CN113355413A

CN113355413A - Application of molecular marker and kit in auxiliary diagnosis of cancer

Info

Publication number: CN113355413A
Application number: CN202010137596.5A
Authority: CN
Inventors: 韦玉杰; 王俊; 狄飞飞
Original assignee: Nanjing Tengchen Biological Technology Co ltd
Current assignee: Nanjing Tengchen Biological Technology Co ltd
Priority date: 2020-03-02
Filing date: 2020-03-02
Publication date: 2021-09-07
Anticipated expiration: 2040-03-02
Also published as: CN113355413B

Abstract

The invention discloses an application of a molecular marker and a kit in auxiliary diagnosis of cancer. The invention provides application of a methylated ACTB gene as a marker in preparation of a product; 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 hypermethylation phenomenon of the ACTB 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

Application of molecular marker and kit in auxiliary diagnosis of cancer

Technical Field

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

Background

Lung cancer is a malignant tumor occurring in the epithelium of bronchial mucosa, and its morbidity and mortality have been on the rise in recent decades, and is the cancer with the highest worldwide morbidity and mortality. 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 main lung cancer diagnosis methods at present 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 diagnosing the esophageal cancer, but the sensitivity is less than 40%, the specificity is low, and the diagnostic value is low particularly 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.

Disclosure of Invention

The invention aims to provide a cytoskeleton actin beta (ACTB) methylation marker and a kit for auxiliary diagnosis of cancer.

In a first aspect, the invention claims the use of a methylated ACTB gene as a marker in the preparation of a product. The use of the product may be at least one of:

(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 pancreatic ductal carcinoma 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 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 the above (1) to (14), the cancer may be a cancer which can cause an increase in methylation level of ACTB gene in the body, such as lung cancer, pancreatic cancer, esophageal cancer, etc.

In a second aspect, the invention features the use of an agent for detecting methylation levels of the ACTB gene in the manufacture 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 features the use of a substance for detecting methylation levels of the ACTB gene and a medium having stored thereon a mathematical modeling method and/or a method of use for making 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 ACTB gene of n 1A-type samples and n 2B-type samples (training set) respectively;

(A2) and (4) taking the methylation level data of the ACTB genes of all the 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 n1 and n2 in (A1) are both positive integers of 50 or more.

The use method of the mathematical model comprises the following steps:

(B1) detecting the methylation level of the ACTB gene of a sample to be detected;

(B2) substituting the ACTB 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, and the grouping of the two classifications, which group is the type A and which group is the type B, 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 methylation level of ACTB 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 methylation levels of the ACTB 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) the ACTB 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 type A and the type B based on the ACTB gene methylation level data of the n1 type A samples and the n2 type B samples collected by the data collection 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 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) the methylation level data of the ACTB gene of the person to be detected, which is obtained by detection;

the data operation module is used for substituting the ACTB gene methylation level data of the person to be detected 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 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.

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

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, and the grouping of the two classifications, which group is the type A and which group is the type B, are determined according to a specific mathematical model without convention.

In the foregoing aspects, the methylation level of the ACTB gene may be the methylation level of all or part of CpG sites in the ACTB gene in fragments shown below (e1) - (e 3). The methylated ACTB gene can be methylated at all or part of CpG sites in the fragments shown below (e1) - (e3) in the ACTB 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) a DNA fragment shown in SEQ ID No.2 or a DNA fragment with more than 80% of identity with the DNA fragment;

(e3) a DNA fragment shown in SEQ ID No.3 or a DNA fragment with more than 80% of identity with the DNA fragment.

Further, the "whole or part of the CpG sites" may be any one or more CpG sites in 3 DNA fragments shown in SEQ ID No.1 to SEQ ID No.3 in the ACTB gene. The upper limit of the "multiple CpG sites" is defined as all CpG sites in 3 DNA fragments shown in SEQ ID No.1 to SEQ ID No.3 in the ACTB gene. All CpG sites in the DNA fragment shown in SEQ ID No.1 are shown in Table 1, all CpG sites in the DNA fragment shown in SEQ ID No.2 are shown in Table 2, and all CpG sites in the DNA fragment shown in SEQ ID No.3 are shown in Table 3.

Or, the "whole or partial CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.1 (see Table 1) and all CpG sites in the DNA fragment shown in SEQ ID No.2 (see Table 2).

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

Or, the "whole or partial CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.2 (see Table 2) and all CpG sites in the DNA fragment shown in SEQ ID No.3 (see Table 3).

Or, the "whole or partial CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.1 (see Table 1), all CpG sites in the DNA fragment shown in SEQ ID No.2 (see Table 2) and all CpG sites in the DNA fragment shown in SEQ ID No.3 (see Table 3).

Or, the "whole or part of CpG sites" may be all or any 30 or any 29 or any 28 or any 27 or any 26 or any 25 or any 24 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 of the DNA fragments shown as SEQ ID No.2 in the ACTB gene.

Or, the "all or part of CpG sites" may be all 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 of the following CpG sites shown in 15 in the DNA fragment shown in SEQ ID No.2 in the ACTB gene:

(f1) the CpG site (ACTB _ B _12) shown by 435 nd and 436 nd bit of the DNA fragment shown in SEQ ID No.2 from the 5' end;

(f2) the CpG site (ACTB _ B _13) shown by 453-454 bits from the 5' end of the DNA fragment shown in SEQ ID No. 2;

(f3) the DNA fragment shown in SEQ ID No.2 has a CpG site (ACTB _ B _14) shown by the 488-489 th site from the 5' end;

(f4) the DNA fragment shown in SEQ ID No.2 has CpG sites (ACTB _ B _15.16) shown by positions 492-493 and 494-495 from the 5' end;

(f5) the DNA fragment shown in SEQ ID No.2 shows CpG sites (ACTB _ B _17.18) from the 514-th 515 th and 518-th 519 th positions of the 5' end;

(f6) the DNA fragment shown in SEQ ID No.2 shows the CpG site (ACTB _ B _19) from 522-523 th site of the 5' end;

(f7) the DNA fragment shown in SEQ ID No.2 shows CpG sites (ACTB _ B _20.21) from the 530 th and 534 th sites of the 5' end;

(f8) the DNA fragment shown in SEQ ID No.2 shows CpG sites (ACTB _ B _22.23) from 560-561 and 563-564 of the 5' end;

(f9) the DNA fragment shown in SEQ ID No.2 shows the CpG site (ACTB _ B _24) from the 575 th and 576 th positions of the 5' end;

(f10) the CpG site (ACTB _ B _25) shown in 592 and 593 of the DNA fragment shown in SEQ ID No.2 from the 5' end;

(f11) the DNA fragment shown in SEQ ID No.2 shows CpG sites (ACTB _ B _26) from 626-627 of the 5' end;

(f12) the DNA fragment shown in SEQ ID No.2 shows the CpG position (ACTB _ B _27) from 638 and 639 th positions of the 5' end;

(f13) the DNA fragment shown in SEQ ID No.2 has CpG sites (ACTB _ B _28.29) from positions 666 and 667 and 672 and 673 of the 5' end;

(f14) the CpG site (ACTB _ B _30) shown in 728 th 729 th site of the 5' end of the DNA fragment shown in SEQ ID No. 2;

(f15) the CpG site (ACTB _ B _31) shown by 757-758 th bits of the 5' end of the DNA fragment shown by SEQ ID No. 2;

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 5), and thus when performing methylation level analysis, and constructing and using related mathematical models. This is the case for (f4), (f5), (f7), (f8) and (f13) described above.

In each of the above aspects, the means for detecting methylation levels of the ACTB gene can comprise (or be) a primer combination for amplifying a full-length or partial fragment of the ACTB gene. The reagent for detecting the methylation level of the ACTB gene can comprise (or be) a primer combination for amplifying the full-length or partial fragment of the ACTB gene; the instrument for detecting the methylation level of the ACTB gene can be a time-of-flight mass spectrometer. Of course, the reagents used to detect the methylation level of the ACTB gene may also include other conventional reagents used to perform time-of-flight mass spectrometry.

Further, the partial fragment may be at least one of:

(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 shown as SEQ ID No.3 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.1 or a DNA fragment contained therein;

(g5) 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;

(g6) a DNA fragment having an identity of 80% or more with the DNA fragment represented by SEQ ID No.3 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 and/or a primer pair C;

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 11 th to 35 th nucleotides of SEQ ID No.4 or SEQ ID No. 4; the primer A2 can be specifically single-stranded DNA shown by the 32 nd to 56 th nucleotides of SEQ ID No.5 or SEQ ID No. 5;

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 11 th to 35 th nucleotides of SEQ ID No.6 or SEQ ID No. 6; the primer B2 can be specifically single-stranded DNA shown by the 32 nd to 57 th nucleotides of SEQ ID No.7 or SEQ ID No. 7;

the primer pair C is a primer pair consisting of a primer C1 and a primer C2; the primer C1 can be specifically single-stranded DNA shown by SEQ ID No.8 or 11 th to 35 th nucleotides of SEQ ID No. 8; the primer C2 can be specifically single-stranded DNA shown by the 32 th-55 th nucleotides of SEQ ID No.9 or SEQ ID No. 9;

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:

Wherein n1 and n2 in (A1) are both 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 ACTB gene of the sample to be detected;

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.

Any of the above mathematical models may be changed in practical application according to the detection method of DNA methylation and the fitting manner, and needs to be determined according to a specific mathematical model without 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 tested, 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 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)) 0.584+ 1.357-ACTB _12+ 1.814-ACTB _ B _ 13-1.357-ACTB _14+ 1.146-ACTB _ B _ 15.16-0.174-ACTB _ B _17.18+ 0.436-ACTB _ 19-0.686-ACTB _ B _ 20.21-6.884-ACTB _ B _22.23+ 3.941-ACTB _24+ 3.047-ACTB _ 25-1.814-ACTB _ 26-2.757-ACTB _27+ 1.136-ACTB _ 28.29-1.815-ACTB _ 30-0.589-ACTB _ 9.790. The ACTB _ B _12 is the methylation level of the CpG sites shown in the 435 nd and 436 nd sites of the DNA fragment shown in SEQ ID No.2 from the 5' end; the ACTB _ B _13 is the methylation level of the CpG sites shown by 453 th and 454 th sites of the 5' end of the DNA fragment shown by SEQ ID No. 2; the ACTB _ B _14 is the methylation level of the CpG sites shown in the 488-489 th site of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ACTB _ B _15.16 is the methylation level of the CpG sites of the DNA fragment shown in SEQ ID No.2 from positions 492 and 494 from the 5' end through 493 and 494; the ACTB _ B _17.18 is the methylation level of the CpG sites of the DNA fragment shown in SEQ ID No.2 from the 514 th and the 518 th 519 th positions of the 5' end; the ACTB _ B _19 is the methylation level of the CpG sites shown by 522-523 th site from the 5' end of the DNA fragment shown by SEQ ID No. 2; the ACTB _ B _20.21 is the methylation level of the CpG sites shown in the 530 th and 534 th 535 nd sites of the DNA fragment shown in SEQ ID No.2 from the 5' end; the ACTB _ B _22.23 is the methylation level of CpG sites of the DNA fragment shown in SEQ ID No.2 from 560-561 and 563-564 sites of the 5' end; the ACTB _ B _24 is the methylation level of the CpG sites shown by the 575 th and 576 th sites of the 5' end of the DNA fragment shown by SEQ ID No. 2; the ACTB _ B _25 is the methylation level of the CpG sites shown in 592 and 593 th sites of the DNA fragment shown in SEQ ID No.2 from the 5' end; the ACTB _ B _26 is the methylation level of the CpG sites shown in 626-627 th site of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ACTB _ B _27 is the methylation level of the CpG sites shown in the 638 and 639 th positions of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ACTB _ B _28.29 is the methylation level of CpG sites of the DNA fragment shown in SEQ ID No.2 from 666 and 667 and 672 and 673 positions of the 5' end; the ACTB _ B _30 is the methylation level of the CpG sites shown in 728-729 th site of the 5' end of the DNA fragment shown in SEQ ID No. 2; the ACTB _ B _31 is the methylation level of the CpG sites of the DNA fragment shown in SEQ ID No.2 from 757-758 th site of the 5' end. 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 each of the above aspects, the detecting the methylation level of the ACTB gene is detecting the methylation level of the ACTB 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.

Any of the ACTB genes described above may specifically include Genbank accession numbers: NM — 001101.5, transcript variant 1. The invention provides hypermethylation of the ACTB 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.

Cytoskeletal actin beta (beta-actin, ACTB) gene quantification experiments in the following examples were performed in triplicate, and the results were averaged.

Example 1 primer design for detection of methylation sites of ACTB Gene

Three segments of the ACTB gene (ACTB _ a, ACTB _ B, and ACTB _ C) were selected for methylation level and cancer-related analysis through extensive sequence and functional analysis.

The ACTB _ A fragment (SEQ ID No.1) is located in the hg19 reference genome c hr7:5567016 and 5567713, sense strand.

The ACTB _ B fragment (SEQ ID No.2) is located in the hg19 reference genome chr7: 5567834-.

The ACTB _ C fragment (SEQ ID No.3) is located in the hg19 reference genome chr7: 5568885-.

The CpG site information in the ACTB _ a fragment is shown in table 1.

The CpG site information in the ACTB _ B fragment is shown in table 2.

The CpG site information in the ACTB _ C fragment is shown in table 3.

TABLE 1 CpG site information in ACTB _ A fragments

CpG sites	Position of CpG sites in the sequence
		ACTB_A_1	97 th to 98 th from 5' end of SEQ ID No.1
ACTB_A_2	153 th and 154 th from 5' end of SEQ ID No.1
		ACTB_A_3	182-183 of SEQ ID No.1 from the 5' end
ACTB_A_4	SEQ ID No.1 at position 191-192 from the 5' end
		ACTB_A_5	SEQ ID No.1 at position 210-211 from the 5' end
ACTB_A_6	311 st and 312 st positions from the 5' end of SEQ ID No.1
		ACTB_A_7	Position 343-344 from the 5' end of SEQ ID No.1
ACTB_A_8	Position 361-362 from the 5' end of SEQ ID No.1
		ACTB_A_9	Position 377-378 from the 5' end of SEQ ID No.1
ACTB_A_10	384-385 th from the 5' end of SEQ ID No.1
		ACTB_A_11	396-397 th site from the 5' end of SEQ ID No.1
ACTB_A_12	Position 402-403 from the 5' end of SEQ ID No.1
		ACTB_A_13	SEQ ID No.1 from the 5' end at position 448-449
ACTB_A_14	SEQ ID No.1 at position 465-466 from 5' end
		ACTB_A_15	468-469 th site from 5' end of SEQ ID No.1
ACTB_A_16	The sequence of SEQ ID No.1 from the 5' end 477-478
		ACTB_A_17	SEQ ID No.1 from the 488-activated 489 th site of the 5' end
ACTB_A_18	581-582 bit from 5' end of SEQ ID No.1
		ACTB_A_19	The sequence No.1 from the 5' end of 673-674

TABLE 2 CpG site information in ACTB _ B fragment

TABLE 3 CpG site information in ACTB _ C fragments

Specific PCR primers were designed for three fragments (ACTB _ A fragment, ACTB _ B fragment, ACTB _ C fragment) as shown in Table 4. Wherein SEQ ID No.4, SEQ ID No.6 and SEQ ID No.8 are forward primers, and SEQ ID No.5, SEQ ID No.7 and SEQ ID No.9 are reverse primers; in SEQ ID No.4, SEQ ID No.6 and SEQ ID No.8, the 1 st to 10 th sites from the 5' end are non-specific tags, and the 11 th to 35 th sites are specific primer sequences; positions 1 to 31 from 5' in SEQ ID No.5, SEQ ID No.7 and SEQ ID No.9 are non-specific tags, positions 32 to 56 in SEQ ID No.5 are specific primer sequences, positions 32 to 57 in SEQ ID No.7 are specific primer sequences, and positions 32 to 55 in SEQ ID No.9 are specific primer sequences. The primer sequence does not contain SNP and CpG sites.

TABLE 4 ACTB methylation primer sequences

Example 2 methylation detection and result analysis of ACTB Gene

First, research sample

With informed consent, ex vivo blood samples were collected from 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., patients who had previously and now had no cancer and had no reported pulmonary nodules and had blood routine indicators within the reference range).

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 3 pairs of specific primers in the table 4 to perform PCR amplification by DNA polymerase according to a reaction system required by a conventional PCR reaction, wherein the 3 pairs of primers all adopt the same conventional PCR system, and the 3 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 reagents used in the flight time mass spectrometry detection are all kits (T-clean Mass clear Reagent Auto Kit, cat # 10129A); 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 69 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. ACTB Gene methylation levels in blood of cancer-free controls, benign nodules, and Lung cancer

The methylation levels of all CpG sites in the ACTB gene were analyzed using 722 lung cancer patients, 152 patients with benign nodules in the lung, and 945 cancer-free controls of blood as the study material (table 5). The results showed that all CpG sites in the ACTB gene had a median methylation level of 0.46(IQR ═ 0.19-0.64) in the cancer-free control group, 0.48 in the benign nodules (IQR ═ 0.20-0.66), and 0.49 in the lung cancer patients (IQR ═ 0.21-0.68).

2. Methylation level of ACTB gene in blood can distinguish between non-cancer control and lung cancer patients

By comparatively analyzing the methylation levels of ACTB genes of 722 lung cancer patients and 945 cancer-free controls, it was found that the methylation levels of all CpG sites in the ACTB genes of lung cancer patients were significantly higher than those of cancer-free controls (p <0.05, table 6). In addition, methylation levels of all CpG sites of ACTB gene in different subtypes of lung cancer (lung adenocarcinoma, lung squamous carcinoma and small cell lung cancer) are respectively obviously different from those of a cancer-free control. Methylation levels of all CpG sites of ACTB gene in different stages (clinical stage I and stage II-III) of lung cancer are respectively and obviously different from those 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 ACTB 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 ACTB gene in blood can distinguish benign nodules of lung from lung cancer patients

By comparing the methylation levels of the ACTB gene in 722 lung cancer patients and 152 benign nodules, it was found that the methylation levels of all CpG sites of the ACTB gene in the benign nodule patients were significantly lower than those of lung cancer patients (p <0.05, table 7). In addition, significant differences were found between the methylation levels of all CpG in the ACTB gene of lung cancer patients of different subtypes (lung adenocarcinoma, squamous cell lung carcinoma, small cell lung carcinoma), different clinical stages (stages I or II-III) and no lymphatic infiltration, respectively, and benign nodules. Therefore, the methylation level of the ACTB 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 ACTB Gene methylation levels in blood

By comparing and analyzing methylation levels of the ACTB gene in different subtype lung cancer patients (lung adenocarcinoma, lung squamous carcinoma and small cell lung cancer) and different stage lung cancer patients, the methylation levels of all CpG sites in the ACTB gene are found to have significant differences (p is less than 0.05, table 8) 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. Thus, the methylation level of the ACTB gene can be used to differentiate between different subtypes or stages of lung cancer.

5. ACTB methylation levels in blood can distinguish pancreatic cancer patients from non-cancerous controls

The difference in methylation level of all CpG sites in ACTB gene was analyzed using blood from 79 pancreatic cancer patients and 945 cancer-free controls as study material (table 9), of which 63 of 79 pancreatic cancer patients were pancreatic ductal adenocarcinoma. The median methylation level of all target CpG sites in 79 pancreatic cancer patients was 0.53(IQR ═ 0.22-0.71), the median methylation level of the no-cancer control group was 0.46(IQR ═ 0.19-0.64), and the methylation level of all CpG sites in pancreatic cancer patients was significantly higher than that of the no-cancer control (p < 0.05). The median of all targeted CpG site methylation levels for 63 patients with pancreatic ductal adenocarcinoma was 0.53(IQR ═ 0.23-0.71), and the methylation levels were significantly higher than the no-cancer control (p < 0.05). Thus, the methylation level of the ACTB gene can be used for clinical diagnosis of pancreatic cancer.

6. Blood ACTB methylation levels can distinguish esophageal patients from cancer-free controls

The differences in methylation levels of CpG sites in the ACTB gene between patients with esophageal cancer and cancer-free controls were analyzed using blood from 118 patients with esophageal cancer and 945 cancer-free controls as study material (Table 10), including 94 esophageal squamous cell carcinoma in 118 esophageal cancers. The results showed that the median methylation level of all target CpG sites in esophageal cancer patients was 0.54(IQR ═ 0.23-0.72), the median methylation level of the cancer-free control group was 0.46(IQR ═ 0.19-0.64), and the methylation level of all CpG sites in esophageal cancer patients was significantly higher than that of the cancer-free control (p < 0.05). The median level of methylation for all targeted CpG sites in esophageal squamous cell carcinoma was 0.54(IQR ═ 0.24-0.73), and the level of methylation was significantly higher than that of the no-cancer control (p <0.05, table 10). Thus, the methylation level of the ACTB gene can be used for clinical diagnosis of esophageal cancer.

7. ACTB methylation levels in blood can distinguish pancreatic cancer patients from lung cancer patients

The difference in methylation level in ACTB gene in blood of pancreatic cancer patients and lung cancer patients was analyzed using blood of 79 pancreatic cancer patients and 722 lung cancer patients as a study material (table 11). The results show that the median methylation level of all target CpG sites in pancreatic cancer patients is 0.53(IQR ═ 0.22-0.71), the median methylation level of lung cancer patients is 0.49(IQR ═ 0.21-0.68), and the methylation level of all CpG sites in pancreatic cancer patients is significantly higher than that of lung cancer patients (p < 0.05). Thus, the methylation level of the ACTB gene can be used to differentiate pancreatic cancer from lung cancer patients.

8. Blood ACTB methylation levels can distinguish esophageal cancer patients from lung cancer patients

The difference in methylation level in ACTB gene in blood was analyzed using blood from 118 patients with esophageal cancer and 722 patients with lung cancer as a study material (table 11). The results show that the median methylation level of all target CpG sites in esophageal cancer patients is 0.54(IQR ═ 0.23-0.72), the median methylation level of lung cancer patients is 0.49(IQR ═ 0.21-0.68), and the methylation level of all CpG sites in esophageal cancer patients is significantly higher than that of lung cancer patients (p < 0.05). Thus, the methylation level of the ACTB gene can be used to differentiate between esophageal and lung cancer patients.

9. Levels of ACTB methylation in blood can distinguish pancreatic and esophageal cancer patients

The difference in methylation levels in ACTB gene in blood was analyzed in 79 pancreatic cancer patients and 118 esophageal cancer patients (table 13). The results show that the median methylation level of all target CpG sites in pancreatic cancer patients is 0.53(IQR ═ 0.22-0.71), the median methylation level of all target CpG sites in esophageal cancer patients is 0.54(IQR ═ 0.23-0.72), 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 ACTB gene can be used to differentiate 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 lung cancer subtypes;

(9) to differentiate the stage 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 (combination of one or more of tables 1-3) 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, the methylation level of one or more CpG sites on the ACTB gene of the sample to be detected is firstly 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 single CpG sites or the methylation level of a combination of multiple CpG sites of ACTB 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 A class from 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 the sample, b0 is a constant, x1 to xn are independent variables about to be the methylation value 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 value 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 respectively assigned to 0 and 1), so that the constant B0 of the mathematical model and the weights B1-bn of each methylation site are determined, and a detection index (in this example, 0.5) corresponding to the maximum john index calculated by the mathematical model is used as a threshold value for division. And (3) the detection index (y value) obtained by testing and substituting the sample to be detected into the model calculation is more than 0.5 and is classified as B, less than 0.5 and is classified as A, and the value is equal to 0.5 and is used as an uncertain gray area. Where class A and class B are two corresponding classifications (a subgroup 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 II lung cancer patients, stage I and III lung cancer patients, and stage II and 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 ACTB gene of the subject is detected by a DNA methylation detection method, and then the detected methylation data is substituted into the mathematical model. If the methylation level of one or more CpG sites of the ACTB gene of the subject is substituted into the mathematical model, and the calculated detection index is larger than a threshold value, the subject judges that the detection index in the training set is larger than 0.5 and belongs to a class (class B); if the methylation level data of one or more CpG sites of the ACTB gene of the subject is substituted into the mathematical model, and the calculated value, namely the detection index is smaller than the threshold value, the subject and the training set are classified into a class (class A) with the detection index smaller than 0.5; if the methylation level data of one or more CpG sites of the ACTB 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 ACTB _ B (ACTB _ B _12, ACTB _ B _13, ACTB _ B _14, ACTB _ B _15.16, ACTB _ B _17.18, ACTB _ B _19, ACTB _ B _20.21, ACTB _ B _22.23, ACTB _ B _24, ACTB _ B _25, ACTB _ B _26, ACTB _ B _27, ACTB _ B _28.29, ACTB _ B _30, ACTB _ B _31) and the use of mathematical modeling for the discrimination of good and malignant nodules in the lung: data on methylation levels of 15 distinguishable preferred CpG site combinations that have been detected in a training set of lung cancer patients and lung benign nodule patients (here: 722 lung cancer patients and 152 lung benign nodule patients) and the ages, sexes (male assigned 1 and female assigned 0) and white blood cell counts of the patients were used to build a mathematical model for distinguishing lung cancer patients from lung benign nodule patients by R software using a two-classification logistic regression formula. 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: the evaluation of the positive cells for the positive cells in the positive cells of the positive cells (0.584 + 1.357-ACTB _ B _12+ 1.814-ACTB _14+ 1.146-ACTB _ B _ 15.16-0.174-ACTB _ B _17.18+ 0.436-ACTB _ 19-0.686-ACTB _ B _ 20.21-6.884-ACTB _ B _22.23+ 3.941-ACTB _ B _24+ 3.047-ACTB _ 25-1.814-ACTB _ 26-2.757-ACTB _27+ 1.136-AC _ B _ 28.29-1.815-ACTB _ 30-0.589-ACTB _ 9-0.0220.0220-ACTB _ B _30 + 1.15-gender-9-gender-0. Under the condition that 0.5 is set as a threshold, methylation levels of 15 distinguishable CpG sites of ACTB _ B _12, ACTB _ B _13, ACTB _ B _14, ACTB _ B _15.16, ACTB _ B _17.18, ACTB _ B _19, ACTB _ B _20.21, ACTB _ B _22.23, ACTB _ B _24, ACTB _ B _25, ACTB _ B _26, ACTB _ B _27, ACTB _ B _28.29, ACTB _ B _30 and ACTB _ B _31 of a sample to be detected are tested and then substituted into a model together with information of age, gender and white cell count of the sample, and the obtained detection index, namely the y value is larger than 0.5 and is classified as a lung cancer patient, smaller than 0.5 is classified as a lung uncertain nodule patient, and equal to 0.5 is classified as a lung cancer patient or a benign lung nodule patient. The area under the curve (AUC) for this model was calculated to be 0.67 (table 15). Specific subject determination methods as shown in fig. 2, for example, DNA is extracted from blood collected from two subjects (a, B), and after the extracted DNA is converted by bisulfite, methylation levels of 15 distinguishable CpG sites, i.e., ACTB _ B _12, ACTB _ B _13, ACTB _ B _14, ACTB _ B _15.16, ACTB _ B _17.18, ACTB _ B _19, ACTB _ B _20.21, ACTB _ B _22.23, ACTB _ B _24, ACTB _ B _25, ACTB _ B _26, ACTB _ B _27, ACTB _ B _28.29, ACTB _ B _30, and ACTB _ B _31, of the subjects are detected 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 (the lung cancer patient accords with the clinical judgment result) if the value calculated by the first test subject through the mathematical model is 0.84 and more than 0.5; and (3) substituting the methylation level data of one or more CpG sites of the ACTB gene of the second subject into the mathematical model to calculate a value of 0.18 to less than 0.5, and judging the patient with the benign pulmonary nodule by the second subject (which is consistent with the clinical judgment result).

(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 12, table 13 and table 14. In tables 12, 13 and 14, 1 CpG site represents a site of any one CpG site in the amplified segment of ACTB _ B, 2 CpG sites represent a combination of any 2 CpG sites in ACTB _ B, 3 CpG sites represent a combination of any 3 CpG sites in ACTB _ B, and so on … …. 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 ACTB 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 sites.

In addition, among the CpG sites shown in tables 1 to 3, there are cases where a combination of a few more excellent sites is better in discrimination than a combination of a plurality of non-excellent sites. For example, the 15 distinguishable optimal sites shown in tables 15, 16, and 17, such as ACTB _ B _12, ACTB _ B _13, ACTB _ B _14, ACTB _ B _15.16, ACTB _ B _17.18, ACTB _ B _19, ACTB _ B _20.21, ACTB _ B _22.23, ACTB _ B _24, ACTB _ B _25, ACTB _ B _26, ACTB _ B _27, ACTB _ B _28.29, ACTB _ B _30, and ACTB _ B _31, are preferred sites for any 15 combinations in ACTB _ B.

In summary, CpG sites in the ACTB gene and various combinations thereof, CpG sites in the ACTB _ A fragment and various combinations thereof, CpG sites in the ACTB _ B fragment and various combinations thereof, ACTB _ B _12, ACTB _ B _13, ACTB _ B _14, ACTB _ B _15.16, ACTB _ B _17.18, ACTB _ B _19, ACTB _ B _20.21, ACTB _ B _22.23, ACTB _ B _24, ACTB _ B _25, ACTB _ B _26, ACTB _ B _27, ACTB _ B _28.29, ACTB _ B _30 and ACTB _ B _31 and various combinations thereof, CpG sites in the ACTB _ C fragment and various combinations thereof, and methylation levels of CpG sites in the ACTB _ A, ACTB _ B and ACTB _ C and various combinations thereof, for lung cancer patients and non-cancer control patients, pancreatic cancer patients and non-pancreatic cancer patients, Patients with esophageal cancer and lung cancer, patients with pancreatic cancer and esophageal cancer, patients with lung adenocarcinoma and lung squamous cancer, patients with lung adenocarcinoma and small cell lung cancer, patients with lung squamous 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 the ability to discriminate.

Table 5 compares methylation levels of non-cancerous controls, benign nodules, and lung cancer

TABLE 6 comparison of methylation level differences between cancer-free controls and lung cancer

Table 7 comparison of methylation level differences between benign nodules and Lung cancer

TABLE 8 comparison of methylation level differences for different subtypes or stages of lung cancer

Table 9 comparison of methylation level differences between cancer-free controls and pancreatic cancer

TABLE 10 comparison of methylation level differences between cancer-free controls and esophageal cancer

TABLE 11 comparison of methylation level differences for Lung, pancreas and esophageal cancers

TABLE 12 CpG sites of ACTB _ 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 13 CpG sites of ACTB _ B and combinations thereof for differentiating between esophageal and non-cancerous controls, esophageal and pancreatic cancer, and esophageal and lung cancer

TABLE 14 CpG sites of ACTB _ B and their free combinations for differentiating patients with adenocarcinoma of the lung and squamous cell carcinoma of the lung, adenocarcinoma of the lung and small cell lung carcinoma of the lung, squamous cell carcinoma of the lung and small cell lung carcinoma of the lung, stage I and II, stage I and III, and stage II and III

TABLE 15 optimal CpG sites of ACTB _ 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 16 optimal CpG sites of ACTB _ B and combinations thereof for differentiating between esophageal and non-cancerous controls, esophageal and pancreatic cancer, and esophageal and lung cancer

TABLE 17 optimal CpG sites of ACTB _ B and combinations thereof 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, patients with stage I and II, stage III, stage II and III, and stage III lung carcinoma

<110> Nanjing Tengthen Biotechnology Co., Ltd

<120> application of molecular marker and kit in auxiliary diagnosis of cancer

<130> GNCLN200562

<160> 9

<170> PatentIn version 3.5

<210> 1

<211> 698

<212> DNA

<213> Artificial sequence

<400> 1

atcacctccc ctgtgtggac ttgggagagg actgggccat tctccttaga gagaagtggg 60

gtggctttta ggatggcaag ggacttcctg taacaacgca tctcatattt ggaatgacta 120

ttaaaaaaac aacaatgtgc aatcaaagtc ctcggccaca ttgtgaactt tgggggatgc 180

tcgctccaac cgactgctgt caccttcacc gttccagttt ttaaatcctg agtcaagcca 240

aaaaaaaaaa aaaaaccaaa acaaaacaaa aaaaacaaat aaagccatgc caatctcatc 300

ttgttttctg cgcaagttag gttttgtcaa gaaagggtgt aacgcaacta agtcatagtc 360

cgcctagaag catttgcggt ggacgatgga ggggccggac tcgtcatact cctgcttgct 420

gatccacatc tgctggaagg tggacagcga ggccaggatg gagccgccga tccacacgga 480

gtacttgcgc tcaggaggag caatgatctg aggagggaag gggacaggca gtgaggaccc 540

tggatgtgac agctccccac acaccacagg accccacagc cgacctgccc aggtcagctc 600

aggcaggaaa gacacccacc ttgatcttca ttgtgctggg tgccagggca gtgatctcct 660

tctgcatcct gtcggcaatg ccagggtaca tggtggtg 698

<210> 2

<211> 786

<212> DNA

<213> Artificial sequence

<400> 2

ttttctggtg tttgtctctc tgactaggtg tctaagacag tgttgtgggt gtaggtacta 60

acactggctc gtgtgacaag gccatgaggc tggtgtaaag cggccttgga gtgtgtatta 120

agtaggtgca cagtaggtct gaacagactc cccatcccaa gaccccagca cacttagccg 180

tgttctttgc actttctgca tgtcccccgt ctggcctggc tgtccccagt ggcttcccca 240

gtgtgacatg gtgtatctct gccttacaga tcatgtttga gaccttcaac accccagcca 300

tgtacgttgc tatccaggct gtgctatccc tgtacgcctc tggccgtacc actggcatcg 360

tgatggactc cggtgacggg gtcacccaca ctgtgcccat ctacgagggg tatgccctcc 420

cccatgccat cctgcgtctg gacctggctg gccgggacct gactgactac ctcatgaaga 480

tcctcaccga gcgcggctac agcttcacca ccacggccga gcgggaaatc gtgcgtgaca 540

ttaaggagaa gctgtgctac gtcgccctgg acttcgagca agagatggcc acggctgctt 600

ccagctcctc cctggagaag agctacgagc tgcctgacgg ccaggtcatc accattggca 660

atgagcggtt ccgctgccct gaggcactct tccagccttc cttcctgggt gagtggagac 720

tgtctcccgg ctctgcctga catgagggtt acccctcggg gctgtgctgt ggaagctaag 780

tcctgc 786

<210> 3

<211> 532

<212> DNA

<213> Artificial sequence

<400> 3

gaaggtgtgg tgccagattt tctccatgtc gtcccagttg gtgacgatgc cgtgctcgat 60

ggggtacttc agggtgagga tgcctctctt gctctgggcc tcgtcgccca cataggaatc 120

cttctgaccc atgcccacca tcacgccctg ggaaggaaag gacaagaagc cctgagcacg 180

ggcgcagccc ccaccccgga aaccgggagg ctcctgtgca gagaaagcgc ccttgcctcc 240

cgcccgctcc cggggctgcc ccacccagcc agctccccta cctggtgcct ggggcgcccc 300

acgatggagg ggaagacggc ccggggggca tcgtcgcccg cgaagccggc cttgcacatg 360

ccggagccgt tgtcgacgac gagcgcggcg atatcatcat ccatggtgag ctgcgagaat 420

agccgggcgc gctgtgagcc gaggtcgccc ccgccctggc cacttccggc gcgccgagtc 480

cttaggccgc cagggggcgc cggcgcgcgc ccagattggg gacaaaggaa gc 532

<210> 4

<211> 35

<212> DNA

<213> Artificial sequence

<400> 4

aggaagagag attatttttt ttgtgtggat ttggg 35

<210> 5

<211> 56

<212> DNA

<213> Artificial sequence

<400> 5

cagtaatacg actcactata gggagaaggc tcaccaccat ataccctaac attacc 56

<210> 6

<211> 35

<212> DNA

<213> Artificial sequence

<400> 6

aggaagagag ttttttggtg tttgtttttt tgatt 35

<210> 7

<211> 57

<212> DNA

<213> Artificial sequence

<400> 7

cagtaatacg actcactata gggagaaggc tacaaaactt aacttccaca acacaac 57

<210> 8

<211> 35

<212> DNA

<213> Artificial sequence

<400> 8

aggaagagag gaaggtgtgg tgttagattt ttttt 35

<210> 9

<211> 55

<212> DNA

<213> Artificial sequence

<400> 9

cagtaatacg actcactata gggagaaggc tacttccttt atccccaatc taaac 55

Claims

1. The application of the methylated ACTB 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;

(11) assisting in distinguishing lung cancer from pancreatic cancer;

2. The application of the substance for detecting the methylation level of the ACTB gene 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;

(11) assisting in distinguishing lung cancer from pancreatic cancer;

3. The application of a substance for detecting the methylation level of the ACTB gene and a medium for 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;

(11) assisting in distinguishing lung cancer from pancreatic cancer;

(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 ACTB gene of n 1A-type samples and n 2B-type samples respectively;

(A2) taking the ACTB 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 type A and the type B, and determining a threshold value for classification judgment;

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

(B2) substituting the ACTB 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;

(11) assisting in distinguishing lung cancer from pancreatic cancer;

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

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 means for detecting the methylation level of the ACTB 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;

(11) assisting in distinguishing lung cancer from pancreatic 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:

(D2) a device comprising a unit A and a unit B;

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 ACTB gene is the methylation level of all or part of CpG sites in the fragments shown in (e1) - (e3) below in the ACTB gene;

the methylated ACTB gene is methylated at all or part of CpG sites in the fragments shown as (e1) - (e3) in the ACTB gene;

9. The use or kit or system according to claim 8, wherein: the 'all or part of CpG sites' is any one or more CpG sites in 3 DNA fragments shown in SEQ ID No.1 to SEQ ID No.3 in the ACTB gene;

or

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 '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. 3;

or

The whole or partial CpG sites are all CpG sites in the DNA segment shown in SEQ ID No. 2; and all CpG sites in the DNA fragment shown in SEQ ID No. 3;

or, the "all or part of the CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.1, all CpG sites in the DNA fragment shown in SEQ ID No.2 and all CpG sites in the DNA fragment shown in SEQ ID No. 3;

or

The "whole or part of CpG sites" may be all or any 30 or any 29 or any 28 or any 27 or any 26 or any 25 or any 24 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 of the DNA fragments shown as SEQ ID No.2 in the ACTB gene;

or

The "whole or partial CpG sites" are all 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 of the following 15 CpG sites in the DNA fragment shown in SEQ ID No. 2:

(f1) the CpG site shown in the 435 nd-436 nd position of the DNA segment shown in SEQ ID No.2 from the 5' end;

(f2) the CpG site shown in 453-bit 454-bit of the DNA segment shown in SEQ ID No.2 from the 5' end;

(f3) the DNA fragment shown in SEQ ID No.2 is from the CpG site shown in the 488-activated 489 th site of the 5' end;

(f4) the DNA segment shown in SEQ ID No.2 has CpG sites shown in positions 492-493 and 494-495 at the 5' end;

(f5) the DNA segment shown in SEQ ID No.2 shows CpG sites from the 514-position 515 and 518-position 519 of the 5' end;

(f6) the DNA segment shown in SEQ ID No.2 shows the CpG site from 522-523 bit of the 5' end;

(f7) the DNA fragment shown in SEQ ID No.2 has CpG sites shown in the 530 th and 534 th and 535 th positions from the 5' end;

(f8) the DNA segment shown in SEQ ID No.2 is from the CpG sites shown in 560-561 and 563-564 of the 5' end;

(f9) the DNA segment shown in SEQ ID No.2 shows CpG sites from 575 rd and 576 nd sites of the 5' end;

(f10) the CpG site is shown in 592-593 th site of the DNA segment shown in SEQ ID No.2 from the 5' end;

(f11) the DNA segment shown in SEQ ID No.2 shows CpG sites from 626-627 of the 5' end;

(f12) the DNA segment shown in SEQ ID No.2 is a CpG site shown in the 638-639 th site of the 5' end;

(f13) the DNA fragment shown in SEQ ID No.2 is provided with CpG sites shown in 666 and 667 and 672 and 673 sites at the 5' end;

(f14) the DNA segment shown in SEQ ID No.2 shows CpG sites at 728-729 th sites from the 5' end;

(f15) the DNA fragment shown in SEQ ID No.2 has CpG sites shown in 757-758 th position 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 ACTB gene comprises a primer combination for amplifying the full length or partial fragment of the ACTB gene;

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

further, the partial fragment is at least one fragment selected from the following fragments:

(g2) a DNA fragment shown as SEQ ID No.2 or a DNA fragment contained therein;

(g3) a DNA fragment shown as SEQ ID No.3 or a DNA fragment contained therein;

(g6) a DNA fragment having an identity of 80% or more to the DNA fragment represented by SEQ ID No.3 or a DNA fragment contained therein;

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

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.4 or SEQ ID No. 4; the primer A2 is single-stranded DNA shown by the 32 nd to 56 th nucleotides of SEQ ID No.5 or SEQ ID No. 5;

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 11 th to 35 th nucleotides of SEQ ID No.6 or SEQ ID No. 6; the primer B2 is SEQ ID No.7 or single-stranded DNA shown by 32 th-57 th nucleotides of SEQ ID No. 7;

the primer pair C is a primer pair consisting of a primer C1 and a primer C2; the primer C1 is single-stranded DNA shown by 11 th to 35 th nucleotides of SEQ ID No.8 or SEQ ID No. 8; the primer C2 is single-stranded DNA shown by the 32 th-55 th nucleotide of SEQ ID No.9 or SEQ ID No. 9.