的技术人员应该明了，对本发明的任何改进，对本发明产品各原料的等效替换及辅助成分的添加、具体方式的选择等，均落在本发明的保护范围和公开范围之内。
在本发明提及的所有文献都在本申请中引用作为参考，就如同每一篇文献被单独引用作为参考那样。此外应理解，在阅读了本发明的上述讲授内容之后，本领域技术人员可以对本发明作各种改动或修改，这些等价形式同样落于本申请所附权利要求书所限定的范围。

Claims (10)

  1. 一种判断子宫内膜容受性的方法，其特征在于，包括步骤：
    (a)提供一样本；
    (b)测定所述样本中的子宫内膜容受性相关基因的表达量；
    (c)将步骤(b)获得的子宫内膜容受性相关基因的表达量与预定值进行比较，从而判断子宫内膜容受性。
  2. 如权利要求1所述的方法，其特征在于，步骤(b)获得的子宫内膜容受性相关基因的表达量高于预定值时，则表明存在子宫内膜容受性。
  3. 如权利要求1所述的方法，其特征在于，所述样本为以下时期的样本：LH+n，LH+n+2，LH+n+4，其中，n为3-7，较佳地，n为4-6。
  4. 如权利要求1所述的方法，其特征在于，所述子宫内膜容受性相关基因包括选自表A的至少70％、较佳地，至少80％，更佳地，至少90％，更佳地，至少95％的基因：
    表A
      名称 1 KRIT1 2 RBM5 3 PAF1 4 RFC1 5 TPR 6 ACAA1 7 SFSWAP 8 SPEN 9 CPSF1 10 XRCC1 11 KDM5A 12 SART3 13 PIK3C3 14 YTHDC1 15 PPIE 16 NFX1 17 CDC5L 18 SF3A1 19 TXN2 20 EIF3D 21 EP300 22 CHD8 23 PNN 24 CSTF1 25 PRPF6
    26 PQBP1 27 CIAO3 28 HMOX2 29 PIH1D1 30 AKAP8 31 BUD31 32 EIF3A 33 CASC3 34 CDK5RAP3 35 SUPT6H 36 CNOT2 37 SUDS3 38 TBCCD1 39 EIF2B4 40 ORC2 41 SRSF4 42 SFPQ 43 SRSF11 44 PRRC2C 45 FBXW2 46 SNX19 47 EPC1 48 TBCC 49 CNOT1 50 GTF2F1 51 KDM5C 52 NSRP1 53 UXT 54 ATG14 55 AKAP9 56 PRKRIP1 57 CCNT1 58 LSM4 59 RLIM 60 ERAL1 61 PTPRA 62 NASP 63 SRRM1 64 PRPF38A 65 CDK5RAP2 66 PRPF4
    67 PPIG 68 SMARCC2 69 TCF25 70 CSNK1D 71 ENSA 72 TEX261 73 FIP1L1 74 CENPC 75 ZMAT2 76 CELF1 77 CPSF7 78 UPF2 79 MMS19 80 SON 81 ADAR 82 MAGOH 83 ELP6 84 NIPBL 85 SLU7 86 PCF11 87 NSD1 88 YWHAB 89 DDB1 90 SF1 91 ATG4B 92 FEM1B 93 SIN3A 94 LUZP1 95 GPS1 96 SF3B5 97 HNRNPA3 98 PYM1 99 RBM4 100 PRPF8 101 ZBTB4 102 CKAP5 103 SMAD2 104 POLR2A 105 RNF135 106 RNF41 107 MRPS11
    108 CEP63 109 EIF3C 110 SF3B3 111 SIAH1 112 SND1 113 UBL5 114 NELFE 115 EIF3CL 116 FIS1 117 TRIM26 118 MRPL20 119 KMT2E 120 AFF4 121 GTF3C1 122 ANAPC5 123 MAEA 124 TOX4 125 GID8 126 ARFGAP1 127 ARHGEF7 128 H2AFV 129 ZNHIT1 130 COA1 131 GBF1 132 GOSR1 133 IFT20 134 ANAPC15 135 IK 136 KANSL3 137 GTF3C2 138 CHMP3 139 FAM20B 140 CHCHD5 141 RPAIN 142 UBE4B 143 C19orf12 144 ANKRD17 145 MED6 146 TMEM258 147 ERCC5 148 ATP5MC2
    149 SMPD4 150 ECPAS 151 DMAC1 152 SEC24B 153 NCOR1 154 PI4KB 155 C1orf43 156 ASXL2 157 VTI1A 158 PPP1R15B 159 SNF8 160 GATD3A 161 MED11 162 RAD21 163 SPIDR 164 ANAPC16 165 VPS39 166 ATP5PD 167 FIBP 168 CORO1B 169 RAB1B 170 RMDN1 171 BET1L 172 ASB8 173 EXOC7 174 UQCR10 175 TOP1 176 SPOUT1 177 ARMCX6 178 PPP1R10 179 LIN52 180 SMIM7 181 TOMM6 182 PDCD6 183 GGNBP2 184 GATD3B 185 KIAA0100 186 ELOA 187 AQR 188 FBXO42 189 LSG1
    190 FAM120A 191 THRAP3 192 ARID4B 193 POLR3E 194 GPBP1 195 RFXANK 196 TAF11 197 BUD23 198 PDCD2 199 BCS1L 200 ZNF638 201 ZNF37A 202 EXOSC7 203 TOP2B 204 DELE1 205 GCN1 206 DDX24 207 DHPS 208 WAC 209 HPS4 210 PPP6R2 211 PACSIN2 212 HMGXB4 213 POLR3H 214 RBM23 215 ZC3H14 216 DCAF11 217 NDRG3 218 GYS1 219 CCDC130 220 DNAJC2 221 CHCHD2 222 TMEM248 223 NUFIP2 224 UBTF 225 MTMR4 226 RSRC2 227 KRR1 228 CHD4 229 ZNF451 230 SENP6
    231 PRPF4B 232 PRKAR2A 233 FXR1 234 HDLBP 235 PPP1R7 236 ASH1L 237 GON4L 238 TSNAX 239 HMGCL 240 MED28 241 NEK9 242 PANK3 243 SPOP 244 MTIF3 245 ZC3H13 246 SMUG1 247 RAB22A 248 STAU1 249 DDX27 250 SERPINB6 251 MEA1 252 COX6B1 253 TIMM17B 254 XPO7 255 SAFB2 256 EIF2S3 257 UBA1 258 RBM39 259 ACLY 260 DHX30 261 SCO1 262 LARS 263 PPHLN1 264 LPIN1 265 TIMM10 266 ARGLU1 267 TFCP2 268 C2orf49 269 SLTM 270 CIR1 271 TMOD3
    272 SBNO1 273 DCAF5 274 ANP32A 275 COMMD4 276 ARHGAP17 277 RHOT2 278 SERBP1 279 STRIP1 280 UFC1 281 MRPL9 282 UBAP2L 283 SDE2 284 SNRNP200 285 C7orf50 286 MDH2 287 NDUFB11 288 TAF1 289 EIF4EBP2 290 MTG1 291 NUDT22 292 VIPAS39 293 KIN 294 ATP5F1A 295 PELO 296 SAR1B 297 HNRNPDL 298 CCDC174 299 LARP1 300 SCAF4 301 APPL1 302 GPBP1L1 303 PSKH1 304 SSU72 305 CCDC12 306 ZYG11B 307 PMVK 308 KIAA1143 309 UBXN7 310 GAPVD1 311 NEMF 312 HIF1AN
    313 MARF1 314 NDUFV1 315 HARS 316 ATF7 317 AKAP13 318 QARS 319 ZNF24 320 FAM192A 321 MRPL57 322 CHD2 323 TOMM20 324 MGA 325 IP6K1 326 DNAJC30 327 IMP3 328 NDUFAF3 329 SPTY2D1 330 CLK3 331 MRPS23 332 TTC3 333 GPATCH8 334 USP7 335 LAMTOR4 336 TBC1D9B 337 GSTK1 338 QRICH1 339 DDX39B 340 GIGYF2 341 BRD2 342 GPANK1 343 PRRC2A 344 DHX16 345 NAP1L4 346 SELENOH 347 RBMXL1 348 ACBD6 349 FAM133B 350 CDKN2AIPNL 351 CDK11B 352 PRKDC 353 MYO19
    354 LAS1L 355 PPP1R12A 356 CCAR1 357 SMC1A 358 ARAF 359 HSP90AA1 360 CHERP 361 SRRT 362 SF3B2 363 HNRNPC 364 HNRNPM 365 RBX1 366 TELO2 367 UBE2I 368 TIMM50 369 PRPF31 370 TCERG1 371 TUSC2 372 EIF4G1 373 NCL 374 PRPF3 375 SNRPB 376 PRKCSH 377 TUBGCP2 378 EIF3G 379 SYNCRIP 380 HUS1 381 ACTR1A 382 MBD1 383 HDGF 384 PARP1 385 RPL7L1 386 RPUSD3 387 ACOX1 388 U2SURP 389 CPSF2 390 TSR1 391 RFWD3 392 CD2BP2 393 PCBP1 394 PA2G4
    395 PPID 396 HCFC1 397 FKBP2 398 BRMS1 399 EIF3K 400 PUF60 401 NOC2L 402 PRPF40A 403 RNPS1 404 DCP1A 405 CWC25 406 MED24 407 PHF20 408 EIPR1 409 KAT6A 410 PSMD8 411 NOP56 412 COPE 413 SSR3 414 COPA 415 THOC6 416 WDR74 417 PSMB7 418 HAX1 419 SURF6 420 VPS28 421 VKORC1 422 PSMD13 423 TMEM222 424 C6orf106 425 MRPL38 426 CSNK2B 427 PSMB3 428 CCDC124 429 RANBP3 430 NOP58 431 ZFR 432 IDH3G 433 HSD17B10 434 MRPL28 435 PSMC5
    436 HSP90AB1 437 L3MBTL2 438 CINP 439 NAA10 440 SGTA 441 EDF1 442 NDUFS8 443 TPI1 444 MFN2 445 DNPEP 446 CLPP 447 RBM42 448 PNKD 449 ILF3 450 COX4I1 451 RBSN 452 ILKAP 453 NIP7 454 THUMPD3 455 CCT7 456 TBRG4 457 DDX56 458 DCAF7 459 YME1L1 460 MAN2C1 461 SCYL1 462 GPN2 463 GMPPA 464 DDX46 465 SRFBP1 466 CXXC1 467 EIF5B 468 GPATCH4 469 EIF4A1 470 UBXN1 471 IWS1 472 PSMC3 473 CIAO2B 474 ZNF592 475 DNAJC7 476 DTYMK
    477 RNF181 478 SLC25A6 479 TRMT112 480 EIF1AD 481 AURKAIP1 482 ACSF3 483 TALDO1 484 COX5A 485 TUFM 486 FARSA 487 MRPL14 488 ARL6IP4 489 EWSR1 490 DDX41 491 CDK10 492 FAAP100 493 RPS19BP1 494 PTMA 495 MRPL21 496 MRPS18B 497 ABCF1 498 MCRIP1 499 CNPY2 500 MRPL12 501 BAZ2A 502 USP4 503 SMG7 504 ARPP19 505 NR1H2 506 NPEPPS 507 BIN3 508 UBE3B 509 WASF2 510 TAGLN2 511 IRF2 512 RELA 513 DCTN2 514 CIB1 515 SPTAN1 516 WWP2 517 MSRB1
    518 DCTN1 519 EIF6 520 CUX1 521 WDR1 522 PDRG1 523 SH3GLB1 524 SNAP29 525 KLHDC3 526 CHMP1A 527 LGALS3 528 GLYR1 529 NOSIP 530 HERC4 531 UBE2J2 532 CHTOP 533 PEF1 534 ZDHHC3 535 ATP5MD 536 SETD3 537 MCRS1 538 AP1G2 539 CHMP1B 540 ARF5 541 RNF10 542 SNX1 543 HAGH 544 FAM50A 545 MYL6 546 NANS 547 LPIN2 548 UBL4A 549 TBCB 550 PRKD2 551 DMAC2 552 RNF7 553 WRAP73 554 PEX16 555 ANXA11 556 CYREN 557 DYNLRB1 558 HECTD3
    559 PGLS 560 COX5B 561 CDK9 562 ARPC5L 563 RTCA 564 UNC45A 565 NARF 566 GUK1 567 CAST 568 NIT1 569 EFCAB14 570 PRMT2 571 FLAD1 572 SLMAP 573 TKT 574 SLFN5 575 CSNK1G1 576 EXOSC10 577 NADSYN1 578 KDM2A 579 KPNA4 580 TMEM120A 581 COX19 582 ARPIN 583 SYNRG 584 LYPLA2 585 TOLLIP 586 CDC37 587 H2AFY 588 RBCK1 589 RAF1 590 GPS2 591 NMT1 592 FLOT1 593 FBXW5 594 SQSTM1 595 DTX3L 596 PPIA 597 SMG5 598 EGLN2 599 ROCK1
    600 PXN 601 RANGAP1 602 PSMA7 603 MBD4 604 ADRM1 605 ARF3 606 SMIM12 607 PPP1CA 608 SMIM29 609 WDR5 610 GRIPAP1 611 CWF19L1 612 MED15 613 TSPO 614 MYH9 615 ITPK1 616 TPD52L2 617 GSDMD 618 PSMD9 619 ADPRHL2 620 CCDC32 621 NSUN5 622 EIF4E2 623 MGST3 624 PCYT1A 625 SAP30BP 626 RNASEK-C17orf49 627 SHISA5 628 BLCAP 629 DDX23 630 FLII 631 GAK 632 PAK2 633 HGS 634 AATF。
  5. 一种生物标志物集合，其特征在于，所述的集合包括选自表A的至少70％、较佳地，至少80％，更佳地，至少90％，更佳地，至少95％的基因。
  6. 一种用于子宫内膜容受性状态判断的试剂组合，其特征在于，所述试剂组合包括用于检测权利要求5所述的集合中各个生物标志物的试剂。
  7. 一种试剂盒，其特征在于，所述的试剂盒包括权利要求5所述的集合和/ 或权利要求6所述的试剂组合。
  8. 一种生物标志物集合的用途，其特征在于，用于制备一试剂盒，所述的试剂盒用于评估待测对象的子宫内膜容受性的状态，其中，所述生物标志物集合包括选自表A的至少70％、较佳地，至少80％，更佳地，至少90％，更佳地，至少95％的基因。
  9. 一种评估待测对象的子宫内膜容受性状态的方法，其特征在于，包括步骤：
    (1)提供一来源于待测对象的样品，对样品中集合中各个生物标记物的水平进行检测，所述集合包括选自表A的至少70％、较佳地，至少80％，更佳地，至少90％，更佳地，至少95％的基因；
    (2)将步骤(1)测得的水平与一预定值进行比较。
  10. 一种评估待测对象的子宫内膜容受性状态的系统，其特征在于，所述系统包括：
    (a)子宫内膜容受性状态的特征输入模块，所述输入模块用于输入待测对象的子宫内膜容受性状态的特征；
    其中所述的子宫内膜容受性状态的特征包括选自表A的至少70％、较佳地，至少80％，更佳地，至少90％，更佳地，至少95％的基因；
    (b)子宫内膜容受性状态的判别处理模块，所述处理模块对于输入的子宫内膜容受性状态的特征，按预定的判断标准进行评分处理，从而获得子宫内膜容受性状态评分；并且将所述子宫内膜容受性状态评分与预定值进行比较，从而得出辅助诊断结果，其中，当所述子宫内膜容受性状态评分高于所述预定值时，则提示该对象具有子宫内膜容受性；和
    (c)辅助诊断结果输出模块，所述输出模块用于输出所述的辅助诊断结果。
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EP3960873A4 (en) 2023-06-21
CN110042156A (zh) 2019-07-23
JP2022530135A (ja) 2022-06-27
CN110042156B (zh) 2021-12-28

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