GAGs marker for breast cancer screening and application thereof

Abstract

The invention discloses a GAGs marker for breast cancer screening and application of the GAGs marker, and belongs to the technical field of biological medicine. According to the method, a GAGs expression spectrum library of breast cancer patients is obtained by using liquid chromatography-tandem mass spectrometry, the GAGs markers are obtained through screening, and a risk assessment model based on the GAGs markers is established. Compared with an existing traditional detection mode, the method has the advantages that the breast cancer can be predicted more accurately, and the early detection rate of the breast cancer can be increased; a stable and good prediction effect can be achieved by using fewer detection indexes, and a new thought is provided for a personalized treatment scheme of a breast cancer patient.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedicine, and specifically relates to a GAGs marker for breast cancer screening and an application thereof. Background Art

[0002] Breast cancer is one of the most common malignant tumors in women worldwide, seriously threatening women's health and quality of life. Breast cancer is a malignant tumor with highly heterogeneous biological characteristics. Although different subtypes have the same clinical symptoms and pathological manifestations, they are very different in terms of treatment effects and disease outcomes.

[0003] Currently, early detection of breast cancer is crucial to improving patient survival rates. However, existing detection methods have problems such as low detection accuracy and limited personalization. Summary of the invention

[0004] The purpose of the present invention is to provide a GAGs marker for breast cancer screening, and another purpose is to provide a specific application of the marker to make up for the deficiencies of the prior art.

[0005] The extracellular matrix (ECM) plays an important role in the occurrence and development of tumors, and its changes can affect the proliferation, migration and invasion of tumor cells. Glycosaminoglycans (GAGs) are important macromolecular components involved in the formation of ECM scaffold structures, regulating biological processes such as cell adhesion, migration, and signal transduction, and play an important role in cell growth and differentiation. The present invention provides a basis for early detection and prognosis of tumors by constructing a tumor marker model based on GAGs, and at the same time provides a new target for anti-tumor treatment.

[0006] In order to achieve the above-mentioned invention object, the present invention adopts the following technical solutions:

[0007] GAGs markers for breast cancer screening include hyaluronic acid (HA), heparan sulfate (HS), chondroitin sulfate (CS), unsulfated CS disaccharide, four-sulfated CS disaccharide, six-sulfated CS disaccharide, two-sulfated CS disaccharide, four-six-position double-sulfated CS disaccharide, two-four-position double-sulfated CS disaccharide, unsulfated HS disaccharide, N-sulfated HS disaccharide, six-position sulfated HS disaccharide, two-sulfated HS disaccharide, N-six-position double-sulfated HS disaccharide, N-two-position double-sulfated HS disaccharide, N-two-six-position tri-sulfated HS disaccharide, CS sulfation, and HS sulfation.

[0008] Furthermore, GAGs markers for breast cancer screening include four-position six-position doubly sulfated CS disaccharides, two-position sulfated CS disaccharides, six-position sulfated CS disaccharides, N-position sulfated HS disaccharides, and N-position six-position doubly sulfated HS disaccharides (serum); or six-position sulfated CS disaccharides, two-position sulfated CS disaccharides, CS sulfation degree, two-position four-position doubly sulfated CS disaccharides, four-position sulfated CS disaccharides, unsulfated CS disaccharides, and N-position two-position doubly sulfated HS disaccharides (tissue).

[0009] Furthermore, GAGs markers for breast cancer screening include four-position six-position disulfated CS disaccharides, six-position sulfated CS disaccharides (serum); or six-position sulfated CS disaccharides and N-position two-position disulfated HS disaccharides (tissue).

[0010] Application of the GAGs marker in breast cancer screening products.

[0011] The application may specifically be: constructing a breast cancer risk assessment system based on the above-mentioned GAGs markers, including the following modules:

[0012] Sample collection module: used to collect samples to be tested and establish training sets and validation sets;

[0013] Mass spectrometry detection module: extract sample tissue protein, release glycosaminoglycan GAGs sugar chains, enzymatically hydrolyze them into disaccharides, and perform mass spectrometry analysis;

[0014] GAGs analysis module: analyze the relative content of GAGs sugar chains, perform ROC curve analysis and random forest analysis to screen GAGs sugar chain markers, and perform logistic regression analysis on the 10 GAGs indicators screened by random forest;

[0015] Scoring prediction model module: Risk score of each sample = 3.56 + 59.16 * percentage of 4-6-bit double sulfated CS disaccharides - 10.46 * percentage of 6-bit sulfated CS disaccharides; or, risk score of each sample = -15.123 + 2.177 * percentage of 6-bit sulfated CS disaccharides + 1.608 * content of N-2 double sulfated HS disaccharides;

[0016] Risk Assessment Module: Train and validate the score prediction model, where a risk score less than zero is defined as non-breast cancer and a risk score greater than zero is defined as breast cancer.

[0017] Furthermore, the sample is serum, plasma, urine, body fluid or tissue.

[0018] Furthermore, in the risk assessment model establishment module: serum samples were analyzed based on logistic regression to determine the coefficients of the two GAGs indicators, and a composite detection scoring model for predicting breast cancer was established:

[0019] The risk score of each sample = 3.56 + 59.16 * percentage of four-digit and six-digit double sulfated CS disaccharides - 10.46 * percentage of six-digit sulfated CS disaccharides;

[0020] The coefficients of two GAGs indicators were determined based on logistic regression analysis of tissue samples, and a composite detection score model for predicting breast cancer was established:

[0021] The risk score of each sample = -15.123 + 2.177 * the percentage of hexasulfated CS disaccharides + 1.608 * the content of disulfated HS disaccharides at the N-position.

[0022] Compared with the prior art, the advantages and positive effects of the present invention are:

[0023] The present invention uses liquid chromatography tandem mass spectrometry to obtain the GAGs expression library of breast cancer patients, screens GAGs markers, and establishes a risk assessment model based on GAGs markers. Compared with existing traditional detection methods, the present invention can more accurately predict breast cancer, which will help improve the early detection rate of breast cancer; it can achieve stable and good prediction effects with fewer detection indicators, providing new ideas for personalized treatment plans for breast cancer patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a random forest analysis diagram of GAGs in serum samples according to an embodiment of the present invention.

[0025] Figure 2 This is a principal component analysis diagram of the 10 components obtained by the random forest analysis of GAGs in serum samples described in the embodiment of the present invention.

[0026] Figure 3 This is a random forest analysis diagram of GAGs of tissue samples according to an embodiment of the present invention.

[0027] Figure 4 This is a principal component analysis diagram of 10 components obtained by random forest analysis of GAGs in tissue samples according to an embodiment of the present invention.

[0028] Figure 5 Confusion matrix of breast cancer probability for internal validation of serum samples.

[0029] Figure 6 Schematic diagram of the area under the ROC curve for internal validation of serum samples.

[0030] Figure 7 Confusion matrix of breast cancer probability for external validation of serum samples.

[0031] Figure 8Confusion matrix of breast cancer probability for internal validation of serum samples.

[0032] Fig. 9 Schematic diagram of the area under the ROC curve for internal validation of serum samples.

[0033] Fig.10 Confusion matrix of breast cancer probability for external validation of serum samples. DETAILED DESCRIPTION

[0034] The technical solution of the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods, but the scope of protection claimed by the present invention is not limited to the scope described in the examples.

[0035] Embodiment 1:

[0036] A method for screening breast cancer markers, comprising the following steps:

[0037] (1) Sample selection

[0038] Clinical samples include serum, plasma, urine, body fluids or tissues, wherein the amount of serum, plasma, urine and body fluids is 20-200 μL, and the amount of tissue is 5-100 mg. 80% of the samples are used as training sets and 20% of the samples are used as validation sets for GAGs omics analysis. In this embodiment, there are 98 clinical serum samples, including 32 normal human serum and 66 breast cancer patient serum. 78 samples are selected as training set clinical samples and 20 samples are validation set clinical samples; there are 196 clinical tissue samples, including 94 breast cancer tissue samples and 102 normal adjacent tissue samples. 157 samples are selected as training set clinical samples and 39 samples are validation set clinical samples.

[0039] (2) Sample preparation and mass spectrometry analysis

[0040] First, add 2-4 times the volume of ice ethanol to 20-200 μL serum or 5-100 mg tissue, centrifuge at 8000 rpm for 10 minutes, blow dry the precipitate, redissolve it in water, add 1-2% Pronase, and react on a shaker at 45-55°C for 4-6 hours. Inactivate at 100°C, add 4-6 times the volume of anhydrous ethanol, and place at -20°C for 2 hours to completely precipitate GAGs. Centrifuge, discard the upper layer, redissolve the precipitate in enzymatic buffer (50 mM ammonium acetate, pH 7.0, containing 2 mM GaCl2), add excess 6 GAG enzymes (chondroitinase AC, B, ABC and heparinase I, II, III) for enzymatic hydrolysis, and react in a 36°C water bath for 4-8 hours. Inactivate at 100°C, and freeze-dry the supernatant for later use.

[0041] Then, 2-aminoacridone (AMAC) derivatization was performed; 20 μL of freshly prepared 0.05 mol / L 2-AMAC was added to the freeze-dried sample, and after standing at room temperature for 20-30 min, 20 μL of freshly prepared 0.5 mol / L sodium cyanoborohydride was added, mixed evenly, and reacted in a 45° C. water bath for 4 to 6 h.

[0042] Secondly, mass spectrometry analysis was performed using a Thermo UltiMate 3000 ultra-high performance liquid chromatograph connected to a TSQQuantiva mass spectrometer. On a 150mm x 2.1mm C18 column, the flow rate was 0.12mL / min, the mobile phases A and B were 10mM ammonium bicarbonate and methanol, respectively, and B increased from 12% to 25% within 12min. The spray voltage was 3kV, the detector was in negative ion mode, the scanning mode was multiple reaction monitoring MRM scanning, and the resolution was 60000.

[0043] Finally, for the data analysis of GAGs, Xcalibur 2.0.7 software was used to collect mass spectrometry data, and then the absolute quantification of GAGs disaccharides was performed using reference standards. The obtained GAGs disaccharide data were statistically analyzed using GraphPad Prism v.5.03 software.

[0044] (3) Screening of GAGs markers

[0045] SPSS v.22.0 was used to perform receiver operating characteristic curve analysis (ROC) and detective analysis was performed on statistically significant (AUC>0.7, p<0.05) GAGs data. The following results were obtained:

[0046] Total amount of hyaluronic acid (HA), total amount of heparan sulfate (HS), total amount of chondroitin sulfate (CS), unsulfated CS disaccharide content, 4-position sulfated CS disaccharide content, 6-position sulfated CS disaccharide content, 2-position sulfated CS disaccharide content, 4-position 6-position double sulfated CS disaccharide content, 2-position 4-position double sulfated CS disaccharide content, unsulfated HS disaccharide content, N-position sulfated HS disaccharide content, 6-position sulfated HS disaccharide content, 2-position sulfated HS disaccharide content, N-position 6-position double sulfated HS disaccharide content, N-position 2-position double sulfated HS disaccharide content, N-position 2-position trisulfated HS disaccharide content, percentage of unsulfated HA disaccharide, percentage of unsulfated CS disaccharide, percentage of 4-position sulfated CS disaccharide, percentage of 6-position sulfated CS disaccharide, 2-position sulfated HS disaccharide content The percentage of acidified CS disaccharides, the percentage of 4- and 6-position double sulfated CS disaccharides, the percentage of 2- and 4-position double sulfated CS disaccharides, the percentage of unsulfated HS disaccharides, the percentage of N-sulfated HS disaccharides, the percentage of 6-sulfated HS disaccharides, the percentage of 2-sulfated HS disaccharides, the percentage of N-6-position double sulfated HS disaccharides, the percentage of N-2-position double sulfated HS disaccharides, the percentage of N-2- and 6-position trisulfated HS disaccharides, CS sulfation level, HS sulfation level, the content of 2-sulfated modified CS disaccharides, the content of 4-sulfated modified CS disaccharides, the content of 6-sulfated modified CS disaccharides, the content of N-sulfated modified HS disaccharides, the content of 2-sulfated modified HS disaccharides, the content of 6-sulfated modified HS disaccharides, and the content of acetylated modified HS disaccharides.

[0047] For samples with AUC>0.7 and p<0.05, random forest analysis was performed using R language, and a total of 10 most important GAGs indicators for breast cancer screening were obtained. Figure 1 , the 10 most important GAGs indicators of serum samples were: the percentage of four- and six-position doubly sulfated CS disaccharides, the content of four- and six-position doubly sulfated CS disaccharides, the percentage of two-position sulfated CS disaccharides, the percentage of six-position sulfated CS disaccharides, the content of two-position sulfated-modified CS disaccharides, the content of six-position sulfated CS disaccharides, the content of two-position sulfated CS disaccharides, the percentage of N-position sulfated HS disaccharides, the content of six-position sulfated-modified CS disaccharides, and the percentage of N-position six-position doubly sulfated HS disaccharides.

[0048] The principal component analysis was performed using these 10 GAGs indicators, such as Figure 2 As shown in the figure, it has a high discriminatory effect on normal human serum and breast cancer serum. Figure 3,The 10 most important GAGs indicators of tissue samples are: the percentage of CS disaccharides sulfated at the six positions, the content of CS disaccharides sulfated at the six positions, the content of CS disaccharides modified at the six positions, the content of CS disaccharides sulfated at the two positions, the degree of CS sulfation, the percentage of CS disaccharides sulfated at the two positions, the percentage of CS disaccharides doubly sulfated at the two positions, the percentage of CS disaccharides sulfated at the four positions, the content of unsulfated CS disaccharides, and the content of HS disaccharides doubly sulfated at the two positions in N. ,The principal component analysis was performed through these 10 GAGs indicators, e.g. Figure 4 As shown, it has a high ability to distinguish breast cancer tissue from adjacent tissue.

[0049] (4) Establishing a prediction model

[0050] Logistic regression analysis was performed on the 10 GAGs indicators screened by random forest. The coefficients of the serum samples including 2 GAGs indicators were determined based on logistic regression analysis, and a composite detection scoring model for predicting breast cancer was established:

[0051] The risk score of each sample = 3.56 + 59.16 * the percentage of four-digit and six-digit double sulfated CS disaccharides - 10.46 * the percentage of six-digit sulfated CS disaccharides.

[0052] The coefficients of two GAGs indicators were determined based on logistic regression analysis of tissue samples, and a composite detection score model for predicting breast cancer was established:

[0053] The risk score of each sample = -15.123 + 2.177 * the percentage of hexasulfated CS disaccharides + 1.608 * the content of disulfated HS disaccharides at the N-position.

[0054] Among them, a risk score less than zero was defined as non-breast cancer, and a risk score greater than zero was defined as breast cancer.

[0055] In one embodiment of the present invention, during the validation process of the model, the training set samples are first used as internal validation. Figure 5 Figure 3. Confusion matrix of breast cancer transfer for internal validation of serum samples. The internal validation model included 78 samples. The specificity of the detection model was 95.6%, the sensitivity was 98.1%, the positive predictive value was 98.1%, and the negative predictive value was 95.6%.

[0056] Figure 6 The figure is a schematic diagram of the area under the ROC curve for internal validation of serum samples. The vertical axis represents the sensitivity value of the detection model, and the horizontal axis represents the specificity value of the detection model. The area under the ROC curve is 0.997. The external validation uses the validation set samples. Figure 7Figure 1. Confusion matrix of breast cancer for external validation of serum samples. The external validation model included 20 samples. The specificity of the detection model was 88.8%, the sensitivity was 100%, the positive predictive value was 92.3%, and the negative predictive value was 100%.

[0057] Figure 8 Figure 3. Confusion matrix of breast cancer for internal validation of tissue samples. The internal validation model included 157 samples. The specificity of the detection model was 92.7%, the sensitivity was 93.5%, the positive predictive value was 92.3%, and the negative predictive value was 93.8%.

[0058] Fig. 9 The area under the ROC curve for internal validation of tissue samples is 0.963. The validation set samples were used for external validation. Fig.10 Figure 1. Breast cancer confusion matrix for external validation of tissue samples. The external validation model included 39 samples. The detection model had a specificity of 90.9%, a sensitivity of 88.2%, a positive predictive value of 88.2%, and a negative predictive value of 90.9%.

[0059] The above results indicate that the GAGs-based detection model has high sensitivity and specificity and has potential application value in the clinical detection of breast cancer.

[0060] The above embodiments only illustrate the technical solutions of the present invention, rather than limiting it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for a person skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to replace some of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions claimed to be protected by the present invention.

Claims

1. A GAGs marker for breast cancer screening, characterized in that: The invention comprises one or more of hyaluronic acid HA, heparan sulfate HS, chondroitin sulfate CS, unsulfated CS disaccharide, four-position sulfated CS disaccharide, six-position sulfated CS disaccharide, two-position sulfated CS disaccharide, four-position six-position double sulfated CS disaccharide, two-position four-position double sulfated CS disaccharide, unsulfated HS disaccharide, N-position sulfated HS disaccharide, six-position sulfated HS disaccharide, two-position sulfated HS disaccharide, N-position six-position double sulfated HS disaccharide, N-position two-position double sulfated HS disaccharide, N-position two-position six-position trisulfated HS disaccharide, CS sulfate, and HS sulfate.

2. The GAGs marker for breast cancer screening according to claim 1, characterized in that: The GAGs markers included two groups: four- and six-position doubly sulfated CS disaccharides, two-position sulfated CS disaccharides, six-position sulfated CS disaccharides, N-position sulfated HS disaccharides, and N- and six-position doubly sulfated HS disaccharides; or hexasulfated CS disaccharide, disulfated CS disaccharide, degree of CS sulfation, di- and tetra-sulfated CS disaccharides, tetra-sulfated CS disaccharides, unsulfated CS disaccharides, and N- and tetra-sulfated HS disaccharides.

3. The GAGs marker for breast cancer screening according to claim 1, characterized in that: GAGs markers used for breast cancer screening include two groups: four-position six-position disulfated CS disaccharides, six-position sulfated CS disaccharides; or six-position sulfated CS disaccharides and N-position two-position disulfated HS disaccharides.

4. Use of the GAGs marker according to any one of claims 1 to 3 in breast cancer screening products.

5. The use according to claim 4, characterized in that The application is specifically to build a breast cancer risk assessment system, including the following modules: Sample collection module: used to collect samples to be tested and establish training sets and validation sets; Mass spectrometry detection module: extract sample tissue protein, release glycosaminoglycan GAGs sugar chains, enzymatically hydrolyze them into disaccharides, and perform mass spectrometry analysis; GAGs analysis module: analyze the relative content of GAGs sugar chains, perform ROC curve analysis and random forest analysis to screen GAGs sugar chain markers, and perform logistic regression analysis on the 10 GAGs indicators screened by random forest; Scoring prediction model module: Risk score of each sample = 3.56 + 59.16 * percentage of 4-bit and 6-bit double sulfated CS disaccharides - 10.46 * percentage of 6-bit sulfated CS disaccharides; or, risk score of each sample = -15.123 + 2.177 * percentage of 6-bit sulfated CS disaccharides + 1.608 * content of N-bit double sulfated HS disaccharides; Risk Assessment Module: Train and validate the score prediction model, where a risk score less than zero is defined as non-breast cancer and a risk score greater than zero is defined as breast cancer.

6. The use according to claim 5, characterized in that In the risk assessment model establishment module: serum samples were analyzed based on logistic regression to determine the coefficients of the two GAGs indicators, and a composite detection scoring model for predicting breast cancer was established: The risk score of each sample = 3.56 + 59.16 * percentage of four-digit and six-digit double sulfated CS disaccharides - 10.46 * percentage of six-digit sulfated CS disaccharides; The coefficients of two GAGs indicators were determined based on logistic regression analysis of tissue samples, and a composite detection score model for predicting breast cancer was established: The risk score of each sample = -15.123 + 2.177 * the percentage of hexasulfated CS disaccharides + 1.608 * the content of disulfated HS disaccharides at the N-position.

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