Metabolic marker for early lung cancer diagnosis and screening method and application thereof

Abstract

The invention relates to the technical field of disease diagnosis, in particular to a metabolic marker for early lung cancer diagnosis and a screening method and application thereof. The metabolic marker for early lung cancer diagnosis comprises O-acetyl-L-carnitine, D-galactobionic acid, fatty acid 20: 3 and arachidonic acid. The metabolic marker provided by the invention is used for distinguishing healthy people from early lung cancer patients (including early patients in the 0 stage and I stage of LC), can predict the early morbidity of lung cancer, has higher potential clinical application value, and is expected to play an important role in lung cancer screening, early diagnosis and lung cancer intervention.

Description

Technical Field

[0001] The present invention relates to the technical field of disease diagnosis, and in particular to metabolic markers for early lung cancer diagnosis and screening methods and applications thereof. Background Art

[0002] Lung cancer (LC) has been one of the most common malignant tumors worldwide, and has been one of the cancers with the highest morbidity and mortality worldwide for many years. Despite the continuous advancement of medical technology, the mortality rate of lung cancer patients remains high. This is mainly because early diagnosis of lung cancer and effective treatment methods are still relatively limited. Early diagnosis and treatment are crucial to improving the survival rate of patients. Studies have shown that the 10-year survival rate after surgery for stage I lung cancer can reach 92%. However, due to the lack of obvious symptoms of early lung cancer, many lung cancer patients have entered the middle and late stages when diagnosed, when the treatment effect is poor and the mortality rate increases accordingly. Therefore, the improvement of lung cancer screening and early diagnosis technology is crucial to improving the survival chances of lung cancer patients, and finding more accurate early diagnosis methods is also an urgent task at present. At present, the main technologies used in clinical screening and diagnosis of early lung cancer include imaging examinations, such as X-rays and computed tomography (CT scans), pathological examinations, and hematological examinations. However, traditional lung cancer screening methods, such as X-rays and CT scans, although they have certain diagnostic capabilities, have high false positive rates and potential radiation risks. In addition, surgery or puncture to obtain lung cancer tissue for pathological examination is currently considered the gold standard for lung cancer diagnosis, but these methods have a series of problems such as cumbersome operation, serious injury to patients, and heterogeneity of tissue samples. Therefore, there is an urgent need to develop more accurate, low-risk and non-invasive methods for early diagnosis and screening of lung cancer to improve the treatment effect and survival rate of lung cancer patients. Metabolomics is an emerging omics technology that can quantitatively describe the overall situation of all metabolites in organisms and study their dynamic changes under external intervention or disease physiological conditions. It has a wide range of applications in revealing disease mechanisms, drug development and personalized medicine. The core focus of cancer metabolomics research is biomarker discovery. Serological tumor markers have attracted much attention in the field of lung cancer diagnosis due to their non-invasive, easy collection, relatively low cost and potential high sensitivity.

[0003] Currently, commonly used serological tumor markers in clinical practice, such as five lung cancer items (CEA, CYFRA21-1, SCC, Pro-GRP, NSE), are used as auxiliary diagnostic methods for lung cancer, but these traditional serological tumor markers have low sensitivity for lung cancer detection and cannot meet the requirements of early lung cancer screening. Although recent studies on lung cancer diagnostic markers have found that xylose and acetyl-ornithine can be used to diagnose the risk of lung cancer, due to the small amount of sample data, low accuracy, and the main focus on the evaluation of lung cancer and healthy groups, the diagnostic advantage for early lung cancer is not significant, and it needs to be combined with other indicators for diagnosis. Therefore, it is urgent to discover new metabolic markers and devote ourselves to lung cancer screening to provide earlier treatment and better prognosis in the early stages, reduce the lethality of lung cancer, and improve patient survival. Therefore, the existing technology still needs to be improved and developed. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide metabolic markers for early lung cancer diagnosis and screening methods and applications thereof, aiming to solve the problem that existing tumor metabolic markers cannot meet the requirements for early lung cancer diagnosis.

[0005] The technical solution of the present invention is as follows:

[0006] In a first aspect of the present invention, a metabolic marker for early lung cancer diagnosis is provided, wherein the metabolic marker comprises: O-acetyl-L-carnitine, D-galactonic acid, fatty acid 20:3 and arachidonic acid.

[0007] Preferably, the metabolic markers further include: 2-amino-3-methyl-1-butanol, cortisol, fatty acid 20:2 and fatty acid 16:0.

[0008] More preferably, the metabolic markers further include: allantoic acid, geraniol, urocanic acid and fatty acid 18:3.

[0009] Optimally, the metabolic markers consist of O-acetyl-L-carnitine, D-galactonic acid, 2-amino-3-methyl-1-butanol, fatty acid 20:3, cortisol, allantoic acid, arachidonic acid, geraniol, fatty acid 20:2, fatty acid 16:0, urocanic acid, and fatty acid 18:3.

[0010] Different from the existing metabolic markers for distinguishing healthy people from lung cancer patients (including patients in middle and late stages such as stage II, stage III and stage IV of LC), the metabolic markers of the present invention are used to distinguish healthy people from patients with early lung cancer (including only stage 0 and early stage I of LC). The metabolic markers of the present invention can predict the early onset of lung cancer and have higher potential clinical application value. These metabolic markers are expected to play an important role in the early diagnosis and intervention of lung cancer.

[0011] That is, the present invention focuses on the diagnosis of early lung cancer. Compared with only distinguishing between healthy and lung cancer patients, the more specific diagnostic classification of the present invention is more conducive to early screening and more accurate diagnosis, early intervention and treatment, and improving the patient's chance of survival, which is more forward-looking.

[0012] The metabolite markers of the present invention include not only lipid metabolites, but also polar metabolites that play a key role in the body, which indicates that the metabolite markers of the present invention cover a wider range of metabolite types. This wide range of metabolite markers makes the present invention more extensive and accurate, and is expected to provide more comprehensive lung cancer diagnosis information.

[0013] The second aspect of the present invention provides a method for screening metabolic markers for early lung cancer diagnosis according to the present invention, which comprises the following steps:

[0014] Plasma samples were collected from healthy subjects and patients with early-stage lung cancer;

[0015] Pre-treating the plasma sample to obtain a test solution;

[0016] Using a chromatography-mass spectrometer to detect the test liquid to obtain detection data;

[0017] Processing the detection data and then identifying the metabolites;

[0018] Metabolic markers for early diagnosis of lung cancer were screened from the identified metabolites.

[0019] Preferably, the step of screening the identified metabolites to obtain metabolite markers for early lung cancer diagnosis specifically comprises:

[0020] The orthogonal partial least squares discriminant analysis (OPLS-DA) model was constructed;

[0021] The VIP value > 1 and P < 0.05 obtained based on the OPLS-DA model were used as the screening conditions for metabolic markers, and metabolic markers for the diagnosis of early lung cancer were screened.

[0022] The third aspect of the present invention provides a use of the metabolic marker for early lung cancer diagnosis according to the present invention in the preparation of an early lung cancer diagnostic reagent.

[0023] A fourth aspect of the present invention provides a use of the metabolic marker for early lung cancer diagnosis according to the present invention in the preparation of an early lung cancer diagnosis kit.

[0024] A fifth aspect of the present invention provides an early lung cancer diagnosis kit, which comprises the metabolic markers for early lung cancer diagnosis described in the present invention.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] (1) At present, the research on metabolic markers in the blood in the diagnosis of early lung cancer is relatively limited, and the research samples are small. The research samples of the present invention are from two central hospitals, including 195 healthy and 300 early lung cancer samples. The large number of samples increases the richness and reliability of the data, and the samples cover healthy people and early cancer (lung cancer not in the middle and late stages) patients. The rich sample types are conducive to screening out metabolic markers with potential advantages in the diagnosis of early lung cancer.

[0027] (2) The present invention studies the differences between healthy people and patients with early lung cancer, and the metabolite markers screened out can be used for the diagnosis of healthy and early lung cancer. These metabolite markers show potential for application in the diagnosis of early lung cancer, meeting the needs of lung cancer patients whose initial symptoms are not obvious. They have broad application prospects and are expected to have a positive impact on the early diagnosis and treatment of lung cancer.

[0028] It should be emphasized that in the screening process of existing biomarkers for lung cancer diagnosis, the samples collected from lung cancer patients include samples of middle and late stages such as LC stage II, stage III and stage IV, while in the screening process of the biomarkers of the present invention, the samples collected from lung cancer patients are only early samples such as LC stage 0 and stage I, and the metabolite changes of early lung cancer (LC stage 0 + stage I) and the metabolite changes of middle and late lung cancer (LC stage II + stage III + stage IV) are completely different. Therefore, the existing biomarkers are used to distinguish healthy people from patients with middle and late lung cancer, while the metabolite markers screened by the present invention are used to distinguish healthy people from patients with early lung cancer, that is, the metabolite markers of the present invention can predict the early onset of lung cancer, can predict the onset stage of early lung cancer with high specificity and high sensitivity, have higher potential clinical application value, and are expected to play an important role in early diagnosis and intervention of lung cancer.

[0029] Although there are existing studies on the diagnosis of early lung cancer, in this study, early lung cancer patients refer to patients with single lung cancer with a diameter of less than 3 cm confirmed by imaging examination and tissue biopsy. However, the clinical staging assessment of lung cancer is a comprehensive process, and it is usually impossible to accurately determine the stage of lung cancer by relying solely on single imaging and pathological results. And according to the data provided, the early lung cancer samples used in this study include lung cancer patients in stages I to IV in the TNM system staging, and these patients are classified as early lung cancer patients in this study. Different from the existing early lung cancer diagnosis research, the present invention uses a comprehensive evaluation of the TNM system staging, and samples from patients with stage 0 and stage I lung cancer are used as lung cancer samples to screen differential metabolites. These patients belong to the early stage of onset in the lung cancer staging, and the method of the present invention has clinical value for early screening. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is the ROC evaluation result of the test set of the early lung cancer diagnosis model based on 12 metabolic markers.

[0031] Figure 2 This is the ROC curve analysis verification result of the early lung cancer diagnosis model validation group data set based on 12 metabolic markers.

[0032] Figure 3 This is the ROC curve analysis verification result of the early lung cancer diagnosis model validation group data set based on 8 metabolic markers.

[0033] Figure 4 This is the ROC curve analysis verification result of the early lung cancer diagnosis model validation group data set based on 4 metabolic markers. DETAILED DESCRIPTION

[0034] The present invention provides metabolic markers for early lung cancer diagnosis and screening methods and applications thereof. To make the purpose, technical solutions and effects 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 intended to limit the present invention.

[0035] Example 1

[0036] 1. Subjects’ conditions and sample collection

[0037] All subjects obtained written informed consent before the study. A total of 300 plasma samples from patients with early lung cancer (LC0 / I) and 195 healthy controls (HC) from two central hospitals (Central Hospital 1 and Central Hospital 2) were collected. The data from Central Hospital 1 were used for the modeling group, including 108 HC samples and 260 LC0 / I samples (Table 1). The data from Central Hospital 2 were used for the validation group, including 87 HC samples and 40 LC0 / I samples (Table 1). The samples used in the modeling group and the validation group were different, and they were all actual samples collected. Plasma samples were collected in the early morning on an empty stomach. All collected plasma samples were stored in a -80 degree refrigerator.

[0038] Table 1 Grouping information of the modeling group dataset and the validation group dataset for healthy control population and early lung cancer patients

[0039] Healthy controls (HC) Early stage lung cancer (LC0 / I) Modeling Group 108 260 Validation Group 87 40 total 195 300

[0040] 2. Detection and identification of metabolites in plasma samples

[0041] 1) Reagents: Methanol, acetonitrile, water, acetic acid, methyl tert-butyl ether, isopropanol of mass spectrometry grade purity, formic acid and ammonium acetate of HPLC grade purity were purchased from Sigma-Aldrich, USA.

[0042] 2) Plasma pretreatment: After taking out the plasma from the -80 degree refrigerator and thawing it, take 100 μL of plasma and place it in 1000 μL of precooled solution (methyl tert-butyl ether: methanol, volume ratio 3:1), vortex mix the extracted plasma to obtain an extract; add 500 μL of solution (methanol: water, volume ratio 3:1) to the extract, sonicate, let stand, vortex and centrifuge to separate the layers, the upper layer is the organic phase, and the lower layer is the aqueous phase;

[0043] Organic phase: 500 μL of the upper organic phase was taken into a centrifuge tube. After the organic phase was dried, 200 μL of a solution (acetonitrile: isopropanol, volume ratio 3:1) was added and incubated at room temperature for 15 minutes. After incubation, the centrifuge tube was vortexed and ultrasonically treated for 5 minutes. The centrifuge tube was then centrifuged at room temperature for 5 minutes (12000 rpm). 180 μL of the supernatant was taken from the centrifuge tube and placed in a 2 mL glass injection vial as the organic phase test solution, which was tested by LC-MS.

[0044] Aqueous phase: 400 μL of the lower aqueous phase was taken into a centrifuge tube, and 1100 μL of ice methanol was added thereto to precipitate the protein; after the protein in the centrifuge tube was precipitated, the centrifuge tube was centrifuged, 1000 μL of the supernatant was transferred to a new centrifuge tube, and dried overnight; 200 μL of water was added to the dried centrifuge tube, and incubated at room temperature for 15 minutes; after incubation, the centrifuge tube was vortexed and ultrasonically treated for 5 minutes, and then the centrifuge tube was centrifuged at room temperature for 5 minutes (12000 rpm); 180 μL of the supernatant was taken from the centrifuge tube and placed in a 2 mL glass injection vial as the aqueous phase test liquid, which was tested by the machine (LC-MS).

[0045] 3) Test solution detection: The organic phase test solution was tested using Waters ACQUTTY BEH C8 1.7μm 2.1*100mm column, the aqueous phase was tested using Waters ACQUTTY HSS T3 1.8μm 2.1*100mmcolumn was used for small molecule separation; the liquid chromatography and mass spectrometry used ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific), respectively;

[0046] The mobile phase parameters are as follows:

[0047] Organic phase test solution: mobile phase A is an aqueous solution containing 0.1% acetic acid and 0.1% ammonium acetate; mobile phase B is an acetonitrile-isopropanol (7:3, v / v) solution containing 0.1% acetic acid and 1% ammonium acetate; the separation elution gradient is as follows: 0-12 minutes is 55%-89% mobile phase B, 12-19.5 minutes is 100% mobile phase B;

[0048] Aqueous phase test solution: mobile phase A is an aqueous solution containing 0.1% formic acid; mobile phase B is an acetonitrile solution containing 0.1% formic acid; the separation elution gradient is as follows: 0-13 minutes is 1%-70% mobile phase B, 13-18 minutes is 99% mobile phase B;

[0049] Wherein, the above % is volume percentage;

[0050] The mass spectrometry parameters are as follows:

[0051] 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 are as follows: the resolution of the Full MS mode is 70,000, the scanning range is 100-1500 m / z, the AGC is 3E+6, and the Maximum IT is 200 milliseconds. In the Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometry is 17,500, the quadrupole window is 1.5 m / z, the AGC is 1E+5, the maximum ion injection time is 50 ms, and the HCD relative collision energy is 30 eV.

[0052] 4) Metabolomics data processing: First, the detection peaks were extracted from all mass spectrometry data, and then baseline correction was applied to remove noise and retain the original signal peaks; the original data were converted into central discrete data; then, the peaks in a single sample were compared with the retention times in the chromatogram for matching; the data set was further removed from the isotope peaks to obtain the final mass spectrometry matrix data; in order to reduce the differences in metabolite concentrations between samples and make the data distribution more symmetrical, NormalizationAutoencoder (NormAE) was used for homogenization.

[0053] 5) Identification of metabolites: Public databases such as the Human Metabolite Database (HMDB; www.hmdb.ca), the Metabolomics Database (Metlin; https: / / metlin.scripps.edu), and the Mass Spectrum Database (http: / / www.massbank.jp), as well as primary and secondary chromatograms and mass spectrometry spectra of standards separated on the same chromatographic column were used; metabolites were identified by matching with the database and standards under the conditions that the retention time was within 0.1 min and the mass-to-charge ratio was less than 10 ppm.

[0054] 3. Metabolic marker screening, model construction and model validation for early lung cancer diagnosis

[0055] 1) Screening of metabolic markers for early diagnosis of lung cancer

[0056] A total of 403 metabolites were successfully identified by matching with the database and standards. In order to screen out important metabolites that can effectively distinguish the healthy control group from the early lung cancer group from the 403 metabolites, supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was performed on the healthy control group and the early lung cancer group, and then based on the OPLS-DA values ​​VIP>1 and P<0.05 as the significant difference as the intersection condition, 12 metabolic markers for diagnosing early lung cancer were screened (Table 2).

[0057] Table 2 12 metabolic markers for early lung cancer diagnosis

[0058]

[0059]

[0060] 2) Construction of early lung cancer diagnosis model

[0061] The early lung cancer diagnosis model was constructed using the modeling group data, and the data set was split. The machine learning support vector machine (SVM) algorithm learned the two-dimensional matrix data. Specifically, 3 / 4 of the healthy control group and early lung cancer group sample data of the modeling group were randomly used as training sets, and the remaining 1 / 4 was used as a test set for learning. The SVM was used to randomly cycle Diego 2000 times, and the early lung cancer diagnosis model was constructed by statistically averaging the accuracy of the final model. The results of the multivariate ROC curve analysis based on 12 metabolic markers for early lung cancer diagnosis showed that AUC = 0.952 ( Figure 1 ), sensitivity = 84.6%, and specificity = 88.9%, indicating that the model has excellent performance in diagnosing early-stage lung cancer patients.

[0062] The ROC curve evaluates the performance of the model in the classification task by using sensitivity and 1-specificity as the coordinate axes. Sensitivity measures the model's ability to identify true positive examples, and specificity measures the model's ability to identify true negative examples. The area under the ROC curve (AUC) is an indicator of the model's ability to distinguish between positive and negative samples, ranging from 0 to 1. The closer the AUC is to 1, the better the model performance is, and it is more effective in distinguishing positive and negative examples. When the AUC is close to or less than 0.5, the model performance is similar to random guessing, and the ability to distinguish is poor. In practical applications, high sensitivity and specificity help reduce missed diagnoses and misjudgments.

[0063] Next, eight well-performing metabolites were screened out from the 12 metabolites for early lung cancer diagnosis, and then four well-performing metabolites were further screened out from these eight metabolites. Finally, an early lung cancer diagnosis model was constructed using a combination of the above metabolites.

[0064] In the modeling group, when the SVM method was used to construct an early lung cancer diagnosis model, 8 metabolic markers, O-acetyl-L-carnitine, D-galactonic acid, 2-amino-3-methyl-1-butanol, fatty acid 20:3, cortisol, arachidonic acid, fatty acid 20:2 and fatty acid 16:0 were combined to construct an early lung cancer diagnosis model. The results showed that AUC = 0.936, sensitivity = 83.1%, specificity = 85.2%, indicating that this metabolic marker combination is highly efficient in the diagnosis of early lung cancer, can provide important support for clinical practice, and has clinical diagnostic value.

[0065] In the modeling group, when the SVM method was used to construct an early lung cancer diagnosis model, four metabolic markers, O-acetyl-L-carnitine, D-galactonic acid, fatty acid 20:3 and arachidonic acid were combined to construct an early lung cancer diagnosis model. The results showed that AUC = 0.918, sensitivity = 80.0%, and specificity = 92.6%, indicating that this metabolic marker combination is highly efficient in the diagnosis of early lung cancer, can provide important support for clinical practice, and has clinical diagnostic value.

[0066] 3) Validation of early lung cancer diagnosis model

[0067] In order to evaluate the effectiveness of the constructed early lung cancer diagnosis model, the validation group data sets of the healthy control group and the early lung cancer group were used, and the validation group samples (Table 1) were used as unknown samples and put into the above-constructed early lung cancer diagnosis model for verification. The performance of metabolic markers in the diagnosis of early lung cancer was evaluated by ROC curve analysis, and the evaluation index was AUC (area under the curve).

[0068] In the early lung cancer diagnosis model constructed by combining 12 metabolic markers, O-acetyl-L-carnitine, D-galactonic acid, 2-amino-3-methyl-1-butanol, fatty acid 20:3, cortisol, allantoic acid, arachidonic acid, geraniol, fatty acid 20:2, fatty acid 16:0, urocanic acid, and fatty acid 18:3, the ROC curve analysis results of the validation group showed that AUC = 0.897 ( Figure 2 ), sensitivity = 82.5%, specificity = 78.2%, indicating that this metabolic marker combination is highly effective in the diagnosis of early lung cancer, can provide important support for clinical practice, and has clinical diagnostic value.

[0069] In the early lung cancer diagnosis model constructed by combining 8 metabolic markers, O-acetyl-L-carnitine, D-galactonic acid, 2-amino-3-methyl-1-butanol, fatty acid 20:3, cortisol, arachidonic acid, fatty acid 20:2 and fatty acid 16:0, the ROC curve analysis results of the validation group showed that AUC = 0.892 ( Figure 3 ), sensitivity = 82.5%, specificity = 80.5%, indicating that this metabolic marker combination is highly effective in the diagnosis of early lung cancer, can provide important support for clinical practice, and has clinical diagnostic value.

[0070] In the early lung cancer diagnosis model constructed by combining four metabolic markers, O-acetyl-L-carnitine, D-galactonic acid, fatty acid 20:3 and arachidonic acid, the ROC curve analysis results of the validation group showed that AUC = 0.889 ( Figure 4), sensitivity = 85.0%, specificity = 83.9%, indicating that this metabolic marker combination is highly effective in the diagnosis of early lung cancer, can provide important support for clinical practice, and has clinical diagnostic value.

[0071] In practical applications, high sensitivity can effectively capture actual positive cases and reduce the possibility of missed diagnosis, while high specificity ensures the effective exclusion of actual non-existent positive cases and reduces the risk of misdiagnosis. The different metabolite marker combinations screened in the present invention all show high sensitivity and high specificity. It shows that the screened metabolite markers show high accuracy and discrimination in the diagnosis of early lung cancer, have high clinical value and application potential, can provide early intervention opportunities and more treatment options for patients with early lung cancer, and are expected to be widely used in lung cancer screening and early diagnosis.

[0072] 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 metabolic marker for early diagnosis of lung cancer, characterized in that: The metabolic markers include: O-acetyl-L-carnitine, D-galactonic acid, fatty acid 20:3 and arachidonic acid.

2. The metabolic marker for early lung cancer diagnosis according to claim 1, characterized in that: The metabolic markers also include: 2-amino-3-methyl-1-butanol, cortisol, fatty acid 20:2 and fatty acid 16:

0.

3. The metabolic marker for early lung cancer diagnosis according to claim 2, characterized in that: The metabolic markers also include: allantoic acid, geraniol, urocanic acid and fatty acid 18:

3.

4. The metabolic marker for early lung cancer diagnosis according to claim 3, characterized in that: The metabolic markers consist of O-acetyl-L-carnitine, D-galactonic acid, 2-amino-3-methyl-1-butanol, fatty acid 20:3, cortisol, allantoic acid, arachidonic acid, geraniol, fatty acid 20:2, fatty acid 16:0, urocanic acid and fatty acid 18:

3.

5. A method for screening metabolic markers for early lung cancer diagnosis according to any one of claims 1 to 4, characterized in that: The following steps are involved: Plasma samples were collected from healthy subjects and patients with early-stage lung cancer; Pre-treating the plasma sample to obtain a test solution; Using a chromatography-mass spectrometer to detect the test liquid to obtain detection data; Processing the detection data and then identifying the metabolites; Metabolic markers for early diagnosis of lung cancer were screened from the identified metabolites.

6. The method for screening metabolic markers for early lung cancer diagnosis according to claim 5, characterized in that: The early stage lung cancer patients are lung cancer patients in stage 0 and stage I in the TNM system staging.

7. The method for screening metabolic markers for early lung cancer diagnosis according to claim 5, characterized in that: The step of screening the identified metabolites to obtain metabolic markers for early lung cancer diagnosis specifically includes: Construct an orthogonal partial least squares discriminant analysis model; The VIP value obtained based on the orthogonal partial least squares discriminant analysis model is greater than 1, and P<0.05 is used as the metabolic marker screening conditions to screen out metabolic markers for the diagnosis of early lung cancer.

8. Use of the metabolic marker for early lung cancer diagnosis according to any one of claims 1 to 4 in the preparation of an early lung cancer diagnostic reagent.

9. Use of the metabolic marker for early lung cancer diagnosis according to any one of claims 1 to 4 in the preparation of an early lung cancer diagnosis kit.

10. An early lung cancer diagnosis kit, characterized in that: The invention comprises the metabolic marker for early lung cancer diagnosis according to any one of claims 1 to 4.

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