CN116465920B - Metabolic markers for diagnosing esophageal cancer - Google Patents

Metabolic markers for diagnosing esophageal cancer Download PDF

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CN116465920B
CN116465920B CN202310346063.1A CN202310346063A CN116465920B CN 116465920 B CN116465920 B CN 116465920B CN 202310346063 A CN202310346063 A CN 202310346063A CN 116465920 B CN116465920 B CN 116465920B
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esophageal cancer
esophageal
metabolic marker
metabolic
level
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CN116465920A (en
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林艳
赵妍
马长春
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Second Affiliated Hospital of Shantou University Medical College
Cancer Hospital of Shantou University Medical College
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Shantou University Medical College
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N24/00—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects
    • G01N24/08—Investigating or analyzing materials by the use of nuclear magnetic resonance, electron paramagnetic resonance or other spin effects by using nuclear magnetic resonance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry

Abstract

The invention discloses a metabolic marker for diagnosing esophageal cancer, and in particular relates to a metabolic marker comprising Sarcosine, preferably the metabolic marker further comprises N, N-Dimethylglycine, choline, methionine, bileacid, glutamate, trimethylamine, citrate, 3-Hydroxybutyrate and/or Histidine. Experiments prove that the metabolic markers show significant differences in esophageal cancer, and the diagnosis of esophageal cancer by using the markers has higher diagnosis efficacy.

Description

Metabolic markers for diagnosing esophageal cancer
Technical Field
The invention belongs to the field of biological medicine, and in particular relates to a metabolic marker for diagnosing esophageal cancer.
Background
Esophageal Cancer (EC) is a common malignancy of the gastrointestinal tract, and more than 90% are Esophageal Squamous Cell Carcinoma (ESCC). Early esophageal cancer symptoms are very hidden, and if diagnosis and treatment are carried out early, the survival rate of more than 85% in 5 years can be achieved; most patients are often found and diagnosed with the disease in the advanced stage, and the survival rate of 5 years is less than 20%. Screening of high-hair area populations typically employs "endoscopic iodine staining+biopsy," but invasive procedures result in poor compliance and low cost-effectiveness and increased false negative rates. Esophageal barium meal and CT imaging have radiation exposure and are easy to miss tiny focus. The accuracy of clinically traditional tumor markers such as CEA and CA199 is also poor. With the development of multiple groups of technology, researchers try to use various novel markers for diagnosis of esophageal cancer, such as serum miRNA, autoantibodies, somatic gene mutation, salivary exosomes, artificial intelligent sponge cells and the like, but the methods are limited by an advanced technology platform and high cost, and are difficult to be used for clinical large-scale screening. The latest Chinese esophageal cancer screening and early diagnosis and early treatment guidelines still show that no recommendable biomarker is used for screening or diagnosing esophageal cancer at the present stage due to insufficient evidence. Therefore, aiming at the important requirement of accurate diagnosis of esophageal squamous carcinoma, the novel method which is simple and noninvasive and can accurately diagnose esophageal squamous carcinoma in early stage is a key clinical problem which accords with national development planning and needs to be solved urgently.
Metabolic reprogramming is one of the important features of tumors, and this reprogramming results in a characteristic metabolic phenotype that can be used in early cancer diagnosis, such as the metabonomics (Metabolomics, metabonomics) method, which systematically measures a variety of metabolites, including nutrients, drugs, signaling mediators, and metabolites of these small molecules in blood, urine, tissue extracts, or other body fluids, as the most direct manifestation of the body phenotype.
Current metabonomics techniques are mainly proton nuclear magnetic resonance spectroscopy (ProtonNuclearMagnetic Resonance, 1 H-NMR) and mass spectrometry (MassSpectrometry, MS). The detection sensitivity of MS is high, but is limited to detecting easily ionized compounds, and the detection is long in time consumption, expensive in standard reagent and complex in sample pretreatment. 1 H-NMR is based on the irradiation of a sample with radio frequency pulses to excite the observed nuclei simultaneously to resonate, in order to quantitatively detect the micro-metabolite changes of the body occurring in pathophysiological states. In contrast to the MS, 1 H-NMR has unique advantages: sample pretreatment is simple, and reagents are cheap; the method has the advantages of stable detection, short time, high precision, good repeatability and no damage; the information of the hydrogen-containing metabolites can be obtained by single detection; the metabolite detection is flexible and efficient due to the multiple spectrum editing technology; advances in magnetic shielding and cryogenic technology have made nuclear magnetic resonance apparatus more compact and cheaper. The practice proves that the preparation method has the advantages that, 1 H-NMR is applicable to clinical multi-center large samples and longitudinal studies, whether tens of thousands of sample sizes or multi-center samples spanning many years, 1 the H-NMR detection is stable and non-dispersive, and is the first technology for screening and researching tumor metabonomics.
Disclosure of Invention
The invention aims to provide a novel method which is simple, convenient and noninvasive and can accurately diagnose esophageal cancer in an early stage.
In order to achieve the above purpose, the invention adopts the following technical scheme:
in one aspect, the invention provides the use of a reagent for determining the level of a metabolic marker in a sample, said metabolic marker comprising Sarcosine, in the preparation of a kit for predicting or diagnosing esophageal cancer.
Further, the metabolic markers further comprise N, N-Dimethylglycine, choline, methionine, bileacid, glutamate, trimethylamine, citrate, 3-Hydroxybutyrate and/or Histine.
The term "and/or" as used herein in phrases such as "a and/or B" is intended to include both a and B; a or B; a (alone); and B (alone). Likewise, the term "and/or" as used in phrases such as "A, B and/or C" is intended to encompass each of the following embodiments: A. b and C; A. b or C; a or C; a or B; b or C; a and C; a and B; b and C; a (alone); b (alone); and C (alone).
"Metabolic markers" or "metabolite markers" are generally used synonymously in the context of the present invention.
In the present invention, "metabolite" means any substance produced by metabolism or necessary for or involved in a specific metabolic process. The term metabolite includes signal transduction molecules and intermediates in chemical reactions that convert energy derived from food into useful forms, including but not limited to: sugars, fatty acids, amino acids, nucleosides, antioxidants, vitamins, cofactors, lipids, intermediates formed during cellular processes (cellular processes), and other small molecules.
In the present invention, the level or content of the metabolite may be one or more of the following: absolute or relative amounts or concentrations of metabolites; the presence or absence of a metabolite; a range of amounts or concentrations of the metabolite; minimum and/or maximum amounts or concentrations of metabolites; average amount or concentration of metabolite; and/or the median number or concentration of metabolites. As an alternative embodiment, the level for a combination of metabolites may also be a ratio of absolute or relative amounts or concentrations of two or more metabolites related to each other. Positive and negative reference levels for a particular disease state, phenotype or lack thereof of an appropriate metabolite may be determined by detecting the level of the desired metabolite in one or more appropriate patients, and such reference levels may be modified to fit a particular patient population (e.g., the reference levels may be age-matched so that a comparison may be made between the metabolite levels in a patient sample from an age and the reference levels for a particular disease state, phenotype or lack thereof in a particular age group). Alternatively, the reference level may be modified to adapt to a particular technique that may be used to detect metabolite levels in the biological sample (e.g., LC-MS, GC-MS, etc.), where the metabolite levels may vary based on the particular technique used.
The metabolic markers described herein may be used alone or in combination in a diagnostic test to assess esophageal cancer status in a subject. Esophageal cancer status includes the presence or absence of esophageal cancer. Esophageal cancer status may also include monitoring the course of esophageal cancer, e.g., monitoring disease progression. Based on the esophageal cancer status of the subject, additional procedures may be indicated, including, for example, additional diagnostic tests or therapeutic procedures.
The ability of a diagnostic test to correctly predict a disease state is typically measured in terms of accuracy of the assay, sensitivity of the assay, specificity of the assay, or "area under the curve" (AUC, e.g., area under the curve of the Receiver Operating Characteristic (ROC)). As used herein, accuracy is a measure of the fraction of misclassified samples. The degree of accuracy may be calculated as, for example, the total number of correctly classified samples in the test population divided by the total number of samples. Sensitivity is a measure of the "true positives" predicted to be positive by the test and can be calculated as the number of correctly identified esophageal cancer samples divided by the total number of esophageal cancer samples. Specificity is a measure of "true negativity" predicted to be negative by the test and can be calculated as the number of correctly identified normal samples divided by the total number of normal samples. AUC is a measure of the area under the receiver operating characteristic curve, which is the curve of sensitivity versus false positive rate (1-specificity). The larger the AUC, the more robust the predictive value tested. Other useful metrics for test utility include both "positive predictive value" which is a percentage of actual positives tested positive and "negative predictive value" which is a percentage of actual negatives tested negative. In a preferred embodiment, the level of the one or more metabolic markers in a sample derived from a subject having a different esophageal cancer status relative to a normal subject exhibits a statistically significant difference of at least p=0.05, e.g., p=0.05, p=0.01, p=0.005, p=0.001, etc., as determined relative to a suitable control. In other preferred embodiments, diagnostic tests using the metabolic markers described herein, alone or in combination, exhibit an accuracy of at least about 75%, e.g., an accuracy of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99%, or about 100%. In other embodiments, diagnostic tests using the metabolic markers described herein, alone or in combination, exhibit a specificity of at least about 75%, for example, a specificity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99%, or about 100%. In other embodiments, diagnostic tests using the metabolic markers described herein, alone or in combination, exhibit a sensitivity of at least about 75%, for example, a sensitivity of at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99%, or about 100%. In other embodiments, diagnostic tests using the metabolic markers described herein, alone or in combination, exhibit a specificity and sensitivity of each of at least about 75%, e.g., at least about 75%, about 80%, about 85%, about 90%, about 95%, about 97%, about 99% or about 100% (e.g., at least about 80% specificity and at least about 80% sensitivity, or e.g., at least about 80% specificity and at least about 95% sensitivity).
Although the biomarkers alone may be used in diagnostic applications for esophageal cancer, as shown herein, combinations of biomarkers may provide higher predictors of esophageal cancer status than the biomarkers alone. In particular, detecting multiple biomarkers may increase the accuracy, sensitivity, and/or specificity of a diagnostic test. The invention includes the individual metabolic markers and combinations of metabolic markers listed in these tables, as well as their use in the methods and kits described herein.
Further, the metabolic markers are a combination of Sarcosine, N-Dimethylglycine, choline, methionine, bileacid, glutamate, trimethylamine, citrate, 3-hydroxybutyl and Histine.
Further, the reagent determines the level of the metabolic marker in the sample by: chromatography, spectroscopy, mass spectrometry, fluorometry, electrophoresis, immunoaffinity, immunohybridization, immunochemistry, ultraviolet (UV) spectroscopy, fluorescence analysis, radiochemical analysis, near infrared (near IR) spectroscopy, nuclear magnetic resonance spectroscopy (NMR), light scattering analysis (LS), nephelometry.
Further, the reagent determines the level of the metabolic marker in the sample by nuclear magnetic resonance spectroscopy (NMR).
As used herein, the term "sample" refers to a biological sample obtained or derived from a source of interest as described herein. In some embodiments, the source of interest comprises an organism, such as an animal or a human. In some embodiments, the biological sample comprises biological tissue or fluid. In some embodiments, the biological sample may be or comprise bone marrow; blood; blood cells; ascites; tissue or fine needle biopsy samples; a body fluid containing cells; free floating nucleic acids; sputum; saliva; urine; cerebrospinal fluid peritoneal fluid; pleural fluid; feces; lymph; a skin swab; oral swab; a nasal swab; washings (washings) or lavages, such as catheter lavages or bronchoalveolar lavages; aspirate; scraping scraps; bone marrow specimens; tissue biopsy specimens; surgical specimens; faeces, other body fluids, secretions and/or excretions; and/or cells therein, and the like. In some embodiments, the biological sample is or comprises cells obtained from an individual. In some embodiments, the sample is a "primary sample" obtained directly from the source of interest by any suitable means. For example, in some embodiments, the primary biological sample is obtained by a method selected from the group consisting of: biopsy (e.g., fine needle aspiration or tissue biopsy), surgical tissue, collection of bodily fluids (e.g., blood, lymph, stool, etc.), and the like. In some embodiments, as will be apparent from the context, the term "sample" refers to a formulation obtained by processing (e.g., by removing one or more components of a primary sample and/or by adding one or more reagents to a primary sample). For example, filtration using a semipermeable membrane. Such "treated samples" may comprise, for example, nucleic acids or proteins extracted from the sample or obtained by subjecting the primary sample to techniques such as amplification or reverse transcription of mRNA, isolation and/or purification of certain components, and the like. As a preferred embodiment, the sample is blood. In a specific embodiment of the invention, the sample is serum.
Further, the esophageal cancer comprises esophageal squamous carcinoma and esophageal adenocarcinoma. In a specific embodiment of the invention, the esophageal cancer is esophageal squamous carcinoma.
In another aspect, the invention provides a kit for predicting or diagnosing esophageal cancer, the kit comprising reagents for determining the level of a metabolic marker in a sample, the metabolic marker comprising Sarcosine.
Further, the metabolic markers further comprise N, N-Dimethylglycine, choline, methionine, bileacid, glutamate, trimethylamine, citrate, 3-Hydroxybutyrate and/or Histine.
Further, the metabolic markers are a combination of Sarcosine, N-Dimethylglycine, choline, methionine, bileacid, glutamate, trimethylamine, citrate, 3-hydroxybutyl and Histine.
Further, the kit also comprises a pretreatment reagent for pretreating the sample.
In another aspect, the invention provides a system for predicting or diagnosing esophageal cancer comprising a computing device for determining a subject's risk of having esophageal cancer based on the level of a metabolic marker comprising Sarcosine.
Further, the metabolic markers further comprise N, N-Dimethylglycine, choline, methionine, bileacid, glutamate, trimethylamine, citrate, 3-Hydroxybutyrate and/or Histine.
Further, the metabolic markers are a combination of Sarcosine, N-Dimethylglycine, choline, methionine, bileacid, glutamate, trimethylamine, citrate, 3-hydroxybutyl and Histine.
Further, the system may further comprise any one or more of the following:
(1) The detection device is used for detecting the level of the metabolic marker; preferably, the detection may be performed for a plurality of non-esophageal cancer healthy persons, a plurality of patients and subjects;
(2) The reference device is used for receiving the level of the metabolic marker output by the detection device; more preferably, all esophageal cancer patients are set as esophageal cancer groups, all non-esophageal cancer healthy persons are set as non-esophageal cancer groups, and the above two groups are set as known grouping information by referring to the device;
(3) The comparison device is used for receiving the level of the metabolic marker output by the detection device and the known grouping information output by the reference device, analyzing and comparing the level detection result of the metabolic marker of the subject with the known grouping information data set, and judging whether the subject belongs to an esophageal cancer group or a non-esophageal cancer group.
In a preferred embodiment, the detection device is a nuclear magnetic resonance spectrometer, the reference device is a computer, and the comparison device is a computer.
The term "subject" refers to any animal (e.g., mammal), including, but not limited to, humans, non-human primates, dogs, cats, rodents, and the like. Further, the subject is a human subject. The terms "subject," "individual," and "patient" are used interchangeably herein. Thus, the terms "subject," "individual," and "patient" encompass individuals having cancer (e.g., esophageal cancer), including those who have undergone or are candidates for excision (surgery) to remove cancerous tissue.
In another aspect, the invention provides the use of a metabolic marker comprising Sarcosine in the construction of a computational model for predicting or diagnosing esophageal cancer.
Further, the metabolic markers further comprise N, N-Dimethylglycine, choline, methionine, bileacid, glutamate, trimethylamine, citrate, 3-Hydroxybutyrate and/or Histine.
Further, the metabolic markers are a combination of Sarcosine, N-Dimethylglycine, choline, methionine, bileacid, glutamate, trimethylamine, citrate, 3-hydroxybutyl and Histine.
Drawings
FIG. 1 is a graph showing metabolic pathway analysis results of changes in the evolution process of differential metabolites of tissues of patients suffering from esophageal squamous carcinoma and normal esophageal epithelium, normal mucosa of esophagus, early esophageal squamous carcinoma tissues and advanced esophageal squamous carcinoma tissues;
FIG. 2 is a graph showing the results of metabolic pathway analysis of changes in serum metabolite differences between patients with squamous cell carcinoma and healthy control subjects, as well as "preoperative tumor-bearing serum→postoperative convalescence serum→healthy subject serum";
FIG. 3 is a statistical graph of the differential expression of N, N-Dimethylglycine;
FIG. 4 is a statistical graph of differential expression of Choline;
FIG. 5 is a statistical graph of the differential expression of metanine;
FIG. 6 is a statistical graph of the differential expression of Bileacid;
FIG. 7 is a graph of differential expression statistics of Glutamate;
FIG. 8 is a statistical graph of the differential expression of Trimethylimine;
FIG. 9 is a statistical graph of differential expression of Citate;
FIG. 10 is a statistical graph of the differential expression of Sarcosine;
FIG. 11 is a statistical graph of differential expression of Histine;
FIG. 12 is a statistical graph of the differential expression of 3-hydroxybutyl;
FIG. 13 is a ROC diagram of Sarcosine for diagnosing esophageal squamous carcinoma;
FIG. 14 is a ROC graph of the combined diagnosis of esophageal squamous carcinoma in a training set of metabolome;
FIG. 15 is a ROC graph of the combined diagnosis of esophageal squamous carcinoma in validating a centralized metabolome;
FIG. 16 is a graph of cross-validation results of combined diagnosis of esophageal squamous carcinoma in validating a concentrated metabolome.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to examples, but it will be understood by those skilled in the art that the following examples are only for illustrating the present invention and should not be construed as limiting the scope of the present invention. The specific conditions are not noted in the examples and are carried out according to conventional conditions or conditions recommended by the manufacturer. The reagents or apparatus used were conventional products commercially available without the manufacturer's attention.
Example 1 screening and efficacy determination of metabolites relevant to diagnosis and prediction of esophageal squamous carcinoma
1. Experimental method
1. Study object and study design
Samples of a strict match of 54 esophageal squamous carcinoma patients on the clinic were collected from a second affiliated hospital of the university of Shanzhi medical school, including preoperative and postoperative 1 week serum (morning fasting serum), surgical tumor tissue and normal esophageal epithelial mucosal tissue 5cm distal to the foci of cancer, and complete clinical phenotype data. Serum from 126 healthy control populations was collected from the subject physical examination center. After the diagnosis marker model is constructed, body fluid of 25 patients with early esophageal squamous carcinoma is taken for model efficacy verification. After sample collection, the samples are subjected to pretreatment, split charging and quick freezing by liquid nitrogen, and then stored in a refrigerator at the temperature of minus 80 ℃, and dry ice is used as a cold chain in the transportation process.
Nano-row standard:
1) Esophageal squamous carcinoma is clearly diagnosed via pathological biopsy; no obvious lower digestive tract symptoms and organic lesions of the stomach and small intestine; no other systemic serious diseases and the like; no radiotherapy/chemotherapy is performed within one year, and no antibiotics are used.
2) Each physical examination index of healthy people (matched with baseline data such as age, sex, BMI and the like) is normal, and abnormality is not found in the endoscopy; there was no history of systemic disease and drug treatment 3 months prior to the study.
3) Clinical sample use in this project was all approved by the ethics committee, and subjects were solicited for consent and signed informed consent prior to sampling.
2. Tissue and serum sample preparation and NMR spectroscopic detection
2.1 metabolite extraction
1) Phosphate heavy water buffer solution PBS/D 2 And (3) preparing:
pH7.4，K 2 HPO 4 and NaH 2 PO 4 The molar ratio of (2) was 4:1. Wherein, the serum PBS90mM, contain 0.9% NaCl; tissue PBS150mM, containing 0.05% TSP.
2) Preparing tissue extract:
methanol/chloroform/water was formulated as extractant in a 2:2:3 ratio. Weighing a certain amount of tissue, cutting into small blocks with the diameter of 1-3 mm, placing the small blocks into a 5mL round bottom centrifuge tube, adding a proper amount of extractant and grinding beads, and grinding at 60Hz for 60-90s. The homogenate was then transferred to a new 10mL glass tube, the remaining required extractant was added, the lid was closed, and vortexed for 60s. After uniform mixing, transferring the homogenate to a new 5mL sharp-bottomed centrifuge tube; standing on ice for 15min, centrifuging at 10000rpm at 4 ℃ for 10min, taking supernatant again, and transferring to a new labeled 5mL pointed bottom centrifuge tube. The lid was opened and placed under flowing nitrogen to remove methanol (the degree of drop in the liquid surface was observed every 10 minutes until the liquid surface was no longer lowered). The obtained liquid is frozen at-80deg.C, frozen completely, and freeze-dried overnight. Dissolving the lyophilized tissue powder in 550 μLPBS/D 2 O buffer solution is fully and uniformly vortex and centrifuged for 5 minutes at 10000rpm at 4 ℃, 500 mu L of supernatant is taken and transferred into a nuclear magnetic tube with 5mm for testing.
3) Serum preparation:
400 mu L of serum and 200 mu L of serum PBS are mixed uniformly by vortex; centrifuge at 10000rpm for 10min at 4deg.C, take 550 μL of supernatant, place in 5mm nuclear magnetic tube and sample.
2.2 metabolites 1 H-NMR detection
Acquisition of one dimension with Bruker600MHz spectrometer 1 H-NMR spectrum. The tissue extract was prepared using a NOESYGPPR1D spin echo pulse sequence: [ RD-90 ° -t1-90 ° -tm-90 ° -ACQ]Samples were taken and serum was collected using cpgpr 1D sequence: [ RD-90 ° - (τ -180 ° - τ) n-ACQ]Sampling is performed. The water peak signal was suppressed using a standard pre-saturation pulse sequence to obtain a free decay signal (FID signal). The FID signal is converted into a one-dimensional NMR spectrogram through Fourier transformation, and chemical shift values are adjusted by taking TSP signal peaks or lactic acid peaks as internal standard 0 points, so that corresponding samples are obtained 1 H-NMR spectrum.
2.3 1 Pretreatment of H-NMR data, spectral analysisAnalysis
The original NMR spectrum is pre-processed by using MestRenova nuclear magnetic resonance spectrum processing software (V14.0 version) due to the problems of large signal quantity, complex noise and the like, and comprises Fourier transformation, phase correction, baseline correction, frequency calibration and spectrum peak attribution. All spectra were multiplied by an exponential window function with a broadening factor of 1Hz when fourier transformed to improve the signal to noise ratio. The chemical shift of the metabolite is determined by an internal standard TSP (delta 0.00 ppm) or lactic acid peak (delta 1.33 ppm), the spectrum in the range of delta 0-9 ppm is subjected to data dimension reduction by piecewise integration every 0.002ppm, the peak intensity of 4.6-5.2 ppm is set to 0 before integration to eliminate the influence of residual water peak on surrounding spectrum peaks, and then the spectrogram is subjected to full spectrum normalization.
3. Potential biomarker screening
3.1 statistical analysis of multivariate variables
And (3) normalizing each group of data in the data matrix according to the total integral area, importing the normalized data into SIMCA14.1 software, and performing Paretor conversion (Paretocaling) to normalize the data so as to eliminate the dimensional relationship among variables. And filtering the signals irrelevant to the model classification and the orthogonal signals by adopting an orthogonal partial least squares discriminant analysis (OPLS-DA) to obtain an OPLS-DA model. And further Cross-Checking (CV) the quality of the model and permutation test (Permutiontest) to verify the validity of the model.
3.2 Metabolic marker screening
Potential markers were screened based on the model's variable importance projection values (VIP values), the statistical differences in metabolites between groups (corrected P values < 0.05). SPSS26.0 is used for non-parametric test and subject work curve (ROC) analysis, and the combination of pattern recognition and machine learning methods is used for extracting metabolites contributing to classification, determining sensitivity and specificity of the metabolites, constructing a diagnosis model and testing the accuracy of early esophageal squamous cell carcinoma prediction.
4. Key metabolic pathways and diagnostic metabolic markers (groups) for esophageal squamous carcinoma
4.1 analysis of key metabolic pathways during esophageal squamous carcinoma evolution
Characteristic variables obtained by a pattern recognition method are combined with the attribution information of an NMR spectrogram to clearly determine characteristic metabolites causing category differences, so that the early screening and standardization of esophageal squamous carcinoma has objective basis. By combining biochemical, molecular biological, pathological and pathophysiological knowledge, HMDB5.0 (TheHumanMetabolome Database) database and other information and utilizing MetaboAnalyst5.0 database and KEGGPathway analysis, the possible mechanism of occurrence and development of esophageal squamous carcinoma is analyzed by using the metabolic pathway changed from 'normal mucosa of esophagus, early stage esophageal squamous carcinoma tissue, progressive stage esophageal squamous carcinoma tissue' evolution process and 'preoperative tumor stage body fluid, postoperative recovery stage body fluid, healthy body fluid' in body fluid.
4.2 evaluation of diagnostic predictive efficacy of Metabolic markers (groups)
1) And (3) respectively calculating the area under the working curve (AUC) of the subjects of the differential metabolites in the tissues and the body fluid (serum), and combining algorithms such as a Support Vector Machine (SVM), a Random Forest (RF), partial least squares discriminant analysis (PLS-DA) and the like to obtain a plurality of alternative diagnosis models.
2) And then, from tissues (the origin of esophageal cancer occurrence), selecting the differential metabolite which can be reflected in serum and has the optimal AUC value from the tissues as a diagnosis marker.
3) Monte Carlo cross-validation (MCCV) was used to further verify the predictive ability of the diagnostic model to early stage esophageal squamous cell carcinoma.
2. Experimental results
1. The metabolic changes of alanine, aspartic acid and glutamic acid are throughout the whole course in the whole course of the changes of normal mucosa of esophagus, early esophageal squamous carcinoma tissue, evolution process of esophageal squamous carcinoma tissue in the progress period, preoperative tumor-bearing body fluid in body fluid, postoperative recovery period body fluid and body fluid of healthy person in the tissue, specifically:
1) Analysis of differential metabolites of tissues of patients suffering from esophageal squamous carcinoma from normal esophageal epithelium, and metabolic pathways altered during the evolution of normal mucosa of esophagus, early esophageal squamous carcinoma tissue, advanced esophageal squamous carcinoma tissue, as shown in fig. 1, including alanine, aspartic acid and glutamic acid metabolism, glycine, serine and threonine metabolism, arginine and proline metabolism, glutathione metabolism, biosynthesis of phenylalanine, tyrosine and tryptophan, valine, leucine and isoleucine metabolism, histidine metabolism, taurine and hypotaurine metabolism, riboflavin metabolism, the citric acid cycle (TCA cycle), fatty acid biosynthesis, pyruvic acid metabolism and other important metabolic pathways. 2) Serum metabolite differences between patients with esophageal squamous carcinoma and healthy control groups, and metabolic pathways changed from preoperative tumor-bearing serum to postoperative convalescence serum to healthy serum (as shown in fig. 2), include changes in fat metabolism such as alanine, aspartic acid and glutamic acid metabolism, glycine, serine and threonine metabolism, arginine and proline metabolism, glutathione metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, valine, leucine and isoleucine metabolism, histidine metabolism, and citric acid cycle (TCA cycle) metabolism, and also changes in cysteine and methionine metabolic pathways, and in inositol phosphate metabolism, ketone body synthesis and degradation, glycerolipid metabolism, glycerophospholipid metabolism, butyric acid metabolism, and the like, which occur in the tissues described in 5.1.
2. And (3) comparing the changed key metabolites in tissues (the origin of esophageal cancer occurrence), screening out the differential metabolites with optimal AUC values, which can be reflected in body fluid, as diagnostic markers, and constructing a serum diagnostic model.
The serum diagnosis model metabolic marker comprises one or a combination of several of N, N-Dimethylglycine, choline, methionine, bileacid, glutamate, trimethylamine, citrate, sarcosine and 3-Hydroxybutyrate, histidine, or a ratio of metabolites. The differences in expression of each metabolite are shown in FIGS. 3-12, the Sarcosine diagnostic efficacy data is shown in FIG. 13, and the efficacy of the serum metabolome in combination with diagnosis of esophageal squamous carcinoma is shown in FIG. 14.
3. Serum sample data of 25 patients with early esophageal squamous carcinoma and 87 healthy control groups are taken, and the serum marker group constructed by the invention is used for verification, and the diagnosis and prediction efficacy are shown in fig. 15 and 16:
serum marker panel: the diagnosis and prediction efficiency is good, and the coverage of the detected tissue differential metabolites is higher. The AUC of the serum marker set combined diagnosis of early esophageal squamous carcinoma can reach 1 (shown in figure 15), and the diagnostic efficacy of the serum marker set combined diagnosis of early esophageal squamous carcinoma is superior to that of single markers. The average accuracy of the prediction of early esophageal squamous carcinoma based on 100 cross-verifications was 0.998 (as shown in fig. 16).
Although specific embodiments of the invention have been described in detail, those skilled in the art will appreciate that: many modifications and variations of the details are possible in light of the above teachings, and such variations are within the scope of the invention. The full scope of the invention is given by the appended claims and any equivalents thereof.

Claims (10)

1. A system for predicting or diagnosing esophageal cancer, comprising a computing device for determining a subject's risk of having esophageal cancer based on the level of a metabolic marker that is a combination of Sarcosine, N-Dimethylglycine, choline, methionine, bile acid, glutamate, trimethylamine, citrate, 3-Hydroxybutyrate, and Histidine, said system comprising:
(1) The detection device is used for detecting the level of the metabolic marker;
(2) The reference device is used for receiving the level of the metabolic marker output by the detection device;
(3) The comparison device is used for receiving the level of the metabolic marker output by the detection device and the known grouping information output by the reference device, analyzing and comparing the level detection result of the metabolic marker of the subject with the known grouping information data set, and judging whether the subject belongs to an esophageal cancer group or a non-esophageal cancer group.
2. The system of claim 1, wherein the system determines the level of the metabolic marker in the sample by: chromatography, mass spectrometry, fluorometry, electrophoresis, immunoaffinity, immunohybridization, immunochemistry, ultraviolet spectroscopy, fluorescence analysis, radiochemical analysis, near infrared spectroscopy, nuclear magnetic resonance spectroscopy, light scattering analysis, or nephelometry.
3. The system of claim 2, wherein the system determines the level of the metabolic marker in the sample by nuclear magnetic resonance spectroscopy.
4. The system of claim 1, wherein the sample used by the system is blood.
5. The system of claim 1, wherein the detection device is capable of detecting a plurality of non-esophageal cancer healthy persons, a plurality of patients and subjects.
6. The system of claim 1, wherein the reference device sets all esophageal cancer patients as esophageal cancer groups, all non-esophageal cancer healthy persons as non-esophageal cancer groups, and both groups as known grouping information.
7. The system of claim 1, further comprising a pretreatment reagent for pretreating the sample.
8. The system of any one of claims 1-7, wherein the esophageal cancer comprises esophageal squamous carcinoma or esophageal adenocarcinoma.
9. The system of claim 8, wherein the esophageal cancer is esophageal squamous carcinoma.
10. The application of the metabolic marker in constructing a calculation model for predicting or diagnosing esophageal cancer is characterized in that the metabolic marker is a combination of Sarcosine, N-Dimethylglycine, choline, methionine, bile acid, glutamate, trimethylamine, citrate, 3-hydroxybutyl rate and Histidine.
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