Plasma metabolism marker combination for distinguishing early lung cancer from pneumonia

Abstract

The invention provides a plasma metabolism marker combination for distinguishing early lung cancer and pneumonia and application of the plasma metabolism marker combination. A metabonomics technology is used for detecting and identifying plasma biomarkers, an efficient method is provided for diagnosis of early lung cancer, a metabolic marker combination for distinguishing early lung cancer and pneumonia is found, and the kit has very high sensitivity and specificity of early lung cancer diagnosis and can be used for diagnosis of early lung cancer and pneumonia. Therefore, the patient can know the risk in the early stage of cancer and take corresponding measures. The plasma metabolism marker provided by the invention has micro-invasiveness and high efficiency, can enable patients to accept detection more conveniently and safely, and is expected to improve the diagnosis rate of early lung cancer and improve the overall survival rate of lung cancer patients.

Description

Technical Field

[0001] The present invention relates to the field of metabolic marker analysis and detection, and in particular to a plasma metabolic marker combination and application thereof for distinguishing early lung cancer from pneumonia. Background Art

[0002] Lung cancer (LC) is one of the malignant tumors with the highest morbidity and mortality rates worldwide. Early lung cancer is difficult to detect and diagnose because its symptoms are often not obvious or similar to other common diseases. Therefore, many patients are diagnosed only when lung cancer has developed to the late stage. The survival rate of patients with advanced lung cancer is short, usually only a few months to one or two years, which directly leads to the high mortality rate of lung cancer. Although the treatment of lung cancer continues to improve, early diagnosis and timely treatment are still the key to reducing the mortality rate of lung cancer. In China, lung cancer is a common malignant tumor, and the incidence and mortality of lung cancer in men ranks first among all cancers, and the incidence and mortality of lung cancer in women are also increasing year by year. Therefore, early diagnosis of lung cancer is crucial for the majority of lung cancer patients and can significantly improve the survival rate.

[0003] Pneumonia is a disease caused by lung infection, and common symptoms include cough, sputum, chest pain, dyspnea, and fever. However, because lung cancer can cause lung lesions and inflammatory responses, these symptoms may also occur in early lung cancer. This makes it difficult to distinguish them clinically, increasing the difficulty of diagnosing early lung cancer. At the same time, because the two may overlap in clinical manifestations and imaging features, the accurate diagnosis and timely treatment of early lung cancer become complicated and critical. For patients and clinicians, accurately distinguishing early lung cancer from pneumonia is a major challenge and pain point in current clinical practice.

[0004] At present, the clinical diagnosis of lung cancer is mainly based on imaging and histology. Although these methods have certain effects and reference values ​​in the diagnosis and pathological staging of lung cancer, they still have some shortcomings. Imaging diagnostic methods such as X-rays and CT scans have limitations, including high early missed detection rates, difficulty in detecting small tumors, and difficulty in distinguishing between tumor lesions and inflammatory lesions. In addition, radiation issues have always been a difficult problem that imaging needs to solve. Traditional histological methods, such as surgical lung tissue biopsy and bronchoscopy, are relatively invasive and invasive, resulting in low patient acceptance. In view of this, in current tumor biology research, the discovery of diagnostic biomarkers for early lung cancer has become a hot topic. Metabolomics is a detection technology that has emerged in recent years. By studying the changes in metabolites in organisms, new biomarkers are discovered. As a non-invasive or minimally invasive detection technology, it has been widely used in the diagnosis and prediction of diseases. Currently, the commonly used early lung cancer biomarkers in clinical practice, such as CEA, CA19-9, and CA72-4, are widely used, but their sensitivity and specificity are low, and their role in distinguishing lung cancer from pneumonia is limited.

[0005] Therefore, there is an urgent need for new biomarkers with high specificity and sensitivity that can effectively distinguish early lung cancer from pneumonia and improve the detection rate of early lung cancer. This has important clinical significance for the diagnosis and timely treatment of early lung cancer. Summary of the invention

[0006] In order to make up for the shortcomings of the existing technology, the present invention uses metabolomics technology to detect and identify plasma biomarkers, aiming to discover a specific combination of biomarkers that distinguish patients with early lung cancer and pneumonia, and provide an efficient method for the diagnosis of early lung cancer. Specifically, in the present invention, through a detailed analysis and study of plasma samples of the early lung cancer group (Lung Cancer, stage 0 and stage I, recorded as LC group) and the pneumonia group (Pneumonia, PNA), a specific combination of biomarkers for distinguishing early lung cancer from pneumonia was discovered, which significantly improved the sensitivity and specificity of early lung cancer diagnosis. This allows patients to understand the risks and take corresponding measures in the early stages of the disease.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention discloses plasma metabolic markers for distinguishing early lung cancer from pneumonia. The metabolic markers include phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0) and phosphatidylcholine 38:6 (16:0 / 22:6).

[0008] Preferably, the metabolic markers also include: arachidic acid, phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6) and chenodeoxycholic acid.

[0009] Preferably, the metabolic markers further include: phosphatidylcholine 32:0 (16:0 / 16:0), phosphatidylethanolamine 38:7e (16:1e / 22:6), leucyl-leucine and glutamyl-glutamine.

[0010] Preferably, the metabolic markers further include: gluconolactone, phosphatidylcholine 42:10 (20:4 / 22:6), hexadecanedioic acid and diene nicotine.

[0011] The present invention discloses plasma metabolic markers for distinguishing early lung cancer from pneumonia. The metabolic markers include: arachidic acid, phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6) and chenodeoxycholic acid.

[0012] The present invention discloses plasma metabolic markers for distinguishing early lung cancer from pneumonia. The metabolic markers include: arachidic acid, phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 32:0 (16:0 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6), phosphatidylethanolamine 38:7e (16:1e / 22:6), leucyl-leucine, glutamylglutamine and glycochenodeoxycholic acid.

[0013] The invention discloses plasma metabolic markers for distinguishing early lung cancer from pneumonia. The metabolic markers include: gluconolactone, arachidic acid, phosphatidylcholine 42:10 (20:4 / 22:6), phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 32:0 (16:0 / 16:0, phosphatidylcholine 38:6 (16:0 / 22:6), phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6), phosphatidylethanolamine 38:7e (16:1e / 22:6), leucyl-leucine, hexadecanedioic acid, glutamylglutamine, glycochenodeoxycholic acid and diene nicotine.

[0014] The invention discloses use of the metabolic marker in preparing a reagent for distinguishing early lung cancer from pneumonia.

[0015] Preferably, the sample used in the differentiation process is selected from serum, plasma or blood.

[0016] The invention discloses use of the metabolic marker in preparing a kit for distinguishing early lung cancer from pneumonia.

[0017] Preferably, the sample used in the differentiation process is selected from serum, plasma or blood.

[0018] The present invention discloses a kit for distinguishing early lung cancer from pneumonia groups, wherein the kit comprises the metabolic markers according to any one of claims 1 to 4.

[0019] Preferably, the kit further comprises quality control products and standard products.

[0020] The present invention uses metabolomics technology to analyze and screen out a combination of plasma metabolite markers, thereby improving the accuracy of the diagnosis results of patients with early lung cancer and providing more sensitive and reliable plasma metabolite markers for early lung cancer screening. Current research has little differentiated diagnosis for patients with early lung cancer and pneumonia, and lacks a high-accuracy plasma marker combination. At the same time, the plasma metabolite markers provided by the present invention are minimally invasive and highly efficient, enabling patients to be tested more conveniently and safely, and are expected to improve the diagnosis rate of patients with early lung cancer and improve the overall survival rate of patients with lung cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 . Multivariate ROC curves of 15 plasma metabolic markers that distinguished the early lung cancer group from the pneumonia group in the modeling group.

[0022] Figure 2 . Multivariate ROC curves of 15 plasma metabolic markers for distinguishing early lung cancer group from pneumonia group in the validation group.

[0023] Figure 3 . Multivariate ROC curves of 11 plasma metabolic markers for distinguishing early lung cancer group from pneumonia group in the validation group.

[0024] Figure 4 . Multivariate ROC curves of 7 plasma metabolic markers for distinguishing early lung cancer group from pneumonia group in the validation group.

[0025] Figure 5 . Multivariate ROC curves of three plasma metabolic markers for distinguishing early lung cancer group from pneumonia group in the validation group. DETAILED DESCRIPTION

[0026] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0027] Example 1 Subject Condition and Sample Collection Subjects’ situation: The samples collected by the institute include the modeling group and the validation group, both of which were collected by us. The modeling group includes 225 plasma samples from early lung cancer (LC) and 118 plasma samples from pneumonia (PNA); the validation group includes 75 plasma samples from lung cancer and 40 plasma samples from pneumonia (see Table 1). Plasma samples were collected in the early morning on an empty stomach. All collected plasma was centrifuged and stored in a -80 degree refrigerator.

[0028] Table 1 Subjects Pneumonia (PNA) Early stage lung cancer (LC 0 / I) total Modeling team number 118 225 343 Number of people in the verification group 40 75 115 total 158 300 458 Written informed consent was obtained from all subjects before participating in the study.

[0029] Inclusion criteria for pneumonia group: (1) Male or female aged ≥18 years; (2) Patients who are confirmed to have pneumonia or who are clinically diagnosed with pneumonia through comprehensive evaluation by clinicians; (3) Patients who are hospitalized for surgery or undergo screening at medical institutions participating in clinical research; (4) The diagnosis does not include cancer.

[0030] Lung cancer group inclusion criteria: Inclusion criteria: (1) male or female aged ≥18 years; (2) patients diagnosed with primary early lung cancer by biopsy, postoperative pathological confirmation, or comprehensive evaluation by clinicians.

[0031] Exclusion criteria: (1) pregnancy or lactation; (2) emergency or rescue; (3) history of malignant tumor or any anti-tumor treatment before sampling; (4) concurrent multiple primary malignant tumors.

[0032] Example 2 Detection of small molecule metabolites in plasma samples (1) Analytical reagents Methanol, acetonitrile, water, isopropanol, acetic acid, and ammonium acetate of mass spectrometry grade purity, and formic acid and methyl tert-butyl ether of chromatographic grade purity (HPLC) were purchased from Sigma-Aldrich, USA; (2) Sample preparation The plasma sample was taken out from the -80 degree refrigerator, thawed on ice, and 100 μL of plasma was taken and placed in 1000 μL of pre-cooled (methyl tert-butyl ether: methanol, volume ratio of 3:1) solution. The extracted blood sample was vortexed to obtain the sample extract. 500 μL of (methanol: water, volume ratio of 3:1) solution was added to the sample extract, sonicated, allowed to stand, vortexed and centrifuged to separate layers.

[0033] After the samples were separated, 500 μL of the upper layer was taken into a centrifuge tube as the organic phase. After the organic phase was dried, 200 μL of (acetonitrile:isopropanol, volume ratio 3:1) was added and allowed to stand at room temperature for 15 minutes. The mixture was then vortexed and ultrasonically treated for 5 minutes, followed by centrifugation at room temperature for 5 minutes (12,000 rpm). 180 μL of the supernatant was taken into a 2 mL glass injection vial as the organic phase, which was tested by LC-MS.

[0034] During sample stratification, 400 μL of the lower aqueous phase was taken to a centrifuge tube, and 1100 μL of ice methanol was added to precipitate the protein. After protein precipitation, 1000 μL of the supernatant was taken after centrifugation and transferred to a new centrifuge tube and dried overnight. 200 μL of water was added to the dried centrifuge tube and allowed to stand at room temperature for 15 minutes. Then, the tube was vortexed and ultrasonically treated for 5 minutes, followed by centrifugation at room temperature for 5 minutes (12000 rpm). 180 μL of the supernatant was taken from the centrifuge tube and placed in a 2 mL glass injection vial, which was the aqueous phase, and was detected by LC-MS.

[0035] Small molecule metabolite detection The organic phase used a Waters ACQUTTY UPLC ® BEH C8 1.7µm 2.1*100mm column, and the aqueous phase used a Waters ACQUTTYUPLC ® HSS T3 1.8µm 2.1*100mm column for small molecule separation; the liquid chromatography and mass spectrometer used ACQUITY UPLC I-Class liquid chromatography system (Waters) and Q-Exactive mass spectrometry system (Thermo Fisher Scientific).

[0036] The mobile phase parameters are as follows: Organic phase: Mobile phase A was an aqueous solution containing 0.1% acetic acid and 10 mM ammonium acetate, and mobile phase B was an acetonitrile:isopropanol (7:3, v / v) solution containing 0.1% acetic acid and 10 mM ammonium acetate. The separation gradient was as follows: 55% B for 0-1 min, 55%-75% B for 1-4 min, 75%-89% B for 4-12 min, 89%-100% B for 12-15 min, 100% B for 15-19.5 min, 100%-55% B for 19.5-19.51 min, and 55% B for 19.51-24 min.

[0037] Aqueous phase: Mobile phase A was an aqueous solution containing 0.1% formic acid, and mobile phase B was an acetonitrile solution containing 0.1% formic acid. The separation gradient was as follows: 1% B for 0-1 min, 1%-40% B for 1-11 min, 40%-70% B for 11-13 min, 70%-99% B for 13-15 min, 99% B for 15-18 min, 99%-1% B for 18-19 min, and 1% B for 19-22 min.

[0038] The mass spectrometry parameters are as follows: The mass spectrometry data were collected in Full MS and Full MS / dd-MS2 modes (each including positive and negative modes). The parameters used by QExactive are as follows: the resolution of the Full MS mode is 70,000, the scanning range is 100-1500m / z, the AGC is 3E+6, and the Maximum IT is 200 ms. In the Full MS / dd-MS2 mode, the resolution of the secondary mass spectrometry is 17,500, the quadrupole window is 1.5m / z, the AGC is 1E+5, the Maximum IT is 20050 ms, and the HCD relative collision energy is 30eV.

[0039] (4) Metabolomics data processing Firstly, effective peak signals were extracted from all mass spectrometry data, and then baseline correction was applied to remove noise and retain the original signal peak. The original data were converted into central discrete data. The retention time in the chromatogram corresponding to each mass spectrometry peak in the sample was corrected, and the peaks with differences between samples within the error range were defined as the same peak, thus obtaining a matrix data set. In order to make the data distribution more reflect the real differences between samples, the Normalization Autoencoder (NormAE) was used for homogenization.

[0040] (5) Identification of metabolites Public databases such as the Human Metabolite Database (HMDB; www.hmdb.ca), the Metabolomics Database (Metlin; (https: / / metlin.scripps.edu), and the Mass Spectrum Database (http: / / www.massbank.jp / ) were used, as well as the primary, secondary chromatograms, and mass spectra of the standards separated on the same chromatographic column; the retention times were within 0.1 min and the mass-to-charge ratio was less than 10 ppm, and the identification was matched with the databases and standards.

[0041] Example 3 Screening of plasma markers for distinguishing early lung cancer from pneumonia (1) Screening of differential metabolites for distinguishing early lung cancer group from pneumonia group Based on the modeling group data of plasma samples, we used Lasso (Least Absolute Shrinkage and Selection Operator) to screen differential metabolites for the early lung cancer group and the pneumonia group. By shrinking the coefficients of other metabolites that contribute little to the difference between the two to zero, only the important metabolites that contribute highly to distinguishing the early lung cancer group from the pneumonia group are retained. After model optimization, 15 plasma metabolite marker combinations with significant differences in distinguishing early lung cancer from pneumonia were finally determined (Table 2). The area under the ROC curve (AUC) of the metabolite was used as a diagnostic evaluation indicator.

[0042] Table 2 Fifteen differential metabolic markers distinguishing early lung cancer group and pneumonia group Metabolites (Chinese name) Metabolites (English name) Weight coefficient 1 Gluconolactone Gluconolactone -0.01 2 Arachidic acid Arachidic acid 0.003 3 Phosphatidylcholine 42:10 (20:4 / 22:6) PC 42:10 (20:4 / 22:6) -0.001 4 Phosphatidylethanolamine 40:5p (18:0p / 22:5) PE 40:5p (18:0p / 22:5) -0.035 5 Disodium hexosylceramide d34:1 (d18:1 / 16:0) Hex2Cer d34:1 (d18:1 / 16:0) 0.005 6 Phosphatidylcholine 32:0 (16:0 / 16:0) PC 32:0 (16:0 / 16:0) 0.011 7 Phosphatidylcholine 38:6 (16:0 / 22:6) PC 38:6 (16:0 / 22:6) -0.031 8 Phosphatidylcholine 38:7 (16:1 / 22:6) PC 38:7 (16:1 / 22:6) -0.003 9 Phosphatidylcholine 40:7 (18:1 / 22:6) PC 40:7 (18:1 / 22:6) -0.028 10 Phosphatidylethanolamine 38:7e (16:1e / 22:6) PE 38:7e (16:1e / 22:6) -0.032 11 Leucyl-Leucine Leucyl-leucine -0.005 12 Hexadecanedioic acid Hexadecanedioate 0.014 13 Glutamine Glutamylglutamine 0.026 14 Glycoside Glycochenodeoxycholic acid 0.02 15 Nicotinamide Nicotyrine -0.007 The ROC curve is a method used to study the relationship between model sensitivity and specificity. In the ROC curve, the ordinate represents sensitivity and the abscissa represents 1-specificity. The performance of the model is evaluated by comparing the AUC (area under the curve) below the curve. The AUC value is between 0 and 1. When the AUC value is less than 0.5, it means that the accuracy of the diagnostic test is lower than random guessing, that is, the diagnosis is meaningless; and when the AUC value is closer to 1, it means that the accuracy of the diagnostic test is higher, that is, the ability to distinguish between positive and negative examples is stronger.

[0043] Example 4: Construction of a plasma metabolic marker model for distinguishing early lung cancer and pneumonia groups (1) Construction of a model using 15 plasma metabolite markers to distinguish between early lung cancer and pneumonia groups Multivariate ROC analysis was used to verify the effectiveness of the 15 differential metabolites screened by Lasso in distinguishing the early lung cancer group from the pneumonia group. We randomly divided 3 / 4 of the sample data of the modeling group into a training set and 1 / 4 as a test set. The training set was used to build and train the machine learning classification model, and the test set was used to verify the discriminative ability of the model. The SVM (Support Vector Machine, SVM) was trained 1000 times by random cycle iteration and the final model accuracy was averaged. Fifteen plasma metabolic markers were used, including gluconolactone, arachidic acid, phosphatidylcholine 42:10 (20:4 / 22:6), phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 32:0 (16:0 / 16:0, phosphatidylcholine 38:6 (16:0 / 22:6), phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6), and phosphatidylethanolamine 38:7e (18:1 / 22:6). The evaluation model was constructed by combining leucyl-leucine, hexadecanedioic acid, glutamylglutamine, chenodeoxycholic acid and diene nicotine. The results showed that AUC = 0.865 ( Figure 1 ), sensitivity = 78.9%, specificity = 75.9%. This shows that the use of a combination of 15 metabolic markers has significant clinical diagnostic value, can effectively distinguish early lung cancer patients from pneumonia patients, and improve the sensitivity and specificity of diagnosis, providing important support for clinical diagnosis.

[0044] This example uses the same research subjects and the same detection methods as Example 1 and Example 2, except that different numbers of metabolite markers are used for SVM classifier modeling. We selected 11, 7, and 3 metabolite markers from 15 plasma metabolite markers as metabolite marker combinations, and performed multivariate ROC analysis in the modeling group samples.

[0045] (2) Construction of a model for distinguishing early lung cancer and pneumonia using 11 plasma metabolic markers In this embodiment, the research object is the same as that of embodiment 1, and the detection and analysis method of embodiment 2 is adopted. When constructing the diagnostic model for early lung cancer, the SVM method was used, and 11 plasma metabolic markers were selected, including arachidic acid, phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 32:0 (16:0 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6), phosphatidylethanolamine 38:7e (16:1e / 22:6), leucyl-leucine, glutamylglutamine and chenodeoxycholic acid. The results of the combination in distinguishing early lung cancer from pneumonia showed that AUC=0.854, sensitivity=70.2%, and specificity=82.8%.

[0046] (3) Construction of a model for distinguishing early lung cancer and pneumonia using seven plasma metabolic markers In this embodiment, the research object is the same as that in Example 1, and the detection and analysis method of Example 2 is used. When constructing the early lung cancer diagnosis model, the SVM method is used, and 7 plasma metabolite markers are selected, including arachidic acid, phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6) and chenodeoxycholic acid. The results of the combination of these seven metabolite markers in distinguishing early lung cancer from pneumonia showed that AUC = 0.836, sensitivity = 83.9%, and specificity = 72.4%.

[0047] (4) Construction of a model for distinguishing early lung cancer and pneumonia using three plasma metabolic markers In this embodiment, the research object is the same as that in Example 1, and the detection and analysis method of Example 2 is used. When constructing the early lung cancer diagnosis model, the SVM method is used, and three plasma metabolic markers are selected, including phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0) and phosphatidylcholine 38:6 (16:0 / 22:6). The results of the combination of these three markers in distinguishing early lung cancer from pneumonia show that AUC=0.835, sensitivity=71.9%, and specificity=86.2%.

[0048] The above results show that in model construction, the use of 15, 11, 7 and 3 metabolic markers can show good effects in distinguishing early lung cancer and pneumonia patients, and have high diagnostic accuracy.

[0049] Example 5: Model validation using validation group plasma samples (1) Model validation using a combination of plasma metabolic markers This example has the same research subjects as Example 1, and adopts the detection and analysis method of Example 2. To verify the effectiveness of the constructed diagnostic model in distinguishing early lung cancer and pneumonia patients, we used an independent data set and used the plasma samples of the validation group as unknown samples for validation.

[0050] The diagnostic model for distinguishing early lung cancer and pneumonia patients constructed using a combination of 15 plasma metabolic markers was validated with an AUC of 0.838 ( Figure 2 ), sensitivity = 84.0%, specificity = 72.5%. The validation results of the combination of 11 plasma metabolic markers were as follows: AUC = 0.854 ( Figure 3 ), sensitivity = 74.7%, specificity = 82.5%. The validation results of the combination of 7 plasma metabolic markers were as follows: AUC = 0.834 ( Figure 4 ), sensitivity = 78.7%, specificity = 75.0%. The validation results of the combination of three plasma metabolic markers were as follows: AUC = 0.831 ( Figure 5 ), sensitivity = 81.3%, specificity = 75.0%. These results show that the model we established showed good diagnostic performance on unknown samples using 15, 11, 7 and 3 metabolite markers, which can support its application in clinical practice.

[0051] In summary, the different metabolic marker combinations we screened showed good diagnostic effects in distinguishing early lung cancer from pneumonia, with high sensitivity and specificity, and can provide efficient and accurate diagnostic support for patients with early lung cancer.

[0052] The present invention illustrates the process method of the present invention through the above-mentioned embodiments, but the present invention is not limited to the above-mentioned process steps, that is, it does not mean that the present invention must rely on the above-mentioned process steps to be implemented. Those skilled in the art should understand that any improvement of the present invention, equivalent replacement of the raw materials selected by the present invention, addition of auxiliary components, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A plasma metabolic marker for distinguishing early lung cancer from pneumonia, characterized in that: The metabolic markers include: phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0) and phosphatidylcholine 38:6 (16:0 / 22:6).

2. The metabolic marker according to claim 1, characterized in that The metabolic markers also include: eicosanoid, phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6) and glycochenodeoxycholic acid.

3. The metabolic marker according to claim 2, characterized in that The metabolic markers also include: phosphatidylcholine 32:0 (16:0 / 16:0), phosphatidylethanolamine 38:7e (16:1e / 22:6), leucyl-leucine and glutamyl-glutamine.

4. The metabolic marker according to claim 3, characterized in that The metabolic markers also include: gluconolactone, phosphatidylcholine 42:10 (20:4 / 22:6), hexadecanedioic acid and diene nicotine.

5. Use of the metabolic marker according to any one of claims 1 to 4 in the preparation of a reagent for distinguishing early lung cancer from pneumonia.

6. The use according to claim 6, characterized in that The sample used in the differentiation process is selected from serum, plasma or blood.

7. Use of the metabolic marker according to any one of claims 1 to 4 in preparing a kit for distinguishing early lung cancer from pneumonia.

8. The use according to claim 7, characterized in that The sample used in the differentiation process is selected from serum, plasma or blood.

9. A kit for distinguishing early lung cancer from pneumonia, characterized in that: The kit comprises the metabolic marker according to any one of claims 1 to 4.

10. The kit according to claim 9, characterized in that The kit also includes quality control products and standard products.

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