Method for determining endometrial receptivity

Abstract

The application provides a method for judging endometrial receptivity. The method comprises the following steps: extracting total RNA of endometrium related tissue sample; reverse transcribing the total RNA of the sample into cDNA and amplifying and constructing a library; performing high-throughput sequencing on the library to obtain transcriptome data, analyzing and obtaining the expression level of genes related to endometrial receptivity; inputting the expression level of the related genes into an endometrial receptivity prediction model, and judging the endometrial receptivity according to the calculation result. The method can quickly and accurately judge the endometrial receptivity, and predict the endometrium related tissue of the subject suitable for the implantation window period of embryo transplantation.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics, specifically to a method, product, computer device, storage medium, and program product for determining the receptivity of the endometrium. Background Technology

[0002] The uterine endometrium (also known as the lining of the uterus) is the inner lining of the uterus in mammals. It responds to both estrogen and progesterone, and therefore undergoes significant changes throughout the menstrual cycle. The endometrium consists of three layers: the compact layer, the spongy layer, and the basal layer. The outer two-thirds of the endometrium, comprised of the compact and spongy layers (collectively known as the functional layer), undergoes cyclical changes and sheds under the influence of ovarian hormones. The basal layer, the one-third of the endometrium closest to the myometrium, is not affected by ovarian hormones and does not undergo cyclical changes.

[0003] In human reproduction, a fertilized egg locates, adheres to, and implants in the mother's uterus, eventually developing into a mature fetus. The implantation process has a significant impact on successful pregnancy. Successful clinical pregnancy requires not only a high-quality embryo but also good endometrial receptivity (ER) and synchronized development of the endometrium and embryo. With the development of assisted reproductive technology, embryo quality has been further improved. Therefore, endometrial receptivity has become a research hotspot in recent years. Endometrial receptivity refers to the endometrium's ability to accept an embryo. Only during a short, specific period does the endometrium allow the embryo to implant; this period is called the "implantation window," which for adult women corresponds to days 20-24 of the menstrual cycle or 6-8 days after ovulation.

[0004] In the field of in vitro fertilization-embryo transfer (IVF-ET), the implantation window is not accurately determined for women with repeated implantation failures or other secondary infertility. Calculating the implantation window based on the menstrual cycle or ovulation date carries a significant risk of implantation failure. While the specific mechanisms of endometrial receptivity are not fully understood, it is clear that various methods exist for assessing endometrial receptivity in current clinical practice, but their accuracy is unsatisfactory. This is a major reason for embryo implantation failure in IVF-ET. With advancements in molecular biology, the clinical application of genes as molecular markers to aid in the assessment of various physiological and pathological states is becoming increasingly mature. However, existing technologies for detecting endometrial receptivity are mostly cumbersome and time-consuming, requiring at least 10 days from sample acquisition to the output of receptivity results, resulting in low timeliness. Therefore, establishing a rapid, stable, and accurate biomarker and assessment method for endometrial receptivity can help medical personnel clarify the receptivity of the endometrium and accurately help subjects find the implantation window, which is of great significance for improving the success rate of in vitro fertilization-embryo transfer. Summary of the Invention

[0005] To address the aforementioned problems, this invention provides a method, product, computer device, storage medium, and program product for determining endometrial receptivity. This method can quickly and accurately determine endometrial receptivity and predict the suitable implantation window for embryo transfer in the subject's endometrial tissue.

[0006] This invention provides a method for determining the receptivity of the uterine lining, which includes the following steps:

[0007] S11: Extract total RNA from samples of endometrial-related tissues;

[0008] S12: Reverse transcribe the total RNA of the sample into cDNA and amplify it to construct a library;

[0009] S13: Perform high-throughput sequencing on the library to obtain transcriptome data, analyze and obtain the expression levels of genes related to endometrial receptivity;

[0010] S14: The expression levels of relevant genes are incorporated into the endometrial receptivity prediction model, and the endometrial receptivity is determined based on the calculation results.

[0011] In the above-mentioned determination method, preferably, the endometrial-related tissue mentioned in step S11 is one or a combination of two or more of the following: endometrial tissue, uterine fluid, uterine irrigation fluid, vaginal exfoliated cells, vaginal secretions, biopsy products of the endometrium, serum, and plasma.

[0012] In the above-mentioned determination method, preferably, the endometrial-related tissue mentioned in step S11 is endometrial biopsy tissue.

[0013] In the above-mentioned judgment method, preferably, the endometrial-related tissue mentioned in step S11 is a sample of the subject at the following time: female subjects in a natural menstrual cycle, on the 7th day after the luteinizing hormone peak;

[0014] or;

[0015] The endometrial tissue mentioned in step S11 is a sample taken from the subject at the following time: female subjects in a hormone replacement cycle, on day 5 after ovulation.

[0016] In the above determination method, preferably, step S11 includes the following steps:

[0017] S101: Homogenize the tissue sample to obtain homogenized tissue;

[0018] S102: Add 300 μL of cell lysis buffer to the homogenized tissue, shake vigorously for 30 seconds to 1 minute, centrifuge briefly, add 350 μL of freshly prepared 70% ethanol, shake to mix, and centrifuge briefly again to obtain a mixed liquid.

[0019] S103: Transfer 650 μL of the mixed liquid to the adsorption column, centrifuge at 10000×g for 1 minute, discard the centrifugation waste liquid, add 500 μL of washing buffer to the adsorption column membrane, centrifuge at 10000×g for 1 minute, discard the centrifugation waste liquid, carefully drop 50 μL of DNA digesting enzyme onto the adsorption column membrane, incubate at 25℃ for 15 minutes to obtain the DNA digested sample;

[0020] S104: Add 500 μL of washing buffer to the DNA-digested sample, centrifuge at 12000×g for 1 minute, discard the centrifugation waste liquid, add another 500 μL of washing buffer to the adsorption column, centrifuge at 12000×g for 1 minute, discard the centrifugation waste liquid, add 500 μL of 80% ethanol to the tube, centrifuge at 12000×g for 1 minute, discard the centrifugation waste liquid; centrifuge again at 12000×g for 1 minute, discard the collection tube, insert the adsorption column into a 1.5 mL centrifuge tube, open the cap and let it air dry for 1 minute, add 15 μL of DEPC water, incubate at 25℃ for 1 minute, centrifuge at 13000×g for 2 minutes, repeat once more, collect a total of 50 μL of elution buffer to obtain total RNA.

[0021] In the above determination method, preferably, step S12 includes the following steps:

[0022] S201: Add total RNA to the reverse transcription reaction mixture to obtain the first strand of cDNA;

[0023] S202: Using the first strand of cDNA as a template, a linear pre-amplification reaction is completed in the pre-amplification reaction mixture to obtain the pre-amplification product;

[0024] S204: Using the pre-amplified product as a template, Illumina sequencing universal primers and tag sequences are introduced into the exponential amplification reaction mixture. After a limited number of cycles, the cDNA signal is further amplified, and a library that can be used for sequencing is obtained.

[0025] The reagents used in the above steps are from the Yikang Micro RNA Amplification Kit KT110700724.

[0026] In the above-mentioned judgment method, preferably, step S13 uses the following methods to detect the expression level of genes related to the endometrial receptivity: second-generation sequencing, expression profiling chip, methylation chip, third-generation sequencing, RT-qPCR, RT-qPCR chip, or a combination thereof.

[0027] This invention also provides a method for constructing a predictive model of endometrial receptivity, which includes the following steps:

[0028] S301: Extract total RNA from relevant samples from different optimal receptivity windows, construct libraries and perform transcriptome sequencing to obtain sequencing data; wherein, the relevant samples are from endometrial tissue, uterine fluid and other reproductive endocrine-related body fluids or exfoliated products;

[0029] S302: Perform quality control and filtering on the sequencing data to obtain processed sequencing data;

[0030] S303: Align the processed sequencing data to the human reference genome and transcriptome to obtain annotated sequencing data;

[0031] S304: Calculate the contrast ratio, exon / intron ratio, and gene expression normalization of the annotated sequencing data to obtain gene expression normalization data; wherein, the gene expression normalization data is one or a combination of two or more of FPKM, TPM, and RPKM data;

[0032] S305: Compare the standardized gene expression data of different genes in multiple samples of different classes to analyze differential gene expression;

[0033] S306: Through feature engineering selection, biomarkers are screened from the list of differentially expressed genes to obtain a set of feature biomarkers;

[0034] S307: Extract the expression levels corresponding to the characteristic biomarker set in the gene expression normalization data, and use the random forest algorithm to construct a prediction model for predicting receptivity, thus obtaining the endometrial receptivity prediction model.

[0035] The present invention also provides a product for determining the receptivity of the endometrium, wherein the product comprises the following modules:

[0036] RNA Acquisition Module: Extracts total RNA from samples of endometrial tissues. Specifically, this module can be implemented using a kit or test strip product.

[0037] Library construction module: The total RNA of the sample is reverse transcribed into cDNA and amplified to construct a library. Specifically, this module can be implemented through chips, kits, test strips or high-throughput sequencing platform products.

[0038] High-throughput sequencing module: Performs high-throughput sequencing on the library to obtain transcriptome data, analyzes and obtains the expression levels of genes related to endometrial receptivity;

[0039] Judgment module: The expression levels of relevant genes are input into the endometrial receptivity prediction model, and the endometrial receptivity is judged based on the calculation results.

[0040] Preferably, the products described above include chips, high-throughput sequencing platforms, or prediction devices.

[0041] This invention also provides a product for constructing a predictive model of endometrial receptivity, wherein the product includes the following modules:

[0042] Sequencing module: Total RNA is extracted from relevant samples with different optimal receptivity windows, a library is constructed, and transcriptome sequencing is performed to obtain sequencing data; wherein, the relevant samples are from endometrial tissue, uterine fluid, and other reproductive endocrine-related fluids or exfoliated products; specifically, this module can be implemented through a kit or test strip product;

[0043] Preprocessing module: Performs quality control and filtering on sequencing data to obtain processed sequencing data;

[0044] Annotation module: Aligns the processed sequencing data to the human reference genome and transcriptome to obtain annotated sequencing data;

[0045] Normalization module: Calculates the contrast ratio, exon / intron ratio, and gene expression normalization of the annotated sequencing data to obtain gene expression normalized data; wherein, the gene expression normalized data is one or a combination of two or more of FPKM, TPM, and RPKM data;

[0046] Differential expression analysis module: compares the standardized gene expression data of different genes in multiple samples of different classes to perform differential gene expression analysis;

[0047] Feature selection module: Through feature engineering selection, biomarkers are selected from the list of differentially expressed genes to obtain a set of feature biomarkers;

[0048] Model building module: Extract the expression levels of characteristic biomarkers from the gene expression normalization data, and use the random forest algorithm to build a prediction model for predicting receptivity, thus obtaining the endometrial receptivity prediction model.

[0049] Preferably, the products described above include chips, high-throughput sequencing platforms, or prediction devices.

[0050] The present invention also provides a computer device comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to achieve:

[0051] The endometrial receptivity status is determined based on the methods described above.

[0052] The present invention also provides a non-transitory computer-readable storage medium storing a computer program, which, when executed by a processor, performs the following: determining the endometrial receptivity state based on the above method.

[0053] The present invention also provides a computer program product comprising computer instructions, which, when executed by a processor, implement the following: determining the endometrial receptivity state based on the above method.

[0054] The technical solution of the present invention has the following beneficial technical effects:

[0055] The method for determining endometrial receptivity provided by this invention can rapidly construct a transcriptome library and accurately determine endometrial receptivity. It can predict the suitable implantation window for embryo transfer from endometrial biopsy tissue (or uterine fluid and other reproductive endocrine-related fluids or exfoliated cells) of the subject, thereby guiding embryo transfer and significantly shortening the time to pregnancy.

[0056] Current conventional RNA-seq procedures are lengthy, requiring over 12 hours from total RNA extraction to reverse transcription and RNA sequencing library preparation. This invention can complete the entire experimental process within 6 hours, significantly reducing the time required compared to conventional methods.

[0057] This invention uses total RNA extracted from a sample as a template. mRNA is reverse transcribed using reverse transcriptase to obtain the first strand of cDNA. Then, the first strand of cDNA is used as a template for pre-amplification and exponential amplification reactions. Adapter and tag sequences are added to both ends of the cDNA amplification product to obtain a high-quality sequencing library. Conventional RNA-seq workflows include "total RNA extraction - reverse transcription - library preparation," with the library preparation stage alone requiring at least four steps, multiple rounds of reactions and purification, and a turnaround time of over 7 hours. This invention is simple and rapid, completing the reverse transcription of mRNA to form cDNA in a short time, and directly amplifying the cDNA to construct a sequencing library. Amplification and sequencing library construction takes only 2 hours, and the operation is simpler, requiring only two steps.

[0058] This invention achieves a reverse transcription and amplification library construction success rate of over 95%, and the cDNA library can be seamlessly integrated with mainstream sequencing platforms. On average, more than 16,000 gene expressions can be detected in the sequencing data, reaching a level comparable to that of conventional RNA-seq data.

[0059] In addition, based on the high-throughput sequencing data of the library prepared by this method, characteristic genes for predicting acceptability can be screened and models can be constructed, which can accurately determine the acceptability of the sample and has certain clinical application value. Attached Figure Description

[0060] Figure 1 Here is an example electrophoresis image of a cDNA library for quality control.

[0061] Figure 2 A flowchart for constructing a predictive model of endometrial receptivity and its application;

[0062] Figure 3 Flowchart for constructing a predictive model for endometrial receptivity;

[0063] Figure 4 The results of regression analysis on the prediction accuracy of the endometrial receptivity prediction model on the test set;

[0064] Figure 5 Flowchart for predicting endometrial receptivity;

[0065] Figure 6 A schematic diagram showing the results of the endometrial receptivity prediction for the subjects. Detailed Implementation

[0066] In order to provide a clearer understanding of the technical features, objectives and beneficial effects of the present invention, the technical solution of the present invention will now be described in detail below, but it should not be construed as limiting the scope of implementation of the present invention.

[0067] Example 1:

[0068] This embodiment provides a method and product for constructing a predictive model of endometrial receptivity, the process of which is as follows:

[0069] I. Synthesizing cDNA libraries:

[0070] The poly(A) tail of mRNA (messenger RNA) in total RNA can bind specifically to the poly(dT) tail of specially designed reverse transcription primers, and then undergo reverse transcription in a reaction mixture containing reverse transcriptase to synthesize the first strand of cDNA.

[0071] Among them, the poly(dT) on the specially designed primers can contain 25 to 30 T bases.

[0072] In some embodiments of this application, the reagents used for reverse transcription of RNA may include one or more components selected from the group consisting of: MMLV reverse transcriptase, AMV reverse transcriptase, HIV reverse transcriptase, and SuperScript. TM II Reverse Transcriptase, SuperScript TM III Reverse Transcriptase, RevertAid TM Reverse transcriptase, Maxima H Minus reverse transcriptase.

[0073] In one specific embodiment of this application, the first strand of cDNA in the purified product is subjected to a linear pre-amplification reaction and an exponential amplification reaction in sequence.

[0074] The linear pre-amplification reaction includes: using the first strand of cDNA as a template, performing a linear pre-amplification reaction in a pre-amplification reaction mixture; wherein the pre-amplification reaction mixture includes: pre-amplification primers, a mixture of nucleotide monomers, and a nucleic acid polymerase; wherein the pre-amplification primers include a universal sequence and a variable sequence from the 5' end to the 3' end.

[0075] In one specific embodiment of this application, the pre-amplification reaction mixture includes: sequences that are complementary to or identical to some or all of the universal sequencing primers.

[0076] Performing an exponential amplification reaction includes: adding an exponential amplification reaction mixture to a linear pre-amplification reaction product and performing an exponential amplification reaction; wherein the exponential amplification reaction mixture includes: exponential amplification primers, a mixture of nucleotide monomers and a nucleic acid polymerase, wherein the exponential amplification primers include specific sequences and universal sequences from the 5' end to the 3' end.

[0077] That is, the first strand of synthesized cDNA can be used as a template to complete a linear pre-amplification reaction in a pre-amplification reaction mixture; wherein, the pre-amplification reaction mixture includes: pre-amplification primers, a mixture of nucleotide monomers, and a nucleic acid polymerase. The pre-amplification primers include universal and variable sequences from the 5' end to the 3' end.

[0078] The nucleic acid polymerase can be selected from: Phi29 DNA polymerase, Bst DNA polymerase, Pyrophage 3137, Vent polymerase, TOPO Taq DNA polymerase, 9°Nm polymerase, Klenow Fragment DNA polymerase I, MMLV reverse transcriptase, AMV reverse transcriptase, HIV reverse transcriptase, T7phase DNA polymerase variant, ultra-fidelity DNA polymerase, Taq polymerase, Bst DNA polymerase, E. coli DNA polymerase, LongAmp Taq DNA polymerase, OneTaq DNA polymerase, DeepVent DNA polymerase, Vent(exo-) DNA polymerase, Deep Vent(exo-) DNA polymerase, and any combination thereof.

[0079] Using the synthesized pre-amplified product as a template, the first strand of cDNA is further amplified in an exponential amplification reaction mixture after a limited number of cycles, simultaneously obtaining a library suitable for sequencing. The exponential amplification reaction mixture includes: exponential amplification primers, a mixture of nucleotide monomers, and a nucleic acid polymerase. The exponential amplification primers consist of specific and universal sequences from the 5' to the 3' end. Specifically, the exponential amplification primers include sequences that are partially or entirely complementary to or identical to those of the sequencing primers.

[0080] Specifically, the reagents used for first-strand amplification of cDNA may include one or more components selected from the group consisting of: a mixture of nucleotide monomers (e.g., dATP, dGTP, dTTP and dCTP, for example, a total concentration between 1 mmol and 8 mmol / μL), dTT (e.g., a concentration between 1 mmol and 7 mmol / μL), Mg2+ solution (e.g., a concentration between 2 mmol and 8 mmol / μL), bovine serum albumin (BSA), pH adjuster (e.g., Tris HCl), DNase inhibitor, RNase, SO42-, Cl-, K+, Ca2+, Na+, and / or (NH4)+.

[0081] The specific implementation process of synthesizing a cDNA library includes:

[0082] (1) Total RNA extraction from endometrial tissue:

[0083] 1) Homogenize the endometrial tissue samples;

[0084] 2) Add 300 μL of cell lysis buffer to the homogenized tissue, shake vigorously for 30 seconds to 1 minute, and then centrifuge briefly;

[0085] 3) Add 350 μL of freshly prepared 70% ethanol, shake to mix, and then centrifuge briefly;

[0086] 4) Transfer 650 μL of the above mixed liquid to the adsorption column, centrifuge at 10000×g for 1 minute, and discard the centrifugation waste liquid;

[0087] 5) Add 500 μL of washing buffer to the adsorption column membrane, centrifuge at 10000×g for 1 minute, and discard the centrifugation waste liquid;

[0088] 6) Carefully drop 50 μL of DNA digesting enzyme onto the adsorption column membrane and incubate at 25°C for 15 minutes;

[0089] 7) Add 500 μL of washing buffer, centrifuge at 12000×g for 1 minute, and discard the centrifugation waste liquid; add another 500 μL of washing buffer to the adsorption column, centrifuge at 12000×g for 1 minute, and discard the centrifugation waste liquid;

[0090] 8) Add 500 μL of 80% ethanol to the tube, centrifuge at 12000×g for 1 minute, and discard the waste liquid; centrifuge again at 12000×g for 1 minute, and discard the collection tube;

[0091] 9) Insert the adsorption column into a 1.5 mL centrifuge tube, open the cap and let it air dry for 1 minute;

[0092] 10) Add 15 μL of DEPC water, incubate at 25°C for 1 minute, centrifuge at 13000×g for 2 minutes, and repeat once more. Collect a total of 50 μL of eluent, which is the total RNA. The concentration and quality control of the extracted total RNA were determined using a NanoDrop micro-spectrophotometer.

[0093] The results of RNA concentration and quality testing are shown in Table 1:

[0094] Table 1

[0095]

[0096]

[0097] (2) Pre-denaturation of RNA template:

[0098] 1) Prepare the reaction system shown in Table 2 on ice:

[0099] Table 2

[0100] Components volume Total RNA template (50–100 ng) 2μL Oligo d(T)NVN(50μM) 2μL 10mM dNTPs 1μL Nuclease-free water 5μL Total volume 10μL

[0101] The reaction was carried out in a preheated PCR instrument under the conditions shown in Table 3:

[0102] Table 3

[0103] temperature time 70℃ 10min Place immediately on ice (0°C) >5min

[0104] (3) Reverse transcription to obtain the first strand of synthetic cDNA:

[0105] 1) The reaction system for synthesizing the first strand of cDNA is shown in Table 4:

[0106] Table 4

[0107] Components volume Step 1 reaction solution 10μL Reverse transcription buffer 4μL reverse transcriptase 1μL Nuclease-free H2O 5μL Total volume 20μL

[0108] 2) Vortex the reaction system and centrifuge it. Then, carry out the following reaction in a preheated PCR instrument under the conditions shown in Table 5.

[0109] Table 5

[0110] temperature time 42℃ 2min 50℃ 50min 80℃ 5min 4℃ Hold

[0111] (4) Purification of cDNA first-strand products:

[0112] 1) Take 1.8×(36μL) Ampure XP magnetic beads, mix them with the amplification product, and place the PCR tube on a magnetic rack and let it stand for 5 minutes.

[0113] 2) After the magnetic beads are completely adsorbed onto the tube wall, discard the supernatant and wash the magnetic beads twice with freshly prepared 80% ethanol, discarding the supernatant again.

[0114] 3) Let stand at room temperature for 3-5 minutes until the magnetic beads are dry (be careful not to let the magnetic beads dry out too much, so as not to affect the recovery efficiency). Then, add 50 μL of LTE buffer, EB buffer or nuclease-free water to resuspend the magnetic beads according to the needs of downstream experiments.

[0115] 4) After standing at room temperature for 3-5 minutes, place the PCR tube on a magnetic rack and aspirate 48 μL of supernatant, which contains the first strand of cDNA.

[0116] (5) Pre-amplification:

[0117] After purification, 1 μL of the product was placed in a PCR tube. 30 μL of the pre-amplification reaction mixture was added to each sample. After vortexing and momentary infiltration, the following reaction was performed in a preheated PCR instrument under the conditions shown in Table 6:

[0118] Table 6

[0119]

[0120] (6) Exponential expansion:

[0121] Add 30 μL of the exponential amplification reaction mixture to the product of the previous step. Simultaneously add 1 μL of the tag sequence primer to each reaction tube. After mixing and centrifugation, perform the following reaction in a PCR instrument under the conditions shown in Table 7:

[0122] Table 7

[0123]

[0124] (7) Purification index amplification products:

[0125] 1) Take 1×(65μL) Ampure XP magnetic beads, mix them with the amplification product, and place the PCR tube on a magnetic rack and let it stand for 5 minutes.

[0126] 2) After the magnetic beads are completely adsorbed onto the tube wall, discard the supernatant and wash the magnetic beads twice with freshly prepared 80% ethanol, discarding the supernatant again.

[0127] 3) Let stand at room temperature for 3-5 minutes until the magnetic beads are dry (be careful not to let the magnetic beads dry out too much, so as not to affect the recovery efficiency). Then, add 25 μL of TE buffer, EB buffer or nuclease-free water to resuspend the magnetic beads according to the needs of downstream experiments.

[0128] 4) After standing at room temperature for 3-5 minutes, place the PCR tube on a magnetic rack and aspirate 20 μL of supernatant, which is the sequencing library.

[0129] (8) cDNA library quality testing:

[0130] Take 5 μL of the purified amplification product and add 1 μL of 6× DNA loading buffer (Kangwei Century Biotechnology Co., Ltd., catalog number CW0610A) for sample loading. Use a 1% agarose gel, and use GeneRuler 100bp Plus (Thermo Scientific) as the label. TM (SM0323). Example electrophoresis image of cDNA library quality control: Figure 1 As shown, the first lane from left to right is the molecular weight marker, and lanes 2 to 6 are the library products formed by reverse transcription and amplification of RNA from 6 examples of endometrial tissue, with the products concentrated in the range of 300-600bp.

[0131] Use Qubit TM The concentration of the library was determined using a 1X dsDNA quantitative kit (Invitrogen, catalog number Q33230). Take 199 μL of Qubit... TM Add 1 μL of purified amplification product to dsDNA 1× working solution, mix well on a vortex mixer, and then transfer to a Qubit array. TMThe library concentrations were measured using a 4.0 fluorometer, and the library concentrations for each sample are shown in Table 8.

[0132] Table 8

[0133]

[0134]

[0135] II. Sequencing of the cDNA library:

[0136] The experimental procedures were performed in accordance with the instruction manual for the MGISEQ-T7 sequencer from BGI Genomics.

[0137] Computer application strategy: Single-ended or dual-ended is acceptable, with a read length greater than 45 and a minimum of 2.5M reads.

[0138] III. Construction of a predictive model for endometrial receptivity:

[0139] In a specific embodiment of the present invention, the method for constructing a predictive model of endometrial receptivity using transcriptome data after rapid library construction and sequencing includes the following steps:

[0140] The construction process of the endometrial receptivity prediction model of this invention mainly employs a machine learning algorithm. Machine learning algorithms are used in various specific fields, such as image classification, speech recognition, and disease prediction. This algorithm achieves efficient and accurate prediction or classification through steps such as data preprocessing, feature engineering, model training, model evaluation, and deployment. This invention uses the gene expression profiles of the endometrium of patients with clear clinical outcomes as the training set, and utilizes supervised learning methods in machine learning to build an analytical model. This model then predicts the position of unknown test samples. The flowchart of the endometrial receptivity prediction model and its application is shown below. Figure 2 As shown.

[0141] (1) The training set used in this invention for building the model consists of endometrial tissue, intrauterine fluid, or other reproductive endocrine-related fluids or exfoliated material from different optimal receptivity windows, representing expression profiles under different receptivity states. These samples were tested using an RNA sequencing-based endometrial receptivity test to determine the number of hours remaining until the optimal receptivity period. A period between -18 and 18 hours was considered the receptivity period, less than -18 hours was the late receptivity period, and more than 18 hours was the early receptivity period. Transcriptome sequencing data from these sample sets were obtained, and the sequencing data underwent quality control processing to obtain high-quality data. This data was then aligned to the human reference genome and transcriptome. QC indicators such as the number of valid sequences, the proportion of aligned sequences, and the proportion of aligned introns / exons were calculated. Samples that passed QC were obtained, and the gene expression count for each sample was calculated. Further standardization of gene length and sequencing depth resulted in an FPKM (Fragments Per Kilobase Million) used for downstream model building analysis to eliminate the influence of sequencing depth. Other available standardization methods include RPKM (Reads Per Kilobase Million) and TPM (Trans Per Million), but this invention preferably uses FPKM. The formula for calculating FPKM is as follows:

[0142]

[0143] Where F1 = the total number of fragments aligned to the gene; L1 = the length of the gene; FT = the total number of fragments aligned to the genome.

[0144] (2) Compare the expression levels of different genes in (multiple) samples of different classes to perform differential gene expression analysis. For all genes, identify the intersection of differentially expressed genes between the classes labeled "pre-receptivity phase" vs. "receptivity phase," "pre-receptivity phase" vs. "post-receptivity phase," and "receptivity phase" vs. "post-receptivity phase" (those satisfying p < 0.05 and fold change > 2 or < 0.5 meet the selection criteria). Feature engineering is then used to select more suitable biomarkers for model construction. This approach aims to eliminate the influence of genes that are consistently highly or consistently poorly expressed in different classes on the analytical model. This ensures a good fit for the subsequent model analysis while preventing overfitting.

[0145] (3) In the supervised learning method of the predictive model of endometrial receptivity in this invention, the receptivity status and the number of hours away from optimal receptivity of the "training data" are known. The inclusion criteria of the "training data" or "training set" are: healthy Chinese women with no history of disease, no primary infertility, and a body mass index of 19-25 kg / m². 2Between these points, with the accumulation of a large number of sample enrollments, tests, and clinical outcomes of enrolled cases, the expression profiles of the "receptivity window" in endometrial tissue, uterine fluid, or other reproductive endocrine-related fluids or exfoliated material from these cases were obtained. After determining the specific number of hours away from optimal receptivity through endometrial receptivity testing based on RNA sequencing, embryo transfer was guided according to the results, and clinical outcomes were tracked. If all embryos implanted during this period could effectively implant and develop, the number of hours away from optimal receptivity for that sample could be confirmed as the gold standard.

[0146] (4) Extract the expression levels corresponding to the feature values ​​in the dataset. Randomly select 70% of the sample data as the training set for the model. Use the random forest algorithm in R to construct a predictive model for predicting endometrial receptivity, thus completing the model construction. The flowchart for constructing the endometrial receptivity prediction model is as follows: Figure 3 As shown. The script input is as follows:

[0147] boston<-ERTdata

[0148] colnames(boston)[1]<-"Label"

[0149] Skim (Boston)

[0150] plot_missing(boston)

[0151] hist(boston$Label,breaks=50)

[0152] set.seed(80)

[0153] trains<-createDataPartition(#from package caret

[0154] y=boston$Label,#cut consistency into fragments

[0155] p = 0.7,

[0156] list = F )

[0158] traindata<-boston[trains,

[0159] colnames(boston)

[0160] form_reg <- as.formula(

[0161] paste0("Label~",

[0162] paste(colnames(traindata)[2:161],collapse="+") ) )

[0165] set.seed(80)

[0166] fit_rf_reg<-randomForest(

[0167] form_reg,

[0168] data = traindata,

[0169] ntree=500,

[0170] mtry=6,

[0171] importance=T, )

[0173] (5) Randomly select 70% of the dataset and use the remaining 30% as the test set. Input the test set into the model for prediction. The correlation coefficient R between the predicted result and the original result is calculated. 2 =0.958, the regression analysis results of the test set prediction accuracy of the endometrial receptivity prediction model are as follows: Figure 4 As shown.

[0174] Example 2:

[0175] This embodiment provides a method and product for determining endometrial receptivity. The flowchart for predicting endometrial receptivity is shown below. Figure 5 As shown.

[0176] The endometrial receptivity prediction model trained in Example 1 is used to predict the optimal receptivity hours for the test sample, thereby determining the receptivity status.

[0177] The specific methods for determining the receptivity of the uterine lining are as follows:

[0178] (1) On the 5th day after the patient started using progesterone, endometrial tissue was taken, homogenized, RNA was extracted, reverse transcribed, and library constructed. Transcriptome sequencing was performed to obtain transcriptome sequencing data. After sequencing data control filtering, it was compared with the human reference genome and transcriptome. The proportion of sequencing data compared with the genome and the proportion of sequencing data compared with exons and introns were calculated to obtain gene expression data of the sample to be tested.

[0179] (2) After obtaining the transcriptome sequencing data of the sample to be tested, calculate the gene expression count value of the sample to be tested, and then convert it into a standardized FPKM value. Input the value into the prediction model to obtain the number of hours away from the optimal acceptance. Then, according to the rule: the acceptance period is within -18 to -18 hours, the late acceptance period is less than -18 hours, and the early acceptance period is greater than 18 hours, the acceptance status classification can be inferred. Alternatively, the optimal transfer window hours can be inferred based on the progesterone usage time or the peak time of luteinizing hormone, thereby guiding embryo transfer.

[0180] Includes the following modules:

[0181] RNA Acquisition Module: Extracts total RNA from samples of endometrial tissues. Specifically, this module can be implemented using a kit or test strip product.

[0182] Library construction module: The total RNA of the sample is reverse transcribed into cDNA and amplified to construct a library. Specifically, this module can be implemented through chips, kits, test strips or high-throughput sequencing platform products.

[0183] High-throughput sequencing module: Performs high-throughput sequencing on the library to obtain transcriptome data, analyzes and obtains the expression levels of genes related to endometrial receptivity;

[0184] Judgment module: The expression levels of relevant genes are input into the endometrial receptivity prediction model, and the endometrial receptivity is judged based on the calculation results.

[0185] The endometrial tissue of a newly input case was used to assess its receptivity using a machine learning model. The results of the subject's endometrial receptivity prediction are illustrated in the diagram below. Figure 6 As shown. The subject's test results indicate receptivity, and it is recommended that blastocyst implantation be performed on the same day in the next corresponding cycle. For example, if the sample was taken 5 days after progesterone administration at 12:00, then blastocyst implantation should be performed 5 days after progesterone administration and 12 hours after that in the next cycle. If cleavage-stage embryos are required, please implant them 2 days in advance and monitor clinical outcomes.

[0186] In further embodiments, the following is also provided:

[0187] A computer device includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to determine the endometrial receptivity state based on the method of Embodiment 2.

[0188] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0189] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0190] A non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, performs the following: determining the endometrial receptivity state based on the method described in Embodiment 2.

[0191] This method can be directly implemented by a hardware processor, or it can be implemented using a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0192] Those skilled in the art will recognize that the units, i.e., algorithm steps, of the various examples described in connection with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0193] A computer program product includes computer instructions that, when executed by a processor, perform the following: determining the endometrial receptivity state based on the method described in Embodiment 2.

[0194] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0195] The units or modules described in the embodiments of this disclosure can be implemented in software or programmable hardware. The described units or modules can also be located in a processor, and the names of these units or modules do not necessarily constitute a limitation on the unit or module itself.

[0196] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for determining the receptivity of the uterine lining, comprising the following steps: S11: Extract total RNA from samples of endometrial-related tissues; S12: Reverse transcribe the total RNA of the sample into cDNA and amplify it to construct a library; S13: Perform high-throughput sequencing on the library to obtain transcriptome data, analyze and obtain the expression levels of genes related to endometrial receptivity; S14: The expression levels of relevant genes are incorporated into the endometrial receptivity prediction model, and the endometrial receptivity is determined based on the calculation results.

2. The determination method according to claim 1, wherein, The endometrial-related tissues mentioned in step S11 are one or more of the following: endometrial tissue, uterine fluid, uterine irrigation fluid, vaginal exfoliated cells, vaginal secretions, endometrial biopsy products, serum, and plasma.

3. The determination method according to claim 1 or 2, wherein, The endometrial-related tissue mentioned in step S11 is endometrial biopsy tissue.

4. The determination method according to any one of claims 1-3, wherein, The endometrial-related tissue mentioned in step S11 is a sample taken by the subject at the following time: female subjects in a natural menstrual cycle, on the 7th day after the luteinizing hormone peak; or; The endometrial tissue mentioned in step S11 is a sample taken from the subject at the following time: female subjects in a hormone replacement cycle, on day 5 after ovulation.

5. The determination method according to claim 1, wherein, Step S11 includes the following steps: S101: Homogenize the tissue sample to obtain homogenized tissue; S102: Add 300 μL of cell lysis buffer to the homogenized tissue, shake vigorously for 30 seconds to 1 minute, centrifuge briefly, add 350 μL of freshly prepared 70% ethanol, shake to mix, and centrifuge briefly again to obtain a mixed liquid. S103: Transfer 650 μL of the mixed liquid to the adsorption column, centrifuge at 10000×g for 1 minute, discard the centrifugation waste liquid, add 500 μL of washing buffer to the adsorption column membrane, centrifuge at 10000×g for 1 minute, discard the centrifugation waste liquid, carefully drop 50 μL of DNA digesting enzyme onto the adsorption column membrane, incubate at 25℃ for 15 minutes to obtain the DNA digested sample; S104: Add 500 μL of washing buffer to the DNA-digested sample, centrifuge at 12000×g for 1 minute, discard the centrifugation waste liquid, add another 500 μL of washing buffer to the adsorption column, centrifuge at 12000×g for 1 minute, discard the centrifugation waste liquid, add 500 μL of 80% ethanol to the tube, centrifuge at 12000×g for 1 minute, discard the centrifugation waste liquid; centrifuge again at 12000×g for 1 minute, discard the collection tube, insert the adsorption column into a 1.5 mL centrifuge tube, open the cap and let it air dry for 1 minute, add 15 μL of DEPC water, incubate at 25℃ for 1 minute, centrifuge at 13000×g for 2 minutes, repeat once more, collect a total of 50 μL of elution buffer to obtain total RNA.

6. The determination method according to claim 1, wherein, Step S12 includes the following steps: S201: Add total RNA to the reverse transcription reaction mixture to obtain the first strand of cDNA; S202: Using the first strand of cDNA as a template, a linear pre-amplification reaction is completed in the pre-amplification reaction mixture to obtain the pre-amplification product; S204: Using the pre-amplified product as a template, Illumina sequencing universal primers and tag sequences are introduced into the exponential amplification reaction mixture. After a limited number of cycles, the cDNA signal is further amplified, and a library that can be used for sequencing is obtained. The reagents used in the above steps are from the Yikang Micro RNA Amplification Kit KT110700724.

7. The method according to claim 1, wherein, Step S13 uses the following methods to detect the expression levels of genes related to the endometrial receptivity: next-generation sequencing, expression profiling chip, methylation chip, third-generation sequencing, RT-qPCR, RT-qPCR chip, or a combination thereof.

8. A method for constructing a predictive model of endometrial receptivity, comprising the following steps: S301: Extract total RNA from relevant samples from different optimal receptivity windows, construct libraries and perform transcriptome sequencing to obtain sequencing data; wherein, the relevant samples are from endometrial tissue, uterine fluid and other reproductive endocrine-related body fluids or exfoliated products; S302: Perform quality control and filtering on the sequencing data to obtain processed sequencing data; S303: Align the processed sequencing data to the human reference genome and transcriptome to obtain annotated sequencing data; S304: Calculate the contrast ratio, exon / intron ratio, and gene expression normalization of the annotated sequencing data to obtain gene expression normalization data; wherein, the gene expression normalization data is one or a combination of two or more of FPKM, TPM, and RPKM data; S305: Compare the standardized gene expression data of different genes in multiple samples of different classes to analyze differential gene expression; S306: Through feature engineering selection, biomarkers are screened from the list of differentially expressed genes to obtain a set of feature biomarkers; S307: Extract the expression levels corresponding to the characteristic biomarker set in the gene expression normalization data, and use the random forest algorithm to construct a prediction model for predicting receptivity, thus obtaining the endometrial receptivity prediction model.

9. A product for determining the receptivity of the uterine lining, wherein, The product includes the following modules: RNA acquisition module: Extracts total RNA from samples of endometrial-related tissues; Library construction module: Reverse transcribes total RNA from the sample into cDNA and amplifies it to construct a library; High-throughput sequencing module: Performs high-throughput sequencing on the library to obtain transcriptome data, analyzes and obtains the expression levels of genes related to endometrial receptivity; Judgment module: The expression levels of relevant genes are input into the endometrial receptivity prediction model, and the endometrial receptivity is judged based on the calculation results.

10. The product according to claim 9, wherein the product comprises a chip, a high-throughput sequencing platform, or a prediction device.

11. A product for constructing a predictive model of endometrial receptivity, wherein, The product includes the following modules: Sequencing module: Total RNA is extracted from relevant samples with different optimal receptivity windows, libraries are constructed and transcriptome sequencing is performed to obtain sequencing data; wherein, the relevant samples are from endometrial tissue, uterine fluid and other reproductive endocrine-related body fluids or exfoliated products; Preprocessing module: Performs quality control and filtering on sequencing data to obtain processed sequencing data; Annotation module: Aligns the processed sequencing data to the human reference genome and transcriptome to obtain annotated sequencing data; Normalization module: Calculates the contrast ratio, exon / intron ratio, and gene expression normalization of the annotated sequencing data to obtain gene expression normalized data; wherein, the gene expression normalized data is one or a combination of two or more of FPKM, TPM, and RPKM data; Differential expression analysis module: compares the standardized gene expression data of different genes in multiple samples of different classes to perform differential gene expression analysis; Feature selection module: Through feature engineering selection, biomarkers are selected from the list of differentially expressed genes to obtain a set of feature biomarkers; Model building module: Extract the expression levels of characteristic biomarkers from the gene expression normalization data, and use the random forest algorithm to build a prediction model for predicting receptivity, thus obtaining the endometrial receptivity prediction model.

12. The product according to claim 11, wherein the product comprises a chip, a high-throughput sequencing platform, or a prediction device.

13. A computer device comprising: A memory and a processor are communicatively connected. The memory stores computer instructions, and the processor executes these computer instructions to achieve the following: The endometrial receptivity status is determined based on the method described in any one of claims 1-7.

14. A non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, performs the following: determining the endometrial receptivity state based on the method described in any one of claims 1-7.

15. A computer program product comprising computer instructions that, when executed by a processor, perform: determining endometrial receptivity based on the method of any one of claims 1-7.

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