Metabolic markers for early lung cancer diagnosis and screening methods and applications thereof

Abstract

The present application relates to the technical field of disease diagnosis, and particularly relates to metabolic markers for early lung cancer diagnosis, and a screening method and application thereof. The metabolic markers for early lung cancer diagnosis of the present application comprise O-acetyl-L-carnitine, D-galactonic acid, fatty acid 20:3 and arachidonic acid. The metabolic markers of the present application are used for distinguishing healthy people and early lung cancer patients (including 0 stage and I stage early patients of LC), the metabolic markers can predict early onset of lung cancer, have higher potential clinical application value, and are expected to play an important role in lung cancer screening, early diagnosis and intervention of lung cancer.

Description

Technical Field

[0001] This invention relates to the field of disease diagnostic technology, and in particular to metabolic biomarkers for early lung cancer diagnosis, as well as their screening methods and applications. Background Technology

[0002] Lung cancer (LC) has consistently been one of the most common malignant tumors worldwide, and for many years has been one of the leading causes of cancer morbidity and mortality globally. Despite continuous advancements in medical technology, the mortality rate for lung cancer patients remains high. This is primarily due to the relatively limited availability of early diagnosis and effective treatment methods. Early diagnosis and treatment are crucial for improving patient survival rates. Studies show that the 10-year survival rate after surgery for stage I lung cancer can reach 92%. However, because early-stage lung cancer often presents with subtle symptoms, many patients are diagnosed at an advanced stage, at which point treatment outcomes are poor, and mortality rates rise accordingly. Therefore, improvements in lung cancer screening and early diagnosis technologies are essential for enhancing the survival chances of lung cancer patients, and finding more accurate early diagnostic methods is currently an urgent task. Currently, the main technologies used clinically for early lung cancer screening and diagnosis 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, while possessing certain diagnostic capabilities, suffer from high false-positive rates and potential radiation risks. Furthermore, obtaining lung cancer tissue for pathological examination via surgery or biopsy is currently considered the gold standard for lung cancer diagnosis. However, these methods suffer from a series of problems, including cumbersome procedures, significant patient trauma, and tissue sample heterogeneity. Therefore, there is an urgent need to develop more precise, low-risk, and non-invasive methods for early diagnosis and screening of lung cancer to improve treatment outcomes and survival rates. Metabolomics is an emerging omics technology that can quantitatively describe the overall state of all metabolites in an organism and study their dynamic changes under external intervention or disease physiological conditions. It has wide applications in elucidating disease mechanisms, drug development, and personalized medicine. The core focus of cancer metabolomics research is biomarker discovery. Serological tumor markers have attracted considerable attention in the field of lung cancer diagnosis due to their non-invasiveness, ease of collection, relatively low cost, and potentially high sensitivity.

[0003] Currently, commonly used serological tumor markers in clinical practice, such as the five lung cancer markers (CEA, CYFRA21-1, SCC, Pro-GRP, and NSE), serve as auxiliary diagnostic tools for lung cancer. However, these traditional serological tumor markers have low sensitivity for lung cancer detection and cannot meet the requirements for early lung cancer screening. Although recent studies have found that xylose and acetylornithine can be used to diagnose the risk of lung cancer, the limited sample data and low accuracy, coupled with a primary focus on assessing lung cancer patients versus healthy individuals, mean that their diagnostic advantage for early-stage lung cancer is not significant, requiring the combination of other indicators for diagnosis. Therefore, there is an urgent need to discover new metabolic markers for lung cancer screening, enabling earlier treatment and better prognosis in the early stages, reducing the mortality rate of lung cancer, and improving patient survival. Thus, existing technologies still require improvement and development. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide metabolic biomarkers for early lung cancer diagnosis, as well as their screening methods and applications, in order to solve the problem that existing tumor metabolic biomarkers 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, the present invention provides metabolic biomarkers for the early diagnosis of lung cancer, wherein the metabolic biomarkers include: O-acetyl-L-carnitine, D-galactobionic 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, uric acid, and fatty acids in a 18:3 ratio.

[0009] Preferably, the metabolic markers consist of O-acetyl-L-carnitine, D-galactobionic 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, uric acid, and fatty acid 18:3.

[0010] Unlike existing metabolic biomarkers that distinguish between healthy individuals and lung cancer patients (including patients with intermediate and advanced stages such as stage II, III, and IV of LC), the metabolic biomarkers of this invention are used to distinguish between healthy individuals and early-stage lung cancer patients (only including early-stage patients with stage 0 and I of LC). The metabolic biomarkers of this invention can predict the early onset of lung cancer and have higher potential clinical application value. These metabolic biomarkers are expected to play an important role in the early diagnosis and intervention of lung cancer.

[0011] In other words, this invention focuses on the diagnosis of early-stage lung cancer. Compared to simply distinguishing between healthy individuals and lung cancer patients, this more specific diagnostic classification facilitates earlier screening and more accurate diagnosis, enabling timely intervention and treatment, improving patients' survival chances, and demonstrating greater foresight.

[0012] The metabolic biomarkers of this invention include not only lipid metabolites but also polar metabolites that play a crucial role in the body, indicating that the metabolic biomarkers of this invention cover a wider range of metabolites. This broad range of metabolic biomarkers makes this invention more comprehensive and accurate, and is expected to provide more complete information for lung cancer diagnosis.

[0013] A second aspect of the present invention provides a method for screening metabolic biomarkers for early lung cancer diagnosis, comprising the following steps:

[0014] Plasma samples were collected from healthy individuals and patients with early-stage lung cancer, respectively.

[0015] The plasma sample was pretreated to obtain the test solution;

[0016] The test solution was analyzed using chromatography-mass spectrometry to obtain the test data;

[0017] The detection data is processed, and then the metabolites are identified.

[0018] Metabolic biomarkers for early lung cancer diagnosis were obtained by screening the identified metabolites.

[0019] Preferably, the step of screening from identified metabolites to obtain metabolic biomarkers for early lung cancer diagnosis specifically includes:

[0020] Construct an orthogonal partial least squares discriminant analysis (OPLS-DA) model;

[0021] Based on the VIP value > 1 obtained from the OPLS-DA model and P < 0.05 as screening conditions for metabolic biomarkers, metabolic biomarkers for early lung cancer diagnosis were screened.

[0022] A third aspect of the present invention provides the application of the metabolic biomarker for early lung cancer diagnosis described in the present invention in the preparation of early lung cancer diagnostic reagents.

[0023] A fourth aspect of the present invention provides the application of the metabolic biomarker for early lung cancer diagnosis described in the present invention in the preparation of an early lung cancer diagnostic kit.

[0024] A fifth aspect of the present invention provides an early lung cancer diagnostic kit, comprising the metabolic biomarkers for early lung cancer diagnosis described in the present invention.

[0025] Compared with existing technologies, the present invention has the following advantages:

[0026] (1) Currently, research on metabolic biomarkers in blood for the early diagnosis of lung cancer is relatively limited, and the sample size is small. The research samples of this invention come from two central hospitals, including 195 healthy individuals and 300 early lung cancer samples. The large sample size increases the richness and reliability of the data, and the samples cover both healthy individuals and patients with early-stage cancer (non-advanced lung cancer). The diverse sample types are conducive to screening metabolic biomarkers with potential advantages in the early diagnosis of lung cancer.

[0027] (2) This invention studies the differences between healthy individuals and early-stage lung cancer patients, and the identified metabolic biomarkers can be used for the diagnosis of both healthy individuals and early-stage lung cancer. These metabolic biomarkers show potential in the early diagnosis of lung cancer, meeting the needs of lung cancer patients with mild or no early symptoms. They have broad application prospects and are expected to have a positive impact on the early diagnosis and treatment of lung cancer.

[0028] It is important to emphasize that in the screening process of existing biomarkers for lung cancer diagnosis, the collected lung cancer patient samples include samples from intermediate and advanced stages such as stage II, III, and IV of lung cancer (LC). However, in the screening process of the biomarkers of this invention, the collected lung cancer patient samples are only from early stages such as stage 0 and I of LC. The metabolite changes in early lung cancer (stage 0 + I of LC) are completely different from those in intermediate and advanced lung cancer (stage II + III + IV of LC). Therefore, existing biomarkers are used to distinguish between healthy individuals and patients with intermediate and advanced lung cancer, while the metabolic biomarkers screened in this invention are used to distinguish between healthy individuals and early-stage lung cancer patients. In other words, the metabolic biomarkers of this invention can predict the early onset of lung cancer, and can predict the early stage of lung cancer with high specificity and high sensitivity, possessing greater potential clinical application value and expected to play an important role in the early diagnosis and intervention of lung cancer.

[0029] While existing studies have addressed early lung cancer diagnosis, these studies define early lung cancer patients as those with a single lung lesion less than 3 cm in diameter confirmed by imaging and biopsy. However, clinical lung cancer staging is a comprehensive process, and relying solely on single imaging and pathological results is often insufficient for accurate stage determination. Furthermore, the early lung cancer samples used in this study included patients with lung cancer at stages I to IV according to the TNM staging system; these patients were all classified as early-stage lung cancer in this study. Unlike existing early lung cancer diagnosis studies, this invention utilizes a comprehensive assessment based on the TNM staging system, screening samples from stage 0 and I lung cancer patients for differential metabolites. These patients represent an early stage of lung cancer development, and this method has clinical value for early screening. Attached Figure Description

[0030] Figure 1 The ROC evaluation results are for a test set of an early lung cancer diagnostic model based on 12 metabolic biomarkers.

[0031] Figure 2 The ROC curve analysis of the validation set dataset based on 12 metabolic biomarkers in the early lung cancer diagnosis model validates the results.

[0032] Figure 3 The ROC curve analysis of the validation set dataset based on 8 metabolic biomarkers in the early lung cancer diagnosis model validates the results.

[0033] Figure 4 The ROC curve analysis of the validation set dataset based on four metabolic biomarkers in the early lung cancer diagnosis model validates the results. Detailed Implementation

[0034] This invention provides metabolic biomarkers for early lung cancer diagnosis, screening methods, and applications thereof. To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention is further described in detail below. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0035] Example 1

[0036] 1. Subject information and sample collection

[0037] All participants obtained written informed consent before participating in the study. Plasma samples were collected from 300 patients with early-stage lung cancer (LC0 / I) and 195 healthy controls (HC) from two central hospitals (Central Hospital 1 and Central Hospital 2). Data from Central Hospital 1 were used as the modeling group, including 108 HC samples and 260 LCO / I samples (Table 1). Data from Central Hospital 2 were used as the validation group, including 87 HC samples and 40 LCO / I samples (Table 1). The samples used in the modeling group and the validation group were different, and all were actual samples collected. Plasma samples were collected in the morning on an empty stomach. All collected plasma samples were stored at -80 degrees Celsius.

[0038] Table 1. Grouping information of the modeling and validation datasets for healthy controls and early-stage lung cancer patients.

[0039] Healthy control group (HC) Early-stage lung cancer (LC0 / I) Modeling Group 108 260 Verification 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, and isopropanol of mass spectrometry grade purity, and formic acid and ammonium acetate of HPLC grade purity were all purchased from Sigma-Aldrich, USA.

[0042] 2) Plasma pretreatment: After removing the plasma from the -80°C freezer and thawing it, take 100 μL of plasma and place it in 1000 μL of pre-cooled solution (methyl tert-butyl ether: methanol, volume ratio 3:1). Vortex the extracted plasma to obtain the 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: Take 500 μL of the upper organic phase into a centrifuge tube. After drying the organic phase, add 200 μL of solution (acetonitrile:isopropanol, volume ratio 3:1) and incubate at room temperature for 15 minutes. After incubation, vortex the centrifuge tube to mix, sonicate for 5 minutes, and then centrifuge at room temperature for 5 minutes (12000 rpm). Take 180 μL of the supernatant from the centrifuge tube into a 2 mL glass vial as the organic phase test solution, and analyze it using LC-MS.

[0044] Aqueous phase: Transfer 400 μL of the lower aqueous phase to a centrifuge tube and add 1100 μL of ice-cold methanol to precipitate the protein. After protein precipitation, centrifuge the tube and transfer 1000 μL of the supernatant to a new centrifuge tube, then dry overnight. Add 200 μL of water to the dried centrifuge tube and incubate at room temperature for 15 minutes. After incubation, vortex the centrifuge tube, sonicate for 5 minutes, and then centrifuge at room temperature for 5 minutes (12000 rpm). Transfer 180 μL of the supernatant from the centrifuge tube to a 2 mL glass vial as the aqueous phase test solution for LC-MS analysis.

[0045] 3) Test solution detection: The organic phase test solution was tested using Waters ACQUTTY. BEH C8 1.7μm 2.1*100mm column, Waters ACQUTTY aqueous solution used for the test solution. Small molecule separation was performed using an HSS T3 1.8μm 2.1*100mm column; liquid chromatography and mass spectrometry were performed using an ACQUITY UPLC I-Class liquid chromatography system (Waters) and a 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 55%-89% mobile phase B, 12-19.5 minutes 100% mobile phase B;

[0048] Aqueous 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 and 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 percentages mentioned above are by volume;

[0050] The mass spectrometry parameters are as follows:

[0051] Mass spectrometry data were acquired using Full MS and Full MS / dd-MS2 (each with both positive and negative modes). The parameters used by QExactive were as follows: Full MS mode had a resolution of 70,000, a scan range of 100-1500 m / z, an AGC of 3E+6, and a maximum IT of 200 ms; in Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer was 17,500, the quadrupole window was 1.5 m / z, the AGC was 1E+5, the maximum ion implantation time was 50 ms, and the HCD relative collision energy was 30 eV.

[0052] 4) Metabolomics data processing: First, the detection peaks are extracted from all mass spectrometry data. Then, baseline correction is applied to remove noise and retain the original signal peaks. The raw data is converted into central discrete data. Then, the peak values ​​in a single sample are compared with the retention times in the chromatogram for matching. The dataset is further processed by removing isotope peaks to obtain the final mass spectrometry matrix data. In order to reduce the difference in metabolite concentrations between samples and make the data distribution more symmetrical, normalization is performed using NormalizationAutoencoder (NormAE).

[0053] 5) Identification of metabolites: Using public databases such as the Human Metabolites Database (HMDB; www.hmdb.ca), the Metabolomics Database (Metlin; https: / / metlin.scripps.edu), and the Mass Spectrometry Database (http: / / www.massbank.jp), as well as primary and secondary chromatograms and mass spectra of standards separated under the same chromatographic column; under the conditions that the retention time difference is within 0.1 minutes and the mass-to-charge ratio is less than 10 ppm, the metabolites are identified by matching with the database and standards.

[0054] 3. Screening of metabolic biomarkers, model construction, and model validation for early lung cancer diagnosis.

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

[0056] A total of 403 metabolites were successfully identified through matching with databases and standards. To screen for key metabolites that could effectively distinguish between the healthy control group and the early-stage lung cancer group from these 403 metabolites, supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was performed on both groups. Then, based on the OPLS-DA values ​​VIP>1 and P<0.05 as the intersection condition, 12 metabolic biomarkers for diagnosing early-stage lung cancer were selected (Table 2).

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

[0058]

[0059]

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

[0061] An early lung cancer diagnostic model was constructed using data from the modeling group. The dataset was split, and a support vector machine (SVM) algorithm was used to learn the two-dimensional matrix data. Specifically, three-quarters of the sample data from the healthy control group and the early lung cancer group were randomly selected as the training set, and the remaining one-quarter was used as the test set for learning. The SVM was randomly iterated 2000 times, and the average accuracy of the final model was calculated to construct the early lung cancer diagnostic model. Multivariate ROC curve analysis based on 12 early lung cancer diagnostic metabolic biomarkers showed an AUC of 0.952. Figure 1 The sensitivity was 84.6% and the specificity was 88.9%, indicating that the model has excellent performance in diagnosing patients with early-stage lung cancer.

[0062] The ROC curve evaluates a model's performance in a classification task by using sensitivity and 1-specificity as the axes. Sensitivity measures the model's ability to identify true positives, while specificity measures its ability to identify true negatives. The area under the ROC curve (AUC) is a metric for judging 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's performance, and the more effectively it distinguishes between positive and negative examples. When the AUC is close to or less than 0.5, the model's performance is similar to random guessing, with poor discriminatory ability. In practical applications, high sensitivity and specificity help reduce missed diagnoses and false positives.

[0063] Next, eight promising metabolic biomarkers were selected from 12 early lung cancer diagnostic metabolic biomarkers, and then four more promising biomarkers were further selected from these eight. Finally, an early lung cancer diagnostic model was constructed using a combination of these metabolic biomarkers.

[0064] In the modeling group, when constructing an early lung cancer diagnostic model using the SVM method, eight metabolic biomarkers were combined: O-acetyl-L-carnitine, D-galactobionic acid, 2-amino-3-methyl-1-butanol, fatty acid 20:3, cortisol, arachidonic acid, fatty acid 20:2, and fatty acid 16:0. The results showed that the AUC was 0.936, the sensitivity was 83.1%, and the specificity was 85.2%, indicating that this combination of metabolic biomarkers is highly effective in the early diagnosis of lung cancer and can provide important support for clinical practice, thus possessing clinical diagnostic value.

[0065] In the modeling group, when constructing an early lung cancer diagnostic model using the SVM method, four metabolic markers—O-acetyl-L-carnitine, D-galactobionic acid, fatty acid 20:3, and arachidonic acid—were combined to construct the early lung cancer diagnostic model. The results showed that the AUC was 0.918, the sensitivity was 80.0%, and the specificity was 92.6%, indicating that this combination of metabolic markers demonstrated high efficiency in the early diagnosis of lung cancer, providing important support for clinical practice and possessing clinical diagnostic value.

[0066] 3) Validation of early lung cancer diagnostic models

[0067] To evaluate the effectiveness of the constructed early lung cancer diagnostic model, validation datasets were used from a healthy control group and an early lung cancer group. The validation group samples (Table 1) were treated as unknown samples and included in the constructed early lung cancer diagnostic model for validation. ROC curve analysis was used to evaluate the performance of metabolic biomarkers in early lung cancer diagnosis, with AUC (area under the curve) as the evaluation metric.

[0068] In an early lung cancer diagnostic model constructed using a combination of 12 metabolic markers—O-acetyl-L-carnitine, D-galactobionic 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, uric acid, and fatty acid 18:3—the ROC curve analysis of the validation group showed an AUC of 0.897. Figure 2 The results showed that the combination of metabolic markers had a sensitivity of 82.5% and a specificity of 78.2%, indicating that it was highly effective in the early diagnosis of lung cancer and could provide important support for clinical practice, thus having clinical diagnostic value.

[0069] In an early lung cancer diagnostic model constructed using eight metabolic markers—O-acetyl-L-carnitine, D-galactobionic acid, 2-amino-3-methyl-1-butanol, fatty acid 20:3, cortisol, arachidonic acid, and combinations of fatty acid 20:2 and fatty acid 16:0—ROC curve analysis of the validation group showed an AUC of 0.892. Figure 3 The results showed that the combination of metabolic markers had a sensitivity of 82.5% and a specificity of 80.5%, indicating that it was highly effective in the early diagnosis of lung cancer and could provide important support for clinical practice, thus having clinical diagnostic value.

[0070] In an early lung cancer diagnostic model constructed using a combination of four metabolic markers—O-acetyl-L-carnitine, D-galactobionic acid, fatty acid 20:3, and arachidonic acid—the ROC curve analysis of the validation group showed an AUC of 0.889. Figure 4The results showed that the combination of metabolic markers had a sensitivity of 85.0% and a specificity of 83.9%, indicating that it was highly effective in the early diagnosis of lung cancer and could provide important support for clinical practice, thus having clinical diagnostic value.

[0071] In practical applications, high sensitivity can effectively detect actual positive cases, reducing the possibility of missed diagnoses, while high specificity ensures the effective exclusion of non-existent positive cases, reducing the risk of misdiagnosis. The different combinations of metabolic biomarkers screened in this invention all exhibit high sensitivity and high specificity. This indicates that the screened metabolic biomarkers demonstrate high accuracy and discriminative power in the early diagnosis of lung cancer, possessing significant clinical value and application potential. They can provide early intervention opportunities and more treatment options for early-stage lung cancer patients, 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 examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. Metabolic markers for early lung cancer diagnosis, characterized in that, The metabolic markers include O-acetyl-L-carnitine, D-galactonic acid, fatty acid 20:3 and arachidonic acid. The metabolic markers also include 2-amino-3-methyl-1-butanol, cortisol, fatty acid 20:2 and fatty acid 16:

0.

2. The metabolic marker for early lung cancer diagnosis according to claim 1, characterized by, The metabolic markers also include allantoin, geraniol, urocanic acid and fatty acid 18:

3.

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

3.

4. The screening method for metabolic markers for early lung cancer diagnosis according to any one of claims 1 to 3, characterized in that, The method comprises the following steps: collecting blood plasma samples of healthy people and blood plasma samples of early lung cancer patients respectively; pretreating the blood plasma samples to obtain test liquids; detecting the test liquids by using a chromatograph-mass spectrometer to obtain detection data; processing the detection data and then identifying metabolites; screening metabolic markers for early lung cancer diagnosis from the identified metabolites.

5. The screening method of metabolic markers for early lung cancer diagnosis according to claim 4, characterized by, The early lung cancer patients are lung cancer patients in stage 0 and stage I of the TNM system.

6. The screening method of metabolic markers for early lung cancer diagnosis according to claim 4, characterized by, The step of screening metabolic markers for early lung cancer diagnosis from the identified metabolites specifically comprises: constructing an orthogonal partial least squares discriminant analysis model; a VIP value obtained based on the orthogonal partial least squares discriminant analysis model is greater than 1, and P <0.05 as a metabolic marker screening condition, a metabolic marker for early lung cancer diagnosis is screened.

7. Use of the metabolic markers for early lung cancer diagnosis according to any one of claims 1-3 in the preparation of a reagent for early lung cancer diagnosis.

8. Use of the metabolic markers for early lung cancer diagnosis according to any one of claims 1-3 in the preparation of a kit for early lung cancer diagnosis.

9. An early lung cancer diagnostic kit, characterized by, The method comprises the metabolic markers for early lung cancer diagnosis according to any one of claims 1-3.

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