WO2023083197A1 - 用于肺癌诊断或监测的代谢标志物及其筛选方法和应用 - Google Patents

用于肺癌诊断或监测的代谢标志物及其筛选方法和应用 Download PDF

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WO2023083197A1
WO2023083197A1 PCT/CN2022/130737 CN2022130737W WO2023083197A1 WO 2023083197 A1 WO2023083197 A1 WO 2023083197A1 CN 2022130737 W CN2022130737 W CN 2022130737W WO 2023083197 A1 WO2023083197 A1 WO 2023083197A1
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acid
lung cancer
leucine
metabolic
glutamyl
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French (fr)
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唐堂
张明亮
张卫琴
彭浩文
王琳
娄加陶
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武汉迈特维尔医学科技有限公司
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57484—Immunoassay; Biospecific binding assay; Materials therefor for cancer involving compounds serving as markers for tumor, cancer, neoplasia, e.g. cellular determinants, receptors, heat shock/stress proteins, A-protein, oligosaccharides, metabolites

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  • the invention relates to the technical field of detection and diagnosis, in particular to a metabolic marker for diagnosis or monitoring of lung cancer, a screening method and application thereof.
  • lung cancer According to the data released by the International Agency for Research on Cancer of the World Health Organization, the number of breast cancer cases in the world will be the largest in 2020, and lung cancer will be ranked second, accounting for 11.4% of new tumor cases; its mortality rate will still rank first, accounting for 100% of deaths. 18%. In China, the incidence rate (17.8%) and mortality rate (23.8%) of lung cancer both rank first in 2020 and exceed the world average. Despite extensive efforts to prevent and treat lung cancer, lung cancer remains one of the deadliest forms of cancer, the leading cause of cancer death in men and the second leading cause of cancer death in women.
  • lung cancer is mainly divided into two types: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC).
  • NSCLC non-small cell lung cancer
  • SCLC small cell lung cancer
  • the proportion of non-small cell lung cancer is as high as 85% to 90%.
  • NSCLC non-small cell lung cancer
  • SCLC small cell lung cancer
  • the proportion of non-small cell lung cancer is as high as 85% to 90%.
  • NSCLC non-small cell lung cancer
  • SCLC small cell lung cancer
  • the proportion of non-small cell lung cancer is as high as 85% to 90%.
  • Clinically, non-small cell lung cancer is often diagnosed at an advanced stage. More than half of NSCLC patients die within 1 year after diagnosis, and the 5-year survival rate is less than 20%.
  • the 5-year survival rate of patients with early lung cancer can be as high as 90%. Therefore, early diagnosis of lung cancer is an important method for lung cancer patients to obtain a good prognosis and reduce mortality.
  • the means of clinical diagnosis of lung cancer mainly rely on ultrasound imaging and lung puncture.
  • the sensitivity of ultrasound diagnosis is low, and lung puncture is harmful to the lungs of patients, which is risky and difficult to promote.
  • many patients are not diagnosed until the decompensated stage of lung cancer.
  • gene molecules can be used as markers for the diagnosis of lung cancer, but the sensitivity and specificity of single gene diagnosis need to be improved urgently.
  • LDCT low-dose computed tomography
  • Metabolomics is an emerging discipline after genomics and proteomics, and is an important part of systems biology. Metabolomics has developed and rapidly penetrated into many fields, and its purpose is to study the overall metabolic differences in biological systems by monitoring the levels of small molecule metabolites in biological fluids or tissues, and to find the relative relationship between metabolites and pathophysiological changes. The occurrence of tumors is bound to be accompanied by metabolic changes, but in the early stages, the changes of small molecule metabolites are very weak and not easy to be found (Pei-Hsuan, C., Ling, C., Kenneth, H. et al. Metabolic diversity in human non-small cell lung cancer cells. Molecular Cell. 2019, 76, 1-14.
  • Lung cancer diagnostic biomarkers such as Mathe, E.A., Patterson, A.D., Haznadar, M. et al.
  • Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Res. 2014, 74:3259-3270.
  • the present invention adopts following technical scheme:
  • the present invention provides a metabolic marker for diagnosing or monitoring lung cancer.
  • the metabolic marker is at least selected from stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid, Pine, niacinamide, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine, fumaric acid, lysophosphatidylethanolamine, ⁇ -hydroxyisoamyl Acid, Guanosine, Benzoic Acid, Betaine, Levothyroxine, Dimethylguanosine, 1-Hexadecanoyl-sn-Glycero-3-Phosphocholine, Inosine, 12-Hydroxy-5Z,8Z,10E ,14Z-Eicosatetraenoic acid, Isooctanoic acid, L-pyroglutamic acid, N-L- ⁇ -glutamyl
  • the present invention provides a metabolic marker for diagnosing or monitoring lung cancer.
  • the metabolic marker is at least selected from stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid, Pine, niacinamide, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine, fumaric acid, lysophosphatidylethanolamine, ⁇ -hydroxyisoamyl Acid, Guanosine, Benzoic Acid, Betaine, Levothyroxine, Dimethylguanosine, 1-Hexadecanoyl-sn-Glycero-3-Phosphocholine, Inosine, 12-Hydroxy-5Z,8Z,10E , at least one of 14Z-eicosatetraenoic acid, isooctanoic acid, L-pyroglutamic acid, and N-L- ⁇ -
  • the metabolic marker is preferably selected from stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid ⁇ -glutamyl phenylalanine, L- - at least one of phenylalanyl-L-leucine. Further, the metabolic marker is at least one selected from cortisone, nicotinamide, 2-pyrrolidone, and pyruvate. Further, the metabolic marker is also selected from at least one of fumaric acid, lysophosphatidylethanolamine, ⁇ -hydroxyisovalerate, guanosine, benzoic acid, betaine, and levothyroxine.
  • the metabolic markers are also selected from dimethylguanosine, 1-hexadecanoyl-sn-glycero-3-phosphocholine, inosine, 12-hydroxy-5Z,8Z,10E,14Z-di At least one of stearidonic acid, isooctanoic acid, L-pyroglutamic acid, and N-L- ⁇ -glutamyl-L-leucine. Further, the metabolic marker is selected from at least one of ⁇ -ketoglutarate, uridine, taurine, L-serine and phenylacetylglutamine.
  • the metabolic markers for diagnosing or monitoring lung cancer include stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid, 2-pyrrolidone, ⁇ -Glutamyl phenylalanine, L-phenylalanyl-L-leucine, alpha-hydroxyisovaleric acid, benzoic acid, dimethylguanosine, isooctanoic acid, N-L-gamma-glutamyl-L - at least one of leucine.
  • the metabolic markers for diagnosing or monitoring lung cancer also include nicotinamide, pyruvate, lysophosphatidylethanolamine, betaine, 1-hexadecanoyl-sn-glycero-3-phosphocholine, inosine , ⁇ -ketoglutarate, uridine, L-serine, and at least one of phenylacetylglutamine.
  • the metabolic markers for diagnosing or monitoring lung cancer also include at least one of cortisone, fumaric acid, guanosine, levothyroxine, L-pyroglutamic acid, and taurine kind.
  • the metabolic markers for distinguishing lung cancer from healthy people are at least selected from stachydrine, isosuccinic acid, cortisone, nicotinamide, guanosine, 1-hexadecanoyl-sn-glycerol- At least one of 3-phosphocholine, inosine, 12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, ⁇ -ketoglutarate, uridine, L-serine, phenylacetylglutamine A sort of.
  • the metabolic markers used to distinguish early-stage lung cancer and non-lung cancer patients are at least selected from the group consisting of histidine-tryptophan, glycolic acid, isosuccinic acid, 2-pyrrolidone, and ⁇ -glutamyl amphetamine acid, L-phenylalanyl-L-leucine, benzoic acid, 1-hexadecanoyl-sn-glycero-3-phosphocholine, N-L- ⁇ -glutamyl-L-leucine, alpha - at least one of ketoglutarate.
  • the metabolic markers used to distinguish early lung cancer from benign lung diseases are at least selected from stachydrine, histidine-tryptophan, L-threonine, isosuccinic acid, cortisone, Niacinamide, 2-pyrrolidone, gamma-glutamyl phenylalanine, L-phenylalanyl-L-leucine, fumaric acid, lysophosphatidylethanolamine, guanosine, benzoic acid, levothyroxine, 12 -at least one of hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, isooctanoic acid, N-L- ⁇ -glutamyl-L-leucine.
  • the metabolic markers for distinguishing early lung cancer from healthy people are at least selected from isosuccinic acid, cortisone, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, pyruvate, fumaric acid , ⁇ -hydroxyisovaleric acid, betaine, levothyroxine, dimethylguanosine, 1-hexadecanoyl-sn-glycero-3-phosphocholine, L-pyroglutamic acid, N-L- ⁇ -glutamine At least one of aminoacyl-L-leucine, uridine, taurine, L-serine, and phenylacetylglutamine.
  • the metabolic markers used to distinguish between benign and malignant nodules are at least selected from the group consisting of histidine-tryptophan, glycolic acid, 2-pyrrolidone, L-phenylalanyl-L-leucine, At least one of betaine, levothyroxine, dimethylguanosine, 1-hexadecanoyl-sn-glycero-3-phosphocholine, inosine, N-L- ⁇ -glutamyl-L-leucine kind.
  • the AUC value of the area under the ROC curve of a single metabolic marker in the present invention is 0.702-0.813.
  • the performance of multiple metabolite groups is significantly better than that of a single metabolite, and the area under the ROC curve AUC value can reach up to 0.998, which can effectively diagnose lung cancer patients.
  • the present invention also provides a reagent product or kit, including the above-mentioned standard product of metabolic markers for diagnosing or monitoring lung cancer.
  • the reagent product or kit also includes solvents and/or internal standards for extracting and enriching the metabolic markers.
  • the present invention also provides a method for screening metabolic markers for diagnosing or monitoring lung cancer, comprising the following steps: separately collecting lung cancer group samples and non-lung cancer group samples; constructing a lung cancer serum-specific metabolome database; using LC-MS detection and analysis Obtain spectrogram data; use peak area integral data to conduct differential metabolic analysis between lung cancer group samples and non-lung cancer group samples, and determine differential metabolites; use machine learning random forest algorithm to analyze metabolite integration data between lung cancer group samples and non-lung cancer group samples For differential metabolite analysis, 2/3 of the serum sample data of the lung cancer group and the non-lung cancer group was used as the training set, and 1/3 was used as the test set; decision tree modeling was performed on the training set, and then the predictions of multiple decision trees were combined.
  • the lung cancer group samples include lung cancer samples of different TNM stages.
  • the present invention uses large-scale clinical samples and a lung cancer serum-specific metabolome database to conduct serum metabolomics research and obtain a large number of disease-related specific metabolites. Further use of the isotopic internal standards corresponding to the above metabolites for precise identification and quantification, finding serum metabolic markers with high sensitivity and specificity for the diagnosis of lung cancer, and constructing excellent diagnostic models based on metabolic markers still have important clinical application value. Using the above 30 metabolites for lung cancer diagnosis and analysis is simple, fast, economical and relatively non-invasive, and easy to be widely promoted.
  • peripheral venous blood serum was collected from 20 samples of healthy controls, benign lung diseases, and lung cancer patients.
  • healthy controls are healthy people without lung diseases after physical examination; benign lung diseases are those with pneumonia, hamartoma, emphysema, chronic obstructive pulmonary disease, epithelioid granulomatous lesions, pulmonary Patients with benign nodules and other diseases; the diagnostic criteria for lung cancer patients are confirmed by postoperative pathology. All samples had no history of other malignant tumors, other major systemic diseases, and no chronic medical history of long-term medication.
  • Liquid chromatography tandem mass spectrometry realizes the whole process from material separation by chromatography to material identification by mass spectrometry.
  • the above-mentioned mix detection solution is used to establish a lung cancer serum-specific metabolite ion pair database.
  • the metabolite ion pairs mainly come from the following four sources: MIM-EPI collection, TOF collection, Maiwei standard product database and lung cancer Literature metabolites.
  • This embodiment provides a method for screening metabolic markers for lung cancer diagnosis, comprising the following steps:
  • peripheral venous blood serum samples from 864 patients with lung cancer and 884 samples from the non-lung cancer group were collected from the four centers of Shanghai Chest Hospital, Shanghai Pulmonary Hospital, Anhui Provincial Cancer Hospital, and Gansu Provincial Cancer Hospital. .
  • the diagnostic criteria for lung cancer patients were postoperative pathological diagnosis; the non-lung cancer group samples included healthy people without lung diseases after physical examination and patients with pneumonia, hamartoma, emphysema, chronic obstructive pulmonary disease, Patients with benign lung diseases such as epithelioid granulomatous lesions and benign pulmonary nodules. All lung cancer patients and non-lung cancer group samples had no history of other malignant tumors, no other major systemic diseases, and no chronic medical history of long-term medication.
  • step S1 Take out the sample collected in step S1 from the -80°C refrigerator, and thaw it on ice until there are no ice cubes in the sample (subsequent operations are required to be carried out on ice);
  • Into a numbered centrifuge tube add 300 ⁇ L pure methanol internal standard extraction solution (containing 100 ppm L-phenylalanine internal standard); vortex for 5 min, let stand for 24 h, and then centrifuge at 12000 r/min, 4 °C for 10 min ; Take 270 ⁇ L of the supernatant and concentrate for 24 hours; then add 100 ⁇ L of acetonitrile and water in a complex solution with a volume ratio of 1:1 for LC-MS/MS analysis. 20 ⁇ L of each sample was mixed to form a quality control sample (QC), which was collected every 15 samples.
  • QC quality control sample
  • Chromatographic column WatersACQUITYUPLC HSS T3 C18 1.8 ⁇ m, 2.1mm*100mm; column temperature is 40°C; injection volume is 2 ⁇ L.
  • Phase A is an aqueous solution containing 0.1% acetic acid
  • phase B is an acetonitrile solution containing 0.1% acetic acid.
  • the elution gradient program is: 0min, the volume ratio of phase A to B is 95:5; 11.0min, the volume ratio of phase A to B is 10:90; 12.0min, the volume ratio of phase A to B is 10 :90; 12.1min, the volume ratio of A phase and B phase is 95:5; 14.0min, the volume ratio of A phase and B phase is 95:5V/V.
  • Flow rate 0.4mL/min.
  • each ion pair is scanned in MRM mode according to the optimized declustering potential (DP) and collision energy (collision energy, CE).
  • DP declustering potential
  • CE collision energy
  • the samples were analyzed and detected according to the determined liquid chromatography conditions and mass spectrometry conditions.
  • the metabolites of the samples were qualitatively and quantitatively analyzed by mass spectrometry. Metabolites of different molecular weights can be separated by liquid chromatography. The characteristic ions of each substance are screened out by the multiple reaction monitoring mode (MRM) of the triple quadrupole, and the signal intensity (CPS) of the characteristic ions is obtained in the detector.
  • MRM multiple reaction monitoring mode
  • CPS signal intensity
  • Use MultiQuant3.0.3 software to open the mass spectrum file of the sample off-machine, and perform the integration and correction of the chromatographic peaks.
  • the peak area (Area) of each chromatographic peak represents the relative content of the corresponding substance. If S/N>5 is set, the retention time shift will not The peaks exceeding 0.2min are retained, and finally all chromatographic peak area integration data are exported and saved.
  • the CV value is the coefficient of variation (Coefficient of Variation), which is the ratio of the standard deviation of the original data to the mean of the original data, which can reflect the degree of dispersion of the data.
  • the Empirical Cumulative Distribution Function (ECDF) can be used to analyze the frequency of the CV of substances less than the reference value.
  • the proportion of substances with 0.5 is higher than 85%, indicating that the experimental data is relatively stable; the proportion of substances with QC sample CV value less than 0.3 is higher than 75%, indicating that the experimental data is very stable.
  • the change of the CV value of the internal standard L-phenylalanine during the detection process was monitored, and the change of the CV value of the internal standard was less than 20%, indicating that the instrument was stable during the detection process.
  • the peak area integral data were used to analyze the differential metabolites between the two groups, and P value ⁇ 0.05 was set as the standard of significant difference, and the differential metabolites were screened as candidate metabolic markers for the diagnosis of lung cancer.
  • the machine learning Random Forest (RF) algorithm was used to analyze the metabolite integral data between the two groups, and 2/3 of the above lung cancer patient samples and control serum sample data were used as the training set, and 1/3 was used as the test set. Decision tree modeling is performed on the training set, and then the predictions of multiple decision trees are combined, and the final prediction result is obtained through voting.
  • This metabolite model can effectively diagnose lung cancer patients.
  • the above model is verified by the test set samples, and the screened metabolites become candidate metabolic markers.
  • the union of metabolites screened by intergroup difference analysis and machine learning screened metabolites was used as a set of candidate metabolic markers for the diagnosis of lung cancer.
  • the set of metabolic markers screened by the above difference analysis and random forest model, according to their retention time, primary and secondary speculative molecular mass and molecular formula of the markers, and compared with the spectral information in the metabolite spectrum database, so that Qualitative identification of metabolites.
  • non-isotope standard products of metabolites identified above check the retention time of metabolites in serum samples and corresponding non-isotope standard products in high-performance liquid chromatography tandem mass spectrometry detection, and the consistency of primary and secondary mass spectrometry information to determine Accuracy of metabolite characterization.
  • peripheral venous blood serum was collected from 157 lung cancer patients and 218 non-lung cancer patients in Shanghai Chest Hospital.
  • the diagnostic standard of lung cancer is lung cancer confirmed by postoperative pathology; the samples of non-lung cancer group include healthy people without lung disease after physical examination and patients with pneumonia, hamartoma, emphysema, chronic obstructive pulmonary disease, Patients with benign lung diseases such as epithelioid granulomatous lesions and benign pulmonary nodules. All lung cancer patients and non-lung cancer group samples had no other history of malignant tumors, other major systemic diseases, and no chronic medical history of long-term medication.
  • Table 5 The basic information of these research objects is shown in Table 5:
  • step S1 Take out the sample collected in step S1 from the -80°C refrigerator, and thaw it on ice until there are no ice cubes in the sample (subsequent operations are required to be carried out on ice); after the sample is thawed, vortex for 10 seconds to mix; take 50 ⁇ L of the project sample and add 150 ⁇ L Extract (the extract contains an isotope internal standard with a concentration of 100ppm), vortex for 3min, centrifuge at 12,000rpm at 4°C for 10min, and stand in a refrigerator at -20°C overnight; centrifuge at 12,000rpm and 4°C for 5min, take 170 ⁇ L of the supernatant, and Transfer to a 96-well plate, and seal the plate after the protein precipitation for LC-MS/MS analysis. 20 ⁇ L of each sample was mixed to form a quality control sample (QC), which was collected every 15 samples.
  • QC quality control sample
  • the targeted quantitative detection uses two methods of T3 column and Amide column to separate metabolites to ensure the accuracy of metabolite quantification.
  • Chromatographic column Waters ACQUITY UPLC HSS T3 C18 1.8 ⁇ m, 2.1mm*100mm; column temperature 40°C; injection volume 2 ⁇ L.
  • Phase A is a solution containing 0.1% acetic acid
  • phase B is an acetonitrile solution containing 0.1% acetic acid
  • elution gradient program 0min
  • the volume ratio of phase A to phase B is 95:5; 11.0min, phase A and The volume ratio of phase B is 10:90; 12.0min, the volume ratio of phase A to phase B is 10:90; 12.1min, the volume ratio of phase A to phase B is 95:5; 14.0min, the volume ratio of phase A to phase B
  • the volume ratio is 95:5V/V.
  • Flow rate 0.4mL/min.
  • Chromatographic column Waters ACQUITY UPLC BEH Amide 1.7 ⁇ m, 2.1mm*100mm; column temperature 40°C; injection volume 2 ⁇ L.
  • Phase A is an aqueous solution containing 20mM ammonium formate and 0.4% ammonia water
  • phase B is pure acetonitrile
  • elution gradient program 0min
  • the volume ratio of phase A to phase B is 10:90; 9.0min, phase A to B
  • the volume ratio of phases is 40:60; 10.0min, the volume ratio of A phase to B phase is 60:40; 11.0min, the volume ratio of A phase to B phase is 60:40; 11.1min, the volume ratio of A phase to B phase
  • the volume ratio is 10:90; 15.0min, the volume ratio of phase A and phase B is 10:90.
  • Flow rate 0.4mL/min.
  • T3 column and Amide column have the same mass spectrometry acquisition conditions, mainly including: electrospray ionization (ESI) temperature 500°C, mass spectrometry voltage 5500V (positive), -4500V (negative), ion source gas I (GS I) 55psi, Gas II (GS II) was 60psi, curtain gas (CUR) was 25psi, and the collision-activated dissociation (CAD) parameter was set to high.
  • ESI electrospray ionization
  • GS I mass spectrometry voltage 55psi
  • Gas II GS II
  • CUR curtain gas
  • CAD collision-activated dissociation parameter was set to high.
  • DP declustering potential
  • CE collision energy
  • MultiQuant 3.0.3 software was used to process the mass spectrometry data, referring to the retention time and peak shape information of the standard, the mass spectrum peaks detected in different samples of the analyte were integrated and corrected to ensure the accuracy of qualitative and quantitative.
  • the dilution factor in MultiQuant 3.0.3 is set to 3
  • the concentration obtained by substituting the integrated peak area ratio in the final sample into the standard curve The value (ng/mL) is the content data of the substance in the actual sample.
  • the CV value is the coefficient of variation (Coefficient of Variation), which is the ratio of the standard deviation of the original data to the mean of the original data, which can reflect the degree of dispersion of the data.
  • the Empirical Cumulative Distribution Function (ECDF) can be used to analyze the frequency of the CV of substances less than the reference value.
  • the CV value is less than 0.3, indicating that the experimental data is relatively stable; the proportion of substances with a CV value of less than 0.2 in QC samples is higher than 90, indicating that the experimental data is very stable.
  • the change of the CV value of the isotope internal standard is monitored during the detection process, and the change of the CV value of the internal standard is less than 20%, indicating that the instrument is stable during the detection process.
  • the difference in metabolite concentrations between the lung cancer patient group and the non-lung cancer group was analyzed significantly, and P value ⁇ 0.05 was set as the standard of significant difference for differential metabolite screening.
  • the screened differential metabolism uses the binary logistic regression algorithm to construct a classification model, screens to obtain the optimal classification metabolite combination, and obtains a lung cancer diagnostic model.
  • the fold change results of individual metabolic markers are shown in Table 7:
  • the diagnostic model contains the following 30 metabolites: stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid, cortisone, niacinamide, 2-pyrrolidone, gamma-glutamine Phenylalanine, Pyruvate, L-Phenylalanyl-L-Leucine, Fumaric Acid, Lysophosphatidylethanolamine, Alpha-Hydroxyisovaleric Acid, Guanosine, Benzoic Acid, Betaine, Levothyroxine , dimethylguanosine, 1-hexadecanoyl-sn-glycero-3-phosphocholine, inosine, 12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, isooctanoic acid, L - Pyroglutamic acid, N-L- ⁇ -glutamyl-L-leucine, ⁇ -ketoglutarate
  • Table 8 The AUC value of a single metabolite for the diagnosis of lung cancer
  • the research objects and detection and analysis methods are the same as in Example 3, and only any two serum metabolic markers, such as stachydrine and histidine-tryptophan, are used in the binary logistic regression modeling in step (6).
  • any two differential metabolites have a strong ability to diagnose and distinguish between lung cancer and non-lung cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance.
  • the statistical data of the joint diagnosis of two metabolic markers are as follows: the AUC of stachydrine and histidine-tryptophan in the diagnosis of lung cancer is 0.850; the AUC of guanosine and benzoic acid in the diagnosis of lung cancer is 0.757; L - The AUC of the combination of serine and phenylacetylglutamine in the diagnosis of lung cancer was 0.721.
  • Example 5 Construction of a lung cancer diagnostic model using 5 serum metabolic markers
  • the research objects and detection and analysis methods are the same as in Example 3, and only any five serum metabolic markers, such as stachydrine, histidine-tryptophan, are used in the binary logistic regression modeling in step (6).
  • serum metabolic markers such as stachydrine, histidine-tryptophan
  • Uridine Taurine
  • L-Serine Phenylacetyl Glutamine.
  • any 5 differential metabolites have a strong ability to diagnose and distinguish between lung cancer and non-lung cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance.
  • the statistical data of the joint diagnosis of five metabolic markers are as follows: stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid and L-phenylalanyl-L-leucine
  • the AUC value of ⁇ -hydroxyisovaleric acid, guanosine, benzoic acid, betaine and levothyroxine for the diagnosis of lung cancer was 0.882;
  • the AUC value of uridine, taurine, L-serine and phenylacetylglutamine combined for the diagnosis of lung cancer was 0.735.
  • Example 6 Construction of a lung cancer diagnostic model using 8 serum metabolic markers
  • the research objects and detection and analysis methods are the same as in Example 3, and only any 8 serum metabolic markers, such as stachydrine, histidine-tryptophan, are used in step (6) binary logistic regression modeling , L-threonine, glycolic acid, isosuccinic acid, cortisone, niacinamide, 2-pyrrolidone, or fumaric acid, lysophosphatidylethanolamine, alpha-hydroxyisovaleric acid, guanosine, benzoic acid, betaine , levothyroxine, dimethylguanosine, or isooctanoic acid, L-pyroglutamic acid, N-L- ⁇ -glutamyl-L-leucine, ⁇ -ketoglutarate, uridine, taurine, L-serine, phenylacetyl glutamine.
  • serum metabolic markers such as stachydrine, histidine-tryptophan
  • the AUC value of stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid, cortisone, nicotinamide and 2-pyrrolidone combined for the diagnosis of lung cancer was 0.906.
  • the AUC value of fumaric acid, lysophosphatidylethanolamine, alpha horsehydroxyisovalerate, guanosine, benzoic acid, betaine, levothyroxine and dimethylguanosine in the diagnosis of lung cancer was 0.794.
  • Example 7 Construction of a lung cancer diagnostic model using 11 serum metabolic markers
  • the research objects and detection and analysis methods are the same as in Example 3, and only any 11 serum metabolic markers, such as stachydrine, histidine-tryptophan, are used in the binary logistic regression modeling in step (6).
  • L-threonine glycolic acid, isosuccinic acid, cortisone, niacinamide, 2-pyrrolidone, gamma-glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine , or 1-hexadecanoyl-sn-glycero-3-phosphocholine, inosine, 12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, isooctanoic acid, L-pyroglutamic acid , N-L- ⁇ -glutamyl-L-leucine, ⁇ -ketoglutarate, uridine, taurine, L-serine, phenylacetylglut
  • Example 8 Construction of a lung cancer diagnostic model using 15 serum metabolic markers
  • Example 3 This example is the same as Example 3 with the same research objects and detection and analysis methods, and only any 15 serum metabolic markers, such as stachydrine, histidine-tryptophan, are used in step (6) binary logistic regression modeling , L-threonine, glycolic acid, isosuccinic acid, cortisone, niacinamide, 2-pyrrolidone, gamma-glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine , fumaric acid, lysophosphatidylethanolamine, alpha-hydroxyisovaleric acid, guanosine, or benzoic acid, betaine, levothyroxine, dimethylguanosine, 1-hexadecanoyl-sn-glycerol-3- Phosphocholine, Inosine, 12-Hydroxy-5Z, 8Z, 10E, 14Z-Eicosatetraenoic Acid, Isooctanoi
  • any 15 differential metabolites have a strong ability to diagnose and distinguish between lung cancer and non-lung cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance.
  • AUC area under the ROC curve
  • Benzoic acid betaine, levothyroxine, dimethylguanosine, 1-hexadecanoyl-sn-glycero-3-phosphocholine, inosine, 12-hydroxy-5Z, 8Z, 10E, 14Z-20
  • Carbasatetraenoic acid isooctanoic acid, L-pyroglutamic acid, N-L-, acid glutamyl-L-leucine, ⁇ -aminoketoglutaric acid, uridine, taurine, L-serine, phenylacetyl glutaric acid
  • the AUC value of aminoamide combined for the diagnosis of lung cancer was 0.813.
  • Example 9 Construction of a lung cancer diagnostic model using 18 serum metabolic markers
  • the research objects and detection and analysis methods are the same as in Example 3, and only any 18 serum metabolic markers, such as stachydrine, histidine-tryptophan, are used in step (6) binary logistic regression modeling , L-threonine, glycolic acid, isosuccinic acid, cortisone, niacinamide, 2-pyrrolidone, gamma-glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine , fumaric acid, lysophosphatidylethanolamine, alpha-hydroxyisovaleric acid, guanosine, benzoic acid, betaine, levothyroxine.
  • serum metabolic markers such as stachydrine, histidine-tryptophan
  • Any 18 differential metabolites have a strong ability to diagnose and distinguish between lung cancer and non-lung cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance.
  • the combined AUC of 18 differential metabolites for the diagnosis of lung cancer was further improved to 0.945.
  • Example 10 Construction of a lung cancer diagnostic model using 21 serum metabolic markers
  • the research object and detection and analysis method are the same as in Example 3, and only any 21 serum metabolic markers, such as stachydrine, histidine-tryptophan, are used in step (6) binary logistic regression modeling , L-threonine, glycolic acid, isosuccinic acid, cortisone, niacinamide, 2-pyrrolidone, gamma-glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine , fumaric acid, lysophosphatidylethanolamine, alpha-hydroxyisovaleric acid, guanosine, benzoic acid, betaine, levothyroxine, dimethylguanosine, 1-hexadecanoyl-sn-glycero-3-phosphate Choline, inosine.
  • serum metabolic markers such as stachydrine, histidine-tryptophan
  • Any 21 differential metabolites have a strong ability to diagnose and distinguish between lung cancer and non-lung cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance.
  • the combined AUC of 21 differential metabolites for the diagnosis of lung cancer increased to 0.963.
  • Example 11 Construction of a lung cancer diagnostic model using 25 serum metabolic markers
  • the research objects and detection and analysis methods are the same as in Example 3, and only any 25 serum metabolic markers, such as stachydrine, histidine-tryptophan, are used in step (6) binary logistic regression modeling , L-threonine, glycolic acid, isosuccinic acid, cortisone, niacinamide, 2-pyrrolidone, gamma-glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine , fumaric acid, lysophosphatidylethanolamine, alpha-hydroxyisovaleric acid, guanosine, benzoic acid, betaine, levothyroxine, dimethylguanosine, 1-hexadecanoyl-sn-glycero-3-phosphate Choline, Inosine, 12-Hydroxy-5Z,8Z,10E,14Z-Eicosatetraenoic Acid, Isooctanoic Acid, L-
  • any 25 differential metabolites have a strong ability to diagnose and distinguish between lung cancer and non-lung cancer patients, and the area under the ROC curve (AUC) is greater than 0.7, which has clinical diagnostic significance.
  • AUC area under the ROC curve
  • the samples in this example are from Example 3, 157 cases of lung cancer patients and 70 cases of healthy people.
  • the metabolite detection and analysis method was the same as in Example 3, and the following 30 metabolites were quantitatively detected, including: stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid, Pine, niacinamide, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine, fumaric acid, lysophosphatidylethanolamine, ⁇ -hydroxyisoamyl Acid, Guanosine, Benzoic Acid, Betaine, Levothyroxine, Dimethylguanosine, 1-Hexadecanoyl-sn-Glycero-3-Phosphocholine, Inosine, 12-Hydroxy-5Z,8Z,10E ,14Z-Eicosatetraenoic acid, I
  • metabolic markers are stachydrine, isosuccinic acid, cortisone, nicotinamide, guanosine, 1-hexadecanoyl-sn-glycero-3-phosphocholine, inosine, 12-hydroxy-5Z,8Z ,10E,14Z-Eicosatetraenoic acid, ⁇ -ketoglutarate, L-serine, phenylacetylglutamine.
  • Table 9 Change multiples of metabolites in patients with lung cancer vs. healthy people
  • Example 13 Serum Targeted Metabolome Diagnosis Differentiates Diagnosis Model Construction of Patients with Lung Cancer and Benign Lung Diseases
  • the samples in this example come from Example 3, 157 cases of lung cancer patients, 148 cases of benign lung disease patients, including pneumonia, hamartoma, emphysema, chronic obstructive pulmonary disease, epithelioid granulomatous lesions, lung Lung diseases such as benign nodules.
  • the metabolite detection and analysis method was the same as in Example 3, and the following 30 metabolites were quantitatively detected, including: stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid, Pine, niacinamide, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine, fumaric acid, lysophosphatidylethanolamine, ⁇ -hydroxyisoamyl Acid, Guanosine, Benzoic Acid, Betaine, Levothyroxine, Dimethylguanosine, 1-Hexadecanoyl-sn-Glycero-3-Phosphocholine, Inosine, 12-Hydroxy-5Z, 8Z, 10E , 14Z-eicosatetraenoic acid, isooctanoic acid, L-pyroglutamic acid, N-L- ⁇ -g
  • metabolic markers histidine-tryptophan, L-threonine, glycolic acid, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine acid, fumaric acid, isooctanoic acid, L-pyroglutamic acid, N-L- ⁇ -glutamyl-L-leucine.
  • Example 14 Serum Targeted Metabolome Diagnosis Differentiates Diagnosis Model Construction of Early Lung Cancer and Non-Lung Cancer Patients
  • the samples in this example are derived from Example 3, 218 samples of non-lung cancer patients, including 70 healthy people and 148 patients with benign lung diseases; early lung cancer patients are patients whose TNM staging is stage I and stage II in Example 3 , a total of 65 cases.
  • Benign lung diseases include pneumonia, hamartoma, emphysema, chronic obstructive pulmonary disease, epithelioid granulomatous lesions, benign pulmonary nodules and other lung diseases.
  • the metabolite detection and analysis method was the same as in Example 3, and the following 30 metabolites were quantitatively detected, including: stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid, Pine, niacinamide, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine, fumaric acid, lysophosphatidylethanolamine, ⁇ -hydroxyisoamyl Acid, Guanosine, Benzoic Acid, Betaine, Levothyroxine, Dimethylguanosine, 1-Hexadecanoyl-sn-Glycero-3-Phosphocholine, Inosine, 12-Hydroxy-5Z, 8Z, 10E , 14Z-eicosatetraenoic acid, isooctanoic acid, L-pyroglutamic acid, N-L- ⁇ -g
  • metabolic markers are histidine-tryptophan, glycolic acid, isosuccinic acid, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, L-phenylalanyl-L-leucine, benzoic acid, 1-Hexadecanoyl-sn-glycero-3-phosphocholine, N-L- ⁇ -glutamyl-L-leucine, ⁇ -ketoglutarate.
  • These metabolites changed significantly in patients with early lung cancer, and the specific changes are shown in Table 11:
  • the samples in this example are from Example 3, 70 samples of healthy people; the patients with early lung cancer are patients with TNM stage I and II in Example 3, a total of 65 cases.
  • the metabolite detection and analysis method was the same as in Example 3, and the following 30 metabolites were quantitatively detected, including: stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid, Pine, niacinamide, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine, fumaric acid, lysophosphatidylethanolamine, ⁇ -hydroxyisoamyl Acid, Guanosine, Benzoic Acid, Betaine, Levothyroxine, Dimethylguanosine, 1-Hexadecanoyl-sn-Glycero-3-Phosphocholine, Inosine, 12-Hydroxy-5Z, 8Z, 10
  • metabolic markers are isosuccinic acid, cortisone, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, pyruvate, fumaric acid, ⁇ -hydroxyisovaleric acid, betaine, levothyroxine, dimethyl guanosine, 1-hexadecanoyl-sn-glycero-3-phosphocholine, L-pyroglutamic acid, N-L- ⁇ -glutamyl-L-leucine, uridine, taurine, L - Serine, Phenylacetylglutamine.
  • Table 12 shows that changes were shown in patients with early lung cancer, and the specific changes are shown in Table 12:
  • Table 12 Change multiples of metabolites in patients with early lung cancer VS healthy people
  • Example 16 Serum Targeted Metabolome Diagnosis to Distinguish Patients with Early Lung Cancer and Patients with Benign Lung Diseases Diagnosis Model Construction
  • the samples in this example are derived from Example 3, 148 samples of patients with benign lung diseases, including pneumonia, hamartoma, emphysema, chronic obstructive pulmonary disease, epithelioid granulomatous lesions, benign pulmonary nodules, etc.
  • Patients with lung disease; patients with early lung cancer are patients whose TNM staging is stage I and stage II in Example 3, a total of 65 cases.
  • the metabolite detection and analysis method is the same as in Example 3, and the following 30 metabolites are quantitatively detected, including: stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid, Pine, niacinamide, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine, fumaric acid, lysophosphatidylethanolamine, ⁇ -hydroxyisoamyl Acid, Guanosine, Benzoic Acid, Betaine, Levothyroxine, Dimethylguanosine, 1-Hexadecanoyl-sn-Glycero-3-Phosphocholine, Inosine, 12-Hydroxy-5Z, 8Z, 10E , 14Z-eicosatetraenoic acid, isooctanoic acid, L-pyroglutamic acid, N-L- ⁇ -g
  • metabolic markers are stachydrine, histidine-tryptophan, L-threonine, isosuccinic acid, cortisone, nicotinamide, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, L- Phenylalanyl-L-leucine, fumaric acid, lysophosphatidylethanolamine, guanosine, benzoic acid, levothyroxine, 12-hydroxy-5Z,8Z,10E,14Z-eicosatetraenoic acid, iso Caprylic acid, N-L-gamma-glutamyl-L-leucine. These metabolites changed significantly in patients with early lung cancer, and the specific changes are shown in Table 13:
  • Table 13 Change multiples of metabolites in patients with early lung cancer VS patients with benign lung diseases
  • Example 17 Construction of a diagnostic model for distinguishing between benign and malignant nodules by targeting the metabolome of serum
  • the samples of patients with benign nodules in this example are from Example 3, a total of 88 cases; the patients with malignant nodules are patients with TNM stage IA in Example 3, a total of 55 cases.
  • the metabolite detection and analysis method was the same as in Example 3, and the following 30 metabolites were quantitatively detected, including: stachydrine, histidine-tryptophan, L-threonine, glycolic acid, isosuccinic acid, Pine, niacinamide, 2-pyrrolidone, ⁇ -glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine, fumaric acid, lysophosphatidylethanolamine, ⁇ -hydroxyisoamyl Acid, Guanosine, Benzoic Acid, Betaine, Levothyroxine, Dimethylguanosine, 1-Hexadecanoyl-sn-Glycero-3-Phosphocholine, Inosine, 12-Hy
  • metabolic markers are histidine-tryptophan, glycolic acid, 2-pyrrolidone, L-phenylalanyl-L-leucine, betaine, levothyroxine, dimethylguanosine, 1-hexadecanine Alkanoyl-sn-glycero-3-phosphocholine, inosine, N-L- ⁇ -glutamyl-L-leucine.
  • Example 18 Construction of a lung cancer diagnostic model using tissue samples
  • the metabolite detection and analysis method of this embodiment is the same as that of Example 3, and the following 30 metabolites are quantitatively detected, including: stachydrine, histidine-tryptophan, L-threonine, glycolic acid, iso Succinic acid, cortisone, niacinamide, 2-pyrrolidone, gamma-glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine, fumaric acid, lysophosphatidylethanolamine, alpha -Hydroxyisovaleric Acid, Guanosine, Benzoic Acid, Betaine, Levothyroxine, Dimethylguanosine, 1-Hexadecanoyl-sn-Glycero-3-Phosphocholine, Inosine, 12-Hydroxy-5Z , 8Z, 10E, 14Z-eicosatetraenoic acid, isooctanoic acid, L-py
  • Table 15 The AUC value of a single metabolic marker in the tissue for the diagnosis of lung cancer
  • Example 19 Constructing a lung cancer diagnostic model using urine samples
  • urine samples were collected from 100 lung cancer patients and 100 non-lung cancer patients in Shanghai Chest Hospital under the same conditions, and stored in a -80°C refrigerator for a long time.
  • the metabolite detection and analysis method of this embodiment is the same as that of Example 3, and the following 30 metabolites are quantitatively detected, including: stachydrine, histidine-tryptophan, L-threonine, glycolic acid, iso Succinic acid, cortisone, niacinamide, 2-pyrrolidone, gamma-glutamyl phenylalanine, pyruvate, L-phenylalanyl-L-leucine, fumaric acid, lysophosphatidylethanolamine, alpha -Hydroxyisovaleric Acid, Guanosine, Benzoic Acid, Betaine, Levothyroxine, Dimethylguanosine, 1-Hexadecanoyl-sn-Glycero-3-Phosphocholine, Inosine, 12-Hydroxy-5Z ,8Z,10E,14Z-Eicosatetraenoic acid, Isooctanoic acid, L-p
  • Table 16 The AUC value of a single metabolic marker in urine for the diagnosis of lung cancer
  • more samples can be selected for modeling according to the modeling method of the present invention, so as to increase the accuracy of the model.
  • This embodiment provides a detection kit prepared based on the above metabolic markers, the detection kit includes the following components:
  • Serum sample metabolite extractant 100% pure methanol and 50% acetonitrile aqueous solution are used for sample preparation; 50% acetonitrile aqueous solution can be used as a solvent for dissolving standards.
  • Lung cancer can be diagnosed or monitored by using the detection kit provided in this embodiment.

Abstract

一种用于诊断或监测肺癌的代谢标志物及其筛选方法和应用，该代谢标志物体系选自水苏碱、组氨酸‑色氨酸、L‑苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2‑吡咯烷酮、γ‑谷氨酰苯丙氨酸、丙酮酸、L‑苯丙氨酰‑L‑亮氨酸、富马酸、溶血磷脂酰乙醇胺、α‑羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1‑十六烷酰基‑sn‑甘油‑3‑磷酸胆碱、肌苷、12‑羟基‑5Z,8Z,10E,14Z‑二十碳四烯酸、异辛酸、L‑焦谷氨酸、N‑L‑γ‑谷氨酰‑L‑亮氨酸、α‑酮戊二酸、尿苷、牛磺酸、L‑丝氨酸、苯乙酰谷氨酰胺中的至少一种。所述的代谢标志物能精确诊断肺癌患者，灵敏度和特异性强。

Description

用于肺癌诊断或监测的代谢标志物及其筛选方法和应用 技术领域
本发明涉及检测诊断技术领域，具体涉及一种用于肺癌诊断或监测的代谢标志物及其筛选方法和应用。
背景技术
根据世界卫生组织国际癌症研究机构发布的数据，2020年全球乳腺癌发病例数最多，肺癌处于排名第二，占新发肿瘤病例的11.4％；其死亡率依然排在第一，占死亡病例的18％。而在中国，2020年肺癌的发病率(17.8％)和死亡率(23.8％)均排在第一位，并且超过世界平均水平。尽管针对肺癌的预防和治疗已做出了广泛的努力，肺癌仍然是最致命的癌症之一，是男性癌症死亡的首要原因，也是女性癌症死亡的第二大原因。
根据组织病理学，肺癌主要分为非小细胞肺癌(NSCLC)和小细胞肺癌(SCLC)两类，非小细胞肺癌占比高达85％～90％。临床上，非小细胞肺癌在诊断时常常已进入为晚期。超过半数的非小细胞肺癌患者在确诊后1年内死亡，5年生存率不到20％。但是，早期肺癌患者5年生存率可高达90％以上。因此，对肺癌的早期诊断是肺癌患者获得良好预后以及减少死亡率的重要方法。
临床上确诊肺癌的手段主要依靠超声影像和肺穿刺。其中，超声诊断的灵敏度较低，而肺穿刺对患者的肺部有损伤，存在风险，不易推广，导致很多患者直到肺癌失代偿期才被确诊。有研究发现：基因分子可以作为肺癌诊断的标志物，但是单个基因诊断的敏感度与特异性亟待提高。
另外，美国国家肺部筛查试验(NLST)的数据表明，利用低剂量计算 机断层扫描(LDCT)对高危人群进行早期肺癌筛查，可将肺癌死亡率降低20％，总死亡率降低7％(Bethesda,et al.Reduced lung-cancermortality with low-dose computed tomographic screening.N Engl J Med.2011；365:395-409.)。但是，LDCT存在辐射暴露和假阳性率高等问题，影响基于LDCT筛查在全球范围内的实用性。
代谢组学是继基因组学和蛋白质组学之后的一门新兴学科，是系统生物学的重要组成部分。代谢组学已经发展并迅速渗透到许多领域，其目的是通过监测生物液或组织中小分子代谢物的水平来研究生物系统中的整体代谢差异，并寻找代谢物与病理生理变化的相对关系。肿瘤的发生必然伴随有代谢的改变，但是在早期阶段，小分子代谢物的变化非常微弱，不容易被发现(Pei-Hsuan,C.,Ling,C.,Kenneth,H.et al.Metabolic diversity in human non-small cell lung cancer cells.Molecular Cell.2019,76,1-14.Brandon,F.,Ashley,S.,Ralph,J.D.Metabolic reprogramming and cancer progression.Science.2020,April 10；368.)。大量研究表明，肿瘤的发生和发展与能量代谢密切相关，比如瓦博格效应(Warburg effect、三羧酸循环(TCA)、糖酵解途径等，为满足癌细胞增殖提供能量需求(Vander Heiden,MG.,Cantley,LC.,Thompson,CB.Understanding the Warburg effect:the metabolic requirements ofcell proliferation.Science 2009；324(5930):1029-33.)。因此，基于血液或尿液的生物标记物或多重标记物组合可以补充LDCT筛查的缺陷，可能能够在实施肺癌筛查方面作出重大贡献。
在过去十年中，也有一些科学家尝试在肺癌筛查、诊断、预后等领域应用代谢组学，研究发现了一些在肺癌发生和发展过程中发生改变的代谢物和代谢通路，获得了一些可靠的肺癌诊断生物标志物，例如Mathe,E.A.,Patterson,A.D.,Haznadar,M.et al.Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer.Cancer  Res.2014,74:3259-3270.William,R.W.,Samir,H.,Brian,D.et al.Diacetylspermine is a novel prediagnostic serum biomarker for non-small-cell lung cancer and has additive performance with pro-surfactant protein B.J Clin Oncol.2015,Nov 20；33(33):3880-6.Agnieszka,K.,Szymon,P.,Mariusz,K.et al.Serum lipidome screening in patients with stage I non-small cell lung cancer.Clin Exp Med.2019；19(4):505-513.。但这些研究大多数样本量不大，采用非靶向检测技术，特异性不强，筛选得到的代谢物在临床应用中价值不大。
发明内容
基于此，有必要提供一种用于肺癌诊断或的代谢标志物及其筛选方法和应用，临床样本量大，特异性强，筛选得到的代谢标志物临床应用价值大。
本发明采用如下技术方案：
本发明提供一种用于诊断或监测肺癌的代谢标志物，所述代谢标志物至少选自水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺中的至少一种。
本发明提供一种用于诊断或监测肺癌的代谢标志物，所述代谢标志物至少选自水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨 酸中的至少一种。进一步地，所述代谢标志物优选包括α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺中的至少一种。
在其中一些实施例中，所述代谢标志物优选选自水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸γ-谷氨酰苯丙氨酸、L-苯丙氨酰-L-亮氨酸中的至少一种。进一步地，所述代谢标志物还选自可的松、烟酰胺、2-吡咯烷酮、丙酮酸中的至少一种。进一步地，所述代谢标志物还选自富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素中的至少一种。进一步地，所述代谢标志物还选自二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸中的至少一种。进一步地，所述代谢标志物还选自α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸和苯乙酰谷氨酰胺中的至少一种。
在其中一些实施例中，所述用于诊断或监测肺癌的代谢标志物包括水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、L-苯丙氨酰-L-亮氨酸、α-羟基异戊酸、苯甲酸、二甲基鸟苷、异辛酸、N-L-γ-谷氨酰-L-亮氨酸中的至少一种。进一步地，所述用于诊断或监测肺癌的代谢标志物还包括烟酰胺、丙酮酸、溶血磷脂酰乙醇胺、甜菜碱、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、α-酮戊二酸、尿苷、L-丝氨酸、苯乙酰谷氨酰胺中的至少一种。和/或，进一步地，所述用于诊断或监测肺癌的代谢标志物还包括可的松、富马酸、鸟苷、左旋甲状腺素、L-焦谷氨酸、牛磺酸中的至少一种。
在其中一些实施例中，用于区分肺癌和健康人的代谢标志物至少选自水苏碱、异琥珀酸、可的松、烟酰胺、鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、α-酮戊二酸、尿苷、L-丝氨酸、苯乙酰谷氨酰胺中的至少一种。
在其中一些实施例中，用于区别早期肺癌和非肺癌患者的代谢标志物至少选自组氨酸-色氨酸、乙醇酸、异琥珀酸、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、L-苯丙氨酰-L-亮氨酸、苯甲酸、1-十六烷酰基-sn-甘油-3-磷酸胆碱、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸中的至少一种。
在其中一些实施例中，用于区别早期肺癌和良性肺部疾病的代谢标志物至少选自水苏碱、组氨酸-色氨酸、L-苏氨酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、鸟苷、苯甲酸、左旋甲状腺素、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、N-L-γ-谷氨酰-L-亮氨酸中的至少一种。
在其中一些实施例中，用于区分早期肺癌和健康人的代谢标志物至少选自异琥珀酸、可的松、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、富马酸、α-羟基异戊酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺中的至少一种。
在其中一些实施例中，用于区分良恶性结节的的代谢标志物至少选自组氨酸-色氨酸、乙醇酸、2-吡咯烷酮、L-苯丙氨酰-L-亮氨酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、N-L-γ-谷氨酰-L-亮氨酸中的至少一种。
在ROC曲线评价方法中，本发明中单个代谢标志物在ROC曲线下的面积AUC值为0.702～0.813。多个代谢物组的性能明显优于单个代谢物，ROC曲线下的面积AUC值最高能达到0.998，能够对肺癌患者进行有效诊断。
上述所述用于诊断或监测肺癌的代谢标志物在制备诊断或监测肺癌的代谢物数据库、试剂产品或者试剂盒中的应用。
本发明还提供一种试剂产品或者试剂盒，包括上述所述的用于诊断或监测肺癌的代谢标志物的标准品。
进一步地，所述试剂产品或者试剂盒还包括提取富集所述代谢标志物的溶剂和/或内标物。
本发明还提供一种用于诊断或监测肺癌的代谢标志物的筛选方法，包括如下步骤：分别采集肺癌组样本和非肺癌组样本；构建肺癌血清特异性代谢组数据库；采用LC-MS检测分析获得谱图数据；利用峰面积积分数据进行肺癌组样本和非肺癌组样本的差异代谢分析，确定差异代谢物；使用机器学习随机森林算法对肺癌组样本和非肺癌组样本间代谢物积分数据进行差异代谢物分析，将肺癌组及和非肺癌组血清样本数据的2/3作为训练集，1/3作为测试集；对训练集进行决策树建模，然后组合多个决策树的预测，通过投票得出最终预测结果与有效诊断为肺癌患者一致的代谢物模型；采用测试集对代谢物模型进行验证，确定候选代谢标志物；合并差异分析代谢物和机器学习筛选的候选代谢标志物进行解谱，并通过标准品核对准确性，定量确定用于肺癌诊断的代谢标志物。
具体地，所述肺癌组样本包括不同TNM分期的肺癌样本。
与现有技术相比，本发明采用大规模临床样本和肺癌血清特异性代谢组数据库进行血清代谢组学研究，获得大量与疾病相关的特异性代谢物。进一步利用上述代谢物对应的同位素内标进行精确定性和定量，寻找灵敏度高、特异性好的血清代谢标志物用于肺癌诊断，并依据代谢标志物构建优良诊断模型仍具有重要的临床应用价值。采用上述30种代谢物进行肺癌诊断分析，简便、快速、经济且相对无创，易于广泛推广。
具体实施方式
下面结合具体实施例对本发明作进一步的详细说明，以使本领域的技术人员更加清楚地理解本发明。
以下各实施例，仅用于说明本发明，但不止用来限制本发明的范围。基 于本发明中的具体实施例，本领域普通技术人员在没有做出创造性劳动的情况下，所获得的其他所有实施例，都属于本发明的保护范围。
在本发明实施例中，若无特殊说明，所有原料组分均为本领域技术人员熟知的市售产品；在本发明实施例中，若未具体指明，所用的技术手段均为本领域技术人员所熟知的常规手段。关键仪器信息分别见下表1：
表1实验仪器信息
名称 型号 品牌
HPLC-MS/MS QTRAP 6500+ SCIEX
HPLC-TOF-MS TripleTOF 6600 SCIEX
离心机 5424R Eppendorf
离心浓缩仪 CentriVap LABCONCO
涡旋混合器 VORTEX-5 Kyllin-Be11
实施例1肺癌血清特异性代谢物离子对数据库构建
S1，采集样品
本研究在取得患者同意后，收集健康对照、良性肺部疾病、肺癌患者各20例样本的外周静脉血血清。其中，健康对照为体检后无肺部疾病的健康人群；良性肺部疾病为医院检查后具有包括肺炎、错构瘤、肺气肿、慢性阻塞性肺疾病、上皮样肉芽肿性病变、肺部良性结节等疾病的患者；肺癌患者的诊断标准是经术后病理确诊。所有样本均无其它恶性肿瘤病史，无其他全身性重大疾病，无长期用药的慢性病史。
采血时间均为清晨空腹状态。所有血清样本离心后置于-80℃冰箱内保存，研究时分别取出血清样品解冻后进行后续分析。
S2，样品预处理
从-80℃冰箱中取出样品于冰上解冻至各样本中没有冰块(后续操作都要求在冰上进行)；样本解冻后，涡旋10s混匀，取样本50μL加入到对应编号的离心管中；加入300μL纯甲醇内标提取液；涡旋5min，静置 24h，再置于12000r/min、4℃条件下离心10min；吸取上清液270μL浓缩24h；加入100μL复溶液(乙腈和水的体积比为1:1)，每个样本各取50μL混合成mix检测液。
S3，建库流程
液相色谱串联质谱(LC-MS/MS)实现了从利用色谱进行物质分离到利用质谱进行物质鉴定的整个流程。基于广泛靶向代谢组方法，利用上述mix检测液建立肺癌血清特异性代谢物离子对数据库，代谢物离子对主要有以下四种来源：MIM-EPI采集、TOF采集、迈维标准品数据库和肺癌文献代谢物。其中，通过MIM-EPI检测模式共采集到1100个离子对，TOF检测模式共采集到1300个离子对，迈维标准品数据库共采集到572个离子对，以及肺癌文献相关代谢物113个，汇总和去重上述所有来源离子对信息，最终获得肺癌血清特异性代谢物离子对2988个。
实施例2
本实施例提供一种肺癌诊断代谢标志物的筛选方法，包括如下步骤：
S1，采集样品
本研究在取得患者同意后，收集上海市胸科医院、上海市肺科医院、安徽省肿瘤医院、甘肃省肿瘤医院四个中心的864例肺癌患者和884例非肺癌组样本的外周静脉血血清。其中，肺癌患者的诊断标准是经术后病理确诊；非肺癌组样本包括体检后无肺部疾病的健康人群以及医院检查后具有包括肺炎、错构瘤、肺气肿、慢性阻塞性肺疾病、上皮样肉芽肿性病变、肺部良性结节等良性肺部疾病的患者。所有肺癌患者和非肺癌组样本均无其它恶性肿瘤病史，无其他全身性重大疾病，无长期用药的慢性病史。
这些研究对象的基本信息见表2：
表2研究对象基础信息和病理分期
Figure PCTCN2022130737-appb-000001
Figure PCTCN2022130737-appb-000002
采血时间均为清晨空腹状态。所有血清样本离心后置于-80℃冰箱内保存，研究时分别取出血清样品解冻后进行后续分析。
S2，血清广泛靶向代谢组学分析
(1)样品预处理
从-80℃冰箱中取出步骤S1采集的样品，于冰上解冻至样本中没有冰块(后续操作都要求在冰上进行)；样本解冻后，涡旋10s混匀，取样本50μL加入到对应编号的离心管中；加入300μL纯甲醇内标提取液(含100ppm浓度的L-苯基丙氨酸内标)；涡旋5min，静置24h，再于12000r/min、4℃条件下离心10min；吸取上清液270μL浓缩24h；再加入100μL由乙腈和水按照体积比1:1组成的复溶液中，用于LC-MS/MS分析。每个样本各取20μL混合成质控样本(QC)，每间隔15个样本采集一次。
(2)样品代谢物检测分析
表3实验试剂
化合物 CAS编号 品牌
甲醇 67-56-1 Merck
乙腈 75-05-8 Merck
乙酸 64-19-7 Aladdin
L-苯基丙氨酸 63-91-2 isoreag
确定液相色谱条件如下：
色谱柱：WatersACQUITYUPLC HSS T3 C18 1.8μm，2.1mm*100mm；柱温为40℃；进样量为2μL。
流动相：A相为含0.1％乙酸水溶液，B相为含0.1％乙酸的乙腈溶液。洗脱梯度程序为：0min，A相与B相的体积比为95:5；11.0min，A相与B相的体积比为10:90；12.0min，A相与B相的体积比为10:90；12.1min，A相与B相的体积比为95:5；14.0min，A相与B相的体积比为95:5V/V。流速0.4mL/min。
确定质谱条件如下：
电喷雾离子源(electrospray ionization，ESI)温度500℃，质谱电压5500V(positive)或者-4500V(negative)，离子源气体I(GS I)55psi，气体II(GS II)60psi，气帘气(curtain gas,CUR)25psi，碰撞诱导电离(collision-activated dissociation，CAD)参数设置为高。
在三重四极杆(Qtrap)中，每个离子对是根据优化的去簇电压(declustering potential，DP)和碰撞能(collision energy，CE)进行MRM模式扫描检测。
按照确定的液相色谱条件和质谱条件分别对样本进行分析检测。
(3)图谱峰面积预处理和积分
基于实施例1的肺癌血清特异性代谢物数据库，对样本的代谢物进行质谱定性定量分析。通过液相色谱能够分离不同分子量的代谢物。通过三重四极杆的多反应监测模式(MRM)筛选出每个物质的特征离子，在检测器中获得特征离子的信号强度(CPS)。用MultiQuant3.0.3软件打开样本下机质 谱文件，进行色谱峰的积分和校正工作，每个色谱峰的峰面积(Area)代表对应物质的相对含量，设置S/N>5，保留时间偏移不超过0.2min的峰保留，最后导出所有色谱峰面积积分数据保存。
(4)实验质量控制
通过对不同质控QC样本质谱检测分析的总离子流图进行重叠展示分析，可以判断代谢物提取和检测的重复性，即技术重复。仪器的高稳定性为数据的重复性和可靠性提供了重要的保障。CV值即变异系数(Coefficient of Variation)，是原始数据标准差与原始数据平均数的比，可反映数据离散程度。使用经验累积分布函数(Empirical Cumulative Distribution Function,ECDF)可以分析小于参考值的物质CV出现的频率，QC样本的CV值较低的物质占比越高，代表实验数据越稳定：QC样本CV值小于0.5的物质占比高于85％，表明实验数据较稳定；QC样本CV值小于0.3的物质占比高于75％，表明实验数据非常稳定。同时监测检测过程中L-苯基丙氨酸内标CV值变化情况，内标CV值的变化小于20％，表明检测过程中仪器稳定性好。
(5)数据处理和分析
利用峰面积积分数据在两组间进行差异代谢物分析，并设定P value<0.05为差异显著性标准，筛选差异代谢物作为诊断肺癌的候选代谢标志物。同时使用机器学习随机森林(Random Forest，RF)算法对两组间代谢物积分数据进行分析，将上述肺癌患者样本及对照血清样本数据的2/3作为训练集，1/3作为测试集。对训练集进行决策树建模，然后组合多个决策树的预测，通过投票得出最终预测结果，该代谢物模型可有效诊断肺癌患者。通过测试集样本对上述模型进行验证，所筛选的代谢物成为候选代谢标志物。组间差异分析筛选的代谢物和机器学习筛选代谢物的并集作为诊断肺癌的候选代谢标志物集合。
(6)血清代谢物解谱
上述差异分析和随机森林模型筛选的代谢标志物集合，根据其保留时间，一级和二级推测标志物的分子质量和分子式，并且与代谢物谱图数据库中的谱图信息进行比对，从而对代谢物进行定性鉴定。
进一步采购上述所鉴定代谢物非同位素标准品，核对血清样本中代谢物和对应非同位素标准品在高效液相色谱串联质谱检测中的保留时间，一级和二级质谱信息的一致性，以确定代谢物定性的准确性。
根据上述鉴定方法，我们成功鉴定出30种血清代谢标志物作为适合于肺癌诊断的诊断标志物，见表4：
表4 30种血清代谢标志物
Figure PCTCN2022130737-appb-000003
Figure PCTCN2022130737-appb-000004
这些代谢物可以单独使用或组合使用。
实施例3血清靶向代谢组进行肺癌诊断模型构建
S1，采集样品
本研究在取得患者同意后，收集上海胸科医院的157例肺癌患者和218例非肺癌组样本的外周静脉血血清。其中肺癌的诊断标准是经术后病理确诊的肺癌；非肺癌组样本包括体检后无肺部疾病的健康人群以及医院检查后具有包括肺炎、错构瘤、肺气肿、慢性阻塞性肺疾病、上皮样肉芽肿性病变、肺部良性结节等良性肺部疾病的患者。所有肺癌患者和非肺癌组样本均无其它任何恶性肿瘤病史，无其他全身性重大疾病，无长期用药的慢性病史。这些研究对象的基本信息见表5：
表5研究对象基线信息和病理分期
Figure PCTCN2022130737-appb-000005
Figure PCTCN2022130737-appb-000006
采血时间均为清晨空腹状态。所有血清样本离心后置于-80℃冰箱内保存，研究时取出血清样本解冻后进行后续分析。
S2，样品代谢检测分析
本步骤所采用的实验试剂见下表6：
表6试验试剂
Figure PCTCN2022130737-appb-000007
Figure PCTCN2022130737-appb-000008
Figure PCTCN2022130737-appb-000009
(1)样品预处理
从-80℃冰箱中取出步骤S1采集的样品，于冰上解冻至样本中没有冰块(后续操作都要求在冰上进行)；样本解冻后，涡旋10s混匀；取50μL项目样本加入150μL提取液(提取液含有浓度为100ppm的同位素内标)，涡旋3min，12000rpm，4℃离心10min，-20℃冰箱低温静置过夜；于12000rpm、4℃离心5min，取上清170μL，按顺序转移入96孔板内，过完蛋白沉淀板封口用于LC-MS/MS分析。每个样本各取20μL混合成质控样本(QC)，每间隔15个样本采集一次。
(2)确定检测条件进行检测
鉴于代谢标志物性质的差异，靶向定量检测使用T3柱和Amide柱两种方法进行代谢物分离，以保证代谢物定量的准确性。
确定T3柱液相色谱条件：
色谱柱：Waters ACQUITY UPLC HSS T3 C18 1.8μm，2.1mm*100mm；柱温40℃；进样量2μL。
流动相：A相为含0.1％的乙酸溶液，B相为含0.1％乙酸的乙腈溶液；洗脱梯度程序：0min，A相与B相的体积比为95:5；11.0min，A相与B相的体积比为10:90；12.0min，A相与B相的体积比为10:90；12.1min，A相与B相的体积比为95:5；14.0min，A相与B相的体积比为95:5V/V。流速0.4mL/min。
Amide柱液相色谱条件：
色谱柱：Waters ACQUITY UPLC BEH Amide 1.7μm，2.1mm*100mm；柱温40℃；进样量2μL。
流动相：A相为含20mM甲酸铵和0.4％氨水的水溶液，B相为纯乙腈；洗脱梯度程序：0min，A相与B相的体积比为10:90；9.0min，A相与B相的体积比为40:60；10.0min，A相与B相的体积比为60:40；11.0min，A相与B相的体积比为60:40；11.1min，A相与B相的体积比为10:90；15.0min，A相与B相的体积比为10:90。流速0.4mL/min。
质谱条件：
T3柱和Amide柱质谱采集条件相同，主要包括：电喷雾离子源(electrospray ionization，ESI)温度500℃，质谱电压5500V(positive)、-4500V(negative)，离子源气体I(GS I)55psi，气体II(GS II)60psi，气帘气(curtain gas,CUR)25psi，碰撞诱导电离(collision-activated dissociation，CAD)参数设置为高。在三重四极杆(Qtrap)中，每个离子对是根据优化的去簇电压(declustering potential，DP)和碰撞能(collision energy，CE)进行MRM模式扫描检测。
(3)图谱峰面积预处理和积分
采用MultiQuant 3.0.3软件处理质谱数据，参考标准品的保留时间与峰型信息，对待测物在不同样本中检测到的质谱峰进行积分校正，以确保定性定量的准确。
对所有样本进行定性定量分析，每个色谱峰的峰面积(Area)代表对应物质的相对含量，代入线性方程和计算公式，最终得到所有样本中待测物的定性定量分析结果。
(4)代谢物浓度计算
配制0.01ng/mL、0.05ng/mL、0.1ng/mL、0.5ng/mL、1ng/mL、5ng/mL、 10ng/mL、50ng/mL、100ng/mL、200ng/mL、500ng/mL不同浓度的标准品溶液，获取各个浓度标准品的对应定量信号的质谱峰强度数据；以对应代谢物的外标与内标浓度比(Concentration Ratio)为横坐标，外标与内标峰面积比(Area Ratio)为纵坐标，绘制不同物质的标准曲线。将检测到的所有样本的积分峰面积比值代入标准曲线线性方程进行计算，进一步代入计算公式计算后，MultiQuant 3.0.3中稀释因子设置为3，最终样本中积分峰面积比值代入标准曲线得到的浓度值(ng/mL)即实际样本中该物质的含量数据。
(5)实验质量控制
通过对不同质控QC样本质谱检测分析的总离子流图进行重叠展示分析，可以判断代谢物提取和检测的重复性，即技术重复。仪器的高稳定性为数据的重复性和可靠性提供了重要的保障。CV值即变异系数(Coefficient of Variation)，是原始数据标准差与原始数据平均数的比，可反映数据离散程度。使用经验累积分布函数(Empirical Cumulative Distribution Function,ECDF)可以分析小于参考值的物质CV出现的频率，QC样本的CV值较低的物质占比越高，代表实验数据越稳定：QC样本所有的物质CV值小于0.3，表明实验数据较稳定；QC样本CV值小于0.2的物质占比高于90，表明实验数据非常稳定。同时监测检测过程中同位素内标CV值变化情况，内标CV值的变化小于20％，表明检测过程中仪器稳定性好。
(6)数据处理和分析
肺癌患者组和非肺癌组两组间代谢物浓度进行差异显著性分析，并设定P value<0.05为差异显著性标准，进行差异代谢物筛选。筛选的差异代谢使用二元逻辑回归算法构建分类模型，筛选获得最优分类代谢物组合，得到肺癌诊断模型。单个代谢标志物变化倍数结果见表7：
表7肺癌患者VS非肺癌患者代谢物变化倍数
编号 中文名称 变化倍数 P value
1 水苏碱 1.65 2.37E-04
2 组氨酸-色氨酸 0.78 6.80E-05
3 L-苏氨酸 0.91 3.73E-04
4 乙醇酸 1.54 2.26E-03
5 异琥珀酸 0.91 1.26E-02
6 可的松 0.89 1.73E-03
7 烟酰胺 0.84 1.01E-02
8 2-吡咯烷酮 1.14 1.39E-03
9 γ-谷氨酰苯丙氨酸 1.12 4.50E-03
10 丙酮酸 1.25 7.50E-03
11 L-苯丙氨酰-L-亮氨酸 1.11 3.30E-02
12 富马酸 1.08 1.14E-02
13 溶血磷脂酰乙醇胺 1.11 3.46E-03
14 α-羟基异戊酸 1.10 4.51E-02
15 鸟苷 0.93 1.32E-02
16 苯甲酸 1.07 4.24E-02
17 甜菜碱 1.07 2.89E-02
18 左旋甲状腺素 1.06 4.60E-02
19 二甲基鸟苷 1.11 3.77E-02
20 1-十六烷酰基-sn-甘油-3-磷酸胆碱 1.01 4.54E-02
21 肌苷 0.93 4.87E-02
22 12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸 1.18 3.43E-02
23 异辛酸 0.96 4.19E-02
24 L-焦谷氨酸 0.88 3.94E-02
25 N-L-γ-谷氨酰-L-亮氨酸 0.94 4.74E-02
26 α-酮戊二酸 1.12 1.43E-02
27 尿苷 0.89 2.71E-02
28 牛磺酸 1.05 4.58E-02
29 L-丝氨酸 1.02 4.95E-02
30 苯乙酰谷氨酰胺 0.98 4.84E-02
该诊断模型包含以下30种代谢物：水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、 苯乙酰谷氨酰胺。
这30个差异代谢物单个用于诊断区分肺癌和非肺癌患者的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义；这30个差异代谢物联合用于诊断时，AUC进一步提高，30个联合起来诊断肺癌的AUC达0.996。单个代谢标志物用于肺癌诊断的结果见表8：
表8单个代谢物用于肺癌诊断的AUC值
编号 中文名称 AUC 灵敏度 特异性
1 水苏碱 0.813 80.5％ 81.7％
2 组氨酸-色氨酸 0.802 79.3％ 80.6％
3 L-苏氨酸 0.795 78.4％ 79.9％
4 乙醇酸 0.792 77.6％ 79.4％
5 异琥珀酸 0.790 77.2％ 79.2％
6 可的松 0.786 77.0％ 78.8％
7 烟酰胺 0.776 76.7％ 78.3％
8 2-吡咯烷酮 0.764 74.8％ 77.2％
9 γ-谷氨酰苯丙氨酸 0.759 73.9％ 76.3％
10 丙酮酸 0.754 73.7％ 75.8％
11 L-苯丙氨酰-L-亮氨酸 0.751 73.4％ 75.4％
12 富马酸 0.746 73.0％ 75.0％
13 溶血磷脂酰乙醇胺 0.740 72.9％ 74.8％
14 α-羟基异戊酸 0.738 72.7％ 74.3％
15 鸟苷 0.736 72.6％ 74.2％
16 苯甲酸 0.733 72.5％ 74.0％
17 甜菜碱 0.731 72.3％ 73.8％
18 左旋甲状腺素 0.726 72.1％ 73.3％
19 二甲基鸟苷 0.722 72.0％ 73.1％
20 1-十六烷酰基-sn-甘油-3-磷酸胆碱 0.720 71.8％ 72.9％
21 肌苷 0.718 71.5％ 72.5％
22 12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸 0.717 71.2％ 72.3％
23 异辛酸 0.714 71.0％ 71.9％
24 L-焦谷氨酸 0.712 70.9％ 71.5％
25 N-L-γ-谷氨酰-L-亮氨酸 0.711 70.7％ 71.3％
26 α-酮戊二酸 0.709 70.5％ 70.9％
27 尿苷 0.708 70.3％ 70.8％
28 牛磺酸 0.705 70.2％ 70.8％
29 L-丝氨酸 0.703 70.1％ 70.7％
30 苯乙酰谷氨酰胺 0.702 70.1％ 70.5％
实施例4使用2个血清代谢标志物进行肺癌诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)中二元逻辑回归建模时使用任意2个血清代谢标志物，例如水苏碱和组氨酸-色氨酸，或鸟苷和苯甲酸，或L-丝氨酸和苯乙酰谷氨酰胺。
任意2个差异代谢物单个用于诊断区分肺癌和非肺癌患者的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，示例2个代谢标志物联合诊断的统计数据如下：水苏碱和组氨酸-色氨酸联合起来诊断肺癌的AUC达0.850；鸟苷和苯甲酸联合起来诊断肺癌的AUC达0.757；L-丝氨酸和苯乙酰谷氨酰胺联合起来诊断肺癌的AUC达0.721。
实施例5：使用5个血清代谢标志物进行肺癌诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用任意5个血清代谢标志物，例如水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸和L-苯丙氨酰-L-亮氨酸，或α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素，或α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。
任意5个差异代谢物单个用于诊断区分肺癌和非肺癌患者的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，示例5个代谢标志物联合诊断的统计数据如下：水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸和L-苯丙氨酰-L-亮氨酸联合用于诊断肺癌的AUC值为0.882；α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素联合用于诊断肺癌的AUC值为0.882；α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺联合用于诊断肺癌的AUC值为0.735。
实施例6：使用8个血清代谢标志物进行肺癌诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6) 二元逻辑回归建模时使用任意8个血清代谢标志物，例如水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮，或富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷，或异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。
任意8个差异代谢物单个用于诊断区分肺癌和非肺癌患者的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，示例8个代谢标志物联合诊断的统计数据如下：
水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮联合用于诊断肺癌的AUC值为0.906。
富马酸、溶血磷脂酰乙醇胺、α马羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷联合用于诊断肺癌的AUC值为0.794。
异辛酸、L-焦谷氨酸、N-L-、、谷氨酰-L-亮氨酸、α氨酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺联合用于诊断肺癌的AUC值为0.756。
实施例7：使用11个血清代谢标志物进行肺癌诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用任意11个血清代谢标志物，例如水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸，或1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。
任意11个差异代谢物单个用于诊断区分肺癌和非肺癌患者的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，示例8个代谢标志物联合诊断的统计数据如下：
水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰 胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸联合用于诊断肺癌的AUC达0.918。
1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-、酸谷氨酰-L-亮氨酸、α氨酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺联合用于诊断肺癌的AUC达0.791。
实施例8：使用15个血清代谢标志物进行肺癌诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用任意15个血清代谢标志物，例如水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷，或苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z，8Z，10E，14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。
任意15个差异代谢物单个用于诊断区分肺癌和非肺癌患者的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。其中，示例15个差异代谢物联合用于诊断肺癌的AUC值统计结果如下：
水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷联合用于诊断肺癌的AUC值达0.933。
苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z，8Z，10E，14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-、酸谷氨酰-L-亮氨酸、α氨酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺联合用于诊断肺癌的AUC值达0.813。
实施例9：使用18个血清代谢标志物进行肺癌诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用任意18个血清代谢标志物，例如水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素。
任意18个差异代谢物单个用于诊断区分肺癌和非肺癌患者的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。示例的18个差异代谢物联合用于诊断肺癌的AUC进一步提高至0.945。
实施例10：使用21个血清代谢标志物进行肺癌诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用任意21个血清代谢标志物，例如水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷。
任意21个差异代谢物单个用于诊断区分肺癌和非肺癌患者的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。示例的21个差异代谢物联合用于诊断肺癌的AUC提高至0.963。
实施例11：使用25个血清代谢标志物进行肺癌诊断模型的构建
本实施例与实施例3的研究对象、检测分析方法相同，仅在步骤(6)二元逻辑回归建模时使用任意25个血清代谢标志物，例如水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四 烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸。
任意25个差异代谢物单个用于诊断区分肺癌和非肺癌患者的能力较强，ROC曲线下面积(AUC)均大于0.7，具有临床诊断意义。示例的25个差异代谢物联合用于诊断时，AUC进一步提高，联合起来诊断肺癌的AUC达0.981。
实施例12：血清靶向代谢组诊断区分肺癌和健康人诊断模型构建
本实施例的样本来源于实施例3，肺癌患者157例，健康人70例。代谢物检测和分析方法与实施例3相同，对以下30个代谢物进行定量检测，包括：水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。
进一步优选代谢标志物水苏碱、异琥珀酸、可的松、烟酰胺、鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、α-酮戊二酸、L-丝氨酸、苯乙酰谷氨酰胺。这些代谢物在肺癌患者体内发生显著变化，具体变化结果见表9：
表9肺癌患者VS健康人代谢物变化倍数
中文名称 变化倍数 P value
水苏碱 2.13 3.55E-06
异琥珀酸 0.80 1.46E-05
可的松 0.80 8.74E-06
烟酰胺 0.69 2.42E-05
鸟苷 0.85 2.88E-02
1-十六烷酰基-sn-甘油-3-磷酸胆碱 1.07 3.70E-03
肌苷 0.88 3.51E-02
12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸 1.19 1.99E-02
α-酮戊二酸 1.13 1.20E-02
尿苷 0.89 3.31E-02
L-丝氨酸 1.09 1.58E-02
苯乙酰谷氨酰胺 0.93 2.61E-02
这12个差异代谢物单个用于诊断区分早期肺癌和非肺癌患者的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断早期肺癌的AUC为0.731～0.982。
实施例13：血清靶向代谢组诊断区分肺癌和良性肺部疾病患者诊断模型构建
本实施例的样本来源于实施例3，肺癌患者157例，良性肺部疾病患者148例，包括肺炎、错构瘤、肺气肿、慢性阻塞性肺疾病、上皮样肉芽肿性病变、肺部良性结节等肺部疾病。
代谢物检测和分析方法与实施例3相同，对以下30个代谢物进行定量检测，包括：水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z，8Z，10E，14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。
进一步优选代谢标志物组氨酸-色氨酸、L-苏氨酸、乙醇酸、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸。这些代谢物在肺癌患者体内发生显著变化，具体变化结果见表10：
表10肺癌患者VS良性肺部疾病患者代谢物变化倍数
中文名称 变化倍数 P value
组氨酸-色氨酸 0.69 6.67E-06
L-苏氨酸 0.92 8.39E-03
乙醇酸 1.33 1.31E-02
2-吡咯烷酮 1.13 1.32E-02
γ.谷氨酰苯丙氨酸 1.19 4.48E-03
丙酮酸 1.10 3.20E-02
L-苯丙氨酰-L-亮氨酸 1.22 2.37E-04
富马酸 1.11 3.29E-03
异辛酸 0.88 4.21E-02
L-焦谷氨酸 0.89 4.09E-02
N-L-γ-谷氨酰-L-亮氨酸 0.87 2.27E-02
这11个差异代谢物单个用于诊断区分早期肺癌和非肺癌患者的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断早期肺癌的AUC为0.711～0.906。
实施例14：血清靶向代谢组诊断区分早期肺癌和非肺癌患者诊断模型构建
本实施例的样本来源于实施例3，非肺癌患者样本218例，包括健康人70例，良性肺部疾病患者148例；早期肺癌患者为实施例3中TNM分期为I期和II期的患者，共65例。良性肺部疾病包括肺炎、错构瘤、肺气肿、慢性阻塞性肺疾病、上皮样肉芽肿性病变、肺部良性结节等肺部疾病。
代谢物检测和分析方法与实施例3相同，对以下30个代谢物进行定量检测，包括：水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z，8Z，10E，14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。
进一步优选代谢标志物组氨酸-色氨酸、乙醇酸、异琥珀酸、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、L-苯丙氨酰-L-亮氨酸、苯甲酸、1-十六烷酰基-sn-甘油-3-磷酸胆碱、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸。这些代谢物在早期肺癌患者体内发生显著变化，具体变化结果见表11：
表11早期肺癌患者VS非肺癌患者代谢物变化倍数
中文名称 变化倍数 P value
组氨酸-色氨酸 0.74 1.65E-06
L-苏氨酸 0.94 4.85E-02
乙醇酸 1.32 3.90E-02
异琥珀酸 0.89 2.92E-02
2-吡咯烷酮 1.11 4.52E-02
γ-谷氨酰苯丙氨酸 1.12 1.44E-02
L-苯丙氨酰-L-亮氨酸 1.07 2.94E-02
苯甲酸 1.16 2.21E-02
1-十六烷酰基-sn-甘油-3-磷酸胆碱 1.06 9.20E-03
N-L-γ-谷氨酰-L-亮氨酸 0.92 3.81E-02
α-酮戊二酸 1.15 1.80E-02
这10个差异代谢物单个用于诊断区分早期肺癌和非肺癌患者的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断早期肺癌的AUC为0.713～0.916。
实施例15：血清靶向代谢组诊断区分早期肺癌和健康人诊断模型构建
本实施例的样本来源于实施例3，健康人样本70例；早期肺癌患者为实施例3中TNM分期为I期和II期的患者，共65例。代谢物检测和分析方法与实施例3相同，对以下30个代谢物进行定量检测，包括：水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z，8Z，10E，14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。
进一步优选代谢标志物异琥珀酸、可的松、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、富马酸、α-羟基异戊酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。这些代谢物在早期肺癌患者体内发生显著变化，具体变化结果见表12：
表12早期肺癌患者VS健康人代谢物变化倍数
中文名称 变化倍数 P value
异琥珀酸 0.78 3.32E-05
可的松 0.83 2.29E-03
2-吡咯烷酮 1.15 1.17E-02
γ-谷氨酰苯丙氨酸 1.21 3.30E-03
丙酮酸 1.25 1.91E-03
富马酸 0.91 4.26E-02
α-羟基异戊酸 1.13 3.52E-02
甜菜碱 1.09 4.91E-02
左旋甲状腺素 0.92 4.15E-02
二甲基鸟苷 0.79 2.36E-02
1-十六烷酰基-sn-甘油-3-磷酸胆碱 1.12 2.10E-05
L-焦谷氨酸 1.08 2.75E-02
N-L-γ-谷氨酰-L-亮氨酸 0.91 4.38E-02
尿苷 0.81 3.91E-02
牛磺酸 0.88 3.73E-02
L-丝氨酸 1.05 2.65E-02
苯乙酰谷氨酰胺 0.88 1.67E-02
这17个差异代谢物单个用于诊断区分早期肺癌和健康人的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断早期肺癌的AUC为0.745～0.996。
实施例16：血清靶向代谢组诊断区分早期肺癌患者和良性肺部疾病患者诊断模型构建
本实施例的样本来源于实施例3，良性肺部疾病患者样本148例，包括肺炎、错构瘤、肺气肿、慢性阻塞性肺疾病、上皮样肉芽肿性病变、肺部良性结节等肺部疾病的患者；早期肺癌患者为实施例3中TNM分期为I期和II期的患者，共65例。代谢物检测和分析方法与实施例3相同，对以下30个代谢物进行定量检测，包括：水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z，8Z，10E，14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。
进一步优选代谢标志物水苏碱、组氨酸-色氨酸、L-苏氨酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、鸟苷、苯甲酸、左旋甲状腺素、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、N-L-γ-谷氨酰-L-亮氨酸。这些代谢物在早期肺癌患者体内发生显著变化，具体变化结果见表13：
表13早期肺癌患者VS良性肺部疾病患者代谢物变化倍数
中文名称 变化倍数 P value
水苏碱 1.14 3.28E-02
组氨酸-色氨酸 0.66 4.48E-07
L-苏氨酸 0.95 4.73E-02
异琥珀酸 0.95 3.92E-02
可的松 0.96 4.88E-02
烟酰胺 0.92 3.55E-02
2-吡咯烷酮 1.09 2.76E-02
γ-谷氨酰苯丙氨酸 0.89 3.51E-02
L-苯丙氨酰-L-亮氨酸 1.18 1.84E-02
富马酸 1.05 4.62E-02
溶血磷脂酰乙醇胺 1.13 2.30E-02
鸟苷 1.19 4.50E-01
苯甲酸 1.16 2.37E-02
左旋甲状腺素 1.09 4.08E-02
12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸 0.86 4.60E-02
异辛酸 1.15 1.94E-02
N-L-γ-谷氨酰-L-亮氨酸 0.89 2.07E-02
这17个差异代谢物单个用于诊断区分早期肺癌和健康人的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断早期肺癌的AUC为0.705～0.906。
实施例17：血清靶向代谢组诊断区分良恶性结节诊断模型构建
本实施例的良性结节患者样本来源于实施例3，共88例；恶性结节患者为实施例3中TNM分期为IA期的患者，共55例。代谢物检测和分析方法与实施例3相同，对以下30个代谢物进行定量检测，包括：水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、 γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。
进一步优选代谢标志物组氨酸-色氨酸、乙醇酸、2-吡咯烷酮、L-苯丙氨酰-L-亮氨酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、N-L-γ-谷氨酰-L-亮氨酸。这些代谢物在恶性结节患者体内发生显著变化，具体变化结果见表14：
表14恶性结节患者VS良性结节患者代谢物变化倍数
中文名称 变化倍数 P value
组氨酸-色氨酸 0.58 2.07E-05
乙醇酸 1.34 1.13E-02
2-吡咯烷酮 1.13 3.80E-02
L-苯丙氨酰-L-亮氨酸 1.33 4.18E-04
甜菜碱 1.16 5.87E-04
左旋甲状腺素 1.09 4.06E-02
二甲基鸟苷 1.13 3.86E-02
1-十六烷酰基-sn-甘油-3-磷酸胆碱 1.04 4.36E-01
肌苷 1.14 2.99E-02
N-L-E-谷氨酰-L-亮氨酸 0.85 1.37E-02
这10个差异代谢物单个用于诊断区分良恶性结节患者的能力较强，并且各种代谢物组合用于诊断时，AUC进一步提高，其诊断早期肺癌的AUC为0.753～0.966。
实施例18：使用组织样本构建肺癌诊断模型
1、研究对象
本研究在取得患者同意后，在相同条件下收集上海胸科医院30例肺癌患者肿瘤病灶区组织和30例正常肺组织当做健康对照。分别采集的组织样品先通过纱布蘸取表面的血液，随后迅速转移至液氮中短期保存，最后转移 至-80℃冰箱中长期保存。
2、样品预处理
(1)从-80℃冰箱中取出样品于冰上解冻至能切动的状态(后续操作都要求在冰上进行)，准备好称量组织样本需要用的刀片、镊子、钢珠、滤纸、酒精、水等。
(2)取出样本，用滤纸吸掉样本表面的血液，用手术刀切下一块样本，用镊子夹到去皮后的离心管中，称量50±2mg，记录每个样本的称量重量。
(3)向称量好的样本中加入一粒钢珠，在30HZ的条件下匀浆4次，每次30s，根据匀浆情况，可适当增加匀浆时间.。
(4)向匀浆好的离心管中加入1mL 70％甲醇内标提取液。
(5)振荡5min，冰上静置15min。
(6)在4℃条件下，12000r/min离心10min。
(7)离心后吸取上清液400uL到对应的离心管中。
(8)静置于-20℃冰箱，过夜。
(9)在4℃条件下，12000r/min再离心3min。
(10)离心后取上清200μL按顺序转移入96孔板内，过完蛋白沉淀板封口用于LC-MS/MS分析。
本实施例与实施例3的代谢物检测和分析方法相同，对以下30个代谢物进行定量检测，包括：水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z，8Z，10E，14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。组织中单个代谢标志物用于肺癌诊断的结果见表15：
表15组织中单个代谢标志物用于肺癌诊断的AUC值
编号 中文名称 AUC 灵敏度 特异性
1 水苏碱 0.842 82.0％ 83.3％
2 组氨酸-色氨酸 0.833 80.5％ 82.4％
3 L-苏氨酸 0.824 80.1％ 81.6％
4 乙醇酸 0.816 79.1％ 80.8％
5 异琥珀酸 0.813 78.7％ 80.6％
6 可的松 0.811 78.5％ 80.5％
7 烟酰胺 0.805 78.0％ 80.3％
8 2-吡咯烷酮 0.786 77.1％ 78.7％
9 Γ-谷氨酰苯丙氨酸 0.777 76.1％ 78.3％
10 丙酮酸 0.774 75.5％ 77.8％
11 L-苯丙氨酰-L-亮氨酸 0.771 75.0％ 77.4％
12 富马酸 0.768 74.7％ 77.0％
13 溶血磷脂酰乙醇胺 0.765 74.5％ 76.7％
14 Α-羟基异戊酸 0.760 74.2％ 76.3％
15 鸟苷 0.759 74.1％ 76.2％
16 苯甲酸 0.757 74.0％ 76.0％
17 甜菜碱 0.756 73.8％ 75.8％
18 左旋甲状腺素 0.753 73.6％ 75.3％
19 二甲基鸟苷 0.752 73.5％ 75.1％
20 1-十六烷酰基-sn-甘油-3-磷酸胆碱 0.750 73.3％ 74.9％
21 肌苷 0.748 73.0％ 74.5％
22 12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸 0.747 72.8％ 74.2％
23 异辛酸 0.745 72.6％ 73.8％
24 L-焦谷氨酸 0.743 72.4％ 73.5％
25 N-L-％谷氨酰-L-亮氨酸 0.740 72.2％ 73.3％
26 α6酮戊二酸 0.737 72.0％ 73.0％
27 尿苷 0.735 71.8％ 72.8％
28 牛磺酸 0.732 71.7％ 72.7％
29 L-丝氨酸 0.727 71.5％ 72.6％
30 苯乙酰谷氨酰胺 0.722 71.3％ 72.4％
这30个差异代谢物单个用于诊断区分肺癌和非肺癌患者的能力较强；且各种代谢物组合用于诊断时，AUC进一步提高，其诊断肺癌的AUC为0757～0.998。
实施例19：使用尿液样本构建肺癌诊断模型
1、研究对象
本研究在取得患者同意后，在相同条件下收集上海胸科医院100例肺癌患者和100例非肺癌患者尿液样本，-80℃冰箱中长期保存。
2、样品预处理
(1)从-80℃冰箱中取出样品于冰上解冻至样本中没有冰块(后续操作都要求在冰上进行)。
(2)样本解冻后，涡旋10s混匀，取200uL样本到离心管中。
(3)向离心管中加入400uL纯水提取液。
(4)涡旋3min，在4℃条件下，12000r/min下离心10min。
(5)离心后吸取上清液150uL按顺序转移入96孔板内，过完蛋白沉淀板封口用于LC-MS/MS分析。
本实施例与实施例3的代谢物检测和分析方法相同，对以下30个代谢物进行定量检测，包括：水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺。尿液中单个代谢标志物用于肺癌诊断的结果见表16：
表16尿液中单个代谢标志物用于肺癌诊断的AUC值
编号 中文名称 AUC 灵敏度 特异性
1 水苏碱 0.808 80.0％ 81.2％
2 组氨酸-色氨酸 0.797 78.8％ 80.1％
3 L-苏氨酸 0.790 77.8％ 79.5％
4 乙醇酸 0.783 77.1％ 78.9％
5 异琥珀酸 0.778 76.8％ 78.6％
6 可的松 0.776 76.5％ 78.3％
7 烟酰胺 0.771 76.2％ 77.8％
8 2-吡咯烷酮 0.765 75.2％ 77.1％
9 γ7谷氨酰苯丙氨酸 0.758 74.5％ 76.5％
10 丙酮酸 0.751 74.0％ 75.8％
11 L-苯丙氨酰-L-亮氨酸 0.746 73.6％ 75.3％
12 富马酸 0.741 73.0％ 74.8％
13 溶血磷脂酰乙醇胺 0.733 72.4％ 74.3％
14 α4羟基异戊酸 0.725 72.2％ 73.8％
15 鸟苷 0.724 72.1％ 73.7％
16 苯甲酸 0.722 72.0％ 73.5％
17 甜菜碱 0.721 71.8％ 73.3％
18 左旋甲状腺素 0.718 71.6％ 72.8％
19 二甲基鸟苷 0.717 71.5％ 72.6％
20 1-十六烷酰基-sn-甘油-3-磷酸胆碱 0.715 71.3％ 72.4％
21 肌苷 0.713 71.0％ 72.0％
22 12-羟基-5Z，8Z，10E，14Z-二十碳四烯酸 0.712 70.7％ 71.8％
23 异辛酸 0.709 70.5％ 71.4％
24 L-焦谷氨酸 0.707 70.4％ 71.0％
25 N-L-％谷氨酰-L-亮氨酸 0.706 70.2％ 70.8％
26 α6酮戊二酸 0.704 70.0％ 70.4％
27 尿苷 0.703 69.8％ 70.3％
28 牛磺酸 0.700 69.7％ 70.3％
29 L-丝氨酸 0.698 69.6％ 70.2％
30 苯乙酰谷氨酰胺 0.695 69.1％ 70.0％
这30个差异代谢物单个用于诊断区分肺癌和非肺癌患者的能力较强；且各种代谢物组合用于诊断时，AUC进一步提高，其诊断肺癌的AUC为0.712～0.961。
在实际应用中，可以按照本发明建模方法选取更多的样本进行建模，增加模型的准确度。
实施例20检测试剂盒
本实施例提供一种基于上述代谢标志物制备的检测试剂盒，该检测试剂盒包括如下成分：
代谢标志物的标准品：水苏碱、组氨酸-色氨酸、L-苏氨酸、乙醇酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺，各标准品分别封装或标准品混合溶液封装。
血清样本代谢物提取剂：100％纯甲醇和50％乙腈水溶液用于样品制备；50％乙腈水溶液可以用作溶解标准品的溶剂。
内标物：L-苯基丙氨酸。
当然，设计检测试剂盒时，并不需要完全包含上述30个标志物的标准品，可以仅使用其中几个，还可以使用其中几个或全部与其他标志物进行组合。这些标准品可以单独封装，也可以制成混合物封装。
采用本实施例提供的检测试剂盒，能够拥有诊断或监测肺癌。
在此有必要指出的是，以上实施例仅限于对本发明的技术方案做进一步的阐述和说明，并不是对本发明的技术方案的进一步的限制，本发明的方法仅为较佳的实施方案，并非用于限定本发明的保护范围。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (14)

  1. 一种用于诊断或监测肺癌的代谢标志物，其特征在于，所述代谢标志物至少选自水苏碱、组氨酸-色氨酸、异琥珀酸、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、苯甲酸、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺中的至少一种。
  2. 根据权利要求1所述用于诊断或监测肺癌的代谢标志物，其特征在于，所述代谢标志物还选自L-苏氨酸、乙醇酸、可的松、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、甜菜碱、肌苷、L-焦谷氨酸中的至少一种。
  3. 根据权利要求1所述用于诊断或监测肺癌的代谢标志物，其特征在于，所述代谢标志物至少选自水苏碱、组氨酸-色氨酸、异琥珀酸、γ-谷氨酰苯丙氨酸、L-苯丙氨酰-L-亮氨酸中的至少一种。
  4. 根据权利要求3所述用于诊断或监测肺癌的代谢标志物，其特征在于，所述代谢标志物还选自L-苏氨酸、乙醇酸、可的松、烟酰胺、2-吡咯烷酮、丙酮酸、富马酸、溶血磷脂酰乙醇胺、α-羟基异戊酸、鸟苷、苯甲酸、甜菜碱、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、肌苷、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、L-焦谷氨酸、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸、尿苷、牛磺酸、L-丝氨酸和苯乙酰谷氨酰胺中至少一种。
  5. 根据权利要求1所述用于诊断或监测肺癌的代谢标志物，其特征在于，所述代谢标志物至少选自组氨酸-色氨酸、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、L-苯丙氨酰-L-亮氨酸、异辛酸、N-L-γ-谷氨酰-L-亮氨酸中的至少一种；和/或所述代谢标志物还选自L-苏氨酸、乙醇酸、富马酸、L-焦谷 氨酸中的至少一种。
  6. 根据权利要求1所述的用于诊断或监测肺癌的代谢标志物，其特征在于，所述代谢标志物至少选自水苏碱、异琥珀酸、可的松、烟酰胺、1-十六烷酰基-sn-甘油-3-磷酸胆碱、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、α-酮戊二酸、尿苷、L-丝氨酸、苯乙酰谷氨酰胺中的至少一种；和/或所述代谢标志物还包括鸟苷、肌苷中的至少一种。
  7. 根据权利要求1所述的用于诊断或监测肺癌的代谢标志物，其特征在于，所述代谢标志物至少选自组氨酸-色氨酸、异琥珀酸、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、L-苯丙氨酰-L-亮氨酸、苯甲酸、1-十六烷酰基-sn-甘油-3-磷酸胆碱、N-L-γ-谷氨酰-L-亮氨酸、α-酮戊二酸中的至少一种；和/或所述代谢标志物还包括乙醇酸。
  8. 根据权利要求1所述的用于诊断或监测肺癌的代谢标志物，其特征在于，所述代谢标志物至少选自水苏碱、组氨酸-色氨酸、异琥珀酸、可的松、烟酰胺、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、L-苯丙氨酰-L-亮氨酸、苯甲酸、左旋甲状腺素、12-羟基-5Z,8Z,10E,14Z-二十碳四烯酸、异辛酸、N-L-γ-谷氨酰-L-亮氨酸中的至少一种；和/或所述代谢标志物还包括L-苏氨酸、富马酸、溶血磷脂酰乙醇胺、鸟苷中的至少一种。
  9. 根据权利要求1所述的用于诊断或监测肺癌的代谢标志物，其特征在于，所述代谢标志物至少选自异琥珀酸、可的松、2-吡咯烷酮、γ-谷氨酰苯丙氨酸、丙酮酸、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、N-L-γ-谷氨酰-L-亮氨酸、尿苷、牛磺酸、L-丝氨酸、苯乙酰谷氨酰胺中的至少一种；和/或所述代谢标志物还包括富马酸、α-羟基异戊酸、甜菜碱、L-焦谷氨酸中的至少一种。
  10. 根据权利要求1所述的用于诊断或监测肺癌的代谢标志物，其特征在于，所述代谢标志物至少选自组氨酸-色氨酸、2-吡咯烷酮、L-苯丙氨酰-L- 亮氨酸、左旋甲状腺素、二甲基鸟苷、1-十六烷酰基-sn-甘油-3-磷酸胆碱、N-L-γ-谷氨酰-L-亮氨酸中的至少一种；和/或所述代谢标志物还选自乙醇酸、甜菜碱、肌苷中的至少一种。
  11. 权利要求1至10任一项所述的用于诊断或监测肺癌的代谢标志物在制备诊断或监测肺癌的代谢物数据库、试剂产品或者试剂盒中的应用。
  12. 一种试剂产品或者试剂盒，其特征在于，包括权利要求1至10任一项所述的用于诊断或监测肺癌的代谢标志物的标准品。
  13. 权利要求1至10所述的用于诊断或监测肺癌的代谢标志物的筛选方法，其特征在于，包括如下步骤：
    分别采集肺癌组样本和非肺癌组样本；
    构建肺癌血清特异性代谢物数据库：
    采用LC-MS检测分析获得谱图数据；
    利用峰面积积分数据进行肺癌组样本和非肺癌组样本的差异代谢分析，确定差异代谢物；
    使用机器学习随机森林算法对肺癌组样本和非肺癌组样本间代谢物积分数据进行分析，将肺癌组及和非肺癌组血清样本数据的2/3作为训练集，1/3作为测试集；对训练集进行决策树建模，然后组合多个决策树的预测，通过投票得出最终预测结果与有效诊断为肺癌患者一致的代谢物模型；采用测试集对代谢物模型进行验证，确定候选代谢标志物；
    合并差异分析代谢物和机器学习筛选的候选代谢物进行解谱，并通过标准品核对准确性，确定权利要求1至10任一项所述的代谢标志物。
  14. 根据权利要求13所述的用于诊断或监测肺癌的代谢标志物的筛选方法，其特征在于，所述肺癌组样本包括不同TNM分期的肺癌样本。
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