CN114264719B - Dual-mode detection mass spectrum chip for biological sample lipid, kit and application - Google Patents
Dual-mode detection mass spectrum chip for biological sample lipid, kit and application Download PDFInfo
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Abstract
The invention discloses a mass spectrum chip combination or a mass spectrum chip for carrying out dual-mode detection on a biological sample lipid extract based on a mass spectrum method, and belongs to the field of mass spectrum detection. The mass spectrum chip combination or the mass spectrum chip comprises sample application areas formed by different silicon nano structures, and is used for receiving the biological sample lipid extract and respectively scanning in a positive ion mode and a negative ion mode, further screening data in the same detection mode, and selecting mass spectrum data with the widest peak number coverage range for statistical analysis. The mass spectrum chip combination or the mass spectrum chip for the dual-mode detection of the biological sample lipid is utilized for lipid detection, so that the background interference in the lipid detection range can be reduced, the high coverage rate and the detection sensitivity of the lipid and the metabolite in the positive ion mode and the negative ion mode can be simultaneously considered, the number of detected effective peaks can be increased to 479, and the lipid detection range simultaneously comprises neutral lipid and polar lipid.
Description
Technical Field
The invention belongs to the technical field of mass spectrum detection, and particularly relates to a mass spectrum chip, a kit and application of dual-mode detection of biological sample lipid, in particular to a mass spectrum chip, a kit and application of dual-mode detection of biological sample lipid based on a mass spectrum method.
Background
Cancer is one of the public health problems of global concern. More than 60% of countries in 2019 treat cancer as the first or second leading cause of death in humans. Furthermore, from the world's organization's predictions of cancer, it is expected that the number of cancer patients worldwide will be 47% increased by 2040 years. How to diagnose cancer early non-invasively by means of biomarkers is thus still one of the most promising research areas at the present stage. The tumor markers used as noninvasive diagnosis in clinic at present are few, the sensitivity is low (60% -70%), and the sensitivity and the specificity of diagnosis cannot be well balanced. However, the recent development of a histological marker has been receiving a great deal of attention because of its excellent diagnostic ability by a combination of a plurality of markers.
So-called histology studies typically include genome, transcriptome, proteome and metabolome, where lipidomic is a branch of metabolome. Because the lipid participates in various cell activities and device components, the change of the lipid directly reflects the energy balance, structure and functionalization of cells, so that the lipid can be well related to disease phenotypes, and the excavating of disease markers is facilitated. Among the numerous subjects in lipidomics, serum is the most clinically readily available body fluid that contains abundant lipid information and is the subject of choice for the lipid analysis of body fluids.
At present, most of serum lipids are analyzed under the SALDI-MS platform by adopting a single nano material and being limited in one mode, and the high coverage rate and the detection sensitivity of the lipids and the metabolites under the positive ion mode and the negative ion mode cannot be simultaneously considered. For example, kai Liang et al developed an analysis of serum lipids in positive ion mode using graphene oxide material polymers (AGO) to discriminate between hyperlipidemia patients and healthy controls (Liang, k.; gao, h.; gu, y.; yang, s.; zhang, j.; li, j.; wang, y.; li, y. Animal. The PDA modified anti-reflective material prepared by jin Yang et al was also analyzed for serum lipids in positive ion mode only (Yang, j.; zhang, w.; zhang, h.; zhong, m.; cao, w.; li, z.; huang, x.; nie, z.; liu, j.; li, p.; ma, x.; ouyang, z.acs Appl Mater Interfaces 2019,11,46140-46148). However, for diagnostic analysis of disease, a high coverage lipid data acquisition method would be more useful for statistical analysis and would also be more useful for the construction of superior diagnostic models.
Disclosure of Invention
Aiming at the problems that at present, single nano materials are mostly adopted for analyzing serum lipid under an SALDI-MS platform and are limited in one mode, and high coverage rate and detection sensitivity of lipid and metabolite under a positive ion mode and a negative ion mode can not be simultaneously considered, the first aspect of the invention is to provide a mass spectrum chip combination for carrying out dual-mode detection on a biological sample lipid extract based on a mass spectrum method.
The second aspect of the present invention provides a mass spectrum chip for performing dual-mode detection on a lipid extract of a biological sample based on a mass spectrum method, where the mass spectrum chip is a silicon nano chip whose surface includes at least two different silicon nano structures, the silicon nano structures are not modified or are used as sample application areas after different modifications to respectively receive the lipid extract of the biological sample, and are used for scanning in a positive ion mode and a negative ion mode respectively, so as to screen data in the same detection mode, and select mass spectrum data with the widest coverage of the number of peaks for statistical analysis.
A third aspect of the present invention provides a method for dual mode detection of lipids using a mass spectrometry chip combination according to the first aspect of the present invention or a mass spectrometry chip according to the second aspect of the present invention, comprising the steps of:
s1, respectively dripping lipid extracts of biological samples onto different sample application areas, and drying;
s2, fixing the mass spectrum chip combination or the mass spectrum chip on a target holder;
s3, conveying the target supporting plate into a target chamber, scanning the sample application areas in a positive ion detection mode and a negative ion detection mode respectively, obtaining mass spectrum data of each sample application area in the positive ion detection mode and the negative ion detection mode, and carrying out statistical analysis;
and S5, screening the data under the same detection mode, and selecting mass spectrum data with the widest peak number coverage range for statistical analysis.
In the present invention, preferably, the silicon nanomaterial includes quantum dots, nanowires, nanotubes, and porous silicon.
Further preferably, the silicon nanomaterial is selected from one of a silicon nanowire, a metal-doped silicon nanowire, an organic-doped silicon nanowire, a metal-and organic-co-doped silicon nanowire.
Preferably, the biological sample is selected from cancer biological samples;
further preferably, the cancer biological sample is selected from liver cancer sample, stomach cancer sample, intestinal cancer sample.
Further preferably, the sample type is selected from one of whole blood, serum, plasma, interstitial fluid.
Preferably, the data screening method under the same detection mode is specifically as follows: first, baseline subtraction pretreatment was performed on raw mass spectra using FlexAnalysis 3.4 software (Bruker Daltonics corp.). The intensities of selected peaks at S/N.gtoreq.5 are then normalized to the total ionic strength. Combining the results of orthogonal partial least squares discriminant analysis (OPLS-DA) in dual sample's t-test in MATLAB software and SIMCA software (immetrics AB, umea, sweden), important peak information satisfying P <0.05 and VIP >1 simultaneously is obtained.
According to a fourth aspect of the invention, there is provided the use of a mass spectrometry chip combination according to the first aspect of the invention or a mass spectrometry chip according to the second aspect of the invention for the preparation of a kit for dual mode detection of a biological sample of cancer based on a mass spectrometry method for the discrimination of said cancer.
In a fifth aspect, the invention provides a kit for dual-mode detection of a lipid extract of a biological sample of cancer based on a mass spectrometry method, comprising a mass spectrometry chip combination according to the first aspect of the invention or a mass spectrometry chip according to the second aspect of the invention.
Preferably, the mass spectrometry chip combination or the mass spectrometry chip comprises a first spotting region and a second spotting region, the first spotting region is composed of unmodified silicon nanostructures and is used for receiving the cancer biological sample lipid extract and obtaining mass spectrometry detection data of the cancer biological sample lipid extract in a negative ion mode; the second sample application area consists of a metal and polydopamine modified silicon nanostructure and is used for receiving the cancer biological sample lipid extract and obtaining mass spectrum detection data of the cancer biological sample lipid extract in a positive ion mode; the metal is selected from one of Au, ag, cu, pt.
Preferably, the silicon nanomaterial comprises quantum dots, nanowires, nanotubes, porous silicon.
Further preferably, the silicon nanomaterial is selected from one of a silicon nanowire, a metal-doped silicon nanowire, an organic-doped silicon nanowire, a metal-and organic-co-doped silicon nanowire.
In some embodiments of the present invention, the mass spectrometry chip combination is composed of a silicon nanowire chip and a silicon nanowire chip co-modified by a metal, a metal and polydopamine, wherein at this time, the silicon nanostructure is a silicon nanowire array on the silicon nanowire chip, the silicon nanowire array on the silicon nanowire chip is not modified to form a first spotting region, and the silicon nanowire array co-modified by the metal and polydopamine on the metal and polydopamine co-modified silicon nanowire chip forms a second spotting region.
Preferably, the preparation method of the metal, metal and polydopamine co-modified silicon nanowire chip comprises the following steps: 1) Preparing a silicon nanowire chip with a vertical nanowire array; 2) Performing silanization modification on the silicon nanowire chip obtained in the step 1) by using a silanization reagent to obtain a silicon nanowire chip modified by silane; 3) Performing metal modification on the silicon nanowire chip modified by the silane obtained in the step 2) to obtain a metal-modified silicon nanowire chip; 4) And 3) further performing polydopamine modification on the metal-modified silicon nanowire chip obtained in the step 3) to obtain the metal-polydopamine co-modified silicon nanowire chip.
In some embodiments of the invention, the specific process of step 3) is as follows: immersing the silicon nanowire chip into a metal nanoparticle colloid solution, reacting for 15-180 min, washing with deionized water, and then using N 2 And (5) blow-drying.
In some embodiments of the invention, the specific process of step 4) is as follows: immersing the silicon nanowire core chip into Tris buffer solution containing dopamine, reacting for 15-55 min, washing with deionized water and using N 2 And (5) blow-drying.
In a sixth aspect, the present invention provides a method for dual mode detection of a lipid extract of a biological sample of cancer using the kit for dual mode detection of lipid of a biological sample based on a mass spectrometry method according to the fifth aspect of the present invention, comprising the steps of:
s1', respectively dripping lipid extracts of a cancer biological sample onto the first sample application area and the second sample application area, and drying;
s2', fixing the processed mass spectrum chip combination or mass spectrum chip with the first sample application area and the second sample application area on a target holder;
s3', the target supporting plate is sent into a target chamber, and only the second sample application area is scanned in a positive ion detection mode; and only scanning the first spot area in the negative ion detection mode to obtain the lipid mass spectrum data. Further, the method also comprises the step of carrying out statistical analysis on the mass spectrum data, and the method is specifically as follows:
1) Performing baseline subtraction pretreatment on mass spectrum data, and screening out peaks with signal to noise ratio S/N more than or equal to 5;
2) Normalizing the intensity of a selected peak with the signal to noise ratio S/N more than or equal to 5 to the total ion intensity;
3) Screening out important peak information with P <0.05 and variable projection importance value >1 according to double-sample Student's t-test and orthogonal partial least square discriminant analysis;
4) Further screening peak information with high contribution by using a random forest packet;
5) And constructing an artificial neural network model comprising a training set, a verification set and a test set based on the selected data, and attributing the characteristic peak information to the characteristic lipid.
In some embodiments of the invention, the characteristic lipids include Phosphatidylethanolamine (PE) (O-34:2), phosphatidylethanolamine (PE) (O-36:3), phosphatidylethanolamine (PE) (O-36:2), phosphatidylethanolamine (PE) (O-38:5), phosphatidylethanolamine (PE) (O-40:8), phosphatidylinositol (PI) (34:1), lysophosphatidylcholine (LPC) (20:3), diglyceride (DG) (36:4), cholesterol Ester (CE) (18:2), cholesterol Ester (CE) (20:4), sphingomyelin (SM) (d 34:1), phosphatidylcholine (PC) (36:4), triacylglycerol (TG) (52:3), and Triacylglycerol (TG) (56:6).
In some preferred embodiments of the invention, the lipid extract of the cancer sample in S1' is prepared by the following method: a cancer sample was taken and methyl tert-butyl ether/methanol (MTBE/MeOH) solution was added thereto, vortexed for 10min, and H was added 2 O, continuing to vortex for 10min, centrifuging for 10min, collecting supernatant, repeating twice, drying under nitrogen flow, and re-suspending the precipitate in IPA to obtain lipid extract of cancer sample, and preserving at-20deg.C.
In some preferred embodiments of the invention, the detection conditions for S3' are as follows: the diameter of the circular laser spot is set to be 100 mu m, the relative laser pulse energy is set to be 60% of the maximum energy respectively, and the measurement mode is set to be a reflection mode; carrying out 500 times of laser irradiation and overlapping for 4 times on a single point to obtain a generated spectrum; for the negative ion detection mode, the mass range is set to m/z=400-1000; for the positive ion detection mode, the mass range is set to m/z=400-1000.
Preferably, the cancer biological sample is selected from liver cancer sample, stomach cancer sample, intestinal cancer sample.
The beneficial effects of the invention are that
Compared with the prior art, the invention has the following beneficial effects:
aiming at the problems that at present, single nano materials are adopted for analyzing serum lipid under an SALDI-MS platform and are limited in one mode, and high coverage rate and detection sensitivity of lipid and metabolite under positive ion and negative ion modes cannot be simultaneously considered, the biological sample lipid dual-mode detection kit disclosed by the invention can be used for detecting, so that the background interference under a lipid detection range can be reduced, and the high coverage rate and detection sensitivity of lipid and metabolite under positive ion and negative ion modes can be simultaneously considered, the number of detected effective peaks can be increased to approximately 479, and neutral lipid and polar lipid can be simultaneously contained.
The biological sample lipid dual-mode detection kit provided by the invention is simple and convenient in use method, and can be used for diagnosing diseases or predicting the occurrence risk of diseases.
Drawings
Fig. 1 shows a flow chart of the preparation of SiNW-Au-PDA in an embodiment of the present invention.
Fig. 2 shows a vertical nanowire array of SiNW prepared in an embodiment of the invention.
Fig. 3 shows a top view (a), a cross-sectional view (b) and a 45 ° oblique view (c) of a SiNW-Au-PDA SEM image.
FIG. 4 shows the total peak numbers (S/N.gtoreq.5) measurable in serum lipid LDI-MS analysis for example 3, comparative example 1 and comparative example 2.
Fig. 5 shows the results of (a) the model of comparative example 1, (b) the model of comparative example 2, and (c) the model of example 3 differentiating between the training set HCC patient and the healthy control group OPLS-DA.
Detailed Description
Unless otherwise indicated, implied from the context, or common denominator in the art, all parts and percentages in the present application are based on weight and the test and characterization methods used are synchronized with the filing date of the present application. Where applicable, the disclosure of any patent, patent application, or publication referred to in this application is incorporated by reference in its entirety, and the equivalent of such patent is incorporated by reference, particularly as regards the definitions of synthetic techniques, product and process designs, polymers, comonomers, initiators or catalysts, etc. in the art, as disclosed in such documents. If the definition of a particular term disclosed in the prior art does not conform to any definition provided in this application, the definition of that term provided in this application controls.
Numerical ranges in this application are approximations, so that it may include the numerical values outside of the range unless otherwise indicated. The numerical range includes all values from the lower value to the upper value that increase by 1 unit, provided that there is a spacing of at least 2 units between any lower value and any higher value. For example, if a component, physical or other property (e.g., molecular weight, melt index, etc.) is recited as being 100 to 1000, it is intended that all individual values, e.g., 100, 101, 102, etc., and all subranges, e.g., 100 to 166, 155 to 170, 198 to 200, etc., are explicitly recited. For ranges containing values less than 1 or containing fractions greater than 1 (e.g., 1.1,1.5, etc.), then 1 unit is suitably considered to be 0.0001,0.001,0.01, or 0.1. For a range containing units of less than 10 (e.g., 1 to 5), 1 unit is generally considered to be 0.1. These are merely specific examples of what is intended to be provided, and all possible combinations of numerical values between the lowest value and the highest value enumerated are to be considered to be expressly stated in this application.
As used with respect to chemical compounds, the singular includes all isomeric forms and vice versa unless explicitly stated otherwise (e.g., "hexane" includes all isomers of hexane, either individually or collectively). In addition, unless explicitly stated otherwise, the use of the terms "a," "an," or "the" include plural referents.
The terms "comprises," "comprising," "including," and their derivatives do not exclude the presence of any other component, step or procedure, and are not related to whether or not such other component, step or procedure is disclosed in the present application. For the avoidance of any doubt, all use of the terms "comprising," "including," or "having" herein, unless expressly stated otherwise, may include any additional additive, adjuvant, or compound. Rather, the term "consisting essentially of … …" excludes any other component, step or process from the scope of any of the terms recited below, as those out of necessity for operability. The term "consisting of … …" does not include any components, steps or processes not specifically described or listed. The term "or" refers to the listed individual members or any combination thereof unless explicitly stated otherwise.
In order to make the technical problems, technical schemes and beneficial effects solved by the invention more clear, the invention is further described in detail below with reference to the embodiments.
Examples
The following examples are presented herein to demonstrate preferred embodiments of the present invention. It will be appreciated by those skilled in the art that the techniques disclosed in the examples which follow represent techniques discovered by the inventor to function in the practice of the invention, and thus can be considered to constitute preferred modes for its practice. Those of skill in the art should, in light of the present disclosure, appreciate that many changes can be made in the specific embodiments which are disclosed and still obtain a like or similar result without departing from the spirit or scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, the disclosure of which is incorporated herein by reference as is commonly understood by reference.
Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the claims.
The molecular biology experiments described in the following examples, which are not specifically described, were performed according to the specific methods listed in the "guidelines for molecular cloning experiments" (fourth edition) (j. Sambrook, m.r. Green, 2017) or according to the kit and product specifications. Other experimental methods, unless otherwise specified, are all conventional. The instruments used in the following examples are laboratory conventional instruments unless otherwise specified; the test materials used in the examples described below, unless otherwise specified, were purchased from conventional biochemical reagent stores.
Example 1 preparation of gold nanoparticle and polydopamine Co-modified silicon nanowire chip (SiNW-Au-PDA)
The preparation of the SiNW-Au-PDA is divided into 3 steps, as shown in FIG. 1, firstly, a silicon nanowire chip (SiNW) with a vertical nanowire array is prepared, then a gold nanoparticle (AuNP) solution with uniform particle size is prepared, and modification is carried out by using the AuNP solution and a Dopamine (DA) solution respectively, so that the SiNW-Au-PDA chip is finally obtained.
The detailed steps are as follows:
step one, preparing SiNW chip with vertical nanowire array
The inventor prepares a SiNW chip by using a one-step metal-assisted chemical etching method, and the preparation process is as follows:
the p-type silicon single crystal was cut into square with a size of 2X 2cm by a diamond knife, and then a silicon single crystal was grown in a solution containing 4.8M hydrofluoric acid (HF) and 0.02M silver nitrate (AgNO) 3 ) And (5) performing reactive etching in the solution for 10min. After etching, the etching solution was washed three times with deionized water and then immersed in dilute nitric acid (HNO) 3 ) Soaking for 1h to dissolve silver (Ag) catalyst.
Through this step a SiNW chip with a vertical nanowire array (as shown in fig. 2) can be produced.
Step two, preparing gold nanoparticle (AuNP) colloidal solution with uniform particle size
The inventors utilized conventional sodium citrate (Na 3 Cit) reduction method, the specific preparation process is as follows:
when 50ml of 0.01% tetrachloroauric acid (HAuCl) 4 ) After the solution was boiled, 415. Mu.L of 1% Na was added 3 The Cit solution was boiled for a further 30min, eventually forming a homogeneous AuNP colloidal solution.
Step three, preparing SiNW-Au-PDA chip
(1) The SiNW chip treated by oxygen plasma is reacted with toluene solution (v/v) containing 2% 3-aminopropyl trimethoxysilane (APTES) for 15min at room temperature;
(2) Washing the reaction product with toluene and absolute ethyl alcohol in sequence, and stabilizing the reaction product at 60 ℃ for 60min to obtain SiNW-APTES;
(3) Immersing the SiNW-APTES in AuNP colloid solution for 150min, washing with deionized water, and adding N 2 Drying to obtain a gold modified SiNW chip (SiNW-Au);
(4) Further, siNW-Au was immersed in 50mM Tris buffer (pH=8.5) containing 0.1M Dopamine (DA) and reacted for 15minDeionized water was used for cleaning and washing with N 2 Blow-drying to finally prepare the SiNW-Au-PDA chip, wherein an SEM image is shown in FIG. 3.
EXAMPLE 2 preparation of serum lipid extract
Mu.l of serum was taken and 335. Mu.l of MTBE/MeOH solution was added thereto, vortexed for 10min, and 65ul of H was added 2 O, continuing to vortex for 10min, centrifuging for 10min, finally extracting supernatant, repeatedly extracting supernatant twice, drying under nitrogen flow, and re-suspending precipitate in IPA to obtain serum lipid extract, and preserving at-20deg.C.
Example 3 application of silicon nanomaterial combination for biological sample lipid dual-mode detection based on mass spectrometry in serum lipid extraction substance spectrum detection
S1, cutting the SiNW chip and the SiNW-Au-PDA chip prepared in the example 1 into small chips with the size of 3 multiplied by 3mm, sucking 2 mu l of the serum lipid extract obtained in the example 2, and dripping the serum lipid extract onto the SiNW chip and the SiNW-Au-PDA chip respectively;
s2, fixing the SiNW chip and the SiNW-Au-PDA chip on the same aluminum target plate matched with the matrix-assisted laser desorption ionization time-of-flight mass spectrum target chamber by using carbon conductive adhesive;
s3, an aluminum target plate is sent into a target chamber, the diameter of a circular laser spot is set to be 100 mu m, the energy of relative laser pulses is respectively set to be 60% of the maximum energy, and a measuring mode is set to be a reflecting mode; carrying out 500 times of laser irradiation and overlapping for 4 times on a single point to obtain a generated spectrum; for the negative ion detection mode, the mass range is set to m/z=400-1000; for the positive ion detection mode, the mass range is set to m/z=400-1000, and only the SiNW-Au-PDA chip is detected in the positive ion detection mode; only detecting the SiNW chip in a negative ion detection mode to obtain mass spectrum data; and screening the data under the same detection mode, and finally selecting mass spectrum data with the widest peak quantity coverage range.
Comparative example 1
Comparative example 1 differs from example 3 in that comparative example 1 employs the SiNW chip in example 1 in both the positive and negative ion detection modes.
Comparative example 2
Comparative example 2 differs from example 3 in that comparative example 2 employs the SiNW-Au-PDA chip in example 1 in both the positive and negative ion detection modes.
Analysis of Mass Spectrometry data obtained in example 3 and comparative examples 1-2
The dataset consisted of the average data from three replicates per sample. First, baseline subtraction pretreatment was performed on raw mass spectra using FlexAnalysis 3.4 software (Bruker Daltonics corp.). The intensities of selected peaks at S/N.gtoreq.5 are then normalized to the total ionic strength. And combining the results of orthogonal partial least squares discriminant analysis (OPLS-DA) in double-sample Student's t-test in MATLAB software and SIMCA software (Umetrics AB, umeas, sweden) to obtain important peak information meeting P <0.05 and variable projection importance Value (VIP) >1 simultaneously. Then, the peak information is further screened out by utilizing random forest package (http:// cran. R-project. Org/web/packages/random forest /) in R software, and peak information with high contribution is screened out. Finally, a diagnostic model is constructed based on the selected dataset.
An Artificial Neural Network (ANN) model with a multi-layer perceptual structure (hidden neuron number 10) was built in the pattern recognition tool of MATLAB software. In the ANN model, the training set represents 70% of the total data set, with 15% of the data being used as the validation set. Another 15% of the data was used as a test set to evaluate the sensitivity and specificity of the predictions.
The model of example 3, the model of comparative example 1, and the model of comparative example 2 were respectively constructed based on the above procedure.
First, we split the overall cohort into discovery cohorts and validation cohorts according to the age and sex of the subjects, as shown in table 1.
TABLE 1 basic information of study cohort
The expression format of age is median (range).
As shown in FIG. 4, the three detection methods of example 3, comparative example 1 and comparative example 2 can detect the number of effective peaks (S/N. Gtoreq.5), and it can be seen that example 3 can achieve the maximum detection of serum lipid coverage.
To ensure the reliability of statistical comparisons, statistical models constructed for different chip combinations all contained the same characteristic lipid numbers, as shown in tables 2-4.
TABLE 2 characterization of lipid peaks screened using the model of example 3
TABLE 3 characterization of lipid peaks screened using the model of comparative example 1
TABLE 4 characterization of lipid peaks screened using the model of comparative example 2
The results of the model of example 3, the model of comparative example 1 and the model of comparative example 2 for the training set HCC patient and the healthy control group OPLS-DA were shown in fig. 5, and it was found that the model discrimination ability constructed using a single chip was inferior to that obtained using a combined chip.
Furthermore, we also evaluated the diagnostic accuracy of the 3 models constructed on the dataset using the artificial neural network for the model of example 3, the model of comparative example 1 and the model of comparative example 2, respectively, and the results are shown in table 5.
TABLE 5 sensitivity, specificity and AUC values of different diagnostic models for differentiation of HCC patients from healthy controls
The experimental results show that the high-coverage serum lipid analysis has important significance for constructing a good diagnosis model.
All documents mentioned in this application are incorporated by reference as if each were individually incorporated by reference. Further, it will be appreciated that various changes and modifications may be made by those skilled in the art after reading the above teachings, and such equivalents are intended to fall within the scope of the claims appended hereto.
Claims (4)
1. A method for carrying out lipid dual-mode detection by a mass spectrum chip combination or a mass spectrum chip is characterized in that,
the mass spectrum chip combination comprises two silicon nano chips, wherein the surfaces of different silicon nano chips are provided with different silicon nano structures, the silicon nano structure of one silicon nano chip is SiNW, and a first point-like area is formed; the other silicon nano-chip has a silicon nano-structure of SiNW-Au-PDA, forming a second sample application area,
the mass spectrum chip is a silicon nano chip with two different silicon nano structures on the surface, wherein one silicon nano structure is SiNW, and a first point-like area is formed; the other is SiNW-Au-PDA, forming a second sample application area,
wherein the SiNW is a silicon nanowire with a vertical nanowire array, the SiNW-Au-PDA is a SiNW jointly modified by gold nanoparticles and polydopamine,
the method comprises the following steps:
s1, dripping lipid extracts of a biological sample onto a first sample application area and a second sample application area respectively, and drying;
s2, fixing the mass spectrum chip combination or the mass spectrum chip on a target supporting plate;
s3, sending the target supporting plate into a target chamber, scanning the first sample application area in a negative ion detection mode, and scanning the second sample application area in a positive ion detection mode to obtain mass spectrum data of the first sample application area in the negative ion detection mode and mass spectrum data of the second sample application area in the positive ion detection mode, and performing statistical analysis, wherein the statistical analysis is specifically as follows:
1) Performing baseline subtraction pretreatment on mass spectrum data, and screening out peaks with signal to noise ratio S/N more than or equal to 5;
2) Normalizing the intensity of a selected peak with the signal to noise ratio S/N more than or equal to 5 to the total ion intensity;
3) Screening out important peak information with P <0.05 and variable projection importance value >1 according to double-sample Student's t-test and orthogonal partial least square discriminant analysis;
4) Further screening peak information with high contribution by using a random forest packet;
5) And constructing an artificial neural network model containing a training set, a verification set and a test set based on the selected data, judging the accuracy of the model, and attributing the characteristic peak information to the characteristic lipid.
2. The method of claim 1, wherein the biological sample lipid extract is a cancer biological sample lipid extract, the method being based on non-diagnostic and/or non-therapeutic purposes.
3. The method of claim 2, wherein the cancer biological sample lipid extract is obtained by the method of: adding methyl tert-butyl ether/methanol solution into cancer biological sample, swirling, collecting supernatant, repeating twice, drying under nitrogen flow, and re-suspending precipitate in isopropanol to obtain cancer biological sample lipid extract.
4. The method of claim 1, wherein the characteristic lipid comprises Phosphatidylethanolamine (PE) O-34:2, phosphatidylethanolamine (PE) O-36:3, phosphatidylethanolamine (PE) O-36:2, phosphatidylethanolamine (PE) O-38:5, phosphatidylethanolamine (PE) O-40:8, phosphatidylinositol (PI) 34:1, lysophosphatidylcholine (LPC) 20:3, diglyceride (DG) 36:4, cholesterol Ester (CE) 18:2, cholesterol Ester (CE) 20:4, sphingomyelin (SM) d34:1, phosphatidylcholine (PC) 36:4, triacylglycerol (TG) 52:3, and Triacylglycerol (TG) 56:6.
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