WO2023155471A1 - Product for evaluating responsiveness of breast cancer patient to adjuvant endocrine therapy - Google Patents

Abstract

Disclosed is a product for evaluating the responsiveness of a breast cancer patient to adjuvant endocrine therapy. A first aspect of the present invention provides use of a reagent for detecting a gene in the preparation of a product for evaluating the responsiveness of a breast cancer patient to adjuvant endocrine therapy. The gene comprises at least one of the group consisting of U2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL, and GLUD1. In the present application, a gene model of the responsiveness to adjuvant endocrine therapy is established for a breast cancer patient, which can effectively reflect the degree of responsiveness of a subject to the adjuvant endocrine therapy and effectively guide the post-operative treatment of the breast cancer patient.

Description

Products to evaluate the responsiveness of breast cancer patients to adjuvant endocrine therapy technical field

This application relates to the technical field of molecular diagnosis, in particular to products for evaluating the responsiveness of breast cancer patients to adjuvant endocrine therapy.

Background technique

The treatment of breast cancer first needs to be diagnosed by means of basic pathology and molecular pathology. The basic pathology is mainly clinical indicators, including clear lesion size, histological type, histological grade, presence or absence of vascular invasion, presence or absence of carcinoma in situ, Surgical margins and lymph nodes, etc. Molecular pathology includes the detection of molecular indicators for all invasive lesions. Molecular indicators include ER (estrogen receptor), PR (progesterone receptor), HER2 (human epidermal growth factor receptor 2) and Ki-67 (tumor cell proliferation index), among which ER and PR can be collectively referred to as Hormone receptor (HR), and HR+/HER2-type breast cancer patients account for 60% of the total breast cancer patients. Although most early-stage patients can be cured by surgery, some patients still have local recurrence or distant recurrence after surgery. side transfer. Therefore, in order to judge the prognosis of patients more effectively, in addition to conventional clinical pathology and molecular indicators, it is also necessary to find appropriate molecular indicators based on abnormal gene expression, such as the currently widely used transcriptome multi-gene detection indicators.

The multigene prognosis of breast cancer can be traced back at least to the 70-gene detection system (Mammaprint) developed by researchers at the Netherlands Cancer Institute in 2002, which is also the first multigene detection system approved by the US FDA for clinical use. In addition, the breast cancer 21-gene detection system (Oncotype DX) launched by Genomic Health in the United States in 2005, the nanopore-based 50-gene detection system (PAM50), and the breast cancer index (BCI) have been widely used in the world. )wait. Breast cancer treatment guidelines usually regard endocrine therapy as the basic framework of the treatment plan for HR+/HER2 early breast cancer population, so it is of great practical significance to evaluate the sensitivity of endocrine therapy: on the one hand, patients with high response to endocrine therapy , to establish confidence in the basic treatment plan; on the other hand, for patients with low responsiveness, it can prompt doctors to consider the feasibility of adding other adjuvant treatments on the basis of endocrine therapy as soon as possible. However, most of these multi-gene expression profiling methods are used to assess the risk of recurrence to determine whether adjuvant chemotherapy is needed after endocrine therapy in HR+/HER2- early breast cancer populations, and have no effect on the sensitivity and responsiveness of patients to endocrine therapy itself. Therefore, it is necessary to provide such methods to effectively complement existing recurrence risk assessment.

Contents of the invention

This application aims to solve at least one of the technical problems existing in the prior art. For this reason, the present application proposes a product for evaluating the responsiveness of breast cancer patients to adjuvant endocrine therapy.

The first aspect of this application provides the application of reagents for detecting genes in the preparation of products for evaluating the responsiveness of breast cancer patients to adjuvant endocrine therapy. The genes include: U2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, At least N species from the group consisting of GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL, and GLUD1, where N is optionally a positive integer from 1 to 23 .

According to the application of the embodiment of the present application, it has at least the following beneficial effects:

This application establishes a gene model of responsiveness to adjuvant endocrine therapy for breast cancer patients, and obtains the expression level of related genes in breast cancer patients through detection reagents. The application of this gene model can effectively reflect the response degree of subjects to adjuvant endocrine therapy , so as to effectively guide the postoperative treatment of breast cancer patients.

Among them, U2AF2 (U2 Small Nuclear RNA Auxiliary Factor 2) is the U2 small nuclear RNA cofactor 2 gene, and its encoded protein prevents the incompletely spliced pre-mRNA from the nucleus by preventing the transfer of introns, and helps the completed splicing The mRNA that is completely composed of exons is transferred out, and the process of mRNA molecules being transferred from the nucleus to the cytoplasm is regulated through these two aspects. In addition, related reports have shown that U2AF2 is related to the formation and progression of non-small cell lung cancer and other tumors.

CREBBP (CREB Binding Protein) is a cAMP response element binding protein binding protein gene, which is widely expressed and participates in the transcriptional coactivation of many different transcription factors. This gene plays a key role in embryonic development, growth control and homeostasis by combining chromatin remodeling with transcription factor recognition. The protein encoded by this gene has histone acetyltransferase activity, which can acetylate histone and non-histone proteins, providing specific tags for transcriptional activation.

LATS1 (Large Tumor Suppressor Kinase 1) is a large tumor suppressor kinase 1 gene, and its encoded protein is a serine/threonine kinase that localizes in the mitotic organ and complexes with the cell cycle controller CDC2 kinase in early mitosis. Meanwhile, as a negative regulator of YAP1 in the Hippo signaling pathway, it plays a key role in organ size control and tumor suppression by limiting proliferation and promoting apoptosis.

TOP2A (DNA Topoisomerase II Alpha) is a DNA topoisomerase II alpha gene that regulates the topological conformation of DNA replication or transcription. Anthracycline chemotherapeutic drugs target TOP2A. By inhibiting its expression, the TOP2A-mediated reconnection effect is weakened during DNA uncoiling, causing DNA breakage, thereby initiating programmed cell death, and achieving the purpose of eliminating cancer cells.

SMARCB1 (SWI/SNF Related, Matrix Associated, Actin Dependent Regulator Of Chromatin, Subfamily B, Member 1) is a gene encoded by SWI/SNF Related, Matrix Associated, Actin Dependent Regulator Of Chromatin, Subfamily B, Member 1. Part of a complex that derepresses chromatin structure, allowing the transcriptional machinery to more efficiently access its targets. At the same time, this gene is also a tumor suppressor gene, and its mutation is associated with malignant rhabdoid tumors.

GAPDH (Glyceraldehyde-3-Phosphate Dehydrogenase) is the glyceraldehyde-3-phosphate dehydrogenase gene, and its encoded protein is a typical moonlight protein. In addition to participating in energy production, membrane fusion, endocytosis and iron ion channels, it also Involved in the regulation of mRNA stability, apoptosis, gene transcription, maintenance of DNA stability and transfer of tRNA in the nucleus, etc. Other studies have shown that GAPDH plays an important role in tumor growth and is associated with poor prognosis.

TRAF1 (TNF Receptor Associated Factor 1) is a TNF receptor-associated factor 1 gene. The TRAF protein family to which the encoded protein belongs binds to various receptors of the TNFR superfamily and mediates signal transduction. The heterodimeric complex formed by this protein and TRAF2 is required for TNF-α-mediated activation of MAPK8/JNK and NF-κB, and interacts with inhibitors of apoptosis proteins (IAPs) to mediate activation from TNF receptors. anti-apoptotic signal.

GUSB (Glucuronidase Beta) is the glucuronidase β gene, and its encoded hydrolase can degrade glycosaminoglycans including heparan sulfate, dermatan sulfate and chondroitin sulfate.

TNFSF8 (TNF Superfamily Member 8) is a TNF superfamily member 8 gene, and its encoded protein is the ligand of tumor necrosis factor (TNF) receptor CD30, also known as CD30L/CD153. CD30 is expressed in activated T cells, NK cells and B cells; while TNFSF8 is expressed in activated T cells, resting B cells and macrophages.

UPB1 (Beta-Ureidopropionase 1) is the β-ureidopropionase 1 gene. β-ureidopropionase is involved in the pyrimidine degradation pathway. Uracil and thymine are degraded by the sequential action of dihydropyrimidine dehydrogenase, dihydropyrimidine and β-ureidopropionase on β-alanine and β-aminoisobutyric acid. In some solid tumors, pyrimidine-catalyzed degradation maintains an EMT-driven mesenchymal-like state, thereby affecting cancer metastasis.

MMP7 (Matrix Metallopeptidase 7) is a matrix metallopeptidase 7 gene, and its encoded protein is a member of the M10 family of matrix metalloproteinases (MMPs) peptidases. It participates in the decomposition of extracellular matrix and basement membrane proteins in normal physiological processes, such as fibronectin , type IV collagen, laminin, especially elastin, pentosan, osteopontin and cartilage proteoglycan aggregates, etc. In addition, it is also involved in the promotion of tumor cell invasion and progression, and is highly expressed in a variety of cancers.

DES (Desmin) is the desmin gene, and the encoded protein is the main protein component of solitary fibrous tumor (Solitary Fibrous Tumor) in breast or other parts.

SRD5A2 (Steroid 5 Alpha-Reductase 2) is a steroid 5α-reductase 2 gene that encodes a microsomal protein expressed at high levels in androgen-sensitive tissues. The encoded protein is active at acidic pH and is sensitive to the 4-azasteroid inhibitor finasteride. Like the androgen receptor (AR), it is a decisive regulator of androgens.

S100A9 (S100 Calcium Binding Protein A9) is the S100 Calcium Binding Protein A9 gene, and its encoded protein is a member of the S100 protein family. S100 proteins are localized in the cytoplasm and/or nucleus of a variety of cells and are involved in the regulation of many cellular processes, such as cell cycle progression and differentiation. This protein may play a role in the inhibition of casein kinase, and the altered expression may be associated with cystic fibrosis.

KIT (KIT Proto-Oncogene, Receptor Tyrosine Kinase) is a KIT proto-oncogene receptor tyrosine kinase gene, and its encoded protein phosphorylates multiple intracellular proteins after being activated by its cytokine ligand stem cell factor (SCF). Plays a role in the proliferation, differentiation, migration and apoptosis of a variety of cell types.

ESR1 (Estrogen Receptor 1) is the estrogen receptor 1 gene, its encoded protein is localized in the nucleus, can form a dimer with estrogen receptor 2, and regulate growth, metabolism, sexual development, pregnancy and other reproductive functions. Transcription of many estrogen-inducible genes, and plays a key role in breast cancer, endometrial cancer and osteoporosis.

TSPYL5 (Testis-Specific Y-Encoded-Like Protein 5) is a testis-specific Y-encoded-like protein 5 gene, which may participate in the regulation of cell growth by regulating the Akt signaling pathway, and participate in p53 by inhibiting the protein level of p53/TP53 and promoting its ubiquitination. Regulation of /TP53.

NR4A1 (Nuclear Receptor Subfamily 4 Group A Member 1) is a member 1 gene of the nuclear receptor subfamily 4A group, the encoded protein acts as a nuclear transcription factor, and the transfer of the protein from the nucleus to the mitochondria can induce apoptosis.

WAS (WASP Actin Nucleation Promoting Factor) is the Wiskott-Aldrich syndrome protein actin nucleation promoting factor gene. The small GTPase Cdc42, which regulates actin filament formation, is known to be related to the cytoskeleton organizing complex Arp2/3.

EP300 (E1A Binding Protein P300) is the E1A binding protein P300 gene, which encodes adenovirus E1a-related cellular P300 transcriptional coactivator. This protein acts as a histone acetyltransferase, regulates transcription through chromatin remodeling, and plays an important role in cell proliferation and differentiation, in addition to mediating CAMP gene regulation by specifically binding to phosphorylated CREB protein.

TPX2 (TPX2 Microtubule Nucleation Factor) is the TPX2 microtubule nucleation factor gene, which plays a vital role in spindle assembly during cell mitosis.

VHL (Von Hippel-Lindau Tumor Suppressor) is a Von Hippel-Lindau tumor suppressor gene. The protein encoded by this gene is involved in the ubiquitination and degradation of hypoxia-inducible factor (HIF), thereby affecting cell metabolism and differentiation.

GLUD1 (Glutamate Dehydrogenase 1) is the gene of glutamate dehydrogenase 1. The encoded protein of this gene is a mitochondrial matrix enzyme, which can catalyze the oxidative deamination of glutamate to generate α-ketoglutarate and ammonia. important role in insulin secretion. It is allosterically activated by ADP and inhibited by GTP and ATP.

Adjuvant therapy, also known as additional therapy, refers to the treatment given after surgery to eliminate residual cancer cells in the body, and to reduce the possibility of tumor recurrence, metastasis, and spread through adjuvant therapy. Adjuvant endocrine therapy in the embodiments of the present application refers to endocrine therapy given to breast cancer patients after surgery. The method of endocrine therapy includes removing the stimulation of hormones on tumor cells by means of drugs or endocrine gland excision to play an anti-tumor effect. Traditional endocrine therapy drugs include selective estrogen receptor inhibitors (such as tamoxifen, also known as tamoxifen), selective estrogen receptor down-regulators (such as fulvestrant), aromatase inhibitors (such as dorozole, anastrozole, exemestane), etc., and with the advancement of technical means, it may also include targeted therapy of double-drug and multi-drug combination. In the examples of this application, the responsiveness of breast cancer patients to endocrine therapy refers to the sensitivity of all early breast cancer patients with HR+/HER2- and no lymph node metastasis to endocrine therapy, and whether the disease progression can be delayed or cured by endocrine therapy, but No other factors such as age, menopause, or endocrine therapy regimen were taken into account.

In some embodiments of the present application, the genes comprise at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, At least twenty-one, at least twenty-two, all twenty-three.

In some embodiments of the present application, the reagent detects the mRNA expression level of the gene.

In some embodiments of the present application, the molecular type of the breast cancer patient is HR positive and HER2 negative. Here, HR positive means that at least one of ER (estrogen receptor) and PR (progesterone receptor) is positive.

In some embodiments of the present application, the primary tumor stage of the breast cancer patient is T1-T2, and the regional lymph node stage is N0-N3.

Among them, the primary tumor stage refers to the T stage judged according to the TNM staging rules. Specifically, the tumor can be confirmed according to clinical and/or impact diagnostic methods or according to the pathological size and scope. T1 means that the maximum diameter of the tumor in the breast is 20mm or Smaller, T2 means the tumor diameter is greater than 20mm but not greater than 50mm. Comprehensive T1-T2 means that the maximum diameter of the breast tumor does not exceed 50mm. In breast cancer patients, the lymph nodes located mainly in the armpits, above and below the collarbone, and under the sternum are called regional lymph nodes, while lymph nodes in other parts of the body are called distant lymph nodes. Regional lymph node staging is the staging of the metastasis and spread of cancer cells in lymph nodes. N0 means no evidence of regional lymph node metastasis or only isolated tumor cell groups. N1 corresponds to cancer that has metastasized to 1 to 3 axillary lymph nodes with a diameter of at least For some conditions such as 2mm, N2 can be divided into N2a (such as cancer has spread to 4-9 axillary lymph nodes) and N2b (such as cancer has spread to intramammary lymph nodes, but has not spread to axillary lymph nodes), and N3 can be divided into N3a ( If the cancer has spread to 10 or more underarm or subclavian lymph nodes), N3b (if the cancer has spread to the internal mammary and underarm lymph nodes), and N3c (if the cancer has spread to the supraclavicular lymph nodes).

In some embodiments of the present application, the regional lymph node stage is N0.

The second aspect of the present application provides a kit for evaluating the response of breast cancer patients to adjuvant endocrine therapy, the kit includes reagents for detecting genes, and the genes include: U2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8 , UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1 at least N species in the group consisting of, N is optionally a positive integer from 1 to 23.

In some embodiments of the present application, the genes comprise at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, at least sixteen, at least seventeen, at least eighteen, at least nineteen, at least twenty, At least twenty-one, at least twenty-two, all twenty-three.

In some embodiments of the present application, the reagent detects the mRNA expression level of a gene.

In some embodiments of the present application, the molecular type of the breast cancer patient is HR positive and HER2 negative.

In some embodiments of the present application, the primary tumor stage of the breast cancer patient is T1-T2, and the regional lymph node stage is N0-N3.

In some embodiments of the present application, the regional lymph node stage is N0.

In a third aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to make the computer perform the following operations:

Step 1: Obtain information on the expression levels of genes from breast cancer patient samples, including: U2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT , ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1 at least N species from the group consisting of, N is selected from positive integers from 1 to 23;

Step 2: Expression levels are mathematically correlated to obtain a score; the score is used to indicate responsiveness of breast cancer patients to adjuvant endocrine therapy.

In some embodiments of the present application, the reagent detects the mRNA expression level of a gene.

In some embodiments of the present application, the molecular type of the breast cancer patient is HR positive and HER2 negative.

In some embodiments of the present application, the primary tumor stage of the breast cancer patient is T1-T2, and the regional lymph node stage is N0-N3.

In some embodiments of the present application, the regional lymph node stage is N0.

In some embodiments of the present application,

a _i is the expression level of the gene, b _i is the set weight of the gene, and n is the number of the gene.

In some embodiments of the present application, when the score is higher than the set value, it indicates that the breast cancer patient has a high response to endocrine therapy after surgery.

In some embodiments of the present application, score=0.2492×U2AF2+0.2013×CREBBP+0.2007×LATS1+0.1235×TOP2A+0.1175×SMARCB1+0.1155×GAPDH+0.1155×TRAF1+0.1111×GUSB+0.0969×TNFSF8+0.045×UP B1 +0.0412×MMP7+0.0242×DES-0.0306×SRD5A2-0.0315×S100A9-0.0368×KIT-0.0595×ESR1-0.0711×TSPYL5-0.0765×NR4A1-0.182×WAS-0.1922×EP300-0.192 2×TPX2-0.2026×VHL-0.2642 ×GLUD1, the abbreviation of the gene in the formula indicates the expression level of the corresponding gene.

In a fourth aspect of the present application, an electronic device is provided, the electronic device includes a processor and a memory, the memory stores a computer program that can run on the processor, and the processor implements the following operations when running the computer program:

Step 1: Obtain information on the expression levels of genes from breast cancer patient samples, including: U2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT , ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1 at least N species from the group consisting of, N is selected from positive integers from 1 to 23;

Step 2: Expression levels are mathematically correlated to obtain a score; the score is used to indicate responsiveness of breast cancer patients to adjuvant endocrine therapy.

In some embodiments of the present application,

a _i is the expression level of the gene, b _i is the set weight of the gene, and n is the number of the gene.

In some embodiments of the present application, score=0.2492×U2AF2+0.2013×CREBBP+0.2007×LATS1+0.1235×TOP2A+0.1175×SMARCB1+0.1155×GAPDH+0.1155×TRAF1+0.1111×GUSB+0.0969×TNFSF8+0.045×UP B1 +0.0412×MMP7+0.0242×DES-0.0306×SRD5A2-0.0315×S100A9-0.0368×KIT-0.0595×ESR1-0.0711×TSPYL5-0.0765×NR4A1-0.182×WAS-0.1922×EP300-0.192 2×TPX2-0.2026×VHL-0.2642 ×GLUD1, the abbreviation of the gene in the formula indicates the expression level of the corresponding gene.

Wherein, the memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs, such as the adjuvant endocrine therapy for breast cancer patients described in the embodiments of this application The process of assessing responsiveness. The processor executes the non-transitory software program and instructions stored in the memory, so as to realize the assessment of the responsiveness of the breast cancer patient to the adjuvant endocrine therapy.

The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; the data storage area may store and execute the above-mentioned marker screening method. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage devices.

In some embodiments of the present application, the memory may optionally include a memory remotely located relative to the processor, and these remote memories may be connected to the processor through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The non-transitory software programs and instructions needed to implement the above-described evaluations are stored in memory and, when executed by one or more processors, perform the above-described evaluations.

The implementation of the device described above is only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

It can be understood that all or some of the steps and systems disclosed above can be implemented as software, firmware, hardware and an appropriate combination thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit . Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). It is understood that computer storage media includes volatile and nonvolatile, removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. and non-removable media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, tape, magnetic disk storage or other magnetic storage devices, or can Any other medium used to store desired information and which can be accessed by a computer.

In addition, it is understood that communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and may include any information delivery media.

The scoring model for the above-mentioned endocrine therapy response in breast cancer patients is further discussed as follows:

The model involves 23 genes, in descending order of weight: U2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL, and GLUD1. According to the functions and signaling pathways, these genes can be simply divided into:

Transcription regulation: U2AF2, CREBBP, EP300, ERS1, VHL;

Cell cycle regulation, cell proliferation and differentiation: SMARCB1, S100A9, KIT;

Apoptosis: LATS1, MMP7 related to Hippo signaling pathway, TRAF1 related to TNF-a pathway;

Cytoskeleton and organization framework: GAPDH, DES, WAS, TPX2.

Hormones: SRD5A2, ESR1; etc.

The expression or mutation of U2AF2 may be related to the occurrence, development, recurrence and metastasis of non-small cell lung cancer, prostate cancer and other cancers. In this model, U2AF2 has the largest weight, indicating that the high expression of U2AF2 has a great role in promoting the effect of endocrine therapy.

CREBBP is involved in estrogen receptor signaling in breast cancer by inducing estrogen receptor acetylation and enhancing its transcriptional and DNA-binding activity. Breast cancer pathology studies have found that CREBBP is highly expressed in HR+ breast cancer, and corresponds to a better five-year DFS. However, CREBBP did not show high expression in HER2+ breast cancer, indicating that CREBBP may also be involved in the HER2 signaling pathway. The weight given to CREBBP in this model is the second largest, indicating that the high expression of CREBBP has a great promotion effect on the effect of endocrine therapy. ESR1, which is closely related to it, has a negative weight in the model, indicating that ESR1 has a negative impact on the effectiveness of endocrine therapy, which is also consistent with the results of a large number of ESR1 function studies.

The structure and function of acetylase EP300 is similar to CREBBP, but in the response model of endocrine therapy, it is opposite to CREBBP, its weight is negative, and its absolute value is larger, indicating that EP300 plays a drug-resistant role in response to endocrine therapy. EP300 participates in the Wnt pathway as a beta-catenin transcriptional coactivator and promotes the growth of cancer stem cells (CSC). Case studies of triple-negative breast cancer and basal-like breast cancer have shown that EP300 down-regulates the breast cancer resistance gene ABCG2 and promotes CSC phenotype, tumor growth and transfer. Research results in metaplastic breast cancer have shown that the expression of EP300 and E-cadherin is down-regulated, thereby initiating EMT and promoting drug resistance to paclitaxel. These results suggest that the mechanism of EP300 resistance to endocrine therapy may be consistent with chemotherapy.

On the one hand, the tumor suppressor gene Von Hippel-Lindau (VHL) regulates the accumulation of hypoxia-inducible factor (HIF), thereby affecting cell metabolism and differentiation, including GLUT-1, 6-PFK, and PDK related to glucose metabolism; VEGF, PDGF, CTGF; CA9 that controls extracellular pH; TGFa; erythropoiesis-related genes; CXCR4 and its ligand SDF1 that stimulate chemotaxis, and CXCR4 is related to tumor metastasis; EMT-related MMP1, LOX, TWIST, and HGFR. On the other hand, independent of HIF, VHL is also involved in the regulation of ECM aggregation type IV collagen COL4, fibronectin FN1, motor protein KIF2; cytoskeleton-related aPKC, PAR3/6, GSK3B; apoptosis-related P53, MDM2, JUNB; CK2, NFKCB, CARD9 for cell survival; CDKN1B for cell cycle; TCEB1/2 for protein ubiquitination. The weight of VHL in this model is negative and its absolute value is large, indicating that it has a certain negative impact on the effectiveness of endocrine therapy.

The proto-oncogene KIT has been extensively studied in the field of breast cancer. Immunohistochemical staining results of pathological sections of invasive ductal breast cancer showed that 75% of the samples had low expression of KIT, and the low expression of KIT was related to the number of lymph node spread and poor DFS. In contrast, immunohistochemical staining results were found in triple-negative breast cancer, whereas estrogen-promoted KIT expression resulted in an aggressive phenotype in a model of invasive lobular breast cancer. In this model, the weight of KIT is negative, indicating that KIT negatively affects the effectiveness of endocrine therapy, consistent with the function of KIT in promoting tumor aggressiveness.

LATS1, a negative regulator of YAP1 in the Hippo signaling pathway, plays a key role in organ size control and tumor suppression by limiting proliferation and promoting apoptosis. Knockdown of LATS1 increases cancer cell plasticity, and luminal B cancers are more prone to express basal-like features and increase resistance to endocrine therapy. In ER+ cells, the combination of CRABP and LATS1 stops the ubiquitination of LATS1, thereby activating YAP1/TAZ, starting the Hippo signaling pathway, and inhibiting invasion and metastasis; on the contrary, in ER- cells, the combination of CRABP and LATS1 starts the ubiquitination of LATS1, thereby Down-regulate YAP1/TAZ, terminate Hippo signaling pathway, activate ER-cell invasion and metastasis. Moroishi T found in a breast cancer cell model that knocking out LATS1/2 can increase cancer immune response, thereby inhibiting tumor growth. In this model, the weight of LATS1 is a large positive number, indicating that LATS1 plays an important positive role in endocrine therapy.

MMP7 is another gene related to Hippo signaling pathway in the model. Gene expression analysis found that the expression of YAP1/MMP7/CXCL16 gene axis was strongly associated with Hippo-YAP1 pathway kinase LATS2. High expression of YAP1 leads to high expression of MMP7, which downregulates the chemoattractant CXCL16. The expression of soluble CXCL16 on tumor tissue can attract immune cells with its receptor CXCL6, such as CD4+ or CD8+ T cells and NK cells, thereby stimulating an immune response, so high expression of YAP1/MMP7 prevents immune cells from penetrating on tumor tissue. Penetration and aggregation, leading to immune escape. Treatment of MDA-MB-231 with an MMP7 inhibitor resulted in loss of invasiveness and slowed growth of the cells. In this model, the weight of MMP7 is positive, indicating that MMP7 has a positive effect on endocrine therapy.

In the "S-score" to measure cancer susceptibility defined by combining gene mutation, methylation, gene fold change (CNV) and gene expression data, GUSB has two distinct roles of promoting and suppressing tumors in tumors with different aggressiveness. The opposite effect. In the mechanism of estrogen balance in the mammary gland, GUSB regulates the hydrolysis of estrogen sulfate and glucuronide, regulates the biological conversion of 17β-estradiol and estrogen, and is a direct factor of breast cancer risk. The weight of GUSB7 in this model is positive, indicating that GUSB has a positive effect on endocrine therapy.

The genes related to NF-bK pathway and MAPK pathway in the model include TNFSF8 and TRAF1. TNFSF8 is the ligand of tumor necrosis factor (TNF) receptor CD30, also known as CD30L. Abnormal expression of CD30 is a sign of hematopoietic malignancies, including anaplastic large cell lymphoma and Hodgkin's lymphoma, while TRAF1 is CD30/ Molecules downstream of CD30L signaling. In a retrospective study of 284 cases of breast cancer, IHC staining of pathological sections did not find any ALK-positive or CD30-positive cases, which may indicate the absence of immune cells in the cancer microenvironment. The weights of TNFSF8 and TRAF1 in the model are both positive, indicating that CD30/CD30L/TRAF1 signaling plays a positive role in endocrine therapy.

SMARCB1 is the core subunit of the chromatin remodeling SWI/SNF complex, which is a highly conserved global transcriptional regulator that regulates targets by recruiting transcription factors to target genes or by changing the position of nucleosomes gene expression. The loss of SMARCB1 is the main driver of malignant rhabdoid tumors, leading to the termination of G0/G1 cell cycle transition; misactivation of the sonic hedgehog pathway (SHH) and Wnt/beta-catenin pathway; abnormal expression of embryonic stem cell renewal genes and nerve growth genes. The weight of SMARCB1 in the model is a large positive number, indicating that SMARCB1 plays an important positive role in the response to endocrine therapy of breast cancer, and its mechanism remains to be studied in the future.

DNA topoisomerase TOP2A is a well-known important gene that regulates the topological conformation of DNA replication or transcription. TOP2A amplification is often accompanied by HER2 amplification, and co-amplification of the two accounts for 40-50% of HER2+ breast cancers. The study found that breast cancer with simultaneous amplification of HER2 and TOP2A was also accompanied by high expression of other genes: CASC3, CDC6, RARA and SMARCE1. And SMARCE1 is similar to the SMARCB1 gene in the above model. In addition, TOP2A gene expression is strongly associated with DMFS and associated with complete remission after treatment with the anthracycline chemotherapy drug cyclophosphamide (Cyclophosphamide). The weight of TOP2A in the model is a large positive number, indicating that TOP2A plays an important positive role in the response to endocrine therapy of breast cancer.

The study on the expression of GAPDH in breast cancer showed that the OS and RFS of the population with high GAPDH expression were greatly reduced; the breast cancer cell MCF7 was cultured with estradiol, and the expression of GAPDH was positively correlated with the dose of estradiol, indicating that GAPDH was related to the growth of cancer cells and breast cancer. related to the malignancy of the cancer. Related evidence also shows that GAPDH plays an important role in tumor growth and is associated with poor prognosis. The weight of GAPDH in the model is a large positive number, indicating that it plays a positive role in the response to endocrine therapy for breast cancer.

DES codes for desmin, which is the main protein component of breast or other solitary fibrous tumors (Solitary Fibrous Tumor). Solitary fibrous neoplasms occur in less than 0.2% of breast cancer patients and are mostly benign. The weight of DES in the model is the smallest positive number, indicating that the response to endocrine therapy for breast cancer plays a small positive role.

β-ureidopropionase UPB1 is involved in pyrimidine metabolism, and in some solid tumors, the catalytic degradation of pyrimidine maintains the EMT-driven mesenchymal-like state, which has an impact on cancer metastasis. UPB1 is also involved in the metabolism of fluoropyrimidine chemotherapeutics such as 5-FU, and the weight of UPB1 in the model is positive, indicating that it plays a positive role in the response to endocrine therapy for breast cancer.

Estrogen receptor ESR1 is a marker gene of breast cancer, and the weight of ESR1 in the model is a negative integer, indicating that it plays a negative role in the response to endocrine therapy of breast cancer. Another gene, SRD5A2, is similar to androgen receptor (AR) and is a decisive regulator of androgen. The weight of SRD5A2 in the model is also a negative integer, indicating that it plays a negative role in the response to endocrine therapy for breast cancer.

Calbindin S1008A and S1009A belong to damage-associated molecular pattern (DAMP) molecules, which regulate the immune response of the tumor microenvironment to promote tumor growth and malignant transformation. Their functions are similar to cytokines and chemokines, and they act as inhibitors between tumors and inflammation. The dual role of carcinogens and carcinogens. The weight of S1009A in the model is a negative integer, indicating that it plays a negative role in the response to endocrine therapy in breast cancer.

The testis-specific Y-encoded-like protein TSPYL5 promotes cancer growth by downregulating USP7 to suppress the tumor suppressor gene P53 Studies of postmenopausal early ER+ breast cancer samples have shown that high expression of TSPYL5 leads to high expression of CYP19A1, suggesting that TSPYL5 may be a transcription factor involved in the regulation of CYP19A1 and other genes. However, the weight of TSPYL5 in the model is a negative integer, indicating that it plays a negative role in the response to endocrine therapy in breast cancer.

The microtubule nucleating factor TPX2 is essential for spindle assembly during mitosis. Some studies have shown that chromosomal instability (CIN) cells survive by activating TPX2/AURKA, and TPX2 is highly expressed in many cancers. The weight of TPX2 in the model is a negative integer and its absolute value is large, indicating that it has a great negative effect on the response to endocrine therapy in breast cancer.

The NR4A receptor acts as a transcription factor that alters the expression of downstream genes in multiple cellular functions: apoptosis, proliferation, DNA repair, metabolism, cell migration, inflammation, and angiogenesis. As a potent activator of TGF-b signaling, NR4A1 promotes AXIN2–RNF12/ARKADIA-induced degradation of SMAD7 by binding to SMAD7 and AXIN2, thereby initiating TGF-b signaling and promoting tumor metastasis. Inflammatory cytokines lead to strong expression of NR4A1, thus high expression of NR4A1 in breast cancer with high immune infiltration and corresponding poor prognosis. This is consistent with the finding that NR4A1 plays a negative role in the response to endocrine therapy in the model, with negative weights.

The interaction between tumor-associated macrophages (TAM) and tumor cells promotes tumor invasion and metastasis, through a paracrine closed loop formed by the combination of colony-stimulating factor-1 expressed on TAM and EGFR expressed by tumor cells. Deletion of WASP in the WAS gene family attenuates this paracrine effect, preventing co-migration of TAMs with cancer cells and attenuating metastatic dissemination. The weight of WAS in the model is a negative integer and the absolute value is large, indicating that the response to endocrine therapy for breast cancer has a relatively large negative effect.

Glutamate dehydrogenase GLUD1 is the negative integer with the largest absolute weight in the model, indicating that GLUD1 has the greatest negative effect on the response to endocrine therapy for breast cancer.

It can be understood that the above discussion is based on a model of all 23 genes, and each gene in this model has more or less researches in the field of breast cancer to show that these genes have a related effect on breast cancer, but in the examples of this application Among them, it is not limited to the model of all 23 genes, and other models with response evaluation effects can also be constructed by selecting several genes from them.

Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.

Description of drawings

Figure 1 is the ROC curve corresponding to the maximum value, median value and minimum value of AUC repeated 20 times for the optimal model cross-validation of the present application.

Fig. 2 is the ROC curve verified in all samples of the optimal model of the present application.

Figure 3 is a boxplot of the expression levels of a single gene in different populations in the optimal model of the present application.

Figure 4 is the marker ROC curve of a single gene in the optimal model of the present application, and the 0-expression value in the figure indicates that the gene is negatively correlated with the response to endocrine therapy.

Fig. 5 is the results of the maximum value, median value and minimum value of AUC of cross-validation of multi-gene subsets in the genes of the optimal model of the present application. Among them, a and b are the cross-validation results of the 2-gene model, and c and d are the cross-validation results of the 22-gene model.

Detailed ways

The idea and technical effects of the present application will be clearly and completely described below in conjunction with the embodiments, so as to fully understand the purpose, features and effects of the present application. Apparently, the described embodiments are only some of the embodiments of the present application, not all of them. Based on the embodiments of the present application, other embodiments obtained by those skilled in the art without creative efforts belong to The protection scope of this application.

The following describes the embodiments of the present application in detail, and the described embodiments are exemplary and are only used for explaining the present application, and should not be construed as limiting the present application.

In the description of the present application, several means more than one, and multiple means more than two. Greater than, less than, exceeding, etc. are understood as not including the original number, and above, below, within, etc. are understood as including the original number. If the description of the first and second is only for the purpose of distinguishing the technical features, it cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features relation.

In the description of this application, reference to the terms "one embodiment," "some embodiments," "exemplary embodiments," "example," "specific examples," or "some examples" is intended to mean that the embodiments Specific features, structures, materials, or characteristics described in or examples are included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Example 1

In this embodiment, a group of breast cancer gene markers screened out using mRNA gene expression data, the process is as follows:

1. Data set preparation

The data set is the gene chip data of breast cancer slices (Affymetrix platform) Cancer Genome Atlas TCGA data set: TCGA-BRCA, only select early (T1-T2) breast cancer patients with ER+ or PR+, HER2-, Node=0 and undergo endocrine Treatment, a total of 258 cases were obtained. Among them, 78 cases responded to TCGA-BRCA, and 180 cases did not respond. After removing gene transcripts with extremely low expression (defined as the number of samples with non-zero expression not exceeding 10), miRNA and lncRNA were removed, and the number of genes was 9524.

Data standardization is performed step by step on samples and genes:

(1) Standardize each sample: calculate the median of all gene expression levels for each sample, and then subtract the median of all gene expression levels of the sample from the original expression levels of each gene in the sample , to obtain the once-normalized expression level of each gene of the sample, and the difference in the input amount of the sample mRNA is removed through this normalization method;

(2) Standardize each gene: Based on the gene expression data once standardized in step (1), further calculate the median of the expression level of each gene in all samples, and then divide each Subtract the median expression level of the gene in all samples from the primary normalized expression level of the gene in the sample to obtain the secondary normalized expression level of the gene in each sample.

2. Screening and model of genetic diagnostic markers

Aiming at the response of breast cancer patients to adjuvant endocrine therapy (RESPONSE), the model is established through the following operations:

Identification of genes associated with responsiveness to adjuvant endocrine therapy. Using t-test (t-test), look for genes with statistical significance (p<0.05) that can distinguish different groups of target variables (response vs non-response), and initially obtain differentially expressed genes. Specifically, the treatment response target variable population is:

Response population (Response=1): including patients who were clinically evaluated as complete remission, partial remission, or stable condition after the follow-up visit after treatment;

Non-response population (Response=0): patients with progressive disease.

in:

Complete Response: All cancer or tumors gone; no evidence of disease. Tumor markers (if applicable) may be within normal range.

Partial response: The cancer shrinks by a percentage, but the disease remains. Tumor markers (if applicable) may have declined, but evidence of disease remains.

Stable disease: The cancer has neither grown nor shrunk; the amount of disease has not changed. Tumor markers (if applicable) did not change significantly.

Disease Progression: The cancer has progressed; there is now more disease than before treatment. Tumor marker testing (if applicable) showing elevated tumor markers.

Genes were grouped for up- or down-regulation. Differentially expressed genes were divided into two groups. In the t-test results, positive t represents genes whose expression is down-regulated in patient tissues; negative t represents genes whose expression is up-regulated in patient tissues. Hierarchical correlation coefficient analysis was performed on the two groups of genes respectively.

Hierarchical correlation coefficient analysis. For gene groups with up-regulated or down-regulated expression, hierarchical correlation coefficient clustering is carried out respectively. The purpose is that at a given level of correlation coefficient, the genes in each cluster need to be roughly correlated with each other. The clustering is carried out through the following iterations. First, Obtain the correlation coefficient matrix between any pair of genes in the up-regulated or down-regulated gene group, and set the first correlation coefficient threshold T ₁ =0.75 (note: this threshold can be adjusted by looking at the distribution of correlation coefficients between all gene pairs in advance) , scan the correlation coefficient matrix, and perform the following recursive clustering on all genes greater than the threshold T: First, sort the results of the corresponding t-tests of these genes according to the p value from small to large, and take the first unclassified gene as Candidate genes, classify all genes with a correlation coefficient greater than T and the candidate gene into a cluster, then take the row (or column) average value of the correlation coefficient sub-matrix formed by the genes of this cluster, and start from the large To small sorting, take the first gene (that is, the gene with the largest correlation coefficient in the cluster) as the representative gene of the cluster, that is, the gene most related to all the genes in the cluster; lower the threshold by 0.05 to get the second Correlation coefficient threshold T ₂ =T ₁ -0.05, repeat the above steps for the remaining genes that are not classified into clusters until all genes are exhausted, so that all these differentially expressed genes are classified into clusters, and the representative genes of each cluster constitute the marker The model candidate gene of the animal.

Genomes were identified by iterative linear regression analysis. In the hierarchical correlation coefficient analysis, the number of genes (s) as the model parameter is given in advance, and iterative linear regression analysis is performed. Recycle different s values to find the optimal number of model parameters, which is determined by the maximum value of the corresponding R-squared value (rsq), so as to obtain the optimal model.

Final Endocrine Therapy Response Score for 23 Gene Models: U2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300 , TPX2, VHL and GLUD1.

The parameters of each gene in the final optimal model are shown in Table 1 below:

Table 1. Parameters related to each gene in the 23-gene linear regression model

Cross-validation: Divide the data set equally according to the population of the target variable, half of which is the training set and the other half is the verification set, calculate ROC and AUC, and repeat N (=20) times. And calculate the statistical characteristics of AUC, such as minimum, maximum, median. The median AUC value of the cross-validation is used as an index to evaluate the model results.

The results are shown in Figure 1. It can be seen from the figure that the maximum value of the AUC of the model repeated 20 times is 0.9, the minimum value is 0.7, and the median value is 0.86, indicating that the model has good classification significance and can combine HR+/ HER2-, T1-T2, and N0 breast cancer patients with different sensitivity to adjuvant endocrine therapy are well separated, which can effectively guide postoperative adjuvant therapy for breast cancer patients.

According to the established 23-gene linear regression model (score=0.2492×U2AF2+0.2013×CREBBP+0.2007×LATS1+0.1235×TOP2A+0.1175×SMARCB1+0.1155×GAPDH+0.1155×TRAF1+0.1111×GUSB+0.0969×TNFSF8+0.045 ×UPB1+ 0.0412×MMP7+0.0242×DES-0.0306×SRD5A2-0.0315×S100A9-0.0368×KIT-0.0595×ESR1-0.0711×TSPYL5-0.0765×NR4A1-0.182×WAS-0.1922×EP300-0.192 2×TPX2-0.2026×VHL-0.2642× GLUD1) 258 samples of early breast cancer patients with complete HR+HER2-N0 endocrine therapy receiving endocrine therapy in the TCGA breast cancer data set (a total of 78 samples marked as response Response=1, and a total of 78 cases marked as no response Response=0 180 cases (response rate 30.23%) were drawn as a whole to draw the ROC curve to evaluate the ability of the model to diagnose endocrine therapy response and non-response, and the results are shown in Figure 2. The AUC is 0.9281, the specificity (1-false positive rate) corresponding to the optimal decision point on the ROC curve (shown by the dotted line) is 89%, and the sensitivity is 85%. When using the above linear regression model to calculate the prediction score, calculate the corresponding chi-square coefficient (chi-sq), set the score corresponding to the maximum position as the optimal threshold score, and the prediction threshold score is 0.5219. When the linear regression model is used for evaluation, if the score is greater than the threshold, it is a response, and endocrine therapy can be established; if the score is less than the threshold, it is non-response, and adjuvant chemotherapy or other adjuvant therapy should be considered immediately based on the existing endocrine therapy.

There are 23 genes in the optimal model constructed. For a single gene in the model, the boxplots of its expression in response (1) and non-response (0) populations are shown in Figure 3. The ROC curve used as a marker to distinguish the response degree of breast cancer patients to adjuvant endocrine therapy is shown in Figure 4. It can be seen from Figure 3 and Figure 4 that most genes in the model have independent diagnostic efficacy. The discussion of genes shows that the genes screened out in this example are reasonable. Although the AUC of several genes is only 0.5 or slightly greater than 0.5, which has no obvious diagnostic value, the 23-gene model reflects the synergistic effect with other genes, which improves the diagnostic efficiency of the 23-gene model.

For multiple genes in the model, randomly select K (2, 3...22) genes in the model genome, rebuild the model according to the above method and perform cross-validation, part of the results are shown in Figure 5, and it can be seen from the figure , the model reconstructed by selecting a subset of 2 or more genes in the above 23 gene sets also has a good diagnostic value, but the diagnostic value increases with the number of genes, so it can also be selected from the above Multiple genes in the set of 23 genes were selected as indicators for evaluating the responsiveness of adjuvant endocrine therapy in breast cancer patients, and the closer to all 23 genes, the higher the diagnostic accuracy may be.

Example 2

This embodiment provides a kit for evaluating the responsiveness of adjuvant endocrine therapy in patients with breast cancer, the kit includes reagents capable of quantitatively detecting the mRNA levels of the following 10 genes: U2AF2, SMARCB1, GAPDH, GUSB, KIT, ESR1, WAS, EP300, TPX2 and GLUD1, the reagents include reverse transcriptase, primers, Taq enzymes, fluorescent dyes, etc.

Example 3

This embodiment provides a device for evaluating the responsiveness of adjuvant endocrine therapy for breast cancer patients. The device includes a processor and a memory, and the memory stores a computer program that can be executed by the processor. The method of using this device to assess the responsiveness of adjuvant endocrine therapy for breast cancer patients is as follows:

1. Select postoperative cancer tissue section samples of breast cancer patients to extract mRNA.

2. Send the extracted mRNA into the detection device to obtain the following 23 genes: U2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, Information on the quantitative expression levels of TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1 a _i .

3. According to the scoring formula

Substitute the expression of each gene into the calculation of the response score of adjuvant endocrine therapy; then divide the risk scores of the subjects into different risk groups according to one or more preset thresholds, and consider directly applying endocrine therapy or Other adjuvant therapy, carrying out transcriptome diagnosis for precision treatment of HR+HER2- breast cancer patients.

The present application has been described in detail above in conjunction with the embodiments, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge of those of ordinary skill in the art without departing from the gist of the present application. In addition, the embodiments of the present application and the features in the embodiments can be combined with each other under the condition of no conflict.

Claims (10)

1. Application of reagents for detecting genes in the preparation of products for evaluating the responsiveness of breast cancer patients to adjuvant endocrine therapy, characterized in that the genes include: U2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, At least N species from the group consisting of TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL, and GLUD1, where N is optionally a positive integer from 1 to 23.
2. The use according to claim 1, wherein the reagent detects the mRNA expression level of the gene.
3. The application according to claim 1, characterized in that the molecular typing of breast cancer patients is HR positive and HER2 negative.
4. The application according to claim 1, characterized in that the primary tumor stage of breast cancer patients is T1-T2, and the regional lymph node stage is N0-N3;

   Preferably, the regional lymph node stage is N0.
5. The kit for evaluating the responsiveness of adjuvant endocrine therapy for breast cancer patients is characterized in that it includes reagents for detecting genes, and the genes include: U2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, At least N species from the group consisting of DES, SRD5A2, S100A9, KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL, and GLUD1, N is optionally a positive integer from 1 to 23;

   Preferably, the reagent detects the mRNA expression level of the gene.
6. The kit according to claim 5, wherein the molecular typing of breast cancer patients is HR positive and HER2 negative;

   Preferably, the primary tumor stage of the breast cancer patient is T1-T2, and the regional lymph node stage is N0-N3.
7. The computer-readable storage medium is characterized in that the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to make the computer perform the following operations:

   Step 1: Obtain information on the expression levels of genes from breast cancer patient samples, the genes including: U2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9 , KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1 are at least N species from the group consisting of, N is optionally a positive integer from 1 to 23;

   Step 2: mathematically correlating the expression levels to obtain a score; the score is used to indicate the responsiveness of breast cancer patients to adjuvant endocrine therapy.
8. The computer-readable storage medium according to claim 7, wherein the expression level is the transcription level of the gene.
9. The computer-readable storage medium of claim 7, wherein:

   

   a _i is the expression level of the gene, b _i is the set weight of the gene, and n is the number of genes;

   Preferably, when the score is higher than the set value, it indicates that the breast cancer patient has a high response to endocrine therapy after operation.
10. The electronic device is characterized in that it includes a processor and a memory, the memory stores a computer program that can run on the processor, and the processor implements the following operations when running the computer program:

    Step 1: Obtain information on the expression levels of genes from breast cancer patient samples, the genes including: U2AF2, CREBBP, LATS1, TOP2A, SMARCB1, GAPDH, TRAF1, GUSB, TNFSF8, UPB1, MMP7, DES, SRD5A2, S100A9 , KIT, ESR1, TSPYL5, NR4A1, WAS, EP300, TPX2, VHL and GLUD1 are at least N species from the group consisting of, N is optionally a positive integer from 1 to 23;

    Step 2: mathematically correlating the expression levels to obtain a score; the score is used to indicate the responsiveness of breast cancer patients to adjuvant endocrine therapy.

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