CN105044240A - Diagnosis marker suitable for early-stage esophageal squamous cell cancer diagnosis - Google Patents

Diagnosis marker suitable for early-stage esophageal squamous cell cancer diagnosis Download PDF

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CN105044240A
CN105044240A CN201510498042.7A CN201510498042A CN105044240A CN 105044240 A CN105044240 A CN 105044240A CN 201510498042 A CN201510498042 A CN 201510498042A CN 105044240 A CN105044240 A CN 105044240A
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cancer
esophagus
diagnosis
metabolic
early
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CN105044240B (en
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王家林
张涛
朱正江
薛付忠
赵德利
盛修贵
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Shandong Institute of Cancer Prevention and Treatment
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Shandong Institute of Cancer Prevention and Treatment
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Abstract

The invention discloses a diagnosis marker suitable for an early-stage esophageal squamous cell cancer diagnosis. The diagnosis marker is a composition of two or more than two kinds of materials from the following five kinds of serum metabolic markers including L-tyrosine, L-tryptophan, glycocholic acid, taurocholate and cortisol. When the diagnosis marker provided by the invention is adopted, a diagnosis model can be built. The diagnosis model has the advantages of good effect, high sensitivity and good specificity, is suitable for late-stage and early-stage esophagus cancer diagnoses, and has good clinic use and popularization value.

Description

A kind of diagnostic marker being suitable for esophageal squamous cell carcinoma early diagnosis
Technical field
The present invention relates to the diagnosis of esophageal squamous cell carcinoma, be specifically related to a kind of esophageal squamous cell carcinoma diagnostic marker, the screening technique of diagnostic marker, the diagnostic model built based on this diagnostic marker, and the construction method of diagnostic model, this diagnostic marker and diagnostic model all have good diagnosis effect for oesophagus carcinoma in situ, early stage and late esophagus cancer, be particularly suitable for cancer of the esophagus early diagnosis, belong to esophageal squamous cell carcinoma diagnostic techniques field.
Background technology
The cancer of the esophagus (esophagealcancer) is the malignant change formed by esophagus squameous epithelium or epithelioglandular paraplasm.Show according to World Health Organization's latest data: the whole world about has 400,000 people to die from the cancer of the esophagus every year, and China is the country that Incidence of esophageal cancer and mortality ratio are the highest, and the organization type of 90% patient is squamous cell carcinoma (ESCC).Incidence of Esophageal Cancer is hidden, and asymptomatic or symptom is not true to type very much in early days, has been clinical end-stage during discovery, general prognosis not good (survival rate was about 13% in 5 years).Therefore, early diagnosis and early treatment are the keys improved cancer of the esophagus prognosis, reduce mortality ratio.At present, examine in the morning of Esophageal Cancer in High Risk Areas and early control platform and the early screening commonly used clinically and diagnostic method and comprise cytologic examination by esophageal abrasive balloon, x-ray canel barium meal contrast examination, esophagus ultrasound scope, endoscopy of esophagus etc.But these methods have been wound inspection, complicated operation and price is high, limit its widespread use in cancer of the esophagus examination and early diagnosis.
The cancer of the esophagus relates to multifactor, multistage, polygenic variation accumulation and complex process interactional with environmental factor, comprise the change relating to numerous proto-oncogene, tumor suppressor gene and protein on a molecular scale, and the impact of life bad for a long time or eating habit (feed is containing the more food of nitrosamines, as liked pickling sauerkraut or the food that goes mouldy, liking into boiling hot food, smoking, bad habit etc. of drinking for a long time).Metabolism group carries out qualitative and quantitative detection to all molecular weight in biological sample (as serum, urine, saliva etc.) lower than 1000Da small molecule metabolites (as biological micromolecules such as fatty acid, amino acid, nucleosides and steroidals), thus the metabolism that monitoring body is made by endogenous material after the interference such as disease or hazards accumulation responds.Biological information in body passes to protein by gene through transcribing, and is finally presented as small molecule metabolites.Be different from the biosome inherent difference of genomics and protein science reflection, the research field of metabolism group extend to influencing each other and acting between body and environment.Small molecule metabolites is not only the material base of body vital movement, biochemical metabolism, also embody the change of some foeign element to internal metabolism environment, thus some unique metabolic thing concentration interindividual difference in fact reflect in disease performance and the external cause of disease.Recent study finds, in the disease development processes such as such as metabolic disease and malignant tumour (oophoroma), the metabolism of body basic biochemistry all there occurs significant change, to play a significant role to the metabolic mechanism of human intelligible complex disease, simultaneously for the screening of complex disease and early diagnosis provide brand-new technical method.
The generation development of the cancer of the esophagus is caused by polygenes and environmental factor interact, first be that relevant functional gene expression changes or suddenlys change, then be that a series of cellular signal transduction and protein synthesis change, finally under interacting with environmental factor, metabolic product changed.The generation of the cancer of the esophagus just environmental risk factors accumulates the result that each metabolic pathway stable state of body is hindered in not breakdown gradually.At present, someone utilizes metabolism group to study the cancer of the esophagus, such as (the WuH such as Wu, XueR, LuC, etal.Metabolomicstudyfordiagnosticmodelofoesophagealcanc erusinggaschromatography/massspectrometry.JChromatogrBAn alytTechnolBiomedLifeSci, 2009, 877 (27): 3111-7.), (the ZhangJ such as Zhang, BowersJ, LiuL, etal.EsophagealcancermetabolitebiomarkersdetectedbyLC-MS andNMRmethods.PLoSOne, 2012, 7 (1): e30181.), (the XuJ such as Xu, ChenY, ZhangR, etal.Globalandtargetedmetabolomicsofesophagealsquamousce llcarcinomadiscoverspotentialdiagnosticandtherapeuticbio markers.MolCellProteomics, 2013, 12 (5): 1306-18.) metabonomic technology is all utilized to be studied the cancer of the esophagus.
The generation development of the cancer of the esophagus, along with the change of metabolin multiple in body, generally needs several years even more than ten years, if find in the carcinogenesis stage, carries out early treatment, effectively can improve outcome.And in fact, there are some researches prove before disease incidence or hazards accumulation phase, endogenous material will make corresponding metabolism response.Such as, Zhao etc. disclose the metabolic characteristics of pre-diabetic by Metabolic fingerprinting research, and confirm that the metabolism of fatty acid, tryptophane, uric acid, bile acid etc. changes and betide disease and occur before clinical symptoms in a very long time, for the examination of metabolic disease, early diagnosis and intervention provide new may.But, the mainly advanced esophageal carcinoma patient that the research of above-mentioned cancer of the esophagus metabolism group is included in, and mostly do not include in or little include early stage cancer of the esophagus case in.There is lymph node and DISTANT METASTASES IN in late esophagus cancer, even there is tumor cachexia state, now organism metabolism changes a lot, therefore, these researchs only can find the metabolic profile difference that Incidence of Esophageal Cancer is compared with normal healthy controls late period, difference according to these metabolic profiles only can diagnose out late esophagus cancer preferably, and cannot diagnose for the early stage cancer of the esophagus, namely can not realize the early diagnosis of the cancer of the esophagus.Secondly, the research of above-mentioned cancer of the esophagus metabolism group only obtains little a part of metabolin relevant to Incidence of Esophageal Cancer (be only the analysis of metabolism target from research level, instead of metabolic profile or metabonomic analysis).In addition, translational medicine potentiality and the effect of metabolism group are not mostly evaluated in above-mentioned research from the angle of the objective Molecular screening/early diagnosis master pattern of the cancer of the esophagus, major part does not report the sensitivity of the examination/diagnosis of esophageal cancer of the metabolin of screening, specificity and ROC area under curve AUC value.
Inventor has carried out a series of research for the cancer of the esophagus early stage, adopt the technical research of metabolism group can carry out the metabonomic analysis model of cancer of the esophagus early screening, this technology finds the application that has and promotional value for the people at highest risk of China Esophageal Cancer in High Risk Areas, applies in Feicheng, Shandong pilot at present.But Esophageal Cancer group examination and clinical early diagnosis still have many differences.Esophageal Cancer in High Risk Areas examination be district occurred frequently with healthy or that surface the is healthy artificial object of observation, object is in the crowd of health, find that those surfaces are healthy, but the suspicious people's (high-risk individuals) suffering from esophageal site pathology, Screening tests positive must be done further diagnosis or intervene; And diagnosis be in a clinical setting with patient or suspicious patient for the object of observation, object distinguishes patient whether to have corresponding illness, timely, correct judgement is made to conditions of patients, to take corresponding effective remedy measures, diagnose positive will treat (as operation, chemotherapy or radiotherapy) clinically.At present, hospital generally uses wound, complicated operation and the high imaging examination clinical diagnosis cancer of the esophagus case of price, and patient is initiatively medical is be late period mostly, therefore still lacking can the simple and effective serum biomarker thing for clinical esophagus cancer diagnosis (particularly early stage esophagus cancer diagnosis).Therefore, find special, responsive, economical and noninvasive cancer of the esophagus early diagnosis blood serum metabolic label, and set up a kind of early molecule of the cancer of the esophagus safely and effectively diagnostic model there is important clinical value.
Summary of the invention
Diagnostic operation for esophageal squamous cell carcinoma in prior art (the abbreviation cancer of the esophagus) is complicated, expensive, have traumatic, current label is only highly sensitive to late esophagus cancer, the deficiencies such as the early diagnosis of the cancer of the esophagus can not be realized, the invention provides a kind of diagnostic marker being suitable for esophageal squamous cell carcinoma early diagnosis, this diagnostic marker is for oesophagus carcinoma in situ, the early stage cancer of the esophagus, late esophagus cancer all has good sensitivity and specificity, the diagnosis of late esophagus cancer can not only be used for, can also preferably for the early diagnosis of esophageal squamous cell carcinoma, for improving esophageal squamous cell carcinoma prognosis, reduce mortality ratio to have very important significance.
Present invention also offers the above-mentioned screening technique being suitable for the diagnostic marker of esophageal squamous cell carcinoma early diagnosis, for early stage and late esophagus cancer, all there is good sensitivity and specificity by the label of the method gained, especially be applicable to the early diagnosis of the cancer of the esophagus, the treatment for the cancer of the esophagus has important clinical meaning.
Present invention also offers the construction method of a kind of esophageal squamous cell carcinoma diagnostic model and diagnostic model, this model building method is simple, the diagnostic method having wound now can be replaced, convenient and swift, avoid the misery of personnel to be tested, for oesophagus carcinoma in situ, the early stage cancer of the esophagus, late esophagus cancer, all there is good sensitivity and specificity, examine the morning for esophageal squamous cell carcinoma early to control and provide effective technical support.
Present invention also offers a kind of method adopting this diagnostic model to diagnose esophageal squamous cell carcinoma, adopting model of the present invention just can diagnose by means of only getting blood, convenient and swift, without interior wound, especially highly sensitive for the early stage cancer of the esophagus, specificity is good, has good clinical value.
At present, this area is mostly from gene and macro-molecular protein aspect research screening esophageal squamous cell carcinoma diagnostic marker, the present invention changes Research Thinking in the past, the thinking adopting blood serum metabolic omics technology screening esophageal squamous cell carcinoma diagnostic marker is proposed first, find the label being particularly suitable for esophageal squamous cell carcinoma early diagnosis, made the early stage esophageal squamous cell carcinoma being not easy to find have good diagnostic method.The cancer of the esophagus screening that the present invention relies on " the national cancer of the esophagus is early examined and early controlled Demonstration Base (Feicheng, Shandong Province) " with follow up a case by regular visits to crowd's queue, obtain oesophagus carcinoma in situ and (be called for short carcinoma in situ, 0 phase 39 example), the early stage cancer of the esophagus (is called for short early carcinoma, I phase 17 example, II phase 11 example) and late esophagus cancer (abbreviation late cancer, III phase 30 example) serum specimen of patient, and randomly draw through determining without any esophageal lesion and other metabolic diseases (as hyperthyroidism, first subtracts, hypertension and diabetes, ephrosis etc.) healthy population be normal healthy controls, UPLC-QTOF/MS is used to obtain the Metabolic fingerprinting of 1466 small molecule metabolites, pass through patient with esophageal carcinoma, and the contrast of the Metabolic fingerprinting of the small molecule metabolites of health objects, analyze, obtain the diagnostic marker being suitable for esophageal squamous cell carcinoma early diagnosis, model construction is carried out with these diagnostic markers, obtain esophagus cancer diagnosis model, whether utilize this model to diagnose out is fast the cancer of the esophagus, especially the early stage cancer of the esophagus can be diagnosed out, highly sensitive, specificity is good, there is Clinical practice and promotional value.
In the present invention, described oesophagus carcinoma in situ refers to 0 phase in TNM staging scale, refers in mucous epithelium layer or the holostrome of epithelium is involved in atypical hyperplasia (severe) in epiderm skin, but not yet invades brokenly basilar memebrane and infiltrate the cancer of growth downwards; The early stage cancer of the esophagus refers to I and the II phase in TNM staging scale, refer to without lymph node involvement, without DISTANT METASTASES IN be confined to mucous membrane after or the cancer of submucosa; Late esophagus cancer refers to III phase and IV phase in TNM staging scale, refers to involve muscle layer or reach beyond adventitia or adventitia, has the cancer of local or lymphatic metastasis at a distance.TNM staging scale is according to AmericanjointCommitteeonCancer (AJCC) TNMClassficationofCarcinomaoftheEsophagusandEsophagogast ricJunction (7thed, 2010).
Diagnostic marker of the present invention and diagnostic model can by the asymptomatic or unconspicuous early stage esophagus cancer diagnosis of symptom out, without interior wound, alleviate the misery of tester, and diagnostic procedure is succinct, quick, improve work efficiency, examine the morning for the cancer of the esophagus early control, the improvement of prognosis, the reduction of mortality ratio be all of great significance.Realize concrete technical scheme of the present invention as follows:
A kind of diagnostic marker being suitable for esophageal squamous cell carcinoma early diagnosis, any one or more than one combination in following 25 kinds of blood serum metabolic labels: beta-alanine-lysine (beta-Ala-Lys), L-Carnosine (L-Carnosine), POA (cis-9-Palmitoleicacid), palmitic acid (Palmiticacid), oleic acid (OleicAcid), lysophosphatidic acid LPA (18:1 (9Z)/0:0), lysolecithin LysoPC (14:0/0:0), lysolecithin LysoPC (18:2 (9Z, 12Z)), lysolecithin LysoPC (24:0), phosphatide PC (14:1 (9Z)/P-18:1 (11Z)), phosphatide PC (16:0/18:2 (9Z, 12Z)), phosphatide PC (24:1 (15Z)/22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)), linoleic acid (Linoleicacid), nicotinamide adenine dinucleotide (NADH), cortisol (Cortisol), TYR (L-Tyrosine), L-Trp (L-Tryptophan), glycocholic acid (GlycocholicAcid), taurocholate (Taurocholate), hypoxanthine (Hypoxanthine), allantoic acid (Allantoicacid), inosine (Inosine), S1P (Sphingosine1-phosphate), sulfogalactosylceramide 3-O-Sulfogalactosylceramide (d18:1/20:0), galactosylceramide Lactosylceramide (d18:1/22:0).
In above-mentioned diagnostic marker, can be any one in above-mentioned 25 kinds of blood serum metabolic labels, also can be two kinds between them or two or more random combinations.When using two or more the combination of blood serum metabolic label as diagnostic marker, the effect of diagnosis can be better than the effect of single blood serum metabolic label as diagnostic marker.
Further, above-mentioned diagnostic marker can be the combination of any one blood serum metabolic label in following (a)-(h): (a) can be the combination of beta-alanine-lysine (beta-Ala-Lys) and L-Carnosine (L-Carnosine); (b) or be the combination of POA (cis-9-Palmitoleicacid), palmitic acid (Palmiticacid) and oleic acid (OleicAcid); (c) or be the combination of lysophosphatidic acid LPA (18:1 (9Z)/0:0), lysolecithin LysoPC (14:0/0:0), lysolecithin LysoPC (18:2 (9Z, 12Z)) and lysolecithin LysoPC (24:0); (d) or be phosphatide PC (14:1 (9Z)/P-18:1 (11Z)), phosphatide PC (16:0/18:2 (9Z, 12Z)), phosphatide PC (24:1 (15Z)/22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)) and the combination of linoleic acid (Linoleicacid); (e) or be the combination of nicotinamide adenine dinucleotide (NADH), TYR (L-Tyrosine) and L-Trp (L-Tryptophan); (f) or be the combination of cortisol (Cortisol), glycocholic acid (GlycocholicAcid) and taurocholate (Taurocholate); (g) or be the combination of hypoxanthine (Hypoxanthine), allantoic acid (Allantoicacid) and inosine (Inosine); (h) or be the combination of S1P (Sphingosine1-phosphate), sulfogalactosylceramide 3-O-Sulfogalactosylceramide (d18:1/20:0) and galactosylceramide Lactosylceramide (d18:1/22:0).
Further, above-mentioned diagnostic marker can be two or more the combination in following 15 kinds of blood serum metabolic labels: lysophosphatidic acid LPA (18:1 (9Z)/0:0), lysolecithin LysoPC (14:0/0:0), lysolecithin LysoPC (18:2 (9Z, 12Z)), lysolecithin LysoPC (24:0), phosphatide PC (14:1 (9Z)/P-18:1 (11Z)), phosphatide PC (16:0/18:2 (9Z, 12Z)), phosphatide PC (24:1 (15Z)/22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)), nicotinamide adenine dinucleotide (NADH), cortisol (Cortisol), L-Trp (L-Tryptophan), taurocholate (Taurocholate), hypoxanthine (Hypoxanthine), inosine (Inosine), sulfogalactosylceramide 3-O-Sulfogalactosylceramide (d18:1/20:0), galactosylceramide Lactosylceramide (d18:1/22:0).
Further, above-mentioned diagnostic marker can be two or more the combination in following 7 kinds of blood serum metabolic labels: lysophosphatidic acid LPA (18:1 (9Z)/0:0), lysolecithin LysoPC (14:0/0:0), lysolecithin LysoPC (18:2 (9Z, 12Z)), lysolecithin LysoPC (24:0), phosphatide PC (14:1 (9Z)/P-18:1 (11Z)), phosphatide PC (16:0/18:2 (9Z, 12Z)) and phosphatide PC (24:1 (15Z)/22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)).
Further, above-mentioned diagnostic marker can be two or more the combination in following 5 kinds of blood serum metabolic labels: TYR (L-Tyrosine), L-Trp (L-Tryptophan), glycocholic acid (GlycocholicAcid), taurocholate (Taurocholate) and cortisol (Cortisol).
Preferably, above-mentioned diagnostic marker is that the combination of following 25 kinds of blood serum metabolic labels (is designated as diagnostic marker A, lower same): beta-alanine-lysine (beta-Ala-Lys), L-Carnosine (L-Carnosine), POA (cis-9-Palmitoleicacid), palmitic acid (Palmiticacid), oleic acid (OleicAcid), lysophosphatidic acid LPA (18:1 (9Z)/0:0), lysolecithin LysoPC (14:0/0:0), lysolecithin LysoPC (18:2 (9Z, 12Z)), lysolecithin LysoPC (24:0), phosphatide PC (14:1 (9Z)/P-18:1 (11Z)), phosphatide PC (16:0/18:2 (9Z, 12Z)), phosphatide PC (24:1 (15Z)/22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)), linoleic acid (Linoleicacid), nicotinamide adenine dinucleotide (NADH), cortisol (Cortisol), TYR (L-Tyrosine), L-Trp (L-Tryptophan), glycocholic acid (GlycocholicAcid), taurocholate (Taurocholate), hypoxanthine (Hypoxanthine), allantoic acid (Allantoicacid), inosine (Inosine), S1P (Sphingosine1-phosphate), sulfogalactosylceramide 3-O-Sulfogalactosylceramide (d18:1/20:0), galactosylceramide Lactosylceramide (d18:1/22:0).
Preferably, above-mentioned diagnostic marker is that the combination of following 7 kinds of blood serum metabolic labels (is designated as diagnostic marker B, lower same): lysophosphatidic acid LPA (18:1 (9Z)/0:0), lysolecithin LysoPC (14:0/0:0), lysolecithin LysoPC (18:2 (9Z, 12Z)), lysolecithin LysoPC (24:0), phosphatide PC (14:1 (9Z)/P-18:1 (11Z)), phosphatide PC (16:0/18:2 (9Z, 12Z)) and phosphatide PC (24:1 (15Z)/22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)).
Preferably; above-mentioned diagnostic marker is the combination (being designated as diagnostic marker C, lower same) of following 5 kinds of blood serum metabolic labels: TYR (L-Tyrosine), L-Trp (L-Tryptophan), glycocholic acid (GlycocholicAcid), taurocholate (Taurocholate) and cortisol (Cortisol).
The invention provides multiple serum protein moteblites or serum protein moteblites combination form diagnostic marker, above-mentioned diagnostic marker relates to 25 kinds of blood serum metabolic labels altogether, these 25 kinds of blood serum metabolic labels and 10 kinds of metabolic pathways closely related.Wherein, beta-alanine-lysine (beta-Ala-Lys) and these 2 kinds of blood serum metabolic labels of L-Carnosine (L-Carnosine) and beta alanine metabolism (beta-Alaninemetabolism) metabolic pathway closely related; It is closely related that these 3 kinds of blood serum metabolic labels of POA (cis-9-Palmitoleicacid), palmitic acid (Palmiticacid) and oleic acid (OleicAcid) and fatty acid synthesize (Fattyacidbiosynthesis) metabolic pathway; Lysophosphatidic acid LPA (18:1 (9Z)/0:0), lysolecithin LysoPC (14:0/0:0), lysolecithin LysoPC (18:2 (9Z, 12Z)) and these 4 kinds of blood serum metabolic labels of lysolecithin LysoPC (24:0) and glycerophosphatide metabolism (Glycerophospholipidmetabolism) metabolic pathway closely related; Phosphatide PC (14:1 (9Z)/P-18:1 (11Z)), phosphatide PC (16:0/18:2 (9Z, 12Z)), phosphatide PC (24:1 (15Z)/22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)) and these 4 kinds of blood serum metabolic labels of linoleic acid (Linoleicacid) and glycerophosphatide metabolism (Glycerophospholipidmetabolism) and these two kinds of metabolic pathways of linoleic acid metabolism (Linoleicacidmetabolism) closely related; Nicotinamide adenine dinucleotide (NADH) is closely related with oxidative phosphorylation (Oxidativephosphorylation) metabolic pathway; TYR (L-Tyrosine) and these 2 kinds of blood serum metabolic labels of L-Trp (L-Tryptophan) and phenylalanine/tyrosine and tryptophan metabolism (Phenylalanine, tyrosineandtryptophanbiosynthesis) metabolic pathway closely related; It is closely related that glycocholic acid (GlycocholicAcid) and these 2 kinds of blood serum metabolic labels of taurocholate (Taurocholate) and primary bile acid synthesize (Primarybileacidbiosynthesis) metabolic pathway; Cortisol (Cortisol) and cancer path and choleresis (Pathwaysincancer, andBilesecretion) metabolic pathway closely related; These 3 kinds of blood serum metabolic labels of hypoxanthine (Hypoxanthine), allantoic acid (Allantoicacid) and inosine (Inosine) and purine metabolism (Purinemetabolism) metabolic pathway closely related; These 3 kinds of blood serum metabolic labels of S1P (Sphingosine1-phosphate), sulfogalactosylceramide 3-O-Sulfogalactosylceramide (d18:1/20:0) and galactosylceramide Lactosylceramide (d18:1/22:0) and sphingolipid metabolism (Sphingolipidmetabolism) metabolic pathway closely related.
Present invention also offers the above-mentioned various screening technique being suitable for the diagnostic marker of esophageal squamous cell carcinoma early diagnosis, comprise the following steps:
(1) collect patients with esophageal squamous cell and healthy population serum sample, as analyzing samples, wherein esophageal squamous cell carcinoma serum sample comprises oesophagus carcinoma in situ serum sample, early stage cancer of the esophagus serum sample and late esophagus cancer serum sample;
(2) adopted by each analyzing samples UPLC-QTOF/MS blood serum metabolic omics technology to analyze, obtain the original Metabolic fingerprinting of each serum sample;
(3) use R language XCMS software package that the original Metabolic fingerprinting of esophageal squamous cell carcinoma serum sample and healthy serum sample is carried out collection of illustrative plates pre-service respectively, obtain every behavioural analysis sample, often be classified as the two-dimensional matrix of metabolin information, and use the CAMERA software package of R language to carry out metabolin peak mark, for further statistical study to two-dimensional matrix;
(4) two-dimensional matrix of step (3) is carried out principal component analysis (PCA) and partial least squares discriminant analysis successively, obtain PLS-DA model, this PLS-DA models show patients with esophageal squamous cell and healthy population have metabolic patterns difference and obvious classification trend;
(5) according to PLS-DA model obtained above, variable importance by PLS-DA modeling is marked and the screening of difference metabolin is carried out in univariate non-parametric test, screening criteria is: VIP >=1, and q value is less than 0.05 after the multiple testing adjustment of False discovery rate FDR;
(6) the difference metabolin above-mentioned screening obtained wraps quasi-molecular ion, adduct and the Isotope Information of determining difference metabolin according to the CAMERA of R language, obtain potential metabolic marker thing;
(7) on the basis of above-mentioned potential metabolic marker thing, in conjunction with the one-level of potential metabolic marker thing, second order ms information, quasi-molecular ion information, adduct information and Isotope Information, infer molecular mass and the molecular formula of diagnostic marker, and carry out with existing n-compound contrasting, mating, obtain blood serum metabolic label.Namely single blood serum metabolic label or the combination of blood serum metabolic label can be used as the diagnostic marker being suitable for esophageal squamous cell carcinoma early diagnosis.
In above-mentioned screening technique, described healthy population be without Lesions in Upper Gastrointestinal Tract and other metabolic diseases (as hyperthyroidism, first subtract, hypertension and diabetes, ephrosis etc.) crowd.
In above-mentioned screening technique, when carrying out the analysis of LC-MS blood serum metabolic omics technology, every 10 analyzing samples add a Quality control samples, for Real Time Monitoring sample from sample introduction pre-treatment to the quality control situation analytic process, described Quality control samples is the biased sample of 5 parts of cancer of the esophagus serum samples and 5 parts of healthy serum samples.
In above-mentioned screening technique, before described analyzing samples and Quality control samples sample introduction, carry out following pre-service:
(1) extract 50 μ l analyzing samples or Quality control samples with pipettor, be placed on 96 orifice plates of the automatic sample processing system of Bravo;
(2) add 150 μ l methyl alcohol to extract, vortex 30s, and hatch with protein precipitation at-20 DEG C;
(3) then in supercentrifuge at 4 DEG C with 4000 revs/min of centrifugal 20min;
(4) supernatant of step (3) is poured in LC-MS sample injection bottle, detect in order to LC-MS at being kept at-80 DEG C.
In above-mentioned screening technique, carry out collection of illustrative plates pre-service to original Metabolic fingerprinting to refer to: with Masshunter software, the original Metabolic fingerprinting obtained is converted to MZdata data file, then MZdata data file used XCMS software package to carry out comprising the pretreatment operation of aliging in retention time correction, peak identification, peak match and peak, obtain two-dimensional matrix.
In above-mentioned screening technique, use R software package CAMERA to carry out metabolin peak to two-dimensional matrix and identify the metabolin peak mark comprising isotopic peak, adduct and fragmention.
In above-mentioned screening technique, when adopting LC-MS blood serum metabolic omics technology to analyze to each analyzing samples, liquid chromatography chromatographic column used is WatersACQUITYUPLCHSST3 chromatographic column, and specification is 100mm × 2.1mm, 1.8 μm; Sample size is 6 μ L, and injector temperature is 4 DEG C, and flow velocity is 0.5ml/min; Chromatogram flow phase comprises two kinds of solvent orange 2 As and B: the A under positive ion ESI+ pattern is 0.1wt% aqueous formic acid, A under negative ion ESI-model is 0.5mmol/L ammonium fluoride aqueous solution, B under positive ion ESI+ pattern is the acetonitrile solution of 0.1wt% formic acid, and the B under negative ion ESI-model is pure acetonitrile; Chromatogram condition of gradient elution is: 0-1min is 1%B, 1-8min is that 1%B-100%B increases progressively gradually, and 10-10.1min is that 100%B is kept to 1%B rapidly, and then 1%B continues 1.9min.
In above-mentioned screening technique, when adopting LC-MS blood serum metabolic omics technology to analyze to each analyzing samples, Mass Spectrometer Method uses quadrupole rod time-of-flight mass spectrometry instrument Q-TOF, and adopt positive ion mode ESI+ and the negative ion mode ESI-of electric spray ion source, ion source temperature is 400 DEG C, taper hole airshed is 12L/min, and desolventizing temperature is 250 DEG C, and desolventizing airshed is 16L/min; Under positive ion and negative ion mode, capillary voltage is respectively+3kV and-3kV, and taper hole voltage is 0V; Positive ion mode lower cone hole pressure is 20psi, and negative ion mode lower cone hole pressure is 40psi; The mass charge ratio range of spectrum data collection is 50 ~ 1200m/z, and the sweep frequency of collection is 0.25s.
In preferred version of the present invention, oesophagus carcinoma in situ patient 39 people used during screening, early stage patient with esophageal carcinoma 28 people, late esophagus cancer patient 30 people, healthy population 105 people.
In preferred version of the present invention, the R2X=0.167 of the PLS-DA model obtained in screening process, R2Y=0.569, Q2Y=0.523.
Present invention also offers a kind of construction method of esophageal squamous cell carcinoma diagnostic model, comprise the following steps:
(1) collect patients with esophageal squamous cell and healthy population serum sample, as analyzing samples, wherein esophageal squamous cell carcinoma serum sample comprises oesophagus carcinoma in situ serum sample, early stage cancer of the esophagus serum sample and late esophagus cancer serum sample;
(2) adopted by each analyzing samples LC-MS blood serum metabolic omics technology to analyze, obtain the original Metabolic fingerprinting of each serum sample;
(3) the original Metabolic fingerprinting of R language XCMS software package to each serum sample is used to carry out collection of illustrative plates pre-service respectively, obtain every behavioural analysis sample, often be classified as the two-dimensional matrix of metabolin information, use R software package CAMERA to carry out metabolin peak mark, for further statistical study to two-dimensional matrix simultaneously;
(4) from two-dimensional matrix, filter out according to mass-to-charge ratio and retention time the information that the present invention is suitable for the diagnostic marker of esophageal squamous cell carcinoma early diagnosis, obtain diagnostic marker two-dimensional matrix;
(5) according to this diagnostic marker two-dimensional matrix, use randomForest software package in R language to build Random Forest model, obtain esophageal squamous cell carcinoma diagnostic model.
In above-mentioned construction method, described oesophagus carcinoma in situ refers to 0 phase in TNM staging scale, refers in mucous epithelium layer or the holostrome of epithelium is involved in atypical hyperplasia (severe) in epiderm skin, but not yet invades brokenly basilar memebrane and infiltrate the cancer of growth downwards; The early stage cancer of the esophagus refers to I and the II phase in TNM staging scale, refer to without lymph node involvement, without DISTANT METASTASES IN be confined to mucous membrane after or the cancer of submucosa; Late esophagus cancer refers to III phase and IV phase in TNM staging scale, refers to involve muscle layer or reach beyond adventitia or adventitia, has the cancer of local or lymphatic metastasis at a distance.TNM staging scale is according to AmericanjointCommitteeonCancer (AJCC) TNMClassficationofCarcinomaoftheEsophagusandEsophagogast ricJunction (7thed, 2010).
In preferred version of the present invention, when building Random Forest model, modeling parameters ntree=5000.
In preferred version of the present invention, during model construction, be build based on following number of samples: used states oesophagus carcinoma in situ patient 39 people, early stage patient with esophageal carcinoma 28 people, late esophagus cancer patient 30 people, healthy population 105 people.
In preferred version of the present invention, when the diagnostic marker being suitable for esophageal squamous cell carcinoma early diagnosis is combination (the diagnostic marker A) of 25 kinds of blood serum metabolic labels, the diagnosis dividing value (Threshold) of the diagnostic model of gained is 0.3552; When the diagnostic marker being suitable for esophageal squamous cell carcinoma early diagnosis is combination (the diagnostic marker B) of 7 kinds of blood serum metabolic labels, the diagnosis dividing value (Threshold) of the diagnostic model of gained is 0.7431; When the diagnostic marker being suitable for esophageal squamous cell carcinoma early diagnosis is combination (the diagnostic marker C) of 5 kinds of blood serum metabolic labels, the diagnosis dividing value (Threshold) of the diagnostic model of gained is 0.4943.When the predicted numerical value that diagnostic model provides is more than or equal to diagnosis dividing value, illustrating to suffer from esophageal squamous cell carcinoma, when being less than diagnosis dividing value, illustrating not suffer from esophageal squamous cell carcinoma.
Present invention also offers a kind of esophageal squamous cell carcinoma diagnostic model, this diagnostic model builds according to the construction method of above-mentioned esophageal squamous cell carcinoma diagnostic model and obtains.The same, in preferred version of the present invention, when the diagnostic marker that diagnostic model is used is diagnostic marker A, the diagnosis dividing value of diagnostic model is 0.3552; When for diagnostic marker B, the diagnosis dividing value of diagnostic model is 0.7431; When for diagnostic marker C, the diagnosis dividing value of diagnostic model is 0.4943.
Present invention also offers a kind of using method of esophageal squamous cell carcinoma diagnostic model, namely adopt this esophageal squamous cell carcinoma diagnostic model to diagnose the method for esophageal squamous cell carcinoma, comprise the following steps:
(1) get serum sample to be checked, reach sample introduction requirement by pre-service, adopted by pretreated serum sample to be checked LC-MS blood serum metabolic omics technology to analyze, must the original Metabolic fingerprinting of this serum sample to be checked;
(2) use R language XCMS software package that this original Metabolic fingerprinting is carried out collection of illustrative plates pre-service, and carry out metabolin peak mark, obtain the two-dimensional matrix that may be used for statistical study;
(3) from two-dimensional matrix, filter out according to mass-to-charge ratio and retention time the information that the present invention is suitable for the diagnostic marker of esophageal squamous cell carcinoma early diagnosis, obtain diagnostic marker two-dimensional matrix;
(4) bring in esophageal squamous cell carcinoma diagnostic model by diagnostic marker two-dimensional matrix, the diagnosis dividing value (Threshold) of the numerical value provided according to model and model, determines whether early stage esophageal squamous cell carcinoma.
In preferred version of the present invention, when with the combination of 25 kinds of blood serum metabolic labels (diagnostic marker A) for being suitable for the diagnostic marker of esophageal squamous cell carcinoma early diagnosis time, when the numerical value that diagnostic model provides is more than or equal to 0.3552, be diagnosed as the cancer of the esophagus, otherwise for not to be; When with the combination of 7 kinds of blood serum metabolic labels (diagnostic marker B) for being suitable for the diagnostic marker of esophageal squamous cell carcinoma early diagnosis time, when the numerical value that diagnostic model provides is more than or equal to 0.7431, be diagnosed as the cancer of the esophagus, otherwise for not to be; When with the combination of 5 kinds of blood serum metabolic labels (diagnostic marker C) for being suitable for the diagnostic marker of esophageal squamous cell carcinoma early diagnosis time, when the numerical value that diagnostic model provides is more than or equal to 0.4943, be diagnosed as the cancer of the esophagus, otherwise for not to be.
Advantage of the present invention is: the present invention adopts blood serum metabolic omics technology and data statistic analysis technology to obtain being suitable for diagnostic marker and the esophageal squamous cell carcinoma diagnostic model of esophageal squamous cell carcinoma early diagnosis, and has found there are closely-related 10 metabolic pathways with diagnostic marker.Diagnostic marker screening technique of the present invention is workable, and model building method is simple, and gained diagnostic model is respond well, highly sensitive, specificity is good, is not only applicable to the diagnosis of late esophagus cancer, also be suitable for the diagnosis of the early stage cancer of the esophagus, be particularly suitable for the early diagnosis of the cancer of the esophagus.The present invention just can realize diagnosis by means of only getting blood, low without creating, spending, can be good at substituting wound property diagnostic mode now, significantly reduce the misery of patient, and the present invention diagnoses quick, convenient, required time is short, improves work efficiency, the morning being conducive to the cancer of the esophagus finds, early treatment, has good Clinical practice and promotional value.
Accompanying drawing explanation
Fig. 1. the total ion chromatogram (LC-QTOF/MS of original Metabolic fingerprinting, + ESI is positive ion mode,-ESI is negative ion mode), transverse axis is retention time (RetentionTime, min), the longitudinal axis is metabolin relative concentration, and Normal is healthy serum sample, and ESCC is cancer of the esophagus serum sample.
Fig. 2. the PCA shot chart of metabolic profile preanalysis, wherein Normal is healthy serum sample, and ESCC is cancer of the esophagus serum sample, and QC represents Quality control samples.
Fig. 3 A is the three-dimensional shot chart of PLS-DA that the metabolic profile of the cancer of the esophagus and normal healthy controls compares, the R2X=0.167 of modeling, R2Y=0.569, Q2Y=0.523; Fig. 3 B is accordingly based on the PLS-DA modeling proof diagram of random permutation method.Wherein Normal is healthy serum sample, and ESCC is cancer of the esophagus serum sample.
The identity process figure of Fig. 4 .L-tryptophane (L-Tryptophan), wherein scheming (a): m/z is retention time feature in the chromatogram of 205.0974; Figure (b): retention time is the first mass spectrometric figure of 181.38s; Figure (c): retention time to be 181.38s, m/z be 205.0974 secondary ion MS/MS fragmentation pattern; Figure (d): the fragment cracking mechanism of metabolin (RT:181.38s, m/z:205.0974).
The ROC curve map of the external testing sample of Fig. 5 .25 the Random Forest model constructed by metabolic marker thing.
The external testing sample ROC curve of Fig. 6 .7 the early stage esophagus cancer diagnosis model of the random forest constructed by metabolic marker thing.
The external testing sample ROC curve of Fig. 7 .5 the early stage esophagus cancer diagnosis model of the random forest constructed by metabolic marker thing.
Embodiment
Below, by following embodiment, the present invention is further explained, and advantage of the present invention is further proved.
The screening of diagnostic marker of the present invention, the construction method of diagnostic model and compliance test result are as follows:
1, research object
The cancer of the esophagus screening platform that this research relies on " the national cancer of the esophagus is early examined and early controlled Demonstration Base (Feicheng, Shandong Province) ", for Feicheng, Shandong Province 40-69 year gastroscope under the indicative biopsy object of iodine staining (confirming as goldstandard), gather oesophagus carcinoma in situ (0 phase 39 example), the early stage cancer of the esophagus (I phase 17 example, II phase 11 example) and late esophagus cancer (III phase 30 example); And to randomly draw in screening iodine staining negative subject under gastroscope namely without health objects 105 example of Lesions in Upper Gastrointestinal Tract as healthy sample.
2, the blood serum metabolic group of LC-MS detects
Be put in after the serum sample of all collections is centrifugal in-80 DEG C of refrigerators and preserve, use Ultra Performance Liquid Chromatography-GC-MS
(UPLC-QTOF/MS6550, and the automatic Pretreated system of Bravo (Agilent Agilent), USA) metabolism group detection (point 3 large batch detection is carried out, and carry out quality control), obtain the original Metabolic fingerprinting comprising chromatogram and Information in Mass Spectra of sample.Concrete operations are as follows:
2.1 instrument and equipment
Experimental facilities comprises: UPLC-QTOF/MS6550 system (Agilent, USA), Bravo system (Agilent, USA), high speed low temperature centrifugal machine, vibration scroll machine, nitrogen drying device, 4 DEG C of cold storage refrigerators (Haier), pure water instrument (Siemens).
Experiment consumptive material comprises: WatersACQUITY hSST3 (particlesize, 1.8 μm; 100mm (length) × 2.1mm) chromatographic column, liquid nitrogen, High Purity Nitrogen; Cone end sample injection bottle, 2ml centrifugal rotor, 2ml centrifuge tube (round bottom), pipettor, 1000 μ l rifle heads, 200 μ l rifle heads, marking pen, emgloves, mouth mask.
Experiment reagent comprises: methyl alcohol (enlightening horse, HPLC level is pure), acetonitrile (enlightening horse, HPLC level is pure), formic acid (recovery precision chemical research institute, Tianjin), pure water (TOC<10ppb).
2.2 serum sample pre-service
Before serum sample pre-service, prepare 21 parts of Quality control samples (QC), all early stage cancer of the esophagus serum samples, oesophagus carcinoma in situ serum sample, late esophagus cancer serum sample, healthy serum sample and Quality control samples are carried out random number, using early stage cancer of the esophagus serum sample, oesophagus carcinoma in situ serum sample, late esophagus cancer serum sample, healthy serum sample as analyzing samples, add a Quality control samples every 10 analyzing samples.Early stage cancer of the esophagus serum sample, oesophagus carcinoma in situ serum sample and late esophagus cancer serum sample are referred to as cancer of the esophagus serum sample, and Quality control samples is the biased sample of 5 parts of cancer of the esophagus serum samples and 5 parts of healthy serum samples.Cancer of the esophagus serum sample, healthy serum sample and Quality control samples all carry out pre-service, and pre-service comprises following 4 steps:
(1) extract 50 μ l analyzing samples or Quality control samples with pipettor, be placed on 96 orifice plates of the automatic sample processing system (Agilent, USA) of Bravo;
(2) add 150 μ l methyl alcohol to extract, vortex 30s, and hatch with protein precipitation at-20 DEG C.
(3) then in supercentrifuge at 4 DEG C with 4000 revs/min of centrifugal 20min;
(4) supernatant of step (3) is poured in LC-MS sample injection bottle, detect in order to LC-MS at being kept at-80 DEG C;
2.3 serum UPLC-QTOF/MS detect
The pretreated sample of 6 μ L equal portions is injected ACQUITYUPLCHSST3 (particlesize, 1.8 μm by UPLC system (1290series, Agilent); 100mm (length) × 2.1mm) chromatographic column (Waters, Milford, USA).Loading sequence is completely random sample introduction, to get rid of the bias that Loading sequence brings.It is that (water dilutes 0.1wt% formic acid that chromatogram flow phase comprises two kinds of solvent: A, positive ion ESI+) or (the water dilution of 0.5mM ammonium fluoride, negative ion ESI-), B is 0.1wt% formic acid (dilution in acetonitrile, positive ion ESI+) or 100% acetonitrile (negative ion ESI-).Chromatogram gradient is: 0-1min is 1%B, 1-8min is that 1%B-100%B increases progressively gradually, and then 10-10.1min is that 100%B is kept to 1%B rapidly, and then 1%B continues 1.9min.Flow velocity is 0.5ml/min.Whole sample detection process maintains 4 DEG C.Wherein, the percentage composition of A and B refers to volumn concentration.
Mass Spectrometer Method uses Agilent quadrupole rod time-of-flight mass spectrometry instrument Q-TOF (6550, Agilent), and adopts positive ion mode (ESI+) and the negative ion mode (ESI-) of electric spray ion source.Ion source temperature is set as 400 DEG C, and taper hole airshed is 12L/min.Meanwhile, desolventizing air temperature set is 250 DEG C, and desolventizing airshed 16L/min.Under positive ion and negative ion mode, capillary voltage is respectively+3kV and-3kV, and taper hole voltage is 0V.Taper hole pressure is 20psi (positive ion) and 40psi (negative ion).The mass charge ratio range of spectrum data collection is 50 ~ 1200m/z, and the sweep frequency of collection is 0.25s.In MS/MS second mass analysis, high-purity nitrogen is as collision gas for generating object ion fragment, and collision energy is set to 10,20 or 40eV.
3, XCMS collection of illustrative plates pre-service
UPLC-QTOF/MS serum positive ion ESI+ and negative ion ESI-detects and obtains original Metabolic fingerprinting data (see Fig. 1), Mzdata data file is converted into by the Masshunter software of Agilent company, then use the XCMS software package of R language to carry out the pre-service of XCMS collection of illustrative plates, pre-service comprise retention time correction, peak identification, peak match, peak alignment, filter are made an uproar, Overlapped peak resolution, Threshold selection, standardization etc.The pretreated correlation parameter of XCMS is: peak half waist peak width is 10 (fwhm=10), and retention time window is set to 10 (bw=10), and other parameters are default value.Obtain the two-dimensional matrix that can be used for statistical study after the pre-service of XCMS collection of illustrative plates, wherein every behavior sample (observation), be often classified as metabolin (variable), matrix intermediate value is corresponding metabolite concentration.And each metabolin peak use retention time (retentiontime, RT) and mass-to-charge ratio (mass-to-chargeratio, m/z) qualitative.Then this two-dimensional matrix uses R software package CAMERA to carry out metabolin peak mark (comprising isotopic peak, adduct and fragmention).Carry out standardization to sample before statistical study, retention time range set to be analyzed is 0.5 ~ 10min.Through the pre-service of XCMS collection of illustrative plates, in the data matrix that the UPLC-QTOF/MS spectrum of positive ion detecting pattern generates, comprise 981 metabolin peaks, anionic textiles pattern is 485 metabolin peaks, has 1466 metabolin peaks.
4, LC-MS quality control of the experiment
When serum sample carries out metabolism group detection, arrange the order of 1 QC to insert in analyzing samples equably by every 10 analyzing samples in the QC sample of preparation, thus Real-Time Monitoring is from Sample pretreatment to the quality control situation pattern detection process.The original Metabolic fingerprinting of gained is after the pre-service of XCMS collection of illustrative plates, calculate the %RSD value (coefficient of variation) of each metabolin in QC sample, the %RSD value of most metabolin controls below 30%, illustrate that Sample pretreatment is all right to the quality control in sample product testing process, the metabolism group data obtained are genuine and believable.
5, the metabolic profile preanalysis of Based PC A
Use and come classification trend and outlier between preliminary observation group without supervision analytical approach and principal component analysis (PCA) (principalcomponentanalysis, PCA), see Fig. 2.In figure, the repeatability of QC sample can show that LC-MS quality control of the experiment is good.From figure, it can also be seen that between the cancer of the esophagus and normal healthy controls, to there is certain classification trend, but still have partial intersection, need to adopt supervised learning method to realize further classification.
6, based on the metabolic profiling analysis of PLS-DA
The two-dimensional matrix data Random assignment obtained is become 4/5 as training sample trainingdata, and other 1/5 as external testing sample testdata (see table 1).For the deviation that gap in elimination group of trying one's best causes, obtain trend of comparatively significantly dividing into groups, the difference and the classification trend that have supervision analytical approach and partial least squares discriminant analysis (partialleastsquares-discriminantanalysis, PLS-DA) to show the metabolic profile between the cancer of the esophagus and normal healthy controls is used further for training sample.As shown in Figure 3, the cancer of the esophagus is with having classification trend between metabolic patterns difference and obvious group between normal healthy controls, the R2X=0.167 of its modeling, R2Y=0.569, Q2Y=0.523.
The baseline of the metabolism group research of table 1. cancer of the esophagus early diagnosis and clinical pathologic characteristic
7, the difference metabolin of cancer of the esophagus early diagnosis screens and chemical substance qualification
For filtering out the difference metabolin of early stage esophagus cancer diagnosis, we mark (VIP) by means of the variable importance of PLS-DA modeling and univariate non-parametric test (nonparametricKruskal-Wallisranksumtest) is screened.Variable Selection standard is: VIP >=1; And q value is less than 0.05 after the multiple testing adjustment of False discovery rate FDR.According to this standard, filter out differential expression blood serum metabolic group echo thing 551 between the cancer of the esophagus and normal healthy controls altogether, quasi-molecular ion, adduct and the Isotope Information of determining difference metabolin is wrapped further according to the CAMERA of R language, getting rid of does not have in chemical signal and human body, obtains 242 potential metabolic marker things.
For above-mentioned 242 potential metabolic marker things, according to following chemical substance authentication step (as metabolic marker thing L-Trp RT181.38s, the qualification process of m/z205.0974, is shown in Fig. 4), carry out the qualification of metabolic marker thing:
(1) according to the first mass spectrometric cracking distribution characteristics of potential metabolic marker thing, in conjunction with the CAMERA software package of R language, (CAMERA is R language package software package Collectionofannotationrelatedmethodsformassspectrometryd ata, http://bioconductor.org/packages/release/bioc/html/CAMERA.html) determine the quasi-molecular ion of potential metabolic marker thing, adduct and Isotope Information, infer molecular mass and the molecular formula of potential metabolic marker thing;
(2) search online people's metabolite database HMDB (http://www.hmdb.ca/) and METLIN (http://metlin.scripps.edu/) according to molecular mass, determine some compound candidate;
(3) experiment of RRLC-QTOF/MS/MS second order ms is carried out to 242 potential metabolic marker things, obtain the corresponding mass ions patch information of metabolin further, and carry out second order ms figure fragmention coupling with compound candidate in database; The chromatogram of comparison compound standard sample library and mass spectral characteristic carry out final material and determine.
According to above-mentioned authentication method, when being confirmed by second order ms or standard items, successful identification goes out 25 blood serum metabolic labels altogether, and the common name (CommonName) of these 25 serum size labels is respectively: beta-Ala-Lys; L-Carnosine; Cis-9-Palmitoleicacid; Palmiticacid; OleicAcid; LPA (18:1 (9Z)/0:0); LysoPC (14:0/0:0); LysoPC (18:2 (9Z, 12Z)); LysoPC (24:0); PC (14:1 (9Z)/P-18:1 (11Z)); PC (16:0/18:2 (9Z, 12Z)); PC (24:1 (15Z)/22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)); Linoleicacid; NADH; Cortisol; L-Tyrosine; L-Tryptophan; GlycocholicAcid; Taurocholate; Hypoxanthine; Allantoicacid; Inosine; Sphingosine1-phosphate; 3-O-Sulfogalactosylceramide (d18:1/20:0); Lactosylceramide (d18:1/22:0).
Contrast delivers document, and these 25 blood serum metabolic labels of the present invention are first and find in esophageal squamous cell carcinoma in early days, and this diagnosis for the early stage cancer of the esophagus, treatment are of great significance.Wherein, the Chinese translation of beta-Ala-Lys is beta-alanine-lysine, the Chinese translation of L-Carnosine is L-Carnosine, the Chinese translation of cis-9-Palmitoleicacid is POA, the Chinese translation of Palmiticacid is palmitic acid, the Chinese translation of OleicAcid is oleic acid, the Chinese translation of LPA is lysophosphatidic acid, the Chinese translation of LysoPC is lysolecithin, the Chinese translation of PC is phosphatide, the Chinese translation of Linoleicacid is linoleic acid, the Chinese translation of NADH is nicotinamide adenine dinucleotide, the Chinese translation of Cortisol is cortisol, the Chinese translation of L-Tyrosine is TYR, the Chinese translation of L-Tryptophan is L-Trp, the Chinese translation of GlycocholicAcid is glycocholic acid, the Chinese translation of Taurocholate is taurocholate, the Chinese translation of Hypoxanthine is hypoxanthine, the Chinese translation of Allantoicacid is allantoic acid, the Chinese translation of Inosine is inosine, the Chinese translation of Sphingosine1-phosphate is S1P, the Chinese translation of 3-O-Sulfogalactosylceramide is sulfogalactosylceramide, the Chinese translation of Lactosylceramide is galactosylceramide, may deviation be there is in each Chinese translation because translating, be as the criterion with English standard name.
The database retrieval information of above-mentioned 25 blood serum metabolic labels in HMDB and METLIN is as shown in table 2 below, those skilled in the art can obtain the details of these 25 blood serum metabolic labels according in following table No. HMDBID, No. METLINID, such as chemical structural formula:
A table 225 blood serum metabolic label is in the database retrieval information of HMDB and METLIN
In addition, analyzed by KEGG enrichment (enrichment) and metabolic pathway (pathway), find above-mentioned 25 blood serum metabolic labels and following 10 metabolic pathways closely related: Glycerophospholipidmetabolism; Linoleicacidmetabolism; Beta-Alaninemetabolism; Fattyacidbiosynthesis; Oxidativephosphorylation; Phenylalanine, tyrosineandtryptophanbiosynthesis; Primarybileacidbiosynthesis; Pathwaysincancer, andBilesecretion; Purinemetabolism; Sphingolipidmetabolism.Above-mentioned 10 metabolic pathways are the standard name (http://www.genome.jp/kegg/) in metabolic pathway KEGG, its corresponding Chinese translation is: glycerophosphatide metabolism (Glycerophospholipidmetabolism), linoleic acid metabolism (Linoleicacidmetabolism), beta alanine metabolism (beta-Alaninemetabolism), fatty acid synthesis (Fattyacidbiosynthesis), oxidative phosphorylation (Oxidativephosphorylation), phenylalanine/tyrosine and tryptophan metabolism (Phenylalanine, tyrosineandtryptophanbiosynthesis), primary bile acid synthesis (Primarybileacidbiosynthesis), cancer path and choleresis (Pathwaysincancer, andBilesecretion), purine metabolism (Purinemetabolism), sphingolipid metabolism (Sphingolipidmetabolism).This proves that Incidence of Esophageal Cancer these 10 metabolic pathways early stage there occurs disturbance, and this discovery of the present invention has good directive function for the prevention of the cancer of the esophagus and the research and development of medicine.
Following table 3 is screen the different information of 25 blood serum metabolic labels in patient with esophageal carcinoma and healthy population obtained, and wherein under positive and negative two ion modes, has all found L-Tryptophan.FC is the change multiple (foldchange) that the cancer of the esophagus is compared with normal healthy controls, can find out according to FC information: beta-alanine-lysine (beta-Ala-Lys), lysophosphatidic acid LPA (18:1 (9Z)/0:0), lysolecithin LysoPC (14:0/0:0), phosphatide PC (16:0/18:2 (9Z, 12Z)), nicotinamide adenine dinucleotide (NADH), TYR (L-Tyrosine), L-Trp (L-Tryptophan), glycocholic acid (GlycocholicAcid), allantoic acid (Allantoicacid), inosine (Inosine) and S1P (Sphingosine1-phosphate) are compared healthy group expression and are obviously raised in cancer of the esophagus group, and other metabolic marker thing is compared healthy group expression and is obviously reduced in cancer of the esophagus group.
FDR is the False discovery rate corrected based on non-parametric test Multiple range test, and its value is all less than 0.05; AUC is the ROC area under curve AUC value of the Evaluating Diagnostic Tests of single metabolism group echo thing, when can find out that these 25 blood serum metabolic labels carry out the diagnosis of the cancer of the esophagus and the non-cancer of the esophagus as label separately from this value, minimum AUC value is 0.61, and the highest AUC value is 0.85.This shows, as the diagnostic marker of one-component, the diagnosis effect that the present invention screens 25 the blood serum metabolic labels obtained is comparatively significant, carries out diagnosing having certain value of clinical studies with single blood serum metabolic label.
In order to make diagnosis effect better, blood serum metabolic label can be combined into exercise and use, such as can combine according to the relation between blood serum metabolic label and metabolic pathway, form following 8 kinds of diagnostic markers: the combination of (a) beta-alanine-lysine (beta-Ala-Lys) and L-Carnosine (L-Carnosine); The combination of (b) POA (cis-9-Palmitoleicacid), palmitic acid (Palmiticacid) and oleic acid (OleicAcid); The combination of (c) lysophosphatidic acid LPA (18:1 (9Z)/0:0), lysolecithin LysoPC (14:0/0:0), lysolecithin LysoPC (18:2 (9Z, 12Z)) and lysolecithin LysoPC (24:0); (d) phosphatide PC (14:1 (9Z)/P-18:1 (11Z)), phosphatide PC (16:0/18:2 (9Z, 12Z)), phosphatide PC (24:1 (15Z)/22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)) and the combination of linoleic acid (Linoleicacid); The combination of (e) nicotinamide adenine dinucleotide (NADH), TYR (L-Tyrosine) and L-Trp (L-Tryptophan); The combination of (f) cortisol (Cortisol), glycocholic acid (GlycocholicAcid) and taurocholate (Taurocholate); The combination of (g) hypoxanthine (Hypoxanthine), allantoic acid (Allantoicacid) and inosine (Inosine); The combination of (h) S1P (Sphingosine1-phosphate), sulfogalactosylceramide 3-O-Sulfogalactosylceramide (d18:1/20:0) and galactosylceramide Lactosylceramide (d18:1/22:0).
Several metabolic marker things that AUC can also be selected effective carry out being combined to form diagnostic marker, such as, diagnostic marker can be two or more the combination in following blood serum metabolic label: lysophosphatidic acid LPA (18:1 (9Z)/0:0), lysolecithin LysoPC (14:0/0:0), lysolecithin LysoPC (18:2 (9Z, 12Z)), lysolecithin LysoPC (24:0), phosphatide PC (14:1 (9Z)/P-18:1 (11Z)), phosphatide PC (16:0/18:2 (9Z, 12Z)), phosphatide PC (24:1 (15Z)/22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)), nicotinamide adenine dinucleotide (NADH), cortisol (Cortisol), L-Trp (L-Tryptophan), taurocholate (Taurocholate), hypoxanthine (Hypoxanthine), inosine (Inosine), sulfogalactosylceramide 3-O-Sulfogalactosylceramide (d18:1/20:0), galactosylceramide Lactosylceramide (d18:1/22:0).
Can also be two or more the combination in following 7 kinds of blood serum metabolic labels: lysophosphatidic acid LPA (18:1 (9Z)/0:0), lysolecithin LysoPC (14:0/0:0), lysolecithin LysoPC (18:2 (9Z, 12Z)), lysolecithin LysoPC (24:0), phosphatide PC (14:1 (9Z)/P-18:1 (11Z)), phosphatide PC (16:0/18:2 (9Z, 12Z)) and phosphatide PC (24:1 (15Z)/22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)).
Can also be two or more the combination in following 5 kinds of blood serum metabolic labels: TYR (L-Tyrosine), L-Trp (L-Tryptophan), glycocholic acid (GlycocholicAcid), taurocholate (Taurocholate) and cortisol (Cortisol).
25 kinds of blood serum metabolic labels of differential expression between table 3. cancer of the esophagus and normal healthy controls
FDR is False discovery rate (Multiple range test); AUC is ROC area under curve; FC is that foldchange changes multiple; RSD% is the coefficient of variation calculated based on quality control sample.
Below, itemize the further effect of the present invention's 3 kinds of preferred diagnostic markers, the applicable cases of other diagnostic markers will not enumerate at this.
8, cancer of the esophagus early diagnosis model and external certificate
8.1 using the combination of 25 of above-mentioned qualification blood serum metabolic labels as diagnostic marker, in training sample, build cancer of the esophagus early diagnosis model based on random forest (randomForest).Random forest uses randomForest software package in R language to realize, modeling parameters ntree=5000 (be equal to following b).
Random forest modeling procedure is as follows:
(1) sample content of original training set is N, application bootstrap method has randomly draws the individual new self-service sample set of b with putting back to, and building b classification tree thus, the sample be not at every turn pumped to constitutes the outer data (out-of-bag, OOB) of b bag;
(2) m is provided with allindividual variable, then randomly draw m at each Nodes of every one tree txyindividual variable (m try< < m all), then at m trymiddle selection one has the variable of classification capacity most, and the threshold value of variable classification is by checking that each classification point is determined;
(3) each classification tree in random forest is binary tree, and it generates follows top-down recurrence division principle, namely divides training set successively from root node.Every tree grows to greatest extent, does not do any pruning.
(4) many classification trees composition random forests that will generate, carry out discriminant factor with random forest sorter to new data, and how much the ballot that classification results press Tree Classifier is determined.
(5) the diagnosis dividing value (Threshold) of diagnosis then can be obtained with this ballot score and actual classification situation row ROC tracing analysis.The diagnosis dividing value (Threshold) of this model is 0.3552.
The Random Forest model of above-mentioned structure namely can as esophagus cancer diagnosis model, when adopting the Random Forest model built to diagnose, the data message of the blood serum metabolic label of 25 in test serum is imported in Random Forest model, if the voting results of model classifiers are more than or equal to diagnosis dividing value, then be judged to be diagnosis positive (trouble esophageal squamous cell carcinoma), if lower than diagnosis dividing value, be then judged to be diagnosis negative (not suffering from esophageal squamous cell carcinoma).
The two-dimensional matrix data of 25 of external testing sample blood serum metabolic labels are substituted in the Random Forest model of above-mentioned foundation, obtain the cancer of the esophagus P predicted value of test sample book, and compare with actual pathological examination (cancer of the esophagus or health) and do ROC tracing analysis (see Fig. 5), obtain the sensitivity of Random Forest model, specificity and ROC area under curve AUC value, the results are shown in Table 4.As can be seen from Fig. 5 and table 4, the esophagus cancer diagnosis modelling effect of the above-mentioned structure of the present invention is good, and its ROC area under curve AUC for esophagus cancer diagnosis is 0.895 (0.784 ~ 1), and sensitivity is 85.00%, and specificity is 90.48%.
Further, the difference of test sample book cancer of the esophagus P predicted value is by stages done ROC tracing analysis respectively compared with actual pathological examination (cancer of the esophagus or health), for evaluating the diagnosis effect of this diagnostic model to the difference cancer of the esophagus by stages.Random Forest model is for the sensitivity of the difference cancer of the esophagus by stages, specificity and ROC area under curve AUC value see the following form 4, as can be seen from the table: along with the further deterioration of the cancer of the esophagus, AUC value and specificity have and increase trend, sensitivity in position during cancer and late cancer better, decline to some extent in cancer in early days, this model is better for the diagnosis effect of late esophagus cancer on the whole, but the diagnosis effect of carcinoma in situ and the early stage cancer of the esophagus (AUC) also can reach acceptable more than 0.85, also there is the value of early diagnosis, also illustrate simultaneously the present invention screen the blood serum metabolic label that obtains in early days the cancer of the esophagus even carcinoma in situ stage just had metabolic alterations.
Carcinoma in situ also wants the Zao stage than early stage (I and II phase) cancer of the esophagus, and the diagnosis of the cancer of the esophagus is early stage more difficult, and late period is relatively easy.Seen by the data in table, diagnostic model of the present invention can be good at diagnosing out whether suffer from the cancer of the esophagus, and it is not only good to the diagnosis effect of late esophagus cancer, also better for the accuracy of the early stage cancer of the esophagus and carcinoma in situ, sensitivity and specificity, effectively can diagnose out the unconspicuous carcinoma in situ of symptom and the early stage cancer of the esophagus, reduce cancer rate of missed diagnosis, the morning being very beneficial for the cancer of the esophagus finds, early treatment, for improve the cancer of the esophagus prognosis, reduce the mortality ratio of the cancer of the esophagus and have good help, there is good Clinical practice and promotional value.
The ROC analysis result of the outside popularization of table 4. esophagus cancer diagnosis model
8.2 carry out modeling using the combination of 7 blood serum metabolic labels as diagnostic marker, and for diagnosis of esophageal cancer, specific as follows:
The two-dimensional matrix data Random assignment obtained is become 4/5 as training sample trainingdata, and other 1/5 as external testing sample testdata (see table 1).Only adopt lysophosphatidic acid LPA (18:1 (9Z)/0:0), lysolecithin LysoPC (14:0/0:0), lysolecithin LysoPC (18:2 (9Z, 12Z)), lysolecithin LysoPC (24:0), phosphatide PC (14:1 (9Z)/P-18:1 (11Z)), phosphatide PC (16:0/18:2 (9Z, 12Z)) and phosphatide PC (24:1 (15Z)/22:6 (4Z, 7Z, 10Z, 13Z, 16Z, 19Z)) 7 kinds of metabolic marker things are as diagnostic marker, cancer of the esophagus early diagnosis model is built based on random forest (randomForest) in training sample.Random forest uses randomForest software package in R language to realize, and modeling parameters ntree=5000, random forest modeling procedure is the same.
When adopting the probabilistic model built to diagnose, the data message of the blood serum metabolic label of 7 in test serum is imported in Random Forest model, if the voting results of model classifiers are more than or equal to diagnosis dividing value, then be judged to be diagnosis positive (trouble esophageal squamous cell carcinoma), if lower than diagnosis dividing value, be then judged to be diagnosis negative (not suffering from esophageal squamous cell carcinoma).The diagnosis dividing value (Threshold) of this model is 0.7431.
The two-dimensional matrix data of 7 of external testing sample blood serum metabolic labels are substituted in the Random Forest model of above-mentioned foundation, obtain the cancer of the esophagus P predicted value of test sample book, and compare with actual pathological examination (cancer of the esophagus or health) and do ROC tracing analysis (see Fig. 6), obtain the sensitivity of Random Forest model, specificity and ROC area under curve AUC value, the results are shown in Table 5.As can be seen from Fig. 6 and table 5, the esophagus cancer diagnosis modelling effect of the above-mentioned structure of the present invention is good, and its AUC for esophagus cancer diagnosis is 0.876 (0.752 ~ 1), and sensitivity is 90%, and specificity is 85.71%.
Further, the difference of test sample book cancer of the esophagus P predicted value is by stages done ROC tracing analysis respectively compared with actual pathological examination (cancer of the esophagus or health), for evaluating the diagnosis effect of this diagnostic model to the difference cancer of the esophagus by stages.Random Forest model is for the sensitivity of the difference cancer of the esophagus by stages, specificity and ROC area under curve AUC value see the following form 5, as can be seen from the table: along with the further deterioration of the cancer of the esophagus, AUC value and sensitivity have increases trend, specificity in position during cancer and late cancer better, decline to some extent in cancer in early days, this model is better for the diagnosis effect of late esophagus cancer on the whole, but the diagnosis effect of carcinoma in situ and the early stage cancer of the esophagus (AUC) also can reach acceptable more than 0.83, also there is the value of early diagnosis, also illustrate simultaneously the present invention screen the blood serum metabolic label that obtains in early days the cancer of the esophagus even carcinoma in situ stage just had metabolic alterations.
As can be seen from the data in table, the diagnostic model of the present invention's 7 blood serum metabolic label information architectures compared to employing 25 blood serum metabolic label information architectures diagnostic model weak effect some, but this diagnostic model also can be good at diagnosing out whether suffer from the cancer of the esophagus, and it is not only good to the diagnosis effect of late esophagus cancer, for the accuracy of the early stage cancer of the esophagus and carcinoma in situ, sensitivity and specificity are also better, effectively can diagnose out the unconspicuous carcinoma in situ of symptom and the early stage cancer of the esophagus, reduce cancer rate of missed diagnosis, the morning being very beneficial for the cancer of the esophagus finds, early treatment, for the prognosis improving the cancer of the esophagus, the mortality ratio reducing the cancer of the esophagus has good help, there is good Clinical practice and promotional value.
The ROC analysis result of the outside popularization of table 5 esophagus cancer diagnosis model
8.3, modeling is carried out using the combination of 5 blood serum metabolic labels as diagnostic marker, and for diagnosis of esophageal cancer, specific as follows:
The two-dimensional matrix data Random assignment obtained is become 4/5 as training sample trainingdata, and other 1/5 as external testing sample testdata (see table 1).Adopt TYR (L-Tyrosine), L-Trp (L-Tryptophan), glycocholic acid (GlycocholicAcid), taurocholate (Taurocholate) and cortisol (Cortisol) 5 kinds of blood serum metabolic labels as diagnostic marker, in training sample, build cancer of the esophagus early diagnosis model based on random forest (randomForest).Random forest uses randomForest software package in R language to realize, and modeling parameters ntree=5000, random forest modeling procedure is the same.
When adopting the probabilistic model built to diagnose, the data message of the blood serum metabolic label of 5 in test serum is imported in Random Forest model, if the voting results of model classifiers are more than or equal to diagnosis dividing value, then be judged to be diagnosis positive (trouble esophageal squamous cell carcinoma), if lower than diagnosis dividing value, be then judged to be diagnosis negative (not suffering from esophageal squamous cell carcinoma).The diagnosis dividing value (Threshold) of this model is 0.4943.
The two-dimensional matrix data of 5 of external testing sample blood serum metabolic labels are substituted in the Random Forest model of above-mentioned foundation, obtain the cancer of the esophagus P predicted value of test sample book, and compare with actual pathological examination (cancer of the esophagus or health) and do ROC tracing analysis (see Fig. 7), obtain the sensitivity of Random Forest model, specificity and ROC area under curve AUC value, the results are shown in Table 6.As can be seen from Fig. 7 and table 6, the esophagus cancer diagnosis modelling effect of the above-mentioned structure of the present invention is good, and its AUC for esophagus cancer diagnosis is 0.84 (0.703 ~ 0.978), and sensitivity is 95%, and specificity is 76.19%.
Further, the difference of test sample book cancer of the esophagus P predicted value is by stages done ROC tracing analysis respectively compared with actual pathological examination (cancer of the esophagus or health), for evaluating the diagnosis effect of this diagnostic model to the difference cancer of the esophagus by stages.Random Forest model sees the following form 6 for the sensitivity of the difference cancer of the esophagus by stages, specificity and ROC area under curve AUC value, as can be seen from the table: these 5 kinds of blood serum metabolic labels show different trend for carcinoma in situ, early carcinoma and late cancer.
As can be seen from the data in table, the diagnostic model of the present invention's 5 blood serum metabolic label information architectures compared to employing 25 and 7 blood serum metabolic label information architectures diagnostic model weak effect some, but this diagnostic model also can be good at diagnosing out whether suffer from the cancer of the esophagus, and it is not only good to the diagnosis effect of late esophagus cancer, for the accuracy of the early stage cancer of the esophagus and carcinoma in situ, sensitivity and specificity are also better, effectively can diagnose out the unconspicuous carcinoma in situ of symptom and the early stage cancer of the esophagus, reduce cancer rate of missed diagnosis, the morning being very beneficial for the cancer of the esophagus finds, early treatment, for the prognosis improving the cancer of the esophagus, the mortality ratio reducing the cancer of the esophagus has good help, there is good Clinical practice and promotional value.
The ROC analysis result of the outside popularization of table 6 cancer of the esophagus early diagnosis model
9, conclusion
Any one diagnostic marker as diagnosis of esophageal cancer in 9.1 gained of the present invention, 25 blood serum metabolic labels all has good diagnosis effect, but by the better effects if of multiple blood serum metabolic label Combination application.
The preferred 3 kinds of diagnostic markers (diagnostic marker A, B, C) of 9.2 the present invention and the diagnostic model built have good diagnosis effect for the cancer of the esophagus, have clinical value.
Through checking, gained diagnostic marker of the present invention and diagnostic model have good using value, and diagnostic marker of the present invention and diagnostic model can be adopted clinically to carry out the diagnosis of the cancer of the esophagus, and step is as follows:
(1) gather serum to be checked, pre-service is carried out, in order to sample detection to serum in step (1)-(4) in centrifugal rear employing above-mentioned 2.2;
(2) by pretreated serum sample to be checked according to above-mentioned 2.3 step carry out LC-MS detection, obtain original Metabolic fingerprinting;
(3) original Metabolic fingerprinting is carried out collection of illustrative plates pre-service according to the method for above-mentioned steps 3, and carry out metabolin peak mark, obtain the two-dimensional matrix of this serum to be checked;
(4) from two-dimensional matrix, filter out corresponding diagnostic marker (diagnostic marker A, B or C) information according to mass-to-charge ratio and retention time, obtain diagnostic marker two-dimensional matrix;
(5) bring in corresponding diagnostic model by diagnostic marker two-dimensional matrix, the diagnosis dividing value (Threshold) of the numerical value provided according to model and model, determines whether esophageal squamous cell carcinoma.When the numerical value that model provides is more than or equal to diagnosis dividing value, be judged to be diagnosis positive (trouble esophageal squamous cell carcinoma), if lower than diagnosis dividing value, be then judged to be diagnosis negative (not suffering from esophageal squamous cell carcinoma).
In addition, in order to accelerate efficiency, the serum sample of many people can be gathered simultaneously, and be numbered, carry out LC-MS detection, collection of illustrative plates pre-service, metabolic peak mark, the screening of diagnostic marker two-dimensional matrix and data importing by disposable for multiple sample.
In actual applications, more sample can be chosen according to modeling method of the present invention and carry out modeling, increase the accuracy of model.
Non-limiting for the description to patent of the present invention above, based on other embodiments of patent thought of the present invention, all among scope.

Claims (3)

1. be suitable for a diagnostic marker for esophageal squamous cell carcinoma early diagnosis, it is characterized in that: two or more the combination in following 5 kinds of blood serum metabolic labels: TYR (L-Tyrosine), L-Trp (L-Tryptophan), glycocholic acid (GlycocholicAcid), taurocholate (Taurocholate) and cortisol (Cortisol).
2. diagnostic marker according to claim 1, is characterized in that: the combination for following 5 kinds of blood serum metabolic labels: TYR (L-Tyrosine), L-Trp (L-Tryptophan), glycocholic acid (GlycocholicAcid), taurocholate (Taurocholate) and cortisol (Cortisol).
3. diagnostic marker according to claim 1, it is characterized in that: TYR (L-Tyrosine) and L-Trp (L-Tryptophan) and phenylalanine/tyrosine and tryptophan metabolism (Phenylalanine, tyrosineandtryptophanbiosynthesis) metabolic pathway closely related; It is closely related that glycocholic acid (GlycocholicAcid) and taurocholate (Taurocholate) and primary bile acid synthesize (Primarybileacidbiosynthesis) metabolic pathway; Cortisol (Cortisol) and cancer path and choleresis (Pathwaysincancer, andBilesecretion) metabolic pathway closely related.
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