CN112924684A - Biomarker for distinguishing depression from non-depression and diagnostic kit comprising the same - Google Patents
Biomarker for distinguishing depression from non-depression and diagnostic kit comprising the same Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
- G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
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- C—CHEMISTRY; METALLURGY
- C07—ORGANIC CHEMISTRY
- C07K—PEPTIDES
- C07K14/00—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
- G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
- G01N33/6848—Methods of protein analysis involving mass spectrometry
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/30—Psychoses; Psychiatry
- G01N2800/304—Mood disorders, e.g. bipolar, depression
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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- G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
Abstract
Disclosed is a biomarker for distinguishing between depression and non-depression, the biomarker for distinguishing between depression and non-depression selected from the group consisting of SEQ ID NO: 1. SEQ ID NO: 2. SEQ ID NO: 3 and SEQ ID NO. 4. The kit constructed by the four polypeptide markers can stably and reliably diagnose the depression, can overcome the defect and deficiency of diagnosing the depression by subjective judgment, and has important significance for the diagnosis and clinical intervention of the depression.
Description
Technical Field
The application belongs to the fields of analytical chemistry and clinical examination and diagnosis, and particularly relates to a depression diagnosis marker based on proteomics and a kit comprising the same.
Background
Depression is a common psychological disorder, with over 3 hundred million people predicted worldwide for depressed patients. The disease has a high recurrence rate and a high suicide rate, and thus causes a higher disability-adjusting life year and a serious economic burden compared to other mental diseases. Although effective treatment methods are available for depression, more depression patients in China still cannot receive effective treatment. Clinical diagnosis of depression remains one of the difficulties that restrict effective treatment. At present, the diagnosis of depression is mainly based on the interview of a structural formula or a semi-structural formula, which is very limited by the cultural level of patients and the subjective factors such as the clinical experience of doctors, and the phenomena of misdiagnosis, missed diagnosis and the like often occur. Therefore, hospitals are urgently required to establish an objective marker system for clinical diagnosis of depression.
In fact, the study of pathological mechanisms associated with depression has been kept hot and a number of potential biomarkers have been discovered. These markers are involved in different pathomechanistic hypotheses: monoamine deficiency hypothesis, hypothalamic-pituitary-adrenal axis disorder hypothesis, immunoinflammation hypothesis, nerve regeneration reduction hypothesis, etc., but diagnosis for depression still lacks specificity and sensitivity. While these markers are mainly genes, proteins, metabolites, peptide markers are less studied. The polypeptide as the product of protein synthesis, processing and degradation can reflect the abnormal metabolism of precursor protein and related protease in organisms. After the protein is cut into different peptide fragments, the protein has better tissue permeability and high functionality. Some peptides may be involved in the regulation of enzyme inhibitory activity, cellular activity, and neural activity of the body as messenger molecules. Therefore, it is closely related to the occurrence and development of diseases. However, the complex matrix effect, low physiological concentration and protein adsorption effect in the biological sample seriously interfere the detection of peptide substances. These technical difficulties limit the systematic study of the association of polypeptimics with depression.
Disclosure of Invention
In order to solve the problems in the prior art, the application aims at the problems, successfully realizes the research of the polypeptimics in the biological body fluid, determines the depression biomarker, and develops the depression biomarker into a kit.
The inventor of the invention mainly utilizes salting-out assisted liquid-liquid extraction (SALLE) to purify and enrich biological fluid polypeptide molecules.
The inventor of the invention mainly utilizes a nanoliter high performance liquid chromatography and two-dimensional linear ion trap mass spectrometry combined technology (nanoLC-orbitrap/MS) to collect the polypeptide profile of biological body fluid.
The invention firstly utilizes a nano LC-orbistrap/MS system to measure the polypeptide profile of biological fluid, and selects the polypeptide characteristics with the deletion value of each polypeptide characteristic within 50 percent in all samples and the relative standard deviation within 75 percent in the whole experiment of each polypeptide characteristic in quality control samples as stable polypeptide characteristics.
In the embodiment of the present invention, the inventor aims at multiple charge features, and after aligning all the features, i.e. ensuring that two embodiments have the same feature number, and then performing and normalizing, i.e. summing all the features in the sample to obtain the total feature intensity of the sample, multiplying each feature in the sample divided by the total feature intensity by 100 to obtain the relative intensity of the feature in the sample, and all the samples adopt the same operation, so that the difference between the samples can be partially eliminated.
The inventors of the present invention performed logarithmic transformation and pareto scaling on the normalized features using metaboanalyst software so that the polypeptide features satisfied normal distribution, followed by T-test.
In the examples section, the features of the examples in which the level of significance P in the T-test was less than 0.05 and the mean level in the healthy group was 2-fold or 2-fold higher or lower than the mean level in the depressed patients, and the feature of the level of significance less than 0.05 by the multiple hypothesis test (FDR), were selected as potential biomarkers.
The inventors of the present invention searched the amino acid sequence of polypeptide features using PEAKS and determined potential polypeptide markers by retention time and mass window.
The application selects the polypeptide characteristics of the area under the characteristic curve (AUC) of a subject from 0.6 to 0.999, and establishes a combined model for diagnosing the depression through a binary logistic regression model.
The four polypeptide markers (SEQ ID NO: 1-SEQ ID NO: 4) determined by the invention are used for diagnosing the depression, and the sensitivity and the specificity are better obtained in a discovery group and a verification group. In addition, the compounds are used as combined markers, so that better sensitivity and specificity are obtained.
Although the relative content of the four polypeptide markers is determined by using non-targeted mass spectrometry, the relative content of the four markers can also be determined by an ELISA method, a western-blot method, a fluorescence method, a chromatography method and targeted mass spectrometry.
The kit established by using the single marker or the combined marker can be used for the diagnosis of clinical depression and can provide more objective diagnosis results. The product is not only suitable for plasma system, but also suitable for biological body fluid such as serum, whole blood, urine, sweat, saliva, semen, tears, etc.
One aspect of the present invention provides a biomarker for distinguishing depression from non-depression, the biomarker for distinguishing depression from non-depression selected from the group consisting of SEQ ID NO: 1. SEQ ID NO: 2. SEQ ID NO: 3 and SEQ ID NO. 4.
In a preferred embodiment, the biomarker for distinguishing between depression and non-depression is characteristic of a level of significance in the T-test of less than 0.05 and a mean level in the healthy group that is 2-fold or 2-fold higher than the mean level in the depressed patient.
In a preferred embodiment, the biomarker for distinguishing between depression and non-depression is a biomarker consisting of SEQ ID NO: 1. SEQ ID NO: 2. SEQ ID NO: 3 and the polypeptide shown in SEQ ID NO. 4.
In a preferred embodiment, the combination biomarker predicts an area under the curve of a subject performance curve for a patient with depression between 0.6 and 0.999.
In a preferred embodiment, the relative amounts of the biomarkers for distinguishing between depression and non-depression are determined by non-targeted mass spectrometry, ELISA, western-blot, fluorescence, chromatography or targeted mass spectrometry.
Another aspect of the present invention provides a diagnostic kit for distinguishing depression from non-depression, comprising the above-described biomarker for distinguishing depression from non-depression.
In a preferred embodiment, the diagnostic kit is suitable for the diagnosis of depression in people of different sexes.
In a preferred embodiment, the diagnosis kit is suitable for diagnosing the depression of people of different ages of 18-100 years.
In a preferred embodiment, the diagnostic kit is suitable for the judgment of the severity of depression according to the hamilton scale.
In a preferred embodiment, the diagnostic kit is for the detection of at least one body fluid sample of plasma, serum, whole blood, urine, sweat, saliva, semen or tears.
The beneficial effects that this application can produce include:
1) the invention is based on the polypeptide omics technology, determines 4 polypeptide markers, and can be used as a biomarker of depression and development of a kit for diagnosing the depression. The combined diagnosis model established by the four polypeptide markers can stably and reliably diagnose the depression.
2) The invention can predict depression patients by measuring the relative content of endogenous polypeptide and adopting single or combined markers, can overcome the defect and deficiency of diagnosing depression by subjective judgment, and has important significance for the diagnosis and clinical intervention of depression.
Drawings
FIG. 1 shows the clinical diagnostic performance of the combination markers and the sample distribution information in example 1;
FIG. 2 shows the clinical diagnostic performance of the combination markers and the sample distribution information in example 2;
FIG. 3 shows the clinical diagnostic performance of a single marker in example 3 and sample distribution information;
FIG. 4 shows the clinical diagnostic performance of the combination markers and the sample distribution information in example 4;
fig. 5 shows the clinical diagnostic performance of the combination markers and the sample distribution information in example 5.
Detailed Description
The present application will be described in detail with reference to examples, but the present application is not limited to these examples.
The experimental procedures included in the examples are as follows:
1. grouping samples
The clinical experiment related to the invention passes the examination of ethical committee of the seventh people hospital in Dalian City, and all volunteers sign informed consent. In example 1 below, the age and gender between groups were matched without significant differences. In example 2 below, age and gender matching between groups were not considered.
2. Sample collection
All volunteers collected blood on an empty stomach in the morning. Collecting whole blood with vacuum blood collection tube containing enzyme inhibitor and anticoagulant, centrifuging at 3000rpm for ten minutes, collecting upper layer plasma, subpackaging, and storing at-80 deg.C for use. The diagnosis of patients with depression is evaluated according to the Hamilton Depression Scale (HDRS), and classified into major depression (S-MDD) and moderate depression (M-MDD) according to the evaluation result. The specific sources and groupings of the samples are shown in table 1.
TABLE 1
3. Sample pretreatment
The sample may be pretreated using conventional techniques such as organic solvent protein precipitation, solid-liquid extraction and liquid-liquid extraction. The implementation of the present invention is merely an example of a method for pretreating a sample by using salting-out assisted liquid-liquid extraction (SALLE), and other commonly used pretreatment techniques can also be used to carry out subsequent nano-upgrading ultra-high performance liquid chromatography mass spectrometry for screening and identifying the marker polypeptide. In salting-out assisted liquid-liquid extraction (SALLE): desorbing the body fluid sample with 5% phosphoric acid (v/v); 4mL of 4M dipotassium hydrogen phosphate and 4mL of isopropanol are added for extraction; transferring the extract, and drying at 40 deg.C with nitrogen; redissolving with 1mL of isopropanol to remove salt, transferring supernatant, and drying with nitrogen at 40 ℃; mu.L of 15% acetonitrile (v/v) containing 0.2% formic acid (v/v) and 30. mu.L of dichloromethane were added, and after degreasing, the supernatant was taken for analysis.
4. Nano-upgrading ultra-high performance liquid chromatography mass spectrometry
Chromatographic conditions are as follows: the analytical column used was a PicoFrit series capillary column with an internal diameter of 75 μm and a length of 20cm, with a 10 μm needle at one end, C18(3 μm,
) And (5) filling the filler. The trapping column was a capillary column of 150 μm internal diameter, 5cm length, C18(5 μm,
) And (5) filling the filler. Mobile phase a was 0.1% formic acid (v/v) and mobile phase B was 80% acetonitrile (v/v) (containing 0.2% formic acid (v/v)). The sample loading amount is 5 mu L, the sample is loaded for 7min at the flow rate of 3 mu L/mL by a loading pump, and 2% B is eluted isocratically; after loading, the 6-way valve is switched to an analysis mode, the sample is positively washed onto the analysis column, and the elution gradient is as follows: 0-8 min, 5% B; 8-10 min, 5-25% of B; 25-80% B for 10-47 min; 47-50 min, 80-95% B; 50-52 min, 95% B. The flow rate was 0.28. mu.L/mL.
Mass spectrum conditions: an electrospray ionization (ESI) ion source positive ion mode is adopted, the spraying voltage is 1.5kV, the temperature of a heating capillary tube is 320 ℃, a scan event is set in the FTMS mode, and the scanning ranges are 300-2000 respectively. And recording the total ion flow graph and the MS spectrogram by using Xcalibur software. The secondary analysis used the HCD cleavage mode with collision energy of 18-27V.
5. Pattern recognition and marker screening
5.1 data preprocessing
Introducing original mass spectrum data obtained by Nano-LC/MS into Progenetics QI software (Waters) for peak identification, peak alignment and peak extraction; screening, complementing and deleting redundancy of the data by using Excel, and then carrying out sum normalization, logarithmic conversion and pareto scaling on the data by using MetabioAnalyst 3.0 software.
5.2 statistical analysis
And (3) after data are preprocessed, carrying out T-test single-factor statistical analysis and fold analysis to select the marker with obvious difference.
5.3 marker screening and identification
Screening differential ions according to results of T-test and fold analysis, matching the differential ions with a secondary spectrogram through a time window and a mass window, using PEAKS Studio software to search by taking a protein sequence in a human source database in Swiss-prot as a database to obtain an identification result, and confirming an amino acid sequence of a differential polypeptide molecule.
5.4 Combined diagnostic markers
Selecting the polypeptide markers to predict the characteristics of the area under the curve (AUC) of the operating characteristic curve (ROC curve) of the testee of the depression patients, wherein the characteristic is 0.6-0.999, and simultaneously meeting the requirement in example 1 and example 2, further combining the polypeptide characteristics into a proper number of marker systems by using SPSS software, and establishing a binary logistic regression model. The relative content of these polypeptide markers was regressed to the combined signature variable P, with P closer to 1 indicating a greater likelihood that the signature of the sample is depression, and P closer to 0 indicating a greater likelihood that the signature of the sample is healthy. And (3) making a receiver operating characteristic curve (ROC curve) according to the value of the combined marker variable P, wherein the area AUC value under the curve is closer to 1, the prediction capability of the model is stronger, and the horizontal and vertical coordinates of the curve respectively represent the false positive rate and the true positive rate.
5.5 validation of Combined diagnostic markers
To further verify the markers, the predictive ability of the marker system for depression was verified using the data of example 2, and when the AUC value of the area under the curve of the receiver operating characteristic curve (ROC curve) of the combined marker in example 2, the sensitivity and the specificity were as high, it was considered that the combined marker could reliably and stably predict depression.
5.6 validation of different combinations of diagnostic markers
To further verify the effectiveness and practicability of the diagnostic markers, the data of examples 3, 4 and 5 are used to verify the prediction ability of the marker systems in different combinations on depression, and the marker systems are considered to be capable of reliably and stably predicting depression when the AUC values and the sensitivities of the areas under the curves of the working characteristic curves (ROC curves) of the combined marker subjects in examples 3, 4 and 5 are as high as the specificity.
Example 1
1. Sample collection
60 cases of each of patients with depression and Healthy Controls (HC) were collected, and information on sex, age, and degree of depression was shown in Table 1 above, and there was no significant difference between the groups.
2. Sample pretreatment
Pretreating a sample by adopting a salting-out assisted liquid-liquid extraction method (SALLE):
1) desorption: placing 500 μ L of the subpackaged plasma in a centrifuge tube, adding 200 μ L of 5% phosphoric acid (v/v), and vortexing;
2) and (3) extraction and impurity removal: adding 4mL of 4M dipotassium hydrogen phosphate, swirling, adding 4mL of isopropanol, swirling, centrifuging at 3000rpm for 10min, transferring supernatant to a centrifuge tube, and drying by nitrogen at 40 ℃;
3) desalting: adding 1mL of isopropanol into the sample obtained in the step 2), performing ultrasonic treatment, performing vortex treatment, centrifuging at 7000rpm for 20min, and transferring the supernatant to a centrifuge tube;
4) quality control samples: sucking each sample, mixing the supernatant, and subpackaging into 6 parts as quality control samples;
5) fat removal and redissolution: the supernatants from 3) and 4) were dried under nitrogen at 40 ℃ and 45. mu.L of 15% acetonitrile (v/v) containing 0.2% formic acid (v/v) and 30. mu.L dichloromethane were added, sonicated, vortexed, and centrifuged at 15000rpm to give 20. mu.L of the supernatant for analysis.
3. Nano-grade ultra-high performance liquid chromatography mass spectrometry (Nano-LC/MS)
Chromatographic conditions are as follows: the analytical column used was a PicoFrit series capillary column with an internal diameter of 75 μm and a length of 20cm, with a 10 μm needle at one end, C18(3 μm,
) And (5) filling the filler. The trapping column was a capillary column of 150 μm internal diameter, 5cm length, C18(5 μm,
) And (5) filling the filler. Mobile phase a was 0.1% formic acid (v/v) and mobile phase B was 80% acetonitrile (v/v) (containing 0.2% formic acid (v/v)). The sample loading amount is 5 mu L, the sample is loaded for 7min at the flow rate of 3 mu L/mL by a loading pump, and 2% B is eluted isocratically; after loading, the 6-way valve is switched to an analysis mode, the sample is positively washed onto the analysis column, and the elution gradient is as follows: 0-8 min, 5% B; 8-10 min, 5-25% of B; 25-80% B for 10-47 min; 47-50 min, 80-95% B; 50-52 min, 95% B. The flow rate was 0.28. mu.L/mL.
Mass spectrum conditions: an electrospray ionization (ESI) ion source positive ion mode is adopted, the spraying voltage is 1.5kV, the temperature of a heating capillary tube is 320 ℃, 2 scan events are set in the FTMS mode, and the scanning ranges are 300-2000 respectively. And recording the total ion flow graph and the MS spectrogram by using Xcalibur software. The secondary analysis used the HCD cleavage mode with collision energy of 18-27V.
Each sample was analyzed in duplicate, with sample injection sequence of C1, C1, D1, D1, C2, C2, D2, D2 … … (C is healthy control, D is depression group), 20 samples were analyzed per minute, and quality control samples were analyzed 1 time to monitor system stability and reproducibility.
4. Pattern recognition and marker screening
4.1 data preprocessing
And (3) introducing the original mass spectrum data obtained by the Nano-LC/MS into Progenetics QI software (Waters) for peak identification, peak alignment and peak extraction, wherein the filter length is 0.8, and removing ions with the absolute ion intensity less than 10000. Deleting the multi-charge characteristics (Z >2) which are extracted from the single-charge characteristics and isotope distribution peaks, and combining the characteristics of which the m/Z value is within 10ppm and the retention time is within +/-2 min, wherein the combination rule is as follows: calculating the average intensity of each feature (all samples in example 1), calculating the number of missing values of the samples (samples with characteristic peak response intensity < 10000), calculating the product of the ratio of the number of the samples without missing values in all samples and the average intensity as the optimized average intensity, and keeping the feature with the maximum average intensity. And if the response intensity of the characteristic in the sample is less than 10000, replacing the value in the sample with the maximum peak response intensity in the combined characteristic. And then deleting the characteristics with the total number of the characteristic samples being greater than 50% of the missing values and deleting the characteristics with the quality control samples RSD being greater than 75%. Features with a quality value of 10ppm or less and retention times of 2min or less are combined together, as above, but do not require replacement. Then, the characteristics of example 1, which can be in one-to-one correspondence with those of example 2, are retained by using the value of the characteristic quality within 10ppm and the retention time within ± 2 min.
Ions were introduced into the MetaboAnalyst 3.0 software for sum normalization, log conversion and pareto scaling.
4.2 statistical analysis
After pre-processing the data, a T-test one-way statistical analysis was performed, selecting as potential biomarkers the features in the example where the T-test was less than 0.05 for the level of significance P and 2-fold or 2-fold higher or lower than the mean for depression patients in the healthy group, and the feature where the multiple hypothesis test (FDR) was less than 0.05 for the level of significance.
4.3 marker screening and identification
Screening differential ions according to results of T-test and fold analysis, matching the differential ions with a secondary spectrogram through a time window and a mass window, using PEAKS Studio software to search by taking a protein sequence in a human source database in Swiss-prot as a database to obtain an identification result, and confirming an amino acid sequence of a differential polypeptide molecule.
4.4 Combined diagnostic markers
Selecting the characteristics of the polypeptide markers for predicting the area under the curve (AUC) of the operating characteristic curve (ROC curve) of the testee of the depression patients from 0.6 to 0.99, simultaneously meeting the requirement in the example 1 and the example 2, further combining the polypeptide characteristics into a proper number of marker systems by using SPSS software, and establishing a binary logistic regression model, wherein the 4 polypeptides are respectively: c (+25.00) SVMHEALHNHYTQKSLSLSPG (P1: SEQ ID NO: 1), VLSPADKTNVKAAWGKVGAHA G (-.98) (P2: SEQ ID NO: 2), DAHKSEVAHRFKDLGEENFKAL (P3: SEQ ID NO: 3), LAAPPGHQLHRAHYDLRHTF MG (P4: SEQ ID NO: 4), for specific information see Table 2. The relative content of these 4 polypeptides is shown in Table 3, and all have significant difference compared with the healthy control group. The SPSS software was further used to regress the relative content of these polypeptides to the combined marker variable P, the regression equation was as follows:
P＝1/(1+e-(-1.878+1515.789a-989.186b-13.632c-832.161d)
wherein a, b, c and d represent the relative content of the 4 polypeptides (P1-P4), and the variable can be used for assisting in judging depression. A receiver operating characteristic curve (ROC curve) is made according to the variable P value of the combined marker, as shown in figure 1, the AUC value of the area under the curve is 0.997, the sensitivity and the specificity are 96.7 percent and 100 percent respectively, and the AUC of the combined marker is higher than that of a single polypeptide as a diagnostic marker, as shown in figure 1.
TABLE 2
§Represents FDR in example 1<0.05,*Represents P in example 1<0.05∩(FC>2or FC<0.5), post-translational modification, -.98 (carbon-terminal amination), +25.00 (cysteine cyanation)
TABLE 3
AVE 1: mean content in healthy group (HC), AVE 2: mean content in the disease group (MDD), s.d. standard deviation, AUC: area under the curve, AUC': area under the curve representing the combined diagnostic marker, CI: representing the confidence interval.
Example 2
To further verify the markers, the samples in example 2 were assayed in the same way.
1. Sample collection
The sample collection method is the same as that of example 1, example 2 comprises 58 depression patients and 60 healthy controls, the information of sex, age and degree of depression is shown in the table 1, and the sex and age between groups are significantly different.
2. Sample pretreatment
The same as in example 1.
3. Nano-grade ultra-high performance liquid chromatography mass spectrometry (Nano-LC/MS)
The same as in example 1.
4. Validation of Combined diagnostic markers
Example 2 as a verification group, the same data processing method as that of example 1 was used, and the verification result was consistent with that of example 1, as shown in fig. 2, a dual logistic regression model was established using the combined marker of example 1 to obtain an ROC curve, and as shown in fig. 2, the area under the curve was 0.871, and the sensitivity and specificity were 86.2% and 80%, respectively, indicating that the combined marker of example 1 can reliably and stably predict depression. The SPSS software was further used to regress the relative content of these polypeptides to the combined marker variable P, the regression equation was as follows:
P＝1/(1+e-(0.843+96.78*a-270.532*b-2.359*c-159.722*d))
wherein, a, b, c and d respectively represent the relative content of the 4 polypeptides (SEQ ID NO: 1-SEQ ID NO: 4).
Example 3
To further verify the better diagnostic performance of a single marker on disease, the samples in example 3 were tested in the same way.
1. Sample collection
The same as in example 1.
2. Sample pretreatment
The same as in example 1.
3. Nano-grade ultra-high performance liquid chromatography mass spectrometry (Nano-LC/MS)
The same as in example 1.
4. Validation of Single diagnostic markers
Selecting P3 of the four markers from the four markers as shown in SEQ ID NO: 3, a binary logistic regression model was established to obtain an ROC curve, and the results are shown in fig. 3, wherein the area under the curve is 0.906, and the sensitivity and specificity are 73.3% and 96.7% respectively, which indicates that the P3 marker in example 3 can reliably and stably predict depression. The remaining single markers can be similarly predicted.
Example 4
To further verify the better diagnostic performance of the dual combination markers for disease, the samples in example 4 were tested in the same way.
1. Sample collection
The same as in example 1.
2. Sample pretreatment
The same as in example 1.
3. Nano-grade ultra-high performance liquid chromatography mass spectrometry (Nano-LC/MS)
The same as in example 1.
4. Validation of dual combination diagnostic markers
Selecting P1 of the four markers from the four markers as shown in SEQ ID NO: 1 and P3 SEQ ID NO: 3, a binary logistic regression model is established to obtain an ROC curve, and the result is shown in fig. 4, the area under the curve is 0.973, and the sensitivity and specificity are 93.3% and 90.0% respectively, which indicates that the P1 and P3 combined markers in example 4 can reliably and stably predict the depression. The remaining dual combination markers can be similarly predicted.
Example 5
To further verify the better diagnostic performance of the three combination markers on disease, the samples in example 5 were tested in the same way.
1. Sample collection
The same as in example 1.
2. Sample pretreatment
The same as in example 1.
3. Nano-grade ultra-high performance liquid chromatography mass spectrometry (Nano-LC/MS)
The same as in example 1.
4. Validation of triple combination diagnostic markers
Selecting P1 of the four markers from the four markers as shown in SEQ ID NO: 1. p3 SEQ ID NO: 3 and P4 SEQ ID NO:4, a binary logistic regression model is established to obtain an ROC curve, and the result is shown in fig. 5, the area under the curve is 0.993, and the sensitivity and specificity are 100% and 90.0% respectively, which shows that the combined markers of P1, P3 and P4 in example 5 can reliably and stably predict the depression. The remaining three combined markers can be similarly predicted.
Although the present application has been described with reference to a few embodiments, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the application as defined by the appended claims.
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Claims (10)
1. A biomarker for distinguishing between depression and non-depression, wherein the biomarker for distinguishing between depression and non-depression is selected from the group consisting of SEQ ID NO: 1. SEQ ID NO: 2. SEQ ID NO: 3 and SEQ ID NO. 4.
2. The biomarker for distinguishing between depression and non-depression according to claim 1, wherein the biomarker for distinguishing between depression and non-depression is a feature with a level of significance in the T-test of less than 0.05 and a mean level in healthy groups 2-fold or 2-fold higher or 2-fold lower than the mean level in depressed patients or a feature with a level of significance in the T-test of less than 0.05 on multiple hypothesis tests.
3. The biomarker for distinguishing between depression and non-depression according to claim 1, wherein the biomarker for distinguishing between depression and non-depression is a biomarker consisting of SEQ ID NO: 1. SEQ ID NO: 2. SEQ ID NO: 3 and the polypeptide shown in SEQ ID NO. 4.
4. The biomarker for differentiating between depression and non-depression according to claim 3, wherein the combined biomarker predicts an area under the curve of the subject performance curve for a patient with depression between 0.6 and 0.999.
5. The biomarker for distinguishing between depression and non-depression according to claim 1, wherein the relative content of the biomarker for distinguishing between depression and non-depression is determined by non-targeted mass spectrometry, ELISA, western-blot, fluorescence, chromatography or targeted mass spectrometry.
6. Diagnostic kit for distinguishing between depression and non-depression, characterized in that it comprises a biomarker for distinguishing between depression and non-depression according to any one of claims 1 to 5.
7. The diagnostic kit of claim 6, wherein the diagnostic kit is suitable for diagnosing depression in people of different sexes.
8. The diagnostic kit according to claim 6, wherein the diagnostic kit is suitable for diagnosing depression in people of different ages ranging from 18 to 100 years.
9. The diagnostic kit of claim 6, wherein the diagnostic kit is suitable for use in the determination of the severity of depression according to the Hamilton scale.
10. The diagnostic kit of claim 6, wherein the diagnostic kit is for the detection of at least one body fluid sample of plasma, serum, whole blood, urine, sweat, saliva, semen or tears.
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