Application of plasma metabolism marker in preparation of reagent product for pulmonary nodule diagnosis

Abstract

The invention provides application of a group of plasma metabolism markers in preparation of a reagent product for pulmonary nodule diagnosis, and relates to the technical field of disease diagnosis reagents. The metabolic marker is prepared from trigonelline, 4-methylcatechol, 2, 4-dinitrophenol, 3, 4-dihydroxyphenylacetic acid, N-(2-furfuryl) glycine, mevalonic acid and formononetin, and the metabolic marker is prepared from the following raw materials: the trigonelline, the 4-methylcatechol, the 2, 4-dinitrophenol, the 3, 4-dihydroxyphenylacetic acid, the N-( According to the application, ultra-high performance liquid chromatography-high resolution mass spectrometry (UPLC / MS) is adopted to carry out metabonomics analysis on plasma samples before and after an early pulmonary nodule operation, and a group of plasma metabolites (trigonelline, 4-methylcatechol, 2, 4-dinitrophenol, 3, 4-dihydroxyphenylacetic acid, N-(2-furfuryl) glycine, mevalonic acid and formononetin) are identified; and the group of metabolites is utilized to form a diagnosis model, so that benign and malignant pulmonary nodules can be accurately diagnosed, and early-stage differential diagnosis and risk assessment are facilitated.

Description

Technical Field

[0001] This application relates to the field of disease diagnostic reagents technology, and more specifically, to the application of a group of plasma metabolic markers in the preparation of reagent products for the diagnosis of pulmonary nodules. Background Technology

[0002] Early diagnosis is crucial for improving cancer patient survival. Results from the US National Lung Screening Trial (NLST) and the European NELSON trial both demonstrate that screening high-risk populations with low-dose computed tomography (LDCT) significantly reduces lung cancer mortality. With the widespread use of LDCT in lung cancer screening, the incidental radiographic discovery of asymptomatic pulmonary nodules continues to increase. Pulmonary nodules are defined as focal shadows with a diameter not exceeding 3 cm. Currently, we face the challenge of assessing the malignancy probability of pulmonary nodules and managing the large number of incidentally discovered pulmonary nodules on LDCT. The limitations of CT can lead to frequent follow-up examinations and false positive results, resulting in unnecessary interventions and overtreatment. Therefore, there is an urgent need to develop reliable and convenient diagnostic reagents to accurately identify lung cancer at an early stage and differentiate most benign nodules at initial detection.

[0003] Comprehensive molecular analysis of blood (serum, plasma, peripheral blood mononuclear cells), including metabolomics, genomics, proteomics, and DNA methylation, has attracted increasing attention for the discovery of biomarkers for lung cancer diagnosis. Simultaneously, metabolomics methods, by detecting cellular end products influenced by both endogenous and exogenous activities, have been applied to predict disease occurrence and outcome. Liquid chromatography-tandem mass spectrometry (LC-MS), due to its high sensitivity and wide dynamic range, can cover metabolites with diverse physicochemical properties and is widely used in metabolomics research.

[0004] Although global metabolomics analysis of plasma has been performed to identify biomarkers associated with lung cancer diagnosis and treatment efficacy, the plasma metabolites used for lung cancer diagnosis in large cohort studies remain to be investigated. Conventional studies analyzing lung cancer biomarkers utilize differences between lung cancer patients and healthy controls, which are susceptible to inter-individual variations and yield a large amount of interfering data unrelated to lung cancer biomarkers. Summary of the Invention

[0005] The purpose of this application is to provide a set of plasma metabolic markers for the application in the preparation of reagent products for the diagnosis of pulmonary nodules, which are characterized by high specificity and sensitivity, and are helpful for early-stage differential diagnosis and risk assessment.

[0006] To solve the above-mentioned technical problems, the technical solution adopted in this application is as follows: This application provides the use of a group of plasma metabolic markers in the preparation of reagent products for the diagnosis of pulmonary nodules, said metabolic markers being composed of trigonelline, 4-methylcatechol, 2,4-dinitrophenol, 3,4-dihydroxyphenylacetic acid, N-(2-furfuryl)glycine, mevalonic acid and gentianin.

[0007] Compared with the prior art, the embodiments of this application have at least the following advantages or beneficial effects: This application employs ultra-high performance liquid chromatography-high resolution mass spectrometry (UPLC / MS) to perform metabolomics analysis on plasma samples from early-stage pulmonary nodules before and after surgery, identifying a group of plasma metabolites (trigonelline, 4-methylcatechol, 2,4-dinitrophenol, 3,4-dihydroxyphenylacetic acid, N-(2-furfuryl)glycine, mevalonic acid, and mangiferin). Furthermore, a diagnostic model was established using this group of metabolites, which can accurately diagnose the benign or malignant nature of pulmonary nodules, thus aiding in early-stage differential diagnosis and risk assessment. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a comparison of differentially expressed metabolites among different types of patients in Example 2 of this application; Figure 2 These are metabolic expression profiles of different types of patients in Example 2 of this application; Figure 3 This is an AUC graph showing the accuracy of patient diagnosis analysis using a combination of metabolic biomarkers in Example 2 of this application. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased commercially.

[0011] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present application will now be described in detail with reference to specific embodiments.

[0012] The application of plasma metabolic markers in the preparation of reagent products for the diagnosis of pulmonary nodules, wherein the metabolic markers are composed of trigonelline, 4-methylcatechol, 2,4-dinitrophenol, 3,4-dihydroxyphenylacetic acid, N-(2-furfuryl)glycine, mevalonic acid and gentianin.

[0013] In some embodiments of this application, the screening method for the above-mentioned plasma metabolic markers includes the following steps: S1. Collect peripheral blood from patients with pulmonary nodules and separate the plasma. S2. After extracting, centrifuging, lyophilizing, dissolving, and centrifuging again, take the supernatant to obtain the test solution; S3. Separate and detect metabolites in the test solution, analyze the detection data, and obtain the expression levels of trigonelline, 4-methylcatechol, 2,4-dinitrophenol, 3,4-dihydroxyphenylacetic acid, N-(2-furfuryl)glycine, mevalonic acid, and gentiopicrin.

[0014] In some embodiments of this application, the separation operation in step S1 above specifically involves: taking peripheral blood from the patient, centrifuging at 1600g for 15 minutes at 4°C, taking the supernatant, centrifuging at 12000rpm for 10 minutes, and taking the supernatant, which is plasma.

[0015] In some embodiments of this application, the above-mentioned S2 step is specifically as follows: take plasma into an EP tube, add an extractant made of methanol, acetonitrile and water in a volume ratio of 4:2:1, shake and place at -20°C for 2 hours; take out the EP tube, centrifuge at 25000g for 15 minutes at 4°C, take the supernatant into a new EP tube, freeze dry the liquid, add methanol and water in a volume ratio of 1:1 to the EP tube, shake until a complete solution is formed, and finally centrifuge at 25000g for 15 minutes at 4°C, collect the supernatant, which is the test solution.

[0016] In some embodiments of this application, step S3 above uses a UPLC tandem mass spectrometer to separate and detect metabolites in the test solution.

[0017] In some embodiments of this application, the above-mentioned method for analyzing plasma metabolic markers yields a fitted regression curve, i.e., a diagnostic model, through regression analysis: y = -0.0000002152a - 0.000003366 b - 0.000007265c + 0.00001057d -0.0000007176e + 0.00001126f - 0.00008255g + 6.939; Where y represents the diagnostic probability, and ag are trigonelline, 4-methylcatechol, 2,4-dinitrophenol, 3,4-dihydroxyphenylacetic acid, N-(2-furfuryl)glycine, mevalonic acid, and gentiopicrin, respectively.

[0018] The features and performance of this application will be further described in detail below with reference to the embodiments. Example 1

[0019] This embodiment provides a method for early lung cancer screening based on the levels of a set of metabolic markers, including the following steps: S1. Separating plasma: 1) Draw 2 mL of peripheral blood from patients with pulmonary nodules (including benign and malignant) detected by CT scan, centrifuge at 1600 g for 15 min at 4°C, and separate the supernatant into a clean 1.5 mL EP tube.

[0020] 2) Centrifuge the EP tube containing the supernatant at 12,000 rpm for 10 min at 4℃, and transfer the supernatant to a clean EP tube for metabolite extraction.

[0021] S2, Extraction of plasma metabolites: 1) Take 100 μL of plasma into an EP tube, add 700 μL of methanol:acetonitrile:water in a volume ratio of 4:2:1, shake on a shaker for 1 min, and then place in a -20°C refrigerator for 2 h.

[0022] 2) Remove the EP tube and centrifuge at 25000g for 15 min at 4℃. Remove the EP tube from the centrifuge and transfer 600 μL of the supernatant into a new EP tube.

[0023] 3) Use a freeze dryer to remove the liquid, add 180ul of methanol:water in a volume ratio of 1:1 to the EP tube, and shake until completely dissolved.

[0024] 4) Centrifuge at 25000g for 15 min at 4℃. Collect the supernatant into a new EP tube.

[0025] S3, UPLC / MS analysis: 1) Metabolites were separated and detected using a Waters 2777C UPLC (Waters, USA) tandem Q Exactive HF high-resolution mass spectrometer (Thermo Fisher Scientific, USA).

[0026] 2) The detection data were analyzed to obtain the expression levels of seven metabolites: trigonelline, 4-methylcatechol, 2,4-dinitrophenol, 3,4-dihydroxyphenylacetic acid, N-(2-furfuryl)glycine, mevalonic acid, and gentianin.

[0027] S4. Establish a diagnostic model: 1) A diagnostic index was established using the expression levels of seven metabolites and the logistic regression model. The model formula is: y = [(-0.0000002152 × trigonelline) - (0.000003366 × 4-methylcatechol) - (0.000007265 × 2,4-dinitrophenol) + (0.00001057 × 3,4-dihydroxyphenylacetic acid) - (0.0000007176 × N-(2-furfuryl)glycine) + (0.00001126 × mevalonic acid) - (0.00008255 × gentianin)] + 6.939.

[0028] 2) The diagnosis is determined by the y-value. A y-value greater than 0 indicates lung cancer, while a y-value less than 0 indicates a benign nodule. Example 2

[0029] This embodiment primarily utilizes plasma samples from 200 patients before and after radical surgery for early-stage pulmonary nodules, combined with plasma samples from 167 healthy individuals with histopathologically diagnosed benign nodules, to study lung cancer screening biomarkers. The differences in plasma metabolism between patients undergoing radical surgery and three months post-surgery were significantly smaller than the differences between patients before surgery and healthy individuals. The inventors identified only 51 differentially expressed metabolites between pre- and post-operative groups, while finding 780 differentially expressed metabolites between the pre- and benign groups (e.g., ...). Figure 1 As shown). The metabolic expression profiles were similar before and after surgery, and there were significant differences compared to the benign group (e.g.). Figure 2 (As described above). Among the 51 differentially expressed metabolites found preoperatively and postoperatively, and the 780 differentially expressed metabolites found preoperatively and in the benign group, seven common differentially expressed metabolites were identified. These seven metabolites were identified as: trigonelline; 4-methylcatechol; 2,4-dinitrophenol; 3,4-dihydroxyphenylacetic acid; N-(2-furfuryl)glycine; mevalonic acid; and gentianin. These seven differentially expressed metabolites were then used to diagnose the benign and malignant nature of pulmonary nodules.

[0030] The differential expression characteristics of plasma metabolic markers in the benign nodule and lung cancer groups in this embodiment are shown in Table 1.

[0031] Table 1

[0032] A p-value less than 0.05 indicates a statistically significant difference, while a p-value greater than 1 indicates a difference in metabolite expression between the two groups.

[0033] In this embodiment, the above metabolites were used to perform machine learning modeling on the metabolic samples of lung cancer detection (200 malignant nodule samples and 167 benign nodule samples), and the model formula was obtained as follows: y = [(-0.0000002152 × trigonelline) - (0.000003366 × 4-methylcatechol) - (0.000007265 × 2,4-dinitrophenol) + (0.00001057 × 3,4-dihydroxyphenylacetic acid) - (0.0000007176 × N-(2-furfuryl)glycine) + (0.00001126 × mevalonic acid) - (0.00008255 × gentianin)] + 6.939. The model predicted a value greater than 0 for lung cancer samples and a value less than 0 for healthy samples.

[0034] Subsequently, this embodiment utilizes the model formula of this biomarker combination (7 metabolites) to conduct diagnostic analysis on 273 benign nodules and 300 early-stage lung cancer patients. The results are as follows: Figure 3 As shown, the accuracy AUC reached 0.983.

[0035] The embodiments described above are some, but not all, of the embodiments of this application. The detailed description of the embodiments of this application is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

Claims

1. Use of plasma metabolic markers for the manufacture of a reagent product for the diagnosis of pulmonary nodules, characterized in that, The metabolic markers consist of trigonelline, 4-methylcatechol, 2,4-dinitrophenol, 3,4-dihydroxyphenylacetic acid, N-(2-furoyl)glycine, mevalonic acid and formononetin.

2. Use of the plasma metabolic markers according to claim 1 for the preparation of a reagent product for the diagnosis of pulmonary nodules, characterized in that, The screening method of the plasma metabolic markers comprises the following steps: S1, taking peripheral blood of a patient with pulmonary nodule, and separating to obtain plasma; S2, taking the plasma to perform extraction, centrifugation, freeze-drying, dissolution and re-centrifugation, and then taking supernatant to obtain a to-be-tested liquid; S3, separating and detecting metabolites in the to-be-tested liquid, analyzing detection data, and obtaining expression levels of trigonelline, 4-methylcatechol, 2,4-dinitrophenol, 3,4-dihydroxyphenylacetic acid, N-(2-furoyl)glycine, mevalonic acid and formononetin.

3. Use of the plasma metabolic markers according to claim 2 for the manufacture of a reagent product for the diagnosis of pulmonary nodules, characterized in that, The separation operation in the S1 step is specifically as follows: taking peripheral blood of a patient, centrifuging at 1600g for 15 min at 4℃, centrifuging the supernatant at a speed of 12000 rpm for 10 min, and taking the supernatant, i.e. the plasma.

4. Use of the plasma metabolic markers according to claim 2 for the manufacture of a reagent product for the diagnosis of pulmonary nodules, characterized in that, The S2 step is specifically as follows: taking the plasma to an EP tube, adding an extraction agent mixed by methanol, acetonitrile and water in a volume ratio of 4:2:1, placing in a-20℃ condition for 2h after oscillation; taking out the EP tube, centrifuging at 25000g for 15 min at 4℃, taking the supernatant to a new EP tube, drying the liquid by a freeze dryer, adding methanol and water in a volume ratio of 1:1 to the EP tube, oscillating to a complete solution, and finally centrifuging at 25000g for 15 min at 4℃, and collecting the supernatant, i.e. the to-be-tested liquid.

5. Use of the plasma metabolic markers according to claim 2 for the manufacture of a reagent product for the diagnosis of pulmonary nodules, characterized in that, The S3 step adopts ultra-high performance liquid chromatography-high resolution mass spectrometry to separate and detect metabolites in the to-be-tested liquid.

6. Use of the plasma metabolic markers according to claim 1 for the manufacture of a reagent product for the diagnosis of pulmonary nodules, characterized in that, The analysis method of the plasma metabolic markers, through regression analysis, obtains a fitted regression curve, i.e. a diagnosis model: y = -0.0000002152a - 0.000003366 b - 0.000007265c + 0.00001057d -0.0000007176e + 0.00001126f - 0.00008255g + 6.939; wherein y is a diagnosis probability, and a-g are respectively trigonelline, 4-methylcatechol, 2,4-dinitrophenol, 3,4-dihydroxyphenylacetic acid, N-(2-furoyl)glycine, mevalonic acid and formononetin.

Images