A potential molecular marker to aid in the diagnosis of cancer

Abstract

The application discloses a potential molecular marker for assisting in diagnosing cancer. The application provides application of a methylation CD44 gene as a marker in preparation of a product; the product is used for at least one of the following: assisting in diagnosing cancer or predicting a cancer morbidity risk; assisting in distinguishing a benign nodule and cancer; assisting in distinguishing different subtypes of cancer; assisting in distinguishing different stages of cancer; assisting in distinguishing different cancers; determining whether a to-be-tested substance hinders or promotes occurrence of cancer; and the cancer can be lung cancer or breast cancer. The application finds a low methylation phenomenon of the CD44 gene in blood of lung cancer and breast cancer patients, and has important scientific significance and clinical application value for improving early diagnosis and treatment effect of lung cancer and breast cancer and reducing mortality.

Description

Technical Field

[0001] This invention relates to the medical field, and in particular to a potential molecular marker for aiding in the diagnosis of cancer. Background Technology

[0002] 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 in women worldwide, ranking first in incidence among female malignant tumors. On the other hand, the survival rate of breast cancer is related to the type and stage of the tumor. The 5-year survival prognosis for early-stage breast cancer is usually higher than 60%, but for advanced breast cancer, this figure drops to 40-60%. For metastatic breast cancer, the 5-year survival prognosis is usually about 15%. Therefore, improving the early detection rate of breast cancer is essential for effective diagnosis and treatment in later stages. At present, clinical medicine mainly uses two methods for early screening and diagnosis of breast cancer: imaging and pathology. Among imaging diagnoses, B-mode ultrasound imaging is radiation-free, but due to the limitations of the ultrasound imaging mechanism, this method has poor resolution for small lesions with inconspicuous echo changes, and is prone to missed diagnoses. Mammography is a low-dose mammogram technique that clearly displays the structure of different layers of breast tissue. However, mammography has a high false-positive rate, requiring a breast biopsy for more accurate diagnosis. Furthermore, mammography poses risks such as ionizing radiation. Magnetic resonance imaging (MRI) of the breast uses magnetic energy and radio waves to examine breast tissue and generate internal images; it is primarily used for screening high-risk groups for breast cancer. Pathological diagnosis mainly involves breast biopsy, which involves taking tissue samples for pathological diagnosis. However, biopsy surgery is invasive and often resisted by patients. In addition, some commonly used tumor markers, such as tumor antigen 15-3, tumor antigen 27.29, carcinoembryonic antigen, tumor antigen 125, and circulating tumor cells, are used for breast cancer diagnosis, but their specificity and sensitivity need improvement and they are generally used in conjunction with imaging studies. Therefore, the discovery of more sensitive and specific molecular markers for early breast cancer is urgently needed.

[0003] Lung cancer is a malignant tumor that occurs in the bronchial mucosal epithelium. In recent decades, its incidence and mortality rates have been on the rise, making it the leading cause of cancer-related morbidity and mortality worldwide. Despite recent advancements in diagnostic methods, surgical techniques, and chemotherapy drugs, the overall 5-year survival rate for lung cancer patients is only 16%, primarily because most patients have already experienced metastasis at the time of diagnosis, thus losing the opportunity for radical surgical treatment. Studies show that lung cancer prognosis is directly related to stage: the 5-year survival rate is 83% for stage I lung cancer, 53% for stage II, 26% for stage III, and 6% for stage IV. Therefore, early diagnosis and early treatment are crucial to reducing the mortality rate of lung cancer patients.

[0004] Currently, the main methods for diagnosing lung cancer include: 1. Imaging methods: such as chest X-ray and low-dose spiral CT. However, chest X-ray is difficult to detect early-stage lung cancer. Although low-dose spiral CT can detect small nodules in the lungs, the false positive rate is as high as 96.4%, causing unnecessary psychological burden on the examinee. Furthermore, chest X-ray and low-dose spiral CT should not be used frequently due to radiation exposure. In addition, imaging methods are often affected by equipment, the doctor's experience in interpreting images, and the effective reading time. 2. Cytological methods: such as sputum cytology, bronchoscopic brushing or biopsy, and bronchoalveolar lavage fluid cytology. Sputum cytology and bronchoscopic brushing or biopsy have low sensitivity for peripheral lung cancer. Additionally, bronchoscopic brushing or biopsy and bronchoalveolar lavage fluid cytology are relatively cumbersome and uncomfortable for the examinee. 3. Commonly used serum tumor markers include carcinoembryonic antigen (CEA), carbohydrate antigens (CA125 / 153 / 199), cytokeratin 19 fragment antigen (CYFRA21-1), and neuron-specific enolase (NSE). These serum tumor markers have limited sensitivity for lung cancer, generally 30%-40%, and even lower for stage I tumors. Furthermore, their tumor specificity is also relatively limited, influenced by many benign lesions such as benign tumors, inflammation, and degenerative diseases. Currently, tumor markers are mainly used for malignant tumor screening and monitoring the effectiveness of tumor treatment. Therefore, further development of highly efficient and specific early diagnostic technologies for lung cancer is needed.

[0005] Currently, the most effective internationally recognized method for diagnosing pulmonary nodules is low-dose spiral CT screening of the chest. However, while low-dose spiral CT is highly sensitive and can detect a large number of small nodules, it is difficult to differentiate between benign and malignant nodules. Of the small nodules detected, the proportion of malignant nodules is less than 4%. Currently, clinical identification of benign or malignant pulmonary nodules requires long-term follow-up, repeated CT scans, or invasive methods such as pulmonary nodule biopsy (including fine-needle aspiration biopsy, bronchoscopic biopsy, thoracoscopic or open-chest lung biopsy). CT-guided or ultrasound-guided transthoracic puncture biopsy has high sensitivity, but its diagnostic rate for nodules <2cm is low, with a 30-70% missed diagnosis rate, and a high incidence of pneumothorax and bleeding. Bronchoscopic needle aspiration biopsy has a relatively low complication rate, but its diagnostic rate for peripheral nodules is limited; the diagnostic rate for nodules ≤2cm is only 34%, and for nodules larger than 2cm, it is 63%. Surgical resection has a high diagnostic rate and can directly treat nodules, but it can cause a temporary decline in lung function. If the nodule is benign, the patient undergoes unnecessary surgery, leading to overtreatment. Therefore, there is an urgent need for new in vitro diagnostic molecular markers to assist in the differentiation of lung nodules, reducing the rate of missed diagnoses and minimizing unnecessary punctures or surgeries.

[0006] DNA methylation is an important chemical modification of genes, affecting the regulation of gene transcription and the structure of the cell nucleus. Alterations in DNA methylation are early and associated events in cancer development, primarily manifested as hypermethylation of tumor suppressor genes and hypomethylation of proto-oncogenes in tumor tissue. However, the correlation between blood DNA methylation and tumor development has been less reported. Furthermore, blood is easy to collect, and DNA methylation is relatively stable; therefore, the discovery of tumor-specific blood DNA methylation molecular markers would have significant clinical application value. Thus, exploring and developing blood DNA methylation diagnostic technologies suitable for clinical testing needs has important clinical application value and social significance for improving the early diagnosis and treatment of lung cancer and breast cancer and reducing mortality rates. Summary of the Invention

[0007] The purpose of this invention is to provide a CD44 gene (CD44 molecule, CD44) methylation marker and kit for the auxiliary diagnosis of cancer.

[0008] Firstly, this invention claims the use of the methylated CD44 gene as a marker in the preparation of a product. The product is used in at least one of the following:

[0009] (1) Assist in the diagnosis of cancer or predict the risk of developing cancer;

[0010] (2) To help differentiate between benign nodules and cancer;

[0011] (3) To help differentiate between different subtypes of cancer;

[0012] (4) To help differentiate between different stages of cancer;

[0013] (5) Assist in the diagnosis of lung cancer or predict the risk of developing lung cancer;

[0014] (6) It helps to differentiate between benign lung nodules and lung cancer;

[0015] (7) To help differentiate between different subtypes of lung cancer;

[0016] (8) Assists in differentiating different stages of lung cancer;

[0017] (9) Assist in the diagnosis of breast cancer or predict the risk of developing breast cancer;

[0018] (10) Assists in differentiating different stages of breast cancer;

[0019] (11) To help differentiate between lung cancer and breast cancer;

[0020] (12) Determine whether the test substance has an inhibitory or promoting effect on the occurrence of cancer.

[0021] Furthermore, the auxiliary diagnosis of cancer described in (1) can specifically be manifested in at least one of the following: assisting in the differentiation between cancer patients and cancer-free controls (which can be understood as having neither cancer now nor in the past and having not reported benign lung or breast nodules and whose blood routine indicators are within the reference range); assisting in the differentiation of different cancers.

[0022] Furthermore, the benign nodules mentioned in (2) are the benign nodules corresponding to the cancers mentioned in (2), such as benign lung nodules and lung cancer.

[0023] Furthermore, the different subtypes of cancer mentioned in (3) can be pathological classifications, such as histological classifications.

[0024] Furthermore, the different stages of cancer mentioned in (4) can be clinical staging or TNM staging.

[0025] In a specific embodiment of the present invention, the auxiliary diagnosis of lung cancer mentioned in (5) specifically manifests as at least one of the following: able to help distinguish between lung cancer patients and cancer-free controls; able to help distinguish between lung adenocarcinoma patients and cancer-free controls; able to help distinguish between lung squamous cell carcinoma patients and cancer-free controls; able to help distinguish between small cell lung cancer patients and cancer-free controls; able to help distinguish between stage I lung cancer patients and cancer-free controls; able to help distinguish between stage II-III lung cancer patients and cancer-free controls; able to help distinguish between lung cancer patients without lymph node infiltration and cancer-free controls; able to help distinguish between lung cancer patients with lymph node infiltration and cancer-free controls. Wherein, the cancer-free controls can be understood as those who have neither currently nor previously had cancer and have not reported any benign nodules in the lungs or breast, and whose routine blood tests are within the reference range.

[0026] In a specific embodiment of the present invention, the auxiliary method for distinguishing between benign lung nodules and lung cancer as described in (6) is specifically manifested in at least one of the following: it can assist in distinguishing between lung cancer and benign lung nodules, it can assist in distinguishing between lung adenocarcinoma and benign lung nodules, it can assist in distinguishing between squamous cell carcinoma and benign lung nodules, it can assist in distinguishing between small cell lung cancer and benign lung nodules, it can assist in distinguishing between stage I lung cancer and benign lung nodules, it can assist in distinguishing between stage II-III lung cancer and benign lung nodules, it can assist in distinguishing between lung cancer without lymph node infiltration and benign lung nodules, and it can assist in distinguishing between lung cancer with lymph node infiltration and benign lung nodules.

[0027] In a specific embodiment of the present invention, the auxiliary differentiation of different subtypes of lung cancer described in (7) is specifically manifested as: it can help differentiate any two of lung adenocarcinoma, lung squamous cell carcinoma and small cell lung cancer.

[0028] In a specific embodiment of the present invention, the auxiliary differentiation of different stages of lung cancer described in (8) is specifically manifested in at least one of the following: it can help differentiate any two of T1 stage lung cancer, T2 stage lung cancer and T3 stage lung cancer; it can help differentiate lung cancer without lymph node infiltration and lung cancer with lymph node infiltration; it can help differentiate any two of clinical stage I lung cancer, clinical stage II lung cancer and clinical stage III lung cancer.

[0029] In a specific embodiment of the present invention, the auxiliary diagnosis of breast cancer mentioned in (9) specifically manifests as at least one of the following: it can help distinguish between breast cancer patients and cancer-free female controls. The cancer-free female controls can be understood as those who have never had cancer and have not reported any benign lung or breast nodules, and whose routine blood tests are within the reference range.

[0030] In a specific embodiment of the present invention, the auxiliary differentiation of different stages of breast cancer described in (10) is specifically manifested in at least one of the following: it can help differentiate any two of T1 stage breast cancer, T2 stage breast cancer and T3 stage breast cancer; it can help differentiate breast cancer without lymph node invasion and breast cancer with lymph node invasion; it can help differentiate any two of clinical stage I breast cancer, clinical stage II breast cancer and clinical stage III breast cancer.

[0031] In (1)-(12) above, the cancer may be a cancer that can cause a decrease in the methylation level of the CD44 gene in the body, such as lung cancer, breast cancer, etc.

[0032] Secondly, the present invention claims the use of a substance for detecting the methylation level of the CD44 gene in the preparation of a product; said product being used in at least one of the preceding (1)-(12).

[0033] Thirdly, the present invention claims the use of substances for detecting CD44 gene methylation levels and media containing methods for establishing mathematical models and / or methods of use in the preparation of products; said products are used in at least one of the preceding (1)-(12).

[0034] The mathematical model can be obtained by a method including the following steps:

[0035] (A1) Detect the CD44 gene methylation level in n1 type A samples and n2 type B samples (training set).

[0036] (A2) Take the CD44 gene methylation level data of all samples obtained in step (A1), and establish a mathematical model according to the classification method of type A and type B, and determine the classification threshold by binary logistic regression.

[0037] The method of using the mathematical model may include the following steps:

[0038] (B1) Detect the methylation level of the CD44 gene in the sample to be tested;

[0039] (B2) Substitute the CD44 gene methylation level data of the sample to be tested obtained in step (B1) into the mathematical model to obtain the detection index; then compare the detection index with the threshold, and determine whether the sample to be tested is type A or type B based on the comparison result.

[0040] In a specific embodiment of the present invention, the threshold is set to 0.5. Values ​​greater than 0.5 are classified into one category, values ​​less than 0.5 are classified into another category, and values ​​equal to 0.5 are considered an uncertain gray area. Type A and Type B are corresponding binary classifications. Which group belongs to type A and which to group in a binary classification is determined based on a specific mathematical model and does not require prior agreement.

[0041] In practical applications, the threshold can also be determined based on the maximum Yoden index (specifically, the value corresponding to the maximum Yoden index). Values ​​greater than the threshold are grouped into one category, values ​​less than the threshold are grouped into another, and values ​​equal to the threshold are treated as an uncertain gray area. Type A and Type B are corresponding binary classifications. Which group is type A and which is type B depends on the specific mathematical model and does not require prior agreement.

[0042] The type A sample and the type B sample can be any of the following:

[0043] (C1) Lung cancer samples and cancer-free controls;

[0044] (C2) Lung cancer samples and benign lung nodule samples;

[0045] (C3) Samples of different subtypes of lung cancer;

[0046] (C4) Samples of lung cancer at different stages;

[0047] (C5) Lung cancer and breast cancer samples;

[0048] (C6) Breast cancer samples and cancer-free female controls;

[0049] (C7) Samples of breast cancer at different stages.

[0050] Fourthly, the present invention claims the use of the “medium containing a method for establishing a mathematical model and / or a method for using it” described in the third aspect above in the preparation of a product; the use of the product is at least one of (1)-(12) above.

[0051] Fifthly, the present invention claims protection for a reagent kit.

[0052] The kit claimed in this invention includes a substance for detecting the methylation level of the CD44 gene; the use of the kit is at least one of the preceding (1)-(12).

[0053] Furthermore, the kit also contains the "medium containing the mathematical model establishment method and / or usage method" described in the third or fourth aspect above.

[0054] Sixthly, the present invention claims protection for a system.

[0055] The system claimed in this invention includes:

[0056] (D1) Reagents and / or instruments used to detect the methylation level of the CD44 gene;

[0057] (D2) Device, the device comprising unit X and unit Y.

[0058] The unit X is used to establish a mathematical model, including a data acquisition module, a data analysis and processing module, and a model output module.

[0059] The data acquisition module is configured to acquire CD44 gene methylation level data from n1 type A samples and n2 type B samples obtained by (D1) detection.

[0060] The data analysis and processing module is configured to receive CD44 gene methylation level data of the n1 type A samples and n2 type B samples sent by the data acquisition module, establish a mathematical model according to the classification method of type A and type B, and determine the classification threshold by means of binary logistic regression.

[0061] The model output module is configured to receive the mathematical model established by the data analysis and processing module and output it.

[0062] The unit Y is used to determine the type of the sample to be tested, and includes a data input module, a data processing module, a data comparison module, and a conclusion output module.

[0063] The data input module is configured to input the CD44 gene methylation level data of the subject obtained by (D1) detection.

[0064] The data processing module is configured to receive the CD44 gene methylation level data of the subject sent from the data input module, and substitute the CD44 gene methylation level data of the subject into the mathematical model established by the data analysis and processing module in unit X to calculate the detection index.

[0065] The data comparison module is configured to receive the detection index sent from the data processing module and compare the detection index with the threshold determined in the data analysis and processing module in unit X.

[0066] The conclusion output module is configured to receive the comparison result sent from the data comparison module, and output a conclusion as to whether the type of the sample to be tested is type A or type B based on the comparison result.

[0067] The type A sample and the type B sample are either of the following:

[0068] (C1) Lung cancer samples and cancer-free controls;

[0069] (C2) Lung cancer samples and benign lung nodule samples;

[0070] (C3) Samples of different subtypes of lung cancer;

[0071] (C4) Samples of lung cancer at different stages;

[0072] (C5) Lung cancer and breast cancer samples;

[0073] (C6) Breast cancer samples and cancer-free female controls;

[0074] (C7) Samples of breast cancer at different stages.

[0075] Where n1 and n2 can both be positive integers greater than 50.

[0076] In a specific embodiment of the present invention, the threshold is set to 0.5. Values ​​greater than 0.5 are classified into one category, values ​​less than 0.5 are classified into another category, and values ​​equal to 0.5 are considered an uncertain gray area. Type A and Type B are corresponding binary classifications. Which group belongs to type A and which to group in a binary classification is determined based on a specific mathematical model and does not require prior agreement.

[0077] In practical applications, the threshold can also be determined based on the maximum Yoden index (specifically, the value corresponding to the maximum Yoden index). Values ​​greater than the threshold are grouped into one category, values ​​less than the threshold into another, and values ​​equal to the threshold are treated as an uncertain gray area. Type A and Type B are corresponding binary classifications. Which group belongs to type A and which to group in a binary classification depends on the specific mathematical model and does not require prior agreement.

[0078] In the above aspects, the CD44 gene methylation level refers to the methylation level of all or part of the CpG sites in the segments shown in (e1)-(e3) below in the CD44 gene. The methylated CD44 gene refers to the methylation of all or part of the CpG sites in the segments shown in (e1)-(e3) below in the CD44 gene.

[0079] (e1) The DNA fragment shown in SEQ ID No. 1 or a DNA fragment having more than 80% identity with it;

[0080] (e2) The DNA fragment shown in SEQ ID No. 2 or a DNA fragment having more than 80% identity with it;

[0081] (e3) The DNA fragment shown in SEQ ID No. 3 or a DNA fragment that is more than 80% identical to it.

[0082] Further, the "all or part of the CpG sites" refers to any one or more CpG sites in the three DNA fragments shown in SEQ ID No. 1 to SEQ ID No. 3 of the CD44 gene. The upper limit of "multiple CpG sites" here is all CpG sites in the three DNA fragments shown in SEQ ID No. 1 to SEQ ID No. 3 of the CD44 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, and all CpG sites in the DNA fragment shown in SEQ ID No. 3 are shown in Table 3.

[0083] Alternatively, the “all or part of the CpG sites” refers to 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. 2 (see Table 2);

[0084] Alternatively, the “all or part of the CpG sites” refers to 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);

[0085] Alternatively, the “all or part of the CpG sites” refers to all CpG sites in the DNA fragment shown in SEQ ID No. 2 (see Table 2) and all CpG sites in the DNA fragment shown in SEQ ID No. 3 (see Table 3);

[0086] Alternatively, the “all or part of the CpG sites” refers to all CpG sites in the DNA fragment shown in SEQ ID No. 1 (see Table 1), all CpG sites in the DNA fragment shown in SEQ ID No. 2 (see Table 2), and all CpG sites in the DNA fragment shown in SEQ ID No. 3 (see Table 3).

[0087] Alternatively, the “all or part of the CpG sites” refers to all, any 6, any 5, any 4, any 3, any 2, or any 1 of the DNA fragments shown in SEQ ID No. 3 in the CD44 gene;

[0088] Alternatively, the "all or part of the CpG sites" refers to all, any four, any three, any two, or any one of the following five CpG sites in the DNA fragment shown in SEQ ID No. 3:

[0089] (f1) The CpG site (CD44_C_2) at positions 130-131 from the 5' end of the DNA fragment shown in SEQ ID No. 3;

[0090] (f2) The CpG site (CD44_C_3) at positions 158-159 from the 5' end of the DNA fragment shown in SEQ ID No. 3;

[0091] (f3) The CpG site (CD44_C_4) at positions 198-199 from the 5' end of the DNA fragment shown in SEQ ID No. 3;

[0092] (f4) The CpG site (CD44_C_5) at positions 316-317 from the 5' end of the DNA fragment shown in SEQ ID No. 3;

[0093] (f5) The DNA fragment shown in SEQ ID No. 3 has the CpG site (CD44_C_6) at positions 346-347 from the 5' end.

[0094] In the above aspects, the substance used to detect the methylation level of the CD44 gene may contain (or be) a primer combination for amplifying the full-length or partial fragment of the CD44 gene. The reagent used to detect the methylation level of the CD44 gene may contain (or be) a primer combination for amplifying the full-length or partial fragment of the CD44 gene. The instrument used to detect the methylation level of the CD44 gene may be a time-of-flight mass spectrometer. Of course, the reagent used to detect the methylation level of the CD44 gene may also contain other conventional reagents used in time-of-flight mass spectrometry.

[0095] Furthermore, the partial segment may be at least one of the following segments:

[0096] (g1) The DNA fragment shown in SEQ ID No. 1 or the DNA fragment contained therein;

[0097] (g2) The DNA fragment shown in SEQ ID No. 2 or the DNA fragment contained therein;

[0098] (g3) The DNA fragment shown in SEQ ID No. 3 or the DNA fragment contained therein;

[0099] (g4) A DNA fragment that has more than 80% identity with the DNA fragment shown in SEQ ID No. 1 or the DNA fragment contained therein;

[0100] (g5) A DNA fragment that has more than 80% identity with the DNA fragment shown in SEQ ID No. 2 or the DNA fragment contained therein;

[0101] (g6) A DNA fragment that has more than 80% identity with the DNA fragment shown in SEQ ID No. 3 or the DNA fragment contained therein;

[0102] Furthermore, the primer combination may be primer pair A and / or primer pair B and / or primer pair C.

[0103] The primer pair A is a primer pair consisting of primer A1 and primer A2; primer A1 is the single-stranded DNA represented by nucleotides 11-35 of SEQ ID No. 4 or SEQ ID No. 4; primer A2 is the single-stranded DNA represented by nucleotides 32-56 of SEQ ID No. 5 or SEQ ID No. 5.

[0104] The primer pair B is a primer pair consisting of primer B1 and primer B2; primer B1 is a single-stranded DNA represented by nucleotides 11-35 of SEQ ID No. 6 or SEQ ID No. 6; primer B2 is a single-stranded DNA represented by nucleotides 32-56 of SEQ ID No. 7 or SEQ ID No. 7.

[0105] The primer pair C is a primer pair consisting of primer C1 and primer C2; primer C1 is a single-stranded DNA represented by nucleotides 11-35 of SEQ ID No. 8 or SEQ ID No. 8; primer C2 is a single-stranded DNA represented by nucleotides 32-56 of SEQ ID No. 9 or SEQ ID No. 9.

[0106] Furthermore, this invention also claims a method for distinguishing whether a test sample is a type A sample or a type B sample. This method may include the following steps:

[0107] (A) A mathematical model can be established using the following steps:

[0108] (A1) Detect the CD44 gene methylation level in n1 type A samples and n2 type B samples respectively (training set);

[0109] (A2) Take the CD44 gene methylation level data of all samples obtained in step (A1), and establish a mathematical model according to the classification method of type A and type B, and determine the classification threshold by binary logistic regression.

[0110] In (A1), n1 and n2 are both positive integers greater than 50.

[0111] (B) The test sample may be determined to be either a type A sample or a type B sample by means of the following steps:

[0112] (B1) Detect the CD44 gene methylation level of the test sample;

[0113] (B2) Substitute the CD44 gene methylation level data of the sample to be tested obtained in step (B1) into the mathematical model to obtain the detection index; then compare the detection index with the threshold, and determine whether the sample to be tested is type A or type B based on the comparison result.

[0114] In a specific embodiment of the present invention, the threshold is set to 0.5. Values ​​greater than 0.5 are classified into one category, values ​​less than 0.5 are classified into another category, and values ​​equal to 0.5 are considered an uncertain gray area. Type A and Type B are corresponding binary classifications. Which group belongs to type A and which to group in a binary classification is determined based on a specific mathematical model and does not require prior agreement.

[0115] In practical applications, the threshold can also be determined based on the maximum Yoden index (specifically, the value corresponding to the maximum Yoden index). Values ​​greater than the threshold are grouped into one category, values ​​less than the threshold into another, and values ​​equal to the threshold are treated as an uncertain gray area. Type A and Type B are corresponding binary classifications. Which group belongs to type A and which to group in a binary classification depends on the specific mathematical model and does not require prior agreement.

[0116] The type A sample and the type B sample can be any of the following:

[0117] (C1) Lung cancer samples and cancer-free controls;

[0118] (C2) Lung cancer samples and benign lung nodule samples;

[0119] (C3) Samples of different subtypes of lung cancer;

[0120] (C4) Samples of lung cancer at different stages;

[0121] (C5) Lung cancer and breast cancer samples;

[0122] (C6) Breast cancer samples and cancer-free female controls;

[0123] (C7) Samples of breast cancer at different stages.

[0124] The mathematical models described above may vary in practical applications depending on the DNA methylation detection method and fitting method. They should be determined based on the specific mathematical model and no convention is required.

[0125] In an embodiment of the present invention, the model is specifically log(y / (1-y))=b0+b1x1+b2x2+b3x3+….+bnXn, where y is the dependent variable, which is the detection index obtained by substituting the methylation values ​​of one or more methylation sites of the test sample into the model, b0 is a constant, x1~xn are independent variables, which are the methylation values ​​of one or more methylation sites of the test sample (each value is a value between 0 and 1), and b1~bn are the weights assigned to each site methylation value by the model.

[0126] In embodiments of the present invention, known parameters such as age, gender, and white blood cell count can be added as appropriate to improve the discrimination efficiency of the model. A specific model established in an embodiment of the present invention is a model for assisting in distinguishing between benign pulmonary nodules and lung cancer. The model is specifically: log(y / (1-y))=-0.672-0.711*CD44_C_2-0.076*CD44_C_3+0.082*CD44_C_4+0.918*CD44_C_5-0.052*CD44_C_6+0.027*age+0.747*gender (male assigned 1, female assigned 0)+0.014*white blood cell count. The methylation level of CpG sites at positions 130-131 from the 5' end of the DNA fragment shown in SEQ ID No. 3 is represented by CD44_C_2; the methylation level of CpG sites at positions 158-159 from the 5' end of the DNA fragment shown in SEQ ID No. 3 is represented by CD44_C_4; the methylation level of CpG sites at positions 198-199 from the 5' end of the DNA fragment shown in SEQ ID No. 3 is represented by CD44_C_5; the methylation level of CpG sites at positions 316-317 from the 5' end of the DNA fragment shown in SEQ ID No. 3 is represented by CD44_C_6; and the methylation level of CpG sites at positions 346-347 from the 5' end of the DNA fragment shown in SEQ ID No. 3 is represented by CD44_C_6. The threshold of the model is 0.5. Patient candidates with a detection index greater than 0.5 calculated by the model are lung cancer patients, and patients with a detection index less than 0.5 are benign lung nodules.

[0127] In the above aspects, the detection of CD44 gene methylation level refers to the detection of CD44 gene methylation level in blood.

[0128] In the above aspects, when the type A sample and the type B sample are different subtypes of lung cancer in (C3), the type A sample and the type B sample can specifically be any two of the following: lung adenocarcinoma sample, lung squamous cell carcinoma sample, and small cell lung cancer sample.

[0129] In the above aspects, when the type A sample and the type B sample are lung cancer samples of different stages in (C4), the type A sample and the type B sample can specifically be any two of the clinical stage I lung cancer samples, clinical stage II lung cancer samples, and clinical stage III lung cancer samples.

[0130] In the above aspects, when the type A sample and the type B sample are breast cancer samples of different stages in (C7), the type A sample and the type B sample can specifically be any two of the following: T1 stage breast cancer sample, T2 stage breast cancer sample, and T3 stage breast cancer sample; or non-lymph node invasive breast cancer sample and lymph node invasive breast cancer sample; or any two of the following: clinical stage I breast cancer sample, clinical stage II breast cancer sample, and clinical stage III breast cancer sample.

[0131] The CD44 gene mentioned above may specifically include Genbank accession number: NM_000610.4, transcript variant 1; NM_001001389.2, transcript variant 2; NM_001001390.2, transcript variant 3; NM_001001391.2, transcript variant 4; NM_001001392.2, transcript variant 5; NM_001202555.2, transcript variant 6; NM_001202556.2, transcript variant 7; NM_001202557.2, transcript variant 8.

[0132] This invention provides information on the hypomethylation of the CD44 gene in the blood of lung cancer and breast cancer patients. Experiments have demonstrated that blood samples can differentiate between cancer (lung and breast cancer) patients and cancer-free controls, distinguish between benign lung nodules and lung cancer, differentiate between different subtypes and stages of lung cancer, and differentiate between lung cancer and breast cancer, as well as different stages of breast cancer. This invention has significant scientific and clinical application value for improving the early diagnosis and treatment of lung and breast cancer and reducing mortality rates. Attached Figure Description

[0133] Figure 1 This is a schematic diagram of the mathematical model.

[0134] Figure 2 Examples of mathematical models are provided. Detailed Implementation

[0135] The present invention will now be described in further detail with reference to specific embodiments. The given embodiments are merely illustrative of the invention and not intended to limit its scope. The embodiments provided below can serve as a guide for further improvements by those skilled in the art and do not constitute a limitation on the invention in any way.

[0136] Unless otherwise specified, the experimental methods used in the following examples are conventional methods, performed according to the techniques or conditions described in the literature in this field or according to the product instructions. Unless otherwise specified, the materials and reagents used in the following examples are commercially available.

[0137] In the following examples, the CD44 gene (CD44 molecule, CD44) quantification experiments were all performed in triplicate, and the results were averaged.

[0138] Example 1: Primer design for detecting CD44 gene methylation sites

[0139] After extensive sequence and functional analysis, four fragments (CD44_A, CD44_B, and CD44_C) from the CD44 gene were selected for methylation level and cancer association analysis.

[0140] The CD44_A fragment (SEQ ID No. 1) is located in the hg19 reference genome chr11:35187705-35188468, positive chain.

[0141] The CD44_B fragment (SEQ ID No. 2) is located in the hg19 reference genome chr11:35239651-35240354, in the positive chain.

[0142] The CD44_C fragment (SEQ ID No. 3) is located in the hg19 reference genome chr11:35249620-35250287, in the positive chain.

[0143] The CpG site information in the CD44_A fragment is shown in Table 1.

[0144] The CpG site information in the CD44_B fragment is shown in Table 2.

[0145] The CpG site information in the CD44_C fragment is shown in Table 3.

[0146] Table 1. CpG site information in CD44_A fragment

[0147] CpG site Location of CpG sites in the sequence CD44_A_1 SEQ ID No. 1, from the 26th to the 27th bit at the 5' end CD44_A_2 SEQ ID No. 1, from the 48th to the 49th position of the 5' end CD44_A_3 SEQ ID No. 1, bits 121-122 from the 5' end CD44_A_4 SEQ ID No. 1, bits 127-128 from the 5' end CD44_A_5 SEQ ID No. 1, positions 455-456 from the 5' end CD44_A_6 SEQ ID No. 1, from the 5' end, positions 486-487 CD44_A_7 SEQ ID No. 1, positions 523-524 from the 5' end CD44_A_8 SEQ ID No. 1, positions 531-532 from the 5' end CD44_A_9 SEQ ID No. 1, bits 681-682 from the 5' end CD44_A_10 SEQ ID No. 1, bits 733-734 from the 5' end CD44_A_11 SEQ ID No. 1, positions 738-739 from the 5' end

[0148] Table 2. CpG site information in CD44_B fragment

[0149] CpG site Location of CpG sites in the sequence CD44_B_1 SEQ ID No. 2, positions 26-27 from the 5' end CD44_B_2 SEQ ID No. 2, from the 5' end, positions 66-67 CD44_B_3 SEQ ID No. 2, bits 219-220 from the 5' end CD44_B_4 SEQ ID No. 2, bits 257-258 from the 5' end CD44_B_5 SEQ ID No. 2, positions 350-351 from the 5' end CD44_B_6 SEQ ID No. 2, positions 414-415 from the 5' end CD44_B_7 SEQ ID No. 2, from the 5' end, positions 466-467 CD44_B_8 SEQ ID No. 2, positions 503-504 from the 5' end CD44_B_9 SEQ ID No. 2, positions 593-594 from the 5' end CD44_B_10 SEQ ID No. 2, bits 677-678 from the 5' end

[0150] Table 3. CpG site information in CD44 C fragment

[0151] CpG site Location of CpG sites in the sequence CD44_C_1 SEQ ID No. 3, positions 31-32 from the 5' end CD44_C_2 SEQ ID No. 3, positions 130-131 from the 5' end CD44_C_3 SEQ ID No. 3, positions 158-159 from the 5' end CD44_C_4 SEQ ID No. 3, positions 198-199 from the 5' end CD44_C_5 SEQ ID No. 3, positions 316-317 from the 5' end CD44_C_6 SEQ ID No. 3, positions 346-347 from the 5' end CD44_C_7 SEQ ID No. 3, positions 641-642 from the 5' end

[0152] Specific PCR primers were designed for the three fragments (CD44_A, CD44_B, and CD44_C), as shown in Table 4. SEQ ID No. 4, SEQ ID No. 6, and SEQ ID No. 8 are forward primers, and SEQ ID No. 5, SEQ ID No. 7, and SEQ ID No. 9 are reverse primers. In SEQ ID No. 4, SEQ ID No. 6, and SEQ ID No. 8, positions 1 to 10 from the 5' end are non-specific tags, and positions 11 to 35 are specific primer sequences. In SEQ ID No. 5, SEQ ID No. 7, and SEQ ID No. 9, positions 1 to 31 from the 5' end are non-specific tags, and positions 32 to 56 are specific primer sequences. The primer sequences do not contain SNP or CpG sites.

[0153] Table 4. CD44 methylation primer sequences

[0154]

[0155]

[0156] Example 2: Detection and analysis of CD44 gene methylation

[0157] I. Research Sample

[0158] With informed consent from the patients, a total of 722 lung cancer patients, 152 patients with benign lung nodules, 227 breast cancer patients, and 945 cancer-free controls (cancer-free controls are patients who have never had cancer and have not reported any lung nodules, and whose blood routine indicators are all within the reference range) were collected from ex vivo blood samples.

[0159] All patient samples were collected before surgery and were confirmed by imaging and pathology.

[0160] Lung cancer and breast cancer subtypes are determined based on pathological histology.

[0161] Lung cancer and breast cancer staging is based on the AJCC 8th edition staging system.

[0162] The 722 lung cancer patients were classified into the following types: 619 cases of lung adenocarcinoma, 42 cases of lung squamous cell carcinoma, 49 cases of small cell lung cancer, and 12 cases of other types.

[0163] The 722 lung cancer patients were classified into three stages: 649 in stage I, 41 in stage II, and 32 in stage III.

[0164] The 722 lung cancer patients were divided according to the size of the lung cancer tumor (T): T1 603 cases, T2 83 cases, and T3 36 cases.

[0165] The 722 lung cancer patients were divided into two groups according to whether or not they had lung cancer lymph node infiltration (N): 688 patients had no lung cancer lymph node infiltration, and 34 patients had lung cancer lymph node infiltration.

[0166] The 227 breast cancer patients were classified into the following types: 34 cases of ductal carcinoma in situ, 165 cases of invasive ductal carcinoma, and 28 cases of invasive lobular carcinoma.

[0167] The 227 breast cancer patients were classified into three stages: 198 cases of stage I, 20 cases of stage II, and 9 cases of stage III.

[0168] The 227 breast cancer patients were divided according to lung cancer tumor size (T): T1 189 cases, T2 27 cases, and T3 11 cases.

[0169] The 227 breast cancer patients were divided according to whether or not they had breast cancer lymph node invasion (N): 201 cases had no breast cancer lymph node invasion, and 26 cases had breast cancer lymph node invasion.

[0170] The median ages of cancer-free individuals, patients with benign lung nodules, lung cancer patients, and breast cancer patients were 56, 57, 58, and 56 years, respectively. The male-to-female ratio in each of the three groups—cancer-free individuals, patients with benign lung nodules, and lung cancer patients—was approximately 1:1, while all breast cancer patients were female.

[0171] II. Methylation Detection

[0172] 1. Extract total DNA from blood samples.

[0173] 2. Treat the total DNA from the blood sample prepared in step 1 with bisulfite (refer to the instructions for Qiagen's DNA methylation kit). After bisulfite treatment, unmethylated cytosine (C) is converted to uracil (U), while methylated cytosine remains unchanged. That is, the C bases at the original CpG sites are converted to C or U after bisulfite treatment.

[0174] 3. Using the DNA treated with bisulfite in step 2 as a template, PCR amplification was performed using the three specific primer pairs in Table 4 with DNA polymerase according to the reaction system required for conventional PCR. All three primer pairs used the same conventional PCR system, and all three primer pairs were amplified according to the following procedure.

[0175] The PCR reaction program was as follows: 95℃, 4min → (95℃, 20s → 56℃, 30s → 72℃, 2min) 45 cycles → 72℃, 5min → 4℃, 1h.

[0176] 4. Take the amplification product from step 3 and perform DNA methylation analysis by time-of-flight mass spectrometry. The specific method is as follows:

[0177] (1) Add 2 μl of shrimp alkaline phosphate (SAP) solution (0.3 ml SAP [0.5 U] + 1.7 ml H2O) to 5 μl of PCR product and then incubate in the PCR instrument according to the following procedure (37℃, 20 min → 85℃, 5 min → 4℃, 5 min);

[0178] (2) Take out 2 μl of the SAP-treated product obtained in step (1) and add it to 5 μl of T-Cleavage reaction system according to the instructions, and then incubate at 37°C for 3 h;

[0179] (3) Take the product from step (2), add 19 μl of deionized water, and then incubate with 6 μg of Resin in a rotating shaker for 1 h for deionization.

[0180] (4) Centrifuge at 2000 rpm at room temperature for 5 min, and load a small amount of supernatant onto 384 Spectro CHIP using the Nanodispenser robotic arm;

[0181] (5) Time-of-flight mass spectrometry analysis; the obtained data were collected using SpectroACQUIRE v3.3.1.3 software and visualized using MassArray EpiTyper v1.2 software.

[0182] The reagents used in the above time-of-flight mass spectrometry detection were all from the T-Cleavage Mass CLEAVEReagent Auto Kit (catalog number: 10129A); the detection instrument used in the above time-of-flight mass spectrometry detection was... Analyzer Chip Prep Module 384, model: 41243; the above data analysis software is the software that comes with the testing instrument.

[0183] 5. Analyze the data obtained in step 4.

[0184] Statistical analysis of the data was performed using SPSS Statistics 23.0.

[0185] Nonparametric tests are used for comparative analysis between two groups.

[0186] The effectiveness of combinations of multiple CpG sites in different sample groups for differentiation was determined using statistical methods such as logistic regression and receiver operating curves.

[0187] All statistical tests were two-tailed, and a p-value < 0.05 was considered statistically significant.

[0188] Mass spectrometry experiments yielded peak profiles for 28 distinguishable methylated fragments. Using SpectroACQUIRE v3.3.1.3 software, the methylation level of each sample at each CpG site was automatically calculated based on the formula: "Methylation level = Peak area of ​​methylated fragment / (Peak area of ​​unmethylated fragment + Peak area of ​​methylated fragment)".

[0189] III. Results Analysis

[0190] 1. CD44 gene methylation levels in the blood of cancer-free controls, benign nodules, and lung cancer patients.

[0191] The methylation levels of all CpG sites in the CD44 gene were analyzed using blood samples from 722 lung cancer patients, 152 patients with benign lung nodules, and 945 cancer-free controls (Table 5). The results showed that the median methylation level of all CpG sites in the CD44 gene was 0.48 (IQR = 0.19–0.69) in the cancer-free control group, 0.45 (IQR = 0.17–0.65) in the benign nodules, and 0.44 (IQR = 0.15–0.65) in the lung cancer patients.

[0192] 2. The level of CD44 gene methylation in the blood can distinguish between cancer-free controls and lung cancer patients.

[0193] Comparative analysis of CD44 gene methylation levels in 722 lung cancer patients and 945 cancer-free controls revealed significantly lower methylation levels at all CpG sites in the CD44 gene compared to cancer-free controls (p<0.05, Table 6). Furthermore, the methylation levels at all CpG sites in the CD44 gene were significantly different from those in cancer-free controls across different lung cancer subtypes (adenocarcinoma, squamous cell carcinoma, and small cell lung cancer). The methylation levels at all CpG sites in the CD44 gene were also significantly different from those in cancer-free controls across different lung cancer stages (clinical stage I, II-III) (p<0.05, Table 6). Additionally, the methylation levels in lung cancer patients without lymph node invasion and those with lymph node invasion were significantly different from those in cancer-free controls (p<0.05, Table 6). Therefore, CD44 gene methylation levels can be used for the clinical diagnosis of lung cancer, especially for early diagnosis.

[0194] 3. The level of CD44 gene methylation in the blood can differentiate between benign lung nodules and lung cancer patients.

[0195] By comparing the methylation levels of the CD44 gene in 722 lung cancer patients and 152 benign nodules, the results showed that the methylation levels of all CpG sites in the CD44 gene were significantly higher in patients with benign nodules than in patients with lung cancer (p<0.05, Table 7). Furthermore, it was found that the methylation levels of all CpG sites in the CD44 gene were significantly different between lung cancer patients of different subtypes (adenocarcinoma, squamous cell carcinoma, and small cell lung cancer), different clinical stages (stage I or II-III), and with or without lymph node invasion compared to benign nodules (p<0.05, Table 7). Therefore, the methylation level of the CD44 gene can be used to differentiate between lung cancer patients and patients with benign nodules, and is a biomarker with significant potential value.

[0196] 4. Blood CD44 gene methylation levels can differentiate between different subtypes or stages of lung cancer.

[0197] By comparing and analyzing the methylation levels of the CD44 gene in patients with different subtypes of lung cancer (adenocarcinoma, squamous cell carcinoma, and small cell lung cancer) and at different stages of lung cancer, significant differences were found in the methylation levels of all CpG sites in the CD44 gene across different lung cancer subtypes (adenocarcinoma, squamous cell carcinoma, and small cell lung cancer), tumor size (T1, T2, and T3), different stages (clinical stage I, II, and III), and the presence or absence of lymph node invasion (p<0.05, Table 8). Therefore, the methylation level of the CD44 gene can be used to differentiate between different subtypes or stages of lung cancer.

[0198] 5. The level of CD44 gene methylation in blood can be used to diagnose breast cancer.

[0199] The methylation levels of CpG sites in the CD44 gene were analyzed between 227 breast cancer patients and 472 cancer-free female controls (Table 9). The results showed that the median methylation level of all target CpG sites in breast cancer patients was 0.46 (IQR = 0.19–0.66), while the median methylation level in cancer-free female controls was 0.48 (IQR = 0.19–0.69). The methylation levels of all CpG sites in breast cancer patients were significantly lower than those in cancer-free female controls (p < 0.05). Furthermore, the methylation levels of all CpG sites in the CD44 gene showed significant differences in different breast cancer stages (clinical stage I, II–III), tumor size (T1, T2, and T3), and the presence or absence of lymph node invasion (p < 0.05, Table 10). Therefore, the methylation level of the CD44 gene can be used for the clinical diagnosis of breast cancer.

[0200] 6. The level of CD44 methylation in the blood can distinguish between breast cancer patients and lung cancer patients.

[0201] The methylation levels of the CD44 gene in the blood of 227 breast cancer patients and 722 lung cancer patients were analyzed (Table 11). The results showed that the median methylation level of all target CpG sites was 0.46 (IQR = 0.19–0.66) in breast cancer patients and 0.44 (IQR = 0.15–0.65) in lung cancer patients. The methylation levels of all CpG sites were significantly higher in breast cancer patients than in lung cancer patients (p < 0.05). Therefore, the methylation level of the CD44 gene can be used to distinguish between breast cancer and lung cancer patients.

[0202] 7. Establishment of mathematical models for assisting cancer diagnosis

[0203] The mathematical model established in this invention can be used to achieve the following objectives:

[0204] (1) Differentiate between lung cancer patients and cancer-free controls;

[0205] (2) Differentiate between lung cancer patients and patients with benign lung nodules;

[0206] (3) Differentiate between breast cancer patients and cancer-free women as controls;

[0207] (4) Differentiate between breast cancer patients and lung cancer patients

[0208] (5) Differentiate lung cancer subtypes;

[0209] (6) Differentiate lung cancer stages;

[0210] (7) Differentiate breast cancer stages.

[0211] The mathematical model is established as follows:

[0212] (A) Data source: Methylation levels of target CpG sites (one or more combinations of Tables 1-3) in ex vivo blood samples from 722 lung cancer patients, 152 patients with benign lung nodules, 227 breast cancer patients, and 945 cancer-free controls listed in Step 1 (detection method is the same as in Step 2).

[0213] Data can be supplemented with known parameters such as age, gender, and white blood cell count to improve discrimination efficiency, depending on actual needs.

[0214] (B) Model Establishment

[0215] As needed, select any two different types of patient data, i.e., the training set (e.g., cancer-free controls and lung cancer patients, cancer-free female controls and breast cancer patients, benign lung nodules and lung cancer patients, lung cancer patients and breast cancer patients, lung adenocarcinoma and squamous cell lung cancer patients, lung adenocarcinoma and small cell lung cancer patients, lung squamous cell lung cancer and small cell lung cancer patients, stage I lung cancer and stage II lung cancer patients, stage I lung cancer and stage III lung cancer patients, stage II lung cancer and stage III lung cancer patients, stage I breast cancer and stage II breast cancer patients, stage I breast cancer and stage III breast cancer patients, stage II breast cancer and stage III breast cancer patients, T1 breast cancer and T2 breast cancer patients, T1 breast cancer and T3 breast cancer patients, T2 breast cancer and T3 breast cancer patients, breast cancer without lymph node invasion and breast cancer with lymph node invasion patients) as the data for building the model. Use statistical software such as SAS, R, SPSS to build a mathematical model using the statistical method of binary logistic regression through formulas. The maximum Youden index calculated by the mathematical model formula corresponds to the threshold value, or a threshold of 0.5 can be directly set. Samples tested and substituted into the model calculation yield a detection index greater than the threshold, categorized as Class B; less than the threshold, categorized as Class A; and equal to the threshold, treated as an uncertain gray area. When predicting the category of a new sample, the methylation level of one or more CpG sites on the CD44 gene of the sample is first detected using DNA methylation assays. This methylation level data is then substituted into the mathematical model (if known parameters such as age, sex, and white blood cell count were included in the model construction, this step also substitutes the specific values ​​of the corresponding parameters of the sample into the model formula) to calculate the detection index corresponding to the sample. The detection index of the sample is then compared with the threshold value to determine the category of the sample.

[0216] For example: Figure 1As shown, data on the methylation levels of single CpG sites or combinations of multiple CpG sites in the CD44 gene from the training set were used in statistical software such as SAS, R, and SPSS to establish a mathematical model for distinguishing between classes A and B using a binary logistic regression formula. This mathematical model is a binary logistic regression model, specifically: log(y / 1-y)=b0+b1x1+b2x2+b3x3+….+bnXn, where y is the dependent variable, i.e., the detection index obtained by substituting the methylation values ​​of one or more methylation sites of the test sample into the model; b0 is a constant; x1~xn are the independent variables, i.e., the methylation values ​​of one or more methylation sites of the test sample (each value is a value between 0 and 1); and b1~bn are the weights assigned to each methylation value by the model. In practical applications, a mathematical model is first established based on the methylation degree (x1~xn) of one or more DNA methylation sites in the samples already detected in the training set and their known classification (class A or class B, with y assigned values ​​of 0 and 1 respectively). This model determines the constant b0 of the mathematical model and the weights b1~bn of each methylation site. The detection index (0.5 in this example) corresponding to the maximum Yangen index, calculated by the mathematical model, is used as the threshold for classification. After testing and substituting the y-value into the model, samples with a detection index greater than 0.5 are classified as class B, less than 0.5 as class A, and values ​​equal to 0.5 are considered an uncertain gray area. Classes A and B are corresponding two categories (the grouping of binary categories, where one group is class A and the other is class B, is determined by the specific mathematical model and is not specified here). Examples include cancer-free controls and lung cancer patients, cancer-free female controls and breast cancer patients, patients with benign lung nodules and lung cancer patients, lung cancer patients and breast cancer patients, lung adenocarcinoma and squamous cell carcinoma patients, lung adenocarcinoma and small cell lung cancer patients, lung squamous cell carcinoma and small cell lung cancer patients, stage I lung cancer and stage II lung cancer patients, stage I lung cancer and stage III lung cancer patients, stage II lung cancer and stage III lung cancer patients, stage I breast cancer and stage II breast cancer patients, stage I breast cancer and stage III breast cancer patients, stage II breast cancer and stage III breast cancer patients, T1 breast cancer and T2 breast cancer patients, T1 breast cancer and T3 breast cancer patients, T2 breast cancer and T3 breast cancer patients, and breast cancer without lymph node invasion and breast cancer with lymph node invasion. To predict which category a subject's sample belongs to, blood is first collected, and then DNA is extracted from it. After the extracted DNA was converted by bisulfite, the methylation level of a single CpG site or a combination of multiple CpG sites in the CD44 gene of the subject was detected using a DNA methylation assay. The methylation data obtained were then substituted into the mathematical model described above.If the methylation level of one or more CpG sites in the CD44 gene of the subject, after being substituted into the above mathematical model, results in a detection index greater than the threshold, then the subject is classified into the same category (Category B) as those in the training set with a detection index greater than 0.5. If the methylation level of one or more CpG sites in the CD44 gene of the subject, after being substituted into the above mathematical model, results in a detection index less than the threshold, then the subject is classified into the same category (Category A) as those in the training set with a detection index less than 0.5. If the methylation level of one or more CpG sites in the CD44 gene of the subject, after being substituted into the above mathematical model, results in a detection index equal to the threshold, then it is impossible to determine whether the subject belongs to Category A or Category B.

[0217] For example: Figure 2 The diagram illustrates the application of methylation and mathematical modeling of preferred CpG sites (CD44_C_2, CD44_C_3, CD44_C_4, CD44_C_5, CD44_C_6) of CD44_C in the differentiation of benign and malignant lung nodules: Methylation levels of five distinguishable preferred CpG site combinations detected in a training set of lung cancer patients and patients with benign lung nodules (here: 722 lung cancer patients and 152 patients with benign lung nodules), along with patient age, sex (male assigned 1, female assigned 0), and white blood cell count, were used in R software to build a mathematical model for distinguishing between lung cancer patients and patients with benign lung nodules using a binary logistic regression formula. The mathematical model here is a binary logistic regression model. Therefore, the constant b0 and the weights b1 to bn of each methylation site are determined. In this example, they are: log(y / (1-y))=-0.672-0.711*CD44_C_2-0.076*CD44_C_3+0.082*CD44_C_4+0.918*CD44_C_5-0.052*CD44_C_6+0.027*age+0.747*gender (male is assigned 1, female is assigned 0)+0.014*white blood cell count. Here, y is the dependent variable, which is the detection index obtained by substituting the methylation values ​​of the five distinguishable methylation sites of the sample, along with age, gender, and white blood cell count, into the model. With a threshold of 0.5, the methylation levels of the five distinguishable CpG sites (CD44_C_2, CD44_C_3, CD44_C_4, CD44_C_5, and CD44_C_6) in the test samples were measured and then input into the model along with information such as age, sex, and white blood cell count. The resulting detection index, y-value, was calculated. A value greater than 0.5 was classified as lung cancer, less than 0.5 as benign lung nodules, and equal to 0.5 was indeterminate between lung cancer and benign lung nodules. The area under the curve (AUC) of this model was 0.69 (Table 15). Specific methods for determining the subjects are illustrated below. Figure 2 As shown, blood samples were collected from two subjects (A and B) to extract DNA. After bisulfite conversion, the methylation levels of five distinguishable CpG sites (CD44_C2, CD44_C3, CD44_C4, CD44_C5, and CD44_C6) were measured using a DNA methylation assay. The methylation level data, along with the subjects' age, sex, and white blood cell count, were then input into the mathematical model. Subject A's calculated methylation level was 0.82, greater than 0.5, indicating a diagnosis of lung cancer (consistent with clinical diagnosis). Subject B's methylation level at one or more CpG sites of the CD44 gene, calculated using the mathematical model, was 0.21, less than 0.5, indicating a diagnosis of benign lung nodules (consistent with clinical diagnosis).

[0218] (C) Model Performance Evaluation

[0219] Based on the above methods, mathematical models were established to distinguish between cancer-free controls and lung cancer patients, cancer-free female controls and breast cancer patients, patients with benign lung nodules and lung cancer patients, lung cancer patients and breast cancer patients, lung adenocarcinoma and squamous cell carcinoma patients, lung adenocarcinoma and small cell lung cancer patients, lung squamous cell carcinoma and small cell lung cancer patients, stage I and stage II lung cancer patients, stage I and stage III lung cancer patients, stage II and stage III lung cancer patients, stage I and stage II breast cancer patients, stage I and stage III breast cancer patients, stage II and stage III breast cancer patients, T1 and T2 breast cancer patients, T1 and T3 breast cancer patients, T2 and T3 breast cancer patients, and breast cancer without lymph node invasion and breast cancer with lymph node invasion. The effectiveness of these models was evaluated using receiver operating characteristic (ROC) curves. A larger area under the ROC curve (AUC) indicates better model discrimination and more effective molecular markers. The evaluation results after constructing mathematical models using different CpG sites are shown in Tables 12, 13, and 14. In Tables 12, 13, and 14, one CpG site represents any single CpG site in the CD44_C amplified fragment; two CpG sites represent any combination of two CpG sites in CD44_C; three CpG sites represent any combination of three CpG sites in CD44_C, and so on. The values ​​in the tables represent the range of evaluation results for different site combinations (i.e., the results for any combination of CpG sites are within this range).

[0220] The results show that the discriminative ability of the CD44 gene for different groups (cancerous controls and lung cancer patients, cancer-free female controls and breast cancer patients, patients with benign lung nodules and lung cancer patients, lung cancer patients and breast cancer patients, lung adenocarcinoma and squamous cell carcinoma patients, lung adenocarcinoma and small cell lung cancer patients, lung squamous cell carcinoma and small cell lung cancer patients, stage I lung cancer and stage II lung cancer patients, stage I lung cancer and stage III lung cancer patients, stage II lung cancer and stage III lung cancer patients, stage I breast cancer and stage II breast cancer patients, stage I breast cancer and stage III breast cancer patients, stage II breast cancer and stage III breast cancer patients, T1 breast cancer and T2 breast cancer patients, T1 breast cancer and T3 breast cancer patients, T2 breast cancer and T3 breast cancer patients, breast cancer without lymph node invasion and breast cancer with lymph node invasion) increases with the number of loci.

[0221] In addition, among the CpG sites shown in Tables 1-3, there are a few combinations of superior sites that exhibit better discriminative ability than combinations of multiple inferior sites. For example, the five distinguishable optimal sites CD44_C_2, CD44_C_3, CD44_C_4, CD44_C_5, and CD44_C_6 shown in Tables 15, 16, and 17 are the preferred sites among any five combinations of CD44_C.

[0222] In summary, the methylation levels of CpG sites and their various combinations on the CD44 gene, the CpG sites and their various combinations on the CD44_A fragment, the CpG sites and their various combinations on the CD44_B fragment, the CpG sites and their various combinations on the CD44_C fragment, the CD44_C_2, CD44_C_3, CD44_C_4, CD44_C_5, and CD44_C_6 sites and their various combinations on the CD44_C fragment, as well as the CpG sites and their various combinations on CD44_A, CD44_B, and CD44_C, all have an impact on cancer-free controls and lung cancer patients, cancer-free female controls and breast cancer patients, and benign lung cancer patients. The system has the ability to differentiate between patients with sexual nodules and those with lung cancer, lung cancer and those with breast cancer, lung adenocarcinoma and those with squamous cell carcinoma, lung adenocarcinoma and those with small cell lung cancer, those with squamous cell carcinoma and those with small cell lung cancer, those with stage I lung cancer and those with stage II lung cancer, those with stage I lung cancer and those with stage III lung cancer, those with stage II lung cancer and those with stage III lung cancer, those with stage I breast cancer and those with stage II breast cancer, those with stage I breast cancer and those with stage III breast cancer, those with stage II breast cancer and those with stage III breast cancer, those with stage III breast cancer, those with stage T1 breast cancer and those with stage T2 breast cancer, those with stage T1 breast cancer and those with stage T3 breast cancer, those with stage T2 breast cancer and those with stage T3 breast cancer, and those with breast cancer without lymph node invasion and those with breast cancer with lymph node invasion.

[0223] Table 5. Comparison of methylation levels in cancer-free controls, benign nodules, and lung cancer.

[0224]

[0225]

[0226] Table 6. Comparison of methylation levels between cancer-free controls and lung cancer.

[0227]

[0228] Table 7. Comparison of methylation levels between benign nodules and lung cancer.

[0229]

[0230] Table 8. Comparison of methylation levels among different subtypes or stages of lung cancer.

[0231]

[0232]

[0233] Table 9. Comparison of methylation levels between cancer-free female controls and breast cancer patients.

[0234]

[0235]

[0236] Table 10. Comparison of methylation levels at different stages of breast cancer

[0237]

[0238] Table 11. Comparison of methylation levels between lung cancer and breast cancer

[0239]

[0240]

[0241] Table 12. CpG sites of CD44_C and their combinations used to differentiate between lung cancer and cancer-free controls, lung cancer and benign nodules, breast cancer and cancer-free female controls, and lung cancer and breast cancer.

[0242]

[0243]

[0244] Table 13. CpG sites of CD44_C and their free assortment for differentiating different subtypes and stages of lung cancer patients.

[0245]

[0246] Table 14. CpG sites and their combinations of CD44_C used to differentiate different stages of breast cancer.

[0247]

[0248]

[0249] Table 15. Optimal CpG sites and combinations of CD44_C used to differentiate between lung cancer and cancer-free controls, lung cancer and benign nodules, breast cancer and cancer-free female controls, and lung cancer and breast cancer.

[0250]

[0251] Table 16. Optimal CpG sites and combinations for CD44_C used to differentiate different subtypes and stages of lung cancer patients.

[0252]

[0253] Table 17. Optimal CpG sites and combinations of CD44_C used to differentiate different stages of breast cancer.

[0254]

[0255] The present invention has been described in detail above. For those skilled in the art, the invention can be practiced in a wide range of ways with equivalent parameters, concentrations, and conditions without departing from its spirit and scope, and without requiring unnecessary experiments. Although specific embodiments have been given, it should be understood that further modifications can be made to the invention. In summary, according to the principles of the invention, this application is intended to include any changes, uses, or improvements to the invention, including changes made using conventional techniques known in the art that depart from the scope disclosed herein. Some of the essential features can be applied within the scope of the following appended claims. <110> Nanjing Tengchen Biotechnology Co., Ltd. <120> A potential molecular marker for aiding in cancer diagnosis <130> GNCLN211588 <160> 9 <170> PatentIn version 3.5 <210> 1 <211> 764 <212> DNA <213> Artificial sequence <400> 1 gtcaatctgc taaatcaata agatccgaaa aagcagaaaa caaagatcgt gaggccagag 60 gatttcctga tcaccatggc aacacacagg ggtgggtact gtggtttctc tgcctgaaga 120 cgcactcgtc aaagactact tggtgttaca gaattctggt gcagtgaagc aacagttctg 180 ggacatatac tttcttttgc cagaaagact ctgttggatc caggaccagg agtggggggt 240 taaacagttc tccataccct cacctccaag tccccatcat cccattagga gacagggaca 300 gtactgttct ccaaactctc aaaacaaccc aagagctttc taggctgtgc attgggttga 360 tcatttggtg gccttggctg aagggccagg gtaacagcta ggagaaccct tgggattctt 420 ctccaatgtg acaggcccct ccatgttggt tcagcgttga ctgccatgac aggctccagg 480 ctccacggaa aaaaggcaga gaaatcaggg ggtccagcca ggcgccaaga cgttggggtg 540 ccttgcattt ctaagtgcca agtgatgaat agctcagttg aaagcaaccc agggcacatt 600 ctggaggaaa ttctctgggt atgattgtgc atgactgttt tctctgctca ttaaaccctc 660 atctgcctcc agggcaattc cgggagagac tgagtcactc aagactcatg gtctgcactg 720 ggcagccaaa ggcgattcgg ccaccaagtg tgattctggc ctca 764 <210> 2 <211> 704 <212> DNA <213> Artificial sequence <400> 2 tagaaccatt gtgtctccct catttcgata ggcctccagt tctgacttga ggattgtctg 60 tcttgcgata cttcctccca gtttaaggca ttttgttcat ctccttattt tgctttttag 120 cagtagaaaa tgttgattct tctaacaagg tgtgtttcag taatatatta gcagggactc 180 taatagggtg atttttcata gtcagaaagg gccactgccg ctaaactcct tatttattta 240 ttgtgaggtg gagtctcgct ctgttgccaa gctgtaatgc agaggcacaa tctcagctca 300 ctgcaacctc tgcttcctgg gttcaagcaa ttctcctgcc tcagcctccc gagtagctgg 360 gactacaggc acatgccact atgcctggct aatttttgta tttttagtag agacggggtt 420 tcaccatgtt gactaggttg atctcaatct cttgaccttg tgatccgcct gcttcagcct 480 cccaaagtac tgggattaca ggcgtgagca aactccttat tttatagtgt ttatgttggc 540 tcctatgatg ctattggatg agattttcag gttccctttg ggctttctgg agcgtgtgag 600 tcccctgggg gtcttgttaa aatgaagatt ctgatttggt agctttgaga tgaggcataa 660 gattctgcat ttctagcgat ctccccaggt atgcctgtcc tgat 704 <210> 3 <211> 668 <212> DNA <213> Artificial sequence <400> 3 aactttcctt cctctaagca tagtctgact cgtatatgtg tagtagtgat atgttacagg 60 aaagaggaag cattgcatta ggctgggtgg ggtgaagcaa tggacagtaa ccaggggaaaa 120 gatcaaggtc gtactcttcc atagcaactt tcttttgcgt ttgggtctta agtaatgttt 180 gagaaacctt aaattaacga gaataatttg tcttatgtta ttaaacttcc tatatcaggg 240 ttgtatgagt tagccaatgt taggaaaaca tcactatggc cttatacctc tagaaaaatg 300 ttgttttatt cttctcgata cattcagtaa taatgctgac aaggccgaca aagtgttgac 360 tgggagagga agttatggga ctttctggaa tatacaaatc agaatgattt acaatcatca 420 tgttgatggt ttatctaaat ctgcagaaac tgctaaaatg cttaaaagag gcaaacattt 480 ccattgtgaa gtaagataga aagagaatca gaacttgaga gtcagacaga ccagggtttg 540 aatcctcttt cttacttgct gagttacttg ggggaagtta ctgaattttc tgatccccaa 600 ggtttcttca cttggaatat agaaatat aataaacac cggcttcctg gagtcttagt 660 tgctggtt 668 <210> 4 <211> 35 <212> DNA <213> Artificial sequence <400> 4 35 <210> 5 <211> 56 <212> DNA <213> Artificial sequence <400> 5 cattaatacg actcactata gggagaaggc ttaaaccaa aatcacactt aataac 56 <210> 6 <211> 35 <212> DNA <213> Artificial sequence <400> 6 aggaagagag tagaattat gtgtttttt tattt 35 <210> 7 <211> 56 <212> DNA <213> Artificial sequence <400> 7 cagtaatacg actcactata gggagaaggc tatcaaaca aacataccta aaaaaa 56 <210> 8 <211> 35 <212> DNA <213> Artificial Sequence <400> 8 aggagagag tttttttt ttttaagta tagtt 35 <210> 9 <211> 56 <212> DNA <213> Artificial Sequence <400> 9 cagtaatacg actcactata gggagaaggc taaccaaa ctaaaactcc aaaaaa 56

Claims

1. The application of a substance used to detect CD44 gene methylation levels in the preparation of a product; the product is intended to help distinguish between lung cancer and cancer-free controls; The CD44 gene methylation level is the methylation level of any of the CpG sites shown in (e1)-(e4) of the CD44 gene as follows: (e1) All CpG sites in the two DNA fragments shown in SEQ ID No. 1 and SEQ ID No. 2; (e2) All CpG sites in the two DNA fragments shown in SEQ ID No. 1 and SEQ ID No. 3; (e3) All CpG sites in the two DNA fragments shown in SEQ ID No. 2 and SEQ ID No. 3; (e4) All CpG sites in the three DNA fragments shown in SEQ ID No.1, SEQ ID No.2 and SEQ ID No.

3.

2. The application of substances used to detect CD44 gene methylation levels and media containing mathematical model establishment methods and / or usage methods in the preparation of the product; the product is intended to assist in distinguishing between lung cancer and cancer-free controls; The mathematical model was obtained by a method including the following steps: (A1) The methylation level of the CD44 gene was detected in n1 type A samples and n2 type B samples, respectively; (A2) Take the CD44 gene methylation level data of all samples obtained in step (A1), and establish a mathematical model according to the classification method of type A and type B, and determine the classification threshold by binary logistic regression. The method of using the mathematical model includes the following steps: (B1) Detect the methylation level of the CD44 gene in the sample to be tested; (B2) Substitute the CD44 gene methylation level data of the sample to be tested obtained in step (B1) into the mathematical model to obtain the detection index; then compare the detection index with the threshold, and determine whether the sample to be tested is type A or type B based on the comparison result; The type A samples and the type B samples are lung cancer samples and cancer-free controls, respectively. The CD44 gene methylation level is the methylation level of any of the CpG sites shown in (e1)-(e4) of the CD44 gene as follows: (e1) All CpG sites in the two DNA fragments shown in SEQ ID No. 1 and SEQ ID No. 2; (e2) All CpG sites in the two DNA fragments shown in SEQ ID No. 1 and SEQ ID No. 3; (e3) All CpG sites in the two DNA fragments shown in SEQ ID No. 2 and SEQ ID No. 3; (e4) All CpG sites in the three DNA fragments shown in SEQ ID No.1, SEQ ID No.2 and SEQ ID No.

3.

3. The application of a medium containing a mathematical model establishment method and / or usage method in the preparation of the product; the product is intended to assist in distinguishing between lung cancer and cancer-free controls; The mathematical model was obtained by a method including the following steps: (A1) The methylation level of the CD44 gene was detected in n1 type A samples and n2 type B samples, respectively; (A2) Take the CD44 gene methylation level data of all samples obtained in step (A1), and establish a mathematical model according to the classification method of type A and type B, and determine the classification threshold by binary logistic regression. The method of using the mathematical model includes the following steps: (B1) Detect the methylation level of the CD44 gene in the sample to be tested; (B2) Substitute the CD44 gene methylation level data of the sample to be tested obtained in step (B1) into the mathematical model to obtain the detection index; then compare the detection index with the threshold, and determine whether the sample to be tested is type A or type B based on the comparison result; The type A samples and the type B samples are lung cancer samples and cancer-free controls, respectively. The CD44 gene methylation level is the methylation level of any of the CpG sites shown in (e1)-(e4) of the CD44 gene as follows: (e1) All CpG sites in the two DNA fragments shown in SEQ ID No. 1 and SEQ ID No. 2; (e2) All CpG sites in the two DNA fragments shown in SEQ ID No. 1 and SEQ ID No. 3; (e3) All CpG sites in the two DNA fragments shown in SEQ ID No. 2 and SEQ ID No. 3; (e4) All CpG sites in the three DNA fragments shown in SEQ ID No.1, SEQ ID No.2 and SEQ ID No.

3.

4. The application according to any one of claims 1-3, characterized in that: The substance used to detect the methylation level of the CD44 gene is a primer combination.

5. The application according to claim 4, characterized in that: The primer combination is any of the following: (1) Primer pair A and primer pair B; (2) Primer pair A and primer pair C; (3) Primer pair B and primer pair C; (4) Primer pair A, primer pair B, and primer pair C; The primer pair A is a primer pair consisting of primer A1 and primer A2; primer A1 is the single-stranded DNA represented by nucleotides 11-35 of SEQ ID No. 4 or SEQ ID No. 4; primer A2 is the single-stranded DNA represented by nucleotides 32-56 of SEQ ID No. 5 or SEQ ID No.

5. The primer pair B is a primer pair consisting of primer B1 and primer B2; primer B1 is the single-stranded DNA represented by nucleotides 11-35 of SEQ ID No. 6 or SEQ ID No. 6; primer B2 is the single-stranded DNA represented by nucleotides 32-56 of SEQ ID No. 7 or SEQ ID No.

7. The primer pair C is a primer pair consisting of primer C1 and primer C2; primer C1 is the single-stranded DNA represented by nucleotides 11-35 of SEQ ID No. 8 or SEQ ID No. 8; primer C2 is the single-stranded DNA represented by nucleotides 32-56 of SEQ ID No. 9 or SEQ ID No.

9.

6. The system, including: (D1) Reagents and / or instruments used to detect the methylation level of the CD44 gene; (D2) A device comprising unit X and unit Y; The unit X is used to establish a mathematical model, including a data acquisition module, a data analysis and processing module, and a model output module; The data acquisition module is configured to acquire CD44 gene methylation level data from n1 type A samples and n2 type B samples obtained by (D1) detection; The data analysis and processing module is configured to receive CD44 gene methylation level data of n1 type A samples and n2 type B samples sent by the data acquisition module, establish a mathematical model according to the classification method of type A and type B, and determine the classification threshold by means of binary logistic regression. The model output module is configured to receive the mathematical model established by the data analysis and processing module and output it. The unit Y is used to determine the type of the sample to be tested, including a data input module, a data processing module, a data comparison module, and a conclusion output module; The data input module is configured to input the CD44 gene methylation level data of the subject obtained by (D1) detection; The data processing module is configured to receive the CD44 gene methylation level data of the subject sent from the data input module, and substitute the CD44 gene methylation level data of the subject into the mathematical model established by the data analysis and processing module in unit X to calculate the detection index. The data comparison module is configured to receive the detection index sent from the data processing module and compare the detection index with the threshold determined in the data analysis and processing module in the unit X; The conclusion output module is configured to receive the comparison result sent from the data comparison module, and output a conclusion on whether the type of the sample to be tested is type A or type B based on the comparison result. The type A samples and the type B samples are lung cancer samples and cancer-free controls, respectively. The CD44 gene methylation level is the methylation level of any of the CpG sites shown in (e1)-(e4) of the CD44 gene as follows: (e1) All CpG sites in the two DNA fragments shown in SEQ ID No. 1 and SEQ ID No. 2; (e2) All CpG sites in the two DNA fragments shown in SEQ ID No. 1 and SEQ ID No. 3; (e3) All CpG sites in the two DNA fragments shown in SEQ ID No. 2 and SEQ ID No. 3; (e4) All CpG sites in the three DNA fragments shown in SEQ ID No.1, SEQ ID No.2 and SEQ ID No.

3.

7. The system according to claim 6, characterized in that: The reagent used to detect the methylation level of the CD44 gene is a primer combination.

8. The system according to claim 7, characterized in that: The primer combination is any of the following: (1) Primer pair A and primer pair B; (2) Primer pair A and primer pair C; (3) Primer pair B and primer pair C; (4) Primer pair A, primer pair B, and primer pair C; The primer pair A is a primer pair consisting of primer A1 and primer A2; primer A1 is the single-stranded DNA represented by nucleotides 11-35 of SEQ ID No. 4 or SEQ ID No. 4; primer A2 is the single-stranded DNA represented by nucleotides 32-56 of SEQ ID No. 5 or SEQ ID No.

5. The primer pair B is a primer pair consisting of primer B1 and primer B2; primer B1 is the single-stranded DNA represented by nucleotides 11-35 of SEQ ID No. 6 or SEQ ID No. 6; primer B2 is the single-stranded DNA represented by nucleotides 32-56 of SEQ ID No. 7 or SEQ ID No.

7. The primer pair C is a primer pair consisting of primer C1 and primer C2; primer C1 is the single-stranded DNA represented by nucleotides 11-35 of SEQ ID No. 8 or SEQ ID No. 8; primer C2 is the single-stranded DNA represented by nucleotides 32-56 of SEQ ID No. 9 or SEQ ID No. 9.

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