Biomarkers for repeated implantation failure and diagnostic and therapeutic methods

Abstract

The present invention relates to methods for luteal support therapy in patients with recurrent implantation failure, and to methods and biomarkers for predicting the responsiveness of patients with recurrent implantation failure to luteal support therapy.

Description

Technical Field

[0001] This invention relates to the field of in vitro fertilization-embryo implantation technology. More specifically, this invention relates to luteal support therapy methods for recurrent implantation failure, and methods and biomarkers for predicting luteal support therapy responsiveness in patients with recurrent implantation failure. Background Technology

[0002] Repeated implantation failure (RIF) is a specific disease type in the field of assisted reproductive technology (Bashiri A, Halper KI, Orvieto R. Recurrent Implantation Failure-update overview on etiology, diagnosis, treatment and future directions. Reproductive Biology and Endocrinology 2018; 16:121). Generally, a patient is considered to have repeated implantation failure when they have undergone three or more consecutive cycles of high-quality embryo transfer without achieving clinical pregnancy. The incidence of RIF in in vitro fertilization-embryo transfer (IVF-ET) is 10-15%. One-third of RIF cases are related to embryo quality, and the other two-thirds are related to maternal factors. Current clinical strategies for RIF include improving embryo quality and endometrial receptivity. Methods to improve endometrial receptivity include hormonal therapy (estrogen, progesterone, and bromocriptine), adjuvant therapy (vitamin D, aspirin, low molecular weight heparin, antimicrobial agents, and antibiotics), immunotherapy (prednisone, active immunization, passive immunization, and granulocyte colony-stimulating factor G-CSF), assisted reproductive technology (endometrial window period testing), surgery (hysteroscopy and laparoscopy), traditional Chinese medicine, stem cell therapy, and physiological therapies. While these methods can treat some RIF patients, others still cannot resolve their fertility problems.

[0003] In in vitro fertilization-embryo transfer (IVF-EF), luteal support has been proposed for both fresh and frozen-thawed embryo transfer cycles. It is generally believed that a certain serum progesterone (P) level in the mid-luteal phase is crucial for successful implantation. Two luteal support strategies have been proposed: a conventional strategy involving exogenous progesterone and estrogen supplementation; and a strategy that enhances luteal hormone (progesterone and estrogen) production through exogenous LH-active drugs (e.g., LH or HCG). In IVF-EF patients undergoing fresh embryo transfer cycles, luteal insufficiency is common due to the use of ovulation-inducing drugs; for these patients, progesterone supplementation for luteal support has been shown to provide clear clinical improvement. However, the effectiveness of luteal support depends on the specific luteal support drugs and protocols used, as well as the specific patient circumstances, and remains controversial in this field. Regarding the use of hCG as a luteal support drug, for example, Ludwig M et al. reported that in fresh embryo transfer cycles, supplementing hCG in addition to routine progesterone luteal support did not provide better efficacy than progesterone alone (Ludwig M, Finas A, Katalinic A, Strik D, Kowalcek I, Schwartz P, et al. Prospective, randomized study to evaluate the success rates using hCG, vaginal progesterone or a combination of both for luteal phase support. Acta Obstetricia et Gynecologica Scandinavica 2001; 80:574-82.). Similarly, Linden et al. reported that a meta-analysis showed that for fresh embryo transfer cycles, luteal support with progesterone combined with hCG did not result in statistically significant differences in live birth rate and continued pregnancy rate compared to progesterone alone (Luteal phase support for assisted reproduction cycles (Review), Cochrane Database of Systematic Reviews 2015, Issue 7, Art. No.: CD009154). Currently, research on different luteal support regimens and their suitability in different RIF patient populations remains limited. Furthermore, with the emergence of the concept of precise, personalized luteal support therapy, it is necessary to identify biomarkers indicating the responsiveness of specific luteal support regimens and to select targeted treatment regimens based on these biomarkers.

[0004] In recent years, studies based on microarray chips and differentially expressed genes have been proposed for application in the etiology and treatment response of complex diseases. In the field of recurrent intrauterine febrile fibroid (RIF), functional enrichment studies have also been proposed to stage the endometrial receptivity of RIF patients and analyze gene profiles related to the implantation window. For example, Ercan Bastu et al. (Potential Marker Pathways in the endometrium that may cause recurrent implantation failure, Reproductive Sciences, 2018, pp. 1-12) compared RIF patients with fertile individuals and, through transcriptomics and differential gene functional enrichment, identified and proposed nine KEGG biological pathways that may be related to endometrial receptivity in RIF patients with natural cycles, including circadian rhythm, pathways in cancer, proteasome, complement and coagulation cascades, citrate cycle, adherens junction, immune system and inflammation, cell cycle, and renin-angiotensin system. Similarly, Koot et al. (An endometrial gene expression signature accurately predicts recurrent implantation failure after IVF, Sci. Rep. 2016; 6:19411) also proposed the application of differentially expressed gene microarrays and functional enrichment analysis in RIF prediction. However, these analyses based on differentially expressed genes usually require obtaining endometrial biopsies from patients at specific time points in the cycle; and the analysis results often change with the time of biopsy, thus these methods have certain limitations in application.

[0005] In light of the foregoing, current diagnostic and treatment methods for RIF of unknown etiology are limited. There is a need in this field to differentiate RIF patients and to develop new methods for improving RIF treatment and diagnosis. Invention Overview

[0006] Through clinical research, this invention explores the correlation between luteinizing hormone (LH) levels and RIF patients, and proposes a novel luteal support method suitable for RIF patients with specific low LH serum levels. Furthermore, the inventors performed whole-genome exon sequencing and functional enrichment analysis of variant genes; and based on this, established the correlation between the responsiveness to this luteal support therapy and gene variants in specific biological pathways.

[0007] Based on the above in-depth research at the molecular, genetic, and clinical levels, the inventors have also proposed a new subtype of RIF disease—"subclinical hypopituitarism" recurrent implantation failure. Patients with this subtype of RIF have diagnostic specific biological pathway gene variants and / or optionally, diagnostic low LH levels during luteal phase D2, and show good clinical responsiveness to luteal support regimens supplemented with HCG after transplantation.

[0008] Therefore, in one aspect, the present invention provides a method for diagnosing or classifying patients with recurrent luteal infarction (RIF), comprising detecting variations in characteristic biological pathway-related genes of the patient and / or serum LH levels on day 2 of the luteal phase. In another aspect, the present invention provides a luteal support method comprising, for RIF patients with variations in characteristic biological pathway-related genes and / or serum LH levels on day 2 of the luteal phase below a certain threshold, further supplementing luteinization-related active drugs, especially HCG, in addition to luteal support with progesterone and / or estrogen; preferably, this method improves the patient's clinical pregnancy rate and / or live birth rate. In yet another aspect, the present invention provides a method for predicting the responsiveness of RIF patients to luteal support therapy supplemented with HCG, comprising detecting variations in characteristic biological pathway-related genes of the patient and / or serum LH levels on day 2 of the luteal phase.

[0009] In another aspect, the present invention also provides biomarkers and combinations thereof for use in diagnosing or classifying patients with recurrent inflammatory bowel disease (RIF), as well as biomarkers and combinations thereof for use in predicting the therapeutic response of RIF patients to luteal support methods supplemented with hCG. Furthermore, the present invention also provides the use of the aforementioned biomarkers and combinations thereof in the preparation of kits for use in the diagnostic or predictive methods of the present invention. Brief description of the attached diagram

[0010] Figure 1 The study showed cyclical changes in luteinizing hormone (LH) measured in some patients undergoing IVF-ET due to fallopian tube problems.

[0011] Figure 2 The study showed cyclical changes in luteinizing hormone (LH) detected in some patients with recurrent implantation failure.

[0012] Figure 3This demonstrates the quality control of sequencing data for all samples in Example 3.

[0013] Figure 4 The results show the enrichment of pathways in the case group. The enriched pathways ECM-receptor interaction and extracellular matrix organization in the case group are descriptions of the same pathway in two different databases, KEGG and GO-BP.

[0014] Figure 5 The results show the enrichment of pathways in the control group.

[0015] Figure 6 The study revealed 27 genes associated with the major KEGG / GO enrichment pathways (ECM-receptor interaction / extracellular matrix organization; PI3K-Akt signaling pathway; and Focal adhesion) in the case group.

[0016] Figure 7 The results show the statistical enrichment of treatment-responsive genes identified based on pathway enrichment analysis in both case and control samples.

[0017] Figure 8 The data shows the enrichment of major pathway-related genes in the samples from the 10 case groups.

[0018] Figure 9 This invention demonstrates the characteristic biological pathways and pathway-related genes of the present invention.

[0019] Figure 10 This shows a list of characteristic pathway SNPs identified in patients with responsiveness to HCG luteal support therapy. Invention Details

[0020] Before describing the invention in detail, it should be understood that the invention is not limited to the specific methods and experimental conditions described herein, as these methods and conditions can be modified. Furthermore, the terminology used herein is for illustrative purposes only and is not intended to be restrictive.

[0021] definition

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. For the purposes of this invention, the following terms are defined below.

[0023] The term “about” when used in conjunction with a numeric value means to cover a range of numeric values ​​that have a lower limit of 5% less than the specified numeric value and an upper limit of 5% greater than the specified numeric value.

[0024] When the term “and / or” is used to connect two or more options, it should be understood to mean any one of the options or any two or more of the options.

[0025] As used herein, the terms “comprising” or “including” mean to include the stated elements, integers, or steps, but do not exclude any other elements, integers, or steps. In this document, when the terms “comprising” or “including” are used, unless otherwise specified, they also cover situations consisting of the stated elements, integers, or steps.

[0026] In this text, D0 refers to the endometrial transformation day, which is the day of ovulation in natural cycles and ovulation induction cycles; in artificial cycles, it is the day the endometrial transformation medication is administered. Correspondingly, D2 refers to the second day after D0. Ovulation monitoring can be performed using any known method in this invention. For example, transvaginal ultrasound can be performed daily from day 8 to 10 of the menstrual cycle to monitor follicular development until the dominant follicle disappears, which is considered ovulation.

[0027] In this article, clinical pregnancy is defined as the observation of a gestational sac on an abdominal ultrasound examination performed 30-35 days after embryo transfer.

[0028] In this article, live birth is defined as the delivery of one or more live babies after 20 weeks of gestation.

[0029] In this paper, pathway-related genes are defined as genes annotated to at least one of the following biological pathways and / or biological processes according to KEGG and GO:

[0030] KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0031] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0032] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0033] -KEGG_PATHWAY:Focal Adhesion(hsa04510).

[0034] In some embodiments of the methods, compositions, kits and / or uses of the present invention, the pathway-related genes are selected from: genes annotated with KEGG at hsa04510, hsa04511, hsa04512, and / or genes annotated with GO_BP_DIRECT at GO:0030198.

[0035] In some implementations, the pathway-related genes are selected from those annotated under the KEGG pathway hsa04512, such as COL1A, COL2A, COL4A, COL6A, COL9A, LAMA1_2, LAMA3_5, LAMA4, LAMB1, LAMB2, LAMB3, LAMB4, LAMC1, LAMC2, LAMC3, CHAD, RELN, THBS1, THBS2S, FN1, SPP1, VTN, TN(TNC, tenascin) C),VWF,IBSP,AGRN,HSPG2,ITGA1,ITGA2,ITGA2B,ITGA3,ITGA4,ITGA5,ITGA6,ITGA7,ITGA8,ITGA9,ITGA10,ITGA11,ITGAV,ITGB 1,ITGB3,ITGB4,ITGB5,ITGB6,ITGB7,ITGB8,CD44,SDC1,SDC4,SV2,CD36,GP5,GP1BA,GP1BB,GP9,GP6,DAG1,CD47,HMMR,COL24A.

[0036] In some implementations, the pathway-related genes are selected from those annotated with GO_BP_DIRECT under GO:0030198, such as PECAM1, ITGB2, APP, FBN1, CD44, NCAN, SPOCK2, COL16A1, LAMB2, B4GALT1, DAG1, SLC39A8, JAM2, ITGA8, IBSP, COL28A1, ACAN, ADAMTS18, ITGB6, LAMB3, ITGA9, ITGA5, PGFFB, ITGAE, ITGB5, MPZL3, NF1, PTK2, LAMC1, TGF BI,SOX9,CCN1,ITGAM,EGFLAM,NPNT,VIT,TNF,ITGB3,BSG,FOXF1,LAMA1,VWF,MMP12,NDNF,FBLN1,ITGA4,VCAN,TNR,COL14A1,ITGAV,ICA M3,COMP,ICAM5,LAMA3,LAMA5,FN1,ADAMTS13,CCDC80,COL7A1,COL3A1,COL1A1,FBLN2,LAMA2,THBS1,LAMB1,VCAM1,ICAM1,ITGAD,COL4A3 ,DMP1,DDR1,FGG,FGB,FGA,ITGA1,CDH1,MMP14,VTN,ITGB4,ITGA6,POSTN,MADCAM1,ADAM15,JAM3,ITGA7,MYF5,ITGB8,ITGB7,ITGA3,WNT 3A,TNC,PDGFRA,ADAMTS12,HAPLN1,ECM2,NID1,EGFL6,LAMC2,ITGA2B,ADAMTS9,ITGA10,COL8A2,FBLN5,SERPINE1,NID2,F11R,COL13A1, CD47,COL8A1,HSD17B12,SMOC2,ICAM2,COL4A6,ITGA11,COL6A3,COL6A2,COL6A1,ITGAX,ITGAL,MMP24,SPP1,COL19A1,ICAM4,ITGB1,ADA M12, COL18A1, COL15A1, COL24A, ADAM19, LAMA4, KDR, ITGA2, COL17A1, COL5A1, BCAN, COL5A3, LAMC3, DNAJB6, SPINK5, DDR2, COL9A3, ERCC2.

[0037] In some implementations, the pathway-related genes are selected from those annotated under the KEGG pathway hsa04510, such as COL1A, COL2A, COL4A, COL6A, COL9A, LAMA1_2, LAMA3_5, LAMA4, LAMB1, LAMB2, LAMB3, LAMB4, LAMC1, LAMC2, LAMC3, CHAD, RELN, THBS1, THBS2S, FN1, SPP1, VTN, TN, VWF, IBSP, ITGA1, ITGA2, ITGA2B, ITGA3, ITGA4, ITGA5, I TGA6,ITGA7,ITGA8,ITGA9,ITGA10,ITGA11,ITGAV,ITGB1,ITGB3,ITGB4,ITGB5,ITGB6,ITGB7,ITGB8,PDGFA,PDGFB,PDGFC_D,EGF,IG F1,VEGFA,VEGFB,PGF,VEGFC_D,HGF,PDGFRA,PDGFRB,IGF1R,KDR,EGFR,FLT1,FLT4,MET,ERBB2,SRC,ARHGAP35,ARHGAP5,RHOA,DIAPH1 ,ROCK1,ROCK2,MYL2,MYL5,MYL7,MYL9,MYL10,MYL12,MYLPF,PPP1C,PPP1R12A,PPP1R12B,PPP1R12C,MYLK,ACTB_G1,RASGRF1,CAPN2, ACTN1_4,TLN,FLNA,PXN,ILK,ZYX,VASP,VCL,PARV,PDPK1,AKT,GSK3B,CTNNB1,PRKCA,PRKCB,PRKCG,PTK2,PIK3CA_B_D,PIK3R1_2_3, PTEN,VAV,RAC1,RAC2,RAC3,PAK1,PAK2,PAK3,PAK4,PAK5,PAK6,CDC42,BCAR1,CRK,DOCK1,RAPGEF1,RAP1A,RAP1B,JNK,JUN,BRAF,CA V1,CAV2,CAV3,FYN,SHC1,SHC2,SHC3,SHC4,GRB2,SOS,HRAS,RAF1,MAP2K1,ERK,ELK1,CCND1,CCND2,CCND3,BIRC2_3,XIAP,BAD,BCL2.

[0038] In some implementations, the pathway-related genes are selected from those annotated under the KEGG pathway hsa04511, such as EGF, TGFA, EREG, AREG, FGF1, FGF2, FGF, FGF19, FGF21, FGF23, NGFA, NGFB, BDNF, NTF3, NTF4, INS, IGF1, IGF2, PDGFA, PDGFB, PDGFC_D, CSF1, KITLG, FLT3LG, VEGFA, VEGFB, PGF, VEGFC_D, HGF, ANGPT1, ANGPT2, ANGPT4, EFNA, EGFR, ERBB2, ERBB3, ERBB4, FG FR1,FGFR2,FGFR3,FGFR4,NGFR,NTRK1,NTRK2,INSR,IGF1R,PDGFRA,PDGFRB,CSF1R,KIT,FLT3,FLT1,FLT4,KDR,MET,TEK,EPHA2,GRB2,SOS,HRAS,KRAS ,NRAS,RAF1,MAP2K1,MAP2K2,ERK,IRS1,TLR2,TLR4,RAC1,IGH,SYK,CD19,PIK3AP1,GH,PRL,OSM,IL2,IL3,IL6,IL4,IL7,IFNA,IFNB,EPO,CSF3,GHR,P RLR,OSMR,IL2RA,IL2RB,IL2RG,IL3RA,IL6R,IL4R,IL7R,IFNAR1,IFNAR2,EPOR,CSF3R,JAK1,JAK2,JAK3,COL1A,COL2A,COL4A,COL6A,COL9A,LAMA1_ 2,LAMA3_5,LAMA4,LAMB1,LAMB2,LAMB3,LAMB4,LAMC1,LAMC2,LAMC3,CHAD,RELN,THBS1,THBS2S,FN1,SPP1,VTN,TN,VWF,IBSP,ITGA1,ITGA2,ITGA2B, ITGA3,ITGA4,ITGA5,ITGA6,ITGA7,ITGA8,ITGA9,ITGA10,ITGA11,ITGAV,ITGB1,ITGB3,ITGB4,ITGB5,ITGB6,ITGB7,ITGB8,PTK2,PIK3CA_B_D,PIK3R 1_2_3,F2R,CHRM1,CHRM2,LPAR1,LPAR2,LPAR3,LPAR4,LPAR5,LPAR6,GNB1,GNB2,GNB3,GNB4,GNB5,GNG2,GNG3,GNG4,GNG5,GNG7,GNG8,GNG10,GNG11,GNG12,GNG13,GNGT1,GNGT2,PIK3CG,PIK3R5_6,PDPK1,STK11,PRKAA,DDIT4,TSC1,TSC2,RHEB,MLST8,MTOR,RAPTOR,EIF4EBP1,EIF4E,RPS6KB,EIF4B,RP-S6e,P RKCA,PKN1,SGK1,SGK2,SGK3,AKT,AIP3,AIP1,PTEN,THEM4,PPP2C,PPP2R1,PPP2R2,PPP2R3,PPP2R5,HSP90A,HSP90B,CDC37,CRTC2,PHLPP,TCL1A,TCL1B,MTCP1 ,NOS3,BRCA1,GSK3B,GYS,E4.1.1.32,G6PC,MYC,CCND1,CDKN1A,CDKN1B,CDK2,CDK4,CDK6,CCND2,CCND3,CCNE,FOXO3,RBL2,TNFSF6,BCL2L11,YWHAB_Q_Z,YWHA E,YWHAG_H,BAD,BCL2L1,BCL2,CASP9,CREB1,ATF2,ATF4,CREB3(CREB3L4),CREB5,ATF6B,MCL1,RXRA,NR4A1,IKBKG,IKBKA,IKBKB,RELA,NFKB1,MYB,MDM2,TP53. ,

[0039] In some embodiments of the methods, compositions, kits, and / or uses of the present invention, the pathway-related genes are selected from... Figure 9 The genes listed in the document are one or a combination of the genes for the KEGG pathway hsa04510, hsa04511, hsa04512, and / or the GO_BP_DIRECT biological process GO:0030198.

[0040] In this document, the term "marker" or "biomarker" refers to a biomolecule, or a portion / fragment of a biomolecule, the alteration and / or presence of which is relevant to a particular biological condition or state. In some embodiments, the markers of the present invention are genetic markers, encompassing all biologically relevant forms of the identified gene and its encoded protein, including, for example, gene variations (e.g., missense mutations), gene expression levels, and the mutated form and / or protein expression level and / or activity of the corresponding encoded protein. In some embodiments, genetic markers are sequence variations in gene exons, and / or the corresponding changes in protein amino acids resulting from said variations. In some embodiments, the detection of a biomarker molecule may be the detection of its fragment or portion. The portion may be, for example, a fragment of a gene or a fragment of a protein, such as a gene fragment containing 5-30 nucleotides, or a protein fragment containing 5-30 amino acids.

[0041] The term "biomarker set" or "biomarker group" as used herein refers to a combination, array, or collection comprising one or more biomarkers (e.g., genetic markers). In some embodiments, the number of biomarkers used in the biomarker set depends on the detection sensitivity and specificity for a particular combination of biomarker values. Sensitivity and specificity indicate the ability to correctly classify subjects based on the values ​​of biomarkers detected in a biological sample. In some embodiments, in the methods of the present invention, such as diagnostic and predictive methods, the sensitivity of the diagnostic or predictive method reaches 70%, 80%, or more, for example, 90%, or more, for the biomarker set used. In some embodiments, in the methods of the present invention, such as diagnostic and predictive methods, the specificity of the diagnostic or predictive method reaches 70%, 80%, or more, for example, 90%, or more, for the biomarker set used.

[0042] The term "genetic variation" refers to an alteration in the sequence encoding the gene relative to its canonical sequence, including but not limited to deletions, insertions, and / or substitutions. The term "canonical sequence" herein refers to the most frequently occurring sequence in humans, such as the sequence in the human reference genome (GRCh37 / hg19). Preferably, the allele frequency resulting from the gene variation is no greater than 0.03 in the human population, for example, no greater than 0.02 or 0.01, and, for example, no greater than 0.01 according to the GnomAD database.

[0043] The term "missense mutation" refers to a gene sequence variation that can lead to changes in the amino acid sequence of a polypeptide product or the functional RNA base sequence. In some preferred embodiments, the gene variations of the present invention include missense mutations, preferably said missense mutations that do not lead to complete inactivation of the gene-encoded protein but affect the biological activity of the gene-encoded protein. In one embodiment, said missense mutation is a harmful missense mutation. In this document, a harmful missense mutation is a mutation that affects the structure and / or function of a gene-encoded product (e.g., a polypeptide product); however, said mutation does not lead to complete inactivation of the protein. In one embodiment, harmful missense mutations are harmful missense mutations predicted by SIFT and / or PolyPhen-2 software.

[0044] The term "array" or "microarray" refers to an ordered arrangement of hybridizable array elements, preferably polynucleotide probes (e.g., oligonucleotides), on a matrix. The matrix can be a solid matrix such as a glass slide or a semi-solid matrix such as a nitrocellulose membrane.

[0045] The term “diagnosis” is used herein to refer to the identification or classification of a molecular or pathological state, disease, or condition. For example, “diagnosis” can refer to the classification of a specific subtype of RIF, such as by histopathological criteria (e.g., D2 serum LH levels) and / or by molecular characteristics (e.g., specific gene variants or combinations of specific gene variants, expression of proteins encoded by said genes, or patterns of gene enrichment).

[0046] The term "aided diagnosis" is used herein to refer to methods that assist in making clinical decisions regarding the presence or nature of symptoms or conditions of a specific type of RIF or treatment responsiveness. In some embodiments, the present invention also provides, for example, methods to assist in the diagnosis of RIF subtypes or methods to assist in the diagnosis of treatment responsiveness in RIF patients, said methods may include measuring genetic variations or enrichment patterns of specific genes or combinations of genes in a biological sample from an individual.

[0047] The term “detection” in relation to biomarkers, as used herein, encompasses detection in any manner, including both direct and indirect detection.

[0048] The term "predicted treatment responsiveness" is used herein to refer to the likelihood that a patient will respond favorably or unfavorably to a drug or group of drugs. In one embodiment, prediction involves the degree of said response. In one embodiment, prediction involves whether and / or whether a patient will achieve an improvement in clinical outcomes, such as clinical pregnancy rate, live birth rate, etc., after treatment, for example, with a specific therapeutic agent.

[0049] As used herein, "reference sample" or "control sample" refers to a sample obtained from a source known or considered free from the disease or condition identified by the methods or compositions of the present invention. For example, "reference" may be a group of a majority of individuals blinded from patients with recurrent implantation failure who have not received luteal support therapy with supplemental HCG and / or have not had their LH levels monitored during the luteal phase. Alternatively, "reference" may be a subject who has not been diagnosed with RIF, or a subject with RIF due to embryo quality issues, or an IVF-ET subject with organic lesions such as fallopian tube problems.

[0050] As used herein, the term “sample” refers to a composition obtained or derived from a subject of interest that contains or is suspected of containing, to be characterized and / or identified, for example, based on physical, biochemical, chemical and / or physiological characteristics, cellular entities and / or other molecular entities (in the context of this invention, particularly nucleic acids to be detected, such as whole-exome nucleic acid sequences, or biomarker nucleic acids to be detected, or fragments thereof).

[0051] The term "luteinizing activity-related drugs" in this article refers to drugs with luteinizing biotin-like effects, such as LH and HCG, including recombinant or isolated and purified forms, such as recombinant LH, urinary HCG, and recombinant HCG.

[0052] The term "progesterone medication" in this document refers to progesterone-based medications that can be used for fertility-related treatments, including both natural and synthetic progesterone-based medications. Common progesterone medications include, for example, but not limited to, intramuscular progesterone, such as 17α-hydroxyhexanoate progesterone ester; vaginal progesterone, such as progesterone extended-release gels and micronized progesterone capsules; and oral progesterone, such as dydrogesterone.

[0053] The term "estrogenous drugs" in this article refers to estrogenous drugs that can be used for fertility-related treatments, including but not limited to estradiol valerate and micronized estradiol, which can be administered orally, vaginally, or transdermally.

[0054] The various aspects of the present invention will be described in further detail below.

[0055] This invention is based, at least in part, on the definition of a unique new disease subtype of recurrent miscarriage failure (RIF) (also referred to herein as the subclinical hypopituitarism RIF subtype). This subtype is characterized by variations in genes associated with characteristic biological pathways and / or serum LH levels on day D2 of the luteal phase. This invention also provides biomarkers relevant to the diagnosis and classification of this subtype, novel luteal support methods suitable for improving clinical outcomes of embryo transfer in this subtype, and methods and biomarkers for predicting patient responsiveness to luteal support therapy.

[0056] I. Disease subtypes and characteristic pathway-related gene variations of the present invention

[0057] Based on research at both the genetic and clinical levels, the inventors propose a novel subtype of recurrent implantation failure—subclinical hypopituitarism-related recurrent implantation failure. This subtype exhibits characteristic pathway-related gene variants and demonstrates a favorable clinical pregnancy response to luteal support therapy with HCG supplementation. Preferably, it also possesses one or more of the following features, preferably all of them:

[0058] (1) No clear reason for repeated implantation failure;

[0059] (2) No obvious organic pituitary damage;

[0060] (3) Mild symptoms of hypopituitarism;

[0061] (4) Serum LH level on day D2 of the luteal phase ≤5 IU / L.

[0062] Therefore, in one aspect, the present invention provides methods and compositions for characterizing (including, but not limited to, diagnosing and / or classifying) subclinical hypopituitarism recurrent implantation failure and / or predicting a patient’s responsiveness to luteal support therapy with supplemental HCG by screening for variations in characteristic biological pathway-related genes as biomarkers in biological samples from subjects.

[0063] Mutant gene screening

[0064] The variant gene screening of this invention includes the detection of nucleotide variations in the subject's genome and / or exome. These gene variations include, but are not limited to, polymorphisms, splicing variants, and mutations. Various variant gene screening techniques are known in the art, including, but not limited to, whole-genome sequencing, target region sequencing, whole-exome sequencing, and SNP array hybridization. Furthermore, it is known in the art that variant genes can also be screened by detecting changes (e.g., reductions) in the biological activity / function of gene-encoded products (e.g., proteins). All of these techniques are applicable to this invention. And, as those skilled in the art will understand, this invention is not limited to any specific variant screening technique.

[0065] In some embodiments of the invention, preferably, protein-coding sequences in the subject's genome are analyzed via whole-exome sequencing (WES). This technique allows for the detection of disease-related genetic abnormalities primarily located in exon regions. WES produces high-throughput results. Various techniques exist in the art that can be applied to the analysis of sequencing data to allow the detection of nucleotide variations and nucleotide polymorphisms. Data obtained through these methods can be combined and further applied to applications such as the diagnosis and classification of disease subtypes.

[0066] Both of the existing major second-generation sequencing (NGS) methods can be used to perform WES in this invention: DNA amplification-based sequencing (e.g., available from Illumina, Ion Torrent) and single-molecule real-time sequencing (available from Pacific Biosciences, Oxford Nanopore). Tissue samples used for sequencing can be freshly frozen, formaldehyde-fixed and paraffin-embedded, or liquid-based samples (e.g., blood samples). Kits for isolating nucleic acids from each of these samples are commercially available. Exomes can be captured using array-based or magnetic bead-based methods. Target regions can also be appropriately captured to achieve sufficient sequencing coverage depth. After obtaining WES sequencing data, data processing is typically performed to control data quality and prune low-quality reads. Reads can then be mapped to a selected reference genome to identify and annotate genetic variations. In one embodiment, genetic variations are identified using WES technology, including but not limited to frameshift mutations, stop-gain mutations, stop-loss mutations, and missense mutations. In a preferred embodiment, in the compositions and methods of the present invention, missense mutations that result in amino acid alterations are identified. Tools that can be used to identify single nucleotide variants in WES data include, but are not limited to, varScan2, MuTect, Strelka, Platypus, FreeBayes, and SomaticSniper. Any of these methods and combinations thereof are suitable for the gene variant detection of the present invention.

[0067] Identification of harmful missense mutations

[0068] The inventors have discovered that, in the RIF disease subtype of this invention, consistent with its subclinical manifestations, the gene variants detected in the functional enrichment analysis of variant genes in cases and controls are mainly missense mutations that do not lead to gene inactivation but affect protein biological activity, i.e., harmful missense mutations.

[0069] Therefore, in some preferred embodiments of the present invention, in the compositions and methods of the present invention, harmful missense mutations in pathway-related genes are detected as biomarkers. Harmful missense mutations can be identified using various software known in the art, such as SIFT and PolyPhen-2 software.

[0070] Functional annotation of variant genes

[0071] In this paper, after screening for variant genes in subjects, in some implementations, the KEGG and GO databases can be used to perform functional annotation of the detected variant genes, including KEGG biological pathway annotation and GO biological process annotation. Optionally, functional enrichment analysis can also be performed on the detected variant genes and compared with a reference sample.

[0072] KEGG (Kyoto Encyclopedia of Genes and Genomes) notes

[0073] KEGG is a database for systematic gene function analysis, linking genomic information with more ordered biological functional information. The KEGG database comprises three databases: the GENE database, the PATHWAY database, and the LIGAND database. The GENE database stores genomic information as a collection of gene catalogs, covering fully sequenced genomes as well as some partially sequenced genomes, with real-time updated gene function annotations. The PATHWAY database stores more ordered biological functional information, including graphical representations of cell biological processes such as metabolism, membrane transport, signal transduction, and the cell cycle. KEGG also provides a list of orthologous groups as a supplement to the PATHWAY database, offering information on conserved sub-pathways (pathway motifs), often encoded by multiple genes coupled to a chromosomal location, which is very useful for predicting gene function. The third KEGG database is LIGAND, which provides information on compounds, enzyme molecules, and enzymatic reactions. KEGG offers Java graphical tools for browsing genome maps, comparing two genome maps, and manipulating expression maps, as well as computational tools for sequence comparison, graphical comparison, and pathway calculation. The KEGG database is publicly available at http: / / www.genome.jp / kegg / .

[0074] In some embodiments of the methods and compositions of the present invention, RIF patients having the RIF disease subtype of the present invention and / or exhibiting a therapeutic response to supplemental HCG luteal support exhibit one or more gene variants in the extracellular matrix (ECM)-receptor interaction (KEGG) pathway. The ECM-receptor interaction (KEGG) pathway is described under entry hsa04512.

[0075] In some embodiments, therefore, in the compositions and methods of the present invention, such as diagnostic and predictive methods, the detected genetic marker comprises one or more genes, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, or 65 or more genes, according to KEGG annotations on the ECM-receptor interaction KEGG pathway. In some embodiments, the gene may be selected from genes listed in the supplemental ortholog group list of KEGG database entry hsa0451, such as genes COL1A, COL2A, COL4A, COL6A, COL9A, LAMA1_2, LAMA3_5, LAMA4, LAMB1, LAMB2, LAMB3, LAMB4, LAMC1, LAMC2, LAMC3, CHAD, RELN, THBS1, THBS25, FN1, SPP1, VTN, TN, VWF, IBSP, AGRN. HSPG2, ITGA1, ITGA2, ITGA2B, ITGA3, ITGA4, ITGA5, ITGA6, ITGA7, ITGA7, ITGA8, ITGA9, ITGA10, ITGA11, ITGAV, ITGB1, ITGB3, ITGB4, ITGB5, ITGB6, ITGB7, ITGB8, CD44, SDC1, SDC4, SV2, CD36, GP5, GP1BA, GP1BB, GP9, GP6, DAG1, CD47, HMMR, COL24A. In a preferred embodiment, the biomarker of the present invention comprises EC-receptor interaction pathway genes selected from: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, and COL6A6.

[0076] In some embodiments of the methods and compositions of the present invention, RIF patients having the RIF disease subtype of the present invention and / or exhibiting a therapeutic response to supplemental HCG luteal support exhibit one or more gene variants in the KEGG pathway PI3K-Akt signaling pathway. The PI3K-Akt signaling pathway is described under entry hsa04511.

[0077] In some embodiments, therefore, in the compositions and methods of the present invention, such as diagnostic and predictive methods, the detected genetic marker comprises one or more genes, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, or 65 or more genes, according to KEGG annotations in the PI3K-Akt signaling pathway. In some embodiments, the gene may be selected from genes listed in the supplemental ortholog group list of KEGG database entry hsa04511, such as genes EGF, TGFA, EREG, AREG, FGF1, FGF2, FGF, FGF19, FGF21, FGF23, NGFA, NGFB, BDNF, NTF3, NTF4, INS, IGF1, IGF2, PDGFA, PDGFB, PDGFC_D, CSF1, KITLG, FLT3LG, VEGFA, VEGFB, PGF, VEGFC_D, HGF. ANGPT1,ANGPT2,ANGPT4,EFNA,EGFR,ERBB2,ERBB3,ERBB4,FGFR1,FGFR2,FGFR3,FGFR4,NGFR,NTRK1,NTRK2,INSR,IGF1R,PDGFRA ,PDGFRB,CSF1R,KIT,FLT3,FLT1,FLT4,KDR,MET,TEK,EPHA2,GRB2,SOS,HRAS,KRAS,NRAS,RAF1,MAP2K1,MAP2K2,ERK,IRS1,TLR2 ,TLR4,RAC1,IGH,SYK,CD19,PIK3AP1,GH,PRL,OSM,IL2,IL3,IL6,IL4,IL7,IFNA,IFNB,EPO,CSF3,GHR,PRLR,OSMR,IL2RA,IL2RB ,IL2RG,IL3RA,IL6R,IL4R,IL7R,IFNAR1,IFNAR2,EPOR,CSF3R,JAK1,JAK2,JAK3,COL1A,COL2A,COL4A,COL6A,COL9A,LAMA1_2,L AMA3_5,LAMA4,LAMB1,LAMB2,LAMB3,LAMB4,LAMC1,LAMC2,LAMC3,CHAD,RELN,THBS1,THBS2S,FN1,SPP1,VTN,TN,VWF,IBSP,ITGA 1,ITGA2,ITGA2B,ITGA3,ITGA4,ITGA5,ITGA6,ITGA7,ITGA8,ITGA9,ITGA10,ITGA11,ITGAV,ITGB1,ITGB3,ITGB4,ITGB5,ITGB6,ITGB7,ITGB8,PTK2,PIK3CA_B_D,PIK3R1_2_3,F2R,CHRM1,CHRM2,LPAR1,LPAR2,LPAR3,LPAR4,LPAR5,L PAR6,GNB1,GNB2,GNB3,GNB4,GNB5,GNG2,GNG3,GNG4,GNG5,GNG7,GNG8,GNG10,GNG11,GNG12,GNG13,GN GT1,GNGT2,PIK3CG,PIK3R5_6,PDPK1,STK11,PRKAA,DDIT4,TSC1,TSC2,RHEB,MLST8,MTOR,RAPTOR,EIF 4EBP1,EIF4E,RPS6KB,EIF4B,RP-S6e,PRKCA,PKN1,SGK1,SGK2,SGK3,AKT,AIP3,AIP1,PTEN,THEM4,PPP2 C, PPP2R1, PPP2R2, PPP2R3, PPP2R5, HSP90A, HSP90B, CDC37, CRTC2, PHLPP, TCL1A, TCL1B, MTCP1, NOS3, B RCA1,GSK3B,GYS,E4.1.1.32,G6PC,MYC,CCND1,CDKN1A,CDKN1B,CDK2,CDK4,CDK6,CCND2,CCND3,CCNE,F OXO3, RBL2, TNFSF6, BCL2L11, YWHAB_Q_Z, YWHAE, YWHAG_H, BAD, BCL2L1, BCL2, CASP9, CREB1, ATF2, ATF4, CREB3 (CREB3L4), CREB5, ATF6B, MCL1, RXRA, NR4A1, IKBKG, IKBKA, IKBKB, RELA, NFKB1, MYB, MDM2, TP53. In a preferred embodiment, the biomarker of the present invention comprises genes of the PI3K-Akt signaling pathway, selected from: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, and LPAR6.

[0078] In some embodiments of the methods and compositions of the present invention, RIF patients having the RIF disease subtype of the present invention and / or exhibiting a therapeutic response to supplemental HCG luteal support exhibit one or more gene variants in the KEGG pathway FOCAL ADHENSION. The FOCAL ADHENSION pathway is described under entry hsa04510.

[0079] In some embodiments, therefore, in the compositions and methods of the present invention, such as diagnostic and predictive methods, the detected genetic marker comprises one or more genes annotated according to KEGG on the FOCAL ADHENSION pathway, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, or 65 or more genes.In some embodiments, the gene may be selected from genes listed in the Supplemental Ortholog Group List of KEGG database entry hsa0450, such as genes COL1A, COL2A, COL4A, COL6A, COL9A, LAMA1_2, LAMA3_5, LAMA4, LAMB1, LAMB2, LAMB3, LAMB4, LAMC1, LAMC2, LAMC3, CHAD, RELN, THBS1, THBS2S, FN1, SPP1, VTN, TN, VWF, IBSP, ITGA1, ITGA2, ITGA2B, ITGA3, IT GA4,ITGA5,ITGA6,ITGA7,ITGA8,ITGA9,ITGA10,ITGA11,ITGAV,ITGB1,ITGB3,ITGB4,ITGB5,ITGB6,ITGB7,ITGB8,PDGFA,PDGFB,PDGFC _D,EGF,IGF1,VEGFA,VEGFB,PGF,VEGFC_D,HGF,PDGFRA,PDGFRB,IGF1R,KDR,EGFR,FLT1,FLT4,MET,ERBB2,SRC,ARHGAP35,ARHGAP5,RHO A,DIAPH1,ROCK1,ROCK2,MYL2,MYL5,MYL7,MYL9,MYL10,MYL12,MYLPF,PPP1C,PPP1R12A,PPP1R12B,PPP1R12C,MYLK,ACTB_G1,RASGRF1, CAPN2,ACTN1_4,TLN,FLNA,PXN,ILK,ZYX,VASP,VCL,PARV,PDPK1,AKT,GSK3B,CTNNB1,PRKCA,PRKCB,PRKCG,PTK2,PIK3CA_B_D,PIK3R1_ 2_3,PTEN,VAV,RAC1,RAC2,RAC3,PAK1,PAK2,PAK3,PAK4,PAK5,PAK6,CDC42,BCAR1,CRK,DOCK1,RAPGEF1,RAP1A,RAP1B,JNK,JUN,BRAF, CAV1,CAV2,CAV3,FYN,SHC1,SHC2,SHC3,SHC4,GRB2,SOS,HRAS,RAF1,MAP2K1,ERK,ELK1,CCND1,CCND2,CCND3,BIRC2_3,XIAP,BAD,BCL2.In a preferred embodiment, the biomarker of the present invention comprises FOCAL ADHESION pathway genes selected from: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, SHC4, and MYL12A.

[0080] Gene Ontology

[0081] Gene Ontology (GO) is abbreviated as GO. GO annotations can be divided into three main categories: GO_MF (Molecular Function), GO_BP (Biological Process), and GO_CC (Cellular Component). GO describes a gene through these three categories. For example, in a GO annotation, the gene product "cytochrome c" can be described through its molecular function (oxidoreductase activity), biological process (oxidative phosphorylation), and molecular component (mitochondrial matrix).

[0082] In this paper, GO functional enrichment analysis can be performed at any and / or a combination of three levels: molecular function, biological process, and cellular component, counting the number or composition of genes or proteins (e.g., variant genes or their encoded proteins). Furthermore, specific GO terms can be selected to count the number of genes or proteins directly corresponding to that term.

[0083] In some embodiments of the methods and compositions according to the present invention, RIF patients having the RIF disease subtype of the present invention and / or exhibiting a therapeutic response to supplemental HCG luteal support exhibit one or more gene variants in the GO_BP_DIRECT extracellular matrix organization biological process. In the GO database, the extracellular matrix organization biological process is described under entry GO:0030198.

[0084] Therefore, in some embodiments, in the compositions and methods of the present invention, such as diagnostic and predictive methods, the detected genetic marker comprises one or more genes annotated on extracellular matrix tissue biology process GO:0030198 according to GO_BP_DIRECT, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, or 65 or more genes. In some embodiments, the gene is also annotated on one or more of GO:0005576 (extracellular region), GO:0062023 (collagen-containing extracellular matrix), and GO:0007155 (cell adhesion).

[0085] In some implementations, the detected genetic markers include genes with GO_BP_DIRECT extracellular matrix tissue annotations and are selected from, for example, PECAM1, ITGB2, APP, FBN1, CD44, NCAN, SPOCK2, COL16A1, LAMB2, B4GALT1, DAG1, SLC39A8, JAM2, ITGA8, IBSP, COL28A1, ACAN, ADAMTS18, ITGB6, LAMB3, ITGA9, ITGA5, PGFFB, ITGAE, ITGB5, MPZL3, NF1, PTK2, and LAMC1. ,TGFBI,SOX9,CCN1,ITGAM,EGFLAM,NPNT,VIT,TNF,ITGB3,BSG,FOXF1,LAMA1,VWF,MMP12,NDNF,FBLN1,ITGA4,VCAN,TNR,COL14A1,ITGAV ,ICAM3,COMP,ICAM5,LAMA3,LAMA5,FN1,ADAMTS13,CCDC80,COL7A1,COL3A1,COL1A1,FBLN2,LAMA2,THBS1,LAMB1,VCAM1,ICAM1,ITGAD,C OL4A3,DMP1,DDR1,FGG,FGB,FGA,ITGA1,CDH1,MMP14,VTN,ITGB4,ITGA6,POSTN,MADCAM1,ADAM15,JAM3,ITGA7,MYF5,ITGB8,ITGB7,ITG A3,WNT3A,TNC,PDGFRA,ADAMTS12,HAPLN1,ECM2,NID1,EGFL6,LAMC2,ITGA2B,ADAMTS9,ITGA10,COL8A2,FBLN5,SERPINE1,NID2,F11R,CO L13A1,CD47,COL8A1,HSD17B12,SMOC2,ICAM2,COL4A6,ITGA11,COL6A3,COL6A2,COL6A1,ITGAX,ITGAL,MMP24,SPP1,COL19A1,ICAM4,ITG B1,ADAM12,COL18A1,COL15A1,ADAM19,LAMA4,KDR,ITGA2,COL17A1,COL5A1,BCAN,COL5A3,LAMC3,DNAJB6,SPINK5,DDR2,COL9A3,ERCC2.In a preferred embodiment, the biomarker of the present invention comprises a gene with GO_BP_DIRECT extracellular matrix tissue annotation, selected from: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, and COL9A3.

[0086] In some embodiments, in the compositions and methods of the present invention, such as diagnostic and predictive methods, the detected genetic marker comprises a combination of genes from one or more of (a)-(d):

[0087] (a) Genes annotated with KEGG_PATHWAY: extracellular matrix-receptor interaction pathway (hsa04512), such as Figure 9 The genes shown are classified under hsa04512;

[0088] (b) Genes annotated with GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198), such as Figure 9 The genes shown are classified under GO:0030198;

[0089] (c) Genes annotated with KEGG_PATHWAY:PI3K-Akt signaling pathway (hsa04511), such as Figure 9 The genes shown are classified under hsa04511;

[0090] (d) Genes annotated with KEGG_PATHWAY:Focal Adhesion(hsa04510), for example Figure 9 The genes shown are classified under hsa04510.

[0091] Preferably, the detected genetic marker includes the N gene from (a) or (b); optionally, it also includes the M gene from (c) and / or (d). N can be an integer selected from 2 to 300, 2 to 200, 2 to 150, 2 to 100, 2 to 50, or 2-40, or 2-30, or 2-25, or 2-20, or 2-15, or 2-10, or 2-5. M can be an integer selected from 2 to 300, 2 to 200, 2 to 150, 2 to 100, 2 to 50, or 2-40, or 2-30, or 2-25, or 2-20, or 2-15, or 2-10, or 2-5.

[0092] In a preferred embodiment, in the compositions and methods of the present invention, such as diagnostic and predictive methods, the detected genetic marker comprises one or any combination of the following pathway-related genes: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6, SHC4, and MYL12A. In some embodiments, the detected genetic marker comprises a combination of genetic markers consisting of N pathway-related genes, where N is an integer selected from 2 to 27, or 2-25, or 2-20, or 2-15, or 2-10, or 2-5. In other embodiments, the genetic markers to be detected include LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6, SHC4, and MYL12A.

[0093] II. Biomarkers of the present invention

[0094] In another aspect, the present invention provides for characterizing, classifying, and diagnosing the “subclinical hypopituitarism recurrent implantation failure disease subtype” of the present invention, as well as for biomarkers or combinations of biomarkers that can be used to predict the responsiveness of patients with recurrent implantation failure to luteal support therapy with supplemental HCG, and compositions or products (e.g., microarray chips) containing reagents for their detection, and their respective uses in the diagnostic and predictive methods of the present invention.

[0095] Therefore, in one aspect, the present invention provides isolated biomarkers or combinations of biomarkers comprising or composed of one or more pathway-related genes, wherein said pathway-related genes are selected from genes annotated according to KEGG and GO on at least one or more of the following biological pathways or biological processes:

[0096] (a) KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0097] (b)GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0098] (c) KEGG_PATHWAY: PI3K-Akt signal transduction pathway (hsa04511);

[0099] (d)KEGG_PATHWAY:Focal Adhesion(hsa04510),

[0100] In some implementations, the isolated biomarker combination includes Figure 9 The diagram shows N biomarkers from pathway-related gene biomarkers. In some preferred embodiments, the isolated biomarker combination comprises those selected from... Figure 9 The N biomarkers listed in categories hsa04512 and GO:0030198 shown; optionally, also including biomarkers selected from... Figure 9 The M biomarkers listed in categories hsa04511 and hsa04510 are shown. N can be an integer selected from 2 to 300, 2 to 200, 2 to 150, 2 to 100, 2 to 50, or 2-40, or 2-30, or 2-25, or 2-20, or 2-15, or 2-10, or 2-5. M can be an integer selected from 2 to 300, 2 to 200, 2 to 150, 2 to 100, 2 to 50, or 2-40, or 2-30, or 2-25, or 2-20, or 2-15, or 2-10, or 2-5.

[0101] In a preferred embodiment, the biomarker or combination of biomarkers consists of N pathway-associated genes selected from LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6, SHC4, and MYL12A. N can be an integer selected from 2 to 27, or 2-25, or 2-20, or 2-15, or 2-10, or 2-5.

[0102] In some embodiments, the biomarker combination of the present invention comprises at least one or at least two separate biomarkers selected from the following: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6.

[0103] In some embodiments, the biomarker combination of the present invention comprises at least one or at least two separate biomarkers selected from the following: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, and COL9A3.

[0104] In some embodiments, the biomarker combination of the present invention comprises at least one or at least two separate biomarkers selected from the following: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6.

[0105] In some embodiments, the biomarker combination of the present invention comprises at least one or at least two separate biomarkers selected from the following: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, SHC4, MYL12A.

[0106] In some embodiments, the biomarker combination of the present invention can be used to characterize, diagnose, or classify patients with "subclinical hypopituitarism" and recurrent implantation failure.

[0107] In some embodiments, the biomarker combination of the present invention can be used to predict the therapeutic response to luteal support therapy in patients with recurrent implantation failure. In some embodiments, the luteal support therapy includes, after embryo implantation, the use of a luteinizing activity-related drug, particularly LH or an LH substitute, preferably HCG. In some preferred embodiments, the luteinizing activity-related drug is intramuscularly injected HCG, preferably administered after embryo implantation, preferably in combination with a drug that regulates estrogen and / or progesterone (e.g., progesterone and / or estrogen). Preferably, HCG is administered at a dose of 100-800 IU, or 200-500 IU, more preferably 500 IU of HCG intramuscularly daily the day after implantation.

[0108] In some embodiments, the biomarkers of the present invention can be further combined with other biomarkers indicating individual conditions in the compositions and methods of the present invention. In some preferred embodiments, they are combined with serum LH levels during the luteal phase (D2). Serum LH levels during the D2 phase can be measured using any method known in the art. In a preferred embodiment, in patients with the disease subtype of the present invention, or in patients with a treatment-responsive constitution to the present invention, serum LH levels during the D2 phase are less than 5 IU / L, for example, less than 4 IU / L, 3 IU / L, 2 IU / L, or less than 1 IU / L. IU refers to the International Unit of LH.

[0109] Detection of biomarkers

[0110] In the compositions and methods of the present invention, such as diagnostic and predictive methods, information about the genetic markers of the present invention is detected in a sample from a subject. In some embodiments, the detection includes identifying and comparing genetic variations on the genetic markers between a subject sample and a reference. In some embodiments, genetic variations are detected by detecting the genetic markers at the nucleic acid level, such as sequencing, microarray hybridization, SNP detection, etc. In other embodiments, genetic variations are detected by detecting the protein encoded by the gene of the genetic marker at the protein level, such as detecting the encoded protein in terms of structure (e.g., amino acid sequence), expression level, and / or function or activity, to indicate the presence of genetic variations in the gene associated with the subject's disease subtype and / or treatment responsiveness.

[0111] Various methods for detecting gene variations at the nucleic acid and protein levels are known in the art. These methods are all applicable to this invention. These methods include, but are not limited to, nucleic acid sequencing, polymerase chain reaction (PCR), mass spectrometry-based analysis methods, antibody-based analysis methods, and any combination of two or more of these.

[0112] In some embodiments, the gene-encoded RNA of the genetic marker is detected. RNA detection can be performed by sequencing, microarray, SAGE, blotting, RT-PCR, or quantitative PCR, or a combination thereof, preferably by microarray. In other embodiments, the gene-encoded protein product of the genetic marker is detected. Protein detection can be performed by ELISA, mass spectrometry, blotting, or immunochemistry, preferably by ELISA.

[0113] According to any of the methods described herein, the sample used for the detection of the genetic markers of the present invention can be a nucleic acid or a sample containing nucleic acid. The nucleic acid can be RNA transcribed from genomic DNA or cDNA generated from RNA. The nucleic acid can be derived from suitable biological samples such as cells, tissues, or body fluids of the subject. Prior to performing the detection on the nucleic acid, in some embodiments, obtaining a copy of the nucleic acid, for example, a copy obtained by amplification, may be included. Amplification may be desirable in certain circumstances, for example, to facilitate obtaining the required amount of material for detecting variants. The amplicon can then be subjected to a variant detection method, such as gene variant detection methods known in the art and described herein, such as probe hybridization, etc.

[0114] According to any of the methods described herein, the sample used for the detection of the genetic marker of the present invention can be a protein / peptide / capsule encoded by the genetic marker or a sample containing said protein / peptide / capsule. In some embodiments, prior to detection of the protein / peptide / capsule, the protein / peptide / capsule may be purified or isolated from the sample.

[0115] In some embodiments, the detection and / or quantification of one or more biomarkers includes an assay method utilizing a capture reagent. Examples of capture reagents may include, but are not limited to, oligonucleotides, antibodies, antibody fragments, nucleic acid-based protein conjugates, small molecules, etc. In some embodiments, the capture reagent is an oligonucleotide, polynucleotide, or nucleic acid molecule that hybridizes with a biomarker gene. In some embodiments, the capture reagent is a probe contained in a microarray, and the assay method includes microarray hybridization. In other embodiments, the capture reagent is an antibody, and the assay method may be an immunoassay, such as Western blotting, enzyme immunoassay (EIA), enzyme-linked immunosorbent assay (ELISA), and radioimmunoassay (RIA). The solid matrix used for capture detection may include, but is not limited to, 96-well plates, nitrocellulose membranes, microbeads, and microparticles.

[0116] In a preferred embodiment, the microarray is used to detect variations in the genes of the biomarkers of the present invention. The microarray typically uses thousands of nucleic acid probes arranged in an array, hybridizing with, for example, a cDNA or cRNA sample under highly stringent conditions. Probe-target hybridization is generally detected and quantified by detecting a fluorophore, silver, or chemiluminescently labeled target, thereby determining the relative abundance of the nucleic acid sequence in the target. In a typical microarray, the probes are attached to a solid surface via covalent bonds with a chemical matrix (via epoxysilanes, aminosilanes, lysine, polyacrylamide, or others). The solid surface is, for example, glass, a silicon wafer, or microbeads. Various microarrays are commercially available, including those manufactured, for example, by Affymetrix, Inc., and Illumina, Inc.

[0117] In another preferred embodiment, the detection method is used to detect the protein encoded by the biomarker of the present invention to detect the presence of a protein sequence alteration indicating gene variation. In one embodiment, the detection method is based on an ELISA method. In one embodiment, the ELISA can be a direct ELISA, indirect ELISA, multiplex ELISA, ELISPOT technology, sandwich ELISA, competitive ELISA, or other similar techniques known in the art. Typically, ELISA is performed using antibodies, but it can also be performed using any capture reagent that specifically binds to the biomarker of the present invention or its expression product.

[0118] Many protein biochips can be used in this invention. These include, for example, but not limited to, protein chips manufactured by Packard BioScience Company (Meriden Conn.), Zyomyx (Hayward, Calif.), and Phylos (Lexington, Mass.). Generally, a protein chip comprises a matrix having a surface. A capturing agent or adsorbent is attached to the surface of the matrix. The surface often contains multiple addressable sites, each with a capturing agent bound thereto. These capturing agents can be biomolecules, such as peptides or nucleic acids or small molecules, that capture the biomarkers of this invention in a specific manner.

[0119] For ease of detection, the detectable markers can be used in any of the methods described herein for the direct or indirect detection of the biomarkers of this invention. A variety of detectable markers can be used. The detectable markers can be selected based on factors such as the required sensitivity, the feasibility of conjugation with a capture agent, available detection instruments, etc. Suitable detectable markers include, but are not limited to, fluorescent dyes, chemiluminescent dyes, enzymes, nanoparticles, biotin, digoxigenin, metals, and radioactive isotopes.

[0120] Suitable biological samples for this invention include, but are not limited to, blood, serum, and other bodily fluids or biopsy tissues. Biological samples can be obtained using methods known to those skilled in the art. In some cases, the biological sample is blood, plasma, serum, or peripheral blood mononuclear cells (PBMCs). By screening such samples, simple early diagnosis of diseases such as recurrent acute respiratory infection (RIF) can be achieved. Furthermore, by testing such samples for variations in target nucleic acids (or encoded polypeptides), the progress of treatment can be easily monitored.

[0121] After determining that a subject or tissue or cell sample contains a pattern of enrichment of the variant gene function disclosed herein, and / or a biomarker or combination of biomarkers indicating a disease subtype of the present invention, the administration of an effective amount of an appropriate luteal support therapy (including luteinizing drugs) may be considered to improve the subject’s clinical outcomes of embryo transfer, such as clinical pregnancy or live birth.

[0122] III. The diagnostic and predictive methods of the present invention

[0123] Based on the detection of variants in characteristic biological pathway-related genes of the present invention, and / or based on the biomarkers and their detection of the present invention, the present invention also provides methods for diagnosing and classifying disease subtypes of the present invention, and methods for predicting treatment responsiveness in patients with recurrent implantation failure.

[0124] Therefore, in one aspect, the present invention provides a method for diagnosing or classifying a subject as a subtype of subclinical hypopituitaristic recurrent implantation failure, the method comprising detecting genetic variation information of the subject in a biological sample obtained from the subject, wherein the subject is diagnosed or classified as subclinical hypopituitaristic recurrent implantation failure if the subject exhibits variations in one or more pathway-related genes (preferably at least 2, 3, and more preferably at least 4 or 5 genes) selected from at least one or more KEGG biological pathways or GO biological processes:

[0125] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0126] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0127] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0128] -KEGG_PATHWAY:Focal Adhesion(hsa04510).

[0129] In some embodiments, the diagnostic method of the present invention includes: detecting information on one or more (e.g., 10-30, 30-100, or more than 150-500) pathway-related genes in a biological sample from a subject, wherein said pathway-related genes are selected from genes that, according to KEGG and GO annotations, are associated with at least one or more of the following biological pathways or processes:

[0130] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0131] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0132] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0133] -KEGG_PATHWAY:Focal Adhesion(hsa04510),

[0134] If the information indicates that one or more (preferably two, three, more preferably at least four or five) of the pathway-related genes are mutated, the subject is diagnosed or classified as the disease subtype.

[0135] Preferably, at least one (preferably at least two or three) of the mutated pathway-related genes is associated with a KEGG biological pathway or GO biological process selected from the following, based on KEGG and GO annotations:

[0136] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway;

[0137] -GO_BP_DIRECT: Extracellular matrix tissue;

[0138] More preferably, the gene is annotated on the KEGG_ pathway extracellular matrix-receptor interaction pathway and / or GO_BP_DIRECT extracellular matrix tissue according to KEGG and GO, and is also annotated on the KEGG_ pathway PI3K-Akt signaling pathway and Focal Adhesion.

[0139] In some preferred embodiments, the diagnostic / classification method includes detecting one or any combination of the following pathway-related genes in a biological sample from a subject: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6, SHC4, and MYL12A; wherein, when information indicating the presence of gene variations in one or more pathway-related genes (preferably at least two or three, and more preferably at least four or five genes) is detected in the subject's biological sample, the subject is diagnosed or classified into the disease subtype.

[0140] In some preferred embodiments, subjects diagnosed as the disease subtype according to the method of the present invention have variations in at least one of the following genes: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3.

[0141] In other preferred embodiments, subjects diagnosed with the disease subtype according to the method of the present invention exhibit enrichment of variant genes in the KEGG biological pathway or GO biological process compared to a reference. Preferably, the enrichment is manifested by the subject having more pathway-related gene variants in the biological pathway or biological process than the reference, preferably at least 2, 3, or 4 more pathway-related gene variants than the reference.

[0142] In any implementation of the aforementioned diagnostic method, the subject may have no clear cause for recurrent implantation failure and no obvious organic pituitary lesions, but may exhibit mild symptoms of hypopituitarism.

[0143] In any embodiment of the diagnostic method described above, the diagnostic method further includes detecting the serum level of the subject during the luteal phase D2, preferably in the morning. Preferably, patients diagnosed with the disease subtype of the present invention also have a serum LH level of less than or equal to 5 IU / L on the luteal phase D2, more preferably less than 3 IU / L, 2 IU / L, or 1 IU / L.

[0144] On the other hand, the present invention provides a method for predicting whether a subject with recurrent implantation failure (RIF) is suitable for luteal support therapy, or for predicting the responsiveness of a subject with RIF to luteal support therapy, wherein the luteal support therapy comprises administering a luteinization activity-related drug, such as LH and / or HCG, preferably HCG, after the endometrial transformation day, preferably after embryo implantation.

[0145] The method includes detecting genetic variation information of the subject in a biological sample obtained from the subject, wherein if the subject exhibits variation in one or more pathway-related genes (preferably at least 2, 3, and more preferably at least 4 or 5 genes) selected from at least one or more KEGG biological pathways or GO biological processes, the subject is indicated to be suitable for or may respond to the luteal support therapy.

[0146] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0147] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0148] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0149] -KEGG_PATHWAY:Focal Adhesion(hsa04510).

[0150] In some embodiments of the prediction method of the present invention, the prediction method includes: detecting information on one or more pathway-related genes in a biological sample from a subject, wherein the pathway-related genes are selected from genes annotated according to KEGG and GO on at least one or more of the following biological pathways or biological processes:

[0151] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0152] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0153] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0154] -KEGG_PATHWAY:Focal Adhesion(hsa04510),

[0155] If the information indicates a mutation in one or more (preferably two, three, more preferably at least four or five) of the pathway-related genes, then it is predicted that the subject is suitable for the luteal support therapy, or may have a therapeutic response to the luteal support therapy.

[0156] Preferably, at least one (preferably at least two or three) of the mutated pathway-related genes is associated with a KEGG biological pathway or GO biological process selected from the following, based on KEGG and GO annotations:

[0157] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway;

[0158] -GO_BP_DIRECT: Extracellular matrix tissue;

[0159] More preferably, the gene is annotated on the KEGG_ pathway extracellular matrix-receptor interaction pathway and / or GO_BP_DIRECT extracellular matrix tissue according to KEGG and GO, and is also annotated on the KEGG_ pathway PI3K-Akt signaling pathway and Focal Adhesion.

[0160] In some embodiments of the prediction method of the present invention, the prediction method includes: detecting in a biological sample from a subject one of the following pathway-related genes or any combination thereof: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6, SHC4, and MYL12A; wherein, when information indicating the presence of gene variations in one or more pathway-related genes (preferably at least two or three, and more preferably at least four or five genes) is detected in the subject's biological sample, the subject is predicted to be suitable for the luteal support therapy or may have a therapeutic response to the luteal support therapy.

[0161] Preferably, the subject predicted to be suitable for receiving the treatment or likely to have a therapeutic response to the treatment has a variant in at least one of the following genes: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3.

[0162] In some embodiments of the prediction method according to the present invention, the method includes: if a subject exhibits an enrichment of variant genes in the KEGG biological pathway or GO biological process compared to a reference, preferably the enrichment is manifested as the subject having more pathway-related genes mutated in the biological pathway or biological process than the reference, preferably having at least one, at least two, three, or four more pathway-related genes mutated than the reference, then predicting that the subject is suitable for use with the luteal support therapy, or may have a therapeutic response to the luteal support therapy.

[0163] In any embodiment of the prediction method according to the present invention, the subject may be without a clear cause of recurrent implantation failure and without obvious organic pituitary lesions, but exhibiting mild symptoms of hypopituitarism.

[0164] In any embodiment of the prediction method according to the invention, preferably, the method further includes detecting the subject's serum LH level on day D2 of the luteal phase. Preferably, the subject predicted to be treatment-responsive has a day D2 serum LH level of less than 5 IU / L, more preferably less than 3 IU / L, 2 IU / L, or 1 IU / L.

[0165] In any of the above methods, in some embodiments, the gene mutation is a missense mutation. Preferably, the mutation does not lead to gene inactivation but affects the biological activity of the protein encoded by the gene. Preferably, the allele frequency of the mutation in normal healthy individuals is...

[0166] <0.01.

[0167] In any of the above methods, in some embodiments, the biological sample is selected from blood, serum, other body fluids, or biopsy tissue.

[0168] In any of the above methods, in some implementations, the genetic variation information is measured using PCR or microarray chips.

[0169] In any of the above methods, in some embodiments, the gene variation information is obtained using sequencing methods, preferably exon sequencing methods.

[0170] In any of the above methods, in some embodiments, the gene variation information is obtained by detecting the biological activity of the protein encoded by the gene, preferably, the biological activity is reduced biological activity.

[0171] In any of the above methods, in some embodiments, the measurement includes using an immunoassay, such as an ELISA assay, to detect a mutant protein encoded by a variant gene.

[0172] In any of the above methods, in some embodiments, the luteal support therapy includes: in addition to progesterone and / or estrogen supplementation, further, after the endometrial transformation day, preferably after embryo implantation, supplementation with luteinization-related active drugs, such as LH or HCG, more preferably, intramuscular injection of HCG. Preferably, the luteal support therapy also includes, during luteal support therapy, adjusting the dosage of progesterone and / or estrogen drugs according to the patient's progesterone and / or estrogen levels. Preferably, HCG is administered at a dose of 100-800 IU or 200-500 IU.

[0173] More preferably, starting the day after transplantation, patients should receive an intramuscular injection of 500 IU HCG daily.

[0174] In some embodiments of the diagnostic and prediction method of the present invention, a machine learning-based classifier may also be used for diagnosis and prediction. Machine learning algorithms suitable for the present invention include, but are not limited to, linear discriminant analysis, support vector machines, recursive feature elimination, microarray predictive analysis, logistic regression, CART, FlexTree, LART, random forest, etc. Therefore, in some embodiments, the diagnostic and prediction method of the present invention includes the following steps:

[0175] - In vitro determination of variations in multiple target nucleic acids in samples from several known subclinical hypopituitarism patients with recurrent implantation failure (preferably, the patients have serum LH levels <5 IU / L during the luteal phase D2) to construct a training dataset;

[0176] - Use the training dataset to train a classifier, such as a random forest classifier;

[0177] - By measuring the variation of multiple target nucleic acids from in vitro samples of subjects, and using a trained classifier, predicting, determining, or evaluating the likelihood of subjects being classified as the RIF disease subtype of the present invention or responding to the luteal support therapy of the present invention.

[0178] Classification and prediction can be set to a threshold based on the trained machine learning model; that is, a threshold used to determine the probability that a sample belongs to a given category. This probability threshold can be at least 50%, or at least 60%, or at least 70%, or at least 80% or higher. Classification or prediction can also be performed by comparing the dataset obtained from the subjects with a reference dataset to determine if there is a statistically significant difference between the two. If a significant difference exists, the subject is classified as belonging to a category different from the reference. If no significant difference exists, the subject is classified as belonging to the same category as the reference.

[0179] Through biomarker combinations and machine learning, in some implementations, the diagnostic / classification and prediction methods of the present invention can be provided with high specificity and high sensitivity.

[0180] In any of the diagnostic and predictive methods described above, the detection of variations in multiple biomarkers may include the detection of... Figure 9The N biomarkers are listed in the table. In some embodiments, N can be an integer selected from 2 to 50, or 2-40, or 2-30, or 2-25, or 2-20, or 2-15, or 2-10, or 2-5. In some embodiments, N is an integer selected from 3 to 50, or 3-40, or 3-30, or 3-25, or 3-20, or 3-15, or 3-10, or 3-5. In some embodiments, N is an integer selected from 4 to 50, or 4-40, or 4-30, or 4-25, or 4-20, or 4-15, or 4-10, or 4-5. In some embodiments, N is an integer selected from 5 to 50, or 5-40, or 5-30, or 5-25, or 5-20, or 5-15, or 5-10. In some implementations, N is an integer selected from 6 to 50, or 6-40, or 6-30, or 6-25, or 6-20, or 6-15, or 6-10. Those skilled in the art will understand that N can also be selected from a similar or larger range of values.

[0181] In a preferred embodiment, the detected variants of a plurality of biomarkers, said biomarker combination being composed of N pathway-associated genes selected from LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6, SHC4, and MYL12A. N can be an integer selected from 2 to 27, or 2-25, or 2-20, or 2-15, or 2-10, or 2-5.

[0182] In any of the diagnostic and predictive methods described above, preferably, the gene variant in the biomarker is a missense mutation, especially a harmful missense mutation affecting protein structure and / or function, such as harmful missense mutations predicted by SIFT and PolyPhen-2 software. In some embodiments, the detection of missense mutations includes detecting nucleotide mutations on nucleic acid sequences, including, for example, SNPs, SNVs, insertions and / or deletions. In some embodiments, in any of the diagnostic and predictive methods described above, detecting gene variants in the biomarker includes detecting the present invention. Figure 10The list includes one or more SNVs (single nucleotide variants), for example, 2 to 50, or 2-40, or 2-30, or 2-25, or 2-20, or 2-15, or 2-10, or 2-5 SNVs. SNVs can be detected by microarray hybridization, sequencing, or other methods. They can also be detected by detecting changes in the amino acid sequence of the protein encoded by the SNV. Alternatively, they can be detected by detecting the activity of the protein encoded by the gene containing the SNV. In some preferred embodiments, the compositions and methods of the present invention include the detection of one or more SNVs selected from the following: rs375041472,rs2276331,rs117194484,rs749258477,rs201006742,rs532783486,rs533178276,rs140413590,rs745335790,rs200254355,rs187465892,rs770208004,rs768170625,rs769085031,rs759512073,rs113055208,rs1458 52498, rs147773336, rs779870630, rs200796753, rs529211517, rs201251711, rs140368397, rs749946427, rs181688285, rs139813458, rs754919524, rs754641652, rs199896561, and SNVs on LPAR6, such as the C-to-T mutation at chromosome 13 position 48986219 (this SNV results in an amino acid change: LPAR6:NM_001162498:exon1:c.G341A:p.R114Q).In some further preferred embodiments, the compositions and methods of the present invention include detecting one or more SNVs selected from the following: rs375041472,rs2276331,rs117194484,rs749258477,rs201006742,rs532783486,rs533178276,rs745335790,rs200254355,rs187465892,rs770208004,rs768170625,rs76908503 1, rs759512073, rs145852498, rs147773336, rs779870630, rs200796753, rs529211517, rs201251711, rs140368397, rs749946427, rs181688285, rs139813458, rs754919524, rs754641652, rs199896561, and C-to-T mutation at position 48986219 on chromosome 13 of SNV.

[0183] IV. The Lutein Support RIF Treatment Method of the Invention

[0184] In long-term clinical practice, the inventors have discovered that some patients with pituitary instability (RIF) have significantly low serum LH levels on day 2 (D2) of the luteal phase. That is, in these RIF patients, during the luteal phase after ovulation, in addition to the commonly known abnormalities in estrogen and progesterone, LH levels (in blood drawn at 8:00 AM) are also abnormal. However, clinical examination reveals no obvious organic pituitary lesions in these patients.

[0185] Through their efforts to develop clinical treatment options for patients with RIF (Recurrent Inflammation of Fever), the inventors discovered that in this patient population, supplementing with HCG in addition to routine luteal support using progesterone and estrogen medications can effectively improve clinical outcomes, including clinical pregnancy rates and live birth rates. Further genetic studies revealed characteristic pathway gene variations in patients exhibiting treatment responsiveness.

[0186] Therefore, the inventors propose a novel luteal support treatment method, which includes, in patients with recurrent implantation failure who have characteristic biological pathway gene mutations and / or have LH levels less than 5 IU / L on day D2 of the luteal phase, further supplementing luteal production-related drugs after embryo transfer in addition to luteal support treatment with progesterone and estrogen.

[0187] Therefore, in another aspect, the present invention provides a method for providing luteal support therapy to patients with recurrent implantation failure, the method comprising administering, after the endometrial transformation day, preferably after embryo implantation, an effective amount of a luteinization-related drug, such as hCG and / or LH, preferably hCG, to the patient.

[0188] The patients described herein exhibit variations in one or more pathway-related genes (preferably at least 2, 3, and more preferably at least 4 or 5 genes) selected from at least one or more KEGG biological pathways or GO biological processes:

[0189] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0190] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0191] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0192] -KEGG_PATHWAY:Focal Adhesion(hsa04510);

[0193] Preferably, at least one (preferably at least two or three) of the mutated pathway-related genes is associated with a KEGG biological pathway or GO biological process selected from the following, based on KEGG and GO annotations:

[0194] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway;

[0195] -GO_BP_DIRECT: Extracellular matrix tissue;

[0196] More preferably, the gene is annotated on the KEGG_ pathway extracellular matrix-receptor interaction pathway and / or GO_BP_DIRECT extracellular matrix tissue according to KEGG and GO, and is also annotated on the KEGG_ pathway PI3K-Akt signaling pathway and Focal Adhesion.

[0197] In some preferred embodiments of the luteal support treatment method of the present invention, the patient has a gene variant in one or a combination of the following pathway-related genes: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6, SHC4, and MYL12A.

[0198] In some preferred embodiments of the luteal support treatment method of the present invention, the patient has a gene variation in a gene combination containing at least 2, at least 3, or more preferably at least 4 or 5 pathway genes, and more preferably, the gene combination contains at least one gene selected from the following: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3.

[0199] In some preferred embodiments of the luteal support treatment method of the present invention, the patient's serum level during the luteal phase D2 is ≤5 IU / L, for example, less than 4 IU / L, or 3 IU / L, or 2 IU / L, or less than 1 IU / L.

[0200] In any of the aforementioned embodiments of the luteal support treatment method, preferably, the gene mutation is a missense mutation; preferably, the mutation does not lead to gene inactivation but affects the biological activity of the protein encoded by the gene; preferably, the allele frequency of the mutation in normal healthy individuals is <0.01.

[0201] In one implementation, the RIF patient also has abnormal estrogen and progesterone levels. In other implementations, the luteal support treatment method further includes: adjusting the patient's hormone levels from the endometrial transformation date by administering progesterone and / or estrogen medications based on the patient's luteal phase progesterone, estrogen, and luteinizing hormone levels, referencing a standard hormone curve for normal pregnancy.

[0202] Preferably, in some embodiments, the method of the present invention results in improved clinical outcomes in said RIF patients. In some embodiments, the improved clinical outcome is an improved clinical pregnancy rate. In other embodiments, the improved clinical outcome is an improved live birth rate. In still other embodiments, the improved clinical outcome includes both an improved clinical pregnancy rate and an improved live birth rate. In yet another embodiment, the method of the present invention does not result in an increased rate of multiple pregnancies, miscarriages, and / or ectopic pregnancies.

[0203] Therefore, in some embodiments, the present invention also provides a method for improving clinical outcomes of embryo implantation in patients with recurrent implantation failure, such as clinical pregnancy rate and / or live birth rate, wherein the patients with recurrent implantation failure are diagnosed or classified as subclinical hypopituitaristic recurrent implantation failure according to the method of the present invention, and optionally, the patients also have a luteal phase D2 serum LH level of less than or equal to 5 IU / L, the method comprising: supplementing post-implantation with a luteinizing activity-related drug in addition to conventional luteal support therapy with progesterone and / or estrogen. Preferably, the method of the present invention with supplementation of a luteinizing activity-related drug results in improved clinical outcomes in said patients compared to conventional methods without supplementation of a luteinizing activity-related drug.

[0204] In a preferred embodiment, the supplemental luteinizing activity-related drug is LH and / or HCG, preferably HCG. Currently, luteinizing hormone (LH) is expensive and has a low active ingredient content; directly adding exogenous LH to supplement LH imposes an economic burden on patients and has poor efficacy. HCG, on the other hand, is a human chorionic gonadotropin (hCG) drug with luteinizing hormone-like effects. Considering the structural similarity between the α-subunit of LH and HCG, and their action on the same receptor, the possibility of using exogenous HCG to replace exogenous LH for luteal support has been proposed. However, the addition of HCG interferes with HCG test values ​​on day 7 post-implantation, which is detrimental to the confirmation of biochemical pregnancy. Therefore, designing an economical and practical luteal support protocol using HCG is also an urgent problem to be solved.

[0205] In one embodiment, the present invention therefore provides a luteal support treatment method for RIF patients supplemented with hCG. This method solves the aforementioned problems. In this luteal support treatment method of the present invention, hCG is used after embryo implantation, preferably by intramuscular injection, and more preferably, the hCG is administered at a dose of 100-800 IU, or 200-500 IU, more preferably, starting the day after implantation, the patient receives a daily intramuscular injection of 500 IU hCG.

[0206] In normal individuals, estrogen (E2) levels are not lower than 100 pg / ml on day 2 after ovulation, progesterone (P) levels are not lower than 10 ng / ml, and luteinizing hormone (LH) levels are not lower than 6 miu / ml on day 5. To treat patients with recurrent implantation failure due to subclinical pituitary insufficiency, in some embodiments, the luteal support treatment method of this invention also includes: based on the patient's own luteal phase hormone levels, during the embryo implantation cycle, for example from the endometrial transformation day, referring to a standard hormone curve, administering appropriate progesterone and / or estrogen medications for hormone adjustment treatment. For example, in some embodiments, after luteal phase D2, hormone levels (HCG, E2, LH, and P) are periodically measured and compared with set normal values. Exogenous hormone medications (e.g., estradiol and / or progesterone) are used to supplement the corresponding estrogen and / or progesterone to bring the corresponding hormone levels to the set normal standards.

[0207] In some embodiments of the aforementioned treatment methods, the patient has a biological pathway-related gene variant characteristic of the disease subtype of the present invention described herein (e.g., the aforementioned KEGG PATHWAY and GO pathway-related genes), or a gene variant of one or more biomarkers characteristic of the disease subtype of the present invention described herein (e.g., the gene variants in the aforementioned biomarkers of the present invention), wherein preferably the gene variant is a missense mutation.

[0208] In some embodiments of the aforementioned treatment methods, the methods are used to improve endometrial receptivity in patients with RIF, and / or to improve the likelihood of clinical pregnancy and / or delivery of a live birth in patients with RIF.

[0209] In some embodiments of the aforementioned treatment method, the embryo transfer is either a frozen-thawed embryo transfer or a fresh cycle transfer, preferably a frozen-thawed embryo transfer.

[0210] In some implementations of the aforementioned treatment methods, the method includes regularly checking the patient's LH hormone and supplementing with HCG during the luteal phase.

[0211] In some implementations of the aforementioned treatment methods, RIF patients begin endometrial transformation via a natural cycle, an artificial cycle, or an ovulation induction cycle before embryo transfer. In a preferred embodiment, RIF patients undergo frozen-thawed embryo transfer after ovulation in a natural cycle. In another preferred embodiment, RIF patients undergo frozen-thawed embryo transfer after an ovulation induction cycle. In yet another preferred embodiment, RIF patients undergo frozen-thawed embryo transfer after endometrial transformation via an artificial cycle.

[0212] In one embodiment of the aforementioned treatment method, the treatment method further includes monitoring the patient's hormone levels, including LH, P, E2 and HCG levels, before implantation and during the embryo implantation cycle.

[0213] V. The product and its uses of the present invention

[0214] In one embodiment, the present invention provides a composition comprising reagents for detecting the biomarkers or combinations of biomarkers of the present invention. In another embodiment, the present invention provides a kit comprising the composition of the present invention. In yet another embodiment, the present invention provides the use of the composition in the preparation of a kit for use in the diagnostic and / or predictive methods of the present invention. In still another embodiment, the present invention provides a microarray comprising a combination of reagents for detecting combinations of biomarkers of the present invention.

[0215] In some embodiments, the compositions, kits, and microarrays of the present invention can be used to detect gene variations of N biomarkers listed in any of the embodiments of the biomarkers of the present invention described above. N can be an integer selected from 2 to 50, or 2-40, or 2-30, or 2-25, or 2-20, or 2-15, or 2-10, or 2-5. In some embodiments, N is an integer selected from 3 to 50, or 3-40, or 3-30, or 3-25, or 3-20, or 3-15, or 3-10, or 3-5. In some embodiments, N is an integer selected from 4 to 50, or 4-40, or 4-30, or 4-25, or 4-20, or 4-15, or 4-10, or 4-5. In some embodiments, N is an integer selected from 5 to 50, or 5-40, or 5-30, or 5-25, or 5-20, or 5-15, or 5-10. In some embodiments, N is an integer selected from 6 to 50, or 6-40, or 6-30, or 6-25, or 6-20, or 6-15, or 6-10. Those skilled in the art will understand that N can also be selected from similar or larger numerical ranges. Accordingly, in some embodiments, the compositions, kits, and microarrays of the present invention comprise one or more reagents for detecting the N biomarkers.

[0216] In some embodiments, the kit of the present invention comprises one or more reagents for detecting biomarkers, and optionally, a container for containing biological samples isolated from a subject; and instructions for performing biomarker detection and / or implementing the diagnostic and / or predictive methods of the present invention. The various reagents may be packaged in separate containers. Furthermore, the kit may also contain one or more control reference samples, and optionally, the detection time required to perform the detection (e.g., microarray detection or immunoassay). In some embodiments, the reagents may be antibodies that bind to the biomarker, or nucleic acid probes, or protein microarray chips, or nucleic acid microarray chips.

[0217] In one embodiment, the kit of the present invention may also include reagents for detecting the activity of a biomarker-encoded product to quantitatively or qualitatively indicate whether the biomarker has variations that lead to changes in the product's structure / activity.

[0218] In some embodiments, the present invention provides compositions for diagnosing or classifying RIF patients or for predicting the therapeutic responsiveness of RIF patients to luteal support therapy with supplemental luteinizing activity-related drugs, wherein said compositions comprise reagents or combinations of reagents for detecting information on a set of biomarkers.

[0219] The biomarker set consists of multiple pathway-related genes, which are selected from genes annotated according to KEGG and GO on at least one or more of the following biological pathways or processes:

[0220] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0221] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0222] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0223] -KEGG_PATHWAY:Focal Adhesion(hsa04510),

[0224] For example, the biomarker set consists of at least 2, at least 5, at least 10, at least 15, or all of the pathway-related genes selected from LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6, SHC4, and MYL12A.

[0225] Preferably, the detection provides variation information of pathway-related genes, and the variation is preferably a missense mutation.

[0226] Preferably, the reagent is a polynucleotide for detecting the gene variation, and / or a reagent (e.g., an antibody) for detecting the amount and / or activity of the mutant protein encoded by the gene variation.

[0227] In some preferred embodiments, the composition is in the form of a microarray chip containing a polynucleotide reagent or combination of reagents for providing information about the biomarker set, preferably, the polynucleotide reagent being a polynucleotide that hybridizes to a missense mutation site (e.g., SNP) of the pathway gene.

[0228] In some embodiments, the present invention also provides a kit comprising the diagnostic / predictive composition or microarray chip of the present invention.

[0229] In other embodiments, the present invention also provides for use of the diagnostic / predictive compositions of the present invention, the microarray chips of the present invention, or the kits of the present invention in the preparation of products for diagnosing or classifying RIF patients, or for predicting the therapeutic response of RIF patients to luteal support therapy with supplemental luteinizing activity-related drugs.

[0230] The following abbreviations are used in the examples.

[0231] LH: Luteinizing hormone;

[0232] FSH: Follicle-stimulating hormone;

[0233] P: Progesterone (also known as luteal hormone);

[0234] E2: Estradiol;

[0235] HCG: Human chorionic gonadotropin;

[0236] IVF-ET: In vitro fertilization-embryo transfer;

[0237] FET: Frozen-thawed embryo transfer;

[0238] RIF: Repeated implantation failure;

[0239] M1,2,5,8,11,13,18: Days 1, 2, 5, 8, 11, 13, and 18 of the menstrual cycle.

[0240] D0: Endometrial transformation day, which is the day of ovulation in natural cycles and ovulation induction cycles; and the day on which endometrial transformation drugs are started in artificial cycles.

[0241] D2: The second day calculated from the day of endometrial transformation, D0.

[0242] BMI: Body Mass Index. Example

[0243] Example 1

[0244] To investigate the relationship between luteinizing hormone (LH) and recurrent intrauterine flow (RIF), the cyclical changes in serum LH levels in RIF patients during the menstrual cycle were measured and compared with the cyclical changes in LH levels in other IVF patients.

[0245] Previous studies selected patients with secondary infertility due to isolated tubal factors and no history of embryonic arrest, aged 25-36 years (mean 27.5±2.4), who had not used hormonal drugs within the past three months, and measured LH levels on day 3 and day 5 post-ovulation. The average LH level in this patient group on day 3 was 7.63±3.49 mIU / ml; the average LH level on day 5 was 6.67±4.53 mIU / ml, showing no low LH levels during the luteal phase. In this study, patients undergoing IVF-ET due to isolated tubal factors were selected as controls.

[0246] In summary, in this embodiment, patients with a history of RIF were selected as the experimental group; patients undergoing IVF-ET due to simple tubal factors were selected as the control group. All participants underwent daily transvaginal ultrasound monitoring of follicular development starting on day 8 of their menstrual cycle to determine the ovulation day (D0). Peripheral blood was drawn at 8:00 AM on days 2, 5, 8, 11, and 13 of the menstrual cycle (i.e., M2, M5, M8, M11, M13, with day 1 of the menstrual cycle designated as M1), and on day 2 after ovulation (D2, with day ovulation designated as D0), for serum luteinizing hormone (LH) level measurement. The LH cycle changes in the selected patients were then monitored and plotted.

[0247] The results showed that, compared to the control group, some RIF patients exhibited low LH levels during the luteal phase.

[0248] In Tables 1 and 2 below and Figure 1 and Figure 2 The images show, for example, the cyclical changes in LH levels (LH unit: mIU / ml) detected in some RIF patients and some IVF patients with fallopian tube problems.

[0249] Table 1. Cyclic changes of LH in patients with recurrent implantation failure.

[0250]

[0251] Note: M2: Day 2 of menstruation; D2: Day 2 after ovulation.

[0252] Table 2. LH cycle changes in patients undergoing IVF due to tubal problems.

[0253]

[0254] Note: M2: Day 2 of menstruation; D2: Day 2 after ovulation.

[0255] Clinical observation revealed that RIF patients with low serum LH levels during the luteal phase shared common characteristics: no obvious organic pituitary changes and no detectable specific cause of recurrent implantation failure; in addition to known abnormalities in estrogen and progesterone levels during the luteal phase, their LH levels on day 2 of the luteal phase were also abnormal. Luteinizing hormone (LH) is a glycoprotein gonadotropin secreted by anterior pituitary cells. Previous studies have shown that LH, besides affecting ovarian steroid hormone production through the corpus luteum, can also directly act on the uterus. Abnormal serum LH levels on day 2 in these RIF patients suggest that the patients may have some functional impairment of the pituitary gland, leading to subclinical hypopituitarism and subsequently causing multiple adverse effects on embryo implantation and pregnancy.

[0256] Example 2

[0257] For patients with recurrent implantation failure and low LH serum levels during the luteal phase, a novel luteal support therapy regimen supplemented with the LH functional analog HCG was implemented, and a retrospective observational study was conducted on the patients' clinical treatment responsiveness. In this retrospective observational study, based on previous dynamic LH level observations in RIF patients and tubal factor IVF patients, a serum LH level ≤5 IU / L on day 2 of the luteal phase was used as one of the inclusion criteria.

[0258] All patients enrolled in this study were from the Sixth Medical Center of the General Hospital of the Chinese People's Liberation Army and signed informed consent forms. All clinical studies were approved by the institution's ethics committee before commencement.

[0259] A total of 84 patients were enrolled. The patients were generally in good health, with no history of immune disorders, chromosomal abnormalities, or recurrent miscarriages; and had the following characteristics: (1) diagnosed with a history of RIF according to the "Expert Consensus on Preimplantation Genetic Diagnosis / Screening Technology" (Chinese Journal of Medical Genetics, 2018, 35(2):151-155); (2) aged ≥35 years and <45 years, including 29 patients aged ≥35 and <38, 11 patients aged ≥38 and <40, 25 patients aged ≥40 and <43, and 29 patients aged ≥40 and <45; (3) serum LH level ≤5 IU / L on day D2 of the luteal phase. Upon examination, none of these enrolled patients showed organic pituitary damage and had normal basal follicle-stimulating hormone (FSH), luteinizing hormone (LH), and progesterone (P) levels.

[0260] Patients were divided into an observation group and a control group based on the different luteal support protocols used.

[0261] Observation group Lutein support protocol: For RIF patients (including natural cycles, artificial cycles, and ovulation induction cycles), starting from the endometrial transformation day (D0), use Femoston (yellow tablets), phenobarbital, aspirin, and prednisone. Dosage is as follows: Femoston (yellow tablets) 1 tablet, three times daily, orally (PO); phenobarbital 2 tablets, twice daily, vaginally (PV); aspirin 1 tablet, three times daily, PO; prednisone 5 mg, once daily, PO. On D2, the levels of the three hormones (estradiol E2, luteinizing hormone LH, and progesterone P) are measured; and the dosage of progesterone and estrogen medications is adjusted according to the measured estrogen and progesterone levels.

[0262] Starting the day after transplantation, the patient received an intramuscular injection of 500 IU of HCG daily, with other medications remaining the same. On day 7 post-transplantation, HCG, E2, LH, and P levels were evaluated.

[0263] • If HCG > 50 mIU / mL, pregnancy is confirmed. Subsequently, four hormones (HCG, E2, LH, and P) are monitored every other day to promptly determine the HCG doubling status. Estrogen and progesterone dosages are adjusted, and these hormone levels are checked weekly for stability; follow-up frequency is increased if hormone levels are unstable. 30-35 days post-transfer, a transvaginal ultrasound is performed to assess intrauterine pregnancy and embryonic development. An abdominal ultrasound is performed on day 49 to assess fetal development; estrogen, progesterone, and HCG dosages may be gradually reduced as needed. All medications are discontinued at 12 weeks. Obstetric examinations are performed, and follow-up is conducted for one year.

[0264] If HCG levels are ≤50 mIU / mL, continue administering the medication and monitor the four hormones (HCG, E2, LH, and P) every other day. If pregnancy is confirmed, begin fetal perinatal care. If the HCG level does not double, discontinue the medication.

[0265] • If HCG ≤ 5 mIU / mL, the patient is determined not to be pregnant, and medication administration is discontinued.

[0266] control group The luteal support protocol was the same as that of the observation group, except that HCG was not administered.

[0267] In this study, both the observation and control groups underwent frozen-thaw cycle transplantation, which avoids ovarian hyperstimulation syndrome (OHSS). Furthermore, because LH and HCG have similar α-subunit structures and act on the same receptors, exogenous HCG can be used to replace exogenous LH to compensate for LH deficiency. The half-life of HCG is 25 hours; after an intramuscular injection of 500 IU of HCG, the blood HCG level will not exceed 80 mIU / mL (in fact, observations have shown that most are within 50 mIU / mL). Therefore, daily intramuscular injection of a small dose of 500 IU does not affect the observation of HCG doubling due to early pregnancy. Therefore, pregnancy can be confirmed when the actual measured HCG level is ≥50 mIU / mL.

[0268] Clinical outcome record

[0269] The following clinical outcomes were recorded for both groups: biochemical pregnancy, ectopic pregnancy, clinical pregnancy, multiple pregnancy, miscarriage, and live birth. Because the observation group used hCG, biochemical pregnancy outcomes were not compared between the two groups. Clinical pregnancy was defined as the presence of a gestational sac on abdominal ultrasound 30-35 days post-transfer. Live birth was defined as the delivery of one or more live fetuses after 20 weeks of gestation.

[0270] Statistical analysis

[0271] SPSS version 21.0 was used for statistical analysis. Quantitative data conforming to a normal distribution were expressed as mean ± standard deviation and compared using a two-sample t-test. Quantitative data not conforming to a normal distribution were expressed as median, p-25, and p-75 and compared using a rank-sum test. Qualitative data were expressed as percentages and compared using a rank-sum test. P < 0.05 was considered statistically significant.

[0272] result:

[0273] Table 3 lists the patient characteristics and treatment duration comparisons between the observation group and the control group. Table 4 lists the pregnancy outcomes comparisons between the two groups. No adverse reactions were detected during the study.

[0274] Table 3 Comparison of patient characteristics and treatment duration between the two groups

[0275]

[0276]

[0277] Note: Observation group: luteal support with HCG supplementation every other day after transplantation; Control group: routine luteal support without HCG supplementation. P<0.05 is considered statistically significant.

[0278] Table 4 Comparison of pregnancy outcomes between the two groups

[0279]

[0280] Note: Observation group: luteal support with HCG supplementation every other day after transplantation; Control group: routine luteal support without HCG supplementation; P<0.05 was considered statistically significant.

[0281] As shown in Table 4, no significant differences were found between the observation group and the control group in terms of ectopic pregnancy rate, multiple pregnancy rate, and miscarriage rate (P>0.05). However, statistically significant differences were found between the observation group and the control group in clinical pregnancy rate (51.2% vs. 29.3%, RR = 1.907, 95% CI: 1.106–3.289) and live birth rate (46.5% vs. 24.4%, RR = 1.906, 95% CI: 1.019–3.570) (P<0.05).

[0282] Example 3

[0283] We examined the genetic basis of HCG luteal support therapy responsiveness in RIF patients through whole-genome exome sequencing and functional enrichment analysis.

[0284] In this study, 10 patients with recurrent luteal insufficiency (RIF) who achieved successful clinical pregnancy using the HCG-supplemented luteal support therapy regimen described in Example 2 were randomly selected as the case group. Simultaneously, 10 RIF patients who did not undergo LH hormone level testing and did not receive HCG-supplemented luteal support therapy were randomly selected as the control group. All patients enrolled in the study were from the Sixth Medical Center of the General Hospital of the Chinese People's Liberation Army and signed informed consent forms. The patient characteristics of the case group and the control group are compared in Table 5.

[0285] Table 5 Comparison of general demographic characteristics of patients between the two groups

[0286]

[0287] Note: P < 0.05 is considered statistically significant.

[0288] Gene-level analysis

[0289] Whole genome exon sequencing

[0290] Genomic DNA was extracted from peripheral blood using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany). The quality and quantity of extracted DNA were measured using the Qubit DNA Analysis Kit and Qubit 3.0 fluorometer. >0.6 μg of total DNA from the samples was used for library construction. Human whole-exon region DNA was efficiently enriched using the Agilent SureSelect Human All Exon V6 kit, following the manufacturer's instructions, to generate sequencing libraries. Qubit 2.0 was used for initial library amplification, and insert sizes (180–280 bp) were detected using NGS3K / Caliper. Library concentration was accurately quantified (3 nM) using qPCR to ensure quality. Whole-exome sequencing was performed using a Novaseq 6000 (Illumina) with 150 bp paired-end reads, based on the effective library concentration. Data output was required to be 10 Gbp per sample.

[0291] Sequencing results processing and analysis

[0292] Raw sequencing data were obtained through Bioinformatics. The raw data was filtered, sequencing error rates were checked, and the proportions of Phred values ​​Q20 and Q30, raw data volume, and mapping rate were evaluated to assess whether the data met the standards (average proportion of Q20 bases >90%, average proportion of Q30 bases >80%, average error rate <0.1%). Data quality from all samples is summarized in... Figure 3 The cleaned data was aligned to the reference genome (GRCh37 / hg19) using Burrows-Wheeler Alignment (BWA) to generate initial alignment results in BAM format. SAMtools [Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform] were then used.

[0293] [Bioinformatics (Oxford, England) 2009; 25:1754-60] Data sorting and Sambamba markup (duplicate removal) were performed, and the results were used to calculate sequencing coverage and depth. Generally, a sample's sequencing reads reach a mapping rate of >95% and a read depth of >10X. Under these conditions, single nucleotide polymorphisms (SNPs) detected at that base position are more reliable. Sequencing results from all samples were tested, and the coverage of the exon target region reached over 99%, with at least 95% of the sequenced target regions having a sequencing depth of at least 10X (fraction of target covered with at least 10x).

[0294] Gene mutation screening and correlation prediction with disease treatment response

[0295] Gene mutation screening

[0296] The high-quality sequences obtained above were aligned with the human reference genome (GRCh37 / hg19) to obtain gene variation information in the samples. Statistical analysis and KEGG and GO annotation were performed on the detected variant genes.

[0297] The detected variants were screened based on the following principles:

[0298] 1) The consequences of mutations are frameshift, stop-gain, and stop-loss.

[0299] 2) Missense mutations predicted as harmful by SIFT and PolyPhen-2 software are also retained [Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nature protocols 2009; 4:1073-81.];

[0300] 3) Based on the GnomAD database, the allele frequency is <0.01.

[0301] Functional enrichment analysis

[0302] Using the DAVID bioinformatics resource [Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic acids research 2009; 37: 1-13], functional enrichment analysis was performed on the genes selected according to the above principles.

[0303] The functions of the detected genes were investigated using the KEGG (Kyoto encyclopedia of Genes and Genomes) pathway and Gene Ontology (GO) terms for biological processes (BP), molecular functions (MF), and cellular components (CC). Statistical significance (P-value) was obtained by applying a modified Fisher's exact test and Benjamini's multiple test correction.

[0304] result:

[0305] In both the 10 randomly selected treatment-responsive RIF patients and the 10 randomly selected control RIF patients enrolled in this study, multiple genes with potentially harmful mutations were detected.

[0306] Functional enrichment analysis was performed on the selected variant genes in the case group and the control group, respectively. Figure 4 The results of pathway enrichment analysis for the case group are shown. Figure 5 The results of pathway enrichment analysis in the control group are shown. As shown in the figure, genes in the case group showed significant enrichment in the following three pathways: ECM-receptor-related pathway; PI3K-Akt signaling pathway; and Focal adhesion pathway. In stark contrast, none of these pathways appeared in the list of significantly enriched pathways in the control group.

[0307] The 27 genes identified in the case group that are associated with the three main enrichment pathways mentioned above are shown in Table 6 below. Among them, 17 genes are associated with extracellular gene pathways, and 10 genes are simultaneously annotated to all three pathways according to KEGG / GO. The correlation between each gene and the pathway is also shown in... Figure 6 middle.

[0308] Table 6: 27 genes detected in the case group related to ECM-receptor interaction pathways, PI3K-Akt signaling pathway, and Focaladhesion pathway.

[0309]

[0310]

[0311] The variations detected in these genes were primarily missense mutations. These mutations alter specific amino acids in the protein but do not lead to protein inactivation and are not loss-of-function mutations, such as frameshift mutations or stop codon gain / loss mutations. These findings are consistent with the pituitary subclinical phenotype observed in this study.

[0312] The distribution of the 27 genes mentioned above associated with the three pathways was examined in samples from the case and control groups. The results are as follows: Figure 7 As shown, of the 27 related genes, three genes (COL13A1, COL24A1, and LAMC2) appeared not only in the case group samples but also in the four control samples (WJ02 / YHF09, TY08, and LMY06). The remaining genes appeared only in the case group samples. This analysis result is consistent with the previous pathway enrichment analysis results for the case and control groups. For the four control samples containing the three pathway-related genes, considering that the enrolled control patients were blindly selected from a RIF patient population who had not previously undergone LH hormone level testing and HCG supplementation therapy, the presence of these three pathway-related genes in these individuals may predict a beneficial clinical response to HCG supplementation luteal support therapy.

[0313] In addition, the distribution of the aforementioned 27 three-pathway-related genes in each case sample was examined. The results are as follows: Figure 8 As shown, except for one case sample, all other case group samples showed variations in at least one pathway-related gene. Seven of the cases involved ECM-receptor interaction / extracellular matrix organization pathway molecules, while the other two cases involved PI3K and / or FOCAL adhension pathway molecules.

[0314] The results above indicate that three pathways identified through functional enrichment analysis in a case-control study—the ECM-receptor interaction pathway, the PI3K-Akt signaling pathway, and the Focal adhesion pathway—and 27 pathway genes, are involved in the responsiveness of subclinical RIF patients to HCG supplementation. Missense gene variants in these pathways, particularly those in the ECM-receptor interaction pathway, can be predicted to potentially produce a beneficial clinical response to the HCG supplementation luteal therapy of this invention, for example, improved clinical pregnancy / live birth rates. Therefore, regular luteal phase hormone monitoring and HCG supplementation would be advantageous for RIF patients with such gene variants.

[0315] Based on the findings of the above-mentioned gene-level and clinical studies, the inventors propose a novel subtype of RIF disease: "subclinical hypopituitarism" recurrent implantation failure. This subtype is characterized by specific pathway-related gene variants and low (≤5 IU / L) serum luteinizing hormone (LH) levels during the luteal phase (D2); and exhibits a favorable therapeutic response to HCG supplementation in terms of pregnancy rate and / or live birth rate. Also based on the findings of the above-mentioned gene-level and clinical studies, the inventors have proposed the technical solution of this invention.

[0316] Some embodiments of the present invention

[0317] 1. A method for diagnosing or classifying a subject as a subtype of subclinical hypopituitaristic recurrent implantation failure, the method comprising detecting genetic variation information of the subject in a biological sample obtained from the subject, wherein the subject is diagnosed or classified as subclinical hypopituitaristic recurrent implantation failure if the subject exhibits variations in one or more pathway-related genes (preferably at least 2, 3, and more preferably at least 4 or 5 genes) selected from at least one or more KEGG biological pathways or GO biological processes:

[0318] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0319] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0320] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0321] -KEGG_PATHWAY:Focal Adhesion(hsa04510).

[0322] 2. The method of implementation 1, wherein the method comprises: detecting information on one or more pathway-related genes in a biological sample from a subject, wherein the pathway-related genes are selected from genes annotated according to KEGG and GO on at least one or more of the following biological pathways or biological processes:

[0323] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0324] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0325] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0326] -KEGG_PATHWAY:Focal Adhesion(hsa04510),

[0327] If the information indicates that one or more (preferably two, three, more preferably at least four or five) of the pathway-related genes are mutated, the subject is diagnosed or classified as the disease subtype.

[0328] Preferably, at least one (preferably at least two or three) of the mutated pathway-related genes is associated with a KEGG biological pathway or GO biological process selected from the following, based on KEGG and GO annotations:

[0329] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway;

[0330] -GO_BP_DIRECT: Extracellular matrix tissue;

[0331] More preferably, at least one gene is annotated on the KEGG_ pathway extracellular matrix-receptor interaction pathway and / or GO_BP_DIRECT extracellular matrix tissue according to KEGG and GO, and is also annotated on the KEGG_ pathway PI3K-Akt signaling pathway and Focal Adhesion.

[0332] 3. According to the method of implementation scheme 2, wherein one or any combination of the following pathway-related genes or any combination thereof are detected in a biological sample from the subject: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6, SHC4, and MYL12A; wherein, when information indicating the presence of gene variations in one or more of the pathway-related genes (preferably at least 2 or 3, and more preferably at least 4 or 5 genes) is detected in the subject's biological sample, the subject is diagnosed or classified into the disease subtype.

[0333] 4. According to the method of implementation scheme 3, wherein the subject diagnosed with the disease subtype has a variant in at least one of the following genes: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3.

[0334] 5. The method according to any of the foregoing embodiments, wherein, compared with the reference, the subject exhibits enrichment of variant genes in the KEGG biological pathway or GO biological process, preferably the enrichment is manifested as the subject having more pathway-related gene variants in the biological pathway or biological process than the reference, preferably at least 2, 3 or 4 more pathway-related gene variants than the reference.

[0335] 6. According to any of the aforementioned implementation schemes, the subject has no clear cause for repeated implantation failure and no obvious pituitary organic lesions, but exhibits mild symptoms of pituitary hypofunction.

[0336] 7. The method according to any of the foregoing embodiments, wherein the subject has a serum LH level of less than or equal to 5 IU / L on day D2 of the luteal phase, more preferably less than 3 IU / L, 2 IU / L, or 1 IU / L.

[0337] 8. A method for predicting whether a subject with recurrent implantation failure (RIF) is suitable for treatment with a luteal support therapy, or for predicting the responsiveness of a subject with recurrent implantation failure (RIF) to luteal support therapy, wherein the luteal support comprises administering a luteinization activity-related drug, such as LH and / or HCG, preferably HCG, after the endometrial transformation day, preferably after embryo implantation.

[0338] The method includes detecting genetic variation information of the subject in a biological sample obtained from the subject, wherein if the subject exhibits variation in one or more pathway-related genes (preferably at least 2, 3, and more preferably at least 4 or 5 genes) selected from at least one or more KEGG biological pathways or GO biological processes, the subject is indicated to be suitable for or may respond to the luteal support therapy.

[0339] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0340] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0341] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0342] -KEGG_PATHWAY:Focal Adhesion(hsa04510).

[0343] 9. The method according to embodiment 8, wherein the method comprises: detecting information on one or more pathway-related genes in a biological sample from a subject, wherein the pathway-related genes are selected from genes annotated according to KEGG and GO on at least one or more of the following biological pathways or biological processes:

[0344] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0345] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0346] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0347] -KEGG_PATHWAY:Focal Adhesion(hsa04510),

[0348] If the information indicates a mutation in one or more (preferably two, three, more preferably at least four or five) of the pathway-related genes, then it is predicted that the subject is suitable for the luteal support therapy, or may have a therapeutic response to the luteal support therapy.

[0349] Preferably, at least one (preferably at least two or three) of the mutated pathway-related genes is associated with a KEGG biological pathway or GO biological process selected from the following, based on KEGG and GO annotations:

[0350] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway;

[0351] -GO_BP_DIRECT: Extracellular matrix tissue;

[0352] More preferably, the at least one gene is annotated on the KEGG_ pathway extracellular matrix-receptor interaction pathway and / or GO_BP_DIRECT extracellular matrix tissue according to KEGG and GO, and is also annotated on the KEGG_ pathway PI3K-Akt signaling pathway and Focal Adhesion.

[0353] 10. The method according to embodiment 9, wherein one or any combination of the following pathway-related genes or any combination thereof are detected in a biological sample from a subject: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6, SHC4, and MYL12A; wherein, when information indicating the presence of gene variations in one or more pathway-related genes (preferably at least two or three, and more preferably at least four or five genes) is detected in the subject's biological sample, it is predicted that the subject is suitable for the luteal support therapy or may have a therapeutic response to the luteal support therapy.

[0354] 11. The method according to embodiment 10, wherein a subject predicted to be suitable for receiving the treatment or likely to have a therapeutic response to the treatment has a variant in at least one of the following genes: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITG A9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3.

[0355] 12. The method according to any one of the aforementioned embodiments 8-11, wherein if, compared with a reference, the subject exhibits enrichment of variant genes in the KEG G biological pathway or GO biological process, preferably the enrichment is manifested as the subject having more pathway-related gene variants in the biological pathway or biological process than the reference, preferably having at least 1, at least 2, 3 or 4 more pathway-related gene variants than the reference, then it is predicted that the subject is suitable for the luteal support therapy or may have a therapeutic response to the luteal support therapy.

[0356] 13. The method according to any one of the aforementioned implementation schemes 8-12, wherein the subject has no clear cause for repeated implantation failure and no obvious pituitary organic lesions, but exhibits mild symptoms of pituitary hypofunction.

[0357] 14. The method according to any one of the aforementioned embodiments 8-13, wherein the method further includes detecting the serum LH level of the subject on day D2 of the luteal phase, preferably, the subject has a D2 serum LH level of less than 5 IU / L, more preferably less than 3 IU / L, 2 IU / L, or 1 IU / L.

[0358] 15. The method according to any of the foregoing embodiments, wherein the gene mutation is a missense mutation, preferably, the mutation does not lead to gene inactivation but affects the biological activity of the protein encoded by the gene, preferably, the allele frequency of the mutation in normal healthy individuals is <0.01.

[0359] 16. The method according to any of the foregoing embodiments, wherein the biological sample is selected from blood, serum, other body fluids, or biopsy tissue.

[0360] 17. The method according to any of the foregoing embodiments, wherein the gene variation information is measured using PCR or a microarray chip.

[0361] 18. The method according to any of the foregoing embodiments, wherein the gene variation information is obtained using a sequencing method, preferably an exon sequencing method.

[0362] 19. The method according to any of the foregoing embodiments, wherein the gene variation information is obtained by detecting the biological activity of the protein encoded by the gene, preferably, the biological activity is reduced biological activity.

[0363] 20. The method according to any of the foregoing embodiments, wherein the measurement comprises using an immunoassay, such as an ELISA assay, to detect a mutant protein encoded by a variant gene.

[0364] 21. The method according to any one of the aforementioned embodiments 8-20, wherein the luteal support treatment comprises: administering HCG after embryo implantation, more preferably, injecting HCG intramuscularly, and preferably the luteal support treatment further comprises supplementing with progesterone and / or estradiol drugs, and optionally adjusting the dosage of the drugs according to the patient's progesterone and / or estrogen levels;

[0365] Preferably, HCG is administered at a dose of 100-800 IU or 200-500 IU.

[0366] More preferably, starting the day after transplantation, patients should receive an intramuscular injection of 500 IU HCG daily.

[0367] 22. A method of providing luteal support therapy to a patient with recurrent implantation failure, the method comprising administering, after the endometrial transformation day, preferably after embryo implantation, an effective amount of a luteinization-related drug, such as hCG and / or LH, preferably hCG, to the patient.

[0368] The patients described herein exhibit variations in one or more pathway-related genes (preferably at least 2, 3, and more preferably at least 4 or 5 genes) selected from at least one or more KEGG biological pathways or GO biological processes:

[0369] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0370] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0371] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0372] -KEGG_PATHWAY:Focal Adhesion(hsa04510);

[0373] Preferably, the subject has a variant in at least one or more genes selected from at least one or more of the following KEGG biological pathways or GO biological processes:

[0374] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway;

[0375] -GO_BP_DIRECT: Extracellular matrix tissue;

[0376] More preferably, the subject has variations in at least one or more of the following genes, which are annotated to the KEGG_PATHWAY extracellular matrix-receptor interaction pathway and / or GO_BP_DIRECT extracellular matrix tissue according to KEGG and GO, and are annotated to the PI3K-Akt signaling pathway and Focal Adhesion.

[0377] 23. The method according to embodiment 22, wherein the patient has a gene variant on one or a combination of the following pathway-related genes: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6, SHC4, and MYL12A;

[0378] Preferably, the patient has a gene variation in a gene combination containing at least 2, at least 3, or more preferably at least 4 or 5 pathway genes. More preferably, the gene combination contains at least one gene selected from the following: LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3.

[0379] 24. The method according to any one of embodiments 22-23, wherein the patient’s serum D2 level during the luteal phase is ≤5 IU / L, for example, less than 4 IU / L, or 3 IU / L, or 2 IU / L, or less than 1 IU / L.

[0380] 25. The method according to any one of embodiments 22-24, wherein the gene mutation is a missense mutation, preferably, the mutation does not cause gene inactivation but affects the biological activity of the protein encoded by the gene, preferably, the allele frequency of the mutation in normal healthy individuals is <0.01.

[0381] 26. The method according to any one of embodiments 22-25, wherein the method is used to improve the likelihood of clinical pregnancy and / or delivery of a live birth in the RIF patient.

[0382] 27. The method according to any one of embodiments 22-26, wherein the method comprises: administering HCG after embryo implantation, more preferably, injecting HCG intramuscularly, wherein preferably the luteal support treatment further comprises supplementing with progesterone and / or estradiol drugs, and optionally adjusting the dosage of the drugs according to the patient's progesterone and / or estrogen levels;

[0383] Preferably, HCG is administered at a dose of 100-800 IU or 200-500 IU.

[0384] More preferably, starting the day after transplantation, patients should receive an intramuscular injection of 500 IU HCG daily.

[0385] 28. According to any one of the implementation schemes 22-27, wherein the embryo transfer is a frozen-thaw cycle transfer or a fresh cycle transfer, preferably a frozen-thaw cycle transfer.

[0386] Preferably, the patient undergoes embryo transfer after ovulation in a natural cycle, after the application of an ovulation induction protocol, or after an artificial cycle, especially frozen-thawed embryo transfer.

[0387] 29. The method according to any one of embodiments 22-28, wherein the method includes monitoring the patient’s hormone levels, including LH, P, E2 and HCG levels, before implantation and during the embryo implantation cycle.

[0388] 30. A composition for diagnosing or classifying patients with recurrent luteal insufficiency (RIF) or for predicting the therapeutic response of RIF patients to luteal support therapy with supplemental luteinizing activity-related drugs, wherein the composition comprises reagents or combinations of reagents for detecting information on a set of biomarkers.

[0389] The biomarker set consists of multiple pathway-related genes, which are selected from genes annotated according to KEGG and GO on at least one or more of the following biological pathways or processes:

[0390] -KEGG_PATHWAY: Extracellular matrix-receptor interaction pathway (hsa04512);

[0391] -GO_BP_DIRECT: Extracellular matrix tissue (GO:0030198);

[0392] -KEGG_PATHWAY:PI3K-Akt signal transduction pathway (hsa04511);

[0393] -KEGG_PATHWAY:Focal Adhesion(hsa04510),

[0394] For example, the biomarker set consists of at least 2, at least 5, at least 10, at least 15, or all of the pathway-related genes selected from LAMA5, LAMA2, ITGB4, ITGA11, SPP1, TNC, ITGA9, COL24A, COL6A6, COL18A, COL13A1, NID2, ACAN, CDH1, ERCC2, COL9A3, PHLPP2, IFNA4, CRTC2, CSF3R, CSF1, GYS1, CREB3L4, LPAR6, SHC4, and MYL12A.

[0395] Preferably, the detection provides variation information of the pathway-related genes, such as those described in embodiments 8-11, wherein the variation is preferably a missense mutation.

[0396] Preferably, the reagent is a polynucleotide for detecting the gene variation, and / or a reagent (e.g., an antibody) for detecting the amount and / or activity of the mutant protein encoded by the gene variation.

[0397] 31. The composition of embodiment 30, wherein the composition is present in the form of a microarray chip and comprises a polynucleotide reagent or combination of reagents for providing information on the biomarker set, preferably, the polynucleotide reagent being a polynucleotide that hybridizes to a missense mutation site (e.g., SNP) of the pathway gene.

[0398] 32. A kit comprising the composition of embodiment 30 or 31.

[0399] 33. Use of the composition of embodiment 30, the microarray chip of embodiment 31, or the kit of embodiment 32 in the preparation of products for the diagnosis or classification of RIF patients, or for the prediction of the therapeutic response of RIF patients to luteal support therapy with supplemental luteinizing activity-related drugs.

Claims

1. Use of a reagent for detecting serum luteinizing hormone (LH) levels in the preparation of a product for predicting the responsiveness of patients with recurrent implantation failure (RIF) to luteal support therapy, wherein luteal support therapy refers to the administration of HCG after embryo transfer in addition to progesterone and / or estrogen medication, wherein the reagent comprises a reagent for detecting serum LH levels on day D2 of the luteal phase in RIF patients, wherein the measured LH level is compared to a reference value of 5 IU / L, and when the measured value is less than or equal to 5 IU / L, it indicates that the patient is suitable for luteal support therapy or has a therapeutic response to luteal support therapy.

2. The use according to claim 1, wherein the therapeutic responsiveness is an improved clinical pregnancy rate and / or live birth rate.

3. The use according to claim 1, wherein, The patient had no clear cause for repeated implantation failures and no obvious organic pituitary lesions, but exhibited mild symptoms of hypopituitarism.

4. The use according to claim 1, wherein, When the patient has a serum LH level of less than or equal to 3 IU / L on day 2 of the luteal phase, it indicates that the patient is suitable for luteal support therapy or has a therapeutic response to luteal support therapy.

5. The use according to claim 1, wherein, When the patient has a serum LH level of less than or equal to 2 IU / L on day 2 of the luteal phase, it indicates that the patient is suitable for luteal support therapy or has a therapeutic response to luteal support therapy.

6. The use according to claim 1, wherein, When the patient has a serum LH level of less than or equal to 1 IU / L on day 2 of the luteal phase, it indicates that the patient is suitable for luteal support therapy or has a therapeutic response to luteal support therapy.

7. The use according to any one of claims 1-6, wherein the HCG is intramuscularly injected HCG.

8. The use according to claim 7, wherein the HCG is formulated for intramuscular injection of 500 IU HCG.

9. The use according to any one of claims 1-6, wherein the embryo transfer is a frozen-thaw cycle transfer or a fresh cycle transfer.

10. The use according to claim 9, wherein the embryo transfer is a freeze-thaw cycle transfer.

11. The use according to any one of claims 1-6, wherein the patient undergoes embryo transfer after ovulation in a natural cycle, after the application of an ovulation induction protocol, or after an artificial cycle.

12. The use according to any one of claims 1-6, wherein the luteal support treatment is the supplementation of HCG on the basis of administration of progesterone and / or estradiol.

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