CN112255334B - Small molecule marker for distinguishing junctional ovarian tumor from malignant ovarian tumor and application thereof - Google Patents
Small molecule marker for distinguishing junctional ovarian tumor from malignant ovarian tumor and application thereof Download PDFInfo
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Abstract
The invention belongs to the technical field of biological medicines, and particularly relates to a small molecular marker for distinguishing junctional and malignant ovarian tumors and application thereof. The invention provides a brand-new urine diagnosis marker combination for distinguishing the borderline and the malignant ovarian tumor, which has the characteristics of high accuracy and high sensitivity, the AUC of a training set reaches 0.943, and the AUC of a verification set is 0.836, and the effect is better than that of CA125(AUC: training set: 0.830; verification set: 0.807). The urine diagnosis marker combination belongs to urine micromolecular metabolites and has the advantage of no wound.
Description
Technical Field
The invention relates to the technical field of biological medicines, in particular to a small molecular marker for distinguishing junctional ovarian tumors from malignant ovarian tumors and application thereof.
Background
Ovarian Cancer (OC) is one of the most fatal cancers among gynecological malignancies, with 294,414 new cases of ovarian cancer and 184,799 deaths of ovarian cancer worldwide. IARC predicts 73,300 new cases of ovarian cancer in 2018 china. Since the onset is hidden, there are no effective measures for general investigation and early diagnosis, and therefore, 80% of women have been diagnosed at an advanced stage. The survival rate of ovarian cancer is highly correlated with the stage of the tumor. The 5-year survival rates of early ovarian cancer and late ovarian cancer differ significantly. The 5-year survival rate for patients in stage I of fig. is as high as 92%, while the 5-year survival rate for patients in stage IV of fig. is only 5%. Therefore, early diagnosis is an effective method for improving the prognosis of ovarian cancer.
Current diagnostic methods for female reproductive system malignancies rely primarily on classical symptomatic and screening tests such as gynecological examinations, ultrasound examinations, and tumor markers. At present, the markers clinically used for ovarian cancer or early detection of ovarian cancer mainly comprise serum CA125, HE4 and the like, and CA125 (glycoprotein) serving as a unique index for early diagnosis of ovarian cancer and ovarian cancer still has certain limitations and is increased in benign diseases and other malignant tumors. Human epididymis protein 4(HE4) is a new tumor marker, the value of the tumor marker for differential diagnosis of benign and malignant ovarian tumors is superior to that of CA125, and the combined detection of malignant tumors by using HE4 and CA125 is more accurate than the single use of any marker. However, the specificity and sensitivity of the currently clinically applied indexes are not yet satisfactory, so that it is necessary to develop a novel biomarker or a novel model for the diagnosis and early diagnosis of ovarian cancer. And at present, there is no simple and convenient means for diagnosing whether ovarian diseases (benign tumor, borderline tumor and malignant tumor) exist, and no biomarker for distinguishing the borderline ovarian tumor from the malignant ovarian tumor exists, so that the biomarkers in the two aspects need to be explored.
As one of the markers of tumors, metabolic alterations have been emphasized over the last decades. Metabolomics is a high-throughput bioanalytical method aimed at the identification and quantification of small molecules (molecular weight less than 1500 daltons) present in any biological system or in any particular physiological state, is a comprehensive assessment of endogenous small molecule metabolites in biological systems, with the potential to recognize important metabolic changes in various diseases. It has been used to find new diagnostic markers and to explore the pathogenesis of ovarian and other cancers. These studies suggest that exploring the metabolic characteristics of biological samples may facilitate clinical diagnosis of ovarian cancer and help understand the underlying biological mechanisms of ovarian cancer.
At present, the metabolomics research of ovarian cancer has a plurality of defects, and the further research on the metabolomics of the ovarian cancer, especially on ovarian cancer biomarkers is urgently needed in the field.
Disclosure of Invention
The invention aims to provide a small molecular marker for distinguishing junctional ovarian tumors from malignant ovarian tumors and application thereof. The invention adopts urine metabolic marker detection, can distinguish the junctional and malignant ovarian tumors by a non-invasive means, and has high accuracy and high sensitivity.
The technical scheme of the invention is specifically introduced as follows.
Use of a small molecule marker for the preparation of a diagnostic agent for differentiating junctional and malignant ovarian tumors; the diagnostic reagent comprises a small molecular marker, and the small molecular marker is a urine metabolism marker which comprises 16a-Hydroxyestrone (16a-Hydroxyestrone), Coniferyl alcohol (Coniferyl alcohol), Indoleacrylic acid (Indolacrylic acid), (E) -Carcinodine ((E) -Casimiriodine), and Cerulenin (Cerulenin).
In the invention, the diagnostic reagent diagnoses and distinguishes the borderline ovarian tumor and the malignant ovarian tumor by detecting the concentration of the urine metabolic marker in the urine of a subject.
In the present invention, the diagnostic reagent detects the concentration of the urine metabolic marker based on a chemical analysis method or a mass spectrometry method.
In the invention, the concentration of 16a-hydroxyestrone, coniferyl alcohol, indoleacrylic acid, (E) -cassiodine and Cerulenin in urine of a patient with malignant ovarian tumor is obviously reduced compared with that of a patient with borderline tumor.
The invention has the beneficial effects that:
based on the small molecular marker, the borderline tumor and the malignant ovary can be diagnosed and distinguished; the invention relates to a SVM model for distinguishing the junction and the malignant ovarian tumors, which is established on the basis of 5 indexes of 16a-Hydroxyestrone (16a-Hydroxyestrone), Coniferyl alcohol (Coniferyl alcohol), Indoleacrylic acid (Indoleacetic acid), (E) -casseimeriodine ((E) -Casimiriodine) and Cerulenin (Cerulenin) in urine, and the model parameters are seed 520, gamma 0.1, cost 10, AUC 0.943 and cut-off 0.828, the sensitivity is 98.65%, the specificity is 84.62%, and is obviously higher than CA125(AUC: 0.830; cut-off: 165.5U/ml; sensitivity: 81.08%; specificity: 76.92%). In the verification group, the mathematical diagnosis model predicts that the malignant ovarian tumor AUC is 0.836, the sensitivity is 80.26%, the specificity is 71.43%, and the effect is better than that of CA125 used alone.
Drawings
Fig. 1 shows receiver operating characteristic analysis (ROC) curves of SVM classifiers constructed with 5 urine metabolite features in the discovery and validation sets for testing classification performance of models for distinguishing junctional ovarian tumors from malignant ovarian tumors.
Detailed Description
The technical solution of the present invention is specifically described below with reference to the accompanying drawings and examples.
Example 1
1. Participant and study design
A total of 420 patients with primary Ovarian Cancer (OC), benign ovarian tumors, borderline ovarian tumors, and normal donors were enrolled. Urine and plasma samples from participants were collected from 2016 at 9 months to 2018 at 5 months, with an age between 14 and 84 years. The enrolled patients did not receive any radiation, chemotherapy, or metabolic disease, including but not limited to liver disease, diabetes, and kidney disease. In addition, patients taking drugs that are proven to alter metabolism are excluded. All ovarian cancer patients in this study underwent surgery and the diagnosis was further confirmed by at least two pathologists. The discovery set of our project included 326 samples (plasma and urine: 40 normal controls, 36 benign, 13 junctional, 74 malignant, respectively: 148 participants matched plasma and urine samples). An external cohort (plasma: 36 normal controls, 14 benign, 1 borderline, 21 malignant; urine: 40 normal controls, 45 benign, 7 borderline, 76 malignant) was used as the validation group.
In the morning, fasting abdominal blood was collected using a Vacutainer tube, and fasting midstream urine (fasting urine only in the normal control group) was collected using a 15ml urinary tube. The blood samples were stored at room temperature for 30min and allowed to clot. The coagulated blood sample was centrifuged at 3000rpm for 10min at 4 ℃ to obtain supernatant plasma, which was then dispensed into 1.5ml centrifuge tubes. Blood samples were stored rapidly at-80 ℃ until UPLC-QTOF/MS analysis. Urine was collected for a half hour and stored in a 4 ℃ freezer, followed by centrifugation at-80 ℃ until UPLC-QTOF/MS analysis was performed. Then centrifuged at 4000 rpm for 10 minutes at 4 ℃. Finally, the supernatant was transferred to a 5ml centrifuge tube and stored at-80 ℃.
HPLC-MS analysis
2.1 sample preparation
400 μ L of plasma or urine was added to a 10mL polypropylene bottle, followed by 200 μ L of 5% (v/v) H3PO4 and vigorous stirring for 30 s. Subsequently, 200. mu.L of 5% (v/v) H3PO4 was added and stirred vigorously for 30 s. Then, 4mL of salt solution and 4mL of organic solvent were added to the sample one after the other and mixed with a vortex machine for 30s after each step. The mixture was centrifuged for 10min (3000 rpm). The supernatant (4mL) was transferred to a 5mL vial and dried under stable nitrogen. The dried sample was further desalted with 1ml of isopropyl alcohol. After centrifugation, the supernatant was transferred to a 1.5mL vial and dried. Plasma residues were dissolved in 50. mu.L of 15% acetonitrile and 0.2% FA and 30. mu.L of DCM, and urine residues were dissolved in 80. mu.L of 15% acetonitrile and 0.2% FA and 40. mu.L of DCM. Then centrifuged for 40min (15000 rpm). The supernatant was transferred to a clean sample vial for sample injection. The lower phase 15. mu.l was transferred to a 1.5mL vial and dried. The residue was dissolved in 50. mu.L of 60% acetonitrile and 0.2% FA, and 2. mu.L was injected into the UPLC-QTOF system for analysis. To monitor the robustness of sample preparation and stability of instrumental analysis, we drawn equal amounts of plasma/urine from 20% of randomly screened samples and prepared quality control samples. The pretreatment of the quality control sample is consistent with that of a real sample.
2.2 liquid chromatography-Mass Spectrometry
Metabolic profiling based on non-targeted analysis was performed on the plasma polar sample (PU), plasma non-polar sample (PL), urine polar sample (UU), urine non-polar sample (UL) in the discovery and validation set using agilent 1290Infinity liquid chromatography system in combination with agilent G6200 series time-of-flight mass spectrometer (agilent, usa). The separation was carried out using an Agilent Poroshell120EC-C18(50 mm. times.2.1 mm,2.7 μm) chromatography column. The mobile phase is solvent A, 0.5 percent FA, solvent B and acetonitrile. The gradient adopted for the upper phase is as follows: 0-5min, 2-20% B, 5-9min, 20-80% B, 9-10min, 80-100% B, 10-12.5min, 100% B, 12.5-15min, 2% B, flow rate of 0.3mL/min, and sample injection amount of 5 muL. The gradient applied for the lower phase was as follows: 0-4min, 20-70% B, 4-10min, 70-100% B, 10-12.5min, 100% B, 12.5-15min, 20% B, flow rate of 0.3mL/min, sample size of 2 μ L. ESI-MS experimental source conditions were as follows: atomizer pressure, 40 psi; drying the gas at 9.0L/min; gas temperature, 350 ℃. The capillary voltage is set to be 3.5kV, the degreaser voltage is 65V, the upper phase mass scanning rate is 1500m/z, the lower phase mass scanning rate is 1200m/z, and Agilent MassHunter workstation software is adopted for data acquisition.
The disease samples and normal control samples in the finding and validation groups were alternated in the run order to avoid batch effects. In addition, after 20 authentic samples per group, quality control samples were inserted into the assay sequence.
3. Data statistics and analysis
And (3) importing the original data obtained by the UPLC-QTOF into XCMS for preprocessing, including chromatographic peak extraction, peak type matching, retention time correction, missing value filling and the like. The resulting data included retention time, M/Z values and normalized peak area. Further data preprocessing includes median normalization, missing value estimation, log2 transformation, and Pareto scaling, making features more comparable using R-3.5.3. Variables missing in 20% or more of the samples or variables having a coefficient of variation greater than 30% in the quality control samples were removed from further statistical analysis. The missing value (i.e., 0) is replaced with the 1/2 minimum value. The Pareto scaled unsupervised Principal Component Analysis (PCA) model was used to assess overall metabolome changes among groups and monitor the stability of the study. A supervised model of Pareto scaled orthogonal biased least squares discriminant analysis (OPLS-DA) was used to maximize the distance between groups and to determine important variables that contribute significantly to classification based on their importance in the projection (VIP). 1000 multivariate tests were performed to obtain the risk of overfitting of the model. The nonparametric test (Wilcoxon rank sum test) was used to assess the importance of the variables. False Discovery Rate (FDR) correction is used to estimate the probability of false positives and to correct multiple hypothesis testing.
Univariate analysis was performed using R-Studio software. Statistical analysis was performed using a Wilcoxon Mann-Whitney test based on hairpiece survival of Benjamin-Hochberg, setting P <0.05 and hairpiece survival <0.05 as significance levels. "variable importance in the project" (VIP) values >1 and FDR <0.1 are thresholds for differential metabolites between normal control and benign ovarian tumors, borderline ovarian tumors, and ovarian cancer. VIP >1 and P-value <0.05 are thresholds for differential metabolites between benign ovarian tumors, borderline ovarian tumors and ovarian cancer. Subsequently, the m/z values of the differential metabolic features were searched in the HMDB database (http:// www.hmdb.ca), and metabolites were further determined from the mass spectrometric secondary fragment information. According to the difference metabolites, a Support Vector Machine (SVM) is adopted to establish a model, and the model parameters are seed 520, gamma 0.1 and cost 10. And evaluating the classification analysis result by adopting a receiving operation characteristic curve (ROC). Evaluation was performed by calculating sensitivity and specific classification effect. The categories of differential metabolites were searched by HMDB (http:// www.hmdb.ca).
Sensitivity is true positive/(true positive + false negative)
Specificity ═ true negative/(true negative + false positive)
4. Discussion of results
Urine biomarker combinations for differentiating borderline ovarian and malignant ovarian tumors comprising five metabolite features of 16a-Hydroxyestrone (16 a-hydroxyyestrone), Coniferyl alcohol (Coniferyl alcohol), Indoleacrylic acid (Indoleacrylic acid), (E) -cassimiroridine ((E) -caseiriodidine), Cerulenin (Cerulenin), as shown in table 1:
TABLE 1
The concentrations of five metabolites, 16a-Hydroxyestrone (16a-Hydroxyestrone), Coniferyl alcohol (Coniferyl alcohol), Indoleacrylic acid (Indoleacrylic acid), (E) -cassimiroridine (E) -cassiiroedine), and Cerulenin (Cerulenin), in the urine of patients with malignant ovarian tumors were significantly reduced compared to the concentrations of five metabolites, borderline ovarian tumor.
The invention establishes a Support Vector Machine (SVM) for distinguishing the junction and the malignant ovarian tumor based on 5 indexes of 16a-Hydroxyestrone (16a-Hydroxyestrone), Coniferyl alcohol (Coniferyl alcohol), Indoleacrylic acid (Indreacrylic acid), (E) -cassimedine ((E) -Casimiriodine) and Cerulenin (Cerulenin) in urine, wherein model parameters are seed 520, gamma 0.1 and cost 10, and variables are 16a-Hydroxyestrone (16a-Hydroxyestrone), Coniferyl alcohol (Coniferyl alcohol), Indoleacrylic acid (Indreacrylic acid), (E) -cassimedine ((E) -Casimidyl), Cerulenin (Cerulenin) in urine; a threshold value greater than 0.828 is defined as malignant ovarian tumor, whereas borderline ovarian tumor.
FIG. 1 shows receiver operating characteristic analysis (ROC) curves of SVM classifiers constructed with 5 urine metabolite features in training and validation sets; in the finding group, the sensitivity was 98.65% and the specificity was 84.62% at AUC of 0.943 and cut-off of 0.828, which are significantly higher than those of CA125(AUC: 0.830; cut-off: 165.5U/ml; sensitivity: 81.08%; specificity: 76.92%). In the verification group, the mathematical diagnosis model predicts that the malignant ovarian tumor AUC is 0.836, the sensitivity is 80.26%, the specificity is 71.43%, and the effect is better than that of CA125 used alone.
Claims (4)
1. Application of small molecular marker in preparation of diagnostic reagent for distinguishing borderline ovarian tumor from malignant ovarian tumor
Characterized in that the diagnostic reagent comprises a small molecular marker, the small molecular marker is a urine metabolism marker which comprises 16a-hydroxyestrone, coniferyl alcohol, indoleacrylic acid, (E) -carximidine and Cerulenin.
2. Use according to claim 1, characterized in that: the diagnostic reagent diagnoses and distinguishes the borderline ovarian tumor and the malignant ovarian tumor by detecting the concentration of the urine metabolic marker in the urine of the subject.
3. The use according to claim 2, wherein the diagnostic reagent detects the concentration of a metabolic marker in urine based on chemical analysis or mass spectrometry.
4. The use of claim 2, wherein the urine of a patient with malignant ovarian tumor has a significantly reduced concentration of 16a-hydroxyestrone, coniferyl alcohol, indoleacrylic acid, (E) -cassiodine, and Cerulenin compared to a patient with borderline tumor.
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