CN113122630A - Calcium binding protein methylation marker for auxiliary diagnosis of cancer - Google Patents
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Abstract
The invention discloses a calcium binding protein methylation marker for auxiliary diagnosis of cancer. The invention provides application of a methylated S100P gene as a marker in preparation of products; the use of the product is at least one of the following: auxiliary diagnosis of cancer or prediction of cancer risk; aid in distinguishing benign nodules from cancer; assisting in distinguishing different subtypes of cancer; assisting in distinguishing different stages of cancer; aid in distinguishing between different cancers; determining whether the test substance has a blocking or promoting effect on the occurrence of the 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 the patients with the lung cancer, the pancreatic cancer and the esophageal cancer, and the invention has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of the lung cancer, the pancreatic cancer and the 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 auxiliary diagnosis of cancer.
Background
Lung cancer is a malignant tumor occurring in the epithelium of the bronchial mucosa, and the morbidity and mortality of lung cancer have been on the rise in recent decades, and the lung cancer is the cancer with the highest morbidity and mortality all over the world. Despite recent advances in diagnostic methods, surgical techniques, and chemotherapeutic drugs, the overall 5-year survival rate for lung cancer patients is only 16%, mainly because most lung cancer patients have metastasis at the time of treatment and thus lose the chance of radical surgical intervention. Research shows that the prognosis of lung cancer is directly related to stage, the 5-year survival rate of the lung cancer in stage I 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%. Therefore, early diagnosis and early treatment are key to reducing mortality in lung cancer patients.
The current major lung cancer diagnostic methods are as follows: (1) the imaging method comprises the following steps: such as chest X-ray and low dose helical CT. Early stage lung cancer is difficult to detect by chest X-ray. Although the low-dose spiral CT can find small 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 examined. Meanwhile, chest X-ray and low dose helical CT are not suitable for frequent use due to radiation. In addition, imaging methods are often affected by the equipment and doctor's experience in viewing the film, as well as the time available for reading the film. (2) The cytological method comprises the following steps: such as sputum cytology, bronchoscopic biopsy or biopsy, bronchoalveolar lavage fluid cytology, and the like. Sputum cytology and bronchoscopy swabs or biopsies have low sensitivity for peripheral lung cancer. Meanwhile, the operation of brushing a sheet under a bronchoscope or taking a biopsy and performing bronchoalveolar lavage fluid cytology is complicated, and the comfort level of a physical examiner is poor. (3) Serum tumor markers commonly used at present: carcinoembryonic antigen (CEA), carbohydrate antigen (CA125/153/199), cytokeratin 19 fragment antigen (CYFRA21-1), neuron-specific enolase (NSE), and the like. These serum tumor markers have limited sensitivity to lung cancer, typically 30% -40%, and even lower for stage I tumors. Moreover, tumor specificity is limited, and is affected by many benign diseases such as benign tumors, inflammations, degenerative diseases, and the like. At present, tumor markers are mainly used for screening malignant tumors and rechecking tumor treatment effects. Therefore, there is a need to further develop a highly efficient and specific early diagnosis technique for lung cancer.
The currently internationally accepted most effective method of pulmonary nodule diagnosis is chest low dose helical CT screening. However, low-dose helical CT has high sensitivity, and is difficult to identify benign or malignant nodules, although a large number of nodules can be found. Among the nodules found, the proportion of malignancy is less than 4%. Currently, the clinical identification of benign and malignant pulmonary nodules requires long-term follow-up, repeated CT examination, or invasive examination methods such as biopsy sampling (including fine needle biopsy of chest wall, bronchoscopic biopsy, thoracoscopic or open-chest lung biopsy) of pulmonary nodules. CT-guided or ultrasound-guided transthoracic puncture biopsy has higher sensitivity, but has a lower diagnosis rate for <2cm nodules, a 30-70% missed diagnosis rate, and a higher incidence rate of pneumothorax and hemorrhage. The incidence rate of complication of the bronchoscope needle biopsy is relatively low, but the diagnosis rate of peripheral nodules is limited, the diagnosis rate of the nodules is only 34% when the number of the nodules is less than or equal to 2cm, and the diagnosis rate of the nodules is 63% when the number of the nodules is more than 2 cm. The surgical resection has high diagnosis rate and can directly treat the nodules, but can cause the lung function of the patient to be temporarily reduced, and if the nodules are benign, the patient is subjected to unnecessary operations, thereby resulting in over-treatment. Therefore, there is an urgent need for new in vitro diagnostic molecular markers to assist in the identification of pulmonary nodules, and to minimize unnecessary punctures or surgeries while reducing the rate of missed diagnosis.
Pancreatic cancer is a common malignancy of the digestive tract, with about 90% of pancreatic ductal adenocarcinomas, the fourth most lethal malignancy in the world today. Due to the characteristics of occult pathogenesis, poor specificity of clinical symptoms and early infiltrative, most pancreatic cancer patients are in an advanced stage when discovered, and lose the chance of surgical treatment, so that the 5-year survival rate is only 7 percent. Pancreatic cancer patients can reach 60% of 5-year survival if they can be found early (stage I). Currently, the common diagnostic methods for pancreatic cancer in clinic are: (1) the accuracy of ultrasonic diagnosis is limited by the doctor's experience of seeing the film, the patient's hypertrophic body and the gas in the gastrointestinal tract; generally, the method for diagnosing pancreatic cancer, ultrasound, can be applied as a supplementary examination of CT, but the enhanced CT has large radiation to the human body and is not easy to be frequently used; MRI has no radiation effect, but is not suitable for some people (metal objects, cardiac pacemakers and the like exist in the body), the examination time is long, and some small and medium hospitals are not popularized due to the fact that equipment is expensive. (2) The serum tumor markers such as CA19-9, CA242, CA50 and the like are clinically combined for further detection, have high sensitivity and low specificity and are easily influenced by liver function and cholestasis. (3) And (3) pathological examination: percutaneous aspiration biopsy, biopsy under the guidance of ultrasonic gastroscope, ascites exfoliation cytology and examination biopsy under laparoscopy or open surgery, but this method is invasive and not suitable for early patients. Therefore, more sensitive and specific early pancreatic cancer molecular markers are urgently to be discovered.
Esophageal cancer is a malignant tumor originating from the epithelium of the esophageal mucosa, of which about 80% are squamous cell carcinomas, which are one of the clinically common malignant tumors. Worldwide, the incidence of esophageal cancer is at the 8 th position in malignant tumors and the mortality is at the 6 th position. China is a country with high incidence of esophageal cancer, and the incidence rate of the cancer tends to increase gradually. At present, more than 90% of patients with esophageal cancer have progressed to the middle and advanced stage when the diagnosis is confirmed, and the overall 5-year survival rate is less than 20%. At present, the following methods are mainly used for detecting esophageal cancer clinically. Performing endoscopic ultrasonic examination: due to the low penetration of the high frequency probe, only
Even shorter, the visible range is very limited, and in addition
Patients cannot use this method because of excessive esophageal narrowing. Esophagoscopy: the esophagoscope can observe the position, size and shape of a focus in detail, and can also directly clamp pathological tissues or brush a sample with a brush for cytological examination, but the esophagoscope causes discomfort of a patient. X-ray barium meal radiography: the patient swallows the barium gruel during X-ray examination, and observes the development of the barium gruel when passing through the esophagus, so as to achieve qualitative and positioning diagnosis, which is influenced by the operation and the experience of seeing the slice of the doctor, and the method is not suitable for the patient with very early esophageal cancer. CT scanning: it is possible to show the relationship between the patient's oesophagus and the adjacent organs, but it has the disadvantage of 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) and the like can be used for esophageal cancerThe sensitivity is less than 40%, the specificity is low, and the diagnostic value is low especially for early patients. Therefore, there is a need to further develop highly effective and specific early diagnosis techniques for esophageal cancer.
DNA methylation is an important chemical modification of genes, affecting the regulation 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 proto-oncogenes in tumor tissues. However, the correlation between DNA methylation in blood and tumorigenesis and development has been reported to be relatively small. In addition, blood is easy to collect, DNA methylation is stable, and the clinical application value is huge if a tumor specific blood DNA methylation molecular marker can be found. Therefore, exploring and developing a blood DNA methylation diagnosis technology suitable for clinical detection needs has important clinical application value and social significance for improving early diagnosis and treatment effects of lung cancer and reducing mortality.
Calcium-binding proteins (S100P) are members of the S100 protein family and function primarily 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 auxiliary diagnosis of cancer.
The S100P gene described in any one of the following may specifically include Genbank accession numbers: NM _ 005980.3.
In a first aspect, the invention claims the use of the methylated S100P gene as a marker for the preparation of a product; the use of the product is at least one of the following:
(1) auxiliary diagnosis of cancer or prediction of cancer risk;
(2) aid in distinguishing benign nodules from cancer;
(3) assisting in distinguishing different subtypes of cancer;
(4) assisting in distinguishing 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) the method helps to distinguish different subtypes of the lung cancer;
(8) assisting in distinguishing different stages of lung cancer;
(9) auxiliary diagnosis of pancreatic cancer or prediction of pancreatic cancer risk;
(10) auxiliary diagnosis of esophageal cancer or prediction of esophageal cancer risk;
(11) assisting in distinguishing lung cancer from pancreatic cancer;
(12) the lung cancer and the esophageal cancer are distinguished in an auxiliary mode;
(13) the pancreatic cancer and the esophageal cancer are assisted to be distinguished;
(14) determining whether the test agent has a hindering or promoting effect on the development of the cancer.
Further, the diagnosis assistance for cancer in (1) may be embodied as at least one of the following: aid in distinguishing between cancer patients and non-cancer controls (it can be understood that no cancer has been reported and benign nodules in the lung are reported and that the blood routine is within the reference range); aid in distinguishing between different cancers.
Further, the benign nodules in (2) are benign nodules corresponding to the cancers in (2), such as benign nodules of lung and lung cancer.
Further, the different subtypes of the cancer described in (3) may be pathotyped, such as histological typing.
Further, the different stages of the cancer in (4) may be clinical stages or TNM stages.
In a specific embodiment of the present invention, the diagnosis assistance system of lung cancer in (5) is specifically embodied as at least one of: can help to distinguish lung cancer patients from non-cancer controls, can help to distinguish lung adenocarcinoma patients from non-cancer controls, can help to distinguish lung squamous carcinoma patients from non-cancer controls, can help to distinguish small cell lung cancer patients from non-cancer controls, can help to distinguish stage I lung cancer patients from non-cancer controls, can help to distinguish stage II-III lung cancer patients from non-cancer controls, can help to distinguish lung cancer patients without lymph node infiltration from non-cancer controls, and can help to distinguish lung cancer patients with lymph node infiltration from non-cancer controls. Wherein the cancer-free control is understood as having no cancer at present and once and no reported benign nodules in the lung and having blood routine indicators within a reference range.
In a specific embodiment of the present invention, the auxiliary distinguishing between benign nodules and lung cancer in (6) is embodied as at least one of: the kit can assist in distinguishing lung cancer and benign nodules of the lung, lung adenocarcinoma and benign nodules of the lung, squamous lung cancer and benign nodules of the lung, small cell lung cancer and benign nodules of the lung, stage I lung cancer and benign nodules of the lung, stage II-III lung cancer and benign nodules of the lung, non-lymph node infiltrated lung cancer and benign nodules of the lung, and lymph node infiltrated lung cancer and benign nodules of the lung.
In a specific embodiment of the present invention, the auxiliary differentiation of different subtypes of lung cancer in (7) is embodied as: can help to distinguish any two of lung adenocarcinoma, lung squamous carcinoma and small cell lung cancer.
In a specific embodiment of the present invention, the auxiliary differentiation of different stages of lung cancer in (8) is embodied as at least one of the following: can help to distinguish any two of T1 stage lung cancer, T2 stage lung cancer and T3 lung cancer; can help to distinguish the lung cancer without lymph node infiltration from the lung cancer with lymph node infiltration; can help to distinguish any two of clinical stage I lung cancer, clinical stage II lung cancer and clinical stage III lung cancer.
In a specific embodiment of the present invention, the diagnosis assistance for pancreatic cancer in (9) is embodied as at least one of: can help to distinguish pancreatic cancer patients from non-cancer controls, and can help to distinguish lung pancreatic duct cancer from non-cancer controls. Wherein the cancer-free control is understood as having no cancer at present and once and no reported benign nodules in the lung and having blood routine indicators within a reference range.
In a specific embodiment of the present invention, the diagnosis assistance method in (10) is specifically embodied in at least one of the following: can help distinguish esophageal cancer patients from non-cancer controls, and can help distinguish esophageal squamous cell carcinoma from non-cancer controls. Wherein the cancer-free control is understood to mean that no cancer has been present and ever reported and that benign nodules in the lung are not reported and that the blood routine is within the reference range.
In the above (1) to (14), the cancer may be a cancer capable of causing a reduction in the methylation level of S100P gene in the body, such as lung cancer, pancreatic cancer, esophageal cancer, and the like.
In a second aspect, the invention claims the use of a substance for detecting the methylation level of the S100P gene in the preparation of a product. The use of the product may be at least one of the foregoing (1) to (14).
In a third aspect, the invention claims the use of a substance for detecting the methylation level of the S100P gene and a medium having stored thereon a mathematical modeling method and/or a method of use for the manufacture 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 according to a method comprising the steps of:
(A1) detecting the methylation level of the S100P gene of n 1A type samples and n 2B type samples (training set);
(A2) and (4) taking the methylation level data of the S100P genes 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 a threshold value for classification judgment.
Wherein both n1 and n2 in (A1) can be positive integers of 50 or more.
The use method of the mathematical model comprises the following steps:
(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, which is obtained in the step (B1), into the mathematical model to obtain a detection index; and then comparing the detection index with the threshold value, and determining whether the type of the sample to be detected is the type A or the type B according to the comparison result.
In a specific embodiment of the present invention, the threshold is set to 0.5. Greater than 0.5 is classified as one class and less than 0.5 is classified as another class, equal to 0.5 as an indeterminate gray zone. The type A and the type B are two corresponding classifications, grouping of the two classifications, which group is the type A and which group is the type B, and are determined according to a specific mathematical model without convention.
In practical applications, the threshold may also be determined according to the maximum johning index (specifically, may be a value corresponding to the maximum johning index). The gray areas greater than the threshold are classified into one category, the gray areas less than the threshold are classified into another category, and the gray areas equal to the threshold are regarded as uncertain gray areas. The type A and the type B are two corresponding classifications, grouping of the two classifications, which group is the type A and which group is the type B, and are determined according to a specific mathematical model without convention.
The type a sample and the type B sample may be any one of:
(C1) lung cancer samples and no cancer controls;
(C2) lung cancer samples and benign nodule samples of lung;
(C3) samples of different subtypes of lung cancer;
(C4) samples of different stages of lung cancer;
(C5) lung cancer samples and esophageal cancer samples;
(C6) lung cancer 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 the medium storing the mathematical modeling method and/or the method of use 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 claimed kit of the present invention includes 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 the "medium storing the mathematical model building method and/or the method of use" as described in the third aspect or the fourth aspect.
In a sixth aspect, the invention claims a system.
The claimed system of the present invention comprises:
(D1) reagents and/or instruments 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 (D1) S100P gene methylation level data of n 1A type samples and n 2B type samples obtained by detection;
wherein n1 and n2 can both be positive integers of more than 50.
The data analysis processing module can establish a mathematical model through a two-classification logistic regression method according to the classification modes of the A type and the B type and determine the threshold value of classification judgment based on the S100P gene methylation level data of the n 1A type samples and the n 2B type samples collected by the data collection 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 a 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 (D1) detected S100P gene methylation level data of a person to be detected;
the data operation module is used for substituting the S100P gene methylation level data of the person to be tested 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 the conclusion that the type of the sample to be tested is the type A or the type B 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 benign nodule samples of lung;
(C3) samples of different subtypes of lung cancer;
(C4) samples of different stages of lung cancer;
(C5) lung cancer samples and esophageal cancer samples;
(C6) lung cancer 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. Greater than 0.5 is classified as one class and less than 0.5 is classified as another class, equal to 0.5 as an indeterminate gray zone. The type A and the type B are two corresponding classifications, grouping of the two classifications, which group is the type A and which group is the type B, and are determined according to a specific mathematical model without convention.
In practical applications, the threshold may also be determined according to the maximum johning index (specifically, may be a value corresponding to the maximum johning index). The gray areas greater than the threshold are classified into one category, the gray areas less than the threshold are classified into another category, and the gray areas equal to the threshold are regarded as uncertain gray areas. The type A and the type B are two corresponding classifications, grouping of the two classifications, which group is the type A and which group is the type B, and are determined according to a specific mathematical model without convention.
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 S100P gene in fragments as shown in (e1) to (e2) below. The methylated S100P gene can be methylated at all or part of CpG sites in the S100P gene as shown in the following (e1) to (e2) fragments.
(e1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment with more than 80% of identity with the DNA fragment;
(e2) the DNA fragment shown in SEQ ID No.2 or the DNA fragment with more than 80 percent of identity with the DNA fragment.
Further, said "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 point of all CpG sites in said DNA fragment represented by SEQ ID No. 2.
Alternatively, the "whole 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 the 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 has CpG sites shown at positions 41-42 and positions 44-45 from the 5' end (S100P _ B _1, 2);
(f2) the CpG site shown in the 128 th and 129 th positions from the 5' end of the DNA fragment shown in SEQ ID No.2 (S100P _ B _ 3);
(f3) the DNA fragment shown in SEQ ID No.2 has CpG sites shown at positions 278 and 279 from the 5' end (S100P _ B _ 5);
(f4) the DNA fragment shown in SEQ ID No.2 has CpG sites shown in positions 362-363 and 372-373 from the 5' end (S100P _ B _7, 8);
(f5) the CpG sites indicated by 379-380 th site from the 5' end of the DNA fragment shown in SEQ ID No.2 (S100P _ B _ 9);
(f6) the DNA fragment shown in SEQ ID No.2 shows CpG sites at 473-474 positions from the 5' end (S100P _ B _ 12);
(f7) the DNA fragment shown in SEQ ID No.2 has CpG sites shown in 491-492 at the 5' end (S100P _ B _ 13);
(f8) the DNA fragment shown in SEQ ID No.2 has CpG sites shown in 516-517 bits from the 5' end (S100P _ B _ 14);
(f9) the DNA fragment shown in SEQ ID No.2 has CpG sites shown in the 542-and 551-aspic 552-and 554-and 555-positions from the 5' end (S100P _ B _15,16, 17);
(f10) the DNA fragment shown in SEQ ID No.2 shows CpG sites at positions 649 and 650 from the 5' end (S100P _ B _ 19).
In a specific embodiment of the invention, some adjacent methylation sites are treated as one methylation site when performing DNA methylation analysis using time-of-flight mass spectrometry since several CpG sites are located on one methylation fragment and the peak pattern is indistinguishable (indistinguishable sites are listed in table 4), and thus when performing methylation level analysis, and constructing and using related mathematical models. This is the case for (f1), (f4) and (f9) 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-length 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-length or partial fragment of the S100P gene; the instrument for detecting the methylation level of the S100P gene can be a time-of-flight mass spectrometer. Of course, the reagent for detecting the methylation level of the S100P gene can also comprise other conventional reagents for performing time-of-flight mass spectrometry.
Further, the partial fragment is at least one fragment selected from the following fragments:
(g1) the DNA fragment shown in SEQ ID No.1 or the DNA fragment contained in the DNA fragment;
(g2) a DNA fragment shown as SEQ ID No.2 or a DNA fragment contained therein;
(g3) a DNA fragment having an identity of 80% or more to the DNA fragment represented by SEQ ID No.1 or a DNA fragment contained therein;
(g4) a DNA fragment having an identity of 80% or more with the DNA fragment represented by SEQ ID No.2 or a DNA fragment contained therein.
In the present invention, the primer combination may specifically be 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 can be specifically single-stranded DNA shown by SEQ ID No.3 or 11 th to 35 th nucleotides of SEQ ID No. 3; the primer A2 can be specifically single-stranded DNA shown by 32 th-56 th nucleotides of SEQ ID No.4 or 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 by SEQ ID No.5 or 11 th to 35 th nucleotides of SEQ ID No. 5; the primer B2 can be single-stranded DNA shown by the 32 nd to 56 th nucleotides of SEQ ID No.6 or SEQ ID No. 6.
In addition, the invention also claims a method for distinguishing the sample to be detected as the type A sample or the type B sample. The method may comprise the steps of:
(A) the mathematical model may be established according to a method comprising the steps of:
(A1) detecting the methylation level of the S100P gene of n 1A type samples and n 2B type samples (training set);
(A2) and (4) taking the methylation level data of the S100P genes 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 a threshold value for classification judgment.
Wherein both n1 and n2 in (A1) can be positive integers of 50 or more.
(B) Whether the sample to be tested is a type a sample or a type B sample can be determined according to a method comprising the following steps:
(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, which is obtained in the step (B1), into the mathematical model to obtain a detection index; and then comparing the detection index with the threshold value, and determining whether the type of the sample to be detected is the type A or the type B according to the comparison result.
In a specific embodiment of the present invention, the threshold is set to 0.5. Greater than 0.5 is classified as one class and less than 0.5 is classified as another class, equal to 0.5 as an indeterminate gray zone. The type A and the type B are two corresponding classifications, grouping of the two classifications, which group is the type A and which group is the type B, and are determined according to a specific mathematical model without convention.
In practical applications, the threshold may also be determined according to the maximum johning index (specifically, may be a value corresponding to the maximum johning index). The gray areas greater than the threshold are classified into one category, the gray areas less than the threshold are classified into another category, and the gray areas equal to the threshold are regarded as uncertain gray areas. The type A and the type B are two corresponding classifications, grouping of the two classifications, which group is the type A and which group is the type B, and are determined according to a specific mathematical model without convention.
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 benign nodule samples of lung;
(C3) samples of different subtypes of lung cancer;
(C4) samples of different stages of lung cancer;
(C5) lung cancer samples and esophageal cancer samples;
(C6) lung cancer 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 one of the above mathematical models may be changed according to the detection method of DNA methylation and the fitting manner, and is determined according to a specific mathematical model without any convention.
In the embodiment of the present invention, the model is specifically log (y/(1-y)) ═ b0+ b1x1+ b2x2+ b3x3+ …. + bnXn, where y is a detection index obtained after a dependent variable is substituted into the model for the methylation value of one or more methylation sites of the sample to be detected, b0 is a constant, x1 to xn are independent variables, i.e., the methylation values of one or more methylation sites of the sample to be detected (each value is a value between 0 and 1), and b1 to bn are weights assigned to the methylation values of each site by the model.
In the embodiment of the present invention, the model may be established by adding known parameters such as age, sex, white blood cell count, etc. as appropriate to improve the discrimination efficiency. One particular model established in embodiments of the present invention is a model for assisting in distinguishing benign nodules from lung cancer, the model being specifically: log (y/(1-y)) -0.364+0.556 x S100P _ B _1,2+2.259 x S100P _ B _3-0.686 x S100P _ B _5-3.811 x S100P _ B _7,8+4.102 x S100P _ B _9-2.525 x S100P _ B _12+0.575 x S100P _ B _13-1.496 x S100P _ B _14+6.669 x S100P _ B _15,16,17-4.183 x S100P _ B _19+ 0.022-0.843 female age-gender (assigned 1, 0 assigned to 0.045 white cell count. The threshold of the model is 0.5. Patients with a detection index greater than 0.5 calculated by the model are candidates for lung cancer patients and patients with a detection index less than 0.5 are candidates for benign nodules in the lung.
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 specimen and the type B specimen are specimens of different subtypes of lung cancer (C3), the type a specimen and the type B specimen may be specifically any two of a lung adenocarcinoma specimen, a lung squamous carcinoma specimen, and a small cell lung cancer specimen.
In the above aspects, when the type a specimen and the type B specimen are different stage specimens of (C4), the type a specimen and the type B specimen may be specifically any two of a clinical stage I lung cancer specimen, a clinical stage II lung cancer specimen, and a clinical stage III lung cancer specimen.
The invention provides hypomethylation of the S100P gene in blood of lung cancer patients, pancreatic cancer patients and esophageal cancer patients. Experiments prove that by taking blood as a sample, cancer (lung cancer, pancreatic cancer and esophageal cancer) patients and cancer-free controls can be distinguished, benign nodules and lung cancer of the lung can be distinguished, different subtypes and different stages of the lung cancer can be distinguished, and the lung cancer and the pancreatic cancer, the lung cancer and the esophageal cancer, the pancreatic cancer and the esophageal cancer can be distinguished. The invention has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of lung cancer, pancreatic cancer and esophagus and reducing the death rate.
Drawings
Fig. 1 is a diagram of a mathematical model.
Fig. 2 is an illustration of a mathematical model.
Detailed Description
The experimental procedures used in the following examples are all conventional procedures unless otherwise specified.
Materials, reagents and the like used in the following examples are commercially available unless otherwise specified.
The calcium-binding protein (S100P) gene quantification test in the following examples was performed in triplicate, and the results were averaged.
Example 1 primer design for detecting methylation site of S100P Gene
Through a large number of sequence and functional analyses, two fragments (S100P _ a fragment and S100P _ B fragment) of the S100P gene were selected for methylation level and cancer-related analysis.
The S100P _ A fragment is located in the hg19 reference genome chr4:6694355-6695352, sense strand.
The S100P _ B fragment is located in the hg19 reference genome chr4:6695337 and 6696281, the sense strand.
The CpG site information in the S100P _ A fragment (SEQ ID No.1) is shown in Table 1.
The CpG site information in the S100P _ 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 | 48-49 of SEQ ID No.1 from the 5' end |
| S100P_A_2 | 85-86 th bit from 5' end of SEQ ID No.1 |
| S100P_A_3 | 96-97 th site from 5' end of SEQ ID No.1 |
| S100P_A_4 | 151-152 th site from 5' end of SEQ ID No.1 |
| S100P_A_5 | 189-190 th site from 5' end of SEQ ID No.1 |
| S100P_A_6 | 214-215 from the 5' end of SEQ ID No.1 |
| S100P_A_7 | Position 569-570 from the 5' end of SEQ ID No.1 |
| S100P_A_8 | Position 630-631 from the 5' end of SEQ ID No.1 |
| S100P_A_9 | SEQ ID No.1 at position 695-696 from the 5' end |
| S100P_A_10 | 761-762 of SEQ ID No.1 from the 5' end |
| S100P_A_11 | 765-766 position from 5' end of SEQ ID No.1 |
| S100P_A_12 | Position 774-775 from 5' end of SEQ ID No.1 |
| S100P_A_13 | Position 821-822 from the 5' end of SEQ ID No.1 |
| S100P_A_14 | 849-850 th site from 5' end of SEQ ID No.1 |
| S100P_A_15 | SEQ ID No.1 at position 881-882 from the 5' end |
| S100P_A_16 | Position 891-892 from the 5' end of SEQ ID No.1 |
| S100P_A_17 | 906-907 th site from 5' end of SEQ ID No.1 |
| S100P_A_18 | The sequence shown in SEQ ID No.1 is from the 5' end 932-933 position |
| S100P_A_19 | 966 from the 5' end of SEQ ID No.1 |
TABLE 2 CpG site information in S100P _ B fragment
Specific PCR primers were designed for both fragments (S100P _ 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; positions 1 to 10 from 5' in SEQ ID No.3 and SEQ ID No.5 are non-specific labels, and positions 11 to 35 are specific primer sequences; the 1 st to 31 th sites of the 5' of the sequence 4 and the sequence 6 are non-specific labels, and the 32 nd to 56 th sites are specific primer sequences. The primer sequence does not contain SNP and CpG sites.
TABLE 3S 100P methylated primer sequences
Example 2, detection of methylation of S100P Gene and analysis of the results
First, research sample
With the patient's informed consent, a total of 722 lung cancer patients, 152 patients with benign nodules in the lung, 79 pancreatic cancer patients, 118 esophageal cancer patients, and 945 cancer-free controls (cancer-free controls, i.e., no cancer had been present and had been reported, and no benign nodules in the lung and blood routine indices were within the reference range) were collected from ex vivo blood samples.
All patient samples were collected preoperatively and confirmed both imagewise and pathologically.
The subtypes of lung cancer, pancreatic cancer and esophageal cancer are judged according to the histopathology.
The staging of lung cancer is judged by AJCC 8 th edition staging system.
722 lung cancer patients were classified by type: 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 classified according to stage: 649 cases in stage I, 41 cases in stage II and 32 cases in stage III.
722 lung cancer patients were classified by lung cancer tumor size (T): t1603, T283 and T336.
722 lung cancer patients were classified by the presence or absence of lung cancer lymph node infiltration (N): there were 688 cases of lung cancer lymph node infiltration and 34 cases of lung cancer lymph node infiltration.
79 pancreatic cancer patients were classified by typing: pancreatic ductal adenocarcinoma 63 cases, and 16 other subtypes were counted.
118 patients with esophageal cancer were classified by type: 94 cases of esophageal squamous cell carcinoma, and 24 cases of other subtypes.
The median age of each of the cancer-free population, benign nodules of the lung, pancreatic and esophageal cancer patients was 56, 57, 58 and 57 years of age, respectively, and the ratio of each of these 5 populations was about 1: 1.
Bis, methylation detection
1. Total DNA of the blood sample was extracted.
2. The total DNA of the blood sample prepared in step 1 was treated with bisulfite (see Qiagen for DNA methylation kit instructions). Following 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 following bisulfite treatment.
3. Taking the DNA treated by the bisulfite in the step 2 as a template, adopting 2 pairs of specific primers in the table 3 to perform PCR amplification by DNA polymerase according to a reaction system required by a conventional PCR reaction, wherein the 2 pairs of primers all adopt the same conventional PCR system, and the 2 pairs of primers all perform amplification according to the following procedures.
The PCR reaction program is: 95 ℃, 4min → (95 ℃,20 s → 56 ℃, 30s → 72 ℃, 2min)45 cycles → 72 ℃,5min → 4 ℃,1 h.
4. Taking the amplification product in the step 3, and carrying out DNA methylation analysis by flight time mass spectrum, wherein the specific method comprises the following steps:
(1) mu.l Shrimp Alkaline Phosphate (SAP) solution (0.3ml SAP [0.5U ] was added to 5. mu.l PCR product]+1.7ml H2O) then incubated in a PCR apparatus (37 ℃,20min → 85 ℃,5min → 4 ℃,5min) according to the following procedure;
(2) taking out 2 μ l of SAP treated product obtained in step (1), adding into 5 μ l T-Cleavage reaction system according to the instruction, and incubating at 37 deg.C for 3 h;
(3) adding 19 mu l of deionized water into the product obtained in the step (2), and then performing deionization incubation for 1h by using 6 mu g of Resin in a rotary table;
(4) centrifuging at 2000rpm for 5min at room temperature, and loading the micro-supernatant with 384SpectroCHIP by a Nanodipen mechanical arm;
(5) performing time-of-flight mass spectrometry; the data obtained were collected with the SpectroACQUIRE v3.3.1.3 software and visualized with the MassArray EpiTyper v1.2 software.
The reagent used for the flight time mass spectrometry detection is a reagent kit (T-clean Mass clear read)ent Auto Kit, cat number: 10129A) (ii) a The detection instrument used for the time-of-flight mass spectrometry detection comprises
Analyzer Chip Prep Module 384, model: 41243 by weight; the data analysis software is self-contained software of the detection instrument.
5. And (4) analyzing the data obtained in the step (4).
Statistical analysis of the data was performed by SPSS Statistics 23.0.
The nonparametric test was used for comparative analysis between the two groups.
The discrimination effect of multiple combinations of CpG sites for different sample groupings was achieved by logistic regression and statistical methods of subject curves.
All statistical tests were two-sided, and P values <0.05 were considered statistically significant.
By mass spectrometry experiments, a total of 39 distinguishable methylated fragments were obtained as peak maps. Methylation levels were calculated using the SpectroACQUIRE v3.3.1.3 software based on a comparison of the peak areas of the methylated and unmethylated fragments-containing peaks (SpectroACQUIRE v3.3.1.3 software automatically calculates the peak area to obtain the methylation level at each CpG site for each sample).
Third, result analysis
1. Methylation level of S100P gene in blood of cancer-free control, benign nodules and lung cancer
The methylation levels of all CpG sites in the S100P gene were analyzed using blood from 722 lung cancer patients, 152 patients with benign nodules in the lung, and 945 cancer-free controls as study material (Table 4). The results showed that the median methylation level of the S100P gene of the no-cancer control was 0.51(IQR ═ 0.38 to 0.67), the median methylation level of the S100P gene of the benign nodule was 0.46(IQR ═ 0.33 to 0.62), and the median methylation level of the lung cancer patients was 0.47(IQR ═ 0.34 to 0.64).
2. Methylation level of S100P gene in blood can distinguish non-cancer control from lung cancer patient
By comparatively analyzing the methylation levels of the S100P gene of 722 lung cancer patients and 945 cancer-free controls, it was found that the methylation levels of all CpG sites in the S100P gene of lung cancer patients were significantly lower than those of cancer-free controls (p <0.05, table 5). In addition, the methylation levels of all CpG sites of the S100P gene in different subtypes of lung cancer (lung adenocarcinoma, lung squamous carcinoma and small cell lung cancer) are respectively obviously different from that of a cancer-free control. The methylation levels of all CpG sites of the S100P gene in different stages (clinical stage I and stage II-III) of lung cancer are respectively and remarkably different from that of a cancer-free control. In addition, methylation levels were significantly different between non-lymphoid lung cancer patients and lymphoid lung cancer patients, respectively, compared to non-cancer controls (p < 0.05). Therefore, the methylation level of the S100P gene can be used for clinical diagnosis of lung cancer, and particularly can be used for early diagnosis of lung cancer.
3. Methylation level of S100P gene in blood can distinguish benign nodules of lung from lung cancer patients
We compared and analyzed 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 nodule patients (p <0.05, table 6). Furthermore, we found that the methylation levels of all CpG in the S100P gene were significantly higher in lung cancer patients of different subtypes (lung adenocarcinoma, lung squamous carcinoma, small cell lung carcinoma), different clinical stages (stage I or II-III) and presence or absence of lymphatic infiltration than in benign nodules (p <0.05), respectively. Therefore, the methylation level of the S100P gene can be applied to distinguishing lung cancer patients from benign nodule patients, and is a very potential marker.
4. Differentiation of different subtypes or stages of lung cancer by methylation level of S100P gene in blood
By comparatively analyzing the methylation levels of the S100P genes of different subtypes of lung cancer patients (lung adenocarcinoma, lung squamous carcinoma and small cell lung cancer) and 945 cancer-free controls, the methylation levels of all CpG sites in the S100P gene are found to be significantly different under the conditions of different subtypes of lung cancer (lung adenocarcinoma patients, lung squamous carcinoma patients and small cell lung cancer patients), lung cancer tumor sizes (T1, T2 and T3), different stages of lung cancer (clinical stages I, II and 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 or stages of lung cancer.
5. Levels of S100P methylation in blood can distinguish pancreatic cancer patients from non-cancerous controls
The differences in methylation levels of all CpG sites in the S100P gene were analyzed using blood from 79 pancreatic cancer patients and 945 cancer-free controls as study material (table 8), of which 63 of 79 pancreatic cancer patients were pancreatic ductal adenocarcinoma. The median methylation level of all target CpG sites in pancreatic cancer patients was 0.44(IQR ═ 0.31-0.60), the median methylation level of the no-cancer control group was 0.51(IQR ═ 0.38-0.67), and the methylation level of all CpG sites of the S100P gene was significantly lower in pancreatic cancer patients than in the no-cancer control (p < 0.0001). The median of all target CpG site methylation levels for 63 patients with pancreatic ductal adenocarcinoma was 0.43(IQR ═ 0.30-0.59), and the methylation levels were significantly lower than for the no-cancer control (p < 0.0001). Therefore, the methylation level of the S100P gene can be used for clinical diagnosis of pancreatic cancer.
6. Levels of S100P methylation in blood can distinguish esophageal patients from cancer-free controls
The differences in methylation levels of S100P between esophageal cancer patients and cancer-free controls were analyzed using blood from 118 esophageal cancer patients and 945 cancer-free controls (Table 9), including 94 esophageal squamous cell carcinomas in 118 esophageal cancers. The results showed that the median methylation level of all target CpG sites in esophageal cancer patients was 0.45(IQR ═ 0.32-0.61), the median methylation level of the cancer-free control group was 0.51(IQR ═ 0.38-0.67), and the methylation level of all CpG sites of the S100P gene in esophageal cancer patients was significantly lower than that of the cancer-free control (p < 0.0001). The median level of methylation for all CpG sites in esophageal squamous cell carcinoma was 0.45(IQR ═ 0.32-0.61), and the level of methylation was significantly lower than that of the no-cancer control (p <0.0001, table 9). Therefore, the methylation level of the S100P gene can be used for clinical diagnosis of esophageal cancer.
7. The methylation level of S100P in blood can distinguish pancreatic cancer patients from lung cancer patients
The differences in methylation levels of the S100P gene in the blood of pancreatic cancer patients and lung cancer patients were analyzed using the blood of 79 pancreatic cancer patients and 722 lung cancer patients as the study material (Table 10). The results show that the median methylation level of all CpG sites in pancreatic cancer patients is 0.44(IQR ═ 0.31-0.60), the median methylation level of lung cancer patients is 0.47(IQR ═ 0.34-0.64), and the methylation level of all CpG sites in pancreatic 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 differentiate pancreatic cancer from lung cancer patients.
8. The methylation level of S100P in blood can distinguish esophageal cancer patients from lung cancer patients
The differences in the methylation level of the S100P gene in the blood of esophageal cancer patients and lung cancer patients were analyzed using the blood of 118 esophageal cancer patients and 722 lung cancer patients as the study material (Table 10). The results show that the median methylation level of all target CpG sites in esophageal cancer patients is 0.45(IQR ═ 0.32-0.61), the median methylation level of lung cancer patients is 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). Therefore, the methylation level of the S100P gene can be used to distinguish esophageal cancer from lung cancer patients.
9. The methylation level of S100P in blood can distinguish pancreatic cancer patients from esophageal cancer patients
The differences in the methylation level of the S100P gene in the blood of 79 pancreatic cancer patients and 118 esophageal cancer patients were analyzed (Table 10). The results show that the median methylation level of all target CpG sites in pancreatic cancer patients is 0.44(IQR ═ 0.31-0.60), the median methylation level of all target CpG sites in esophageal cancer patients is 0.45(IQR ═ 0.32-0.61), and the methylation level of all CpG sites in pancreatic cancer patients is significantly lower than that of 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. Establishment of mathematical model for assisting cancer diagnosis
The mathematical model established by the invention can be used for achieving the following purposes:
(1) differentiating lung cancer patients from non-cancer 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 non-cancerous controls;
(5) differentiating pancreatic cancer patients from lung cancer patients;
(6) distinguishing patients with esophageal cancer from patients with lung cancer;
(7) differentiating pancreatic cancer patients from esophageal cancer patients;
(8) distinguishing different subtypes of lung cancer;
(9) differentiate different stages of lung cancer.
The mathematical model is established as follows:
(A) the data source is as follows: 722 lung cancer patients, 152 patients with benign nodules in the lung, 79 pancreatic cancer patients, 118 esophageal cancer patients and 945 patients without cancer control isolated blood samples listed in step one have the target CpG sites (one or more combinations of tables 1-2) methylation level (the detection method is the same as step two).
The data can be added with known parameters such as age, sex, white blood cell count and the like according to actual needs to improve the discrimination efficiency.
(B) Model building
Selecting any two types of patient data of different types, namely training sets (such as non-cancer contrast and lung cancer patients, non-cancer contrast and pancreatic cancer patients, non-cancer contrast 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, stage I lung cancer patients and stage II lung cancer patients, stage I lung cancer patients and stage III lung cancer patients, stage II lung cancer patients and stage III lung cancer patients) as data for establishing models according to needs, and establishing mathematical models by using statistical software such as SAS, R, SPSS and the like through a formula by using a statistical method of two-classification logistic regression. The numerical value corresponding to the maximum Johnson index calculated by the mathematical model formula is a threshold value or 0.5 is directly set as the threshold value, the detection index obtained after the sample to be detected is tested and substituted into the model calculation is classified into one class (B class) when being larger than the threshold value, and classified into the other class (A class) when being smaller than the threshold value, and the detection index is equal to the threshold value and is used as an uncertain gray zone. When a new sample to be detected is predicted to judge which type the sample belongs to, firstly, the methylation level of one or more CpG sites on the S100P gene of the sample to be detected is detected by a DNA methylation determination method, then the data of the methylation level are substituted into the mathematical model (if known parameters such as age, sex, white blood cell count and the like are included in the model construction, the step simultaneously substitutes the specific numerical value of the corresponding parameter of the sample to be detected into the model formula), the detection index corresponding to the sample to be detected is obtained by calculation, then the detection index corresponding to the sample to be detected is compared with the threshold value, and the sample to be detected belongs to which type is determined according to the comparison result.
Examples are: as shown in FIG. 1, the methylation level of a single CpG site or the methylation level of a combination of multiple CpG sites of the S100P gene in the training set is determined by statistical software such as SAS, R, SPSS, etc. by using a formula of two-class logistic regression to establish a mathematical model for distinguishing the A class from the B class. The mathematical model is here a two-class logistic regression model, specifically: log (y/(1-y)) + b0+ b1x1+ b2x2+ b3x3+ …. + bnXn, wherein y is a detection index obtained by substituting a dependent variable into a model about to be detected for the methylation value of one or more methylation sites of a sample, b0 is a constant, x1 to xn are 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 to bn are weights assigned to the methylation values of each site by the model. In specific application, a mathematical model is established according to the methylation degree (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, and the value of y is assigned with 0 and 1 respectively), so that the constant B0 of the mathematical model and the weights B1-bn of each methylation site are determined, and the value corresponding to the maximum johnson index calculated by the mathematical model is used as a threshold value or is directly set as a threshold value divided by 0.5. And (3) after the sample to be detected is tested and substituted into the model for calculation, the detection index (y value) obtained is classified as B when being larger than the threshold, classified as A when being smaller than the threshold, and is equal to the threshold to be used as an uncertain gray area. Where class A and class B are two corresponding classifications (a grouping of two classifications, which group is class A and which group is class B, determined according to a specific mathematical model, not to be construed herein), such as cancer-free controls and lung cancer patients, cancer-free controls and pancreatic cancer patients, cancer-free controls and esophageal cancer patients, benign nodules of the lung 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 squamous carcinoma patients, lung adenocarcinoma and small cell lung cancer patients, squamous carcinoma and small cell lung carcinoma patients, stage I and stage II lung cancer patients, stage I and stage III lung cancer patients, and stage II and stage III lung cancer patients. When a sample of a subject is predicted to determine which class it belongs to, blood of the subject is first collected and then DNA is extracted therefrom. After transforming the extracted DNA with bisulfite, the methylation level of a single CpG site or the methylation level of a combination of multiple CpG sites of the S100P gene of the subject is detected by a DNA methylation detection method, and then the detected methylation data is substituted into the above 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 the calculated value, namely the detection index, is larger than the threshold value, the subject judges that the subject belongs to the class (B class) with the detection index larger than the threshold value in the training set; 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, namely the detection index, is less than the threshold value, the subject belongs to the class (class A) with the detection index less than the threshold value in the training set; if the methylation level data of one or more CpG sites of the S100P gene of the subject is substituted into the above mathematical model, the calculated value, i.e., the detection index, is equal to the threshold, it cannot be determined whether the subject is of class A or B.
Examples are: the schematic diagram of fig. 2 illustrates the methylation of preferred CpG sites of S100P _ B (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 S100P _ B _19) and the application of mathematical modeling for the discrimination of good and malignant nodules in the lung: data of methylation levels of combinations of 10 distinguishable CpG sites of 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 S100P _ B _19, which have been detected in training sets of lung cancer patients and lung benign nodule patients (here: 722 lung cancer patients and 152 lung benign nodule patients), and ages, sexes (male assignment 1, female assignment 0) of patients, white cell counts of patients were established by R software using a formula of two-class logistic regression to establish a mathematical model for distinguishing lung cancer patients from lung nodule patients. The mathematical model is here a two-class logistic regression model, whereby the constant b0 of the mathematical model and the weights b1 to bn of the individual methylation sites are determined, in this case specifically: log (y/(1-y)) -0.364+0.556 x S100P _ B _1,2+2.259 x S100P _ B _3-0.686 x S100P _ B _5-3.811 x S100P _ B _7,8+4.102 x S100P _ B _9-2.525 x S100P _ B _12+0.575 x S100P _ B _13-1.496 x S100P _ B _14+6.669 x S100P _ B _15,16,17-4.183 x S100P _ B _19+ 0.022-0.843 x gender (assigned as 1, assigned as 0) -0.045 x.y) was obtained as a result of the substitution of y into the methyl-based age index of the sample and the gender of the sample to be tested. Under the condition that 0.5 is set as a threshold, methylation levels of 10 distinguishable CpG sites of 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 S100P _ B _19 of a sample to be detected are tested and then are substituted into a model together with information of age, sex and white cell count, and the obtained detection index, namely y value, is more than 0.5 and is classified as a lung cancer patient, less than 0.5 is classified as a lung benign patient, and is not classified as a lung cancer patient or a lung nodule patient if the y value is equal to 0.5. The area under the curve (AUC) calculation for this model was 0.64 (table 14). As an example of the method for determining the subject, as shown in FIG. 2, DNA is extracted from blood collected from two subjects (A, B), and the extracted DNA is converted by bisulfite, and then the methylation levels of 10 distinguishable CpG sites of 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 S100P _ B _19 of the subjects are measured by a DNA methylation measurement method. The detected methylation level data is then substituted into the mathematical model described above, along with information on the age, sex, and white blood cell count of the subject. The first test subject is judged to be a lung cancer patient if the value calculated by the mathematical model of the first test subject is 0.81 and more than 0.5; and B, judging the second subject as a lung benign nodule patient if the value of the second subject calculated by the mathematical model is 0.12 to less than 0.5.
(C) Evaluation of model Effect
According to the above method, mathematical models for distinguishing lung cancer patients and non-cancer controls, lung cancer patients and benign nodule patients, pancreatic cancer patients and non-cancer controls, non-cancer controls and esophageal cancer patients, lung cancer patients and pancreatic cancer patients, lung cancer patients and esophageal cancer patients, pancreatic cancer patients and esophageal cancer patients, lung adenocarcinoma and squamous lung cancer patients, lung adenocarcinoma and small-cell lung cancer patients, squamous 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, respectively, are established, and the effectiveness thereof is evaluated by a subject curve (ROC curve). The larger the area under the curve (AUC) obtained by the ROC curve, the better the discrimination of the model, and the more effective the molecular marker. The results of the evaluation after the mathematical model construction using different CpG sites are shown in table 11, table 12 and table 13. In tables 11, 12 and 13, 1 CpG site represents a site of any CpG site in the amplified fragment of S100P _ B, 2 CpG sites represent a combination of any 2 CpG sites in S100P _ B, 3 CpG sites represent a combination of any 3 CpG sites in S100P _ B, and so on in … …. The values in the table are ranges for the results of different site combinations (i.e., results for any combination of CpG sites are within the range).
The above results show that the discriminatory ability of the S100P gene for each group (lung cancer patient and non-cancer control, lung cancer patient and lung benign nodule patient, pancreatic cancer patient and non-cancer control, esophageal cancer patient and non-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 carcinoma and small cell lung cancer patient, stage I lung cancer and stage II lung cancer patient, stage I lung cancer and stage III lung cancer patient, stage II lung cancer and stage III lung cancer patient) increases with the number of loci.
In addition, among the CpG sites shown in tables 1 to 2, there are cases where a combination of a few preferred sites is better in discrimination than a combination of a plurality of non-preferred sites. Combinations of 10 distinguishable CpG sites, 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 S100P _ B _19 shown in table 14, table 15, and table 16, are preferred sites for any ten combinations in S100P _ B.
Taken together, CpG sites on the S100P gene and various combinations thereof, CpG sites on the S100P _ A fragment and various combinations thereof, CpG sites on the S100P _ B fragment and various combinations thereof, 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 S100P _ B _19 sites and various combinations thereof on the S100P _ B fragment, and methylation levels of CpG sites on the S100P _ A and S100P _ B and various combinations thereof all for lung cancer patients and non-cancer nodule-free controls, lung cancer patients and lung cancer patients, pancreatic cancer patients and non-esophageal cancer controls, pancreatic cancer patients and lung cancer patients, lung cancer patients and lung cancer patients, Patients with squamous cell lung cancer and small cell lung cancer, patients with stage I lung cancer and stage II lung cancer, patients with stage I lung cancer and stage III lung cancer, and patients with stage II lung cancer and stage III lung cancer have discrimination ability.
Table 4 compares methylation levels of non-cancerous controls, benign nodules, and lung cancer
TABLE 5 comparison of methylation level differences between cancer-free controls and lung cancer
Table 6 comparison of methylation level differences between benign nodules and Lung cancer
Table 7 comparison of methylation level differences between different subtypes or stages of lung cancer
Table 8 comparison of methylation level differences between cancer-free controls and pancreatic cancer
TABLE 9 comparison of methylation level differences between cancer-free controls and esophageal cancer
TABLE 10 comparison of methylation level differences for Lung and pancreatic and esophageal cancers
TABLE 11 CpG sites of S100P _ B and combinations thereof for differentiating between lung cancer and non-cancer controls, lung cancer and benign nodules, pancreatic cancer and non-cancer controls, and lung cancer and pancreatic cancer
TABLE 12 CpG sites of S100P _ B and their combinations for differentiating esophageal and non-cancerous controls, lung and esophageal cancer, and pancreatic and esophageal cancer
TABLE 13S 100P _ B CpG sites and combinations thereof for differentiating patients with adenocarcinoma of the lung and squamous cell carcinoma of the lung, adenocarcinoma of the lung and small cell lung carcinoma, squamous cell lung carcinoma and small cell lung carcinoma of the lung, stage I and II lung carcinoma, stage I and III lung carcinoma, stage II and III lung carcinoma
TABLE 14 optimal CpG sites of S100P _ B and their combinations for differentiating lung cancer and non-cancer control, lung cancer and benign nodules, pancreatic cancer and non-cancer control, and lung cancer and pancreatic cancer
TABLE 15S 100P _ B optimal CpG sites and combinations thereof for differentiating esophageal and non-cancerous controls, lung and esophageal cancer, and pancreatic and esophageal cancer
TABLE 16S 100P _ B optimal CpG sites and their combinations for differentiating patients with adenocarcinoma of the lung and squamous cell carcinoma of the lung, patients with adenocarcinoma of the lung and small cell lung carcinoma, patients with squamous cell carcinoma of the lung and small cell lung carcinoma of the lung, patients with stage I and II lung carcinoma, patients with stage I and III lung carcinoma, patients with stage II and III lung carcinoma
<110> Nanjing Tengthen Biotechnology Co., Ltd
<120> a calcium binding protein methylation marker for auxiliary diagnosis of cancer
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Claims (10)
1. The application of the methylated S100P gene as a marker in the preparation of products; the use of the product is at least one of the following:
(1) auxiliary diagnosis of cancer or prediction of cancer risk;
(2) aid in distinguishing benign nodules from cancer;
(3) assisting in distinguishing different subtypes of cancer;
(4) assisting in distinguishing 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) the method helps to distinguish different subtypes of the lung cancer;
(8) assisting in distinguishing different stages of lung cancer;
(9) auxiliary diagnosis of pancreatic cancer or prediction of pancreatic cancer risk;
(10) auxiliary diagnosis of esophageal cancer or prediction of esophageal cancer risk;
(11) assisting in distinguishing lung cancer from pancreatic cancer;
(12) the lung cancer and the esophageal cancer are distinguished in an auxiliary mode;
(13) the pancreatic cancer and the esophageal cancer are assisted to be distinguished;
(14) determining whether the test agent has a hindering or promoting effect on the development of the cancer.
2. The application of a substance for detecting the methylation level of the S100P gene in preparing products; the use of the product is at least one of the following:
(1) auxiliary diagnosis of cancer or prediction of cancer risk;
(2) aid in distinguishing benign nodules from cancer;
(3) assisting in distinguishing different subtypes of cancer;
(4) assisting in distinguishing 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) the method helps to distinguish different subtypes of the lung cancer;
(8) assisting in distinguishing different stages of lung cancer;
(9) auxiliary diagnosis of pancreatic cancer or prediction of pancreatic cancer risk;
(10) auxiliary diagnosis of esophageal cancer or prediction of esophageal cancer risk;
(11) assisting in distinguishing lung cancer from pancreatic cancer;
(12) the lung cancer and the esophageal cancer are distinguished in an auxiliary mode;
(13) the pancreatic cancer and the esophageal cancer are assisted to be distinguished;
(14) determining whether the test agent has a hindering or promoting effect on the development of the cancer.
3. The application of a substance for detecting the methylation level of the S100P gene and a medium storing a mathematical model building method and/or a using method in the preparation of a product; the use of the product is at least one of the following:
(1) auxiliary diagnosis of cancer or prediction of cancer risk;
(2) aid in distinguishing benign nodules from cancer;
(3) assisting in distinguishing different subtypes of cancer;
(4) assisting in distinguishing 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) the method helps to distinguish different subtypes of the lung cancer;
(8) assisting in distinguishing different stages of lung cancer;
(9) auxiliary diagnosis of pancreatic cancer or prediction of pancreatic cancer risk;
(10) auxiliary diagnosis of esophageal cancer or prediction of esophageal cancer risk;
(11) assisting in distinguishing lung cancer from pancreatic cancer;
(12) the lung cancer and the esophageal cancer are distinguished in an auxiliary mode;
(13) the pancreatic cancer and the esophageal cancer are assisted to be distinguished;
(14) determining whether the test substance has a blocking or promoting effect on the occurrence of the cancer;
the mathematical model is obtained according to a method comprising the following steps:
(A1) detecting the methylation level of the S100P gene of n 1A type samples and n 2B type samples respectively;
(A2) taking the methylation level data of the S100P genes 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 A type and the B type;
the use method of the mathematical model comprises the following steps:
(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, which is obtained in the step (B1), into the mathematical model to obtain a detection index; then comparing the detection index with the threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result;
the type A sample and the type B sample are any one of the following:
(C1) lung cancer samples and no cancer controls;
(C2) lung cancer samples and benign nodule samples of lung;
(C3) samples of different subtypes of lung cancer;
(C4) samples of different stages of lung cancer;
(C5) lung cancer samples and esophageal cancer samples;
(C6) lung cancer 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.
4. The application of the medium storing the mathematical model establishing method and/or the using method in the product preparation; the use of the product is at least one of the following:
(1) auxiliary diagnosis of cancer or prediction of cancer risk;
(2) aid in distinguishing benign nodules from cancer;
(3) assisting in distinguishing different subtypes of cancer;
(4) assisting in distinguishing 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) the method helps to distinguish different subtypes of the lung cancer;
(8) assisting in distinguishing different stages of lung cancer;
(9) auxiliary diagnosis of pancreatic cancer or prediction of pancreatic cancer risk;
(10) auxiliary diagnosis of esophageal cancer or prediction of esophageal cancer risk;
(11) assisting in distinguishing lung cancer from pancreatic cancer;
(12) the lung cancer and the esophageal cancer are distinguished in an auxiliary mode;
(13) the pancreatic cancer and the esophageal cancer are assisted to be distinguished;
(14) determining whether the test substance has a blocking or promoting effect on the occurrence of the cancer;
the mathematical model is obtained according to a method comprising the following steps:
(A1) detecting the methylation level of the S100P gene of n 1A type samples and n 2B type samples respectively;
(A2) taking the methylation level data of the S100P genes 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 A type and the B type;
the use method of the mathematical model comprises the following steps:
(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, which is obtained in the step (B1), into the mathematical model to obtain a detection index; then comparing the detection index with the threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result;
the type A sample and the type B sample are any one of the following:
(C1) lung cancer samples and no cancer controls;
(C2) lung cancer samples and benign nodule samples of lung;
(C3) samples of different subtypes of lung cancer;
(C4) samples of different stages of lung cancer;
(C5) lung cancer samples and esophageal cancer samples;
(C6) lung cancer 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.
5. A kit comprising a substance for detecting the methylation level of the S100P gene; the kit is used for at least one of the following purposes:
(1) auxiliary diagnosis of cancer or prediction of cancer risk;
(2) aid in distinguishing benign nodules from cancer;
(3) assisting in distinguishing different subtypes of cancer;
(4) assisting in distinguishing 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) the method helps to distinguish different subtypes of the lung cancer;
(8) assisting in distinguishing different stages of lung cancer;
(9) auxiliary diagnosis of pancreatic cancer or prediction of pancreatic cancer risk;
(10) auxiliary diagnosis of esophageal cancer or prediction of esophageal cancer risk;
(11) assisting in distinguishing lung cancer from pancreatic cancer;
(12) the lung cancer and the esophageal cancer are distinguished in an auxiliary mode;
(13) the pancreatic cancer and the esophageal cancer are assisted to be distinguished;
(14) determining whether the test agent has a hindering or promoting effect on the development of the cancer.
6. The kit of claim 5, wherein: the kit further comprises a medium storing a mathematical model building method and/or a method of use as claimed in claim 3 or 4.
7. A system, comprising:
(D1) reagents and/or instruments 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 (D1) S100P gene methylation level data of n 1A type samples and n 2B type samples obtained by detection;
the data analysis processing module can establish a mathematical model through a two-classification logistic regression method according to the classification modes 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 collected by the data collection 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 a 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 (D1) detected S100P gene methylation level data of a person to be detected;
the data operation module is used for substituting the S100P gene methylation level data of the person to be tested 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 the conclusion that the type of the sample to be tested is the type A or the type B 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 benign nodule samples of lung;
(C3) samples of different subtypes of lung cancer;
(C4) samples of different stages of lung cancer;
(C5) lung cancer samples and esophageal cancer samples;
(C6) lung cancer 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.
8. The use or kit or system according to any one of claims 1 to 7, wherein: the methylation level of the S100P gene is the methylation level of all or part of CpG sites in the fragments shown as (e1) to (e2) in the S100P gene;
the methylated S100P gene is methylated at all or part of CpG sites in the fragment shown as (e1) to (e2) in the S100P gene;
(e1) a DNA fragment shown in SEQ ID No.1 or a DNA fragment with more than 80% of identity with the DNA fragment;
(e2) the DNA fragment shown in SEQ ID No.2 or the DNA fragment with more than 80 percent of identity with the DNA fragment.
9. The use or kit or system according to claim 8, wherein: the 'all or part of CpG sites' are all CpG sites in the DNA segment shown in SEQ ID No.1 and all CpG sites in the DNA segment shown in SEQ ID No. 2;
or
The "whole or partial CpG sites" are 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 or any 1 CpG sites in all CpG sites in the DNA fragment shown in SEQ ID No. 2;
or
The "whole or partial CpG sites" are 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 has CpG sites shown at the 41 th to 42 th positions and the 44 th to 45 th positions from the 5' end;
(f2) the CpG site shown in the 128-th and 129-th position of the 5' end of the DNA segment shown in SEQ ID No. 2;
(f3) the DNA fragment shown in SEQ ID No.2 is a CpG site shown as 278 and 279 positions from the 5' end;
(f4) the DNA segment shown in SEQ ID No.2 is at CpG sites shown in positions 362-363 and 372-373 from the 5' end;
(f5) the CpG site is shown in 379-380 bit from 5' end of the DNA segment shown in SEQ ID No. 2;
(f6) the DNA fragment shown in SEQ ID No.2 shows CpG sites from 473-474 sites of the 5' end;
(f7) the DNA fragment shown in SEQ ID No.2 is from the 491-492 CpG site of the 5' end;
(f8) the DNA segment shown in SEQ ID No.2 shows the CpG site from 516-517 bit of the 5' end;
(f9) the DNA fragment shown in SEQ ID No.2 is from the CpG sites shown in the 542-propanoic 543 position, the 551-propanoic 552 position and the 554-propanoic 555 position of the 5' end;
(f10) the DNA fragment shown in SEQ ID No.2 has CpG sites indicated by 649-650 th site from the 5' end.
10. The use or kit or system according to any one of claims 1 to 9, wherein: the substance for detecting the methylation level of the S100P gene comprises a primer combination for amplifying the full-length 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-length or partial fragment of the S100P gene;
further, the partial fragment is at least one fragment selected from the following fragments:
(g1) the DNA fragment shown in SEQ ID No.1 or the DNA fragment contained in the DNA fragment;
(g2) a DNA fragment shown as SEQ ID No.2 or a DNA fragment contained therein;
(g3) a DNA fragment having an identity of 80% or more to the DNA fragment represented by SEQ ID No.1 or a DNA fragment contained therein;
(g4) a DNA fragment having an identity of 80% or more to the DNA fragment represented by SEQ ID No.2 or a DNA fragment contained therein;
further, 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 single-stranded DNA shown by 11 th to 35 th nucleotides of SEQ ID No.3 or SEQ ID No. 3; the primer A2 is single-stranded DNA shown by 32 th-56 th nucleotides of SEQ ID No.4 or 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 single-stranded DNA shown by SEQ ID No.5 or 11 th to 35 th nucleotides of SEQ ID No. 5; the primer B2 is single-stranded DNA shown by the 32 nd to 56 th nucleotides of SEQ ID No.6 or SEQ ID No. 6.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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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