WO2018049947A1 - Biomarker composition for detection of endometriosis and application thereof - Google Patents

Abstract

A biomarker composition for detection or risk assessment of endometriosis, and application thereof. The biomarker composition comprises at least one of 24 nucleic acids. The 24 nucleic acids have sequences represented by Seq ID No. 1 to Seq ID No. 24 respectively, or sequences having similarity of 97% or more to those represented by Seq ID No. 1 to Seq ID No. 24. The biomarker composition has excellent specificity, and can be used for early diagnosis of endometriosis, thereby providing a new approach for detection or risk assessment of endometriosis. The biomarker composition has high sensitivity and specificity, good reproducibility, and important application value. By using a genital tract specimen as a biomarker detection specimen, the material is easily available, the operation steps are simple, and continuous in-vitro detection can be implemented.

Description

Biomarker combination and application for detection of endometriosis Technical field

The present application relates to the field of biomarkers, and in particular to a biomarker combination for endometriosis detection or risk assessment of disease and its use.

Background technique

Endometriosis is a frequently-occurring disease in women of childbearing age, causing a variety of clinical symptoms. In recent years, the incidence rate has increased significantly, reaching 10%-15%, accounting for more than 30% of gynecological surgery. Endometriosis is closely related to infertility, and about 40%-50% of infertile patients have infertility caused by endometriosis. Current research suggests that microbial infection plays a very important role in infertility caused by endometriosis. In addition, many studies have shown that inflammatory factors in the peritoneal fluid of endometriosis, such as tumor necrosis factor (TNF-α) and interleukin-6 (IL-6), can interfere with certain aspects of the reproductive process. Infertility. Although endometriosis is a benign disease, its lesions are extensive, morphologically diverse, extremely aggressive and recurrent, and the destruction of organ structure and function is comparable to malignant tumors, so it is known as "benign cancer." The clinical manifestations of endometriosis are not directly proportional to the extent of the disease, so there is a lack of effective clinical diagnostic methods.

For endometriosis, although some of the currently used clinical markers, such as carcinoembryonic antigen 125 (CA125), placental protein 14 (PP14), endometrial antibodies and interleukin-6 (IL-6), etc. They have certain reference value for clinical diagnosis and tracking of recurrence, but they still lack specificity.

Therefore, the search for sensitive and specific biomarkers of endometriosis is an urgent problem to be solved.

Summary of the invention

The object of the present application is to provide a biomarker for the detection of endometriosis and a preparation method and application thereof.

In order to achieve the above objectives, the present application adopts the following technical solutions:

One aspect of the present application discloses a biomarker for the detection of endometriosis, the biomarker combination comprising at least one of twenty-four nucleic acids, each of which is Seq ID No. 1 The sequence shown in Seq ID No. 24, or a sequence having 97% or more similarity to the sequence shown in Seq ID No. 1 to Seq ID No. 24, respectively.

It should be noted that the twenty-four nucleic acids of the present application are researched and derived from a nucleic acid sequence associated with endometriosis, wherein each nucleic acid sequence is associated with endometriosis. Cause Therefore, the endometriosis test or the risk assessment of the disease can be used alone or in combination without considering the accuracy of the judgment or when the requirement is low. However, in a preferred embodiment of the present application, not only twenty-four nucleic acids are used together, but also twenty-four nucleic acids are classified according to a specific rule, and are divided into a plurality of marker groups, and each marker group is used together. Endometriosis detection or risk assessment of disease, which will be described in detail in the following preferred technical solutions.

It should also be noted that the twenty-four nucleic acids of the present application are clustered according to 97% or more similarity, and then the most representative sequence is selected from each taxon (abbreviation OTU) as a seed sequence, wherein Endometriosis has twenty-four seed sequences that are related, ie, constitutes a biomarker combination of the present application; therefore, in the biomarker combination of the present application, twenty-four nucleic acids are not limited to Seq ID No. 1 The sequence shown in Seq ID No. 24 may also be a sequence having 97% or more similarity to the sequence shown by Seq ID No. 1 to Seq ID No. 24.

It should be added that the biomarker combination for the detection of endometriosis or the risk assessment of the present application is not directly based on the detection of the combination of biomarkers with or without endometriosis detection. Or the risk assessment, but after detecting the biomarker combination, by analyzing its relative abundance, and taking the relative abundance into the random forest model to judge, the object to be tested is judged according to the probability of the random forest model output. Whether or not there is endometriosis or assessing the risk of endometriosis in the subject to be tested will be described in detail in the following technical solutions.

Preferably, another aspect of the present application discloses a biomarker combination for endometriosis detection or risk assessment, including a first marker panel, a second marker panel, and a third marker panel At least one of the groups; the first marker group consists of fourteen nucleic acids, respectively, Seq ID No. 1, Seq ID No. 2, Seq ID No. 6, Seq ID No. 7, Seq ID No. 12, Seq ID No. 13, Seq ID No. 15, Seq ID No. 17 to Seq ID No. 22, Seq ID No. 24, or Seq ID No. 1, Seq ID No, respectively. .2, Seq ID No. 6, Seq ID No. 7, Seq ID No. 12, Seq ID No. 13, Seq ID No. 15, Seq ID No. 17 to Seq ID No. 22, Seq ID No. 24 The sequence shown has a sequence of 97% or more similarity; the second marker group consists of two nucleic acids, each of which is a sequence of Seq ID No. 1, Seq ID No. 7, or Seq ID No, respectively. 1. The sequence shown by Seq ID No. 7 has a sequence of 97% or more similarity; the third marker group consists of eleven nucleic acids, and the eleven nucleic acids are Seq ID No. 3 to Seq ID No. 5, respectively. S Eq ID No. 8 to Seq ID No. 12, Seq ID No. 14, Seq ID No. 16, Seq ID No. 23, or Seq ID No. 3 to Seq ID No. 5, Seq, respectively. The sequence shown by ID No. 8 to Seq ID No. 12, Seq ID No. 14, Seq ID No. 16, and Seq ID No. 23 has a sequence of 97% or more similarity.

It should be noted that, in a preferred embodiment of the present application, twenty-four nucleic acids are reproducibly divided into three marker groups, namely, a first marker group, a second marker group, and a third marker group; Three standard The comprehensive judgment of the volunteer group can greatly improve the accuracy of detecting the endometriosis of the biomarker combination of the present application or assessing the risk of the disease.

Preferably, the first marker group is a CL marker group for performing endometriosis detection or risk assessment of a sample from the lower third of the vagina.

Preferably, the second marker group is a CU marker group for performing endometriosis detection or risk assessment of a sample from the vaginal posterior iliac crest.

Preferably, the third marker group is a CV marker group for performing endometriosis detection or risk assessment of a sample from the cervical canal.

It should be noted that the twenty-four nucleic acids in the biomarker combination of the present application actually represent 14 microorganisms in the lower third of the vagina, the posterior vaginal canal and the cervical canal; the present application passes under the vagina Twenty-four nucleic acids of 14 microorganisms in 1/3, vaginal posterior fornix and cervical canal were detected, and the relationship between their relative abundance and endometriosis was statistically analyzed to establish a random forest model. In this way, it is judged whether the subject to be tested has endometriosis or is at risk of suffering from endometriosis. Therefore, the three marker groups actually correspond to three sampling sites respectively; the samples from the three sites correspond to the respective marker groups, and are independently analyzed and judged. However, comprehensive judgment based on the results of the three can improve the accuracy of detecting the endometriosis of the biomarker combination of the present application or assessing the risk of the disease.

It should also be noted that in the lower third of the vagina, the posterior vaginal canal and the cervical canal, the number of microorganisms is far more than 14 species, and the nucleic acids of 14 microorganisms are far more than the 24 records described in this application; However, this application screens out 24 nucleic acids of 14 microorganisms according to the random forest model as a biomarker for the detection of endometriosis, providing a new measure for the detection and evaluation of endometriosis. Way.

It should be added that in the three marker groups, the CL marker group is the marker group of the lower third of the vagina, the lower third of the vagina is abbreviated as CL; the marker of the CU marker group is the marker of the posterior vaginal sample. Group, vaginal posterior hernia is abbreviated as CU; CV marker group is the marker group of cervical canal sample, and cervical canal is abbreviated as CV.

Another aspect of the present application discloses a kit for endometriosis detection or risk assessment of a disease, comprising a primer pair for detecting a biomarker combination of the present application, the positive of the primer pair The primer was the sequence shown in SEQ ID No. 25, and the reverse primer was the sequence shown in SEQ ID No. 26.

It should be noted that the biomarker combination of the present application can be present in the kit as a standard reference, and the primer pair is directly used for PCR amplification of the biomarker combination in the sample to be tested.

The other side of the present application discloses that the biomarker combination of the present application should be used in the screening of endometriosis drugs or in the preparation of kits or detection tools for endometriosis detection or risk assessment of disease. use.

It can be understood that the biomarker combination of the present application is itself studied for endometriosis, and can of course be used for the detection or risk assessment of endometriosis; and the biomarker combination of the present application can also be integrated. To some kits or tools specially designed for the detection of endometriosis to facilitate the detection and evaluation of endometriosis, as long as the biomarker combination of the present application is used, it is within the scope of protection of the present application. Inside. At the same time, because the biomarker combination of the present application can detect endometriosis or assess the risk of endometriosis; of course, it can compare the endometriosis before and after treatment. The disease condition or the risk of the disease changes to determine whether the drug used is effective for the purpose of drug screening.

A further aspect of the present application discloses a method for detecting endometriosis, comprising the following steps,

(1) performing sample collection on the object to be tested, detecting the biomarker combination of the present application in the collected sample, and analyzing the level of each nucleic acid in the biomarker combination;

(2) comparing the level of each nucleic acid measured in the step (1) with a reference data set or a reference value to obtain a detection result;

Preferably, the level of each nucleic acid is the relative abundance of each nucleic acid; the reference data set or reference value is the nucleic acid of each of the biomarker combinations derived from the endometriosis population and the non-endometriosis population control Level.

More preferably, the reference data set or reference value in step (2) is at least one of Table 5, Table 6, or Table 7; comparing the level of each nucleic acid with a reference data set or a reference value to obtain a detection result, specifically Including, using multivariate statistical models to calculate the probability of disease, preferably, the multivariate statistical model is a random forest model.

More preferably, the sample to be tested is subjected to sample collection in step (1), including collecting the lower third of the vagina sample, the posterior vaginal sputum sample and the cervical canal sample.

It should be noted that each nucleic acid fragment in the biomarker combination of the present application is a nucleic acid fragment associated with endometriosis, and therefore, by analyzing the level of the biomarker combination in the test subject, that is, relatively abundant In degree, it is possible to detect whether the subject to be tested is sick or to determine the risk of the disease.

A further aspect of the present application discloses a method for preparing endometriosis detection or disease risk assessment kit or tool by detecting a biomarker for determining endometriosis; wherein the biomarker a biomarker combination of the present application;

A method for determining endometriosis by detecting a biomarker includes the following steps,

(1) performing sample collection on the object to be tested, detecting the biomarker combination of the present application in the collected sample, and analyzing the level of each nucleic acid in the biomarker combination;

(2) comparing the level of each nucleic acid measured in the step (1) with a reference data set or a reference value to obtain a detection result;

Preferably, the level of each nucleic acid is the relative abundance of each nucleic acid; the reference data set or reference value is the level of each nucleic acid in the biomarker combination derived from endometriosis patients and non-endometriosis controls. .

More preferably, the reference data set or reference value in step (2) is at least one of Table 5, Table 6, or Table 7; comparing the level of each nucleic acid with a reference data set or a reference value to obtain a detection result, specifically Including, using a multivariate statistical model to calculate the probability of disease, preferably, the multivariate statistical model is a random forest model.

A further aspect of the present application discloses a method of screening for a drug candidate for treating endometriosis, comprising the steps of

1) separately determining the biomarker combination of the present application in the sample before and after administration, and analyzing the level of each nucleic acid in the biomarker combination;

2) judging the candidate drug according to the level of each nucleic acid in the sample before and after the drug is compared;

In step 2), comparing the levels of each nucleic acid in the sample before and after administration, specifically including calculating the probability of disease using a multivariate statistical model, preferably, the multivariate statistical model is a random forest model.

A further aspect of the present application discloses a method for detecting a microbiota in a female reproductive tract, comprising the following steps:

(1) collecting a microbial sample in the reproductive tract of the test subject, detecting the biomarker combination of the present application in the collected sample, and analyzing the level of each nucleic acid in the biomarker combination;

(2) comparing the level of each nucleic acid measured in the step (1) with a reference data set or a reference value to obtain a detection result;

Preferably, the level of each nucleic acid is the relative abundance of each nucleic acid; the reference data set or reference value is the nucleic acid of each of the biomarker combinations derived from the endometriosis population and the non-endometriosis population control Level.

Preferably, the reference data set or reference value in step (2) is at least one of Table 5, Table 6, or Table 7; comparing the level of each nucleic acid with a reference data set or a reference value to obtain a detection result, specifically including The multivariate statistical model is used to calculate the probability of disease. More preferably, the multivariate statistical model is a random forest model.

Preferably, in step (1), the microbial sample in the genital tract of the test subject is collected, specifically comprising collecting the lower third of the vaginal sample, the posterior vaginal sputum sample and the cervical canal sample of the test subject. Among them, the collection of the microbial samples in the reproductive tract can be carried out by using a conventional nylon fluff swab, which is not specifically limited herein.

It should be noted that the biomarker combination of the present application is actually based on the relationship between the microbial DNA in the female reproductive tract and endometriosis, that is, the biomarker of the present application is actually a female. The microbe OTU in the reproductive tract can reflect the state of endometriosis; therefore, the present application proposes a method for detecting microbiota in the female reproductive tract, which provides a judgment for the endometriosis or its risk by detecting the microbial population. And the basis for the assessment.

A further aspect of the present application discloses a method of preparing a biomarker combination of endometriosis comprising the following steps,

(1) Collecting microbial samples in the reproductive tract of endometriosis patients and non-patients, respectively, and performing 16S sequencing on all collected samples;

(2) Clustering the 16S sequencing results to obtain the OTU unit and the seed sequence of each OTU, and calculate the relative abundance of each OTU unit;

(3) Using the random forest model to fit the relative abundance of each OTU unit with the endometriosis state, and perform five 10-fold cross-validation to obtain the optimal OTU combination, and the optimal OTU combination. The seed sequence of OTU, a biomarker combination that constitutes endometriosis.

Preferably, in step (1), the microbial sample is collected in the genital tract, specifically comprising collecting the lower third of the vagina sample, the posterior vaginal sputum sample and the cervical canal sample.

It should be noted that the method for preparing the biomarker combination of endometriosis in the present application is to use a random forest model to fit and verify the association between the microbial DNA in the reproductive tract and endometriosis. A biomarker combination is obtained that is capable of assessing the risk or risk of endometriosis. It will be understood that the preparation method of the present application or its basic idea is not limited to the preparation of a biomarker combination for endometriosis; it can also be used to prepare a biomarker combination of similar conditions associated with the presence of microbial DNA in the reproductive tract. For example, a biomarker combination of adenomyosis.

Due to the adoption of the above technical solutions, the beneficial effects of the present application are:

The biomarker for detecting endometriosis of the present application has good specificity and can detect endometriosis well, and provides for detection or risk assessment of endometriosis. A new approach and can be used for early diagnosis of endometriosis.

Other key advantages of this application include:

(a) The biomarker combination of the present application is used for the detection of endometriosis or the risk assessment of the disease, and has the advantages of high sensitivity and high specificity, and has important application value.

(b) The genital tract sample as a biomarker combination test sample has the advantages of convenient material selection, simple operation steps and continuous in vitro detection.

(c) The biomarker combination of the present application is useful for the detection of endometriosis or for assessing the risk of disease with reproducible characteristics.

DRAWINGS

1 is a graph showing the results of infertility caused by endometriosis based on a marker group of CL at the lower third of the vagina in the embodiment of the present application, wherein a is a random forest identification as the number of OTUs increases. Endometriosis (infertility) 5 times 10 fold cross-validation error rate distribution, b is crossed The verified combined receiver operating curve (abbreviated ROC curve), the area under the curve (abbreviated AUC) is 0.8272, the shaded area represents the 95% confidence interval, and the diagonal represents the curve with an AUC of 0.5;

2 is a diagram showing the results of identifying endometriosis (infertility) based on the marker group of vaginal posterior sacral CU in the embodiment of the present application, in which a is an identification of random forests in the uterus as the number of OTUs increases. Membrane ectopic (infertility) 5 times 10 fold cross-validation error rate distribution, b is the cross-validated combination receiver operating curve (abbreviated ROC curve), the area under the curve (abbreviated AUC) is 0.5919, The shaded area represents a 95% confidence interval and the diagonal represents a curve with an AUC of 0.5;

3 is a diagram showing the results of identifying endometriosis (infertility) based on the CV marker group of the cervical canal in the embodiment of the present application, in which a is an identification of endometrial abnormalities in random forests as the number of OTUs increases. The error rate distribution of 5 cases of 10-fold cross-validation in the case of infertility (b infertility), b is the receiver's operation curve of the cross-validated combination, the area under the curve is 0.8493, the shaded area represents 95% confidence interval, diagonal a curve representing an AUC of 0.5;

4 is a ROC curve for identifying endometriosis (infertility) in a second population of the CL marker group at the lower third of the vagina in the embodiment of the present application;

5 is a ROC curve for identifying endometriosis (infertility) in a second population of the vaginal posterior sputum CU marker group in the examples of the present application;

6 is a ROC curve for identifying endometriosis (infertility) in a second population of the cervical canal CV marker group in the examples of the present application;

In the figure, the number of variables refers to the number of OTUs, where sensitivity = true positive / (true positive + false negative); specificity = true negative / (true negative + false positive).

detailed description

The biomarker of the present application is obtained based on the relationship between the microbial DNA of the three parts of the subject and the endometriosis. The biomarkers of the present application are actually the three parts. Microorganism OTU in the state of endometriosis. Specifically, in a preparation method of the present application, the correspondence or biomarker is obtained by using the relative abundance of the OTU seed sequence as a subject, endometriosis state (sick or non-diseased) For the second object, the two are fitted through a random forest model, and finally obtained through five 10-fold cross-validation. Through rigorous calculations and experimental studies, the present application finally obtained twenty-four nucleic acids of 14 microorganisms in three sites as biomarkers of the present application.

In one implementation of the present application, the marker group of the three sites can independently evaluate the risk or risk of endometriosis, but the probability of combining the three sites is used to determine whether the subject has a uterus. Endometriosis or the risk of endometriosis, the accuracy will be higher.

The terms used in this application are those that are generally understood by those of ordinary skill in the art. For a better understanding of this application, some definitions and related terms are explained as follows:

"Endometriosis" of the present application is a common gynecological disease, defined as a condition caused by the growth of endometrial tissue outside the uterine cavity. The most common endometriosis occurs in the ovaries and fallopian tubes, and may also occur in the myometrium, pelvic peritoneum, and even the bladder and large intestine. Among them, endometriosis of the ovary can form an endometrial tumor with a brown liquid inside, so there is also a "chocolate cyst" or "juliek tumor", which will affect pregnancy. "Endometriosis" and "endometriosis (infertility)" of the present application specifically refer to infertility caused by endometriosis which is common in the art. "Non-endometriosis" of the present application specifically refers to the absence of endometriosis, which is common in the art, and can be pregnant.

The levels of the biomarker materials of the present application are indicated by relative abundance.

In one embodiment of the present application, the reference value refers to a reference value or a normal value of a healthy control. It will be apparent to those skilled in the art that in the case where the number of samples is sufficient, the range of normal values, i.e., absolute values, of each biomarker can be obtained by inspection and calculation.

A "biomarker", also referred to as a "biological marker," as used herein, refers to a measurable indicator of the biological state of an individual. Such a biomarker may be any substance in an individual as long as they are related to a specific biological state of the individual to be examined, such as a disease. Such biomarkers can be, for example, nucleic acid markers (eg, DNA), protein markers, cytokine markers, chemokine markers, carbohydrate markers, antigenic markers, antibody markers, species markers ( Species/genus markers) and functional markers (KO/OG markers). The biomarkers of the present application are specifically DNA nucleic acid markers.

The term "OTU" in this application refers to the operation taxonomic units (OTU), which is in the phylogenetic study or population genetics research. In order to facilitate the analysis, a certain classification unit, such as strain, species, and genus, is artificially given. , grouping, etc., set the same flag. In the present application, the sequence is divided into an OTU according to a 97% similarity threshold, whereby a plurality of OTUs can be obtained for each of the three sites, and each OTU is regarded as a microbial species. The microbial diversity in the samples and the abundance of different microorganisms are based on an analysis of the OTU.

As used herein, "individual" refers to an animal, particularly a mammal, such as a primate, referred to as a human in the examples of the present application.

The present application will be further described in detail below by way of specific embodiments and the accompanying drawings. The following examples are only intended to further illustrate the present application and are not to be construed as limiting the invention.

Example

1. Materials and methods

1.1 Sample Collection

The sample collection in this case was assisted by a gynaecologist at Shenzhen Peking University Hospital. Excluding the cases of inflammation, the subjects were non-menstrual, non-pregnancy, non-lactation women, no endocrine and autoimmune diseases, normal liver and kidney function. No hormones or antibiotics were used for some time before sampling, no vaginal medication, vaginal lavage and cervical treatment, and no sexual life was performed within 48 hours before sampling. According to the above criteria, this example screened 49 women of childbearing age as the first group. All individuals who meet the above criteria are registered with detailed phenotypic information to understand their medical history, family history, medication history and lifestyle, and have signed informed consent.

The lower genital tract sampling is performed after the individual is admitted to the hospital, without disinfection, after emptying the urine, the lower third of the vagina (abbreviation CL), the posterior vagina (abbreviated CU), and the cervical canal (abbreviated CV) are collected in the gynecological examination bed. A sample of secretions at each site. Specifically, the sample numbers and sampling information of the 49 collection objects are number C026, C028, C033, C035, C038, C041, C045, C048, C050, C055, C056, C058, C059, C062, C063, C064, C065. Seventeen subjects were non-endometriosis patients, and seventeen subjects collected samples of CL, CU and CV; number T022, T024, T027, T028, T032, T033, T036, T039 , T041, T042, T045, T053, T056, T058, T059, T061, T062, T063, T067, T069, T070, T076, T078, T084, T085, T086, T087, T088, T090, T092, T094, T095 Twelve subjects were patients with endometriosis, and thirty-two subjects collected samples of CL, CU and CV.

Sample collection was performed using a nylon fluff swab for sample collection. Nylon fluff swabs were purchased from Chenyang Global Group CY-93050 and CY-98000. After sampling, the swab head was quickly frozen with liquid nitrogen, stored at -80 ° C, and transported to Shenzhen Huada Gene Research Institute with dry ice for subsequent experiments.

1.2 DNA extraction and 16S sequencing

In this example, DNA extraction was performed using the QIAamp DNA Mini Kit (purchased from QIAGEN). The specific extraction steps are carried out in accordance with the instructions provided by the manufacturer. The 16S rRNA gene V4-V5 hypervariable region-specific primers were used for amplification. The two primers were V4-515F and V5-907R, V4-515F was the sequence shown by Seq ID No. 25, and V5-907R was Seq ID No. The sequence shown in 26.

Seq ID No. 25: 5'-GTGCCAGCMGCCGCGGTAA-3'

Seq ID No. 26: 5'-CCGTCAATTCMTTTRAGT-3’

The PCR procedure was as follows, denaturation at 94 ° C for 3 min; then into 25 cycles: denaturation at 94 ° C for 45 s, annealing at 50 ° C for 60 s, extension at 72 ° C for 90 s; after the end of the cycle, extension at 72 ° C for 10 min. The obtained PCR product was purified by AMPure Beads (Axygen), and sequencing was carried out by chip lane sequencing, and a plurality of samples were mixed and sequenced. Therefore, the library construction requires the addition of a linker sequence after ligation of a 10 bp barcode sequence at the outer end of the primer sequence of each sample. By adding different barcode sequences to each sample, ie sample identification Sequence, distinguishing between different samples. After the library was constructed, V5-V4 reverse sequencing was performed by Ion torrent PGM sequencing platform. The above library construction and sequencing were carried out by Shenzhen Huada Gene.

1.3 16S sequencing data processing

The raw data was extracted and pre-processed from the PGM system using Mothur software (V1.33.3). The standards for high-quality sequences include: 1) length greater than 200 bp; 2) less than 2 mismatched bases with degenerate PCR; 3) The average quality score is greater than 25. Based on the 16S rRNA gene sequence, the OTU was clustered using the QIIME uclust method, and the similarity threshold was set to 97%. A seed sequence of each OTU was selected and annotated using the reference gene information gg_13_8_otus in the Greengene database. The relative abundance of each OTU in each sample is calculated, where the relative abundance of an OTU is the ratio of the abundance of the OTU in a sample to the sum of all OTU abundances in the sample.

1.4 Analysis of microbial consistency between samples at different sites

Based on the presence or absence of OTU, this example uses the Sorenson index (

–Dice index) to measure the similarity of microbial populations at different sites in the same individual, calculated as follows:

Where A and B represent the number of OTUs in samples A and B, respectively, and C represents the number of OTUs shared in the two samples. QS is a similarity index and ranges from 0 to 1. In this example, the similarity index of CL and CU, the similarity index of CL and CV, and the similarity index of CU and CV were calculated. The similarity index is close to 1, indicating that the similarity of the microbiota at the two sampling sites is higher.

1.5 random forest classifier

In order to establish a model that can identify abnormal state samples, for each sampling site, the relative abundance of each sample and the endometriosis state were fitted using the randomForest toolkit in R software (3.1.2RC). The default parameters are used; wherein the OTU of each sample is an OTU present in at least 10% of the sample, that is, only less than 10% of all samples to be tested in each part can be inspected. Out of the OTU. Then, five 10-fold cross-validation is performed, and the error curves of the five 10-fold cross-validation are averaged, and the lowest error of the average post-curve is added to the standard error of the point as the domain value of the acceptable error. Among the groups of OTUs whose classification error is smaller than the domain value, the least number of OTUs is the optimal OTU combination as a biomarker combination for identifying endometriosis.

1.6 Biomarker verification

In order to verify the biomarkers obtained in this example, this example additionally used an independent test population, that is, the second population for verification. In the second group, there were 11 endometriosis patients and 11 non-endometriosis individuals for CL and CU; for CV, there were 12 patients with endometriosis and 10 A non-endometriosis individual.

2. Experimental results

2.1 Microbial structural characteristics and trends of the same in vivo genital tract

In order to explore the relationship between microbial populations in different regions of the reproductive tract, this example calculates the distance between samples of the same individual. The weighted UniFrac distance from the posterior vagina (CU), cervical (CV) mucus to the uterus and peritoneal fluid increased sequentially relative to the lower vaginal 1/3 (CL) sample. This again indicates that the community structure of the female reproductive tract is continuously changing as the anatomical structure is from bottom to top.

Samples in different parts of the same individual are highly correlated, and the Sorenson indices between samples in different parts are consistent with their anatomical structures. There was a significant correlation between cervical (CV) mucus and peritoneal fluid samples, with an average Sorenson index of 0.255, indicating that the uterine cavity and abdominal cavity health can be evaluated by analyzing readily available cervical mucus samples in the general population.

In addition, in this case, the cervical mucus was sampled through the vagina and the uterine cavity, respectively. It was found that the bacterial distribution of the samples taken by the two routes showed a high degree of similarity, further indicating that the uterine cavity microorganisms can be evaluated by analyzing the easily available cervical tube samples. Case.

2.2 Disease-related microorganisms

In order to obtain the OTU biomarkers used to identify endometriosis, this example establishes a random forest model. The specific steps are as follows: (1) Using the relative abundance of OTU as an input feature, design a random forest model based on the first population; (2) For the random forest model, a 10-fold cross-validation algorithm was designed. The first group was divided into two types: endometriosis individuals and non-endometriosis individuals, and the ROC curves of random forest models were obtained. The area AUC value under each ROC curve was used as an evaluation index.

In this example, a random forest model was used, combined with a 10-fold cross-validation, to obtain the optimal biomarkers for each part, as shown in Table 1, for the identification of endometriosis. Tables 2 to 4 show the enrichment information of the marker group of the three sites in the sample, and Tables 5 to 7 respectively show the relative abundance information of the marker group of the three sites in the first population sample. In this example, the biomarkers at three sites identify the results of endometriosis, as shown in Figures 1 to 3. Figure 1 shows the marker in the lower third of the vagina (CL). Membrane ectopic disease, Figure 2 is the marker group of vaginal posterior iliac crest (CU) to identify endometriosis, and Figure 3 is the marker group of cervical canal (CV) to identify endometriosis.

Table 1 biomarkers and their respective parts

Seq ID No.	OTU number	OTU classification	CL	CU		CV
1	1	Lactobacillus sp.	√	√	--
2	5	Leptotrichiaceae	√	--	--
3	11	Vagococcus sp.	--	--	√
4	12	Delftia sp.	--	--	√

5	27	Dysgonomonas sp.	--	--	√
6	33	Aerococcus sp.	√	--	--
7	35	Prevotella sp.	√	√	--
8	38	Lactobacillus sp.	--	--	√
9	42	Tissierellaceae	--	--	√
10	48	Comamonadaceae	--	--	√
11	54	Erysipelotrichaceae	--	--	√
12	61	Lactobacillus sp.	√	--	√
13	64	Dialister sp.	√	--	--
14	70	Erysipelothrix sp.	--	--	√
15	86	Anaerococcus sp.	√	--	--
16	108	Dysgonomonas sp.	--	--	√
17	221	Lactobacillus sp.	√	--	--
18	233	Lactobacillus sp.	√	--	--
19	344	Lactobacillus sp.	√	--	--
20	424	Lactobacillus iners	√	--	--
twenty one	464	Prevotella sp.	√	--	--
twenty two	520	Lactobacillus iners	√	--	--
twenty three	628	Lactobacillus iners	--	--	√
twenty four	663	Lactobacillus sp.	√	--	--

In Table 1, the markers of the three parts of CL, CU, and CV can be judged separately, and "√" is a biomarker used for judging the part. "--" means that it is not needed. of.

In the sample test, the relative abundance of the OTU of each part is calculated, and the relative abundance is input into the random forest model to obtain the result, and it is judged whether it is endometriosis.

Table 2 OTU abundance information of the marker group in CL

Table 3 OTU abundance information of marker groups in CU

Table 4 OTU abundance information of the marker group in CV

In Tables 2 to 4, the endometriosis group refers to the sample of endometriosis among the 49 subjects in the first group, and the control group refers to the 49 subjects in the first group. A sample of endometriosis.

Table 5 Abundance information of each OTU of the marker group in the first group in CL

Table 6 Abundance information of each OTU of the marker group in the CU in the first population

Table 7 Abundance information of each OTU of the marker group in the CV in the first population

Figure 1 shows the endometriosis identified by the marker group at the lower third of the vagina (CL). In the figure, a is a randomized forest identification of endometriosis 5 times as the number of OTUs increases. 10% cross-validation error rate distribution, the model was trained with the relative abundance of OTU in the sample, using a total of 17 non-endometriosis individuals and 32 endometriosis CL samples, The black line represents the average of 5 trials, the gray line represents 5 trials, the black vertical line represents the number of OTUs in the best combination, and the b plot is the cross-validated combination receiver operating curve with the area under the curve AUC At 0.8272, the shaded area represents a 95% confidence interval and the diagonal represents a curve with an AUC of 0.5.

Figure 2 shows the identification of endometriosis in the marker group of posterior vaginal vault (CU). In the figure, a is a 10-fold crossover of random forest identification of endometriosis with increasing number of OTUs. Test The distribution of error rates, the model was trained with the relative abundance of OTU in the sample, using a total of 17 non-endometriosis individuals and 32 uterine endometriosis CU samples, black lines represent The average of 5 trials, the gray line is 5 trials respectively, the black vertical line represents the number of OTUs in the best combination; the b diagram is the receiver operation curve of the cross-validated combination, the area under the curve AUC is 0.5919, the shaded area Represents a 95% confidence interval and the diagonal represents a curve with an AUC of 0.5.

Figure 3 shows the identification of endometriosis in the cervical canal (CV) marker group. In the figure, a is a 10-fold cross-validation of random forest identification of endometriosis with increasing number of OTUs. The distribution of error rates, the model was trained with the relative abundance of OTU in the sample, using a total of 17 CV samples from individuals with non-endometriosis and 32 individuals with endometriosis, with black lines representing 5 The average of the subtests, the gray line is 5 trials respectively, the black vertical line represents the number of OTUs in the best combination; the b diagram is the receiver operation curve of the cross-validated combination, the area under the curve AUC is 0.8493, and the shaded area represents 95% confidence interval, diagonal represents a curve with an AUC of 0.5.

It can be seen from the results of Fig. 1 to Fig. 3 that the OTU biomarker group at three different sites can identify individuals with endometriosis and non-endometriosis individuals; the area under the curve of the ROC is AUC It is 0.8272 (CL), 0.5919 (CU) and 0.8493 (CV). Among them, AUC is the area under the curve, and the larger the value, that is, the closer to 1, indicating that the judgment ability is stronger, that is, the more accurate the judgment.

2.3 Biomarker verification

The OTU biomarkers obtained from the random forest were verified in the second population samples, and the results are shown in Table 8, Table 9, and Table 10. In Tables 8 to 10, sample numbers such as C003CL, C003CU, and C003CV respectively indicate samples of three parts of CL, CU, and CV collected from the same C003 sampling object. Tables 8 to 10 show the probability that the three marker groups predict individuals with endometriosis, and the ROC curves thus obtained are sequentially shown in Figs. 4 to 6 . In Tables 8 to 10, the probability > 0.5 is considered to be that the individual has a risk of suffering from endometriosis or endometriosis through the marker group at the site.

Table 8 CL marker group at the CL site predicts the probability of a second population sample suffering from endometriosis

Sample serial number	Whether endometriosis (infertility) (N: No; Y is)	Probability
C001CL	N	0.427
C003CL	N	0.428
C004CL	N	0.261
C005CL	N	0.295
C007CL	N	0.427
C008CL	N	0.541
C009CL	N	0.577
C012CL	N	0.337
C014CL	N	0.447
C019CL	N	0.563
C021CL	N	0.649
T000CL	Y	0.575
T001CL	Y	0.691
T003CL	Y	0.851

T005CL	Y	0.526
T006CL	Y	0.731
T008CL	Y	0.366
T015CL	Y	0.808
T017CL	Y	0.746
T081CL	Y	0.330
T082CL	Y	0.784
T083CL	Y	0.761

Table 9 CU marker group at CU site predicts the probability of second population sample suffering from endometriosis

Sample serial number	Whether endometriosis (infertility) (N: No; Y is)	Probability
C001CU	N	0.285
C003CU	N	0.106
C004CU	N	0.369
C005CU	N	0.443
C007CU	N	0.298
C008CU	N	0.588
C009CU	N	0.675
C012CU	N	0.988
C014CU	N	0.121
C019CU	N	0.188
C021CU	N	0.584
T001CU	Y	0.943
T003CU	Y	0.514
T005CU	Y	0.969
T006CU	Y	0.885
T000CU	Y	0.475
T008CU	Y	0.135
T015CU	Y	0.838
T017CU	Y	0.943
T081CU	Y	0.722
T082CU	Y	0.943
T083CU	Y	0.992

Table 10 CV marker group at CV site predicts the probability of second population sample suffering from endometriosis

Sample serial number	Whether endometriosis (infertility) (N: No; Y is)	Probability
C003CV	N	0.415
C004CV	N	0.415
C005CV	N	0.351
C007CV	N	0.388
C008CV	N	0.347
C009CV	N	0.515
C012CV	N	0.418
C014CV	N	0.417
C019CV	N	0.152
C021CV	N	0.152
T000CV	Y	0.546
T001CV	Y	0.286
T003CV	Y	0.632
T004CV	Y	0.395
T005CV	Y	0.633
T006CV	Y	0.723
T008CV	Y	0.642
T015CV	Y	0.554

T017CV	Y	0.717
T081CV	Y	0.448
T082CV	Y	0.573
T083CV	Y	0.408

The results in Figure 4 show that the CL site is based on the CL marker group to determine the probability of endometriosis with an AUC value of 0.8750; the results in Figure 5 show that the CU site is based on the CU marker group to determine the probability of endometriosis, its AUC The value of 0.840; the results of Figure 6 show that the CV site based on the CV marker group to determine the probability of endometriosis, its AUC value is 0.9189; it can be seen that these three marker groups have a higher discriminating ability, can be used in the uterus Detection of endometriosis, the results are consistent with the results of Tables 8 to 10. In the results of Tables 8 to 10, the probability of the three marker groups predicted, at least one of which is greater than 0.5, determines that the individual is at risk of suffering from endometriosis or has endometriosis, thereby obtaining The judgment result is consistent with the actual situation.

The above content is a further detailed description of the present application in conjunction with the specific embodiments, and the specific implementation of the present application is not limited to the description. For the ordinary person skilled in the art to which the present invention pertains, a number of simple deductions or substitutions may be made without departing from the spirit of the present application.

Claims (18)

1. A biomarker combination for endometriosis detection or risk assessment of disease, characterized in that said biomarker combination comprises at least one of twenty-four nucleic acids, said twenty-four nucleic acids The sequences shown in Seq ID No. 1 to Seq ID No. 24, respectively, or sequences having 97% or more similarity to the sequences shown in Seq ID No. 1 to Seq ID No. 24, respectively.
2. A biomarker combination for endometriosis detection or risk assessment of disease, characterized in that the biomarker combination comprises a first marker group, a second marker group and a third marker group At least one of the groups;

   The first marker group is composed of fourteen nucleic acids, which are Seq ID No. 1, Seq ID No. 2, Seq ID No. 6, Seq ID No. 7, and Seq ID No., respectively. 12. The sequence shown by Seq ID No. 13, Seq ID No. 15, Seq ID No. 17 to Seq ID No. 22, Seq ID No. 24, or Seq ID No. 1, Seq ID No. 2, respectively. , Seq ID No. 6, Seq ID No. 7, Seq ID No. 12, Seq ID No. 13, Seq ID No. 15, Seq ID No. 17 to Seq ID No. 22, and Seq ID No. 24. a sequence having a sequence similarity of 97% or more;

   The second marker group is composed of two nucleic acids, respectively, which are sequences shown by Seq ID No. 1, Seq ID No. 7, or Seq ID No. 1, Seq ID No. 7, respectively. The sequence shown has a sequence of 97% or more similarity;

   The third marker group is composed of eleven nucleic acids, which are Seq ID No. 3 to Seq ID No. 5, Seq ID No. 8 to Seq ID No. 12, and Seq ID No., respectively. 14. The sequence shown by Seq ID No. 16, Seq ID No. 23, or Seq ID No. 3 to Seq ID No. 5, Seq ID No. 8 to Seq ID No. 12, Seq ID No. 14, respectively. The sequence shown by Seq ID No. 16, Seq ID No. 23 has a sequence similarity of 97% or more.
3. The biomarker combination according to claim 2, wherein the first marker group is a CL marker group for detecting endometriosis or a sample from a lower third of the vagina. Disease risk assessment.
4. The biomarker combination according to claim 2, wherein the second marker group is a CU marker group for detecting endometriosis or risk of a disease from a vaginal posterior iliac crest. Evaluation.
5. The biomarker combination according to claim 2, wherein the third marker group is a CV marker group for performing endometriosis detection or risk assessment of a sample from the cervical canal. .
6. A kit for detecting endometriosis or risk assessment of a disease, characterized in that the kit comprises a primer pair for detecting the biomarker combination according to any one of claims 1 to 5, Reference The forward primer of the pair is the sequence shown in SEQ ID No. 25, and the reverse primer is the sequence shown in SEQ ID No. 26.
7. Use of the biomarker combination according to any one of claims 1 to 5 for the screening of endometriosis drugs or for the preparation of a kit or detection tool for endometriosis detection or risk assessment of disease.
8. A method for detecting endometriosis characterized by comprising the following steps,

   (1) performing sample collection on the object to be tested, detecting the biomarker combination according to any one of claims 1 to 5 in the collected sample, and analyzing the level of each nucleic acid in the biomarker combination;

   (2) comparing the level of each nucleic acid measured in the step (1) with a reference data set or a reference value to obtain a detection result;

   Preferably, the level of each nucleic acid is the relative abundance of each nucleic acid; the reference data set or reference value is in a biomarker combination derived from a patient with endometriosis and a non-endometriosis control The level of each nucleic acid.
9. The detecting method according to claim 8, wherein the reference data set or reference value in the step (2) is at least one of the table 5, the table 6 or the table 7; the level and reference of each nucleic acid are referred to The data set or the reference value is compared to obtain the detection result, which specifically includes calculating the disease probability by using the multivariate statistical model. Preferably, the multivariate statistical model is a random forest model.
10. The detecting method according to claim 8 or 9, wherein in the step (1), the sample to be measured is subjected to sample collection, including collecting the lower third of the vagina sample, the posterior vaginal sample and the cervical canal sample of the object to be tested. .
11. A method for determining endometriosis by detecting biomarkers for use in preparing a kit or tool for assessing endometriosis detection or disease risk; the biomarker is claim 1-5 a combination of said biomarkers;

    The method for determining endometriosis by detecting a biomarker includes the following steps,

    (1) performing sample collection on the object to be tested, detecting the biomarker combination according to any one of claims 1 to 5 in the collected sample, and analyzing the level of each nucleic acid in the biomarker combination;

    (2) comparing the level of each nucleic acid measured in the step (1) with a reference data set or a reference value to obtain a detection result;

    Preferably, the level of each nucleic acid is the relative abundance of each nucleic acid; the reference data set or reference value is in a biomarker combination derived from a patient with endometriosis and a non-endometriosis control The level of each nucleic acid.
12. The use according to claim 11, wherein the reference data set or reference value in the step (2) is at least one of the table 5, the table 6 or the table 7; the level of each nucleic acid and the reference data The set or reference value is compared to obtain the test result, which specifically includes calculating the disease probability by using the multivariate statistical model. Preferably, the multivariate statistical model is a random forest model.
13. A method for screening a drug candidate for treating endometriosis, comprising the steps of:

    1) separately determining the biomarker combination according to any one of claims 1 to 5 in the sample before and after administration, and analyzing the level of each nucleic acid in the biomarker combination;

    2) judging the candidate drug according to the level of each nucleic acid in the sample before and after the drug is compared;

    In the step 2), comparing the levels of each nucleic acid in the sample before and after administration, specifically including calculating the probability of disease by using a multivariate statistical model, preferably, the multivariate statistical model is a random forest model.
14. A method for detecting a microbiota in a female reproductive tract, comprising: the following steps,

    (1) collecting a microbial sample in the reproductive tract of the test subject, detecting the biomarker combination according to any one of claims 1 to 5 in the collected sample, and analyzing the level of each nucleic acid in the biomarker combination;

    (2) comparing the level of each nucleic acid measured in the step (1) with a reference data set or a reference value to obtain a detection result;

    Preferably, the level of each nucleic acid is the relative abundance of each nucleic acid; the reference data set or reference value is a biomarker combination derived from a population of endometriosis and a population of non-endometriosis controls The level of each nucleic acid.
15. The detecting method according to claim 14, wherein the reference data set or reference value in the step (2) is at least one of the table 5, the table 6 or the table 7; the level and reference of each nucleic acid The data set or the reference value is compared to obtain the detection result, which specifically includes calculating the disease probability by using the multivariate statistical model. Preferably, the multivariate statistical model is a random forest model.
16. The detecting method according to claim 14 or 15, wherein in the step (1), the microbial sample in the genital tract of the object to be tested is collected, which comprises collecting the lower third of the vagina sample, the vaginal posterior iliac sample and the sample to be tested. Cervical tube samples.
17. A method for preparing a biomarker combination of endometriosis, comprising the steps of:

    (1) Collecting microbial samples in the reproductive tract of endometriosis patients and non-patients, respectively, and performing 16S sequencing on all collected samples;

    (2) Clustering the 16S sequencing results to obtain the OTU unit and the seed sequence of each OTU, and calculate the relative abundance of each OTU unit;

    (3) Using the random forest model to fit the relative abundance of each OTU unit with the endometriosis state, and perform five 10-fold cross-validation to obtain the optimal OTU combination, and the optimal OTU combination. The seed sequence of OTU, a biomarker combination that constitutes endometriosis.
18. The method according to claim 17, wherein in the step (1), the microbial sample is collected in the genital tract, and specifically comprises collecting the lower third of the vagina sample, the posterior vaginal sputum sample and the cervical canal sample of the test subject.

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