WO2018133553A1

WO2018133553A1 - Method for establishing quantitative reference range for healthy person urinary proteome and acquiring disease-related urinary protein marker

Info

Publication number: WO2018133553A1
Application number: PCT/CN2017/113550
Authority: WO
Inventors: 秦钧; 冷文川; 甄蓓; 倪晓天; 路天元; 汪宜; 王广舜; 孙长青; 钟博文
Original assignee: 北京蛋白质组研究中心
Priority date: 2017-01-20
Filing date: 2017-11-29
Publication date: 2018-07-26

Abstract

A method for establishing a quantitative reference range for the urinary proteome of a healthy person and for acquiring a disease-related urinary protein marker: acquiring a quantitative reference range for the urinary proteome of a healthy person on the basis of an established urinary proteome data set of the healthy person, using a hypergeometric distribution inspection method in a urinary proteome data set of a patient of a certain disease to screen for an outlier protein to serve as a urinary protein marker related to the disease, and establishing a tumor-related outlier urinary protein library with a tumor as an example. The method eliminates interferences from physiological fluctuations and differential proteins between individuals in a screening process for a urinary protein biomarker.

Description

Establishment of quantitative reference range for healthy human urine proteome and method for obtaining disease-related urinary protein markers

Technical field

The invention belongs to the establishment of biomarker data in the field of medical biology, and relates to a method for establishing a quantitative reference range of a healthy human urine proteome using a urine human proteome data set capable of covering physiological fluctuations and differences between individuals and individuals, and establishing The Healthy Human Urine Proteome Database also relates to a method for obtaining a urine protein marker (ie, an outlier urine protein) of a disease patient by screening a urine proteome of a disease patient using a healthy human urine proteome quantitative reference range data In particular, it relates to the establishment of disease-related outlier urinary protein libraries and tumor-associated outlier urinary protein libraries established by tumors as a representative of diseases.

Background technique

Urine is the most commonly used body fluid sample in clinical tests except for blood. The detection of bilirubin, glucose, ketone body, protein, blood cells and other indicators in urine routine is used for the diagnosis or therapeutic monitoring of various diseases. In view of the important value of urine testing in health medicine, scientists all over the world have been using proteomics technology to try to find new protein markers for disease diagnosis, prognosis and efficacy detection from urine. The current research and development process for finding new biomarkers from urine is usually divided into two stages: discovery and verification: in the discovery stage, proteomics can be used to target several to dozens of cases (usually <50 cases) of target disease groups. The test samples were tested and the significantly different proteins between the two groups became the candidate biomarkers into the validation phase; in the validation phase, candidate biomarkers were tested using large independent samples. Due to the lack of high-throughput, deep quantitative urine proteomic detection methods, candidate markers found through small sample sizes during the discovery phase are actually proteins that differ between individuals, rather than proteins that truly reflect differences in disease and control status. At present, there is no main reason for the successful application of new urine protein markers by proteomics methods. Therefore, it is necessary to establish a quantitative reference range for human urinary proteome that can cover intra- and inter-individual and inter-individual differences and physiological fluctuations, so as to establish a method for obtaining tumor urinary protein markers to be effective. Overcoming the interference caused by physiological fluctuations and differences within and between individuals in the urinary proteome.

Summary of the invention

In order to solve the problems in the prior art, an object of the present invention is to provide a method for establishing a quantitative reference range of a healthy human urine proteome, and further to provide a database of healthy human urine proteomes, which can cover individuals and individuals. Differences and physiological fluctuations in the healthy human urine proteome dataset and the number of healthy human urine proteins determined from the data set and the calculated quantitative reference range of the healthy human urine proteome.

The method for establishing a quantitative reference range for a healthy human urine proteome proposed by the present invention comprises the following steps:

1) Sampling: collecting urine samples of a healthy number of healthy people;

2) preparing a urine protein sample: each urine sample collected is made into a urine protein sample;

3) Detection: mass spectrometry is performed on each urine protein sample to obtain mass spectrometry data of each urine protein sample;

4) Search and quantification: perform database search, peptide quantification and protein splicing assembly on the mass spectrometry data of each urine protein sample, determine the protein species in each urine protein sample and quantify each protein to form a urine proteome data;

5) Different sub-data sets are determined for different people and different sampling time spans, including: urinary proteome data of all urine protein samples of individual individuals with different sampling time spans are collected to obtain the individual's intra-urine proteome sub-data set. (BCM); urinary proteome data of all urine protein samples collected by a small number of people or a single sample is collected to obtain an inter-individual urinary proteome sub-data set (BPRC);

6) Calculate the distribution range of the coefficient of variation of all urine protein quantitative data in each sub-data set to assess physiological fluctuations in the individual;

7) Using the method of random resampling, analyze the sub-datasets of the two individuals with the longest sampling time span, and determine the number of samples needed to cover the physiological fluctuations or differences in the healthy human urine proteome;

8) Collect the total number of sub-data (BCM and BPRC) and obtain the total data set A of healthy human urine proteome data; at least 10% of the urine samples in each sub-dataset or total data set have quantitative information To participate in the assessment of the assessment of physiological fluctuations and differences between individuals in the urinary proteome of each sub-dataset or total dataset;

9) Calculate the quantitative reference range of the healthy human urine proteome using the data of the total data set A.

Wherein, when the data in step 9) conforms to the normal distribution, the quantitative reference range is established by the parameter method, and the upper and lower limits of the reference range of the population covering the target percentage are calculated according to the statistical parameters (mean and standard deviation) of the data (eg, the mean plus Reduce the standard deviation by 2 times to cover 95% of individuals). When the data in step 9) is inconsistent with the normal distribution, the non-parametric method is used to establish the quantitative reference range, and the upper and lower limits of the reference range are determined according to the percentile method to actually cover the target percentage (such as 2.5 and 97.5). The percentile covers 95% of the individuals).

Among them, different sub-data sets are determined for different people and different sampling time spans, and sub-data sets formed by urine samples with a small number of sampling times are used to evaluate the physiological fluctuations of the urine proteome of a plurality of samples repeatedly. Differences; sub-data sets formed by urine samples with a small number of fewer samples were used to assess physiological fluctuations and differences between individuals in the urinary proteome with fewer or single samplings for most people; sub-data for male and female urine proteomes The set can be used to assess physiological fluctuations and differences between individuals of different urinary proteome groups.

The method of evaluation is to calculate the coefficient of variation of each eligible protein in the corresponding sub-data set or the total data set, and then display the distribution range of the coefficient of variation of the desired protein in each sub-data set or total data set in a box plot. To assess the physiological fluctuations and differences between individuals in the corresponding urinary proteome.

The step 2) uses a method based on ultracentrifugation and reduction to obtain a urine protein sample, that is, the precipitate after centrifugation of the urine sample is resuspended in a resuspension buffer (50 mM Tris, 250 mM sucrose, pH 8.5), and then dithiothreose is added. Alcohol, Most of the urinary protein in the sample was removed by heating, washed with a washing buffer (10 mM triethanolamine, 100 mM sodium chloride, pH 7.4), and then centrifuged to obtain a urine sample of the urine sample.

The step 3) separates the urine protein sample by polyacrylamide gel electrophoresis (SDS-PAGE), gel-cut into 6 bands for in-gel digestion, and then combines into a 2-component peptide sample as a urine protein. In the group, the two-component peptide samples were detected by LC-MS/MS to obtain the urine protein sample mass spectrum data for each urine sample; and the purpose of the search was to analyze the data produced by the mass spectrometry to determine the mass spectrometry production. The protein contained in the data is obtained, and a first-order quantitative result of all the peptides is obtained, thereby obtaining corresponding proteome data for each urine protein sample.

Among them, the physiological fluctuations and differences in the urinary proteome of healthy individuals in three different sampling time spans (24 hours, 3 consecutive days and more than 2 months) were evaluated by determining the protein quantification in the corresponding sub-data sets. The distribution of the coefficient of variation of the data (the standard deviation of the protein quantitation data / the mean of the protein quantification data); each sub-data set of 24 hours or 3 consecutive days of sampling includes 3-5 urine proteome data, for those in 3- The quantitative data of the five urine samples were calculated, and the coefficient of variation was calculated. Finally, the distribution range of the coefficient of variation of all the required proteins in each sub-data set was obtained, and displayed by box-plot; each sampling time span Sub-data sets greater than 2 months include 6-62 urinary proteome data for those at least 3 (<30 urinary proteome sub-data sets) or 10% urine samples (>30 urinary proteome subgroups) The data with quantitative data in the data set calculates the coefficient of variation, and finally obtains the distribution range of the coefficient of variation of all the required proteins in each sub-data set, and displays it in a box-plot.

Among them, the total data set and the gender sub-dataset in it are used to evaluate the physiological fluctuations and differences between healthy human urine proteome individuals, and the protein with quantitative data for more than 10% urine samples in each data set or sub-data set is calculated. The coefficient of variation of the quantitative data, and box-plot is used to display the coefficient of variation distribution of all eligible proteins in each data set and sub-data set.

The present invention still further provides a healthy human urine proteome database, comprising the aforementioned identified sub-data sets, total data sets, and healthy human urine protein types determined according to the data set and the calculated quantitative reference range of healthy human urine proteome; The quantitative reference range for the healthy human urine proteome includes 2025 urine proteins and their values listed in Table 7-1 or Table 7-2.

More broadly, the present invention also provides a method of establishing a quantitative reference range for healthy human urine protein, comprising the following steps:

Sampling: collecting urine samples from healthy people;

Sample preparation: the collected urine sample is made into a urine protein sample;

Detection: detection of urine protein samples to obtain protein detection data of urine protein samples;

Determination: The protein detection data is classified, and each category selects the upper limit value and the lower limit value from the plurality of protein detection data to form a quantitative reference range, and the plurality of types are combined to form a quantitative reference range for healthy human urine protein.

According to this method, a healthy human urine protein quantitative reference range established based on healthy human urine protein data is within the scope of the present disclosure. The human urine protein data includes, but is not limited to, protein species and individual protein content.

Another object of the present invention is to provide a method of obtaining a urine protein marker associated with a disease. The disease can be any kind of disease. The following is a case in which the tumor is taken as an example, and the expression about the tumor can be applied to a certain disease.

The method for obtaining a disease-related (taking a tumor as an example) urine protein marker proposed by the present invention comprises the following steps:

(1) The healthy human urine proteome data set A is randomly divided into three sub-data sets A1, sub-data sets A2 and sub-data sets A3, based on the non-parametric percentage of the healthy human urine proteome sub-data set A1. The number of digits method determines the quantitative reference range of the healthy human urine proteome, and the quantitative value of the 99.5th percentile of each urine protein in the data set is the upper limit of the quantitative reference range;

(2) The training sub-data set B1 is formed from the urine proteome data set B of the tumor patient, and each of the urine proteome data is screened by the upper limit of the reference range established in (1), if a certain protein is At least two samples exceeding the upper limit of the reference range are included in the candidate tumor-associated outlier urinary protein pool; all training data are screened to produce a candidate tumor-associated outlier urinary protein library C1;

(3) Extracting part of the validation sub-dataset B2 from the urinary proteome dataset B of the tumor patient, and screening each of the urinary proteome data in the sub-datasets A2 and B2 with the upper limit of the reference range established in (1) Each urinary proteome (sample) produces a sample-specific outlier urinary protein pool C2; each sample is specifically isolated from the urinary protein pool C2 and (2) the candidate tumor-associated outlier urinary protein The proteins in library C1 are compared to determine the same protein and quantity in the two pools. The more the same protein, the closer the sample is to the tumor patient sample;

The hypergeometric test was used to calculate the p-values of the same protein overlap in the two libraries C1 and C2. The ROC curve was used to investigate the generation of the ROC curve (2). The ability of the candidate tumor-associated outlier urinary protein pool C1 to distinguish the urinary proteome of healthy people and tumor patients in the sub-data sets A2 and B2;

(4) Randomly sampling the urinary proteome data set B of the tumor patient N times (N is a natural number greater than 10) to form an N pair training sub-data set B1 and a verification sub-data set B2, and performing the above for each pair of sub-data sets (3) In the same analysis, N candidate tumor-associated outliers urinary protein pool C1 and N ROC curves were obtained, and the candidate tumor-associated outlier urinary protein pool C1 corresponding to the area under the maximum ROC curve was determined as the final tumor correlation. The group urine protein library C, which contains the outlier protein, is a tumor urine protein marker.

The above method also includes the step of verifying the established tumor-associated outlier urine protein library C:

(5) Extracting the sub-data set B3 from the urinary proteome data set B of the tumor patient completely independent (referring to the process of never participating in the training and verification process), using the sub-data sets A3 and B3 to obtain the above (4) Most The final tumor-associated outlier urinary protein library C is tested for the ability to distinguish between healthy and tumor patients. The method is the same as (3) above, and the p-value of the hypergeometric distribution test of the urine proteome of each healthy person and tumor patient is obtained. The card value Pc determined in the above (4) is compared to determine whether each urinary proteome belongs to a healthy person or a tumor patient, and the tumor-related outlier urinary protein pool is determined according to the false positive rate and the false negative rate to distinguish the sensitivity of the healthy person and the tumor patient. Sex and specificity.

The step (1) determines that the quantitative reference range of the healthy human urine proteome is calculated by the non-parametric method using the data of the sub-data set A1, and the individual who actually covers the target percentage according to the upper and lower limits of the reference range according to the percentile method ( For example, the 2.5th and 97.5th percentiles cover 95% of individuals).

The process of establishing the urine proteome data set B of the tumor patient in the step (2) comprises:

1) Sampling: collecting urine samples from tumor patients;

5) Collecting the urine proteomic data of all urine protein samples to obtain the urine proteome data set B of the tumor patient.

The tumor-associated outlier urine protein library obtained by the above method and the outlier urine protein contained therein, that is, the tumor urine protein marker are also in the present invention.

The tumor-associated outlier urine protein library includes 509 urine proteins, specifically A1BG, A2M, ABCB7, ABCD4, ABCE1, ABHD11, ABHD12, ABHD14B, ACADM, ACADSB, ACE2, ACO2, ACOT9, ACSL3, ACSM2A, ACSM2B, ACTR1B, ADD1, AGT, AHNAK2, AHSG, ALDH1L2, ALDH3A1, ALDH3A2, ALDH3B1, ALDH4A1, ALDOC, AMY2A, AMY2B, ANGPTL6, ANK1, ANPEP, ANXA1, ANXA10, ANXA2, ANXA3, ANXA4, ANXA5, ANXA6, APMAP, APOB, APP, AQP7, ARFIP1, ARG1, ARHGAP1, ARL13B, ARL6IP5, ARL8A, ARMC9, ARRDC1, ASNA1, ASPH, ATP13A3, ATP2A2, ATP6AP1, ATP6V0A1, ATP6V0C, ATP6V1B1, ATP6V1B2, AZU1, B3GNT3, BBOX1, BLMH, BPI, BPIFB1 C14orf166, C19orf59, C1orf123, C1QB, C1QC, C3, C4A, C4BPA, C5, C6orf211, C8A, C8B, C9, CAMK2G, CANT1, CCDC22, CCDC64B, CCT4, CCT6A, CDC42BPA, CDH11, CEACAM1, CEACAM6, CEACAM8, CECR5, CERS3, CFB, CHCHD3, CHI3L1, CHP1, CLCA4, CLCN7, CLPTM1, CLRN3, CMBL, CNDP2, CNN3, COL12A1, COL4A2, COMP, COPA, COPS7A, CORO1C, CPT1A, CPVL, CRAT, CRHBP, CRP, CRYAB, CRYL1, CTBS, CTNNA1, CTSG, CTSH, CWH43, CYP1A1, DDAH2, DDOST, DDX6, DERL1, DHCR24, DHX15, DIRC2, DKC1, DMTN, DNAJA1 DNAJB1, DNAJC7, DNASE1, DNM2, DOCK2, DOCK5, DOCK9, DPM1, DPP4, DPT, DSCR3, ECHS1, EEF1A1, EIF4A1, ELMO1, ELMO3, EMC1, EMD, EMILIN1, ENO1, ENOPH1, ENPEP, ENPP4, ENPP6, ENPP7, EPB41, EPB42, EPCAM, EPDR1, EPHA2, EPPK1, EPX, ERAP1, ERLIN1, ERLIN2, ERMP1, ESRP1, ETF1, FAM151A, FARP1, FCAR, FCN2, FDFT1, FDXR, FGA, FGB, FGG, FGL1, FGL2, FGR, FLOT2, FN3KRP, FNBP1L, FOLH1, FRK, GALK2, GALM, GALNT1, GDF15, GIPC2, GLDC, GLUD1, GLYAT, GNS, GOLM1, GOLT1B, GPD2, GPR110, GPR126, GPR137B, GPR56, GPX3, GSTK1, GSTT1, HBB, HDLBP, HECTD3, HEXA, HEXB, HGD, HLA-A, HLA-B, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB5, HMOX2, HNRNPA0, HNRNPA1, HNRNPD, HNRNPF, HNRNPH3, HNRNPK, HNRNPM, HNRNPU, HSP90B1, HSPA2, HSPA6, HSPA8, HSPA9 HYOU1, IDH2, IDH3A, IGFBP7, IGLL1, ILF3, IMPDH2, IQCC, ITIH1, ITIH2, KCNJ15, KIAA0319L, KIF13B, KRT18, KRT20, KRT7, KRT8, LAMB1, LAMP1, LAMTOR1, LBR, LLGL2, LMF2, LMNB2, LONP1 LPCAT3, LRG1, LRRK2, LZTFL1, MAL2, MARVELD3, MCCC1, MCRS1, MFSD1, MGAM, MITD1, MLYCD, MMP7, MNDA, MPO, MPP1, MRPL12, MRPL39, MRPS18B, MRPS22, MSMO1, MTAP, MTCH1, MTHFD1, MTOR, MUC20, MUT, MVP, MYO18A, MYO1B, MYO1D, NAMPT, NCF1, ND4, NDFIP1, NDUFA10, NDUFA9, NDUFS3, NDUFS8, NNT, NPTN, NSDHL, NT5C3A, NUDT9, NUMA1, OAT, OGFOD3, OGN, OLA1, ORM1 P4HB, PAPSS2, PCCA, PCCB, PCK1, PCK2, PDHA1, PDIA3, PDP1, PEF1, PFKFB2, PFKL, PGD, PHB2, PHPT1, PI4K2A, PICALM, PIP4K2A, PIP4K2C, PITRM1, PLA2G15, PLAU, PLCG2, PLEKHF2, PNKD, PON1, PPP2R2A, PRDX4, PRKAB1, PRKACA, PSMA5, PSMA7, PSMC3, PSMC4, PSMD3, PSMD7, PSMF1, PTPLAD1, PTPRC, RAB11B, RAB12, RAB24, RAB32, RAB7A, RAB9A, RDH13, RELA, RILPL2, RNASE3, RNA SET2, ROGDI, RPN1, RPN2, RPS3, RPS6KA1, RPS9, RPTOR, RRAGC, RRAS2, S100A7, SCAMP3, SCARB1, SCARB2, SEC22B, SEPT9, SERPINA1, SERPINA3, SERPINA7, SERPIND1, SF3B1, SF3B3, SGPL1, SIAE, SIRT5, SLC12A6, SLC12A7, SLC12A9, SLC13A2, SLC15A1, SLC15A4, SLC17A1, SLC17A3, SLC17A5, SLC1A5, SLC22A11, SLC25A10, SLC25A13, SLC25A24, SLC25A4, SLC25A5, SLC26A11, SLC26A4, SLC2A1, SLC30A2, SLC34A2, SLC35F2, SLC35F6, SLC38A7, SLC3A1, SLC46A1, SLC4A1, SLC6A19, SLC6A8, SLC7A9, SLC9A3, SLFN5, SMPDL3A, SMPDL3B, SNRNP200, SNX27, SORT1, SPAG9, SPARCL1, SPNS1, SPRYD4, SPTA1, SPTB, SPTLC1, SSR1, ST13, STAP1, STARD3NL, STC1, STIM1, STK11IP, STOM, STRN, STT3A, STUB1, STX3, STX7, STX8, STXBP1, SUCLA2, SUCLG2, SULT1A1, SULT1C2 SUN2, SVIL, TACSTD2, TAOK1, TARDBP, TBC1D9B, TBCB, TBL2, TCIRG1, TFRC, TGM2, TGM3, TIMM44, TIMM50, TM9SF2, TM9SF3, TM9SF4, TMBIM1, TMED2, TMED4, TMEM104, TMEM106A, TMEM160, TMEM176A, TMEM176B, TMEM192, TMEM205, TMEM27, TMEM40, TMEM55B, TMEM9, TMLHE, TOMM40L, TOP2B, TPCN1, TPCN2, TPM3, TRIM14, TRIP10, TST, TTC38, TUFM, TXNDC5, TYMP, UGT1A6, UGT1A9, UGT2B7, UPK3B, VAC14, VAMP7, VAPA, VAPB, VASN, VIM, VKORC1L1, VNN2, VPS37C, VPS4A, VSNL1, VTI1B, VWA5A, WASH1, WASL, ZADH2, ZNRF2.

Still another object of the present invention is to provide a tumor correlation degree judgment for a urine sample to be tested by using the tumor-related out-of-group urine protein library by obtaining a proteomic data of the urine sample to be tested, and using a hypergeometric distribution test method Calculating the p value of the same protein overlap in the urine sample and the tumor urine protein outlier protein library, determining the Pc value when the specificity is 95%, and determining the urine to be tested when the p-value of the hypergeometric distribution test is greater than Pc The sample is a healthy human sample. When the p value is less than Pc, the urine sample to be tested is judged to be a tumor patient sample.

Similarly, other disease-related outlier urine protein stores can be used to determine the disease correlation degree of the urine sample to be tested. The process is: obtaining proteomic data of the urine sample to be tested, and calculating the urine by using a hypergeometric distribution test method. Determining the p value of the same protein in the urinary protein outlier protein library, and determining the Pc value when the specificity is 95%. When the p-value of the hypergeometric distribution test is greater than Pc, the urine sample to be tested is determined to be a healthy person. The sample, when the p value is less than Pc, determines that the urine sample to be tested is a patient sample of the disease.

The effect of the invention: a urine protein set dataset capable of covering intra- and inter-individual differences and physiological fluctuations is established by collecting large-scale human urine proteome data, and a quantitative reference range of urine proteome is established by using the data set. . According to the reference range, the urinary proteome data of a patient (in the case of a tumor) is screened to obtain an off-colon urinary protein marker related to the disease, and the screening process can be well excluded from the discovery of urinary protein biomarkers. Interference from physiological fluctuations and differential proteins between individuals.

DRAWINGS

Figure 1 is a graph showing the coefficient of variation of physiological fluctuation ranges for 24 hours and 3 consecutive days in a healthy human urine proteome group. The 24-hour data comes from 2 volunteers (U001 and U002), and the data for 3 consecutive days comes from 16 voluntary (U001-U005, U007-U017). The vertical axis is the coefficient of variation, and the horizontal axis is the different sub-data sets of different individuals.

Figure 2 is a graph showing the coefficient of variation of physiological fluctuation ranges greater than 60 days in a healthy human urine proteome group. In addition to U10, U015 and U017, the sampling time span of the other 14 volunteers was 61-314 days. The vertical axis is the coefficient of variation, and the horizontal axis is the sub-data set of different individuals.

Figure 3 is a graph showing the relationship between the number of samples and the physiological fluctuations in the healthy human urine proteome. A is the relationship between the number of samples of the volunteer U001 and the physiological fluctuations of the urine proteome. B is the relationship between the number of samples of the volunteer U002 and the physiological fluctuations of the urine proteome; the vertical axis is the variation. The coefficient, the horizontal axis is the number of samples in the sub-data set.

Figure 4 is a graph showing the coefficient of variation of the range of physiological fluctuations between individuals in the healthy human urine proteome. Vertical axis: coefficient of variation; horizontal axis: BCM, BPRC, A1, Female, and Male are sub-data sets, A is the total data set, and the numbers in parentheses are the median coefficient of variation in the distribution of proteome variation coefficients in each data set.

Figure 5 is a total ion chromatogram generated by liquid chromatography tandem mass spectrometry (LC-MS) of a urine protein sample (including a two-component peptide sample) of volunteer U001. The vertical axis is the signal intensity and the horizontal axis is the retention. time.

Figure 6 is a flow chart showing the process of establishing a tumor-associated outlier urine protein library,

A is the training data set and the generation of candidate tumor-associated alien protein pools;

B is the generation of the validation data set and the evaluation of the candidate tumor-associated alien protein pool;

Panel C is the generation of test data sets and testing of the final tumor-associated alien protein pool.

detailed description

One aspect of the present invention provides a method for establishing a quantitative reference range of a healthy human urine proteome, and further provides a healthy human urine protein group database; and another method for obtaining a disease-related urine protein marker, and taking a tumor as an example Further, a tumor-related outlier urinary protein pool was proposed. The invention utilizes the quantitative reference range of the healthy human urine proteome to screen the urinary proteome data of a disease patient to find outliers, through three stages of discovery, verification and testing (to randomly divide the urine proteome data of healthy people and patients into training) The analysis of the validation and test sub-data sets separately determines the disease (in the case of tumors) associated with the outlier urinary protein pool. Generally, the concept of a proteome refers to a collection of all kinds of proteins in a cell, within a tissue, within a body fluid, or within an individual. In the present invention, the urinary proteome refers to all of the different kinds of proteins included in each urine sample.

In order to achieve the above results, the present invention explains the following aspects:

First, the preparation of urine protein samples

For the collection of healthy human urine samples and urine samples of patients with certain diseases, the following urine protein samples were obtained by ultracentrifugation and reduction methods:

(1) 10 ml of urine sample, centrifuged at 100 ° C for 10 minutes at 4 ° C, discard the supernatant, leaving a precipitate;

(2) Transfer the above precipitate to a centrifuge tube, add 60 μl of resuspension buffer (50 mM Tris, 250 mM sucrose, pH 8.5) to the centrifuge tube, let stand at room temperature for 10 minutes, and thoroughly resuspend the pellet with a pipette. ;

(3) adding dithiothreitol to the above resuspended precipitate to a final concentration of 50 mM, and heating at 80 ° C for 10 minutes to remove most of the urinary protein in the sample;

(4) A washing buffer (10 mM triethanolamine, 100 mM sodium chloride, pH 7.4) was added to 400 ul, and then centrifuged at 100,000 for 20 minutes under a centrifugal force of 100,000, and the supernatant was discarded to leave a precipitate.

This precipitate was used as a urine protein sample of the urine sample.

Second, the mass spectrometric detection of urine protein samples

In the present invention, each urine protein sample prepared by the above ultracentrifugation method is dissolved in 60 μl of 1% sodium dodecyl sulfate buffer (1% SDS, 50 mM Tris, pH 8.5), and 30 μl of the sample is used for polypropylene. After separation by amide gel electrophoresis (SDS-PAGE), the gel was cut into 6 bands for in-gel digestion, and then combined into a 2-component peptide sample as a urine proteome, using LC-MS/MS for 2 components. Peptide samples were tested to obtain urine protein sample data for each urine sample (mass data, see Figure 5 for the spectrum). The specific operation is:

The peptide sample obtained after digestion was dissolved in 20 μl of loading buffer (5% methanol, 0.1% formic acid), and then 5 μl was applied for loading, using a ThermoScientific nanoscale liquid chromatography tandem high resolution mass spectrometry system (nLC-Easy1000-Q Exactive- HF) for data collection.

The specifications of the nanoliter liquid phase loading column are as follows: the inner diameter is 100 μm, the packing is the C18 packing of Dr. Maisch GmbH (particle diameter is 3 μm, the particle diameter is 120 nm), the packed bed length is 2 cm; the nanoliter liquid phase The separation column specifications were as follows: an inner diameter of 150 μm, a filler of Dr. Maisch GmbH, a C18 filler (particle diameter of 1.9 μm, a particle diameter of 120 nm), and a packed bed length of 12 cm. Mobile phase A was 0.1% formic acid; mobile phase B was acetonitrile and 0.1% formic acid. The peptide separation elution gradient was as follows: 0-69 minutes for 5%-31% mobile phase B and 70-75 minutes for 95% mobile phase B.

The mass spectrometry data was collected by Data Dependent Acquisition. The parameters used for Q Exactive-HF were as follows: the first-order mass spectrometer resolution was 120,000, the scanning range was 300-1400 m/z, the AGC was 3E+6, and the maximum ion implantation time was 80 msec. The secondary mass spectrometry separates the fragmentation according to the signal intensity of the peptide fragment in the first-order mass spectrum from high to low (in Top 20 mode), the resolution of the secondary mass spectrometer is 15,000, and the mass separation window of the secondary mass spectrometer is 3 m/z. The AGC is 2E+4, the maximum ion implantation time is 20ms, the HCD relative collision energy is 27%, and the data acquisition uses 12s dynamic elimination.

3. Analysis of mass spectrometry data of urine protein samples

Mass spectrometry data from each urine protein sample was searched using bioinformatics tools and methods. The purpose of the database search is to analyze the data produced by the mass spectrometry and determine the proteins contained in the data produced by the mass spectrometry. The process is to analyze the secondary spectrum of the parent ion in the data produced by the mass spectrometer within a certain mass deviation range. The intensity distribution of the fragment ions was compared with the theoretical intensity, and the mother ions were scored by the fragment ions not exceeding the mass deviation range to obtain the identification results of the parent ions (short peptides). The short peptide fragment is matched with a known protein amino acid sequence library to determine the protein information of the detected short peptide segment, and the protein identification result is obtained. The specific process and parameters used are as follows:

The obtained mass spectral data was subjected to peptide sequence database search analysis using the Proteome Discoverer V2.0 software of the Mascot 2.3 search engine. In the "Mascot" template, the parameters of the database search are set: the human protein sequence database is selected in the "Protein Database", and the database used is the human body of the National Center for Biotechnology Information (NCBI). Protein reference sequence database; select Trypsin in "Enzyme Name"; fill in 2 in "Maximum Missed Cleavage" (representing the maximum number of missed sites allowed to be 2); select Default in "Instrument"; select All in "Taxonomy" Entries; fill 20ppm in "Precursor Mass Tolerance"; fill 50mmu in "Precursor Mass Tolerance"; select False in "Use Average Precursor Mass"; select None in "From Quan Method"; select False in "Show All Modifications"; In "Dynamic Modification", except for the commonly available Acetyl (Protein N-term), DeStreak (C), Oxidation (M), and Carbamidomethyl (C); the false positive identification of the peptide level is less than 1%.

The first-order spectra in the original data were calculated by the peptide-matching map information generated by the database search, and the first-order quantitative results of all the peptides were obtained. The batch calculation program uses the existing protein abundance quantification software based on high-resolution mass spectrometry data peptide cross-regression [referred to as: PQPCR] V 1.0 (National Copyright Administration of the People's Republic of China computer software copyright registration number: soft boarding No. 0451332, registration number 2012SR083269, registration date: September 4, 2012, copyright owner: Beijing Proteome Research Center). The quantified peptides are spliced into corresponding proteins according to the amino acid sequence of the proteins in the database, and the corresponding urine proteome data of each urine protein sample is obtained. The concept of the urinary proteome refers to all the different kinds of proteins included in each urine sample, and all the proteins identified in one urine sample are called a urinary proteome.

Fourth, healthy human urine proteome dataset and corresponding sub-data sets, establish a urine protein set data set for cancer patients

Each healthy human urine proteome data obtained by the above method analysis was sequentially combined to obtain healthy human proteome data set A (integrated Table 4 and Table 5, containing data sets of 497 urine proteomes of 167 healthy persons), Each tumor urinary proteome data obtained (with tumors as an example of disease) was combined to obtain the patient's tumor urinary proteome data set B (as shown in Table 8-2, including 17 solid tumors - bladder cancer 17 cases, breast cancer) 4 cases, 25 cases of cervical cancer, 22 cases of colorectal cancer, 14 cases of esophageal cancer, 47 cases of gastric cancer and 25 cases of lung cancer, 154 urine proteome data sets).

The data in the Healthy Human Urine Proteome Dataset A is used to assess the individual and individual of the healthy human urine proteome. Physiological fluctuations and differences between the organisms and establish a quantitative reference range for healthy human urine proteome. The data in data set A can be divided into different sub-data sets for the purpose of assessing physiological fluctuations and differences in different types of urinary proteomes. For example, data used to assess intra-individual differences in an individual can constitute a sub-data set (see Table 3); the data in this sub-data set can also be subdivided into corresponding sub-data sets based on the sampling time span. To assess physiological fluctuations and differences in urinary proteome at different time spans within healthy individuals. In addition, sub-data sets can be created based on factors such as gender.

The data system of the data set or sub-dataset is used to evaluate the intra- and inter-individual differences and physiological fluctuations of the healthy human urine proteome, and the quantitative reference range of the healthy human urine proteome is calculated by the percentile method. (See Table 6).

The data in the tumor urinary proteome dataset B were randomly divided into training, validation, and test sub-data sets for the detection, validation, and testing of the ability of healthy and tumor patients to be diagnosed.

V. Assessment of physiological fluctuations and differences in healthy human urine proteome

The physiologic fluctuations and differences in the urinary proteome of healthy individuals in three different sampling time spans (24 hours, 3 consecutive days, and more than 2 months) were evaluated by determining the quantitative data of each protein in the corresponding sub-dataset. The distribution range of the coefficient of variation (the standard deviation of protein quantitative data/the mean of protein quantitation data). The sub-dataset sampled every 24 hours or 3 consecutive days includes 3-5 urine proteomic data, and for those proteins with quantitative data in 3-5 urine samples, calculate the coefficient of variation and finally obtain each sub- The data set all met the distribution of the coefficient of variation of the required protein and was presented in a box-plot. The sub-dataset with a sampling time span of more than 2 months includes 6-62 urinary proteome data for those at least 3 (<30 urinary proteome subdata sets) or 10% urine samples (>30) The protein with quantitative data in the sub-dataset of the urinary proteome) calculates the coefficient of variation, and finally obtains the distribution range of the coefficient of variation of all the required proteins in each sub-data set, and displays it in a box-plot.

6. To assess the physiological fluctuations and differences among individuals in the urine protein group of healthy people.

Data set A of 497 urinary proteomes containing 167 healthy individuals and their gender subsets were used to assess physiological fluctuations and differences between healthy human urinary proteome individuals, over 10 for each data set or sub-data set. % urine samples have quantitative data, calculate the coefficient of variation of their quantitative data, and use box-plot to display the distribution of coefficient of variation for all eligible proteins in each data set and sub-data set.

VII. Establishment of quantitative reference range for urinary proteome in healthy people

Through the systematic evaluation of the physiological fluctuations and differences between individuals and individuals in the urine protein group of healthy people, it is proved that the established 497 urine proteome data sets A containing 167 healthy people can cover the urine group of healthy people. And physiological fluctuations and differences between individuals. The protein in the data set A was determined by the percentile method based on its quantitative data in 497 urine samples to determine the quantitative value of the protein in different percentiles as the protein. Quantitative reference range in the urine proteome of healthy populations. For example, a quantitative value for the 2.5th and 97.5th percentile levels of a protein covers the quantitative fluctuation range of the protein in 95% of the 497 urine samples. The quantitative reference range of all proteins in this data set A can be used to exclude the interference caused by physiological fluctuations or inter-individual differences in the development of urine protein biomarkers; it can also help to find out in the process of health management using urine proteome information. Outliers that are outside the quantitative reference range.

Eight, screening outliers and establishing tumor-related outlier urine protein libraries

The Healthy Human Urine Proteome Dataset A (dataset containing 497 urinary proteomes from 167 healthy individuals) was randomly divided into 3 sub-data sets. The first sub-dataset A1 includes 350 urine proteome data from healthy individuals to establish a quantitative reference range for healthy human urine proteome (using the percentile method); the second sub-dataset A2 includes 100 healthy individuals. Urine proteomic data was used to validate the ability of screened tumor-associated outliers to differentiate between healthy and tumor patients; the third sub-dataset A3 included 47 healthy urinary proteome data from healthy individuals for final independent testing of validated tumor-associated The group urine protein library distinguishes the ability of healthy people and tumor patients. Test sub-dataset A3 is no longer involved in the discovery and validation of tumor-associated outliers to ensure independence from the ability of the ultimately established tumor-associated outlier urinary protein pool to differentiate between healthy and cancer patients. . The urinary proteome dataset of tumor patients was also randomly divided into training subdataset B1, validation subdataset B2, and test subdataset B3 according to the corresponding number of 7 tumors for the corresponding healthy human urine proteome subdataset. (A1-A3) jointly completed the establishment of a tumor-associated outlier urinary protein pool. The B1, B2, and B3 sub-data sets included urine proteomic data for 45, 61, and 48 tumor patients, respectively. Test sub-dataset B3 is no longer involved in the discovery and validation of tumor-associated outliers to ensure independence from the ability of the ultimately established tumor-associated outlier urinary protein pool to differentiate between healthy and tumor patients. .

a) Using sub-dataset A1 (including 350 urinary proteome data) to determine the quantitative reference range for healthy human urine proteome:

Through a systematic assessment of the physiological fluctuations and differences between individuals and individuals in the urinary proteome of healthy people (see methods of five and six), it is proved that the established 350 urinary proteome dataset A1 of healthy people can cover the urine protein of healthy people. Physiological fluctuations and differences within and between individuals. The quantitative value of the protein in different percentiles was determined by using the percentile method for each protein in the data set based on its quantitative data in 350 urine samples as a quantitative reference for the protein in the urine proteome of healthy people. range.

b) The specific process of tumor-related outlier screening and database construction is as follows (all processes are shown in Figure 6):

(1) Establish a quantitative reference range for healthy human urine proteome using the nonparametric percentile method and the Healthy Human Urine Proteome Subset A1. The determination method is as described in a), wherein the quantitative value of the 99.5th percentile of the quantitative data of each urine protein in the 350 urine proteome of the sub-data set A1 is the upper limit of the quantitative reference range;

(2) Each urinary protein in the training sub-dataset B1 that will include urinary proteome data from 45 tumor patients The genomic data is screened using the upper limit of the reference range established in (1). If a protein exceeds the upper limit of the reference range in at least two samples, it is included in the post-candidate tumor-associated urinary protein pool. When all training data were screened, a candidate tumor-associated outlier urine protein library C1 was generated.

(3) A sub-data set A2 including 100 healthy human urine proteome data and a validation reference set established in (1) for each urine proteome data in the validation sub-dataset B2 of the urine data of the 61 tumor patients. The upper limit is screened so that each urine proteome produces a sample-specific outlier urine protein library C2. The total protein in each sample specific outlier urine protein library C2 was compared with the protein in the candidate tumor associated outlier urine protein library C1 generated in (2) to see how many identical proteins were in the two pools. The more the sample-specific outlier urinary protein pool C2 is the same as the candidate tumor-associated urinary protein pool C1, the closer the sample is to the tumor patient's sample. The hypergeometric test was used to calculate (the calculation method is shown in Table 9, the formula is as follows). The p values of the same protein overlap in the two libraries.

The sub-dataset A2 of the healthy human urine proteome data and the urinary proteome validation subdataset B2 of the tumor patient obtained a total of 161 corresponding hypergeometric distribution test p values, and the ROC curve was drawn using these p values (receiver operating characteristic curve, ROC). ) was used to examine the ability of the candidate tumor-associated outlier urinary protein pool C1 generated in (2) to verify the urinary proteome of healthy and tumor patients in the sub-datasets A2 and B2. The vertical axis of the ROC curve has a scale of 0-1, no unit, which is used to measure the sensitivity of the urine protein group in healthy people and tumor patients. The closer to 1 is, the higher the sensitivity is; the horizontal axis is the false positive rate, and the scale is also 0-1, no unit, distinguish the specificity of the urine proteome of healthy people and tumor patients = (1 - false positive rate), the closer the difference is to 1 means the higher the specificity. The ideal sensitivity and specificity are 1, and the area under the ROC curve is 1, so the area under the ROC curve can be used to measure the level of discrimination. In addition, the corresponding hypergeometric distribution test p value can be determined according to the expected sensitivity or specificity as a card value (Pc value) to distinguish between healthy people and tumor patients. In this application, the corresponding card value Pc is determined with a specificity of 95%.

(4) The above (3) is the data of 106 urine proteomes of tumor patients (48 urinary proteome data were randomly selected from the data of 154 tumor urinary proteome data of B data set to generate tumor test sub-data sets. A training sub-data set B1 (containing 45 tumor urine proteome data) and a corresponding validation sub-data set B2 (containing 61 tumor urine proteome data) randomly generated in the remaining data after B3). In order to avoid the sampling error caused by a random sampling, a total of 20 random samples were taken from 106 urinary proteome data of tumor patients, and 20 pairs of training sub-data sets and verification sub-data sets (20 pairs B1 to B2) were obtained. The same analysis in (3) above was performed for each pair of sub-data sets (B1 to B2), and 20 candidate tumor-associated outlier urinary protein pools C1 and 20 ROC curves were obtained, which corresponded to the area under the maximum ROC curve (0.957). The candidate tumor-associated outlier urinary protein pool C1 was identified as the final tumor-associated outlier urinary protein pool C (containing 509 tumor-associated outliers, see Table 10), and the Pc value at specificity of 95% was 1.78×10 ^-8 The corresponding sensitivity (=1 - false negative rate) is 85.2% as shown in Figure 6 B. When the hypergeometric distribution test p value of the sample to be analyzed is greater than Pc, the sample is considered to be a healthy human sample, and when it is smaller than Pc, the sample is considered to be a tumor patient sample.

(5) Finally, using the test sub-dataset A3 (containing 47 urinary proteome data from healthy people) and B3 (including 48 urine proteomic data from tumor patients), which are completely independent (referring to the training and verification process), (4) The final tumor-associated outlier urinary protein pool C obtained in the healthy human and tumor patients was tested by the method of the above (3) to obtain the hypergeometric distribution of the urine proteome of each healthy person and tumor patient. The p value is tested and compared with the card value Pc determined in (4) above to determine whether each urinary proteome belongs to a healthy person or a tumor patient, and the tumor-related outlier urinary protein pool is determined according to the false positive rate and the false negative rate. Sensitivity and specificity of human and cancer patients. For example, 2 of 47 healthy people were misclassified to the tumor group (false positive rate was 4.26%), and 8 of 48 tumor patients were misclassified into the healthy group (false negative rate 16.67%), according to the test As a result of the dataset, the tumor-associated outlier urinary protein pool distinguishes the sensitivity (=1-false negative rate) of healthy people and tumor patients by about 85%, and the specificity (1-false positive rate) is greater than 95%, as shown in Fig. 6. C frame.

The present invention will be further described in detail below in conjunction with specific embodiments. The methods used in the examples are all conventional methods unless otherwise specified; the terms referred to are intended to be unless otherwise specified. The tumors mentioned in the examples are merely representative of the disease and are not limiting, and the method of the present invention is applicable to all diseases.

Example 1. Establish a data set for assessing physiological fluctuations and differences within a healthy human urinary proteome, and assess physiological fluctuations in the urinary proteome

The process of creating a data set includes:

1) Sampling: Continuously collect 17 urine samples of informed consent volunteers at different time spans. See Table 1 for sampling time and quantity;

2) preparing a urine protein sample: each urine sample collected is prepared into a urine protein sample according to the method described above, and each urine sample is made into a urine protein sample (a peptide sample containing two components));

3) Detection: Each urine protein sample was tested according to the method of the above two, and the mass spectrometry data of each urine protein sample was obtained, and the first row of U001-1 in Table 1 (one of the U001 volunteers collected in 24 hours) For example, the urine protein sample prepared by urine sample is shown in Fig. 5 (the upper and lower spectra correspond to the peptide samples of the two components, respectively);

4) Search and quantification: According to the above three methods, database search, peptide quantification and protein splicing assembly of each urine protein sample are performed to determine the protein species and the quantification of each protein in each urine protein sample. Urinary proteome data, for example, U001-1 (a urine protein sample made from four urine samples collected by volunteers U001 for 24 hours), the urine proteome data are shown in Table 2, which includes 4 collected in 24 hours. The sample involves quantitative data of 1615 proteins, limited to the length, in which only part of the protein data is extracted;

5) Each urinary proteome data was sequentially combined according to the method of the foregoing four to obtain an intra-individual urinary proteome data set for each volunteer of 17 healthy volunteers with different sampling time spans. Take U001 volunteer as an example. The sub-data set of the individual urinary proteome is shown in Table 3. It contains the quantitative data of 3264 proteins involved in the 62 samples collected from the volunteer for 314 days, which is limited to the space. Take some protein data;

6) According to the method of the foregoing four, different sub-data sets are determined according to different people and different sampling time spans (as shown in Table 3), and the distribution range of the coefficient of variation of all urine protein quantitative data in each sub-data set is calculated. Assessing physiological fluctuations or differences within the individual of a healthy human urine proteome with different sampling time spans;

7) Using the random resampling method, the sub-datasets of the two volunteers with the longest sampling time span (314 and 264 days) (including 62 and 51 urine proteome data, respectively), as shown in Table 3, U001 The volunteer sub-dataset, as well as the sub-dataset of U002 volunteers (limited to the data omitted here), were analyzed to determine the number of samples required to cover physiological fluctuations or differences within the healthy human urine proteome.

The data set of this embodiment includes short-term (24 hours, 3 consecutive days) or long-term sampling (over 60 days) data of 17 volunteers, and the total sampling time span of each volunteer is 5 days to 314 days. Daily morning urine samples or 24-hour urine samples; results obtained a sub-data set BCM containing a total of 319 urine proteome data (see Table 4).

According to different volunteers, the sub-data set BCM is divided into sub-data sets of different individuals (see Table 3); in these sub-data sets, according to whether it is continuous sampling within 24 hours or sampling for 3 consecutive days, further Divided into different sub-data sets. These sub-datasets can be used to assess the range or differences in physiologic fluctuations of urinary proteome within 24 hours, consecutive 3 days, and greater than 60 days in healthy individuals. The results are shown in Figures 1 and 2 (horizontal axis is the different sub-data sets for different individuals) The vertical axis is the coefficient of variation). among them:

Figure 1 shows the 24-hour urinary proteome physiological fluctuation data for individuals from four volunteers (U001 and U002) for a total of four 24-hour sub-data sets, each sub-data set including 3-5 urine proteome data ( For example, Table 2 shows data for 4 urine samples of volunteer U001 collected within 24 hours, each urine sample having 1 proteomic data, and then combined into a 24-hour sub-data set). For each sub-data, the protein with quantitative data in all urine samples is obtained, and the coefficient of variation of the quantitative data (the standard deviation of the quantitative data/the mean of the quantitative data) is obtained, and the coefficient of variation of all the proteins in the sub-data set meets the requirements. The range is displayed using a box-plot to represent the physiological fluctuation range of the 24-hour urine proteome within the individual. The median coefficient of variation of the physiological fluctuations of the 24-hour urine proteome of the four sub-datasets ranged from 0.29 to 0.33, and the coefficient of variation of the most variable protein was 2.0 (see U11-2 in the left section of Figure 1). .

The physiological volatility data of the urinary proteome in three consecutive days was from 35 sub-datasets of 16 volunteers (U001-U005, U007-U017), and each sub-dataset included 3 urinary proteome data (sampled every morning) The composition of the urine proteome data). Use and evaluate the same physiological fluctuations in the 24-hour urine proteome Methods The distribution coefficient of variation coefficient of each sub-dataset urinary proteome was obtained to represent the physiological fluctuation range of the urinary proteome in the individual for 3 consecutive days (see the right part of Figure 1). The median coefficient of variation of physiological fluctuations in the urinary proteome for 3 consecutive days was 0.23-0.5, slightly higher than the quantitative fluctuation of the urinary proteome within 24 hours.

The urinary proteome physiological fluctuation data for more than 60 days in vivo was from 17 sub-datasets of 17 volunteers, and each sub-dataset included 6-62 urine proteome data with a sampling time span of 61-314 days. For sub-data sets that include less than 30 urinary proteome data, when a protein has quantitative information in at least 3 urine samples, the coefficient of variation is calculated (if a protein cannot be detected in at least 3 urine samples, then This protein is not considered to be a common protein in the healthy human urinary proteome and therefore does not assess its physiological fluctuations; for sub-data sets including 30 or more urinary proteome data, when a protein is present in at least 10% of the urine sample In the case of quantitative information (proteins that cannot be detected in at least 10% of urine samples, this protein is not considered to be a common protein in the healthy human urine proteome, so its physiological fluctuations are not evaluated) and its coefficient of variation is calculated. The physiological fluctuation range of the urine proteome in each sub-dataset is expressed by the distribution range of the coefficient of variation of all eligible proteins (see Figure 2). The median coefficient of variation of physiological fluctuations in the long-term urinary proteome in vivo was 0.45-0.87 (see Figure 2), which was significantly higher than the physiological fluctuations in the urinary proteome of individuals within 24 hours and 3 consecutive days.

The data in Figure 2 also shows that there is no linear relationship between the physiological fluctuations of the urinary proteome in the individual and the time span of sampling, which indicates that the physiological fluctuations of the urinary proteome in the individual do not change indefinitely with time, but in a limited Within a stable range. Therefore, it is feasible to establish a quantitative reference range for the individual urine proteome according to the physiological fluctuation range of a person's intra-urine proteome.

Furthermore, this example also uses two of the largest individual urinary proteome sub-datasets (containing 62 and 51 urinary proteome data, respectively) to analyze at least how many different samples are needed to cover a stable urinary proteome physiology in an individual. Range of sexual fluctuations. In each sub-data set, only proteins with quantitative information in at least 10% of the urine samples were involved in the analysis. Using random resampling methods, 3-25 urinary proteome data were randomly selected from each sub-data set to form a sub-data set with sample sizes of 3-25. In order to avoid the interference caused by sampling error, this process is repeated 100 times, so that each sample size will get 100 sub-data sets generated by repeated random extraction, and calculate the quantitative mean of each protein in each sub-data set ( In this way, each protein will have 100 mean values. Then, based on the 100 mean values of each protein, the mean value of the quantitative mean and the standard deviation of the quantitative mean are calculated, and the coefficient of variation of the quantitative mean is further obtained. Finally, the box plot is displayed. The distribution range of the quantitative mean coefficient of variation of all proteins under a certain sample size (see Figure 3). Figure 3 is a separate data set from two independent individuals (A from U001, B from U002). The results clearly show the quantitation of protein in the urine proteome after detecting about 15 urine protein groups in a person. The mean value began to stabilize, indicating that the physiological fluctuation range of the individual's urinary proteome has been substantially covered.

The types of proteins included in each sub-data set used to assess physiological fluctuations in healthy individuals The meter information is shown in Table 1.

Table 1. Sub-dataset statistics used to assess physiological fluctuations in healthy individuals

Table 2: Urine proteome data for U001-1 urine protein samples

Table 3: Urinary Proteome Subset of U001 (containing 3264 proteins out of 62 samples collected on 314 days)

Table 4.17 Volunteers 319 urine proteome sub-data sets BCM

Example 2, establishing a data set for assessing physiological fluctuations and differences between healthy human urinary proteome individuals, and assessing physiological fluctuations between urinary proteome individuals

The data collection of the healthy human urine proteome was the same as in Example 1.

This example collected a sub-dataset BPRC consisting of 178 urine proteome data from 150 volunteers (see Table 5).

Table 5. 178 urinary proteome data sub-data sets BPRC including 150 healthy volunteers

The sub-dataset BPRC and the sub-dataset BCM were combined to obtain 497 urinary proteome data sets A including 167 healthy volunteers (integration Tables 4 and 5, omitted here). Data set A can also be based on the gender of the volunteer Divided into male and female urine proteome sub-data sets or other sub-data sets. Sub-dataset BCM (including 319 urine proteomic data from 17 healthy volunteers) can be used to assess physiologic fluctuations and differences between urinary proteomes that have been sampled by a small number of individuals; sub-dataset BPRC (including 150 healthy volunteers) The 178 urinary proteome data can be used to assess physiologic fluctuations and differences between individuals in the urinary proteome with a small or single sampling of the majority; Male (including 343 urinary proteome data from 98 healthy volunteers) And the female (Female, including 154 urine proteomics data from 69 healthy volunteers) urinary proteome sub-datasets can be used to assess physiological fluctuations and differences between individuals of different sex urinary proteomes. Only proteins with quantitative information in at least 10% of the urine samples in each sub-data set were involved in the assessment of physiological fluctuations and differences in the urine proteome of each sub-dataset. The method of evaluation is still to calculate the coefficient of variation of each protein in the corresponding sub-data set, and then display the distribution range of the coefficient of variation of the desired protein in each sub-data set in a box plot to evaluate the corresponding urinary proteome Physiological fluctuations and differences (see Figure 4).

Data set A composed of 497 urine proteome data of healthy people and 350 urinary proteome data of healthy people sub-data set A1 can be used to establish quantitative reference range of healthy human urine proteome. Figure 4 shows 6 sub-data sets and The inter-individual physiological fluctuation range of the middle urinary proteome is very similar, and the median coefficient of variation is between 1.01-1.19, which also indicates that the data set A or the sub-data set A1 basically covers the physiological fluctuations and differences among the healthy urinary proteome individuals. . However, the range of physiological fluctuations between individuals is significantly higher than the range of physiological fluctuations within individuals (Figure 4, Figure 2 and Figure 1).

See Table 6 for statistical information such as the types of proteins included in each sub-dataset used to assess physiological fluctuations and differences between healthy individuals.

Table 6. Statistical data for each sub-set used to assess physiological fluctuations and differences between individuals in healthy individuals

Example 3: Establishing a Healthy Human Urine Proteome Quantitative Reference Range and a Healthy Human Urine Proteome Database

The above Examples 1 and 2 systematically evaluated the physiological fluctuations and differences between individuals and individuals in the healthy human urine proteome, and showed that the collected data can cover the individuals and individuals of the healthy human urine proteome. Physiological fluctuations and differences.

The Healthy Human Urine Proteome Total Data Set A (integrated Tables 4 and 5, containing a data set of 497 urinary proteomes from 167 healthy individuals) was randomly divided into three sub-data sets, of which the first sub-data set A1 Including 350 urine proteome data for healthy people, the second sub-data set A2 includes 100 urine proteome data for healthy people, and the third sub-data set A3 includes 47 urine proteome data for healthy people. In this example, the quantitative data of the healthy human urine proteome is established using the data of the total data set A and the sub-data set A1, respectively.

The method of establishing the quantitative reference range is divided into two types: parameter and non-parameter. The parameter reference method is used to establish the quantitative reference range. The data must conform to the normal distribution, so that the percentage of the coverage target can be calculated according to the statistical parameters (mean and standard deviation) of the data. The upper and lower limits of the reference range of the population, such as the mean plus or minus 2 times the standard deviation covers 95% of the individuals. However, the parameter method cannot be used when it is not clear whether the data conforms to the normal distribution.

The nonparametric method does not require statistical distribution of the data. Individuals that actually cover the target percentage by the upper and lower limits of the reference range according to the percentile method, such as the 2.5th and 97.5th percentiles, cover 95% of the individuals. In view of the fact that the quantitative data of some proteins in the data set conform to the normal distribution and some do not conform to it, for the convenience of calculation, this example adopts the non-parametric method to establish a quantitative reference range for the healthy human urine proteome. The specific results are shown in Table 7-1 and Table 7-2.

According to the data in Table 7-1 (using the data of data set A to establish a quantitative reference range for healthy human urine proteome), the healthy human urine protein DYNC1H1 is taken as an example, and the quantitative values of the 2.5th and 97.5th percentile levels (0.024-11.344) Covers the quantitative fluctuation range of the protein in 95% of the 497 urine samples; the quantitative value of the 5th and 95th percentile levels (0.918-8.964) covers 90% of the protein in 497 urine samples. The range of quantitative fluctuations.

According to the data in Table 7-2 (using the data of sub-dataset A1 to establish a quantitative reference range for healthy human urine proteome), and the quantitative value of the 99.5th percentile is the upper limit of the quantitative reference range, and the healthy human urine protein DYNC1H1 is For example, the quantitative values of the 2.5th and 97.5th percentile levels (0.044-10.962) cover the quantitative fluctuation range of the protein in 95% of the 350 urine samples; the quantitative value of the 99.5th percentile (19.279) The upper limit of the reference range is quantified.

In the case where the study is not divided into two stages of discovery and verification, the range of values established by data set A can be used; in the case where the research must use two stages of discovery and verification, the range of values established by A1 can be used.

The Healthy Human Urine Proteome Database is established according to the above quantitative reference range of the healthy human urine proteome, and the database includes the above identified sub-data sets (such as Table 1 - Table 5), the total data set A (such as Table 6), and The healthy human urine protein species determined by total data set A or sub-data set A1 and the calculated healthy human urine proteome quantitative reference range (as in Table 7-1 or Table 7-2).

Example 4: Establishing a urinary proteome data set B of a tumor patient and establishing a tumor-associated outlier urinary protein pool C The data set process for establishing a urine proteome of a tumor patient is the same as in Example 1.

This example collected 154 urinary proteome data from 154 patients including 7 solid tumor types to establish a urinary proteome data set B for tumor patients (see Table 8-2). Among them, 17 cases of bladder cancer, 4 cases of breast cancer, 25 cases of cervical cancer, 22 cases of colorectal cancer, 14 cases of esophageal cancer, 47 cases of gastric cancer and 25 cases of lung cancer. Tumors were established using the Healthy Human Urine Proteome Total Data Set A in Example 2 (integration of Tables 4 and 5, including 497 urine proteomic data of 167 persons) and the urinary proteome data set B of tumor patients in this example. Related outliers urinary protein library C, the specific process is as follows:

The Healthy Human Urine Proteome Dataset A (dataset containing 497 urinary proteomes from 167 healthy individuals) was randomly divided into three sub-data sets. The first sub-dataset A1 includes 350 healthy human urine proteome data to establish a quantitative reference range for healthy human urine proteome (using the percentile method); the second sub-dataset A2 includes 100 healthy individuals. Urine proteomic data was used to validate the ability of screened tumor-associated outlier urine proteins to differentiate between healthy and tumor patients; the third sub-dataset A3 included 47 healthy human urine proteomic data for final independent testing of validated tumor-associated The group urine protein library distinguishes the ability of healthy people and tumor patients. The urinary proteome dataset of tumor patients was also randomly divided into training subdataset B1, validation subdataset B2, and test subdataset B3 (see Table 8-1) for the corresponding health according to the corresponding number of 7 tumors. The Human Urine Proteome Subset (A1-A3) together complete the establishment of a tumor-associated outlier urinary protein pool. The B1, B2, and B3 sub-data sets included urine proteomic data for 45, 61, and 48 tumor patients, respectively. Test sub-dataset B3 is no longer involved in the discovery and validation of tumor-associated outliers to ensure independence from the ability of the ultimately established tumor-associated outlier urinary protein pool to differentiate between healthy and tumor patients. .

Table 8-1. B distribution of urinary proteome datasets in tumor patients

The data of 154 tumor urinary proteomes are shown in Table 8-2.

Table 8-2. Urine Proteome Dataset B for Tumor Patients

The specific process of tumor-related outlier screening and database construction is as follows:

(1) A healthy human urine proteome quantitative reference range was established based on the first healthy human urinary proteome sub-data set A1 using the method of Example 3. Here, the quantitative value of the 99.5th percentile of the quantitative data of each urine protein in the 350 urine proteome of the first sub-data set A1 is the upper limit of the quantitative reference range;

(2) Each urine proteome data in the training sub-data set B1 including 45 urine proteome data of the tumor patient is screened using the upper limit of the reference range established in (1), if a protein is in at least two samples The upper limit of the reference range is included in the post-candidate tumor-associated outlier urinary protein pool. When all training data were screened, a candidate tumor-associated outlier urine protein library C1 was generated.

(3) A reference range established in (1) for each urinary proteome data in the validation sub-dataset B2 including the sub-dataset A2 of 100 urinary proteome data of healthy persons and 61 urinary proteome data of tumor patients The upper limit is screened so that each urine proteome produces a sample-specific outlier urine protein library C2. The total protein in each sample specific outlier urine protein library C2 was compared with the protein in the candidate tumor associated outlier urine protein library C1 generated in (2) to see how many identical proteins were in the two pools. The more the sample-specific outlier urinary protein pool C2 is the same as the candidate tumor-associated urinary protein pool C1, the closer the sample is to the tumor patient's sample. The p-value of the same protein overlap in the two pools was calculated using the hypergeometric test (calculated as shown in Table 9).

The sub-dataset A2 of the healthy human urine proteome data and the urinary proteome validation subdataset B2 of the tumor patient obtained a total of 161 corresponding hypergeometric distribution test p values, and the ROC curve was drawn using these p values (receiver operating characteristic curve, ROC). ) was used to examine the ability of the candidate tumor-associated outlier urinary protein pool C1 generated in (2) to verify the urinary proteome of healthy and tumor patients in the sub-dataset B2. ROC song The vertical axis of the line is 0-1, no unit, which is used to measure the sensitivity of the urine protein group in healthy people and tumor patients. The closer to 1 is, the higher the sensitivity is; the horizontal axis is the false positive rate, and the scale is also 0. -1, no unit, distinguishing the specificity of the urine proteome of healthy people and tumor patients = (1 - false positive rate), the closer the difference is to 1 the higher the specificity. The ideal sensitivity and specificity are 1, and the area under the ROC curve is 1, so the area under the ROC curve can be used to measure the level of discrimination. In addition, the corresponding hypergeometric distribution test p value can be determined according to the expected sensitivity or specificity as a card value (Pc value) to distinguish between healthy people and tumor patients. In this application, the corresponding card value Pc is determined with a specificity of 95%.

Table 9. Hypergeometric distribution test contingency table

q(C1∩C2)q(C1∩C2)	m-q(C1-C1∩C2)M-q(C1-C1∩C2)	m(C1)m(C1)
k-q(C2-C1∩C2)K-q(C2-C1∩C2)	n-k+q(T-C1-C2+C1∩C2)N-k+q(T-C1-C2+C1∩C2)	n(T-C1)n(T-C1)
k(C2)k(C2)	15447-k(T-C2)15447-k(T-C2)	15447(T)15447(T)

Note: C1-tumor related outlier protein library, the number of proteins included is m;

C2-sample specific outlier protein library, the number of proteins included is k;

T-protein detected in the urine proteome of all healthy people and tumor patients, the number of proteins included is 15447;

C1∩C2- represents the intersection of C1 and C2, and the number of proteins included is q.

(4) The above (3) is the urinary proteome data of 106 tumor patients (48 urinary proteome data were randomly selected from the data of 154 tumor urinary proteome data in B data set to generate tumor test sub-data sets. A training sub-data set B1 (containing 45 tumor urine proteome data) and a corresponding validation sub-data set B2 (containing 61 tumor urine proteome data) randomly generated in the remaining data after B3). In order to avoid the sampling error caused by a random sampling, 100 random samplings were performed on the urinary proteome data of 106 tumor patients, and 100 pairs of training sub-data sets and verification sub-data sets (100 pairs B1 to B2) were obtained. The same analysis in the above (3) was performed for each pair of sub-data sets (B1 to B2), and 100 candidate tumor-associated outlier urine protein pools C1 and 100 ROC curves were obtained, which corresponded to the area under the maximum ROC curve (0.957). The candidate tumor-associated outlier urinary protein pool C1 was identified as the final tumor-associated outlier urinary protein pool C (containing 509 tumor-associated outliers, see Table 10), and the Pc value at specificity of 95% was 1.78×10 ^-8 The corresponding sensitivity (=1 - false negative rate) is 85.2% as shown in Figure 6 B. When the hypergeometric distribution test p value of the sample to be analyzed is greater than Pc, the sample is considered to be a healthy human sample, and when it is smaller than Pc, the sample is considered to be a tumor patient sample.

(5) Finally, using the test sub-data sets A3 and B3 (containing urinary proteome data of 47 healthy people and 48 tumor patients) that are completely independent (referring to the training and verification process never participated), obtained in (4) above. The final tumor-associated outlier urinary protein pool area C is tested for the ability of healthy people and tumor patients. The method is the same as (3) above, and the p-value of the hypergeometric distribution test of the urine proteome of each healthy person and tumor patient is obtained. And comparing with the card value Pc determined in the above (4) to determine that each urine proteome belongs to a healthy person or a tumor patient, according to the false positive Sex and false negative rates determine the sensitivity and specificity of tumor-associated outlier urinary protein pools to distinguish healthy and cancer patients. For example, 2 of 47 healthy people were misclassified to the tumor group (false positive rate was 4.26%), and 8 of 48 tumor patients were misclassified into the healthy group (false negative rate 16.67%), according to the test As a result of the dataset, the tumor-associated outlier urinary protein pool distinguishes the sensitivity (=1-false negative rate) of healthy people and tumor patients by about 85%, and the specificity (1-false positive rate) is greater than 95%, as shown in Fig. 6. C frame.

Table 10. Tumor-associated outlier urine protein library C

Note: The number in parentheses after the first line of various cancers is the number of cases of the tumor urine sample;

The numbers in the table represent the number of times the corresponding protein is an outlier in the corresponding tumor sample.

The identified 509 tumor-associated outliers were: A1BG, A2M, ABCB7, ABCD4, ABCE1, ABHD11, ABHD12, ABHD14B, ACADM, ACADSB, ACE2, ACO2, ACOT9, ACSL3, ACSM2A, ACSM2B, ACTR1B, ADD1, AGT, AHNAK2 AHSG, ALDH1L2, ALDH3A1, ALDH3A2, ALDH3B1, ALDH4A1, ALDOC, AMY2A, AMY2B, ANGPTL6, ANK1, ANPEP, ANXA1, ANXA10, ANXA2, ANXA3, ANXA4, ANXA5, ANXA6, APMAP, APOB, APP, AQP7, ARFIP1, ARG1 ARHGAP1, ARL13B, ARL6IP5, ARL8A, ARMC9, ARRDC1, ASNA1, ASPH, ATP13A3, ATP2A2, ATP6AP1, ATP6V0A1, ATP6V0C, ATP6V1B1, ATP6V1B2, AZU1, B3GNT3, BBOX1, BLMH, BPI, BPIFB1, C14orf166, C19orf59, C1orf123, C1QB, C1QC, C3, C4A, C4BPA, C5, C6orf211, C8A, C8B, C9, CAMK2G, CANT1, CCDC22, CCDC64B, CCT4, CCT6A, CDC42BPA, CDH11, CEACAM1, CEACAM6, CEACAM8, CECR5, CERS3, CFB, CHCHD3, CHI3L1 CHP1, CLCA4, CLCN7, CLPTM1, CLRN3, CMBL, CNDP2, CNN3, COL12A1, COL4A2, COMP, COPA, COPS7A, CORO1C, CPT1A, CPVL, CRAT, CRHBP, CRP, CRYAB, CRYL1 CTBS, CTNNA1, CTSG, CTSH, CWH43, CYP1A1, DDAH2, DDOST, DDX6, DERL1, DHCR24, DHX15, DIRC2, DKC1, DMTN, DNAJA1, DNAJB1, DNAJC7, DNASE1, DNM2, DOCK2, DOCK5, DOCK9, DPM1, DPP4, DPT, DSCR3, ECHS1, EEF1A1 EIF4A1, ELMO1, ELMO3, EMC1, EMD, EMILIN1, ENO1, ENOPH1, ENPEP, ENPP4, ENPP6, ENPP7, EPB41, EPB42, EPCAM, EPDR1, EPHA2, EPPK1, EPX, ERAP1, ERLIN1, ERLIN2, ERMP1, ESRP1, ETF1 FAM151A, FARP1, FCAR, FCN2, FDFT1, FDXR, FGA, FGB, FGG, FGL1, FGL2, FGR, FLOT2, FN3KRP, FNBP1L, FOLH1, FRK, GALK2, GALM, GALNT1, GDF15, GIPC2, GLDC, GLUD1, GLYAT, GNS, GOLM1, GOLT1B, GPD2, GPR110, GPR126, GPR137B, GPR56, GPX3, GSTK1, GSTT1, HBB, HDLBP, HECTD3, HEXA, HEXB, HGD, HLA-A, HLA-B, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB5, HMOX2, HNRNPA0, HNRNPA1, HNRNPD, HNRNPF, HNRNPH3, HNRNPK, HNRNPM, HNRNPU, HSP90B1, HSPA2, HSPA6, HSPA8, HSPA9, HYOU1, IDH2, IDH3A, IGFBP7, IGLL1, ILF3, IMPDH2 IQCC, ITIH1, ITIH2, KCNJ15, KIAA0319L, KIF13B, KRT18, KRT20, KRT7, KRT8, LAMP1, LAMP1, LAMTOR1, LBR, LLGL2, LMF2, LMNB2, LONP1, LPCAT3, LRG1, LRRK2, LZTFL1, MAL2, MARVELD3, MCCC1 MCRS1, MFSD1, MGAM, MITD1, MLYCD , MMP7, MNDA, MPO, MPP1, MRPL12, MRPL39, MRPS18B, MRPS22, MSMO1, MTAP, MTCH1, MTHFD1, MTOR, MUC20, MUT, MVP, MYO18A, MYO1B, MYO1D, NAMPT, NCF1, ND4, NDFIP1, NDUFA10, NDUFA9 , NDUFS3, NDUFS8, NNT, NPTN, NSDHL, NT5C3A, NUDT9, NUMA1, OAT, OGFOD3, OGN, OLA1, ORM1, P4HB, PAPSS2, PCCA, PCCB, PCK1, PCK2, PDHA1, PDIA3, PDP1, PEF1, PFKFB2, PFKL , PGD, PHB2, PHPT1, PI4K2A, PICALM, PIP4K2A, PIP4K2C, PITRM1, PLA2G15, PLAU, PLCG2, PLEKHF2, PNKD, PON1, PPP2R2A, PRDX4, PRKAB1, PRKACA, PSMA5, PSMA7, PSMC3, PSMC4, PSMD3, PSMD7, PSMF1 , PTPLAD1, PTPRC, RAB11B, RAB12, RAB24, RAB32, RAB7A, RAB9A, RDH13, RELA, RILPL2, RNASE3, RNASET2, ROGDI, RPN1, RPN2, RPS3, RPS6KA1, RPS9, RPTOR, RRAGC, RRAS2, S100A7, SCAMP3, SCARB1 , SCARB2, SEC22B, SEPT9, SERPINA1, SERPINA3, SERPINA7, SERPIND1, SF3B1, SF3B3, SGPL1, SIAE, SIRT5, SLC12A6, SLC12A7, SLC12A9, SLC13A2, SLC15A1, SLC15A4, SLC17A1, SLC17A3, SLC17A 5. SLC1A5, SLC22A11, SLC25A10, SLC25A13, SLC25A24, SLC25A4, SLC25A5, SLC26A11, SLC26A4, SLC2A1, SLC30A2, SLC34A2, SLC35F2, SLC35F6, SLC38A7, SLC3A1, SLC46A1, SLC4A1, SLC6A19, SLC6A8, SLC7A9, SLC9A3, SLFN5, SMPDL3A, SMPDL3B, SNRNP200, SNX27, SORT1, SPAG9, SPARCL1, SPNS1, SPRYD4, SPTA1, SPTB, SPTLC1, SSR1, ST13, STAP1, STARD3NL, STC1, STIM1, STK11IP, STOM, STRN, STT3A, STUB1, STX3, STX7, STX8, STXBP1, SUCLA2, SUCLG2, SULT1A1, SULT1C2, SUN2, SVIL, TACSTD2, TAOK1, TARDBP, TBC1D9B, TBCB, TBL2, TCIRG1, TFRC, TGM2, TGM3, TIMM44, TIMM50, TM9SF2, TM9SF3, TM9SF4, TMBIM1, TMED2, TMED4, TMEM104, TMEM106A, TMEM160, TMEM176A, TMEM176B, TMEM192, TMEM205, TMEM27, TMEM40, TMEM55B, TMEM9, TMLHE, TOMM40L, TOP2B, TPCN1, TPCN2, TPM3, TRIM14, TRIP10, TST, TTC38, TUFM, TXNDC5, TYMP, UGT1A6, UGT1A9, UGT2B7, UPK3B, VAC14, VAMP7, VAPA, VAPB, VASN, VIM, VKORC1L1, VNN2, VPS37C, VPS4A, VSNL1, VTI1B, VWA5A, WASH1, WASL, ZADH2, ZNRF2.

The 509 outliers in the cancer outlier protein library (C) identified in this example are tumor-specific proteins, which can be used as tumor markers for research and development of various early detection or monitoring of cancer based on urine protein detection. In a service, kit or other product.

By using the method of the present embodiment, the type of disease to which the urine sample is directed can be adjusted, and it can be used to develop services and products (such as protein markers of specific diseases) for classifying different diseases and conditions, which are not enumerated here, but Similar changes made by those skilled in the art with reference to this embodiment also belong to the present disclosure.

Industrial applicability

The invention provides a method for establishing a quantitative reference range of a healthy human urine proteome and a database of healthy human urine proteome, and further provides a method for obtaining a disease-related urine protein marker and a tumor-related outlier urine protein library, which can be better excluded. Interference from physiological fluctuations and inter-individual differential proteins during the discovery of urinary protein biomarkers provides a basis for clinical testing and scientific experiments, and is suitable for industrial applications.

Claims

A method of establishing a quantitative reference range for a healthy human urine proteome includes the following steps:

1) Sampling: collecting urine samples of a healthy number of healthy people;

2) preparing a urine protein sample: each urine sample collected is made into a urine protein sample;

3) Detection: mass spectrometry is performed on each urine protein sample to obtain mass spectrometry data of each urine protein sample;

4) Search and quantification: perform database search, peptide quantification and protein splicing assembly on the mass spectrometry data of each urine protein sample, determine the protein species in each urine protein sample and quantify each protein to form a urine proteome data;

5) Different sub-data sets are determined for different people and different sampling time spans, including: urinary proteome data of all urine protein samples of individual individuals with different sampling time spans are collected to obtain the individual's intra-urine proteome sub-data set. (BCM); urinary proteome data of all urine protein samples collected by a small number of people or a single sample is collected to obtain an inter-individual urinary proteome sub-data set (BPRC);

6) Calculate the distribution range of the coefficient of variation of all urine protein quantitative data in each sub-data set to assess physiological fluctuations in the individual;

7) Using the method of random resampling, analyze the sub-datasets of the two individuals with the longest sampling time span, and determine the number of samples needed to cover the physiological fluctuations or differences in the healthy human urine proteome;

8) Collect the total number of sub-data (BCM and BPRC) and obtain the total data set A of healthy human urine proteome data; at least 10% of the urine samples in each sub-dataset or total data set have quantitative information To participate in the assessment of the assessment of physiological fluctuations and differences between individuals in the urinary proteome of each sub-dataset or total dataset;

9) Calculate the quantitative reference range of the healthy human urine proteome using the data of the total data set A.
The method according to claim 1, wherein when the data in step 9) conforms to the normal distribution, the quantitative reference range is established by the parameter method, and the reference range of the population covering the target percentage is calculated according to the statistical parameter (mean and standard deviation) of the data according to the formula. Upper and lower limits (if the mean plus or minus 2 times the standard deviation covers 95% of the individuals).
According to the method of claim 1, when the data in step 9) is inconsistent with the normal distribution, the quantitative reference range is established by the non-parametric method, and the upper and lower limits of the reference range are determined according to the percentile method to actually cover the target percentage. Individuals (eg, the 2.5th and 97.5th percentiles cover 95% of individuals).
The method according to claim 1 or 2 or 3, wherein different sub-data sets are determined for different people and different sampling time spans, and sub-data sets formed by urine samples with a small number of sampling times are used to evaluate a minority number of times. Sampling urinary proteome physiologic fluctuations and differences; sub-data sets formed by urine samples with a small number of sampling times are used to assess physiological fluctuations between urinary proteome individuals with a small or single sampling of most people And differences; male and female urinary proteome sub-data sets can be used to assess physiologic waves between urinary proteomes of different genders Movement and difference.
The method according to claim 4, wherein the method of evaluating is to calculate a coefficient of variation of each of the eligible proteins in the corresponding sub-data set or the total data set, and then displaying the sub-data sets or the total data sets in a box plot to meet the requirements. The distribution of the coefficient of variation of the protein is used to assess the physiological fluctuations and differences between the corresponding urinary proteome.
The method according to any one of claims 1 to 5, wherein the step 2) uses a method based on ultracentrifugation and reduction to obtain a urine protein sample, that is, a sediment suspension resuspension buffer (50 mM Tris, 250 mM sucrose, pH 8) after centrifugation of the urine sample. .5) Resuspend, add dithiothreitol, heat to remove most of the urinary protein in the sample, wash with washing buffer (10 mM triethanolamine, 100 mM sodium chloride, pH 7.4) and centrifuge. The set of precipitates is a urine protein sample of the urine sample.
The method according to claim 6, wherein the urine protein sample is separated by polyacrylamide gel electrophoresis (SDS-PAGE), cut into 6 strips for in-gel digestion, and then combined into two components. The peptide sample is used as a urine proteome, and the 2-component peptide sample is detected by LC-MS/MS to obtain the urine protein sample mass spectrum data for each urine sample; and the step 4) is for the mass spectrometry output. The data was analyzed to determine the proteins contained in the data produced by the mass spectrometry and to obtain a first-order quantitative result for all peptides, thereby obtaining corresponding proteome data for each urine protein sample.
The method according to claim 4, wherein physiologic fluctuations and differences in the urinary proteome of healthy individuals in three different sampling time spans (24 hours, 3 consecutive days, and more than 2 months) are evaluated, and the evaluation method is determined. The distribution range of the coefficient of variation (the standard deviation of the protein quantitative data/the mean of the protein quantitative data) of the quantitative data of each protein in the corresponding sub-data set;

The sub-dataset sampled every 24 hours or 3 consecutive days includes 3-5 urine proteomic data, and for those proteins with quantitative data in 3-5 urine samples, calculate the coefficient of variation and finally obtain each sub- The data set all meets the distribution range of the coefficient of variation of the required protein and is displayed in a box-plot;

The sub-dataset with a sampling time span of more than 2 months includes 6-62 urinary proteome data for those at least 3 (<30 urinary proteome subdata sets) or 10% urine samples (>30) The protein with quantitative data in the sub-dataset of the urinary proteome) calculates the coefficient of variation, and finally obtains the distribution range of the coefficient of variation of all the required proteins in each sub-data set, and displays it in a box-plot.
The method according to claim 4, wherein the total data set and the gender sub-data set thereof are used to assess physiological fluctuations and differences between healthy human urine proteome individuals, and more than 10% urine samples per data set or sub-data set. A protein with quantitative data, the coefficient of variation of its quantitative data is calculated, and a box-plot is used to display the coefficient of variation distribution of all eligible proteins in each data set and sub-data set.
a healthy human urine proteome database comprising the sub-data set identified in any one of claims 1 to 9, The total data set, and the healthy human urine protein type determined according to the data set and the calculated quantitative reference range of the healthy human urine proteome; the healthy human urine proteome quantitative reference range includes Table 7-1 or Table 7-2 And covering 2025 urine proteins and their values.
A method of establishing a quantitative reference range for healthy human urine protein, comprising the following steps:

Sampling: collecting urine samples from healthy people;

Sample preparation: the collected urine sample is made into a urine protein sample;

Detection: detection of urine protein samples to obtain protein detection data of urine protein samples;

Determination: The protein detection data is classified, and each category selects the upper limit value and the lower limit value from the plurality of protein detection data to form a quantitative reference range, and the plurality of types are combined to form a quantitative reference range for healthy human urine protein.
The method of claim 11 wherein said protein detection data comprises, but is not limited to, protein species and individual protein content.
Methods for obtaining urinary protein markers associated with disease are obtained by establishing a disease-associated urinary protein library, including the following steps:

(1) The healthy human urine proteome data set A according to any one of claims 1 to 10 is randomly divided into three sub-data sets A1, sub-data sets A2 and sub-data sets A3, based on healthy human urine proteome sub-data. Set A1 uses a nonparametric percentile method to determine a quantitative reference range for a healthy human urinary proteome, with the quantitative value of the 99.5th percentile of each urinary protein in the data set being the upper limit of the quantitative reference range;

(2) extracting a part of the training sub-data set B1 from the patient's urine proteome data set B, and screening each of the urine proteome data by the upper limit of the reference range established in (1), if a certain protein is at least Two samples exceeded the upper limit of the reference range and were included in the candidate disease-associated outlier urinary protein pool; all training data were screened to produce a candidate disease-associated outlier urinary protein library C1;

(3) extracting part of the validation sub-data set B2 from the patient's urinary proteome dataset B, and screening each of the urinary proteome data in the sub-datasets A2 and B2 with the upper limit of the reference range established in (1), Each urinary proteome (sample) produces a sample-specific outlier urinary protein pool C2; each sample specific outlier urinary protein pool C2 is associated with the candidate disease generated in (2) outlier urinary protein library The proteins in C1 are compared to determine the same protein and quantity in the two libraries. The more the same protein, the closer the sample is to the patient's sample;

The hypergeometric test was used to calculate the p-values of the same protein overlap in the two libraries C1 and C2. The ROC curve was used to investigate the generation of the ROC curve (2). The candidate disease-associated outlier urinary protein pool C1 is capable of distinguishing between healthy human and patient urinary proteome in sub-data sets A2 and B2;

(4) Randomly sampling the patient's urine proteome data set B N times (N is a natural number greater than 10) to form an N pair training sub-data set B1 and a verification sub-data set B2, and performing the above (3) on each pair of sub-data sets. Same point in Analysis of N candidate disease-associated outliers urinary protein pool C1 and N ROC curves, wherein the candidate disease-associated outlier urinary protein pool C1 corresponding to the area under the maximum ROC curve was identified as the final disease-related outlier urinary protein bank. C, the outlier contained therein is a disease-associated urinary protein marker.
The method of claim 13 further comprising the step of verifying the established disease-related outlier urine protein library C:

(5) Extracting the sub-data set B3 from the patient's urinary proteome data set B completely independent (referring to the process of never participating in the training and verification process), using the sub-data sets A3 and B3 to obtain the final result in (4) above. The disease-associated outlier urinary protein library C is tested for the ability to distinguish between healthy humans and patients. The method is the same as the method of (3) above, and the hypergeometric distribution test p value of each healthy person and patient urine proteome is obtained, and the above (4) The card value Pc determined in the comparison is determined to determine whether each urinary proteome belongs to a healthy person or patient, and the sensitivity and specificity of the disease-associated outlier urinary protein pool to distinguish healthy humans and patients are determined according to the false positive rate and the false negative rate.
The method according to claim 13 or 14, wherein the step (1) determines that the quantitative reference range of the healthy human urine proteome is calculated by the non-parametric method using the data of the sub-data set A1, and the upper and lower limits of the reference range are determined according to the percentile method. Individuals who actually covered the target percentage (eg, the 2.5th and 97.5th percentiles covered 95% of the individuals).
The method according to claim 13 or 14 or 15, wherein the process of establishing the patient urinary proteome data set B in step (2) comprises:

1) Sampling: collecting urine samples of patients;

2) preparing a urine protein sample: each urine sample collected is made into a urine protein sample;

3) Detection: mass spectrometry is performed on each urine protein sample to obtain mass spectrometry data of each urine protein sample;

4) Search and quantification: perform database search, peptide quantification and protein splicing assembly on the mass spectrometry data of each urine protein sample, determine the protein species in each urine protein sample and quantify each protein to form a urine proteome data;

5) Collecting the urine proteomic data of all urine protein samples to obtain the patient urine proteome data set B.
The method according to any one of claims 13 to 16, wherein the disease is any disease, and in the embodiment, a tumor is exemplified.
The disease-related outlier urine protein library obtained by the method according to any one of claims 13 to 16 and an outlier urine protein contained therein, that is, a disease-associated urine protein marker.
The tumor-associated outlier urinary protein library obtained in the method of claim 17 and an outlier urinary protein contained therein, that is, a tumor urinary protein marker.
The tumor-associated outlier urine protein library according to claim 19, comprising 509 urine proteins, specifically A1BG, A2M, ABCB7, ABCD4, ABCE1, ABHD11, ABHD12, ABHD14B, ACADM, ACADSB, ACE2, ACO2, ACOT9, ACSL3, ACSM2A, ACSM2B, ACTR1B, ADD1, AGT, AHNAK2, AHSG, ALDH1L2, ALDH3A1, ALDH3A2, ALDH3B 1 , ALDH4A1, ALDOC, AMY2A, AMY2B, ANGPTL6, ANK1, ANPEP, ANXA1, ANXA10, ANXA2, ANXA3, ANXA4, ANXA5, ANXA6, APMAP, APOB, APP, AQP7, ARFIP1, ARG1, ARHGAP1, ARL13B, ARL6IP5, ARL8A, ARMC9 , ARDDC1, ASNA1, ASPH, ATP13A3, ATP2A2, ATP6AP1, ATP6V0A1, ATP6V0C, ATP6V1B1, ATP6V1B2, AZU1, B3GNT3, BBOX1, BLMH, BPI, BPIFB1, C14orf166, C19orf59, C1orf123, C1QB, C1QC, C3, C4A, C4BPA, C5 , C6orf211, C8A, C8B, C9, CAMK2G, CANT1, CCDC22, CCDC64B, CCT4, CCT6A, CDC42BPA, CDH11, CEACAM1, CEACAM6, CEACAM8, CECR5, CERS3, CFB, CHCHD3, CHI3L1, CHP1, CLCA4, CLCN7, CLPTM1, CLRN3 , CMBL, CNDP2, CNN3, COL12A1, COL4A2, COMP, COPA, COPS7A, CORO1C, CPT1A, CPVL, CRAT, CRHBP, CRP, CRYAB, CRYL1, CTBS, CTNNA1, CTSG, CTSH, CWH43, CYP1A1, DDAH2, DDOST, DDX6, DERL1, DHCR24, DHX15, DIRC2, DKC1, DMTN, DNAJA1, DNAJB1, DNAJC7, DNASE1, DNM2, DOCK2, DOCK5, DOCK9, DPM1, DPP4, DPT, DSCR3, ECHS 1, EEF1A1, EIF4A1 , ELMO1, ELMO3, EMC1, EMD, EMILIN1, ENO1, ENOPH1, ENPEP, ENPP4, ENPP6, ENPP7, EPB41, EPB42, EPCAM, EPDR1, EPHA2, EPPK1, EPX, ERAP1, ERLIN1, ERLIN2, ERMP1, ESRP1, ETF1, FAM151A , FARP1, FCAR, FCN2, FDFT1, FDXR, FGA, FGB, FGG, FGL1, FGL2, FGR, FLOT2, FN3KRP, FNBP1L, FOLH1, FRK, GALK2, GALM, GALNT1, GDF15, GIPC2, GLDC, GLUD1, GLYAT, GNS , GOLM1, GOLT1B, GPD2, GPR110, GPR126, GPR137B, GPR56, GPX3, GSTK1, GSTT1, HBB, HDLBP, HECTD3, HEXA, HEXB, HGD, HLA-A, HLA-B, HLA-DQB1, HLA-DRA, HLA -DRB1, HLA-DRB5, HMOX2, HNRNPA0, HNRNPA1, HNRNPD, HNRNPF, HNRNPH3, HNRNPK, HNRNPM, HNRNPU, HSP90B1, HSPA2, HSPA6, HSPA8, HSPA9, HYOU1, IDH2, IDH3A, IGFBP7, IGLL1, ILF3, IMPDH2, IQCC , ITIH1, ITIH2, KCNJ15, KIAA0319L, KIF1 3B, KRT18, KRT20, KRT7, KRT8, LAMB1, LAMP1, LAMTOR1, LBR, LLGL2, LMF2, LMNB2, LONP1, LPCAT3, LRG1, LRRK2, LZTFL1, MAL2, MARVELD3, MCCC1, MCRS 1, MFSD1, MGAM, MITD1, MLYCD , MMP7, MNDA, MPO, MPP1, MRPL12, MRPL39, MRPS 18B, MRPS22, MSMO1, MTAP, MTCH1, MTHFD1, MTOR, MUC20, MUT, MVP, MYO18A, MYO1B, MYO1D, NAMPT, NCF1, ND4, NDFIP1, NDUFA10, NDUFA9, NDUFS3, NDUFS8, NNT, NPTN, NSDHL, NT5C3A, NUDT9, NUMA1, OAT, OGFOD3, OGN, OLA1, ORM1, P4HB, PAPSS2, PCCA, PCCB, PCK1 PCK2, PDHA1, PDIA3, PDP1, PEF1, PFKFB2, PFKL, PGD, PHB2, PHPT1, PI4K2A, PICALM, PIP4K2A, PIP4K2C, PITRM1, PLA2G15, PLAU, PLCG2, PLEKHF2, PNKD, PON1, PPP2R2A, PRDX4, PRKAB1, PRKACA, PSMA5, PSMA7, PSMC3, PSMC4, PSMD3, PSMD7, PSMF1, PTPLAD1, PTPRC, RAB11B, RAB12, RAB24, RAB32, RAB7A, RAB9A, RDH13, RELA, RILPL2, RNASE3, RNASET2, ROGDI, RPN1, RPN2, RPS3, RPS6KA1 RPS9, RPTOR, RRAGC, RRAS2, S100A7, SCAMP3, SCARB1, SCARB2, SEC22B, SEPT9, SERPINA1, SERPINA3, SERPINA7, SERPIND1, SF3B1, SF3B3, SGPL1, SIAE, SIRT5, SLC12A6, SLC12A7, SLC12A9, SLC13A2, SLC15A1, SLC15A4, SLC17A1, SLC17A3, SLC17A5, SLC1A5, SLC22A11, SLC25A10, SLC25A13, SLC25A24, SLC25A4, SLC25A5, SLC26A11, SLC26A4, SLC2A1, SLC30A2, SLC34A2, SLC35F2, SLC35F 6. SLC38A7, SLC3A1, SLC46A1, SLC4A1, SLC6A19, SLC6A8, SLC7A9, SLC9A3, SLFN5, SMPDL3A, SMPDL3B, SNRNP200, SNX27, SORT1, SPAG9, SPARCL1, SPNS1, SPRYD4, SPTA1, SPTB, SPTLC1, SSR1, ST13, STAP1 STARD3NL, STC1, STIM1, STK11IP, STOM, STRN, STT3A, STUB1, STX3, STX7, STX8, STXBP1, SUCLA2, SUCLG2, SULT1A1, SULT1C2, SUN2, SVIL, TACSTD2, TAOK1, TARDBP, TBC1D9B, TBCB, TBL2, TCIRG1 TFRC, TGM2, TGM3, TIMM44, TIMM50, TM9SF2, TM9SF3, TM9SF4, TMBIM1, TMED2, TMED4, TMEM104, TMEM106A, TMEM160, TMEM176A, TMEM176B, TMEM192, TMEM205, TMEM27, TMEM40, TMEM55B, TMEM9, TMLHE, TOMM40L, TOP2B, TPCN1, TPCN2, TPM3, TRIM14, TRIP10, TST, TTC38, TUFM, TXNDC5, TYMP, UGT1A6, UGT1A9, UGT2B7, UPK3B, VAC14, VAMP7, VAPA, VAPB, VASN, VIM, VKORC1L1, VNN2, VPS37C, VPS4A, VSNL1 VTI1B, VWA5A, WASH1, WASL, ZADH2, ZNRF2.
The use of the tumor-associated outlier urinary protein library according to claim 19 or 20, wherein the proteomic data of the urine sample to be tested is obtained by using the steps 2) to 4) of claim 1 or claim 16, and using a method of hypergeometric distribution test Calculating the p value of the same protein overlap in the urine sample and the tumor urine protein outlier protein library, determining the Pc value when the specificity is 95%, and determining the urine sample to be tested when the p-value of the hypergeometric distribution test is greater than Pc For a healthy human sample, when the p value is less than Pc, it is judged that the urine sample to be tested is a tumor patient sample.
Use of the disease-related outlier urine protein library of claim 18, with claim 1 or claim Step 2) - 4) Obtain proteomic data of the urine sample to be tested, and calculate the p value of the same protein overlap in the urine sample and the disease-related urinary protein outlier protein library by using a hypergeometric distribution test method to determine The Pc value when the specificity is 95%, when the p-value of the hypergeometric distribution test is greater than Pc, it is judged that the urine sample to be tested is a healthy human sample, and when the p value is smaller than Pc, the urine sample to be tested is determined as the patient sample of the disease. .