CN114965801A

CN114965801A - Application of metabolic marker in preparation of oral cancer diagnosis kit

Info

Publication number: CN114965801A
Application number: CN202210520741.7A
Authority: CN
Inventors: 杨溪; 马海龙; 张新宇
Original assignee: Ninth Peoples Hospital Shanghai Jiaotong University School of Medicine
Current assignee: Ninth Peoples Hospital Shanghai Jiaotong University School of Medicine
Priority date: 2022-05-13
Filing date: 2022-05-13
Publication date: 2022-08-30

Abstract

The invention discloses an application of a metabolic marker in preparation of an oral cancer diagnosis kit, wherein the metabolic marker is selected from one or more of sphingosine, O-phosphoethanolamine, dihydrosphingosine, 3-dehydrodihydrosphingosine, D-maltose, dextrin, L-kynurenine, 5-hydroxytryptophan, L-glutamic acid, glutathione, gamma-glutamylcysteine, L-aspartic acid, L-arginine, N-acetyl-L-glutamic acid 5-semialdehyde, argininosuccinic acid, PC (18:1(11Z)/16:1(9Z)) and 12, 13-DHOME. The metabolic marker can be used for quickly identifying and identifying oral cancer and tissues beside the oral cancer, is beneficial to quickly identifying surgical incisal margins in oral cancer operation, and can also assist in early diagnosis of the oral cancer.

Description

Application of metabolic marker in preparation of oral cancer diagnosis kit

Technical Field

The invention relates to a metabolic marker, in particular to application of the metabolic marker in preparation of an oral cancer diagnosis kit.

Background

Metabolomics (metabolomics) is an important component of system biology following genomics, transcriptomics and proteomics, and is also one of the research hotspots in the field of omics today. The Nicholson research group in UK proposes the concept of metabonomics when analyzing the urine components of rats from the toxicological point of view, and considers that the metabonomic group is a technology for researching the metabolic pathways of a biological system by observing the change of metabolites or the change of metabolites along with time after the biological system is disturbed or stimulated (such as certain gene variation or environmental variation).

Compared with the traditional metabolic research, the metabonomics integrates multidisciplinary knowledge of physics, biology, analytical chemistry and the like, the modern advanced instrument combined analysis technology is utilized to detect the change of the whole metabolite spectrum of an organism under specific conditions, and the whole biological function condition is researched by a special multivariate statistical analysis method. Because the research objects of metabonomics are all metabolites of organisms, and the metabolites are generated by the reaction of endogenous substances in organisms, the change of the metabolites reveals the change of the endogenous substances or gene level, so that the research objects change from micro genes into macro metabolites and macro metabolic phenotype, and the research objects are more visual. The advantages of metabolomics are mainly the following 5 aspects: 1. genomics and proteomics analyze biological activities at the gene and protein level, respectively, and actually many vital activities in cells occur at the metabolite level, such as cell signal release, energy transfer, intercellular communication and the like are all regulated by metabolites; 2. compared with transcriptomics and proteomics, the metabonomics specially research the change of metabolites of organisms caused by gene modification or external environment change, is the omics closest to phenotype, and is the final embodiment of the whole function or state of a biological system; 3. effective small changes in gene and protein expression can be amplified in the process of generating metabolites through protein cascade reaction, so that the detection is easier; 4. the metabolome does not need to establish a sequence tag (EST) database; 5. metabolites and metabolic pathways share similarities in different organisms and can be mapped.

Although the liquid chromatography-mass spectrometry (LC-MS) technology is started later, the technology has obvious advantages compared with other metabonomics technologies. LC-MS is suitable for analyzing metabolites which are difficult to volatilize or have poor thermal stability, and has the characteristics of high flux, high resolution and high sensitivity. The ultra-high pressure liquid chromatography or ultra-high performance liquid chromatography (UPLC) realizes better separation by adopting chromatographic packing with smaller particle size to match with higher pressure, and specifically comprises higher separation capacity, faster separation speed and better sensitivity; the tandem mass spectrometer can realize high-low collision energy fast switching in an MS scanning mode, simultaneously collects secondary mass spectrum information of metabolites, analyzes the mass spectrum information by combining with Progenesis QI v2.3, and can detect and identify hundreds to thousands of metabolites in single analysis. The metabolomics protocol includes: sample pretreatment, metabolite extraction, LC-MS full-scan detection, data pretreatment, statistical analysis and the like.

Oral cancer is a general term of malignant tumors occurring in oral cavity, and is one of the most common malignant tumor diseases, the treatment method adopts comprehensive treatment mainly based on operation, if the surgical resection is not thorough, the disease is easy to recur after the operation, the prognosis of early oral cancer treatment is better, and the prognosis of middle and late stages is worse, so that the rapid identification and identification in the operation of oral cancer tissues and the early diagnosis of oral cancer are key factors for obtaining good prognosis. Metabonomic detection provides a convenient and feasible means for the rapid diagnosis of oral cancer.

Disclosure of Invention

In order to facilitate the rapid identification and characterization of oral cancer and paracancerous tissues, the present invention provides the use of a metabolic marker in the preparation of a diagnostic kit for oral cancer, said metabolic marker being selected from one or more of sphingosine, O-phosphoethanolamine, sphinganine, 3-dehydrosphinganine, D-maltose, dextrin, L-kynurenine, 5-hydroxytryptophan, L-glutamic acid, glutathione, γ -glutamylcysteine, L-aspartic acid, L-arginine, N-acetyl-L-glutamic acid 5-semialdehyde, arginosuccinic acid, PC (18:1(11Z)/16:1(9Z)) and 12, 13-DHOME.

Preferably, the metabolic marker is selected from one or more of sphingosine, O-phosphoethanolamine, sphinganine, 3-dehydrosphinganine, D-maltose, dextrin, L-kynurenine, 5-hydroxytryptophan, L-glutamic acid, gamma-glutamylcysteine, L-aspartic acid, L-arginine, N-acetyl-L-glutamic acid 5-semialdehyde, arginosuccinic acid and PC (18:1(11Z)/16:1 (9Z)).

Further preferably, the metabolic marker is selected from one or more of 3-dehydrosphinganine, D-maltose, dextrin, L-kynurenine, L-glutamic acid, L-aspartic acid and N-acetyl-L-glutamic acid 5-semialdehyde.

Preferably, the metabolic markers comprise at least two of the above compounds.

Preferably, the metabolic markers are from tissue samples, including cancer tissue and normal tissue samples.

In one embodiment of the invention, the metabolic markers include sphingosine, O-phosphoethanolamine, sphinganine and 3-dehydrosphinganine.

In one embodiment of the invention, the metabolic markers comprise D-maltose and dextrin.

In one embodiment of the invention, the metabolic markers include L-kynurenine and 5-hydroxytryptophan.

In one embodiment of the invention, the metabolic markers include L-glutamic acid, glutathione and γ -glutamylcysteine.

In one embodiment of the invention, the metabolic markers include L-glutamic acid, L-aspartic acid, L-arginine, N-acetyl-L-glutamic acid 5-semialdehyde, and argininosuccinic acid.

In one embodiment of the invention, the metabolic markers include PC (18:1(11Z)/16:1(9Z)) and 12, 13-DHOME.

Further, the invention provides an oral cancer diagnosis kit, which comprises reagents required by liquid chromatography-mass spectrometry combined detection of the metabolic markers, such as methanol, formic acid, water, acetonitrile, an internal standard and the like.

Preferably, the oral cancer diagnosis kit further comprises consumables, such as a chromatographic column and the like, required by the liquid chromatography-mass spectrometry combined detection of the metabolic markers.

Preferably, the test sample of the oral cancer diagnosis kit is a tissue sample.

Preferably, the kit is a sphingolipid metabolism pathway combined index oral cancer diagnostic kit, wherein the metabolic markers include sphingosine, O-phosphoethanolamine, sphinganine and 3-dehydrosphinganine.

Preferably, the kit is a starch and sucrose metabolic pathway combined index oral cancer diagnosis kit, wherein the metabolic markers comprise D-maltose and dextrin.

Preferably, the kit is a kynurenine metabolic pathway combined index oral cancer diagnosis kit, wherein the metabolic markers comprise L-kynurenine and 5-hydroxytryptophan.

Preferably, the kit is an iron death pathway combined index oral cancer diagnosis kit, wherein the metabolic markers comprise L-glutamic acid, glutathione and gamma-glutamylcysteine.

Preferably, the kit is an arginine metabolic pathway combined index oral cancer diagnosis kit, wherein the metabolic markers comprise L-glutamic acid, L-aspartic acid, L-arginine, N-acetyl-L-glutamic acid 5-semialdehyde and argininosuccinic acid.

Preferably, the kit is a linoleic acid metabolism pathway combined index oral cancer diagnosis kit, wherein the metabolic markers comprise PC (18:1(11Z)/16:1(9Z)) and 12, 13-DHOME.

The invention provides a group of metabolic markers for identifying and identifying oral cancer and tissues beside the oral cancer, wherein the content of the markers in the oral cancer tissues and the tissues beside the oral cancer (normal tissues) is remarkably different, the cancer tissues and the normal tissues can be quickly identified by detecting the content of the markers through LC-MS (liquid chromatography-mass spectrometry), the method is very suitable for quickly identifying surgical incisal margins in operation or after the operation, and is favorable for completely excising tumor tissues, and in addition, the method can also be used for assisting the early diagnosis of the oral cancer so as to obtain good prognosis.

The oral cancer diagnosis kit designed according to the method can further improve the detection efficiency of the metabolic marker and accelerate the intraoperative identification of cancer tissues. The invention divides the metabolic markers into groups, designs detection kits aiming at different pathways, and can select different metabolite detection kits for detection according to needs before tumor screening or determination.

The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.

Drawings

FIG. 1 is a diagram of a PCA model obtained by 7 rounds of cycle cross validation in an embodiment of the present invention;

FIG. 2 is a volcano plot visualized by p-value and VIP fold change values in an embodiment of the present invention;

FIG. 3 is a ROC plot of sphingosine, a differential metabolite in an example of the invention;

FIG. 4 is a ROC plot of the differential metabolite O-phosphoethanolamine in examples of the present invention;

FIG. 5 is a ROC plot of the differential metabolite sphingan in examples of the present invention;

FIG. 6 is a ROC plot of the differential metabolite 3-dehydrosphinganine in examples of the present invention;

FIG. 7 is a ROC plot of the differential metabolite D-maltose in the examples of the present invention;

FIG. 8 is a ROC plot of the differential metabolite dextrin in the examples of the present invention;

FIG. 9 is a ROC plot of the differential metabolite L-kynurenine in examples of the present invention;

FIG. 10 is a ROC plot of the differential metabolite 5-hydroxytryptophan in examples of the present invention;

FIG. 11 is a ROC plot of the differential metabolite L-glutamic acid in the examples of the present invention;

FIG. 12 is a ROC plot of the differential metabolite glutathione in the examples of the present invention;

FIG. 13 is a ROC plot of the differential metabolite γ -glutamylcysteine in examples of the present invention;

FIG. 14 is a ROC plot of the differential metabolite L-aspartic acid in the examples of the present invention;

FIG. 15 is a ROC plot of the differential metabolite L-arginine in examples of the present invention;

FIG. 16 is a ROC plot of the differential metabolite N-acetyl-L-glutamic acid 5-semialdehyde in the examples of the present invention;

FIG. 17 is a ROC plot of the different metabolite argininosuccinic acid in the examples of the present invention;

FIG. 18 is a ROC plot of the differential metabolite PC (18:1(11Z)/16:1(9Z)) in the examples of the present invention;

FIG. 19 is a ROC plot of the differential metabolite 12,13-DHOME in an example of the present invention;

FIG. 20 is a ROC graph showing the association index of sphingolipid metabolism pathways in the example of the present invention;

FIG. 21 is a ROC plot of starch and sucrose metabolic pathway association indicators in accordance with an embodiment of the present invention;

FIG. 22 is a ROC plot of the joint index of kynurenine metabolic pathways in the examples of the present invention;

FIG. 23 is a ROC plot of the iron death pathway combination index in an example of the present invention;

FIG. 24 is a ROC plot of the combined indicators of arginine metabolic pathways in an example of the invention;

FIG. 25 is a ROC plot of the combined indicators of linoleic acid metabolic pathways in an example of the present invention.

Detailed Description

EXAMPLE 1 LC-MS quantitative detection of tissue samples

In this example, 69 samples of tissues surgically removed from oral cancer patients were collected, and after pathological diagnosis, the samples were divided into cancer tissue samples (T group) and corresponding tissue samples adjacent to cancer (N group), and LC-MS quantitative detection was performed on 138 samples.

Reagent: methanol, formic acid, water, and acetonitrile were all obtained from Thermo, and L-2-chlorophenylalanine was obtained from Hengchuang Biotech, Inc. All chemicals and solvents were either analytically pure or in chromatographic grade.

The instrument comprises the following steps: the full-automatic sample rapid grinding apparatus (JXFSTPRP-24/32) was purchased from Shanghai Jingxin industry development Co., Ltd, the ultrasonic cleaning machine (F-060SD) was purchased from Shenzhen Fuyang science and technology group Co., Ltd, the desktop high-speed refrigerated centrifuge (TGL-16MS) was purchased from Shanghai Luxiang apparatus centrifuge Co., Ltd, the high-resolution mass spectrometer (QE plus) and the high performance liquid chromatograph (Dionex U3000 UHPLC) were purchased from Sammer Feishel science and the ACQUITY UPLC HSS T3(100 mm. times.2.1 mm,1.8um) column was purchased from Waters.

The method comprises the following steps:

first, pretreatment

1. Weigh 30mg of tissue sample into a 1.5mL EP tube, add 20 μ L of internal standard (L-2-chlorophenylalanine, 0.3mg/mL, in methanol), add 600 μ L of methanol-water (V: V ═ 4: 1);

2. adding two small steel balls, precooling in a refrigerator at-20 deg.C for 2min, and grinding in a grinder (60Hz for 2 min);

3. standing at-20 deg.C for 2 hr;

4. centrifuging for 10min (13000rpm, 4 deg.C), sucking 150 μ L of supernatant with a syringe, filtering with 0.22 μm organic phase pinhole filter, transferring to LC injection vial, and storing at-80 deg.C until LC-MS analysis;

5. the quality control sample (QC) is prepared by mixing the extracting solutions of all samples in a cancer sample group and a paracancer sample group in equal volumes, and the volume of the QC is the same as that of the samples. And inserting QC samples among the samples in the mass spectrum loading process, wherein the QC samples are used for evaluating the stability of a system mass spectrum platform in the whole experiment process.

Remarking: all extraction reagents were pre-cooled at-20 ℃ before use.

Second, liquid chromatogram-mass spectrum analysis condition

The analytical instrument of the experiment is a liquid mass spectrometry system consisting of a Dionex U3000 UHPLC ultra-high performance liquid phase tandem QE plus high resolution mass spectrometer.

Chromatographic conditions are as follows: a chromatographic column: ACQUITY UPLC HSS T3(100 mm. times.2.1 mm,1.8 um); column temperature: 45 ℃; mobile phase: a-water (containing 0.1% formic acid), B-acetonitrile (containing 0.1% formic acid); flow rate: 0.35 mL/min; sample injection volume: 2 μ L.

Elution gradient:

time (min)	A％	B％
				0	95	5
2	95	5
			4	70	30
8	50	50
			10	20	80
14	0	100
			15	0	100
15.1	95	5
			16	95	5

Mass spectrum conditions: an ion source: ESI; and the sample mass spectrum signal acquisition respectively adopts a positive and negative ion scanning mode.

Mass spectrum parameters:

example 2 data analysis and differential metabolite screening

1. Base peak diagram

The BPC (Base Peak Chromatogram) is a graph obtained by continuously plotting the intensity of the strongest ion in the mass spectrogram at each time point, and the content of various metabolites in different samples can be calculated according to the proportion of the ion intensity of various metabolites in the BPC to that of an internal standard compound.

QC sample quality control

QC samples were used to balance the "chromatography-mass spectrometry" system prior to sample detection and to assess the stability of the mass spectrometry system during sample detection.

(1) RSD screening of QC samples

Ion peaks of QC set RSD >0.4 were deleted. Relative Standard Deviation (RSD) is the ratio of the standard deviation to the arithmetic mean of the measured results, a measure of the degree of dispersion of the data distribution, and is used to measure the degree of deviation of the data values from the arithmetic mean. The smaller the standard deviation, the less the values deviate from the mean and vice versa.

(2) QC sample principal component analysis

The principal Component Analysis (principal Component Analysis) is an unsupervised data Analysis method, which converts the original random vector related to its Component into a new random vector unrelated to its Component by means of an orthogonal transformation, so that they reflect the original variable information as much as possible, thereby achieving the purpose of reducing dimension. Meanwhile, the stability of the system is evaluated, as shown in fig. 1, a PCA model diagram obtained by 7-fold cross-validation (7-cycle cross validation) is shown, wherein light-color triangles are cancer tissue (T) samples, dark squares are cancer tissue (N) samples, and QC samples are closely gathered together, which shows that the instrument detection stability is better in the experimental process.

3. Data pre-processing

(1) Qualitative analysis of prognesis QI v 2.3:

data preprocessing prior to pattern recognition, raw data were subjected to baseline filtering, peak recognition, integration, retention time correction, peak alignment and normalization by metabolomics processing software prognostiis QI v2.3 software (Nonlinear Dynamics, Newcastle, UK), with the main parameters:

precursor tolerance:5ppm；product tolerance:10ppm；product ion threshold:5％。

identification of compounds was based on exact mass number, secondary fragment and isotope distribution, and was characterized using the EMDB database. The EMDB database establishes a special metabolite database for people and animals, wherein the database comprises 3600+ metabolites, including amino acids, lipids, nucleotides, carbohydrates, vitamins, auxiliary factors, hormones and the like, and comprises metabolite structures, mass spectrum data and the like, and aims to solve biological problems through metabonomics in a more professional way.

(2) Qualitative and quantitative results

For the extracted data, deleting ion peaks with deletion values (0 value) > 50% in the group, replacing the 0 value by half of the minimum value, screening the qualitatively obtained compounds according to the rating (Score) of the qualitative results of the compounds, wherein the screening standard is 36 points (full Score is 60 points), and the qualitative results are regarded as inaccurate and deleted when the 36 points are less than 36 points. And finally, combining the positive and negative ion data into a data matrix table, wherein the matrix contains all information which can be used for analysis and is extracted from the original data, and the subsequent analysis is based on the information.

4. Differential metabolite screening

(1) And screening differential metabolites among groups by adopting a method of combining multidimensional analysis and single-dimensional analysis. In the OPLS-DA analysis, a Variable import in project (VIP) can be used to measure the influence strength and the interpretation ability of the expression pattern of each metabolite on the classification and discrimination of each group of samples, so as to mine the differential metabolites with biological significance, the greater the VIP, the greater the contribution of the Variable to the group, and further verify whether the differential metabolites among the groups have significance by using the T test. The criteria for the screening were VIP value >1 for the first principal component of the OPLS-DA model and p-value <0.05 for the T-test.

(2) QC sample hierarchical clustering

In order to more intuitively show the relationship between the QC samples and other samples and the stability among the QC samples, Hierarchical Clustering (Hierarchical Clustering) is carried out on all metabolite expression quantities.

(3) VIP and Fold change value visualization

The p value, VIP and Fold change (Fold change) values can be visualized by using a volcano map, and screening of differential metabolites is facilitated. As shown in fig. 2, where the dark gray origin represents significantly up-regulated differential metabolites in the experimental group, the black origin represents significantly down-regulated differential metabolites, and the light gray dots represent insignificant differential metabolites.

The differential metabolites screened in this example include: sphingosine, O-phosphoethanolamine, sphinganine, 3-dehydrosphinganine, D-maltose, dextrin, L-kynurenine, 5-hydroxytryptophan, glutamic acid, glutathione, gamma-glutamylcysteine, L-aspartic acid, arginine, N-acetyl-L-glutamic acid 5-semialdehyde, arginosuccinic acid, PC (18:1(11Z)/16:1(9Z)) and 12, 13-DHOME.

The detection potency (AUC value) of the above single index of the differential metabolites is shown in the following table:

the terms "sensitivity" and "specificity" in the figures of the present invention mean what is known in the art, the AUC value is the area under the ROC curve of the metabolites, theoretically the AUC value is between 0.5 and 1, the diagnostic value is greater the closer the value is to 1, the higher the predictive value of these metabolites can be seen from the data in the above table, wherein the predictive value of the metabolites with AUC value exceeding 0.7 is higher, especially the best with AUC value exceeding 0.85.

5. Metabolic pathway enrichment assay

Differential metabolite pathway enrichment assays are helpful in understanding the metabolic pathway change mechanisms in differential samples. Metabolic pathway enrichment analysis was performed on differential metabolites based on the KEGG database. The KEGG (https:// www.kegg.jp /) database was constructed to understand the functions and interactions of genes, proteins and metabolites in biological systems (e.g., cells, tissues, etc.). The information of metabolic pathways, human diseases, drug research and development and the like related to the metabolites can be inquired.

In this example, the pathway enrichment analysis of the above differential metabolites found out the combined metabolite indexes of several groups of pathways, as shown in the following table:

from the above table, the AUC values of the combined indicators of all the pathways are all greater than the AUC value of a single indicator in the group, and the combined AUC values of all the pathways are all greater than 0.85, and they can be used as combined detection markers for oral cancer diagnosis, especially for rapid identification and identification of oral cancer and tissues beside the cancer.

The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims

1. Use of a metabolic marker in the manufacture of a diagnostic kit for oral cancer, wherein the metabolic marker is selected from one or more of sphingosine, O-phosphoethanolamine, dihydrosphingosine, 3-dehydrodihydrosphingosine, D-maltose, dextrin, L-kynurenine, 5-hydroxytryptophan, L-glutamic acid, glutathione, γ -glutamylcysteine, L-aspartic acid, L-arginine, N-acetyl-L-glutamic acid 5-semialdehyde, arginosuccinic acid, PC (18:1(11Z)/16:1(9Z)) and 12, 13-DHOME.

2. Use of a metabolic marker according to claim 1 in the manufacture of a diagnostic kit for oral cancer, wherein the metabolic marker comprises at least two of the compounds listed in claim 1.

3. Use of a metabolic marker according to claim 1 or 2 in the manufacture of a diagnostic kit for oral cancer, wherein the metabolic marker comprises sphingosine, O-phosphoethanolamine, sphinganine and 3-dehydrosphinganine.

4. Use of a metabolic marker according to claim 1 or 2 in the preparation of a diagnostic kit for oral cancer, wherein the metabolic marker comprises D-maltose and dextrin.

5. Use of a metabolic marker according to claim 1 or 2 in the preparation of a diagnostic kit for oral cancer, wherein said metabolic marker comprises L-kynurenine and 5-hydroxytryptophan.

6. Use of a metabolic marker according to claim 1 or 2 in the manufacture of a diagnostic kit for oral cancer, wherein the metabolic marker comprises L-glutamic acid, glutathione and γ -glutamylcysteine.

7. Use of a metabolic marker according to claim 1 or 2 in the preparation of a diagnostic kit for oral cancer, wherein said metabolic marker comprises L-glutamic acid, L-aspartic acid, L-arginine, N-acetyl-L-glutamic acid 5-semialdehyde and argininosuccinic acid.

8. Use of a metabolic marker in the preparation of a diagnostic kit for oral cancer according to claim 1 or 2, wherein the metabolic marker comprises PC (18:1(11Z)/16:1(9Z)) and 12, 13-DHOME.

9. Use of a metabolic marker according to claim 1 or 2 for the preparation of a diagnostic kit for oral cancer for the rapid identification and characterization of oral cancer and tissues adjacent to the cancer.

10. A diagnostic kit for oral cancer comprising reagents required for the combined liquid chromatography-mass spectrometry detection of a metabolic marker according to any one of claims 1 to 9.