Use of metabolic marker for diagnosis of lung cancer staging and kit

Abstract

The present application relates to a use of a metabolic marker for diagnosis of lung cancer staging and a kit. Further provided are a use of one or more of a metabolic marker for diagnosis of lung cancer staging and a detection reagent for a metabolic marker for diagnosis of lung cancer staging in preparation of a kit for diagnosis of lung cancer staging; and the kit for diagnosis of lung cancer staging, wherein the kit comprises one or more of the metabolic marker for diagnosis of lung cancer staging and the detection reagent for the metabolic marker for diagnosis of lung cancer staging.

Description

Application and kit of metabolic markers for diagnosing lung cancer staging

[0001] Related applications

[0002] This application claims priority to Chinese patent application No. 202311708074.6, filed on December 13, 2023, entitled “Application and kit of metabolic markers for diagnosing lung cancer staging,” the entire text of which is hereby incorporated by reference. Technical Field

[0003] The present application relates to the field of biomedical technology, and in particular to the application of metabolic markers and a kit for diagnosing lung cancer staging. Background Art

[0004] Lung cancer is one of the most common cancers with a high mortality rate worldwide, with a five-year survival rate of only 54%. Accurate diagnosis of lung cancer is crucial to improving patient survival. However, currently, only 15% of lung cancer patients are detected at an early stage. Most patients are diagnosed at an incurable late stage, which is also the cause of most lung cancer deaths. Treatment goals and approaches vary at different stages of lung cancer. The primary goal of early-stage treatment is surgical removal of the tumor to prevent the spread and metastasis of cancer cells, while preserving as much normal lung tissue as possible. Mid-stage surgery is less common, and a combination of treatments is generally used, including chemotherapy, radiotherapy, targeted drug therapy, immunotherapy, and Traditional Chinese Medicine (TCM). For patients with advanced lung cancer, the primary goals of treatment are to alleviate symptoms, improve quality of life, and prolong survival. Therefore, accurate diagnosis and staging of lung cancer provide important guidance for standardized and personalized treatment, helping to improve treatment efficacy, enhance patient quality of life, and prolong survival.

[0005] Currently, lung cancer screening primarily relies on low-dose spiral CT (LDCT), bronchoscopy, and cytology. LDCT is primarily used to screen high-risk individuals for lung cancer. While it has achieved some success, its widespread adoption is limited by its high cost, increased false-positive rates, and potential radiation exposure. Bronchoscopy, cytology, and pathology are the primary methods for lung cancer diagnosis. However, these methods have limitations in accurately diagnosing tumors located at the lung margins and occult lung cancer. Bronchoscopy sometimes fails to fully visualize the lung margins, while cytology may not be sensitive enough for certain tumor types. Pathology can be challenging for specific lung cancer types, such as neuroendocrine carcinomas, requiring multiple biopsies for a definitive diagnosis, increasing patient suffering, time, and costs. Therefore, finding more cost-effective, accurate, and non-invasive or minimally invasive lung cancer screening methods is urgently needed for accurate lung cancer diagnosis.

[0006] Metabolomics studies analyze changes in metabolites produced by organisms during disease or specific physiological states, revealing biological processes associated with metabolic changes. Metabolite analysis can better select appropriate treatments for patients. In lung cancer diagnosis, existing lung cancer biomarkers such as CEA (carcinoembryonic antigen), NSE (neuron-specific enolase), SCC (squamous cell carcinoma antigen), CA125 (carbohydrate antigen 125), and CYFRA21-1 (soluble cytokeratin 19 fragment), while valuable, have limitations. These markers are primarily used to aid diagnosis and monitor lung cancer progression and cannot accurately predict lung cancer stage. Pathological diagnosis is still required for definitive lung cancer stage determination. Therefore, clinical laboratories urgently need to develop more specific and sensitive tumor markers to improve lung cancer diagnosis and treatment. The development of new tumor markers is particularly important given the challenges of atypical histological morphology and the difficulty of differential diagnosis. The introduction of new tumor markers can improve the accuracy of lung cancer diagnosis and staging, providing patients with more accurate and effective treatment options and expanding the possibilities for personalized treatment. It has important clinical significance for improving the treatment effect and survival rate of lung cancer patients.

[0007] Summary of the Invention

[0008] The purpose of the embodiments of the present application includes providing an application and a kit of metabolic markers for diagnosing lung cancer staging. The technical solution is:

[0009] The embodiments of the present application provide the use of metabolic markers for diagnosing lung cancer staging or / and detection reagents thereof in preparing a kit for diagnosing lung cancer staging;

[0010] The metabolic markers for diagnosing lung cancer staging include dehydroepiandrosterone sulfate, 4-hydroxy-1-(3-pyridine)-1-butanone, L-leucyl-L-leucine, octanoyl-L-carnitine, phosphatidylcholine 34:3e, acylcarnitine 10:0, N-formyl-L-methionine, 5'-methylthioadenosine, phosphatidylcholine 36:3e, fatty acid 22:6, hydrocinnamic acid, acylcarnitine 10:1, hypoxanthine, L-alanyl-L-aspartic acid, iminodiacetic acid, betaine, choline, O-palmitoyl-L-carnitine and L-glutamate.

[0011] Optionally, the detection reagent detects the metabolic marker for diagnosing lung cancer staging by liquid chromatography-mass spectrometry.

[0012] Optionally, the sample types detected by the detection reagent include blood samples.

[0013] Optionally, the blood sample comprises plasma.

[0014] Optionally, the step of detecting the metabolic marker for diagnosing lung cancer staging by the detection reagent by liquid chromatography-mass spectrometry includes:

[0015] separating an organic phase and an aqueous phase from the plasma; and,

[0016] The liquid chromatography-mass spectrometry method is used to detect the organic phase and the aqueous phase respectively, thereby realizing the detection of the metabolic markers for diagnosing lung cancer staging.

[0017] Optionally, the liquid chromatography conditions for detecting the organic phase include:

[0018] The stationary phase was a C8 column;

[0019] The mobile phase includes mobile phase A and mobile phase B, wherein the mobile phase A includes an aqueous solution containing 0.08% (w / v)-0.12% (w / v) acetic acid and 0.08% (w / v)-0.12% (w / v) ammonium acetate, and the mobile phase B includes 0.08% (w / v)-0.12% (w / v) acetic acid, 0.8% (w / v)-1.2% (w / v) ammonium acetate, and a mixture of acetonitrile and isopropanol in a volume ratio of (6.5-7.5): (2.5-3.5);

[0020] The elution method includes gradient elution, and the gradient elution procedure includes:

[0021] From 0 minutes to 12 minutes, the volume proportion of the mobile phase B increased from 55% to 89%;

[0022] From 12 minutes to 19.5 minutes, the volume proportion of the mobile phase B increased from 89% to 100%.

[0023] Optionally, the liquid chromatography conditions for detecting the aqueous phase include:

[0024] The stationary phase was a T3 column;

[0025] The mobile phase comprises a mobile phase A and a mobile phase B, wherein the mobile phase A comprises an aqueous solution containing 0.08%-0.12% (w / v) formic acid; and the mobile phase B comprises an acetonitrile solution containing 0.08%-0.12% (w / v) formic acid.

[0026] The elution method includes gradient elution, and the gradient elution procedure includes:

[0027] From 0 to 13 minutes, the volume proportion of the mobile phase B increased from 1% to 70%;

[0028] From 13 to 18 minutes, the volume proportion of the mobile phase B increased from 70% to 99%.

[0029] Optionally, the mass spectrometry conditions for detection include:

[0030] Acquisition was performed in Full MS and Full MS / dd-MS2 modes, each with positive and negative modes;

[0031] In Full MS mode, the resolution is 35,000-70,000, the scan range is 100 m / z-1500 m / z, the AGC is 3E+6, and the Maximum IT is 150 ms-250 ms;

[0032] In Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer is 17,500-35,000, the quadrupole window is 1.2m / z-1.6m / z, the AGC is 1E+5, the maximum ion injection time is 45ms-55ms, and the HCD relative collision energy is 10eV-45eV.

[0033] Optionally, the step of separating the organic phase and the aqueous phase from the plasma comprises:

[0034] Extracting the plasma with solvent 1 to prepare an extract; the solvent 1 comprises methyl tert-butyl ether and methanol;

[0035] The extract was extracted with solvent 2, and after separation, the upper layer was collected to obtain an organic phase, and the lower layer was collected to obtain an aqueous phase.

[0036] The embodiments of the present application further provide a kit for diagnosing lung cancer staging, which comprises the metabolic markers for diagnosing lung cancer staging as defined above and / or a detection reagent thereof.

[0037] The details of one or more embodiments of the present application are set forth in the description below, and other features, objects, and advantages of the present application will be apparent from the specification and its claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application and to more fully understand the present application and its beneficial effects, the following is a brief introduction to the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present application. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0039] FIG1 is a linear correlation analysis diagram of dehydroepiandrosterone sulfate;

[0040] FIG2 is a linear correlation analysis diagram of 4-hydroxy-1-(3-pyridine)-1-butanone;

[0041] FIG3 is a linear correlation analysis diagram of L-leucyl-L-leucine;

[0042] FIG4 is a linear correlation analysis diagram of octanoyl-L-carnitine;

[0043] FIG5 is a linear correlation analysis diagram of phosphatidylcholine 34:3e;

[0044] FIG6 is a linear correlation analysis diagram of acylcarnitine 10:0;

[0045] FIG7 is a linear correlation analysis diagram of N-formyl-L-methionine;

[0046] FIG8 is a linear correlation analysis diagram of 5'-methylthioadenosine;

[0047] FIG9 is a linear correlation analysis diagram of phosphatidylcholine 36:3e;

[0048] Figure 10 is a linear correlation analysis diagram of fatty acid 22:6;

[0049] FIG11 is a linear correlation analysis diagram of hydrocinnamic acid;

[0050] Figure 12 is a linear correlation analysis diagram of acylcarnitine 10:1;

[0051] FIG13 is a linear correlation analysis diagram of hypoxanthine;

[0052] FIG14 is a linear correlation analysis diagram of L-alanyl-L-aspartic acid;

[0053] FIG15 is a linear correlation analysis diagram of iminodiacetic acid;

[0054] FIG16 is a linear correlation analysis diagram of betaine;

[0055] Figure 17 is a linear correlation analysis diagram of choline;

[0056] Figure 18 is a linear correlation analysis diagram of O-palmitoyl-L-carnitine;

[0057] FIG19 is a linear correlation analysis diagram of L-glutamic acid;

[0058] FIG20 is a diagram showing the ROC evaluation results of a lung cancer diagnosis model based on 19 metabolic markers;

[0059] FIG21 is a diagram showing the ROC curve analysis results of 19 metabolic markers in the multi-classification lung cancer staging training set;

[0060] FIG22 is a diagram showing the ROC curve analysis results of 19 metabolic markers in a multi-classification lung cancer staging test set;

[0061] FIG23 is a diagram showing the ROC curve analysis results of 19 metabolic markers in the lung cancer diagnosis validation group;

[0062] FIG24 is a diagram showing the multi-classification ROC curve analysis results of 19 metabolic markers in the lung cancer staging validation group. DETAILED DESCRIPTION

[0063] The goal of one or more embodiments of the present application is to diagnose lung cancer and predict clinical staging through metabolic markers, and to provide more targeted guidance and reference for lung cancer prevention strategies and treatment plans, thereby reducing the mortality rate of lung cancer and making positive contributions to improving the quality of life of patients, reducing medical costs and reducing the impact of lung cancer. Early stage lung cancer diagnosis can take targeted treatment measures before the tumor has further spread or worsened. The lung cancer metabolic markers explored in the embodiments of the present application can achieve lung cancer diagnosis and clinical staging prediction at one time. Compared with traditional technical solutions, the technical solutions provided by one or more embodiments of the present application have the following advantages: the lung cancer metabolic markers screened by the embodiments of the present application have the advantages of non-invasiveness, high prediction accuracy, rapid acquisition of test results, early diagnosis, and clinical staging prediction; they help monitor tumor growth and changes in treatment effects, increase the chance of early diagnosis, and reduce the risk of death caused by late prognosis. The advantages of some embodiments of the present application are reflected in: (1) a large number of samples, which increases the richness and reliability of data. (2) In the lung cancer diagnosis model of the present application, the highest AUC prediction value is 0.968, which can show good prediction performance and accuracy. (3) The screening source process of the metabolite markers of the present application is more standardized and reliable, the screening process is clear, and the steps of the screened metabolite markers are clear, which can be used for one-time lung cancer diagnosis and clinical staging prediction. (4) The screened metabolite markers are statistically significant. The P value of the metabolite markers screened in the examples of the present application is significant, which means that the changes in the metabolite markers selected in the examples of the present application are significantly different between lung cancer patients and healthy individuals, and can provide more accurate and reliable information for lung cancer diagnosis and prediction. (5) The examples of the present application conducted a multi-classification ROC analysis, which more comprehensively explored the potential of metabolic markers in lung cancer staging prediction, and has obvious advantages.

[0064] The first aspect of the embodiments of the present application

[0065] The present application provides an embodiment of a method for diagnosing lung cancer staging using a metabolic marker or / and a detection reagent thereof in preparing a kit for diagnosing lung cancer staging.

[0066] The metabolic markers for diagnosing lung cancer staging include dehydroepiandrosterone sulfate, 4-hydroxy-1-(3-pyridine)-1-butanone, L-leucyl-L-leucine, octanoyl-L-carnitine, phosphatidylcholine 34:3e, acylcarnitine 10:0, N-formyl-L-methionine, 5'-methylthioadenosine, phosphatidylcholine 36:3e, fatty acid 22:6, hydrocinnamic acid, acylcarnitine 10:1, hypoxanthine, L-alanyl-L-aspartic acid, iminodiacetic acid, betaine, choline, O-palmitoyl-L-carnitine and L-glutamate.

[0067] The present application does not particularly limit the detection reagent. Optionally, the detection reagent detects the metabolic marker for diagnosing lung cancer staging by liquid chromatography-mass spectrometry.

[0068] In some embodiments, the sample type detected by the detection reagent includes a blood sample. Optionally, the blood sample includes plasma or serum. The present application does not specifically limit the sample form of the blood sample, and it can be, for example, a dried blood smear.

[0069] In some embodiments, the step of using the detection reagent to detect the metabolic marker for diagnosing lung cancer staging by liquid chromatography-mass spectrometry comprises:

[0070] separating an organic phase and an aqueous phase from the plasma; and,

[0071] The liquid chromatography-mass spectrometry method is used to detect the organic phase and the aqueous phase respectively, thereby realizing the detection of the metabolic markers for diagnosing lung cancer staging.

[0072] In some embodiments, the liquid chromatography conditions for detecting the organic phase include:

[0073] The stationary phase was a C8 column;

[0074] The mobile phase includes a mobile phase A and a mobile phase B, wherein the mobile phase A includes an aqueous solution containing 0.08% (w / v)-0.12% (w / v) (e.g., 0.08%, 0.09%, 0.1%, 0.11%, 0.12%) of acetic acid and 0.08% (w / v)-0.12% (w / v) (e.g., 0.08%, 0.09%, 0.1%, 0.11%, 0.12%) of ammonium acetate, and the mobile phase B includes 0.08% (w / v)-0.12% (w / v) (e.g., 0.08%, 0.09%, 0.1%, 0.11%, 0.12%) of acetic acid, 0.8% (w / v)-1.2% (w / v) (e.g., 0.8%, 0.9%, 1%, 1.1%, 1.2%) ammonium acetate and a mixture of acetonitrile and isopropanol in a volume ratio of (6.5-7.5):(2.5-3.5) (e.g., 6.5:2.5, 6.5:3, 6.5:3.5, 7:2.5, 7:3, 7:3.5, 7.5:2.5, 7.5:3, 7.5:3.5);

[0075] The elution method includes gradient elution, and the gradient elution procedure includes:

[0076] From 0 minutes to 12 minutes, the volume proportion of the mobile phase B increased from 55% to 89%;

[0077] From 12 minutes to 19.5 minutes, the volume proportion of the mobile phase B increased from 89% to 100%.

[0078] In some embodiments, the liquid chromatography conditions for detecting the aqueous phase include:

[0079] The stationary phase was a T3 column;

[0080] The mobile phase comprises a mobile phase A and a mobile phase B, wherein the mobile phase A comprises an aqueous solution containing 0.08%-0.12% (w / v) formic acid; and the mobile phase B comprises an acetonitrile solution containing 0.08%-0.12% (w / v) formic acid.

[0081] The elution method includes gradient elution, and the gradient elution procedure includes:

[0082] From 0 to 13 minutes, the volume proportion of the mobile phase B increased from 1% to 70%;

[0083] From 13 to 18 minutes, the volume proportion of the mobile phase B increased from 70% to 99%.

[0084] In some specific embodiments of the present application, the mass spectrometry conditions for detection include:

[0085] Acquisition was performed in Full MS and Full MS / dd-MS2 modes, each with positive and negative modes;

[0086] In Full MS mode, the resolution is 35,000-70,000, the scan range is 100 m / z-1500 m / z, the AGC is 3E+6, and the Maximum IT is 150 ms-250 ms;

[0087] In Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometer is 17,500-35,000, the quadrupole window is 1.2m / z-1.6m / z, the AGC is 1E+5, the maximum ion injection time is 45ms-55ms, and the HCD relative collision energy is 10eV-45eV.

[0088] In some embodiments, the step of separating the organic phase and the aqueous phase from the plasma comprises:

[0089] Extracting the plasma with solvent 1 to prepare an extract; the solvent 1 comprises methyl tert-butyl ether and methanol;

[0090] The extract is extracted with solvent 2, and after separation, the upper layer is collected to obtain an organic phase, and the lower layer is collected to obtain an aqueous phase; the solvent 2 includes methanol and water.

[0091] In some embodiments, the extracting step satisfies one or more of the following conditions:

[0092] (1) Extraction methods include: vortexing;

[0093] (2) The amount of the solvent 1 per 100 μL of the plasma is 800 μL-1200 μL (e.g., 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200 μL);

[0094] (3) In the solvent 1, the volume ratio of methyl tert-butyl ether and methanol includes (2.5-3.5):1 (2.5:1, 2.6:1, 2.7:1, 2.8:1, 2.9:1, 3.0:1, 3.1:1, 3.2:1, 3.3:1, 3.4:1, 3.5:1).

[0095] In some embodiments, the extraction step satisfies one or more of the following conditions:

[0096] 1) In the solvent 2, the volume ratio of methanol to water is (2.5-3.5):1;

[0097] 2) The extraction methods and steps include: ultrasonication, standing, and vortexing.

[0098] In the second aspect of the embodiment of the present application

[0099] An embodiment of the present application provides a kit for diagnosing lung cancer staging, which includes the metabolic marker for diagnosing lung cancer staging and / or its detection reagent as defined in the first aspect above.

[0100] In the third aspect of the embodiment of the present application

[0101] The present invention provides a method for diagnosing lung cancer by staging, which comprises the following steps:

[0102] The level of the metabolic marker for diagnosing lung cancer staging defined in the first aspect is detected in a sample isolated from a subject, and the subject is determined to have lung cancer based on the obtained detection result.

[0103] In the embodiment of the present application, determining whether the subject has lung cancer based on the obtained test results includes: determining whether the subject has lung cancer, and, if so, determining the stage of the lung cancer.

[0104] In the third aspect, the definitions of "sample" and "detection reagent" used for detection are the same as those in the first aspect.

[0105] The embodiments of the present application will be described in detail below with reference to the examples. It should be understood that these examples are intended to illustrate the present application only and are not intended to limit the scope of the present application. The experimental methods for which specific conditions are not specified in the following examples are preferably referred to the guidance provided in the present application, and can also be based on the experimental manuals or conventional conditions in this area, or according to the conditions recommended by the manufacturer, or with reference to experimental methods known in the art.

[0106] In the following specific examples, the measured parameters of raw material components may have slight deviations within the range of weighing accuracy unless otherwise specified. For temperature and time parameters, acceptable deviations caused by instrument testing accuracy or operational accuracy are allowed.

[0107] Example 1: Screening of differential metabolites in plasma between healthy and lung cancer groups

[0108] 1. Detection objects and methods

[0109] (1) All subjects obtained written informed consent before participating in the study.

[0110] (2) Inclusion criteria for healthy group and lung cancer group and staging:

[0111] a. The healthy group was selected under three conditions: (1) According to the questionnaire, subjects with common chronic diseases (hypertension, diabetes, coronary heart disease), a history of tumor treatment, and a history of major surgery were excluded; (2) subjects reported no obvious clinical symptoms; (3) physical examination results were within the normal range and no obvious abnormalities were found; and (4) physical examination results were outside the normal range or abnormal but judged by the doctor to be clinically insignificant. (The physical examination items included: LDCT, abdominal color Doppler ultrasound, 4 tumor markers, blood pressure, etc., as shown in the figure). Subjects who met all three of the above conditions were considered to be in the healthy group.

[0112] b. Lung cancer eligibility criteria were as follows: Lung cancer histological classification was based on the World Health Organization's Classification of Tumors of the Lung, Pleura, Thymus, and Heart, Fourth Edition (published in 2015). Lung cancer staging was based on the American Joint Committee of Cancer (AJCC) Eighth Edition, according to the TNM staging system. Histopathological examination served as the gold standard, with the final diagnosis made by experienced clinicians.

[0113] Exclusion criteria: under 18 years old; pregnant; duplicate samples, undiagnosed, or unqualified quality inspection.

[0114] (3) Reagents: Formic acid, acetic acid, methanol, ammonium acetate, acetonitrile, methyl tert-butyl ether, and isopropanol of HPLC grade were purchased from Sigma-Aldrich, USA; deionized water was prepared using an ultrapure water system from Millipore, USA.

[0115] (4) Sample preparation:

[0116] 100 μL of plasma was placed in 1000 μL of pre-cooled (methyl tert-butyl ether: methanol, volume ratio 3:1) solution, and the extracted blood sample was vortexed to obtain a sample extract; 500 μL of (methanol: water, volume ratio 3:1) solution was added to the sample extract, sonicated, allowed to stand, vortexed, and centrifuged to separate layers;

[0117] Organic phase: After the sample is separated, 500 μL of the upper layer is transferred to a centrifuge tube (the organic phase). After the organic phase is dried, 200 μL of (acetonitrile:isopropanol, volume ratio 3:1) is added and incubated at room temperature for 15 minutes. After incubation, the centrifuge tube is vortexed and ultrasonically treated for 5 minutes. The centrifuge tube is then centrifuged at room temperature for 5 minutes (12,000 rpm). 180 μL of the supernatant from the centrifuge tube is transferred to a 2 mL glass vial (the organic phase) for analysis by LC-MS.

[0118] -Aqueous phase: After the sample is separated, remove 400 μL of the lower aqueous phase to a centrifuge tube and add 1100 μL of ice-cold methanol to precipitate the protein. After the protein in the centrifuge tube is precipitated, centrifuge the tube and transfer 1000 μL of the supernatant to a new centrifuge tube and 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, ultrasonically treat it for 5 minutes, and then centrifuge it at room temperature for 5 minutes (12000 rpm). Take 180 μL of the supernatant from the centrifuge tube and transfer it to a 2 mL glass injection vial. This is the aqueous phase and is detected by LC-MS.

[0119] (5) Small molecule metabolite detection:

[0120] Organic phase substances were detected using Waters ACQUTTY BEH C8 1.7μm 2.1*100mm column, water phase material detection using Waters ACQUTTY An HSS T3 1.8 μm 2.1*100 mm column was used for small molecule separation. 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).

[0121] The mobile phase parameters corresponding to organic and aqueous phase substances are as follows:

[0122] Organic phase material: Mobile phase A is an aqueous solution containing 0.1% (w / v) acetic acid and 0.1% (w / v) ammonium acetate; mobile phase B is an acetonitrile-isopropanol (7:3, v / v) solution containing 0.1% (w / v) acetic acid and 1% (w / v) ammonium acetate, and the separation elution gradient is as follows: 55%-89% (v / v) mobile phase B from 0 to 12 minutes, and 100% (v / v) mobile phase B from 12 to 19.5 minutes;

[0123] Aqueous phase: Mobile phase A is an aqueous solution containing 0.1% (w / v) formic acid; mobile phase B is an acetonitrile solution containing 0.1% (w / v) formic acid. The separation elution gradient is as follows: 1%-70% (v / v) mobile phase B from 0-13 minutes, 99% (v / v) mobile phase B from 13-18 minutes;

[0124] The mass spectrometry parameters are as follows:

[0125] Mass spectrometric data were acquired using Full MS and Full MS / dd-MS2 modes (each in both positive and negative modes). The Q Exactive parameters used were as follows: Full MS mode with a resolution of 70,000 s, a scan range of 100–1500 m / z, an AGC of 3E+6, and a Maximum Time Interval of 200 ms. In Full MS / dd-MS2 mode, the secondary mass spectrometer had a resolution of 17,500 s, a quadrupole window of 1.5 m / z, an AGC of 1E+5, a maximum ion injection time of 50 ms, and an HCD relative collision energy of 30 eV.

[0126] The embodiments of the present application have significant advantages in terms of metabolite extraction:

[0127] This embodiment can extract metabolites from only 100 μL of plasma. This process includes the treatment of organic and aqueous phase substances, and the use of efficient stratification technology to separate the two substances at the same time and test them separately, ultimately obtaining detection data for all metabolites in the organic and aqueous phases. This embodiment of the application refers to this unique process as "all-in-one extraction", which is also one of the significant features of the embodiment of the application in terms of metabolite extraction. Compared with the traditional two-extraction method, this method is efficient and convenient, providing a more convenient way for metabolite analysis. In addition, it can also improve the stability of the test results to a certain extent and ensure the reliability of the data.

[0128] (6) 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 processed to remove 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, the Normalization Autoencoder (NormAE) was used for normalization processing.

[0129] (7) Identification of metabolites: Utilize 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 the primary and secondary chromatographic and mass spectrometric spectra of the standards separated on the same chromatographic column; identify by matching with the database and the standards under the conditions that the retention time difference is within 0.2 min and the mass-to-charge ratio is less than 10 ppm.

[0130] (8) In the metabolomics data analysis, this example performed the following five steps:

[0131] The first step was to classify the data from the two centers into a modeling group (Table 1) and a validation group (Table 2). The samples used in both the modeling and validation groups were actual samples collected by us, and the modeling and validation group samples were different samples.

[0132] In the second step, this example screened out metabolites with significant differences between the healthy group (HC) and the lung cancer group (LC) using the modeling group samples to construct a lung cancer diagnostic model and identify potential metabolite markers for lung cancer diagnosis;

[0133] In the third step, lung cancer patients were divided into early stage (0+I+II) and late stage (III+IV) according to the stage of lung cancer ( Table 1 );

[0134] Table 1. Modeling group health and lung cancer group information

[0135] Then, linear correlation analysis was performed using the above-mentioned metabolic markers, and the Pearson correlation coefficient was calculated to evaluate the closeness of the linear correlation between the two variables;

[0136] In the fourth step, in this embodiment, metabolites with a Pearson correlation coefficient |R| greater than 0.3 were selected for multivariate and multi-classification ROC analysis to evaluate their performance in classifying lung cancer stages.

[0137] In the fifth step, the validation group data (Table 2) were used to independently verify the above-mentioned metabolic markers in the diagnosis and clinical staging prediction of lung cancer.

[0138] Table 2. Validation group health and lung cancer group information

[0139] 2. Results Analysis

[0140] (1) Screening of metabolic markers for lung cancer diagnosis

[0141] By matching with the database and standards, this example successfully identified a total of 404 metabolites. To screen out the key metabolites that can effectively distinguish the healthy group (HC) and lung cancer group (LC) in the modeling group, ROC curve analysis was used.

[0142] The ROC curve is a method for studying the relationship between model sensitivity and specificity. With sensitivity as the vertical axis and 1-specificity as the horizontal axis, the area under the ROC curve (AUC) can be used as an indicator of the model's ability to discriminate between metabolites. The AUC value reflects the model's ability to distinguish between positive and negative samples, ranging from 0 to 1. A values ​​closer to 1 indicate better performance in the classification task, better able to distinguish between positive and negative samples. A values ​​close to or less than 0.5 indicate that the model's performance is close to random guessing, indicating poor discriminatory ability and inaccuracy.

[0143] In this example, univariate ROC curve analysis was performed for each of the 404 metabolites. From the analysis results, 24 metabolites with AUC values ​​greater than 0.70 were retained. T-test P values ​​for all 24 metabolites between the healthy control group (HC) and lung cancer patients (LC) were less than 0.05.

[0144] (2) Screening of metabolic markers for lung cancer staging

[0145] According to the stage of lung cancer, this example re-divided the cancer patients in the modeling group into early stage (0+I+II) and late stage (III+IV) (Table 1). Then, 24 significantly different metabolite markers were used for lung cancer diagnosis, and linear correlation analysis was performed, including the correlation between health, early stage lung cancer, and late stage lung cancer. After analysis, 19 lung cancer metabolite markers with a correlation coefficient |R|>0.3 were selected (Figures 1 to 19 and Table 3). Figures 1 to 19 correspond to the metabolic markers numbered 1 to 19 in Table 3, respectively.

[0146] (3) ROC analysis of lung cancer diagnosis and lung cancer staging

[0147] Unlike considering the performance of individual metabolites in isolation, multivariate ROC analysis considers the interrelationships between multiple metabolites, providing a more comprehensive and accurate assessment of lung cancer detection. This approach leverages the correlations between metabolites and can more effectively capture combinations of disease-related biomarkers.

[0148] This example uses a machine learning support vector machine (SVM) algorithm to learn two-dimensional matrix data. Three-quarters of the sample data from the healthy group and lung cancer group of the modeling group are randomly used as the training set, and the remaining 1 / 4 is used as the test set for learning. The SVM is randomly cycled 2000 times. By calculating the average value of the final model accuracy, a lung cancer diagnosis model based on 19 significantly different metabolites is constructed. Based on the 19 significantly different metabolites selected, this example uses the test set to perform multivariate ROC analysis to evaluate the effect of the combined effect of these metabolites in lung cancer diagnosis. The classification model evaluation indicator is the AUC value (area under the curve) in the ROC analysis. The results show that the AUC value of the ROC multivariate analysis is 0.968 (Figure 20), indicating that the metabolites selected in this example have high accuracy and discriminatory power in the lung cancer diagnosis task.

[0149] Subsequently, multivariate and multi-classification ROC curve analysis was performed based on the lung cancer stage. Through multivariate ROC analysis and machine learning, the training set result was AUC = 0.96 (Figure 21), and the test set result was AUC = 0.844 (Figure 22), demonstrating that the metabolic markers screened in this example can efficiently achieve early diagnosis of lung cancer and prediction of cancer clinical stage in one go.

[0150] (4) Independent verification using validation group data

[0151] Using the validation data, the model was used to diagnose HC vs. LC, as well as HC and LC 0+I+II, and LC III+IV. The results showed that the AUC value for multivariate ROC analysis of HC vs. LC was 0.97 (Figure 23). For HC and LC 0+I+II, and LC III+IV, the AUC value for multivariate multiclassification ROC analysis was 0.87 (Figure 24). These results demonstrate that the model has high diagnostic performance across different classifications, providing potential clinical application value for the early diagnosis of lung cancer and prediction of lung cancer clinical stages.

[0152] Table 3. Absolute values ​​of R and P values ​​for linear correlation of 19 metabolic markers of lung cancer

[0153] Overall, the lung cancer metabolic markers screened in this example offer multiple advantages, including non-invasiveness, high predictive accuracy, rapid test results, and advantages in early diagnosis and clinical staging prediction. These advantages help monitor tumor growth and changes in treatment efficacy, increasing the chances of early diagnosis and reducing the risk of death associated with late-stage prognosis.

[0154] The various technical features of the above-mentioned implementation modes and examples can be combined in any appropriate manner. In order to make the description concise, not all possible combinations of the various technical features in the above-mentioned implementation modes and examples are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of the description in this specification.

[0155] The above-described embodiments only express several implementation methods of the present application, which facilitate a specific and detailed understanding of the technical solutions of the present application, but cannot be understood as limiting the scope of protection of the patent application. It should be pointed out that, for ordinary technicians in this field, without departing from the concept of the present application, several variations and improvements can be made, which all fall within the scope of protection of the present application. In addition, it should be understood that after reading the above-mentioned teaching content of the present application, those skilled in the art can make various changes or modifications to the present application, and the equivalent forms obtained also fall within the scope of protection of the present application. It should also be understood that the technical solutions obtained by those skilled in the art through logical analysis, reasoning or limited experiments on the basis of the technical solutions provided in the present application are all within the scope of protection of the claims attached to the present application. Therefore, the scope of protection of the patent application of the present application shall be based on the content of the attached claims, and the description and drawings can be used to interpret the content of the claims.

Claims

1. Use of one or more of the metabolic markers for diagnosing lung cancer staging and the detection reagents for the metabolic markers for diagnosing lung cancer staging in the preparation of a kit for diagnosing lung cancer staging; The metabolic markers for diagnosing lung cancer staging include dehydroepiandrosterone sulfate, 4-hydroxy-1-(3-pyridine)-1-butanone, L-leucyl-L-leucine, octanoyl-L-carnitine, phosphatidylcholine 34:3e, acylcarnitine 10:0, N-formyl-L-methionine, 5'-methylthioadenosine, phosphatidylcholine 36:3e, fatty acid 22:6, hydrocinnamic acid, acylcarnitine 10:1, hypoxanthine, L-alanyl-L-aspartic acid, iminodiacetic acid, betaine, choline, O-palmitoyl-L-carnitine and L-glutamic acid.

2. The use according to claim 1, wherein: The detection reagent detects the metabolic markers for diagnosing lung cancer staging by liquid chromatography-mass spectrometry.

3. The use according to claim 1 or 2, wherein: The sample types detected by the detection reagent include blood samples.

4. The use according to claim 3, wherein: The blood sample includes plasma or serum.

5. The use according to any one of claims 1 to 4, wherein: The step of using the detection reagent to detect the metabolic marker for diagnosing lung cancer staging by liquid chromatography-mass spectrometry comprises: separating an organic phase and an aqueous phase from the plasma; and, The liquid chromatography-mass spectrometry method is used to detect the organic phase and the aqueous phase respectively, so as to detect the metabolic markers used for diagnosing lung cancer staging.

6. The use according to claim 5, wherein: The liquid chromatography conditions for detecting the organic phase include: The stationary phase was a C8 column; The mobile phase comprises a mobile phase A and a mobile phase B, wherein the mobile phase A comprises an aqueous solution containing about 0.08% (w / v) to about 0.12% (w / v) acetic acid and about 0.08% (w / v) to about 0.12% (w / v) ammonium acetate, and the mobile phase B comprises a mixture of about 0.08% (w / v) to about 0.12% (w / v) acetic acid, about 0.8% (w / v) to about 1.2% (w / v) ammonium acetate, and acetonitrile and isopropanol in a volume ratio of about (6.5-7.5): (2.5-3.5); The elution method includes gradient elution, and the procedure of gradient elution includes: From about 0 minutes to about 12 minutes, the volume proportion of the mobile phase B increases from about 55% to about 89%; From about 12 minutes to about 19.5 minutes, the volume proportion of the mobile phase B increases from about 89% to about 100%.

7. The use according to any one of claims 5 to 6, wherein: The liquid chromatography conditions for detecting the aqueous phase include: The stationary phase was a T3 column; The mobile phase comprises a mobile phase A and a mobile phase B, wherein the mobile phase A comprises an aqueous solution containing about 0.08% to about 0.12% (w / v) formic acid; and the mobile phase B comprises an acetonitrile solution containing about 0.08% to about 0.12% (w / v) formic acid; The elution method includes gradient elution, and the procedure of gradient elution includes: From about 0 to about 13 minutes, the volume percentage of the mobile phase B increases from about 1% to about 70%; At about 13 to about 18 minutes, the volume proportion of the mobile phase B increases from about 70% to about 99%.

8. The use according to any one of claims 5 to 7, wherein: The mass spectrometry conditions for detection include: Acquisition was performed in Full MS and Full MS / dd-MS2 modes, each with positive and negative modes; In FullMS mode, the resolution is about 35,000 to about 70,000, the scan range is about 100 m / z to about 1500 m / z, the AGC is about 3E+6, and the Maximum IT is about 150 milliseconds to about 250 milliseconds; In FullMS / dd-MS2 mode, the resolution of the secondary mass spectrometer is about 17,500 to about 35,000, the quadrupole window is about 1.2m / z to about 1.6m / z, the AGC is 1E+5, the maximum ion injection time is about 45ms to about 55ms, and the HCD relative collision energy is about 10eV to about 45eV.

9. The use according to any one of claims 5 to 8, wherein: The step of separating the organic phase and the aqueous phase from the plasma comprises: Extracting the plasma with solvent 1 to prepare an extract; the solvent 1 comprises methyl tert-butyl ether and methanol; The extract is extracted with solvent 2, and after stratification, the upper layer is collected to obtain an organic phase, and the lower layer is collected to obtain an aqueous phase; the solvent 2 includes methanol and water.

10. A kit for diagnosing lung cancer staging, comprising one or more of the metabolic markers for diagnosing lung cancer staging and the detection reagents for the metabolic markers for diagnosing lung cancer staging as defined in any one of claims 1 to 9.

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