CN118186078A

CN118186078A - Novel marker combination for auxiliary diagnosis of multi-target lung cancer and application thereof

Info

Publication number: CN118186078A
Application number: CN202410180308.2A
Authority: CN
Inventors: 余韶华; 张琼; 朱友杰; 徐博; 郑文渊
Original assignee: Hangzhou Aorui Gene Technology Co ltd
Current assignee: Hangzhou Aorui Gene Technology Co ltd
Priority date: 2024-02-18
Filing date: 2024-02-18
Publication date: 2024-06-14

Abstract

The invention provides a novel multi-target lung cancer diagnosis combined marker and application thereof, and a series of novel methylation sites capable of efficiently distinguishing lung cancer and lung benign lesions from high-depth whole genome sulfite sequencing (WGBS) data of lung cancer, lung benign lesions and healthy human samples are found. By detecting the markers in peripheral blood, the kit has good diagnosis performance on early lung cancer. Meanwhile, 3 protein markers (CEA, CYFRA21-1 and ProGRP) commonly used in lung cancer clinic are introduced on the basis of methylation detection, and the accuracy of diagnosis can be further improved through multi-group combination detection.

Description

Novel marker combination for auxiliary diagnosis of multi-target lung cancer and application thereof

Technical Field

The invention relates to the field of cancer detection, in particular to a novel marker combination for multi-target lung cancer auxiliary diagnosis and application thereof.

Background

Lung cancer is one of the most common and fatal cancers worldwide. Global CANCER STATISTICS 2020 shows that about 220.7 thousands of new cases of lung cancer occur worldwide, with the second place; about 179.6 ten thousand cases of lung cancer death, the first (CA: a cancer journal for clinicians,2021,71 (3): 209-249). The incidence and death numbers of Chinese lung cancer respectively account for 37% and 39.8% of the world, and the prevention and control of lung cancer are important challenges facing the prevention and control of malignant tumors in China. In recent years, the lung cancer treatment has been greatly advanced, the survival rate of the lung cancer in China in 5 years is improved from 16.1% to 19.7%, but about 75% of patients are in the advanced stage of lung cancer in diagnosis, and the optimal radical surgery treatment time is missed (THE LANCET Global Health,2018,6 (5): e555-e 567). The early diagnosis can obviously improve the prognosis survival of the lung cancer patient, and the survival rate of the lung cancer patient in 5 years after operation can be seen that the survival rate of the patient in the period I in 5 years after operation is 77% -92%, while the survival rate of the patient in the period IIIA-IVA is only 10% -36%, and the survival rate in 5 years is obviously different (Chin J Tuberc RESPIR DIS, january 2023, vol.46, no. 1). In summary, the key to increase lung cancer survival is early discovery, early diagnosis and early treatment, which is important.

The lung imaging examination method mainly comprises chest X-ray, chest CT, magnetic Resonance Imaging (MRI), positron emission computed tomography (PET-CT) and the like, and is a clinically common lung cancer early diagnosis and diagnosis method. Chest X-ray is simple and easy to implement, but has low resolution, is not easy to detect the lung nodules and lesions of hidden parts such as mediastinum, pericardium and the like, and has certain limitation on detection of early lung cancer (Jama, 2021,325 (10): 962-970). Chest CT is the most important and common image inspection method in the current lung cancer diagnosis, stage, curative effect evaluation and follow-up visit after treatment, has higher resolution, and can detect micro focus of lung and lesions of difficult-to-display parts of common X-ray chest radiography. Among them, the diagnostic value of low-dose helical CT (low-dose computed tomography, LDCT) has been confirmed by studies such as national lung diagnostic test (NLST), dutch-Belgium random lung cancer diagnostic test (NELSON) and multi-center Italian lung detection test (MILD). However, with the wide application of LDCT at home and abroad, many problems are gradually highlighted, including different definition standards of high risk groups, excessively high false positive rate, excessive diagnosis, cost effectiveness and the like. MRI and PET-CT have the characteristics of high sensitivity and specificity, but have higher detection cost and difficult clinical popularization.

Liquid biopsy is a minimally invasive method that monitors and early identifies changes in cells or cellular products transferred from malignant lesions into body fluids in real time. Meanwhile, due to the minimally invasive nature of the liquid biopsy, complications caused by the tissue biopsy can be prevented. Liquid biopsies include enrichment and isolation of Circulating Tumor Cells (CTCs), circulating tumor DNA (ctDNA) and other tumor genetic material, such as extracellular vesicles (Evs) (Mol Cancer,2023.22 (1): p.7). ctDNA has become a research hotspot for early detection of cancer due to simple collection and abundant signal features. ctDNA contains a number of changes, including methylation, mutation, and copy number changes, and can be used for early cancer detection. However, methylation changes in ctDNA occur earlier than genomic changes (such as mutations and copy number changes), exhibit abundant cancer and tissue specificity, and exhibit significant stability in body fluids (NAT REV CLIN Oncol,2018.15 (5): p.292-309). Zhao et al (ADVANCED SCIENCE, 2023:2206518) developed a multiplex digital methylation specific PCR (multiplex digital methylation-SPECIFIC PCR, MDMSP) method with a minimum detection limit of 0.0005% methylation detection method based on the 4 methylation markers (SOX 17, CDO1, TAC1, and HOXA 7) previously found by the research team, and with an AUC of 0.86 for lung cancer. This study demonstrates the feasibility of early diagnosis of lung cancer based on blood free DNA methylation.

Although single-panel detection techniques have been widely studied and applied, the occurrence of malignant tumors involves multiple pathological processes of different levels and dimensions, such as genes, epigenetic, transcriptome, microorganisms, proteins and metabolism, and if only single-panel features are analyzed, the screening of targets is greatly limited. On one hand, the multi-group characteristic integration analysis can mutually verify, and the persuasion of the detection result is enhanced; on the other hand, the method can capture early cancer signals in a multi-dimensional and omnibearing way, and improves the detection sensitivity. In terms of multiple sets of single cancer detection, the U.S. FDA approved the first worldwide product Cologuard for colorectal cancer diagnosis by fecal occult blood and gene multi-target combined detection (FIT-DNA) in 2014. The product is recommended by a plurality of authoritative organizations such as the American disease prevention working group and the like to be applied to early diagnosis of colorectal tumor of asymptomatic people of proper age. As a core product of Exact Science, cologuard utilized three types of biomarkers: methylation of NDRG4 and BMP3 genes, point mutation of KRAS gene and hemoglobin in fecal occult blood. The end product exhibited a sensitivity of 92.3% for colorectal cancer and 42.4% for progressive adenomas, with a specificity of 87% (N Engl J Med,2014.371 (2): p.187). Meanwhile, the company in 2022 ESMO releases liver cancer detection platformLiver is on line. The product is used for early diagnosis of liver cancer by detecting methylation of free DNA and protein markers in peripheral blood. In 2022, exact Sciences published/>, journal Clinical Gastroenterology and HepatologyRecent studies by Liver, chalasani et al (Clin Gastroenterol Hepatol,2022.20 (1): p.173-182.e7), incorporated blood samples from 540 patients (136 cases+404 controls), tested for inclusion of 3 DNA methylation markers (HOXA 1, TSTYL 5 and B3GALT 6) and 1 protein marker (AFP). Analysis results displayThe sensitivity of Liver cancer detection by Liver is 88%, the specificity is 87%, and prospective verification tests are currently being developed. The above studies indicate that a multi-group chemical combination detection technique based on ctDNA methylation can be used as an advantageous tool for cancer detection.

At present, the obtained lung cancer methylation detection products in China have human SHOX2 and RASSF1A gene methylation DNA detection kit (PCR fluorescence method) of a perspective organism and Ai Kelun SHOX2/RASSF1A/PTGER4 gene methylation detection kit (PCR-fluorescence probe method) of a living organism, and the lung cancer products have seven lung cancer related antibody detection kits (enzyme-linked immunosorbent assay) of Kevlar, but the products are only based on methylation or detection of single groups such as patient autoantibodies, and the accuracy of clinical lung cancer diagnosis is not enough.

Disclosure of Invention

Aiming at the problems in the prior art, the invention provides a novel multi-target lung cancer auxiliary diagnosis combined marker, a kit and application thereof in the field of lung cancer diagnosis. The methylation sites which are novel in series and can efficiently distinguish lung cancer and benign lesion patients are found out from the high-depth whole genome methylation (WGBS) data of lung cancer and tissues beside the lung cancer, so that the method can be used for efficiently detecting the lung cancer; the novel gene methylation locus is combined with clinically common protein markers (CEA, CYFRA21-1 and ProGRP), so that the AUC value can be further improved, and the sensitivity and specificity of lung cancer diagnosis can be improved.

By detecting the methylation level of the series of novel methylation sites and combining the CEA, CYFRA21-1 and ProGRP protein levels in serum samples, lung cancer patients and non-lung cancer patients can be distinguished more sensitively and specifically. The data of the clinical lung cancer and non-lung cancer patient samples are detected, so that the combination provided by the invention can effectively distinguish the lung cancer patient from the non-lung cancer patient, the AUC value can reach 0.984 at the highest, the sensitivity reaches 89.5%, and the specificity reaches 99.0%.

Methylation refers to the process of catalytically transferring methyl groups from an active methyl compound to other compounds, which may form various methyl compounds, or which may chemically modify certain proteins or nucleic acids to form methylated products. In biological systems, methylation is enzymatically catalyzed, and involves heavy metal modification, regulation of gene expression, regulation of protein function, and ribonucleic acid processing.

CpG is an abbreviation for cytosine (C) -phosphate (p) -guanine (G), and in mammals CpG exists in two forms: one is dispersed in the DNA sequence; another type of CpG island, known as CpG island (CPG ISLAND), is highly aggregated, in normal tissues, 70% -90% of the scattered CpG is methyl-modified, whereas CpG islands of about 100-1000bp and rich in CpG dinucleotides are often unmethylated. CpG islands often occur in regulatory regions of eukaryotic coding genes, where they are readily methylated to form 5' -methylcytosine due to the presence of C in CpG, deaminated to form uracil, and converted to thymine (T) by DNA replication or amplification, where T itself is not readily repaired due to its presence in DNA. CpG is therefore distributed in the genome in islands.

Methylation-modified C is not converted by bisulfite, unmethylated cytosine (C) is converted by bisulfite to uracil (U), U pairs with A and C pairs with G when the bases are complementary paired. In theory, all the sites of a tumor patient are methylated, so that methylation modification of the sites of a tumor patient is a common phenomenon, only partial modification is not generated, and CpG sites which are closer to each other on a genome are methylated or unmethylated at the same time, which means that only one cluster of CpG sites can be analyzed as a whole.

Methylation modification of cytosine number 5 is a DNA modification mode widely existing in eukaryotic cell organisms, and the methylation modification on DNA plays an important role in the growth and development of organisms and in the canceration process of cells. Because of the same base-pairing properties as cytosine, 5-methylcytosine cannot be directly measured by means of one-generation sequencing or high-throughput sequencing. The most common method for detecting 5-methylcytosine (C) is to convert the DNA to be detected by bisulfite, and after alkaline hydrolysis, unmethylated cytosine is converted to uracil (U), while 5-methylcytosine (C) is not converted. Uracil, when paired with an adenine, is distinguished from the complementary pairing of cytosine and guanine, and therefore, by detection of bisulfite treated DNA, it is possible to determine, by PCR techniques, which cytosines (C) have been methylated in the original DNA molecule without conversion. Thus, a methylation site marker is a methylation of all the C's of the nearer CpG sites on the entire sequence genome, all the C's being methylation sites.

The invention also introduces protein markers to improve the accuracy of the detection, the protein markers consisting of carcinoembryonic antigen (CEA), soluble fragment of cytokeratin 19 (CYFRA 21-1) and gastrin releasing peptide precursor (ProGRP). Because the protein markers are all common detection indexes in clinic of lung cancer, corresponding detection information can be provided.

In one aspect, the present invention provides the use of a marker selected from any one or more of the nucleotide sequences of Seq ID No.1 to Seq ID No.18 or a combination of the complete complementary sequences thereof for the preparation of a reagent for detecting lung cancer.

The invention screens out methylation sites of lung cancer and benign lung lesions patients by carrying out high-depth genome sulfite sequencing on lung cancer tissues, corresponding paracancerous tissues and normal human cfDNA samples, and then further verifies by a large number of lung cancer and benign lung lesions patients plasma samples, finally discovers and determines 18 target sequences with abnormal methylation in lung cancer patients. Clinical verification shows that the 18 novel target sequences provided by the invention have obvious methylation degree differences in lung cancer patients and lung benign lesions patients.

The 18 target sequences are double-stranded DNA and have complementary sequences, and it is understood that the methylation site target sequences provided by the invention can be sense strands or antisense strands.

In some embodiments, the marker is a combination of 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, or 18 nucleotide sequences selected from the sequence listing Seq ID No. 1-Seq ID No.18, or the complete complement thereof.

The invention finds 18 methylation sites with obvious difference in methylation degree in lung cancer and lung benign lesion patients, and further precisely screens out the combination of 2 methylation sites and 6 methylation sites, thereby realizing the efficient detection of lung cancer under the condition of less marker detection.

Further, the marker combination is a combination of nucleotide sequences shown in any 2 or any 6 selected from the group consisting of Seq ID No.1 to Seq ID No.18 or the complete complementary sequences thereof.

Screening any 2 or any 6 combinations of methylation sites gives 11 sets of methylation sites with high AUC values, high sensitivity and specificity, wherein 3 sets of 2 methylation sites are included, and the marker combination can be selected from the nucleotide sequences shown in any one of the following 3 sets or the combination of complete complementary sequences thereof:

1、Seq ID NO.3、Seq ID NO.7

2、Seq ID NO.7、Seq ID NO.18

3、Seq ID NO.13、Seq ID NO.18

wherein there are 8 sets of 6 methylation sites, and the marker combination may be selected from the group consisting of a combination of nucleotide sequences shown in any one of the following 8 sets or the complete complement thereof:

4. Seq ID No.5, seq ID No.7, seq ID No.10, seq ID No.12, seq ID No.15 and Seq ID No.18

5. Seq ID No.2, seq ID No.7, seq ID No.8, seq ID No.11, seq ID No.14 and Seq ID No.15

6. Seq ID No.3, seq ID No.8, seq ID No.10, seq ID No.11, seq ID No.13 and Seq ID No.17

7. Seq ID No.1, seq ID No.4, seq ID No.12, seq ID No.14, seq ID No.15 and Seq ID No.17

8. Seq ID No.5, seq ID No.7, seq ID No.8, seq ID No.12, seq ID No.13 and Seq ID No.17

9. Seq ID No.1, seq ID No.3, seq ID No.6, seq ID No.8, seq ID No.10 and Seq ID No.11

10. Seq ID No.3, seq ID No.7, seq ID No.13, seq ID No.8, seq ID No.9 and Seq ID No.16

11、Seq ID NO.3、Seq ID NO.4、Seq ID NO.5、Seq ID NO.10、Seq ID NO.13、Seq ID NO.15

Preferably, the marker is a combination comprising the nucleotide sequence of Seq ID No.7 and/or Seq ID No.18 or the complete complement thereof. The AUC value, detection sensitivity and specificity of the methylation sites corresponding to Seq ID No.7 or Seq ID No.18 are significantly higher than those of the other 16 methylation sites.

Preferably, the marker is a combination comprising 2, 6 or 18 nucleotide sequences of Seq ID No.7 and/or Seq ID No.18 or the complete complement thereof.

Further, the marker is a combination comprising the nucleotide sequences shown by Seq ID No.5, seq ID No.7, seq ID No.10, seq ID No.12, seq ID No.15 and Seq ID No.18 or the complete complement thereof.

Through a large number of clinical lung cancer and lung benign samples, the invention finds 6 brand-new hypermethylation sites which can particularly sensitively and specifically distinguish lung cancer patients, lung benign patients and benign control individuals from the 18 target sequences, and the sequences are shown as sequence tables Seq ID No.5, seq ID No.7, seq ID No.10, seq ID No.12, seq ID No.15 and Seq ID No.18 respectively. The data of clinical plasma samples for detecting lung cancer and lung benign diseases show that the AUC value can reach 0.918 by only adopting the 6 methylation sites for lung cancer detection, the sensitivity reaches 85.2%, and the specificity reaches 99.0%; experimental data show that the lung cancer diagnosis performance of the 6 methylation sites is similar to the performance of the combination of the 18 methylation sites, and the detection process is simpler, more convenient and efficient and has the most application prospect.

Further, the markers also include protein markers consisting of carcinoembryonic antigen (CEA), soluble fragment of cytokeratin 19 (CYFRA 21-1), and gastrin releasing peptide precursor (ProGRP)

Due to technical limitations, the ability to detect cancer solely through methylation sites has been a growing trend in a number of industries, such as genomics, epigenetics, proteomics, and the like. Several clinical studies have shown that the sensitivity and specificity of the detection of multiple sets of chemical markers is superior to that of a single set of chemical markers. Therefore, on the basis of the methylation site provided by the invention, the detection of the level of the binding protein further improves the AUC value of lung cancer detection and improves the detection sensitivity and specificity.

The protein markers CEA, CYFRA21-1 and ProGRP provided by the invention have significant differences in the serum levels of lung cancer patients compared with the serum levels of protein markers of lung benign lesions patients. Research proves that by detecting the methylation level of the series of novel methylation sites and combining CEA, CYFRA21-1 and ProGRP in a serum sample, lung cancer patients and non-cancer individuals can be distinguished more accurately. The data of clinical lung cancer and lung benign disease samples are detected, so that the combination provided by the invention can effectively distinguish lung cancer patients from non-cancer patients, the AUC value can reach 0.984 at the highest, the sensitivity reaches 89.5%, and the specificity reaches 99.0%.

In another aspect, the present invention provides a primer combination for detecting lung cancer, wherein the primer combination is any one or more of 18 groups of primers and probe combinations shown in the following table:

Group of	Target sequence	Forward primer	Reverse primer	Probe with a probe tip
					1	Seq ID NO.1	Seq ID NO.23	Seq ID NO.25	Seq ID NO.24
2	Seq ID NO.2	Seq ID NO.26	Seq ID NO.28	Seq ID NO.27
					3	Seq ID NO.3	Seq ID NO.29	Seq ID NO.31	Seq ID NO.30
4	Seq ID NO.4	Seq ID NO.32	Seq ID NO.34	Seq ID NO.33
					5	Seq ID NO.5	Seq ID NO.35	Seq ID NO.37	Seq ID NO.36
6	Seq ID NO.6	Seq ID NO.38	Seq ID NO.40	Seq ID NO.39
					7	Seq ID NO.7	Seq ID NO.41	Seq ID NO.43	Seq ID NO.42
8	Seq ID NO.8	Seq ID NO.44	Seq ID NO.46	Seq ID NO.45
					9	Seq ID NO.9	Seq ID NO.47	Seq ID NO.49	Seq ID NO.48
10	Seq ID NO.10	Seq ID NO.50	Seq ID NO.52	Seq ID NO.51
					11	Seq ID NO.11	Seq ID NO.53	Seq ID NO.55	Seq ID NO.54
12	Seq ID NO.12	Seq ID NO.56	Seq ID NO.58	Seq ID NO.57
					13	Seq ID NO.13	Seq ID NO.59	Seq ID NO.61	Seq ID NO.60
14	Seq ID NO.14	Seq ID NO.62	Seq ID NO.64	Seq ID NO.63
					15	Seq ID NO.15	Seq ID NO.65	Seq ID NO.67	Seq ID NO.66
16	Seq ID NO.16	Seq ID NO.68	Seq ID NO.70	Seq ID NO.69
					17	Seq ID NO.17	Seq ID NO.71	Seq ID NO.73	Seq ID NO.72
18	Seq ID NO.18	Seq ID NO.74	Seq ID NO.76	Seq ID NO.75

In some embodiments, the primer combination is any 2 or 6 selected from the group consisting of the primers and probe combinations shown in table 2.

Further, the primer combination is 6 groups of primer and probe combinations shown in the following table:

Group of	Target sequence	Forward primer	Reverse primer	Probe with a probe tip
					4	Seq ID NO.5	Seq ID NO.35	Seq ID NO.37	Seq ID NO.36
10	Seq ID NO.7	Seq ID NO.41	Seq ID NO.43	Seq ID NO.42
					12	Seq ID NO.10	Seq ID NO.50	Seq ID NO.52	Seq ID NO.51
13	Seq ID NO.12	Seq ID NO.56	Seq ID NO.58	Seq ID NO.57
					15	Seq ID NO.15	Seq ID NO.65	Seq ID NO.67	Seq ID NO.66
18	Seq ID NO.18	Seq ID NO.74	Seq ID NO.76	Seq ID NO.75

Further, the primer combination also comprises a primer and a probe of an internal reference gene, wherein the internal reference gene COL2A1 has a sequence shown as a sequence table Seq ID No.19, a forward primer has a sequence shown as a sequence table Seq ID No.20, a reverse primer has a sequence shown as a sequence table Seq ID No.22, and a probe has a sequence shown as a sequence table Seq ID No. 21.

In yet another aspect, the invention provides a kit for detecting lung cancer, the kit comprising any one of the primer and probe combinations described above.

Further, the kit or chip also includes reagents or materials for detecting proteins including CEA, CYFRA21-1 and ProGRP.

In yet another aspect, the present invention provides a marker combination for detecting lung cancer, the marker combination comprising a methylation site and a protein marker, the methylation site being any one or more selected from the group consisting of Seq ID No.1 to Seq ID No.18 of the sequence listing; the protein markers include CEA, CYFRA21-1 and ProGRP.

In some embodiments, the methylation site in the marker combination is a combination comprising 2, 6 or 18 nucleotide sequences of Seq ID No.7 and/or Seq ID No.18 or the complete complement thereof.

In some embodiments, the methylation site in the marker combination is a combination comprising the nucleotide sequences shown by Seq ID No.3 and Seq ID No.7 or the complete complement thereof, or a combination comprising the nucleotide sequences shown by Seq ID No.7 and Seq ID No.18 or the complete complement thereof, or a combination comprising the nucleotide sequences shown by Seq ID No.13 and Seq ID No.18 or the complete complement thereof, or a combination comprising the nucleotide sequences shown by Seq ID No.5, seq ID No.7, seq ID No.10, seq ID No.12, seq ID No.15 and Seq ID No.18 or the complete complement thereof, or a combination comprising the nucleotide sequences shown by Seq ID No.2, seq ID No.7, seq ID No.8, seq ID No.11, seq ID No.14 and Seq ID No.15, or a combination of nucleotide sequences shown by Seq ID No.3, seq ID No.8, seq ID No.10, seq ID No.11, seq ID No.13 and Seq ID No.17 or their complete complements, or a combination of nucleotide sequences shown by Seq ID No.1, seq ID No.4, seq ID No.12, seq ID No.14, seq ID No.15 and Seq ID No.17 or their complete complements, or a combination of nucleotide sequences shown by Seq ID No.5, seq ID No.7, seq ID No.8, seq ID No.12, seq ID No.13 and Seq ID No.17 or their complete complements, or a combination of nucleotide sequences shown by Seq ID No.1, seq ID No.3, seq ID No.6, seq ID No.8, seq ID No.10 and Seq ID No.11 or their complete complements, or a combination of nucleotide sequences shown by Seq ID No.5, seq ID No.7, seq ID No.8, seq ID No.12, seq ID No.13 and Seq ID No.17, or their complete complements, or a combination of nucleotide sequences shown by Seq ID No.1, seq ID No.6, seq ID No.8, seq ID No.10 and Seq ID No.11, the nucleotide sequences shown by Seq ID No.13 and Seq ID No.16 or the combination of the complete complementary sequences thereof, or the nucleotide sequences shown by Seq ID No.3, seq ID No.4, seq ID No.5, seq ID No.10, seq ID No.13 and Seq ID No.15 or the combination of the complete complementary sequences thereof, or the nucleotide sequences shown by Seq ID No.1 to Seq ID No.18 or the combination of the complete complementary sequences thereof.

In some embodiments, the methylation site in the marker combination is a combination comprising the nucleotide sequences set forth in Seq ID No.5, seq ID No.7, seq ID No.10, seq ID No.12, seq ID No.15, and Seq ID No.18, or the complete complement thereof.

In yet another aspect, the present invention provides a marker combination for detecting lung cancer, comprising a combination of nucleotide sequences shown as Seq ID No.5, seq ID No.7, seq ID No.10, seq ID No.12, seq ID No.15 and Seq ID No.18 or the complete complement thereof.

The method for in vitro detection of lung cancer through methylation loci provided by the invention comprises the following steps:

1) Separating genomic DNA or plasma free DNA in a biological sample to be detected;

2) Detecting the methylation state of the methylation site or combination of methylation sites;

3) And judging the state of the biological sample through the methylation site state of the target gene, and realizing in-vitro detection of lung cancer.

The method for in vitro detection of lung cancer by the methylation site and protein combination provided by the invention comprises the following steps:

1) Separating genomic DNA or plasma free DNA and serum in the biological sample to be detected;

2) Detecting the methylation state of the methylation site or combination of methylation sites and serum CEA, CYFRA21-1 and ProGRP protein levels;

3) And judging the state of the biological sample through the methylation site state of the target gene and the protein marker level, and realizing in-vitro detection of lung cancer.

In some aspects, the method further comprises the steps of:

1) Separating serum and plasma of the biological sample to be detected, and extracting plasma free DNA of the biological sample to be detected;

2) Treating the DNA sample obtained in step 1) with a reagent to convert the 5-unmethylated cytosine base into uracil, the base after conversion to uracil being different from the 5-unmethylated cytosine in hybridization ability and being detectable;

3) Combining the DNA sample treated in step 2) with a polymerase chain reaction system comprising one or more of the following components: DNA polymerase, the primer or primer combination of the target sequence, the corresponding probe or probe combination, and a polymerase chain reaction buffer solution, and generating an amplification product after the polymerase chain reaction;

4) Detecting the amplified product with a fluorescent-labeled probe or a combination of probes, which can generate a fluorescent signal if combined with the amplified product; if the probe cannot bind to the amplification product, a fluorescent signal cannot be generated;

5) Determining the methylation status of at least one CpG of the target sequence of the target gene of interest based on whether a fluorescent signal is generated;

6) Measuring the concentration of CEA, CYFRA21-1 and ProGRP in human serum by adopting a magnetic particle chemiluminescence immunoassay sandwich method;

In some embodiments, the polymerase chain reaction system wherein the DNA polymerase comprises a thermostable DNA polymerase, a hot-start DNA polymerase, or a polymerase lacking 5'-3' exonuclease activity.

The methylation status of at least one CpG in the target sequence of the target gene is determined by the difference between the cycle threshold Ct value of the PCR reaction or the Ct value of the target gene. Detection of the methylation state of one or more target sequences of a target gene of interest can be conveniently achieved by analyzing the methylation state of DNA in a biological sample using a PCR reaction.

In some embodiments, the reagent used to convert unmethylated cytosine at position 5 of the DNA to uracil is preferably bisulfite.

Methylation modification of cytosine number 5 is a DNA modification mode widely existing in eukaryotic cell organisms, and methylation modification on DNA plays an important role in the growth and development of organisms and in the process of cell proto-canceration. Because of the same base-pairing properties as cytosine, 5-methylcytosine cannot be directly measured by means of one-generation sequencing or high-throughput sequencing. The most commonly used method for detecting 5-methylcytosine is to convert the DNA to be detected by bisulphite, and after alkaline hydrolysis, unmethylated cytosine is converted into uracil, while 5-methylcytosine is not converted. Uracil will pair complementarily to adenine when base paired, unlike cytosine paired complementarily to guanine, so that by detecting bisulfite treated DNA, the remaining unconverted cytosine can be determined by sequencing techniques, polymerase chain reaction techniques or DNA molecule hybridization-related techniques to determine which cytosine is methylated in the original DNA molecule. Therefore, the invention preferably adopts bisulphite as a methylation conversion reagent, and after the DNA sample to be detected is treated, the methylation state of the CpG dinucleotide sequence in the target sequence of the target gene is determined through relevant technologies such as sequencing, polymerase chain reaction or DNA molecular hybridization.

In some embodiments, the methods of the invention are applicable to analyzing samples in a mixed state, such as low concentrations of tumor cells present in blood, stool, or tissue. Thus, when analyzing the methylation status of CpG dinucleotide sequences in such samples, a person skilled in the art can use quantitative determination methods to determine the methylation level, such as percentage, ratio, fraction or degree, of CpG dinucleotide sequences, and the like, rather than the methylation state of a single nucleotide molecule. Accordingly, the methylation state described herein should be considered to include methylation modified states of single nucleotide molecules, including methylation states that are reacted by quantifying the level of methylation.

In some modes, the present invention employs a real-time fluorescent quantitative PCR mode to determine methylation status, such as: real-time fluorescent quantitative PCR using Taqman probes, real-time fluorescent quantitative PCR using fluorescent dyes, methylation-specific PCR (MSP) and the like are used to determine the methylation status of at least one CpG dinucleotide of a target sequence of a target gene. Because of the different base complementary pairing abilities of the gene target sequences of different methylation states, quantitative testing of methylation states in genomic DNA samples can be performed by real-time fluorescent quantitative PCR, where sequence discrimination occurs at the level of probe hybridization.

As a control, the COL2A1 gene was used in the present invention, and the genomic DNA after the reagent treatment was subjected to the test by designing the primer probe so as not to cover any CpG dinucleotide position. Real-time fluorescent quantitative PCR can be used with any suitable probe, such as Taqman probes, MGB probes, scorpion probes, and the like. The fluorescent probe conventionally comprises a luminescent group, a nucleic acid sequence, a quenching group, and if necessary, some chemical modifications or special nucleotides such as a thioate nucleotide, a locked nucleic acid, etc.

In general, in real-time fluorescent quantitative PCR detection, the probe will be designed to have a melting temperature exceeding 10℃for the forward and reverse primers, which allows complete binding of the probe to the PCR product during annealing and extension. Typically, for example, a Taqman probe will hydrolyze during extension by a DNA polymerase having 5'-3' exonuclease activity, so that the fluorescent groups and quenching groups in the probe are far away, and resonance energy transfer between the fluorescent groups and quenching groups is destroyed, so that fluorescence emitted by the fluorescent groups can be detected by an instrument, and at the same time, as the PCR product increases gradually, the fluorescence signal will exhibit an exponential level rise over a certain period of time, and finally an "S" -shaped amplification curve is presented on a fluorescent quantitative PCR instrument.

Reagents for real-time fluorescent quantitative PCR include, but are not limited to: forward and reverse primers for target sequence of target gene, taqman fluorescent probe, optimized PCR buffer, deoxynucleotide triphosphate, DNA polymerase with 5'-3' exonuclease activity, etc.

The invention adopts but not limited to a magnetic particle chemiluminescence immunoassay sandwich method for detecting the target protein level, and other common detection methods such as a flow fluorescence luminescence method, an enzyme-linked immunosorbent method and the like.

In some embodiments, the presence or absence of a positive biological sample is determined by combining the methylation of the above sites or combinations of sites with the status of protein marker levels.

The methylation site for lung cancer auxiliary diagnosis provided by the invention has the following beneficial effects:

1. provides 18 brand new differential methylation sites, and the methylation state of the methylation sites in lung cancer plasma free DNA is obviously different from that of plasma free DNA of patients with benign lung lesions;

2. from the 18 methylation sites, 2 or 6 methylation sites in combination can be used for detecting lung cancer with high sensitivity and high specificity;

3. From the 18 methylation sites, the lung cancer detection AUC values of the two methylation sites of the Seq ID NO.7 and the Seq ID NO.18 are found, and the sensitivity and the specificity are good;

4. Finding 6 methylation sites (Seq ID No.5, seq ID No.7, seq ID No.10, seq ID No.12, seq ID No.15 and Seq ID No. 18), optionally in combination with protein markers CEA, CYFRA21-1 and ProGRP for noninvasive, rapid in vitro detection of lung cancer, capable of more effectively distinguishing lung cancer from other lung cancer patients, AUC value up to 0.984, sensitivity up to 89.5% and specificity up to 99.0%;

5. The method is convenient and quick, and the detection result is highly consistent with the clinical gold standard detection result.

Drawings

FIG. 1 is a thermal map of sequencing data of lung cancer, paracancestral tissue, and normal human blood WGBS in example 1, wherein tumor is a lung cancer tissue sample; adjacent is a paracancerous tissue sample, and normal is a normal cfDNA sample;

FIG. 2 is a graph showing methylation of all cfDNA samples in example 1 within the screened target region;

FIG. 3 shows methylation rate distribution of CpG sites in candidate target regions of lung adenocarcinoma/paracancestor samples in the TCGA database;

FIG. 4 shows methylation rate distribution of CpG sites in candidate target regions for lung squamous carcinoma/paracarcinoma samples in the TCGA database;

FIG. 5 is a box plot of methylation level comparisons for 18 methylation sites in example 1 in different types of samples;

FIG. 6 is a schematic diagram of the result of the fluorescent quantitative PCR reaction for lung cancer patient samples in example 2;

FIG. 7 is a schematic representation of the results of fluorescent quantitative PCR assays for samples of patients with benign lung disease in example 2;

FIG. 8 is a ROC curve obtained by normalization of detection of 2 methylation site binding CEA, CYFRA21-1 and ProGRP proteins in example 2;

FIG. 9 is a ROC curve obtained by normalization of detection of 6 methylation sites binding CEA, CYFRA21-1 and ProGRP proteins in example 3.

Detailed Description

The invention will be described in further detail below with reference to the drawings and examples, it being noted that the examples described below are intended to facilitate an understanding of the invention and are not intended to limit the invention in any way. The reagents used in this example are all known products and are obtained by purchasing commercially available products.

The following examples were run in parallel and repeated several times, and the tests were time-consuming and required a large sample size, so that lung cancer patients, lung benign disease patients, normal human lung cancer tissues and other tissue samples were all from different batches.

EXAMPLE 1 screening of methylation sites of genes

This example, starting from the actual clinical application of the product, found and determined 18 target sequences that were abnormal in methylation in lung cancer patients. The specific screening process for lung cancer methylation qPCR candidate targets is as follows:

The screening process includes two stages, the first stage: performing high-depth whole genome sulfite sequencing on lung cancer tissues, corresponding paracancestral tissues and normal human cfDNA samples (WGBS), and analyzing the data to obtain a genome region which shows abnormal hypermethylation in lung cancer tissues and extremely low methylation in normal human cfDNA; and a second stage: the methylation capture probes for the above regions were synthesized, the target regions of cfDNA samples of lung cancer and benign lung patients were highly sequenced, and by analyzing these data, genomic regions were obtained that were effective in distinguishing cfDNA from benign lung disease patients and the distinguishing performance was evaluated.

In the first stage, we performed high depth WGBS on 18 pairs of lung cancer tissues and their paracancerous tissue samples, 20 normal human cfDNA samples. The WGBS data can cover more methylation information at CpG sites than using 450K chip data and sequencing data based on methylation sensitive restriction enzyme treatment. Meanwhile, the lung cancer tissue data and the beside-cancer tissue data are compared, so that the defect of a normal tissue specific methylation region caused by directly comparing the lung cancer tissue data with the normal cfDNA data can be effectively avoided. In screening for Differential Methylation Regions (DMR), we cut the genome into a sliding window of 100bp (50 bp overlap), compare their methylation differences in each pair of lung cancer tissue/paracancestral tissue for each region, and then select regions that exhibit differential methylation in at least half of the paired lung cancer tissue/paracancel tissue (and the methylation changes are consistent in both samples, i.e., exhibit higher methylation in lung cancer tissue and an average methylation rate of less than 0.1 in paracancel tissue). This is FDMR (Frequently Differential Methylation Region), and compared with direct comparison of different types of samples into two sets, our method can make full use of the information of paired samples, and can avoid the influence of outliers of individual samples on the final result. Furthermore, for the sliding window region obtained by the above method, we performed a second step of screening with WGBS data of 20 normal human cfDNA, i.e. we only needed a sliding window with an average methylation rate of less than 0.01 in normal human cfDNA, which could exclude possible confusion of the result for the common hypermethylated region in cfDNA. By the above method we obtained a batch of regions that show abnormal hypermethylation in lung cancer tissue while showing very low methylation in normal human cfDNA. Figure 1 shows the average methylation of these sliding window regions in lung cancer tissue, paracancestral tissue, and normal human cfDNA samples.

In the second stage, we combine the abnormal hypermethylation region related to lung cancer obtained by literature investigation with the candidate sliding window region, and design 5 methylation capture probes for the combined region, namely a probe for complete methylation of the original sequence, a probe for complete methylation of the positive strand CpG site of the genome and a probe for complete demethylation, and a probe for complete methylation and complete demethylation of the negative strand CpG site of the genome, and design the probes only for the conditions of complete methylation and complete demethylation of the CpG site, so that the biological characteristics of cooperative methylation of tumor DNA are met, and the complexity of analysis data of us is simplified. Using these probes we captured and sequenced high depth cfDNA samples from 90 lung cancer patients and 82 lung benign disease patients, with average coverage depth of target area exceeding 10000X. Because the peripheral blood of the early tumor patient only contains a trace amount of ctDNA, the ultra-high depth sequencing of the sample is beneficial to capturing a small early tumor methylation change signal and improving the sensitivity.

In analyzing these captured sequencing data, we consider that, in each candidate sliding window region, arbitrary consecutive 3CpG sites are taken as one marker (3 CpG), and for each marker, the number of 3CpG sites that are methylated or that are unmethylated is counted, considering that CpG sites that are adjacent to each other tend to exhibit similar methylation changes. Specifically, a. Find the coordinates of all 3CpG sites in the region; b. for any group of 3 continuous CpG sites, extracting corresponding methylation state information (methylation state character strings in the Bam file XM tag) from the Bam file, when only read1 is compared to the 3CpG sites, using the methylation state information of the read1, when only read2 is compared to the 3CpG sites, using the methylation state information of the read2, when the read1 and the read2 are simultaneously compared to the 3CpG sites, using the methylation state information of the read1 according to the proposal of bismark, and for the condition that index exists in the read, correcting the position of the methylation state character strings in the XM tag to match genome coordinates; c. simultaneously using information aligned to the positive and negative strands; d. since our probes are only directed to fully methylated and fully unmethylated states, the above described methylation rate is calculated without regard to read heads that exhibit various intermediate methylation states at the 3 successive CpG sites (e.g., methylated-unmethylated-methylated, unmethylated-methylated, etc.). The treatment method effectively integrates the co-methylation information of adjacent CpG sites, so that the obtained characteristic signal is more robust.

Considering that the blood levels of ctDNA in lung cancer patients are different, the methylation rates calculated from cfDNA samples cannot be directly compared, so we coded 0/1 for 3CpG data, i.e. if at least one read is methylated at all 3 consecutive CpG sites, this marker is coded 1; if all reads are demethylated at the 3 consecutive CpG sites, the marker is encoded as 0; if no read covers these 3 consecutive CpG sites, it is marked as NA (deletion value). Thus, we have obtained the encoded values of 172 cfDNA samples on all 3CpG markers within the target region. All samples were divided into two batches according to the number of deletion values of the samples, i.e. the ratio of deletion values was less than or equal to (all 3CpG markers) 1/10 and the ratio was greater than (all 3CpG markers) 1/10. To select genomic regions that can be used to effectively distinguish lung cancer patients cfDNA from lung benign disease patients cfDNA, we performed the following 10-fold cross-validation. Firstly, randomly dividing a first batch of samples into 10 samples, constructing a random forest model by 9 samples each time, calculating importance indexes of different candidate target areas, and predicting the types of the rest samples; this process was performed 10 times in total. Second, the number of times that the importance index of the different candidate target areas is greater than 1 is accumulated in 10 times of cross validation, and candidate target areas with the accumulated number of times being greater than 5 are selected. Third, the number of cfDNA samples of lung benign disease patients showing significant methylation within these target regions was counted, and target regions with a number of no more than 5 were selected as final candidate regions. Figure 2 shows methylation of all cfDNA samples within these target regions. In fig. 2, each row represents one cfDNA sample, each column represents one 3CpG marker, and a plurality of consecutive 3 cpgs belonging to different candidate target regions are pooled together.

Meanwhile, to verify the accuracy of these regions, we collected 450K methylation chip data of lung adenocarcinoma/paracarcinoma and lung squamous carcinoma/paracarcinoma samples in TCGA database, respectively, and examined the distribution of methylation rate of CpG sites intersected with the final candidate target region. As shown in fig. 3 and 4, lung cancer samples have a higher methylation ratio for CpG sites within the candidate target region. Using data from lung cancer tissue, paracancestral tissue, lung benign nodular plasma, lung cancer plasma, normal plasma in combination with literature studies, we screened 36 promoter regions from NGS test data for lung cancer hypermethylation regions.

Depending on the region settings, we downloaded the sequences from the NCBI database website and converted the sequences to methylated sequences using METHYL PRIMER Express v1.0 software, i.e., preserving "C" in the "CG" sequence and converting all other "C" to "T". As shown below. To ensure a well balanced GC content distribution in the design region sequence, the antisense strand is considered as a template for the sequence selection. Corresponding detection primers and probes were designed for the 36 hypermethylated regions according to the general primer design principle. Designed primers were screened using the following concentrations of template:

1) A pure positive template MJ is selected, the template is obtained by purifying after SSSI enzyme treatment, all CG sites have methylation modification, the template is a positive substance template verified by the optimal methylation test, and medium concentration and low concentration levels of 100pg/ul and 10pg/ul characterization are selected.

2) Selecting a pure negative template WBC, wherein the template is taken from peripheral blood leukocyte gDNA of healthy people, contains the whole set of gDNA background, and better simulates the state of human cfDNA after ultrasonic disruption. 1ng/ul was chosen as a simulated true high concentration sample.

3) A low abundance mixed template MJ/WBC, which is used to simulate the mixed state in a real sample, typically 5% as the state simulating a low abundance sample.

According to the design combination of primer screening, each combination is respectively prepared into a methylation detection system for comparison, and 100pg/ul and 10pg/ul methylation positive templates, 1ng/ul methylation negative templates and 5% abundance methylation mixed templates are used for comprehensive screening, wherein screening standards are shown in the following table 1. And finally, selecting the primer combination by taking the specificity, the detection rate and the Ct value of the amplification effect as judging indexes.

Table 1 sequence combination screening criteria

Numbering device	Template type	Concentration of	Performance requirements
				1	WBC	1ng/ul	Is not detected
2	MJ	100pg/ul	CT value is 29-31
				3	MJ	10pg/ul	Can be detected
4	5％MJ/WBC	1ng/ul	Can be detected

The screening results are shown in table 2:

Table 2: results of regional screening

UD: no detection of

Through screening, the non-specific amplification problem exists in the regions 1, 5, 21 and 22, the above 4 regions are eliminated, the insufficient amplification sensitivity problem exists in the regions 2,6, 11, 12, 25, 26, 29 and 30, and the above 8 regions are also eliminated. The remaining 24 regions all reached a better level in detection performance. Clinical performance verification of the target was then performed on clinical samples, including 50 lung cancer samples and 50 lung benign nodule samples. And (3) performing ROC analysis of a single region on the detection result, and removing the region with the AUC of less than 0.7 of the single region to remove the region with poor clinical performance, and reducing the number of the regions so as to meet qPCR detection, and simultaneously, ensuring that the better clinical performance is maintained when the subsequent region combination verification is performed.

Table 3: single area AUC

After the above screening, 18 target sequences (having nucleotide sequences shown as Seq ID No.1 to SeqID No.18, see Table 1 in the specification) hypermethylated in lung cancer patients were finally found and determined. The box line graphs of the comparison of methylation levels of the 18 abnormal methylation sites in the plasma samples of the lung cancer patient and the normal person are shown in fig. 5, and it can be seen that the 18 methylation sites obtained by screening according to the invention can effectively distinguish the lung cancer from the normal person. Furthermore, the target sequences of regions 15, 16, 17, 18, 31, 32 with a single region AUC <0.7 have the nucleotide sequences shown in SEQ ID nos. 77 to 82.

Example 2 diagnosis of Lung cancer Using 2 methylation sites (or binding protein markers)

This example selects 2 of the 18 methylation sites screened in example 1 for detection of lung cancer. And the detection is carried out in two ways respectively: 1. detecting lung cancer using 2 methylation sites; 2. lung cancer was detected using a combination of 2 methylation sites and protein markers.

1. Detection of lung cancer Using 2 methylation sites

The specific method for detecting lung cancer by using 2 methylation sites comprises the following steps:

Step 1, separating serum and plasma of a blood sample, and extracting plasma free DNA of a biological sample to be detected by using a magnetic bead method extraction reagent, wherein 20 cases are lung cancer patients and 20 cases are lung benign patients.

Step 2, methylation conversion treatment is carried out on the plasma free DNA sample extracted in the step 1 by using a methylation conversion reagent with bisulphite as a main component, and 5ng of plasma free DNA is added to convert unmethylated cytosine into uracil.

And 3, putting the converted plasma free DNA into a reaction system containing real-time fluorescence quantitative PCR for detecting the gene target sequence. Wherein the fluorescent probes for detecting 2 target sequences are respectively marked by FAM and ROX fluorescent dyes, and the fluorescent probes for detecting the internal reference gene COL2A1 are marked by VIC fluorescent dyes. Wherein, the upstream primer, the downstream primer and the probe refer to the upstream primer, the downstream primer and the probe corresponding to the 2 target sequences respectively as shown in Table 2.

The system for fluorescence quantitative PCR detection is a multiplex PCR system formed by mixing a plurality of target genes with primer probes of internal reference COL2A1, wherein the target genes are detected simultaneously with the internal reference COL2A 1. And (3) detecting at most 4 target genes and internal references simultaneously by a single tube, and detecting multiple tubes when the number of the target genes is more. In the reaction system, the forward and reverse primer input concentration of the target gene sequence is 0.167 mu M, the probe input concentration is 0.167 mu M, the real-time fluorescence quantitative PCR reaction system is 30 mu L, and the forward and reverse primer input concentration of the internal reference gene sequence is 0.083 mu M.

Step 4, a fluorescent quantitative PCR reaction detection procedure is set forth in table 4:

TABLE 4 multiplex PCR reaction procedure

And step 5, obtaining a fluorescent quantitative PCR reaction detection result.

In this example, the following groups of combinations comprising 2 methylation sites were used for the detection:

1、Seq ID NO.3、Seq ID NO.7

2、Seq ID NO.7、Seq ID NO.18

3、Seq ID NO.13、Seq ID NO.18

When two methylation sites corresponding to the nucleotide sequences shown in the SEQ ID NO.7 and the SEQ ID NO.18 of the group 2 are selected, the detection results are shown in the figures 6 and 7, wherein the figure 6 is a lung cancer patient sample result, and all fluorescence signals of the methylation sites to be detected are detected to be positive; FIG. 7 shows a sample of a patient with benign lung disease, in which only the fluorescent signal of the control gene COL2A1 was detected, and the fluorescent signal of the remaining sites to be detected was not detected, and was negative.

The analysis and judgment method of the lung cancer result by adopting two methylation sites comprises the following steps: 1) Recording the Ct value of each methylation site automatically output by software; 2) Respectively calculating the Ct value of each site in the sample and the internal reference COL2A1, and then carrying out normalization processing on the Ct: Δct (target sequence) = |ct (COL 2 A1) -Ct (target sequence) |; 3) M methylation sites, score for the ith methylation site is Mi. Mi is determined by a value of 0 or 1, respectively, based on the ΔCt (target sequence) value and the corresponding Youden's index. Let Mi=1 if ΔCt (target sequence) > Youden's index, and Mi=0 if ΔCt (target sequence) < Youden's index. Methylated M-score=sum_i++m (Mi). The test results are shown in Table 2.

The results obtained using the kit detection were corrected and calculated analytically according to logistic regression analysis. The threshold setting for M-score is set according to the ROC curve. And by synthesizing the detection results of the two target methylation sites, carrying out 10 times cross validation on the 40 samples, taking an average value to obtain a classified ROC curve, and calculating an AUC value, detection sensitivity and specificity.

The classification ROC curve was obtained by combining the two target sequences SEQ ID No.7, SEQ ID No.18 methylation test results shown in Table 5, performing 10-fold cross-validation on these 40 samples, and taking the average.

TABLE 5 methylation level detection results for 2 methylation sites

For the sample detection result, the lung cancer detection level of the combination of two methylation sites of SEQ ID NO.7 and SEQ ID NO.18 is adopted, and the AUC=0.879 effect is obviously better than that of the combination of other two methylation sites. The results of the detection and analysis of groups 1, 2 and 3 are shown in the following examples 5 and Table 8.

2. Method for detecting lung cancer by adopting combination of two methylation sites and protein markers

In this example, two methylation sites of groups 1,2 and 3 are used to detect lung cancer by combining protein markers including CEA, CYFRA21-1 and ProGRP.

The detection method of 2 methylation sites refers to the steps 1 to 5, and the detection of protein markers CEA, CYFRA21-1 and ProGRP also needs to add the step 6: taking the serum sample separated in the step 1, and measuring the concentration of the protein marker in the human serum by adopting a magnetic particle chemiluminescence immunoassay sandwich method.

The sandwich method process of the magnetic particle chemiluminescence immunoassay comprises the following steps: mixing and incubating R1 (antibodies corresponding to CEA, CYFRA21-1 and ProGRP respectively), a sample to be tested and M magnetic particles. Different sites of the protein marker in the sample are combined with the antibody coupled on the magnetic beads to form a solid-phase antibody-antigen complex; through cleaning, adding R2 reagent (CEA, CYFRA21-1 and ProGRP corresponding secondary antibodies respectively) for mixed incubation, combining the complex with a labeled tumor marker antibody to form a solid-phase antibody-antigen-antibody sandwich complex; by washing, unbound antibodies and other substances are removed. Chemiluminescent substrates 1 and 2 are added to the reaction complex, and the chemiluminescent reaction is measured by relative luminescence intensities, which are proportional to the concentration of the tumor marker in the sample. The protein markers in the sample were subjected to a magnetic particle chemiluminescent immunoassay sandwich assay and the score was determined as P-value. The test results are shown in Table 6.

The analysis and judgment method comprises the following steps: in combination with the above analysis and judgment method for detecting lung cancer by using 2 methylation sites alone, the following steps are also needed:

1) The CEA, CYFRA21-1 and ProGRP detection values of each sample are normalized respectively: p1=log ₁₀P_CEA,P2＝log₁₀P_CYFRA21-1,P3＝log₁₀P_ProGRP; p-score = sum_j P (Pj) of the protein;

2) The results obtained using the kit detection were corrected and calculated analytically according to logistic regression analysis. The threshold settings for M-score and P-score are set according to the ROC curve.

Detection performance is enhanced by integrating two complementary dimensions of methylation and protein markers. The integrated model was LC-score=m-score+p-score. In some embodiments, when the LC-score value is equal to or greater than a set threshold, the result indicates a positive detection of lung cancer and/or early stage lung cancer in the patient. In some embodiments, when the LC-score value is less than a threshold value, the result indicates a negative detection of lung cancer and/or early stage lung cancer in the patient.

3) The 40 samples were cross-validated by 10-fold by combining the two target methylation sites and the normalization of CEA, CYFRA21-1 and ProGRP protein detection results and averaged to obtain a classification ROC curve.

When two methylation sites corresponding to the nucleotide sequences shown in Seq ID No.7 and Seq ID No.18 of group 2 were selected, 10-fold cross-validation was performed on these 40 samples in combination with the normalized results after CEA, CYFRA21-1 and ProGRP protein detection, and the average was taken to obtain a classification ROC curve as shown in FIG. 8, wherein CEA, CYFRA21-1 and ProGRP protein detection results are shown in Table 6.

TABLE 6 results of different sample protein level assays

When two methylation sites of group 2 are adopted and combined with CEA, CYFRA21-1 and ProGRP proteins for detection, the lung cancer detection effect can be further improved, the AUC=0.940, the sensitivity is 81.5%, and the specificity is 94.5% (figure 8).

The normalization results of groups 1, 2, and 3 after detection of the binding CEA, CYFRA21-1, and ProGRP proteins are detailed in example 5 below.

Example 3 diagnosis of Lung cancer Using 6 methylation sites (or binding protein markers)

This example selects 6 out of the 18 methylation sites screened in example 1 for detection of lung cancer. And the detection is carried out in two ways respectively: 1. detecting lung cancer using 6 methylation sites; 2. lung cancer was detected using a combination of 6 methylation sites and protein markers.

1. Detection of lung cancer Using 6 methylation sites

In this example, the following groups of combinations comprising 6 methylation sites were used for the detection:

The specific method for detecting lung cancer by using 6 methylation sites comprises the following steps:

In the first step, 40 patients with benign lung diseases and lung cancer are obtained, wherein the number of lung benign disease samples is 20, and the number of lung cancer samples is 20. Serum and free plasma DNA of the extracted samples were separated.

In the second step, methylation reagent using bisulphite as main component is used to carry out methylation treatment on the plasma free DNA sample, and then plasma free DNA is added to convert unmethylated cytosine into uracil.

Third, referring to the upstream primer, the downstream primer and the probe corresponding to the 6 target sequences, respectively, as shown in Table 2, real-time fluorescent quantitative PCR detection was performed according to the method of example 2.

Aiming at the sample detection result of this time, the effect of adopting the 6 methylation sites of the 7 th group is obviously better than the lung cancer detection level of other groups. As available from ROC curves, auc=0.918.

The results of the detection analysis of 6 methylation sites from groups 4-11 are detailed in example 5, which follows.

2. Lung cancer detection using 6 methylation sites in combination with protein markers

On the basis of adopting 6 methylation sites to detect lung cancer in the detection, the number of the methylation sites is also increased:

fourth, taking the serum sample separated in the first step, measuring CEA, CYFRA21-1 and ProGRP protein marker levels in human serum by adopting a magnetic particle chemiluminescence immunoassay sandwich method, and analyzing the results.

Fifth, the methylation of each gene and the value of the protein in the detected results were normalized, and 10-fold cross-validation was performed on these 40 samples by combining the delta Ct (target gene) results of 6 methylation sites and the detection results of three protein marker levels, and the average was taken to obtain a classification ROC curve (fig. 9).

The normalization results after detection of 6 methylation sites of groups 4-11, binding CEA, CYFRA21-1 and ProGRP proteins are detailed in example 5 below.

When 6 methylation sites of the 7 th group are adopted and combined with the normalization results after CEA, CYFRA21-1 and ProGRP protein detection, the lung cancer detection effect can be further improved, the AUC=0.984, the sensitivity is 89.5%, and the specificity is 99.0%.

Example 4 diagnosis of Lung cancer Using 18 methylation sites (or binding protein markers)

This example uses the 18 methylation sites screened in example 1 for lung cancer detection. And the detection is carried out in two ways respectively: 1. detecting lung cancer using 18 methylation sites; 2. lung cancer was detected using a combination of 18 methylation sites and protein markers.

1. Detection of lung cancer Using 18 methylation sites

The specific method for detecting lung cancer by adopting 18 methylation sites comprises the following steps:

Third, referring to the 18 target sequences shown in Table 2, the upstream primer, downstream primer and probe were subjected to real-time fluorescent quantitative PCR detection in accordance with the method of example 2.

AUC for detection using 18 methylation sites reached 0.919, as described in example 5.

2. Lung cancer detection using 18 methylation sites in combination with protein markers

The specific method for detecting lung cancer by adopting the method of combining 18 methylation sites and protein markers comprises the following steps: on the basis of adopting 18 methylation sites to detect lung cancer in the detection, the number of the methylation sites is increased:

Fifth, the methylation of each gene and the protein value in the detected results are normalized, 10 times cross validation is performed on the 40 samples by combining the delta Ct (target gene) results of 18 target genes and the detection results of three protein marker levels, and an average value is taken to obtain a classification ROC curve, wherein AUC-methylation+protein=0.982, the sensitivity reaches 89.0%, and the specificity reaches 99.0%, and the description is shown in example 5.

Example 5 Performance comparative analysis Using combinations of different methylation sites

Mathematical modeling analysis of different combinations of loci was performed on the relative cycle number Δct values of 18 methylation loci (SEQ ID nos. 1-18) of the 40 lung cancer and lung benign patient samples obtained in example 1 to investigate the use of 18 methylation loci and proteins as biomarker combinations for detecting lung cancer.

First, we evaluated the performance of the above model of a single site of 18 methylation sites for diagnosing lung cancer occurrence, and calculated AUC values, respectively, and the results are shown in table 7.

TABLE 7 Performance comparison of single methylation site diagnosis of lung cancer

As can be seen from Table 7, the 18 methylation sites provided in example 1 have higher AUC values for diagnosing lung cancer, have better diagnostic performance, and are particularly methylation sites corresponding to the nucleotide sequences shown by Seq ID No.7 and Seq ID No.18, which are brand-new methylation sites found to be used for efficiently distinguishing lung cancer from other lung diseases.

Next, the diagnostic efficacy of the different combinations of methylation sites listed in examples 2-4, or combinations of binding protein markers, was compared, together with a set of methylation sites encompassing the 24 regions of example 1, including 18 methylation sites of SEQ ID NOS.1-18 and 6 methylation sites (regions 15, 16, 17, 18, 31, 32) that were deleted in example 1 due to the lower AUC values, corresponding to the specific sequences shown in SEQ ID NOS.77-82, respectively; the final results are shown in Table 8.

TABLE 8 comparison of the models of different combinations of methylation sites for diagnosing the occurrence of lung cancer

Group of	Combination (SEQ ID NO)	AUC-methylation	AUC-methylation+protein
				1 (Example 2)	3+7	0.825	0.877
2 (Example 2)	7+18	0.879	0.940
				3 (Example 2)	13+18	0.858	0.910
4 (Example 3)	5+7+10+12+15+18	0.918	0.984
				5 (Example 3)	2+7+8+11+14+15	0.902	0.952
6 (Example 3)	3+8+10+11+13+17	0.911	0.961
				7 (Example 3)	1+4+12+14+15+17	0.907	0.957
8 (Example 3)	5+7+8+12+13+17	0.917	0.967
				9 (Example 3)	1+3+6+8+10+11	0.899	0.949
10 Example 3	3+7+13+8+9+16	0.907	0.954
				11 Example 3	3+4+5+10+13+15	0.909	0.949
12 Example 4	1～18	0.919	0.982
				13	1～18+77～82	0.919	0.982

As can be seen from tables 7 and 11, the diagnostic performance of the comparative multiple methylation site combination model using a single methylation site as the diagnostic model was lower, the diagnostic performance of the multiple group of methylation site combination models using 2 methylation site combinations as the diagnostic model was lower than the diagnostic performance of the 6 methylation site combination model, and the diagnostic performance of the multiple group of methylation binding protein markers was significantly better than that of the single group of markers; however, the 18 methylation sites of SEQ ID NO. 1-18 are adopted to cooperatively diagnose with the 6 methylation sites shown by 18 methylation sites and SEQ ID NO. 77-82, so that the diagnosis performance is not obviously different, but the 18 methylation sites are selected, 6 methylation sites are reduced, and the quantity of test indexes and analysis data in the diagnosis process can be reduced.

When the methylation site cooperative protein is selected for diagnosis, the diagnostic performance of the cooperative diagnosis of partial methylation site combinations and proteins is remarkably improved, for example, the nucleotide sequences shown by Seq ID No.5, seq ID No.7, seq ID No.10, seq ID No.12, seq ID No.15 and Seq ID No.18 are adopted in group 4, when the combined protein is used for diagnosis of a plurality of chemical models, the AUC value reaches 0.984, which is the highest in all models, and compared with 18 methylation sites and proteins, 12 methylation sites are reduced, and the test index amount and the analysis data amount of the diagnosis process can be reduced, which is preferable.

When 2 methylation site combinations were chosen as diagnostic models, both AUC-methylation and AUC-methylation + protein were highest for group 2, and it was seen that diagnostic performance was significantly higher when group 2 employed 2 methylation sites corresponding to the nucleotide sequences shown by Seq ID No.7, seq ID No.18 than the other 2 methylation site combinations.

When 6 methylation site combinations were chosen as diagnostic models, both AUC-methylation and AUC-methylation +proteins were highest for group 4, and it was seen that when 6 methylation sites corresponding to the nucleotide sequences shown by Seq ID No.5, seq ID No.7, seq ID No.10, seq ID No.12, seq ID No.15 and Seq ID No.18 were used for group 4, diagnostic performance was significantly higher than for the other 6 methylation site combinations.

When 18 methylation site combinations are selected as diagnostic models, the diagnostic performance is very close to that of the 6 methylation site combinations of group 4, so that the 6 methylation site combinations of group 4 are most preferably used.

This example further analyzes the diagnostic properties of the different methylation site combinations preferred therefrom, respectively, sensitivity = number of patients diagnosed/total number of patients; specificity refers to the ability of a diagnostic test to exclude a disease when the real situation is not diseased, specificity = number of non-diseased/total number of non-diseased not diagnosed diseased; youden index = sensitivity + specificity-1, the results are shown in table 9.

TABLE 9 comparison of preferred multiple methylation site models for diagnosing lung cancer occurrence

Combination (SEQ ID NO)	AUC	Sensitivity of	Specificity of the sample	Youden index
					7+ Proteins	0.881	74.1％	100％	0.741
18+ Proteins	0.861	74.1％	94.7％	0.688
					7+18+ Proteins	0.940	81.5％	94.5％	0.760
5+7+10+12+15+18+ Proteins	0.984	89.5％	99.0％	0.885
					2+7+8+11+14+15+ Proteins	0.952	92.5％	92.8％	0.853
3+8+10+11+13+17+ Proteins	0.961	93.5％	93.0％	0.865
					1+4+12+14+15+17+ Proteins	0.957	92.6％	92.9％	0.855
5+7+8+12+13+17+ Proteins	0.967	93.9％	93.3％	0.872
					1+3+6+8+10+11+ Proteins	0.949	91.3％	91.6％	0.829
3+7+8+9+13+16+ Proteins	0.954	91.7％	92.4％	0.841
					3+4+5+10+13+15+ Proteins	0.949	91.1％	92.2％	0.833
1-18+ Protein	0.982	89.0％	99.0％	0.880
					1-18+77-82+ Protein	0.983	89.2％	99.0％	0.882

As can be seen from Table 9, the combination of several groups of methylation sites and proteins provided by the invention can be used for high-efficiency detection of lung cancer, wherein a plurality of groups of chemical diagnosis models of Seq ID No.5, seq ID No.7, seq ID No.10, seq ID No.12, seq ID No.15 and Seq ID No.18 and proteins are preferred, the highest AUC can reach 0.984, the sensitivity reaches 89.5%, the specificity reaches 99.0%, and the noninvasive, global and higher-sensitivity and higher-specificity lung cancer diagnosis is truly realized, so that the clinical requirements are completely met.

Example 7 diagnosis of Lung cancer Using 6 methylation sites (or binding protein markers) in a training set clinical sample

This example utilized the 6 methylation sites screened in example 1 and employed a method of methylation site and protein marker combination detection for lung cancer detection in a training set clinical sample.

The specific method comprises the following steps:

Step 1, separating serum and plasma of a blood sample, extracting plasma free DNA of a biological sample to be detected by using a magnetic bead method extraction reagent, wherein 185 cases are lung cancer patients, and 70 cases are normal human controls.

And 3, putting the converted plasma free DNA into a reaction system containing real-time fluorescence quantitative PCR for detecting the gene target sequence. Wherein the fluorescent probes for detecting 6 target sequences are respectively marked by TXD, FAM and CY5 fluorescent dyes, and the fluorescent probes for detecting the internal reference gene COL2A1 are marked by VIC fluorescent dyes. Wherein, the upstream primer, the downstream primer and the probe refer to the upstream primer, the downstream primer and the probe corresponding to the 6 target sequences respectively as shown in Table 2.

Step 4, setting a fluorescence quantitative PCR reaction detection program as follows:

And step 5, obtaining a fluorescent quantitative PCR reaction detection result.

Step 6: taking the serum sample separated in the step 1, and measuring the concentration of the protein marker in the human serum by adopting a magnetic particle chemiluminescence immunoassay sandwich method.

And referring to the instruction book of the protein detection kit corresponding to the Beijing thermal scenery organism, performing a magnetic particle chemiluminescence immunoassay sandwich method test on the protein markers in the sample, and determining the score as a P value.

The analysis and judgment method for the result of detecting lung cancer by using the methylation combined protein marker comprises the following steps:

1) Recording the Ct value of each methylation site automatically output by software;

2) Respectively calculating the Ct value of each site in the sample and the internal reference COL2A1, and then carrying out normalization processing on the Ct: Δct (target sequence) = |ct (COL 2 A1) -Ct (target sequence) |;

3) 15 methylation sites, score for the ith methylation site being Mi. Mi is determined by a value of 0 or 1, respectively, based on the ΔCt (target sequence) value and the corresponding Youden's index. Let Mi=1 if ΔCt (target sequence) > Youden's index, and Mi=0 if ΔCt (target sequence) < Youden's index. Methylated M-score=sum_i++m (Mi) (i=1-15).

4) The CEA, CYFRA21-1 and ProGRP detection values of each sample are normalized respectively: p1=log ₁₀P_CEA,P2＝log₁₀P_CYFRA21-1,P3＝log₁₀P_ProGRP; p-score=sum_j P (Pj) of the protein.

5) The results obtained using the kit detection were corrected and calculated analytically according to logistic regression analysis. The threshold settings for M-score and P-score are set according to the ROC curve.

For the clinical sample detection results of the training set of this time, the marker combination of 6 methylation sites and combined with CEA, CYFRA21-1 and ProGRP proteins has auc=0.982, sensitivity of 90.5% and specificity of 98.4% for the lung cancer detection level.

Example 8 diagnosis of Lung cancer Using 6 methylation sites (or binding protein markers) in clinical samples of test set

The present example uses the 6 methylation sites screened in example 1 and uses a method of combined detection of methylation sites and protein markers for lung cancer detection in a test set of clinical samples, wherein 158 cases are lung cancer patients, 70 cases are normal human controls, and other experiments and detection methods are as shown in example 7.

For the clinical sample detection results of the present test set, the marker combination of 6 methylation sites and combined with CEA, CYFRA21-1 and ProGRP proteins has auc=0.983, sensitivity of 91.1% and specificity of 98.2% for the lung cancer detection level.

The preferred combination of 6 methylation sites and 3 proteins can be used for high-efficiency detection of lung cancer, in a clinical sample of a test set, the AUC reaches 0.983, the sensitivity reaches 91.1%, the specificity reaches 98.2%, the noninvasive, global and higher-sensitivity and higher-specificity lung cancer diagnosis is truly realized, and the clinical requirements can be met.

Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the invention, and the scope of the invention should be assessed accordingly to that of the appended claims.

Sequence listing

Seq ID NO.1

Methylation site 1:

AGAGTTAGGAGATCGGAAAGTTGAAGTAGTTGTTTTATTAAGTTATAGAGGAAGTGGTTGAGTTAAGGGTTCGGGAGGTAGTTAGTTTACGGTAATACGAGAAAATTCGGGGTTCGTTGGTGGTTTAGGTTTAGGTTTGGGGTTAGGAGTTAAAGGTTTTGT

Seq ID NO.2

Methylation site 2:

GAGGCGGTGGTCGAGGAGCGTCGTCGGAGCGGGGATTTGGTCGTTTAGGTAGTCGAATAAGAGCGTTAGGTTAGCGAGATGCGGGGGCGTTTCGAGTAGTTTGAGAAAACGGTAGAGTTGTTGAAAGAGAAGATGGAGTATTTTATTGGGGTTTGTCGAGA

Seq ID NO.3

Methylation site 3:

GTTAAGGTTTTCGCGGGAGGAGAGGTTTTCGTAGGAGGAGACGTTAGAGGGTTTTATCGACGTTTTCGCGGTTGACGAGTCGTTTTCGGAGTTCGTTTTCGGGTTTTCGTGCGTGGCGAACGTTTCGGCGAACGTTACGGTCGATTTCGAGTAGTTGTTCGC

Seq ID NO.4

Methylation site 4:

GTAAGAGCGTAGTTACGGCGGGCGGTTAGTGCGTCGTTTTTTTTTATTGGGTATTTCGGGTTTCGAGGACGAGGCGCGCGCGGAGCGGTTGGCGGAGTTGGTGGCGTTGGAGGCGCGCGAGTACGGCGACGTGTTGTAGTGGGTTTTCGCGGATATTTTTT

Seq ID NO.5

Methylation site 5:

CGCGATGTAAGTAATTGAGATCGGGAGTTGTTTTCGGTAGAGCGTATTTATTTCGGTTTTAGGTGGATTGAAGTTTAGAGCGGCGTTGTGTAGTTGGAAGGGCGCGCGATAGTTTAAGTTAGAGGCGGTTTCGGGGCGCGGCGTAGGATATAAGATTTTAAA

Seq ID NO.6

Methylation site 6:

TTAGAATAGTAGGTTTTTGGAGTTTTTTTCGGGTTTTGGGTTTGTTCGTAGGAGCGCGAGTATAGCGCGTAGTTCGGCGTTCGTACGTACGGTGTTGAGCGTTTTATTTCGTAGGATGTAGGTTTTTTCGGTCGTTTGTCGTTTTATGGTAGCGTGGTGTAG

Seq ID NO.7

Methylation site 7:

TTTTCGGCGTTTAGTTCGTTTTATGCGGTTCGTAGTTTTAAGTATAGTTGTTTTTAGGGTTGGTGGCGTAGGTTTTGTTATACGTCGAAGATTTTTTAGTCGGTCGGCGGCGTTTTTTGCGGGTTTAGGGTTCGCGCGTTTAGTAGTAGGGGCGAAAGGTA

Seq ID NO.8

Methylation site 8:

TTGGTGTATGGCGCGGGGAAGGGGGCGCGTTAGGACGGGTTTTTTTTTTCGCGTTAGTTTCGGATTTTTAGGGCGGAAGTCGGTGGTTGTTGGCGAGGCGGCGGCGGCGGTTTATCGGGTTTTTTTTTTAAAAGGTTTCGTAATTATTAATGTGTTTTTTGT

Seq ID NO.9

Methylation site 9:

GGTTCGTAGTAGTTAGGGGTCGAGTTACGGTTACGGACGTTTTGGTGTTTCGGTTCGTGTCGGGTTTTTAGGCGGAGGAGGCGTTCGTTGGGTTTAGATTTTCGATTTTAGTTTCGGTTTTTCGGCGTTTGGGTTGCGCGGAGTTTTTGTTTCGCGTTTCGG

Seq ID NO.10

Methylation site 10:

TTGTTTTTGTGTTGTTTTTTAGTTAGGGTTCGATCGGTTAGGTTTCGGTGAATTTTAGGTAATTTTAGTTTTGTTTAGGGATTTGTTCGTTTCGTTGGGCGTCGTAAAGGTATATAGATGCGAGGGAATTTTGCGGTTTGGATTTTTTGGGGATTTGTAATT

Seq ID NO.11

Methylation site 11:

AGTATGTTTTTTTTAAAGTTTTTTTGGGTGTTTTTTTTTATCGTTATCGTCGTCGGTTGTCGTTTTATTATTTTTGTTTTTATTTTTTTTTTATATGTTTTATTTTTAGACGGTGGTTTTTAGAAGTTTTTGTTTTTTTGATAGTTGTCGTTTGGGTAGTTC

Seq ID NO.12

methylation site 12:

CGTCGGGAACGGAGCGCGTTTAATTTTTAGCGGGAGTCGTTAGGTTTGGTTTGGTCGGGGTTTTTTTTCGTTCGTTATTTTCGGATAAAGTATAGTCGAGTTCGGTTGGAAGGTAGAGTTTCGAAGTAGGTAGGACGGAGCGGAGTAAAAGAATGCGGTTT

Seq ID NO.13

Methylation site 13:

CGGTTTCGCGTTTCGCGTTTCGCGTTTTTTAGCGTTGCGTTTTTCGTTTTCGGTTTCGTTTCGTTAGTTTGGTTCGTTTAGCGATTGCGTTTATTTGAAGATCGTATTTAGGGGTAGATGCGGAAATTGGTTTTAGTCGCGTTATGTAGCGCGTTTTCGTT

Seq ID NO.14

methylation site 14:

TAGGCGTCGGCGTTTAGGTCGTAGTCGGACGTAAAGGGGTTTTGATAAAGGGGGTTGTTGATATTGTATAAGTTCGGAACGGTCGAGGCGAAGGCGTCGGCGTTCGTTTCGTAGTCGTTTTTTTGTGAGTTG

Seq ID NO.15

methylation site 15:

TAGGTGGTGGAGGTGGAGGTGGAGGACGACGAGAGGTTGGTCGCGGAGGGCGCGAGGGGGTCGGTCGGGGAGCGCGCGGGCGAGTTTAGTACGCGCGTCGAAGGGTTGTAGATAGCGGTCGGTCGGGCGTTTAGTTGTAGTTTTAGGGGTCGGATGGGGTTGACGAAGTCGGTTTGTATTGTTTGGTCGCGACGATTTAGGTTTCGTCGGTTTTGTTTATTTTTTTTTAGCGTTTGTTGTAGT

Seq ID NO.16

Methylation site 16:

AGTACGTTGATGTTGGAGTTTAGGTTATTGCGGAGGGTTATGGCGGTAGGCGGGGTCGGGTTCGGTTCGGGCGGCGAGTAGGTAGTAGGGTCGGTCGGCGAGAGAGGCGTCGGGGGTAAGTTTTTTTTTTCGGCGAAGTGGTCGTTTTTTAGTGAGTTTTTT

Seq ID NO.17

Methylation site 17:

GGGCGTGTAGGTTGAGTGTCGCGGGATAGGCGCGATATTGGTGTTGGCGTTGGCGTTATAGATTTAGGGTTGCGGCGTGTTTTTTGGTTTTTAGTTTGAGATCGGCGATGTTGGAGTTTTTGTTGGTGGTTTTGGCGGTTGTTGCGGTCGTTATTATCGAGG

Seq ID NO.18

methylation site 18:

CGGCGGAAGTCGAATTCGCGGTTAGCGTGGCGAGCGGTAGTTCGAAGGGCGGTGTTGGGAATATTATGTAGGGCGCGTGCGCGGTTAGGTGCGGATGTAGGTGGTGGTGCGCGTGCGTTATAGCGTTGTTTAGTTGTAGTTGCGTTTGAATTTGAAAGGATA

Seq ID NO.19

COL2A1：

TTTTTTGTAAGGAGGGATGTGGAGGGATAGAGGAGTAGTAGGTAAGGTTAGTAGGAGGTGATATAGGTAGGGAGGATTAGGTTAAGGTTGGGAGGAGTTTATATTTGGTGTT

Seq ID NO.20

forward primer of COL2 A1:

ATGTGGAGGGATAGAGGAGTA

Seq ID NO.21

Probes for COL2 A1:

CACCTCCTACTAACCTTACC

Seq ID NO.22

Reverse primer of COL2 A1:

CCTCCCAACCTTAACCTAAT

Seq ID NO.23

forward primer for methylation site 1:

GGTTGAGTTAAGGGTTCG

Seq ID NO.24

probe of methylation site 1:

AGTTTACGGTAATACGAGAA

Seq ID NO.25

Reverse primer of methylation site 1:

CACCAACGAACCCCGAA

Seq ID NO.26

Forward primer for methylation site 2:

GTCGTCGGAGCGGGGATT

Seq ID NO.27

probe of methylation site 2:

TCGTTTAGGTAGTCGAATAA

Seq ID NO.28

Reverse primer of methylation site 2:

CGCCCCCGCATCTCGCT

Seq ID NO.29

forward primer for methylation site 3:

ATCGACGTTTTCGCGGTT

Seq ID NO.30

probe of methylation site 3:

CGAGTCGTTTTCGGAGTTC

Seq ID NO.31

Reverse primer for methylation site 3:

GTTCGCCACGCACGAAAACCCGAA

Seq ID NO.32

Forward primer for methylation site 4:

GGCGCGCGCGGAGCG

Seq ID NO.33

Probe of methylation site 4:

GGCGGAGTTGGTGGCGTT

Seq ID NO.34

reverse primer of methylation site 4:

CACTACAACACGTCGCCGTAC

Seq ID NO.35

Forward primer for methylation site 5:

GAGCGTATTTATTTCGGTTTTAG

Seq ID NO.36

probe of methylation site 5:

TAAACTATCGCGCGCCCTTCCA

Seq ID NO.37

reverse primer of methylation site 5:

CGCCGCGCCCCGAAACCG

Seq ID NO.38

forward primer for methylation site 6:

GGAGCGCGAGTATAGCGC

Seq ID NO.39

probe of methylation site 6:

AGTTCGGCGTTCGTACG

Seq ID NO.40

Reverse primer of methylation site 6:

CTACGAAATAAAACGCTCAAC

Seq ID NO.41

forward primer for methylation site 7:

CGTCGAAGATTTTTTAGTCG

Seq ID NO.42

probe of methylation site 7:

TCGGCGGCGTTTTTTGCG

Seq ID NO.43

Reverse primer for methylation site 7:

CCTACTACTAAACGCGCGAACC

Seq ID NO.44

Forward primer for methylation site 8:

GCGCGTTAGGACGGGTTTTT

Seq ID NO.45

probe of methylation site 8:

TTCGCGTTAGTTTCGGATT

Seq ID NO.46

reverse primer of methylation site 8:

CGCCGCCGCCTCGCCAACAA

Seq ID NO.47

forward primer for methylation site 9:

GGTCGAGTTACGGTTACGGAC

Seq ID NO.48

probe of methylation site 9:

GTTTCGGTTCGTGTCGG

Seq ID NO.49

reverse primer of methylation site 9:

CGCCGAAAAACCGAAACTAAAATCGA

Seq ID NO.50

forward primer for methylation site 10:

GGGTTCGATCGGTTAGGTT

Seq ID NO.51

probe of methylation site 10:

ACGCCCAACGAAACGAA

Seq ID NO.52

reverse primer of methylation site 10:

TTCCCTCGCATCTATATACC

Seq ID NO.53

forward primer for methylation site 11:

GGGTGTTTTTTTTTATCG

Seq ID NO.54

probe of methylation site 11:

TCGTCGTCGGTTGTCGTTT

Seq ID NO.55

Reverse primer of methylation site 11:

CACCGTCTAAAAATAAAACATATAA

Seq ID NO.56

forward primer for methylation site 12:

GGAGCGCGTTTAATTTTTAGC

Seq ID NO.57

probe of methylation site 12:

GTCGTTAGGTTTGGTTTGG

Seq ID NO.58

reverse primer of methylation site 12:

TTATCCGAAAATAACGAACGA

Seq ID NO.59

forward primer for methylation site 13:

CGCGTTTCGCGTTTCGCG

Seq ID NO.60

probe of methylation site 13:

TTAGCGTTGCGTTTTTCG

Seq ID NO.61

reverse primer of methylation site 13:

CGCAATCGCTAAACGAAC

Seq ID NO.62

forward primer for methylation site 14:

GCGTCGGCGTTTAGGTCGTAG

Seq ID NO.63

probes for methylation site 14:

CTTCGCCTCGACCGTTCCGAAC

Seq ID NO.64

reverse primer of methylation site 14:

CGACTACGAAACGAACGCCGACG

Seq ID NO.65

Forward primer for methylation site 15:

GCGCGCGGGCGAGTTTAGT

Seq ID NO.66

probe of methylation site 15:

CGCGCGTCGAAGGGTTG

Seq ID NO.67

reverse primer of methylation site 15:

CGTCGCGACCAAACAATACAAAC

Seq ID NO.68

Forward primer for methylation site 16:

CGGTTCGGGCGGCGAGTAG

Seq ID NO.69

probes for methylation site 16:

CTCTCGCCGACCGACCC

Seq ID NO.70

reverse primer of methylation site 16:

ACGACCACTTCGCCGAAAAAA

Seq ID NO.71

Forward primer for methylation site 17:

GGCGTTGGCGTTATAGATTTAGG

Seq ID NO.72

Probe of methylation site 17:

TGCGGCGTGTTTTTTGGTT

Seq ID NO.73

reverse primer of methylation site 17:

CTCCAACATCGCCGATCTCAAAC

Seq ID NO.74

Forward primer for methylation site 18:

AGCGTGGCGAGCGGTAGTTCG

Seq ID NO.75

Probes for methylation site 18:

GCACCTAACCGCGCACGCGC

Seq ID NO.76

reverse primer of methylation site 18:

GCTATAACGCACGCGCACCAC

Seq ID NO.77

Region 15 methylation site

AGTTGCGATAGAGTGTGGGGTTGTTGATAAATGAATTGGTTATGGAGAAGGAGGTTATAGAGAAGTTGCGGAAGTTTTTGGTTTTTTAGAGTAGCGGTTTTCGAGGGTTGTGGGATTGTTTGTTCGTAGATTTAGTGGGCGAGAGGAGTGTATAAAGTAAAG

Seq ID NO.78

Region 16 methylation site

TTAAAATTTGGTATTTAGATTTAGAAGTAGTGTGTTTAGGGTTGTTATTAGTGTAGTATTATCGTTTATTAGTATTTGGATTTGAATTGTATATTAGTTTGGGAAGAGGTTTAATAGTATGATGGGTTGTTGTGGTTTTATTGAAGTTTTGAGGAGGTTATT

Seq ID NO.79

Region 17 methylation site

GTTTTTTTTTAGTTCGAAGTTCGTAGTATATTCGTAGGTAAAGTTTTTTAAGTCGTTTAGGTAGTTAGGGAGTTTTGCGTATTTGTTAGTACGGAGGTATTTTTCGGGGTAGGGATATAATATATCGTTCGAGAGTTTGTTTTAGCGAGCGTCGATTTCGTT

Seq ID NO.80

Region 18 methylation site

TTGGAGTATCGCGTATTTTCGCGCGGTGTAGGAGCGTTGGGGTTTTTTTATTTATTGTAGCGTGTCGTTTTCGAGATCGTCGGGGTCGGAGGATAGTTAGGAGAAATTTCGTAAAGGTTCGTTTTTTAGGGTGTAGTGGGAACGTTTGCGTTTTAGTGCGAT

Seq ID NO.81

Region 31 methylation site

AGGGAAGAGTTGTACGTGGAGCGGTTCGGTTGGTGGTTTTTAGGGCGTTGAGGGCGTTTGGCGAAAGAGTCGTAGTTTCGCGTTTATTAGTTTTTTTTTGTTTGAGAGTATGGATATATTAAATAAATATTTTTGTTTTCGGAGAGGAGTGTGCGAGAGATC

Seq ID NO.82

Region 32 methylation sites

TTTGCGAGATAAAAAATAATTATAGTTAGTTTTATTTAAGGGGGAGATTAGTTCGGTGTTTTTCGGTCGTTTCGGGAGGAAAAGGGCGGGGAGTGGGGGTAGGTCGGTCGGGTAGTTTAGTTTGTTCGGTTTAGGGTTTGATTATTTCGGTTTTTTATTTGG

Claims

1. Use of a marker combination for the preparation of an auxiliary diagnostic reagent for lung cancer, characterized in that the marker combination comprises a combination of any one or more of the nucleotide sequences shown in Seq ID No.1 to Seq ID No.18 or the complete complement thereof.

2. The use according to claim 1, wherein the marker combination is a combination of nucleotide sequences selected from any 2 or any 6 of Seq ID No.1 to Seq ID No.18 or the complete complement thereof.

3. Use according to claim 1, wherein the marker combination comprises a combination of nucleotide sequences as shown in Seq ID No.7 and/or Seq ID No.18 or the complete complement thereof.

4. The use according to claim 1, wherein the marker combination comprises a combination of nucleotide sequences as shown in any one or more of Seq ID No.5, seq ID No.7, seq ID No.10, seq ID No.12, seq ID No.15 and Seq ID No.18 or the complete complement thereof.

5. The use according to claim 1, wherein the marker combination is a combination of the nucleotide sequences represented by Seq ID No.1 to Seq ID No.18 or the complete complement thereof.

6. The use of any one of claims 1 to 5, wherein the marker further comprises a protein marker consisting of CEA, CYFRA21-1, and ProGRP.

7. A primer combination for detecting lung cancer, which is characterized in that the primer combination is any one or more groups selected from 18 groups of primers and probe combinations shown in the following table:

8. A kit for detecting lung cancer comprising the primer and probe combination of claim 7.

9. A marker combination for detecting lung cancer, comprising a methylation site and a protein marker, wherein the methylation site is any one or more selected from the group consisting of Seq ID No.1 to Seq ID No. 18; the protein markers include CEA, CYFRA21-1 and ProGRP.

10. A marker combination for detecting lung cancer, comprising a combination of nucleotide sequences represented by Seq ID No.5, seq ID No.7, seq ID No.10, seq ID No.12, seq ID No.15 and Seq ID No.18 or the complete complement thereof.