A urinary metabolic biomarker composition for early lung cancer diagnosis and its application

Abstract

This invention discloses a urinary metabolic biomarker composition for early lung cancer diagnosis and its application. The urinary metabolic biomarker composition for early lung cancer diagnosis includes ethanolamine, catechol, isomaltose, and nicotinamide. The urinary metabolic biomarker composition provided by this invention offers rapid detection, high specificity, high sensitivity, and high predictive accuracy for early lung cancer diagnosis. It is non-invasive and non-surgical, facilitating earlier intervention and targeted, individualized precision treatment for patients, thereby improving patient survival rates. Furthermore, the urinary metabolic biomarker composition uses urine samples for early lung cancer diagnosis, which has advantages such as easy collection, low processing costs, and long-term cryopreservation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of disease diagnosis, and particularly relates to a urine metabolic marker composition for early lung cancer diagnosis. BACKGROUND

[0002] Lung cancer is one of the most common malignant tumors and is also the main cause of cancer death. However, there is currently a lack of effective means for early non-invasive diagnosis of lung cancer, and in general, lung cancer is often diagnosed at the late stage when the tumor has metastasized to other parts. Because the treatment strategies for lung cancer at different stages of development are significantly different, early detection and accurate diagnosis of lung cancer development are crucial for taking more effective treatment strategies.

[0003] The existing lung cancer screening and diagnosis techniques mainly include low-dose computed tomography (LDCT), liquid biopsy, sputum cytology and tumor marker detection. LDCT is a screening technique commonly used for high-risk groups of lung cancer, and although it has been proven to reduce lung cancer mortality by 20%-39% in high-risk patients compared to chest X-ray or non-invasive detection. However, due to the high cost of LDCT, there are many false positive results (about one-fifth of LDCT screening results), which have significant drawbacks in clinical practice, may require further diagnosis and treatment, and even more invasive detection, which brings unnecessary burden and discomfort to patients. In addition, the radiation risk associated with LDCT screening has not been fully addressed. Liquid biopsy and sputum cytology detection have strong invasiveness, which may bring certain surgical risks and complexity, cause postoperative discomfort and a long recovery period, etc. Moreover, sputum cytology detection cannot cover all areas of the lungs, especially for early lesions, which may be limited. Tumor markers have certain limitations in specificity, which may be interfered by other factors, leading to misdiagnosis or missed diagnosis, which makes early detection of lung cancer face serious challenges.

[0004] Therefore, in order to provide more treatment opportunities for lung cancer patients, improve treatment effectiveness, and improve patient survival rate, it is urgent to develop more accurate, convenient and non-invasive means for early diagnosis of lung cancer. SUMMARY

[0005] In view of the deficiencies of the prior art described above, the purpose of the present application is to provide a urine metabolic marker composition for early lung cancer diagnosis and its application, aiming to provide more accurate, convenient and non-invasive means for early diagnosis of lung cancer, so as to provide more treatment opportunities for lung cancer patients, improve treatment effectiveness, and improve patient survival rate.

[0006] The technical solution of the present application is as follows:

[0007] In a first aspect of the present application, a urine metabolic marker composition for early lung cancer diagnosis is provided, wherein the urine metabolic marker composition for early lung cancer diagnosis comprises ethanolamine, catechol, isomaltose and nicotinamide.

[0008] Optionally, the urine metabolic marker composition for early lung cancer diagnosis further comprises at least one of hypoxanthine, uracil, N-methylproline, dihydroxyacetone.

[0009] Optionally, the urine metabolic marker composition for early lung cancer diagnosis further comprises melibiose.

[0010] Optionally, the urine metabolic marker composition for early lung cancer diagnosis further comprises 3-methyl adipic acid.

[0011] Optionally, the urine metabolic marker composition for early lung cancer diagnosis further comprises hippuric acid.

[0012] Optionally, the urine metabolic marker composition for early lung cancer diagnosis further comprises 4-methyl catechol.

[0013] Optionally, the urine metabolic marker composition for early lung cancer diagnosis is composed of hypoxanthine, ethanolamine, uracil, catechol, N-methylproline, dihydroxyacetone, isomaltose, melibiose, 3-methyl adipic acid, hippuric acid, 4-methyl catechol and nicotinamide.

[0014] In a second aspect of the present application, use of the urine metabolic marker composition for early lung cancer diagnosis of the present application in the preparation of a product for diagnosing early lung cancer is provided.

[0015] Optionally, the product is a reagent or a kit.

[0016] Optionally, the sample used in the product for diagnosing early lung cancer is urine.

[0017] Beneficial effects: The urine metabolic marker composition provided by the present application has rapid detection, high specificity, high sensitivity, high prediction accuracy when used for the diagnosis of early lung cancer, and is non-invasive and non-invasive, which helps to provide more early intervention treatment and targeted individualized precision treatment for patients, and improves the survival rate of patients. In addition, the sample used in the urine metabolic marker composition for the diagnosis of early lung cancer is a urine sample, which has the advantages of easy collection, low processing cost, long-term frozen storage, etc. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The ROC evaluation results of the lung cancer early diagnosis of the modeling group urine sample in Example 3 of the present application.

[0019] Figure 2 The ROC evaluation result of the early diagnosis of lung cancer for the urine sample of the verification group in Example 3 of the present application.

[0020] Figure 3 The ROC evaluation result of the early diagnosis of lung cancer for the urine sample of the verification group in Example 4 of the present application.

[0021] Figure 4 The ROC evaluation result of the early diagnosis of lung cancer for the urine sample of the verification group in Example 5 of the present application. DETAILED DESCRIPTION

[0022] The present application provides a urine metabolic marker composition for early lung cancer diagnosis and its application. In order to make the purpose, technical scheme and effect of the present application more clear and definite, the present application is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present application.

[0024] As a new research method, metabolomics comprehensively understands the metabolic state of individuals by analyzing metabolites in vivo, and becomes a powerful tool for disease diagnosis. Under the pathophysiological state, metabolomics directly reveals the changes in the nature and quantity of metabolites in cells. Biomarkers play a key role in the application of metabolomics and can measure the degree of disease. Metabolomics can not only be used for disease screening, diagnosis, characterization and monitoring, but also provide support for developing personalized treatment interventions, thereby improving the targeting of treatment. Urine is considered a non-invasive alternative matrix with potential significance. Urine has a number of advantages among different biological fluids, including large-scale homogenous group collection, non-invasive collection, low processing cost and long-term frozen storage. These characteristics make urine an ideal choice for studying metabolomics and finding metabolic markers, providing a basis for disease diagnosis and personalized treatment.

[0025] TNM staging of lung cancer is a commonly used cancer staging system for describing the tumor size, lymph node involvement and distant metastasis of lung cancer. There are 5 groups of staging, of which stage 0 and stage I are early stage, stage II is intermediate stage, and stage III and stage IV are advanced stage. The metabolic profile of lung cancer changes significantly from early stage to advanced stage. By studying the metabolite composition of early stage lung cancer, the biological characteristics of early stage lung cancer can be more comprehensively understood, and metabolic markers related to this stage are expected to be found. Through large-scale biological sample collection, systematic experiments and metabolic analysis of urine, the metabolic state of the individual is comprehensively understood, and metabolic markers related to early stage lung cancer are screened out, thereby providing a new direction for developing more accurate and non-invasive early diagnosis methods for lung cancer, while reducing the burden of invasive detection for patients, and providing important support for early diagnosis and treatment of lung cancer and other diseases, and helping to improve the level of early diagnosis. Specifically, the urine metabolic marker composition for early lung cancer diagnosis provided by the present application comprises ethanolamine, catechol (i.e. o-diphenol), isomaltose and nicotinamide.

[0026] The urine metabolic marker composition provided by the present application for the diagnosis of early lung cancer has rapid detection, high specificity, high sensitivity, high prediction accuracy, non-invasiveness and non-invasiveness, which helps to provide more early intervention treatment and targeted individualized precision treatment for patients, and improves the survival rate of patients. In addition, the urine sample used for the diagnosis of early lung cancer has the advantages of easy collection, low processing cost and long-term frozen storage.

[0027] In some embodiments, the urine metabolic marker composition for early lung cancer diagnosis further comprises at least one of hypoxanthine, uracil, N-methyl proline and dihydroxyacetone. That is, the urine metabolic marker composition for early lung cancer diagnosis comprises ethanolamine, catechol, isomaltose and nicotinamide, and at least one of hypoxanthine, uracil, N-methyl proline and dihydroxyacetone.

[0028] In some embodiments, the urine metabolic marker composition for early lung cancer diagnosis further comprises melibiose. That is, the urine metabolic marker composition for early lung cancer diagnosis comprises ethanolamine, catechol, isomaltose, nicotinamide, hypoxanthine, uracil, N-methyl proline, dihydroxyacetone and melibiose, or the urine metabolic marker composition for early lung cancer diagnosis is composed of ethanolamine, catechol, isomaltose, nicotinamide, hypoxanthine, uracil, N-methyl proline, dihydroxyacetone and melibiose.

[0029] In some embodiments, the urinary metabolic marker composition for the diagnosis of early stage lung cancer comprises ethanolamine, catechol, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone, melibiose, and 3-methyl adipic acid, or consists of ethanolamine, catechol, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone, melibiose, and 3-methyl adipic acid.

[0030] In some embodiments, the urinary metabolic marker composition for the diagnosis of early stage lung cancer further comprises hippurate. That is, the urinary metabolic marker composition for the diagnosis of early stage lung cancer comprises ethanolamine, catechol, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone, melibiose, 3-methyl adipic acid, and hippurate, or consists of ethanolamine, catechol, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone, melibiose, 3-methyl adipic acid, and hippurate.

[0031] In some embodiments, the urinary metabolic marker composition for the diagnosis of early stage lung cancer further comprises hippurate. That is, the urinary metabolic marker composition for the diagnosis of early stage lung cancer comprises ethanolamine, catechol, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone, melibiose, 3-methyl adipic acid, and hippurate, or consists of ethanolamine, catechol, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone, melibiose, 3-methyl adipic acid, and hippurate.

[0032] In some embodiments, the urinary metabolic marker composition for the diagnosis of early stage lung cancer consists of hypoxanthine, ethanolamine, uracil, catechol, N-methylproline, dihydroxyacetone, isomaltose, melibiose, 3-methyl adipic acid, hippurate, 4-methyl catechol, and nicotinamide.

[0033] The present application also provides use of the urinary metabolic marker composition for the diagnosis of early stage lung cancer as described above in the present application in the manufacture of a product for the diagnosis of early stage lung cancer. Specifically, the present application provides use of a reagent for detecting the urinary metabolic marker composition for the diagnosis of early stage lung cancer as described above in the present application in the manufacture of a product for the diagnosis of early stage lung cancer.

[0034] In some embodiments, the product is a reagent or a kit.

[0035] In some embodiments, the product employs a sample of urine when used for the diagnosis of early stage lung cancer.

[0036] The present application will be described in detail below with specific examples.

[0037] The samples selected in the following examples are early-stage lung cancer patients, i.e., the focus of the study is to identify possible metabolic markers in stage 0 and stage I lung cancer, with a focus on early-stage diagnosis, aiming to find biomarkers that can provide key information at the early stage of lung cancer.

[0038] Example 1

[0039] The histological classification of lung cancer adopts the classification standard of lung, pleura, thymus and heart tumors in the fourth edition of the World Health Organization (published in 2015). Histopathological examination is used as the gold standard, and the final diagnosis relies on the professional judgment of experienced clinicians. The exclusion criteria for lung cancer patients include: under the age of 18, pregnant, duplicate samples, not diagnosed, and unqualified quality control.

[0040] A total of 526 samples were collected from two medical centers, and all subjects obtained written informed consent before participating in the study. This includes 235 urine samples from healthy people (Healthy Controls, denoted as HC group) and 291 urine samples from early-stage lung cancer patients (Lung Cancer, stage 0 and stage I, denoted as LC group). All urine samples were collected in the morning on an empty stomach, and after centrifugation, they were stored in a refrigerator at -80°C. The 235 urine samples of the HC group were divided into modeling and validation groups, and the 291 urine samples of the LC group were also divided into modeling and validation groups, and the modeling group urine samples were different from the validation group urine samples. Specifically, the number of modeling group urine samples: 180 urine samples from healthy people, 221 urine samples from early-stage lung cancer patients; the number of validation group urine samples: 55 urine samples from healthy people, 70 urine samples from early-stage lung cancer patients (see Table 1 for details). First, use the modeling group samples to screen out significantly different metabolic markers, use the metabolic marker combination to construct an early-stage lung cancer diagnosis model, and finally use the validation group samples to evaluate the effectiveness of the constructed model.

[0041] Table 1, Group Information of Healthy People and Early-Stage Lung Cancer Patients

[0042] HC group LC group Total number Number of urine samples in modeling group 180 221 401 Number of urine samples in validation group 55 70 125 Total number 235 291 526

[0043] Example 2 Detection and identification of small molecule metabolites in urine samples

[0044] (1) Reagents

[0045] Mass spectrometry grade methanol, acetonitrile, water, acetic acid, and chromatography (HPLC) grade formic acid and methyl tert-butyl ether were purchased from Sigma-Aldrich, USA.

[0046] (2) Sample preparation

[0047] After the urine sample was thawed from the -80℃ refrigerator, 40 μL of the urine sample was taken in an extraction tube, 400 μL of pre-cooled methyl tert-butyl ether and methanol mixed extraction solution (volume ratio of methyl tert-butyl ether and methanol was 3:1) was added, and after vortex ultrasonic mixing, 360 μL of methanol and water mixed solution (volume ratio of methanol and water was 3:1) was added, and vortex centrifugation was performed to separate the layers; 300 μL of the lower aqueous phase solution was taken, 900 μL of pre-cooled methanol was added, and after protein precipitation, 1000 μL of supernatant was spin-dried, 200 μL of water was added for reconstitution, and after reconstitution (water phase polar substance) was used for LC-MS (liquid chromatography-mass spectrometry) on-machine detection.

[0048] (3) Small molecule metabolite detection

[0049] Waters ACQUTTY HSS T3 1.8 μm 2.1 mm x 100 mm column was used for small molecule separation; liquid chromatography and mass spectrometry used ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific);

[0050] The mobile phase parameters are as follows: the mobile phase A is a water solution containing 0.1% formic acid (mass content); the mobile phase B is an acetonitrile solution containing 0.1% formic acid (mass content). The separation elution gradient is as follows: 0-13 minutes is 1%-70% mobile phase B, 13-18 minutes is 99% mobile phase B.

[0051] The mass spectrometry parameters are as follows:

[0052] The mass spectrometry data were collected in Full MS and Full MS / dd-MS2 modes (each containing positive and negative modes), and the parameters used by QExactive were as follows: the resolution of Full MS mode was 70,000, the scanning range was 100-1500 m / z, AGC (automatic gain control) was 3E6 (i.e. 3 x 10 6 ), Maximum IT (maximum injection time) was 200 ms; in Full MS / dd-MS2 mode, the resolution of secondary mass spectrometry was 17,500, the quadrupole window was 1.5 m / z, AGC was 1E5 (i.e. 1 x 10 5 ), the maximum ion injection time was 50 ms, and the relative collision energy of HCD (high-energy collisional dissociation) was 30 eV.

[0053] (4) Metabolomics data processing

[0054] Firstly, the detection peaks were extracted from all mass spectra, and then baseline correction was applied to remove noise and retain original signal peaks. The original data were converted into central discrete data. Then, the peaks in individual samples were matched with the retention time in the chromatogram. After further removing isotope peaks from the data set, the final mass spectrum matrix data were obtained. In order to reduce the differences in metabolite concentrations between samples and make the data distribution more symmetrical, normalization processing was performed using a Normalization Autoencoder (NormAE).

[0055] (5) Identification of metabolites

[0056] Using public databases such as the Human Metabolome Database (HMDB; www.hmdb.ca), the Metlin database (Metlin database; https: / / metlin.scripps.edu), the Mass Spectrometry Database (http: / / www.massbank.jp / ), the Lipid Database (https: / / www.lipidmaps.org / ), and the primary chromatogram, secondary chromatogram, and mass spectrum of the standard under the same chromatographic column, the retention time within 0.1 min difference, and the mass-to-charge ratio less than 10 ppm, the metabolites were identified by matching with the database and the standard.

[0057] Example 3 Screening of early lung cancer metabolic markers and construction of diagnostic model

[0058] In this embodiment, the research subjects in Example 1 (the setting method of the modeling group and the validation group is the same as that in Example 1) and the detection analysis and identification method in Example 2 were used to analyze the obtained urine sample data (metabolomics data). Specifically, the metabolites that were significantly different between healthy people and early lung cancer patients were screened using the modeling group samples, and the potential metabolic markers for early lung cancer diagnosis were identified. Then, in order to evaluate the diagnostic effect of the screened metabolic markers in distinguishing healthy people from early lung cancer patients, multivariate ROC analysis was performed to construct an early lung cancer diagnosis model. Finally, the validation group samples were used to verify the effectiveness and accuracy of the diagnosis model for distinguishing healthy people from early lung cancer patients.

[0059] 1) Screening of early lung cancer diagnostic metabolic markers

[0060] A total of 360 small molecule metabolites were successfully identified by matching with databases and standards. To screen important metabolites that can effectively distinguish healthy people from early lung cancer patients from the 360 small molecule metabolites, supervised OPLS-DA was first performed in the HC and LC groups, and then the differential metabolites were screened based on the conditions of OPLS-DA VIP>1 and T test P<0.05. Through screening, it was found that 100 metabolites in the urine of early lung cancer patients changed significantly compared with healthy people (OPLS-DA VIP>1&P<0.05), of which 15 metabolites were significantly up-regulated and 85 metabolites were significantly down-regulated. Then the 100 metabolites were subjected to univariate ROC (Receiver Operating Characteristic) curve analysis, and finally 12 metabolite markers with AUC values greater than 0.65 were obtained (see Table 2). Among them, the VIP (Variable important in the projection) value refers to the variable importance value.

[0061] The ROC curve is used to evaluate the relationship between the sensitivity and specificity of the model, with sensitivity as the vertical coordinate and 1-specificity as the horizontal coordinate. The area under the ROC curve (AUC) reflects the classification performance of the model. The AUC value ranges from 0 to 1, and a value close to 1 indicates excellent classification performance and can effectively distinguish between positive and negative samples. A value close to or less than 0.5 indicates poor performance, similar to random guessing.

[0062] Table 2, 12 differential metabolites of healthy people and early lung cancer patients

[0063] Serial number Metabolite (Chinese name) Metabolite (English name) HMDB ID AUC 1 Hypoxanthine Hypoxanthine HMDB0000157 0.78051 2 Ethanolamine Ethanolamine HMDB0000149 0.6968 3 Uracil Uracil HMDB0000300 0.6958 4 Pyrocatechol Pyrocatechol HMDB0000957 0.69202 5 N-methylproline N-methylproline HMDB0242151 0.68404 6 Dihydroxyacetone Dihydroxyacetone HMDB0001882 0.68366 7 Isomaltose Isomaltose HMDB0002923 0.68271 8 Melibiose Melibiose HMDB0000048 0.67788 9 3-Methyladipic acid 3-Methyladipic acid HMDB0000555 0.67294 10 Hippuric acid Hippuric acid HMDB0000714 0.66629 11 4-Methylcatechol 4-Methylcatechol HMDB0000873 0.6538 12 Niacinamide Niacinamide HMDB0001406 0.65373

[0064] 2) Lung cancer early diagnosis model construction and effectiveness verification

[0065] A multivariate ROC analysis was used to construct the model, and 3 / 4 of the urine samples in the modeling group were randomly selected as the training set and the remaining 1 / 4 as the test set. The training set was used to construct and train the model, and then the test set was used to evaluate the discrimination ability of the trained model. In model construction, the SVM (Support Vector Machine) algorithm was used, and through 2000 iterations of random loop training, the statistical average accuracy method was used to construct a lung cancer early diagnosis model based on 12 significant differential metabolite markers. This process helps to ensure that the model has robust and reliable performance. The results show that the constructed lung cancer early diagnosis model has excellent accuracy and discrimination ability, with an AUC of 0.956 (as shown in Figure 1 the figure), a sensitivity of 90.7%, and a specificity of 84.1%, which has practical clinical diagnostic value and can provide reliable support for accurate diagnosis of early lung cancer, and is expected to become an important reference for clinical diagnosis.

[0066] To evaluate the effectiveness of the constructed diagnostic model, the urine samples of the validation group were taken as unknown samples and put into the lung cancer early diagnosis model constructed using 12 urine metabolic markers for verification. The verification result was: AUC was 0.952, sensitivity was 89.0%, and specificity was 87.9% (as shown in Figure 2 ).

[0067] Example 4: Construction of a lung cancer early diagnosis model using 8 metabolic markers

[0068] The research object of this example and the setting method of the modeling group and the validation group in Example 1 are the same as in Example 1, and the detection and analysis method is the same as in Example 2. The difference between the use of the SVM method to construct a lung cancer early diagnosis model using the urine sample data of the modeling group in this example and Example 3 is that 8 metabolic markers, namely hypoxanthine, ethanolamine, uracil, catechol, N-methyl proline, dihydroxyacetone, isomaltose, and nicotinamide, are combined to construct a diagnostic model.

[0069] The results show that the lung cancer early diagnosis result of the diagnostic model constructed in this example is AUC = 0.946, sensitivity = 89.1%, and specificity = 81.8%, indicating that the combination of urine metabolic markers has practical clinical diagnostic value in lung cancer early diagnosis and can provide reliable support for accurate diagnosis of lung cancer in the early stage, and is expected to become an important reference for clinical diagnosis.

[0070] To evaluate the effectiveness of the constructed diagnostic model, the urine samples of the validation group were taken as unknown samples and put into the lung cancer early diagnosis model constructed using 8 urine metabolic markers for verification. The verification result was: AUC = 0.941, sensitivity = 87.7%, and specificity = 82.8% (as shown in Figure 3 ).

[0071] Example 5: Construction of a lung cancer early diagnosis model using 4 metabolic markers

[0072] The research object of this example and the setting method of the modeling group and the validation group in Example 1 are the same as in Example 1, and the detection and analysis method is the same as in Example 2. The difference between the use of the SVM method to construct a lung cancer early diagnosis model using the urine sample data of the modeling group in this example and Example 3 is that 4 metabolic markers, namely ethanolamine, catechol, isomaltose, and nicotinamide, are combined to construct a diagnostic model.

[0073] The results show that the diagnostic model constructed in this embodiment has an AUC of 0.943, a sensitivity of 87.3%, and a specificity of 90.9% in early diagnosis of lung cancer, indicating that the urine metabolite marker composition has practical clinical diagnostic value in early diagnosis of lung cancer and can provide reliable support for accurate diagnosis of early lung cancer and is expected to become an important reference for clinical diagnosis.

[0074] To evaluate the effectiveness of the constructed diagnostic model, the urine samples in the validation set were used as unknown samples and put into the lung cancer early diagnosis model constructed using the four urine metabolite markers for verification. The verification results were as follows: AUC = 0.916, sensitivity = 82.2%, and specificity = 82.8% Figure 4 ).

[0075] Sensitivity refers to the result that in all cases of true disease, the test can correctly identify positive results. Specificity refers to the result that in all non-disease cases of true non-disease, the test can correctly identify negative results. In the independent validation data set, the lung cancer early diagnosis model has good sensitivity and specificity, and can accurately identify diseased and non-diseased individuals. It is shown that the urine metabolite composition exhibits high prediction accuracy and discrimination ability in early diagnosis of lung cancer and has potential clinical application value. These results show that the diagnostic model provided by the present application can be a more reliable choice in actual clinical application.

[0076] It should be understood that the application of the present application is not limited to the above examples, and those skilled in the art can make improvements or changes according to the above description, and all these improvements and changes shall belong to the protection scope of the appended claims of the present application.

Claims

1. A urinary metabolic marker composition for early lung cancer diagnosis, characterized by, The urine metabolic marker composition for early lung cancer diagnosis comprises ethanolamine, pyrocatechol, isomaltose and nicotinamide.

2. The urinary metabolite marker composition for early lung cancer diagnosis according to claim 1, characterized by, The urine metabolic marker composition for early lung cancer diagnosis further comprises at least one of hypoxanthine, uracil, N-methylproline, dihydroxyacetone.

3. The urinary metabolite marker composition for early lung cancer diagnosis according to claim 1 or 2, characterized by, The urine metabolic marker composition for early lung cancer diagnosis further comprises melibiose.

4. The urinary metabolic marker composition for early lung cancer diagnosis according to claim 3, characterized by, The urine metabolic marker composition for early lung cancer diagnosis further comprises 3-methyl adipic acid.

5. The urinary metabolic marker composition for early lung cancer diagnosis according to claim 4, characterized by, The urine metabolic marker composition for early lung cancer diagnosis further comprises hippuric acid.

6. The urinary metabolite marker composition for early lung cancer diagnosis according to claim 5, characterized by, The urine metabolic marker composition for early lung cancer diagnosis further comprises 4-methyl catechol.

7. The urinary metabolite marker composition for early lung cancer diagnosis according to claim 6, characterized by, The urine metabolic marker composition for early lung cancer diagnosis consists of hypoxanthine, ethanolamine, uracil, pyrocatechol, N-methylproline, dihydroxyacetone, isomaltose, melibiose, 3-methyl adipic acid, hippuric acid, 4-methyl catechol and nicotinamide.

8. Use of the urine metabolic marker composition for early lung cancer diagnosis according to any one of claims 1-7 in the manufacture of a product for diagnosing early lung cancer.

9. Use according to claim 8, characterized in that, The product is a reagent or a kit.

10. Use according to claim 8, characterized in that, The product employs urine as the sample for diagnosing early lung cancer.

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