CN117568470A

CN117568470A - Molecular marker and kit for auxiliary diagnosis of cancer

Info

Publication number: CN117568470A
Application number: CN202210241990.2A
Authority: CN
Inventors: 张筝; 张晶; 狄飞飞
Original assignee: Tengchen Biotechnology Shanghai Co ltd
Current assignee: Tengchen Biotechnology Shanghai Co ltd
Priority date: 2022-03-11
Filing date: 2022-03-11
Publication date: 2024-02-20

Abstract

The invention discloses a molecular marker and a kit for auxiliary diagnosis of cancer. The invention provides an application of a methylation ARHGAP35 gene serving 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 research of the invention discovers the hypermethylation phenomenon of ARHGAP35 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

Molecular marker and kit for auxiliary diagnosis of cancer

Technical Field

The invention relates to the field of medicine, in particular to a molecular marker and a kit for auxiliary diagnosis of cancer.

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.

At present, the main lung cancer diagnosis methods are as follows: 1) The imaging method comprises the following steps: 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 or taking biopsy and bronchoalveolar lavage liquid cytology under the bronchoscope 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 effective 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%. At present, clinical identification of benign and malignant lung nodules requires long-term follow-up, repeated CT examination or a traumatic examination method such as biopsy of lung nodules (including chest wall fine needle puncture biopsy, bronchoscopy tissue biopsy, thoracoscopy or open chest operation lung biopsy) and the like. CT guided or ultrasound guided transthoracic biopsy has higher sensitivity, but has a lower diagnosis rate for <2cm nodules, 30-70% missed diagnosis rate, and higher pneumothorax and hemorrhage incidence. 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 molecular markers for in vitro diagnosis to assist 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 place of female malignant tumors. 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 late stage breast cancer this number falls to 40-60%. For metastatic breast cancer, the prognosis for 5 years survival is typically about 15%. Therefore, it is necessary to increase the early detection rate of breast cancer for effective diagnosis and treatment in the later stage of 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 refers to a method for taking pathological tissues for pathological diagnosis, however, biopsy operation is very resistant to patients because of trauma to people. 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 diagnosis of 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 RhoGTPase activating protein 35 (Rho GTPase Activating Protein, ARHGAP35) methylation marker and a kit for assisting in diagnosing cancers.

In a first aspect, the invention claims the use of a methylated ARHGAP35 gene as a marker in the preparation of a product. The use of the product may be 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 assist in distinguishing breast cancer patients from non-cancerous female controls, can assist in distinguishing breast duct in-situ cancer patients from non-cancerous female controls, can assist in distinguishing breast invasive duct cancer patients from non-cancerous female controls, can assist in distinguishing breast invasive lobular cancer patients from non-cancerous female controls, can assist in distinguishing stage I breast cancer patients from non-cancerous female controls, can assist in distinguishing stage II-III breast cancer patients from non-cancerous female controls, can assist in distinguishing lymph node-infiltrated breast cancer patients from non-cancerous female controls, and can assist in distinguishing lymph node-infiltrated breast cancer patients from non-cancerous female controls. Wherein, the non-cancerous female control is understood to be no cancer and no benign nodules of the breast are reported at present and once 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 ARHGAP35 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 the ARHGAP35 gene in 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 the ARHGAP35 gene and a medium storing a mathematical modeling method and/or a method of use 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 may be obtained by a method comprising the steps of:

(A1) Detecting the methylation level of the ARHGAP35 gene of n 1A type samples and n 2B type samples respectively (training set);

(A2) Taking ARHGAP35 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 ARHGAP35 gene methylation level of a sample to be detected;

(B2) Substituting the ARHGAP35 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 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 the ARHGAP35 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 the ARHGAP35 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 (D1) ARHGAP35 gene methylation level data of n 1A type samples and n 2B type samples detected;

the data analysis processing module is configured to receive ARHGAP35 gene methylation level data from n 1A type samples and n 2B type samples acquired by the data acquisition module, establish a mathematical model according to a classification mode of the A type and the B type by 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 (D1) detected ARHGAP35 gene methylation level data of the to-be-detected person;

The data operation module is configured to receive ZPBP2 gene methylation level data of the to-be-detected person from the data input module, and substitute ARHGAP35 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 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.

Wherein, n1 and n2 can be positive integers more than 50.

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 ARHGAP35 gene may be the methylation level of all or part of CpG sites in the fragments as shown in the following (e 1) to (e 4) in the ARHGAP35 gene. The methylated ARHGAP35 gene may be all or part of CpG sites methylated in the fragments as shown in the following (e 1) to (e 4) in the ARHGAP35 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) The DNA fragment shown in SEQ ID No.4 or a DNA fragment having 80% or more identity thereto.

Further, the "all or part of the CpG sites" may be any one or more CpG sites of 4 DNA fragments shown in SEQ ID No.1 to SEQ ID No.4 in ARHGAP35 gene. The upper limit of "multiple CpG sites" as used herein is all CpG sites in the 4 DNA fragments shown in SEQ ID No.1 to SEQ ID No.4 in the ARHGAP35 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, and all CpG sites in the DNA fragment shown in SEQ ID No.4 are shown in Table 4.

Or, the "all or part of CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.4 (see Table 4) and all CpG sites in the DNA fragment shown in SEQ ID No.1 (see Table 1).

Or, the "all or part of CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.4 (see Table 4) and all CpG sites in the DNA fragment shown in SEQ ID No.3 (see Table 3).

Or, the "all or part of CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.1 (see Table 1) and all CpG sites in the DNA fragment shown in SEQ ID No.3 (see Table 3).

Or, the "all or part of CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.4 (see Table 4) and all CpG sites in the DNA fragment shown in SEQ ID No.1 (see Table 1) and all CpG sites in the DNA fragment shown in SEQ ID No.3 (see Table 3).

Alternatively, the "all or part of the CpG sites" may be all or any 11 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 DNA fragments shown in SEQ ID No.4 in the ARHGAP35 gene.

Or, the "all or part of the CpG sites" may be all 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 CpG sites shown in the following 9 in the DNA fragment shown in SEQ ID No.4 in the ARHGAP35 gene:

(f1) The CpG site (ARHGAP35_D_1) shown in the 26 th-27 th position of the 5' end of the DNA fragment shown in SEQ ID No. 4;

(f2) The DNA fragment shown in SEQ ID No.4 shows the CpG sites (ARHGAP35_D_2) from 135 th to 136 th positions of the 5' end;

(f3) The DNA fragment shown in SEQ ID No.4 shows the CpG sites (ARHGAP35_D_3) at positions 154-155 from the 5' end;

(f4) The CpG site (ARHGAP35_D_4) shown in 172-173 of the DNA fragment shown in SEQ ID No.4 from the 5' end;

(f5) The DNA fragment shown in SEQ ID No.4 shows the CpG sites (ARHGAP35_D_5) from 258 to 259 of the 5' end;

(f6) The DNA fragment shown in SEQ ID No.4 shows the CpG site (ARHGAP35_D_6) from 327 th to 328 th positions of the 5' end;

(f7) The CpG sites (ARHGAP35_D_7) shown in 357-358 of the DNA fragment shown in SEQ ID No.4 from the 5' end;

(f8) The CpG site (ARHGAP35_D_8) shown in 449-450 of the 5' end of the DNA fragment shown in SEQ ID No. 4;

(f9) The DNA fragment shown in SEQ ID No.4 shows the CpG sites (ARHGAP35_D_9) from 537-538 at 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 6), 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 ARHGAP35 gene may comprise (or be) a primer combination for amplifying a full or partial fragment of the ARHGAP35 gene. The reagent for detecting the methylation level of the ARHGAP35 gene may comprise (or be) a primer combination for amplifying a full or partial fragment of the ARHGAP35 gene; the instrument for detecting the methylation level of the ARHGAP35 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 the ARHGAP35 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 having an identity of 80% or more to the DNA fragment shown in SEQ ID No.1 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.2 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.3 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.4 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.

The primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 can be specifically single-stranded DNA shown in SEQ ID No.5 or 11-35 nucleotides of SEQ ID No. 5; the primer A2 can be specifically a single-stranded DNA shown in SEQ ID No.6 or 32-56 nucleotides of SEQ ID No. 6;

The primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 can be specifically a single-stranded DNA shown in SEQ ID No.7 or 11-35 nucleotides of SEQ ID No. 7; the primer B2 can be specifically single-stranded DNA shown in SEQ ID No.8 or 32-56 nucleotides of SEQ ID No. 8;

the primer pair C is a primer pair consisting of a primer C1 and a primer C2; the primer C1 can be specifically a single-stranded DNA shown in SEQ ID No.9 or 11-35 nucleotides of SEQ ID No. 9; the primer C2 can be specifically single-stranded DNA shown in SEQ ID No.10 or 32-56 nucleotides of SEQ ID No. 10;

the primer pair D is a primer pair consisting of a primer D1 and a primer D2; the primer D1 can be specifically a single-stranded DNA shown in SEQ ID No.11 or 11-35 nucleotides of SEQ ID No. 11; the primer D2 can be specifically a single-stranded DNA shown in SEQ ID No.12 or 32-56 nucleotides of SEQ ID No. 12.

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:

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 ARHGAP35 gene methylation level of the sample to be detected;

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))=0.167-2.229*ARHGAP35_D_1+2.621*AR HGAP35_D_2-0.049×arhgap35_d_3-0.159×arhgap35_d_4-0.684×arhgap35_d_5+0.715×arhgap35_d_6+1.775×arhgap35_d_7-1.008×arhgap35_d_8-0.708*ARHGAP 35_D_9+0.001 ×age (integer) +0.146×sex (male assignment 1, female assignment 0) +0.011×white cell count (unit 10≡9/L). The ARHGAP35_D_1 is the methylation level of the CpG site shown in the 26 th-27 th position of the 5' end of the DNA fragment shown in SEQ ID No. 4; the ARHGAP35_D_2 is the methylation level of CpG sites shown in 135 th-136 th positions of a DNA fragment shown in SEQ ID No.4 from the 5' end; the ARHGAP35_D_3 is the methylation level of the CpG site shown in the 154 th-155 th position of the 5' end of the DNA fragment shown in SEQ ID No. 4; the ARHGAP35_D_4 is the methylation level of the CpG site shown in 172-173 th position of the 5' end of the DNA fragment shown in SEQ ID No. 4; the ARHGAP35_D_5 is the methylation level of the CpG site shown in the 258 th site to the 259 th site of the 5' end of the DNA fragment shown in SEQ ID No. 4; the ARHGAP35_D_6 is the methylation level of the CpG site shown in 327 th to 328 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 4; the ARHGAP35_D_7 is the methylation level of CpG sites shown in 357-358 th position of the 5' end of the DNA fragment shown in SEQ ID No. 4; the ARHGAP35_D_8 is the methylation level of CpG sites shown in 449-450 positions of a DNA fragment shown in the S EQ ID No.4 from the 5' end; the ARH GAP35_D_9 is the methylation level of the CpG site shown in 537-538 of the 5' end of the DNA fragment shown in SEQ ID No. 4. 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 ARHGAP35 gene is detecting the methylation level of the ARHGAP35 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.

Any of the above ARHGAP35 genes specifically may include Genbank accession numbers: NM-004491.5 (2019, 5, 8).

The invention provides hypermethylation of ARHGAP35 gene in blood of lung cancer patients and breast cancer patients. 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 quantitative assay of RhoGTPase activator protein 35 (Rho GTPase Activating Protein, ARHGAP35) gene in the following examples was performed in triplicate, and the results averaged.

Example 1 primer design for detection of methylation site of ARHGAP35 Gene

Four fragments in the ARHGAP35 gene (ARHGAP 35_a fragment, ARHGAP35_b fragment, ARHGAP35_c fragment and ARHGAP35_d fragment) were selected for methylation level and cancer correlation analysis through a number of sequence and functional analyses.

The ARHGAP35_A fragment (SEQ ID No. 1) is located in the hg19 reference genome chr19:47454219-47454700, sense strand;

the ARHGAP35_B fragment (SEQ ID No. 2) is located in the hg19 reference genome chr19:47457678-47457978, sense strand;

the ARHGAP 35-C fragment (SEQ ID No. 3) is located in the hg19 reference genome chr19:47473658-47474170, sense strand;

the ARHGAP35_D fragment (SEQ ID No. 4) is located in the hg19 reference genome chr19:47480385-47481084, sense strand.

CpG site information in ARHGAP35_A fragment is shown in Table 1;

CpG site information in ARHGAP35_B fragment is shown in Table 2;

CpG site information in ARHGAP35_C fragment is shown in Table 3;

CpG site information in ARHGAP35_D fragment is shown in Table 4.

Table 1 CpG site information in ARHGAP35_A fragment

CpG sites	CpG sites in sequenceIs the position of (2)
		ARHGAP35_A_1	SEQ ID No.1 from positions 51-52 of the 5' end
ARHGAP35_A_2	SEQ ID No.1 from position 64-65 of the 5' end
		ARHGAP35_A_3	152 th to 153 th positions of SEQ ID No.1 from 5' end
ARHGAP35_A_4	SEQ ID No.1 from position 233-234 of the 5' end
		ARHGAP35_A_5	270 th to 271 th bit from 5' end of SEQ ID No.1
ARHGAP35_A_6	351-352 th bit of SEQ ID No.1 from 5' end
		ARHGAP35_A_7	The 384 th to 385 th positions of SEQ ID No.1 from the 5' end
ARHGAP35_A_8	SEQ ID No.1 from position 401 to 402 at the 5' end
		ARHGAP35_A_9	SEQ ID No.1 from the 5' end at positions 452-453

Table 2, cpG site information in ARHGAP35_B fragment

CpG sites	Position of CpG sites in the sequence
		ARHGAP35_B_1	SEQ ID No.2 from position 48-49 of the 5' end
ARHGAP35_B_2	54 th to 55 th positions of SEQ ID No.2 from 5' end
		ARHGAP35_B_3	SEQ ID No.2 from positions 78-79 of the 5' end
ARHGAP35_B_4	SEQ ID No.2 from positions 101-102 of the 5' end
		ARHGAP35_B_5	SEQ ID No.2 from position 118-119 of the 5' end
ARHGAP35_B_6	230 th to 231 th positions of SEQ ID No.2 from 5' end
		ARHGAP35_B_7	272-273 th bit of SEQ ID No.2 from 5' end

Table 3, cpG site information in ARHGAP35_C fragment

CpG sites	Position of CpG sites in the sequence
		ARHGAP35_C_1	SEQ ID No.3 from 35 th to 36 th position of 5' end
ARHGAP35_C_2	SEQ ID No.3 from position 100-101 of the 5' end
		ARHGAP35_C_3	SEQ ID No.3 from the 5' end at positions 196-197
ARHGAP35_C_4	SEQ ID No.3 from position 240-241 of the 5' end
		ARHGAP35_C_5	SEQ ID No.3 from positions 265 to 266 of the 5' end
ARHGAP35_C_6	Positions 281-282 of SEQ ID No.3 from the 5' end
		ARHGAP35_C_7	SEQ ID No.3 from position 423-424 of the 5' end
ARHGAP35_C_8	SEQ ID No.3 from position 474-475 of 5' end

Table 4, cpG site information in ARHGAP35_D fragment

CpG sites	Position of CpG sites in the sequence
		ARHGAP35_D_1	SEQ ID No.4 from positions 26-27 of the 5' end
ARHGAP35_D_2	SEQ ID No.4 from positions 135-136 of the 5' end
		ARHGAP35_D_3	SEQ ID No.4 from position 154-155 of the 5' end
ARHGAP35_D_4	172-173 th position from 5' end of SEQ ID No.4
		ARHGAP35_D_5	SEQ ID No.4 from position 258-259 at the 5' end
ARHGAP35_D_6	327 th to 328 th positions of SEQ ID No.4 from 5' end
		ARHGAP35_D_7	SEQ ID No.4 from position 357-358 on the 5' end
ARHGAP35_D_8	From the 5' end, SEQ ID No.4, positions 449-450
		ARHGAP35_D_9	SEQ ID No.4 from 5' end 537-538 bits
ARHGAP35_D_10	622 th to 623 th positions of SEQ ID No.4 from 5' end
		ARHGAP35_D_11	631 th to 632 th positions of SEQ ID No.4 from 5' end
ARHGAP35_D_12	SEQ ID No.4 from 5' end at positions 666-667

Specific PCR primers were designed for four fragments (ARHGAP35_A fragment, ARHGAP35_B fragment, ARHGAP35_C fragment and ARHGAP35_D fragment) as shown in Table 5. Wherein SEQ ID No.5, SEQ ID No.7, SEQ ID No.9 and SEQ ID No.11 are forward primers, and SEQ ID No.6, SEQ ID No.8, SEQ ID No.10 and SEQ ID No.12 are reverse primers; positions 1 to 10 from the 5' end in SEQ ID No.5, SEQ ID No.7, SEQ ID No.9 and SEQ ID No.11 are nonspecific labels, and positions 11 to 35 are specific primer sequences; the non-specific tags are located at positions 1 to 31 from 5' in SEQ ID No.6, SEQ ID No.8, SEQ ID No.10 and SEQ ID No.12, and the specific primer sequences are located at positions 32 to 56. The primer sequences do not contain SNPs and CpG sites.

TABLE 5 ARHGAP35 methylation primer sequences

Example 2 ARHGAP35 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, 4 pairs of specific primer pairs in the table 5 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 36 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 ARHGAP35 gene methylation levels in lung cancer blood

Methylation levels of all CpG sites in the ARHGAP35 gene were analyzed using blood of 426 lung cancer patients, 286 lung benign nodule patients and 816 cancer-free controls as study materials (Table 6). The results showed that all CpG sites in the ARHGAP35 gene had a median methylation level of 0.71 (iqr=0.54-0.85) in the cancer-free control group, 0.74 (iqr=0.55-0.87) in benign nodules in the lung, and 0.73 (iqr=0.54-0.86) in lung cancer patients.

2. Blood ARHGAP35 gene methylation level can distinguish cancer-free control and lung cancer patients

As a result of comparative analysis of methylation levels of ARHGAP35 genes in 426 lung cancer patients and 816 cancer-free controls, it was found that methylation levels of all CpG sites in the ARHGAP35 genes were significantly higher in lung cancer patients than in the cancer-free controls (p <0.05, table 7). Furthermore, methylation levels of all CpG sites of the ARHGAP35 gene in different subtypes of lung cancer (lung adenocarcinoma, lung squamous carcinoma and small cell lung carcinoma), respectively, were significantly different from the non-cancerous control (p <0.05, table 7). Methylation levels of all CpG sites of the ARHGAP35 gene in different stages (clinical stage I and stage II-III) of lung cancer were significantly different from that of the cancer-free control (p <0.05, table 7), respectively. Furthermore, there was a significant difference between methylation levels in non-lymphoblastic lung cancer patients and in lymphoblastic lung cancer patients, respectively, and non-cancerous controls (p <0.05, table 7). Therefore, the methylation level of the ARHGAP35 gene can be used for clinical diagnosis of lung cancer, and especially for early diagnosis of lung cancer.

3. The methylation level of ARHGAP35 gene in blood can distinguish benign nodule of lung and lung cancer patient

As a result of comparative analysis of methylation levels of ARHGAP35 gene in 426 lung cancer patients and 286 lung benign nodules, it was found that methylation levels of all CpG sites of ARHGAP35 gene were significantly higher in lung benign nodules patients than in lung cancer patients (p <0.05, table 8). Furthermore, it was found that methylation levels of all CpG in ARHGAP35 gene of lung cancer 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 lymphoinfiltrating lung cancer patients were significantly different from that of benign nodules of lung, respectively (p <0.05, table 8). Thus, the methylation level of the ARHGAP35 gene can be used to distinguish lung cancer patients from benign nodule patients in the lung, and is a very valuable marker.

4. The methylation level of ARHGAP35 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 the ARHGAP35 gene in different subtype lung cancer patients and different stage lung cancer patients, the methylation level of all CpG sites in the ARHGAP35 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), different tumor sizes of lung cancer (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 9). Thus, the methylation level of the ARHGAP35 gene can be used to distinguish between different subtypes of lung cancer or different stages of lung cancer.

5. Methylation level of ARHGAP35 Gene in blood of cancer-free female control, benign nodules of mammary glands and breast cancer

The CpG site methylation level in the ARHGAP35 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 materials (table 10). The results showed that the cancer-free female control group had a median methylation level of 0.71 (iqr=0.53-0.84), the benign breast nodules had a median methylation level of 0.76 (iqr=0.56-0.89), and the breast cancer patients had a median methylation level of 0.77 (iqr=0.58-0.92).

6. Blood ARHGAP35 gene methylation level can distinguish cancer-free female control and breast cancer patients

By comparative analysis of methylation levels of the ARHGAP35 gene in 292 breast cancer patients and 408 non-cancerous female controls, it was found that the methylation levels of all CpG sites in the ARHGAP35 gene were significantly higher in breast cancer patients than in non-cancerous female controls (p <0.05, table 11). Furthermore, methylation levels of all CpG sites of the ARHGAP35 gene in different subtypes of breast cancer (ductal carcinoma in situ, ductal invasive carcinoma of the breast, and lobular invasive carcinoma of the breast) were significantly different from that of non-cancerous female controls, respectively. Methylation levels of all CpG sites of the ARHGAP35 gene in different stages (clinical stage I and stage II-III) of breast cancer were significantly different from that of a cancer-free female control (p <0.05, table 11), 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 11). Therefore, the methylation level of the ARHGAP35 gene can be used for clinical diagnosis of breast cancer, and particularly can be used for early diagnosis of breast cancer.

7. The methylation level of ARHGAP35 gene in blood can distinguish benign nodule of breast from breast cancer patient

By comparative analysis of the methylation level of the ARHGAP35 gene in 292 breast cancer patients and 82 breast benign nodules, it was found that the methylation level of all CpG sites of the ARHGAP35 gene in breast benign nodules patients was significantly lower than in breast cancer patients (p <0.05, table 12). Furthermore, methylation levels of all CpG sites in the ARHGAP35 gene of breast cancer patients of different subtypes (ductal carcinoma in situ, ductal invasive carcinoma of the breast and invasive lobular carcinoma of the breast), different clinical stages (stage I and stages II-III) and the presence or absence of lymphoid infiltration were found to be significantly different from benign nodules of the breast, respectively (p <0.05, table 12). Thus, the methylation level of the ARHGAP35 gene can be used to distinguish breast cancer patients from breast benign nodule patients, and is a very valuable marker.

8. The methylation level of ARHGAP35 gene in blood can be used for distinguishing different subtypes of breast cancer or different stages of breast cancer

By comparing the methylation levels of the ARHGAP35 gene in breast cancer patients of different subtypes and breast cancer patients of different stages, it was found that the methylation levels of all CpG sites in the ARHGAP35 gene are significantly different under the conditions of different subtypes of breast cancer (breast ductal in situ breast cancer, breast invasive ductal carcinoma and breast invasive lobular carcinoma), breast cancer tumor sizes (T1, T2 and T3), different stages of breast cancer (clinical stage I, stage II and stage III), and the presence or absence of lymph node infiltration (p <0.05, table 13). Thus, the methylation level of the ARHGAP35 gene can be used to distinguish between different subtypes of breast cancer or different stages of breast cancer.

9. ARHGAP35 methylation level in blood can distinguish breast cancer patients from lung cancer patients

Blood from 292 breast cancer patients and 426 lung cancer patients was used as a study material to analyze methylation level differences in ARHGAP35 gene in blood from breast cancer patients and lung cancer patients (Table 14). The results show that the methylation level of all target CpG sites in breast cancer patients is median 0.77 (iqr=0.58-0.92), the methylation level of lung cancer patients is median 0.73 (iqr=0.54-0.86), and the methylation level of all CpG sites in breast cancer patients is significantly higher than that in lung cancer patients (p < 0.05). Thus, the methylation level of the ARHGAP35 gene can be used to distinguish breast cancer from 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 1-4) 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, i.e., training sets (e.g., 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 patients 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 stage I and lung cancer stage II, lung cancer stage I and lung cancer stage III, lung cancer stage II and lung cancer stage III, breast catheter carcinoma in situ and breast invasive catheter cancer patients, breast catheter carcinoma in situ and breast invasive lobular carcinoma, breast invasive catheter carcinoma and breast invasive lobular carcinoma patients, breast cancer stage I and breast cancer stage II, breast cancer stage I and breast cancer stage III, breast cancer stage II and breast cancer stage III) are selected as needed as data for establishing a model, and statistical methods using statistical software such as SAS, R, SPSS and SPSS establish a mathematical model using a statistical method of two-classification logistic regression by using statistical methods of the equation. 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 ARHGAP35 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 blood 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 methylation level of a single CpG site or the methylation level of a combination of multiple CpG sites in the training set of the ARHGAP35 gene is used to establish a mathematical model for distinguishing between class a and class B by using a formula of two classification logistic regression through statistical software such as SAS, R, SPSS. 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, no convention is made 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 and breast cancer patients, lung adenocarcinoma and lung squamous cell lung cancer patients, lung squamous cell 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 in stage III, lung cancer in stage II, lung cancer in stage III, duct in situ and duct in breast invasive lobular cancer patients, duct in breast invasive duct cancer and duct in breast invasive lobular cancer patients, stage I and stage II breast cancer patients, stage I and III breast cancer patients, stage II and stage III breast cancer patients. 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 ARHGAP35 genes 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 ARHGAP35 gene of the subject is substituted into the mathematical model and then the calculated detection index is larger than the 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 ARHGAP35 gene of the subject are 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 ARHGAP35 gene of the subject is substituted into the above mathematical model, and the calculated value, i.e., the detection index, is equal to the threshold value, it is impossible to determine whether the subject is class A or class B.

Examples: the schematic diagram of fig. 2 illustrates the methylation level of the preferred CpG sites of ARHGAP35_d (ARHGAP 35_d_1, ARHGAP35_d_2, ARHGAP35_d_3, ARHGAP35_d_4, ARHGAP35_d_5, ARHGAP35_d_6, ARHGAP35_d_7, ARHGAP35_d_8, and ARHGAP 35_d_9) and the application of mathematical modeling for pulmonary benign and malignant nodule discrimination: the methylation level data of the 9 distinguishable preferred CpG site combinations that have been detected in the lung cancer patient and lung benign nodule patient training set (here: 426 lung cancer patients and 286 lung benign nodule patients) are used to build a mathematical model for distinguishing lung cancer patients from lung benign nodule patients by R software using a formula of a two-class logistic regression with age, sex (male assigned 1, female assigned 0) and white blood cell count of the patients. 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))=0.167-2.229×arhgap35_d_1+2.621×arhgap35_d_2-0.049×arhgap35_d_3-0.159×arhgap35_d_4-0.684×arhgap35_d_5+0.715×arhgap35_d_6+1.775×arhgap35_d_7-1.008×arhgap35_d_8-0.708*ARHGAP 35_D_9+0.001 ×age (integer) +0.146×gender (male 1×female 0) +0.011×white blood cell count (unit 10++9/L). Where y is the detection index obtained by substituting the methylation values of 9 distinguishable methylation sites of the sample to be tested and 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 levels of the 9 distinguishable CpG sites of the ARHGAP35_D_1, the ARHGAP35_D_2, the ARHGAP35_D_3, the ARHGAP35_D_4, the ARHGAP35_D_5, the ARHGAP35_D_6, the ARHGAP35_D_7, the ARHGAP35_D_8 and the ARHGAP35_D_9 of the samples to be tested are tested and then are calculated together with the information substitution model of age, sex and white cell count of the samples to be tested, the y value which is the y value is larger than 0.5 is classified as lung cancer patients, the y value which is smaller than 0.5 is classified as lung benign nodule patients, and the lung benign nodule patients are not determined as lung cancer patients or lung benign nodule patients if the y value is equal to 0.5. The area under the curve (AUC) calculation for this model was 0.82 (table 18). Specific subject judgment method for example, as shown in FIG. 2, blood was collected from two subjects (A, B), DNA was extracted from the blood, the extracted DNA was converted by bisulfite, and the methylation level of 9 distinguishable CpG sites of the subjects, ARHGAP35_D_1, ARHGAP35_D_2, ARHGAP35_D_3, ARHGAP35_D_4, ARHGAP35_D_5, ARHGAP35_D_6, ARHGAP35_D_7, ARHGAP35_D_8 and ARHGAP35_D_9, was detected by a DNA methylation assay 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 0.81 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 the ARHGAP35 gene of the subject B into the mathematical model to calculate a value of 0.23 to be less than 0.5, and judging the benign nodule patient of the lung (consistent with clinical judgment result) by the subject B.

(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 patient and a breast cancer patient, a lung cancer patient and a breast cancer patient, a lung adenocarcinoma and a lung squamous carcinoma patient, a lung adenocarcinoma and a small cell lung cancer patient, a lung cancer patient of stage I and a lung cancer patient of stage II, a lung cancer patient of stage I and a lung cancer patient of stage III, a lung cancer patient of stage II and a lung cancer patient of stage III, a ductal carcinoma in situ and a ductal invasive, ductal carcinoma in situ and lobular invasive, ductal invasive carcinoma in situ and lobular invasive, ductal carcinoma in stage I and II, ductal carcinoma in stage I and III, and ductal carcinoma in stage II, and ductal carcinoma in 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 15, 16 and 17. In tables 15, 16 and 17, 1 CpG site represents the site of any one CpG site in the ARHGAP35_D amplified fragment, 2 CpG sites represent the combination of any 2 CpG sites in ARHGAP35_D, 3 CpG sites represent the combination … … of any 3 CpG sites in ARHGAP35_D, and so on. 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 the ARHGAP35 gene for each group (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 and breast cancer patients, lung adenocarcinoma and lung squamous carcinoma patients, lung adenocarcinoma and small cell lung carcinoma patients, lung squamous carcinoma and small cell lung carcinoma patients, lung cancer and lung cancer patients in stage I and stage II, lung cancer and lung cancer patients in stage III, lung cancer and lung cancer patients in stage II, ductal carcinoma in situ and ductal invasive, ductal carcinoma in situ and invasive lobular carcinoma of the breast, ductal invasive duct carcinoma in invasive lobular carcinoma of the breast, ductal breast cancer in stage I and II, breast cancer in stage I and III, breast cancer in stage II and breast cancer in stage III) increases with increasing numbers of spots.

In addition, among the CpG sites shown in tables 1 to 4, there are cases where combinations of a few preferred sites are better in discrimination than combinations of a plurality of non-preferred sites. The combination of 9 distinguishable optimal sites, e.g., ARHGAP35_d_1, ARHGAP35_d_2, ARHGAP35_d_3, ARHGAP35_d_4, ARHGAP35_d_5, ARHGAP35_d_6, ARHGAP35_d_7, ARHGAP35_d_8, and ARHGAP35_d_9 shown in tables 18, 19, and 20 is the preferred site combination of any 9 distinguishable sites in ARHGAP 35_d.

In summary, cpG sites on the ARHGAP35 gene and combinations thereof, cpG sites on the ARHGAP 35A fragment and combinations thereof, cpG sites on the ARHGAP 35B fragment and combinations thereof, cpG sites on the ARHGAP 35C fragment and combinations thereof, cpG sites on the ARHGAP 35D fragment and combinations thereof, ARHGAP35 D_1, ARHGAP35 D_2, ARHGAP35 D_3, ARHGAP35 D_4, ARHGAP35 D_5, ARHGAP35 D_6, ARHGAP35 D_7, HGAP35 D_8 and ARHGAP35 D_9 sites on the ARHGAP 35D fragment and combinations thereof, methylation levels of CpG sites and various combinations thereof on ARHGAP35_ A, ARHGAP _ B, ARHGAP _c and ARHGAP35_d are all 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 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 III, lung cancer and lung cancer patients in stage II, breast ductal carcinoma in situ and breast invasive ductal carcinoma patients, ductal carcinoma in situ and breast invasive lobular carcinoma patients, ductal carcinoma in stage I and II, ductal carcinoma in stage I and III, ductal carcinoma in stage II and breast cancer in stage III.

TABLE 6 methylation levels comparing benign nodules in lung and lung cancer for cancer-free controls

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TABLE 7 methylation level differences comparing cancer-free control and lung cancer

Table 8, comparison of methylation level differences between benign nodules in the lung and lung cancer

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TABLE 9 comparison of methylation level differences for different subtypes of lung cancer or different stages of lung cancer

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Table 10, comparison of methylation levels in non-cancerous female controls, benign nodules of breast and breast cancer

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Table 11, methylation level differences for comparison of cancer-free female controls and breast cancer

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Table 12, comparison of methylation level differences between benign nodules of breast and breast cancer

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TABLE 13 comparison of methylation level differences for different subtypes of breast cancer or different stages of breast cancer

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TABLE 14 comparison of methylation level differences for lung and breast cancers

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Table 15, cpG sites of ARHGAP35_D and combinations thereof for distinguishing lung cancer and non-cancerous controls, lung cancer and benign nodules of lung, breast cancer and non-cancerous female controls, breast cancer and benign nodules of breast, lung cancer and breast cancer

Table 16, cpG sites of ARHGAP35_D and free combinations thereof for distinguishing between different subtypes and different stages of a lung cancer patient

Table 17, cpG sites of ARHGAP35_D and free combinations thereof for distinguishing between different subtypes and different stages of breast cancer patients

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Table 18, ARHGAP35_D optimal CpG sites and combinations thereof for distinguishing lung cancer and non-cancerous controls, lung cancer and benign nodules of lung, breast cancer and non-cancerous female controls, breast cancer and benign nodules of breast, lung cancer and breast cancer

Table 19, ARHGAP35_D optimal CpG sites and combinations thereof for discriminating between different subtypes and different stages of lung cancer patients

Table 20, optimal CpG sites of ARHGAP35_D and combinations thereof for distinguishing between different subtypes and different stages of breast cancer patients

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> a molecular marker and kit for aiding diagnosis of cancer

<130> GNCLN220986

<160> 12

<170> PatentIn version 3.5

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Claims

1. Application of methylation ARHGAP35 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;

2. Use of a substance for detecting the methylation level of the ARHGAP35 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;

3. Use of a substance for detecting the methylation level of the ARHGAP35 gene and 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 the methylation level of the ARHGAP35 gene of n1 type A samples and n2 type B samples respectively;

(A2) Taking ARHGAP35 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 threshold value of classification judgment;

the using method of the mathematical model comprises the following steps:

(B1) Detecting the ARHGAP35 gene methylation level of a sample to be detected;

(B2) Substituting the ARHGAP35 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;

the using method of the mathematical model comprises the following steps:

(B1) Detecting the ARHGAP35 gene methylation level of a sample to be detected;

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 the ARHGAP35 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;

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:

(D2) A device comprising a unit X and a unit Y;

the data analysis processing module is configured to receive ARHGAP35 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 data operation module is configured to receive the ARHGAP35 gene methylation level data of the person to be detected from the data input module, and substitute the ARHGAP35 gene methylation level data of the person to be detected into the mathematical model to calculate a detection index;

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 ARHGAP35 gene is the methylation level of all or part of CpG sites in fragments shown in the following (e 1) - (e 4) in the ARHGAP35 gene;

the methylation ARHGAP35 gene is the methylation of all or part of CpG sites in fragments shown in the following (e 1) - (e 4) in the ARHGAP35 gene;

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 4 DNA fragments shown in SEQ ID No.1 to SEQ ID No.4 in ARHGAP35 gene;

Or (b)

The 'all or part of CpG sites' are all CpG sites in a DNA fragment shown in SEQ ID No.4 and all CpG sites in a DNA fragment shown in SEQ ID No.1 in the ARHGAP35 gene;

or (b)

The 'all or part of CpG sites' are all CpG sites in a DNA fragment shown in SEQ ID No.4 and all CpG sites in a DNA fragment shown in SEQ ID No.3 in the ARHGAP35 gene;

or (b)

The 'all or part of CpG sites' are all CpG sites in the DNA fragment shown in SEQ ID No.1 and all CpG sites in the DNA fragment shown in SEQ ID No.3 in the ARHGAP35 gene;

or (b)

The 'all or part of CpG sites' are all CpG sites in a DNA fragment shown as SEQ ID No.4, all CpG sites in a DNA fragment shown as SEQ ID No.1 and all CpG sites in a DNA fragment shown as SEQ ID No.3 in the ARHGAP35 gene;

or (b)

The 'all or part of CpG sites' are all or any 11 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 all CpG sites in the DNA fragment shown in SEQ ID No.4 in the ARHGAP35 gene;

or (b)

The whole or part of CpG sites are all 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 shown in the following 9 items in the DNA fragment shown in SEQ ID No.4 in the ARHGAP35 gene:

(f1) The DNA fragment shown in SEQ ID No.4 shows CpG sites from 26 th to 27 th positions of the 5' end;

(f2) The DNA fragment shown in SEQ ID No.4 shows CpG sites from 135 th to 136 th positions of the 5' end;

(f3) The DNA fragment shown in SEQ ID No.4 shows CpG sites from 154 th to 155 th positions of the 5' end;

(f4) The DNA fragment shown in SEQ ID No.4 shows CpG sites from 172 to 173 of the 5' end;

(f5) The DNA fragment shown in SEQ ID No.4 shows CpG sites from 258 to 259 positions of the 5' end;

(f6) The DNA fragment shown in SEQ ID No.4 shows CpG sites from 327 th to 328 th positions of the 5' end;

(f7) The DNA fragment shown in SEQ ID No.4 shows CpG sites from 357 to 358 positions of the 5' end;

(f8) The DNA fragment shown in SEQ ID No.4 shows CpG sites from 449 to 450 of the 5' end;

(f9) The DNA fragment shown in SEQ ID No.4 is CpG sites shown at 537-538 from 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 ARHGAP35 gene comprises a primer combination for amplifying a full or partial fragment of the ARHGAP35 gene;

the reagent for detecting the methylation level of the ARHGAP35 gene comprises a primer combination for amplifying a full or partial fragment of the ARHGAP35 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;

(g8) 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;

still further, the primer combination is primer pair a and/or primer pair B and/or primer pair C or primer pair D;

the primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.5 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 5; the primer A2 is SEQ ID No.6 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 6;

the primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is SEQ ID No.7 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 7; the primer B2 is single-stranded DNA shown in SEQ ID No.8 or 32-56 nucleotides of SEQ ID No. 8;

The primer pair C is a primer pair consisting of a primer C1 and a primer C2; the primer C1 is SEQ ID No.9 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 9; the primer C2 is SEQ ID No.10 or single-stranded DNA shown in 32-56 th nucleotide of SEQ ID No. 10.

The primer pair D is a primer pair consisting of a primer D1 and a primer D2; the primer D1 is SEQ ID No.11 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 11; the primer D2 is SEQ ID No.12 or single-stranded DNA shown in 32-56 nucleotides of SEQ ID No. 12.