CN116500270A - Early warning metabolic marker for malignant esophageal lesions - Google Patents
Early warning metabolic marker for malignant esophageal lesions Download PDFInfo
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- CN116500270A CN116500270A CN202310786064.8A CN202310786064A CN116500270A CN 116500270 A CN116500270 A CN 116500270A CN 202310786064 A CN202310786064 A CN 202310786064A CN 116500270 A CN116500270 A CN 116500270A
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- acid
- esophageal
- metabolic
- metabolic pathway
- early warning
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Abstract
The invention provides an early warning metabolic marker for malignant esophageal lesions. Early warning metabolic markers for esophageal malignant lesions include multiple of 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, ribose, phosphatidylserine, leucyl glutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxoretinoic acid, dihydroorotic acid, azelaic acid, cetrimide, sebacic acid, 1, 2-dipalmitoyl glycerophospholipidylcholine, eicosatrienoic acid (20:3N 9), 1-oleoyl glycerophospholipidylcholine, pentosan, chenodeoxycholic acid, N-methyl proline, undecanoic acid, glucosyl-N-neuroyl sphingosine (d18:1/24:1), galactonic acid. The marker provided by the application can provide accurate screening conditions for screening esophageal cancers. The marker provided by the application can effectively increase the accuracy of early warning of esophageal cancer.
Description
Technical Field
The invention relates to the field of clinical examination, in particular to a metabolic marker for early warning of various malignant esophageal lesions.
Background
The esophagus is a key organ that helps transport food into the stomach, which belongs to the digestive system. More than 90% of esophageal cancers in China belong to esophageal squamous cell carcinoma. A series of precancerous lesion stages are experienced before esophageal squamous carcinoma develops: mild dysplasia, moderate dysplasia, severe dysplasia, and carcinoma in situ. All of the above-mentioned precancerous lesions are believed to increase the risk of progression to invasive squamous carcinoma, where severe dysplasia, carcinoma in situ, and esophageal squamous carcinoma are collectively referred to as esophageal malignant lesions. Although decades of esophageal cancer-related studies have been conducted, the main causative factors of esophageal cancer have remained elusive until now, and no effective intervention targets have been found to achieve primary prevention (etiology prevention). Therefore, a secondary prevention strategy taking screening and early diagnosis and early treatment as cores becomes a main grip for preventing and controlling esophageal cancer in China. Currently, endoscopic esophageal mucosa iodine staining and indicative biopsy are gold standard technical means for early esophageal cancer and precancerous lesion screening and diagnosis in China. The means is as follows: in the upper gastrointestinal endoscopy, the esophageal mucosa is dyed by spraying iodine liquid, and then the possibility of the existence of the tumor is judged according to the color depth, the color range and the edge of the esophageal mucosa.
In the existing mode, screening work for high risk groups has been developed in order to increase the probability of finding early cancer patients. According to the national expert consensus for early diagnosis and early treatment of esophageal cancer, the endoscope screening strategy in China mainly comprises the following steps: screening work is carried out on the crowd aged 40-69 years in the high-incidence area of the esophagus cancer; and evaluating high-risk factors of the esophageal cancer of the daily endoscope outpatient in the low-risk area of the esophageal cancer, and carrying out screening work aiming at high-risk groups in the high-risk factors. However, even if screening work is performed in a high-incidence area, the absolute detection rate of malignant lesions of esophagus is still low (about 2%), so that most of the participants cannot directly benefit from the current endoscopy, and are faced with a series of risks such as perforation, bleeding, psychological trauma and the like, and a great deal of manpower and material resources are consumed, thus causing a heavy economic burden to individuals and local governments. Therefore, the method effectively identifies high-risk individuals in the age-appropriate population, develops targeted screening, has important significance for accurate prevention and control of esophageal cancer, and is also a development direction in the future.
At present, the risk assessment and prediction model construction aiming at the esophageal cancer is mainly based on macroscopic variables of the traditional questionnaires, and the early warning biomarker for identifying and incorporating the malignant lesions of the esophagus is expected to further improve the distinguishing capability of the prediction model.
The invention provides an early warning metabolic marker for malignant esophageal lesions and application thereof, and the marker has good performance in the aspect of identifying malignant esophageal lesions and can assist in accurate screening of esophageal cancers in China.
Furthermore, there are differences in one aspect due to understanding to those skilled in the art; on the other hand, since the applicant has studied a lot of documents and patents while making the present invention, the text is not limited to details and contents of all but it is by no means the present invention does not have these prior art features, but the present invention has all the prior art features, and the applicant remains in the background art to which the right of the related prior art is added.
Disclosure of Invention
With the continuous and intensive research, it is gradually recognized that malignant tumors are indistinguishable from metabolic abnormalities in the body: disorders of energy metabolism of cells may cause deregulation of cell proliferation. Previous metabonomics studies have reported that abnormalities in specific metabolite levels are associated with progression of esophageal cancer, such as phosphatidylserine, phosphatidic acid, phosphatidylcholine, phosphatidylinositol, phosphatidylethanolamine, sphinganine-1, glycolic acid, oxalic acid, glyceric acid, malic acid, alpha-tocopherol, tryptophan, citrulline, l-carnitine, lysine, acetylcarnitine, and the like. However, most of researches are carried out on the basis of hospitals, and most of esophageal cancer patients are middle and late stages, on one hand, selection bias of research samples exists, and on the other hand, the problem of reverse causality of abnormal metabolite levels and malignant lesions of the esophagus is difficult to avoid, so that the crowd representativeness and the authenticity of past research results still need to be further verified.
At present, early warning biomarkers for esophageal cancer are relatively few, and the invention provides a group of metabolic markers suitable for early diagnosis of esophageal cancer.
Aiming at the defects of the prior art, the invention provides an early warning metabolic marker for malignant esophageal lesions. Early warning metabolic markers for esophageal malignant lesions include multiple of 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, ribose, phosphatidylserine, leucyl glutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxoretinoic acid, dihydroorotic acid, azelaic acid, cetrimide, sebacic acid, 1, 2-dipalmitoyl glycerophospholipidylcholine, eicosatrienoic acid (20:3N 9), 1-oleoyl glycerophospholipidylcholine, pentosan, chenodeoxycholic acid, N-methyl proline, undecanoic acid, glucosyl-N-neuroyl sphingosine (d18:1/24:1), galactonic acid.
According to a preferred embodiment, the esophageal malignant lesion early warning metabolic marker is determined using a blood-derived sample. Preferably, the metabolite is derived from serum in the blood.
Malignant esophageal lesions are the phenomenon of canceration of normal esophageal tissues of a body caused by the action of various cancerogenic substances and cancerogenic factors. Malignant lesions of the esophagus include severe dysplasia of the esophagus, carcinoma in situ of the esophagus and squamous carcinoma of the esophagus.
According to a preferred embodiment, the early warning metabolic markers of esophageal malignant lesions include 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, phosphatidylserine, leucyl glutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxotretinoin, dihydroorotic acid, azelaic acid, cetylated carnitine, sebacic acid, eicosatrienoic acid (20:3N 9), 1-oleoyl glycerophospholipidcholine, pentose acid, chenodeoxycholic acid, N-methyl proline, undecanoic acid, glucosyl-N-neuroyl sphingosine (d18:1/24:1), galactaric acid.
According to a preferred embodiment, the early warning metabolic marker of esophageal malignant lesions comprises 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, phosphatidylserine, leucyl glutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxoretinoic acid, dihydroorotic acid, azelaic acid, cetylated carnitine, sebacic acid, eicosatrienoic acid (20:3N 9), 1-oleoyl glycerophospholipidcholine.
According to a preferred embodiment, the early warning metabolic marker of esophageal malignant lesions comprises 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, phosphatidylserine, leucyl glutamine.
According to a preferred embodiment, the early warning metabolic marker of esophageal malignant lesions comprises 4-cholesten-3-one, heptanoic acid (7:0), glycerophosphatidylinositol, N6-methyladenosine.
The present invention provides a marker for marking malignant lesions of esophagus, which is a differentially expressed metabolite based on a sterol metabolic pathway, a phospholipid metabolic pathway, a purine metabolic pathway, a pentose metabolic pathway, a phospholipid metabolic pathway, a dipeptide metabolic pathway, a phosphatidylserine metabolic pathway, a vitamin a metabolic pathway, a pyrimidine metabolic pathway, a fatty acid metabolic pathway, a phosphatidylcholine metabolic pathway, a long chain polyunsaturated fatty acid metabolic pathway, a lysophospholipid metabolic pathway, a partially characterized compound metabolic pathway, a primary bile acid metabolism, a urea cycle/arginine/proline metabolic pathway, a medium chain fatty acid metabolic pathway, a hexosyl ceramide metabolic pathway, and a fructose/mannose/galactose metabolic pathway.
Preferably, the metabolite differentially expressed in the sterol metabolic pathway refers to a metabolite whose differential expression occurs in the sterol metabolic pathway under the influence of 4-cholesten-3-one expression.
Preferably, said metabolite being differentially expressed in the medium chain fatty acid metabolic pathway refers to a metabolite which is differentially expressed in the medium chain fatty acid metabolic pathway under the influence of heptanoic acid (7:0) and/or undecanoic acid expression.
Preferably, the metabolite differentially expressed in the phospholipid metabolic pathway refers to a metabolite whose differential expression occurs in the phospholipid metabolic pathway under the influence of the expression of glycerophosphatidylinositol or phosphatidylserine.
Preferably, the metabolite differentially expressed in the purine (adenine-containing) metabolic pathway refers to a metabolite which is differentially expressed in the purine metabolic pathway under the influence of N6-methyladenosine expression.
Preferably, the metabolite differentially expressed in the pentose metabolic pathway refers to a metabolite whose differential expression occurs in the pentose metabolic pathway under the influence of ribose expression.
Preferably, the metabolite differentially expressed in the dipeptide metabolic pathway refers to a metabolite that is differentially expressed in the dipeptide metabolic pathway as affected by leucyl glutamine expression.
Preferably, the metabolite differentially expressed in the phosphatidylserine metabolic pathway refers to a metabolite which is differentially expressed in the phosphatidylserine metabolic pathway under the influence of 1-stearoyl-2-oleoyl glycerophosphatidylserine expression.
Preferably, the metabolites differentially expressed in the vitamin a metabolic pathway are those which are differentially expressed in the vitamin a metabolic pathway as affected by the expression of 4-oxoretinoic acid.
Preferably, the metabolite differentially expressed in the pyrimidine (orotic acid-containing) metabolic pathway refers to a metabolite which is differentially expressed in the pyrimidine (orotic acid-containing) metabolic pathway as affected by the expression of dihydroorotic acid.
Preferably, said metabolite differentially expressed in the fatty acid (dicarboxylic acid) metabolic pathway refers to a metabolite which is differentially expressed in the fatty acid (dicarboxylic acid) metabolic pathway under the influence of azelaic acid and/or sebacic acid expression.
Preferably, the metabolite differentially expressed in the fatty acid (acyl carnitine, monounsaturated) metabolic pathway refers to a metabolite which is differentially expressed in the fatty acid (acyl carnitine, monounsaturated) metabolic pathway under the influence of the expression of sirnoyl carnitine.
Preferably, the metabolite differentially expressed in the phosphatidylcholine metabolic pathway refers to a metabolite which is differentially expressed in the phosphatidylcholine metabolic pathway under the influence of 1, 2-dipalmitoyl glycerophosphorylcholine expression.
Preferably, the metabolites that are differentially expressed in the metabolic pathways of long chain polyunsaturated fatty acids (n 3 and n 6) are those that are differentially expressed in the metabolic pathways of long chain polyunsaturated fatty acids (n 3 and n 6) under the influence of eicosatrienoic acid (20:3n9) expression.
Preferably, the metabolite differentially expressed in the lysophospholipid metabolic pathway refers to a metabolite which is differentially expressed in the lysophospholipid metabolic pathway under the influence of the expression of 1-oleoyl glycerophosphorylcholine.
Preferably, the metabolite that is differentially expressed in the partially characterized compound metabolic pathway refers to a metabolite that is differentially expressed in the partially characterized compound metabolic pathway as affected by pentose acid expression.
Preferably, the metabolite differentially expressed in the primary bile acid metabolic pathway refers to a metabolite whose differential expression occurs in the primary bile acid metabolic pathway under the influence of chenodeoxycholic acid expression.
Preferably, the metabolite differentially expressed in the urea cycle/arginine/proline metabolic pathway refers to a metabolite which is differentially expressed in the urea cycle/arginine/proline metabolic pathway as affected by N-methyl proline expression.
Preferably, the metabolite that is differentially expressed in the hexosyl ceramide metabolic pathway is a metabolite that is differentially expressed in the hexosyl ceramide metabolic pathway under the influence of the expression of glucosyl-N-ceramide (d18:1/24:1).
Preferably, the metabolites differentially expressed in the fructose/mannose/galactose metabolic pathway refer to metabolites that are differentially expressed in the fructose/mannose/galactose metabolic pathway as affected by the expression of galactosylic acid.
The present invention provides a combination of metabolic pathways for characterizing malignant lesions of the esophagus. The metabolic pathway is a differential metabolic pathway in the blood sample. The metabolic pathway comprises a plurality of sterols, medium chain fatty acids, phospholipid metabolism, purine metabolism, pentose metabolism, phospholipid metabolism, dipeptides, phosphatidylserine, vitamin a metabolism, pyrimidine metabolism, fatty acid metabolism, phosphatidylcholine, long chain polyunsaturated fatty acids, lysophospholipids, partially characterized compounds, primary bile acid metabolism, urea cycle/arginine/proline metabolism, hexosyl ceramide, fructose/mannose/galactose metabolism.
The invention provides application of an early warning metabolic marker of malignant esophageal lesions in preparation of a diagnostic reagent or a diagnostic kit, wherein the marker can be one of the following compositions 1,2, 3, 4, 5 or 6:
composition 1: 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, ribose, phosphatidylserine, leucylglutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxoretinoic acid, dihydroorotic acid, azelaic acid, cetyloxycarnitine, sebacic acid, 1, 2-dipalmitoyl glycerophospholipidcholine, eicosatrienoic acid (20:3N 9), 1-oleoyl glycerophospholipidcholine, valeric acid, chenodeoxycholic acid, N-methylproline, undecanoic acid, glucosyl-N-neuroyl sphingosine (d18:1/24:1), galactaric acid;
composition 2: 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, phosphatidylserine, leucylglutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxoretinoic acid, dihydroorotic acid, azelaic acid, cetrimide, sebacic acid, eicosatrienoic acid (20:3N 9), 1-oleoyl glycerophospholipidylcholine, pentose acid, chenodeoxycholic acid, N-methylproline, undecanoic acid, glucosyl-N-ceramide (d18:1/24:1), galactonic acid;
composition 3: 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, phosphatidylserine, leucylglutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxoretinoic acid, dihydroorotic acid, azelaic acid, cetrimide, sebacic acid, eicosatrienoic acid (20:3N 9), 1-oleoyl glycerophospholipidylcholine;
composition 4: 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, phosphatidylserine, leucyl glutamine;
composition 5: 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidylinositol, N6-methyl adenosine;
composition 6: is a differential metabolic pathway in a blood sample comprising sterols, medium chain fatty acids, phospholipid metabolism, purine metabolism, pentose metabolism, phospholipid metabolism, dipeptides, phosphatidylserine, vitamin a metabolism, pyrimidine metabolism, fatty acids, fatty acid metabolism, fatty acids, phosphatidylcholine, long chain polyunsaturated fatty acids, lysophospholipids, partially characterized compounds, primary bile acid metabolism, urea cycle/arginine/proline metabolism, medium chain fatty acids, hexosyl ceramide, fructose/mannose/galactose metabolism.
According to a preferred embodiment, the malignant esophageal lesions comprise severe dysplasia of the esophagus, carcinoma in situ of the esophagus, and squamous carcinoma of the esophagus. Severe dysplasia of the esophagus means that the atypical cells involve more than 2/3 of the epithelium but not the whole epithelium. Esophageal carcinoma in situ refers to the involvement of abnormal cells in the full epithelium without breakthrough of the basal membrane. Esophageal squamous carcinoma refers to squamous cell carcinoma of the esophagus (breakthrough of basal lamina, invasion of lamina propria).
According to a preferred embodiment, the esophageal malignant lesion is an early esophageal malignant lesion. Early esophageal malignancy refers to cancer cells that only involve superficial areas above the submucosa.
The present invention provides the use of an agent for determining the level of a metabolite in the preparation of a kit for use in a method for determining whether an individual has or is at risk of having esophageal cancer, said method comprising the steps of:
obtaining a biological sample from an individual; determining the level of one or more of 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, ribose, phosphatidylserine, leucylglutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxotretinoin, dihydroorotic acid, azelaic acid, cetyloxycarnitine, sebacic acid, 1, 2-dipalmitoyl glycerophospholipidylcholine, eicosatrienoic acid (20:3n9), 1-oleoyl glycerophospholipidylcholine, pentose acid, chenodeoxycholic acid, N-methyl proline, undecanoic acid, glucosyl-N-neurolysphingosine (d18:1/24:1), galacturonic acid in a biological sample from the subject; comparing the level of the metabolite measured above with a metabolite reference level; differential metabolites were obtained.
The marker provided by the application can provide accurate screening conditions for screening esophageal cancers. The marker provided by the application can effectively increase the accuracy of early warning of esophageal cancer. The marker provided by the application can contribute to promoting the accuracy of the esophageal cancer screening mode in China, and can improve the detection rate of early esophageal cancer and precancerous lesions while saving medical and social resources. The invention relies on a prospective screening crowd queue and a high-flux metabonomics technology platform, aims to select a serum metabolite combination with early warning value of malignant lesions of esophagus, and is applied to identification of individuals with high risk of malignant lesions of esophagus before downstream endoscopy in a screening scene.
Metabonomics is a science of qualitatively and quantitatively analyzing biological samples (such as plasma, serum, urine, feces, saliva, etc.) or all small molecule metabolites (such as amino acids, fatty acids, lipids, etc.) in cells, and finding the relative relationship between the metabolites and pathophysiological changes. Since in vivo information transfer is stepwise in terms of DNA, mRNA, protein, metabolite, cell, tissue, organ, individual direction, metabolomics can be seen as an extension and manifestation of genomics and proteomics. Genomics and proteomics, while revealing inherent differences in organisms, benefit from the organism's powerful compensatory mechanisms, which do not necessarily lead to phenotypic differences. The generation and metabolism of small molecules can reflect the inherent differences of organisms, and can reflect the interference and influence of external factors on the organisms.
According to a preferred embodiment, the method for screening individuals at high risk for esophageal cancer is:
collecting a blood sample from the subject and comparing the level of the metabolite in the blood sample from the patient with esophageal malignancy with the level of the metabolite in the blood sample from the healthy subject;
and (II) acquiring differential metabolite data, and determining whether the screened object is an esophagus cancer high-risk individual or not based on the differential metabolite data.
Drawings
FIG. 1 is a graph of subject operating characteristics of an MRS model constructed based on composition 1;
FIG. 2 is a graph of subject performance characteristics for internal validation of MRS models constructed based on composition 1 (based on "leave-one-out" cross-validation);
FIG. 3 is a graph of subject operating characteristics of an MRS model constructed based on composition 2;
FIG. 4 is a graph of subject operating characteristics of an MRS model constructed based on composition 3;
FIG. 5 is a graph of subject operating characteristics of an MRS model constructed based on composition 4;
fig. 6 is a graph of subject operating characteristics of an MRS model constructed based on composition 5.
Detailed Description
The following detailed description refers to the accompanying drawings.
The full-member screening mode of the age-appropriate population developed in the high-incidence area of esophageal cancer in China consumes a great deal of medical and social resources. Meanwhile, due to the low detection rate (about 2%) of malignant lesions of esophagus, most of the participants cannot directly benefit from endoscopy, but are instead at risk of screening for collateral damage (such as perforation, bleeding, psychological trauma, etc.).
The current prediction of risk for esophageal cancer is limited by a range of macroscopic variables (e.g., personal disease history, family history, poor lifestyle, etc.) obtained via questionnaires, and the ability to distinguish people with malignant lesions from normal people remains relatively limited. It is necessary to supplement and integrate important biomarkers with early esophageal cancer warning effect to further improve the accuracy of risk prediction tools.
Based on this, the present application provides metabolic markers for aiding diagnosis or early warning of esophageal cancer.
Example 1
In this embodiment, a technical scheme is described in detail by taking an esophageal cancer high-incidence area as an example. Based on a local two-major prospective esophageal cancer screening crowd queue and a high-throughput metabonomics detection technology platform, a nest type case control research design is adopted to identify serum differential metabolites of malignant lesions of the esophagus and screen metabolite combinations with independent prediction effects, so that the method is used for early warning of malignant lesions of the esophagus.
1. Sample source
1. Serum sample collection
When the blood sample is put into a group (before endoscopic examination), fasting blood is collected for a recruited subject, a blood sample is placed in a refrigerator at-4 ℃ for overnight in the same day, after the blood is completely coagulated, the blood sample is placed in a centrifuge for centrifugation, light yellow liquid (serum) is extracted, the supernatant is temporarily stored in a refrigerator at-20 ℃ and is placed in the refrigerator at-80 ℃ for long-term storage after the blood sample is filled in the box.
2. End event acquisition
The outcome events (esophageal malignancy) included: severe dysplasia of the esophagus, esophageal carcinoma in situ, and esophageal squamous carcinoma. The members of the cohort did not develop malignancy prior to entry into the cohort.
Acquisition of esophageal malignant lesion outcome events for the members of the cohort is mainly by the following 3 pathways:
(1) Baseline or rechecked endoscopic iodine staining combined with abnormal region indicative biopsy and performing pathological diagnosis;
(2) The village doctor actively accesses the home for follow-up in the year of the queue member;
(3) The database is reimbursed against the local medical insurance system.
3. Case, control selection
Individuals in the cohort diagnosed with esophageal malignancy between the time of entry into the cohort and the time of follow-up are included in the cohort. Taking a design framework of a nest type case control study, selecting a control group from the rest non-case queue members according to a 1:1 morbidity density matching mode, wherein matching variables comprise: groups (e.g., screening group, non-screening group), age (+ -1 year), gender, year of blood collection, date of blood collection (+ -30 days).
2. Serometabonomic assay
Serum samples (250 μl/sample) from the case group and the control group were subjected to non-targeted metabonomic detection.
1. Sample processing
And (3) processing the serum sample by using an automatic pipetting pretreatment system of MicroLabSTAR of Hamiltonian. Methanol extract was added to the sample, followed by centrifugation after shaking vigorously in a sample extractor GenoGrinder for 2 minutes, and the supernatant (containing small molecule metabolites) was extracted. The obtained supernatant was transferred to a sample plate by a MicroLabSTAR automatic pipetting system and blow-dried by a nitrogen blower.
2. Quality control
And taking a small amount of mixed materials from each sample to form a quality control sample, and inserting the quality control samples into an actual sample detection sequence for multiple times (random sample introduction of the actual samples and uniform interval of the quality control samples). The same concentration of quality control standard that does not interfere with metabolite measurement was added in advance to each sample. The Relative Standard Deviation (RSD) of each internal standard signal in all quality control samples was calculated for the quality control samples and instrument stability was assessed based on the median RSD of each internal standard signal (qualifying standard: median RSD < 5%).
3. Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) system
UPLC-MS/MS analysis was performed using the ACQUITY binary ultra high performance liquid chromatography (2D UPLC) from Volter and the Q-exact high resolution/accurate mass orbitrap mass spectrometer from Siemens. The sample extracts were divided into 4 parts: 3 parts were analyzed by reverse phase liquid chromatography using a C18 column (UPLC BEH C18-2.1X100 mm,1.7 μm; waters) for gradient elution of the sample; 1 part was analyzed by hydrophilic interaction chromatography using a HILIC column (UPLC BEH Amide 2.1X106 mm,1.7 μm; waters) for gradient elution of the sample. The mass spectrum system detects in positive and negative ion modes, and the scanning range is 70-1000 m/z, and the resolution is 35000.
4. Raw data extraction and compound identification
Internal standard pre-quality control, chromatographic/mass spectrometry information extraction, database retrieval, metabolite identification and metabolite control assessment were performed on the raw data using CalOmics (soft-written dendric 7126986). Based on the information of a metabonomics database built in the software, calculating total scores of a retention time score, an MS error score, an isotope matching degree, an MS1 positive/inverse score and an MS2 positive/inverse score in a specific m/z range according to preset weights, and identifying the metabolites based on preset thresholds.
5. Metabonomics data preprocessing
The method for preprocessing the original data of the detection result mainly comprises the following steps:
(1) Normalization: converting the median of each compound concentration in all samples per day to 1, and scaling the data points of the same metabolite on different days to correct the errors introduced by the instrument performance fluctuation on different days for each compound;
(2) And (5) scaling: the data meet the distribution with the mean value of 0 and the variance of 1 through standardization so as to eliminate the difference of different metabolite concentration orders;
(3) Data conversion: by logarithmic conversion, the data of the bias distribution is converted into the data of the symmetrical distribution, so that the normalization is improved to meet the requirement of the statistical analysis on the linearity.
3. Statistical analysis
The odds ratio was calculated for each metabolite increase by 1 standard deviation using one-factor conditional logistic regression (OR per one standard deviation increase). The p-value was corrected for False Discovery Rate (FDR) using the method of Benjamini-Hochberg, taking into account multiple hypothesis testing. FDR-p < 0.2 is determined as a differential metabolite of malignant lesions of the esophagus. Performing LASSO regression on the differential metabolites, and identifying the optimal combination of the metabolites with independent prediction effect through 10-fold cross validation.
The products of the standardized concentration of each metabolite as an independent predictor and the corresponding single factor analyzed effect value (beta coefficient) are summed to obtain the metabolic risk score (Metabolic Risk Score, MRS) of the individual. And constructing an esophageal malignant lesion risk prediction model based on MRS by using conditional logistic regression. The differentiating ability of the MRS model was evaluated using the area under the subject's working characteristics curve (AUC). The degree of overfitting of the MRS model was evaluated using "leave-one-out" cross-validation.
4. Example results
Table 1 shows early warning serum metabolites of esophageal malignancy. Based on single factor analysis and LASSO regression, 22 esophageal malignant lesions serum differential metabolites with independent prediction function were identified in total.
And calculating MRS by using the concentration (standardized) of the 22 metabolites and the effect value of the corresponding single factor analysis, and further constructing an esophageal malignant lesion risk prediction model based on the MRS. The AUC of the MRS model is as high as 0.815 (internal validation AUC is as high as 0.813), as shown in fig. 1-2. AUC of the MRS model in fig. 1 represents the ability of MRS to distinguish esophageal malignant lesions from healthy individuals, whereas AUC values as high as 0.815 represent high accuracy of MRS in distinguishing esophageal malignant lesions from healthy individuals. The MRS model internal validation results shown in fig. 2, which were used to evaluate the repeatability of the MRS model, and the AUC values thereof were up to 0.813, indicate that the repeatability of model construction was high.
Compared with the marker composition which is obtained by the serum sample and is obtained by the aid of informatics software in the prior art, the marker composition obtained by the method has the advantages that: (1) Serum samples come from community natural crowd screening disease-specific queues, and are more suitable for early warning metabolite identification compared with the analysis based on hospital source samples (mostly middle and late patients) in the prior art; (2) The design of the nest type case control study is adopted, so that the serum sample collection is ensured to be carried out before the ending event, and the demonstration intensity is higher compared with the existing case control study.
The result shows that the combined use of the 22 serum differential metabolites has good distinguishing capability on esophageal cancer patients and healthy individuals, and can be used for early diagnosis and early warning of esophageal malignant lesions.
MRS was calculated using 20 of the previously unreported esophageal cancer differential metabolites (4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, phosphatidylserine, leucyl glutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxotretinoin, dihydroorotic acid, azelaic acid, hydroxamic acid carnitine, sebacic acid, eicosatrienoic acid (20:3N 9), 1-oleoyl glycerophospholipidcholine, pentosic acid, chenodeoxycholic acid, N-methyl proline, undecanoic acid, glucosyl-N-neuroylsphingosine (d18:1/24:1), and galactonic acid), and an MRS-based esophageal malignant lesion risk prediction model was constructed, with AUC of 0.813, as shown in FIG. 3. The constructed MRS model has higher accuracy for distinguishing the esophageal cancer from healthy individuals, and can realize early warning of the esophageal cancer.
MRS was calculated using 14 of the esophageal cancer differential metabolites (4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, phosphatidylserine, leucylglutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxotretinoin, dihydroorotic acid, azelaic acid, cetyloxycarnitine, sebacic acid, eicosatrienoic acid (20:3n9), 1-oleoyl glycerophospholipidylcholine), and thus an esophageal malignant lesion risk prediction model based on MRS was constructed with an AUC of 0.791, as shown in FIG. 4. The constructed MRS model has higher accuracy for distinguishing the esophageal cancer from healthy individuals, and can realize early warning of the esophageal cancer.
MRS was calculated using 6 of the esophageal cancer differential metabolites (4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, phosphatidylserine, leucyl glutamine), and an esophageal malignant lesion risk prediction model based on MRS was further constructed with an AUC of 0.742, as shown in fig. 5. The constructed MRS model has higher accuracy for distinguishing the esophageal cancer from healthy individuals, and can realize early warning of the esophageal cancer.
MRS was calculated using 4 of the esophageal cancer differential metabolites (4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine), and an MRS-based esophageal malignant lesion risk prediction model was further constructed with an AUC of 0.726, as shown in fig. 6. The constructed MRS model has higher accuracy for distinguishing the esophageal cancer from healthy individuals, and can realize early warning of the esophageal cancer.
It should be noted that the above-described embodiments are exemplary, and that a person skilled in the art, in light of the present disclosure, may devise various solutions that fall within the scope of the present disclosure and fall within the scope of the present disclosure. It should be understood by those skilled in the art that the present description and drawings are illustrative and not limiting to the claims. The scope of the invention is defined by the claims and their equivalents. The description of the invention encompasses multiple inventive concepts, such as "preferably," "according to a preferred embodiment," or "optionally," all means that the corresponding paragraph discloses a separate concept, and that the applicant reserves the right to filed a divisional application according to each inventive concept. Throughout this document, the word "preferably" is used in a generic sense to mean only one alternative, and not to be construed as necessarily required, so that the applicant reserves the right to forego or delete the relevant preferred feature at any time.
Claims (10)
1. Early warning metabolic markers for malignant esophageal lesions are characterized by comprising a plurality of 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, ribose, phosphatidylserine, leucyl glutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxotretinoin, dihydroorotic acid, azelaic acid, hydroxamic acid, sebacic acid, 1, 2-dipalmitoyl glycerophospholipidylcholine, eicosatrienoic acid (20:3n9), 1-oleoyl glycerophospholipidylcholine, pentosan, chenodeoxycholic acid, N-methyl proline, undecanoic acid, glucosyl-N-ceramide (d18:1/24:1), and galactonic acid.
2. The esophageal malignant lesion early-warning metabolic marker of claim 1, wherein the esophageal malignant lesion early-warning metabolic marker is derived from a blood sample.
3. The early warning metabolic marker for esophageal malignant lesions according to claim 1, comprising 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, phosphatidylserine, leucyl glutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxotretinoin, dihydroorotic acid, azelaic acid, hydroxamic carnitine, sebacic acid, eicosatrienoic acid (20:3N 9), 1-oleoyl glycerophospholipidcholine, pentose acid, chenodeoxycholic acid, N-methylproline, undecanoic acid, glucosyl-N-neuroyl sphingosine (d18:1/24:1), galactonic acid.
4. The early warning metabolic marker for esophageal malignant lesions according to claim 1, comprising 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, phosphatidylserine, leucyl glutamine, 1-stearoyl-2-oleoyl glycerophospholipidserine, 4-oxotretinoin, dihydroorotic acid, azelaic acid, hydroxamic acid carnitine, sebacic acid, eicosatrienoic acid (20:3N 9), 1-oleoyl glycerophospholipidcholine.
5. The early warning metabolic marker for esophageal malignant lesions according to claim 1, comprising 4-cholesten-3-one, heptanoic acid (7:0), glycerophospholipidinositol, N6-methyladenosine, phosphatidylserine, leucyl glutamine.
6. The early warning metabolic marker for esophageal malignant lesions according to claim 1, comprising 4-cholesten-3-one, heptanoic acid (7:0), glycerophosphatidyl inositol, N6-methyladenosine.
7. An early warning metabolic marker for esophageal malignant lesions, characterized in that the marker comprises differentially expressed metabolites selected based on a sterol metabolic pathway, a phospholipid metabolic pathway, a purine metabolic pathway, a pentose metabolic pathway, a dipeptide metabolic pathway, a phosphatidylserine metabolic pathway, a vitamin a metabolic pathway, a pyrimidine metabolic pathway, a fatty acid metabolic pathway, a phosphatidylcholine metabolic pathway, a long chain polyunsaturated fatty acid metabolic pathway, a lysophospholipid metabolic pathway, a partially characterized compound metabolic pathway, a primary bile acid metabolism, a urea cycle/arginine/proline metabolic pathway, a medium chain fatty acid metabolic pathway, a hexosyl ceramide metabolic pathway, and a fructose/mannose/galactose metabolic pathway.
8. The use of an early warning metabolic marker for esophageal malignant lesions according to any one of claims 1-7 in the preparation of a diagnostic reagent or a diagnostic kit.
9. The use of early warning metabolic markers for esophageal malignant lesions according to claim 8, wherein the esophageal malignant lesions comprise severe dysplasia of the esophagus, carcinoma in situ of the esophagus, and squamous carcinoma of the esophagus.
10. The use of early warning metabolic markers for esophageal malignant lesions according to claim 8, wherein the esophageal malignant lesions are early esophageal malignant lesions.
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