Biomarker composition and application thereof in glioblastoma screening

Abstract

The invention belongs to the technical field of biological detection, and particularly relates to a biomarker composition and application thereof in glioblastoma screening. The biomarker combination is prepared from one or more of the following metabolites: L-aspartic acid, 3-phosphoric acid-D-glyceric acid, 2-phosphoric acid glyceric acid, hexadecanamide ethanol, D-red sphingosine-1-phosphoric acid, L-goose carnosine, L-alanine-L-lysine, docosahexaenoic acid, leukotriene C4 and urea glutaric acid. According to the invention, a patient with glioblastoma is identified through a plurality of biomarkers, so that the accuracy and comprehensiveness of diagnosis are improved. And the detection can be completed by acquiring body fluid, the sampling is convenient, and the kit is suitable for screening the glioblastoma patients in a large scale, and can effectively overcome the individual difference of the glioblastoma patients.

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 glioblastoma screening. 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. The median age of GBM diagnosis is about 65 years old, and the global annual incidence is about 6 per 100,000, accounting for nearly half (about 48%) of primary malignant tumors of the central nervous system. The World Health Organization (WHO) divides these tumors into grades I and II (considered to be low-grade gliomas), grade III (anaplastic), and grade IV (glioblastoma) based on their histopathological characteristics. These classifications indicate different degrees of malignancy, among which grade IV glioblastoma (GBM) is the most dangerous among adult intracranial tumors, with strong invasiveness, high difficulty in treatment, and high recurrence rate. The median overall survival (OS) of its patients is generally short, usually 14.6-20.5 months. Therefore, the screening and identification of glioblastoma has the following significance:

[0003] 1. Early detection: Through screening, lesions can be found in the early stages of glioblastoma, thereby improving the success rate of treatment and the survival rate of patients. Early detection means that the tumor has not yet spread widely, the difficulty of treatment is relatively low, and the patient's prognosis is better. For high-risk groups, regular screening can detect and prevent glioblastoma earlier.

[0004] 2. Designate personalized treatment plans: The information obtained during the screening process helps doctors understand the patient's tumor type and other characteristics more accurately, so as to develop a more personalized treatment plan. This helps improve treatment effectiveness and reduce unnecessary drug use and side effects.

[0005] 3. Improve the quality of life of patients: Early detection and treatment can alleviate the symptoms of patients and improve their quality of life. At the same time, screening can also timely detect and treat tumor-related complications, such as cerebral edema and increased intracranial pressure.

[0006] 4. Follow-up monitoring: Monitoring glioblastoma metastasis helps optimize the follow-up plan. For patients known to be at high risk of glioblastoma metastasis, the frequency of follow-up can be strengthened to detect potential recurrence or metastasis risks early.

[0007] At present, the initial diagnosis of GBM is through neuroimaging examination, followed by tumor tissue removal or biopsy to clarify the diagnosis, grading and tumor characteristics. Tissue biopsy is the gold standard for diagnosing glioblastoma, but tissue biopsy sometimes cannot predict the heterogeneity of the entire tumor mass and may not be able to truly reflect the activity of the tumor in real time.

[0008] On the other hand, in order to accurately assess the prognosis, patients need to undergo a brain magnetic resonance imaging scan (MRI) after treatment. When interpreting these effects, contrast-enhanced lesions may indicate actual tumor progression, but it is also important to note that these changes may also be pseudoprogression caused by radiotherapy, which may resolve on its own over time. Studies have shown that approximately 10% to 30% of glioblastoma patients will experience pseudoprogression at the time of their first MRI review, and this often occurs within the first 12 weeks of treatment. Therefore, accurately distinguishing pseudoprogression from true tumor progression is crucial for clinical decision-making. It can effectively avoid unnecessary surgical interventions and inappropriate treatment plans, ensuring that patients receive the most appropriate medical care.

[0009] Therefore, it is of great significance to develop more accurate glioblastoma identification and screening methods to improve the accuracy of positive prediction of glioblastoma. Summary of the invention

[0010] Purpose of the invention: To solve the above-mentioned technical problems, the present application has developed a biomarker combination and its application in glioblastoma screening through biological detection technology. It can help to quickly and accurately identify and screen glioblastoma through a combination of one or more biomarkers. Compared with traditional imaging examinations and MRI, this method has good sensitivity and specificity, and can identify glioblastoma patients through multiple biomarkers, thereby improving the accuracy and comprehensiveness of diagnosis.

[0011] Technical solution: In order to achieve the above invention objectives, this application adopts the following technical solution:

[0012] In a first aspect, the present invention provides a biomarker combination, which comprises one or more of the following metabolites: L-Aspartic acid, 3-phospho-D-glyceric acid, 2-phosphoglyceric acid, Palmitoylethanolamide, D-Erythro-sphingosine 1-phosphate, Anserine, L-Alanine-L-Lysine, Docosahexaenoic acid, Leukotriene C4, and Ureidoglutaric acid.

[0013] In a second aspect, the present invention provides a reagent for detecting the above-mentioned biomarker combination, wherein the reagent is used to detect the metabolic level of the biomarker combination. The metabolic level of the biomarker combination includes the metabolic level in a cancer tissue sample or a paracancerous tissue sample, or the metabolic level in a sample such as blood or urine.

[0014] In a third aspect, the present invention provides the above-mentioned biomarker combination and the use of the above-mentioned reagents in the preparation of a glioblastoma screening kit.

[0015] In a fourth aspect, the present invention provides a kit comprising reagents for detecting the above-mentioned biomarker combination.

[0016] As a specific embodiment, the kit also includes extraction reagents, purification reagents, enzymatic hydrolysis reagents and quality control reagents of the above-mentioned biomarker combination.

[0017] In a fifth aspect, the present invention provides a method for constructing a glioblastoma screening model, comprising the following steps:

[0018] The metabolites of cancer tissue samples and adjacent tissue samples of glioblastoma-positive patients were detected, and metabolites with significantly differential expression were screened. The machine learning algorithm of logistic regression was used to build a model, and the optimal model was selected. The model with the largest AUC of the test set among the preferred models was used as the optimal screening model, which is the screening model. The metabolites include the above-mentioned biomarker combination.

[0019] As a specific implementation scheme, the detection is carried out by liquid chromatography-mass spectrometry; the selection of the preferred model is carried out by grid search to select the optimal modeling parameters.

[0020] More specifically, the mass spectrometer uses a data-independent scanning mode for substance identification, which divides the entire scanning range of the mass spectrometer into several windows according to the mass-to-charge ratio (m / z) of the substance, and then fragments and detects all parent ions in each window, and collects the fragment ion information of all parent ions for qualitative and quantitative analysis.

[0021] In a sixth aspect, the present invention provides a glioblastoma screening model, which is obtained by the above-mentioned construction method.

[0022] As a specific implementation scheme, in the screening model, the risk value of glioblastoma Risk-score=K1*X1+K2*X2+…+Kn*Xn+M, where n is the number of biomarkers, K is the weight coefficient corresponding to each biomarker, X is the mass spectrometry quantitative detection value corresponding to each biomarker, and M is a constant; if the subject's Risk-score ≥ threshold, it indicates that the subject may be a glioblastoma-positive patient; if the subject's Risk-score < threshold, the subject is a non-positive patient;

[0023] Preferably, the number of X is 10, and X1-X10 correspond to the mass spectrometry quantitative detection values ​​of L-aspartic acid, 3-phospho-D-glyceric acid, 2-phosphoglyceric acid, palmitoylethanolamide, D-erythrosine-1-phosphate, L-angesine, L-alanine-L-lysine, docosahexaenoic acid, leukotriene C4, and ureaglutaric acid, respectively, and the corresponding weight coefficient K values ​​are: -0.76, -0.72, 0.96, 0.63, 0.79, -0.61, -0.90, 0.33, 0.36, -0.96, and the constant M is 0.11523259. Risk-score ≥ 0.51 is considered positive, and Risk-score < 0.51 is considered negative.

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

[0025] 1) The biomarker combination of the present invention is used to identify and screen glioblastoma patients. Compared with traditional imaging examinations and MRI examinations, the present invention uses multiple biomarkers to identify glioblastoma patients, which improves the accuracy and comprehensiveness of diagnosis. The detection can be completed by obtaining body fluids, and the sampling is convenient, which is suitable for screening glioblastoma patients in a large population.

[0026] 2) The combination of multiple biomarkers provided by the present invention can effectively overcome the individual differences among glioblastoma patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1Provides a technical route for biomarker screening and positive predictive screening application for glioblastoma;

[0028] Figure 2 ROC curve of the prediction model test set. DETAILED DESCRIPTION

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Example 1 Screening of Positive Marker Combinations for Glioblastoma

[0034] See attached for technical route Figure 1 :

[0035] 1) Sample collection: Cancer tissue samples from 117 patients with glioblastoma-positive lung cancer and adjacent tissue samples from 100 of these patients were collected;

[0036] 2) Sample testing: Utilize liquid chromatography-mass spectrometry technology to detect metabolites and obtain biomarker detection values ​​by relying on its high-resolution mass analyzer;

[0037] 3) Data preprocessing: The raw data is converted into a specific format by software. Then peak alignment, retention time correction and peak area extraction are performed. ;

[0038] 4) Experimental quality evaluation: Perform correlation analysis on the QC samples added to the experiment, and the correlation coefficient is required to be >0.9.

[0039] 5) Marker combination screening, the specific process is as follows:

[0040] a) The data were split in a ratio of 7:3, that is, 151 samples were used as the training data set and 56 samples were used as the test data set. The matrix results of the metabolite detection values ​​of the training data set were grouped into positive group and control group, and the variable weight value VIP obtained by the OPLS-DA model was calculated. The t hypothesis test P value and median difference fold FC were calculated. Among them, VIP>1.0, FC>1.5 or FC<0.667 and Pvalue<0.05 were considered to be significantly differentially expressed metabolites; the detailed list is shown in Table 1.

[0041] b) Based on the significantly different metabolites screened in step a) of step 5, modeling is performed using a machine learning algorithm of logistic regression, and a grid search is performed to select the optimal modeling parameters. The grid search is a parameter adjustment technique in machine learning, which is used to systematically traverse a combination of multiple parameters and determine the optimal effect parameters through cross-validation.

[0042] c) Further screening the model with the largest test set AUC among the preferred models obtained in step 5 b) as the optimal screening model. According to the AUC curve of the model, the Youden index is calculated as the threshold for judging whether the glioblastoma patient is positive. The threshold is 0.51. The AUC curve is shown in the attached Figure 2 .

[0043] d) The model screened out 10 positive marker combinations in total. The positive marker combinations are: L-Aspartic acid (L-aspartic acid), 3-Phospho-D-glyceric acid, 2-Phosphoglyceric acid, Palmitoylethanolamide, D-Erythro-sphingosine 1-phosphate, Anserine (L-angerine), L-Alanyl-L-Lysine, Docosahexaenoic acid (docosahexaenoic acid), Leukotriene C4 (leukotriene C4), Ureidoglutaric acid. The risk value of each subject was calculated according to the positive risk formula for glioblastoma: Risk-score = K1*X1+K2*X2+…+Kn*Xn+M, where n is the number of biomarkers, K is the weight coefficient corresponding to each biomarker, X is the mass spectrometry quantitative detection value corresponding to each biomarker, and M is a constant; the weight coefficient K values ​​corresponding to each marker are: -0.76, -0.72, 0.96, 0.63, 0.79, -0.61, -0.90, 0.33, 0.36, -0.96, the constant M is 0.11523259, and the risk value ≥ 0.51 is considered positive, and the risk value < 0.51 is considered negative. The sensitivity and specificity of this marker combination in the test set are 0.81 and 0.80, respectively, with good identification effect.

[0044] Table 1 Results of differential analysis of metabolic markers

[0045]

[0046]

[0047]

[0048]

[0049] Example 2 Application of Glioblastoma Positive Marker Combination Screening

[0050] 1. Sample collection: Cancer tissue samples from 5 glioblastoma patients (numbered Ca01-Ca05) and adjacent cancer tissue samples from 4 glioblastoma subjects (numbered N01-N04) were collected.

[0051] 2. Biomarker detection: The samples were tested by liquid chromatography-mass spectrometry to obtain the measured values ​​of the biomarkers: L-Aspartic acid, 3-phospho-D-glyceric acid, 2-phosphoglyceric acid, Palmitoyl ethanolamide, D-Erythro-sphingosine 1-phosphate, Anserine, L-Alanyl-L-Lysine, Docosahexaenoic acid, Leukotriene C4, and Ureidoglutaric acid. The results are shown in Table 2.

[0052] Table 2. Biomarker measurements

[0053]

[0054]

[0055] 3. Calculate the subject's Risk-score according to the formula: Risk-score = K1*X1+K2*X2+…+Kn*Xn+M, where n is the number of biomarkers, K is the weight coefficient corresponding to each biomarker, X is the mass spectrometry quantitative detection value corresponding to each biomarker, and M is a constant; the weight coefficient K values ​​corresponding to each marker are: -0.76, -0.72, 0.96, 0.63, 0.79, -0.61, -0.90, 0.33, 0.36, -0.96, and the constant M is 0.11523259. The screening results are as follows:

[0056] Table 3. Screening results

[0057]

[0058]

[0059] The above results show that when the screening results of 9 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 75%, 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.

[0060] 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 metabolites: L-Aspartic acid, 3-phospho-D-glyceric acid, 2-phosphoglyceric acid, palmitoylethanolamide, D-erythrosine-1-phosphate, L-angesone, L-alanine-L-lysine, docosahexaenoic acid, leukotriene C4, urea glutaric acid.

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. Use of the biomarker combination according to claim 1 and the reagent according to claim 2 in the preparation of a glioblastoma screening kit.

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

5. 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.

6. A method for constructing a glioblastoma screening model, characterized in that: The following steps are involved: Metabolite detection is performed on cancer tissue samples and adjacent tissue samples of glioblastoma-positive patients, and metabolites with significantly differential expression are screened. Modeling is performed using a machine learning algorithm of logistic regression, and a preferred model is selected. The model with the largest test set AUC among the preferred models is used as the optimal screening model, which is the screening model; the metabolites include the biomarker combination described in claim 1.

7. The method for constructing a glioblastoma screening model according to claim 6, characterized in that: The detection is performed by using liquid chromatography-mass spectrometry technology; the selection of the preferred model is performed by performing a grid search to select the optimal modeling parameters.

8. A glioblastoma screening model, characterized in that: The screening model is obtained by the construction method described in claim 6 or 7.

9. The glioblastoma screening model according to claim 8, characterized in that: In the screening model, the risk value of glioblastoma Risk-score = K1*X1+K2*X2+…+Kn*Xn+M, where n is the number of biomarkers, K is the weight coefficient corresponding to each biomarker, X is the mass spectrometry quantitative detection value corresponding to each biomarker, and M is a constant; if the subject's Risk-score ≥ threshold, it indicates that the subject may be a glioblastoma-positive patient; if the subject's Risk-score < threshold, the subject is a non-positive patient.

10. The glioblastoma screening model according to claim 9, characterized in that: The number of X is 10, and X1-X10 correspond to the mass spectrometry quantitative detection values ​​of L-aspartic acid, 3-phospho-D-glyceric acid, 2-phosphoglyceric acid, palmitoylethanolamide, D-erythrosine-1-phosphate, L-angesone, L-alanine-L-lysine, docosahexaenoic acid, leukotriene C4, and urea glutaric acid, and the corresponding weight coefficient K values ​​are as follows: -0.76, -0.72, 0.96, 0.63, 0.79, -0.61, -0.90, 0.33, 0.36, -0.96, the constant M is 0.11523259, and the risk value ≥ 0.51 is considered positive, and the risk value < 0.51 is considered negative.

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