KR102077058B1 - A method and kit for assessing risk of breast cancer using metabolite profiling - Google Patents

Abstract

The present invention relates to a diagnostic method for assessing a breast cancer related risk based on a specific biomarker, the method comprising: (a) taking a biological sample from a subject; (b) leucine (leucine), tyrosine (tyrosine), arachidonic acid (arachidonic acid), prostaglandin J ₂ (prostaglandin J _2), prostaglandin E ₂ (prostaglandin E ₂₎ and gamma-linolenic acid (γ-linolenic acid) in the biological sample Measuring at least one metabolite concentration selected from the group consisting of; And (c) comparing the measured level of the metabolite with the measured value in a normal control sample. A method for assessing the prognosis or risk of breast cancer is provided.

Description

METHODS AND KIT FOR ASSESSING RISK OF BREAST CANCER USING METABOLITE PROFILING}

The present invention relates to methods and kits for assessing the risk of developing breast cancer by measuring the concentration of certain biomarkers.

Cancer is usually called a tumor and refers to a mass that has grown abnormally due to autonomous overgrowth of body tissues. When cancer develops, the cell cycle is not regulated, resulting in continued cell division, rapid growth as it infiltrates surrounding tissues, and spreads or metastasizes to parts of the body, threatening life.

When cancer develops in the ducts and lobules of the breast, it is called breast cancer. Breast cancer is one of the highest mortality cancers among women worldwide.

According to 2012 statistics, women's incidence of breast cancer is increasing due to changes in living conditions caused by industrial development. Although the cause of breast cancer is not clear, various factors, such as hormonal overproduction, westernized eating habits, childbirth and genetic causes, are being discussed.

Most cancers, including breast cancer, have a significantly increased therapeutic potential upon early diagnosis. In general, the diagnosis of cancer depends on diagnostic information obtained by x-ray, CAT scan, NMR, and the like. However, this method is expensive in diagnosis and invasive to the human body.

Meanwhile, new biomarkers are being researched as a method for early diagnosis of cancer. Biomarker refers to an indicator that can detect changes in the body using proteins, DNA, RNA, metabolites, etc. Biomarkers can be used to objectively measure the normal or pathological condition of a subject and the degree of response to drugs. Can be.

The biomarker is called the internal phenotype of a particular disease and can be used to find the cause of a particular disease or measure the risk of developing it. Metabolomics, which collectively analyze metabolites and metabolic circuits in cells, are widely used to detect biomarkers in diseases.

Metabolites that are present in specific or large amounts at the onset of cancer may be used as cancer-specific markers, and biological samples may be taken from patients suspected of developing cancer and observed for changes in cancer-specific markers. You can predict the presence or absence.

The present inventors have attempted to identify various metabolites associated with breast cancer and to correlate with the development of breast cancer to provide a method for diagnosing the risk or prognosis of breast cancer.

Throughout this specification, many papers and patent documents are referenced and their citations are indicated. The disclosures of cited papers and patent documents are incorporated herein by reference in their entirety, so that the level of the technical field to which the present invention belongs and the contents of the present invention are more clearly explained.

The present invention is to solve the above-mentioned problems of the prior art, an object of the present invention to provide a method for diagnosing the risk or prognosis of breast cancer by measuring the level of a specific metabolite.

According to one aspect of the invention, (a) taking a biological sample from the subject (subject); (b) leucine (leucine), tyrosine (tyrosine), arachidonic acid (arachidonic acid), prostaglandin J ₂ (prostaglandin J _2), prostaglandin E ₂ (prostaglandin E ₂₎ and gamma-linolenic acid (γ-linolenic acid) in the biological sample Measuring at least one metabolite concentration selected from the group consisting of; And (c) comparing the measured level of the metabolite with the measured value in a normal control sample. A method for assessing the prognosis or risk of breast cancer is provided.

In one embodiment, phenylalanylphenylalanine (phenylalanylphenylalanine), phenylalanine (phenylalanine), tryptophan (tryptophan), stearonic acid (Stearidonic acid), prostaglandin F _2α (prostaglandin F _2α) ) And one or more metabolite concentrations selected from the group consisting of retinoic acid.

In one embodiment, eicosapentaenoic acid (docospentaenoic acid), docosahexaenoic acid (docosahexaenoic acid), lysoPC (lysophosphatidylcholine) in the method for assessing the prognosis or risk of the breast cancer ) At least one metabolite concentration selected from the group consisting of 18: 3, lysoPC 20: 3, lysoPC 20: 4, and lysoPC 20: 5.

In one embodiment, the method of evaluating the prognosis or risk of breast cancer may determine the possibility of developing breast cancer 7 years after the baseline.

According to another aspect of the invention, leucine (leucine), tyrosine (tyrosine), arachidonic acid (arachidonic acid), prostaglandin J ₂ (prostaglandin J _2), prostaglandin E ₂ (prostaglandin E ₂₎ and gamma-linolenic acid (γ-linolenic acid Provided is a kit for diagnosing breast cancer risk including a quantitative device for at least one metabolite selected from the group consisting of:

In one embodiment, the kit for diagnosing breast cancer risk may include phenylalanylphenylalanine, phenylalanine, tryptophan, stearridonic acid, prostaglandin F _2α and prostaglandin F _2α . (Retinoic acid) may further include a quantification device for one or more metabolites selected from the group consisting of.

In one embodiment, the kit for diagnosing breast cancer risk is eicosapentaenoic acid, docosapentaenoic acid, docosahexaenoic acid, lysoPC (lysophosphatidylcholine) 18: 3 , lysoPC 20: 3, lysoPC 20: 4 and lysoPC 20: 5 may further include a quantification device for one or more metabolites selected from the group consisting of.

According to one aspect of the invention, by measuring the concentration of a specific metabolite in the serum, it is possible to effectively predict the onset of breast cancer.

In particular, the method provides proactive response with information about the likelihood of developing breast cancer after an average of seven years from baseline.

The effects of the present invention are not limited to the above-described effects, but should be understood to include all the effects deduced from the configuration of the invention described in the detailed description or claims of the present invention.

FIG. 1 compares plasma metabolites between a control group (n = 88) and a breast cancer group (n = 84). Score plots of cation mode (a) and anion mode (b) obtained from the OPLS-DA model, and covariance [p] and reliability of cation mode (c) and anion mode (d) obtained from the OPLS-DA model correlation) S-plot for [p (corr)].
2 is a metabolic pathway analysis result showing pathways arranged according to scores based on enrichment analysis (y-axis) and topology analysis (x-axis). The color and size of each circle represent the P -value and the path impact value, respectively.
3 is a matrix showing the correlation between age, weight, and major metabolites in the control group and the breast cancer group. Correlation was derived using Pearson's correlation coefficient, red for positive correlation and blue for negative correlation.

Hereinafter, with reference to the accompanying drawings will be described the present invention. As those skilled in the art would realize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. In the drawings, parts irrelevant to the description are omitted in order to clearly describe the present invention, and like reference numerals designate like parts throughout the specification.

When a part is said to "include" a certain component, it means that it can be provided with other components rather than excluding the other components unless otherwise stated.

Unless defined otherwise, molecular biology, microbiology, protein purification, protein engineering, and DNA sequencing can be performed by conventional techniques commonly used in the field of recombinant DNA, within the capabilities of those skilled in the art. These techniques are known to those skilled in the art and are described in many standardized textbooks and references.

Unless defined otherwise herein, all technical and scientific terms used have the same meaning as commonly understood by one of ordinary skill in the art.

Various scientific dictionaries that include the terms included herein are well known and available in the art. Although any methods and materials similar or equivalent to those described herein are found to be used in the practice or testing herein, some methods and materials have been described. Depending on the context used by those skilled in the art, the present invention is not limited to specific methodologies, protocols, and reagents, because of the variety of uses.

Hereinafter, with reference to the accompanying drawings will be described the present invention in more detail.

According to one aspect of the invention, (a) taking a biological sample from the subject (subject); (b) leucine (leucine), tyrosine (tyrosine), arachidonic acid (arachidonic acid), prostaglandin J ₂ (prostaglandin J _2), prostaglandin E ₂ (prostaglandin E ₂₎ and gamma-linolenic acid (γ-linolenic acid) in the biological sample Measuring at least one metabolite concentration selected from the group consisting of; And (c) comparing the measured level of the metabolite with the measured value in a normal control sample. A method for assessing the prognosis or risk of breast cancer is provided.

The subject may be a human as a subject to be diagnosed, and the biological sample is a sample separated from the subject to evaluate the risk of a breast cancer-related disease, and includes tissue, cells, blood, plasma, peritoneal fluid, synovial fluid, and saliva. , Urine, stool, and the like. Preferably the biological sample may be blood, and specifically, may be serum isolated from blood.

The normal control may be a human who does not have cancer or has no history of cancer for an average of seven years.

In addition, each of the metabolites may be analyzed individually to diagnose the prognosis or risk of breast cancer, but the overall accuracy of the diagnosis may be improved by analyzing the levels of the related metabolites as a whole.

The metabolites can be determined by studying the pathological pathway of breast cancer through metabolism. The metabolism is one of the fields of biology that analyzes and studies metabolites and metabolic circuits in cells collectively.

The "metabolite" refers to the metabolites of small molecules that occur mainly in cellular processes that can be classified in the blood and the like, and the metabolism is based on analyzing the metabolites extracted from blood. Its application is mainly to study metabolic diseases.

Physiological amino acid concentrations, determined by organ function and pathological conditions, can alter individual metabolism. Cancer cells need more amino acids to synthesize nucleic acids and proteins. For that reason, cancer is considered a metabolic syndrome with increased specific metabolites.

In a recent study, eight of the 15 amino acids, including valine, phenylalanine, isoleucine and leucine, were measured higher in breast cancer patients compared to healthy controls (Poschke et al.). In addition, plasma amino acid profiles in premenopausal Japanese women have been reported to be associated with breast cancer risk biomarkers, including free testosterone (Nagata et al.).

The "leucine" is regulated by the ratio of protein assimilation, catabolism and turnover of protein synthesis. Side chain amino acids, including leucine, are also associated with obesity. In addition, "tyrosine" is a kind of aromatic amino acid and plays an important role in hormone biosynthesis such as adrenaline (adrenal cortex hormone) and thyroxine (thyroid hormone).

In the study of the present invention, plasma leucine and tyrosine levels were markedly higher in the breast cancer group (not having baseline cancer but developing breast cancer for 7 years) compared to the control group (not having baseline and 7 years of breast cancer).

The "arachidonic acid" is a polyunsaturated omega-6 fatty acid consisting of 20 carbons. The gamma-linolenic acid is an unsaturated product of linolenic acid.

In the study of the present invention, the level of gamma linolenic acid was significantly higher in the breast cancer group than in the control group. On the other hand, the level of arachidonic acid was significantly lower in the breast cancer group than in the control group.

The "prostaglandin J ₂ (prostaglandin J _2)" and the "prostaglandin E ₂ (prostaglandin E _2)" is related to the metabolism of arachidonic acid.

Prostaglandin E ₂ is a subgroup of lipid mediators known as prostanoids. Previous studies have reported that prostanoids comprising prostaglandin E ₂ may increase the risk of breast cancer invention.

Plasma arachidonic acid levels in the study of the present invention were significantly lower in the breast cancer group compared to the control group, while plasma prostaglandin J ₂ and prostaglandin E ₂ levels were significantly higher in the breast cancer group compared to the control group.

Therefore, any selected from the group consisting of leucine (leucine), tyrosine (tyrosine), prostaglandin J ₂ (prostaglandin J _2), prostaglandin E ₂ (prostaglandin E ₂₎ and gamma-linolenic acid (γ-linolenic acid) in the step (c) When the concentration of one or more metabolites is higher than the measured value in the normal control sample, it may be determined that the risk of breast cancer in the subject is high, and when the concentration of arachidonic acid is low, the risk of developing breast cancer in the subject Can be judged to be high.

On the other hand, in the step (b) phenylalanylphenylalanine (phenylalanylphenylalanine), phenylalanine (phenylalanine), tryptophan (tryptophan), stearic acid (Stearidonic acid), prostaglandin F _2α (prostaglandin F _2α ) and retinoic acid (retinoic acid) One or more metabolite concentrations selected from the group consisting of can be measured further.

In addition, in step (b), eicosapentaenoic acid (eicosapentaenoic acid), docosapentaenoic acid (docosapentaenoic acid), docosahexaenoic acid (docosahexaenoic acid), lysoPC (lysophosphatidylcholine) 18: 3, lysoPC 20: 3 , lysoPC 20: 4 and lysoPC 20: 5 can be measured more than one metabolite concentration selected from the group consisting of.

Further analysis of the levels of the metabolites and comparison with the normal control can further improve the reliability of the diagnosis.

In the study of the present invention, the level of phenylalanylphenylalanine was significantly higher in the breast cancer group (the baseline cancer did not develop at baseline cancer but developed breast cancer for 7 years) compared to the control group (not at baseline and 7 years of breast cancer). It became.

In addition, correlations with plasma amino acids and body weight, including phenylalanine, tyrosine and tryptophan, were found in the breast cancer group in the study of the present invention but not in the control group.

Stearic acid (Stearidonic acid) is an unsaturated product of alpha-linolenic acid (α-linolenic acid). In the study of the present invention, the level of plasma stearic acid was measured significantly in the breast cancer group compared to the control group.

The prostaglandin F _2α (prostaglandin F _2α) is a marker of oxidative stress. In the present study, the level of plasma prostaglandin F _2α was measured significantly higher in the breast cancer group than in the control group.

In addition, in the study of the present invention, the level of plasma retinol acid was markedly higher in the breast cancer group than in the control group.

In the study of the present invention, the levels of plasma eicosapentaenoic acid, docosapentaenoic acid and docosahexaenoic acid were measured significantly in the breast cancer group compared to the control group.

In addition, the levels of plasma lysoPC 18: 3, lysoPC 20: 3, lysoPC 20: 4 and lysoPC 20: 5 in the study of the present invention were measured significantly lower in the breast cancer group than in the control group.

The LysoPC is metabolized by lysophospholipase and further metabolized to fatty acids and choline. Low levels of the LysoPCs suggest a high metabolic rate in breast cancer patients.

Thus, in step (c), phenylalanylphenylalanine, phenylalanine, phenylalanine, tryptophan, stearidonic acid, stearonic acid, prostaglandin F _2α and prostaglandin F _2α and retinoic acid. When the concentration of any one or more metabolites selected from the group consisting of higher than the measured value in the normal control sample it can be determined that the risk of breast cancer in the subject is high.

In addition, in step (c), eicosapentaenoic acid (eicosapentaenoic acid), docosapentaenoic acid (docosapentaenoic acid), docosahexaenoic acid (docosahexaenoic acid), lysoPC (lysophosphatidylcholine) 18: 3, lysoPC 20: 3 , if the concentration of any one or more metabolites selected from the group consisting of lysoPC 20: 4 and lysoPC 20: 5 is lower than the measured value in the normal control sample, the subject may be determined to have a high risk of developing breast cancer.

Meanwhile, the method may determine the possibility of developing breast cancer 7 years after the baseline. The “baseline point of time” means a specific time point at which metabolite profiling, that is, concentration measurement of the metabolite, was performed.

On the other hand, another aspect of the present invention is leucine (leucine), tyrosine (tyrosine), arachidonic acid (arachidonic acid), prostaglandin J ₂ (prostaglandin J _2), prostaglandin E ₂ (prostaglandin E ₂₎ and gamma-linolenic acid (γ-linolenic acid The present invention provides a kit for diagnosing breast cancer risk including a quantitative device for at least one metabolite selected from the group consisting of:

The quantitative apparatus is used to measure the concentration of each metabolite, and may include chromatography, mass spectrometry, and the like.

The chromatography is liquid chromatography (LC), liquid-solid chromatography (LSC), paper chromatography (PC), thin layer chromatography (TLC), gas-solid chromatography (GSC), liquid-liquid chromatography (LLC). ), Foam chromatography (FC), emulsion chromatography (EC), gas-liquid chromatography (GLC), ion chromatography (IC), gel filtration chromatography (GFC) or gel permeation chromatography (GPC) For example, the liquid chromatography may be, but more preferably, ultra-performance liquid chromatography (UPLC), but is not limited thereto.

In addition, the mass spectrometer may use a Fourier transform mass spectrometer (FTMS), and specifically, may use an LTQ-Orbitrap-XL mass spectrometer.

The chromatography may separate each metabolite using different mobility for each molecule, and the mass spectrometer may identify metabolites through structural information as well as molecular weight information of the analyte.

The diagnostic kit is selected from the group consisting of phenyl-alanyl-phenylalanine (phenylalanylphenylalanine), phenylalanine (phenylalanine), tryptophan (tryptophan), stearyl donsan (Stearidonic acid), prostaglandin F _2α (prostaglandin F _2α) and retinol acid (retinoic acid) It may further comprise a quantification device for one or more metabolites.

In addition, the diagnostic kit is eicosapentaenoic acid, docosapentaenoic acid, docosapentaenoic acid, docosahexaenoic acid, lysoPC (lysophosphatidylcholine) 18: 3, lysoPC 20: 3, lysoPC 20: 4 and lysoPC 20: 5 may further comprise a quantification device for one or more metabolites selected from the group consisting of.

Hereinafter, the present invention will be described in more detail with reference to Examples.

Experiment method

Test subject

The subjects of the present invention were breast cancer patients and healthy volunteers identified in the Korean Cancer Prevention Study-II (KCPS-II) biobank cohort.

KCPS-II Biobank is a large-scale blood-based cohort study that followed a long-term follow-up through a unique link of routine national health screenings at health promotion centers across Korea with mortality and hospitalization records.

The cohort consisted of 156,701 participants (94,840 males and 61,861 females) who agreed to provide regular health checks, provide blood samples, and provide information for long-term follow-up.

Among 61,861 female participants who agreed to provide blood samples, 84 breast cancer patients (breast cancer group) and 88 healthy volunteers (normal control group) were selected.

The research of the present invention was approved by Yonsei University Institutional Review Board and all participating health promotion centers.

Survey and anthropometry

All of the selected subjects were surveyed for detailed information such as smoking (non-smokers, smokers, current smokers) and drinking history.

Height and weight according to the light clothing of all subjects were measured, and body mass index (BMI) was calculated by dividing body weight (kg) by height squared (m ² ).

Systolic and diastolic blood pressures were measured after a minimum of 15 minutes of rest and waist circumference was measured between the rib bottom and iliac crest.

Blood collection and biochemical analysis

For clinical and chemical analysis, serum isolated from peripheral venous blood was obtained from each participant after at least 12 hours of fasting and stored at −70 ° C. until analysis.

Fasting blood sugar, total cholesterol, triglycerides, low density lipoprotein-cholesterol (LDL-cholesterol), high density lipoprotein-cholesterol (HDL-cholesterol), aspartate aminotransferase (AST), alanine aminotransaminase (ALT) and serum high- sensitivity C-reactive protein) was measured using COBAS INTEGRA 800 and Hitachi 7600 Analyzer (Hitachi, Tokyo, Japan).

Oxidized low density lipoprotein (ox-LDL) was measured using enzyme immunoassay (Mercodia AB, Uppsala, Sweden). The results of the reaction colors were monitored at 450 nm using a Wallac 1420 Victor ² multilabel counter (PerkinElmer Life Sciences, Boston, Mass., USA).

Each measurement was performed in accordance with internal and external quality control procedures required by the Korean Association of Laboratory Quality Control.

The agreement for each biomarker between individual hospitals was high (correlation coefficient ranged from 0.96 to 0.99).

Breast cancer diagnosis

Data on breast cancer incidence was provided by the National Cancer Registry and from the hospital where the subject was treated. For data not reported to the National Cancer Registry, refer to the subject's hospitalization record.

Global (non-targeting) serum metabolite profiling

sample preparation

Serum samples were prepared according to the following preparation procedure.

100 μl aliquots of each sample were mixed with 800 μl of acetonitrile, stored at 4 ° C. for 10 minutes, and centrifuged at 10,000 ° C. for 5 minutes at 4 ° C.

The remaining 820 μl of the supernatant was dried with nitrogen gas, the pellet was dissolved in 10% methanol, mixed, and centrifuged at 10,000 ° C. for 5 minutes at 4 ° C. 85 μl of the finally produced supernatant was prepared in a vial.

UPLC-LTQ-Orbitrap XL MS Analysis

Samples of each serum extract (5 x 2.1 mm; 1.7 μm; Waters Milford, MA, USA) installed on an Acquity UPLC-BEH-C18 column installed in a Thermo UPLC system (Ultimate 3000 BioRS, Dionex-Thermo Fischer Scienrific, Bremen, Germany) Μl) was injected.

LC-MS grade water (Fisher Scientific, Fair Lawn, NJ, USA) containing 0.1% formic acid in cationic mode and LC-MS grade methanol (Fisher Scientific, Fair Lawn, NJ) containing 0.1% formic acid , USA), each sample was measured for 22 minutes.

In anion mode the mode is LC-MS grade water (Fisher Scientific, Fair Lawn, NJ, USA) containing 6.5 mM ammonium bicarbonate and eluted 95 containing 6.5 mM ammonium bicarbonate. Each sample was measured for 24 minutes using Solution B,% methanol.

In this process, the flow rates of Solution A and Solution B were fixed at 0.4 mL / min (cationic mode; ES +) and 0.35 mL / min (anion mode; ES-), and the concentration of Solution B over time was Changed.

Cation Mode (ESI +): 0% Solution B at 0 min; Solution B 0% when 1 min; Solution B 100% at 16 minutes; Solution B 100% at 17 minutes; Solution B 100% at 20 minutes; 22 min when solution B 0%.

Negative ion mode (ESI-): 0% solution B at 0 min; Solution B 0% when 2 min; Solution B 100% at 17 minutes; Solution B 100% at 22 minutes; Solution B 0% at 24 min.

Metabolites were sent to LTQ-Orbitrap-MS (Thermo Fisher Scientific, Waltham, Mass., USA), operating in each ion mode and full scan mode for Fourier transform MS. Mass resolution is 60,000 and spray voltage is 5.0 kV (ES +) or 4.0 kV (ES-). Nitrogen sheath gas and auxiliary gas flow rates are 50 and 5 (arbitrary units), respectively.

Capillary voltage, tube-lens voltage, and capillary temperature were kept constant at 35 V, 100 V, and 360 ° C., respectively. MS data was collected in the range m / z 50-1,000. For quality control, an integrated control sample was prepared by mixing with each sample and a sample was injected every fifth. MS / MS spectra of metabolites were obtained via collision energy ramps at 20-50%.

Data processing and metabolite identification

All relevant data including retention time, m / z ratio, and ionic strength were collected using SIEVE 2.2 data analysis software (Thermo Fisher Scientific, Waltham, Mass., USA).

Analytical parameters are as follows: m / z range 50-1,000 m / z; 5 ppm wide; And holding time width 2.5 minutes.

Metabolites were searched using the following database: Human Metabolome (www.hmdb.ca); Lipid MAPS (www.lipidmaps.org); KEGG (www.genome.jp/kegg); MassBank (www.massbank.jp); And ChemSpider (www.chemspider.com).

Potential metabolites were identified via MS / MS. MS / MS spectra were sent from Xcalibur 2.1 software (Thermo Fisher Scientific, Waltham, Mass., USA) to MassFrontier software (Thermo Fisher Scientific, Waltham, Mass., USA). The spectra were then compared to the Mass Frontier software database, Human Metabolome, Lipid MAPS, mzCloud and MassBank MS / MS spectra databases to identify potential metabolites.

Statistical analysis

Statistical analysis was performed using SPSS v. 24.0 (IBM / SPSS, Chicago, IL, USA) was used. The distorted variables were converted logarithmically. Mean values are expressed as unconverted values. The results are expressed as mean ± standard error (SE). Independent t-tests were used to compare the parameters between the two groups. Both sides P - the test-values (two-tailed P -value) < 0.05 was considered statistically significant.

In order to compare the number of metabolites several times, using an error detection rate q is corrected to (FDR, False discovery rate-corrected) the R package 'fdrtool' - was calculated values, q is less than 0.05 - the value of the significance Considered to have.

In addition, Pearson's correlation coefficient was used to evaluate the relationship between metabolites and biochemical features in the study population.

Heat maps were generated using Multiple Experiment Viewer (MeV) version 4.9.0 (http://www.tm4.org) to visualize and evaluate the relationship between metabolites and biochemical features in the study population. Managed hierarchical clustering was assessed using mean linkage and Spearman's rank correlation distance metrics.

A stepwise regression analysis was performed to identify key independent predictors of breast cancer development.

In addition, metabolic pathway analysis was performed using MetaboAnalyst 3.0 (http://metaboanalyst.ca).

Spectra data was exported to SIMCA-P ⁺ 14.0 (Umetrics, Umea, Sweden) for multivariate analysis. Prior to multivariate statistical analysis, Pareto scaling was applied to all data.

Models were compared using Orthogonal partial least squares discriminant analysis (OPLS-DA). The validity of the model was evaluated through the parameters of R ² Y and Q ² Y and cross-validation analysis of variance (CV-ANOVA).

Experiment result

Experimental Example 1 Clinical Characteristics of the Baseline

In an average of seven years, 84 female subjects out of 156,701 volunteers developed breast cancer. 84 patients who were admitted for breast cancer and 88 volunteers who did not have cancer and matched BMI were classified into breast cancer group and normal control group.

After correcting age, smoking, and drinking status, the breast cancer group had higher blood sugar and lower blood levels of total cholesterol, LDL-cholesterol, AST, and hs-CRP than the control group (Table 1).

division Control group (n = 88) Breast cancer (n = 84) P ^a P ^b Age (years) 43.4 ± 0.74 45.7 ± 0.86 0.039 - Current smoker n, (%) 1 (1.4) 2 (3.2) 0.483 - Drinker n, (%) 45 (56.3) 29 (43.3) 0.117 - Weight (kg) 56.4 ± 0.72 56.9 ± 0.85 0.642 0.883 Height (cm) 73.8 ± 0.73 75.1 ± 0.88 0.242 0.283 BMI (kg / m ² ) 22.4 ± 0.27 22.7 ± 0.31 0.433 0.832 Systolic Blood Pressure (mmHg) 112.2 ± 1.51 117.0 ± 1.60 0.030 0.054 Diastolic blood pressure (mmHg) 70.9 ± 0.99 71.7 ± 1.15 0.601 0.460 Glucose (mg / dL) ^∮ 84.2 ± 0.90 89.5 ± 1.45 0.002 0.024 Triglycerides (mg / dL) ^∮ 106.7 ± 6.82 109.2 ± 9.75 0.829 0.969 Total Cholesterol (mg / dL) ^∮ 191.4 ± 3.05 181.6 ± 3.58 0.038 0.005 HDL-cholesterol (mg / dL) ^∮ 55.0 ± 0.94 57.7 ± 1.40 0.111 0.179 LDL-cholesterol (mg / dL) ^∮ 116.2 ± 3.08 104.0 ± 3.38 0.009 <0.001 AST (IU / L) ^∮ 21.0 ± 0.56 19.3 ± 0.60 0.037 0.020 ALT (IU / L) ^∮ 17.4 ± 0.90 16.7 ± 0.89 0.578 0.556 GGT (U / L) ^∮ 19.0 ± 1.37 19.9 ± 2.06 0.730 0.626 White blood cell count (x10 ³ / μL) ^∮ 5.45 ± 0.16 5.65 ± 0.18 0.398 0.527 hs-CRP (mg / L) ^∮ 0.54 ± 0.53 0.10 ± 0.02 0.469 0.016

Each value is expressed as mean ± standard error; ^∮ is the value checked by log conversion. P ^a -value derived from the independent t-test. P ^b -value derived as an independent t-test after correction for age, drinking and smoking.

Experimental Example 2 Serum Metabolite Profiling Using UPLC-LTQ-orbitrap MS

Total ion chromatography data (cationic and anion modes) for serum metabolites obtained at baseline were analyzed using OPLS-DA score plots.

In particular, OPLS-DA was the baseline between the control group (baseline and 7 years without cancer; n = 88) and the breast cancer group (baseline cancer but with breast cancer for 7 years; n = 84) in cation mode. Metabolites were compared (FIG. 1A).

The quality of OPLS-DA was tested using R ² and Q ² values to evaluate the suitability of the model and the predictive power of each model.

Referring to FIG. 1, the model has a goodness of fit of 80.8% (R ² Y = 0.808) and a predictive power of 70.6% (Q ² Y = 0.706). The results suggest that the OPLS-DA model has a high degree of conjugation and an acceptable predictive power.

OPLS-DA also compared the metabolites at baseline between control and breast cancer groups with statistical parameters of goodness of fit R ² Y = 0.762 and predictive power Q ² Y = 0.575 in anion mode (FIG. 1B).

The results suggest that the two groups can be distinguished based on differences in the abundance of metabolites.

To extract potential variables that contribute to the identified differences, covariance p (1) and reliability relationships from the OPLS-DA model using Pareto scaling in cationic mode (FIG. 1C) and anion mode (FIG. 1D). An S-plot of p (corr) (1) was generated.

When the metabolite has a high or low p (corr) value, it has a higher relationship in distinguishing the two groups.

Experimental Example 3 Identification of Serum Metabolites

Variables (metabolics) that play an important role in the classification of each group were selected according to the Variable Important in the Projection (VIP) parameter. When the VIP value exceeds 1.0 it means that there is a high correlation to the difference between sample groups.

In comparison of baseline metabolite levels between the control and breast cancer groups, a total of 535 metabolites (both cation and anion modes) have been identified previously, the remainder being new.

Tables 2 and 3 show serum metabolites in control and breast cancer groups. Compared with the control group, the breast cancer group had a significantly higher level of 20 metabolites (Table 2) and a lower level of 22 metabolites (Table 3).

Identification of Upregulated Serum Metabolites in Control and Breast Cancer Groups m / z
[M + H] †
[M-H] ‡ Chemical formula Metabolite VIP t -Test Cohen's d P q 132.102 C ₆ H ₁₃ NO ₂ L-Leucine † 5.130 <0.001 9.404.E-05 0.684 143.107 C ₈ H ₁₆ O ₂ Caprylic acid ‡ 1.162 <0.001 1.844.E-04 0.735 166.086 C ₉ H ₁₁ NO ₂ L-Phenylalanine † 2.639 0.192 0.090 0.200 182.081 C ₉ H ₁₁ NO ₃ L-Tyrosine † 2.564 <0.001 3.366.E-04 0.604 201.112 C ₁₀ H ₁₆ O ₄ cis-4-Decenedioic acid † 1.665 <0.001 1.358.E-06 1.132 205.097 C ₁₁ H ₁₂ N ₂ O ₂ L-Tryptophan † 1.925 0.402 0.140 0.129 217.179 C ₁₂ H ₂₄ O ₃ 3-Hydroxydodecanoic acid † 1.299 0.032 0.026 0.331 219.122 C ₁₀ H ₁₈ O ₅ 3-Hydroxysebacic acid † 1.116 <0.001 4.418.E-04 0.784 247.154 C ₁₂ H ₂₂ O ₅ 3-Hydroxydodecanedioic acid † 1.934 0.002 0.002 0.699 255.232 C ₁₆ H ₃₂ O ₂ Palmitic acid ‡ 5.647 0.863 0.254 0.026 275.185 C ₁₄ H ₂₆ O ₅ 3-Hydroxytetradecanedioic acid † 2.014 0.016 0.015 0.545 277.216 C ₁₈ H ₂₈ O ₂ Stearidonic acid † 1.751 0.005 0.005 0.563 279.232 C ₁₈ H ₃₀ O ₂ γ-Linolenic acid † 3.037 0.002 0.003 0.553 281.247 C ₁₈ H ₃₄ O ₂ Elaidic acid ‡ 1.177 0.003 0.004 0.457 282.279 C ₁₈ H ₃₅ NO Oleamide † 1.146 0.038 0.029 0.455 295.226 C ₁₈ H ₃₀ O ₃ 9-HOTE † 2.494 0.003 0.003 0.563 295.227 C ₁₈ H ₃₂ O ₃ 13S-Hydroxyoctadecadienoic acid ‡ 3.917 0.002 0.002 0.591 301.216 C ₂₀ H ₂₈ O ₂ All-trans-Retinoic acid † 1.954 0.027 0.022 0.373 313.155 C ₁₈ H ₂₀ N ₂ O ₃ Phenylalanylphenylalanine † 1.870 0.001 0.002 0.537 317.211 C ₂₀ H ₃₀ O ₃ 9-HEPE ‡ 2.008 0.120 0.066 0.271 319.227 C ₂₀ H ₃₂ O ₃ 12-HETE ‡ 3.646 0.028 0.023 0.409 321.242 C ₂₀ H ₃₄ O ₃ 15 (S) -Hydroxyeicosatrienoic acid ‡ 2.040 0.001 0.002 0.656 333.206 C ₂₀ H ₃₀ O4 Prostaglandin J ₂ ‡ 1.555 0.133 0.071 0.232 335.221 C ₂₀ H ₃₂ O ₄ 8,15-DiHETE ‡ 2.103 0.113 0.063 0.256 343.227 C ₂₂ H ₃₂ O ₃ 17-HDoHE ‡ 4.067 0.066 0.045 0.346 351.216 C ₂₀ H ₃₂ O ₅ Prostaglandin E ₂ ‡ 1.034 0.213 0.096 0.193 353.232 C ₂₀ H ₃₄ O ₅ 8-Isoprostaglandin F _2α ‡ 1.204 0.001 0.002 0.580 375.216 C ₂₂ H ₃₂ O ₅ Resolvin D ₁ ‡ 1.238 0.406 0.141 0.129 518.322 C ₂₆ H ₄₈ NO ₇ P LysoPC (18: 3) † 2.355 0.249 0.105 0.178 546.355 C ₂₈ H ₅₂ NO ₇ P LysoPC (20: 3) † 1.579 0.104 0.060 0.259

† is metabolite obtained in ESI-cation mode ( M / Z [M + H]), and ‡ is a metabolite obtained in ESI-anion mode ( M / Z [MH]). VIP is a variable importance measure. P -values were derived from an independent t-test between the control group (n = 88) and the breast cancer group (n = 84). The q -value is the P -value that corrected the error detection rate (FDR). Cohen's d is the magnitude of the effect on the comparison of the differences between the quantity means divided by the pooled standard deviation. If d = 0.20 we define the magnitude of the influence as “small”, d = 0.50 as “medium”, and d = 0.80 as “big”.

Identification of Downregulated Serum Metabolites in Control and Breast Cancer Groups m / z
[M + H] †
[M-H] ‡ Chemical formula Metabolite VIP t -Test Cohen's d P q 227.201 C ₁₄ H ₂₈ O ₂ Myristic acid ‡ 1.466 0.979 0.279 -0.004 253.216 C ₁₆ H ₃₀ O ₂ Palmitoleic acid ‡ 2.837 0.443 0.151 -0.117 271.227 C ₁₆ H ₃₂ O ₃ 3-Hydroxyhexadecanoic acid ‡ 1.350 0.002 0.003 -0.478 279.232 C ₁₈ H ₃₂ O ₂ Linoleic acid ‡ 5.202 0.328 0.122 -0.150 301.216 C ₂₀ H ₃₀ O ₂ Eicosapentaenoic acid ‡ 3.869 0.006 0.006 -0.428 303.232 C ₂₀ H ₃₂ O ₂ Arachidonic acid ‡ 8.133 <0.001 1.051.E-05 -0.752 305.247 C ₂₀ H ₃₄ O ₂ 8,11,14-Eicosatrienoic acid ‡ 3.368 0.003 0.003 -0.467 327.232 C ₂₂ H ₃₂ O ₂ Docosahexaenoic acid ‡ 9.506 <0.001 1.831.E-06 -0.833 329.247 C ₂₂ H ₃₄ O ₂ Docosapentaenoic acid ‡ 3.292 <0.001 9.613.E-05 -0.649 367.157 C ₁₉ H ₂₈ O ₅ S Dehydroepiandrosterone sulfate ‡ 1.314 0.065 0.044 -0.283 369.172 C ₁₉ H ₃₀ O ₅ S Androsterone sulfate ‡ 1.070 0.068 0.045 -0.281 468.307 C ₂₂ H ₄₆ NO ₇ P LysoPC (14: 0) † 1.575 0.026 0.022 -0.343 480.345 C ₂₄ H ₅₀ NO ₆ P LysoPC (P-16: 0) † 2.448 <0.001 2.913.E-04 -0.597 481.234 C ₂₅ H ₃₉ O ₇ P 1- (Docosahexaenoyl) -glycero-3-phosphate ‡ 1.291 0.008 0.008 -0.413 482.323 C ₂₃ H ₄₈ NO ₇ P LysoPC (15: 0) † 1.595 0.003 0.004 -0.455 494.322 C ₂₄ H ₄₈ NO ₇ P LysoPC (16: 1) † 2.092 0.036 0.028 -0.323 496.338 C ₂₄ H ₅₀ NO ₇ P LysoPC (16: 0) † 7.701 0.030 0.024 -0.335 506.359 C ₂₆ H ₅₂ NO ₆ P LysoPC (P-18: 1) † 1.161 <0.001 0.001 -0.561 510.355 C ₂₅ H ₅₂ NO ₇ P LysoPC (17: 0) † 2.444 <0.001 0.001 -0.561 520.339 C ₂₆ H ₅₀ NO ₇ P LysoPC (18: 2) † 3.546 0.025 0.021 -0.352 522.355 C ₂₆ H ₅₂ NO ₇ P LysoPC (18: 1) † 4.281 <0.001 2.122.E-05 -0.715 538.386 C ₂₇ H ₅₆ NO ₇ P LysoPE (22: 0) † 1.037 0.022 0.019 -0.353 542.322 C ₂₈ H ₄₈ NO ₇ P LysoPC (20: 5) † 1.461 0.994 0.282 -0.001 544.340 C ₂₈ H ₅₀ NO ₇ P LysoPC (20: 4) † 1.327 0.221 0.098 -0.192 548.370 C ₂₈ H ₅₄ NO ₇ P LysoPC (20: 2) † 1.342 <0.001 1.913.E-09 -1.094 568.337 C ₃₀ H ₅₀ NO ₇ P LysoPC (22: 6) † 3.962 <0.001 2.828.E-09 -1.066 570.354 C ₃₀ H ₅₂ NO ₇ P LysoPC (22: 5) † 2.259 <0.001 1.880.E-10 -1.171

† is metabolite obtained in ESI-cation mode ( M / Z [M + H]), and ‡ is a metabolite obtained in ESI-anion mode ( M / Z [MH]). VIP is a variable importance measure.

P -values were derived from an independent t-test between the control group (n = 88) and the breast cancer group (n = 84). The q -value is the P -value that corrected the error detection rate (FDR). Cohen's d is the magnitude of the effect on the comparison of the differences between the quantity means divided by the pooled standard deviation. If d = 0.20 we define the magnitude of the influence as “small”, d = 0.50 as “medium”, and d = 0.80 as “big”.

Experimental Example 4: Metabolic pathways associated with the development of breast cancer in subjects

To identify the most relevant pathways in selected metabolites, metabolic pathway analysis was performed using MetaboAnalyst 3.0, a web-based analysis module (FIG. 2).

All subjects with or without breast cancer had valine, leucine and isoleucine degradation at baseline; Valine, leucine and isoleucine biosynthesis; Tyrosine metabolism; Arachidonic acid metabolism; Phenylalanine, tyrosine and tryptophan biosynthesis; Phenylalanine metabolism; Tryptophan metabolism; Fatty acid metabolism; Linoleic acid and retinol metabolism were associated (Table 4)

Metabolic pathway analysis Metabolic pathways Hits P FDR Impact Valine, leucine and isoleucine degradation L-Leucine 1.216E-09 1.216E-08 0.022 Valine, leucine and isoleucine biosynthesis L-Leucine 1.216E-09 1.216E-08 0.013 Tyrosine metabolism L-Leucine 8.353E-09 3.341E-08 0.047 Arachidonic acid metabolism Arachidonic acid 1.420E-05 4.057E-05 0.260 Prostaglandin J2 Prostaglandin E2 Phenylalanine, tyrosine and tryptophan biosynthesis L-Tyrosine 1.890E-05 4.199E-05 0.008 L-Phenylalanine L-Tryptophan Phenylalanine metabolism L-Tyrosine 2.501E-05 5.001E-05 0.119 L-Phenylalanine Tryptophan metabolism L-Tryptophan 3.075E-04 5.125E-04 0.109 Fatty acid metabolism Palmitic acid 4.674E-04 6.678E-04 0.030 Linoleic acid metabolism 13S-Hydroxyoctadecadienoic acid 0.007 0.008 0.656 γ-Linolenic acid Linoleic acid Retinol metabolism Retinoic acid 0.036 0.040 0.175

Hits actually matches the metabolism uploaded to MetaboAnalyst. FDR is a P -value corrected for the false discovery rate. Impact is a path impact value calculated from path topology analysis.

The degradation and biosynthesis of valine, leucine and isoleucine were all associated with leucine, and the influence coefficients based on metabolic pathway analysis were 0.022 and 0.013, respectively.

Metabolic pathway analysis revealed that tyrosine belongs to tyrosine metabolism and the influence coefficient was 0.047.

Arachidonic acid (C20: 4n-6), prostaglandin J ₂ and prostaglandin E ₂ were associated with arachidonic acid metabolism and the influence coefficient was 0.260.

Phenylalanine, tyrosine and tryptophan biosynthesis were associated with tyrosine, phenylalanine and tryptophan, with an influence factor of 0.008. Phenylalanine metabolism was associated with tyrosine and phenylalanine and the influence factor based on metabolic pathway analysis was 0.119.

Palmitic acid was associated with fatty acid metabolism and the coefficient of influence was 0.030. Linoleic acid metabolism was associated with 13S-hydroxyoctadecadienoic acid, gamma linolenic acid (C18: 3n-6) and linoleic acid (C18: 2n-6), with an coefficient of influence of 0.656. Retinol metabolism was associated with retinolic acid and the coefficient of influence was 0.175.

Experimental Example 5: Association between the pattern and major metabolites according to the frequency of breast cancer

A correlation matrix, including age, weight, and major metabolites (major metabolites) according to the incidence of breast cancer is shown in FIG. 3.

To analyze the independent effects of different variables on the development of breast cancer, stepwise regression analysis was performed on the following variables: body weight, leucine, tyrosine, arachidonic acid, prostaglandin J ₂ , prostaglandin E ₂ , phenylalanine 12 metabolites (key metabolites), including tryptophan, palmitic acid, 13S-hydroxyoctadecadienoic acid, gamma linolenic acid, linoleic acid and retinolic acid.

The incidence of breast cancer in all subjects was independently affected by leucine, tyrosine, arachidonic acid, prostaglandin J ₂ , prostaglandin E ₂ and gammalinolenic acid.

The results show valine, leucine and isoleucine degradation; Valine, leucine and isoleucine biosynthesis; Tyrosine metabolism; Arachidonic acid metabolism; Phenylalanine, tyrosine and tryptophan biosynthesis; Phenylalanine metabolism; Tryptophan metabolism; Fatty acid metabolism; Clinical relevance of linoleic acid and retinol metabolism. The results suggest that dysregulation of the metabolic process is a key mechanism for the development of breast cancer.

The results also suggest that leucine, tyrosine, arachidonic acid, prostaglandin J ₂ , prostaglandin E ₂ and gammalinolenic acid are independent influencers of breast cancer incidence.

The foregoing description of the present invention is intended for illustration, and it will be understood by those skilled in the art that the present invention may be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the invention is indicated by the following claims, and it should be construed that all changes or modifications derived from the meaning and scope of the claims and their equivalents are included in the scope of the invention.

Claims (8)

(a) taking a biological sample from a subject;
(b) measuring a metabolite concentration of leucine (leucine), prostaglandin J ₂ (prostaglandin J ₂₎ and prostaglandin E ₂ (prostaglandin E ₂₎ in the biological sample; And
(c) measuring a metabolite concentration of phenyl-alanyl-phenylalanine (phenylalanylphenylalanine), phenylalanine (phenylalanine), tryptophan (tryptophan), stearyl donsan (Stearidonic acid), prostaglandin F _2α (prostaglandin F _2α) and retinol acid (retinoic acid) Making; And
(d) comparing the level of the measured metabolite with a measured value in a normal control sample, the method for evaluating the prognosis or risk of breast cancer comprising
When the level of the metabolite of step (b) and (c) is upregulated, the risk of developing breast cancer for 7 years from the time of the measurement is to evaluate the prognosis or risk of breast cancer. delete The method of claim 1,
In step (b), eicosapentaenoic acid (eicosapentaenoic acid), docosapentaenoic acid (docosapentaenoic acid), docosahexaenoic acid (docosahexaenoic acid), lysoPC (lysophosphatidylcholine) 18: 3, lysoPC 20: 3, lysoPC 20: 4 and lysoPC 20: 5, wherein the method further measures one or more metabolite concentrations selected from the group consisting of: delete Leucine (leucine), prostaglandin J ₂ (prostaglandin J _2), and prostaglandin E ₂ (prostaglandin E _2), phenyl-alanyl-phenylalanine (phenylalanylphenylalanine), phenylalanine (phenylalanine), tryptophan (tryptophan), stearyl donsan (Stearidonic acid), prostaglandin F _2α (prostaglandin F _2α) and retinol acid risk of developing breast cancer diagnosis kit comprises a metering device for the metabolites of (retinoic acid). The method of claim 5,
The quantification device is a chromatography and mass spectrometer kit. delete The method of claim 5,
Eicosapentaenoic acid, docosapentaenoic acid, docosahexaenoic acid, lysophosphatidylcholine 18: 3, lysoPC 20: 3, lysoPC 20: 4 and lysoPC 20 A kit further comprising a quantification device for one or more metabolites selected from the group consisting of: 5.

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