Urine metabolism marker composition for early lung cancer diagnosis and application thereof

Abstract

The invention discloses a urine metabolism marker composition for early lung cancer diagnosis and application of the urine metabolism marker composition. The urine metabolism marker composition for early lung cancer diagnosis comprises ethanolamine, catechinic acid, isomaltose and nicotinamide. The urine metabolism marker composition provided by the invention is rapid in detection, high in specificity, high in sensitivity and high in prediction accuracy when being used for diagnosis of early lung cancer, has non-invasiveness and noninvasiveness, is beneficial to providing early intervention treatment and targeted individualized precise treatment for patients, and improves the survival rate of the patients. In addition, when the urine metabolism marker composition is used for diagnosing the early lung cancer, an adopted sample is a urine sample, and the urine metabolism marker composition has the advantages of being easy to collect, low in treatment cost, capable of being frozen and stored for a long time and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of disease diagnosis, and in particular to a urine metabolic marker composition for diagnosing early lung cancer. Background Art

[0002] Lung cancer is one of the most common malignant tumors and the leading cause of cancer death. However, there is currently a lack of effective non-invasive means for early diagnosis of lung cancer. Usually, lung cancer can only be diagnosed in the late stage when the tumor has already metastasized to other parts of the body. Because the treatment strategies used for lung cancer at different stages of development vary significantly, it is crucial to detect and accurately diagnose lung cancer in its early stages in order to adopt more effective treatment strategies.

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

[0004] Therefore, in order to provide more treatment opportunities for lung cancer patients, improve treatment outcomes, and increase patient survival rates, it is urgently necessary to develop more accurate, convenient, and non-invasive methods for early diagnosis of lung cancer. Summary of the invention

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

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

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

[0008] Optionally, the urine metabolic marker composition for early lung cancer diagnosis further includes at least one of hypoxanthine, uracil, N-methylproline, and 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 also includes 3-methyladipic acid.

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

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

[0013] Optionally, the urine metabolic marker composition for early lung cancer diagnosis consists of hypoxanthine, ethanolamine, uracil, catechin, N-methylproline, dihydroxyacetone, isomaltose, melibiose, 3-methyladipic acid, hippuric acid, 4-methylcatechol and nicotinamide.

[0014] The second aspect of the present invention provides a use of the urine metabolite marker composition for early lung cancer diagnosis as described above in the preparation of a product for diagnosing early lung cancer.

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

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

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

[0018] Figure 1 This is the ROC evaluation result of early diagnosis of lung cancer in urine samples of the modeling group in Example 3 of the present invention.

[0019] Figure 2 This is the ROC evaluation result of early diagnosis of lung cancer in the urine samples of the validation group in Example 3 of the present invention.

[0020] Figure 3 This is the ROC evaluation result of early diagnosis of lung cancer in the urine samples of the validation group in Example 4 of the present invention.

[0021] Figure 4 This is the ROC evaluation result of early diagnosis of lung cancer in the urine samples of the validation group in Example 5 of the present invention. DETAILED DESCRIPTION

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

[0023] Unless otherwise defined, all technical terms and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0024] As an emerging research method, metabolomics has become a powerful tool for disease diagnosis by analyzing the full spectrum of metabolites in organisms and comprehensively understanding the metabolic status of individuals. Under pathological and physiological conditions, metabolomics intuitively 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 extent of the disease. By finding and verifying metabolite markers, metabolomics can not only be used for disease screening, diagnosis, qualitative analysis and monitoring, but also provide support for the development of personalized therapeutic interventions, thereby improving the targeted treatment. Urine is considered to be a non-invasive alternative matrix with potential significance. Urine has many advantages among different biological fluids, including large-scale collection of similar groups, non-invasive collection, low processing costs and long-term frozen storage. These characteristics make urine an ideal choice for studying metabolomics and finding metabolic markers, providing a basis for personalized disease diagnosis and treatment.

[0025] The TNM staging of lung cancer is a commonly used cancer staging system used to describe the tumor size, lymph node involvement, and whether there is distant metastasis of lung cancer. There are 5 groups of stages, of which stage 0 and stage I are early, stage II is mid-stage, and stage III and stage IV are late. The metabolic profile of lung cancer changes significantly from early to late stages. By studying the metabolite composition of early lung cancer, the biological characteristics of early lung cancer can be more comprehensively understood, and metabolic markers related to this stage are expected to be discovered. The embodiment of the present invention comprehensively understands the metabolic state of individuals through large-scale biological sample collection, systematic experiments, and metabolomics analysis of urine, thereby screening out metabolic markers related to early lung cancer, thereby providing a new direction for the development of more accurate and non-invasive early diagnosis methods for lung cancer, while reducing the burden of invasive testing for patients, and is expected to provide important support for the early diagnosis and treatment of diseases such as lung cancer, and help improve the level of early diagnosis. Specifically, the embodiment of the present invention provides a urine metabolic marker composition for the diagnosis of early lung cancer, wherein the urine metabolic marker composition for the diagnosis of early lung cancer includes ethanolamine, catechin (also known as catechol), isomaltose, and nicotinamide.

[0026] The urine metabolite marker composition provided in the embodiment of the present invention is used for the diagnosis of early lung cancer, and has rapid detection, high specificity, high sensitivity, high prediction accuracy, and is non-invasive and non-invasive, which helps to provide patients with earlier intervention treatment and targeted individualized precision treatment, thereby improving the survival rate of patients. In addition, the sample used for the diagnosis of early lung cancer by the urine metabolite marker composition is a urine sample, which has the advantages of easy collection, low processing cost, and long-term frozen storage.

[0027] In some embodiments, the urine metabolite marker composition for early lung cancer diagnosis also includes at least one of hypoxanthine, uracil, N-methylproline, and dihydroxyacetone. That is, the urine metabolite marker composition for early lung cancer diagnosis includes ethanolamine, catechin, isomaltose and nicotinamide and at least one of hypoxanthine, uracil, N-methylproline, and dihydroxyacetone.

[0028] In some embodiments, the urine metabolite marker composition for early lung cancer diagnosis also includes melibiose. That is, the urine metabolite marker composition for early lung cancer diagnosis includes ethanolamine, catechin, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone and melibiose, or the urine metabolite marker composition for early lung cancer diagnosis consists of ethanolamine, catechin, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone and melibiose.

[0029] In some embodiments, the urine metabolite marker composition for the diagnosis of early lung cancer includes ethanolamine, catechin, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone, melibiose and 3-methyladipic acid, or the urine metabolite marker composition for the diagnosis of early lung cancer consists of ethanolamine, catechin, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone, melibiose and 3-methyladipic acid.

[0030] In some embodiments, the urine metabolite marker composition for early lung cancer diagnosis also includes hippuric acid. That is, the urine metabolite marker composition for early lung cancer diagnosis includes ethanolamine, catechin, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone, melibiose, 3-methyladipic acid and hippuric acid, or the urine metabolite marker composition for early lung cancer diagnosis consists of ethanolamine, catechin, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone, melibiose, 3-methyladipic acid and hippuric acid.

[0031] In some embodiments, the urine metabolite marker composition for early lung cancer diagnosis also includes hippuric acid. That is, the urine metabolite marker composition for early lung cancer diagnosis includes ethanolamine, catechin, isomaltose, nicotinamide, hypoxanthine, uracil, N-methylproline, dihydroxyacetone, melibiose, 3-methyladipic acid, hippuric acid and 4-methylcatechol.

[0032] In some embodiments, the urine metabolic marker composition for the diagnosis of early lung cancer consists of hypoxanthine, ethanolamine, uracil, catechin, N-methylproline, dihydroxyacetone, isomaltose, melibiose, 3-methyladipic acid, hippuric acid, 4-methylcatechol and nicotinamide.

[0033] The embodiment of the present invention also provides an application of the urine metabolite marker composition for early lung cancer diagnosis as described above in the embodiment of the present invention in the preparation of a product for diagnosing early lung cancer. Specifically, the embodiment of the present invention provides an application of a reagent for detecting the urine metabolite marker composition for early lung cancer diagnosis as described above in the embodiment of the present invention in the preparation of a product for diagnosing early lung cancer.

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

[0035] In some embodiments, the sample used by the product for diagnosing early-stage lung cancer is urine.

[0036] The following describes it in detail through specific embodiments.

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

[0038] Example 1

[0039] The histological classification of lung cancer adopts the fourth edition of the World Health Organization's classification of lung, pleural, thymus and heart tumors (published in 2015). Histopathological examination is used as the gold standard, and the diagnosis ultimately relies on the professional judgment of senior clinicians. The exclusion criteria for lung cancer patients include: age under 18 years, pregnancy, duplicate samples, undiagnosed, and unqualified quality inspection.

[0040] A total of 526 samples were collected from two medical centers, and all subjects obtained written informed consent before participating in the study. These included urine samples from 235 healthy people (Healthy Controls, recorded as the HC group) and urine samples from 291 patients with early lung cancer (Lung Cancer, stage 0 and stage I, recorded as the 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 in the HC group were divided into a modeling group and a validation group, and the 291 urine samples in the LC group were also divided into a modeling group and a validation group, and the urine samples in the modeling group were different from those in the validation group. Specifically, the number of urine samples in the modeling group: 180 urine samples from healthy people, 221 urine samples from patients with early lung cancer; the number of urine samples in the validation group: 55 urine samples from healthy people, 70 urine samples from patients with early lung cancer (see Table 1 for details). First, the modeling group samples were used to screen out significantly different metabolic markers, and a combination of metabolic markers was used to construct an early diagnosis model for lung cancer. Finally, the validation group samples were used to evaluate the effectiveness of the constructed model.

[0041] Table 1. Group information of healthy people and patients with early lung cancer

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

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

[0044] (1) Reagents

[0045] Methanol, acetonitrile, water, acetic acid of mass spectrometry grade purity, formic acid and methyl tert-butyl ether of HPLC grade purity were purchased from Sigma-Aldrich, USA.

[0046] (2) Sample preparation

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

[0048] (3) Detection of small molecule metabolites

[0049] Using Waters ACQUTTY HSS T3 1.8μm2.1mm×100mm 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: mobile phase A is an aqueous solution containing 0.1% formic acid (mass content); 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 including positive and negative modes). The parameters used by QExactive were as follows: Full MS mode resolution was 70,000, the scan range was 100-1500 m / z, and the AGC (automatic gain control) was 3E6 (i.e., 3×10 6 ), Maximum IT (maximum injection time) is 200 ms; in Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer is 17,500, the quadrupole window is 1.5 m / z, and the AGC is 1E5 (i.e. 1×10 5 ), the maximum ion injection time is 50ms, and the HCD (high energy collision dissociation) relative collision energy is 30eV.

[0053] (4) Metabolomics data processing

[0054] First, the detection peaks were extracted from all mass spectra, and then baseline correction was applied to remove noise, retaining the original signal peaks, and the raw data were converted into central discrete data. Then, the peaks in a single sample were compared with the retention times in the chromatograms for matching, and the data set was further removed from the isotope peaks to obtain the final mass spectrum matrix data. In order to reduce the differences in metabolite concentrations between samples and make the data distribution more symmetrical, the Normalization Autoencoder (NormAE) was used for homogenization.

[0055] (5) Identification of metabolites

[0056] Public databases such as the Human Metabolite Database (HMDB; www.hmdb.ca), the Metabolomics Database (Metlin Database; https: / / metlin.scripps.edu), the Mass Spectrum Database (http: / / www.massbank.jp / ), and the Lipid Database (https: / / www.lipidmaps.org / ), as well as the primary chromatogram, secondary chromatogram, and mass spectrometry spectra of the standards separated on the same chromatographic column were used; the retention times were within 0.1 min and the mass-to-charge ratio was less than 10 ppm, and the identification was carried out by matching with the database and the standards.

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

[0058] This embodiment adopts the research object in Example 1 (the setting method of the modeling group and the verification group is the same as that of Example 1), the detection analysis and identification method in Example 2, and analyzes the obtained urine sample data (metabolomics data). Specifically, the modeling group samples are first used to screen out metabolites with significant differences between healthy people and early lung cancer patients, and identify potential metabolic markers for early lung cancer diagnosis. Then, in order to evaluate the screened metabolite markers, a multivariate ROC analysis was performed to construct an early lung cancer diagnosis model in distinguishing healthy people from early lung cancer patients. Finally, the validation group samples were used to verify the effectiveness and accuracy of the diagnostic model for distinguishing healthy people from early lung cancer patients.

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

[0060] A total of 360 small molecule metabolites were successfully identified by matching with the database and standard samples. In order to screen out important metabolites that can effectively distinguish healthy people from early lung cancer patients from these 360 ​​small molecule metabolites, supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was first performed in the HC group and the LC group, and then 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 compared with healthy people, 100 metabolites in the urine of early lung cancer patients changed significantly (OPLS-DA VIP>1&P<0.05), of which 15 metabolites were significantly upregulated and 85 metabolites were significantly downregulated. Then, the univariate ROC (receiver operating characteristic) curve analysis method was used for the 100 metabolites, and finally 12 metabolite markers with AUC values ​​greater than 0.65 were obtained (see Table 2). The VIP (Variable importance 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 ordinate and 1-specificity as the abscissa. The area under the ROC curve (AUC) reflects the classification performance of the model. The AUC value ranges from 0 to 1. A value close to 1 indicates excellent classification performance, which can effectively distinguish 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 between healthy subjects and patients with early lung cancer

[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 Catechin 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) Construction of early diagnosis model for lung cancer and validation of its effectiveness

[0065] The model was constructed using multivariate ROC analysis. Three-quarters of the urine samples in the modeling group were randomly selected as the training set, and the remaining one-quarter was used as the test set. The training set was used to build and train the model, and then the test set was used to evaluate the discriminative ability of the trained model. In model construction, the support vector machine (SVM) algorithm was used. By performing 2000 random cycle iterations of SVM training, a model for early diagnosis of lung cancer based on 12 significantly different metabolic markers was constructed using the statistical average accuracy method. This process helps ensure that the model has robust and reliable performance. The results showed that the constructed model for early diagnosis of lung cancer has excellent accuracy and discrimination ability, with an AUC of 0.956 (e.g. Figure 1 The sensitivity is 90.7% and the specificity is 84.1%, which has practical clinical diagnostic value, can provide reliable support for the accurate diagnosis of early lung cancer, and is expected to become an important reference for clinical diagnosis.

[0066] In order to evaluate the effectiveness of the constructed diagnostic model, the urine samples of the validation group were used as unknown samples and put into the early diagnosis model of lung cancer constructed using the above 12 urine metabolic markers for validation. The validation results were: AUC was 0.952, sensitivity was 89.0%, and specificity was 87.9% (e.g. Figure 2 shown).

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

[0068] This embodiment has the same research objects as those in Example 1 and the same setting methods for the modeling group and the verification group in Example 1, and the same detection and analysis methods as those in Example 2. The difference from Example 3 in using the urine sample data of the modeling group to construct an early diagnosis model for lung cancer by the SVM method is that 8 metabolic markers, namely hypoxanthine, ethanolamine, uracil, catechin, N-methylproline, dihydroxyacetone, isomaltose, and nicotinamide, are used together to construct a diagnostic model.

[0069] The results showed that the diagnostic model constructed in this embodiment had an AUC of 0.946, a sensitivity of 89.1%, and a specificity of 81.8% for early diagnosis of lung cancer, indicating that the urine metabolic marker composition has practical clinical diagnostic value in the early diagnosis of lung cancer, can provide reliable support for the accurate diagnosis of early lung cancer, and is expected to become an important reference for clinical diagnosis.

[0070] In order to evaluate the effectiveness of the constructed diagnostic model, the urine samples of the validation group were used as unknown samples and put into the early diagnosis model of lung cancer constructed using the above-mentioned 8 urine metabolic markers for validation. The validation results were: AUC = 0.941, sensitivity = 87.7%, specificity = 82.8% ( Figure 3 ).

[0071] Example 5 Construction of an early diagnosis model for lung cancer using four metabolic markers

[0072] This embodiment has the same research objects as Example 1 and the same setting methods of the modeling group and the verification group in Example 1, and the same detection and analysis methods as Example 2. When the urine sample data of the modeling group is used to construct an early diagnosis model for lung cancer by the SVM method, the only difference from Example 3 is that four metabolic markers, namely ethanolamine, catechins, isomaltose, and nicotinamide, are used together to construct a diagnostic model.

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

[0074] In order to evaluate the effectiveness of the constructed diagnostic model, the urine samples of the validation group were used as unknown samples and put into the early diagnosis model of lung cancer constructed using the above four urine metabolic markers for validation. The validation results were: AUC = 0.916, sensitivity = 82.2%, specificity = 82.8% ( Figure 4 ).

[0075] Sensitivity refers to the ability of the test to correctly identify positive results in all cases that actually have the disease. Specificity refers to the ability of the test to correctly identify negative results in all non-cases that are actually not sick. In an independent validation data set, the early diagnosis model for lung cancer performed well in both sensitivity and specificity, with good sensitivity and specificity, and was able to accurately identify sick and non-sick individuals. This indicates that the urine metabolite composition exhibits high predictive accuracy and discrimination ability in the early diagnosis of lung cancer, and has potential clinical application value. These results indicate that the diagnostic model provided by the present invention may be a more reliable choice in actual clinical applications.

[0076] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A urine metabolic marker composition for early lung cancer diagnosis, characterized in that: The urine metabolic marker composition for early lung cancer diagnosis comprises ethanolamine, catechin, isomaltose and nicotinamide.

2. The urine metabolic marker composition for early lung cancer diagnosis according to claim 1, characterized in that: The urine metabolic marker composition for early lung cancer diagnosis also includes at least one of hypoxanthine, uracil, N-methylproline, and dihydroxyacetone.

3. The urine metabolic marker composition for early lung cancer diagnosis according to claim 1 or 2, characterized in that: The urine metabolic marker composition for early lung cancer diagnosis also includes melibiose.

4. The urine metabolic marker composition for early lung cancer diagnosis according to claim 3, characterized in that: The urine metabolic marker composition for early lung cancer diagnosis also includes 3-methyladipic acid.

5. The urine metabolic marker composition for early lung cancer diagnosis according to claim 4, characterized in that: The urine metabolic marker composition for early lung cancer diagnosis also includes hippuric acid.

6. The urine metabolic marker composition for early lung cancer diagnosis according to claim 5, characterized in that: The urine metabolic marker composition for early lung cancer diagnosis also includes 4-methylcatechol.

7. The urine metabolic marker composition for early lung cancer diagnosis according to claim 6, characterized in that: The urine metabolic marker composition for early lung cancer diagnosis consists of hypoxanthine, ethanolamine, uracil, catechin, N-methylproline, dihydroxyacetone, isomaltose, melibiose, 3-methyladipic acid, hippuric acid, 4-methylcatechol and nicotinamide.

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

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

10. The use according to claim 8, characterized in that: The sample used by the product for diagnosing early lung cancer is urine.

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