CN116790752A - Molecular marker for early screening and early diagnosing lung cancer - Google Patents
Molecular marker for early screening and early diagnosing lung cancer Download PDFInfo
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Abstract
The invention discloses a lung cancer early screening and early diagnosing molecular marker. The invention provides application of PRTN3 gene methylation as a marker in preparation of products; wherein the methylation PRTN3 gene comprises methylation of any one or more CpG sites selected from 3 DNA fragments shown in SEQ ID No.1, SEQ ID No.2 and SEQ ID No. 3; the use of the product is at least one of the following: auxiliary diagnosis of lung cancer or prediction of lung cancer risk; assisting in distinguishing benign nodules of the lung from lung cancer; assisting in distinguishing different subtypes of lung cancer; assisting in distinguishing different stages of lung cancer. The study of the invention discovers the hypomethylation phenomenon of PRTN3 gene in the blood of lung cancer patients, and the invention has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of lung cancer and reducing the death rate.
Description
Technical Field
The invention relates to the field of medicine, in particular to a lung cancer early screening and early diagnosing molecular marker.
Background
Lung cancer is a malignant tumor that occurs in the epithelium of the bronchial mucosa, and in recent decades, the morbidity and mortality rate have been on the rise, being the cancer with the highest morbidity and mortality rate worldwide. Although new progress has been made in diagnostic methods, surgical techniques, and chemotherapeutics in recent years, the overall 5-year survival rate of lung cancer patients is only 16%, mainly because most lung cancer patients have been shifted at the time of diagnosis and have lost the opportunity for radical surgery. The study shows that the prognosis of lung cancer is directly related to stage, the survival rate of lung cancer in stage I for 5 years is 83%, the survival rate in stage II is 53%, the survival rate in stage III is 26%, and the survival rate in stage IV is 6%. Thus, the key to reducing mortality in lung cancer patients is early diagnosis and early treatment.
The main lung cancer diagnosis methods at present are as follows: (1) imaging method: such as chest X-rays and low dose helical CT. However, early lung cancer is difficult to detect by chest X-ray. Although low-dose spiral CT can find nodules in the lung, the false positive rate is as high as 96.4%, and unnecessary psychological burden is brought to a person to be checked. At the same time, chest X-rays and low dose helical CT are not suitable for frequent use due to radiation. In addition, imaging methods are also often affected by equipment and physician experience, as well as effective film reading time. (2) cytological methods: such as sputum cytology, bronchoscopy brush or biopsy, bronchoalveolar lavage cytology, etc. Sputum cytology and bronchoscopy have less sensitivity to peripheral lung cancer. Meanwhile, the operation of brushing a piece under a bronchoscope or taking a biopsy and performing cytological examination on bronchoalveolar lavage fluid is complicated, and the comfort level of a physical examination person is poor. (3) serum tumor markers commonly used at present: carcinoembryonic antigen (CEA), carbohydrate antigen (CA 125/153/199), cytokeratin 19 fragment antigen (CYFRA 21-1), and Neuron Specific Enolase (NSE), etc. These serum tumor markers have limited sensitivity to lung cancer, typically 30% -40%, and even lower for stage I tumors. Furthermore, the tumor specificity is limited, and is affected by many benign diseases such as benign tumor, inflammation, degenerative diseases and the like. At present, the tumor markers are mainly used for screening malignant tumors and rechecking tumor treatment effects. Therefore, further development of a highly efficient and specific early diagnosis technique for lung cancer is required.
The most effective method of pulmonary nodule diagnosis currently internationally accepted is chest low dose helical CT screening. However, the low-dose helical CT has high sensitivity, and a large number of nodules can be found, but it is difficult to determine whether or not the subject is benign or malignant. In the found nodules, the proportion of malignancy was still less than 4%. Currently, clinical identification of benign and malignant lung nodules requires long-term follow-up, repeated CT examination, or invasive examination methods relying on biopsy sampling of lung nodules (including chest wall fine needle biopsy, bronchoscopy biopsy, thoracoscopy or open chest lung biopsy), and the like. CT guided or ultrasound guided transthoracic biopsy has higher sensitivity, but has lower diagnosis rate for <2cm nodules, 30-70% missed diagnosis rate, and higher pneumothorax and hemorrhage incidence rate. The incidence rate of the aspiration biopsy complications of the bronchoscope needle is relatively low, but the diagnosis rate of the surrounding nodules is limited, the diagnosis rate of the nodules less than or equal to 2cm is only 34%, and the diagnosis rate of the nodules greater than 2cm is 63%. Surgical excision has a high diagnostic rate and can directly treat the node, but can cause a transient decline in patient lung function, and if the node is benign, the patient performs unnecessary surgery, resulting in excessive medical treatment. Therefore, there is a strong need for new in vitro diagnostic molecular markers to aid in the identification of pulmonary nodules, while reducing the rate of missed diagnosis and minimizing unnecessary punctures or surgeries.
DNA methylation is a chemical modification important on genes that affects the regulatory process of gene transcription and nuclear structure. Alterations in DNA methylation are early events and concomitant events in cancer progression, and are mainly manifested by hypermethylation of oncogenes and hypomethylation of protooncogenes on tumor tissues, etc. However, there is less reported correlation between DNA methylation in blood and tumorigenesis development. In addition, blood is easy to collect, DNA methylation is stable, and if a tumor-specific blood DNA methylation molecular marker can be found, the DNA methylation molecular marker has great clinical application value. Therefore, the research and development of blood DNA methylation diagnosis technology suitable for clinical detection has important clinical application value and social significance for improving early diagnosis and treatment effect of lung cancer and reducing death rate.
Disclosure of Invention
The invention aims to provide a lung cancer early screening and early diagnosing molecular marker.
In a first aspect, the invention claims the use of PRTN3 gene methylation as a marker for the preparation of a product. The application of the product is at least one of the following:
(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;
(2) Aiding in distinguishing benign nodules from cancers;
(3) Aiding in distinguishing between different subtypes of cancer;
(4) Aiding in differentiating different stages of cancer;
(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;
(6) Assisting in distinguishing benign nodules of the lung from lung cancer;
(7) Assisting in distinguishing different subtypes of lung cancer;
(8) Auxiliary differentiation of different stages of lung cancer;
(9) Determining whether the test agent has an inhibitory or promoting effect on the occurrence of cancer.
Further, the auxiliary diagnosis of cancer described in (1) may be embodied as at least one of the following: aiding in distinguishing cancer patients from non-cancerous controls (it is understood that no cancer is present and ever and no benign nodules are reported and blood normative indicators are within the reference range); helping to distinguish between different cancers.
Further, the benign nodule in (2) is a benign nodule corresponding to the cancer in (2), such as: when the cancer is lung cancer, the benign nodule is a pulmonary benign nodule.
Further, the different subtypes of cancer described in (3) may be pathological, such as histological, types.
Further, the different stage of the cancer in (4) may be a clinical stage or a TNM stage.
In a specific embodiment of the present invention, the auxiliary diagnosis of lung cancer described in (5) is embodied as at least one of the following: can be used for assisting in distinguishing lung cancer patients from non-cancer controls, assisting in distinguishing lung adenocarcinoma patients from non-cancer controls, assisting in distinguishing lung squamous cancer patients from non-cancer controls, assisting in distinguishing small cell lung cancer patients from non-cancer controls, assisting in distinguishing stage I lung cancer patients from non-cancer controls, assisting in distinguishing stage II-III lung cancer patients from non-cancer controls, assisting in distinguishing lung cancer patients without lymph node infiltration from non-cancer controls, and assisting in distinguishing lung cancer patients with lymph node infiltration from non-cancer controls. Wherein, the cancer-free control is understood to be that no cancer is present and no benign nodules of the lung are reported and the blood routine index is within the reference range.
In a specific embodiment of the present invention, the assisting in distinguishing benign nodules of the lung from lung cancer in (6) is embodied as at least one of: can help to distinguish lung cancer from benign lung nodules, can help to distinguish lung adenocarcinoma from benign lung nodules, can help to distinguish lung squamous cell carcinoma from benign lung nodules, can help to distinguish small cell lung cancer from benign lung nodules, can help to distinguish stage I lung cancer from benign lung nodules, can help to distinguish stage II-III lung cancer from benign lung nodules, can help to distinguish lung cancer without node infiltration from benign lung nodules, can help to distinguish lung cancer with node infiltration from benign lung nodules.
In a specific embodiment of the present invention, the assisting in differentiating between different subtypes of lung cancer described in (7) is embodied as: can help to distinguish any two of lung adenocarcinoma, lung squamous carcinoma and small cell lung carcinoma.
In a specific embodiment of the present invention, the assisting in differentiating different stages of lung cancer described in (8) is embodied as at least one of: any two of the lung cancer of the T1 stage, the lung cancer of the T2 stage and the lung cancer of the T3 stage can be assisted to be distinguished; can help to distinguish lung cancer without lymph node infiltration from lung cancer with lymph node infiltration; can help to distinguish any two of clinical lung cancer in stage I, clinical lung cancer in stage II and clinical lung cancer in stage III.
In the above (1) - (4) and (9), the cancer may be a cancer capable of causing a decrease in the methylation level of PRTN3 gene in the body, such as lung cancer, etc.
In a second aspect, the invention claims the use of a substance for detecting the methylation level of the PRTN3 gene in the preparation of a product. The use of the product may be at least one of the foregoing (1) - (9).
In a third aspect, the invention claims the use of a substance for detecting the methylation level of the PRTN3 gene and a medium storing a mathematical model for the preparation of a product. The use of the product may be at least one of the foregoing (1) - (9).
The mathematical model may be obtained by a method comprising the steps of:
(A1) Detecting PRTN3 gene methylation levels (training set) of n1 type a samples and n2 type B samples, respectively;
(A2) And (3) taking PRTN3 gene methylation level data of all samples obtained in the step (A1), establishing a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type, and determining a threshold value of classification judgment.
The method for using the mathematical model can comprise the following steps:
(B1) Detecting the PRTN3 gene methylation level of a sample to be detected;
(B2) Substituting PRTN3 gene methylation level data of the sample to be detected obtained in the step (B1) into the mathematical model to obtain a detection index; and then comparing the detection index with a threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result.
In a specific embodiment of the present invention, the threshold is set to 0.5. More than 0.5 is classified as one type, less than 0.5 is classified as another type, and 0.5 is equal as an undefined gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.
In practical applications, the threshold may also be determined according to the maximum approximate sign-up index (specifically, may be a value corresponding to the maximum approximate sign-up index). Greater than the threshold is classified as one class, less than the threshold is classified as another class, and equal to the threshold as an indeterminate gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group A type and which group B type are determined according to a specific mathematical model, and no convention is needed.
The type a sample and the type B sample may be any one of the following:
(C1) Lung cancer samples and no cancer controls;
(C2) Lung cancer samples and lung benign nodule samples;
(C3) Samples of different subtypes of lung cancer and no cancer controls;
(C4) Samples of different stages of lung cancer and no-cancer controls;
(C5) Different subtype samples of lung cancer and benign nodule samples of lung;
(C6) Different stage samples of lung cancer and benign nodule samples of lung;
(C7) A sample of different subtypes of lung cancer;
(C8) Samples of lung cancer in different stages.
In a fourth aspect, the invention claims the use of a medium storing a mathematical model 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) - (9).
In a fifth aspect, the invention claims a kit.
The claimed kit comprises a substance for detecting the methylation level of the PRTN3 gene. The use of the kit may be at least one of the foregoing (1) to (9).
Further, the kit may further comprise a medium storing a mathematical model as described in the third or fourth aspect.
In a sixth aspect, the invention claims a system.
The system claimed by the present invention may include:
(D1) Reagents and/or instrumentation for detecting the methylation level of the PRTN3 gene;
(D2) The device comprises a unit X and a unit Y.
The unit X is used for establishing a mathematical model and comprises a data acquisition module, a data analysis processing module and a model output module.
The data acquisition module is configured to acquire (D1) PRTN3 gene methylation level data of the detected n1 type a samples and n2 type B samples.
The data analysis processing module is configured to receive PRTN3 gene methylation level data of the n 1A type samples and the n 2B type samples sent by the data acquisition module, establish a mathematical model according to the PRTN3 gene methylation level data of the n 1A type samples and the n 2B type samples and a classification mode of the A type and the B type by a two-classification logistic regression method, and determine a threshold value of classification judgment.
The model output module is configured to receive the mathematical model established by the data analysis processing module and output the mathematical model.
The unit Y is used for determining the type of the sample to be detected and comprises a data input module, a data operation module, a data comparison module and a conclusion output module.
The data input module is configured to input (D1) the detected PRTN3 gene methylation level data of the subject.
The data operation module is configured to receive PRTN3 gene methylation level data of the tested person sent by the data input module, and substitute the PRTN3 gene methylation level data of the tested person into the mathematical model established by the data analysis processing module in the unit X, so as to calculate a detection index.
The data comparison module is configured to receive the detection index calculated from the data operation module and compare the detection index with the threshold value determined in the data analysis processing module in the unit X.
The conclusion output module is configured to receive the comparison result from the data comparison module and output a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result.
The type a sample and the type B sample are any one of the following:
(C1) Lung cancer samples and no cancer controls;
(C2) Lung cancer samples and lung benign nodule samples;
(C3) Samples of different subtypes of lung cancer and no cancer controls;
(C4) Samples of different stages of lung cancer and no-cancer controls;
(C5) Different subtype samples of lung cancer and benign nodule samples of lung;
(C6) Different stage samples of lung cancer and benign nodule samples of lung;
(C7) A sample of different subtypes of lung cancer;
(C8) Samples of lung cancer in different stages.
Wherein, n1 and n2 can be positive integers more than 50.
In a specific embodiment of the present invention, the threshold is set to 0.5. More than 0.5 is classified as one type, less than 0.5 is classified as another type, and 0.5 is equal as an undefined gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.
In practical applications, the threshold may also be determined according to the maximum approximate sign-up index (specifically, may be a value corresponding to the maximum approximate sign-up index). Greater than the threshold is classified as one class, less than the threshold is classified as another class, and equal to the threshold as an indeterminate gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.
In various aspects of the foregoing, the PRTN3 gene methylation may be methylation of all or part of CpG sites in a fragment of the PRTN3 gene as shown in (e 1) - (e 3) below.
(e1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment having 80% or more identity thereto;
(e2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment having 80% or more identity thereto;
(e3) The DNA fragment shown in SEQ ID No.3 or a DNA fragment having 80% or more identity thereto.
Further, the 'all or part of CpG sites' are any one or more CpG sites in 3 DNA fragments shown in SEQ ID No.1 to SEQ ID No.3 in the PRTN3 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 "all or part of CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.1 (Table 1) and all CpG sites in the DNA fragment shown in SEQ ID No.2 (Table 2) in the PRTN3 gene.
Or, the "all or part of CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.1 (Table 1) and all CpG sites in the DNA fragment shown in SEQ ID No.3 (Table 3) in PRTN3 gene.
Or, the "all or part of CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.2 (Table 2) and all CpG sites in the DNA fragment shown in SEQ ID No.3 (Table 3) in PRTN3 gene.
Or, the "all or part of CpG sites" are all CpG sites in the DNA fragment shown in SEQ ID No.1 (Table 1), all CpG sites in the DNA fragment shown in SEQ ID No.2 (Table 2) and all CpG sites in the DNA fragment shown in SEQ ID No.3 (Table 3) in the PRTN3 gene.
Or, the whole or part of CpG sites are all 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 in SEQ ID No.2 in the PRTN3 gene.
Or, the whole or part of CpG sites are all or any 11 or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 of the following 12 CpG sites in the DNA fragment shown in SEQ ID No.2 in the PRTN3 gene:
(f1) The CpG site (PRTN3_B_14) shown in 331-332 th position of the DNA fragment shown in SEQ ID No.2 from the 5' end;
(f2) The DNA fragment shown in SEQ ID No.2 shows CpG sites (PRTN3_B_15) from 401-402 positions of the 5' end;
(f3) The CpG site (PRTN3_B_16) shown in 436-437 bits of the DNA fragment shown in SEQ ID No.2 from the 5' end;
(f4) The DNA fragment shown in SEQ ID No.2 has CpG sites (PRTN3_B_17) shown in 469-470 th positions from the 5' end;
(f5) The DNA fragment shown in SEQ ID No.2 contains CpG sites (PRTN3_B_18.19) shown at 491 to 492 and 495 to 496 from the 5' end;
(f6) The CpG site (PRTN3_B_20) shown in 519-520 th position of the DNA fragment shown in SEQ ID No.2 from the 5' end;
(f7) The CpG site (PRTN3_B_21) shown in 533-534 of the DNA fragment shown in SEQ ID No.2 from the 5' end;
(f8) The CpG site (PRTN3_B_22) shown in 561-562 of the DNA fragment shown in SEQ ID No.2 from the 5' end;
(f9) The CpG site (PRTN3_B_23) shown in 588-589 of the 5' end of the DNA fragment shown in SEQ ID No. 2;
(f10) The CpG site (PRTN3_B_24) shown in 662-663 of the 5' end of the DNA fragment shown in SEQ ID No. 2;
(f11) The CpG site (PRTN3_B_25) shown in 671-672 of the DNA fragment shown in SEQ ID No.2 from the 5' end;
(f12) The DNA fragment shown in SEQ ID No.2 shows the CpG sites (PRTN3_B_26) at the 743-744 positions from the 5' -end.
In particular embodiments of the invention, some adjacent methylation sites are treated as one methylation site when analyzed for DNA methylation using time-of-flight mass spectrometry, because several CpG sites are located on one methylation fragment, the peak pattern is indistinguishable (indistinguishable sites are set forth in Table 5), and thus the methylation level analysis is performed, and related mathematical models are constructed and used. This is the case with (f 5) described above.
In each of the above aspects, the means for detecting the methylation level of the PRTN3 gene may comprise (or be) a primer combination for amplifying a full or partial fragment of the PRTN3 gene. The reagent for detecting the methylation level of the PRTN3 gene may comprise (or be) a primer combination for amplifying a full or partial fragment of the PRTN3 gene; the instrument for detecting the methylation level of the PRTN3 gene may be a time-of-flight mass spectrometry detector. Of course, other conventional reagents for performing time-of-flight mass spectrometry may also be included in the reagents for detecting the methylation level of the PRTN3 gene.
Further, the partial fragment may be at least one fragment of:
(g1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;
(g2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;
(g3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;
(g4) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;
(g5) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;
(g6) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same.
Still further, the primer combination is primer pair a and/or primer pair B and/or primer pair C.
The primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.4 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 4; the primer A2 is SEQ ID No.5 or single-stranded DNA shown in 32 th-56 th nucleotides of 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 SEQ ID No.6 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 6; the primer B2 is SEQ ID No.7 or single-stranded DNA shown in 32 th-56 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 in SEQ ID No.8 or 11 th-35 th nucleotide of SEQ ID No. 8; the primer C2 is SEQ ID No.9 or single-stranded DNA shown in 32-56 th nucleotide of SEQ ID No. 9.
In addition, the invention also discloses a method for distinguishing whether the sample to be detected is an A type sample or a B type sample. The method may comprise the steps of:
(A) The mathematical model may be built as a method comprising the steps of:
(A1) Detecting PRTN3 gene methylation levels (training set) of n1 type a samples and n2 type B samples, respectively;
(A2) And (3) taking PRTN3 gene methylation level data of all samples obtained in the step (A1), establishing a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type, and determining a threshold value of classification judgment.
Wherein n1 and n2 in (A1) are positive integers of 50 or more.
(B) The sample to be tested may be determined as a type a sample or a type B sample according to a method comprising the steps of:
(B1) Detecting the PRTN3 gene methylation level of the sample to be detected;
(B2) Substituting PRTN3 gene methylation level data of the sample to be detected obtained in the step (B1) into the mathematical model to obtain a detection index; and then comparing the detection index with a threshold value, and determining whether the type of the sample to be detected is A type or B type according to the comparison result.
In a specific embodiment of the present invention, the threshold is set to 0.5. More than 0.5 is classified as one type, less than 0.5 is classified as another type, and 0.5 is equal as an undefined gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.
In practical applications, the threshold may also be determined according to the maximum approximate sign-up index (specifically, may be a value corresponding to the maximum approximate sign-up index). Greater than the threshold is classified as one class, less than the threshold is classified as another class, and equal to the threshold as an indeterminate gray zone. Wherein the A type and the B type are two corresponding classifications, the two classifications are grouped, which group is the A type and which group is the B type, and the A type and the B type are determined according to a specific mathematical model without convention.
The type a sample and the type B sample may be any one of the following:
(C1) Lung cancer samples and no cancer controls;
(C2) Lung cancer samples and lung benign nodule samples;
(C3) Samples of different subtypes of lung cancer and no cancer controls;
(C4) Samples of different stages of lung cancer and no-cancer controls;
(C5) Different subtype samples of lung cancer and benign nodule samples of lung;
(C6) Different stage samples of lung cancer and benign nodule samples of lung;
(C7) A sample of different subtypes of lung cancer;
(C8) Samples of lung cancer in different stages.
Any of the above mathematical models may be changed in practical application according to the detection method and the fitting mode of DNA methylation, and the mathematical model is determined according to a specific mathematical model without any convention.
In the embodiment of the invention, the model is specifically log (y/(1-y))=b0+b1x1+b2x2+b3x3+ … +bnxn, where y is a detection index obtained after substituting a methylation value of one or more methylation sites of a sample to be tested into the model by a dependent variable, b0 is a constant, x1-xn is an independent variable which is a methylation value of one or more methylation sites of the sample to be tested (each value is a value between 0 and 1), and b1-bn is a weight given by the model to the methylation value of each site.
In the embodiment of the invention, the model can be established by adding known parameters such as age, sex, white blood cell count and the like as appropriate to improve the discrimination efficiency. One specific model established in the embodiments of the present invention is a model for assisting in distinguishing lung cancer-free controls from lung cancer, the model specifically being: lg (y/(1-y))=1.409+4.152×prtn3_b_14-0.079×prtn3_b_15-1.203×prtn3_b_16+2.953×prtn3_b_17-3.902×prtn3_b_18.19+0.835×prtn3_b_20+0.186×prtn3_b_21-4.075×prtn3_b_22+1.562×prtn3_b_23-2.238×prtn3_b_24+0.186×prtn3_b_25-1.341×prtn3_b_26-0.007×age (integer) -0.052×sex (male assigning 1, female assigning 0) -0.076×white blood cell number (10 units) 9 /L). PRTN3_B_14 is the methylation level of CpG sites shown in the 331 st-332 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; PRTN3_B_15 is the methylation level of CpG sites shown in 401-402 th positions of a DNA fragment shown in SEQ ID No.2 from the 5' end; PRTN3_B_16 is the methylation level of CpG sites shown in 436-437 bits of the DNA fragment shown in SEQ ID No.2 from the 5' end; PRTN3_B_17 is the methylation level of CpG sites shown in 469-470 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; the PRTN3_B_18.19 is the methylation level of CpG sites shown in 491 to 492 and 495 to 496 of the DNA fragment shown in SEQ ID No.2 from the 5' end; the PRTN3_B_20 is SEThe methylation level of the CpG sites shown in the 519-520 th position of the 5' end of the DNA fragment shown in the Q ID No. 2; PRTN3_B_21 is the methylation level of CpG sites shown at 533-534 th position of a DNA fragment shown in SEQ ID No.2 from the 5' end; PRTN3_B_22 is the methylation level of CpG sites shown in 561-562 bits of a DNA fragment shown in SEQ ID No.2 from the 5' end; PRTN3_B_23 is the methylation level of CpG sites shown in 588-589 th position of the 5' end of the DNA fragment shown in SEQ ID No. 2; PRTN3_B_24 is the methylation level of CpG sites shown in positions 662-663 of the 5' end of the DNA fragment shown in SEQ ID No. 2; PRTN3_B_25 is the methylation level of CpG sites shown in 671-672 th site of the 5' end of the DNA fragment shown in SEQ ID No. 2; PRTN3_B_26 is the methylation level of CpG sites shown in 743-744 from the 5' end of the DNA fragment shown in SEQ ID No. 2. The threshold of the model was 0.5. Patient candidates with a detection index greater than 0.5 calculated by the model were lung cancer patients, and patient candidates with less than 0.5 were lung cancer-free controls.
In the above aspects, the detecting the PRTN3 gene methylation level is detecting the PRTN3 gene methylation level in blood.
In the above aspects, in a specific embodiment of the present invention, the lung cancer different subtype sample and the cancer-free control in (C3) may specifically be any of the following: lung adenocarcinoma samples and no-cancer controls, lung squamous carcinoma samples and no-cancer controls, small cell lung carcinoma samples and no-cancer controls. Wherein, the cancer-free control is understood to be that no cancer is present and no benign nodules of the lung are reported and the blood routine index is within the reference range.
In the above aspects, in a specific embodiment of the present invention, the lung cancer differential stage sample and the cancer-free control in (C4) may specifically be any of the following: clinical phase I lung cancer samples and no cancer controls, clinical phase II lung cancer samples and no cancer controls, clinical phase III lung cancer samples and no cancer controls. Wherein, the cancer-free control is understood to be that no cancer is present and no benign nodules of the lung are reported and the blood routine index is within the reference range.
In the above aspects, in a specific embodiment of the present invention, the lung cancer subtype sample and the lung benign nodule sample described in (C5) may be specifically any of the following: lung adenocarcinoma samples and lung benign nodule samples, lung squamous carcinoma samples and lung benign nodule samples, small cell lung carcinoma samples and lung benign nodule samples.
In the above aspects, in a specific embodiment of the present invention, the lung cancer differential stage sample and the lung benign nodule sample in (C6) may be specifically any of the following: clinical stage I lung cancer samples and lung benign nodule samples, clinical stage II lung cancer samples and lung benign nodule samples, clinical stage III lung cancer samples and lung benign nodule samples.
In the above aspects, when the type a sample and the type B sample are different subtype samples of lung cancer in (C7), the type a sample and the type B sample may specifically be any two of a lung adenocarcinoma sample, a lung squamous carcinoma sample, and a small cell lung cancer sample.
In the above aspects, when the type a sample and the type B sample are different stage samples of lung cancer in (C8), the type a sample and the type B sample may specifically be any two of a clinical stage I lung cancer sample, a clinical stage II lung cancer sample, and a clinical stage III lung cancer sample.
The PRTN3 gene of any of the above may specifically comprise Genbank accession No.: NM-002777.4 (GI: 1519314665).
The invention provides hypomethylation of PRTN3 gene in blood of lung cancer patients. Experiments prove that the blood can be used as a sample to distinguish cancer (lung cancer) patients from cancer-free controls, lung benign nodules from lung cancer, and different subtypes and different stages of lung cancer. The invention has important scientific significance and clinical application value for improving the early diagnosis and treatment effect of lung cancer and reducing the death rate.
Drawings
FIG. 1 is a schematic diagram of a mathematical model.
Fig. 2 is an illustration of a mathematical model.
Detailed Description
The following detailed description of the invention is provided in connection with the accompanying drawings that are presented to illustrate the invention and not to limit the scope thereof. The examples provided below are intended as guidelines for further modifications by one of ordinary skill in the art and are not to be construed as limiting the invention in any way.
The experimental methods in the following examples, unless otherwise specified, are conventional methods, and are carried out according to techniques or conditions described in the literature in the field or according to the product specifications. Materials, reagents and the like used in the examples described below are commercially available unless otherwise specified.
In the following examples, PRTN3 gene quantification tests were carried out in three replicates, and the results were averaged.
Example 1 primer design for detection of PRTN3 Gene methylation site
Through a number of sequence and functional analyses, three fragments in the PRTN3 gene (prtn3_a fragment, prtn3_b fragment, prtn3_c fragment) were selected for methylation level and cancer correlation analysis.
The PRTN3_A fragment (SEQ ID No. 1) is located in the hg19 reference genome chr19:839833-840570, sense strand.
The PRTN3_B fragment (SEQ ID No. 2) is located in the sense strand of the hg19 reference genome chr19:840550-843177.
The PRTN3_C fragment (SEQ ID No. 3) is located on the sense strand of the hg19 reference genome chr19:848410181-842271.
CpG site information in PRTN3_A fragment is shown in Table 1.
CpG site information in PRTN3_B fragments is shown in Table 2.
CpG site information in PRTN3_C fragment is shown in Table 3.
Table 1, cpG site information in PRTN 3A fragment
| CpG sites | Position of CpG sites in the sequence |
| PRTN3_A_1 | SEQ ID No.1 from positions 26-27 of the 5' end |
| PRTN3_A_2 | SEQ ID No.1 from position 33-34 of the 5' end |
| PRTN3_A_3 | SEQ ID No.1 from position 50-51 of the 5' end |
| PRTN3_A_4 | SEQ ID No.1 from position 61-62 of the 5' end |
| PRTN3_A_5 | SEQ ID No.1 from positions 65-66 of the 5' end |
| PRTN3_A_6 | SEQ ID No.1 from position 73-74 of the 5' end |
| PRTN3_A_7 | SEQ ID No.1 from position 93 to 94 of the 5' end |
| PRTN3_A_8 | SEQ ID No.1 from the 5' end at positions 196-197 |
| PRTN3_A_9 | Positions 219-220 of SEQ ID No.1 from the 5' end |
| PRTN3_A_10 | 278 th to 279 th positions of SEQ ID No.1 from 5' end |
| PRTN3_A_11 | 282-283 bits of SEQ ID No.1 from the 5' end |
| PRTN3_A_12 | SEQ ID No.1 from position 290-291 of the 5' end |
| PRTN3_A_13 | 292 th to 293 th positions of SEQ ID No.1 from 5' end |
| PRTN3_A_14 | SEQ ID No.1 shows the 371-372 th position from the 5' end |
| PRTN3_A_15 | SEQ ID No.1 from positions 380-381 of the 5' end |
| PRTN3_A_16 | 385 th to 386 th positions of SEQ ID No.1 from 5' end |
| PRTN3_A_17 | Position 423-424 of SEQ ID No.1 from 5' end |
| PRTN3_A_18 | 487-488 of SEQ ID No.1 from the 5' end |
| PRTN3_A_19 | SEQ ID No.1 from positions 516-517 of the 5' end |
| PRTN3_A_20 | SEQ ID No.1 from the 5' end at positions 587-588 |
| PRTN3_A_21 | SEQ ID No.1 from positions 615-616 of the 5' end |
| PRTN3_A_22 | SEQ ID No.1 from position 712-713 of the 5' end |
Table 2, cpG site information in PRTN 3B fragment
| CpG sites | Position of CpG sites in the sequence |
| PRTN3_B_1 | SEQ ID No.2 from positions 28-29 of the 5' end |
| PRTN3_B_2 | SEQ ID No.2 from position 49-50 of the 5' end |
| PRTN3_B_3 | SEQ ID No.2 from positions 75-76 of the 5' end |
| PRTN3_B_4 | SEQ ID No.2 from position 91-92 of the 5' end |
| PRTN3_B_5 | SEQ ID No.2 from positions 112-113 of the 5' end |
| PRTN3_B_6 | SEQ ID No.2 from position 128-129 of the 5' end |
| PRTN3_B_7 | SEQ ID No.2 from position 132-133 of the 5' end |
| PRTN3_B_8 | SEQ ID No.2 from position 158-159 of the 5' end |
| PRTN3_B_9 | SEQ ID No.2 from position 169 to 170 of the 5' end |
| PRTN3_B_10 | SEQ ID No.2 from positions 188-189 of the 5' end |
| PRTN3_B_11 | SEQ ID No.2 from positions 246-247 of the 5' end |
| PRTN3_B_12 | 275 th to 276 th positions of SEQ ID No.2 from 5' end |
| PRTN3_B_13 | SEQ ID No.2 from position 324-325 of the 5' end |
| PRTN3_B_14 | SEQ ID No.2 from position 331 to 332 at the 5' end |
| PRTN3_B_15 | SEQ ID No.2 from position 401 to 402 at the 5' end |
| PRTN3_B_16 | SEQ ID No.2 from positions 436-437 at the 5' end |
| PRTN3_B_17 | SEQ ID No.2 from the 5' end at positions 469-470 |
| PRTN3_B_18 | SEQ ID No.2 from the 5' end at 491 to 492 bits |
| PRTN3_B_19 | 495-496 bits of SEQ ID No.2 from the 5' end |
| PRTN3_B_20 | 519 to 520 th position from the 5' end of SEQ ID No.2 |
| PRTN3_B_21 | SEQ ID No.2 from position 533-534 of the 5' end |
| PRTN3_B_22 | 561-562 bits of SEQ ID No.2 from 5' end |
| PRTN3_B_23 | SEQ ID No.2 from the 5' end at positions 588-589 |
| PRTN3_B_24 | SEQ ID No.2 from positions 662-663 of the 5' end |
| PRTN3_B_25 | SEQ ID No.2 from the 5' end 671 st to 672 nd |
| PRTN3_B_26 | SEQ ID No.2 from position 743-744 of the 5' end |
| PRTN3_B_27 | SEQ ID No.2 from the 5' end at positions 802-803 |
Table 3, cpG site information in PRTN 3C fragment
| CpG sites | Position of CpG sites in the sequence |
| PRTN3_C_1 | SEQ ID No.3 from position 33-34 of the 5' end |
| PRTN3_C_2 | 117 th to 118 th positions of SEQ ID No.3 from 5' end |
| PRTN3_C_3 | SEQ ID No.3 shows positions 162-163 from the 5' end |
| PRTN3_C_4 | 270 th to 271 th bit from 5' end of SEQ ID No.3 |
| PRTN3_C_5 | 278 th to 279 th positions of SEQ ID No.3 from 5' end |
| PRTN3_C_6 | SEQ ID No.3 from position 286-287 of the 5' end |
| PRTN3_C_7 | SEQ ID No.3 from position 308 to 309 of the 5' end |
| PRTN3_C_8 | SEQ ID No.3 from position 310 to 311 of the 5' end |
| PRTN3_C_9 | SEQ ID No.3 from positions 316-317 of the 5' end |
| PRTN3_C_10 | SEQ ID No.3 from the 5' end at positions 340-341 |
| PRTN3_C_11 | SEQ ID No.3 from position 348 to 349 of the 5' end |
| PRTN3_C_12 | 372-373 th bit of SEQ ID No.3 from 5' end |
| PRTN3_C_13 | SEQ ID No.3 from positions 392-393 of the 5' end |
| PRTN3_C_14 | From the 5' end, SEQ ID No.3, positions 437-438 |
| PRTN3_C_15 | SEQ ID No.3 from position 468-469 of the 5' end |
| PRTN3_C_16 | SEQ ID No.3 from the 5' end at positions 482-483 |
| PRTN3_C_17 | 489-490 from 5' end of SEQ ID No.3 |
| PRTN3_C_18 | SEQ ID No.3 from position 526-527 of the 5' end |
| PRTN3_C_19 | SEQ ID No.3 shows positions 536-537 from the 5' end |
| PRTN3_C_20 | SEQ ID No.3 from the 5' end at positions 538-539 |
| PRTN3_C_21 | SEQ ID No.3 from position 568-569 of the 5' end |
| PRTN3_C_22 | 584-585 th bit of SEQ ID No.3 from 5' end |
| PRTN3_C_23 | SEQ ID No.3 from 5' end 663-664 bits |
| PRTN3_C_24 | SEQ ID No.3 from the 5' end at positions 747-748 |
| PRTN3_C_25 | SEQ ID No.3 from position 765-766 of the 5' end |
Specific PCR primers were designed for three fragments (prtn3_a fragment, prtn3_b fragment and prtn3_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; positions 1 to 10 from the 5' end in SEQ ID No.4, SEQ ID No.6 and SEQ ID No.8 are nonspecific tags, and positions 11 to 35 are specific primer sequences; the non-specific tags are located at positions 1 to 31 from 5' in SEQ ID No.5, SEQ ID No.7 and SEQ ID No.9, and the specific primer sequences are located at positions 32 to 56. The primer sequences do not contain SNPs and CpG sites.
TABLE 4 PRTN3 methylation primer sequences
Example 2 PRTN3 Gene methylation detection and analysis of results
1. Study sample
In total, 426 lung cancer patients, 286 lung benign nodule patients and 816 cancer-free controls (no cancer control was previously and now cancer-free and no lung nodules were reported and blood routine index was within the reference range) were collected with informed consent of the patients.
All patient samples were collected preoperatively and were subjected to imaging and pathological confirmation.
Lung cancer subtypes are judged according to histopathology.
The stage of lung cancer takes an AJCC 8 th edition stage system as a judgment standard.
426 lung cancer patients were classified according to type: 319 cases of lung adenocarcinoma, 47 cases of lung squamous carcinoma, 52 cases of small cell lung carcinoma and 8 other cases.
426 lung cancer patients were divided according to stage: 338 cases in stage I, 49 cases in stage II, 39 cases in stage III.
426 lung cancer patients were classified according to lung cancer tumor size (T): 306 cases in T1, 72 cases in T2 and 48 cases in T3.
426 cases of lung cancer patients were classified according to the presence or absence of lung cancer lymph node infiltration (N): there were 394 cases of lung cancer lymph node infiltration, and 32 cases of lung cancer lymph node infiltration.
The median age of patients with benign nodules in the cancer-free population, lung cancer and lung is 54, 55 and 58, respectively, and the ratio of men to women in each of these 3 populations is about 1:1.
2. Methylation detection
1. Total DNA of the blood sample is extracted.
2. The total DNA of the blood samples prepared in step 1 was subjected to bisulfite treatment (see DNA methylation kit instructions for Qiagen). After bisulfite treatment, unmethylated cytosines (C) in the original CpG sites are converted to uracil (U), while methylated cytosines remain unchanged.
3. And (3) performing PCR amplification by using the DNA treated by the bisulfite in the step (2) as a template and adopting 3 pairs of specific primers in the table (4) through DNA polymerase according to a reaction system required by a conventional PCR reaction, wherein 3 pairs of primers adopt the same conventional PCR system, and 3 pairs of primers are amplified according to the following procedure.
The PCR reaction procedure was: 95 ℃,4 min- & gt (95 ℃,20 s- & gt 56 ℃,30 s- & gt 72 ℃ 2 min) 45 cycles- & gt 72 ℃,5 min- & gt 4 ℃ for 1h.
4. Taking the amplified product of the step 3, and carrying out DNA methylation analysis by a time-of-flight mass spectrum, wherein the specific method is as follows:
(1) Mu.l of Shrimp Alkaline Phosphate (SAP) solution (0.3 ml SAP [ 0.5U) was added to 5. Mu.l of PCR product]+1.7ml H 2 O) then incubated in a PCR apparatus (37 ℃,20 min. Fwdarw. 85 ℃,5 min. Fwdarw. 4 ℃,5 min) according to the following procedure;
(2) Taking out 2 mu.l of the SAP treated product obtained in the step (1), adding the product into a 5 mu l T-clear reaction system according to the instruction, and then incubating for 3 hours at 37 ℃;
(3) Taking the product of the step (2), adding 19 mu l of deionized water, and then carrying out deionized incubation on a rotary shaking table for 1h by using 6 mu g of Resin;
(4) Centrifuging at 2000rpm at room temperature for 5min, and loading 384SpectroCHIP with the micro supernatant by a Nanodispenser mechanical arm;
(5) Time-of-flight mass spectrometry; the data obtained were collected with the spectroacquisition v3.3.1.3 software and visualized by MassArray EpiTyper v 1.2.1.2 software.
Reagents used for the time-of-flight mass spectrometry detection are all kits (T-Cleavage MassCLEAVE Reagent Auto Kit, cat# 10129A); the detection instrument used for the time-of-flight mass spectrometry detection is MassARRAY Analyzer Chip Prep Module 384, model: 41243; the data analysis software is self-contained software of the detection instrument.
5. And (5) analyzing the data obtained in the step (4).
Statistical analysis of the data was performed by SPSS Statistics 23.0.
Non-parametric tests were used for comparative analysis between the two groups.
The identification effect of a combination of multiple CpG sites on different sample groupings is achieved by logistic regression and statistical methods of the subject curves.
All statistical tests were double-sided, with P values <0.05 considered statistically significant.
Through mass spectrometry experiments, peak patterns of 64 distinguishable methylated fragments were obtained in total. The spectrobactire v3.3.1.3 software can automatically calculate the methylation level at each CpG site for each sample by calculating the peak area according to the methylation level = peak area of the methylated fragments/(peak area of the unmethylated fragments + peak area of the methylated fragments) formula.
3. Analysis of results
1. PRTN3 gene methylation levels in blood of cancer-free controls, lung benign nodules and lung cancer
Methylation levels of all CpG sites in the PRTN3 gene were analyzed using blood from 426 lung cancer patients, 286 lung benign nodule patients, and 816 cancer-free controls as study materials (Table 5). The results show that all CpG sites in the PRTN3 gene have a methylation level median of 0.37 (iqr=0.32-0.58), a methylation level median of 0.36 (iqr=0.30-0.57), a methylation level median of 0.33 (iqr=0.26-0.55), a phase i methylation level median of 0.35 (iqr=0.28-0.56), a phase ii methylation level median of 0.25 (iqr=0.18-0.47), a phase iii methylation level median of 0.24 (iqr=0.17-0.45), a lung adenocarcinoma methylation level median of 0.31 (iqr=0.23-0.51), a squamous carcinoma methylation level median of 0.29 (iqr=0.20-0.50), and a small cell lung cancer methylation level median of 0.27 (iqr=0.19-0.48) in the cancer-free control group. It can be seen that the cancer-free control methylation level is higher than that of benign lung nodules, which are higher than that of lung cancer. The methylation level of lung cancer is lower when the lung cancer is in different stages, the methylation level of lung cancer is higher than that of lung squamous carcinoma, and the methylation level of lung squamous carcinoma is higher than that of small cell lung cancer.
Table 5, comparison of methylation levels for cancer-free controls, benign nodules in the lung, lung cancer, different stages and different subtypes of lung cancer
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2. PRTN3 gene methylation level in blood distinguishes cancer-free control from lung cancer patients
As a result of comparative analysis of methylation levels of PRTN3 genes in 426 lung cancer patients and 816 cancer-free controls, it was found that methylation levels of all CpG sites in the PRTN3 genes were significantly lower in lung cancer patients than in the cancer-free controls (p <0.05, table 5 and Table 6). In addition, methylation levels of all CpG sites of the PRTN3 gene in different subtypes of lung cancer (lung adenocarcinoma, lung squamous carcinoma, small cell lung carcinoma) were significantly different from that of a non-cancerous control, respectively. Methylation levels of all CpG sites of PRTN3 gene in different stages (clinical stage I, stage II and stage III) of lung cancer are respectively and remarkably different from that of a cancer-free control. Furthermore, there was a significant difference (p < 0.05) in methylation levels between lung cancer patients without lymph node infiltration and lung cancer patients with lymph node infiltration, respectively, and non-cancerous controls. Therefore, the methylation level of PRTN3 gene can be used for clinical diagnosis of lung cancer, and especially for early diagnosis of lung cancer.
Table 6 methylation level differences between different subtypes of cancer-free control and lung cancer, and between different stages of cancer-free control and lung cancer
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3. PRTN3 gene methylation level in blood to distinguish benign nodules in lung from lung cancer patients
As a result of comparative analysis of methylation levels of the PRTN3 gene in 426 lung cancer patients and 286 lung benign nodules, it was found that methylation levels of all CpG sites of the PRTN3 gene were significantly higher in lung benign nodules patients than in lung cancer patients (p <0.05, table 5 and Table 7). Furthermore, it was found that the methylation levels of all CpG in PRTN3 genes of lung cancer patients of different subtypes (lung adenocarcinoma, lung squamous carcinoma and small cell lung carcinoma), different clinical stages (stage I or stage II-III) and the presence or absence of lymphocytic infiltration, respectively, were significantly different from those of benign nodules in the lung. Thus, the methylation level of the PRTN3 gene can be used to distinguish lung cancer patients from benign nodule patients, and is a very valuable marker.
TABLE 7 comparison of methylation level differences between benign lung nodules and lung cancer, different subtypes of benign lung nodules and lung cancer, and different stages of benign lung nodules and lung cancer
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4. Differentiation of different subtypes of lung cancer or different stages of lung cancer by PRTN3 gene methylation level in blood
By comparing and analyzing the methylation level of the PRTN3 gene in different subtypes of lung cancer patients (lung adenocarcinoma, lung squamous carcinoma and small cell lung cancer) and different stages of lung cancer patients, it was found that the methylation level of all CpG sites in the PRTN3 gene was significantly different under the conditions of different lung cancer subtypes (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 stage I, stage II and stage III), and the presence or absence of lymph node infiltration (p <0.05, table 8). Thus, the methylation level of the PRTN3 gene can be used to distinguish between different subtypes of lung cancer or different stages of lung cancer.
TABLE 8 comparison of methylation level differences between different subtypes and different stages of lung cancer
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5. Modeling of mathematical models for aiding in cancer diagnosis
The mathematical model established by the invention can be used for achieving the following purposes:
(1) Distinguishing lung cancer patients from non-cancerous controls;
(2) Distinguishing lung cancer patients from lung benign nodule samples;
(3) Distinguishing different subtypes of lung cancer from a cancer-free control;
(4) Distinguishing different stages of lung cancer from a cancer-free control;
(5) Distinguishing lung cancer different subtypes from lung benign nodule samples;
(6) Distinguishing lung cancer different stages from lung benign nodule samples;
(7) Distinguishing different subtypes of lung cancer;
(8) Different stages of lung cancer are distinguished.
The mathematical model is established as follows:
(A) Data sources: methylation levels of target CpG sites (combinations of one or more of tables 1-3) in 816 isolated blood samples of 426 lung cancer patients listed in step one, as well as in step two, of a cancer-free control.
The data can be added with known parameters such as age, sex, white blood cell count and the like according to actual needs to improve the discrimination efficiency.
(B) Model building
Any two different types of patient data (such as cancer-free control and lung cancer patients, lung benign nodule patients and lung cancer patients, lung adenocarcinoma and lung squamous cancer patients, lung adenocarcinoma and small cell lung cancer patients, lung squamous cancer and small cell lung cancer patients, lung cancer stage I and II lung cancer patients, lung cancer stage I and III lung cancer patients and lung cancer stage II and III lung cancer patients) are selected as required to be used as data for establishing a model, and statistical software such as SAS, R, SPSS and the like is used for establishing a mathematical model through a formula by using a statistical method of two types of logistic regression. The numerical value corresponding to the maximum approximate dengue index calculated by the mathematical model formula is a threshold value or is directly set to be 0.5 as the threshold value, the detection index obtained by the sample to be tested after the sample is tested and substituted into the model calculation is more than the threshold value and is classified into one type (B type), less than the threshold value and is classified into the other type (A type), and the detection index is equal to the threshold value and is used as an uncertain gray area. When a new sample to be detected is predicted to judge which type is the sample, firstly, detecting methylation levels of one or more CpG sites on the PRTN3 gene of the sample to be detected by a DNA methylation determination method, then substituting data of the methylation levels into the mathematical model (if known parameters such as age, sex, white cell count and the like are included in the model construction, the specific numerical value of the corresponding parameter of the sample to be detected is substituted into a model formula at the same time in the step), calculating to obtain a detection index corresponding to the sample to be detected, comparing the detection index corresponding to the sample to be detected with the threshold value, and determining which type of sample the sample to be detected is the sample to be detected according to the comparison result.
Examples: as shown in fig. 1, the methylation level of a single CpG site or the methylation level of a combination of multiple CpG sites in the PRTN3 gene in the training set is used to establish a mathematical model for distinguishing between class a and class B by using a formula of two classification logistic regression through statistical software such as SAS, R, SPSS. The mathematical model is herein a two-class logistic regression model, specifically: log (y/1-y) =b0+b1x1+b2x2+b3x3+ … +bnxn, where y is a detection index obtained by substituting a dependent variable, i.e., a methylation value of one or more methylation sites of a sample to be tested, into a model and then converting the value, b0 is a constant, x1-xn is an independent variable, i.e., a methylation value (each value is a value between 0 and 1) of one or more methylation sites of the sample to be tested, and b1-bn is a weight given to each methylation value of the site by the model. In specific application, a mathematical model is established according to methylation degrees (x 1-xn) of one or more DNA methylation sites of a sample detected in a training set and known classification conditions (class A or class B, respectively, assigning 0 and 1 to y), so that a constant B0 of the mathematical model and weights B1-bn of each methylation site are determined, and a threshold value divided by a detection index (0.5 in the example) corresponding to the maximum sign index is calculated by the mathematical model. And the detection index, namely y value, obtained by testing the sample to be tested and substituting the sample into the model for calculation is classified into B class, less than 0.5 is classified into A class, and the y value is equal to 0.5 as an uncertain gray area. Wherein class a and class B are the corresponding two classes (two classes of groups, which group a is class B, which group is to be determined according to a specific mathematical model, without convention herein), such as cancer-free control and lung cancer patients, lung benign nodule and lung cancer patients, lung adenocarcinoma and squamous cell lung cancer patients, adenocarcinoma and small cell lung cancer patients, squamous cell lung cancer and small cell lung cancer patients, stage I lung cancer and stage II lung cancer patients, stage I lung cancer and stage III lung cancer patients, stage II lung cancer and stage III lung cancer patients. When predicting a sample of a subject to determine which category the sample belongs to, blood of the subject is collected first, and then DNA is extracted therefrom. After the extracted DNA is converted by bisulfite, the methylation level of single CpG sites or the methylation level of a plurality of CpG sites of the PRTN3 gene of a subject is detected by using a DNA methylation determination method, and methylation data obtained by detection are substituted into the mathematical model. If the methylation level of one or more CpG sites of the PRTN3 gene of the subject is substituted into the mathematical model and then the calculated detection index is larger than a threshold value, the subject is judged to be classified as a class (B class) with the detection index in the training set being larger than 0.5; if the methylation level data of one or more CpG sites of the PRTN3 gene of the subject is substituted into the mathematical model and then the calculated value, namely the detection index, is smaller than a threshold value, the subject belongs to a class (A class) with the detection index in the training set smaller than 0.5; if the methylation level data of one or more CpG sites of the PRTN3 gene of the subject is substituted into the mathematical model and the calculated value, i.e., the detection index, is equal to the threshold value, then the subject cannot be judged to be class A or class B.
Examples: methylation of preferred CpG sites of PRTN3_B (PRTN3_B_14, PRTN3_B_15, PRTN3_B_16, PRTN3_B_17, PRTN3_B_18.19, PRTN3_B_20, PRTN3_B_21, PRTN3_B_22, PRTN3_B_23, PRTN3_B_24, PRTN3_B_25, and PRTN3_B_26) and mathematical modeling for lung malignancy determination: patients with lung cancer and cancer-free pairsData on methylation levels of the above 12 distinguishable preferred CpG site combinations that have been tested in the training set (426 lung cancer patients and 816 cancer-free controls herein) were taken as an indication of age (integer), sex (male assignment 1, female assignment 0), white blood cell count (unit 10) 9 L) a mathematical model for distinguishing lung cancer patients from cancer-free controls was established by R software using a formula of two-classification logistic regression. The mathematical model is here a two-class logistic regression model, whereby the constants b0 of the mathematical model and the weights b1-bn of the individual methylation sites are determined, in this case in particular: lg (y/(1-y))=1.409+4.152×prtn3_b_14-0.079×prtn3_b_15-1.203×prtn3_b_16+2.953×prtn3_b_17-3.902×prtn3_b_18.19+0.835×prtn3_b_20+0.186×prtn3_b_21-4.075×prtn3_b_22+1.562×prtn3_b_23-2.238×prtn3_b_24+0.186×prtn3_b_25-1.341×prtn3_b_26-0.007×age (integer) -0.052×sex (male assigning 1, female assigning 0) -0.076×white blood cell number (10 units) 9 /L). Where y is the detection index obtained by substituting the methylation values of the 12 distinguishable methylation sites of the sample to be tested into the model according to the dependent variables such as age, sex and white blood cell count. With a threshold of 0.5 set, the methylation level of the 12 distinguishable CpG sites PRTN3_B_14, PRTN3_B_15, PRTN3_B_16, PRTN3_B_17, PRTN3_B_18.19, PRTN3_B_20, PRTN3_B_21, PRTN3_B_22, PRTN3_B_23, PRTN3_B_24, PRTN3_B_25 and PRTN3_B_26 of the sample to be tested is tested together with its age (integer), sex, white cell count (unit 10) 9 and/L) substituting the information into the model to calculate, wherein the obtained detection index, namely the y value, is more than 0.5 and is classified as a lung cancer patient, less than 0.5 and is classified as a cancer-free control, and if the y value is equal to 0.5, the detection index is not determined as the lung cancer patient or the cancer-free control. The area under the curve (AUC) calculation for this model was 0.76 (table 12). Specific subject judgment method is exemplified by, for example, as shown in FIG. 2, blood is collected from two subjects (A, B) to extract DNA, the extracted DNA is converted by bisulfite, and then the methyl groups of 12 distinguishable CpG sites, namely PRTN3_B_14, PRTN3_B_15, PRTN3_B_16, PRTN3_B_17, PRTN3_B_18.19, PRTN3_B_20, PRTN3_B_21, PRTN3_B_22, PRTN3_B_23, PRTN3_B_24, PRTN3_B_25 and PRTN3_B_26, of the subjects are measured by DNA methylation The level of chemosis is detected. The methylation level data obtained from the detection is then combined with the age (integer), sex and white blood cell count of the subject (unit 10 9 and/L) are substituted into the mathematical model. The value calculated by the first test subject after the mathematical model is more than 0.85 and is more than 0.5, the first test subject is judged to be a lung cancer patient (which accords with the clinical judgment result); and substituting methylation level data of one or more CpG sites of the PRTN3 gene of the subject B into the mathematical model to calculate a value of 0.21 to be less than 0.5, and judging the subject B to be a cancer-free control (consistent with clinical judgment results).
(C) Model Effect evaluation
According to the above method, mathematical models for distinguishing between a cancer-free control and a lung cancer patient, a lung benign nodule patient and a lung cancer patient, a lung adenocarcinoma and a lung squamous carcinoma patient, a lung adenocarcinoma and a small cell lung cancer patient, a lung squamous carcinoma and a small cell lung cancer patient, a lung cancer patient of stage I and a lung cancer patient of stage II, a lung cancer patient of stage I and a lung cancer patient of stage III, a lung cancer patient of stage II and a lung cancer patient of stage III, respectively, are established, and the effectiveness thereof is evaluated by a subject curve (ROC curve). The larger the area under the curve (AUC) from the ROC curve, the better the differentiation of the model, the more efficient the molecular marker. The evaluation results after construction of mathematical models using different CpG sites are shown in tables 9, 10 and 11. In tables 9, 10 and 11, 1 CpG site represents the site of any one CpG site in the prtn3_b amplified fragment, 2 CpG sites represent the combination of any 2 CpG sites in prtn3_b, 3 CpG sites represent the combination of any 3 CpG sites in prtn3_b, … … and so on. The values in the table are the range of values for the combined evaluation of the different sites (i.e., the results for any combination of CpG sites are within this range).
The above results show that the discrimination ability of the PRTN3 gene for each group (cancer-free control and lung cancer patients, lung benign nodule and lung cancer patients, lung adenocarcinoma and lung squamous cancer patients, lung adenocarcinoma and small cell lung cancer patients, lung squamous cancer and small cell lung cancer patients, stage I and II lung cancer patients, stage I and III lung cancer patients, stage II and III lung cancer patients) increases with increasing number of sites.
In addition, among the CpG sites shown in tables 1-3, there are cases where combinations of a few preferred sites are better identified than combinations of a plurality of non-preferred sites. The combination of 12 distinguishable optimal sites, such as prtn3_b_14, prtn3_b_15, prtn3_b_16, prtn3_b_17, prtn3_b_18.19, prtn3_b_20, prtn3_b_21, prtn3_b_22, prtn3_b_23, prtn3_b_24, prtn3_b_25, and prtn3_b_26 shown in tables 12, 13, and 14 is the preferred site for any 12 distinguishable sites in prtn3_b.
In summary, cpG sites on the PRTN3 gene and various combinations thereof, cpG sites on the PRTN3_A fragment and various combinations thereof, cpG sites on the PRTN3_B fragment and various combinations thereof, prtn3_b_14, prtn3_b_15, prtn3_b_16, prtn3_b_17, prtn3_b_18.19, prtn3_b_20, prtn3_b_21, prtn3_b_22, prtn3_b_23, prtn3_b_24, prtn3_b_25, and prtn3_b_26 sites and various combinations thereof, cpG sites on the prtn3_c fragment and various combinations thereof, methylation levels of CpG sites and various combinations thereof on PRTN3_ A, PRTN _B and PRTN3_C are all discriminatory for cancer-free control and lung cancer patients, lung benign nodule and lung cancer patients, lung adenocarcinoma and squamous cell lung cancer patients, squamous cell lung cancer and small cell lung cancer patients, stage I and stage II lung cancer patients, stage I and stage III lung cancer patients, stage II and stage III lung cancer patients.
Table 9, cpG sites of PRTN3_B and combinations thereof for differentiating between non-cancerous control and lung cancer, non-cancerous control and different stages of lung cancer, non-cancerous control and different subtypes of lung cancer
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Table 10, cpG sites of PRTN3_B and combinations thereof for distinguishing benign lung nodules from lung cancer, benign lung nodules from different stages of lung cancer, benign lung nodules from different subtypes of lung cancer
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Table 11, cpG sites of PRTN 3B and free combinations thereof for distinguishing between different subtypes and different stages of a lung cancer patient
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Table 12, optimal CpG sites of PRTN3_B and combinations thereof for differentiating between non-cancerous control and lung cancer, non-cancerous control and different stages of lung cancer, non-cancerous control and different subtypes of lung cancer
Note that: cpG sites in the tables are all distinguishable sites.
Table 13, optimal CpG sites of PRTN3_B and combinations thereof for differentiating benign lung nodules and lung cancer, benign lung nodules and different stages of lung cancer, benign lung nodules and different subtypes of lung cancer
Note that: cpG sites in the tables are all distinguishable sites.
Table 14, optimal CpG sites of PRTN3_B and combinations thereof for differentiating between different stages and different subtypes of lung cancer patients
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Note that: cpG sites in the tables are all distinguishable sites.
The present application is described in detail above. It will be apparent to those skilled in the art that the present application can be practiced in a wide range of equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the application and without undue experimentation. While the application has been described with respect to specific embodiments, it will be appreciated that the application may be further modified. In general, this application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains.
Claims (10)
- Application of PRTN3 gene methylation as a marker in the preparation of products; the application of the product is at least one of the following:(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;(2) Aiding in distinguishing benign nodules from cancers;(3) Aiding in distinguishing between different subtypes of cancer;(4) Aiding in differentiating different stages of cancer;(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;(6) Assisting in distinguishing benign nodules of the lung from lung cancer;(7) Assisting in distinguishing different subtypes of lung cancer;(8) Auxiliary differentiation of different stages of lung cancer;(9) Determining whether the test agent has an inhibitory or promoting effect on the occurrence of cancer.
- 2. Use of a substance for detecting the methylation level of the PRTN3 gene in the preparation of a product; the application of the product is at least one of the following:(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;(2) Aiding in distinguishing benign nodules from cancers;(3) Aiding in distinguishing between different subtypes of cancer;(4) Aiding in differentiating different stages of cancer;(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;(6) Assisting in distinguishing benign nodules of the lung from lung cancer;(7) Assisting in distinguishing different subtypes of lung cancer;(8) Auxiliary differentiation of different stages of lung cancer;(9) Determining whether the test agent has an inhibitory or promoting effect on the occurrence of cancer.
- 3. Use of a substance for detecting the methylation level of the PRTN3 gene and a medium storing a mathematical model for the preparation of a product; the application of the product is at least one of the following:(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;(2) Aiding in distinguishing benign nodules from cancers;(3) Aiding in distinguishing between different subtypes of cancer;(4) Aiding in differentiating different stages of cancer;(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;(6) Assisting in distinguishing benign nodules of the lung from lung cancer;(7) Assisting in distinguishing different subtypes of lung cancer;(8) Auxiliary differentiation of different stages of lung cancer;(9) Determining whether the analyte has an inhibitory or promoting effect on the occurrence of cancer;the mathematical model is obtained according to a method comprising the following steps:(A1) Detecting PRTN3 gene methylation levels of n1 type A samples and n2 type B samples, respectively;(A2) Taking PRTN3 gene methylation level data of all samples obtained in the step (A1), establishing a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type, and determining a classification judgment threshold;the using method of the mathematical model comprises the following steps:(B1) Detecting the PRTN3 gene methylation level of a sample to be detected;(B2) Substituting PRTN3 gene methylation level data of the sample to be detected obtained in the step (B1) into the mathematical model to obtain a detection index; then comparing the detection index with a threshold value, and determining whether the type of the sample to be detected is A type or B type according to a comparison result;the type a sample and the type B sample are any one of the following:(C1) Lung cancer samples and no cancer controls;(C2) Lung cancer samples and lung benign nodule samples;(C3) Samples of different subtypes of lung cancer and no cancer controls;(C4) Samples of different stages of lung cancer and no-cancer controls;(C5) Different subtype samples of lung cancer and benign nodule samples of lung;(C6) Different stage samples of lung cancer and benign nodule samples of lung;(C7) A sample of different subtypes of lung cancer;(C8) Samples of lung cancer in different stages.
- 4. Use of a medium storing a mathematical model for the preparation of a product; the application of the product is at least one of the following:(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;(2) Aiding in distinguishing benign nodules from cancers;(3) Aiding in distinguishing between different subtypes of cancer;(4) Aiding in differentiating different stages of cancer;(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;(6) Assisting in distinguishing benign nodules of the lung from lung cancer;(7) Assisting in distinguishing different subtypes of lung cancer;(8) Auxiliary differentiation of different stages of lung cancer;(9) Determining whether the analyte has an inhibitory or promoting effect on the occurrence of cancer;the mathematical model is obtained according to a method comprising the following steps:(A1) Detecting PRTN3 gene methylation levels of n1 type A samples and n2 type B samples, respectively;(A2) Taking PRTN3 gene methylation level data of all samples obtained in the step (A1), establishing a mathematical model by a two-classification logistic regression method according to classification modes of A type and B type, and determining a classification judgment threshold;the using method of the mathematical model comprises the following steps:(B1) Detecting the PRTN3 gene methylation level of a sample to be detected;(B2) Substituting PRTN3 gene methylation level data of the sample to be detected obtained in the step (B1) into the mathematical model to obtain a detection index; then comparing the detection index with a threshold value, and determining whether the type of the sample to be detected is A type or B type according to a comparison result;the type a sample and the type B sample are any one of the following:(C1) Lung cancer samples and no cancer controls;(C2) Lung cancer samples and lung benign nodule samples;(C3) Samples of different subtypes of lung cancer and no cancer controls;(C4) Samples of different stages of lung cancer and no-cancer controls;(C5) Different subtype samples of lung cancer and benign nodule samples of lung;(C6) Different stage samples of lung cancer and benign nodule samples of lung;(C7) A sample of different subtypes of lung cancer;(C8) Samples of lung cancer in different stages.
- 5. A kit comprising a substance for detecting the methylation level of the PRTN3 gene; the application of the kit is at least one of the following:(1) Aiding in diagnosing cancer or predicting the risk of developing cancer;(2) Aiding in distinguishing benign nodules from cancers;(3) Aiding in distinguishing between different subtypes of cancer;(4) Aiding in differentiating different stages of cancer;(5) Auxiliary diagnosis of lung cancer or prediction of lung cancer risk;(6) Assisting in distinguishing benign nodules of the lung from lung cancer;(7) Assisting in distinguishing different subtypes of lung cancer;(8) Auxiliary differentiation of different stages of lung cancer;(9) Determining whether the test agent has an inhibitory or promoting effect on the occurrence of cancer.
- 6. The kit of claim 5, wherein: the kit further comprises a medium storing a mathematical model as set forth in claim 3 or 4.
- 7. A system, comprising:(D1) Reagents and/or instrumentation for detecting the methylation level of the PRTN3 gene;(D2) A device comprising a unit X and a unit Y;The unit X is used for establishing a mathematical model and comprises a data acquisition module, a data analysis processing module and a model output module;the data acquisition module is configured to acquire PRTN3 gene methylation level data of n 1A type samples and n 2B type samples obtained by (D1) detection;the data analysis processing module is configured to receive PRTN3 gene methylation level data of the n 1A type samples and the n 2B type samples sent by the data acquisition module, establish a mathematical model according to the PRTN3 gene methylation level data of the n 1A type samples and the n 2B type samples and a classification mode of the A type and the B type by a two-classification logistic regression method, and determine a threshold value of classification judgment;the model output module is configured to receive the mathematical model established by the data analysis processing module and output the mathematical model;the unit Y is used for determining the type of the sample to be detected and comprises a data input module, a data operation module, a data comparison module and a conclusion output module;the data input module is configured to input PRTN3 gene methylation level data of the tested person detected by the (D1);the data operation module is configured to receive PRTN3 gene methylation level data of the to-be-detected person, which is sent by the data input module, and substitutes the PRTN3 gene methylation level data of the to-be-detected person into the mathematical model established by the data analysis processing module in the unit X, so as to calculate a detection index;The data comparison module is configured to receive the detection index calculated by the data operation module and compare the detection index with the threshold value determined by the data analysis processing module in the unit X;the conclusion output module is configured to receive the comparison result from the data comparison module and output a conclusion of whether the type of the sample to be tested is A type or B type according to the comparison result;the type a sample and the type B sample are any one of the following:(C1) Lung cancer samples and no cancer controls;(C2) Lung cancer samples and lung benign nodule samples;(C3) Samples of different subtypes of lung cancer and no cancer controls;(C4) Samples of different stages of lung cancer and no-cancer controls;(C5) Different subtype samples of lung cancer and benign nodule samples of lung;(C6) Different stage samples of lung cancer and benign nodule samples of lung;(C7) A sample of different subtypes of lung cancer;(C8) Samples of lung cancer in different stages.
- 8. The use or kit or system according to any one of claims 1-7, wherein: methylation of the PRTN3 gene into methylation of all or part of CpG sites in fragments shown in the following (e 1) - (e 3) in the PRTN3 gene;(e1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment having 80% or more identity thereto;(e2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment having 80% or more identity thereto;(e3) The DNA fragment shown in SEQ ID No.3 or a DNA fragment having 80% or more identity thereto.
- 9. The use or kit or system according to claim 8, wherein: the 'all or part of CpG sites' are any one or more CpG sites in 3 DNA fragments shown in SEQ ID No.1 to SEQ ID No.3 in the PRTN3 gene;or (b)The 'all or part of CpG sites' are all CpG sites in the DNA fragment shown in SEQ ID No.1 and all CpG sites in the DNA fragment shown in SEQ ID No.2 in the PRTN3 gene;or (b)The 'all or part of CpG sites' are all CpG sites in the DNA fragment shown in SEQ ID No.1 and all CpG sites in the DNA fragment shown in SEQ ID No.3 in the PRTN3 gene;or (b)The 'all or part of CpG sites' are all CpG sites in a DNA fragment shown in SEQ ID No.2 and all CpG sites in a DNA fragment shown in SEQ ID No.3 in the PRTN3 gene;or (b)The 'all or part of 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 in the PRTN3 gene;Or (b)The 'all or part of CpG sites' are all 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 in SEQ ID No.2 in the PRTN3 gene;or (b)The whole or part of CpG sites are all or any 11 or any 10 or any 9 or any 8 or any 7 or any 6 or any 5 or any 4 or any 3 or any 2 or any 1 of the following 12 CpG sites in the DNA fragment shown in SEQ ID No.2 in the PRTN3 gene:(f1) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 331 to 332 th positions of the 5' end;(f2) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 401-402 positions of the 5' end;(f3) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 436 to 437 positions of the 5' end;(f4) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 469 th to 470 th positions of the 5' end;(f5) The DNA fragment shown in SEQ ID No.2 contains CpG sites shown in 491 to 492 and 495 to 496 from the 5' end;(f6) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 519 to 520 positions of the 5' end;(f7) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 533 to 534 positions of the 5' end;(f8) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 561-562 positions of the 5' end;(f9) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 588 to 589 of the 5' end;(f10) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 662 to 663 of the 5' end;(f11) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 671 to 672 of the 5' end;(f12) The DNA fragment shown in SEQ ID No.2 shows CpG sites from 743-744 at the 5' end.
- 10. The use or kit or system according to any one of claims 1-9, wherein: the substance for detecting the methylation level of the PRTN3 gene comprises a primer combination for amplifying the full length or partial fragment of the PRTN3 gene;the reagent for detecting the methylation level of the PRTN3 gene comprises a primer combination for amplifying the full length or partial fragment of the PRTN3 gene;further, the partial fragment is at least one fragment of:(g1) A DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;(g2) A DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;(g3) A DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;(g4) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.1 or a DNA fragment comprising the same;(g5) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.2 or a DNA fragment comprising the same;(g6) A DNA fragment having an identity of 80% or more to the DNA fragment shown in SEQ ID No.3 or a DNA fragment comprising the same;still further, the primer combination is primer pair a and/or primer pair B and/or primer pair C;the primer pair A is a primer pair consisting of a primer A1 and a primer A2; the primer A1 is SEQ ID No.4 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 4; the primer A2 is SEQ ID No.5 or single-stranded DNA shown in 32 th-56 th nucleotides of 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 SEQ ID No.6 or single-stranded DNA shown in 11 th-35 th nucleotides of SEQ ID No. 6; the primer B2 is SEQ ID No.7 or single-stranded DNA shown in 32 th-56 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 in SEQ ID No.8 or 11 th-35 th nucleotide of SEQ ID No. 8; the primer C2 is SEQ ID No.9 or single-stranded DNA shown in 32-56 th nucleotide of SEQ ID No. 9.
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| CN117368479A (en) * | 2023-11-13 | 2024-01-09 | 郑州大学 | Biomarker and detection kit for lung adenocarcinoma diagnosis |
| CN117368479B (en) * | 2023-11-13 | 2024-07-02 | 郑州大学 | Biomarker and detection kit for lung adenocarcinoma diagnosis |
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| CN117368479A (en) * | 2023-11-13 | 2024-01-09 | 郑州大学 | Biomarker and detection kit for lung adenocarcinoma diagnosis |
| CN117368479B (en) * | 2023-11-13 | 2024-07-02 | 郑州大学 | Biomarker and detection kit for lung adenocarcinoma diagnosis |
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