Biomarker combination and application thereof in IDH-1 gene mutation detection

Abstract

The invention belongs to the technical field of biological detection, and particularly relates to a biomarker combination and application thereof in IDH-1 gene mutation detection. The biomarker combination comprises one or more of the following lipid metabolites: a third component of high-density lipoprotein-triglyceride, a fourth component of high-density lipoprotein-triglyceride, high-density lipoprotein-triglyceride, lactic acid and sphingomyelin. Whether a glioblastoma patient carries IDH-1 gene mutation or not is judged through a plurality of biomarkers, the method has the advantages of being good in sensitivity and specificity, small in patient trauma, capable of overcoming individual differences and suitable for large-scale screening, and a new solution is provided for clinical patient diagnosis.

Description

Technical Field

[0001] The present application belongs to the field of biological detection technology, and specifically relates to a biomarker combination and its application in the detection of IDH-1 gene mutation. Technical Background

[0002] Glioblastoma, also known as glioblastoma multiforme (GBM), is a rapidly growing and aggressive brain tumor that invades nearby brain tissue. It is known for its high mortality rate and poor prognosis, and the number of glioma patients is increasing year by year as the aging population increases. The World Health Organization (WHO) classifies these tumors into grades I and II (considered to be low-grade gliomas), III (anaplastic), and IV (glioblastoma) based on their histopathological characteristics. Gliomas develop by infiltrating into normal brain tissue and are difficult to completely remove surgically. Therefore, efforts are being made to improve the degree of surgical resection through various means. In addition, chemotherapy, immunotherapy, molecular targeted drugs, and radiation therapy have also made progress. These therapies have improved the prognosis of gliomas.

[0003] Isocitrate dehydrogenase (IDH) is a key enzyme in the tricarboxylic acid cycle, and the NADPH produced by IDH1 is involved in lipid metabolism, which helps cells defend against reactive oxygen species (ROS) induced during lipid oxidation. In recent years, IDH gene mutations have been found to be early and key mutations in gliomas. These gene mutations specifically change the catalytic activity of the enzyme and are considered to be related to the occurrence and development of tumors. Related studies have reported that patients with gliomas with isocitrate dehydrogenase (IDH) mutations have a better prognosis than patients with wild-type IDH. In glioblastoma patients, the median survival of the IDH normal group was about 15 months, while the median survival of the IDH mutant group was about 31 months. This mechanism of better prognosis may help develop new treatment strategies for gliomas.

[0004] At present, the diagnosis of IDH gene mutation in glioblastoma is mainly performed by obtaining tissue samples through surgery and then detecting them through gene sequencing. This method may bring risks to patients because it requires tissue biopsy of brain tumors, such as brain swelling inside and around the tumor mass, and may even affect neurological function. Therefore, the use of non-invasive methods to detect IDH gene mutations is of great significance for the early diagnosis, prognosis observation and treatment plan selection of glioblastoma. At present, the non-invasive examination of IDH mainly uses magnetic resonance imaging (MRI) to determine the imaging characteristics of IDH mutant gliomas. However, MRI lacks sensitivity and specificity in describing biological characteristics at the molecular level. Summary of the invention

[0005] Purpose of the invention: To solve the above technical problems, the present invention provides a biomarker combination and its application in the detection of IDH-1 gene mutation. The present invention helps to quickly and accurately identify whether glioblastoma carries IDH gene mutation through a combination of one or more biomarkers.

[0006] Technical solution: In order to achieve the above invention objectives, this application adopts the following technical solution:

[0007] In a first aspect, the present invention provides a biomarker combination, wherein the biomarker combination comprises one or more of the following lipid metabolites:

[0008] High-density lipoprotein-triglycerides-fraction 3, high-density lipoprotein-triglycerides-fraction 4, high-density lipoprotein-triglycerides, lactic acid, and sphingomyelin.

[0009] In a second aspect, the present invention provides a reagent for detecting the biomarker combination, wherein the reagent is used to detect the metabolic level of the biomarker combination.

[0010] As a specific embodiment, the metabolic level of the biomarker combination is detected, and the sample detected includes serum, plasma, whole blood or urine sample of the subject.

[0011] In a third aspect, the present invention provides the biomarker combination and the use of the reagent in the preparation of a product for detecting IDH-1 gene mutations in glioblastoma patients.

[0012] As a specific implementation scheme, the product includes a kit or a chip; the IDH-1 gene mutation is an IDH gene R132H point mutation.

[0013] In a fourth aspect, the present invention provides a kit comprising reagents for detecting the biomarker combination.

[0014] As a specific embodiment, the kit also includes extraction reagents, purification reagents, enzymatic hydrolysis reagents and quality control reagents of the biomarker combination.

[0015] In a fifth aspect, the present invention provides a method for constructing an IDH-1 gene mutation detection model for glioblastoma patients, comprising the following steps:

[0016] IDH-1 mutant samples and IDH-1 wild-type samples from glioblastoma patients are collected for lipid metabolite detection, metabolites with significantly differential expression are screened, and modeling is carried out using a machine learning algorithm of logistic regression to select a preferred model, and the model with the largest test set AUC among the preferred models is used as the optimal screening model, which is the detection model; the lipid metabolites include the biomarker combination described in claim 1.

[0017] Preferably, the significant differential expression is that the metabolic level is at least upregulated or downregulated by 1.1 times compared with the control sample.

[0018] In a further specific embodiment, the kit is used in nuclear magnetic resonance spectroscopy, which provides a unique chemical fingerprint through a series of proton nuclear magnetic spectra to reflect basic functional information of the physiological state of cells.

[0019] In a sixth aspect, the present invention provides an IDH-1 gene mutation detection model for glioblastoma patients, wherein the detection model is obtained by the above-mentioned construction method.

[0020] As a specific implementation scheme, in the detection model, whether a glioblastoma patient carries an IDH-1 mutant gene is calculated by the following formula: mutation index = w0 + w1x1 + w2x2 + ... + w n x n , where W is the weight coefficient corresponding to each biomarker, X is the quantitative detection value corresponding to each biomarker, and w0 is a constant; if the subject's mutationindex ≥ threshold, it indicates that the subject may carry the IDH-1 mutant gene; if the subject's mutation index < threshold, the subject carries the wild-type IDH gene;

[0021] Preferably, the number of X is 5, X1-X5 correspond to the mass spectrometry quantitative detection values ​​of high-density lipoprotein-triglyceride-component 3, high-density lipoprotein-triglyceride-component 4, high-density lipoprotein-triglyceride, lactic acid and sphingomyelin, respectively, and the corresponding weight coefficient W values ​​are 0.0536416, 0.05207003, 0.05370309, -0.05035875, and 0.05104326, respectively; the constant w0 is -0.036, and the threshold is 0.52. If the subject's mutation index ≥ 0.52, it indicates that the subject may carry the IDH-1 mutant gene; if the subject's mutation index < 0.52, the subject carries the wild-type IDH gene.

[0022] More specifically, the method for obtaining the mass spectrometry quantitative detection value includes the following:

[0023] The off-machine spectra were normalized by the QuantRef management system built into the Topspin software of Bruker, so that the intensity of the spectra was normalized to the proton concentration of mmol / L. Chemical shifts were corrected using TSP (3-Trimethylsilylpropanoic acid) and the double peak signal of alanine at 1.48ppm. The spectra detected by NMR were qualitatively analyzed based on the Bruker NMR self-built library. Lipoproteins were quantified using the integral of the -CH3 and -CH2- signal groups of the lipoprotein chains at 0.8ppm and 1.25ppm.

[0024] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0025] 1) The present application discloses a group of lipid metabolism markers for glioblastoma patients carrying IDH-1 gene mutations and their uses. The present invention can help diagnose whether the IDH-1 gene of glioblastoma patients has mutated, providing a new diagnostic kit for patient diagnosis.

[0026] 2) The kit can complete the detection by obtaining body fluids, which is convenient for sampling and suitable for screening of glioblastoma patients in large populations.

[0027] 3) The combination of multiple biomarkers provided by the present invention can effectively overcome the differences between individual patients and improve the detection sensitivity and specificity. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Provides a technical route for biomarker screening and IDH-1 positive predictive screening application in glioblastoma;

[0029] Figure 2 ROC curve of the prediction model test set. DETAILED DESCRIPTION

[0030] The embodiments of the present application will be described in detail below in conjunction with the examples, but it will be appreciated by those skilled in the art that the following examples are only used to illustrate the present application and should not be considered as limiting the scope of the present application. In the examples, if no specific conditions are specified, the conditions are carried out according to normal conditions or manufacturer recommendations. If the manufacturer is not specified for the reagents or instruments used, they are all conventional products that can be purchased from the market.

[0031] Partial term definition, unless otherwise defined below, the meaning of all technical terms and scientific terms used in the specific embodiments of the present application is intended to be the same as that commonly understood by those skilled in the art. Although it is believed that the following terms are well understood by those skilled in the art, the following definitions are still set forth to better explain the present application.

[0032] The term "approximately" in this application indicates an accuracy range that can be understood by those skilled in the art and still ensures the technical effect of the characteristic in question. The term generally indicates ±10%, preferably ±5%, of the indicated value.

[0033] As used in this application, the terms "comprises", "comprising", "having", "containing" or "involving" are inclusive or open-ended and do not exclude other unrecited elements or method steps. The term "consisting of" is considered a preferred embodiment of the term "comprising". If a group is defined below as comprising at least a certain number of embodiments, this should also be understood to disclose a group that preferably consists of only these embodiments.

[0034] Example 1 Screening of lipid metabolism marker combinations in glioblastoma patients carrying IDH-1 gene mutations

[0035] See attached for technical route Figure 1 :

[0036] We used nuclear magnetic resonance spectroscopy (NMR) to detect lipid metabolites in body fluids, screened metabolites associated with glioblastoma IDH gene mutations through statistical analysis, and constructed a model based on machine learning to screen out appropriate marker combinations.

[0037] Materials and Reagents:

[0038] 1) Instrument: Nuclear magnetic resonance spectrometer (600MHZ) (Bruker Biospin AG)

[0039] 2) Main reagents: Plasma bufer (Bruker), NMR Tubes / cap3 (Bruker)

[0040] 3) Samples: A total of 58 cases of glioblastoma of lung cancer were collected. The status of the IDH gene was determined by gene sequencing of the patient's blood after surgery. Among them, 23 cases were IDH-1 mutated samples (IDH gene R132H point mutation) and 35 cases were IDH-1 wild-type samples.

[0041] 1.1. Sample testing

[0042] During the test, take the sample out of the refrigerator, wait for it to be completely thawed, take 340 μl of plasma and mix it with 340 μl of NMR lipid buffer (BrukerPlasma buffer) in a 1:1 ratio. After thorough mixing, take 600 μl and place it in a 5 mm NMR tube. Place it in the NMR tube rack according to the order of loading the machine, load it into the automatic sampler and wait for testing.

[0043] 1.2. Quantification of lipid metabolites

[0044] The off-machine spectra were normalized by the QuantRef management system built into the Topspin software of Bruker, so that the intensity of the spectra was normalized to the proton concentration of mmol / L. Chemical shifts were corrected using TSP (3-Trimethylsilylpropanoic acid) and the double peak signal of alanine at 1.48ppm. The spectra detected by NMR were qualitatively analyzed based on the Bruker NMR self-built library. Lipoproteins were quantified using the integral of the -CH3 and -CH2- signal groups of the lipoprotein chains at 0.8ppm and 1.25ppm.

[0045] 1.3. Difference Analysis

[0046] Based on the offline files obtained by the test, the data results were exported, and then the quality of the data was evaluated based on the quality control data of the samples, and the available sample data was screened. The detected blood lipid indicators were differentially analyzed. The matrix results of the lipid metabolite detection values ​​were grouped into positive group and control group, and the Wilcoxon rank sum test P value and median difference fold FC were calculated. Among them, FC>1.1 or FC<0.917 and Pvalue<0.05 were considered to be significantly differentially expressed lipid metabolites; the detailed list is shown in Table 1.

[0047] Table 1. Analysis of significant differences in lipid metabolism

[0048]

[0049]

[0050] 1.4. Marker combination screening, the specific process is as follows:

[0051] 1.4.1. Based on the significantly differentially expressed lipid metabolites screened in 1.3, the detection value matrix corresponding to the corresponding metabolites is extracted.

[0052] 1.4.2. The data was divided into 7:3 ratios, i.e. 40 samples were used as training data sets and 18 samples were used as test data sets. Modeling was performed using the machine learning algorithm of logistic regression, and grid search was performed to select the optimal modeling parameters. The grid search is a parameter adjustment technique in machine learning, which is used to systematically traverse the combination of multiple parameters and determine the best effect parameters through cross-validation.

[0053] 1.4.3. Further select the model with the largest test set AUC among the preferred models obtained in step 1.4.2 as the optimal screening model. According to the AUC curve of the model, calculate its Youden index as the threshold for judging whether the glioblastoma patient is positive. The AUC curve is attached. Figure 2 .

[0054] 1.4.4. Based on the model building results, calculate the mutation index of the IDH-1 gene mutation of each patient in the test data: mutation index = w0 + w1x1 + w2x2 + ... + w n x n , where W is the weight coefficient corresponding to each biomarker, X is the quantitative detection value corresponding to each biomarker, and w0 is a constant; if the subject's mutation index ≥ threshold, it indicates that the subject may carry the IDH-1 mutant gene; if the subject's mutation index < threshold, the subject carries the wild-type IDH gene. The weight coefficient values ​​corresponding to each marker are: 0.0536416, 0.05207003, 0.05370309, -0.05035875, 0.05104326, and the constant w0 is -0.036. For mutation index ≥ 0.52, it is considered positive, and mutation index < 0.52 is negative. The sensitivity and specificity of this marker combination in the test set are 0.86 and 0.78, respectively, with good identification effect.

[0055] Example 2 Application of lipid metabolism marker combination screening in glioblastoma patients with IDH-1 gene mutation

[0056] 1. Sample collection: Blood samples were collected from 5 patients with glioblastoma with IDH-1 gene mutation (numbered S01-S05) and 5 patients with glioblastoma with IDH-1 gene wild type (numbered N01-N04).

[0057] 2. Biomarker detection: The samples were tested using nuclear magnetic resonance spectroscopy to obtain the measured values ​​of the biomarkers: high-density lipoprotein-triglyceride-3rd component (H3TG), high-density lipoprotein-triglyceride-4th component (H4TG), high-density lipoprotein-triglyceride (HDTG), lactic acid, and sphingomyelin (SPC). The results are shown in Table 2.

[0058] Table 2. Biomarker measurements

[0059]

[0060] 3. Calculate the subject's mutation index according to the formula = w0+w1x1+w2x2+...+w n x n, where W is the weight coefficient corresponding to each biomarker, X is the quantitative detection value corresponding to each biomarker, and w0 is a constant; the weight coefficient values ​​corresponding to each marker are: 0.0536416, 0.05207003, 0.05370309, -0.05035875, 0.05104326, and the constant w0 is -0.036. The screening results are as follows:

[0061] Table 3. Screening results

[0062]

[0063]

[0064] The above results show that when the screening results of 10 glioblastoma cancer patients were compared with the actual clinical diagnosis information, 2 patients were misjudged. In the small sample size test, the sensitivity was 80% and the specificity was 80%, which was similar to the performance of the test set when constructing the screening method in Example 2, indicating that the method has good repeatability.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A biomarker combination, characterized in that: The biomarker combination includes one or more of the following lipid metabolites: High-density lipoprotein-triglycerides-fraction 3, high-density lipoprotein-triglycerides-fraction 4, high-density lipoprotein-triglycerides, lactic acid, and sphingomyelin.

2. A reagent for detecting the biomarker combination according to claim 1, characterized in that: The reagent is used to detect the metabolic level of the biomarker combination.

3. The reagent for detecting the biomarker combination according to claim 1 according to claim 2, characterized in that: The metabolic level of the biomarker combination is detected, and the sample detected includes serum, plasma, whole blood or urine sample of the subject.

4. Use of the biomarker combination according to claim 1 and the reagent according to claim 2 or 3 in the preparation of a product for detecting IDH-1 gene mutation in glioblastoma patients.

5. The use according to claim 4, characterized in that: The product includes a kit or a chip.

6. A kit, characterized in that: The kit comprises reagents for detecting the biomarker combination of claim 1.

7. The kit according to claim 4, characterized in that The kit also includes an extraction reagent, a purification reagent, an enzymatic hydrolysis reagent and a quality control reagent of the biomarker combination according to claim 1.

8. A method for constructing an IDH-1 gene mutation detection model for glioblastoma patients, characterized in that: The following steps are involved: IDH-1 mutant samples and IDH-1 wild-type samples from glioblastoma patients are collected for lipid metabolite detection, metabolites with significantly differential expression are screened, and modeling is carried out using a machine learning algorithm of logistic regression to select a preferred model, and the model with the largest test set AUC among the preferred models is used as the optimal screening model, which is the detection model; the lipid metabolites include the biomarker combination described in claim 1.

9. A model for detecting IDH-1 gene mutation in glioblastoma patients, characterized in that: The detection model is obtained by the construction method described in claim 8.

10. The IDH-1 gene mutation detection model according to claim 9, characterized in that: In the detection model, whether a glioblastoma patient carries an IDH-1 mutant gene is calculated by the following formula: mutation index = w0 + w1x1 + w2x2 + ... + w n x n , where W is the weight coefficient corresponding to each biomarker, X is the quantitative detection value corresponding to each biomarker, and w0 is a constant; if the subject's mutation index ≥ threshold, it indicates that the subject may carry the IDH-1 mutant gene; if the subject's mutation index < threshold, the subject carries the wild-type IDH gene; Preferably, the number of X is 5, X1-X5 correspond to the mass spectrometry quantitative detection values ​​of high-density lipoprotein-triglyceride-component 3, high-density lipoprotein-triglyceride-component 4, high-density lipoprotein-triglyceride, lactic acid and sphingomyelin, respectively, and the corresponding weight coefficient W values ​​are 0.0536416, 0.05207003, 0.05370309, -0.05035875, and 0.05104326, respectively; the constant w0 is -0.036, and the threshold is 0.

52. If the subject's mutation index ≥ 0.52, it indicates that the subject may carry the IDH-1 mutant gene; if the subject's mutation index < 0.52, the subject carries the wild-type IDH gene.

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