CN117604094A - Methylation marker and application of kit in auxiliary diagnosis of cancer - Google Patents

Methylation marker and application of kit in auxiliary diagnosis of cancer Download PDF

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CN117604094A
CN117604094A CN202210242006.4A CN202210242006A CN117604094A CN 117604094 A CN117604094 A CN 117604094A CN 202210242006 A CN202210242006 A CN 202210242006A CN 117604094 A CN117604094 A CN 117604094A
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zpbp2
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韦玉杰
王俊
狄飞飞
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Tengchen Biotechnology Shanghai Co ltd
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Abstract

The invention discloses an application of a methylation marker and a kit in auxiliary diagnosis of cancer. The invention provides an application of a methylation ZPBP2 gene as a marker in the preparation of products; the use of the product is at least one of the following: aiding in diagnosing cancer or predicting the risk of developing cancer; aiding in distinguishing benign nodules from cancers; aiding in distinguishing between different subtypes of cancer; aiding in differentiating different stages of cancer; aiding in differentiating between different cancers; determining whether the analyte has an inhibitory or promoting effect on the occurrence of cancer; the cancer may be lung cancer or breast cancer. The study of the invention discovers the hypermethylation phenomenon of ZPBP2 gene in the blood of patients with lung cancer and breast cancer, and the invention has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of lung cancer and breast cancer and reducing the death rate.

Description

Methylation marker and application of kit in auxiliary diagnosis of cancer
Technical Field
The invention relates to the field of medicine, in particular to an application of a methylation marker and a kit in auxiliary diagnosis of cancers.
Background
Lung cancer is a malignant tumor that occurs in the epithelium of the bronchial mucosa, and in recent decades, the morbidity and mortality rate have been on the rise, being the cancer with the highest morbidity and mortality rate worldwide. Although new progress has been made in diagnostic methods, surgical techniques, and chemotherapeutics in recent years, the overall 5-year survival rate of lung cancer patients is only 16%, mainly because most lung cancer patients have been shifted at the time of diagnosis and have lost the opportunity for radical surgery. The study shows that the prognosis of lung cancer is directly related to stage, the survival rate of lung cancer in stage I for 5 years is 83%, the survival rate in stage II is 53%, the survival rate in stage III is 26%, and the survival rate in stage IV is 6%. Thus, the key to reducing mortality in lung cancer patients is early diagnosis and early treatment.
The main lung cancer diagnosis methods at present are as follows: (1) imaging method: such as chest X-rays and low dose helical CT. However, early lung cancer is difficult to detect by chest X-ray. Although low-dose spiral CT can find nodules in the lung, the false positive rate is as high as 96.4%, and unnecessary psychological burden is brought to a person to be checked. At the same time, chest X-rays and low dose helical CT are not suitable for frequent use due to radiation. In addition, imaging methods are also often affected by equipment and physician experience, as well as effective film reading time. (2) cytological methods: such as sputum cytology, bronchoscopy brush or biopsy, bronchoalveolar lavage cytology, etc. Sputum cytology and bronchoscopy have less sensitivity to peripheral lung cancer. Meanwhile, the operation of brushing a piece under a bronchoscope or taking a biopsy and performing cytological examination on bronchoalveolar lavage fluid is complicated, and the comfort level of a physical examination person is poor. (3) serum tumor markers commonly used at present: carcinoembryonic antigen (CEA), carbohydrate antigen (CA 125/153/199), cytokeratin 19 fragment antigen (CYFRA 21-1), and Neuron Specific Enolase (NSE), etc. These serum tumor markers have limited sensitivity to lung cancer, typically 30% -40%, and even lower for stage I tumors. Furthermore, the tumor specificity is limited, and is affected by many benign diseases such as benign tumor, inflammation, degenerative diseases and the like. At present, the tumor markers are mainly used for screening malignant tumors and rechecking tumor treatment effects. Therefore, further development of a highly efficient and specific early diagnosis technique for lung cancer is required.
The most effective method of pulmonary nodule diagnosis currently internationally accepted is chest low dose helical CT screening. However, the low-dose helical CT has high sensitivity, and a large number of nodules can be found, but it is difficult to determine whether or not the subject is benign or malignant. In the found nodules, the proportion of malignancy was still less than 4%. Currently, clinical identification of benign and malignant lung nodules requires long-term follow-up, repeated CT examination, or invasive examination methods relying on biopsy sampling of lung nodules (including chest wall fine needle biopsy, bronchoscopy biopsy, thoracoscopy or open chest lung biopsy), and the like. CT guided or ultrasound guided transthoracic biopsy has higher sensitivity, but has lower diagnosis rate for nodules <2cm, 30-70% missed diagnosis rate, and higher occurrence rate of pneumothorax and hemorrhage. The incidence rate of the aspiration biopsy complications of the bronchoscope needle is relatively low, but the diagnosis rate of the surrounding nodules is limited, the diagnosis rate of the nodules less than or equal to 2cm is only 34%, and the diagnosis rate of the nodules greater than 2cm is 63%. Surgical excision has a high diagnostic rate and can directly treat the node, but can cause a transient decline in patient lung function, and if the node is benign, the patient performs unnecessary surgery, resulting in excessive medical treatment. Therefore, there is a strong need for new in vitro diagnostic molecular markers to aid in the identification of pulmonary nodules, while reducing the rate of missed diagnosis and minimizing unnecessary punctures or surgeries.
Breast cancer is a malignant tumor caused by uncontrolled proliferation of mammary epithelial cells. On the one hand, breast cancer is one of the most common malignant tumors of females worldwide, and the incidence rate is the first malignant tumor of females. On the other hand, the survival rate of breast cancer is related to the class and stage of the tumor. The 5-year survival prognosis for early stage breast cancer is generally higher than 60%, but for advanced breast cancer this number falls to 40-60%. For metastatic breast cancer, the prognosis for 5 years survival is typically about 15%. Therefore, the improvement of the early detection rate of the breast cancer is necessary for effective diagnosis and treatment of the later stage of the breast cancer. At present, clinical medicine mainly has two modes of imaging and pathology for early screening diagnosis of breast cancer. The B-type ultrasonic imaging in the imaging diagnosis is non-radiative, but is limited by the mechanism of ultrasonic imaging, and the method has poor resolution for lesions with smaller volume and insignificant echo change and is easy to miss. The breast molybdenum target examination technology is a technology for shooting breasts by using low-dose breast X-rays, can clearly display the structural condition of each tissue of the breasts, but has higher false positive rate in breast molybdenum target examination, and needs to puncture the breasts of a patient to perform more accurate judgment, and in addition, the breast molybdenum target has damages such as ionizing radiation and the like to the patient. The breast nuclear magnetic resonance imaging technology for checking breast tissues and generating internal images by using magnetic energy and radio waves is mainly suitable for screening high-risk groups of breast cancer. The pathological diagnosis mainly comprises breast biopsy, which is a method for taking pathological tissues for pathological diagnosis, however, the biopsy operation is very resistant to patients due to human trauma. In addition, some common tumor markers such as tumor antigen 15-3, tumor antigen 27.29, carcinoembryonic antigen, tumor antigen 125 and circulating tumor cells are used for diagnosing breast cancer, but the specificity and sensitivity thereof are required to be improved, and are generally used in combination with imaging studies. Therefore, more sensitive and specific early breast cancer molecular markers are urgently discovered.
DNA methylation is a chemical modification important on genes that affects the regulatory process of gene transcription and nuclear structure. Alterations in DNA methylation are early events and concomitant events in cancer progression, and are mainly manifested by hypermethylation of oncogenes and hypomethylation of protooncogenes on tumor tissues, etc. However, there is less reported correlation between DNA methylation in blood and tumorigenesis development. In addition, blood is easy to collect, DNA methylation is stable, and if a tumor-specific blood DNA methylation molecular marker can be found, the DNA methylation molecular marker has great clinical application value. Therefore, the research and development of blood DNA methylation diagnosis technology suitable for clinical detection has important clinical application value and social significance for improving early diagnosis and treatment effect of lung cancer and reducing death rate.
Disclosure of Invention
The invention aims to provide a zona pellucida binding protein 2 (Zona Pellucida Binding Protein, ZPBP 2) methylation marker and a kit for assisting in diagnosing cancers.
In a first aspect, the invention claims the use of a methylated ZPBP2 gene as a marker in the preparation of a product; the application of the product is at least one of the following:
(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;
(2) Aiding in distinguishing benign nodules from cancers;
(3) Aiding in distinguishing between different subtypes of cancer;
(4) Aiding in differentiating different stages of cancer;
(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;
(6) Assisting in distinguishing benign nodules of the lung from lung cancer;
(7) Assisting in distinguishing different subtypes of lung cancer;
(8) Auxiliary differentiation of different stages of lung cancer;
(9) Auxiliary diagnosis of breast cancer or prediction of breast cancer risk;
(10) Aiding in distinguishing benign nodules of breast from breast cancer;
(11) Assisting in distinguishing different subtypes of breast cancer;
(12) Assisting in distinguishing different stages of breast cancer;
(13) Auxiliary differentiation between lung and breast cancer;
(14) Determining whether the test agent has an inhibitory or promoting effect on the occurrence of cancer.
Further, the auxiliary diagnosis of cancer described in (1) may be embodied as at least one of the following: aiding in distinguishing cancer patients from non-cancerous controls (it is understood that no cancer is present and ever and no benign nodules of the lung and breast are reported and blood normative indicators are within reference); helping to distinguish between different cancers.
Further, the benign nodules in (2) are benign nodules corresponding to the cancer in (2), such as benign nodules of the lung and lung cancer; or benign nodules of the breast and breast cancer.
Further, the different subtypes of cancer described in (3) may be pathological, such as histological, types.
Further, the different stage of the cancer in (4) may be a clinical stage or a TNM stage.
In a specific embodiment of the present invention, the auxiliary diagnosis of lung cancer described in (5) is embodied as at least one of the following: can be used for assisting in distinguishing lung cancer patients from non-cancer controls, assisting in distinguishing lung adenocarcinoma patients from non-cancer controls, assisting in distinguishing lung squamous cancer patients from non-cancer controls, assisting in distinguishing small cell lung cancer patients from non-cancer controls, assisting in distinguishing stage I lung cancer patients from non-cancer controls, assisting in distinguishing stage II-III lung cancer patients from non-cancer controls, assisting in distinguishing lung cancer patients without lymph node infiltration from non-cancer controls, and assisting in distinguishing lung cancer patients with lymph node infiltration from non-cancer controls. Wherein, the cancer-free control is understood to be that no cancer is present and no benign nodules of the lung are reported and the blood routine index is within the reference range.
In a specific embodiment of the present invention, the assisting in distinguishing benign nodules of the lung from lung cancer in (6) is embodied as at least one of: can help to distinguish lung cancer from benign lung nodules, can help to distinguish lung adenocarcinoma from benign lung nodules, can help to distinguish lung squamous cell carcinoma from benign lung nodules, can help to distinguish small cell lung cancer from benign lung nodules, can help to distinguish stage I lung cancer from benign lung nodules, can help to distinguish stage II-III lung cancer from benign lung nodules, can help to distinguish lung cancer without node infiltration from benign lung nodules, can help to distinguish lung cancer with node infiltration from benign lung nodules.
In a specific embodiment of the present invention, the assisting in differentiating between different subtypes of lung cancer described in (7) is embodied as: can help to distinguish any two of lung adenocarcinoma, lung squamous carcinoma and small cell lung carcinoma.
In a specific embodiment of the present invention, the assisting in differentiating different stages of lung cancer described in (8) is embodied as at least one of: any two of the lung cancer of the T1 stage, the lung cancer of the T2 stage and the lung cancer of the T3 stage can be assisted to be distinguished; can help to distinguish lung cancer without lymph node infiltration from lung cancer with lymph node infiltration; can help to distinguish any two of clinical lung cancer in stage I, clinical lung cancer in stage II and clinical lung cancer in stage III.
In a specific embodiment of the present invention, the auxiliary diagnosis of breast cancer described in (9) is embodied as at least one of the following: can be used for assisting in distinguishing breast cancer from non-cancer female controls, can be used for assisting in distinguishing breast duct in-situ cancer patients from non-cancer female controls, can be used for assisting in distinguishing breast invasive duct cancer patients from non-cancer female controls, can be used for assisting in distinguishing breast invasive lobular cancer patients from non-cancer female controls, can be used for assisting in distinguishing stage I breast cancer from non-cancer female controls, can be used for assisting in distinguishing stage II-III breast cancer from non-cancer female controls, can be used for assisting in distinguishing breast cancer without lymph node infiltration from non-cancer female controls, and can be used for assisting in distinguishing breast cancer with lymph node infiltration from non-cancer female controls. Wherein, the cancer-free control can be understood as having no cancer at present and once and no benign nodules of the mammary glands are reported and the blood routine index is within the reference range.
In a specific embodiment of the present invention, the assisting in distinguishing benign nodules of breast from breast cancer in (10) is embodied as at least one of: can help distinguish breast cancer and benign nodules of breast, can help distinguish ductal carcinoma in situ and benign nodules of breast, can help distinguish ductal carcinoma of breast from benign nodules of breast, can help distinguish lobular carcinoma of breast from benign nodules of breast, can help distinguish stage I breast cancer from benign nodules of breast, can help distinguish stage II-III breast cancer from benign nodules of breast, can help distinguish breast cancer without lymph node infiltration from benign nodules of breast, can help distinguish breast cancer with lymph node infiltration from benign nodules of breast.
In a specific embodiment of the present invention, the assisting in distinguishing between different subtypes of breast cancer described in (11) is embodied as: can help to distinguish any two of breast duct carcinoma in situ, breast invasive duct carcinoma and breast invasive lobular carcinoma.
In a specific embodiment of the present invention, the assisting in distinguishing between different stages of breast cancer in (12) is embodied as at least one of: any two of the T1-stage breast cancer, the T2-stage breast cancer and the T3-stage breast cancer can be assisted to be distinguished; can help to distinguish breast cancer without lymph node infiltration from breast cancer with lymph node infiltration; can help to distinguish any two of clinical stage I breast cancer, clinical stage II breast cancer and clinical stage III breast cancer.
In the above (1) - (14), the cancer may be a cancer capable of causing an increase in the methylation level of ZPBP2 gene in the body, such as lung cancer, breast cancer, etc.
In a second aspect, the invention claims the use of a substance for detecting the methylation level of ZPBP2 gene for the preparation of a product; the use of the product may be at least one of the foregoing (1) to (14).
In a third aspect, the invention claims the use of a substance for detecting the methylation level of ZPBP2 gene and a medium storing mathematical modeling methods and/or usage methods for the preparation of a product. The use of the product may be at least one of the foregoing (1) to (14).
The mathematical model is obtained according to a method comprising the following steps:
(A1) Detecting ZPBP2 gene methylation levels (training set) of n1 type a samples and n2 type B samples, respectively;
(A2) Taking ZPBP2 gene methylation level data of all samples obtained in the step (A1), establishing a mathematical model according to classification modes of A type and B type by a two-classification logistic regression method, and determining a threshold value of classification judgment.
Wherein n1 and n2 in (A1) are positive integers of 50 or more.
The using method of the mathematical model comprises the following steps:
(B1) Detecting the methylation level of ZPBP2 genes of a sample to be detected;
(B2) Substituting the ZPBP2 gene methylation level data of the sample to be detected obtained in the step (B1) into the mathematical model to obtain a detection index; and then comparing the detection index with the threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result.
In a specific embodiment of the present invention, the threshold is set to 0.5. More than 0.5 is classified as one type, less than 0.5 is classified as another type, and 0.5 is equal as an undefined gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.
In practical applications, the threshold may also be determined according to the maximum approximate sign-up index (specifically, may be a value corresponding to the maximum approximate sign-up index). Greater than the threshold is classified as one class, less than the threshold is classified as another class, and equal to the threshold as an indeterminate gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group A type and which group B type are determined according to a specific mathematical model, and no convention is needed.
The type a sample and the type B sample may be any one of the following:
(C1) Lung cancer samples and no cancer controls;
(C2) Lung cancer samples and lung benign nodule samples;
(C3) A sample of different subtypes of lung cancer;
(C4) Samples of lung cancer at different stages;
(C5) Breast cancer samples and cancer-free female controls;
(C6) Breast cancer samples and breast benign nodule samples;
(C7) A sample of different subtypes of breast cancer;
(C8) Breast cancer samples of different stages;
(C9) Lung cancer samples and breast cancer samples.
In a fourth aspect, the invention claims the use of a medium storing a mathematical model building method and/or a use method as described in the third aspect above for the manufacture of a product. The use of the product may be at least one of the foregoing (1) to (14).
In a fifth aspect, the invention claims a kit.
The kit claimed in the present invention comprises a substance for detecting the methylation level of ZPBP2 gene; the use of the kit may be at least one of the foregoing (1) to (14).
Further, the kit may further comprise a medium storing the mathematical model creation method and/or the use method described in the third or fourth aspect.
In a sixth aspect, the invention claims a system.
The claimed system includes:
(D1) Reagents and/or instrumentation for detecting the methylation level of ZPBP2 gene;
(D2) A device comprising a unit X and a unit Y;
the unit X is used for establishing a mathematical model and comprises a data acquisition module, a data analysis processing module and a model output module;
the data acquisition module is configured to acquire ZPBP2 gene methylation level data of n 1A type samples and n 2B type samples obtained by (D1) detection;
the data analysis processing module is configured to receive ZPBP2 gene methylation level data of the n 1A type samples and the n 2B type samples from the data acquisition module, establish a mathematical model according to a classification mode of the A type and the B type through a two-classification logistic regression method, and determine a threshold value of classification judgment;
the model output module is configured to receive the mathematical model established by the data analysis processing module and output the mathematical model;
the unit Y is used for determining the type of the sample to be detected and comprises a data input module, a data operation module, a data comparison module and a conclusion output module;
the data input module is configured to input ZPBP2 gene methylation level data of the to-be-detected person detected by (D1);
The data operation module is configured to receive the ZPBP2 gene methylation level data of the to-be-detected person from the data input module, and substitutes the ZPBP2 gene methylation level data of the to-be-detected person into the mathematical model to calculate a detection index;
the data comparison module is configured to receive the detection index calculated by the data operation module and compare the detection index with the threshold value determined by the data analysis processing module in the unit X;
the conclusion output module is configured to receive the comparison result from the data comparison module and output a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result of the data comparison module;
the type a sample and the type B sample are any one of the following:
(C1) Lung cancer samples and no cancer controls;
(C2) Lung cancer samples and lung benign nodule samples;
(C3) A sample of different subtypes of lung cancer;
(C4) Samples of lung cancer at different stages;
(C5) Breast cancer samples and cancer-free female controls;
(C6) Breast cancer samples and breast benign nodule samples;
(C7) A sample of different subtypes of breast cancer;
(C8) Breast cancer samples of different stages;
(C9) Lung cancer samples and breast cancer samples.
Wherein, n1 and n2 can be positive integers more than 50.
In a specific embodiment of the present invention, the threshold is set to 0.5. More than 0.5 is classified as one type, less than 0.5 is classified as another type, and 0.5 is equal as an undefined gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.
In practical applications, the threshold may also be determined according to the maximum approximate sign-up index (specifically, may be a value corresponding to the maximum approximate sign-up index). Greater than the threshold is classified as one class, less than the threshold is classified as another class, and equal to the threshold as an indeterminate gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.
In the foregoing aspects, the methylation level of the ZPBP2 gene may be the methylation level of all or part of CpG sites in fragments of the ZPBP2 gene as shown in (e 1) - (e 5) below. The methylated ZPBP2 gene can be a ZPBP2 gene that is methylated at all or part of the CpG sites in the fragments shown below in (e 1) - (e 5).
(e1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment having 80% or more identity thereto;
(e2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment having 80% or more identity thereto;
(e3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment having 80% or more identity thereto;
(e4) A DNA fragment shown in SEQ ID No.4 or a DNA fragment having 80% or more identity thereto;
(e5) The DNA fragment shown in SEQ ID No.5 or a DNA fragment having 80% or more identity thereto.
Further, the 'all or part of CpG sites' are any one or more CpG sites in 5 DNA fragments shown in SEQ ID No.1 to SEQ ID No.5 in the ZPBP2 gene. The upper limit of "multiple CpG sites" as used herein is all CpG sites in the 5 DNA fragments shown in SEQ ID No.1 to SEQ ID No.5 in the ZPBP2 gene. All CpG sites in the DNA fragment shown in SEQ ID No.1 are shown in Table 1, all CpG sites in the DNA fragment shown in SEQ ID No.2 are shown in Table 2, all CpG sites in the DNA fragment shown in SEQ ID No.3 are shown in Table 3, all CpG sites in the DNA fragment shown in SEQ ID No.4 are shown in Table 4, and all CpG sites in the DNA fragment shown in SEQ ID No.5 are shown in Table 5.
Or, the "all or part of CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.2 (see Table 2) and all CpG sites in the DNA fragment shown in SEQ ID No.3 (see Table 3) in the ZPBP2 gene.
Or, the "all or part of CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.2 (see Table 2) and all CpG sites in the DNA fragment shown in SEQ ID No.4 (see Table 4) in the ZPBP2 gene.
Or, the "all or part of CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.3 (see Table 3) and all CpG sites in the DNA fragment shown in SEQ ID No.4 (see Table 4) in the ZPBP2 gene.
Or, the "all or part of CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.2 (see Table 2), all CpG sites in the DNA fragment shown in SEQ ID No.3 (see Table 3) and all CpG sites in the DNA fragment shown in SEQ ID No.4 (see Table 4) in the ZPBP2 gene.
Or, the whole or part of CpG sites are all or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 of CpG sites as shown in the following 11 items in the DNA fragment shown in SEQ ID No.2 in the ZPBP2 gene:
(f1) The DNA fragment shown in SEQ ID No.2 shows the CpG site (ZPBP2_B_1) from 29 th to 30 th positions of the 5' end;
(f2) The DNA fragment shown in SEQ ID No.2 shows the CpG sites (ZPBP2_B_2) from 57-58 th position of the 5' end;
(f3) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ZPBP2_B_3) from 90 th to 91 th positions of the 5' end;
(f4) The CpG site (ZPBP2_B_4) shown in 113-114 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2;
(f5) The DNA fragment shown in SEQ ID No.2 shows the CpG sites (ZPBP2_B_5) from 119 th to 120 th positions of the 5' end;
(f6) The DNA fragment shown in SEQ ID No.2 contains CpG sites (ZPBP2_B_6.7) shown in 190-191 and 192-193 from the 5' end;
(f7) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ZPBP2_B_8) from 213 th to 214 th positions of the 5' end;
(f8) The DNA fragment shown in SEQ ID No.2 shows CpG sites (ZPBP2_B_9) from 229-230 th position of the 5' end;
(f9) The DNA fragment shown in SEQ ID No.2 contains CpG sites (ZPBP2_B_10) shown in 247-248 th positions of the 5' end;
(f10) The DNA fragment shown in SEQ ID No.2 contains CpG sites (ZPBP2_B_11.12) shown in 262-263 th and 264-265 th of the 5' end;
(f11) The DNA fragment shown in SEQ ID No.2 shows the CpG sites from position 281 to 282 (ZPBP2_B_13) from the 5' end.
In particular embodiments of the invention, some adjacent methylation sites are treated as one methylation site when analyzed for DNA methylation using time-of-flight mass spectrometry, because several CpG sites are located on one methylation fragment, the peak pattern is indistinguishable (indistinguishable sites are set forth in Table 7), and thus the methylation level analysis is performed, and related mathematical models are constructed and used.
In the above aspects, the means for detecting the methylation level of the ZPBP2 gene may comprise (or be) a primer combination for amplifying a full or partial fragment of the ZPBP2 gene. The reagent for detecting the methylation level of ZPBP2 gene may comprise (or be) a primer combination for amplifying a full or partial fragment of ZPBP2 gene; the instrument for detecting the methylation level of ZPBP2 gene may be a time-of-flight mass spectrometry detector. Of course, other conventional reagents for performing time-of-flight mass spectrometry may also be included in the reagents for detecting the methylation level of ZPBP2 gene.
Further, the partial fragment may be at least one fragment of:
(g1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;
(g2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;
(g3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;
(g4) A DNA fragment shown in SEQ ID No.4 or a DNA fragment comprising the same;
(g5) A DNA fragment shown in SEQ ID No.5 or a DNA fragment comprising the same;
(g6) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;
(g7) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;
(g8) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same.
(g9) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.4 or a DNA fragment comprising the same;
(g10) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.5 or a DNA fragment comprising the same.
In the present invention, the primer combination may specifically be primer pair a and/or primer pair B and/or primer pair C and/or primer pair D and/or primer pair E.
The primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.6 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 6; the primer A2 is SEQ ID No.7 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 7;
the primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is single-stranded DNA shown in SEQ ID No.8 or 11 th-35 th nucleotide of SEQ ID No. 8; the primer B2 is SEQ ID No.9 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 9;
the primer pair C is a primer pair consisting of a primer C1 and a primer C2; the primer C1 is SEQ ID No.10 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 10; the primer C2 is SEQ ID No.11 or single-stranded DNA shown in 32-56 nucleotides of SEQ ID No. 11.
The primer pair D is a primer pair consisting of a primer D1 and a primer D2; the primer D1 is SEQ ID No.12 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 12; the primer D2 is SEQ ID No.13 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 13;
the primer pair E is a primer pair consisting of a primer E1 and a primer E2; the primer E1 is SEQ ID No.14 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 14; the primer E2 is SEQ ID No.15 or single-stranded DNA shown in 32-56 nucleotides of SEQ ID No. 15.
In addition, the invention also discloses a method for distinguishing whether the sample to be detected is an A type sample or a B type sample. The method may comprise the steps of:
(A) The mathematical model may be built as a method comprising the steps of:
(A1) Detecting ZPBP2 gene methylation levels (training set) of n1 type a samples and n2 type B samples, respectively;
(A2) Taking ZPBP2 gene methylation level data of all samples obtained in the step (A1), establishing a mathematical model according to classification modes of A type and B type by a two-classification logistic regression method, and determining a threshold value of classification judgment.
Wherein n1 and n2 in (A1) are positive integers of 50 or more.
(B) The sample to be tested may be determined as a type a sample or a type B sample according to a method comprising the steps of:
(B1) Detecting the methylation level of the ZPBP2 gene of the sample to be detected;
(B2) Substituting the ZPBP2 gene methylation level data of the sample to be detected obtained in the step (B1) into the mathematical model to obtain a detection index; and then comparing the detection index with a threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result.
In a specific embodiment of the present invention, the threshold is set to 0.5. More than 0.5 is classified as one type, less than 0.5 is classified as another type, and 0.5 is equal as an undefined gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.
In practical applications, the threshold may also be determined according to the maximum approximate sign-up index (specifically, may be a value corresponding to the maximum approximate sign-up index). Greater than the threshold is classified as one class, less than the threshold is classified as another class, and equal to the threshold as an indeterminate gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.
The type a sample and the type B sample may be any one of the following:
(C1) Lung cancer samples and no cancer controls;
(C2) Lung cancer samples and lung benign nodule samples;
(C3) A sample of different subtypes of lung cancer;
(C4) Samples of lung cancer at different stages;
(C5) Breast cancer samples and cancer-free female controls;
(C6) Breast cancer samples and breast benign nodule samples;
(C7) A sample of different subtypes of breast cancer;
(C8) Breast cancer samples of different stages;
(C9) Lung cancer samples and breast cancer samples.
Any of the above mathematical models may be changed in practical application according to the detection method and the fitting mode of DNA methylation, and the mathematical model is determined according to a specific mathematical model without any convention.
In the embodiment of the invention, the model is specifically log (y/(1-y))=b0+b1x1+b2x2+b3x3+ … +bnxn, where y is a detection index obtained after substituting a methylation value of one or more methylation sites of a sample to be tested into the model by a dependent variable, b0 is a constant, x1 to xn are independent variables which are methylation values of one or more methylation sites of the sample to be tested (each value is a value between 0 and 1), and b1 to bn are weights given by the model to the methylation values of each site.
In the embodiment of the invention, the model can be established by adding known parameters such as age, sex, white blood cell count and the like as appropriate to improve the discrimination efficiency. One specific model established in embodiments of the present invention is a model for assisting in distinguishing benign nodules of the lung from lung cancer, the model being specifically: log (y/(1-y))=0.22+0.068×zpbp2_b_1+1.606×zpbp2_b_2-1.043×zpbp2_b_3-0.313×zpbp2_b_4+0.367×zpbp2_b_5-1.216×zpbp2_b_6.7+0.072×zpbp2_b_8-0.282×zpbp2_b_9-0.182×zpbp2_b_10+0.842×zpbp2_b_11.12-0.122×zpbp2_b_13+0.002×age (integer) +0.142×gender (male assigned 1, female assigned 0) +0.005×white cell count (unit 10 ζ9/L). The ZPBP2_B_1 is the methylation level of the CpG site shown in the 29 th-30 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ZPBP2_B_2 is the methylation level of CpG sites shown in 57-58 th positions of a DNA fragment shown in SEQ ID No.2 from the 5' end; the ZPBP2_B_3 is the methylation level of CpG sites shown in the 90 th-91 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ZPBP2_B_4 is the methylation level of CpG sites shown in 113-114 th positions of a DNA fragment shown in SEQ ID No.2 from the 5' end; the ZPBP2_B_5 is the methylation level of CpG sites shown in 119 th-120 th positions of a DNA fragment shown in SEQ ID No.2 from the 5' end; the ZPBP2_B_6.7 is the methylation level of CpG sites shown in 190-191 th and 192-193 th positions of the DNA fragment shown in SEQ ID No.2 from the 5' end; the ZPBP2_B_8 is the methylation level of CpG sites shown in the 213 th-214 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ZPBP2_B_9 is the methylation level of CpG sites shown in 229-230 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ZPBP2_B_10 is the methylation level of CpG sites shown in 247-248 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ZPBP2_B_11.12 is the methylation level of CpG sites shown in 262 th to 263 th and 264 th to 265 th of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ZPBP2_B_13 is the methylation level of CpG sites shown in positions 281-282 of the DNA fragment shown in SEQ ID No.2 from the 5' end. The threshold of the model was 0.5. Patient candidates with a detection index greater than 0.5 calculated by the model are lung cancer patients, and patient candidates with less than 0.5 are lung benign nodule patients.
In the above aspects, the detecting the methylation level of the ZPBP2 gene is detecting the methylation level of the ZPBP2 gene in blood.
In the above aspects, when the type a sample and the type B sample are different subtype samples of lung cancer in (C3), the type a sample and the type B sample may specifically be any two of a lung adenocarcinoma sample, a lung squamous carcinoma sample, and a small cell lung cancer sample.
In the above aspects, when the type a sample and the type B sample are different stage samples of lung cancer in (C4), the type a sample and the type B sample may specifically be any two of a clinical stage I lung cancer sample, a clinical stage II lung cancer sample, and a clinical stage III lung cancer sample.
In the above aspects, when the type a sample and the type B sample are different subtype samples of breast cancer in (C8), the type a sample and the type B sample may specifically be: any two of breast ductal carcinoma in situ, breast invasive ductal carcinoma, and breast invasive lobular carcinoma.
In the above aspects, when the type a sample and the type B sample are different stage samples of breast cancer in (C9), the type a sample and the type B sample may specifically be: any two of clinical stage I breast cancer, clinical stage II breast cancer and clinical stage III breast cancer.
The ZPBP2 gene described above may specifically include Genbank accession No.: NM_199321.3 (2019, 6, 2), NM_198844.3 (2019, 6, 2).
The present invention provides hypermethylation of ZPBP2 gene in blood of lung cancer patient and breast cancer. Experiments prove that the blood can be used as a sample to distinguish cancer (lung cancer and breast cancer) patients from cancer-free controls, lung benign nodules and lung cancer, different subtypes and different stages of lung cancer, breast benign nodules and breast cancer, different subtypes and different stages of breast cancer, and lung cancer and breast cancer. The invention has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of lung cancer and breast cancer and reducing the death rate.
Drawings
FIG. 1 is a schematic diagram of a mathematical model.
Fig. 2 is an illustration of a mathematical model.
Detailed Description
The following detailed description of the invention is provided in connection with the accompanying drawings that are presented to illustrate the invention and not to limit the scope thereof. The examples provided below are intended as guidelines for further modifications by one of ordinary skill in the art and are not to be construed as limiting the invention in any way.
The experimental methods in the following examples, unless otherwise specified, are conventional methods, and are carried out according to techniques or conditions described in the literature in the field or according to the product specifications. Materials, reagents and the like used in the examples described below are commercially available unless otherwise specified.
The zona pellucida binding protein 2 (Zona Pellucida Binding Protein, zpbp 2) gene quantification assays in the examples below were all performed in triplicate and the results averaged.
Example 1 primer design for detection of methylation site of ZPBP2 Gene
Five fragments (zpbp2_a fragment, zpbp2_b fragment, zpbp2_c fragment, zpbp2_d fragment and zpbp2_e fragment) of ZPBP2 gene were selected for methylation level and cancer correlation analysis through a number of sequence and functional analyses.
The ZPBP2_A fragment (SEQ ID No. 1) is located on the sense strand of the hg19 reference genome chr17: 38023450-38023800.
The ZPBP2_B fragment (SEQ ID No. 2) is located on the sense strand of the hg19 reference genome chr17: 38023802-38024112.
The ZPBP2_C fragment (SEQ ID No. 3) is located on the sense strand of the hg19 reference genome chr17: 38024118-38024543.
The ZPBP2_D fragment (SEQ ID No. 4) is located on the sense strand of the hg19 reference genome chr17: 38024550-38025100.
The ZPBP2_E fragment (SEQ ID No. 5) is located on the sense strand of the hg19 reference genome chr17: 38025109-38025522.
CpG site information in ZPBP2_A fragment is shown in Table 1.
CpG site information in ZPBP2_B fragment is shown in Table 2.
CpG site information in the ZPBP2_C fragment is shown in Table 3.
CpG site information in the ZPBP2_D fragment is shown in Table 4.
CpG site information in the ZPBP2_E fragment is shown in Table 5.
Table 1 CpG site information in ZPBP2_A fragment
TABLE 2 CpG site information in ZPBP2_B fragments
CpG sites Position of CpG sites in the sequence
ZPBP2_B_1 SEQ ID No.2 from position 29 to 30 of the 5' end
ZPBP2_B_2 SEQ ID No.2 from positions 57-58 of the 5' end
ZPBP2_B_3 SEQ ID No.2 from positions 90-91 of the 5' end
ZPBP2_B_4 SEQ ID No.2 from positions 113-114 of the 5' end
ZPBP2_B_5 119 th to 120 th positions of SEQ ID No.2 from 5' end
ZPBP2_B_6 SEQ ID No.2 from position 190-191 of the 5' end
ZPBP2_B_7 192-193 th bit of SEQ ID No.2 from 5' end
ZPBP2_B_8 SEQ ID No.2 from positions 213-214 of the 5' end
ZPBP2_B_9 229-230 th position from 5' end of SEQ ID No.2
ZPBP2_B_10 SEQ ID No.2 from position 247 to 248 of the 5' end
ZPBP2_B_11 SEQ ID No.2 from positions 262-263 of the 5' end
ZPBP2_B_12 SE264 th to 265 th bits of Q ID No.2 from 5' end
ZPBP2_B_13 Positions 281-282 of SEQ ID No.2 from the 5' end
TABLE 3 CpG site information in ZPBP2_C fragment
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Table 4, cpG site information in ZPBP 2D fragment
TABLE 5 CpG site information in ZPBP2_E fragment
CpG sites Position of CpG sites in the sequence
ZPBP2_E_1 SEQ ID No.5 from positions 45-46 of the 5' end
ZPBP2_E_2 SEQ ID No.5 from position 47-48 of the 5' end
ZPBP2_E_3 SEQ ID No.5 from positions 58-59 of the 5' end
ZPBP2_E_4 SEQ ID No.5 from position 133 to 134 of the 5' end
ZPBP2_E_5 282-283 bits of SEQ ID No.5 from the 5' end
ZPBP2_E_6 SEQ ID No.5 from position 305-306 of the 5' end
ZPBP2_E_7 SEQ ID No.5 from position 384-385 of the 5' end
Specific PCR primers were designed for five fragments (zpbp2_a fragment, zpbp2_b fragment, zpbp2_c fragment, zpbp2_d fragment and zpbp2_e fragment) as shown in table 6. Wherein SEQ ID No.6, SEQ ID No.8, SEQ ID No.10, SEQ ID No.12 and SEQ ID No.14 are forward primers, SEQ ID No.7, SEQ ID No.9, SEQ ID No.11, SEQ ID No.13 and SEQ ID No.15 are reverse primers; positions 1 to 10 from the 5' end in SEQ ID No.6, SEQ ID No.8, SEQ ID No.10, SEQ ID No.12 and SEQ ID No.14 are nonspecific tags, and positions 11 to 35 are specific primer sequences; SEQ ID No.7, SEQ ID No.9, SEQ ID No.11, SEQ ID No.13 and SEQ ID No.15 show non-specific tags at positions 1 to 31 and specific primer sequences at positions 32 to 56 from 5'. The primer sequences do not contain SNPs and CpG sites.
TABLE 6 ZPBP2 methylation primer sequences
Example 2 ZPBP2 Gene methylation detection and analysis of results
1. Study sample
After informed consent of the patients, in total, ex-vivo blood samples of 426 lung cancer patients, 286 lung benign nodule patients, 292 breast cancer patients, 82 breast benign nodule patients and 816 cancer-free controls (in which the sexes of men and women are half, and 408 are all) were collected.
All patient samples were collected preoperatively and were subjected to imaging and pathological confirmation.
Lung cancer and breast cancer subtypes are judged according to histopathology.
No cancer controls, i.e., previous and current cancer and no lung benign nodules or breast benign nodules were reported and blood normative indicators were within the reference range.
The stage of lung cancer and breast cancer takes an AJCC 8 th edition stage system as a judgment standard.
426 lung cancer patients were classified according to type: 319 cases of lung adenocarcinoma, 47 cases of lung squamous carcinoma, 52 cases of small cell lung carcinoma and 8 other cases.
426 lung cancer patients were divided according to stage: 338 cases in stage I, 49 cases in stage II, 39 cases in stage III.
426 lung cancer patients were classified according to lung cancer tumor size (T): 306 cases in T1, 72 cases in T2 and 48 cases in T3.
426 cases of lung cancer patients were classified according to the presence or absence of lung cancer lymph node infiltration (N): there were 394 cases of lung cancer lymph node infiltration, and 32 cases of lung cancer lymph node infiltration.
292 breast cancer patients were classified according to typing: 93 cases of breast ductal carcinoma in situ, 183 cases of breast invasive ductal carcinoma, and 16 cases of breast invasive lobular carcinoma.
292 breast cancer patients were divided by stage: 225 cases in phase I, 49 cases in phase II, and 18 cases in phase III.
292 breast cancer patients were classified according to lung cancer tumor size (T): 238 cases in T1, 41 cases in T2 and 13 cases in T3.
292 breast cancer patients were classified according to the presence or absence of breast cancer lymph node infiltration (N): there were no breast cancer lymph node infiltrates 266 cases and there were breast cancer lymph node infiltrates 26 cases.
The median ages of the cancer-free population, lung cancer, benign lung nodules, breast cancer and benign breast nodules patients were 54, 55, 58, 56 and 56 years old, respectively, and the ratio of men and women in each of these 5 populations was about 1:1.
The median age of the cancer-free female control was 55 years.
2. Methylation detection
1. Total DNA of the blood sample is extracted.
2. The total DNA of the blood samples prepared in step 1 was subjected to bisulfite treatment (see DNA methylation kit instructions for Qiagen). After bisulfite treatment, unmethylated cytosines (C) in the original CpG sites are converted to uracil (U), while methylated cytosines remain unchanged.
3. The DNA treated by the bisulfite in the step 2 is used as a template, 5 pairs of specific primer pairs in the table 6 are adopted to carry out PCR amplification by DNA polymerase according to a reaction system required by a conventional PCR reaction, and all primers adopt a conventional standard PCR system and are amplified according to the following procedure.
The PCR reaction procedure was: 95 ℃,4 min- & gt (95 ℃,20 s- & gt 56 ℃,30 s- & gt 72 ℃ 2 min) 45 cycles- & gt 72 ℃,5 min- & gt 4 ℃ for 1h.
4. Taking the amplified product of the step 3, and carrying out DNA methylation analysis by a time-of-flight mass spectrum, wherein the specific method is as follows:
(1) Mu.l of Shrimp Alkaline Phosphate (SAP) solution (0.3 ml SAP [ 0.5U) was added to 5. Mu.l of PCR product]+1.7ml H 2 O) then incubated in a PCR apparatus (37 ℃,20 min. Fwdarw. 85 ℃,5 min. Fwdarw. 4 ℃,5 min) according to the following procedure;
(2) Taking out 2 mu.l of the SAP treated product obtained in the step (1), adding the product into a 5 mu l T-clear reaction system according to the instruction, and then incubating for 3 hours at 37 ℃;
(3) Taking the product of the step (2), adding 19 mu l of deionized water, and then carrying out deionized incubation on a rotary shaking table for 1h by using 6 mu g of Resin;
(4) Centrifuging at 2000rpm at room temperature for 5min, and loading 384SpectroCHIP with the micro supernatant by a Nanodispenser mechanical arm;
(5) Time-of-flight mass spectrometry; the data obtained were collected with the spectroacquisition v3.3.1.3 software and visualized by MassArray EpiTyper v 1.2.1.2 software.
Reagents used for the time-of-flight mass spectrometry were all from the kit (T-Cleavage MassCLEAVE Reagent Auto Kit, cat# 10129A); the detection instrument used for the time-of-flight mass spectrometry detection isAnalyzer Chip Prep Module 384, model: 41243; the data analysis software is self-contained software of the detection instrument.
5. And (5) analyzing the data obtained in the step (4).
Statistical analysis of the data was performed by SPSS Statistics 23.0.
Non-parametric tests were used for comparative analysis between the two groups.
The identification effect of a combination of multiple CpG sites on different sample groupings is achieved by logistic regression and statistical methods of the subject curves.
All statistical tests were double-sided, with p-values <0.05 considered statistically significant.
Through mass spectrometry experiments, a total of 72 distinguishable peak patterns of methylated fragments were obtained. The methylation level at each CpG site of each sample can be automatically obtained by calculating the peak area according to the "methylation level=peak area of methylated fragments/(peak area of unmethylated fragments+peak area of methylated fragments)" formula using SpectroAcquin v3.3.1.3 software.
3. Analysis of results
1. Cancer-free control, lung benign nodules and blood level of ZPBP2 gene methylation in lung cancer
Methylation levels of all CpG sites in the ZPBP2 gene were analyzed using blood of 426 lung cancer patients, 286 lung benign nodule patients and 816 cancer-free controls as study materials (Table 7). The results show that all CpG sites in ZPBP2 gene have a median methylation level of 0.67 (iqr=0.60-0.77), 0.70 (iqr=0.62-0.80) in benign nodules in lung, and 0.71 (iqr=0.63-0.81) in lung cancer patients.
2. Blood ZPBP2 gene methylation level can distinguish cancer-free control and lung cancer patients
As a result of comparative analysis of methylation levels of ZPBP2 genes in 426 lung cancer patients and 816 cancer-free controls, it was found that methylation levels of all CpG sites in ZPBP2 genes were significantly higher in lung cancer patients than in cancer-free controls (p <0.05, table 8). Furthermore, methylation levels of all CpG sites of ZPBP2 gene in different subtypes of lung cancer (lung adenocarcinoma, lung squamous carcinoma and small cell lung carcinoma) were significantly different from that of the non-cancerous control (p < 0.05), respectively. Methylation levels of all CpG sites of ZPBP2 gene in different stages (clinical stage I and stage II-III) of lung cancer are significantly different from that of a cancer-free control (p < 0.05). Furthermore, there was a significant difference in methylation levels between non-lymphoblastic lung cancer patients and lymphoblastic lung cancer patients, respectively, and non-cancerous controls (p < 0.05). Therefore, the methylation level of ZPBP2 gene can be used for clinical diagnosis of lung cancer, and especially for early diagnosis of lung cancer.
3. The methylation level of ZPBP2 gene in blood can distinguish benign nodule of lung and lung cancer patient
As a result of comparative analysis of methylation levels of ZPBP2 gene in 426 lung cancer patients and 286 lung benign nodules, it was found that methylation levels of all CpG sites of ZPBP2 gene were significantly lower in lung benign nodules patients than in lung cancer patients (p <0.05, table 9). Furthermore, it was found that the methylation level of all CpG in ZPBP2 gene of lung cancer patients of different subtypes (lung adenocarcinoma, lung squamous carcinoma and small cell lung carcinoma), different clinical stages (stage I and stage II-III) and the presence or absence of lymphocytic infiltration, respectively, were significantly different from that of benign nodules in the lung (p <0.05, table 9). Therefore, the methylation level of ZPBP2 gene can be used to distinguish lung cancer patients from benign nodule patients, and is a very valuable marker.
4. The methylation level of ZPBP2 gene in blood can be used for distinguishing different subtypes of lung cancer or different stages of lung cancer
By comparing and analyzing the methylation level of ZPBP2 gene in different subtype lung cancer patients and different stage lung cancer patients, the methylation level of all CpG sites in the ZPBP2 gene is found to have significant differences under the conditions of different lung cancer subtypes (lung adenocarcinoma, lung squamous carcinoma and small cell lung carcinoma), lung cancer tumor sizes (T1, T2 and T3), different stages of lung cancer (clinical stage I, stage II and stage III) and the presence or absence of lymph node infiltration (p <0.05, table 10). Thus, the methylation level of ZPBP2 gene can be used to distinguish between different subtypes of lung cancer or different stages of lung cancer.
5. Cancer-free female control, benign nodules of breast and methylation level of ZPBP2 gene in blood of breast cancer
The level of CpG site methylation in ZPBP2 gene between breast cancer patients, breast benign nodule patients and cancer-free female controls was analyzed using blood from 292 breast cancer patients, 82 breast benign nodule patients and 408 cancer-free female controls as study material (table 11). The results showed that the cancer-free female control group had a median methylation level of 0.67 (iqr=0.60-0.76), the benign nodules of the breast had a median methylation level of 0.72 (iqr=0.64-0.82), and the breast cancer patients had a median methylation level of 0.69 (iqr=0.61-0.79).
6. Blood ZPBP2 gene methylation level can distinguish between cancer-free female control and breast cancer patients
As a result of comparative analysis of methylation levels of ZPBP2 genes in 292 breast cancer patients and 408 non-cancerous female controls, it was found that methylation levels of all CpG sites in ZPBP2 genes were significantly higher in breast cancer patients than in non-cancerous female controls (p <0.05, table 12). Furthermore, the methylation levels of all CpG sites of ZPBP2 gene in different subtypes of breast cancer (ductal carcinoma in situ, ductal invasive carcinoma in breast and lobular invasive carcinoma in breast) were significantly different from that of non-cancerous female controls, respectively (p <0.05, table 12). Methylation levels of all CpG sites of the ZPBP2 gene in different stages of breast cancer (clinical stage I and stage II-III) were significantly different from that of the cancer-free female control (p <0.05, table 12), respectively. Furthermore, there was a significant difference in methylation levels between non-lymphotic breast cancer patients and those with lymphotic breast cancer, respectively, and non-cancerous female controls (p <0.05, table 12). Therefore, the methylation level of ZPBP2 gene can be used for clinical diagnosis of breast cancer, and especially for early diagnosis of breast cancer.
7. The methylation level of ZPBP2 gene in blood can distinguish benign breast nodule from breast cancer patient
As a result of comparative analysis of the methylation levels of ZPBP2 gene in 292 breast cancer patients and 82 breast benign nodules, it was found that the methylation levels of all CpG sites of ZPBP2 gene were significantly higher in breast benign nodules patients than in breast cancer patients (p <0.05, table 13). Furthermore, it was found that methylation levels of all CpG sites in ZPBP2 gene of breast cancer patients of different subtypes (ductal carcinoma in situ, ductal invasive carcinoma in breast and lobular invasive carcinoma in breast), different clinical stages (stage I and stage II-III) and the presence or absence of lymphoid infiltration were significantly different from benign nodules of breast, respectively (p <0.05, table 13). Therefore, the methylation level of ZPBP2 gene can be applied to distinguish breast cancer patients from breast benign nodule patients, and is a very valuable marker.
8. The methylation level of ZPBP2 gene in blood can distinguish different subtypes of breast cancer or different stages of breast cancer
By comparing the methylation levels of ZPBP2 gene in breast cancer patients of different subtypes and breast cancer patients of different stages, it was found that there was a significant difference in methylation levels of all CpG sites in ZPBP2 gene under the conditions of different subtypes of breast cancer (ductal carcinoma in situ, ductal carcinoma of mammary gland and lobular carcinoma of mammary gland), tumor size of breast cancer (T1, T2 and T3), different stages of breast cancer (clinical stage I, stage II and stage III), the presence or absence of lymph node infiltration (p <0.05, table 14), respectively. Thus, the methylation level of ZPBP2 gene can be used to distinguish between different subtypes of breast cancer or different stages of breast cancer.
9. The methylation level of ZPBP2 in blood can distinguish breast cancer patients from lung cancer patients
Blood of 292 breast cancer patients and 426 lung cancer patients was used as a study material to analyze methylation level differences in ZPBP2 genes in blood of breast cancer patients and lung cancer patients (Table 15). The results show that the methylation level of all target CpG sites in breast cancer patients is median 0.69 (iqr=0.61-0.79), the methylation level of lung cancer patients is median 0.71 (iqr=0.63-0.81), and the methylation level of all CpG sites in breast cancer patients is significantly lower than that in lung cancer patients (p < 0.05). Thus, the methylation level of ZPBP2 gene can be used to distinguish breast and lung cancer patients.
10. Modeling of mathematical models for aiding in cancer diagnosis
The mathematical model established by the invention can be used for achieving the following purposes:
(1) Distinguishing lung cancer patients from non-cancerous controls;
(2) Distinguishing lung cancer patients from lung benign nodule patients;
(3) Distinguishing breast cancer patients from cancer-free female controls;
(4) Distinguishing breast cancer patients from breast benign nodule patients;
(5) Distinguishing breast cancer patients from lung cancer patients
(6) Distinguishing lung cancer subtypes;
(7) Differentiating lung cancer stage;
(8) Distinguishing breast cancer subtypes;
(9) Differentiation of breast cancer stage.
The mathematical model is established as follows:
(A) Data sources: methylation levels of target CpG sites (combinations of one or more of tables 1-5) of the isolated blood samples of 426 lung cancer patients, 286 lung-developed benign nodule patients, 292 breast cancer patients, 82 breast benign nodule patients, and 816 cancer-free controls (including 408 cancer-free female controls) listed in step one (test method is the same as step two).
The data can be added with known parameters such as age, sex, white blood cell count and the like according to actual needs to improve the discrimination efficiency.
(B) Model building
Any two different types of patient data, namely training sets (for example, cancer-free control and lung cancer patients, cancer-free female control and breast cancer patients, lung benign nodule patients and lung cancer patients, breast benign nodule and breast cancer patients, lung cancer patients and breast cancer patients, lung adenocarcinoma and lung squamous carcinoma patients, lung adenocarcinoma and small cell lung cancer patients, lung squamous carcinoma and small cell lung cancer patients, lung cancer patients I and lung cancer patients II, lung cancer patients I and lung cancer patients III, lung cancer patients II and lung cancer patients III, breast duct in-situ cancer and breast invasive duct cancer patients, breast invasive duct cancer and breast invasive lobular cancer patients, breast duct in-situ cancer and breast invasive lobular cancer patients I and II breast cancer patients, breast cancer patients I and III, breast cancer patients II and breast cancer patients III) are selected as required to serve as data for establishing a model, and statistical software such as SAS, R, SPSS and the like is used for establishing a mathematical model by using a statistical method of two-class logistic regression through formulas. The numerical value corresponding to the maximum approximate dengue index calculated by the mathematical model formula is a threshold value or is directly set to be 0.5 as the threshold value, the detection index obtained by the sample to be tested after the sample is tested and substituted into the model calculation is more than the threshold value and is classified into one type (B type), less than the threshold value and is classified into the other type (A type), and the detection index is equal to the threshold value and is used as an uncertain gray area. When a new sample to be detected is predicted to judge which type belongs to, firstly, detecting methylation levels of one or more CpG sites on the ZPBP2 gene of the sample to be detected by a DNA methylation determination method, then substituting data of the methylation levels into the mathematical model (if known parameters such as age, sex, white cell count and the like are included in the model construction, the specific numerical value of the corresponding parameter of the sample to be detected is substituted into a model formula at the same time in the step), calculating to obtain a detection index corresponding to the sample to be detected, comparing the detection index corresponding to the sample to be detected with the threshold value, and determining which type of sample the sample to be detected belongs to according to the comparison result.
Examples: as shown in fig. 1, the data of the methylation level of single CpG sites or the methylation level of a plurality of CpG sites in the training set ZPBP2 gene are established by using a formula of two-class logistic regression through SAS, R, SPSS and other statistical software to establish a mathematical model for distinguishing class a and class B. The mathematical model is herein a two-class logistic regression model, specifically: log (y/1-y) =b0+b1x1+b2x2+b3x3+ … +bnxn, where y is a detection index obtained by substituting a dependent variable, i.e., a methylation value of one or more methylation sites of a sample to be tested, into a model, b0 is a constant, x 1-xn are independent variables, i.e., methylation values (each value is a value between 0 and 1) of one or more methylation sites of the sample to be tested, and b 1-bn are weights given to each methylation site by the model. In specific application, a mathematical model is established according to methylation degrees (x 1-xn) of one or more DNA methylation sites of a sample detected in a training set and known classification conditions (class A or class B, respectively assigning 0 and 1 to y), so that a constant B0 of the mathematical model and weights B1-bn of each methylation site are determined, and a threshold value divided by a detection index (0.5 in the example) corresponding to the maximum sign index is calculated by the mathematical model. And the detection index, namely y value, obtained by testing the sample to be tested and substituting the sample into the model for calculation is classified into B class, less than 0.5 is classified into A class, and the y value is equal to 0.5 as an uncertain gray area. Wherein the class a and the class B are corresponding two classifications (two classification groups, which group a and which group B are to be determined according to a specific mathematical model, without convention herein), such as cancer-free control and lung cancer patients, cancer-free female control and breast cancer patients, lung benign nodule and lung cancer patients, breast benign nodule and breast cancer patients, lung cancer patients and breast cancer patients, lung adenocarcinoma and lung squamous carcinoma patients, lung adenocarcinoma and small cell lung cancer patients, lung squamous carcinoma and small cell lung cancer patients, lung cancer and lung cancer patients in stage I, lung cancer and stage II, lung cancer and lung cancer patients in stage I, lung cancer in stage II, lung cancer and stage III, breast ductal carcinoma in situ and duct of breast, duct of breast invasive lobular carcinoma patients, breast cancer in stage I and stage II, breast cancer in stage I and stage III, breast cancer in stage II and stage III). When predicting a sample of a subject to determine which category the sample belongs to, blood of the subject is collected first, and then DNA is extracted therefrom. After the extracted DNA is converted by bisulfite, the methylation level of single CpG sites or the methylation level of a plurality of CpG sites of ZPBP2 gene of a subject is detected by using a DNA methylation determination method, and methylation data obtained by detection are substituted into the mathematical model. If the methylation level of one or more CpG sites of the ZPBP2 gene of the subject is substituted into the mathematical model and then the calculated detection index is larger than a threshold value, the subject judges that the detection index in the training set is more than 0.5 and belongs to a class (B class); if the methylation level data of one or more CpG sites of the ZPBP2 gene of the subject is substituted into the mathematical model and then the calculated value, namely the detection index, is smaller than a threshold value, the subject belongs to a class (A class) with the detection index in the training set smaller than 0.5; if the methylation level data of one or more CpG sites of the ZPBP2 gene of the subject is substituted into the mathematical model and the calculated value, i.e. the detection index, is equal to the threshold value, then the subject cannot be judged to be class A or class B.
Examples: the schematic diagram of fig. 2 illustrates the methylation levels of CpG sites of zpbp2_b (zpbp2_b_1, zpbp2_b_2, zpbp2_b_3, zpbp2_b_4, zpbp2_b_5, zpbp2_b_6.7, zpbp2_b_8, zpbp2_b_9, zpbp2_b_10, zpbp2_b_11.12 and zpbp2_b_13) and the application of mathematical modeling for lung benign and malignant nodule discrimination: the methylation level data of 11 distinguishable CpG site combinations already detected in the lung cancer patient and lung benign nodule patient training set (426 lung cancer patients and 286 lung benign nodule patients herein) and the ages, sexes (male assigned 1 and female assigned 0) and white blood cell counts of the patients were used to establish a mathematical model for distinguishing lung cancer patients from lung benign nodule patients by using a formula of a two-class logistic regression through R software. The mathematical model is here a two-class logistic regression model, whereby the constant b0 of the mathematical model and the weights b1 to bn of the individual methylation sites are determined, in this example in particular: log (y/(1-y))=0.22+0.068×zpbp2_b_1+1.606×zpbp2_b_2-1.043×zpbp2_b_3-0.313×zpbp2_b_4+0.367×zpbp2_b_5-1.216×zpbp2_b_6.7+0.072×zpbp2_b_8-0.282×zpbp2_b_9-0.182×zpbp2_b_10+0.842×zpbp2_b_11.12-0.122×zpbp2_b_13+0.002×age (integer) +0.142×gender (male assigned 1, female assigned 0) +0.005×white cell count (unit 10 ζ9/L). Where y is a dependent variable, i.e., the methylation values of 11 distinguishable methylation sites of the sample to be tested, and the detection index obtained after substituting the age, sex and white blood cell count into the model. Under the condition that 0.5 is set as a threshold value, the methylation level of the 11 distinguishable CpG sites of ZPBP2_B_1, ZPBP2_B_2, ZPBP2_B_3, ZPBP2_B_4, ZPBP2_B_5, ZPBP2_B_6.7, ZPBP2_B_8, ZPBP2_B_9, ZPBP2_B_10, ZPBP2_B_11.12 and ZPBP2_B_13 of the sample to be tested is tested and then is substituted into a model together with information of age, sex and white cell count of the sample to be tested, the obtained detection index, namely y value, is more than 0.5 and classified as lung cancer patients, less than 0.5 and classified as lung benign nodule patients, and if the methylation level is equal to 0.5, the detection index is not determined as lung cancer patients or lung benign nodule patients. The area under the curve (AUC) calculation for this model was 0.78 (table 16). Specific subject judgment method examples are shown in fig. 2, in which blood is collected from two subjects (a, B) to extract DNA, the extracted DNA is converted by bisulfite, and the methylation level of 11 distinguishable CpG sites of the subjects zpbp2_b_1, zpbp2_b_2, zpbp2_b_3, zpbp2_b_4, zpbp2_b_5, zpbp2_b_6.7, zpbp2_b_8, zpbp2_b_9, zpbp2_b_10, zpbp2_b_11.12 and zpbp2_b_13 is detected by a DNA methylation measurement method. The methylation level data obtained from the detection together with the information on age, sex and white blood cell count of the subject are then substituted into the mathematical model described above. The value calculated by the first test subject after the mathematical model is more than 0.73 and is more than 0.5, and the first test subject is judged to be a lung cancer patient (which accords with the clinical judgment result); and substituting methylation level data of one or more CpG sites of ZPBP2 genes of the second subject into the mathematical model to calculate a value of 0.16 to be less than 0.5, and judging the benign nodular patients of the lung (conforming to clinical judgment results) by the second subject.
(C) Model Effect evaluation
According to the above method, mathematical models for distinguishing between a cancer-free control and a lung cancer patient, a cancer-free female control and a breast cancer patient, a lung benign nodule patient and a lung cancer patient, a breast benign nodule and breast cancer patient, a lung cancer patient and a breast cancer patient, a lung adenocarcinoma and lung squamous carcinoma patient, a lung adenocarcinoma and small cell lung cancer patient, a lung squamous carcinoma and small cell lung cancer patient, a lung cancer patient of stage I and stage II, a lung cancer patient of stage I and stage III, a lung cancer patient of stage II and stage III, a ductal carcinoma in situ and a ductal breast cancer patient, a ductal breast cancer and a lobular breast invasive cancer patient, a ductal carcinoma in situ and a lobular breast cancer patient, a breast cancer patient of stage I and II, a breast cancer patient of stage I and III, a breast cancer patient of stage II and a breast cancer patient of stage III are established, respectively, and the effectiveness thereof is evaluated by a subject curve (ROC curve). The larger the area under the curve (AUC) from the ROC curve, the better the differentiation of the model, the more efficient the molecular marker. The evaluation results after construction of mathematical models using different CpG sites are shown in tables 16, 17 and 18. In tables 16, 17 and 18, 1 CpG site represents the site of any one CpG site in the zpbp2_b amplified fragment, 2 CpG sites represent the combination of any 2 CpG sites in zpbp2_b, 3 CpG sites represent the combination of any 3 CpG sites in zpbp2_b. The values in the table are the range of values for the combined evaluation of the different sites (i.e., the results for any combination of CpG sites are within this range).
The above results show that the discrimination ability of ZPBP2 gene for each group (no cancer control and lung cancer patient, no cancer female control and breast cancer patient, lung benign nodule and lung cancer patient, breast benign nodule and breast cancer patient, lung cancer patient and breast cancer patient, lung adenocarcinoma and lung squamous carcinoma patient, lung adenocarcinoma and small cell lung cancer patient, lung squamous carcinoma and small cell lung cancer patient, lung cancer and lung cancer patient in stage I, lung cancer and lung cancer patient in stage III, lung cancer and lung cancer patient in stage II, ductal carcinoma in situ and ductal invasive, ductal invasive carcinoma in breast and lobular invasive carcinoma in breast, ductal carcinoma in situ and lobular invasive carcinoma in breast, ductal breast cancer in stage I and breast cancer in stage II, breast cancer in stage I and breast cancer in stage III, breast cancer in stage II and breast cancer in stage III) increases with increasing number of loci.
In summary, the CpG sites on the ZPBP2 gene and various combinations thereof, the CpG sites on the ZPBP2_A fragment and various combinations thereof, the CpG sites on the ZPBP2_B fragment and various combinations thereof, the CpG sites on the ZPBP2_C fragment and various combinations thereof, the CpG sites on the ZPBP2_D fragment and various combinations thereof, the CpG sites on the ZPBP2_E fragment and various combinations thereof, and the methylation levels of CpG sites on zpbp2_ A, ZPBP2_ B, ZPBP _ C, ZPBP2_d and zpbp2_e and various combinations thereof are capable of discriminating between cancer-free control and lung cancer patients, cancer-free female control and breast cancer patients, lung benign nodule and lung cancer patients, breast benign nodule and breast cancer patients, lung cancer patients and breast cancer patients, lung adenocarcinoma and lung squamous carcinoma patients, lung adenocarcinoma and small cell lung cancer patients, lung squamous carcinoma and small cell lung cancer patients, lung cancer and lung cancer patients in stage I, lung cancer and stage II, lung cancer and lung cancer patients in stage I, lung cancer and stage III, breast ductal carcinoma in situ and breast invasive ductal carcinoma patients, breast invasive ductal carcinoma and breast invasive lobular carcinoma patients, breast cancer and stage II breast cancer patients in stage I, breast cancer and stage III, breast cancer patients in stage II and breast cancer patients in stage III.
TABLE 7 methylation levels comparing benign nodules in lung and lung cancer for cancer-free controls
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Table 8 methylation level differences for comparison of cancer-free controls and lung cancer
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Table 9, comparison of methylation level differences between benign nodules in the lung and lung cancer
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TABLE 10 comparison of methylation level differences for different subtypes of lung cancer or different stages of lung cancer
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Table 11, comparison methylation levels for non-cancerous female controls, benign nodules of breast and breast cancer
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Table 12, methylation level differences for comparison of cancer-free female controls and breast cancer
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TABLE 13 comparison of methylation level differences between benign nodules of breast and breast cancer
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TABLE 14 comparison of methylation level differences for different subtypes and different stages of breast cancer
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TABLE 15 comparison of methylation level differences for lung and breast cancers
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Table 16, distinguishable CpG sites of ZPBP2_B and combinations thereof for distinguishing lung cancer and non-cancerous controls, lung cancer and benign nodules of the lung, breast cancer and non-cancerous female controls, breast cancer and benign nodules of the breast, and lung cancer and breast cancer
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Table 17, distinguishable CpG sites of ZPBP2_B and combinations thereof for distinguishing between different subtypes and different stages of a lung cancer patient
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Table 18, distinguishable CpG sites of ZPBP2_B and combinations thereof for distinguishing between different subtypes and different stages of breast cancer patients
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The present invention is described in detail above. It will be apparent to those skilled in the art that the present invention can be practiced in a wide range of equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the invention and without undue experimentation. While the invention has been described with respect to specific embodiments, it will be appreciated that the invention may be further modified. In general, this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. The application of some of the basic features may be done in accordance with the scope of the claims that follow.
<110> Nanjing Techno Biotechnology Co., ltd
<120> use of methylation marker and kit for auxiliary diagnosis of cancer
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Claims (10)

1. Application of methylation ZPBP2 gene as a marker in preparation of products; the application of the product is at least one of the following:
(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;
(2) Aiding in distinguishing benign nodules from cancers;
(3) Aiding in distinguishing between different subtypes of cancer;
(4) Aiding in differentiating different stages of cancer;
(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;
(6) Assisting in distinguishing benign nodules of the lung from lung cancer;
(7) Assisting in distinguishing different subtypes of lung cancer;
(8) Auxiliary differentiation of different stages of lung cancer;
(9) Auxiliary diagnosis of breast cancer or prediction of breast cancer risk;
(10) Aiding in distinguishing benign nodules of breast from breast cancer;
(11) Assisting in distinguishing different subtypes of breast cancer;
(12) Assisting in distinguishing different stages of breast cancer;
(13) Auxiliary differentiation between lung and breast cancer;
(14) Determining whether the test agent has an inhibitory or promoting effect on the occurrence of cancer.
2. Use of a substance for detecting the methylation level of ZPBP2 gene in the preparation of a product; the application of the product is at least one of the following:
(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;
(2) Aiding in distinguishing benign nodules from cancers;
(3) Aiding in distinguishing between different subtypes of cancer;
(4) Aiding in differentiating different stages of cancer;
(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;
(6) Assisting in distinguishing benign nodules of the lung from lung cancer;
(7) Assisting in distinguishing different subtypes of lung cancer;
(8) Auxiliary differentiation of different stages of lung cancer;
(9) Auxiliary diagnosis of breast cancer or prediction of breast cancer risk;
(10) Aiding in distinguishing benign nodules of breast from breast cancer;
(11) Assisting in distinguishing different subtypes of breast cancer;
(12) Assisting in distinguishing different stages of breast cancer;
(13) Auxiliary differentiation between lung and breast cancer;
(14) Determining whether the test agent has an inhibitory or promoting effect on the occurrence of cancer.
3. Use of a substance for detecting the methylation level of ZPBP2 gene and a medium storing mathematical modeling methods and/or usage methods for the preparation of a product; the application of the product is at least one of the following:
(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;
(2) Aiding in distinguishing benign nodules from cancers;
(3) Aiding in distinguishing between different subtypes of cancer;
(4) Aiding in differentiating different stages of cancer;
(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;
(6) Assisting in distinguishing benign nodules of the lung from lung cancer;
(7) Assisting in distinguishing different subtypes of lung cancer;
(8) Auxiliary differentiation of different stages of lung cancer;
(9) Auxiliary diagnosis of breast cancer or prediction of breast cancer risk;
(10) Aiding in distinguishing benign nodules of breast from breast cancer;
(11) Assisting in distinguishing different subtypes of breast cancer;
(12) Assisting in distinguishing different stages of breast cancer;
(13) Auxiliary differentiation between lung and breast cancer;
(14) Determining whether the analyte has an inhibitory or promoting effect on the occurrence of cancer;
The mathematical model is obtained according to a method comprising the following steps:
(A1) Detecting ZPBP2 gene methylation levels of n1 type a samples and n2 type B samples, respectively;
(A2) Taking ZPBP2 gene methylation level data of all samples obtained in the step (A1), establishing a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type, and determining a classification judgment threshold value;
the using method of the mathematical model comprises the following steps:
(B1) Detecting the methylation level of ZPBP2 genes of a sample to be detected;
(B2) Substituting the ZPBP2 gene methylation level data of the sample to be detected obtained in the step (B1) into the mathematical model to obtain a detection index; then comparing the detection index with the threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result;
the type a sample and the type B sample are any one of the following:
(C1) Lung cancer samples and no cancer controls;
(C2) Lung cancer samples and lung benign nodule samples;
(C3) A sample of different subtypes of lung cancer;
(C4) Samples of lung cancer at different stages;
(C5) Breast cancer samples and cancer-free female controls;
(C6) Breast cancer samples and breast benign nodule samples;
(C7) A sample of different subtypes of breast cancer;
(C8) Breast cancer samples of different stages;
(C9) Lung cancer samples and breast cancer samples.
4. Use of a medium storing a mathematical model building method and/or a use method for the preparation of a product; the application of the product is at least one of the following:
(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;
(2) Aiding in distinguishing benign nodules from cancers;
(3) Aiding in distinguishing between different subtypes of cancer;
(4) Aiding in differentiating different stages of cancer;
(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;
(6) Assisting in distinguishing benign nodules of the lung from lung cancer;
(7) Assisting in distinguishing different subtypes of lung cancer;
(8) Auxiliary differentiation of different stages of lung cancer;
(9) Auxiliary diagnosis of breast cancer or prediction of breast cancer risk;
(10) Aiding in distinguishing benign nodules of breast from breast cancer;
(11) Assisting in distinguishing different subtypes of breast cancer;
(12) Assisting in distinguishing different stages of breast cancer;
(13) Auxiliary differentiation between lung and breast cancer;
(14) Determining whether the analyte has an inhibitory or promoting effect on the occurrence of cancer;
the mathematical model is obtained according to a method comprising the following steps:
(A1) Detecting ZPBP2 gene methylation levels of n1 type a samples and n2 type B samples, respectively;
(A2) Taking ZPBP2 gene methylation level data of all samples obtained in the step (A1), establishing a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type, and determining a classification judgment threshold value;
The using method of the mathematical model comprises the following steps:
(B1) Detecting the methylation level of ZPBP2 genes of a sample to be detected;
(B2) Substituting the ZPBP2 gene methylation level data of the sample to be detected obtained in the step (B1) into the mathematical model to obtain a detection index; then comparing the detection index with the threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result;
the type a sample and the type B sample are any one of the following:
(C1) Lung cancer samples and no cancer controls;
(C2) Lung cancer samples and lung benign nodule samples;
(C3) A sample of different subtypes of lung cancer;
(C4) Samples of lung cancer at different stages;
(C5) Breast cancer samples and cancer-free female controls;
(C6) Breast cancer samples and breast benign nodule samples;
(C7) A sample of different subtypes of breast cancer;
(C8) Breast cancer samples of different stages;
(C9) Lung cancer samples and breast cancer samples.
5. A kit comprising a substance for detecting the methylation level of ZPBP2 gene; the application of the kit is at least one of the following:
(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;
(2) Aiding in distinguishing benign nodules from cancers;
(3) Aiding in distinguishing between different subtypes of cancer;
(4) Aiding in differentiating different stages of cancer;
(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;
(6) Assisting in distinguishing benign nodules of the lung from lung cancer;
(7) Assisting in distinguishing different subtypes of lung cancer;
(8) Auxiliary differentiation of different stages of lung cancer;
(9) Auxiliary diagnosis of breast cancer or prediction of breast cancer risk;
(10) Aiding in distinguishing benign nodules of breast from breast cancer;
(11) Assisting in distinguishing different subtypes of breast cancer;
(12) Assisting in distinguishing different stages of breast cancer;
(13) Auxiliary differentiation between lung and breast cancer;
(14) Determining whether the test agent has an inhibitory or promoting effect on the occurrence of cancer.
6. The kit of claim 5, wherein: the kit further comprises a medium storing a mathematical model establishing method and/or a using method as set forth in claim 3 or 4.
7. A system, comprising:
(D1) Reagents and/or instrumentation for detecting the methylation level of ZPBP2 gene;
(D2) A device comprising a unit X and a unit Y;
the unit X is used for establishing a mathematical model and comprises a data acquisition module, a data analysis processing module and a model output module;
the data acquisition module is configured to acquire ZPBP2 gene methylation level data of n 1A type samples and n 2B type samples obtained by (D1) detection;
The data analysis processing module is configured to receive ZPBP2 gene methylation level data of the n 1A type samples and the n 2B type samples from the data acquisition module, establish a mathematical model according to a classification mode of the A type and the B type through a two-classification logistic regression method, and determine a threshold value of classification judgment;
the model output module is configured to receive the mathematical model established by the data analysis processing module and output the mathematical model;
the unit Y is used for determining the type of the sample to be detected and comprises a data input module, a data operation module, a data comparison module and a conclusion output module;
the data input module is configured to input ZPBP2 gene methylation level data of the to-be-detected person detected by (D1);
the data operation module is configured to receive the ZPBP2 gene methylation level data of the to-be-detected person from the data input module, and substitutes the ZPBP2 gene methylation level data of the to-be-detected person into the mathematical model to calculate a detection index;
the data comparison module is configured to receive the detection index calculated by the data operation module and compare the detection index with the threshold value determined by the data analysis processing module in the unit X;
The conclusion output module is configured to receive the comparison result from the data comparison module and output a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result of the data comparison module;
the type a sample and the type B sample are any one of the following:
(C1) Lung cancer samples and no cancer controls;
(C2) Lung cancer samples and lung benign nodule samples;
(C3) A sample of different subtypes of lung cancer;
(C4) Samples of lung cancer at different stages;
(C5) Breast cancer samples and cancer-free female controls;
(C6) Breast cancer samples and breast benign nodule samples;
(C7) A sample of different subtypes of breast cancer;
(C8) Breast cancer samples of different stages;
(C9) Lung cancer samples and breast cancer samples.
8. The use or kit or system according to any one of claims 1-7, wherein: the methylation level of the ZPBP2 gene is the methylation level of all or part of CpG sites in fragments shown in the following (e 1) - (e 5) in the ZPBP2 gene;
the methylated ZPBP2 gene is formed by methylation of all or part of CpG sites in fragments shown in the following (e 1) - (e 5) in the ZPBP2 gene;
(e1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment having 80% or more identity thereto;
(e2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment having 80% or more identity thereto;
(e3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment having 80% or more identity thereto;
(e4) A DNA fragment shown in SEQ ID No.4 or a DNA fragment having 80% or more identity thereto;
(e5) The DNA fragment shown in SEQ ID No.5 or a DNA fragment having 80% or more identity thereto.
9. The use or kit or system according to claim 8, wherein: the 'all or part of CpG sites' are any one or more CpG sites in 5 DNA fragments shown in SEQ ID No.1 to SEQ ID No.5 in the ZPBP2 gene;
or (b)
The 'all or part of CpG sites' are all CpG sites in a DNA fragment shown in SEQ ID No.2 and all CpG sites in a DNA fragment shown in SEQ ID No.3 in the ZPBP2 gene;
or (b)
The 'all or part of CpG sites' are all CpG sites in a DNA fragment shown in SEQ ID No.2 and all CpG sites in a DNA fragment shown in SEQ ID No.4 in the ZPBP2 gene;
or (b)
The 'all or part of CpG sites' are all CpG sites in a DNA fragment shown in SEQ ID No.3 and all CpG sites in a DNA fragment shown in SEQ ID No.4 in the ZPBP2 gene;
Or (b)
The 'all or part of CpG sites' are all CpG sites in a DNA fragment shown as SEQ ID No.2, all CpG sites in a DNA fragment shown as SEQ ID No.3 and all CpG sites in a DNA fragment shown as SEQ ID No.4 in the ZPBP2 gene;
or (b)
The whole or part of CpG sites are all or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 of the following 11 CpG sites in the DNA fragment shown in SEQ ID No.2 in the ZPBP2 gene:
(f1) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 29 th to 30 th positions of the 5' end;
(f2) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 57-58 positions of the 5' end;
(f3) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 90 th to 91 st positions of the 5' end;
(f4) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 113-114 positions of the 5' end;
(f5) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 119 th to 120 th positions of the 5' end;
(f6) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 190-191 and 192-193 of the 5' end;
(f7) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 213 th to 214 th positions of the 5' end;
(f8) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 229 th to 230 th positions of the 5' end;
(f9) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 247 th to 248 th positions of the 5' end;
(f10) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 262 to 263 and 264 to 265 of the 5' end;
(f11) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 281-282 positions of the 5' end.
10. The use or kit or system according to any one of claims 1-9, wherein: the substance for detecting the methylation level of the ZPBP2 gene comprises a primer combination for amplifying a full or partial fragment of the ZPBP2 gene;
the reagent for detecting the methylation level of the ZPBP2 gene comprises a primer combination for amplifying the full length or partial fragment of the ZPBP2 gene;
further, the partial fragment is at least one fragment of:
(g1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;
(g2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;
(g3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;
(g4) A DNA fragment shown in SEQ ID No.4 or a DNA fragment comprising the same;
(g5) A DNA fragment shown in SEQ ID No.5 or a DNA fragment comprising the same;
(g6) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;
(g7) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;
(g8) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same.
(g9) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.4 or a DNA fragment comprising the same;
(g10) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.5 or a DNA fragment comprising the same;
still further, the primer combination is primer pair a and/or primer pair B and/or primer pair C or primer pair D and/or primer pair E;
the primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.6 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 6; the primer A2 is SEQ ID No.7 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 7;
the primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is single-stranded DNA shown in SEQ ID No.8 or 11 th-35 th nucleotide of SEQ ID No. 8; the primer B2 is SEQ ID No.9 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 9;
the primer pair C is a primer pair consisting of a primer C1 and a primer C2; the primer C1 is SEQ ID No.10 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 10; the primer C2 is SEQ ID No.11 or single-stranded DNA shown in 32-56 nucleotides of SEQ ID No. 11.
The primer pair D is a primer pair consisting of a primer D1 and a primer D2; the primer D1 is SEQ ID No.12 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 12; the primer D2 is SEQ ID No.13 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 13;
the primer pair E is a primer pair consisting of a primer E1 and a primer E2; the primer E1 is SEQ ID No.14 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 14; the primer E2 is SEQ ID No.15 or single-stranded DNA shown in 32-56 nucleotides of SEQ ID No. 15.
CN202210242006.4A 2022-03-11 2022-03-11 Methylation marker and application of kit in auxiliary diagnosis of cancer Pending CN117604094A (en)

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