CN113122630B - Calbindin methylation markers for use in aiding diagnosis of cancer - Google Patents
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Abstract
The present invention discloses a calbindin methylation marker for use in the assisted diagnosis of cancer. The invention provides an application of methylation S100P gene as a marker in the preparation of products; the use of the product is at least one of the following: aiding in diagnosing cancer or predicting the risk of developing cancer; aiding in distinguishing benign nodules from cancers; aiding in distinguishing between different subtypes of cancer; aiding in differentiating different stages of cancer; aiding in differentiating between different cancers; determining whether the analyte has an inhibitory or promoting effect on the occurrence of cancer; the cancer may be lung cancer, pancreatic cancer or esophageal cancer. The research of the invention discovers the hypomethylation phenomenon of the S100P gene in the blood of patients with lung cancer, pancreatic cancer and esophageal cancer, and the invention has important scientific significance and clinical application value for improving the early diagnosis and treatment effects of lung cancer, pancreatic cancer and esophageal cancer and reducing the death rate.
Description
Technical Field
The invention relates to the field of medicine, in particular to a calcium-binding protein (S100P) methylation marker for assisting in diagnosing cancers.
Background
Lung cancer is a malignant tumor occurring in the epithelium of bronchial mucosa, and in recent decades, the incidence and mortality rate of lung cancer have been on the rise, and is the cancer with the highest incidence and mortality rate worldwide. Despite recent advances in diagnostic methods, surgical techniques, and chemotherapeutics, 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 their visit and have lost the opportunity for radical surgery. The study shows that the prognosis of lung cancer is directly related to stage, the survival rate of lung cancer in stage I for 5 years is 83%, the survival rate in stage II is 53%, the survival rate in stage III is 26%, and the survival rate in stage IV is 6%. Thus, the key to reducing mortality in lung cancer patients is early diagnosis and early treatment.
The main lung cancer diagnosis method at present is as follows: (1) imaging method: such as chest X-rays and low dose helical CT. However, early lung cancer is difficult to detect by chest X-ray. Although low-dose spiral CT can find nodules in the lung, the false positive rate is as high as 96.4%, and unnecessary psychological burden is brought to a person to be checked. At the same time, chest X-rays and low dose helical CT are not suitable for frequent use due to radiation. In addition, imaging methods are also often affected by equipment and physician experience, as well as effective film reading time. (2) cytological methods: such as sputum cytology, bronchoscopy brush or biopsy, bronchoalveolar lavage cytology, etc. Sputum cytology and bronchoscopy have less sensitivity to peripheral lung cancer. Meanwhile, the operation of brushing a piece under a bronchoscope or taking a biopsy and performing cytological examination on bronchoalveolar lavage fluid is complicated, and the comfort level of a physical examination person is poor. (3) serum tumor markers commonly used at present: carcinoembryonic antigen (CEA), carbohydrate antigen (CA 125/153/199), cytokeratin 19 fragment antigen (CYFRA 21-1), and Neuron Specific Enolase (NSE), etc. These serum tumor markers have limited sensitivity to lung cancer, typically 30% -40%, and even lower for stage I tumors. Furthermore, the tumor specificity is limited, and is affected by many benign diseases such as benign tumor, inflammation, degenerative diseases and the like. At present, the tumor markers are mainly used for screening malignant tumors and rechecking tumor treatment effects. Therefore, further development of a highly efficient and specific early diagnosis technique for lung cancer is required.
The most effective method of pulmonary nodule diagnosis currently internationally accepted is chest low dose helical CT screening. However, the low-dose helical CT has high sensitivity, and a large number of nodules can be found, but it is difficult to determine whether or not the subject is benign or malignant. In the found nodules, the proportion of malignancy was still less than 4%. Currently, clinical identification of benign and malignant lung nodules requires long-term follow-up, repeated CT examination, or invasive examination methods relying on biopsy sampling of lung nodules (including chest wall fine needle biopsy, bronchoscopy biopsy, thoracoscopy or open chest lung biopsy), and the like. CT guided or ultrasound guided transthoracic biopsy has higher sensitivity, but has lower diagnosis rate for nodules <2cm, 30-70% missed diagnosis rate, and higher pneumothorax and hemorrhage incidence rate. The incidence rate of the aspiration biopsy complications of the bronchoscope needle is relatively low, but the diagnosis rate of the surrounding nodules is limited, the diagnosis rate of the nodules less than or equal to 2cm is only 34%, and the diagnosis rate of the nodules greater than 2cm is 63%. Surgical excision has a high diagnostic rate and can directly treat the node, but can cause a transient decline in patient lung function, and if the node is benign, the patient performs unnecessary surgery, resulting in excessive medical treatment. Therefore, there is a strong need for new in vitro diagnostic molecular markers to aid in the identification of pulmonary nodules, while reducing the rate of missed diagnosis and minimizing unnecessary punctures or surgeries.
Pancreatic cancer is a common malignancy of the digestive tract, of which about 90% are pancreatic ductal adenocarcinomas, the fourth most lethal malignancy in the world today. Because of the characteristics of hidden onset, poor specificity of clinical symptoms and early infiltration, most pancreatic cancer patients are in late stage when they find, and lose the opportunity of surgical treatment, resulting in survival rate of only 7% in 5 years. If the patient can find out in early stage (stage I), the survival rate of pancreatic cancer patients can reach 60% in 5 years. The current common diagnostic methods for pancreatic cancer are: (1) Imaging methods such as ultrasound, enhanced CT and Magnetic Resonance Imaging (MRI), the accuracy of ultrasound diagnosis is limited by the physician's experience, the body shape of the patient's hypertrophy and the gas in the gastrointestinal tract; generally, the method for diagnosing pancreatic cancer by ultrasonic treatment can be used as a supplementary examination of CT, but the method for enhancing CT has larger radiation to human body and is not easy to frequently use; MRI has no radiation effect, but it is not suitable for some people (metal objects, cardiac pacemakers, etc. are in the body), the time required for examination is long, and some middle and small hospitals have not been popular because the equipment is expensive. (2) Clinically, some serum tumor markers such as CA19-9, CA242, CA50 and the like can be combined for further detection, and the tumor markers have higher sensitivity, lower specificity and are easily influenced by liver function and cholestasis. (3) pathology examination: percutaneous aspiration biopsy, biopsy under ultrasound gastroscopy guidance, ascites abscission cytology, and laparoscopic or open surgery probe biopsy, but this method is a invasive examination and is not suitable for early patients. Therefore, more sensitive and specific early pancreatic cancer molecular markers are urgently discovered.
Esophageal cancer is a malignancy that originates in the epithelium of the esophageal mucosa, of which about 80% are squamous cell carcinomas, one of the clinically common malignancies. Worldwide, the incidence of esophageal cancer is at position 8 among malignant tumors, and mortality is at position 6. China is a country with high incidence of esophageal cancer, and the incidence rate of the esophageal cancer tends to be gradually increased. At present, more than 90% of esophageal cancer patients progress to middle and late stages when diagnosed, and the overall survival rate of 5 years is less than 20%. At present, the clinical esophageal cancer detection mainly comprises the following methods. Endoscopic ultrasound examination: because the penetration force of the high-frequency probe is low, onlyEven shorter, the range of visibility is very limited, furthermore there isPatients cannot use this method due to excessive esophageal stenosis. Esophagoscopy: the esophagoscope can observe the position, size and shape of the focus in detail, and can also directly clamp pathological tissues or brush samples for cytological examination, but can cause discomfort to patients. X-ray barium meal radiography: the patient swallows the barium porridge during X-ray examination, the barium porridge is observed to pass through the development of esophagus, the qualitative and positioning diagnosis is achieved, the influence of doctor operation and film-viewing experience is avoided, and the method is not suitable for patients with early-stage esophagus cancer. CT scanning: the relationship between the patient's esophagus and adjacent organs can be shown, but it suffers from low sensitivity for early patients. In addition, some common tumor markers, such as CA72-4, CA19-9, CEA, CYFRA21-1, squamous cell carcinoma-associated antigen (SCC), etc., can be used for diagnosis of esophageal cancer, but have sensitivity of less than 40%, and have lower specificity and lower diagnostic value, especially for early patients. Therefore, there is a need for further development of a highly effective and specific technique for early diagnosis of esophageal cancer.
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.
Calbindin (S100P) is a member of the S100 protein family, the main function of which is to be involved in the calcium-dependent signal transduction pathway.
Disclosure of Invention
The invention aims to provide a calcium-binding protein (S100P) methylation marker and a kit for assisting in diagnosing cancers.
The S100P gene of any one of the following may specifically include Genbank accession No.: NM_005980.3.
In a first aspect, the invention claims the use of a methylated S100P gene as a marker in the preparation of a product; the application of the product is at least one of the following:
(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;
(2) Aiding in distinguishing benign nodules from cancers;
(3) Aiding in distinguishing between different subtypes of cancer;
(4) Aiding in differentiating different stages of cancer;
(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;
(6) Assisting in distinguishing benign nodules of the lung from lung cancer;
(7) Assisting in distinguishing different subtypes of lung cancer;
(8) Auxiliary differentiation of different stages of lung cancer;
(9) Aiding in diagnosing pancreatic cancer or predicting pancreatic cancer risk;
(10) Auxiliary diagnosis of esophageal cancer or prediction of esophageal cancer risk;
(11) Auxiliary differentiation between lung and pancreatic cancer;
(12) Auxiliary differentiation between lung cancer and esophageal cancer;
(13) Assist in distinguishing pancreatic cancer from esophageal 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 are reported and blood normative indicators are within the reference range); 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.
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 stage I, clinical lung cancer stage II and clinical lung cancer stage III.
In a specific embodiment of the present invention, the auxiliary diagnosis of pancreatic cancer described in (9) is embodied as at least one of: can help to distinguish pancreatic cancer patients from non-cancerous controls, and can help to distinguish lung pancreatic ductal cancer from non-cancerous 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 auxiliary diagnosis of esophageal cancer described in (10) is embodied as at least one of the following: can help to distinguish esophageal cancer patients from non-cancerous controls, and can help to distinguish esophageal squamous cell carcinoma from non-cancerous controls. Wherein, the non-cancerous control is understood to be understood to mean that no cancer is present and ever has been reported and that benign nodules in the lungs have not been reported and that blood normative indicators are within reference ranges.
In the above (1) - (14), the cancer may be a cancer capable of causing a decrease in the methylation level of the S100P gene in the body, such as lung cancer, pancreatic cancer, esophageal cancer, etc.
In a second aspect, the invention claims the use of a substance for detecting the methylation level of the S100P gene for the preparation of a product. The use of the product may be at least one of the foregoing (1) to (14).
In a third aspect, the invention claims the use of a substance for detecting the methylation level of the S100P gene and a medium storing mathematical modeling methods and/or usage methods for the preparation of a product. The use of the product may be at least one of the foregoing (1) to (14).
The mathematical model may be obtained by a method comprising the steps of:
(A1) Detecting S100P gene methylation levels (training set) of n1 type a samples and n2 type B samples, respectively;
(A2) Taking the S100P 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 the classification modes of the A type and the B type, and determining the threshold value of classification judgment.
Wherein, n1 and n2 in (A1) can be positive integers more than 50.
The using method of the mathematical model comprises the following steps:
(B1) Detecting the methylation level of an S100P gene of a sample to be detected;
(B2) Substituting the S100P 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 A type and which group B type are determined according to a specific mathematical model, and no convention is needed.
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) Lung cancer samples and esophageal cancer samples;
(C6) Lung cancer samples and pancreatic cancer samples;
(C7) Pancreatic cancer samples and esophageal cancer samples;
(C8) Pancreatic cancer samples and no cancer controls;
(C9) Esophageal cancer samples and no cancer controls.
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 S100P 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 S100P gene;
(D2) A device comprising a unit a and a unit B;
the unit A 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 used for acquiring S100P gene methylation level data of n 1A type samples and n 2B type samples obtained by the detection of the (D1);
Wherein, n1 and n2 can be positive integers more than 50.
The data analysis processing module can establish a mathematical model through a two-class logistic regression method according to the classification mode of the A type and the B type based on the S100P gene methylation level data of the n 1A type samples and the n 2B type samples acquired by the data acquisition module, and determine the threshold value of classification judgment;
the model output module is used for outputting the mathematical model established by the data analysis processing module;
The unit B 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 used for inputting the S100P gene methylation level data of the to-be-detected person obtained by the detection of (D1);
The data operation module is used for substituting the S100P gene methylation level data of the testee into the mathematical model, and calculating to obtain a detection index;
The data comparison module is used for comparing the detection index with a threshold value;
the conclusion output module is used for outputting a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result of the data comparison module;
the type a sample and the type B sample are any one of the following:
(C1) Lung cancer samples and no cancer controls;
(C2) Lung cancer samples and lung benign nodule samples;
(C3) A sample of different subtypes of lung cancer;
(C4) Samples of lung cancer at different stages;
(C5) Lung cancer samples and esophageal cancer samples;
(C6) Lung cancer samples and pancreatic cancer samples;
(C7) Pancreatic cancer samples and esophageal cancer samples;
(C8) Pancreatic cancer samples and no cancer controls;
(C9) Esophageal cancer samples and no cancer controls.
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 A type and which group B type are determined according to a specific mathematical model, and no convention is needed.
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.
In the foregoing aspects, the methylation level of the S100P gene may be the methylation level of all or part of CpG sites in the fragment of the S100P gene as shown in (e 1) - (e 2) below. The methylated S100P gene can be all or part of CpG sites in the fragments shown in (e 1) - (e 2) below in the S100P gene.
(E1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment having 80% or more identity thereto;
(e2) The DNA fragment shown in SEQ ID No.2 or a DNA fragment having 80% or more identity thereto.
Further, the "all or part of CpG sites" may be all or any 23 or any 22 or any 21 or any 20 or any 19 or any 18 or any 17 or any 16 or any 15 or any 14 or any 13 or any 12 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 points or any 1 of all CpG sites in the DNA fragment shown in the SEQ ID No. 2.
Alternatively, the "all or part of the CpG sites" may be 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).
Or, the "all or part of CpG sites" may be all or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 of the following ten CpG sites in the DNA fragment shown in SEQ ID No. 2:
(f1) The DNA fragment shown in SEQ ID No.2 contains CpG sites (S100deg.P_B_1, 2) shown in positions 41-42 and 44-45 from the 5' end;
(f2) The DNA fragment shown in SEQ ID No.2 shows the CpG sites (S100deg.P_B_3) at the 128-129 th position from the 5' end;
(f3) The DNA fragment shown in SEQ ID No.2 shows CpG sites (S100deg.P_B_5) from 278 to 279 positions of the 5' end;
(f4) The DNA fragment shown in SEQ ID No.2 has CpG sites (S100deg.P_B_7, 8) shown at positions 362-363 and 372-373 from the 5' end;
(f5) The CpG site (S100deg.P_B_9) shown in 379-380 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2;
(f6) The DNA fragment shown in SEQ ID No.2 shows CpG sites (S100deg.P_B_12) from 473 th to 474 th positions of the 5' end;
(f7) The CpG site (S100deg.P_B_13) shown in 491-492 of the 5' end of the DNA fragment shown in SEQ ID No. 2;
(f8) The DNA fragment shown in SEQ ID No.2 shows the CpG sites (S100deg.P_B_14) from 516-517 from the 5' end;
(f9) The CpG sites shown in the 542-543, 551-552 and 554-555 of the DNA fragment shown in SEQ ID No.2 (S100deg.P_B_15, 16, 17) from the 5' end;
(f10) The DNA fragment shown in SEQ ID No.2 shows the CpG sites (S100deg.P_B_19) from 649-650 th position of 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 4), and thus the methylation level analysis is performed, and related mathematical models are constructed and used. This is the case with (f 1), (f 4) and (f 9) described above.
In the above aspects, the substance for detecting the methylation level of the S100P gene comprises a primer combination for amplifying a full or partial fragment of the S100P gene. The reagent for detecting the methylation level of the S100P gene comprises a primer combination for amplifying the full or partial fragment of the S100P gene; the instrument for detecting the methylation level of the S100P 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 S100P 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 having an identity of 80% or more to the DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;
(g4) 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.
In the present invention, the primer combination may specifically be primer pair a and/or primer pair B;
the primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 can be specifically a single-stranded DNA shown in SEQ ID No.3 or 11-35 nucleotides of SEQ ID No. 3; the primer A2 can be specifically a single-stranded DNA shown in SEQ ID No.4 or 32-56 nucleotides of SEQ ID No. 4;
The primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 can be specifically single-stranded DNA shown in SEQ ID No.5 or 11-35 nucleotides of SEQ ID No. 5; the primer B2 can be specifically a single-stranded DNA shown in SEQ ID No.6 or 32-56 nucleotides of SEQ ID No. 6.
In addition, the invention also discloses a method for distinguishing whether the sample to be detected is an A type sample or a B type sample. The method may comprise the steps of:
(A) The mathematical model may be built as a method comprising the steps of:
(A1) Detecting S100P gene methylation levels (training set) of n1 type a samples and n2 type B samples, respectively;
(A2) Taking the S100P 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 the classification modes of the A type and the B type, and determining the threshold value of classification judgment.
Wherein, n1 and n2 in (A1) can be positive integers more than 50.
(B) The sample to be tested may be determined as a type a sample or a type B sample according to a method comprising the steps of:
(B1) Detecting the methylation level of the S100P gene of the sample to be detected;
(B2) Substituting the S100P 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 A type and which group B type are determined according to a specific mathematical model, and no convention is needed.
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 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) Lung cancer samples and esophageal cancer samples;
(C6) Lung cancer samples and pancreatic cancer samples;
(C7) Pancreatic cancer samples and esophageal cancer samples;
(C8) Pancreatic cancer samples and no cancer controls;
(C9) Esophageal cancer samples and no cancer controls.
In practical applications, any of the above mathematical models may be changed according to the detection method and the fitting method 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 as 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 to the methylation values of each site by the model.
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 the examples of the present invention is a model for assisting in distinguishing benign nodules from lung cancer in the lung, specifically ：log(y/(1-y))＝-0.364+0.556*S100P_B_1,2+2.259*S100P_B_3-0.686*S100P_B_5-3.811*S100P_B_7,8+4.102*S100P_B_9-2.525*S100P_B_12+0.575*S100P_B_13-1.496*S100P_B_14+6.669*S100P_B_15,16,17-4.183*S100P_B_19+0.022* years old-0.843 sex (male assigned 1, female assigned 0) -0.045 white blood cell count. 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 less than 0.5 are lung benign nodule patients.
In the above aspects, the detecting the methylation level of the S100P gene is detecting the methylation level of the S100P 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.
The invention provides hypomethylation of S100P gene in blood of lung cancer patients, pancreatic cancer patients and esophageal cancer patients. Experiments prove that the blood can be used as a sample to distinguish cancer (lung cancer, pancreatic cancer and esophageal cancer) patients from cancer-free controls, lung benign nodules and lung cancer, different subtypes and different stages of lung cancer, lung cancer and pancreatic cancer, lung cancer and esophageal cancer. The invention has important scientific significance and clinical application value for improving the early diagnosis and treatment effects of lung cancer, pancreatic cancer and esophagus 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 experimental methods used in the following examples are conventional methods unless otherwise specified.
Materials, reagents and the like used in the examples described below are commercially available unless otherwise specified.
The calcium-binding protein (S100P) gene quantification assays in the examples below were all performed in triplicate and the results averaged.
Example 1 primer design for detection of methylation site of S100P Gene
Through a number of sequence and functional analyses, two fragments in the S100P gene (s100deg.P_A fragment and s100deg.P_B fragment) were selected for methylation level and cancer correlation analysis.
The S100deg.P_A fragment is located on the sense strand of hg19 reference genome chr4: 6694355-6695352.
The S100deg.P_B fragment is located on the sense strand of hg19 reference genome chr4: 6695337-6696281.
CpG site information in the S100deg.P_A fragment (SEQ ID No. 1) is shown in Table 1.
CpG site information in the S100deg.P_B fragment (SEQ ID No. 2) is shown in Table 2.
TABLE 1 CpG site information in S100P_A fragment
| CpG sites | Position of CpG sites in the sequence |
| S100P_A_1 | SEQ ID No.1 from position 48-49 of the 5' end |
| S100P_A_2 | SEQ ID No.1 from positions 85-86 of the 5' end |
| S100P_A_3 | 96 Th to 97 th positions from 5' end of SEQ ID No.1 |
| S100P_A_4 | 151 Th to 152 th positions from 5' end of SEQ ID No.1 |
| S100P_A_5 | SEQ ID No.1 from position 189-190 of the 5' end |
| S100P_A_6 | SEQ ID No.1 from position 214-215 of the 5' end |
| S100P_A_7 | SEQ ID No.1 from position 569-570 of the 5' end |
| S100P_A_8 | 630 Th to 631 th positions of SEQ ID No.1 from 5' end |
| S100P_A_9 | SEQ ID No.1 from the 5' end at positions 695-696 |
| S100P_A_10 | SEQ ID No.1 from position 761-762 of the 5' end |
| S100P_A_11 | SEQ ID No.1 from position 765-766 of the 5' end |
| S100P_A_12 | SEQ ID No.1 from positions 774-775 of the 5' end |
| S100P_A_13 | SEQ ID No.1 from position 821-822 of the 5' end |
| S100P_A_14 | SEQ ID No.1 from position 849-850 of the 5' end |
| S100P_A_15 | 881-882 Bits from the 5' end of SEQ ID No.1 |
| S100P_A_16 | SEQ ID No.1 from position 891 to 892 at the 5' end |
| S100P_A_17 | SEQ ID No.1 from 5' end position 906-907 |
| S100P_A_18 | Positions 932-933 of SEQ ID No.1 from the 5' end |
| S100P_A_19 | SEQ ID No.1 from position 965 to 966 of the 5' end |
TABLE 2 CpG site information in S100P_B fragment
Specific PCR primers were designed for two fragments (S100 P_A fragment and S100P_B fragment) as shown in Table 3. Wherein SEQ ID No.3 and SEQ ID No.5 are forward primers, and SEQ ID No.4 and SEQ ID No.6 are reverse primers; non-specific tags are arranged at positions 1 to 10 from 5' in SEQ ID No.3 and SEQ ID No.5, and specific primer sequences are arranged at positions 11 to 35; the non-specific tags are arranged at the 1 st to 31 st positions of the 5' in the sequence 4 and the sequence 6, and the specific primer sequences are arranged at the 32 nd to 56 th positions. The primer sequences do not contain SNPs and CpG sites.
TABLE 3 S100P methylation primer sequences
Example 2, detection of methylation of S100P Gene and analysis of results
1. Study sample
With patient informed consent, ex vivo blood samples of 722 lung cancer patients, 152 lung benign nodule patients, 79 pancreatic cancer patients, 118 esophageal cancer patients, and 945 cancer-free controls (no cancer controls, i.e., no cancer is present and once, no lung benign nodules are reported and blood routine indexes are all within the reference range) were collected.
All patient samples were collected preoperatively and were subjected to imaging and pathological confirmation.
Lung cancer, pancreatic cancer and esophageal cancer subtypes are judged according to histopathology.
The stage of lung cancer takes an AJCC 8 th edition stage system as a judgment standard.
722 Cases of lung cancer patients were classified according to types: 619 cases of lung adenocarcinoma, 42 cases of lung squamous carcinoma, 49 cases of small cell lung carcinoma and 12 other cases.
722 Lung cancer patients were divided according to stage: 649 cases in stage I, 41 cases in stage II, and 32 cases in stage III.
722 Cases of lung cancer patients were classified according to lung cancer tumor size (T): t1, 603, T2, 83, T3 and 36.
722 Cases of lung cancer patients were classified according to the presence or absence of lung cancer lymph node infiltration (N): 688 cases were not infiltrated by lung cancer lymph nodes, and 34 cases were infiltrated by lung cancer lymph nodes.
79 Pancreatic cancer patients were classified according to the type: pancreatic ductal adenocarcinoma was 63 and the other subtypes amounted to 16.
118 Cases of esophageal cancer patients were classified according to types: 94 cases of esophageal squamous cell carcinoma, a total of 24 cases of other subtypes.
The median ages of the cancer-free population, benign lung nodules, lung cancer, pancreatic cancer and esophageal cancer patients were 56, 57, 58 and 57 years old, respectively, and the ratio of men and women in each of these 5 populations was about 1:1.
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 cytosine (C) is converted to uracil (U), while methylated cytosine remains unchanged, i.e., the C base of the original CpG site is converted to C or U after bisulfite treatment.
3. And 2 pairs of specific primers in the table 3 are adopted to carry out PCR amplification by using the DNA treated by the bisulfite in the step 2 as a template according to a reaction system required by a conventional PCR reaction by DNA polymerase, 2 pairs of primers are all adopted by the same conventional PCR system, and 2 pairs of primers are all 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) To 5. Mu.l of the PCR product was added 2. Mu.l of Shrimp Alkaline Phosphate (SAP) solution (0.3 ml SAP [0.5U ] +1.7ml H 2 O) and 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 a5 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 from Nanodispenser robot;
(5) Time-of-flight mass spectrometry; the data obtained were collected with SpectroACQUIRE v3.3.1.3 software and visualized by MASSARRAY EPITYPER V1.2 software.
Reagents used for the time-of-flight mass spectrometry detection were all kits (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 number 384: 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 23.0.0.
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 39 distinguishable peak patterns of methylated fragments were obtained. Methylation levels were calculated using SpectroACQUIRE v3.3.1.3 software based on peak area comparisons of methylated and unmethylated fragments (SpectroACQUIRE v3.3.1.3 software can automatically calculate peak areas to obtain methylation levels for each sample at each CpG site).
3. Analysis of results
1. Methylation level of S100P gene in blood of cancer-free control, benign nodules and lung cancer
Methylation levels of all CpG sites in the S100P gene were analyzed using blood of 722 lung cancer patients, 152 lung benign nodule patients and 945 cancer-free controls as study materials (Table 4). The results showed that the methylation level of the S100P gene of the cancer-free control was 0.51 (iqr=0.38-0.67), the methylation level of the benign nodule S100P gene was 0.46 (iqr=0.33-0.62), and the methylation level of the lung cancer patient was 0.47 (iqr=0.34-0.64).
2. Blood methylation level of S100P gene can distinguish cancer-free control from lung cancer patients
As a result of comparative analysis of methylation levels of the S100P gene in 722 lung cancer patients and 945 cancer-free controls, it was found that methylation levels of all CpG sites in the S100P gene were significantly lower in lung cancer patients than in cancer-free controls (P <0.05, table 5). In addition, methylation levels of all CpG sites of the S100P gene in different subtypes of lung cancer (lung adenocarcinoma, lung squamous carcinoma, small cell lung carcinoma) are significantly different from that of a non-cancer control. Methylation levels of all CpG sites of the S100P gene in different stages (clinical stage I and stage II-III) of lung cancer are significantly different from that of a cancer-free control. Furthermore, there was a significant difference in methylation levels between non-lymphoblastic lung cancer patients and lymphoblastic lung cancer patients, respectively, and non-cancerous controls (p < 0.05). Therefore, the methylation level of the S100P gene can be used for clinical diagnosis of lung cancer, and especially can be used for early diagnosis of lung cancer.
3. Blood methylation level of S100P gene can distinguish benign nodule of lung and lung cancer patient
We compared the methylation level differences of the S100P gene between 722 lung cancer patients and 152 benign nodules, and found that the methylation level of all CpG sites in the S100P amplified fragment was significantly higher in lung cancer patients than in benign nodules patients (P <0.05, table 6). Furthermore, we found that the methylation level of all CpG in the S100P gene was significantly higher than that of benign nodules (P < 0.05) in different subtypes of lung cancer (lung adenocarcinoma, lung squamous carcinoma, small cell lung cancer), different clinical stages (stage I or II-III), and lung cancer patients with or without lymphocytic infiltration, respectively. Thus, the methylation level of the S100P gene can be applied to distinguish lung cancer patients from benign nodule patients, and is a very valuable marker.
4. The methylation level of S100P 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 S100P gene of different subtypes of lung cancer patients (lung adenocarcinoma, lung squamous carcinoma, small cell lung cancer) and 945 cancer-free controls, the methylation level of all CpG sites in the S100P gene is found to have significant differences under the conditions of different subtypes of lung cancer (lung adenocarcinoma patients, lung squamous carcinoma patients, small cell lung cancer patients), lung cancer tumor sizes (T1, T2 and T3), different stages of lung cancer (clinical stage I, stage II and stage III) and the presence or absence of lymph node infiltration (P <0.05, table 7). Thus, the methylation level of the S100P gene can be used to distinguish between different subtypes of lung cancer or different stages of lung cancer.
5. The methylation level of S100P in blood can distinguish pancreatic cancer patients from cancer-free controls
The difference in methylation levels of all CpG sites in the S100P gene between 79 pancreatic cancer patients and 945 cancer-free control was analyzed using blood as a study material (table 8), of which 63 of 79 pancreatic cancer patients were pancreatic ductal adenocarcinoma. The methylation level of all target CpG sites in pancreatic cancer patients is median of 0.44 (IQR=0.31-0.60), the methylation level of the cancer-free control group is median of 0.51 (IQR=0.38-0.67), and the methylation level of all CpG sites of the S100P gene in pancreatic cancer patients is significantly lower than that of the cancer-free control group (P < 0.0001). The median methylation level of all target CpG sites in 63 pancreatic ductal adenocarcinoma patients was 0.43 (iqr=0.30-0.59), and methylation levels were significantly lower than that of the no-cancer control (p < 0.0001). Thus, the methylation level of the S100P gene can be used for clinical diagnosis of pancreatic cancer.
6. Blood S100P methylation level can distinguish esophageal patients from cancer-free controls
The difference in S100P methylation level between esophageal cancer patients and no-cancer controls was analyzed using blood of 118 esophageal cancer patients and 945 no-cancer controls as a study material (table 9), and 94 esophageal squamous cell carcinomas were included in 118 esophageal cancers. The results show that the methylation level of all the target CpG sites in the esophageal cancer patients is 0.45 (IQR=0.32-0.61), the methylation level of the cancer-free control group is 0.51 (IQR=0.38-0.67), and the methylation level of all the CpG sites of the S100P gene in the esophageal cancer patients is significantly lower than that of the cancer-free control group (P < 0.0001). The median methylation level for all CpG sites in esophageal squamous cell carcinoma was 0.45 (iqr=0.32-0.61), and methylation levels were significantly lower than for the no-cancer control (p <0.0001, table 9). Thus, the methylation level of the S100P gene can be used for clinical diagnosis of esophageal cancer.
7. Blood S100P methylation level can distinguish pancreatic cancer patients from lung cancer patients
The difference in methylation level of the S100P gene in the blood of pancreatic cancer patients and lung cancer patients was analyzed using the blood of 79 pancreatic cancer patients and 722 lung cancer patients as a study material (table 10). The results showed that the methylation level of all CpG sites in pancreatic cancer patients was median 0.44 (iqr=0.31-0.60), the methylation level of lung cancer patients was median 0.47 (iqr=0.34-0.64), and the methylation level of all CpG sites in pancreatic cancer patients was significantly lower than that in lung cancer patients (p < 0.05). Thus, the methylation level of the S100P gene can be used to distinguish pancreatic and lung cancer patients.
8. Blood S100P methylation level can distinguish esophageal cancer patients from lung cancer patients
Blood of 118 patients with esophageal cancer and 722 patients with lung cancer was used as a study material to analyze the difference in methylation level of S100P gene in blood of patients with esophageal cancer and lung cancer (Table 10). The results show that the methylation level of all target CpG sites in esophageal cancer patients is median of 0.45 (IQR=0.32-0.61), the methylation level of lung cancer patients is median of 0.47 (IQR=0.34-0.64), and the methylation level of all CpG sites in esophageal cancer patients is significantly lower than that of lung cancer patients (p < 0.05). Thus, the methylation level of the S100P gene can be used to distinguish between patients with esophageal cancer and lung cancer.
9. The methylation level of S100P in blood can distinguish pancreatic cancer patients from esophageal cancer patients
The blood of 79 pancreatic cancer patients and 118 esophageal cancer patients were analyzed for differences in the methylation level of the S100P gene (table 10). The results show that the methylation level of all target CpG sites in pancreatic cancer patients is median 0.44 (iqr=0.31-0.60), the methylation level of all target CpG sites in esophageal cancer patients is median 0.45 (iqr=0.32-0.61), and the methylation level of all CpG sites in pancreatic cancer patients is significantly lower than that in esophageal cancer patients (p < 0.05). Thus, the methylation level of the S100P gene can be used to distinguish pancreatic cancer patients from esophageal 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) Differentiating pancreatic cancer patients from non-cancerous controls;
(4) Distinguishing esophageal cancer patients from cancer-free controls;
(5) Differentiating between pancreatic cancer patients and lung cancer patients;
(6) Distinguishing patients with esophageal cancer from patients with lung cancer;
(7) Differentiating between pancreatic cancer patients and esophageal cancer patients;
(8) Distinguishing different subtypes of lung cancer;
(9) Different stages of lung cancer are distinguished.
The mathematical model is established as follows:
(A) Data sources: methylation levels of target CpG sites (combinations of one or more of tables 1-2) in isolated blood samples of 722 lung cancer patients, 152 lung benign nodule patients, 79 pancreatic cancer patients, 118 esophageal cancer patients, and 945 cancer-free controls listed in step one (test method is the same as in step two).
The data can be added with known parameters such as age, sex, white blood cell count and the like according to actual needs to improve the discrimination efficiency.
(B) Model building
Any two different types of patient data, namely training sets (such as cancer-free control and lung cancer patients, cancer-free control and pancreatic cancer patients, cancer-free control and esophageal cancer patients, lung benign nodule patients and lung cancer patients, lung cancer patients and pancreatic cancer patients, lung cancer patients and esophageal cancer patients, esophageal cancer patients and pancreatic cancer patients, lung adenocarcinoma and lung squamous carcinoma patients, lung adenocarcinoma and small cell lung cancer patients, lung squamous cell lung cancer 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) are selected as required to serve as data for establishing a model, and statistical software such as SAS, R, SPSS and the like is used for establishing a mathematical model through a formula by using a statistical method of two-class logistic regression. 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 an S100P gene of the sample to be detected by a DNA methylation determination method, then substituting data of the methylation levels into the mathematical model (if known parameters such as age, sex, white cell count and the like are included in the model construction, the step simultaneously substitutes specific numerical values of corresponding parameters of the sample to be detected into a model formula), calculating to obtain a detection index corresponding to the sample to be detected, and then comparing the detection index corresponding to the sample to be detected with a threshold value, and determining which type of sample the sample to be detected belongs to according to a 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 S100P 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 SAS, R, SPSS et al statistical software. 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 after 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, x1 to 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 b1 to 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 numerical value corresponding to a maximum approximate dengue index calculated by the mathematical model is used as a threshold value or a threshold value divided by 0.5 is directly set. And the detection index, namely the y value, obtained after the sample to be detected is tested and calculated by substituting the sample into the model is classified as B when the y value is larger than the threshold value, and classified as A when the y value is smaller than the threshold value, and the y value is equal to the threshold value and is used as an uncertain gray area. Wherein class a and class B are the corresponding two classifications (two classification groups, which group a, which group B is based on a specific mathematical model, are not defined herein), such as cancer-free control and lung cancer patients, cancer-free control and pancreatic cancer patients, cancer-free control and esophageal cancer patients, lung benign nodule patients and lung cancer patients, lung cancer patients and pancreatic cancer patients, lung cancer patients and esophageal cancer patients, esophageal cancer patients and pancreatic 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 stage I and II patients, lung cancer stage I and III patients, lung cancer stage II and lung cancer stage III 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 the S100P gene of a subject is detected by using a DNA methylation determination method, and methylation data obtained by detection are substituted into the mathematical model. If the methylation level of one or more CpG sites of the S100P gene of the subject is substituted into the mathematical model and then the calculated value, namely the detection index, is larger than the threshold value, the subject judges the class (B class) with the detection index in the training set larger than the threshold value; if the methylation level data of one or more CpG sites of the S100P gene of the subject is substituted into the mathematical model and then the calculated value, namely the detection index, is smaller than the threshold value, the subject belongs to the class (class A) with the detection index in the training set smaller than the threshold value; if the methylation level data of one or more CpG sites of the S100P gene of the subject is substituted into the mathematical model, and the calculated value, i.e. the detection index, is equal to the threshold value, the subject cannot be judged to be A class or B class.
Examples: the schematic diagram of fig. 2 illustrates the use of methylation of the preferred CpG sites (S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17 of s100deg.P_B and s100deg.P_B_19) and mathematical modeling for pulmonary benign and malignant nodule discrimination: the methylation level data of the 10 distinguishable CpG site combinations S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17 and S100deg.P_B_19, which have been detected in the lung cancer patient and lung benign nodule patient training set (722 lung cancer patients and 152 lung benign nodule patients herein), the age, sex (male assigned 1, female assigned 0) and white blood cell count of the patient were combined, and a mathematical model for distinguishing lung cancer patients from lung benign nodule patients was established by R software using a formula of a two-class logistic regression. The mathematical model is here a two-class logistic regression model, from which the constants b0 of the mathematical model and the weights b1 to bn of the individual methylation sites are determined, in this case specifically ：log(y/(1-y))＝-0.364+0.556*S100P_B_1,2+2.259*S100P_B_3-0.686*S100P_B_5-3.811*S100P_B_7,8+4.102*S100P_B_9-2.525*S100P_B_12+0.575*S100P_B_13-1.496*S100P_B_14+6.669*S100P_B_15,16,17-4.183*S100P_B_19+0.022* age-0.843 x sex (male assigned 1, female assigned 0) -0.045 x white blood cell count, where y is the methylation value of the 10 distinguishable methylation sites of the dependent variable i.e. the sample to be tested and the detection index obtained after substitution of age, sex, white blood cell count into the model. Under the condition that 0.5 is set as a threshold value, the methylation levels of the S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17 distinguishable CpG sites and S100deg.P_B_19 of the sample to be tested are tested and then are substituted into the model together with information of age, sex and white blood cell count of the sample to be tested, the obtained detection index, namely y value is more than 0.5 and is classified as lung cancer patients, less than 0.5 is classified as lung benign nodule patients, and if the detection index is equal to 0.5, the detection index is not determined as lung cancer patients or lung benign nodule patients. The area under the curve (AUC) calculation for this model was 0.64 (table 14). Specific subject judgment methods are shown in FIG. 2, for example, blood is collected from two subjects (A, B) to extract DNA, the extracted DNA is converted by bisulfite, and methylation levels of 10 distinguishable CpG sites, S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17 and S100deg.P_B_19, of the subjects are detected by a DNA methylation assay. 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 mathematical model of the first test subject is greater than 0.81 and is greater than 0.5, and the first test subject is judged to be a lung cancer patient; and if the value calculated by the mathematical model of the subject B is less than 0.5, judging the subject B as a benign nodule patient of the lung.
(C) Model Effect evaluation
According to the above method, mathematical models for distinguishing a lung cancer patient and a cancer-free control, a lung cancer patient and a benign nodule patient, a pancreatic cancer patient and a cancer-free control, a cancer-free control and an esophageal cancer patient, a lung cancer patient and a pancreatic cancer patient, a lung cancer patient and an esophageal cancer patient, a lung adenocarcinoma and a lung squamous carcinoma patient, a lung adenocarcinoma and a small cell lung cancer patient, a lung squamous carcinoma and a small cell lung cancer patient, a lung cancer patient in stage I and stage II, a lung cancer patient in stage I and stage III, a lung cancer patient in stage II and stage III are respectively established, 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 11, 12 and 13. In tables 11, 12 and 13, 1 CpG site represents the site of any one CpG site in the S100deg.P_B amplified fragment, 2 CpG sites represent the combination of any 2 CpG sites in S100deg.P_B, 3 CpG sites represent the combination of any 3 CpG sites in S100deg.P_B, … … 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 S100P gene for each group (lung cancer patient and no cancer control, lung cancer patient and lung benign nodule patient, pancreatic cancer patient and no cancer control, esophageal cancer patient and no cancer control, pancreatic cancer patient and lung cancer patient, esophageal cancer patient and lung cancer patient, pancreatic cancer patient and esophageal cancer patient, lung adenocarcinoma and lung squamous carcinoma patient, lung adenocarcinoma and small cell lung cancer patient, lung squamous cell lung cancer and small cell lung cancer patient, lung cancer and lung cancer patient in stage I and II, lung cancer and lung cancer patient in stage I and III, lung cancer in stage II and lung cancer patient in stage III) increases with increasing number of loci.
In addition, among the CpG sites shown in tables 1 to 2, there are cases where combinations of a few preferred sites are better in discrimination ability than combinations of a plurality of non-preferred sites. The 10 distinguishable CpG site combinations, such as S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17 and S100deg.P_B_19 shown in Table 14, table 15 and Table 16, are preferred sites for any ten combinations in S100deg.P_B.
In summary, the CpG sites on the S100P gene and combinations thereof, the CpG sites on the S100deg P_A fragment and combinations thereof, the CpG sites on the S100deg P_B fragment and combinations thereof, the S100P_B_1,2、S100P_B_3、S100P_B_5、S100P_B_7,8、S100P_B_9、S100P_B_12、S100P_B_13、S100P_B_14、S100P_B_15,16,17 and S100deg P_B_19 sites on the S100deg P_B fragment and combinations thereof, and the methylation levels of the CpG sites on the S100deg P_A and S100deg P_B sites and combinations thereof are capable of discriminating between a lung cancer patient and a cancer-free control, a lung cancer patient and a lung benign nodule patient, a pancreatic cancer patient and a cancer-free control, an esophageal cancer and cancer-free control, a pancreatic cancer patient and a lung cancer patient, an esophageal cancer patient and a lung cancer patient, a pancreatic cancer patient and an esophageal cancer patient, a lung adenocarcinoma and a small cell lung cancer patient, a lung squamous cell carcinoma and a lung cancer patient, a stage I lung cancer and stage II lung cancer patient, a stage I lung cancer and a stage III lung cancer patient.
Table 4 compares methylation levels of non-cancerous controls, benign nodules, and lung cancer
Table 5 compares methylation level differences between non-cancerous and lung cancer controls
Table 6 compares methylation level differences between benign nodules and lung cancer
Table 7 compares methylation level differences for different subtypes of lung cancer or different stages of lung cancer
Table 8 compares methylation level differences between cancer-free controls and pancreatic cancer
Table 9 compares methylation level differences between cancer-free controls and esophageal cancer
Table 10 compares methylation level differences between lung and pancreatic cancer and esophageal cancer
Table 11 CpG sites of S100P_B and combinations thereof are used to distinguish lung cancer from non-cancerous controls, lung cancer from benign nodules, pancreatic cancer from non-cancerous controls, and lung cancer from pancreatic cancer
Table 12 CpG sites of S100P_B and combinations thereof for distinguishing esophageal cancer from non-cancerous controls, lung cancer and esophageal cancer, and pancreatic cancer and esophageal cancer
CpG sites of S100P_B of Table 13 and combinations thereof for differentiating 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 stage I and lung cancer stage II patients, lung cancer stage I and lung cancer stage III patients, lung cancer stage II and lung cancer stage III patients
Table 14 optimal CpG sites of S100P_B and combinations thereof for differentiating lung cancer and non-cancerous controls, lung cancer and benign nodules, pancreatic cancer and non-cancerous controls, and lung cancer and pancreatic cancer
Table 15 optimal CpG sites of S100P_B and combinations thereof for distinguishing esophageal cancer from non-cancerous controls, lung cancer and esophageal cancer, and pancreatic cancer and esophageal cancer
Table 16 optimal CpG sites of S100P_B and combinations thereof for differentiating lung adenocarcinoma and squamous cell carcinoma patients, lung adenocarcinoma and small cell lung carcinoma patients, squamous cell lung carcinoma and small cell lung carcinoma patients, stage I lung carcinoma and stage II lung carcinoma patients, stage I lung carcinoma and stage III lung carcinoma patients, stage II lung carcinoma and stage III lung carcinoma patients
<110> Nanjing Techno Biotechnology Co., ltd
<120> A calbindin methylation marker for aiding in the diagnosis of cancer
<130> GNCLN200265
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Claims (8)
1. Use of a substance for detecting the methylation level of the S100P gene in the preparation of a product; the use of the product is to assist in distinguishing lung cancer patients from non-cancerous controls;
the methylation level of the S100P gene is that all CpG sites in fragments shown in the following (e 1) - (e 2) in the S100P gene are methylated;
(e1) A DNA fragment shown in SEQ ID No. 1;
(e2) The DNA fragment shown in SEQ ID No. 2.
2. Use of a substance for detecting the methylation level of the S100P gene and a medium storing a mathematical model building method and/or a use method for the preparation of a product; the use of the product is to assist in distinguishing lung cancer patients from non-cancerous controls;
The mathematical model is obtained according to a method comprising the following steps:
(A1) Detecting S100P gene methylation levels of n1 type a samples and n2 type B samples, respectively;
(A2) Taking the S100P gene methylation level data of all samples obtained in the step (A1), and establishing a mathematical model by a two-classification logistic regression method according to the classification modes of the type A and the type B;
The using method of the mathematical model comprises the following steps:
(B1) Detecting the methylation level of an S100P gene of a sample to be detected;
(B2) Substituting the S100P 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 a threshold value, and determining whether the type of the sample to be detected is A type or B type according to a comparison result;
The type a sample and the type B sample are lung cancer samples and non-cancer controls;
the methylation level of the S100P gene is that all CpG sites in fragments shown in the following (e 1) - (e 2) in the S100P gene are methylated;
(e1) A DNA fragment shown in SEQ ID No. 1;
(e2) The DNA fragment shown in SEQ ID No. 2.
3. Use of a medium storing a mathematical model building method and/or a use method for the preparation of a product; the use of the product is to assist in distinguishing lung cancer patients from non-cancerous controls;
The mathematical model is obtained according to a method comprising the following steps:
(A1) Detecting S100P gene methylation levels of n1 type a samples and n2 type B samples, respectively;
(A2) Taking the S100P gene methylation level data of all samples obtained in the step (A1), and establishing a mathematical model by a two-classification logistic regression method according to the classification modes of the type A and the type B;
The using method of the mathematical model comprises the following steps:
(B1) Detecting the methylation level of an S100P gene of a sample to be detected;
(B2) Substituting the S100P 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 a threshold value, and determining whether the type of the sample to be detected is A type or B type according to a comparison result;
The type a sample and the type B sample are lung cancer samples and non-cancer controls;
the methylation level of the S100P gene is that all CpG sites in fragments shown in the following (e 1) - (e 2) in the S100P gene are methylated;
(e1) A DNA fragment shown in SEQ ID No. 1;
(e2) The DNA fragment shown in SEQ ID No. 2.
4. Use according to claim 1 or 2, characterized in that: the substance for detecting the methylation level of the S100P gene is a primer combination.
5. The use according to claim 4, characterized in that: the primer combination is a primer pair A and/or a primer pair B;
The primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.3 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 3; the primer A2 is SEQ ID No.4 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 4;
the primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is SEQ ID No.5 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 5; the primer B2 is SEQ ID No.6 or single-stranded DNA shown in 32-56 th nucleotide of SEQ ID No. 6.
6. A system, comprising:
(D1) Reagents and/or instrumentation for detecting the methylation level of the S100P gene;
(D2) A device comprising a unit a and a unit B;
the unit A 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 used for acquiring S100P gene methylation level data of n 1A type samples and n 2B type samples obtained by the detection of the (D1);
The data analysis processing module can establish a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type based on S100P gene methylation level data of n 1A type samples and n 2B type samples acquired by the data acquisition module;
the model output module is used for outputting the mathematical model established by the data analysis processing module;
The unit B 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 used for inputting the S100P gene methylation level data of the to-be-detected person obtained by the detection of (D1);
The data operation module is used for substituting the S100P gene methylation level data of the testee into the mathematical model, and calculating to obtain a detection index;
The data comparison module is used for comparing the detection index with a threshold value;
the conclusion output module is used for outputting a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result of the data comparison module;
The type a sample and the type B sample are lung cancer samples and non-cancer controls;
the methylation level of the S100P gene is that all CpG sites in fragments shown in the following (e 1) - (e 2) in the S100P gene are methylated;
(e1) A DNA fragment shown in SEQ ID No. 1;
(e2) The DNA fragment shown in SEQ ID No. 2.
7. The system according to claim 6, wherein: the reagent for detecting the methylation level of the S100P gene is a primer combination.
8. The system according to claim 7, wherein: the primer combination is a primer pair A and/or a primer pair B;
The primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.3 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 3; the primer A2 is SEQ ID No.4 or single-stranded DNA shown in 32 th-56 th nucleotides of SEQ ID No. 4;
the primer pair B is a primer pair consisting of a primer B1 and a primer B2; the primer B1 is SEQ ID No.5 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 5; the primer B2 is SEQ ID No.6 or single-stranded DNA shown in 32-56 th nucleotide of SEQ ID No. 6.
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| CN1650033A (en) * | 2002-03-07 | 2005-08-03 | 约翰霍普金斯大学医学院 | Genomic screen for epigenetically silenced genes associated with cancer |
| CN107406880A (en) * | 2015-02-24 | 2017-11-28 | 海德堡鲁普雷希特卡尔斯大学 | For detecting the biomarker group of cancer |
| CN110331207A (en) * | 2019-08-27 | 2019-10-15 | 北京泱深生物信息技术有限公司 | Adenocarcinoma of lung biomarker and related application |
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| CN1650033A (en) * | 2002-03-07 | 2005-08-03 | 约翰霍普金斯大学医学院 | Genomic screen for epigenetically silenced genes associated with cancer |
| CN107406880A (en) * | 2015-02-24 | 2017-11-28 | 海德堡鲁普雷希特卡尔斯大学 | For detecting the biomarker group of cancer |
| CN110331207A (en) * | 2019-08-27 | 2019-10-15 | 北京泱深生物信息技术有限公司 | Adenocarcinoma of lung biomarker and related application |
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