Detection reagent for breast cancer risk related SNP sites after hormone therapy in perimenopausal period and application of detection reagent

Abstract

The invention belongs to the technical field of gene detection, and particularly relates to a detection reagent for a breast cancer risk related SNP site after hormone therapy in the perimenopausal period and application of the detection reagent. According to the method, the SNP site combination related to the breast cancer disease risk after hormone treatment is screened out through screening of the SNP site combination related to the specific phenotype and further analysis deduplication, the optimal site set is further screened through multiple iterations, and finally 128 SNP effective sites are obtained; the invention further provides a detection reagent for the SNP sites related to the breast cancer risk after hormone therapy in the perimenopausal period. The SNP site obtained on the basis of the research can effectively predict the risk of breast cancer of the perimenopausal women after hormone treatment, can provide reference for treatment means of the perimenopausal women, and provides precise diagnosis and treatment opinions for clinicians.

Description

Technical Field

[0001] The present application belongs to the field of gene detection technology, and specifically relates to a detection reagent for SNP sites related to breast cancer risk after perimenopausal hormone therapy and its application. Background Art

[0002] Breast cancer (BC) is a malignant tumor caused by breast cancer due to genetic factors, hormone disorders and other reasons. It is a multi-gene genetic disease and has become one of the most common cancers in the world, posing a serious threat to human life and health. According to the characteristics of breast cancer, researchers have found that the occurrence of breast cancer is closely related to genetic factors. The single nucleotide polymorphism (SNP) of certain genes in breast cancer patients is significantly different from that in normal people. Studies have shown that its onset is also highly correlated with estrogen levels.

[0003] Perimenopause, also known as the menopausal transition period, refers to a transitional period in which female ovarian function gradually declines from a vigorous state to complete disappearance, including menopause and a period before and after menopause. This period usually occurs between women in their 40s and 50s and is a sign of the end of childbearing age. The exact time of perimenopause varies slightly from individual to individual, but generally starts at around 40 years old, when ovarian function declines, and lasts for about 1 year after the last menstruation. Perimenopause women often show symptoms of physical and psychological discomfort, and are the main group of menopausal syndrome. This period generally lasts for several years, bringing a heavy burden to patients and their families. Among them, hormone replacement therapy (HRT) is currently a common method for treating menopausal syndrome, that is, by supplementing exogenous estrogen, treating various symptoms in perimenopausal women and preventing the resulting diseases. However, the main application of hormone replacement therapy, estrogen, as a risk factor for breast cancer lesions, has always been controversial in its clinical use, and whether hormone replacement therapy can be adopted varies greatly among different individuals.

[0004] Although hormone replacement has been able to alleviate the symptoms of perimenopausal women to a certain extent, it is uncertain whether different populations can undergo hormone replacement therapy due to individual differences. Some susceptible populations may be at high risk of developing breast cancer due to the use of hormone replacement therapy.

[0005] Therefore, accurate prediction of whether perimenopausal women are at risk of developing breast cancer after hormone therapy has become an urgent problem to be solved in this field. Summary of the invention

[0006] Based on this, an embodiment of the present application provides a detection reagent for SNP sites associated with breast cancer risk after hormone therapy in the perimenopausal period and its application.

[0007] On the one hand, the present application provides a detection reagent for SNP sites related to the risk of breast cancer after hormone therapy in perimenopause, and the SNP sites include: rs10082840, rs10403928, rs10489087, rs10768696, rs10769150, rs10838333, rs10852141, rs11025022, rs111627363, rs11204592, rs114539816, rs114927745, rs117210868, rs11727167, rs1175258701, rs117692047, rs11775523, rs11775524 1781891, rs117880422, rs117901631, rs118075465, rs12204978, rs12332546, rs12464448, rs12492798, rs12525909, rs12735947, rs12960996, rs1 2976608, rs13061279, rs13122745, rs13433498, rs13894, rs141748731, rs143544469, rs143828073, rs1459208292, rs1477085, rs149700109, rs15 62313、rs1567114、rs16901318、rs16934213、rs16950399、rs17151641、rs1716274、rs17246923、rs17569415、rs17662348、rs1867283、rs1869964、r s2039810, rs2044903375, rs2074504534, rs2292003, rs237622, ​​rs2424994, rs2808707, rs2836528, rs28406708, rs28673315, rs2879577, rs292969 , rs306168, rs34776629, rs35496203, rs3757472, rs37806, rs3814518, rs4111077, rs41377246, rs4698166, rs4712619, rs4744852, rs4759042, rs4 860731, rs4948185, rs532362620, rs551289, rs56876325, rs58179804, rs58283036, rs59647289, rs59853970, rs6018151, rs6043600, rs60630240,rs61638329、rs61745795、rs61787362、rs62186486、rs62383866、rs62407746、rs6495459、rs6723590、rs6784670、rs68119480、r s7162001, rs7193204, rs7264562, rs72783061, rs72832116, rs7294628, rs74426730, rs75837374, rs76276364, rs77313983, rs77 35699, rs77508708, rs7751374, rs77584189, rs77667857, rs77983269, rs782913, rs78393626, rs78407620, rs7923425, rs7946997, rs79677303, rs7990128, rs80051555, rs8007554, rs8096471, rs894276, rs923937, rs9303523, rs9527245 and rs9947513.

[0008] On the other hand, the present application provides the use of the above-mentioned SNP site detection reagent associated with the risk of breast cancer after perimenopausal hormone therapy in the preparation of a breast cancer detection product after perimenopausal hormone therapy.

[0009] On the other hand, the present application provides a kit for detecting the risk of breast cancer after perimenopausal hormone therapy, wherein the kit comprises the above-mentioned detection reagent of the SNP site associated with the risk of breast cancer after perimenopausal hormone therapy.

[0010] In some of the embodiments, the kit further comprises one or both of a nucleic acid extraction reagent and a nucleic acid purification reagent.

[0011] In some of these embodiments, the kit further comprises a capture probe, a capture chip, a primer set, and one or more methods and devices for SNP detection based on human nucleic acid samples.

[0012] On the other hand, the present application provides the use of the above-mentioned SNP site detection reagent associated with the risk of breast cancer after perimenopausal hormone therapy in constructing a model for detecting the risk of breast cancer after perimenopausal hormone therapy or preparing a device for predicting the risk of breast cancer after perimenopausal hormone therapy.

[0013] Another aspect of the present application provides a method for constructing a model for predicting the risk of breast cancer after perimenopausal hormone therapy, comprising:

[0014] S10, obtaining samples to be tested that are diagnosed with breast cancer and samples to be tested that are not diagnosed with breast cancer within a certain period, performing whole genome sequencing to screen and analyze differential SNP sites associated with breast cancer, and obtaining differential SNP sites associated with breast cancer as set A;

[0015] S20, obtaining samples from perimenopausal syndrome patients who have breast cancer after hormone therapy and perimenopausal syndrome patients who have not breast cancer after hormone therapy, performing whole genome sequencing, screening and analyzing differential SNP sites related to breast cancer and hormone therapy, and obtaining differential SNP sites related to breast cancer and hormone therapy as set B;

[0016] S30, taking the intersection of the set A and the set B as set C, removing the set C from the set of SNP sites associated with breast cancer and hormone therapy, and obtaining SNP sites associated with breast cancer after hormone replacement therapy as set D;

[0017] S40, calculating the SNP site goodness of fit value according to the result of step S30, and constructing a model for predicting the risk of breast cancer after perimenopausal hormone therapy using a machine learning algorithm;

[0018] In some embodiments, the sample to be tested includes one or more of a peripheral blood sample, a tissue sample, a body fluid, an amniotic fluid, and a single cell amplification product sample;

[0019] In some of these embodiments, the machine learning algorithm includes one or more of logistic regression, support vector machine, random forest, decision tree, linear regression and naive Bayes.

[0020] In some embodiments, step S10 includes:

[0021] S101. Perform whole genome association analysis on whole genome sequencing data of breast cancer population and normal population to obtain the correlation and significance matrix between genetic loci and breast cancer;

[0022] S102, removing the genetic loci whose population allele frequency is lower than a preset threshold in the association matrix between all genetic loci and breast cancer obtained in step S101, and obtaining the association and significance matrix between non-rare genetic loci and breast cancer;

[0023] S103, filtering the genetic loci whose statistical significance is higher than a preset threshold from the non-rare genetic loci and breast cancer association matrix obtained in step S102, and extracting the remaining genetic loci as a differential SNP locus set A related to breast cancer.

[0024] In some embodiments, step S20 includes:

[0025] S201. Calculate the allele frequency and deletion rate of genetic loci, extract genetic loci with minor allele frequency higher than rare frequency, and remove genetic loci with too high deletion rate in the sample.

[0026] S202, performing a chi-square test of the observed and expected allele frequencies on the genetic loci remaining after screening in step S201, and extracting genetic loci whose significance in the Hardy-Weinberg equilibrium test is higher than the significance threshold.

[0027] S203, calculating the linkage correlation of all genetic loci in each genomic region for the genetic loci remaining after screening in step S202, and removing genetic loci in linkage disequilibrium.

[0028] S204, performing regression analysis on the genetic loci remaining after screening in step S203 and the trait of whether or not to develop breast cancer after hormone therapy, to obtain a difference test table of genetic loci related to breast cancer and hormone therapy;

[0029] S205. Extract genetic loci whose hypothesis test significance values ​​are lower than a preset threshold from the difference test table obtained in step S204 as a differential SNP locus set B related to breast cancer and hormone therapy.

[0030] In some embodiments, step S30 includes:

[0031] S301 calculates the intersection of the obtained breast cancer related SNP site set A and the obtained breast cancer and hormone therapy related SNP site set B as set C.

[0032] S302 removes the set C obtained in step S301 from the set of SNP sites associated with breast cancer and hormone therapy, and obtains a set D of SNP sites associated with breast cancer after hormone replacement therapy.

[0033] In some embodiments, step S40 includes:

[0034] S401. Calculate the number N of elements in the SNP set D generated in step 302, and arrange the elements in the set D in ascending order of significance.

[0035] S402, split the ordered element set obtained in step 401 into N groups of subsets, each group of subsets Ni contains the elements of the i-th element in the ordered set and all the elements before the sequence number, generating N sets containing different SNP site combinations.

[0036] S403, performing multiple linear regression on each group of SNP sets obtained in step S402 and the phenotype of breast cancer after hormone therapy, and calculating and obtaining the R of each group of regression equations. 2 .

[0037] S404: Extract the R of each group of SNPs obtained in step S403 2 The highest group of SNPs in the 14 samples were identified to identify single nucleotide polymorphisms (SNPs) for predicting the risk of breast cancer in perimenopausal women after hormone therapy.

[0038] On the other hand, the present application provides a model for predicting the risk of breast cancer after hormone therapy in perimenopause, and the model is constructed by the above-mentioned method for constructing a model for predicting the risk of breast cancer after hormone therapy in perimenopause.

[0039] Another aspect of the present application provides a device for predicting the risk of breast cancer after hormone therapy in the perimenopausal period, the device comprising:

[0040] Model building module and prediction module.

[0041] The model building module is used to execute the steps of the above-mentioned method for building a model for predicting the risk of breast cancer after perimenopausal hormone therapy.

[0042] The prediction module is used to execute the following steps: calculating the risk of breast cancer after perimenopausal hormone therapy for the sample to be tested using the model for predicting the risk of breast cancer after perimenopausal hormone therapy constructed by the model construction module.

[0043] The details of one or more embodiments of the present application are set forth in the description which follows, and other features, objects, and advantages of the present application will be apparent from the description and its claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application and to more completely understand the present application and its beneficial effects, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can obtain other drawings based on these drawings without creative work.

[0045] Figure 1 Flow chart of SNP site screening for this application;

[0046] Figure 2 The polygenic risk score results of 128 SNP sites provided in one embodiment of the present application;

[0047] Figure 3 The area under the curve (AUC) of the receiver operating curve of the prediction model provided in one embodiment of the application. DETAILED DESCRIPTION

[0048] Below in conjunction with embodiment and example, the application is further described in detail.It should be understood that these embodiments and examples are only used to illustrate the application and are not used to limit the scope of the application, and the purpose of providing these embodiments and examples is to make the understanding of the disclosure of the application more thorough and comprehensive.It should also be understood that the application can be implemented in many different forms, is not limited to the embodiment and example described herein, and those skilled in the art can make various changes or modifications without violating the connotation of the application, and the equivalent form obtained also falls within the protection scope of the application.In addition, in the description below, a large number of specific details are given in order to provide a more comprehensive understanding of the application, and it should be understood that the application can be implemented without one or more of these details.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0050] the term

[0051] Unless otherwise specified or incompatible herewith, the terms and phrases used herein shall have the following meanings:

[0052] The terms "and / or", "or / and", and "and / or" used in this article include any one of two or more related listed items, and also include any and all combinations of related listed items, and the arbitrary and all combinations include any two related listed items, any more related listed items, or a combination of all related listed items. It should be noted that when at least three items are connected by at least two conjunctions selected from "and / or", "or / and", and "and / or", it should be understood that in this application, the technical solution undoubtedly includes technical solutions that are all connected by "logical and", and undoubtedly includes technical solutions that are all connected by "logical or". For example, "A and / or B" includes three parallel solutions of A, B and A+B. For example, the technical solution of "A, and / or, B, and / or, C, and / or, D" includes any one of A, B, C, and D (that is, the technical solution that is all connected by "logical OR"), and also includes any and all combinations of A, B, C, and D, that is, the combination of any two or any three of A, B, C, and D, and also includes the combination of four of A, B, C, and D (that is, the technical solution that is all connected by "logical AND").

[0053] In the present application, "plurality", "multiple", "multiple times", "multiples", etc., unless otherwise specified, refer to a number greater than 2 or equal to 2. For example, "one or more" means one or greater than or equal to two.

[0054] As used herein, "combination thereof", "any combination thereof", "any combination thereof" etc. include all suitable combinations of any two or more of the listed items.

[0055] Herein, the “suitable” mentioned in “suitable combination”, “suitable method”, “any suitable method”, etc., shall be based on the ability to implement the technical solution of this application, solve the technical problems of this application, and achieve the expected technical effects of this application.

[0056] In the present application, "further", "further", "particularly" and the like are used for descriptive purposes to indicate differences in content, but should not be construed as limiting the scope of protection of the present application.

[0057] In this application, "optionally", "optional", and "optional" mean optional or dispensable, that is, any one of the two parallel schemes of "yes" or "no". If multiple "options" appear in a technical solution, unless otherwise specified and there is no contradiction or mutual restriction, each "optional" is independent.

[0058] In the present application, the technical features described in an open manner include closed technical solutions composed of the listed features, and also include open technical solutions containing the listed features.

[0059] In the present application, when it comes to numerical intervals (i.e., numerical ranges), unless otherwise specified, the optional numerical distribution is considered continuous within the above numerical interval, and includes the two numerical endpoints (i.e., the minimum and maximum values) of the numerical range, and each numerical value between the two numerical endpoints. Unless otherwise specified, when the numerical interval only refers to the integers within the numerical interval, it includes the two endpoint integers of the numerical range, and each integer between the two endpoints. In this article, it is equivalent to directly listing each integer, such as t is an integer selected from 1 to 10, indicating that t is any integer selected from the group of integers consisting of 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10. In addition, when multiple ranges are provided to describe features or characteristics, these ranges can be combined. In other words, unless otherwise specified, the ranges disclosed herein should be understood to include any and all subranges included therein.

[0060] The temperature parameters in this application, unless otherwise specified, are allowed to be either constant temperature treatment or to vary within a certain temperature range. It should be understood that the constant temperature treatment allows the temperature to fluctuate within the accuracy range controlled by the instrument. Fluctuations within the range of ±5°C, ±4°C, ±3°C, ±2°C, and ±1°C are allowed.

[0061] In the present application, % (w / w) and wt% both represent weight percentage, % (v / v) refers to volume percentage, and % (w / v) refers to mass volume percentage.

[0062] All documents mentioned in this application are cited as references in this application, just as each document is cited as reference separately. Unless they conflict with the invention purpose and / or technical solution of this application, the cited documents involved in this application are cited with all contents and all purposes. When the cited documents are involved in this application, the definitions of relevant technical features, terms, nouns, phrases, etc. in the cited documents are also cited. When the cited documents are involved in this application, the examples and preferred methods of the cited relevant technical features can also be incorporated into this application as references, but are limited to the implementation of this application. It should be understood that when the cited content conflicts with the description in this application, the present application shall prevail or be modified adaptively according to the description of this application.

[0063] The term "SNP" Single Nucleotide Polymorphism (SNP) is one of the most common forms of genetic variation in the genome. It refers to the difference in a single nucleotide (adenine [A], cytosine [C], guanine [G] or thymine [T]) at a specific position in the genome. This difference can exist between different individuals and usually manifests as two or more nucleotide forms at that position. Characteristics of SNP sites: Position specificity: Each SNP site has a precise position in the genome, which makes them useful as genetic markers for genetic research. Genetic stability: SNPs usually remain unchanged throughout an individual's life and can be stably passed from parents to offspring. Diversity: There are millions of SNPs in the human genome, and these genetic variations are an important source of genetic differences between individuals. Functionality: Although many SNPs are located in non-coding regions, some SNPs are located in gene coding regions and may affect gene expression or protein function.

[0064] A study published by Dorling et al. in 2021 showed that an analysis of 34 preset breast cancer-related genes in 60,466 breast cancer patients and 53,461 normal people in the European population identified 9 breast cancer susceptibility genes, such as BRCA1, BRCA2, etc. In addition, by using the individual's genetic genetic variation, that is, the changes in SNP sites in the individual's genome, the effect value of genetic variation, that is, the SNP sites closely related to breast cancer obtained through genome-wide association analysis (GWAS) studies and estimating the degree of influence of the SNP site on the disease, and then using a specific calculation formula, the individual's polygenic risk score (PRS) for breast cancer can be obtained. PRS can be used to assess an individual's genetic risk of breast cancer, thereby preventing or intervening in breast cancer early. Currently, there are many studies underway using PRS for multi-gene disease risk prediction. For example, in 2019, Mavaddat et al. studied 11,428 breast cancer patients and 18,323 normal controls in the European population, and used 313 SNP sites closely related to the incidence of breast cancer to perform PRS prediction of breast cancer. The area under the curve (AUC) value of the receiver operating characteristic (ROC) of this prediction reached 0.6311; The study published by Zhao et al. in 2021 showed that in breast cancer patients with increased estrogen content, the PRS risk prediction based on 6 breast cancer-related SNP sites added an estrogen metabolism-related SNP site (SULT1A1: rs1042028), and the prediction accuracy increased from 54.56% to 62.18%. The above results indicate that estrogen metabolism-related SNP sites are associated with the incidence of breast cancer, and adding breast cancer susceptibility SNP sites can increase its risk prediction accuracy.

[0065] The present application provides a set of single nucleotide polymorphism sites for predicting the risk of breast cancer in perimenopausal women after hormone therapy. Based on this set of polymorphism sites, the risk of breast cancer in perimenopausal women after hormone therapy can be assessed, so as to adopt more reasonable and more effective treatment for patients with menopausal syndrome and alleviate the pain of perimenopausal women.

[0066] On the one hand, the present application provides a detection reagent for SNP sites related to breast cancer risk after hormone therapy in perimenopause, wherein the SNP sites are shown in Table 1:

[0067] Table 1

[0068]

[0069]

[0070]

[0071] On the other hand, the present application provides the use of the detection reagent of the SNP site associated with the risk of breast cancer after perimenopausal hormone therapy in the preparation of a breast cancer detection product after perimenopausal hormone therapy.

[0072] On the other hand, the present application provides a kit for detecting the risk of breast cancer after perimenopausal hormone therapy, wherein the kit comprises a detection reagent for the SNP site associated with the risk of breast cancer after perimenopausal hormone therapy.

[0073] Optionally, the kit further comprises one or more of a nucleic acid extraction reagent and a nucleic acid purification reagent.

[0074] Nucleic acid extraction reagents are experimental tools used to extract, purify and detect nucleic acids (DNA or RNA) from biological samples. They contain a series of reagents and buffers that can remove contamination from macromolecules such as proteins, polysaccharides and lipids, while excluding contamination from other nucleic acid molecules, ensuring the integrity of the primary structure of nucleic acids, and avoiding the presence of organic solvents and high concentrations of metal ions that have inhibitory effects on enzymes. The nucleic acid extraction reagents described in this application include nucleic acid extraction reagents commonly used on the market.

[0075] Nucleic acid purification reagents are experimental tools specially designed for extracting and purifying nucleic acids (DNA and RNA) from various biological samples. These reagents usually contain a series of chemical components and buffers, which can efficiently separate nucleic acids from cells and tissues while removing proteins, polysaccharides, lipids and other impurities that may interfere with downstream experiments. The nucleic acid purification reagents described in this application include nucleic acid extraction reagents commonly used on the market.

[0076] On the other hand, the present application provides the use of the detection reagent of the SNP site related to the risk of breast cancer after perimenopausal hormone therapy in constructing a model for detecting the risk of breast cancer after perimenopausal hormone therapy or preparing a device for predicting the risk of breast cancer after perimenopausal hormone therapy.

[0077] The present application also provides a method for constructing a model for predicting the risk of breast cancer after perimenopausal hormone therapy, comprising:

[0078] S10, obtaining samples to be tested from patients with breast cancer and patients without breast cancer within a certain period, performing whole genome sequencing to screen and analyze differential SNP sites related to breast cancer, and obtaining differential SNP sites related to breast cancer as set A. That is, obtaining blood from all patients with breast cancer and normal people within a certain period, performing whole genome sequencing, obtaining effective single nucleotide polymorphism sites in the whole genome, and screening differential SNP sites related to breast cancer based on association analysis.

[0079] S20. Obtain samples to be tested from patients with perimenopausal syndrome who have undergone hormone therapy and are suffering from breast cancer and patients with perimenopausal syndrome who have undergone hormone therapy and are not suffering from breast cancer, perform whole genome sequencing, screen and analyze differential SNP sites related to breast cancer and hormone therapy, and obtain differential SNP sites related to breast cancer and hormone therapy as set B. That is, obtain blood from patients with perimenopausal syndrome who have undergone hormone therapy and are suffering from breast cancer and patients with perimenopausal syndrome who have undergone hormone therapy and are not suffering from breast cancer, perform whole genome sequencing, obtain effective single nucleotide polymorphism sites in the whole genome, and screen differential SNP sites related to breast cancer and hormone therapy based on association analysis.

[0080] S30, taking the intersection of the set A and the set B as set C, removing the set C from the set of SNP sites associated with breast cancer and hormone therapy, and obtaining SNP sites associated with breast cancer after hormone replacement therapy as set D. That is, the SNP sites associated with breast cancer are removed from the SNP sites associated with breast cancer and hormone therapy, that is, the SNP sites associated with breast cancer after HRT treatment are obtained.

[0081] S40, calculating the goodness of fit value of the SNP site according to the result of step S30, and constructing a model for predicting the risk of breast cancer after hormone therapy in perimenopause using a machine learning algorithm. The index R used in the regression model to evaluate the degree of conformity between the predicted value and the actual value is used. 2 , and further screened and determined the optimal set of SNP sites associated with breast cancer after hormone therapy.

[0082] Optionally, the sample to be tested includes one or more of a peripheral blood sample, a tissue sample, a body fluid, an amniotic fluid and a single cell amplification product sample.

[0083] Optionally, the machine learning algorithm includes one or more of logistic regression, support vector machine, random forest, decision tree, linear regression and naive Bayes.

[0084] It is understandable that when conducting the test, the single nucleotide polymorphism sites screened out above for predicting the risk of breast cancer in perimenopausal women after hormone therapy are used to predict the risk of breast cancer in perimenopausal women after hormone therapy, and to predict the risk of breast cancer in perimenopausal women after hormone therapy.

[0085] In a specific example, step S10 includes:

[0086] S101. Perform whole genome association analysis on the whole genome sequencing data of breast cancer population and normal population to obtain the correlation and significance matrix between genetic loci and breast cancer.

[0087] S102, removing the genetic loci whose population allele frequency is lower than a preset threshold in the association matrix between all genetic loci and breast cancer obtained in step S101, and obtaining the association and significance matrix between non-rare genetic loci and breast cancer.

[0088] S103, filtering the genetic loci whose statistical significance is higher than a preset threshold from the non-rare genetic loci and breast cancer association matrix obtained in step S102, and extracting the remaining genetic loci as a differential SNP locus set A related to breast cancer.

[0089] In a specific example, step S20 includes:

[0090] S201. Calculate the allele frequency and deletion rate of genetic loci, extract genetic loci with minor allele frequency higher than rare frequency, and remove genetic loci with too high deletion rate in the sample.

[0091] S202, performing a chi-square test of the observed and expected allele frequencies on the genetic loci remaining after screening in step S201, and extracting genetic loci whose significance in the Hardy-Weinberg equilibrium test is higher than the significance threshold.

[0092] S203, calculating the linkage correlation of all genetic loci in each genomic region for the genetic loci remaining after screening in step S202, and removing genetic loci in linkage disequilibrium.

[0093] S204, performing regression analysis on the genetic loci remaining after screening in step S203 and the trait of whether or not to develop breast cancer after hormone therapy, and obtaining a difference test table of genetic loci related to breast cancer and hormone therapy.

[0094] S205. Extract genetic loci whose hypothesis test significance values ​​are lower than a preset threshold from the difference test table obtained in step S204 as a differential SNP locus set B related to breast cancer and hormone therapy.

[0095] In a specific example, step S30 includes:

[0096] S301 calculates the intersection C of the breast cancer related SNP site set A obtained in the above step and the breast cancer and hormone therapy related SNP site set B obtained in the above step.

[0097] S302 removes the set C obtained in step S301 from the set of SNP sites associated with breast cancer and hormone therapy, and obtains a set D of SNP sites associated with breast cancer after hormone replacement therapy.

[0098] In a specific example, step S40 includes:

[0099] S401. Calculate the number N of elements in the SNP set D generated in step 302, and arrange the elements in the set D in ascending order of significance.

[0100] S402, split the ordered element set obtained in step 401 into N groups of subsets, each group of subsets Ni contains the elements of the i-th element in the ordered set and all the elements before the sequence number, generating N sets containing different SNP site combinations.

[0101] S403, performing multiple linear regression on each group of SNP sets obtained in step S402 and the phenotype of breast cancer after hormone therapy, and calculating and obtaining the R of each group of regression equations. 2 .

[0102] S404: Extract the R of each group of SNPs obtained in step S403 2 The highest group of SNPs in the study were used to identify single nucleotide polymorphisms (SNPs) for predicting the risk of breast cancer in perimenopausal women after hormone therapy.

[0103] On the other hand, the present application provides a model for predicting the risk of breast cancer after perimenopausal hormone therapy, and the model is constructed by the method for constructing a model for predicting the risk of breast cancer after perimenopausal hormone therapy.

[0104] On the other hand, the present application also provides a device for predicting the risk of breast cancer after hormone therapy in the perimenopausal period, the device comprising:

[0105] Model building module and prediction module.

[0106] The model building module is used to execute the steps in the method for building a model for predicting the risk of breast cancer after perimenopausal hormone therapy.

[0107] The prediction module is used to execute the following steps: calculating the risk of breast cancer after perimenopausal hormone therapy for the sample to be tested using the model for predicting the risk of breast cancer after perimenopausal hormone therapy constructed by the model construction module.

[0108] On the other hand, the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned method of constructing a model for predicting the risk of breast cancer after hormone therapy in the perimenopausal period and the steps of predicting the risk.

[0109] On the other hand, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method of constructing a model for predicting the risk of breast cancer after perimenopausal hormone therapy and the steps of predicting the risk.

[0110] On the other hand, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned method of constructing a model for predicting the risk of breast cancer after perimenopausal hormone therapy and the steps of predicting the risk.

[0111] This application screened out SNP site combinations related to the risk of breast cancer after hormone therapy through screening and further analysis and deduplication of SNP site combinations related to specific phenotypes (breast cancer and breast cancer after hormone therapy), and further performed multiple iterations to screen the optimal site set, and finally obtained 128 SNP effective sites, and thereby provided a detection reagent for SNP sites related to the risk of breast cancer after hormone therapy in perimenopause. Based on the SNP sites obtained in this study, the risk of breast cancer in perimenopausal women after hormone therapy can be effectively predicted, and the treatment methods for perimenopausal women can be used as a reference, providing clinicians with precise diagnosis and treatment advice.

[0112] In addition, the 128 SNP sites provided in this application can be used to accurately solve the problem of whether each individual perimenopausal woman can undergo hormone therapy, solving the technical problem that the existing technology cannot accurately detect.

[0113] The embodiments of the present application will be described in detail below in conjunction with examples. It should be understood that these examples are only used to illustrate the present application and are not intended to limit the scope of the present application. The experimental methods for which specific conditions are not specified in the following examples are preferably referred to the guidance provided in the present application, and can also be based on the experimental manual or normal conditions in this area, can also be based on the conditions recommended by the manufacturer, or refer to experimental methods known in the art.

[0114] In the following specific embodiments, the measured parameters of raw material components may have slight deviations within the range of weighing accuracy unless otherwise specified. For temperature and time parameters, acceptable deviations caused by instrument test accuracy or operation accuracy are allowed.

[0115] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0116] Example 1

[0117] This example provides a screening process for SNP sites associated with breast cancer risk after perimenopausal hormone therapy

[0118] 1. Screening of SNP loci associated with breast cancer after hormone therapy

[0119] There are 128 SNP sites used to predict the probability of breast cancer in perimenopausal patients after hormone therapy. The specific screening process includes the following steps (see the detailed process Figure 1 )

[0120] 1. Based on the sequencing data of 360 breast cancer patients and normal people, the effective single nucleotide polymorphism sites in the whole genome were analyzed and obtained. The differential SNP sites related to breast cancer were screened based on association analysis. The steps for screening and analyzing differential SNP sites related to breast cancer are as follows:

[0121] 1.1. We used GEMMA software (v0.98.5) to perform genome-wide association analysis on the whole genome sequencing data of 360 breast cancer patients and normal people, and obtained a total of 8.14 million genetic loci and their association with breast cancer and significance matrix;

[0122] 1.2. Remove all genetic loci obtained in step 1.1 from the breast cancer association matrix with population allele frequencies lower than 0.5%, and obtain a total of 6.83 million non-rare genetic loci and their association and significance matrix with breast cancer;

[0123] 1.3. The non-rare genetic loci and breast cancer association matrix obtained in step 1.2 was filtered out for genetic loci with a statistical significance higher than 0.05, and the remaining genetic loci were extracted to obtain a total of 7812 breast cancer-related differential SNP loci sets, see Table 2.

[0124] Table 2: Preliminary screening results of SNP sites related to breast cancer (partial)

[0125] 14:22576148:A:G 2:228243028:C:T 20:35046949:G:A 4:7297506:T:C 19:22729122:A:G 19:37005998:A:G 17:14629746:G:A 11:126370918:C:T 1:210417098:G:A 13:83787475:G:A 9:1641933:C:T 19:55493441:C:T 7:123922971:G:T 17:65343997:T:C 7:69238158:T:C 3:20083881:C:T 13:99384317:A:G 3:62122176:T:C 2:55229942:T:C 18:50124500:C:T

[0126] 2. Based on the sequencing data of 191 perimenopausal patients with breast cancer who were treated with hormone therapy and perimenopausal patients who were treated with hormone therapy but did not have breast cancer, we analyzed and obtained effective single nucleotide polymorphism sites in the whole genome, and performed a preliminary screening of differential SNP sites related to breast cancer and hormone therapy based on association analysis. The steps for screening and analyzing differential SNP sites related to breast cancer and hormone therapy are as follows:

[0127] 2.1. Calculate the allele frequency and deletion rate of genetic loci, extract genetic loci with minor allele frequency > 0.5%, remove genetic loci with deletion ratio > 10% in the sample, and obtain 700,000 filtered genetic loci;

[0128] 2.2. Perform a chi-square test on the observed and expected allele frequencies of the genetic loci left after screening in step 2.1, extract the genetic loci with a Hardy-Weinberg equilibrium test significance higher than 0.000001, and obtain 500,000 filtered genetic loci;

[0129] 2.3. Use plink software (v1.9) to calculate the linkage correlation of all genetic loci in each genomic region for the genetic loci left after screening in step 2.2, remove the genetic loci with lower frequency whose correlation with other loci is >0.25 within each 250-locus window, and obtain 70,000 filtered genetic loci;

[0130] 2.4. Perform regression analysis on the 70,000 genetic loci left after screening in step 2.3 and the trait of breast cancer after hormone therapy to obtain a difference test table of genetic loci related to breast cancer and hormone therapy;

[0131] 2.5. Genetic loci with hypothesis test significance values ​​< 0.05 were extracted from the difference test table obtained in step 2.4, and 2516 SNP loci significantly associated with hormone therapy were obtained as differential SNP loci associated with breast cancer and hormone therapy, see Table 3.

[0132] Table 3: Preliminary screening results of SNP sites associated with breast cancer and hormone therapy (partial)

[0133] 13:80715893:G:A 1:204197356:A:G 22:39323164:A:G 11:41543073:T:C 14:90301035:A:G 3:156253015:A:G 18:71222207:A:G 20:62187187:T:C 22:48598852:C:T 1:20557582:A:G 8:3268884:C:T 3:161847918:C:T 2:159882085:T:C 3:108993:A:G 6:117119661:T:C 11:94355268:A:G 5:3429364:C:T 3:97188153:G:A 12:110234698:C:T 1:34976763:A:G

[0134] 3. Remove the SNP sites associated with breast cancer from the SNP sites associated with breast cancer and hormone therapy to obtain the SNP sites associated with breast cancer after HRT treatment. The steps for obtaining the SNP sites associated with breast cancer after HRT treatment are as follows:

[0135] 3.1. Calculate the intersection of the 7812 SNP sites associated with breast cancer obtained in step 1.3 and the 2516 SNP sites associated with breast cancer and hormone therapy obtained in step 2.5, which contains a total of 302 common genetic sites;

[0136] 3.2. The 302 common genetic loci obtained in step 3.1 were removed from the 2516 SNP loci associated with breast cancer and hormone therapy to obtain SNP loci associated with breast cancer after HRT treatment, a total of 2214 SNP loci associated with breast cancer after HRT treatment, see Table 4.

[0137] Table 4: Screening results of differential SNP sites associated with breast cancer after hormone therapy (partial)

[0138]

[0139]

[0140] 4. Finally, based on the index R used in the regression model to evaluate the degree of consistency between the predicted value and the actual value 2, when 128 characteristic SNPs were selected, R2 was the highest, so these 128 SNPs were retained as risk sites to be detected. 128 candidate SNP sites that were strongly associated with breast cancer after hormone therapy were identified. The final set of single nucleotide polymorphism sites for predicting the risk of breast cancer in perimenopausal women after hormone therapy was obtained in the following steps:

[0141] 4.1. Arrange the 2214 SNP sites related to breast cancer after HRT treatment generated in step 3.2 in ascending order of correlation significance.

[0142] 4.2. The set of 2214 ranked SNP sites obtained in step 4.1 is divided into 2214 groups, and the elements contained in the i-th group are the SNP sites ranked in the top i in terms of significance.

[0143] 4.3. Use R software (v4.1.1) to perform multiple linear regression on each SNP set obtained in step 4.2 and the phenotype of breast cancer after hormone therapy, and calculate and obtain the R of each regression equation. 2 , R 2 It is generally used in regression models to evaluate the degree of consistency between predicted values ​​and actual values. 2 The definition of is as follows: the ratio of the regression sum of squares of the variation in y caused by variable x to the total sum of squares of the variation in y, also known as goodness of fit, reflects the proportion of the independent variable that can be explained by the dependent variable.

[0144] 4.4. Extract the R of each group of SNPs obtained in step 4.3 2 The highest group of SNPs in the 10 genes were finally determined, and a group of 128 single nucleotide polymorphisms for predicting the risk of breast cancer in perimenopausal women after hormone therapy was finally determined, as shown in Table 5.

[0145] Table 5

[0146]

[0147]

[0148] As shown in Table 5, this example obtains a group of 128 SNP sites that are strongly associated with breast cancer after hormone therapy. Through the detection of these sites, the risk of breast cancer in perimenopausal women after hormone therapy can be assessed, so as to adopt more reasonable and effective treatment for menopausal syndrome patients and alleviate the pain of perimenopausal women.

[0149] Verification results:

[0150] 1. Clinical Sample Risk Analysis and Validation

[0151] In this example, 128 loci strongly associated with breast cancer after hormone therapy were implemented in a cohort of 157 patients with breast cancer after hormone therapy and patients without breast cancer after hormone therapy, verifying the accuracy between the risk analysis results and the actual disease results and the clinical significance of the risk analysis.

[0152] Among them, the analysis and verification steps of 157 samples of breast cancer after hormone therapy and those without breast cancer are as follows:

[0153] 1. 157 patients with breast cancer after hormone therapy and patients without breast cancer after hormone therapy were obtained and sequenced to obtain the 128 single nucleotide polymorphism sites in Example 1 for each patient. These 157 patients all had clinical diagnosis results of breast cancer.

[0154] 2. 128 SNPs were analyzed in 157 perimenopausal patients with breast cancer who were treated with hormone therapy and those who were not treated with hormone therapy. The polygenic risk score (PGS) was calculated using the following formula. Figure 2 .

[0155]

[0156] Where: i represents the i-th individual, j is the j-th SNP, Wj is the weight of the SNP, and a is the number of allele copies of the j-th SNP in the i-th individual.

[0157] 3. According to the actual clinical conditions of the 157 samples, the accuracy of the prediction of the 157 samples in this embodiment was analyzed. The results showed that the accuracy reached 84.7%, and the area under the curve (AUC) of the receiver operating curve was 81.3%. Figure 3 shown.

[0158] The above results show that the accuracy of predicting the risk of breast cancer after hormone therapy in perimenopausal women using the 128 SNP sites strongly associated with breast cancer after hormone therapy obtained in this patent is 84.7%, and the AUC value of the risk model is 81.3%, indicating that this test is helpful to help perimenopausal women choose the appropriate treatment method.

[0159] The above-described embodiments only express several implementation methods of the present application, which is convenient for understanding the technical solution of the present application in detail, but it cannot be understood as a limitation on the scope of protection of the patent application. It should be pointed out that for ordinary technicians in this field, without departing from the concept of the present application, several deformations and improvements can be made, which all belong to the protection scope of the present application. In addition, it should be understood that after reading the above-mentioned teaching content of the present application, the technicians in this field can make various changes or modifications to the present application, and the equivalent forms obtained also fall within the protection scope of the present application. It should also be understood that the technical solutions obtained by the technicians in this field through logical analysis, reasoning or limited experiments on the basis of the technical solutions provided in the present application are all within the protection scope of the claims attached to the present application. Therefore, the protection scope of the patent of the present application shall be based on the content of the attached claims, and the description can be used to interpret the content of the claims.

Claims

1. A detection reagent for SNP sites associated with breast cancer risk after perimenopausal hormone therapy, characterized in that: The SNP sites include: rs10082840, rs10403928, rs10489087, rs10768696, rs10769150, rs10838333, rs10852141, rs11025022, rs111627363, rs11204592, rs114539816, rs114927745, rs117210868, rs11727167, rs1175258701, rs117692047, rs117755523, rs11781891, rs117880422, rs117901631, rs118075465, rs12204978, rs12332546, rs12464448, rs12492798, rs12525909, rs12735947, rs12960996, rs12976608, rs13061279, rs13122745, rs13433498, rs13894, rs141748731, rs143544469, rs143828073, rs1459208292, rs1477085, rs149700109, rs1562313, rs1567114, rs16901318, rs16934213, rs16950399, rs17151641, rs1716274, rs17246923, rs17569415, rs17662348, rs1867283, rs1869964, rs2039810, rs2044903375, rs2074504534, rs2292003, rs237622, rs2424994, rs2808707, rs2836528, rs28406708, rs28673315, rs2879577, rs292969, rs306168, rs34776629, rs35496203, rs3757472, rs37806, rs3814518, rs4111077, rs41377246, rs4698166, rs4712619, rs4744852, rs4759042, rs4860731, rs4948185, rs532362620, rs551289, rs56876325, rs58179804, rs58283036, rs59647289, rs59853970, rs6018151, rs6043600, rs60630240, rs61638329, rs61745795, rs61787362, rs62186486,rs62383866, rs62407746, rs6495459, rs6723590, rs6784670, rs68119480, rs7162001, rs7193204, rs7264562, rs72 783061, rs72832116, rs7294628, rs74426730, rs75837374, rs76276364, rs77313983, rs7735699, rs77508708, rs775 1374, rs77584189, rs77667857, rs77983269, rs782913, rs78393626, rs78407620, rs7923425, rs7946997, rs79677303, rs7990128, rs80051555, rs8007554, rs8096471, rs894276, rs923937, rs9303523, rs9527245 and rs9947513.

2. Use of the detection reagent for SNP sites associated with the risk of breast cancer after perimenopausal hormone therapy as described in claim 1 in the preparation of a product for detecting breast cancer after perimenopausal hormone therapy.

3. A kit for detecting the risk of breast cancer after hormone therapy in perimenopause, characterized in that: The kit comprises the detection reagent for the SNP site associated with breast cancer risk after perimenopausal hormone therapy according to claim 1; Optionally, the kit further comprises one or both of a nucleic acid extraction reagent and a nucleic acid purification reagent; Optionally, the kit further comprises one or more of a capture probe, a capture chip, a primer set, and a device for performing SNP detection based on a human nucleic acid sample.

4. Use of the detection reagent for SNP sites associated with the risk of breast cancer after perimenopausal hormone therapy as described in claim 1 in constructing a model for detecting the risk of breast cancer after perimenopausal hormone therapy or in preparing a device for predicting the risk of breast cancer after perimenopausal hormone therapy.

5. A method for constructing a model for predicting the risk of breast cancer after perimenopausal hormone therapy, characterized in that: include: S10, obtaining samples to be tested that are diagnosed with breast cancer and samples to be tested that are not diagnosed with breast cancer within a certain period, performing whole genome sequencing to screen and analyze differential SNP sites associated with breast cancer, and obtaining differential SNP sites associated with breast cancer as set A; S20, obtaining samples from perimenopausal syndrome patients who have breast cancer after hormone therapy and perimenopausal syndrome patients who have not breast cancer after hormone therapy, performing whole genome sequencing, screening and analyzing differential SNP sites related to breast cancer and hormone therapy, and obtaining differential SNP sites related to breast cancer and hormone therapy as set B; S30, taking the intersection of the set A and the set B as set C, removing the set C from the set of SNP sites associated with breast cancer and hormone therapy, and obtaining SNP sites associated with breast cancer after hormone replacement therapy as set D; S40, calculating the SNP site goodness of fit value according to the result of step S30, and constructing a model for predicting the risk of breast cancer after perimenopausal hormone therapy using a machine learning algorithm; Optionally, the sample to be tested includes one or more of a peripheral blood sample, a tissue sample, a body fluid, an amniotic fluid, and a single cell amplification product sample; Optionally, the machine learning algorithm includes one or more of logistic regression, support vector machine, random forest, decision tree, linear regression and naive Bayes.

6. The method according to claim 5, characterized in that Step S10 includes: S101. Perform whole genome association analysis on whole genome sequencing data of breast cancer population and normal population to obtain the correlation and significance matrix between genetic loci and breast cancer; S102, removing the genetic loci whose population allele frequency is lower than a preset threshold in the association matrix between all genetic loci and breast cancer obtained in step S101, and obtaining the association and significance matrix between non-rare genetic loci and breast cancer; S103, filtering the genetic loci whose statistical significance is higher than a preset threshold from the non-rare genetic loci and breast cancer association matrix obtained in step S102, and extracting the remaining genetic loci as a differential SNP locus set A related to breast cancer.

7. The method according to claim 6, characterized in that Step S20 includes: S201, calculate the allele frequency and deletion rate of genetic loci, extract genetic loci with minor allele frequency higher than rare frequency, and remove genetic loci with too high deletion rate in the sample; S202, performing a chi-square test of the observed and expected allele frequencies on the genetic loci remaining after screening in step S201, and extracting genetic loci whose significance in the Hardy-Weinberg equilibrium test is higher than the significance threshold; S203, calculating the linkage correlation of all genetic loci in each genomic region for the genetic loci remaining after screening in step S202, and removing genetic loci in linkage disequilibrium; S204, performing regression analysis on the genetic loci remaining after screening in step S203 and the trait of whether or not to develop breast cancer after hormone therapy, to obtain a difference test table of genetic loci related to breast cancer and hormone therapy; S205. Extract genetic loci whose hypothesis test significance values ​​are lower than a preset threshold from the difference test table obtained in step S204 as a differential SNP locus set B related to breast cancer and hormone therapy.

8. The method according to claim 7, characterized in that Step S30 includes: S301 calculates the intersection of the obtained breast cancer related SNP site set A and the obtained breast cancer and hormone therapy related SNP site set B as set C; S302 removes the set C obtained in step S301 from the set of SNP sites associated with breast cancer and hormone therapy, and obtains a set D of SNP sites associated with breast cancer after hormone replacement therapy.

9. The method according to claim 8, characterized in that Step S40 includes: S401, calculating the number N of elements in the SNP set D generated in step 302, and arranging the elements in the set D in ascending order of significance; S402, splitting the ordered element set obtained in step 401 into N groups of subsets, each group of subsets Ni includes the elements of the i-th element in the ordered set and all the elements before the sequence number, generating N sets containing different SNP site combinations; S403, performing multiple linear regression on each group of SNP sets obtained in step S402 and the phenotype of breast cancer after hormone therapy, and calculating and obtaining the R of each group of regression equations. 2 ; S404: Extract the R of each group of SNPs obtained in step S403 2 The highest group of SNPs in the study were used to identify single nucleotide polymorphisms (SNPs) for predicting the risk of breast cancer in perimenopausal women after hormone therapy.

10. A model for predicting breast cancer risk after perimenopausal hormone therapy, characterized in that: The model is constructed by the method for constructing a model for predicting the risk of breast cancer after hormone therapy in perimenopause as described in any one of claims 5 to 9.

11. A device for predicting the risk of breast cancer after hormone therapy in perimenopause, characterized in that: The device comprises: Model building module and prediction module; The model building module is used to execute the steps of the method for building a model for predicting the risk of breast cancer after perimenopausal hormone therapy according to any one of claims 5 to 9; The prediction module is used to execute the following steps: calculating the risk of breast cancer after perimenopausal hormone therapy for the sample to be tested using the model for predicting the risk of breast cancer after perimenopausal hormone therapy constructed by the model construction module.

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