Biomarkers beta-sitosterol and D-ribose for diagnosing lung cancer

Abstract

The invention discloses biomarkers beta-sitosterol and D-ribose for diagnosing lung cancer, and relates to the technical field of biomedicine. The two small molecular biomarkers beta-sitosterol and D-ribose provided by the invention are natural compounds existing in human blood and other tissues, have significant differences in the blood level of lung cancer patients and healthy people, and can be used as novel, simple and non-invasive biomarkers for diagnosing lung cancer; and a feasible auxiliary method is provided for lung cancer diagnosis. When the two indexes are jointly used, the accuracy of lung cancer diagnosis is better than that when the two indexes are independently used, and the combined diagnosis effect is better.

Description

Technical Field

[0001] The invention relates to the field of biomedical technology, and in particular to biomarkers beta-sitosterol and D-ribose for diagnosing lung cancer. Background Art

[0002] At present, the diagnostic methods for lung cancer can be divided into two categories, one is non-invasive diagnosis, which mainly includes imaging examination and sputum cytology examination, and the other is invasive diagnosis, which mainly includes bronchoscopy and surgical diagnosis methods. Among them, imaging examination mainly includes X-ray, CT, MRI, etc. Surgical diagnosis methods include transbronchial biopsy (TBLB), percutaneous puncture (NB), mediastinoscopy, video-assisted thoracoscopy, and lymph node biopsy. Although recent studies have found that superoxide dismutase and monoamine oxidase combined enzyme detection have certain significance for the diagnosis of lung cancer, and the serum SOD activity of lung cancer patients is significantly lower than that of benign lung lesions and normal healthy people, due to its single evaluation index and low accuracy, it needs to be combined with other indicators for diagnosis. Lung cancer is a malignant tumor that seriously endangers human health and life. Screening out new tumor metabolic markers with the value of early diagnosis and prognosis of lung cancer for early diagnosis and treatment of lung cancer patients, thereby improving the survival rate of lung cancer patients, is an urgent problem to be solved in the diagnosis and treatment of lung cancer.

[0003] The invention application with publication number CN11 8362727A discloses a lung cancer diagnostic biomarker and its application. The application screens suitable lung cancer diagnostic biomarkers through metabolomics methods. Based on the detection of these lung cancer diagnostic biomarkers, it can effectively distinguish healthy people from lung cancer patients with high sensitivity and specificity, providing a more reliable and convenient diagnostic method for lung cancer diagnosis, and has broad application prospects in the field of lung cancer diagnosis.

[0004] The invention application with publication number CN11 3811767A discloses a biomarker panel for serum test for detecting lung cancer, wherein the biomarker is selected from the group of biomarkers consisting of: arginine, C18.2, decaenoylcarnitine (C10:2), LYSOC18.2, methionine, ornithine, PC32:2AA, PC36.0AA, PC36.0AE, putrescine, spermidine, spermine and valine. The serum test for diagnosing lung cancer can take into account the smoking history.

[0005] Tumor markers are molecules that appear abnormally in body fluids during the occurrence, development or existence of tumors, and can be used for tumor screening, diagnosis, prognosis assessment and treatment monitoring. These markers can be present in some body fluids such as blood, urine, and tissues. At present, some common tumor markers (such as CEA, CYFRA21-1, SCC, Pro-GRP, NSE, etc.) are widely used in physical examinations. However, the current tumor markers used in clinical practice have problems with insufficient sensitivity and specificity, and are in urgent need of further improvement. In order to improve the reliability of lung cancer diagnosis, it is urgent to seek more accurate, sensitive and specific biomarkers. Summary of the invention

[0006] Based on this, the present invention provides a biomarker β-sitosterol and D-ribose for diagnosing lung cancer, which can diagnose lung cancer with higher accuracy, sensitivity and specificity, and improve the reliability of lung cancer diagnosis.

[0007] In one aspect, the present invention provides the use of a reagent or method for detecting a biomarker in the preparation of a product for diagnosing lung cancer, wherein the biomarkers are β-sitosterol and D-ribose.

[0008] Preferably, the reagent or method for detecting biomarkers is used to detect the concentration of β-sitosterol and D-ribose in the blood or tissue of the individual to be detected.

[0009] Preferably, if the test results of the two biomarkers in the blood or tissue of the individual to be tested are positive, the individual to be tested has a high possibility of suffering from lung cancer.

[0010] Preferably, when the two biomarkers are detected jointly, y=1.471x1+4.293x2-4.231, wherein x1 is the concentration of β-sitosterol, x2 is the concentration of D-ribose, the concentration units are both μmol / L, and y is the score after fitting of the two biomarkers. If the value of y is greater than or equal to 0, the result is positive, otherwise it is negative.

[0011] The concentration of the two biomarkers can be detected by all possible detection methods, namely, by at least one of UPLC, HPLC, GC-MS, LC-MS, GC-MS-MS, LC-MS-MS, LC-NMR, LC-MS-NMR and antibody or antigen detection methods. Preferably, the method for detecting the biomarkers is UPLC method (ultra-high performance liquid chromatography).

[0012] On the other hand, the biomarkers for diagnosing lung cancer according to the present invention are β-sitosterol and D-ribose.

[0013] β-Sitosterol, also known as β-sitosterol, CAS registration number: 83-46-5, molecular formula: C 29 H 50 O is a white crystalline solid with a molecular weight of 414.707.

[0014] D-Ribose is an important five-carbon monosaccharide with a chemical formula of C5H 10 O5, CAS registration number 50-69-1, molecular weight 150.13.

[0015] In yet another aspect, the present invention also provides a product for diagnosing lung cancer, the product comprising reagents for respectively detecting β-sitosterol and D-ribose in a sample or reagents involved in a method for detecting β-sitosterol and D-ribose.

[0016] The product for diagnosing lung cancer may be a product available on the market, including a kit, a chip, a membrane strip, a protein array or a detection system. Preferably, the product is a kit.

[0017] Finally, the present invention also provides a device for diagnosing lung cancer, the device comprising:

[0018] Detection unit: used to detect the concentrations of β-sitosterol and D-ribose in the sample to be detected, respectively, wherein the concentration of β-sitosterol is x1 μmol / L, and the concentration of D-ribose is x2 μmol / L;

[0019] Processing unit: used to process the input concentration data of β-sitosterol and D-ribose in the sample to be tested, and y represents the scores of the two biomarkers after fitting. The y value is calculated according to the formula y=1.471x1+4.293x2-4.231. If the value of y is greater than or equal to 0, the result is positive, otherwise it is negative;

[0020] Result output unit: used to output the results obtained by the processing unit.

[0021] The two small molecule biomarkers β-sitosterol and D-ribose provided by the present invention are natural compounds existing in human blood and other tissues. The levels in the blood of lung cancer patients are significantly different from those of healthy people. They can be used as new, simple, and non-invasive biomarkers for diagnosing lung cancer, which helps to provide a feasible auxiliary method for lung cancer diagnosis. The diagnosis of lung cancer when the two indicators are used in combination is more accurate than when the two indicators are used alone, and the combined diagnosis effect is better. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1The results of the individual diagnosis of β-sitosterol are shown in Figure 1, where A: the ROC curve of the training set of β-sitosterol; B: the ROC curve of the validation set of β-sitosterol.

[0023] Figure 2 These are the result diagrams of D-ribose diagnosis alone, where A: ROC curve of the training set of D-ribose; B: ROC curve of the validation set of D-ribose.

[0024] Figure 3 The results of drawing a quadrant diagram are shown with the β-sitosterol content as the ordinate and the D-ribose content as the abscissa.

[0025] Figure 4 The results were determined using high performance liquid chromatography for blood samples from 30 healthy people and lung cancer patients.

[0026] Figure 5 The test results were obtained by using the kit on blood samples from 15 healthy people and lung cancer patients. DETAILED DESCRIPTION

[0027] Example 1

[0028] (1) Chemical reagents

[0029] All targeted metabolite standards used in the Q300 method were purchased from Sigma-Aldrich (St. Louis, MO, USA), Steraloids (Newport, RI, USA) and TRC (Toronto, ON, Canada). All standards were accurately weighed and dissolved in water, methanol, sodium hydroxide solution or hydrochloric acid solution to obtain a single standard stock solution with a concentration of 5.0 mg / mL. An appropriate amount of each standard stock solution was mixed to prepare a mixed standard stock solution.

[0030] Formic acid (mass spectrometry grade) was purchased from Sigma-Aldrich (St. Louis, MO, USA). Methanol (mass spectrometry grade), acetonitrile (mass spectrometry grade), and isopropanol (mass spectrometry grade) were purchased from Thermo-Fisher Scientific (Fair Lawn, NJ, USA). Ultrapure water for the experiment was prepared by a Mill-Q Reference Ultrapure Water System (Millipore, Billerica, MA, USA) equipped with a 0.22 μm filter head for LC-MS.

[0031] (2) Sample preparation

[0032] The samples to be tested in this application are from Zhejiang Hospital, 45 lung cancer patients are from Zhejiang Hospital clinical samples, specifically lung adenocarcinoma, and 45 healthy people are from Zhejiang Hospital Physical Examination Center.

[0033] The sample preparation process of the present application is specifically as follows: thaw the plasma / serum sample in an ice bath, accurately pipette 20 μL of plasma / serum and place it in a pre-cooled 96-well plate, then place the 96-well plate in an Eppendorf epMotion workstation (Eppendorf Inc., Humburg, Germany), automatically add 120 μL of pre-cooled methanol solution containing the internal standard to each sample well, vortex vigorously for 20 min to mix, and centrifuge at 4000×g for 20 min using a (Allegra X-15R, Beckman Coulter, Inc., Indianapolis, IN, USA) centrifuge, then take out the plate and place it in an Eppendorf epMotion workstation, pipette 30 μL of the supernatant and transfer it to a new 96-well plate, add 20 μL of freshly prepared derivatization reagent to each well, seal the plate, and place it at 30°C for 60 min of derivatization. Then, 330 μL of 50% methanol solution in an ice bath was added to dilute the sample, followed by centrifugation at 4°C (4000 g, 30 min), 135 μL of supernatant was aspirated and transferred to a new 96-well plate, and 10 μL of internal standard was added to each well. Serial dilutions of the derivatized standard stock solution were added to the left well, and the plate was finally sealed for LC-MS analysis.

[0034] (3) Instruments and equipment

[0035] Ultra-high performance liquid chromatography tandem mass spectrometer (UPLC-MS / MS, ACQUITY UPLC-Xevo TQ-S, Waters Corp., Milford, MA, USA) was used for quantification of functional small molecule metabolites. The instrument parameters and method settings are shown in Table 1. The instrument was optimized and maintained every 24 hours.

[0036] Table 1 UPLC-MS / MS instrument settings

[0037]

[0038] (4) Analytical quality control procedures

[0039] The rapid turnover of many intracellular metabolites necessitates immediate metabolic quenching. The extraction solvent was stored in a -20°C freezer overnight and added immediately after the sample was thawed. During sample preparation, we used an ice-salt bath to keep the sample cold and reduce degradation. All prepared samples were analyzed within 48 hours after extraction and derivatization.

[0040] This project uses a complete set of strict quality control / assurance procedures to control every step from receiving samples in the laboratory to final delivery to ensure continuous high-quality analytical results. QA / QC provides reliable data for biomarker discovery research and / or to help molecular biology research. Therefore, we use three types of quality control samples, including test mixtures, internal standards, and mixed biological samples. In addition to quality control, adjustment samples and solvent blanks are also required to optimize instrument performance.

[0041] The test mixture contains a set of commercial standards with a mass range spanning the entire system for the mass range of the samples being studied. These samples are analyzed at the beginning and end of each batch run to ensure that the instrument is operating within the laboratory's specified range (retention time stability, chromatographic peak shape, and peak signal intensity). The retention time shift for LCMS should be within 4 seconds and the peak intensity difference should be within 15%.

[0042] Internal standards were added to the test samples to monitor analytical variation throughout sample preparation and analysis. Pooled QC samples were prepared by pooling a portion of the study samples so that the QCs roughly represented the biological mean of the entire sample set. QC samples were prepared along with the test samples and injected periodically throughout the analysis (after every 14 test samples by LC-MS).

[0043] A reagent blank is a mixture of solvents used in sample preparation and is usually processed in the same manner as the samples to be analyzed. Reagent blanks can serve as an effective system contamination alarm. Since reagent blanks are composed of high-purity solvents and analyzed in the same manner as the study samples, they are also used to clean the column and remove accumulated matrix effects throughout the study.

[0044] The calibrator consists of a blank sample (matrix sample not treated with an internal standard), a zero sample (matrix sample treated with an internal standard), and a series of seven concentrations covering the expected range of metabolites in a specific biological sample. The LLOQ and ULOQ are the lowest and highest concentrations of the standard curve that can be measured with acceptable accuracy and precision.

[0045] (5) Multivariate statistical analysis software

[0046] Software: QuanMET software (v2.0, Metabo-Profile, Shanghai, China) was used to process the raw data files generated by UPLC-MS / MS and to perform peak integration, calibration, and quantification for each metabolite.

[0047] Quantitative: Quantitative mass spectrometry metabolomics refers to the determination of the concentration of an unknown substance by comparing it to a set of standard samples of known concentration (calibration curve). The calibration curve is a graph of the analytical signal as the concentration of the analyte (substance being measured) changes. For most analyses, the instrument response will be linear with the concentration, which results in a model presented by the equation y=ax+b, where y is the instrument response, such as the height or area of ​​the peak, a is the slope / sensitivity, and b is a constant that describes the background. The analyte concentration (x) of the unknown sample can be calculated from this equation.

[0048] Statistics: Principal Components Analysis (PCA) is an unsupervised modeling method that is often used to detect data outliers, clusters, and categorical trends in a sample set without prior knowledge. The principal components (PCs) from a dataset are orthogonal to each other, reflecting the level of variable reduction of the dataset. Therefore, the first principal component (PC1) has more variance than the second principal component (PC2), which in turn has more variance than the third principal component (PC3), and so on. In this way, PCA greatly reduces the number of variables (scores) required to represent the dataset and allows visualization of data clusters and outliers based on multiple variables in two or three-dimensional space. Since metabolomics data usually contain very complex biological information such as dietary intervention, lifestyle, age, sex, region-dependent environmental factors, and diseases, it is difficult to distinguish the effects of a single factor from the broader biological information. Therefore, complex multivariate statistical models such as partial least squares discriminant analysis (PLS-DA) and / or orthogonal partial least squares discriminant analysis (OPLS-DA) are widely used for multi-class classification and identification of different metabolites.

[0049] Partial Least Square Discriminant Analysis (PLS-DA) is a generalized multiple regression method that can handle multicollinear X (mass spectrometry data) and Y (class) variables. This method has two goals: one is to approximate X and Y, and the other is to model the relationship between them. Since this method can maximize the separation between classes using predefined methods, it is widely used in the field of metabolomics to handle complex data sets generated by complex biological matrices. In this project, PLS-DA modeling was used as a multi-class classifier to observe the differences in the overall metabolic profiles between groups, which provided more valuable information than PCA.

[0050] The OPLS method is the latest improvement of the PLS method. The Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) algorithm decomposes the original data set into three parts: system variables, orthogonal / irrelevant information, and residuals. This results in a model with the least number of predictive parts, defined by the degrees of freedom (k-1 dimensions) between the group variances (k classes). This division of the X-data facilitates model interpretation and model prediction.

[0051] Model interpretation. In PCA, PLS-DA, or OPLS-DA, each spatial point in the k-dimensional space represents a separate sample, and the samples are color-coded according to the grouping information. R2X and R2Y represent the fractions of the variance of the X matrix and the Y matrix, respectively, and Q2Y represents the prediction accuracy of the model. The cumulative values ​​of R2X, R2Y, and Q2Y are close to 1.0, indicating that the model has reliable predictive ability. Variables with a variable influence projection (VIP) greater than 1.0 are considered to be significantly different between classes.

[0052] Data quality control and modeling verification. In order to verify the overfitting of the model, 7 rounds of cross-validation were performed, and 1 / 7 of the sample set was eliminated in each round. Using this method, the Y value of each subject was predicted using the model that was excluded during the modeling process, and all the prediction results were sorted. The obtained R2Y estimates the degree of match between the model and the Y data (here is the classification information), and Q2Y estimates the degree of prediction of the model for the Y data. The cumulative values ​​of R2Ycum and Q2Ycum are close to 1.0, indicating that the model is reliable and the prediction ability meets the requirements.

[0053] Example 2

[0054] The samples to be tested in this application are from Zhejiang Hospital, 45 lung cancer patients are from Zhejiang Hospital clinical samples, specifically lung adenocarcinoma, and 45 healthy people are from Zhejiang Hospital Physical Examination Center.

[0055] Through the metabolomics screening of Example 1, two potential biomarkers were obtained, namely β-Sitosterol (β-sitosterol) and D-Ribose (D-ribose). The β-sitosterol and D-ribose biomarkers were subjected to binary logistic regression analysis for joint diagnosis using SPSS Statistics 19 statistical software, with the independent variables being the disease type (lung cancer and normal control) and the covariates being the two potential markers, to obtain y=1.471x1+4.293x2-4.231, where x1 is β-sitosterol and x2 is D-ribose, and the concentration units of both are μmol / L. y is the score after fitting of the two potential biomarkers. A y value greater than or equal to 0 indicates a positive joint diagnosis, and a y value less than 0 indicates a negative. According to the above formula, the scores of a single marker and the combined diagnosis of the two markers were obtained for each sample, and the ROC curve was obtained using the fitted scores. The results are as follows: Figure 1 , Figure 2 And as shown in Table 2.

[0056] Table 2

[0057] sample Sensitivity Specificity AUC β-Sitosterol 0.67 0.87 0.7778 D-Ribose 0.77 0.77 0.7722 β-Sitosterol combined with D-ribose 0.73 0.90 0.8744

[0058] The results obtained by using β-sitosterol alone as a biomarker and arranging the patient data are shown in Table 3. Among them, the sensitivity is 66.7%; the specificity is 86.7%; the false positive rate is 13.3%; and the false negative rate is 33.3%.

[0059] Table 3

[0060] β-Sitosterol patient healthy total Positive 20 4 24 Negative 10 26 36 total 30 30

[0061] Using D-ribose alone as a biomarker, the results obtained after collating the patient data are shown in Table 4. Among them, the sensitivity is 76.7%; the specificity is 76.7%; the false positive rate is 23.3%; and the false negative rate is 23.3%.

[0062] Table 4

[0063] D-Ribose patient healthy total Positive 23 7 30 Negative 7 23 30 total 30 30

[0064] The results obtained by combining β-sitosterol and D-ribose as biomarkers and sorting the patient data are shown in Table 5. Among them, the sensitivity is 73.3%; the specificity is 90%; the false positive rate is 10%; and the false negative rate is 26.7%.

[0065] Table 5

[0066] joint patient healthy total Positive 22 3 25 Negative 8 27 35 total 30 30

[0067] Example 3

[0068] Thirty lung cancer patients were selected to divide the diagnostic thresholds of β-sitosterol and D-ribose, where the threshold of β-sitosterol was 0.7 μmol / L, β-sitosterol values ​​greater than or equal to 0.7 μmol / L were positive, and values ​​less than 0.7 μmol / L were negative. The threshold of D-ribose was 0.68 μmol / L, D-ribose values ​​greater than or equal to 0.68 μmol / L were positive, and values ​​less than 0.68 μmol / L were negative.

[0069] With β-sitosterol content as the ordinate and D-ribose content as the abscissa, a quadrant graph was drawn using Graphpad 6.0. The results are shown in Figure 3 As shown. Each point in the figure is a patient. The points on the left side of x=0.68 are patients who cannot be diagnosed by D-ribose, and the points below y=0.7 are patients who cannot be diagnosed by β-sitosterol. Among the 7 D-ribose negative patients, 4 were diagnosed by β-sitosterol (blue part). Among the 10 β-sitosterol negative patients, 7 were diagnosed by D-ribose (pink part). This shows that the two indicators can make up for each other's patients left out during diagnosis, and the combined diagnosis effect is better.

[0070] Example 4

[0071] Kit for improving high performance liquid chromatography separation efficiency.

[0072] Plasma samples:

[0073] (1) Collect whole blood from 15 healthy people and lung cancer patients in EDTA anticoagulant tubes and separate the plasma as soon as possible: centrifuge at 3000 rpm for 10 minutes at room temperature

[0074] (2) Take the upper layer, dispense 0.2 mL / tube into 1.5 mL centrifuge tubes, and store at -80°C.

[0075] 1. Take 200 μl of the suspension from the above centrifuge tubes, add 400 μl of buffer P1 (methanol-acetonitrile 1:1) to precipitate the protein in the sample, and centrifuge at 4°C, 14000 rpm for 10 min;

[0076] 2. Take the supernatant, add 600 μl buffer P2 (β-sitosterol: acetic anhydride; D-ribose: 1-(4-carboxyphenyl)-3-methyl-5-pyrazolone (MOPBA)), mix by pipetting, and incubate in a 70°C water bath for 90 min;

[0077] 3. After the sample has cooled to room temperature, add 150 μl buffer P3 (HCL), vortex mix for 30 seconds, centrifuge at 14,000 rpm for 10 minutes, take the supernatant, and measure by HPLC.

[0078] The high performance liquid chromatography method in Example 1 was used for determination as a comparison, and the samples were blood samples from 30 healthy subjects and lung cancer patients.

[0079] The results of the two determination methods are as follows Figure 4 and Figure 5 As shown, the results show that the improved separation efficiency using the kit resulted in more accurate measurements and better results when calculating the fit score.

Claims

1. Use of a reagent or method for detecting biomarkers in the preparation of a product for diagnosing lung cancer, wherein the biomarkers are β-sitosterol and D-ribose.

2. The use according to claim 1, characterized in that The reagent or method for detecting biomarkers is used to detect the concentrations of β-sitosterol and D-ribose in the blood or tissue of the individual to be detected.

3. The use according to claim 2, characterized in that If the test results of the two biomarkers in the blood or tissue of the individual to be tested are positive, the individual to be tested has a high possibility of suffering from lung cancer.

4. The use according to claim 3, characterized in that When the two biomarkers are detected jointly, y=1.471x1+4.293x2-4.231, where x1 is the concentration of β-sitosterol, x2 is the concentration of D-ribose, and the concentration units are μmol / L. y is the score after fitting of the two biomarkers. If the value of y is greater than or equal to 0, the result is positive, otherwise it is negative.

5. The use according to claim 2, characterized in that The method for detecting biomarkers is ultra-performance liquid chromatography.

6. A biomarker for diagnosing lung cancer, characterized in that: The biomarkers are β-sitosterol and D-ribose.

7. A product for diagnosing lung cancer, characterized in that: The product comprises reagents for respectively detecting β-sitosterol and D-ribose in a sample or reagents involved in a method for detecting β-sitosterol and D-ribose.

8. The product for diagnosing lung cancer according to claim 7, characterized in that: The product is a test kit.

9. A device for diagnosing lung cancer, characterized in that: The device comprises: Detection unit: used to detect the concentrations of β-sitosterol and D-ribose in the sample to be detected, respectively, wherein the concentration of β-sitosterol is x1 μmol / L, and the concentration of D-ribose is x2 μmol / L; Processing unit: used to process the input concentration data of β-sitosterol and D-ribose in the sample to be tested, and y represents the scores of the two biomarkers after fitting. The y value is calculated according to the formula y=1.471x1+4.293x2-4.

231. If the value of y is greater than or equal to 0, the result is positive, otherwise it is negative; Result output unit: used to output the results obtained by the processing unit.

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