Organ transplantation rejection risk prediction marker combination and application thereof

Abstract

The invention belongs to the technical field of biology, and particularly relates to an organ transplantation rejection risk prediction marker combination and application thereof. The marker combination comprises: an SNP marker for predicting the risk of organ transplantation rejection, which can be used for predicting the risk of organ transplantation rejection; furthermore, the marker combination further comprises a cell-specific methylation marker used for locating the damaged organ or cell type, and a cell-specific methylation marker used for locating the damaged organ or cell type, therefore, under the plasma single sample detection of the transplantation receptor, the marker combination not only can evaluate whether the transplantation receptor has rejection reaction, but also can locate rejected organs and damaged cell types, and assists clinicians in making finer treatment schemes.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and in particular relates to a combination of markers for predicting organ transplant rejection risk and an application thereof. Background Art

[0002] Organ transplantation is the only definitive treatment for end-stage organ failure, and 100,000 patients receive organ transplants every year worldwide, and graft rejection is a major problem for these patients. When the transplant is recognized as foreign by the recipient's immune system, it is attacked and rejected, leading to cell damage and graft failure. Currently, there are two main methods for monitoring organ transplant rejection. One method monitors through tissue biopsy, which is the gold standard for detecting transplant rejection. However, it is highly invasive, has certain damage to the patient and the transplant, requires hospitalization and highly specialized staff, and has inter-observer variability. Another method uses biomarkers for monitoring, but its sensitivity and specificity are not high, and its efficiency varies with different transplanted organs and also depends on patient characteristics. For example, depending on gender, muscle mass or race, about 50% of the function of the transplanted kidney may have been lost before the kidney biomarker serum creatinine can be measured. Therefore, in solid organ transplantation, therapeutic intervention may be delayed due to late diagnosis.

[0003] Free DNA (cfDNA) can reflect the physiological and pathological state of its source cells and has been widely used in the field of tumor and prenatal diagnosis. In the field of organ transplantation, donor-derived cfDNA is increasingly valued as a non-invasive liquid biopsy marker. Many studies have shown that the detection and quantification of donor-derived cfDNA can be used to monitor allogeneic transplant damage and rejection. At present, most studies are based on SNP markers to characterize the genome of transplant recipients (or combined with transplant donors), and a small number of studies will also combine markers such as TTV, metabolic composition or transcriptome to detect and quantify donor-derived cfDNA. However, the current single detection method of donor-derived cfDNA represents the damage of the entire transplanted organ and cannot accurately locate the damaged cell type. When infection or primary disease damage exists, it is difficult to distinguish it from rejection; therefore, the combined cell-derived methylation markers can better predict the risk of cell rejection, which is beneficial to the diagnosis and treatment of patients. Summary of the invention

[0004] The first aspect of the present invention aims to provide a marker combination.

[0005] The second aspect of the present invention aims to provide a product.

[0006] The purpose of the third aspect of the present invention is to provide an application of the marker combination of the first aspect of the present invention.

[0007] The fourth aspect of the present invention aims to provide a system or device.

[0008] The fifth aspect of the present invention aims to provide a method.

[0009] For relevant contents such as methylation marker screening and quantification, please refer to CN115497561A and CN118280448A, the disclosures of which are fully incorporated herein by reference.

[0010] In order to achieve the above object, the technical solution adopted by the present invention is:

[0011] In a first aspect of the present invention, a marker combination is provided, which comprises: a SNP marker for predicting the risk of organ transplant rejection, wherein the SNP marker comprises rs4076816, rs7533583, rs16835469, rs12075194, rs3134613, rs2780948, rs8179495, rs642307, rs12408340, rs58290373, rs595289, rs12092943, rs11261296, rs12561761, rs10926011, rs1494121, rs2463458, rs1545256, rs759436, rs s2540217,rs17030203,rs1214780,rs35414849,rs76469070,rs12712079,rs34986597,rs4663094,rs7563574,rs4668025,rs2368423,rs11894064 ,rs10203123,rs719418,rs2323142,rs7623055,rs417123,rs4858477,rs6796757,rs4074363,rs9790198,rs2872373,rs4856379,rs2131110,rs110 8898,rs7648579,rs6773068,rs997369,rs4311171,rs1079377,rs7660793,rs4631056,rs1565572,rs11099544,rs10014477,rs7682218,rs111320 83,rs2135075,rs12697263,rs983061,rs6877675,rs4835850,rs1864974,rs6891770,rs4868700,rs7741539,rs1358869,rs11751412,rs2274588, rs4714795,rs4714890,rs9400993,rs9388388,rs2160621,rs4147298,rs11771651,rs11762979,rs691130,rs2465910,rs8192865,rs2536088,rs6 990513,rs7825970,rs3888293,rs10095135,rs2926426,rs16916,rs7825817,rs722084,rs3102478,rs2447962,rs4736684,rs6988568,rs6476874,rs2820957,rs4451372,rs6597555,rs10905916,rs10795948,rs2804831,rs35544747,rs11817790,rs16919890,rs1404504,rs566778,rs11211902,rs11214261,rs948091,rs7963653,rs4761932,rs115314238,rs10778158,rs9510760,rs73479322,rs7981226,rs4771948,rs11627187,rs12050132,rs1676243,rs654527,rs1706814,rs2469558,rs4778836,rs2447285,rs7402264,rs7171883,rs4777774,rs6496016,rs350249,rs3093322,rs4785466,rs7197885,rs2161741,rs16960333,rs4565,rs301138,rs12931391,rs3751797,rs1111434,rs1879488,rs3752963,rs12451747,rs11654444,rs9899645,rs2332598,rs2592208,rs206435,rs1196944,rs4359513,rs6510724,rs8109003,rs11667570,rs678401,rs6132116,rs6046115,rs208815,rs4811260,rs6099870,rs6100135,rs2294992,rs76747837,rs2017582,rs1867353,rs5752495,rs5765703,rs5769888,rs874881,rs492594,rs4865615,rs2071888,rs4288409,rs2509943,rs936019,rs4940019,rs475002,rs3829655,rs236152,rs804133,rs553253,rs12063251,rs7547176,rs3815228,rs3126020,rs6676020,rs284174,rs2806878,rs6427392,rs1034463,rs544013,rs28768068,rs296553,rs12023412,rs10779598,rs12029576,rs10915694,rs2007619,rs6679209,rs4658857,rs34097650,rs72785267,rs887843,rs12476837,rs1176785,rs3731611,rs3768699,rs4954799,rs16836609,rs788164,rs4972845,rs6724378,rs421636,rs16867057,rs10933305,rs10184738,rs55761135,rs842263,rs2076993,rs1506691,rs17024184,rs17027453,rs652889,rs869364,rs56286049,rs6766396,rs1156884,rs6438720,rs9860731,rs7624529,rs6768608,rs522838,rs6444036,rs13090240,rs11917154,rs1317285,rs10212990,rs9306942,rs28433701,rs298471,rs6855202,rs7684755,rs7721079,rs10053782,rs32274,rs11133784,rs16903723,rs4702090,rs7718456,rs10805605,rs6859125,rs271243,rs288873,rs4704126,rs171661,rs7703598,rs1540574,rs11743913,rs4976242,rs262716,rs31736,rs1484802,rs7722055,rs1345733,rs4544835,rs17081213,rs2282821,rs6458232,rs10948707,rs4706552,rs1231499,rs1029321,rs9489314,rs7758642,rs6931683,rs6917940,rs9648612,rs16874238,rs2357843,rs77138421,rs7782130,rs650974,rs1428069,rs6976507,rs6962291,rs2373814,rs1016103,rs34401170,rs1033345,rs2294064,rs3020245,rs6468360,rs10503926,rs10958803,rs4737263,rs12550039,rs4733790,rs58858645,rs4909628,rs4909472,rs2297081,rs7847789,rs7033226,rs2281729,rs10976440,rs7037899,rs3860991,rs10781099,rs10867213,rs11139648,rs17144411,rs2208892,rs12217971,rs10826350,rs10829015,rs7907500,rs10763696,rs2437934,rs2587471,rs655489,rs4919104,rs180703,rs2421125,rs4482024,rs41534749,rs7107350,rs493442,rs10898294,rs61908115,rs11063380,rs1894331,rs10772826,rs36112053,rs7971974,rs11174450,rs1904568,rs1908667,rs4554966,rs7295786,rs10745586,rs1609944,rs9567762,rs9563201,rs9569578,rs9544749,rs9546531,rs1328833,rs157028,rs73264730,rs1884621,rs61607981,rs2356535,rs17116374,rs2798386,rs4900251,rs4377134,rs35798799,rs8032702,rs8030155,rs17236509,rs62025547,rs7183553,rs4781403,rs6498312,rs1794311,rs2819693,rs34652009,rs73550818,rs2526057,rs12444602,rs820808,rs1870924,rs4843188,rs9905366,rs1981659,rs7222971,rs9891244,rs2013788,rs12327412,rs1401461,rs9947527,rs2042714,rs10439060,rs12968605,rs1458223,rs8183455,rs6072981,rs195022,rs2070579,rs2105406,rs12466272,rs77559470,rs2336049,rs6550023,rs2660783,rs517255,rs9689772,rs968371,rs10962874,rs9406647,rs10116779,rs11200732,rs12423490,rs7203147,rs3883321,rs4797088,rs9864927,and rs6451159.

[0012] In some embodiments, the marker combination further comprises: a cell-specific methylation marker for locating damaged organs or cell types.

[0013] In some embodiments, the cell-specific methylation markers used to locate damaged organs or cell types are obtained by the method of hierarchical screening of methylation markers in patent document CN115497561A.

[0014] In some embodiments, the screening method for cell-specific methylation markers for localizing damaged organs or cell types comprises:

[0015] Stratify the sample;

[0016] The first-level screening step includes grouping the samples into N groups according to the above-mentioned stratification, and screening the methylation markers between the N groups, which are the first-level methylation markers;

[0017] The second-layer screening step includes grouping the samples into N groups according to the above stratification, and screening the methylation markers of different types of samples in each group, which are the second-layer methylation markers;

[0018] The stratification of samples comprises:

[0019] A data acquisition step, comprising acquiring methylation modification data of the sample;

[0020] A preprocessing step, comprising preprocessing the methylation modification data to obtain preprocessed samples of various types;

[0021] The dimensionality reduction step includes performing dimensionality reduction on each type of sample after preprocessing;

[0022] The stratification step includes clustering the samples after dimensionality reduction and determining the optimal number of clusters to achieve stratification of the samples.

[0023] In some embodiments, in the preprocessing step, the preprocessing includes probe filtering and sample filtering.

[0024] In some embodiments, in the dimensionality reduction step, the method for performing dimensionality reduction processing on each type of sample includes: calculating the degree of discreteness of each probe in the target type sample, sorting the probes from large to small according to the degree of discreteness, taking the probes whose discreteness ranks before a preset ranking as effective features, clustering the samples, determining the optimal number of clusters based on the indicators, and achieving dimensionality reduction for each type of sample.

[0025] In some embodiments, in the dimensionality reduction processing step, when the probes with discreteness ranking before the preset ranking are taken as effective features, the preset ranking (K) can be set according to the actual application, including but not limited to 10%, 20%, 30%, etc.

[0026] In some embodiments, in the dimensionality reduction step, the indicators include but are not limited to at least one of variance ratio criterion (VRC; also known as Calinsky criterion), gap statistic (GS), silhouette coefficient (Av erage silhouette method), etc.

[0027] In some embodiments, in the dimensionality reduction processing step, the indicators include but are not limited to at least two of the variance ratio criterion (VRC; also known as Calinsky criterion), gap statistic (GS), silhouette coefficient (Average silhouette method), etc.

[0028] In some embodiments, in the dimensionality reduction processing step, if the optimal number of clusters for two or more indicators is consistent, the optimal number of clusters is used as the final optimal number of clusters; otherwise, the optimal number of clusters determined by the silhouette coefficient is selected as the final optimal number of clusters to achieve dimensionality reduction for each type of sample.

[0029] In some embodiments, in the dimensionality reduction step, the method of clustering samples includes but is not limited to at least one of a hierarchical clustering algorithm and a density-based clustering algorithm.

[0030] In some embodiments, the dimension reduction step further comprises calculating the methylation level of each probe capture region for all samples in each category.

[0031] In some embodiments, the methylation level comprises an average methylation rate, methylation entropy, epi-polymorphism, methylation haplotype load (MHL), or haplotype counts.

[0032] In some embodiments, the methylation level comprises a mean beta value.

[0033] In some embodiments, the dimensionality reduction processing step further includes calculating the mean beta value of all samples in each probe capture region in each category; wherein beta value = M / (M+U+offset), U represents the non-methylated signal intensity, M represents the methylated signal intensity, and offset is the offset. The offset is to prevent the denominator from being 0. The beta value is the percentage of the methylated signal intensity.

[0034] The beta value is applicable to chip data. If it is sequencing data, other indicators can be used to replace the beta value, including but not limited to methylation entropy, epi-polymorphism, methylation haplotype load (MHL) or haplotype counts.

[0035] In some embodiments, in the stratification step, the degree of discreteness of each probe in all samples is calculated, the probes are sorted from large to small according to the degree of discreteness, the probes with a discreteness ranking before a preset ranking are taken as effective features, the samples are clustered, and the optimal number of clusters is determined based on the indicator.

[0036] In some embodiments, in the stratification step, the method for clustering samples includes at least one of the following methods: unweighted pair-group method with arithmetic means (UPGMA), phylogenetic tree neighbor joining method, partition-based clustering algorithm, hierarchical-based clustering algorithm, and network-based clustering algorithm.

[0037] In some embodiments, in the stratification step, the indicators include but are not limited to at least two of the variance ratio criterion (VRC; also known as Calinsky criterion), gap statistic (GS), silhouette coefficient (Average silhouette method), etc.

[0038] In some embodiments, in the stratification step, if the optimal number of clusters of two or more indicators is consistent, the optimal number of clusters is used as the final optimal number of clusters; otherwise, the optimal number of clusters N determined by the silhouette coefficient is used as the final optimal number of clusters.

[0039] In some implementations, in the stratification step, the samples of multiple types are finally divided into N groups, each of which contains at least one type of sample. Here, "multiple" means two or more.

[0040] In some embodiments, the type comprises a tissue type or a cell type.

[0041] In some embodiments, the samples include samples of organs that have undergone organ damage or cell damage after organ transplantation and samples of organs that have not undergone organ damage or cell damage after organ transplantation.

[0042] In some embodiments, in the data acquisition step, the methylation modification data of the sample comes from a database.

[0043] In some embodiments, in the data acquisition step, the database comprises a public database.

[0044] In some embodiments, in the data acquisition step, the methylation modification data of the sample may also be derived from self-test data.

[0045] In some embodiments, in the data acquisition step, the sample includes but is not limited to at least one of a tissue sample and a body fluid sample.

[0046] In some embodiments, in the preprocessing step, the probe filtering rules include: if the probe contains a SNP site within 10bp upstream and downstream, the probe is eliminated; at the same time, probes on sex chromosomes and probes with a sample missing value ratio exceeding a preset threshold are eliminated.

[0047] The sample missing value ratio refers to the ratio of the number of samples with no signal detected on a certain probe to the total number of samples. For example, for a probe, if 30 samples out of 100 samples have no signal detected on this probe, it will be represented by a missing value NA. Here, the sample missing value ratio of this probe = 30 / 100.

[0048] In some embodiments, in the preprocessing step, the sample filtering rule includes: using at least one algorithm to identify abnormal samples, and if the identification result of at least one of the algorithms used shows that the sample is abnormal, then the sample is eliminated.

[0049] In some embodiments, if the identification results of two or more algorithms show that a sample is abnormal, the sample is discarded.

[0050] In principle, only one algorithm can be used to identify abnormal samples, but it is more reliable if a sample is identified as an abnormal sample by multiple methods. If only one algorithm is used, more samples may be eliminated. Usually, the algorithm can be adjusted according to actual needs, and the number of algorithms can be selected according to the number of samples finally included in the analysis.

[0051] In some embodiments, the algorithm for identifying abnormal samples includes but is not limited to at least one of Isolation Forest, Local Outlier Factor Detection Algorithm (LOF), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), partition-based clustering algorithm, hierarchical-based clustering algorithm, and network-based clustering algorithm, preferably at least two of the aforementioned algorithms.

[0052] In some embodiments, the first level screening step comprises:

[0053] A sample selection step, comprising obtaining N grouped samples for first-level methylation marker screening;

[0054] a methylation region division step, wherein the methylation regions in which the correlation coefficient between any two adjacent CpG sites is greater than a preset value and the number of CpG sites is greater than a preset number are regarded as the same methylation region;

[0055] The methylation marker screening step includes testing whether there is a significant statistical difference between each methylation region among N groups. If yes, the methylation levels of any two groups among the N groups are compared pairwise to determine whether there is a significant statistical difference between the methylation levels of the methylation region between the two groups. If there is a significant statistical difference in the methylation levels of a specific group sample and the other M group samples in a methylation region, the methylation region is determined to be a specific methylation marker for the specific group, and M is any natural number less than N. If no, the subsequent steps are not performed.

[0056] In some embodiments, in the methylation region division step, the correlation coefficient includes but is not limited to the Pearson correlation coefficient or the Spearman correlation coefficient.

[0057] In some embodiments, in the methylation marker screening step, according to actual applications, M can be set to N-1, N-2, etc., and when M is N-1, the specificity of the methylation marker is the best.

[0058] In some embodiments, in the methylation marker screening step, the test method includes but is not limited to at least one of analysis of variance and Kruskal-Wallis test.

[0059] In some embodiments, in the methylation marker screening step, the p-value is corrected using a multiple comparison method to obtain a corrected p-value (padj). If the corrected p-value is less than a preset threshold, it is determined that there is a significant statistical difference in the methylation level of the methylation region between the N groups.

[0060] In some embodiments, in the methylation marker screening step, when the p-value of the pairwise comparison is less than a preset threshold and the absolute value of the methylation level difference is greater than the preset threshold, it is determined that there is a significant statistical difference in the methylation level of the methylation region between the two groups.

[0061] In some embodiments, the methylation level comprises an average methylation rate, methylation entropy, epi-polymorphism, methylation haplotype load (MHL), or haplotype counts.

[0062] In some embodiments, the methylation level comprises a mean beta value.

[0063] In some embodiments, the methylation level difference comprises the difference in methylation level of each CpG site in the region between two groups of samples.

[0064] In some embodiments, the methylation level comprises the mean beta value of all CpG sites contained in the methylated region.

[0065] In some embodiments, in the methylation marker screening step, when any two groups among the N groups are compared pairwise, the statistical methods used include but are not limited to at least one of Tukey's Honest Significant Difference (Tukey's HSD), Least Significance Difference (LSD), Dunnett-t test, Student New man Keuls (SNK) method, Duncan's new multiple range test, etc.

[0066] In some embodiments, the second-tier screening step is performed with reference to the first-tier screening step.

[0067] In some embodiments, the cell-specific methylation markers for locating damaged organs or cell types include at least one of a renal epithelial cell-specific methylation marker, a renal endothelial cell-specific methylation marker, an alveolar epithelial methylation marker, and a bronchial epithelial methylation marker.

[0068] In some embodiments, the renal epithelial cell-specific methylation marker comprises chr13:76871766-76871871, chr22:32841838-32841950, chr5:130707029-130707674, chr10:117857749-117857831, chr10:45261838-45261910, chr16:72038677-72039042, chr16:87693751-87693831, chr18:42290985-42291186, chr8:144017883-144018008, chr2:127 895700-127895954, chr2:240723076-240723297, chr4:1534879-1535047, chr4:129735232-129735504, chr5:115783394-115783531, chr17:32956139-32956404, chr19:8064907-8065003, chr19:13879658-13880041, chr19:31734430-31735122, chr21:46848068-46848236 and chr8:1132771-1132948.

[0069] In some embodiments, the renal endothelial cell-specific methylation markers include chr1:45378247-45378362, chr13:114457850-114457938, chr14:64394967-64395037, chr8:1284044-1284109, chr16:1591461-1591493, chr22:47566542-47566650, chr1:117074709-117074799, chr1:117074875-117074939, chr7:2853725-2853755, chr21:33396275-33396613 and chr10:22888747-22888997.

[0070] In some embodiments, the alveolar epithelial methylation marker comprises chr4:100865925-100866026, chr12:102276167-102276184, chr12:102298280-102298321, chr9:102907198-102907558, chr10:103549045-103549212, chr10:1038055 77-103805618、chr1:104080755-104081097、chr6:108559696-108559896、chr1:109965889-1099 66010、chr12:111024162-111024235、chr12:112082519-112082664、chr12:112402450-112402639 , chr2:112627023-112627069, chr13:113347960-113348198, chr16:11398225-11398438, chr10: 114129859-114129929, chr10:115950269-115950333, chr11:117029637-117029651, chr16:1198 9202-11989519, chr16:12011174-12011244, chr12:120369895-120370135, chr7:120959369-120959437, chr12:122950462-122950678, chr1:12525734-12525958, and chr10:126161887-126162253.

[0071] In some embodiments, the lung bronchial epithelial methylation marker comprises chr2:100026253-100026342, chr12:102240433-102240450, chr19:10271710-10271765, chr13:105669421-105669509, chr11:107666587-107666748, chr8:1082693 29-108269378, chr2:109130778-109130902, chr3:112308465-112308621, chr6:113335181-113 335340, chr10:114084100-114084313, chr3:11454735-11454953, chr12:117153241-117153639 , chr18:11942307-11942409, chr11:120078015-120078074, chr3:122875162-122875295, chr8: 123097187-123097279, chr4:124427917-124428037, chr4:124428157-124428276, chr10:12495 429-12495664, chr8:126844893-126845111, chr9:130262034-130262136, chr5:134124045-134124535, chr7:140615525-140615745, chr16:14140076-14140132, and chr10:15164370-15164566.

[0072] In some embodiments, the organ comprises at least one of kidney, liver, pancreas, intestine, heart, lung, and stomach; and further comprises kidney and lung.

[0073] In some embodiments, the organ is from a mammal, such as a human, a non-human primate (e.g., a gorilla, ape), a rodent (e.g., a rat, a mouse, a guinea pig), a pet (e.g., a cat, a dog), a livestock (e.g., a horse, a cow, a sheep, a pig, a rabbit); further a human.

[0074] In some embodiments, the cells include at least one of renal endothelial cells, renal epithelial cells, hepatic parenchymal cells, hepatic endothelial cells, pancreatic duct cells, pancreatic acinar cells, pancreatic δ cells, pancreatic β cells, pancreatic α cells, intestinal epithelial cells, cardiomyocytes, cardiac fibroblasts, alveolar epithelial cells, alveolar endothelial cells, lung bronchial epithelial cells, and gastric epithelial cells; and further include at least one of renal endothelial cells, renal epithelial cells, alveolar epithelial cells, and lung bronchial epithelial cells.

[0075] In some embodiments, the marker combination is used for any one of a1)-a4):

[0076] a1) Prediction of risk of organ transplant rejection;

[0077] a2) Prediction and location of organ transplant rejection risk;

[0078] a3) Immunosuppressive drug toxicity monitoring;

[0079] a4) Monitoring of complications and injuries after organ transplantation.

[0080] In some embodiments, the organ transplantation described in a1) and a2) comprises at least one of single organ transplantation, multi-organ transplantation, and relative transplantation.

[0081] In some embodiments, the prediction and localization of organ transplant rejection risk described in a2) refers to assessing the risk of organ transplant rejection and locating the rejected organ and / or damaged cell type.

[0082] In some embodiments, the marker combination is derived from a biological sample of the subject to be tested; and further derived from free DNA in the biological sample of the subject to be tested.

[0083] In some embodiments, the biological sample comprises at least one of body fluid, tissue, cell, and excrement; further is body fluid.

[0084] In some embodiments, the body fluid comprises at least one of blood, lymph, pleural effusion, cerebrospinal fluid, joint fluid, ascites, saliva, lymph, alveolar lavage fluid, and body fluid; further comprising blood.

[0085] In some embodiments, the blood comprises at least one of serum, plasma, dried blood spots, and whole blood; further comprising plasma.

[0086] In some embodiments, the excreta comprises at least one of urine, feces, tears, and sweat.

[0087] In some embodiments, the subject to be tested comprises mammals, such as humans, non-human primates (such as gorillas, apes), rodents (such as rats, mice, guinea pigs), pets (such as cats, dogs), and livestock (such as horses, cows, sheep, pigs, rabbits).

[0088] In some embodiments, the subject comprises a human.

[0089] In some embodiments, the subject to be tested is a transplant recipient after organ transplantation.

[0090] In some embodiments, the SNP markers are used to calculate the content of donor-derived cell-free DNA (ddcfDNA) in the recipient's free DNA, thereby determining whether there is organ transplant rejection.

[0091] In some embodiments, the methylation markers are used to calculate the absolute amount of cell-derived cfDNA, thereby locating the organ or cell type where damage has occurred.

[0092] The second aspect of the present invention provides a product comprising a substance for detecting the SNP marker for predicting the risk of organ transplant rejection according to the first aspect of the present invention.

[0093] In some embodiments, the product further comprises a substance for detecting the cell-specific methylation marker for locating the damaged organ or cell type in the first aspect of the present invention.

[0094] In some embodiments, the material for detecting the SNP marker for predicting the risk of organ transplant rejection in the first aspect of the present invention comprises a reagent for determining the amount of the DNA sequence of the SNP marker for predicting the risk of organ transplant rejection in the first aspect of the present invention.

[0095] In some embodiments, the substance for detecting the cell-specific methylation marker for locating damaged organs or cell types in the first aspect of the present invention comprises a reagent for measuring the amount of the DNA sequence of the cell-specific methylation marker for locating damaged organs or cell types.

[0096] In some embodiments, the determining the amount of the DNA sequence of the SNP marker or the methylation marker is performed by molecular counting.

[0097] In some embodiments, the determination of the amount of the DNA sequence of the SNP marker or the methylation marker is based on the detection of the nucleic acid sequence.

[0098] In some embodiments, the determining the amount of DNA sequence of the SNP marker or methylation marker comprises digital polymerase chain reaction, real-time quantitative polymerase chain reaction, array capture, massively parallel genome sequencing, single molecule sequencing, or multiplex detection of polynucleotides using color-coded probe pairs.

[0099] In some embodiments, the determining the amount of the DNA sequence of the SNP marker or methylation marker comprises mass spectrometry or hybridization with a microarray, a fluorescent probe, or a molecular beacon.

[0100] In some embodiments, the substance for detecting the cell-specific methylation marker for locating damaged organs or cell types in the first aspect of the present invention further comprises a reagent for differentially modifying methylated and unmethylated DNA.

[0101] In some embodiments, the reagent that differentially modifies methylated and unmethylated DNA comprises bisulfite or a protein or chemical that binds to DNA based on methylation status.

[0102] In some embodiments, the reagent that differentially modifies methylated and unmethylated DNA comprises an enzyme that preferentially cleaves methylated DNA.

[0103] In some embodiments, the reagent that differentially modifies methylated and unmethylated DNA comprises an enzyme that preferentially cleaves unmethylated DNA.

[0104] In some embodiments, the material for detecting the SNP markers for predicting the risk of organ transplant rejection in the first aspect of the present invention further comprises primers and reagents for amplifying the DNA sequence of the SNP markers.

[0105] In some embodiments, the material for detecting cell-specific methylation markers for locating damaged organs or cell types in the first aspect of the present invention further comprises primers and reagents for amplifying the DNA sequence of the methylation markers.

[0106] In some embodiments, the amplifying is by polymerase chain reaction (PCR).

[0107] In some embodiments, the PCR is methylation-specific PCR.

[0108] In some embodiments, the amplification is nucleic acid sequence-specific amplification.

[0109] In some embodiments, the material for detecting the SNP markers for predicting the risk of organ transplant rejection in the first aspect of the present invention and the material for detecting the cell-specific methylation markers for locating damaged organs or cell types in the first aspect of the present invention further comprise a DNA extraction reagent.

[0110] In some embodiments, the product comprises at least one of a kit, a chip, a device, and a system.

[0111] In some embodiments, the product is used for any one of a1)-a4):

[0112] a1) Prediction of risk of organ transplant rejection;

[0113] a2) Prediction and location of organ transplant rejection risk;

[0114] a3) Immunosuppressive drug toxicity monitoring;

[0115] a4) Monitoring of complications and injuries after organ transplantation.

[0116] In some embodiments, the organ transplantation described in a1) and a2) comprises at least one of single organ transplantation, multi-organ transplantation, and relative transplantation; further, it is single organ transplantation.

[0117] In some embodiments, the prediction and localization of organ transplant rejection risk described in a2) refers to assessing the risk of organ transplant rejection and locating the rejected organ and / or damaged cell type.

[0118] In some embodiments, the test sample of the product comes from a biological sample of the subject to be tested; further comes from free DNA in the biological sample of the subject to be tested.

[0119] In some embodiments, the biological sample comprises at least one of body fluid, tissue, cell, and excrement; further is body fluid.

[0120] In some embodiments, the subject to be detected, body fluid, tissue, cell, or excrement is the subject to be detected, body fluid, tissue, cell, or excrement in the first aspect of the present invention.

[0121] The third aspect of the present invention provides the use of a substance for detecting the marker combination of the first aspect of the present invention in the preparation of a product, wherein the product is used for any one of a1) to a4):

[0122] a1) Prediction of risk of organ transplant rejection;

[0123] a2) Prediction and location of organ transplant rejection risk;

[0124] a3) Immunosuppressive drug toxicity monitoring;

[0125] a4) Monitoring of complications and injuries after organ transplantation.

[0126] In some embodiments, the product is the product in the second aspect of the present invention.

[0127] A fourth aspect of the present invention provides:

[0128] An apparatus or system comprising:

[0129] Detection module: used for detecting the SNP markers in the first aspect of the present invention in free DNA of a recipient after organ transplantation; and

[0130] Calculation module: used to calculate the content of donor-derived cell-free DNA (ddcfDNA) in the recipient free DNA; and

[0131] Analysis module: compares ddcfDNA with reference thresholds to determine whether organ transplant rejection exists;

[0132] The device or system is used in a1) in the first aspect of the present invention;

[0133] An apparatus or system comprising:

[0134] Detection module: used for detecting the SNP markers and methylation markers in the first aspect of the present invention in free DNA of a recipient after organ transplantation; and

[0135] Calculation module: used to calculate the content of donor-derived cell-free DNA (ddcfDNA) and the absolute amount of cell-derived cfDNA in the recipient free DNA;

[0136] and

[0137] Analysis module: comparing the absolute amounts of ddcfDNA and cfDNA derived from the cells with respective reference thresholds to determine whether there is organ transplant rejection and locate the damaged organ or cell type;

[0138] The device or system is used in any one of a2) to a4) in the first aspect of the present invention.

[0139] In some embodiments, the acceptor cell-free DNA comprises donor-derived cell-free DNA and acceptor-derived cell-free DNA.

[0140] In some embodiments, the recipient cell-free DNA comes from at least one of the body fluids, tissues, cells, and excreta of the transplant recipient; further, body fluids.

[0141] In some embodiments, the body fluid, tissue, cell, or excrement is the body fluid, tissue, cell, or excrement in the first aspect of the present invention.

[0142] In some embodiments, the transplant recipient is an allogeneic transplant recipient.

[0143] In some embodiments, the transplant recipient comprises a mammal, such as a human, a non-human primate (e.g., a gorilla, ape), a rodent (e.g., a rat, a mouse, a guinea pig), a pet (e.g., a cat, a dog), a livestock (e.g., a horse, a cow, a sheep, a pig, a rabbit); further a human.

[0144] In some embodiments, the method for detecting SNP markers and / or methylation markers in the first aspect of the present invention in cell-free DNA of a recipient after organ transplantation comprises: performing methylation sequencing on the cell-free DNA of the recipient.

[0145] In some embodiments, the methylation sequencing comprises at least one of whole genome sequencing and targeted enrichment sequencing.

[0146] In some embodiments, the targeted enrichment sequencing (ie, specific enrichment, capture and sequencing of a target region where a specific methylation marker is located) comprises at least one of targeted capture sequencing and multiplex amplicon sequencing.

[0147] In some embodiments, the methylation sequencing comprises at least one of WGBS (Whole Genome Bisulfite Sequencing), EM-seq (Enzymatic Methyl-sequencing), and GM-seq (the Next Generation of Methylome Analysis).

[0148] In some embodiments, the SNP markers and / or methylation markers in the first aspect of the present invention for detecting recipient free DNA after organ transplantation are performed by using the product of the second aspect of the present invention.

[0149] In some embodiments, the content of donor-derived cell-free DNA (ddcfDNA) in the recipient free DNA is the average value of the ratio of the sequencing depth of low-depth bases at the effective SNP site to the total sequencing depth of the site.

[0150] In some embodiments, the method for determining the SNP site of the effective site is as follows: if the ratio of the sequencing depth of the low-depth bases of the SNP site to the total sequencing depth of the site is (0, 0.2; excluding 0 and 0.2), then it is a SNP site of a valid site; otherwise, it is a SNP site of an invalid site.

[0151] In some embodiments, the method for calculating the absolute amount of the cell-derived cfDNA refers to patent document CN118280448A.

[0152] In some embodiments, the absolute amount of cfDNA derived from the cell = the number of positive signal fragments / the number of all fragments, wherein the number of all fragments is the "count of all fragments mapped to the marker genomic region"; the number of positive signal fragments is the "count of fragments with a positive signal pattern".

[0153] In some embodiments, the positive signal refers to a methylation region containing at least n methylated CpG sites, n≤all CpG sites in the methylation region. In some embodiments, for a hypermethylated marker, when all CpG sites in the methylation region corresponding to the fragment are methylated, the fragment is a fragment with a positive signal pattern; for a hypomethylated marker, when all CpG sites in the methylation region corresponding to the fragment are unmethylated, the fragment is a fragment with a positive signal pattern.

[0154] In some embodiments, the method for classifying hypermethylation markers / hypomethylation markers refers to patent document CN118280448A.

[0155] The classification method of hypermethylation markers / hypomethylation markers is as follows:

[0156] Based on the obtained methylation data of the sample to be tested, a region including more than two CpG sites and a Pearson correlation coefficient between any two CpG sites greater than a preset value is divided into a methylation region, and the beta values ​​of all CpG sites in the methylation region are calculated as the methylation level of the methylation region; and

[0157] When the first quantile of the methylation level of a methylation region of the sample to be tested is ≥ the high methylation threshold, the methylation region is judged to be a high methylation marker; and / or, when the second quantile of the methylation level of a methylation region of the sample to be tested is ≤ the low methylation threshold, the methylation region is judged to be a low methylation marker, wherein the high methylation threshold is selected from the range of 0.5-1, and the low methylation threshold is selected from the range of 0-0.4, wherein the first quantile is 0 to 50 percentile, and the second quantile is 50 to 100 percentile.

[0158] In some embodiments, the reference threshold value of the ddcfDNA is 0.1%-1% (for example, it can be 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, 1%, etc.); further, it is 0.4%-0.6%; further, it is 0.5%.

[0159] In some embodiments, the reference threshold value of the absolute amount of the cell-derived cfDNA is defined based on the N quantile of a control sample (preferably body fluids, tissues, cells, or excrement of a healthy person), where N is 90%-98%.

[0160] In some embodiments, the N is 94%-96%; further 95%.

[0161] In some embodiments, when the ddcfDNA is greater than its reference threshold, organ transplant rejection is present.

[0162] In some embodiments, when the absolute amount of the cell-derived cfDNA is greater than its reference threshold, organ or cell damage is present.

[0163] A fifth aspect of the present invention provides a method comprising the following steps:

[0164] The SNP marker of the first aspect of the present invention for detecting free DNA of a recipient after organ transplantation; and

[0165] Calculating the content of donor-derived cell-free DNA (ddcfDNA) in the recipient free DNA; and

[0166] Analysis module: compares ddcfDNA with reference thresholds to determine whether organ transplant rejection exists;

[0167] The method is used in a1) of the first aspect of the present invention;

[0168] A method comprising the following steps:

[0169] Detection of the SNP markers and methylation markers in the first aspect of the present invention in cell-free DNA of a recipient after organ transplantation; and

[0170] Calculating the content of donor-derived cell-free DNA (ddcfDNA) and the absolute amount of cell-derived cfDNA in the recipient free DNA; and

[0171] Comparing the absolute amounts of ddcfDNA and cfDNA derived from the cells with respective reference thresholds to determine whether there is organ transplant rejection and to locate the damaged organ or cell type;

[0172] The method is used in any one of a2) to a4) in the first aspect of the present invention.

[0173] In some embodiments, the receptor free DNA is the receptor free DNA in the fourth aspect of the present invention.

[0174] In some embodiments, the method for detecting methylation markers in cell-free DNA of a recipient after organ transplantation in the first aspect of the present invention is the method for detecting methylation markers in cell-free DNA of a recipient after organ transplantation in the first aspect of the present invention in the fourth aspect of the present invention.

[0175] In some embodiments, the content of donor-derived cfDNA in the recipient free DNA is the content of donor-derived cfDNA in the recipient free DNA in the fourth aspect of the present invention.

[0176] In some embodiments, the method for calculating the absolute amount of cell-derived cfDNA is the method for calculating the absolute amount of cell-derived cfDNA in the fourth aspect of the present invention.

[0177] In some embodiments, the reference threshold value of ddcfDNA is the reference threshold value of ddcfDNA in the fourth aspect of the present invention.

[0178] In some embodiments, the reference threshold value of the absolute amount of cell-derived cfDNA is the reference threshold value of the absolute amount of cell-derived cfDNA in the fourth aspect of the present invention.

[0179] In some embodiments, when the ddcfDNA is greater than its reference threshold, organ transplant rejection is present.

[0180] In some embodiments, when the absolute amount of the cell-derived cfDNA is greater than its reference threshold, organ or cell damage is present.

[0181] The beneficial effects of the present invention are:

[0182] The present invention provides a marker combination, which comprises: a SNP marker for predicting the risk of organ transplant rejection, wherein the SNP marker can be used to predict the risk of organ transplant rejection.

[0183] Furthermore, the marker combination also includes: cell-specific methylation markers for locating damaged organs or cell types; this marker combination does not rely on transplant donor information, and can be completed with only a single sample of transplant recipient plasma testing, and makes up for the defect that ddcfDNA cannot accurately locate damaged cell types. Combined with ddcfDNA quantification, it can better predict the risk of cell rejection, which is beneficial to the diagnosis and treatment of patients. DETAILED DESCRIPTION

[0184] The present invention is further described in detail below through specific examples.

[0185] It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0186] The experimental methods in the following examples without specifying specific conditions are usually carried out under conventional conditions or under conditions recommended by the manufacturer. The materials and reagents used in the examples are commercially available unless otherwise specified. The manufacturers of the reagents used are indicated, and similar products from other manufacturers are substitutes.

[0187] In the present invention, the sequence numbers of all methylation markers refer to hg19.

[0188] Example 1 Assessing the risk of graft rejection after kidney transplantation

[0189] In kidney transplantation, the transplanted kidney is placed in the iliac fossa on one side instead of the original location of the kidney, so the original kidneys of most kidney transplant patients can be retained. SNP-based ddcfDNA represents the cfDNA content from the donor kidney, and the renal epithelium and renal endothelium quantification based on cfDNA methylation traceability represents the cfDNA content from the donor kidney and the recipient kidney. When rejection occurs, the donor kidney will release more cfDNA from the donor, and both the renal epithelium and the renal endothelium may be damaged. Therefore, when ddcfDNA is positive and the renal epithelium or renal endothelium is positive, it is considered to be rejection damage; when rejection does not occur but there is renal damage caused by infection or other factors, the ddcfDNA content does not reach the threshold of rejection damage and is negative. BK virus infection, which is common after kidney transplantation, can cause renal tubular epithelial cell damage; therefore, when ddcfDNA is negative but the renal epithelium is positive, blood BK virus load detection can be combined to consider whether it may be BK virus infection.

[0190] A retrospective cohort was constructed based on patients who received only kidney transplantation: peripheral blood samples were collected from 26 patients after kidney transplantation, and plasma was separated and DNA molecules were extracted, and then methylation sequencing (targeted bisulfite sequencing) was performed, as follows: ① 10-20mL of peripheral blood samples were collected in disposable free DNA storage tubes, stored and transported at 6-37℃, and sent to the testing laboratory within 72h after blood collection; ② Peripheral blood was separated to obtain plasma; then, free DNA (cfDNA) was extracted from plasma samples using magnetic beads; ③ cfDNA was treated with bisulfite (unmethylated cytosine C was converted to uracil U), and then PCR amplification (uracil U was converted to thymine T); ④ Then, SNP markers and probes corresponding to renal epithelial and renal endothelial methylation markers were added for hybridization capture; ⑤ Finally, sequencing was performed on the machine. Based on SNP markers and cell-specific methylation markers, each sample was analyzed to obtain quantitative results of ddcfDNA and cfDNA derived from kidney-related cells, and compared with reference thresholds.

[0191] Among them, SNP markers were screened by the following methods: 1) downloading SNP sets from the 1000 Genomes Database; 2) selecting SNP information of East Asian populations; 3) retaining SNP sites with high polymorphism in East Asian populations; 4) removing SNP sites that are easily converted by methylation sequencing. The specific SNP markers are: rs4076816, rs7533583, rs16835469, rs12075194, rs3134613, rs2780948, rs8179495, rs642307, rs12408340, rs58290373, rs595289, rs12092943, rs11261296, rs12561 761,rs10926011,rs1494121,rs2463458,rs1545256,rs759436,rs2540217,rs17030203,rs1214780,rs35414849,rs76469070,rs12712079,rs34986 597,rs4663094,rs7563574,rs4668025,rs2368423,rs11894064,rs10203123,rs719418,rs2323142,rs7623055,rs417123,rs4858477,rs6796757,r s4074363,rs9790198,rs2872373,rs4856379,rs2131110,rs1108898,rs7648579,rs6773068,rs997369,rs4311171,rs1079377,rs7660793,rs46310 56,rs1565572,rs11099544,rs10014477,rs7682218,rs11132083,rs2135075,rs12697263,rs983061,rs6877675,rs4835850,rs1864974,rs6891770 ,rs4868700,rs7741539,rs1358869,rs11751412,rs2274588,rs4714795,rs4714890,rs9400993,rs9388388,rs2160621,rs4147298,rs11771651,rs 11762979,rs691130,rs2465910,rs8192865,rs2536088,rs6990513,rs7825970,rs3888293,rs10095135,rs2926426,rs16916,rs7825817,rs722084,rs3102478,rs2447962,rs4736684,rs6988568,rs6476874,rs2820957,rs4451372,rs6597555,rs10905916,rs10795948,rs2804831,rs35544747,rs11817790,rs16919890,rs1404504,rs566778,rs11211902,rs11214261,rs948091,rs7963653,rs4761932,rs115314238,rs10778158,rs9510760,rs73479322,rs7981226,rs4771948,rs11627187,rs12050132,rs1676243,rs654527,rs1706814,rs2469558,rs4778836,rs2447285,rs7402264,rs7171883,rs4777774,rs6496016,rs350249,rs3093322,rs4785466,rs7197885,rs2161741,rs16960333,rs4565,rs301138,rs12931391,rs3751797,rs1111434,rs1879488,rs3752963,rs12451747,rs11654444,rs9899645,rs2332598,rs2592208,rs206435,rs1196944,rs4359513,rs6510724,rs8109003,rs11667570,rs678401,rs6132116,rs6046115,rs208815,rs4811260,rs6099870,rs6100135,rs2294992,rs76747837,rs2017582,rs1867353,rs5752495,rs5765703,rs5769888,rs874881,rs492594,rs4865615,rs2071888,rs4288409,rs2509943,rs936019,rs4940019,rs475002,rs3829655,rs236152,rs804133,rs553253,rs12063251,rs7547176,rs3815228,rs3126020,rs6676020,rs284174,rs2806878,rs6427392,rs1034463,rs544013,rs28768068,rs296553,rs12023412,rs10779598,rs12029576,rs10915694,rs2007619,rs6679209,rs4658857,rs34097650,rs72785267,rs887843,rs12476837,rs1176785,rs3731611,rs3768699,rs4954799,rs16836609,rs788164,rs4972845,rs6724378,rs421636,rs16867057,rs10933305,rs10184738,rs55761135,rs842263,rs2076993,rs1506691,rs17024184,rs17027453,rs652889,rs869364,rs56286049,rs6766396,rs1156884,rs6438720,rs9860731,rs7624529,rs6768608,rs522838,rs6444036,rs13090240,rs11917154,rs1317285,rs10212990,rs9306942,rs28433701,rs298471,rs6855202,rs7684755,rs7721079,rs10053782,rs32274,rs11133784,rs16903723,rs4702090,rs7718456,rs10805605,rs6859125,rs271243,rs288873,rs4704126,rs171661,rs7703598,rs1540574,rs11743913,rs4976242,rs262716,rs31736,rs1484802,rs7722055,rs1345733,rs4544835,rs17081213,rs2282821,rs6458232,rs10948707,rs4706552,rs1231499,rs1029321,rs9489314,rs7758642,rs6931683,rs6917940,rs9648612,rs16874238,rs2357843,rs77138421,rs7782130,rs650974,rs1428069,rs6976507,rs6962291,rs2373814,rs1016103,rs34401170,rs1033345,rs2294064,rs3020245,rs6468360,rs10503926,rs10958803,rs4737263,rs12550039,rs4733790,rs58858645,rs4909628,rs4909472,rs2297081,rs7847789,rs7033226,rs2281729,rs10976440,rs7037899,rs3860991,rs10781099,rs10867213,rs11139648,rs17144411,rs2208892,rs12217971,rs10826350,rs10829015,rs7907500,rs10763696,rs2437934,rs2587471,rs655489,rs4919104,rs180703,rs2421125,rs4482024,rs41534749,rs7107350,rs493442,rs10898294,rs61908115,rs11063380,rs1894331,rs10772826,rs36112053,rs7971974,rs11174450,rs1904568,rs1908667,rs4554966,rs7295786,rs10745586,rs1609944,rs9567762,rs9563201,rs9569578,rs9544749,rs9546531,rs1328833,rs157028,rs73264730,rs1884621,rs61607981,rs2356535,rs17116374,rs2798386,rs4900251,rs4377134,rs35798799,rs8032702,rs8030155,rs17236509,rs62025547,rs7183553,rs4781403,rs6498312,rs1794311,rs2819693,rs34652009,rs73550818,rs2526057,rs12444602,rs820808,rs1870924,rs4843188,rs9905366,rs1981659,rs7222971,rs9891244,rs2013788,rs12327412,rs1401461,rs9947527,rs2042714,rs10439060,rs12968605,rs1458223,rs8183455,rs6072981,rs195022,rs2070579,rs2105406,rs12466272,rs77559470,rs2336049,rs6550023,rs2660783,rs517255,rs9689772,rs968371,rs10962874,rs9406647,rs10116779,rs11200732,rs12423490,rs7203147,rs3883321,rs4797088,rs9864927,and rs6451159;

[0192] The renal epithelial cell-specific methylation markers are 20 markers: chr13:76871766-76871871, chr22:32841838-32841950, chr5:130707029-130707674, chr10:117857749-117857831, chr10:45261838-45261910, chr16:72038677-72039042, chr16:87693751-87693831, chr18 :42290985-42291186, chr8:144017883-144018008, chr2:127895700-127895954, chr2:240723076-240723297, chr4 :1534879-1535047, chr4:129735232-129735504, chr5:115783394-115783531, chr17:32956139-32956404, chr19:8 064907-8065003, chr19:13879658-13880041, chr19:31734430-31735122, chr21:46848068-46848236 and chr8:1132771-1132948; the renal endothelial cell-specific methylation markers are 11 markers chr1:45378247-45378362, chr13:114457850-114457938, chr14:64394967-6 4395037, chr8:1284044-1284109, chr16:1591461-1591493, chr22:47566542-47566650, chr1:117074709-117074799, chr1:117074875-117074939, chr7:2853725-2853755, chr21:33396275-33396613 and chr10:22888747-22888997. The probe design method includes but is not limited to tiled, exhaustive and shingled, and probes are designed for the positive / negative strands of the reference genome and the methylation / unmethylation status of the CpG sites.

[0193] The calculation and analysis method of ddcfDNA is as follows: ① For each SNP site: calculate the ratio of the SNP site (sequencing depth of low-depth bases / total sequencing depth of the site). If the ratio is (0, 0.2; excluding 0 and 0.2), the site is a valid quantitative site, that is, the donor and recipient are homozygous and inconsistent or the donor is homozygous and the recipient is heterozygous. At this time, the low-depth base is considered to be derived from the donor; if the ratio is greater than or equal to 0.2, it is an invalid quantitative site; ② Select all SNPs that are judged to be valid sites. P site, calculate the average value of the ratio of the sequencing depth of the low-depth base of the effective site to the total sequencing depth of the site, which is ddcfDNA; ③ compare the ddcfDNA with the reference threshold (according to the actual situation, the reference threshold of ddcfDNA can be set to 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, 1%, etc., and the reference threshold of this embodiment is set to 1%.) to determine whether there is transplant rejection in the transplant recipient (if it exceeds the reference threshold, it is judged as positive "+", otherwise it is judged as negative "-"); the absolute amount of cfDNA derived from kidney-related cells is calculated with reference to patent document CN118280448A (absolute amount of cfDNA of renal epithelial cells = number of positive signal fragments / number of all fragments; absolute amount of cfDNA of renal endothelial cells = number of positive signal fragments / number of all fragments; markers of renal epithelial cells and renal endothelial cells are all low methylation markers The number of all fragments is "the count of all fragments mapped to the marker genomic region"; the number of positive signal fragments is "the count of fragments with positive signal patterns"; and all CpG sites on a fragment are completely unmethylated, that is, they have positive signal patterns); the reference threshold of cfDNA derived from kidney-related cells can be defined using the N quantile of the control group samples, where N can be 90%, 95%, 98%, etc. In this embodiment, the reference threshold of the absolute amount of cfDNA derived from kidney-related cells is based on the 95% quantile of healthy plasma samples (select healthy people over 18 years old without major diseases, serious underlying diseases or chronic diseases, chronic viral infections, family genetic histories and family genetic diseases) (i.e., the 95th percentile, taking the amount of cfDNA derived from renal epithelial cells as an example: each healthy plasma sample can be calculated to obtain a value, and then sorted in order from small to large, and the value ranked at the 95th% is the 95th percentile).

[0194] Combined with the clinical pathological diagnosis results, the 26 renal transplant patients were divided into three groups: 11 rejection group samples, 7 infection injury group samples (patients without transplant rejection but with infection injury), and 8 stable group samples (patients without transplant rejection and no injury indications). The detailed results of each sample are shown in Table 1.

[0195] In this embodiment, when ddcfDNA is positive and at least one of the renal epithelium or renal endothelium is positive, the rejection detection sensitivity is 82% (9 / 11) and the specificity is 93% (14 / 15), and the rejection damage can be further located based on the cfDNA quantification of the renal epithelium and renal endothelium. When ddcfDNA is negative, the sensitivity of judging infection damage by at least one of the renal epithelium or renal endothelium is 67% (4 / 6) and the specificity is 78% (7 / 9). Since BK virus infection can cause renal tubular epithelial damage, when only the renal epithelium is positive, the presence of BKV infection can be further determined in combination with the blood BK virus load.

[0196] Overall, by combining the methylation quantification of ddcfDNA, renal epithelium and renal endothelium (a general term for glomerular endothelium and tubular endothelium), it is possible to better predict whether the transplant recipient will have a transplant rejection reaction. At the same time, the use of cell-specific methylation markers can also locate the damaged cell type, which helps assist doctors in making more refined treatment plans.

[0197] Table 1 ddcfDNA and methylation quantitative results of kidney transplant samples

[0198]

[0199]

[0200] Note: Positive "+" indicates positive, i.e., exceeding the reference threshold; "-" indicates negative, i.e., not exceeding the reference threshold.

[0201] Example 2 Assessment of the risk of graft rejection after lung transplantation

[0202] A retrospective cohort was constructed based on patients who received only lung transplantation: peripheral blood samples were collected from 10 patients after lung transplantation, and plasma was separated and DNA molecules were extracted, and then methylation sequencing (targeted bisulfite sequencing) was performed as follows: ① 10-20 mL of peripheral blood samples were collected in disposable free DNA storage tubes, stored and transported at 6-37°C, and sent to the testing laboratory within 72 hours after blood collection; ② The peripheral blood was separated to obtain plasma; then, free DNA (cfDNA) was extracted from the plasma samples using the magnetic bead method; ③ The cfDNA was treated with bisulfite (unmethylated cytosine C was converted to uracil U), and then PCR amplification was performed (uracil U was converted to thymine T); ④ Then, SNP markers and probes corresponding to methylation markers of alveolar epithelium and bronchial epithelium were added for hybridization capture; ⑤ Finally, sequencing was performed on the machine. Based on SNP markers and cell-specific methylation markers, each sample was analyzed to obtain quantitative results of ddcfDNA, alveolar epithelial cells, and cfDNA derived from lung bronchial epithelial cells, and compared with reference thresholds.

[0203] Among them, SNP markers were screened by the following methods: 1) downloading SNP sets from the 1000 Genomes Database; 2) selecting SNP information of East Asian populations; 3) retaining SNP sites with high polymorphism in East Asian populations; 4) removing SNP sites that are easily converted by methylation sequencing. The specific SNP markers are: rs4076816, rs7533583, rs16835469, rs12075194, rs3134613, rs2780948, rs8179495, rs642307, rs12408340, rs58290373, rs595289, rs12092943, rs11261296, rs12561 761,rs10926011,rs1494121,rs2463458,rs1545256,rs759436,rs2540217,rs17030203,rs1214780,rs35414849,rs76469070,rs12712079,rs34986 597,rs4663094,rs7563574,rs4668025,rs2368423,rs11894064,rs10203123,rs719418,rs2323142,rs7623055,rs417123,rs4858477,rs6796757,r s4074363,rs9790198,rs2872373,rs4856379,rs2131110,rs1108898,rs7648579,rs6773068,rs997369,rs4311171,rs1079377,rs7660793,rs46310 56,rs1565572,rs11099544,rs10014477,rs7682218,rs11132083,rs2135075,rs12697263,rs983061,rs6877675,rs4835850,rs1864974,rs6891770 ,rs4868700,rs7741539,rs1358869,rs11751412,rs2274588,rs4714795,rs4714890,rs9400993,rs9388388,rs2160621,rs4147298,rs11771651,rs 11762979,rs691130,rs2465910,rs8192865,rs2536088,rs6990513,rs7825970,rs3888293,rs10095135,rs2926426,rs16916,rs7825817,rs722084,rs3102478,rs2447962,rs4736684,rs6988568,rs6476874,rs2820957,rs4451372,rs6597555,rs10905916,rs10795948,rs2804831,rs35544747,rs11817790,rs16919890,rs1404504,rs566778,rs11211902,rs11214261,rs948091,rs7963653,rs4761932,rs115314238,rs10778158,rs9510760,rs73479322,rs7981226,rs4771948,rs11627187,rs12050132,rs1676243,rs654527,rs1706814,rs2469558,rs4778836,rs2447285,rs7402264,rs7171883,rs4777774,rs6496016,rs350249,rs3093322,rs4785466,rs7197885,rs2161741,rs16960333,rs4565,rs301138,rs12931391,rs3751797,rs1111434,rs1879488,rs3752963,rs12451747,rs11654444,rs9899645,rs2332598,rs2592208,rs206435,rs1196944,rs4359513,rs6510724,rs8109003,rs11667570,rs678401,rs6132116,rs6046115,rs208815,rs4811260,rs6099870,rs6100135,rs2294992,rs76747837,rs2017582,rs1867353,rs5752495,rs5765703,rs5769888,rs874881,rs492594,rs4865615,rs2071888,rs4288409,rs2509943,rs936019,rs4940019,rs475002,rs3829655,rs236152,rs804133,rs553253,rs12063251,rs7547176,rs3815228,rs3126020,rs6676020,rs284174,rs2806878,rs6427392,rs1034463,rs544013,rs28768068,rs296553,rs12023412,rs10779598,rs12029576,rs10915694,rs2007619,rs6679209,rs4658857,rs34097650,rs72785267,rs887843,rs12476837,rs1176785,rs3731611,rs3768699,rs4954799,rs16836609,rs788164,rs4972845,rs6724378,rs421636,rs16867057,rs10933305,rs10184738,rs55761135,rs842263,rs2076993,rs1506691,rs17024184,rs17027453,rs652889,rs869364,rs56286049,rs6766396,rs1156884,rs6438720,rs9860731,rs7624529,rs6768608,rs522838,rs6444036,rs13090240,rs11917154,rs1317285,rs10212990,rs9306942,rs28433701,rs298471,rs6855202,rs7684755,rs7721079,rs10053782,rs32274,rs11133784,rs16903723,rs4702090,rs7718456,rs10805605,rs6859125,rs271243,rs288873,rs4704126,rs171661,rs7703598,rs1540574,rs11743913,rs4976242,rs262716,rs31736,rs1484802,rs7722055,rs1345733,rs4544835,rs17081213,rs2282821,rs6458232,rs10948707,rs4706552,rs1231499,rs1029321,rs9489314,rs7758642,rs6931683,rs6917940,rs9648612,rs16874238,rs2357843,rs77138421,rs7782130,rs650974,rs1428069,rs6976507,rs6962291,rs2373814,rs1016103,rs34401170,rs1033345,rs2294064,rs3020245,rs6468360,rs10503926,rs10958803,rs4737263,rs12550039,rs4733790,rs58858645,rs4909628,rs4909472,rs2297081,rs7847789,rs7033226,rs2281729,rs10976440,rs7037899,rs3860991,rs10781099,rs10867213,rs11139648,rs17144411,rs2208892,rs12217971,rs10826350,rs10829015,rs7907500,rs10763696,rs2437934,rs2587471,rs655489,rs4919104,rs180703,rs2421125,rs4482024,rs41534749,rs7107350,rs493442,rs10898294,rs61908115,rs11063380,rs1894331,rs10772826,rs36112053,rs7971974,rs11174450,rs1904568,rs1908667,rs4554966,rs7295786,rs10745586,rs1609944,rs9567762,rs9563201,rs9569578,rs9544749,rs9546531,rs1328833,rs157028,rs73264730,rs1884621,rs61607981,rs2356535,rs17116374,rs2798386,rs4900251,rs4377134,rs35798799,rs8032702,rs8030155,rs17236509,rs62025547,rs7183553,rs4781403,rs6498312,rs1794311,rs2819693,rs34652009,rs73550818,rs2526057,rs12444602,rs820808,rs1870924,rs4843188,rs9905366,rs1981659,rs7222971,rs9891244,rs2013788,rs12327412,rs1401461,rs9947527,rs2042714,rs10439060,rs12968605,rs1458223,rs8183455,rs6072981,rs195022,rs2070579,rs2105406,rs12466272,rs77559470,rs2336049,rs6550023,rs2660783,rs517255,rs9689772,rs968371,rs10962874,rs9406647,rs10116779,rs11200732,rs12423490,rs7203147,rs3883321,rs4797088,rs9864927,and rs6451159;

[0204] The methylation markers of alveolar epithelium are 25 markers: chr4:100865925-100866026, chr12:102276167-102276184, chr12:102298280-102298321, chr9:102907198-102907558, chr10:103549045-103549212, chr10:103805577-1 03805618、chr1:104080755-104081097、chr6:108559696-108559896、chr1:109965889-10996601 0. chr12:111024162-111024235, chr12:112082519-112082664, chr12:112402450-112402639, ch r2:112627023-112627069, chr13:113347960-113348198, chr16:11398225-11398438, chr10:114 129859-114129929, chr10:115950269-115950333, chr11:117029637-117029651, chr16:1198920 The combination of 2-11989519, chr16:12011174-12011244, chr12:120369895-120370135, chr7:120959369-120959437, chr12:122950462-122950678, chr1:12525734-12525958, chr10:126161887-126162253;The methylation markers of lung bronchial epithelium are 25 markers: chr2:100026253-100026342, chr12:102240433-102240450, chr19:10271710-10271765, chr13:105669421-105669509, chr11:107666587-107666748, chr8:108269329-108269378, ch r2:109130778-109130902, chr3:112308465-112308621, chr6:113335181-113335340, chr10:11408410 0-114084313, chr3:11454735-11454953, chr12:117153241-117153639, chr18:11942307-11942409, ch r11:120078015-120078074, chr3:122875162-122875295, chr8:123097187-123097279, chr4:124427917-124428037, chr4:124428157-124428276, chr10:12495429-12495664, c hr8:126844893-126845111, chr9:130262034-130262136, chr5:134124045-134124535, chr7:140615525-140615745, chr16:14140076-14140132, chr10:15164370-15164566. Probe design methods include but are not limited to tiling, exhaustive and shingled, and probes are designed for the positive / negative strands of the reference genome and the methylation / unmethylation status of the CpG sites. ;

[0205] The calculation and analysis method of ddcfDNA is as follows: ① For each SNP site: calculate the ratio of the SNP site (sequencing depth of low-depth bases / total sequencing depth of the site). If the ratio is (0, 0.2; excluding 0 and 0.2), the site is a valid quantitative site, that is, the donor and recipient are homozygous and inconsistent or the donor is homozygous and the recipient is heterozygous. At this time, the low-depth base is considered to be derived from the donor; if the ratio is greater than or equal to 0.2, it is an invalid quantitative site; ② Select all SNPs that are judged to be valid sites. P site, calculate the average value of the ratio of the sequencing depth of the low-depth bases of the effective site to the total sequencing depth of the site, which is ddcfDNA; ③ compare the ddcfDNA with the reference threshold (according to the actual situation, the reference threshold of ddcfDNA can be set to 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 0.6%, 0.7%, 0.8%, 0.9%, 1%, etc., and the reference threshold of this embodiment is set to 1%.) to determine whether there is transplant rejection in the transplant recipient (if it exceeds the reference threshold, it is judged as positive "+", otherwise it is judged as negative "-"); the absolute amount of cfDNA derived from lung-related cells is calculated by referring to patent document CN118280448A (absolute amount of cfDNA of alveolar epithelial cells = number of positive signal fragments / number of all fragments; absolute amount of cfDNA of lung bronchial epithelial cells = number of positive signal fragments / The number of all fragments; the markers of alveolar epithelial and bronchial epithelial cells are all low-methylation markers, and the number of all fragments is "the count of all fragments aligned to the genomic region of the marker"; the number of positive signal fragments is "the count of fragments with positive signal patterns"; and all CpG sites on a fragment are completely unmethylated, that is, they have positive signal patterns); the reference threshold of cfDNA derived from lung-related cells can be defined using the N quantile of the control group sample, where N can be 90%, 95%, 98%, etc. In this embodiment, the reference threshold of the absolute amount of cfDNA derived from lung-related cells is based on the 95% quantile of healthy plasma samples (select healthy people over 18 years old without major diseases, serious underlying diseases or chronic diseases, chronic viral infections, family genetic history and family genetic diseases) (i.e., the 95th percentile, taking the amount of cfDNA derived from alveolar epithelial cells as an example: each healthy plasma sample can be calculated to obtain a value, and then sorted in order from small to large, and the value ranked at the 95th% is the 95th percentile).

[0206] Combined with the clinical pathological diagnosis results, the 10 lung transplant patients were divided into three groups: 3 rejection group samples, 6 lung infection samples, and 1 stable group sample (patients without transplant rejection and no injury indications). The detailed results of each sample are shown in Table 2.

[0207] In this example, when ddcfDNA is positive, the sensitivity of rejection detection is 67% (2 / 3) and the specificity is 71% (5 / 7). The rejection damage can be further located according to the detection status of lung bronchial epithelium and alveolar epithelium. When ddcfDNA is negative, the detection of alveolar epithelium or lung bronchial epithelium indicates possible infection. When ddcfDNA, alveolar epithelial cell cfDNA, and lung bronchial epithelium are all negative, it indicates a stable state.

[0208] Table 2 ddcfDNA and methylation quantitative results of lung transplant samples

[0209] Sample No. Grouping ddcfDNA Alveolar epithelial cell cfDNA Lung bronchial epithelial cell cfDNA 1 Rejection + - + 2 Rejection + + - 3 Rejection - + - 4 Infect + - - 5 Infect + + + 6 Infect - - + 7 Infect - - + 8 Infect - + - 9 Infect - - + 10 Stablize - - -

[0210] Note: Positive "+" indicates positive, i.e., exceeding the reference threshold; "-" indicates negative, i.e., not exceeding the reference threshold.

[0211] The technical solution of the present invention is not limited to the above-mentioned specific embodiments. All technical variations made according to the technical solution of the present invention fall within the protection scope of the present invention.

Claims

1. A marker combination, comprising: a SNP marker for predicting the risk of organ transplant rejection, wherein the SNP marker comprises rs4076816, rs7533583, rs16835469, rs12075194, rs3134613, rs2780948, rs8179495, rs642307, rs12408340, rs58290373, rs595289, rs12092943, rs11261296, rs12561761, rs10926011, rs1494121, s2463458,rs1545256,rs759436,rs2540217,rs17030203,rs1214780,rs35414849,rs76469070,rs12712079,rs34986597,r s4663094,rs7563574,rs4668025,rs2368423,rs11894064,rs10203123,rs719418,rs2323142,rs7623055,rs417123,rs4858 477,rs6796757,rs4074363,rs9790198,rs2872373,rs4856379,rs2131110,rs1108898,rs7648579,rs6773068,rs997369,r s4311171,rs1079377,rs7660793,rs4631056,rs1565572,rs11099544,rs10014477,rs7682218,rs11132083,rs2135075,rs1 2697263,rs983061,rs6877675,rs4835850,rs1864974,rs6891770,rs4868700,rs7741539,rs1358869,rs11751412,rs22745 88,rs4714795,rs4714890,rs9400993,rs9388388,rs2160621,rs4147298,rs11771651,rs11762979,rs691130,rs2465910,r s8192865,rs2536088,rs6990513,rs7825970,rs3888293,rs10095135,rs2926426,rs16916, rs7825817,rs722084,rs3102478,rs2447962,rs4736684,rs6988568,rs6476874,rs2820957,rs4451372,rs6597555,rs10905916,rs10795948,rs2804831,rs35544747,rs11817790,rs16919890,rs1404504,rs566778,rs11211902,rs11214261,rs948091,rs7963653,rs4761932,rs115314238,rs10778158,rs9510760,rs73479322,rs7981226,rs4771948,rs11627187,rs12050132,rs1676243,rs654527,rs1706814,rs2469558,rs4778836,rs2447285,rs7402264,rs7171883,rs4777774,rs6496016,rs350249,rs3093322,rs4785466,rs7197885,rs2161741,rs16960333,rs4565,rs301138,rs12931391,rs3751797,rs1111434,rs1879488,rs3752963,rs12451747,rs11654444,rs9899645,rs2332598,rs2592208,rs206435,rs1196944,rs4359513,rs6510724,rs8109003,rs11667570,rs678401,rs6132116,rs6046115,rs208815,rs4811260,rs6099870,rs6100135,rs2294992,rs76747837,rs2017582,rs1867353,rs5752495,rs5765703,rs5769888,rs874881,rs492594,rs4865615,rs2071888,rs4288409,rs2509943,rs936019,rs4940019,rs475002,rs3829655,rs236152,rs804133,rs553253,rs12063251,rs7547176,rs3815228,rs3126020,r s6676020,rs284174,rs2806878,rs6427392,rs1034463,rs544013,rs28768068,rs296553,rs12023412,rs10779598,rs12029576,rs10915694,rs2007619,rs6679209,rs4658857,rs34097650,rs72785267,rs887843,rs12476837,rs1176785,rs3731611,rs3768699,rs4954799,rs16836609,rs788164,rs4972845,rs6724378,rs421636,rs16867057,rs10933305,rs10184738,rs55761135,rs842263,rs2076993,rs1506691,rs17024184,rs17027453,rs652889,rs869364,rs56286049,rs6766396,rs1156884,rs6438720,rs9860731,rs7624529,rs6768608,rs522838,rs6444036,rs13090240,rs11917154,rs1317285,rs10212990,rs9306942,rs28433701,rs298471,rs6855202,rs7684755,rs7721079,rs10053782,rs32274,rs11133784,rs16903723,rs4702090,rs7718456,rs10805605,rs6859125,rs271243,rs288873,rs4704126,rs171661,rs7703598,r s1540574,rs11743913,rs4976242,rs262716,rs31736,rs1484802,rs7722055,rs1345733,rs4544835,rs17081213,rs2282821,rs6458232,rs10948707,rs4706552,rs1231499,rs1029321,rs9489314,rs7758642,rs6931683,rs6917940,rs9648612,rs16874238,rs2357843,rs77138421,rs7782130,rs650974,rs1428069,rs6976507,rs6962291,rs2373814,rs1016103,rs34401170,rs1033345,rs2294064,rs3020245,rs6468360,rs10503926,rs10958803,rs4737263,rs12550039,rs4733790,rs58858645,rs4909628,rs4909472,rs2297081,rs7847789,rs7033226,rs2281729,rs10976440,rs7037899,rs3860991,rs10781099,rs10867213,rs11139648,rs17144411,rs2208892,rs12217971,rs10826350,rs10829015,rs7907500,rs10763696,rs2437934,rs2587471,rs655489,rs4919104,rs180703,rs2421125,rs4482024,rs41534749,rs7107350,rs493442,rs10898294,rs61908115,rs11063380,rs1894331,rs10772826,rs36112053,rs7971974,rs11174450,rs1904568,rs1908667,rs4554966,rs7295786,rs10745586,rs1609944,rs9567762,rs9563201,rs9569578,rs9544749,rs9546531,rs1328833,rs157028,rs73264730,rs1884621,rs61607981,rs2356535,rs17116374,rs2798386,rs4900251,rs4377134,rs35798799,rs8032702,rs8030155,rs17236509,rs62025547,rs7183553,rs4781403,rs6498312,rs1794311,rs2819693,rs34652009,rs73550818,rs2526057,rs12444602,rs820808,rs1870924,rs4843188,rs9905366,rs1981659,rs7222971,rs9891244,rs2013788,rs12327412,rs1401461,rs9947527,rs2042714,rs10439060,rs12968605,rs1458223,rs8183455,rs6072981,rs195022,rs2070579,rs2105406,rs12466272,rs77559470,rs2336049,rs6550023,rs2660783,rs517255,rs9689772,rs968371,rs10962874,rs9406647,rs10116779,rs11200732,rs12423490,rs7203147,rs3883321,rs4797088,rs9864927,and rs6451159. , 2. The marker combination according to claim 1, characterized in that: The marker combination further comprises: a cell-specific methylation marker for locating damaged organs or cell types; Preferably, the screening method for cell-specific methylation markers for locating damaged organs or cell types comprises: Stratify the sample; The first-level screening step includes grouping the samples into N groups according to the above-mentioned stratification, and screening the methylation markers between the N groups, which are the first-level methylation markers; The second-layer screening step includes grouping the samples into N groups according to the above stratification, and screening the methylation markers of different types of samples in each group, which are the second-layer methylation markers; Preferably, the stratification of samples comprises: A data acquisition step, comprising acquiring methylation modification data of the sample; A preprocessing step, comprising preprocessing the methylation modification data to obtain preprocessed samples of various types; The dimensionality reduction step includes performing dimensionality reduction on each type of preprocessed samples; The stratification step includes clustering the samples after dimensionality reduction and determining the optimal number of clusters to achieve stratification of the samples; Preferably, the first-level screening step comprises: A sample selection step, comprising obtaining N grouped samples for first-level methylation marker screening; a methylation region division step, wherein the methylation regions in which the correlation coefficient between any two adjacent CpG sites is greater than a preset value and the number of CpG sites is greater than a preset number are regarded as the same methylation region; The methylation marker screening step includes testing whether there is a significant statistical difference in each methylation region between N groups. If yes, the methylation levels of any two groups in the N groups are compared pairwise to determine whether there is a significant statistical difference in the methylation level of the methylation region between the two groups. If there is a significant statistical difference in the methylation level of a methylation region between a specific group sample and the other M group samples, the methylation region is determined to be a specific methylation marker for the specific group, where M is any natural number less than N. If not, the subsequent steps are not performed. Preferably, the second layer screening step is performed with reference to the first layer screening step; Preferably, the cell-specific methylation marker for locating the damaged organ or cell type comprises at least one of a renal epithelial cell-specific methylation marker, a renal endothelial cell-specific methylation marker, an alveolar epithelial methylation marker, and a lung bronchial epithelial methylation marker; Preferably, the renal epithelial cell-specific methylation markers include chr13:76871766-76871871, chr22:32841838-32841950, chr5:130707029-130707674, chr10:117857749-117857831, chr10:45261838-45261910, chr16:72038677-72039042, chr16:87693751-87693831, chr18:42290985-42291186, chr8:144017883-144018008, chr2:12789 5700-127895954, chr2:240723076-240723297, chr4:1534879-1535047, chr4:129735232-129735504, chr5:115783394-115783531, chr17:32956139-32956404, chr19:8064907-8065003, chr19:13879658-13880041, chr19:31734430-31735122, chr21:46848068-46848236 and chr8:1132771-1132948; Preferably, the renal endothelial cell-specific methylation markers include chr1:45378247-45378362, chr13:114457850-114457938, chr14:64394967-64395037, chr8:1284044-1284109, chr16:1591461-1591493, chr 22:47566542-47566650, chr1:117074709-117074799, chr1:117074875-117074939, chr7:2853725-2853755, chr21:33396275-33396613 and chr10:22888747-22888997; Preferably, the alveolar epithelial methylation markers include chr4:100865925-100866026, chr12:102276167-102276184, chr12:102298280-102298321, chr9:102907198-102907558, chr10:103549045-103549212, chr10:103805577-1 03805618、chr1:104080755-104081097、chr6:108559696-108559896、chr1:109965889-1099660 10. chr12:111024162-111024235, chr12:112082519-112082664, chr12:112402450-112402639, c hr2:112627023-112627069, chr13:113347960-113348198, chr16:11398225-11398438, chr10:1 14129859-114129929, chr10:115950269-115950333, chr11:117029637-117029651, chr16:11989 202-11989519, chr16:12011174-12011244, chr12:120369895-120370135, chr7:120959369-120959437, chr12:122950462-122950678, chr1:12525734-12525958, and chr10:126161887-126162253; Preferably, the lung bronchial epithelial methylation markers include chr2: 100026253-100026342, chr12: 102240433-102240450, chr19: 10271710-10271765, chr13: 105669421-105669509, chr11: 107666587-107666748, chr8: 108269329- 108269378, chr2:109130778-109130902, chr3:112308465-112308621, chr6:113335181-11333 5340, chr10:114084100-114084313, chr3:11454735-11454953, chr12:117153241-117153639, c hr18:11942307-11942409, chr11:120078015-120078074, chr3:122875162-122875295, chr8:1 23097187-123097279, chr4:124427917-124428037, chr4:124428157-124428276, chr10:124954 29-12495664, chr8:126844893-126845111, chr9:130262034-130262136, chr5:134124045-134124535, chr7:140615525-140615745, chr16:14140076-14140132, and chr10:15164370-15164566; Preferably, the organ comprises at least one of kidney, liver, pancreas, intestine, heart, lung, and stomach; Preferably, the cells include at least one of renal endothelial cells, renal epithelial cells, hepatic parenchymal cells, hepatic endothelial cells, pancreatic duct cells, pancreatic acinar cells, pancreatic δ cells, pancreatic β cells, pancreatic α cells, intestinal epithelial cells, cardiomyocytes, cardiac fibroblasts, alveolar epithelial cells, alveolar endothelial cells, lung bronchial epithelial cells, and gastric epithelial cells.

3. A product comprising a substance for detecting the SNP marker for predicting the risk of organ transplant rejection as claimed in claim 1.

4. The product according to claim 3, characterized in that The product further comprises a substance for detecting the cell-specific methylation marker for locating the damaged organ or cell type as described in claim 2; Preferably, the substance for detecting the SNP marker for predicting the risk of organ transplant rejection as claimed in claim 1 comprises a reagent for determining the amount of the DNA sequence of the SNP marker for predicting the risk of organ transplant rejection as claimed in claim 1; Preferably, the substance for detecting the cell-specific methylation marker for locating the damaged organ or cell type as described in claim 2 comprises a reagent for determining the amount of the DNA sequence of the cell-specific methylation marker for locating the damaged organ or cell type as described in claim 2; Preferably, the determination of the amount of the DNA sequence of the SNP marker or the methylation marker is performed by molecular counting; Preferably, the determination of the amount of the DNA sequence of the SNP marker or methylation marker is based on the detection of the nucleic acid sequence; Preferably, the determination of the amount of DNA sequence of the SNP marker or methylation marker comprises digital polymerase chain reaction, real-time quantitative polymerase chain reaction, array capture, massively parallel genome sequencing, single molecule sequencing, or multiplex detection of polynucleotides using color-coded probe pairs; Preferably, the determination of the amount of the DNA sequence of the SNP marker or methylation marker comprises mass spectrometry or hybridization with a microarray, a fluorescent probe, or a molecular beacon; Preferably, the substance for detecting the cell-specific methylation marker for locating damaged organs or cell types as described in claim 2 further comprises a reagent for differentially modifying methylated and non-methylated DNA; Preferably, the substance for detecting the SNP marker for predicting the risk of organ transplant rejection as described in claim 1 further comprises primers and reagents for amplifying the DNA sequence of the SNP marker; Preferably, the substance for detecting the cell-specific methylation marker for locating damaged organs or cell types as described in claim 2 further comprises primers and reagents for amplifying the DNA sequence of the methylation marker.

5. The product according to any one of claims 3-4, characterized in that: The product comprises at least one of a kit, a chip, a device, and a system.

6. Use of a substance for detecting the marker combination according to any one of claims 1 to 2 in the preparation of a product, wherein the product is used for any one of a1) to a4): a1) Prediction of risk of organ transplant rejection; a2) Prediction and location of organ transplant rejection risk; a3) Immunosuppressive drug toxicity monitoring; a4) Monitoring of complications and injuries after organ transplantation; Preferably, the product comprises at least one of a kit, a chip, a device, and a system; Preferably, the organ transplantation in a1) and a2) comprises at least one of single organ transplantation, multi-organ transplantation, and relative transplantation; Preferably, a2) the prediction and localization of organ transplant rejection risk refers to assessing the risk of organ transplant rejection and localizing the rejected organ and / or damaged cell type; Preferably, the test sample of the product is from a biological sample of the subject to be tested; further from free DNA in the biological sample of the subject to be tested; Preferably, the biological sample comprises at least one of body fluids, tissues, cells and excrement.

7. Any device or system of b1) to b2) that: b1) A device or system comprising: Detection module: the SNP marker described in claim 1 for detecting free DNA of a recipient after organ transplantation; and A calculation module: used to calculate the content of donor-derived cfDNA (ddcfDNA) in the recipient free DNA; and Analysis module: compares ddcfDNA with reference thresholds to determine whether organ transplant rejection exists; The device or system is used for a1) as described in claim 6; b2) An apparatus or system comprising: Detection module: used for detecting the SNP marker and methylation marker described in claim 2 for detecting free DNA of a recipient after organ transplantation; and A calculation module: used to calculate the content of donor-derived cfDNA and the absolute amount of cell-derived cfDNA in the recipient's free DNA; and Analysis module: comparing the absolute amounts of ddcfDNA and cfDNA derived from the cells with respective reference thresholds to determine whether there is organ transplant rejection and locate the damaged organ or cell type; The device or system is used for any one of a2) to a4) described in claim 6.

8. The device or system according to claim 7, characterized in that: The recipient free DNA in b1) and b2) comprises donor-derived free DNA and recipient-derived free DNA; Preferably, the recipient free DNA comes from at least one of the body fluids, tissues, cells, and excreta of the transplant recipient; Preferably, the method of detecting the SNP marker and / or methylation marker of the free DNA of the recipient after organ transplantation as described in b1) and b2) of claim 2 comprises: performing methylation sequencing on the free DNA of the recipient; Preferably, the methylation sequencing comprises at least one of whole genome sequencing and targeted enrichment sequencing; Preferably, the targeted enrichment sequencing comprises at least one of targeted capture sequencing and multiplex amplicon sequencing; Preferably, the methylation sequencing comprises at least one of WGBS, EM-seq, and GM-seq.

9. The device or system according to any one of claims 7-8, characterized in that: The content of donor-derived cfDNA (ddcfDNA) in the recipient free DNA in b1) and b2) is the average value of the ratio of the depth of the low-depth base of the effective SNP site to the total depth of the site; Preferably, the method for determining the effective SNP site is as follows: if the average value of the ratio of the SNP site is (0, 0.2), it is an effective SNP site; otherwise, it is an invalid SNP site; Preferably, the absolute amount of the cell-derived cfDNA in b2) = the number of positive signal fragments / the number of all fragments, wherein the number of all fragments is "the count of all fragments aligned to the marker genomic region"; the number of positive signal fragments is "the count of fragments with a positive signal pattern"; Preferably, for a high methylation marker, when all CpG sites in the methylation region corresponding to the fragment are methylated, the fragment is a fragment with a positive signal pattern; for a low methylation marker, when all CpG sites in the methylation region corresponding to the fragment are unmethylated, the fragment is a fragment with a positive signal pattern.

10. The device or system according to any one of claims 7 to 9, characterized in that: The reference threshold of ddcfDNA in b1) and b2) is 0.1%-1%; Preferably, the reference threshold of the absolute amount of cell-derived cfDNA in b2) is defined according to the N quantile of the control sample, wherein N is 90%-98%; Preferably, when the ddcfDNA in b1) and b2) is greater than its reference threshold, there is organ transplant rejection; Preferably, when the absolute amount of cfDNA derived from the cells in b2) is greater than its reference threshold, organ or cell damage is present.

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