Plasma metabolic marker combination for distinguishing early-stage lung cancer from pneumonia

Abstract

The present invention provides a combination of plasma metabolite markers for distinguishing early lung cancer from pneumonia and its application. The present invention uses metabolomics technology to detect and identify plasma biomarkers, providing an efficient method for the diagnosis of early lung cancer. It has discovered a combination of metabolite markers for distinguishing early lung cancer from pneumonia, which has high sensitivity and specificity for early lung cancer diagnosis, so that patients can understand the risks and take corresponding measures in the early stages of cancer. The plasma metabolite markers provided by the present invention are minimally invasive and highly efficient, enabling patients to undergo testing more conveniently and safely, and are 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-stage 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 the 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 advance, early diagnosis and timely treatment remain 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 rank 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 vast majority of lung cancer patients and can significantly improve survival rates.

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

[0004] Currently, clinical lung cancer diagnosis is primarily based on imaging and histology. While these methods have demonstrated some utility and value in lung cancer diagnosis and pathological staging, they still have several drawbacks. Imaging diagnostic methods such as X-rays and CT scans have limitations, including a high rate of missed early detection, difficulty detecting small tumors, and difficulty distinguishing between neoplastic and inflammatory lesions. Furthermore, radiation exposure remains a persistent challenge for imaging. Traditional histological methods, such as surgical lung biopsy and bronchoscopy, are invasive and relatively unreliable, leading to low patient acceptance. Consequently, the discovery of diagnostic biomarkers for early lung cancer has become a hot topic in current tumor biology research. Metabolomics is a recently emerging technology that investigates changes in metabolic products within an organism to identify new biomarkers. As a non-invasive or minimally invasive technique, it has been widely used in the diagnosis and prognosis of disease. Currently, commonly used early lung cancer biomarkers, such as CEA, CA19-9, and CA72-4, are widely used, but their sensitivity and specificity are low, limiting their effectiveness in distinguishing lung cancer from pneumonia.

[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 is of great clinical significance for the diagnosis and timely treatment of early lung cancer. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention uses metabolomics technology to detect and identify plasma biomarkers, aiming to discover a specific biomarker combination that distinguishes patients with early-stage lung cancer from those with pneumonia, providing a highly efficient method for diagnosing early-stage lung cancer. Specifically, through detailed analysis of plasma samples from a group of patients with early-stage lung cancer (stage 0 and stage 1, denoted as the LC group) and pneumonia (PNA) patients, the present invention discovered a specific biomarker combination for distinguishing early-stage lung cancer from pneumonia, significantly improving the sensitivity and specificity of early-stage lung cancer diagnosis. This allows patients to understand their risk early in the disease and take appropriate measures.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] 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).

[0009] Preferably, the metabolic markers further include: eicosanoid, phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6) and chenodeoxycholic acid.

[0010] 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.

[0011] Preferably, the metabolic markers further include: gluconolactone, phosphatidylcholine 42:10 (20:4 / 22:6), hexadecanedioic acid and dienenicotine.

[0012] The present invention discloses plasma metabolic markers for distinguishing early lung cancer from pneumonia. The metabolic markers include: eicosanoid, 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.

[0013] The present invention discloses plasma metabolic markers for distinguishing early lung cancer from pneumonia. The metabolic markers include: eicosanoid, 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.

[0014] The present invention discloses plasma metabolic markers for distinguishing early lung cancer from pneumonia. The metabolic markers include: gluconolactone, eicosanoid, 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 dienenicotine.

[0015] The present invention discloses use of the metabolic marker in preparing a reagent for distinguishing early lung cancer from pneumonia.

[0016] Preferably, the sample used in the differentiation process is selected from serum, plasma or blood.

[0017] The present invention discloses use of the metabolic marker in preparing a kit for distinguishing early lung cancer from pneumonia.

[0018] Preferably, the sample used in the differentiation process is selected from serum, plasma or blood.

[0019] The present invention discloses a kit for distinguishing early lung cancer from pneumonia groups, wherein the kit comprises the metabolic marker according to any one of claims 1 to 4.

[0020] Preferably, the kit further comprises quality control products and standard products.

[0021] The present invention uses metabolomics technology to analyze and screen a combination of plasma metabolic markers, thereby improving the accuracy of diagnostic results for patients with early-stage lung cancer and providing more sensitive and reliable plasma metabolic markers for early-stage lung cancer screening. Current research on the differentiated diagnosis of patients with early-stage lung cancer and pneumonia is limited, and there is a lack of highly accurate plasma marker combinations. At the same time, the plasma metabolic markers provided by the present invention are minimally invasive and highly efficient, enabling patients to undergo testing more conveniently and safely, and are expected to improve the diagnosis rate of patients with early-stage lung cancer and improve the overall survival rate of lung cancer patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Multivariate ROC curves of 15 plasma metabolic markers for differentiating early lung cancer and pneumonia groups in the modeling group.

[0023] Figure 2 Multivariate ROC curves of 15 plasma metabolic markers for differentiating early lung cancer and pneumonia groups in the validation group.

[0024] Figure 3 Multivariate ROC curves of 11 plasma metabolic markers for differentiating early lung cancer and pneumonia groups in the validation group.

[0025] Figure 4 Multivariate ROC curves of seven plasma metabolic markers for differentiating early lung cancer and pneumonia groups in the validation group.

[0026] Figure 5 Multivariate ROC curves of three plasma metabolic markers for differentiating early lung cancer and pneumonia groups in the validation group. DETAILED DESCRIPTION

[0027] The following describes the specific embodiments of the present invention in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention and are not intended to limit the present invention.

[0028] Example 1 Subject Conditions and Sample Collection

[0029] Subject information:

[0030] The samples collected by the research institute included a modeling group and a validation group, both of which were collected in-house. The modeling group included 225 plasma samples from patients with early-stage lung cancer (LC) and 118 patients with pneumonia (PNA); the validation group included 75 plasma samples from patients with lung cancer and 40 from patients with pneumonia (see Table 1). All plasma samples were collected in the early morning on an empty stomach. All collected plasma was centrifuged and stored in a -80°C freezer.

[0031] Table 1 Subjects

[0032] Pneumonia (PNA) Early lung cancer (LC 0 / I) total Modeling team members 118 225 343 Number of people in the verification group 40 75 115 total 158 300 458

[0033] Written informed consent was obtained from all subjects before participating in the study.

[0034] Inclusion criteria for pneumonia group:

[0035] (1) Male or female aged ≥18 years;

[0036] (2) Patients diagnosed with pneumonia through comprehensive evaluation by clinicians;

[0037] (3) Patients who are hospitalized for surgery or undergo screening at medical institutions participating in clinical research;

[0038] (4) The diagnosis does not include cancer.

[0039] Lung cancer group inclusion and exclusion criteria:

[0040] Inclusion criteria: (1) male or female aged ≥18 years; (2) patients diagnosed with primary early lung cancer by biopsy, postoperative pathological confirmation, or clinical evaluation by a clinician.

[0041] Exclusion criteria: (1) pregnancy or lactation; (2) emergency or emergency treatment; (3) history of malignant tumor or any anti-tumor treatment before sampling; (4) concurrent multiple primary malignant tumors.

[0042] Example 2 Detection of small molecule metabolites in plasma samples

[0043] (1) Analytical reagents

[0044] Methanol, acetonitrile, water, isopropanol, acetic acid, and ammonium acetate of mass spectrometry grade purity, and formic acid and methyl tert-butyl ether of HPLC grade purity were purchased from Sigma-Aldrich, USA;

[0045] (2) Sample preparation

[0046] The plasma sample was taken out of the -80 degree freezer, 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 the layers.

[0047] After the sample was separated into layers, 500 μL of the upper layer was transferred to 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 transferred to a 2 mL glass injection vial as the organic phase for LC-MS analysis.

[0048] 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. The tube 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 from the centrifuge tube and transferred to a 2 mL glass injection vial, which was the aqueous phase and was detected by LC-MS.

[0049] Small molecule metabolite detection

[0050] A Waters ACQUTTY UPLC® BEH C8 1.7µm 2.1*100mm column was used for the organic phase, and a Waters ACQUTTY UPLC® HSS T3 1.8µm 2.1*100mm column was used for the aqueous phase for small molecule separation. The liquid chromatography and mass spectrometry were both performed using an ACQUITY UPLC I-Class liquid chromatography system (Waters) and a Q-Exactive mass spectrometry system (Thermo Fisher Scientific).

[0051] The mobile phase parameters are as follows:

[0052] Organic Phase: Mobile phase A consisted of 0.1% acetic acid and 10 mM ammonium acetate in water. Mobile phase B consisted of 0.1% acetic acid and 10 mM ammonium acetate in acetonitrile:isopropanol (7:3, v / v). The separation gradient was as follows: 55% B (0-1 minute), 55%-75% B (1-4 minutes), 75%-89% B (4-12 minutes), 89%-100% B (12-15 minutes), 100% B (15-19.5 minutes), 100%-55% B (19.5-19.51 minutes), and 55% B (19.51-24 minutes).

[0053] Aqueous phase: Mobile phase A was 0.1% formic acid in water, and mobile phase B was 0.1% formic acid in acetonitrile. The separation gradient was as follows: 1% B for 0-1 minute, 1% to 40% B for 1-11 minutes, 40% to 70% B for 11-13 minutes, 70% to 99% B for 13-15 minutes, 99% to 1% B for 15-18 minutes, 99% to 1% B for 18-19 minutes, and 1% B for 19-22 minutes.

[0054] The mass spectrometry parameters are as follows:

[0055] Mass spectrometric data were acquired in Full MS and Full MS / dd-MS2 modes (each in positive and negative modes). The QExactive parameters used were as follows: Full MS mode with a resolution of 70,000, a scan range of 100–1500 m / z, an AGC of 3E+6, and a Maximum Time Intensity (IT) of 200 ms. In Full MS / dd-MS2 mode, the secondary mass spectrometer had a resolution of 17,500, a quadrupole window of 1.5 m / z, an AGC of 1E+5, a Maximum Time Intensity (IT) of 200–50 ms, and an HCD relative collision energy of 30 eV.

[0056] (4) Metabolomics data processing

[0057] First, valid peak signals were extracted from all mass spectrometry data. Then, baseline correction was applied to remove noise and retain the original signal peaks. The raw data were converted into central discrete data. The retention time of each mass spectrometry peak in the sample was corrected in the chromatogram, and 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 actual differences between samples, the Normalization Autoencoder (NormAE) was used for homogenization.

[0058] (5) Identification of metabolites

[0059] 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 primary and secondary chromatographic and mass spectrometric spectra of standards separated on the same chromatographic column. Identification was performed by matching with the databases and standards under the conditions that the retention time difference was within 0.1 min and the mass-to-charge ratio was less than 10 ppm.

[0060] Example 3 Screening of plasma markers for distinguishing early lung cancer from pneumonia

[0061] (1) Screening of differential metabolites for distinguishing early lung cancer and pneumonia groups

[0062] Based on the modeled data from plasma samples, we used the Lasso (Least Absolute Shrinkage and Selection Operator) to screen for differentially expressed metabolites in the early-stage lung cancer and pneumonia groups. By shrinking the coefficients of other metabolites with minimal contributions to the difference between the two groups to zero, we retained only the key metabolites that contributed significantly to the differentiation between the early-stage lung cancer and pneumonia groups. After model optimization, we ultimately identified 15 plasma metabolite marker combinations that showed significant differences in distinguishing early-stage lung cancer from pneumonia (Table 2). The area under the receiver operating characteristic (ROC) curve (AUC) of the metabolites was used as a diagnostic evaluation metric.

[0063] Table 2 Fifteen differential metabolic markers distinguishing early lung cancer and pneumonia groups

[0064] Metabolite (Chinese name) Metabolite (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 Dishexosylceramide 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 Ganchenodeoxycholic acid Glycochenodeoxycholic acid 0.02 15 Nicotinamide Nicotyrine -0.007

[0065] The ROC curve is a method used to study the relationship between model sensitivity and specificity. In an ROC curve, the vertical axis represents sensitivity, and the horizontal axis represents 1-specificity. Model performance is evaluated by comparing the area under the curve (AUC). AUC values ​​range from 0 to 1. When the AUC is less than 0.5, the diagnostic accuracy is lower than random guessing, meaning the diagnosis is meaningless. AUC values ​​closer to 1 indicate higher diagnostic accuracy, meaning the test is better at distinguishing positive from negative examples.

[0066] Example 4: Construction of a plasma metabolic marker model for distinguishing early lung cancer and pneumonia groups

[0067] (1) Construction of a model using 15 plasma metabolite markers to distinguish early lung cancer and pneumonia groups

[0068] Multivariate ROC analysis validated the effectiveness of the 15 differential metabolites identified by Lasso in distinguishing early-stage lung cancer from pneumonia. We randomly divided the modeling group data into a training set (3 / 4) and a test set (1 / 4). The training set was used to build and train the machine learning classification model, while the test set was used to verify the model's discriminative ability. The SVM (Support Vector Machine, SVM) was trained 1000 times through randomized iterations and the final model accuracy was averaged. Fifteen plasma metabolic markers were used, including gluconolactone, eicosanoid, 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 patients with early lung cancer from patients with pneumonia, and improve diagnostic sensitivity and specificity, providing important support for clinical diagnosis.

[0069] This example used the same research subjects and detection methods as Example 1 and Example 2, except that different numbers of metabolite markers were used for SVM classifier modeling. We selected 11, 7, and 3 metabolite marker combinations from the 15 plasma metabolite markers and performed multivariate ROC analysis on the modeling group samples.

[0070] (2) Construction of a model for distinguishing early lung cancer from pneumonia using 11 plasma metabolic markers

[0071] In this embodiment, the research object is the same as that of Example 1, and the detection and analysis method of Example 2 is adopted. When constructing a diagnostic model for early lung cancer, the SVM method was used, and 11 plasma metabolic markers were selected, including eicosanoid, 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 showed that the combination of eicosanoids and leucine had an AUC of 0.854, a sensitivity of 70.2%, and a specificity of 82.8% in distinguishing early lung cancer from pneumonia.

[0072] (3) Construction of a model for distinguishing early lung cancer from pneumonia using seven plasma metabolic markers

[0073] In this example, the subjects were the same as those in Example 1, and the detection and analysis method of Example 2 was used. When constructing a diagnostic model for early lung cancer, the SVM method was used, and seven plasma metabolite markers were selected, including eicosanoid, 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 this combination of seven metabolite markers in distinguishing early lung cancer from pneumonia showed an AUC of 0.836, a sensitivity of 83.9%, and a specificity of 72.4%.

[0074] (4) Construction of a model for distinguishing early lung cancer from pneumonia using three plasma metabolic markers

[0075] In this example, the subjects were the same as in Example 1, and the detection and analysis method of Example 2 was used. When constructing a diagnostic model for early lung cancer, the SVM method was used, and three plasma metabolic markers were 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 this three-marker combination in distinguishing early lung cancer from pneumonia showed an AUC of 0.835, a sensitivity of 71.9%, and a specificity of 86.2%.

[0076] The above results show that in model construction, the use of 15, 11, 7 and 3 metabolic markers can show good effects in distinguishing patients with early lung cancer and pneumonia, and have high diagnostic accuracy.

[0077] Example 5: Model validation using validation group plasma samples

[0078] (1) Model validation using a combination of plasma metabolic markers

[0079] This example used the same research subjects as Example 1 and employed the detection and analysis methods of Example 2. To validate the effectiveness of the constructed diagnostic model in distinguishing patients with early-stage lung cancer and pneumonia, we used an independent dataset and validated the plasma samples from the validation group as unknown samples.

[0080] A diagnostic model for distinguishing early lung cancer and pneumonia patients was constructed using a combination of 15 plasma metabolic markers. The validation results were AUC = 0.838 ( Figure 2 ), sensitivity = 84.0%, specificity = 72.5%. The validation results of the combination of 11 plasma metabolic markers were AUC = 0.854 ( Figure 3 ), sensitivity = 74.7%, specificity = 82.5%. The validation results of the combination of 7 plasma metabolic markers were AUC = 0.834 ( Figure 4 ), sensitivity = 78.7%, specificity = 75.0%. The results of the combination of three plasma metabolic markers were validated as AUC = 0.831 ( Figure 5 ), sensitivity = 81.3%, and specificity = 75.0%. These results indicate that our established model exhibits good diagnostic performance in unknown samples using 15, 11, 7, and 3 metabolite markers, respectively, supporting its application in clinical practice.

[0081] 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.

[0082] While the present invention is illustrated by the above-described embodiments, the present invention is not limited to the above-described process steps, and implementation of the present invention is not necessarily dependent on the above-described process steps. Those skilled in the art will appreciate that any improvements to the present invention, equivalent substitutions for the raw materials used, additions of auxiliary components, and selection of specific methods, etc., fall within the scope of protection and disclosure of the present invention.

Claims

1. Use of a metabolic marker combination in the preparation of a reagent for distinguishing early lung cancer from pneumonia, characterized in that: The metabolic marker combination is: phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0) and phosphatidylcholine 38:6 (16:0 / 22:6); or phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), eicosanoid, phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6) and glycochenodeoxycholic acid; or phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), eicosanoid, phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6), glycochenodeoxycholic acid, phosphatidylcholine 32:0 (16:0 / 16:0), phosphatidylethanolamine 38:7e (16:1e / 22:6), leucyl-leucine and glutamylglutamine; or phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), eicosanoid, phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6), glycochenodeoxycholic acid, phosphatidylcholine 32:0 (16:0 / 16:0), phosphatidylethanolamine 38:7e (16:1e / 22:6), leucyl-leucine, glutamylglutamine, gluconolactone, phosphatidylcholine 42:10 (20:4 / 22:6), hexadecanedioic acid, and dienicotine.

2. The use according to claim 1, characterized in that The sample used in the differentiation process is selected from serum, plasma or blood.

3. Use of a metabolic marker combination in the preparation of a kit for distinguishing early lung cancer from pneumonia, characterized in that: The metabolic marker combination is: phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0) and phosphatidylcholine 38:6 (16:0 / 22:6); or phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), eicosanoid, phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6) and glycochenodeoxycholic acid; or phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), eicosanoid, phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6), glycochenodeoxycholic acid, phosphatidylcholine 32:0 (16:0 / 16:0), phosphatidylethanolamine 38:7e (16:1e / 22:6), leucyl-leucine and glutamylglutamine; or phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), eicosanoid, phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6), glycochenodeoxycholic acid, phosphatidylcholine 32:0 (16:0 / 16:0), phosphatidylethanolamine 38:7e (16:1e / 22:6), leucyl-leucine, glutamylglutamine, gluconolactone, phosphatidylcholine 42:10 (20:4 / 22:6), hexadecanedioic acid, and dienicotine.

4. The use according to claim 3, characterized in that The sample used in the differentiation process is selected from serum, plasma or blood.

5. A kit for distinguishing early lung cancer from pneumonia, characterized in that: The kit includes quality control substances and standard substances, and the quality control substances are: phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0) and phosphatidylcholine 38:6 (16:0 / 22:6); or phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), eicosanoid, phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6) and chenodeoxycholic acid; or phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), eicosanoid, phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6), glycochenodeoxycholic acid, phosphatidylcholine 32:0 (16:0 / 16:0), phosphatidylethanolamine 38:7e (16:1e / 22:6), leucyl-leucine and glutamylglutamine; or phosphatidylethanolamine 40:5p (18:0p / 22:5), dihexosylceramide d34:1 (d18:1 / 16:0), phosphatidylcholine 38:6 (16:0 / 22:6), eicosanoid, phosphatidylcholine 38:7 (16:1 / 22:6), phosphatidylcholine 40:7 (18:1 / 22:6), glycochenodeoxycholic acid, phosphatidylcholine 32:0 (16:0 / 16:0), phosphatidylethanolamine 38:7e (16:1e / 22:6), leucyl-leucine, glutamylglutamine, gluconolactone, phosphatidylcholine 42:10 (20:4 / 22:6), hexadecanedioic acid, and dienicotine.

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