A set of methylation markers for differential diagnosis of benign and malignant pulmonary nodules and their screening methods and applications

Abstract

The present invention discloses a set of methylation markers for differentiating benign and malignant pulmonary nodules, as well as a screening method and application thereof. The screening method comprises: constructing a local pulmonary nodule methylation dataset by performing RRBS analysis on pulmonary nodules and adjacent tissues, and performing methylation differential analysis on the two datasets based on the DNA methylation dataset of lung adenocarcinoma in the TCGA to obtain a first screening DMR; performing reverse screening on the first screening DMR to obtain a second screening DMR; further analyzing and filtering the tissues and paired plasma for consistency to obtain a set of candidate methylation markers; and performing machine learning on the aforementioned marker set in a plasma training set and a validation set to screen for methylation markers for differentiating benign and malignant pulmonary nodules. The present invention ultimately screened 78 methylation markers for differentiating benign and malignant pulmonary nodules. These 78 markers or their combination have high sensitivity and good specificity in differentiating benign and malignant pulmonary nodules.

Description

Technical Field

[0001] The present invention belongs to the field of analytical technology, and specifically relates to a group of methylation markers for differential diagnosis of benign and malignant pulmonary nodules, and a screening method and application thereof. Background Art

[0002] Lung cancer has no obvious early symptoms, and 75% of cancer patients are diagnosed in the middle or late stages. The detection rate of early lung cancer is less than 25%, but the 5-year survival rate for early lung cancer can reach over 90%.

[0003] Currently, there is consensus on the use of low-dose helical computed tomography (LDCT) for early-stage lung cancer screening. However, with the widespread use of LDCT screening, 50% of people undergoing LDCT will be diagnosed with pulmonary nodules. A pulmonary nodule is a focal, rounded, increased-density lung opacity with a diameter of 30 mm or less. 95% of pulmonary nodules are caused by benign lesions, including granulomas, lymph nodes, chronic inflammation, and hamartomas. Malignant lesions primarily include adenocarcinomas and squamous cell carcinomas. To ensure the accurate diagnosis and treatment of pulmonary nodules, major clinical centers around the world have developed a series of diagnostic and treatment pathways and risk prediction models. However, an overdiagnosis rate of 18-25% still exists. In particular, in patients with sub-centimeter solid lesions on imaging, the proportion of patients with postoperative pathologically confirmed benign disease is as high as 30%. This not only places a significant psychological burden on patients but also results in a significant waste of national medical resources. Therefore, differential diagnosis of benign and malignant pulmonary nodules has become a clinical pain point and research hotspot, and a major need for the development of a healthy China and economic development.

[0004] Currently, a variety of markers and techniques are available for the differential diagnosis of benign and malignant lung nodules. While routinely used serological tumor markers such as CEA, SCC, Cfra21-1, ProGRP, and NSE have some value in the auxiliary diagnosis and differential diagnosis of tumors, their sensitivity and specificity when used alone are very low. Combined testing for the detection rate of clinical stage I lung cancer does not exceed 20%, far from meeting clinical needs. While sputum cytology is convenient, economical, non-invasive, and highly accepted by patients, it has a very low sensitivity and can only serve as a guide for lung cancer diagnosis. Circulating tumor cells (CTCs) are associated with lung cancer staging, with a diagnostic sensitivity of 67.2% for early lung cancer. However, in vitro CTC detection technology is easily limited by sample size and has been less studied in early diagnosis and screening.

[0005] Emerging evidence suggests that epigenetic methylation abnormalities are more prevalent than somatic mutations during tumor development. DNA methylation of different genes is highly correlated with tumor type, exhibiting high tissue specificity within the same individual and high consistency in the same tissue across different individuals. Because epigenetic modifications, such as DNA methylation, often occur early in cancer and are highly tissue-specific compared to somatic mutations and copy number variations, they are, in principle, more suitable for use as differential diagnostic markers for lung nodules.

[0006] Circulating tumor DNA (ctDNA), a type of cell-free DNA (cfDNA) in liquid biopsies, carries tumor-specific genetic and epigenetic alterations. Compared to tissue biopsies, ctDNA offers advantages such as real-time, convenience, and non-invasiveness, making it a continuously attracting attention as a novel tumor marker. A growing number of studies have demonstrated that characteristic methylation "fingerprints" can be used for early cancer diagnosis and staging, therapeutic efficacy assessment, recurrence monitoring, and prognosis. However, currently, there are few ctDNA methylation-based markers for lung nodules, and their diagnostic performance is limited.

[0007] Therefore, the development of high-specificity and high-sensitivity markers based on ctDNA methylation for distinguishing benign and malignant lung nodules that are low-cost, non-invasive, and suitable for clinical promotion is of great significance for the scientific management of people with lung nodules and the effective control of the incidence of lung cancer. Summary of the Invention

[0008] To address the problems of the prior art, the present invention discloses a set of methylation markers for differential diagnosis of benign and malignant pulmonary nodules, as well as a screening method and application thereof. This screening method ultimately identified 78 methylation markers that can be used to differentiate between benign and malignant pulmonary nodules and / or lung cancer. Model construction was then used to evaluate the differential diagnostic performance of the methylation markers provided by the present invention. These 78 methylation markers, or combinations thereof, demonstrate high sensitivity and specificity in differential diagnosis of benign and malignant pulmonary nodules and / or lung cancer, making them suitable for widespread application in the differential diagnosis of benign and malignant pulmonary nodules.

[0009] To solve the above problems, the present invention first provides a set of methylation markers for differential diagnosis of benign and malignant pulmonary nodules, wherein the methylation markers include any one or more combinations of the following 39 methylation regions:

[0010] chr7:143059947-143060107;chr5:178487412-178487572;chr18:49867019-49867179;chr11:15136199-15136359;chr17:43972911-43973071;chr14:74892573-74892733;chr8:104383554-104383714;chr12:64062951-64063111;chr3:87039622-87039782;chr22:44420459-44420619;chr6:27835283-27835443;chr17:77020105-77020265;chr20:62283562-62283722;chr4:54966998-54967158;chr5:146257858-146258018;chr7:98467849-98468009;chr16:28075032-28075192;chr17:47307472-47307632;chr12:54427101-54427261;chr2:182321922-182322082;chr13:28674645-28674805;chr17:45810438-45810598;chr6:29760212-29760372;chr4:48485804-48485964;chr19:37407294-37407454;chr1:179545118-179545278;chr5:128797252-128797412;chr18:43652068-43652228;chr13:20806316-20806476;chr8:116660588-116660748;chr5:153784739-153784899;chr7:132261297-132261457;chr19:56904958-56905118;chr19:53636048-53636208;chr5:33936140-33936300;chr1:54204130-54204290;chr4:17783234-17783394;chr1:67773558-67773718;chr10:105037463-105037623。

[0011] Preferably, the methylation marker further comprises any one or more combinations of the following 39 methylation regions:

[0012] chr19: 2290434-2290594; chr12: 52400797-52400957; chr5: 374080-374240; chr19: 7735166-7735326; chr7: 134143579-134143739; chr 6: 26199948-26200108; chr6: 26273357-26273517; chr14: 52781261-52781421; chr6: 26204595-26204755; chr19: 58951468-58951628; ch r5: 157098320-157098480; chr14: 77228082-77228242; chr12: 4381931-4382091; chr10: 135050089-135050249; chr7: 30722046-307222 06; chr5: 175792494-175792654; chr6: 27100719-27100879; chr11: 69590360-69590520; chr17: 47074677-47074837; chr8: 143858458-14 3858618; chr6: 26189078-26189238; chr4: 110224117-110224277; chr15: 83316243-83316403; chr14: 59104962-59105122; chr11: 13414 6152-134146312; chr6: 29716362-29716522; chr4: 57976315-57976475; chr8: 53852244-53852404; chr14: 52734595-52734755; chr11: 12 4735024-124735184; chr20: 45338378-45338538; chr3: 50242683-50242843; chr4: 39529282-39529442; chr7: 151107115-151107275; ch r7: 55259381-55259541; chr5: 112073416-112073576; chr10: 90343107-90343267; chr1: 29586353-29586513; chr2: 43451673-43451833.

[0013] Preferably, the methylation markers include the following 30 methylation regions:

[0014] chr7: 143059947-143060107; chr5: 178487412-178487572; chr11: 15136199-15136359; chr17: 43972911- 43973071; chr12: 64062951-64063111; chr3: 87039622-87039782; chr6: 27835283-27835443; chr5: 14625 7858-146258018; chr16: 28075032-28075192; chr17: 47307472-47307632; chr19: 37407294-37407454; ch r1: 179545118-179545278; chr13: 20806316-20806476; chr7: 132261297-132261457; chr4: 17783234-1778 3394; chr1: 67773558-67773718; chr10: 105037463-105037623; chr19: 7735166-7735326; chr6: 26199948 -26200108; chr6: 26273357-26273517; chr12: 4381931-4382091; chr7: 30722046-30722206; chr5: 1757924 94-175792654; chr11: 69590360-69590520; chr8: 143858458-143858618; chr14: 59104962-59105122; chr 4: 57976315-57976475; chr4: 39529282-39529442; chr7: 55259381-55259541; chr10: 90343107-90343267.

[0015] Preferably, the methylation markers include the following 42 methylation regions:

[0016] chr18:49867019-49867179;chr11:15136199-15136359;chr14:74892573-74892733;chr22:44420459-44420619;chr20:62283562-62283722;chr4:54966998-54967158;chr12:54427101-54427261;chr2:182321922-182322082;chr13:28674645-28674805;chr17:45810438-45810598;chr6:29760212-29760372;chr1:179545118-179545278;chr5:128797252-128797412;chr13:20806316-20806476;chr8:116660588-116660748;chr5:153784739-153784899;chr19:56904958-56905118;chr1:54204130-54204290;chr10:105037463-105037623;chr19:2290434-2290594;chr12:52400797-52400957;chr5:374080-374240;chr19:7735166-7735326;chr7:134143579-134143739;chr6:26199948-26200108;chr6:26273357-26273517;chr14:52781261-52781421;chr6:26204595-26204755;chr19:58951468-58951628;chr5:157098320-157098480;chr14:77228082-77228242;chr6:27100719-27100879;chr6:26189078-26189238;chr4:110224117-110224277;chr15:83316243-83316403;chr6:29716362-29716522;chr14:52734595-52734755;chr11:124735024-124735184;chr20:45338378-45338538;chr3:50242683-50242843;chr7:151107115-151107275;chr1:29586353-29586513。

[0017] Preferably, the methylation markers include the following 45 methylation regions:

[0018] chr7:143059947-143060107;chr5:178487412-178487572;chr11:15136199-15136359;chr17:43972911-43973071;chr8:104383554-104383714;chr12:64062951-64063111;chr3:87039622-87039782;chr6:27835283-27835443;chr17:77020105-77020265;chr4:54966998-54967158;chr7:98467849-98468009;chr16:28075032-28075192;chr4:48485804-48485964;chr19:37407294-37407454;chr1:179545118-179545278;chr18:43652068-43652228;chr13:20806316-20806476;chr8:116660588-116660748;chr7:132261297-132261457;chr19:53636048-53636208;chr5:33936140-33936300;chr4:17783234-17783394;chr1:67773558-67773718;chr10:105037463-105037623;chr12:52400797-52400957;chr5:374080-374240;chr19:7735166-7735326;chr6:26199948-26200108;chr6:26273357-26273517;chr12:4381931-4382091;chr10:135050089-135050249;chr7:30722046-30722206;chr5:175792494-175792654;chr11:69590360-69590520;chr17:47074677-47074837;chr8:143858458-143858618;chr14:59104962-59105122;chr4:57976315-57976475;chr8:53852244-53852404;chr3:50242683-50242843;chr4:39529282-39529442;chr7:55259381-55259541;chr5: 112073416-112073576; chr10: 90343107-90343267; chr2: 43451673-43451833. ;

[0019] Another aspect of the present invention provides the use of any of the above-mentioned methylation markers in detecting benign and malignant pulmonary nodules and / or lung cancer.

[0020] Another aspect of the present invention provides a reagent for detecting benign or malignant pulmonary nodules and / or lung cancer, wherein the reagent is used to detect the methylation level of any of the above methylation markers.

[0021] Another aspect of the present invention provides a kit for detecting benign or malignant pulmonary nodules and / or lung cancer, wherein the kit comprises the aforementioned reagents for detecting benign or malignant pulmonary nodules and / or lung cancer.

[0022] Another aspect of the present invention provides a method for screening methylation markers for differential diagnosis of benign and malignant pulmonary nodules as described in any of the above items, comprising the following steps:

[0023] S1, by performing RRBS analysis on lung nodules and paranodular tissues, a local lung nodule methylation dataset was constructed. Methylation differential analysis was performed on the two datasets based on the Illumina 450K LUAD DNA Methylation (lung adenocarcinoma Illumina 450k methylation chip) dataset in TCGA, and methylation regions specific to malignant lung nodules were screened and recorded as the first screening DMRs;

[0024] S2, reverse screening is performed on the first screening DMR obtained in step S1, and the methylated regions meeting the following criteria are removed to obtain the second screening DMR:

[0025] (1) Methylated regions from blood cells and other organs;

[0026] (2) the targeted detection region is less than 4 CpGs;

[0027] (3) methylated regions with an average methylation value greater than 0.03 in blood cells;

[0028] S3, based on the second screening DMRs obtained in step S2, obtaining a set of candidate methylation markers by analyzing and filtering the consistency between tissue and paired plasma;

[0029] S4, performing machine learning on the candidate methylation marker set obtained in step S3 in the plasma training set and test set, screening methylation regions with significant differences, and obtaining methylation markers for differential diagnosis of benign and malignant lung nodules.

[0030] Preferably, in step S1, the first screening DMR meets the following criteria in the methylation difference analysis of the two datasets:

[0031] (a) The average methylation value in the control samples was less than 0.02;

[0032] (b) the ratio of the average methylation value between the positive sample and its control sample is greater than 3.0;

[0033] (c) the average methylation value in positive samples was greater than 0.05;

[0034] (d) DMR region is larger than 20 bp;

[0035] The positive samples are malignant nodule tissues; the control samples are malignant nodule adjacent tissues, benign nodule tissues and benign nodule adjacent tissues.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention discloses a set of methylation markers for differentiating benign from malignant pulmonary nodules, as well as a screening method and application thereof. This screening method ultimately identified 78 methylation markers that can be used for differential diagnosis of benign and malignant pulmonary nodules and / or lung cancer. Furthermore, a model was constructed to evaluate the differential diagnostic performance of the methylation markers provided by the present invention. These 78 methylation markers, or combinations thereof, demonstrate high sensitivity and specificity in differential diagnosis of benign and malignant pulmonary nodules and / or lung cancer, making them suitable for widespread application in this field. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a heat map of P201 in different paired samples of 30 lung cancer patients in Example 1 of the present invention.

[0039] Figure 2 is the ROC curve of the diagnostic model of Example 2, Example 3 and Example 4, wherein,

[0040] A is the ROC curve of the diagnostic model of Example 2 in the training set and the validation set;

[0041] B is the ROC curve of the diagnostic model of Example 3 in the training set and the validation set;

[0042] C is the ROC curve of the diagnostic model of Example 4 in the training set and the validation set. DETAILED DESCRIPTION

[0043] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0044] The experimental methods in the following examples of the present invention, for which specific conditions are not specified, are generally carried out under conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or according to the conditions recommended by the manufacturer. The various commonly used chemical reagents used in the examples are all commercially available products. Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0045] The terms "comprise," "comprising," and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps is not limited to the listed steps or modules but may optionally include steps not listed, or other steps inherent to the process, method, product, or device.

[0046] In this application, "plurality" refers to two or more. "And / or" describes the relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. The character / generally indicates that the related objects are in an "or" relationship.

[0047] The patients with malignant lung nodules described in the present invention are patients with lung cancer. The two terms have the same meaning and can be used interchangeably.

[0048] As mentioned above, in view of the shortcomings of the existing technology, the applicant of the present invention first performed RRBS analysis on 50 pairs of clinical lung nodules and para-nodule tissues to construct a local lung nodule methylation dataset, and based on the illumina 450K LUADDNA Methylation dataset in TCGA, performed methylation difference analysis on the two datasets, and screened out 376 malignant lung nodule tissue-specific methylation regions, which were recorded as the first screening DMRs; the first screening DMRs were further reversely screened to obtain 201 second screening DMRs: the second screening DMRs were analyzed and filtered for consistency between tissue and paired plasma to obtain a set of candidate methylation markers; the candidate methylation marker set was subjected to machine learning in the plasma training set and validation set to screen methylation regions with significant differences, and 78 methylation markers for the differential diagnosis of benign and malignant lung nodules were obtained.

[0049] The technical solution of the present invention is described in detail below through specific embodiments and accompanying drawings.

[0050] Materials and experimental methods involved in this application:

[0051] 1. Research subjects

[0052] This study was conducted from February 2016 to August 2021, and a total of 347 subjects from Shanghai Chest Hospital were enrolled. The relevant information of the subjects is shown in Table 1. Among them, 296 patients with benign and malignant pulmonary nodules on CT (including 106 patients with benign lung diseases and 190 patients with lung cancer) and 51 healthy controls. Patients with benign lung diseases include pneumonia, chronic obstructive pulmonary disease, tuberculosis, etc. Lung cancer patients were confirmed by histopathology and / or cytopathology, and the staging was based on the 8th edition of the TNM staging standard. Healthy controls were those who underwent outpatient physical examinations. Patients without histopathological diagnosis, acute medical history, and other malignant tumors were excluded. All subjects signed informed consent, and the study has completed clinical trial registration (ChiCTR2000036938). All subjects in this study were randomly assigned to training and validation sets. The enrolled lung cancer patients were mainly early-stage lung cancer patients (Tis, stage I and stage II), accounting for 72.6% (138 / 190) of the subjects.

[0053] Table 1 Relevant information of 296 patients with pulmonary nodules

[0054]

[0055]

[0056] 2. Methylation Sequencing and Analysis

[0057] 2.1 Extraction of cfDNA from plasma samples

[0058] For each participant, 10 mL of peripheral blood was collected using an EDTA anticoagulant tube. After centrifugation to separate the plasma (approximately 4 mL), cell-free DNA was extracted using a dedicated nucleic acid extraction reagent (APG-61001-050, Shanghai Yiming Biotechnology Co., Ltd.). The sample was then lyophilized to a volume of 20 μL. Qubit quantification was performed at a concentration between 1 and 5 ng, and 15 μL was used for subsequent library construction.

[0059] 2.2 Methylation library construction

[0060] The main reagents used in the methylation library construction process of this application are shown in Table 2. TM Kit purchased from the United States The company can efficiently perform bisulfite conversion of DNA; The linear amplification primer premix contains 201 customized premixed primers for specific linear amplification of 201 candidate methylation markers (the screening process of the 201 candidate methylation markers will be described later); OPERA The Linear Amplification Kit is a kit specifically designed for linear amplification of DNA samples after bisulfite conversion; Universal library preparation reagents (for ) is a universal library construction kit that matches single primer amplification; Single-stranded index adapters (Set 1 or 2, for ) is a labeled adapter kit used in the ligation reaction during library construction; Pre-amplification index primers for library construction (Set 1 or 2, for ) is the index primer kit used in the pre-amplification reaction during library construction; Library Quantification Reagents (for ) is a kit for quantifying the library molecules after library construction.

[0061] Table 2 Main reagents used in methylation library construction

[0062]

[0063] The specific process is as follows:

[0064] 2.2.1 Bisulfite conversion of sample DNA

[0065] The commercial conversion kit EZ DNA Methylation-Lightning was used TM Kit (ZYMO) was used to convert cfDNA samples. The specific operation steps were carried out according to the product instructions.

[0066] 2.2.2 Single-strand linear amplification

[0067] The single-strand linear amplification system preparation method and reaction procedure refer to OPERA The linear amplification kit instructions were followed, and bisulfite conversion product, customized linear amplification primer premix and linear amplification reagent were added.

[0068] The linear amplification program settings and machine operation methods refer to OPERA Linear amplification reagents (for ) kit instructions.

[0069] Purify the linear amplification product by magnetic beads. For specific purification methods, refer to OPERA Universal library preparation reagents (for ) kit instructions, and 20 μL of the purified eluted product was used for subsequent experiments.

[0070] 2.2.3 Single-strand ligation reaction

[0071] The purified product obtained in step 2.2.2 was ligated with the labeled adapter. The preparation of the ligation reaction system and the reaction steps were referred to OPERA Universal library preparation reagents (for ) Kit instructions and Single-stranded index adapter Set 1 or 2 (for ) instructions to obtain a ligation product.

[0072] 2.2.4 Pre-amplification reaction

[0073] The ligation product obtained in step 2.2.3 was subjected to pre-amplification reaction. The preparation of the pre-amplification reaction system and the reaction steps were referred to OPERA Universal library preparation reagents (for ) Kit instructions and Pre-amplification index primers Set 1 or 2 (for ) kit instructions to obtain pre-amplification products, i.e., library molecules containing index primer sequences.

[0074] 2.2.5 Database Expansion

[0075] The pre-expansion product obtained in step 2.2.5 was subjected to expansion reaction. The expansion reaction system preparation and reaction steps were referred to OPERA Universal library preparation reagents (for ) kit instructions to obtain the amplified library products. The PCR quality control and quantification method of the library products refer to the OPERA library quantification reagent (for ) instructions. After quantification, the library was mixed as required and then the sequencing reaction was performed on the machine.

[0076] 2.3 Library sequencing and bioinformatics analysis of offline data

[0077] The library was sequenced using the Illumina NovaSeq6000 platform for 150 bp paired-end sequencing. Off-line data were quality-checked using FastP software and analyzed using SYMPHO BcDNA Methylation Analysis (PR) (Cat. No. APG_81002, Version v0.2), a software developed by Shanghai Yiming Biotechnology. Primer information was extracted from FastQ data using cut adapters, followed by bismark alignment to generate BAM files. Duplicates were removed using UMIcollapse, and the results of all primer calculations were summarized to obtain the deduplicated CpG depth and haploid methylation level (MHC). Finally, the normalized number of haploid methylated molecules (nMHC) in the sample was calculated for subsequent analysis.

[0078] Regarding the above-mentioned methylation sequencing and analysis process, the applicant would like to explain that it is well known in the art that when the cfDNA in the peripheral blood of the sample to be tested is converted, whether bisulfite, bisulfite, bisulfate, bisulfite, etc. are used, the purpose of the above-mentioned conversion treatment can be achieved, that is, the unmethylated cytosine in the free DNA in the peripheral blood is converted into unmethylated thymine to obtain a converted sample. Therefore, no matter which of these reagents is used for the conversion treatment, it is included in the protection scope of the present invention. In addition, the reagents used can be directly purchased from commercial products or prepared by themselves.

[0079] When building a library, sequencing, and obtaining the methylation results of each methylation region for the cfDNA of the peripheral blood of the test sample, common technical means in the field can also be used, such as: targeted methylation sequencing method based on hybridization capture, methylation sequencing method based on multiplex PCR, or methylation detection based on fluorescent quantitative PCR, but are not limited to the above methods of this application.

[0080] Example 1 Screening of candidate methylation markers for benign and malignant pulmonary nodules

[0081] 1. Initial screening of specific methylation markers for benign and malignant lung nodules

[0082] A pulmonary nodule methylation dataset was constructed by performing genome-wide reduced representation bisulfite sequencing (RRBS) on 50 pairs of clinical pulmonary nodule lesions and adjacent tissue samples (40 malignant and 10 benign). Simultaneously, the two datasets were analyzed based on the Illumina 450K LUAD DNA Methylation dataset from TCGA according to the following criteria: (a) the average methylation value of the candidate marker in control samples (including adjacent tissues of malignant lesions, benign lesions, and adjacent tissues of benign lesions) was less than 0.02; (b) the ratio of the average methylation value of the candidate marker in positive samples (malignant lesions) to their control samples was greater than 3.0; (c) the average methylation value of the candidate marker in positive samples (malignant lesions) was greater than 0.05; and (d) the distance-reduced molecular mismatch (DMR) region of the candidate marker was greater than 20 bp. A total of 376 DMRs with significant differential methylation in malignant nodule tissues (lung cancer) compared with adjacent / benign nodule tissues were identified, representing the first screening DMRs.

[0083] It should be noted that the analysis of DMR average methylation values ​​adopts standard methods in the field of methylation analysis.

[0084] 2. Reverse screening of methylation markers

[0085] Based on the tissue-specific methylation database in published literature (Moss, J., et al. Nat Commun, 2018), the 376 DMRs were reversely screened and methylated regions derived from blood cells and other organs were removed. DMRs with less than 4 CpGs in the target detection region were also removed. Then, using whole blood samples from healthy controls, single primer panels were designed for the obtained DMRs and library sequencing was performed according to the methylation sequencing method described above. DMRs with an average methylation value greater than 0.03 in blood cells were removed. Finally, 201 candidate methylation markers with significant differences in malignant lung nodules (hereinafter referred to as P201) were screened, which are the second screening DMRs.

[0086] 3. Verification of the consistency of P201 in tissue and plasma samples of patients with malignant pulmonary nodules

[0087] Thirty patients with malignant lung nodules (i.e., lung cancer patients) were randomly selected for the study. Paired tumor tissue DNA, adjacent paracancerous tissue DNA, paired plasma cfDNA, and blood cell DNA were extracted and sequenced according to the aforementioned methylation sequencing method. The results are as follows: Figure 1As shown, the methylation level of P201 in lung cancer tissue and plasma samples was significantly higher than that in adjacent tissue and blood cells, and P201 was highly consistent in tumor tissue and plasma samples. After this step, P201 became a candidate methylation marker set.

[0088] Example 2 Screening of methylation markers for benign and malignant lung nodules using different machine learning methods

[0089] All subjects were randomly divided into a training set and a validation set. Using the methods described above, cfDNA was extracted from plasma samples from all subjects, and a single-primer amplified methylation library targeting P201 was constructed. Using three different screening strategies, methylation signatures were obtained in the training set that were significantly different between lung cancer and benign and healthy controls. Three different models for distinguishing benign and malignant lung nodules were then constructed, and their performance was evaluated in the validation set. The details are as follows:

[0090] 1. Determination, Modeling, and Performance of the P30 Methylation Marker Set

[0091] 80% of the samples from all subjects were used as the training set, and the remaining 20% ​​were used as the validation set. LASSO analysis (α = 0.03) was used to screen the following 30 key methylation markers (denoted as P30) in the training set:

[0092] chr7: 143059947-143060107; chr5: 178487412-178487572; chr11: 15136199-15136359; chr17: 43972911- 43973071; chr12: 64062951-64063111; chr3: 87039622-87039782; chr6: 27835283-27835443; chr5: 14625 7858-146258018; chr16: 28075032-28075192; chr17: 47307472-47307632; chr19: 37407294-37407454; ch r1: 179545118-179545278; chr13: 20806316-20806476; chr7: 132261297-132261457; chr4: 17783234-1778 3394; chr1: 67773558-67773718; chr10: 105037463-105037623; chr19: 7735166-7735326; chr6: 26199948 -26200108; chr6: 26273357-26273517; chr12: 4381931-4382091; chr7: 30722046-30722206; chr5: 1757924 94-175792654; chr11: 69590360-69590520; chr8: 143858458-143858618; chr14: 59104962-59105122; chr 4: 57976315-57976475; chr4: 39529282-39529442; chr7: 55259381-55259541; chr10: 90343107-90343267.

[0093] A Gaussian process was further used to construct a P30-based differential diagnosis model for benign and malignant pulmonary nodules, and the performance of the model in differential diagnosis of benign and malignant pulmonary nodules in the training set and validation set was evaluated.

[0094] The results are as follows Figure 2 As shown in Figure 3A, the model built using a Gaussian process for P30 demonstrated excellent performance in differentiating benign from malignant pulmonary nodules. As shown in Table 3, in the training set, the AUC reached 0.943 (95% CI 0.918-0.967), with a sensitivity of 86.9% and a specificity of 83.9%. In the validation set, the AUC reached 0.910 (95% CI 0.845-0.974), with a sensitivity of 79.5% and a specificity of 87.1%.

[0095] 2. Determination, Modeling, and Performance of the P45 Methylation Marker Set

[0096] 80% of the samples from all subjects were used as the training set, and the remaining 20% ​​were used as the validation set. LASSO analysis (α = 0.02) was used to screen the following 45 key methylation markers (denoted as P45) in the training set:

[0097] chr7:143059947-143060107;chr5:178487412-178487572;chr11:15136199-15136359;chr17:43972911-43973071;chr8:104383554-104383714;chr12:64062951-64063111;chr3:87039622-87039782;chr6:27835283-27835443;chr17:77020105-77020265;chr4:54966998-54967158;chr7:98467849-98468009;chr16:28075032-28075192;chr4:48485804-48485964;chr19:37407294-37407454;chr1:179545118-179545278;chr18:43652068-43652228;chr13:20806316-20806476;chr8:116660588-116660748;chr7:132261297-132261457;chr19:53636048-53636208;chr5:33936140-33936300;chr4:17783234-17783394;chr1:67773558-67773718;chr10:105037463-105037623;chr12:52400797-52400957;chr5:374080-374240;chr19:7735166-7735326;chr6:26199948-26200108;chr6:26273357-26273517;chr12:4381931-4382091;chr10:135050089-135050249;chr7:30722046-30722206;chr5:175792494-175792654;chr11:69590360-69590520;chr17:47074677-47074837;chr8:143858458-143858618;chr14:59104962-59105122;chr4:57976315-57976475;chr8:53852244-53852404;chr3:50242683-50242843;chr4:39529282-39529442;chr7:55259381-55259541;chr5: 112073416-112073576; chr10: 90343107-90343267; chr2: 43451673-43451833. ;

[0098] A support vector machine was further used to construct a P45-based differential diagnosis model for benign and malignant pulmonary nodules, and the performance of the model in differential diagnosis of benign and malignant pulmonary nodules in the training set and validation set was evaluated.

[0099] The results are as follows Figure 2 As shown in Figure B, the P45 model built using support vector machines demonstrated excellent performance in differentiating benign from malignant pulmonary nodules. As shown in Table 3, in the training set, the AUC reached 0.969 (95% CI 0.948-0.990), with a sensitivity of 89.5% and a specificity of 93.5%. In the validation set, the AUC reached 0.913 (95% CI 0.848-0.978), with a sensitivity of 69.2% and a specificity of 96.8%.

[0100] 3. Determination, Modeling, and Performance of the P42 Methylation Marker Set

[0101] 45% of the samples from all subjects were used as the training set, and the remaining 55% were used as the validation set. The Xgboost method was further used to screen the following 42 key methylation markers (denoted as P42) in the training set:

[0102] chr18:49867019-49867179;chr11:15136199-15136359;chr14:74892573-74892733;chr22:44420459-44420619;chr20:62283562-62283722;chr4:54966998-54967158;chr12:54427101-54427261;chr2:182321922-182322082;chr13:28674645-28674805;chr17:45810438-45810598;chr6:29760212-29760372;chr1:179545118-179545278;chr5:128797252-128797412;chr13:20806316-20806476;chr8:116660588-116660748;chr5:153784739-153784899;chr19:56904958-56905118;chr1:54204130-54204290;chr10:105037463-105037623;chr19:2290434-2290594;chr12:52400797-52400957;chr5:374080-374240;chr19:7735166-7735326;chr7:134143579-134143739;chr6:26199948-26200108;chr6:26273357-26273517;chr14:52781261-52781421;chr6:26204595-26204755;chr19:58951468-58951628;chr5:157098320-157098480;chr14:77228082-77228242;chr6:27100719-27100879;chr6:26189078-26189238;chr4:110224117-110224277;chr15:83316243-83316403;chr6:29716362-29716522;chr14:52734595-52734755;chr11:124735024-124735184;chr20:45338378-45338538;chr3:50242683-50242843;chr7:151107115-151107275;chr1:29586353-29586513。

[0103] Logistic regression was further used to construct a P42-based differential diagnosis model for benign and malignant pulmonary nodules, and the performance of the model in differential diagnosis of benign and malignant pulmonary nodules in the training set and validation set was evaluated.

[0104] The results are as follows Figure 2 As shown in Figure C, the model established by P42 using logistic regression demonstrated excellent performance in differentiating benign from malignant pulmonary nodules. As shown in Table 3, in the training set, the AUC reached 0.936 (95% CI 0.897-0.976), with a sensitivity of 80% and a specificity of 98%. In the validation set, the AUC reached 0.888 (95% CI 0.838-0.987), with a sensitivity of 73% and a specificity of 99%.

[0105] Table 3 Performance of methylation marker models established by P30, P45, and P42 in the training and validation sets

[0106]

[0107] A total of 78 statistically significant methylation markers were identified using the above screening strategies, of which 39 had good independent discrimination performance. See Table 4 for details.

[0108] Table 4 78 methylation markers, their specific locations, and independent diagnostic performance

[0109]

[0110]

[0111]

[0112] In summary, the present invention discloses a set of methylation markers for differentiating benign from malignant pulmonary nodules, as well as a screening method and application thereof. This screening method identified 78 methylation markers that can be used for differential diagnosis of benign and malignant pulmonary nodules and / or lung cancer. Model construction was used to evaluate the differential diagnostic performance of the methylation markers provided by the present invention. The methylation markers or combinations thereof provided by the present invention demonstrate high sensitivity and good specificity in differential diagnosis of benign and malignant pulmonary nodules and / or lung cancer, and are therefore suitable for widespread application in the differential diagnosis of benign and malignant pulmonary nodules.

[0113] Although the present invention has been described in detail through the above preferred embodiments, it should be understood that the above description is not intended to limit the present invention. After reading the above description, various modifications and substitutions of the present invention will become apparent to those skilled in the art. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. Use of a reagent for detecting the methylation level of a methylation marker in the preparation of a product for detecting benign or malignant pulmonary nodules, characterized in that: The methylation regions of the methylation markers on chromosomes are as follows: chr7: 143059947-143060107; chr5: 178487412-178487572; chr11: 15136199-15136359; chr17: 43972911-43973071; chr8: 104383554-104383714; chr12: 64062951-64063111; chr3: 87039622-87039782; chr6: 27835283-27835443; chr17: 77020 105-77020265; chr4: 54966998-54967158; chr7: 98467849-98468009; chr16: 28075032-28075192; chr4: 48485804-48485964; chr19: 3 7407294-37407454; chr1: 179545118-179545278; chr18: 43652068-43652228; chr13: 20806316-20806476; chr8: 116660588-116660748 ; chr7: 132261297-132261457; chr19: 53636048-53636208; chr5: 33936140-339 36300; chr4: 17783234-17783394; chr1: 67773558-67773718; chr10: 105037463- 105037623; chr12: 52400797-52400957; chr5: 374080-374240; chr19: 7735166- 7735326; chr6: 26199948-26200108; chr6: 26273357-26273517; chr12: 4381931- 4382091; chr10: 135050089-135050249; chr7: 30722046-30722206; chr5: 17579 2494-175792654; chr11: 69590360-69590520; chr17: 47074677-47074837; chr8: 143858458-143858618; chr14: 59104962-59105122; chr4: 57976315-57976475; c hr8: 53852244-53852404; chr3: 50242683-50242843; chr4: 39529282-39529442;chr7: 55259381-55259541; chr5: 112073416-112073576; chr10: 90343107-90343267 and chr2: 43451673-43451833; wherein the methylation region of the methylation marker is determined based on the human genome hg19 alignment.

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