Blood marker combination for distinguishing lung non-cancerous diseases and early lung cancer as well as screening method and application of blood marker combination

Abstract

The invention provides a blood marker combination for distinguishing lung non-cancerous diseases and early lung cancer as well as a screening method and application thereof, and belongs to the technical field of in-vitro diagnosis. By analyzing clinical indexes and metabonomics data of blood samples of patients with lung non-cancerous diseases and early-stage lung cancer, 27 specific blood marker combinations related to lung cancer are found, and can be used for developing a non-invasive, non-invasive and high-accuracy diagnostic product for distinguishing lung non-cancerous diseases and early-stage lung cancer. Effective clinical treatment strategies and guidance are provided for early discovery, early diagnosis and early intervention of the lung cancer.

Description

Technical Field

[0001] The present invention belongs to the technical field of in vitro diagnosis, and in particular relates to a blood marker combination for distinguishing non-cancerous lung diseases and early lung cancer, and a screening method and application thereof. Background Art

[0002] Lung cancer is a malignant tumor. Most lung cancer patients have no obvious clinical symptoms in the early stage of the disease, and about 75% of patients are already in the advanced stage of lung cancer when diagnosed. Due to the lack of timely interventional surgical treatment, lung cancer patients miss the best time for treatment, which is also the reason why the 5-year survival rate of most patients with advanced lung cancer is low after surgery. Therefore, early diagnosis and early treatment are crucial to improve the prognosis of lung cancer patients and improve their quality of life.

[0003] At present, the commonly used techniques for lung cancer diagnosis mainly include imaging examination, sputum cytology, pathological examination and serum tumor markers. Among them, imaging examination, such as low-dose CT (LDCT), is the most commonly used technique for lung cancer diagnosis. However, due to the insufficient clarity of LDCT images, it is only suitable for the initial screening and detection of lung nodules. Among the suspicious lung nodules detected by LDCT, the proportion of lung cancer finally confirmed is less than 10%. Sputum cytology is one of the simplest and most convenient non-invasive diagnostic methods for central lung cancer, but it has certain false positive and false negative possibilities, and its classification is more difficult. Pathological examination diagnosis is considered to be the gold standard for clinical diagnosis of many cancers. Due to its invasiveness, it is more harmful to the patient's body and is expensive, so it is not suitable for early diagnosis and screening of large-scale populations of lung cancer. At present, the commonly used serum markers of primary lung cancer in clinical practice include carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin fragment 19 (CYFRA21-1), pro-gastrin-releasing peptide (Pro-GRP) and squamous cell carcinoma antigen (SCC). Among them, NSE and Pro-GRP are related to small cell lung cancer, and elevated CEA levels are helpful in the diagnosis of lung adenocarcinoma. However, the sensitivity and specificity of serum markers for early lung cancer are not ideal.

[0004] Since the above-mentioned technologies for diagnosing and screening early lung cancer have certain limitations, in order to further improve the survival rate of lung cancer patients, it is urgently necessary to develop a non-invasive, non-invasive, and highly accurate early screening method, which is of great significance to improving the treatment effect of lung cancer and improving patient prognosis. Summary of the invention

[0005] The purpose of the present invention is to provide a blood marker combination for distinguishing non-cancerous lung diseases and early lung cancer, as well as a screening method and application thereof. The present invention provides a non-invasive, non-invasive, and highly accurate blood biomarker that can be used for the diagnosis and screening of non-cancerous lung diseases and early lung cancer.

[0006] The present invention provides a blood marker combination for distinguishing non-cancerous lung diseases from early lung cancer, including 13 clinical indicators and 14 metabolomics features;

[0007] The clinical indicators include: the ratio of monocyte ratio to potassium concentration, the ratio of monocyte ratio to activated partial thromboplastin time, the ratio of red blood cell count to activated partial thromboplastin time, the ratio of potassium concentration to international normalized ratio, the ratio of potassium concentration to thrombin time, the ratio of activated partial thromboplastin time to thrombin time, the ratio of carcinoembryonic antigen to serum globulin concentration, the ratio of international normalized ratio to activated partial thromboplastin time, the ratio of carcinoembryonic antigen to potassium concentration, the ratio of carcinoembryonic antigen concentration to prothrombin time, the ratio of carcinoembryonic antigen concentration to activated partial thromboplastin time, the ratio of carcinoembryonic antigen concentration to lactate dehydrogenase concentration, and the ratio of red blood cell count to potassium concentration;

[0008] The metabolome features include: the peak intensity ratio of phosphatidylethanolamine 38:3p and sebacic acid, the peak intensity ratio of phosphatidylinositol 38:3 and sebacic acid, the peak intensity ratio of sphingomyelin d38:5 and sebacic acid, the peak intensity ratio of phosphatidylethanolamine 38:5 and L-prolyl-L-threonine, the peak intensity ratio of gluconolactone and hexadecanedioic acid, the peak intensity ratio of phosphatidylcholine 38:5 and sebacic acid, the peak intensity ratio of gluconolactone and sebacic acid, the peak intensity ratio of gluconolactone and trans-cyclohexane-1,2- The peak intensity ratio of dicarboxylic acids, the peak intensity ratio of phosphatidylcholine 38:6e and trans-cyclohexane-1,2-dicarboxylic acid, the peak intensity ratio of phosphatidylethanolamine 36:1p and trans-cyclohexane-1,2-dicarboxylic acid, the peak intensity ratio of phosphatidylethanolamine 38:5 and nerolidol, the peak intensity ratio of phosphatidylserine 38:0 and sebacic acid, the peak intensity ratio of dimethylphosphatidylethanolamine 38:4 and sebacic acid, and the peak intensity ratio of phosphatidylcholine 38:6e and D-glucosamine-6-phosphate.

[0009] The present invention also provides the use of a reagent or a kit for detecting the blood marker combination described in the above scheme in the preparation of a product for distinguishing non-cancerous lung diseases and early lung cancer.

[0010] Preferably, the non-cancerous lung disease includes one or more of pneumonia, tuberculosis and lung infection.

[0011] The present invention also provides the use of a reagent or a kit for detecting the blood marker combination described in the above scheme in the preparation of a lung disease diagnosis product.

[0012] Preferably, the reagent or kit for detecting the blood marker combination described in the above scheme comprises a liquid chromatography-mass spectrometry detection reagent.

[0013] The present invention also provides a screening method for a combination of blood markers for distinguishing non-cancerous lung diseases from early lung cancer, comprising the following steps:

[0014] Plasma samples were collected from patients with non-cancerous lung diseases and early-stage lung cancer;

[0015] Separating the organic phase and the aqueous phase from plasma samples of patients with non-cancerous lung diseases and early lung cancer patients; wherein the organic phase is non-polar metabolites and the aqueous phase is polar metabolites;

[0016] Liquid chromatography-mass spectrometry was used to detect polar metabolites in the aqueous phase and non-polar metabolites in the organic phase, respectively, to obtain metabolome detection data of plasma samples from patients with non-cancerous lung diseases and early lung cancer patients;

[0017] Preprocessing and metabolite identification are performed on the metabolomics detection data of the patients with non-cancerous lung diseases and early lung cancer patients to obtain metabolomics data;

[0018] Collect clinical indicators of patients with non-cancerous lung diseases and early-stage lung cancer to obtain clinical indicator data;

[0019] Performing data standardization on the metabolomics data and clinical indicator data of the patients with non-cancerous lung diseases and early lung cancer patients, respectively, to obtain standardized metabolomics data and clinical indicator data;

[0020] The standardized metabolomics data and clinical index data are used to calculate the ratio of all possible combinations of two variables, and the combinations of the top 100 feature pairs are selected based on the P-values ​​between the variables from small to large, and the clinical indexes and metabolomics features after the ratios are calculated are respectively used to form new data sets;

[0021] The new data set consisting of clinical indicators and metabolomics characteristics was subjected to Pearson correlation analysis and Lasso regression feature screening, respectively, to obtain a combination of blood markers for distinguishing non-cancerous lung diseases and early lung cancer.

[0022] Preferably, the clinical index detection includes routine blood test and blood biochemistry test.

[0023] Preferably, the Pearson correlation analysis includes: extracting all clinical indicator pairs and metabolic feature pairs with an absolute value of the correlation coefficient R greater than or equal to 0.9, and eliminating them starting from the ones with the largest number of participations, until the correlation between any pair of clinical indicators and metabolic group features is less than a threshold value of 0.9.

[0024] The present invention also provides a method for constructing a diagnostic model for non-cancerous lung diseases and early lung cancer, comprising the following steps:

[0025] A multivariate ROC curve analysis was performed on the blood biomarkers screened by the screening method described in the above scheme.

[0026] Preferably, the multivariate ROC curve analysis includes: selecting 3 / 4 samples from the modeling group data as a training set, the remaining 1 / 4 samples as a test set, and using a support vector machine to randomly iterate 1000 times, and constructing a diagnostic model for non-cancerous lung diseases and early lung cancer by statistically calculating the average value of the final model accuracy.

[0027] The present invention provides a blood marker combination for distinguishing non-cancerous lung diseases from early lung cancer. The present invention analyzes the clinical indicators and metabolomics data of blood samples of patients with non-cancerous lung diseases and early lung cancer, and finds 27 specific blood marker combinations related to lung cancer. This can be used to develop a non-invasive, non-invasive, and highly accurate diagnostic product for distinguishing non-cancerous lung diseases from early lung cancer, providing effective clinical treatment strategies and guidance for early detection, early diagnosis, and early intervention of lung cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0029] Figure 1 This is a multivariate ROC curve analysis result diagram for distinguishing between non-cancerous lung disease group and early lung cancer group based on 27 blood biomarkers in the modeling group;

[0030] Figure 2 This is the result of multivariate ROC curve analysis based on 27 blood biomarkers to distinguish between non-cancerous lung disease group and early lung cancer group in the validation group. DETAILED DESCRIPTION

[0031] The present invention provides a blood marker combination for distinguishing non-cancerous lung diseases from early lung cancer, including 13 clinical indicators and 14 metabolomics features;

[0032] The clinical indicators include: the ratio of monocyte ratio to potassium concentration, the ratio of monocyte ratio to activated partial thromboplastin time, the ratio of red blood cell count to activated partial thromboplastin time, the ratio of potassium concentration to international normalized ratio, the ratio of potassium concentration to thrombin time, the ratio of activated partial thromboplastin time to thrombin time, the ratio of carcinoembryonic antigen to serum globulin concentration, the ratio of international normalized ratio to activated partial thromboplastin time, the ratio of carcinoembryonic antigen to potassium concentration, the ratio of carcinoembryonic antigen concentration to prothrombin time, the ratio of carcinoembryonic antigen concentration to activated partial thromboplastin time, the ratio of carcinoembryonic antigen concentration to lactate dehydrogenase concentration, and the ratio of red blood cell count to potassium concentration;

[0033] The metabolome features include: the peak intensity ratio of phosphatidylethanolamine 38:3p and sebacic acid, the peak intensity ratio of phosphatidylinositol 38:3 and sebacic acid, the peak intensity ratio of sphingomyelin d38:5 and sebacic acid, the peak intensity ratio of phosphatidylethanolamine 38:5 and L-prolyl-L-threonine, the peak intensity ratio of gluconolactone and hexadecanedioic acid, the peak intensity ratio of phosphatidylcholine 38:5 and sebacic acid, the peak intensity ratio of gluconolactone and sebacic acid, the peak intensity ratio of gluconolactone and trans-cyclohexane-1,2- The peak intensity ratio of dicarboxylic acids, the peak intensity ratio of phosphatidylcholine 38:6e and trans-cyclohexane-1,2-dicarboxylic acid, the peak intensity ratio of phosphatidylethanolamine 36:1p and trans-cyclohexane-1,2-dicarboxylic acid, the peak intensity ratio of phosphatidylethanolamine 38:5 and nerolidol, the peak intensity ratio of phosphatidylserine 38:0 and sebacic acid, the peak intensity ratio of dimethylphosphatidylethanolamine 38:4 and sebacic acid, and the peak intensity ratio of phosphatidylcholine 38:6e and D-glucosamine-6-phosphate.

[0034] In the specific implementation of the present invention, the blood marker combination consists of the 13 clinical indicators and 14 metabolomics features described in the above scheme.

[0035] In the specific implementation of the present invention, the units of the clinical indicators are as follows: monocyte ratio: %; red blood cell count: ×10 12 / L; Potassium: mmol / L; International Normalized Ratio: no unit; Activated Partial Thromboplastin Time: sec; Thrombin Time: sec; Carcinoembryonic Antigen: ng / mL; Serum Globulin: g / L; Prothrombin Time: sec; Lactate Dehydrogenase: U / L.

[0036] In the specific implementation of the present invention, the monocyte ratio is the same as the monocyte percentage or the monocyte ratio.

[0037] In the specific implementation of the present invention, the peak intensity is the relative concentration content.

[0038] The present invention also provides the use of a reagent or a kit for detecting the blood marker combination described in the above scheme in the preparation of a product for distinguishing non-cancerous lung diseases and early lung cancer.

[0039] In the specific implementation of the present invention, the non-cancerous lung disease includes one or more of pneumonia, tuberculosis and lung infection.

[0040] In the specific implementation of the present invention, the product includes a reagent or a kit.

[0041] The present invention also provides the use of a reagent or a kit for detecting the blood marker combination described in the above scheme in the preparation of a lung disease diagnosis product.

[0042] In the specific implementation of the present invention, the reagent or kit for detecting the blood marker combination described in the above scheme includes a liquid chromatography-mass spectrometry detection reagent.

[0043] In the specific implementation of the present invention, the mononuclear cell ratio, potassium concentration, activated partial thromboplastin time, red blood cell count, international normalized ratio, thrombin time, carcinoembryonic antigen and serum globulin prothrombin time and lactate dehydrogenase concentration are clinically tested;

[0044] The peak intensities of phosphatidylethanolamine 38:3p, sebacic acid, phosphatidylinositol 38:3, sphingomyelin d38:5, phosphatidylethanolamine 38:5, L-prolyl-L-threonine, gluconolactone, hexadecanedioic acid, phosphatidylcholine 38:5, trans-cyclohexane-1,2-dicarboxylic acid, phosphatidylcholine 38:6e, phosphatidylethanolamine 36:1p, nerolidol, phosphatidylserine 38:0, dimethylphosphatidylethanolamine 38:4 and D-glucosamine-6-phosphate were detected by liquid chromatography-mass spectrometry.

[0045] The present invention also provides a screening method for a combination of blood markers for distinguishing non-cancerous lung diseases from early lung cancer, comprising the following steps:

[0046] Plasma samples were collected from patients with non-cancerous lung diseases and early-stage lung cancer;

[0047] Separating the organic phase and the aqueous phase from plasma samples of patients with non-cancerous lung diseases and early lung cancer patients; wherein the organic phase is non-polar metabolites and the aqueous phase is polar metabolites;

[0048] Liquid chromatography-mass spectrometry was used to detect polar metabolites in the aqueous phase and non-polar metabolites in the organic phase, respectively, to obtain metabolome detection data for patients with non-cancerous lung diseases and early lung cancer.

[0049] Preprocessing and metabolite identification are performed on the metabolomics detection data of the patients with non-cancerous lung diseases and early lung cancer patients to obtain metabolomics data;

[0050] Collect clinical indicators of patients with non-cancerous lung diseases and early-stage lung cancer to obtain clinical indicator data;

[0051] Performing data standardization on the metabolomics data and clinical indicator data of the patients with non-cancerous lung diseases and early lung cancer patients, respectively, to obtain standardized metabolomics data and clinical indicator data;

[0052] The standardized metabolomics data and clinical index data are used to calculate the ratio of all possible combinations of two variables, and the combinations of the top 100 feature pairs are selected based on the P-values ​​between the variables from small to large, and the clinical indexes and metabolomics features after the ratios are calculated are respectively used to form new data sets;

[0053] The new data set consisting of clinical indicators and metabolomics characteristics was subjected to Pearson correlation analysis and Lasso regression feature screening, respectively, to obtain a combination of blood markers for distinguishing non-cancerous lung diseases and early lung cancer.

[0054] The present invention first collects plasma samples from patients with non-cancerous lung diseases and patients with early lung cancer.

[0055] In the specific implementation of the present invention, the inclusion criteria for patients with non-cancerous lung diseases and early lung cancer are as follows: (1) age ≥ 18 years old; (2) patients with early lung cancer are diagnosed with lung cancer by the gold standard method of histopathological examination, stage I, and are considered as patients with early lung cancer; (3) patients diagnosed with benign lung diseases by biopsy / postoperative pathology or clinically diagnosed by comprehensive evaluation by clinicians, including pneumonia, tuberculosis, lung infection and other diseases. The exclusion criteria for patients with non-cancerous lung diseases and early lung cancer are as follows: (1) pregnancy or lactation; (2) emergency or rescue; (3) history of malignant tumors or any anti-tumor treatment before sampling; (4) patients with multiple primary malignant tumors at the same time. Patient clinical data: When collecting plasma samples from subjects, the basic information and health status of the subjects are collected through questionnaires, including basic demographic characteristics, lifestyle, medical history, medication history and other information. At the same time, the specific clinical index data of the subjects (including blood routine, blood biochemistry and tumor markers, etc.) are collected for subsequent statistical analysis and model construction.

[0056] In the specific implementation of the present invention, the plasma is preferably plasma obtained after thawing of a frozen plasma sample. In the specific implementation of the present invention, the frozen plasma sample is stored at -80°C. In the specific implementation of the present invention, the thawing is preferably thawing on ice; after the thawing, vortex mixing for 10 seconds is also included.

[0057] In the specific implementation process of the present invention, blood collection, analysis and preservation are carried out, and the steps are as follows: use purple-headed EDTA anticoagulant blood collection tubes and red-headed blood collection tubes to collect fasting blood samples from patients in the morning. For the blood sample collected by the purple-headed blood collection tube, gently invert the blood collection tube several times to ensure that the blood and the anticoagulant are fully mixed to avoid blood coagulation, and then centrifuge at 4°C and 3500rpm for 10 minutes to separate the plasma for routine blood analysis. After analysis, it is stored in a -80°C refrigerator for subsequent metabolomics testing. After the red-headed blood collection tube collects blood, it is placed at room temperature for 30 minutes until the blood is completely coagulated to form a blood clot, and then centrifuged at 4°C and 3500rpm for 10 minutes to separate the serum for blood biochemical analysis.

[0058] After collecting plasma samples from patients with non-cancerous lung diseases and patients with early lung cancer respectively, the present invention separates an organic phase and an aqueous phase from the plasma samples from patients with non-cancerous lung diseases and patients with early lung cancer; wherein the organic phase is a non-polar metabolite and the aqueous phase is a polar metabolite.

[0059] In the specific implementation of the present invention, the step of separating the organic phase and the aqueous phase from the plasma includes: mixing the plasma and the solvent, extracting to obtain an extract; adding a mixture of methanol and water to the extract, centrifuging, and separating the upper organic phase and the lower aqueous phase. In the specific implementation of the present invention, the volume ratio of the plasma to the solvent is 1:10; the solvent is a mixture of methyl tert-butyl ether and methanol; the volume ratio of methyl tert-butyl ether and methanol is 3:1. In the specific implementation of the present invention, based on the volume of 100 μL of plasma, the volume of the mixture of methanol and water added to the extract is 500 μL; the volume ratio of methanol to water is 3:1; the speed of the centrifugation is 12700 rpm, and the time is 5 min.

[0060] After obtaining the aqueous phase, the present invention obtains polar metabolites from the aqueous phase.

[0061] In the specific implementation process of the present invention, obtaining polar metabolites from the aqueous phase includes the following steps: mixing the aqueous phase and ice methanol, shaking, incubating and centrifuging to precipitate protein; after protein precipitation, collecting the supernatant into a polar centrifuge tube and spinning it dry, adding mass spectrometry grade water to the polar centrifuge tube sample after spinning dryness, and vortex mixing, sonication and centrifugation are performed in sequence to obtain polar metabolites in the aqueous phase.

[0062] In the specific implementation of the present invention, the volume ratio of the water phase and ice methanol is 4:11; the water phase and ice methanol are mixed in a centrifuge tube; the water phase and ice methanol are mixed in a manner including shaking, incubation and centrifugation; the incubation time is 60 minutes; the incubation temperature is -20°C, and the centrifugation time is 10 minutes; the spin-drying device is a vacuum concentrator (Speed-Vac). In the specific implementation of the present invention, the volume ratio of the supernatant and the mass spectrometry-grade water used for re-dissolving the polar centrifuge tube sample after spin-drying is 5:1; the ultrasonic time after re-dissolution is 5 minutes; the speed of centrifugation after ultrasound is 12700rpm; the centrifugation time after ultrasound is 5 minutes.

[0063] After obtaining the organic phase, the present invention obtains non-polar metabolites from the organic phase.

[0064] In the specific implementation process of the present invention, obtaining non-polar metabolites from the organic phase includes the following steps: after the organic phase is spin-dried, it is re-dissolved with a mixture of acetonitrile and isopropanol, vortexed, ultrasonicated and centrifuged in sequence, and the supernatant is collected to obtain the non-polar metabolites of the organic phase.

[0065] In the specific implementation process of the present invention, the volume of the organic phase is 350 μL, the amount of the organic phase and the mixed solution of acetonitrile and isopropanol is 200 μL; the spin drying is performed using a vacuum concentrator (Speed-Vac); the volume ratio of acetonitrile to isopropanol in the mixed solution of acetonitrile and isopropanol is 3:1; the ultrasonic time is 5 min; the centrifugal temperature is 4°C, the time is 5 min, and the speed is 12700 rpm.

[0066] The present application adopts liquid chromatography-mass spectrometry to detect polar metabolites in the aqueous phase and non-polar metabolites in the organic phase, respectively, to obtain metabolome detection data of patients with non-cancerous lung diseases and early lung cancer patients.

[0067] In the specific implementation of the present invention, the liquid chromatography-mass spectrometry is preferably high-resolution liquid chromatography-mass spectrometry (UHPLC-MS).

[0068] In the specific implementation process of the present invention, the loading amount of the polar metabolites in the aqueous phase is 180 μL, which is loaded into a 2 mL glass injection bottle, and the polar metabolites in the aqueous phase are detected by a LC-MS machine.

[0069] In the specific implementation of the present invention, the liquid chromatography conditions for detecting polar metabolites in the aqueous phase include: the chromatographic column is Waters ACQUTTY HSS T3 chromatographic column, specification: 1.8μm2.1*100mm, system: ACQUITY UPLC I-Class liquid chromatography system (Waters), for small molecule separation; the column temperature is 40°C; the mobile phase includes mobile phase A and mobile phase B, the mobile phase A is an aqueous solution containing 0.1% formic acid by volume, and the mobile phase B is an acetonitrile solution containing 0.1% formic acid by volume; the flow rate of the mobile phase is 0.4mL / min; the separation elution gradient is: 0min, 1% mobile phase B by volume; 11min, 40% mobile phase B by volume, 13min, 70% mobile phase B by volume; 15min, 99% mobile phase B by volume; 18min, 99% mobile phase B by volume; 19min, 1% mobile phase B by volume; the sample injection volume is 3μL; the automatic sampler temperature is 10°C. The mass spectrometer used was a Q-Exactive mass spectrometer system (Thermo Fisher Scientific).

[0070] In the specific implementation of the present invention, the liquid chromatography conditions for detecting the non-polar metabolites (lipid metabolites) in the organic phase include: the chromatographic column is Waters ACQUTTY BEH C8, specifications: 1.7μm 2.1*100mm, system: ACQUITY UPLC I-Class liquid chromatography system (Waters) for small molecule separation; column temperature is 60°C; mobile phase includes mobile phase A and mobile phase B, mobile phase A is water; mobile phase B is an acetonitrile / isopropanol solution containing 0.1% acetic acid and 1% ammonium acetate (the volume ratio of acetonitrile and isopropanol is 7:3); the flow rate of the mobile phase is 0.4mL / min; the separation elution gradient is: 0-1min, 55% mobile phase B by volume; 4min, 75% mobile phase B by volume; 12min, 89% mobile phase B by volume; 15min, 100% mobile phase B by volume; 19.5min, 100% mobile phase B by volume; 19.51min, 55% mobile phase B by volume; 24min, 55% mobile phase B by volume; injection volume is 2μL; the temperature of the automatic sampler is 10°C. The mass spectrometer used was a Q-Exactive mass spectrometer system (Thermo Fisher Scientific).

[0071] In the specific implementation process of the present invention, the mass spectrometry conditions for detecting polar metabolites in the aqueous phase and detecting non-polar metabolites in the organic phase include: the system used is: Q-Exactive mass spectrometry system (Thermo Fisher Scientific), the MS instrument uses Orbitrap high-resolution mass spectrometry (Thermo Fisher) equipped with an electrospray ionization source (ESI); full scan and data-dependent acquisition are used to obtain primary and secondary mass spectrometry data; the full scan mass spectrum range is 100-1500Da; the scanning range of the secondary scan mode (Full MS / dd-MS2) is 100-310, 300-710 and 700-1500Da; positive and negative ionization modes are used to collect data; automatic gain control (AGC) is 3E+6; Maximum IT is 200ms; full scan ... scan) with a resolution of 70,000 FWHM (@200 m / z); in the secondary scan mode (FullMS / dd-MS2), the resolution of the secondary mass spectrometer was 17,500, the quadrupole window was 1.5 m / z, the automatic gain control (AGC) was 1E+5, the maximum ion injection time was 50 ms, the relative collision energy (HCD) was 30 eV; the ion spray voltage was +3,500 V in positive mode; the ion spray voltage was -3,000 V in negative mode; the nebulizer was 20 psi; the sheath gas temperature was 400°C, and the sheath gas flow rate was 10 L / min.

[0072] After obtaining the metabolomic detection data of patients with non-cancerous lung diseases and early lung cancer, the present invention performs preprocessing and metabolite identification on the metabolomic detection data of patients with non-cancerous lung diseases and early lung cancer to obtain metabolomic data.

[0073] In the specific implementation process of the present invention, the preprocessing includes: peak extraction, peak alignment, peak filtering and filling of missing values; first, the original mass spectrometry data is subjected to peak extraction, peak alignment and peak filtering, aiming to reduce the systematic data interference caused by instrument detection time fluctuations. The polar metabolite detection platform and the lipid metabolite detection platform are respectively subjected to the above-mentioned processing to avoid mutual interference between different detection platforms. The retention time of the data set is calibrated according to the historical detection conditions of the instrument. After calibration, different filtering criteria are applied to remove the following interfering peaks: (i) isotope peaks; (ii) fragments caused by the cleavage of the analyte source due to the ionization method; (iii) redundant peaks, such as additional low-intensity adducts of the same analyte and redundant derivatives, to ensure the quality of the analyzable data set, and to eliminate the missing values ​​of characteristic peaks > 50% in all samples. The random forest chain equation (MICE Forest) is used to fill the missing values, and the processed data matrix is ​​used as the raw data for statistical analysis, and a series of data preprocessing such as homogenization and standardization can be further performed to obtain the final mass spectrometry matrix data.

[0074] In the specific implementation process of the present invention, the identification of the metabolites includes the following steps: according to the original data, the spectrum information of the compound parent ion and the secondary fragment ion is obtained by software analysis, such as the mass-to-charge ratio (m / z) of the primary mass spectrum, the fragments of the secondary ion, and the retention time (Retention time), and the metabolites are qualitatively identified by matching the spectrum information of the primary and secondary metabolites in the database; the commonly used metabolite databases include HMDB ( www.hmdb.ca )、PubChem( https: / / pubchem.ncbi.nlm.nih.gov )、MassBank( http: / / www.massbank.jp )、Mass Bank of North America( https: / / massbank.us ), and the lipid database Lipidmap ( https: / / www.lipidmaps.org ) and Lipidblast( https: / / fiehnlab.ucdavis.edu / projects / LipidBlast ); Based on the metabolites identified in the relevant database, the metabolites are finally verified according to the retention time, MS1, and MS2 mass spectrometry information when the standard is separated under the same chromatographic column and mass spectrometry conditions. The standard for metabolite identification is that the retention time is within 0.1min deviation and the mass accuracy deviation of the metabolite is less than 10ppm.

[0075] The present invention collects clinical indicators of patients with non-cancerous lung diseases and early lung cancer patients to obtain clinical indicator data of patients with non-cancerous lung diseases and lung cancer patients. In the specific implementation of the present invention, clinical indicator detection includes blood routine, blood biochemical detection and tumor markers.

[0076] The present invention performs data standardization processing on the metabolomics data of the patients with non-cancerous lung diseases and early lung cancer and the clinical indicator data of the patients with non-cancerous lung diseases and early lung cancer, respectively, to obtain standardized metabolomics data and clinical indicator data.

[0077] In the specific implementation process of the present invention, logarithmic transformation (Log-transformation) and standard score (Z-score) processing are performed on metabolomics data and clinical index data respectively. Log-transformation scales the original data to a specific interval, eliminating the order of magnitude difference between different samples; Z-score converts a certain original score into a standard score, which can make the originally incomparable values ​​comparable. After the data is preprocessed, two sets of standardized data matrices are obtained.

[0078] After obtaining the standardized metabolomics data and clinical indicator data, the present invention calculates the ratio of all possible combinations of two variables for the standardized metabolomics data and clinical indicator data, selects the combinations of the TOP 100 feature pairs based on the P-values ​​between the variables from small to large, and respectively forms new data sets with the clinical indicators and metabolomics features after calculating the ratios.

[0079] The present invention performs Pearson correlation analysis and Lasso regression feature screening on a new data set composed of clinical indicators and metabolomics characteristics, respectively, to obtain a blood marker combination for distinguishing non-cancerous lung diseases and early lung cancer.

[0080] In the specific implementation of the present invention, highly correlated variables may cause the model to overfit, and may also cause large fluctuations in actual application. In order to avoid the overfitting of the model and improve the prediction stability of the model, features with correlations higher than the preset threshold will be eliminated to ensure that the model has good discrimination and stability. Specifically: |R x,y |≥β, for x,y=1,2,…,n; where the 1st, 2nd,…,nth features are K1, K2,…,K n , the correlation coefficient between the two features is R, Rx,y represents the correlation coefficient between features Kx and Ky. The threshold β is set according to the correlation coefficient. When the absolute value of the correlation coefficient Rx,y between the two features Kx and Ky is ≥β, the two features are considered to be a feature pair with high correlation. Features that appear in multiple high-correlation feature pairs are preferentially eliminated; the Pearson correlation analysis includes: extracting all clinical indicator pairs and metabolic feature pairs with an absolute value of the correlation coefficient R greater than or equal to 0.9, and eliminating them starting from the ones with the most participation times until the correlation between any pair of clinical indicators and metabolomics features is less than the threshold of 0.9.

[0081] In the specific implementation process of the present invention, Lasso regression, also known as the Least Absolute Shrinkage and Selection Operator, adds a penalty term in the model estimation, compresses some unnecessary variable regression coefficients to zero and then removes them from the model, thereby achieving the purpose of variable screening, and can effectively reduce data dimensions in high-dimensional data analysis.

[0082] The present invention also provides a method for constructing a diagnostic model for non-cancerous lung diseases and early lung cancer, comprising the following steps:

[0083] A multivariate ROC curve analysis was performed on the blood biomarkers screened by the screening method described in the above scheme.

[0084] In the specific implementation process of the present invention, the multivariate ROC curve analysis includes: selecting 3 / 4 samples from the modeling group data as a training set, the remaining 1 / 4 samples as a test set, and randomly iterating the support vector machine (SVM) for 1000 times, and constructing a diagnostic model for non-cancerous lung diseases and early lung cancer by statistically calculating the average value of the final model accuracy.

[0085] The present invention calculates the ratio of the combination of all possible two variables of clinical indicators and metabolomic characteristics based on the preliminary screened clinical indicator data and metabolomic characteristic data, selects the combination of the TOP 100 feature pairs from small to large according to the significance arrangement order based on the P-value, and forms a data set of clinical indicators and metabolomic characteristics after calculating the ratio.

[0086] In the specific implementation process of the present invention, the BiomarkerAnalysis module tool in the MetaboAnalyst website is used to calculate the composition ratios between all possible two variables of clinical indicators and metabolomics characteristics.

[0087] After obtaining the data set, the present invention uses Lasso regression to screen out a blood marker combination for distinguishing non-cancerous lung diseases and early lung cancer from the data set.

[0088] The present invention uses Lasso regression to reduce the dimension of characteristic variables in the data set and further screen out the optimal blood marker combination.

[0089] In order to further illustrate the present invention, a blood marker combination for distinguishing non-cancerous lung diseases and early lung cancer provided by the present invention, as well as its screening method and application are described in detail below in conjunction with the accompanying drawings and examples, but they should not be construed as limiting the scope of protection of the present invention.

[0090] Example 1

[0091] Step 1: Test object and sample information

[0092] 1. Subjects

[0093] 1) The inclusion criteria for patients with non-cancerous lung diseases and early-stage lung cancer are as follows:

[0094] (1) Male or female aged ≥18 years; (2) patients with early lung cancer were diagnosed by the gold standard method of histopathological examination, and patients with lung cancer stage I were included and considered as patients with early lung cancer; (3) patients with benign lung diseases diagnosed by biopsy / postoperative pathology or clinically diagnosed by comprehensive evaluation by clinicians, including patients with pneumonia, tuberculosis, lung infection and other diseases.

[0095] 2) The exclusion criteria for patients with non-cancerous lung diseases and early lung cancer are as follows:

[0096] (1) Pregnancy or lactation; (2) Emergency or rescue; (3) History of malignant tumor or any anti-tumor treatment before sampling; (4) Patients with multiple primary malignant tumors at the same time.

[0097] 3) Obtaining clinical information of subjects: When collecting plasma samples from subjects, collect basic information such as the subjects' basic situation and health status through questionnaires, including basic demographic characteristics, lifestyle, medical history, medication history, etc. At the same time, collect specific clinical indicator data of the subjects (including blood routine, blood biochemistry and tumor markers, etc.) for subsequent statistical analysis and model construction.

[0098] Step 2: Plasma sample pretreatment and metabolite detection

[0099] 1. Plasma sample pretreatment

[0100] After the plasma sample was taken out of the -80°C freezer, it was thawed on ice and then vortexed for 10 seconds. Subsequently, 100 μL of plasma was taken and placed in 1000 μL of pre-cooled methyl tert-butyl ether and methanol mixture, and vortexed to obtain a sample extract. Next, 500 μL of a mixture of methanol and water (the volume ratio of methanol to water was 3:1) was added to the sample extract, and centrifuged at 12700 rpm for 5 minutes to separate the upper organic phase and the lower aqueous phase.

[0101] - Aqueous phase: 400 μL of hydrophilic metabolites were taken from the lower layer and transferred to a centrifuge tube, 1100 μL of ice methanol was added, shaken, incubated at -20°C for 60 min, and centrifuged for 10 min to precipitate the protein; after protein precipitation, 1000 μL of the supernatant was transferred to the corresponding polar centrifuge tube, and then spun dry in a vacuum concentrator (Speed-Vac). 200 μL of mass spectrometry-grade water was added to the dried polar centrifuge tube for reconstitution, the centrifuge tube was vortexed, ultrasonic-assisted treatment was performed for 5 min, and then the centrifuge tube was centrifuged at 4°C for 5 min (12700 rpm); 180 μL of supernatant was taken from the centrifuge tube and transferred to a 2 mL glass injection bottle for polar metabolite detection (LC-MS).

[0102] -Organic phase: Take 350 μL of organic phase from the upper layer and transfer it to a centrifuge tube, and use a vacuum concentrator (Speed-Vac) to spin dry. Add 200 μL of acetonitrile / isopropanol (v / v, 3:1) to the dried sample, vortex mix the centrifuge tube, treat it with ultrasound assistance (ultrasonic treatment at room temperature) for 5 minutes, and then centrifuge the centrifuge tube at 4°C for 5 minutes (12700rpm); take 180 μl of supernatant from the centrifuge tube into a 2mL glass injection vial, which is a non-polar substance, and detect it on the machine (LC-MS).

[0103] 2.UHPLC-MS detection:

[0104] (1) Polar metabolite chromatographic parameters: Polar metabolite analysis was performed using Waters ACQUTTY HSS T3 chromatographic column (1.8 μm 2.1*100 mm, 1.8 μm) was used for small molecule separation, and the column temperature was 40°C; the liquid chromatography and mass spectrometer used ACQUITYUPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific), respectively; the mobile phase composition was as follows: mobile phase A was an aqueous solution containing 0.1% formic acid; mobile phase B was an acetonitrile solution containing 0.1% formic acid, and the flow rate was 0.4 mL / min; the separation elution gradient was as follows: 0-1 min, 1% mobile phase B; 11-13 min, 40%-70% mobile phase B, 13-15 min, 70%-99% mobile phase B; 15-18 min, 99% mobile phase B; 19 min, 1% mobile phase B; the sample injection volume was 3 μL, and the autosampler temperature was 10°C;

[0105] (2) Chromatographic parameters of non-polar metabolites: Lipid metabolites were analyzed using Waters ACQUTTY A BEHC8 (2.1*100 mm, 1.7 μm) chromatographic column was used for small molecule separation, and the column temperature was 60°C. The liquid chromatography and mass spectrometry used ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific), respectively. The mobile phase composition was as follows: mobile phase A was water; mobile phase B was acetonitrile / isopropanol (7 / 3, V / V), both containing 0.1% acetic acid and 1% ammonium acetate, and the flow rate was 0.4 mL / min. The separation elution gradient was as follows: 0-1 min, 55% mobile phase B; 1-4 min, 55%-75% mobile phase B; 4-12 min, 75%-89% mobile phase B; 12-15 min, 89%-100% mobile phase B; 15-19.5 min, 100% mobile phase B; 19.51-24 min, 55% mobile phase B; the injection volume was 2 μL, and the autosampler temperature was 10°C;

[0106] (3) LC-MS mass spectrometry parameters: Full scan and data-dependent acquisition (DDA) were used to obtain primary (MS1) and secondary mass spectrometry (MS2) data for both polar and non-polar metabolites. The full scan mass spectrum range was 100-1500 Da. The secondary scan mode (Full MS / dd-MS2) scan range was 100-310, 300-710 and 700-1500 Da. The MS instrument used an Orbitrap high-resolution mass spectrometer (Thermo Fisher) equipped with an electrospray ionization source (ESI), and data were collected in positive and negative ionization modes. The specific parameters are as follows: automatic gain control (AGC) is 3E+6, Maximum IT is 200ms, the first-level full scan (Full scan) resolution is 70,000FWHM (@200m / z), in the second-level scan mode (Full MS / dd-MS2), the resolution of the secondary mass spectrometer is 17,500, the quadrupole window is 1.5m / z, the automatic gain control (AGC) is 1E+5, the maximum ion injection time is 50ms, and the relative collision energy (HCD) is 30eV; the ion source voltage is +3,500V in positive ion mode and -3,500V in negative ion mode, the nebulizer is 20psi, the sheath gas temperature is 400℃, and the sheath gas flow rate is 10L / min.

[0107] Step 3: Metabolomics data preprocessing and metabolite identification

[0108] 1. Metabolomics data preprocessing:

[0109] The metabolomics data preprocessing steps include peak extraction, peak alignment, peak filtering, and missing value filling. First, the original mass spectrometry data is subjected to peak extraction, peak alignment, and peak filtering to reduce the systematic data interference caused by instrument detection time fluctuations. The polar metabolite detection platform and the lipid metabolite detection platform are processed separately to avoid mutual interference between different detection platforms. The retention time of the data set is calibrated according to the historical detection of the instrument. After calibration, different filtering criteria are applied to remove the following interfering peaks: (i) isotope peaks; (ii) fragments caused by the ionization method that cause the analyte to be broken into fragments in the source; (iii) redundant peaks, such as additional low-intensity adducts of the same analyte and redundant derivatives, to ensure the quality of the analyzable data set, and to remove the missing values ​​of characteristic peaks > 50% in all samples. The random forest chain equation (MICEForest) is used to fill the missing values. The processed data matrix is ​​used as the raw data for statistical analysis, and a series of data preprocessing such as homogenization and standardization can be further performed to obtain the final mass spectrometry matrix data.

[0110] 2. Identification of metabolites:

[0111] The original data is analyzed by the software to obtain the spectral information of the compound parent ion and secondary fragment ion, such as the mass-to-charge ratio (m / z) of the primary mass spectrum, the fragments of the secondary ion, and the retention time. The metabolites are qualitatively identified by matching the spectral information of the primary and secondary metabolites in the database. Commonly used metabolite databases include HMDB ( www.hmdb.ca )、PubChem( https: / / pubchem.ncbi.nlm.nih.gov )、MassBank( http: / / www.massbank.jp )、MassBank ofNorthAmerica( https: / / massbank.us ), and the lipid database Lipidmap ( https: / / www.lipidm aps.org ) and Lipidblast( https: / / fiehnlab.ucdavis.edu / projects / LipidBlast ); Based on the metabolites identified in the relevant database, the metabolites are finally verified according to the retention time, MS1, and MS2 mass spectrometry information of the standard when separated under the same chromatographic column and mass spectrometry conditions. The standard for metabolite identification is that the retention time is within 0.2min deviation and the mass accuracy deviation of the metabolite is less than 10ppm.

[0112] Step 4: Diagnostic methods for distinguishing non-cancerous lung diseases from early lung cancer based on clinical indicators and metabolomics characteristics

[0113] 1. Subject information

[0114] A total of 556 plasma samples were included, all of which were actual samples collected from two medical centers, and the samples in the modeling group and the validation group were different samples. The specific distribution is as follows: the modeling group had 417 plasma samples, including 222 subjects with early lung cancer and 195 subjects with non-cancer lung diseases; the validation group had 139 plasma samples, including 74 subjects with early lung cancer and 65 subjects with non-cancer lung diseases (Table 1). Plasma samples were collected in the early morning on an empty stomach, and all collected plasma samples were stored in a -80°C refrigerator.

[0115] Table 1 Sample information

[0116] Modeling Group Validation Group Number of people Total number (N=179) 417 139 Grouping Early stage lung cancer group 222 74 Non-cancerous lung diseases group 195 65 gender male(%) 54.9％ 56.1％ female(%) 45.1％ 43.9％ age Mean(Standard Deviation) 56.94±11.55 58.33±11.74

[0117] 2. Blood marker combination screening method

[0118] 1) Data standardization: Log-transformation and Z-score processing were performed on metabolomics data and clinical index data, respectively. Log-transformation scales the original data to a specific interval, eliminating the order of magnitude differences between different samples; Z-score converts a certain original score into a standard score, which can make the originally incomparable values ​​comparable. After preprocessing, two sets of standardized data matrices were obtained.

[0119] 2) Calculation of feature comparison values: The metabolomics and clinical indicator data after standardization were used to calculate the ratio of all possible combinations of two variables. The top 100 feature pairs were selected based on the P-value between the variables from small to large. The clinical indicators and metabolomics features after the calculated ratios were combined into new data sets.

[0120] 3) Correlation analysis: Pearson correlation analysis was performed on clinical indicators and metabolomic features, respectively. Then, all pairs of clinical indicators and metabolic features with an absolute value of the correlation coefficient R greater than or equal to 0.9 were extracted and eliminated starting from the ones with the largest number of participations until the correlation of any pair of clinical indicators and metabolomic features was less than the threshold value of 0.9.

[0121] 4) Lasso regression feature screening.

[0122] The metabolomics and clinical index data were screened by Lasso regression, and 14 metabolomics feature pairs were finally screened out (phosphatidylethanolamine 38:3p / sebacic acid, phosphatidylinositol 38:3 / sebacic acid, sphingomyelin d38:5 / sebacic acid, phosphatidylethanolamine 38:5 / L-prolyl-L-threonine, gluconolactone / hexadecandioic acid, phosphatidylcholine 38:5 / sebacic acid, gluconolactone / sebacic acid, gluconolactone / trans-cyclohexane-1,2-dicarboxylic acid, phosphatidylcholine 38:6e / trans-cyclohexane-1,2-dicarboxylic acid, phosphatidylethanolamine 36:1p / trans-cyclohexane-1,2-dicarboxylic acid, phosphatidylethanolamine 38:5 / nerolidol, phosphatidylserine 38:0 =The blood biomarker combinations were categorized into 27 pairs of characteristic variables (monocyte ratio / potassium, monocyte ratio / activated partial thromboplastin time, red blood cell count / activated partial thromboplastin time, potassium / international normalized ratio, potassium / thrombin time, activated partial thromboplastin time / thrombin time, carcinoembryonic antigen / serum globulin, international normalized ratio / activated partial thromboplastin time, carcinoembryonic antigen / potassium, carcinoembryonic antigen / prothrombin time, carcinoembryonic antigen / activated partial thromboplastin time, carcinoembryonic antigen / lactate dehydrogenase, and red blood cell count / potassium).

[0123] Table 227 blood marker combinations for distinguishing lung non-cancer disease group from early lung cancer group

[0124]

[0125]

[0126] 3. Establish a diagnostic model for non-cancerous lung diseases and early lung cancer

[0127] In order to evaluate the diagnostic effect of the 27 blood biomarkers screened out in distinguishing the lung non-cancerous disease group and the early lung cancer group, a multivariate ROC curve analysis was performed on the 27 blood biomarkers. Randomly select 3 / 4 samples from the modeling group data as the training set (training), and the remaining 1 / 4 samples as the test set (test), and use SVM random loop iteration 1000 times. By statistically calculating the average value of the final model accuracy, a diagnostic model for lung non-cancerous diseases and early lung cancer was constructed. The ROC analysis results show that ( Figure 1 ), AUC = 0.816 (sensitivity: 74.6%, specificity: 71.4%, accuracy: 73.1%, precision: 74.6%), indicating that the constructed model has good predictive efficiency.

[0128] Generally speaking, the quality of the model is evaluated based on the area under the ROC curve (AUC), which is usually between 0.5 and 1. The closer the AUC value is to 1, the better the model performance and the better the diagnostic effect. If the AUC value is less than 0.5, it means that the model is not accurate. In addition to common parameters such as the receiver operating curve (ROC) and the area under the curve (AUC), the ROC classification prediction model also includes sensitivity, specificity, accuracy, and precision. Among them, sensitivity and specificity reflect the model's ability to identify positive and negative samples, respectively.

[0129] Sensitivity, also known as the true positive rate (TPF or TPR), indicates the probability that the model correctly diagnoses a positive case in the case group; specificity, also known as the true negative rate (TNF or TNR), indicates the probability that the model correctly diagnoses a negative case in the control group; the higher the sensitivity, the stronger the model's ability to identify positive samples. The higher the specificity, the stronger the model's ability to distinguish negative samples.

[0130] The sensitivity is:

[0131]

[0132] Specificity is:

[0133]

[0134] The accuracy is:

[0135]

[0136] The accuracy is:

[0137]

[0138] in,

[0139] TP (True positive): True positive, the number of samples that are actually positive examples that are correctly predicted as positive examples;

[0140] TN (Ture Negative): True negative, the number of samples that are actually negative examples but are correctly predicted as negative examples;

[0141] FP (False Positive): False positive, the number of samples that are actually negative examples but are mistakenly predicted as positive examples;

[0142] FN (False Negative): False negatives, the number of samples that are actually positive examples but are mistakenly predicted as negative examples.

[0143] 4. External validation of the diagnostic model for non-cancerous lung diseases and early lung cancer

[0144] In order to further verify the predictive effect of the lung non-cancer disease and early lung cancer diagnosis model constructed based on 27 blood biomarkers. The validation group data set is used as an unknown sample and put into the diagnostic model of the lung non-cancer disease group and early lung cancer group constructed by the modeling group to evaluate the independent verification effect of the model on the unknown data set other than the modeling group data set. According to the external validation data set, each sample will output a probability value (Probability), that is, the diagnostic threshold. The sensitivity and specificity can be calculated according to the formula. In the ROC analysis diagram with sensitivity as the ordinate and 1-specificity as the abscissa, different sensitivities and 1-specificities have corresponding points in the ROC curve. Similarly, the probability value of each sample can be used as a diagnostic threshold. Therefore, multiple different points can be obtained from the ROC analysis diagram, and these points are connected to present line segments with different gradients to draw the final ROC curve. Then, the diagnostic threshold of the model is selected based on the optimal sensitivity and specificity values. The results show that the diagnostic threshold of the model is 0.5531. The confusion matrix results in Table 3 show that among the 74 patients with early lung cancer, 58 were diagnosed as early lung cancer group, and 16 were misclassified as non-cancer lung disease group; among the 65 patients with non-cancer lung disease, 44 were diagnosed as non-cancer lung disease group, and 21 were misclassified as early lung cancer disease group. The ROC analysis results of external validation showed that ( Figure 2 ), AUC = 0.815 (sensitivity: 78.4%, specificity: 67.7%, accuracy: 73.4%, precision: 73.4%), indicating that the diagnostic model for early lung cancer established by the 27 clinical indicators and metabolomics characteristics screened out also has good predictive ability.

[0145] Table 3 Confusion matrix of the diagnostic model for distinguishing non-cancerous lung diseases from early lung cancer

[0146] Early stage lung cancer Non-cancerous lung diseases Early stage lung cancer, N=74 58(TP) 16(FN) Non-cancerous lung diseases, N=65 21(FP) 44(TN)

[0147] In summary, compared with the existing technology, the present invention not only uses the characteristics of the plasma metabolome, but also incorporates blood clinical indicators through routine blood tests and blood biochemical tests. Compared with other methods, it more comprehensively reflects the physiological and pathological states of the body and reduces the possibility of bias in the results. Therefore, the use of multi-dimensional blood biomarkers can more accurately diagnose patients with non-cancerous lung diseases and early lung cancer.

[0148] Although the above embodiment describes the present invention in detail, it is only a part of the embodiments of the present invention, not all of the embodiments. People can also obtain other embodiments based on this embodiment without creativity, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A blood marker combination for distinguishing non-cancerous lung diseases from early lung cancer, characterized in that: It includes 13 clinical indicators and 14 metabolomic features; The clinical indicators include: the ratio of monocyte ratio to potassium concentration, the ratio of monocyte ratio to activated partial thromboplastin time, the ratio of red blood cell count to activated partial thromboplastin time, the ratio of potassium concentration to international normalized ratio, the ratio of potassium concentration to thrombin time, the ratio of activated partial thromboplastin time to thrombin time, the ratio of carcinoembryonic antigen to serum globulin concentration, the ratio of international normalized ratio to activated partial thromboplastin time, the ratio of carcinoembryonic antigen to potassium concentration, the ratio of carcinoembryonic antigen concentration to prothrombin time, the ratio of carcinoembryonic antigen concentration to activated partial thromboplastin time, the ratio of carcinoembryonic antigen concentration to lactate dehydrogenase concentration, and the ratio of red blood cell count to potassium concentration; The metabolome features include: the peak intensity ratio of phosphatidylethanolamine 38:3p and sebacic acid, the peak intensity ratio of phosphatidylinositol 38:3 and sebacic acid, the peak intensity ratio of sphingomyelin d38:5 and sebacic acid, the peak intensity ratio of phosphatidylethanolamine 38:5 and L-prolyl-L-threonine, the peak intensity ratio of gluconolactone and hexadecanedioic acid, the peak intensity ratio of phosphatidylcholine 38:5 and sebacic acid, the peak intensity ratio of gluconolactone and sebacic acid, the peak intensity ratio of gluconolactone and trans-cyclohexane-1,2- The peak intensity ratio of dicarboxylic acids, the peak intensity ratio of phosphatidylcholine 38:6e and trans-cyclohexane-1,2-dicarboxylic acid, the peak intensity ratio of phosphatidylethanolamine 36:1p and trans-cyclohexane-1,2-dicarboxylic acid, the peak intensity ratio of phosphatidylethanolamine 38:5 and nerolidol, the peak intensity ratio of phosphatidylserine 38:0 and sebacic acid, the peak intensity ratio of dimethylphosphatidylethanolamine 38:4 and sebacic acid, and the peak intensity ratio of phosphatidylcholine 38:6e and D-glucosamine-6-phosphate.

2. Use of a reagent or kit for detecting the blood marker combination according to claim 1 in the preparation of a product for distinguishing non-cancerous lung diseases from early lung cancer.

3. The use according to claim 1, characterized in that: The non-cancerous lung disease includes one or more of pneumonia, tuberculosis and lung infection.

4. Use of a reagent or kit for detecting the blood marker combination according to claim 1 in the preparation of a lung disease diagnosis product.

5. The use according to any one of claims 2 to 4, characterized in that: The reagent or kit for detecting the blood marker combination according to claim 1 comprises a liquid chromatography-mass spectrometry detection reagent.

6. A screening method for a combination of blood markers for distinguishing non-cancerous lung diseases from early lung cancer, characterized in that: The following steps are involved: Plasma samples were collected from patients with non-cancerous lung diseases and early-stage lung cancer; Separating the organic phase and the aqueous phase from plasma samples of patients with non-cancerous lung diseases and early lung cancer patients; wherein the organic phase is non-polar metabolites and the aqueous phase is polar metabolites; Liquid chromatography-mass spectrometry was used to detect polar metabolites in the aqueous phase and non-polar metabolites in the organic phase, respectively, to obtain metabolome detection data of plasma samples from patients with non-cancerous lung diseases and early lung cancer patients; Preprocessing and metabolite identification are performed on the metabolomics detection data of the patients with non-cancerous lung diseases and early lung cancer patients to obtain metabolomics data; Collect clinical indicators of patients with non-cancerous lung diseases and early-stage lung cancer to obtain clinical indicator data; Performing data standardization on the metabolomics data and clinical indicator data of the patients with non-cancerous lung diseases and early lung cancer patients, respectively, to obtain standardized metabolomics data and clinical indicator data; The standardized metabolomics data and clinical index data are used to calculate the ratio of all possible combinations of two variables, and the combinations of the top 100 feature pairs are selected based on the P-values ​​between the variables from small to large, and the clinical indexes and metabolomics features after the ratios are calculated are respectively used to form new data sets; The new data set consisting of clinical indicators and metabolomics characteristics was subjected to Pearson correlation analysis and Lasso regression feature screening, respectively, to obtain a combination of blood markers for distinguishing non-cancerous lung diseases and early lung cancer.

7. The screening method according to claim 6, characterized in that Clinical indicator tests include routine blood tests and blood biochemistry tests.

8. The screening method according to claim 6, characterized in that The Pearson correlation analysis includes: extracting all clinical indicator pairs and metabolic feature pairs with an absolute value of the correlation coefficient R greater than or equal to 0.9, and eliminating them starting from the ones with the largest number of participations, until the correlation between any pair of clinical indicators and metabolic group characteristics is less than a threshold value of 0.

9.

9. A method for constructing a diagnostic model for non-cancerous lung diseases and early lung cancer, characterized in that: The following steps are involved: A multivariate ROC curve analysis is performed on the blood biomarkers screened by the screening method according to any one of claims 6 to 8.

10. The method according to claim 9, characterized in that The multivariate ROC curve analysis includes: selecting 3 / 4 samples from the modeling group data as a training set, the remaining 1 / 4 samples as a test set, and using a support vector machine to randomly iterate 1000 times, and constructing a diagnostic model for non-cancerous lung diseases and early lung cancer by statistically calculating the average value of the final model accuracy.

Images