Marker for detecting caries occurrence risk and application thereof

Abstract

The invention provides a marker for detecting caries occurrence risk and application thereof, the marker comprises a saliva lipid marker, ceramide Cer (d24: 1 / 18: 0 (2OH)), sphingosine SPH (d18: 0), phosphatidic acid PA (21: 024: 1), PA (25: 020: 1) and phosphatidylcholine PC (16: 024: 0), and the plurality of markers are closely associated with caries risk. The marker provided by the invention can detect the caries occurrence risk in a non-invasive and low-cost manner, so that accurate prevention and control of caries can be realized, and finally, the aim of reducing the incidence of caries is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of detection, in particular to a marker for detecting the risk of caries and application thereof. BACKGROUND

[0002] Caries is one of the most common chronic oral diseases in the world, which seriously affects oral health and can cause complications such as pulpitis and periapical periodontitis.

[0003] At present, the early diagnosis of caries mainly relies on clinical examination, and there is a lack of efficient and non-invasive biomarkers. In recent years, saliva has become an important sample for oral disease research due to its non-invasive, easy-to-obtain and rich-biomarker characteristics. Studies have shown that saliva microbiome and proteome are closely related to caries. However, there is still a blank in the research on the characteristics of saliva lipid metabolism of caries population. Lipids, as the main component of cell membranes, are involved in key physiological processes such as signal transduction and energy metabolism, and their abnormal metabolism may be related to the occurrence and development of caries. Therefore, the applicant uses the widely targeted lipid metabolomics technology to analyze the differences in saliva lipid spectrum between young caries population and non-caries population, in order to find potential lipid biomarkers and provide new ideas for early warning and precise intervention of caries. SUMMARY

[0004] The present application is to overcome the deficiencies in the prior art, and provides a marker for detecting the risk of caries and application thereof.

[0005] The present application provides the following technical solution: the application of a reagent for detecting ceramide Cer (d24:1 / 18:0 (2OH)), sphingosine SPH (d18:0), phosphatidic acid PA (21:0_24:1), PA (25:0_20:1) and phosphatidylcholine PC (16:0_24:0) in the preparation of a kit for detecting the risk of caries in a subject, wherein the risk of caries is the risk of caries from 3 months to 12 months from the date of detection, and the sample for detection of the kit is saliva.

[0006] On the basis of the above technical solution, the following further technical solutions can also be provided: The application is suitable for adults.

[0007] The saliva is non-stimulating saliva.

[0008] The sample for detection of the kit is selected from saliva.

[0009] The kit, wherein the risk of caries is the risk of caries from 3 months to 12 months from the date of detection.

[0010] Advantages of the application: The application provides several saliva metabolites as markers for detecting the risk of caries, so that the risk of caries can be detected non-invasively and at low cost, early prevention and precise intervention of caries can be realized, precise prevention and control of caries can be achieved, and finally the incidence of caries can be reduced. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1 is a blood lipid screening difference chart of the DC group and the CF group: wherein (a) is an OPLS-DA model, (b) is OPLS-DA model verification, (c) is a difference lipid volcano chart, and (d) is a comparison of the relative abundance of 13 different metabolites in the DC group and the CF group; Figure 2 is a chart for establishing and verifying the OPLS-DA model: wherein a and b are DC1 group vs. CF group, c and d are DC2 group vs. CF group, e and f are DC1 group vs. DC2 group, and g is a lipid screened for dynamic changes; Figure 3 is a chart of the expression levels of five different lipids in the CF, DC1 and DC2 groups; Figure 4 is a chart of KEGG pathway enrichment analysis of different lipids; Figure 5 is the relative expression level of different metabolites and the ROC curve. DETAILED DESCRIPTION

[0012] The application will be further described below in conjunction with examples, and it should be understood that the examples are only used to further illustrate and explain the application, and are not used to limit the application.

[0013] Unless otherwise defined, technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present application, illustrative methods and materials are described below. In case of conflict, the patent specification, including that defined in the appended claims, determines. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting. The present application will be further described with reference to the following examples, but the scope of the application is not limited to the examples.

[0014] The behavior of the several markers of the detection sample of the present application is "detection", that is, the marker is determined to be absent or below the sensitivity level. Detection can be quantitative, semi-quantitative or non-quantitative observation, and can be based on comparison with one or more control samples.

[0015] Based on the problems existing in the prior art, the application provides the application of saliva protein markers in detecting the risk of caries in a subject.

[0016] In one embodiment, the application provides use of ceramide Cer(d24:1 / 18:0(2OH)) and sphingosine SPH(d18:0) and phosphatidic acid PA(21:0_24:1) and PA(25:0_20:1) and phosphatidylcholine PC(16:0_24:0) in detecting the risk of dental caries in a subject.

[0017] In one embodiment, the application provides use of ceramide Cer(d24:1 / 18:0(2OH)) and sphingosine SPH(d18:0) and phosphatidic acid PA(21:0_24:1) and PA(25:0_20:1) and phosphatidylcholine PC(16:0_24:0) in a kit for detecting the risk of dental caries in a subject.

[0018] In the above use, the subject is an adult with a permanent dentition.

[0019] A marker for detecting the risk of dental caries, comprising use of ceramide Cer(d24:1 / 18:0(2OH)), sphingosine SPH(d18:0), phosphatidic acid PA(21:0_24:1), PA(25:0_20:1), phosphatidylcholine PC(16:0_24:0) in the preparation of a kit for detecting the risk of dental caries in a subject.

[0020] Saliva sample collection and processing: non-stimulated saliva samples of 957 volunteers were collected and transported to the laboratory as soon as possible, and stored in a -80°C freezer for testing.

[0021] Sample pretreatment: the sample was thawed on ice until completely dissolved, then 200ul of the sample was mixed with 1ml of lipid extract (methyl tert-butyl ether methanol = 3:1, V / V), vortexed and mixed, then 100ul of purified water was added, centrifuged at 12,000r for 10min, 500ul of supernatant was taken, and vacuum dried. For mass spectrometry analysis, vortex and centrifuge at 12,000r for 3min, take the supernatant for sample analysis.

[0022] The saliva samples of the dental caries group (Dental Caries group, DC group for short) with a decayed missing filled index (DMFT) >3 and the caries free group (caries free, CF group for short) with DMFT = 0 were subjected to lipid metabolomics analysis by liquid chromatography-electrospray tandem mass spectrometry (LC-ESI-MS / MS).

[0023] First, quality control analysis was performed: the metabolite-related data files from LC-ESI-MS / MS analysis were processed using Analyst 1.6.3 software. This included the total ion current (TIC) plot of the pooled QC sample (the sum of the intensities of all ions in the mass spectrum at each time point plotted against time) and the multi-peak plot of MRM lipid detection (XIC plot of ion currents extracted from multiple substances), including the retention time (Rt) for lipid detection and the ion current intensity for ion detection (intensity unit: cps, count per second). Finally, the identification and relative quantification results of the metabolites were obtained.

[0024] The analysis is mainly divided into qualitative and quantitative analyses. Qualitative identification is performed using the retention time (RT) and characteristic ion pairs of compounds, based on a self-constructed MWDB database. Quantitative analysis of lipids employs selective response monitoring (MRM) technology in triple quadrupole mass spectrometry. In this mode, the first-stage quadrupole first selects the precursor ion of the target compound, eliminating background interference; subsequently, the precursor ion undergoes collision-induced dissociation to generate fragment ions, from which the third-stage quadrupole screens for specific daughter ions for detection, thus significantly improving the selectivity and accuracy of detection. Finally, the peak areas of lipid characteristic peaks in each sample are integrated, and data standardization is performed using a cross-sample peak area correction method.

[0025] Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA): This supervised multivariate analysis method effectively distinguishes differences between groups by extracting the components of X (quantitative data) and Y (group information) and calculating their correlations. According to OPLS-DA (… Figure 1 a) The model analyzes the lipidomics data and plots score maps for each group to further illustrate the differences between them. The closer the spatial distance between sample points, the higher the data similarity. The horizontal axis (predicted principal components) reflects inter-group differences, the vertical axis (orthogonal principal components) shows intra-group variation, and the percentage represents the explained value of each component. Different colors in the figure represent different groups; samples within a group should be concentrated and clearly distinguishable from other groups. The OPLS-DA model uses R... 2 X, R 2 Y and Q 2 The evaluation values ​​represent the explanatory power and predictive ability of the X / Y matrix, respectively. The closer the value is to 1, the more reliable the model (Q). 2 >0.5 is considered valid, >0.9 is excellent. Metabolite screening can be assessed using variable projective importance (VIP), where VIP values ​​reflect the contribution of each metabolite to sample classification (VIP > 1.0 is significant). Figure 1 b is the OPLS-DA validation plot, with the horizontal axis representing model R. 2 Y, Q 2Values, different colors represent sample groups, and each point represents the distribution location of the sample in the reduced-dimensional space. R 2 Y and Q 2 Y represents the model's classification and discrimination abilities on the training and test sets, respectively. The horizontal axis represents R. 2 Y and Q 2 The value, Q, represents the frequency of the model's classification effect on the vertical axis, i.e., the frequency of the model's classification effect after 1000 random permutations and combinations of data. 2 of p = 0.02, indicating that in this permutation detection, 10 randomly grouped models outperformed the OPLS-DA model in prediction. Generally, p The model is optimal when the value is less than 0.05.

[0026] Differential metabolites were identified based on orthogonal partial least squares discriminant analysis (OPLS-DA). Screening criteria included: VIP > 1, Fold Change ≥ 1.8 or ≤ 0.56, and FDR < 0.05. Differential metabolites in oral saliva from the DC and CF groups were then screened. Volcano plots and histograms were used to analyze the metabolites. Figure 1 c, d) to visually display differentially metabolites. Volcano plot ( Figure 1 Each point in (c) represents a lipid, where green points represent downregulated differentially regulated lipids, red points represent upregulated differentially regulated lipids, and gray points represent lipids detected but with no significant difference. The ordinate represents the logarithm of the relative content difference of a lipid between the two groups of samples (log FC). The larger the absolute value of the ordinate, the greater the relative content difference of the substance between the two groups of samples. Under the FC + FDR screening condition: the ordinate represents the significance level of the difference (-log FDR). Histogram ( Figure 1 d) shows the comparison of the contents of 13 lipids in the two groups. Simultaneously, a comparison was established and validated between the DC1 (DMFT=4-6) group and the CF group. Figure 2 a,b), DC2 (DMFT>6) Figure 2 Groups c and d vs. Group CF, Group DC1 vs. Group DC2 Figure 2 The OPLS-DA model (e, f) and the intersection of differentially expressed lipids in groups DC1 and DC2 were used to screen for dynamically changing lipids, resulting in a total of 5 lipids (e, f). Figure 2 g). Line graphs and wine glass plots were used to visually compare the expression trends and levels of five different lipids in the CF, DC1, and DC2 groups to see if there were any differences. Figure 3 ).

[0027] By enrichment analysis of differential metabolites, the differential metabolites screened out were enriched in KEGG database for enrichment analysis by hypergeometric test, and the metabolic pathways involved were determined. The hypergeometric distribution test was used to calculate P the value to evaluate the enrichment degree of differential metabolites in a specific pathway. KEGG pathway enrichment showed that the differential lipids were mainly enriched in the following pathways (P<0.05): pancreatic cancer pathway, FcγR-mediated phagocytosis, GnRH signaling pathway, cAMP signaling pathway, phospholipase D signaling pathway, and choline metabolism in cancer. P <0.05): pancreatic cancer pathway, FcγR-mediated phagocytosis, GnRH signaling pathway, cAMP signaling pathway, phospholipase D signaling pathway, and choline metabolism in cancer. Figure 4 aThe ordinate is the name of the KEGG metabolic pathway, and the abscissa is the number of differential lipids annotated to the pathway and the proportion of the number of differential lipids annotated to the total number of differential lipids. As Figure 4 bshown, the bubble chart directly displays the enrichment results: the vertical axis is the pathway name, and the horizontal axis is the rich factor (reflecting the relative enrichment degree of differential metabolites in the pathway). The bubble size represents the number of differential metabolites contained in the pathway, and the color depth corresponds p to the value (the darker the color, P the smaller the value, the more significant the enrichment). This analysis helps to reveal the possible effects of differential metabolites between samples on cellular pathways.

[0028] ROC curve is a quantitative analysis method for evaluating the performance of binary classification model, which reflects the diagnostic accuracy by plotting the relationship curve between true positive rate and false positive rate (AUC>0.9 is high diagnostic value). The AUC area of the combined diagnostic model of 5 kinds of lipids is close to 1 ( Figure 5 ).

[0029] In the DC group, the ceramide Cer(d24:1 / 18:0(2OH)) in the saliva of the patients 6 to 12 months before the onset of the disease was higher than 217915, the sphingosine SPH(d18:0) was lower than 111166, the phosphatidic acid PA(21:0_24:1) was higher than 8529, the PA(25:0_20:1) was higher than 5245, and the phosphatidylcholine PC(16:0_24:0) was higher than 9865, so when the threshold data of the above markers reaches the above threshold, the possibility of caries increases.

[0030] This indicates that the above metabolites can be used as biomarkers for evaluating the status of constant pressure column caries.

[0031] Ceramide (d24:1 / 18:0(2OH)), sphingosine (SPH) (d18:0), phosphatidic acid (PA) (21:0-24:1), PA (25:0-20:1), and phosphatidylcholine (PC) (16:0-24:0) were detected as biomarkers for detecting the risk of dental caries. These biomarkers were incorporated into the kit. The kit, which detects five biomarkers, can effectively determine the status of permanent caries dentition. It can be used in clinical diagnosis to help clinicians predict the status of permanent caries dentition in patients, identify high-risk groups as early as possible, provide early intervention guidance, and improve the treatment effect for clinical patients.

Claims

1. The application of reagents for detecting ceramide Cer (d24:1 / 18:0(2OH)), sphingosine SPH (d18:0), phosphatidic acid PA (21:0-24:1), PA (25:0-20:1) and phosphatidylcholine PC (16:0-24:0) in the preparation of a kit for detecting the risk of dental caries in a subject, wherein the risk of dental caries is the risk of dental caries occurring 3 to 12 months from the date of detection, and the sample used for testing with the kit is saliva.

2. The application according to claim 1, characterized in that: The application is intended for adults.

3. The application according to claim 1, characterized in that: The saliva in question is non-irritating.

4. The application according to claim 1, characterized in that: The kit is used to detect samples selected from saliva.

5. The application according to any one of claims 1-5, characterized in that: The kit specifies the risk of dental caries as the risk of dental caries occurring 3 to 12 months from the date of testing.

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