Methylated biomarker for distinguishing benign and malignant pulmonary nodules and application thereof

Abstract

The invention belongs to the technical field of disease diagnosis and molecular biology, and particularly relates to a methylated biomarker for distinguishing benign and malignant pulmonary nodules and application of the methylated biomarker. According to the method, DNA differential methylation analysis is carried out on a pulmonary nodule tissue sample, and 20 optimal methylation biomarkers are selected according to sensitivity to construct a prediction model. The prediction model has high sensitivity and specificity, can distinguish lung malignant nodules (namely lung tumors) and lung benign nodules based on tissue samples and predict the lung cancer occurrence risk, is high in model accuracy and good in sensitivity, and has important value for early screening of lung cancer patients and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of disease diagnosis and molecular biology, and specifically relates to a methylation biomarker for distinguishing benign and malignant pulmonary nodules and an application thereof. Background Art

[0002] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention, and should not necessarily be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art.

[0003] Lung cancer is one of the most common cancers in the world and the leading cause of cancer-related deaths worldwide. Since early lung cancer has basically no typical symptoms, most patients are diagnosed in the late stage, and the survival rate is significantly reduced. Studies have shown that the 5-year survival rate of stage I lung cancer is greater than 80%, while the 5-year survival rate of stage IV lung cancer is almost 0. Therefore, early lung cancer screening and early diagnostic tools with high sensitivity and specificity are crucial for the prognosis and treatment of lung cancer patients.

[0004] At present, low-dose spiral CT (LDCT) screening is widely used in the early detection of lung cancer and has become an effective method for early lung cancer screening. It can detect potential lung nodules at an early stage and can effectively reduce the mortality rate of lung cancer. However, due to the consideration of only the size and density of nodules and the neglect of other risk factors such as basic lung disease history, LDCT screening has the characteristics of high sensitivity and low specificity, with a false positive rate of up to 96.4%, and can detect many non-neoplastic lung nodules. False positive results can cause unnecessary intervention and excessive anxiety, thus causing potential harm to patients. To solve the current dilemma, people have tried to use commonly used tumor markers for diagnosis, such as squamous cell carcinoma antigen (SCC-Ag), carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125) and cytokeratin 19 fragment (CYFRA21-1), but they lack the corresponding sensitivity, and early screening of lung cancer remains a huge challenge.

[0005] As research continues to deepen, abnormal DNA methylation is considered to be an important cause of cancer. DNA methylation is an epigenetic modification of genes by covalently adding methyl groups to the carbon 5 position of cytosine bases through DNA methyltransferases, and mainly occurs in CpG islands rich in CpG dinucleotides. Most tumors are characterized by genome-wide DNA hypomethylation and localized hypermethylation. Suzuki et al. found that this is particularly prominent in the early stages of non-small cell lung cancer. Hypermethylation in the promoter region usually silences tumor suppressor genes, while DNA hypomethylation activates proto-oncogenes, leading to genetic instability and promoting the growth of tumor cells. Therefore, early detection of DNA methylation has the potential to predict the occurrence of cancer. However, there are still few reports on lung cancer screening based on DNA methylation. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides methylation biomarkers for distinguishing benign and malignant pulmonary nodules and their applications. The present invention constructs a prediction model by performing DNA differential methylation analysis on pulmonary nodule tissue samples and selecting the best 20 methylation biomarkers according to sensitivity. The prediction model has high sensitivity and specificity, and can distinguish malignant pulmonary nodules (i.e., lung tumors) from benign pulmonary nodules based on tissue samples, and predict the risk of lung cancer. The model has high accuracy and good sensitivity. Based on the above research results, the present invention is completed.

[0007] Specifically, the present invention relates to the following technical solutions:

[0008] In a first aspect of the present invention, a methylation biomarker for distinguishing benign and malignant pulmonary nodules is provided, wherein the methylation biomarker is selected from one or more of the following gene fragments:

[0009] chr5:93905357-93905359、chr5:134259480-134259482、chr5:134259489-134259491 , chr5:93905256-93905258, chr11:67247741-67247743, chr1:243645964-243645966, chr5:134259522-134259524, chr15:45928449-45928451, chr5:154026439-15402644 1. chr5:154026479-154026481, chr8:42398664-42398666, chr1:47899804-47899806,

[0010] chr2:85362588-85362590, chr2:130875117-130875119, chr11:71189489-71189491, chr11:76887102-76887104, chr5:134259500-134259502, chr5:134259527-134259529, chr11:18808624-18808626 and chr15:25093827-25093829.

[0011] Furthermore, the methylation biomarker is a group consisting of all the above gene fragments.

[0012] The present invention has found through research that the sensitivity of a single gene may not be significant, but by selecting the above 20 genes with the highest sensitivity for combination under the premise of high specificity, the benign and malignant pulmonary nodule prediction model of the present invention has extremely high sensitivity and specificity, thereby greatly improving the prediction ability of the model.

[0013] The second aspect of the present invention provides the use of a reagent for detecting the methylation level of the above-mentioned methylation biomarker in the preparation of a product for distinguishing benign and malignant pulmonary nodules.

[0014] A third aspect of the present invention provides a system for distinguishing benign and malignant pulmonary nodules, the system comprising:

[0015] i) an analysis module, the analysis module comprising: a detection reagent for determining the methylation biomarkers selected from the above in the test sample of the subject;

[0016] ii) an evaluation module, the evaluation module comprising: distinguishing and judging the benign or malignant nature of the sample to be tested according to the expression level of the methylation biomarker determined in i).

[0017] More specifically, the random forest model is a random forest classification model constructed by RandomForestClassifier, and its parameters are set as: criterion = 'entropy', max_depth = 5, n_estimators = 10, oob_score = True, random_state = 124. The threshold for malignant prediction probability is 0.7. The malignant prediction probability of each sample is calculated based on the DNA methylation level of its marker combination using the constructed random forest regression model. If the malignant prediction probability is greater than the threshold, it is judged as a malignant nodule, otherwise it is judged as a benign nodule.

[0018] Beneficial technical effects of one or more of the above technical solutions:

[0019] The above technical scheme uses bisulfite sequencing to perform DNA methylation analysis on the actual collected lung nodule tissue samples. The optimal markers are screened according to the sensitivity and specificity of differentially methylated genes in patients with benign nodules and malignant nodules, and the random forest algorithm is used to construct and verify the prediction model. Finally, the GO and KEGG databases are used for enrichment analysis of differentially methylated genes. The model has high accuracy and good sensitivity. The sensitivity of the model in the training set is 100%, the specificity is 100%, and the AUC is 1.000. In the test set, the sensitivity is 100%, the specificity is 91%, and the AUC is 0.99. The ability to distinguish between lung malignancies and benign lung nodules based on tissue samples is of great value for early screening of lung cancer patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0021] Figure 1 This is the basic analysis of methylation sequencing data of tissue samples of patients with lung nodules in the embodiment of the present invention. (A) Data quality control is performed from the perspective of effective data ratio. (B) Data quality control is performed from the perspective of sequencing coverage depth.

[0022] Figure 2 The receiver operating characteristic curve and area under the curve of the training set and the test set under different numbers of markers in the embodiment of the present invention are shown in Figure 1. According to the sensitivity, the markers are sorted in descending order, and the optimal n (10-100, every 10 as a gradient) markers are selected, and the random forest algorithm is used to perform binary classification algorithm training on the training set (A) and model evaluation on the test set (B).

[0023] Figure 3 This is a heat map of the methylation levels of the 100 markers with the most significant differences in the training set in all samples (training set & test set) in the embodiment of the present invention. The color bar represents the methylation level of the corresponding marker, the bluer the color, the lower the methylation level, and the redder the color, the higher the methylation level.

[0024] Figure 4 This is the performance test of the model under the Top20 markers in the embodiment of the present invention. (A) Heat map of the methylation levels of the 20 markers with the most significant differences in the training set in all samples (training set & test set). The color bar represents the methylation level of the corresponding marker. The bluer the color, the lower the methylation level, and the redder the color, the higher the methylation level. (B) The receiver operating characteristic curve and area under the curve (C and D) of the training set and test set under the Top20 markers. The sensitivity and specificity of the training set (C) and the test set (D).

[0025] Figure 5GO functional enrichment analysis of differentially methylated genes in the embodiments of the present invention. (A and B) Cellular components. GO enrichment bubble map (A) and directed acyclic graph (DAG) (B) (C and D) Molecular functions. GO enrichment bubble map (C) and directed acyclic graph (D) (E and F) Biological processes. GO enrichment bubble map (E) and directed acyclic graph (F). GO enrichment bubble map of differentially methylated genes, reflecting the number distribution of related genes on the GO items enriched in biological processes and the enrichment significance p-value; directed acyclic graph, branches represent inclusion relationships, the functional range defined from top to bottom becomes smaller and smaller, and the depth of color represents the degree of enrichment.

[0026] Figure 6 This is the KEGG pathway enrichment analysis of the differentially methylated genes in the examples of the present invention. DETAILED DESCRIPTION

[0027] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0028] It should be noted that the terms used here are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should also be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof. If the experimental methods of specific conditions are not indicated in the following specific embodiments, the conventional methods and conditions of molecular biology in the art are usually followed, and such techniques and conditions are fully explained in the literature. See, for example, Sambrook et al., the techniques and conditions described in "Molecular Cloning: Laboratory Manual", or according to the conditions recommended by the manufacturer.

[0029] The present invention is further described in conjunction with specific examples. The following examples are only for explaining the present invention and are not intended to limit the content thereof. If the specific experimental conditions are not specified in the examples, they are usually carried out under conventional conditions or under conditions recommended by the sales company; the materials and reagents used in the examples, unless otherwise specified, can be purchased through commercial channels.

[0030] The term "biomarker" refers to "a characteristic that can be objectively detected and evaluated and can serve as an indicator of normal biological processes, pathological processes, or pharmacological responses to therapeutic interventions." For example, nucleic acid markers (also known as gene markers, such as DNA), protein markers, cytokine markers, chemokine markers, carbohydrate markers, antigen markers, antibody markers, etc. In the present invention, the biomarker is a methylation biomarker.

[0031] In the present invention, the "benign" and "malignant" indicate the nature of lung nodules. Usually, benign lung nodules are round or quasi-circular in shape, with good mobility, most of them are non-lobed, and the edges may have sharp corners or fibrous cords, etc. The edges of inflammatory lung nodules are mostly fuzzy, while the edges of benign non-inflammatory lung nodules are mostly clear, neat or even smooth. Malignant lung nodules are manifested by irregular shapes, mostly lobed, or with burr signs (or thorny protrusions), pleural indentation signs and vascular clustering signs, uncontrollable malignant cell growth, spread and metastasis, and the edges are mostly clear but not smooth, and the nodule-lung interface is rough or even has burrs. In a specific embodiment of the present invention, malignant lung nodules are lung cancer.

[0032] The present invention distinguishes between benign and malignant lung nodules, which can not only be used for early screening of lung cancer patients, but also include late-stage diagnosis of lung cancer, as well as risk assessment, prognosis, disease identification, etc. of lung cancer.

[0033] The term early screening refers to the possibility of detecting lung cancer before it metastasizes, particularly before morphological changes in tissues or cells are observable.

[0034] In a typical embodiment of the present invention, a methylation biomarker for distinguishing benign and malignant pulmonary nodules is provided, wherein the methylation biomarker is selected from one or more of the following gene fragments: chr5:93905357-93905359, chr5:134259480-134259482, chr5:134259489-134259491, chr5:93905256-93905258, chr11:67247741-67 247743, chr1:243645964-243645966, chr5:134259522-134259524, chr15:45928449-45928451, chr5:15 4026439-154026441, chr5:154026479-154026481, chr8:42398664-42398666, chr1:47899804-47899806,

[0035] chr2:85362588-85362590, chr2:130875117-130875119, chr11:71189489-71189491, chr11:76887102-76887104, chr5:134259500-134259502, chr5:134259527-134259529, chr11:18808624-18808626 and chr15:25093827-25093829.

[0036] Furthermore, the methylation biomarker is a group consisting of all the above gene fragments.

[0037] The present invention has found through research that the sensitivity of a single gene may not be significant, but by selecting the above 20 genes with the highest sensitivity for combination under the premise of high specificity, the benign and malignant pulmonary nodule prediction model of the present invention has extremely high sensitivity and specificity, thereby greatly improving the prediction ability of the model.

[0038] The second aspect of the present invention provides the use of a reagent for detecting the methylation level of the above-mentioned methylation biomarker in the preparation of a product for distinguishing benign and malignant pulmonary nodules.

[0039] Among them, reagents for detecting the methylation level of methylation biomarkers include reagents required for bisulfite conversion and DNA methylation sequencing.

[0040] The product for distinguishing benign and malignant pulmonary nodules may specifically be a detection kit.

[0041] A third aspect of the present invention provides a system for distinguishing benign and malignant pulmonary nodules, the system comprising:

[0042] i) an analysis module, the analysis module comprising: a detection reagent for determining the methylation biomarkers selected from the above in the test sample of the subject;

[0043] ii) an evaluation module, the evaluation module comprising: distinguishing and judging the benign or malignant nature of the sample to be tested according to the methylation expression level of the methylation biomarker determined in i);

[0044] In the analysis module of i), the sample to be tested is a lung nodule tissue sample.

[0045] The specific evaluation process of the evaluation module of ii) includes: according to the methylation level of the methylation biomarker determined in i), distinguishing and judging the benign or malignant nature of the sample to be tested based on a prediction model; the prediction model is a random forest model.

[0046] More specifically, the random forest model is a random forest classification model constructed by RandomForestClassifier, and its parameters are set as: criterion = 'entropy', max_depth = 5, n_estimators = 10, oob_score = True, random_state = 124. The threshold for malignant prediction probability is 0.7. The malignant prediction probability of each sample is calculated based on the DNA methylation level of its marker combination using the constructed random forest regression model. If the malignant prediction probability is greater than the threshold, it is judged as a malignant nodule (i.e., lung cancer), otherwise it is judged as a benign nodule.

[0047] The present invention is further explained by the following examples, but they are not intended to limit the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0048] Example

[0049] 1. Test method:

[0050] 1.1 Study population

[0051] A total of 81 patients who underwent surgery in Shandong Provincial Hospital from January 2020 to June 2022 were selected, including 48 patients with lung cancer and 33 patients with benign pulmonary nodules. This study was conducted in accordance with the Declaration of Helsinki. Patients enrolled in this study were aged 18 years and above, with positive pulmonary nodules (diameter <3 cm) on CT scan and subsequently underwent surgical resection. The enrolled patients denied the presence of other malignancies and had no history of cancer. All patients did not receive any preoperative cancer treatment. Clinical information of the patients was collected, including the age, sex, smoking history, nodule size, nodule location, pathological type and stage of the nodules. The pathological type of all surgically removed tissue sections was clarified according to the 2015 WHO histological classification of lung cancer. All patients with pathologically confirmed malignant tumors were staged according to the eighth edition of the TNM guideline classification criteria. This study has been approved by the Ethics Committee of the Provincial Hospital Affiliated to Shandong First Medical University, and all participants signed informed consent.

[0052] 1.2 Sample collection and tissue DNA isolation

[0053] Formalin-fixed paraffin-embedded (FFPE) tissue sample sections were obtained from surgical resections. Tissue genomic DNA (gDNA) was extracted from FFPE tissue specimens using the Qiagen QIAamp DNA FFPE Tissue Kit (Qiagen, Cat. No. 56404). The gDNA fragments were split into 200 bp using an M220 Focused-ultrasonicator (Covaris, Inc.). According to the manufacturer's protocol, 50 ng of gDNA fragments were used for bisulfite conversion.

[0054] 1.3 Bisulfite conversion and DNA methylation sequencing

[0055] EZ DNA Methylation-Gold was used according to the manufacturer's protocol. TM Kit (Zymo Research, Cat. No. D5005) was used for bisulfite conversion. Briefly, 130 μl of CT Conversion Reagent was added to 20 μl of DNA sample, placed in a thermal cycler, and incubated as follows: 98°C for 10 min, 64°C for 2.5 h, and 4°C for up to 20 h. The bisulfite-converted DNA was mixed with 600 μl of M-Binding Buffer and the mixed sample was added to the Zymo-Spin TM The IC Column was centrifuged and 100 μl of M-Wash Buffer, 200 μl of M-Desulphonation Buffer, 200 μl of M-Wash Buffer and 15 μl of LOW EDTA Buffer were added for washing and elution, respectively.

[0056] SWIFT libraries were constructed using the Accel-NGS Methyl-Seq DNA Library Kit (SWIFT, Cat. No. SW 30096). Briefly, purified DNA was denatured at 95°C for 2 minutes and immediately cooled on ice after incubation. Adapter ligation was performed using Adaptase mix at 37°C for 15 minutes and 95°C for 2 minutes. Extension was performed using Enzyme Y2 at 98°C for 1 minute + 62°C for 2 minutes + 65°C for 5 minutes, followed by purification using AMPure XP Beads (Beckman Coulter, Cat. No. A63881) and elution in a volume of 15 μl. Adapter ligation was performed using Enzyme B3 at 25°C for 15 minutes, followed by purification using AMPure XP Beads (Beckman Coulter, Cat. No. A63881) and elution in a volume of 20 μl. The library was amplified using KAPA HiFi HotStart Uracil ReadyMix. Amplification conditions were: one cycle of 95°C for 30 seconds and repeated cycles of 98°C for 10 seconds + 60°C for 30 seconds + 72°C for 1 minute. Amplified DNA was purified with AMPure XP Beads and eluted in a volume of 22 μl. Qubit concentration was determined and quality checked using Agilent 2100 Bioanalyzer, and pre-libraries containing more than 500 ng DNA were considered eligible for subsequent targeted enrichment.

[0057] Next, targeted enrichment was performed using the Twist Fast Hybridization and Wash Kit, 96 Reactions (Twist, Catalog No. 101175). Briefly, 4 μl of Twist Methylation Custom Panel, 96 Reactions (Twist, Catalog No. 104408), 13 μl of Twist Universal Blockers, 96 Reactions (Twist, Catalog No. 100767), and 2 μl of Hybridization Enhancer (Twist, Catalog No. 100968) were added to the DNA pre-library and heated at low temperature in a vacuum concentrator for drying. 20 μl of Fast Hybridization Mix (Twist, Catalog No. 100968) and 30 μl of Hybridization Enhancer were added to the freeze-dried sample in the previous step, and hybridization was performed in a PCR instrument at 95°C for 5 minutes and 60°C for 15 minutes to 4 hours.

[0058] After hybridization, use streptavidin magnetic beads (Twist, Cat. No. 100984) to pull down the DNA pre-library bound to the probe. Use 100 μL of streptavidin magnetic beads, add 200 μL Fast Binding Buffer (Twist, Cat. No. 100972) to wash three times, and then resuspend in 200 μL Fast Binding Buffer. Transfer all the hybridization solution to the balanced magnetic beads and mix thoroughly at room temperature on a rotator for 30 minutes. Add 200 μL preheated Fast Wash Buffer I (Twist, Cat. No. 100972), incubate at 65℃ for 5 minutes, place on the magnetic stand for 1 minute, remove the supernatant, and repeat once. Add 200 μL preheated Wash buffer II (Twist, Cat. No. 100972), incubate at 48℃ for 5 minutes, place on the magnetic stand for 1 minute, remove the supernatant, and repeat twice. Finally, the cells were eluted in 45 μl of water and the solution was incubated on ice.

[0059] The library was further amplified using KAPA HiFi HotStart Ready Mix and P5 and P7 primers. The amplification program was as follows: 98°C for 45 seconds per cycle, 98°C for 15 seconds + 60°C for 30 seconds + 72°C for 30 seconds for 15 cycles, and 72°C for 1 minute per cycle. The amplified library was purified with DNA Purification Beads (Twist, Cat. No. 100984) and washed in 80% ethanol solution. The library was finally sequenced on Illumina Nova 6000.

[0060] 1.4 Quality Control

[0061] Fastp (v0.21.0) and trimmomatic (v0.39) were used to preprocess the sequencing data to obtain clean data. Next, bismark (v0.23.0) was used to align the clean data to the hg19 reference genome and remove duplicates from the alignment results. Then bismark_methylation_extractor was used to extract the methylation identification results. Finally, bamdst (v1.0.9) was used to perform statistics on the alignment results to obtain basic statistical information such as sequencing coverage.

[0062] 1.5 Differential methylation analysis

[0063] The samples were split into training and test sets at a ratio of 2:1. The R package DSS (version 2.42.0) was used to perform differential methylation analysis on the training set to obtain the difference level diff and difference significance p_value between the two groups for each CpG site. According to the methylation quantification of each sample between the two groups, the AUC and sensitivity (under the premise of specificity 0.9) were calculated. The differential methylation sites were screened, and the screening criteria were: p_value<1e-2&abs(diff)>0.1&sensitivity>0.6.

[0064] 1.6 Marker selection and model construction

[0065] Sort in descending order according to sensitivity, select the best n markers, and use the random forest algorithm (R package randomForest_4.7-1.1) for binary classification algorithm training (training set) and model evaluation (test set). Random forest, as the name implies, is to build a forest in a random way. The forest consists of many decision trees, and there is no correlation between each decision tree in the random forest. After obtaining the forest, when a new input sample enters, each decision tree in the forest is allowed to make a judgment to see which category the sample should belong to (for the classification algorithm), and then see which category is selected the most, and predict the sample to be that category. Select the 20 markers with the most significant differences for modeling, and use the ROC method to evaluate the performance of the model in the training set and test set.

[0066] Plasma samples from 48 patients with malignant pulmonary nodules and 33 patients with benign pulmonary nodules were collected and divided into training and test sets at a ratio of 2:1. The training set contained 32 samples of malignant pulmonary nodules and 22 samples of benign pulmonary nodules. The methylation levels of 20 differential intervals of the training set samples were calculated as molecular features. The random forest machine learning classification algorithm, that is, the RandomForestClassifier function of sklearn.ensemble, was used to construct a random forest model to obtain a discriminant model for benign and malignant pulmonary nodules.

[0067] The specific construction method is as follows: Random forest uses N (N=10 in this embodiment) decision trees as basic learners to form an integrated learner. Randomness is added during the decision tree training process, including that the samples of each decision tree are obtained by bootstrap sampling (i.e., the sampling process of the samples used by each decision tree belongs to random sampling with replacement), and the classification attributes of each decision tree are randomly selected (i.e., the methylation sites used for random forest decision classification are randomly selected from 20 difference intervals). The input training data are the label (label, i.e., positive or negative sample) to which each sample belongs and the methylation value of the 20 difference intervals. The random forest algorithm is used to evaluate the importance of each methylation feature and the importance of each sample to the model construction. The classification voting results of the N decision trees in the random forest are displayed in the form of percentages, i.e., the results of each sample and each label are expressed in percentage probability.

[0068] When applying this model, the predict function of the Python language is used to judge the test set samples, and it can be judged whether the test samples are classified as malignant or benign. The predict_proba function can be used to calculate the probability of the test sample being classified as a malignant nodule, which is determined by the classification votes of the N decision trees in the random forest. The calculation formula is as follows:

[0069] The probability of a sample being classified as a malignant nodule: P1 = N1 / N;

[0070] Where N1 represents the number of decision trees that predict samples as malignant nodules, and N is the number of decision trees in the random forest.

[0071] When the probability P1 of the test sample being classified as a malignant nodule is ≥ 0.7, the test sample is judged to be a positive sample (ie, a malignant nodule); when the probability P1 of the test sample being classified as a malignant nodule is < 0.7, the test sample is judged to be a negative sample (ie, a benign nodule).

[0072] 1.7 Enrichment analysis of functions and pathways of differentially methylated genes

[0073] GO enrichment analysis was performed using the R package clusterProfiler (v4.2.2), and KEGG pathway enrichment analysis was performed using WebGestalt (https: / / www.webgestalt.org / ).

[0074] 2 Test results

[0075] 2.1 Clinical cohort

[0076] All patients underwent pulmonary nodule resection between January 10, 2020, and June 27, 2022. A total of 81 patients' tissue samples (33 benign and 48 malignant) were used for DNA methylation analysis, model development, and validation. The demographic and clinical characteristics of the patients are shown in Table 1. Specifically, stage I and stage II cancer accounted for 56.3% and 6.2% of the total cancer patients, respectively. The average size of benign nodules was 0.96 cm (0.50-1.65 cm), and the average size of malignant nodules was 0.94 cm (0.50-1.75 cm).

[0077] Table 1 Demographic and clinical characteristics of the patients

[0078]

[0079]

[0080] 2.2 Quality control of methylation sequencing data

[0081] Fastp and trimmomatic software were used to preprocess the raw sequencing data of tissue samples from 81 patients with pulmonary nodules ( Figure 1 Figure A performs data quality control from the perspective of effective data ratio, and evaluates data quality from the perspectives of raw_q20_rate (ratio of bases with Q20 (Q value greater than 20) in raw data), mapped_bases_rate (ratio of bases with successful alignment), target_bases_rate (ratio of bases falling in the target area of ​​the panel), dedup_mapped_bases_rate (ratio of bases with successful alignment after deduplication), and dedup_target_bases_rate (ratio of bases falling in the target area of ​​the panel after deduplication). Figure B performs data quality control from the perspective of sequencing coverage depth, and evaluates data quality from the perspectives of target_average_depth (average coverage depth of bases in the target area of ​​the panel) and dedup_target_average_depth (average coverage depth of bases in the target area of ​​the panel after deduplication). The overall results showed that the average values ​​of raw_q20_rate in benign nodules and malignant nodules were 0.955 and 0.959 (>0.95), respectively, indicating that the tissue sample quality was good and the probability of error was low, which increased the false positive variant detection and made the diagnosis more accurate. The mapped_bases_rate and target_bases_rate indicated that the sequencing quality was good, and the higher the capture efficiency, the lower the sequencing cost.

[0082] 2.3 Identification of differentially methylated regions

[0083] The 81 tissue samples were divided into a training set (22 benign and 32 malignant) and a test set (11 benign and 16 malignant) in a ratio of 2:1, so that the distribution of malignant tumors, age, and gender in the test set was balanced with the distribution of samples in the training set, as shown in Table 2. In the training set and the test set, the percentages of benign nodules and malignant nodules were 40.7% and 59.3%, respectively, with no significant differences in gender and age (P>0.05).

[0084] Table 2 Comparison of clinical information and differences between training set and test set

[0085]

[0086] The DSS method was used to calculate the difference level diff and difference significance p_value between the two groups of 984796 CpG sites. After screening based on the screening criteria: p_value<1e-2&abs(diff)>0.1&sensitivity>0.6, 885 differentially methylated sites were finally obtained. The 885 differentially methylated sites that met the screening criteria were sorted from large to small according to sensitivity, and the performance of the model under different numbers of markers was evaluated to select the optimal marker ( Figure 2 ). The top 10 markers in the model showed an AUC value of 1 in the training set and an AUC value of 0.97 in the test set. The receiver operating characteristic curve-AUC (ROC-AUC) of 60 methylation target regions reached 1 in the test set, showing good model performance. According to the methylation level heat map ( Figure 3 ) showed that there were significant differences in the methylation levels of these 100 markers between the two groups. The methylation levels of most of the markers in the upper part were higher in malignant nodules than in benign nodules, while the methylation levels of most of the markers in the lower part were lower in malignant nodules than in benign nodules, which can clearly distinguish benign and malignant nodules.

[0087] 2.4 Screening of diagnostic markers and construction of diagnostic models for benign and malignant nodules

[0088] Finally, the 20 most significantly different markers (Top20, Figure 4 A) Model construction, see Table 3 for details. In order to test the diagnostic ability of the model under the Top20 markers in distinguishing malignant and benign pulmonary nodules, the model performance was evaluated in the training set and the test set. The model had a specificity of 1 (0.846-1) and a sensitivity of 1 (0.891-1) in the training set, and a specificity of 0.91 (0.587-0.998) and a sensitivity of 0.933 (0.794-1) in the test set ( Figure 4C and D). Its receiver operating characteristic curve-AUC (ROC-AUC) was 1.000 in the training set and reached 0.99 in the test set (Figures B and C), indicating that the model under the Top20 markers has a great advantage in distinguishing benign and malignant nodules, further demonstrating the good performance of the designed model.

[0089] Table 3 Top 20 markers for the diagnosis of benign and malignant pulmonary nodules

[0090]

[0091]

[0092]

[0093] 2.5 Enrichment analysis of differentially methylated genes’ functions and pathways

[0094] In order to explore the biological pathways that differentially methylated genes may affect, gene enrichment analysis was performed on the Top 100 markers. GO enrichment analysis showed that multiple cellular components ( Figure 5 A and B), molecular function ( Figure 5 C and D) and biological processes ( Figure 5 E and F) were significantly enriched. In the cell component category, adhesion plaques, cell-base junctions, cell fronts, etc. were enriched. In the molecular function category, DNA binding transcription factors, extracellular matrix structural components, and catalytic activity acting on glycoproteins were enriched. In the biological process category, embryonic epithelial morphogenesis, myotube differentiation, tubule formation, etc. were enriched. KEGG pathway analysis showed that ( Figure 6 ), multiple pathways may be involved: cancer-related pathways, signal transduction pathways, and infection pathways. These pathways may be involved in the occurrence of lung cancer.

[0095] The present invention uses a random forest algorithm to process the expression of differentially methylated genes in the construction of a prediction model, and selects Top20 genes as candidate markers for the diagnosis of benign and malignant pulmonary nodules. The results show excellent diagnostic performance.

[0096] In this study, the methylation data of benign and malignant pulmonary nodules were systematically analyzed, and malignant tumors were mainly concentrated in early lung cancer. In the screening of differentially methylated sites, the sensitivity and specificity of each gene were analyzed to select the optimal marker. The sensitivity of a single gene may not be significant, but by combining several genes with the highest sensitivity under the premise of high specificity, the diagnostic model for benign and malignant pulmonary nodules has extremely high sensitivity and specificity, which greatly improves the predictive ability of the model. Among the 885 differentially methylated sites that met the screening criteria, the Top20 genes were selected as candidate genes. The sensitivity and specificity of the diagnostic model for benign and malignant pulmonary nodules constructed by the Top20 markers were 100% and 91%, respectively. This method can be used as a supplement to LDCT screening and is of great value for early screening of lung cancer patients.

[0097] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A methylation biomarker for distinguishing benign and malignant pulmonary nodules, characterized in that: The methylation biomarker is selected from one or more of the following gene fragments: chr5:93905357-93905359、chr5:134259480-134259482、 chr5:134259489-134259491、chr5:93905256-93905258、 chr11:67247741-67247743、chr1:243645964-243645966、 chr5:134259522-134259524、chr15:45928449-45928451、 chr5:154026439-154026441, chr5:154026479-154026481, chr8:42398664-42398666, chr1:47899804-47899806, chr2:85362588-85362590、chr2:130875117-130875119、 chr11:71189489-71189491、chr11:76887102-76887104、 chr5:134259500-134259502, chr5:134259527-134259529, chr11:18808624-18808626 and chr15:25093827-25093829.

2. The methylation biomarker according to claim 1, characterized in that The methylation biomarker is a group consisting of all gene fragments in claim 1.

3. Use of a reagent for detecting the methylation level of the methylation biomarker according to claim 1 or 2 in the preparation of a product for distinguishing benign and malignant pulmonary nodules.

4. The use according to claim 3, characterized in that: Reagents for detecting the methylation level of a methylation biomarker include reagents required for bisulfite conversion and DNA methylation sequencing.

5. The use according to claim 3, characterized in that: The product for distinguishing benign and malignant pulmonary nodules is a detection kit.

6. A system for distinguishing benign and malignant pulmonary nodules, characterized in that: The system comprises: i) an analysis module, the analysis module comprising: a detection reagent for determining a methylation biomarker selected from the group consisting of the methylation biomarkers described in claim 1 or 2 in a test sample of a subject; ii) an evaluation module, the evaluation module comprising: distinguishing and judging the benign or malignant nature of the sample to be tested according to the methylation expression level of the methylation biomarker determined in i).

7. The system according to claim 6, characterized in that In the analysis module of i), the detection reagent package includes reagents required for bisulfite conversion and DNA methylation sequencing.

8. The system according to claim 6, characterized in that In the analysis module of i), the sample to be tested is a lung nodule tissue sample.

9. The system according to claim 6, characterized in that The specific evaluation process of the evaluation module of ii) includes: according to the methylation level of the methylation biomarker determined in i), distinguishing and judging the benign or malignant nature of the sample to be tested based on a prediction model; the prediction model is a random forest model.

10. The system according to claim 9, characterized in that The random forest model is a random forest classification model, and its malignancy prediction probability threshold is 0.7.

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