CN109387635A - Gastric cancer detection system and method based on the extracellular vesica detection of thermophoresis - Google Patents

Abstract

A kind of gastric cancer detection system and method based on the extracellular vesica detection of thermophoresis, comprising: to the heating unit heated to vesica extracellular in person under test's blood；The sample bin chamber unit of the heating unit side is set；The signal processing unit of sample bin chamber unit side is set, the signal processing unit obtains at least one optical signal parameter, and by quantization and using without weighting and/or thering is weighted model to calculate corresponding optical parameter, single kind protein expression intensity is obtained.By the present invention in that carrying out signal to the extracellular vesica in blood samples of patients cell with aptamer or antibody, and the optical parameter in signal is detected and handled using detection unit, by quantization optical parameter and by using without weighting and/or there is weighted model to calculate corresponding optical parameter, it can fast and accurately show that the expression albumen intensity of extracellular vesica, detection accuracy are high.

Description

Gastric cancer detection system and method based on thermophoretic extracellular vesicle detection

technical field

The invention relates to the technical field of cancer diagnosis, in particular to a gastric cancer detection system and method based on thermophoretic extracellular vesicle detection.

Background technique

For the treatment of gastric cancer, endoscopic treatment, surgery, chemotherapy, radiation therapy, and the like are known. It is administered taking into consideration the stage of disease, the size and/or depth of invasion of the tumor, the degree of metastasis, and the like. Treatment guidelines are determined in accordance with the "Stomach Cancer Treatment Guidelines" established by the Japan Gastric Cancer Society in 2004. In the case of early gastric cancer, it can be completely removed by endoscopic resection or surgery, and the recurrence rate is also very low. On the other hand, in the case of advanced gastric cancer, even if the lesion is removed, there are microscopic metastatic lesions that cannot be found at the time of surgery, and there are many cases of recurrence. Gastric cancer has a good prognosis when detected early, and usually more than 90% can be completely cured. But larger tumors and/or metastases have poorer outcomes after treatment, with a 5-year survival rate of about 70%. Therefore, its early detection is of great importance.

However, most gastric cancers are asymptomatic in the early stage, and in many cases no clear subjective symptoms appear unless the cancer progresses. Therefore, early detection of gastric cancer by subjective symptoms is difficult. As subjective symptoms, as gastric cancer progresses, soft stools, black stools, nausea, stomach discomfort, etc. may be seen, and as systemic symptoms, fatigue, fever, weight loss, anemia, and the like may be seen. As the disease progresses and the tumor grows, a lump in the abdomen will be felt. Furthermore, even after such subjective symptoms appear, patients often tend to leave the disease unattended, and there are many cases where the state of progress is only discovered for the first time through X-ray photography at the time of physical examination. Therefore, it is important to develop a detection method for detecting gastric cancer with high sensitivity and accuracy at an early stage.

As an examination method for gastric cancer, there are image diagnosis methods using ultrasound examination, CT examination, angiography examination, X-ray photography, and the like. Although the image diagnosis method is a useful method for detecting small gastric cancers at an early stage, it cannot be said to be an effective method when a large number of subjects are to be tested, such as a health diagnosis, and there is a problem that the cost required for the diagnosis is relatively high.

With recent advances in genome analysis and proteome analysis, various new tumor marker candidates have been gradually discovered as research results in the field of cancer.

International Publication No. WO2005/001126 and International Publication No. WO2003/060121 disclose that blood markers that are specific and highly sensitive to specific cancers are considered to be capable of relatively inexpensive and high-throughput examination and/or diagnosis. Development is expected to be high. Examples of methods for finding markers include methods for comparing the amounts of gene expression and/or protein or cell metabolites in cancer cells and non-cancer cells, and/or measurement of the amount of cancer and non-cancer patients in body fluids. Methods for the amount of mRNA, protein, or metabolites, etc. Currently, CEA, BFP, NCC-ST-439, CA72-4, CA19-9 and the like are known as gastric cancer tumor markers clinically used. In addition, pepsinogen C (Melle, C. et al., Journal of proteome research, 2005, vol. 5, p. 1799-1804), hnRNP A2/B1 (Lee, C. et al., Proteomics, 2005, Vol. 5, p. 1160-1166), NSP3, transgelin, antiproliferative protein, HSP27, protein disulfide isomerase A3, GRP58 (Ryu, J.W. et al., Journal Korean Medical Science, 2003, Vol. 18, p.505-509) and other marker candidates.

However, the lack of specificity and/or detection sensitivity of these markers and marker candidates has prevented the establishment of efficient detection methods utilizing these markers and marker candidates from living samples. Therefore, its use is limited to a limited purpose such as follow-up observation after treatment, so a gastric cancer detection method with higher specificity and detection sensitivity is desired.

SUMMARY OF THE INVENTION

Therefore, the present invention provides a gastric cancer detection system and method based on thermophoretic extracellular vesicle detection, to overcome the problem that the detection method in the prior art cannot accurately detect a certain type of cancer cells.

In one aspect, the present invention provides a gastric cancer detection system based on thermophoretic extracellular vesicle detection, comprising:

A heating unit for heating extracellular vesicles in the blood of the subject;

A sample compartment unit arranged on one side of the heating unit and used to load extracellular vesicles, and the expressed protein that is highly expressed to gastric cancer in the extracellular vesicles in the sample compartment unit can be specific to aptamers or antibodies After the heating unit heats the sample compartment unit, thermophoresis effect and convection are generated in the sample compartment unit, and extracellular vesicles are gathered in the sample compartment unit the cooler side;

a signal amplifying unit arranged on the side of the sample chamber unit away from the heating unit for amplifying and reflecting the light signal, and the signal amplifying unit will reflect the light signal to a designated position;

A signal processing unit arranged on one side of the sample chamber unit for collecting and calculating the amplified optical signal, the signal processing unit acquires at least one optical signal parameter, and quantifies the optical parameter and adopts the signal processing unit. Unweighted and/or weighted models determine the corresponding light signal parameters, and obtain the expression intensity of the expressed proteins that are highly expressed in gastric cancer in a single case of extracellular vesicles.

Further, the chemiluminescence process is as follows: firstly incubate the aptamer or antibody with the luminescent catalyst and the extracellular vesicles, label the luminescent catalyst on the surface of the extracellular vesicle through specific binding, and send the luminescent catalyst to the surface of the extracellular vesicle. It adds a light-emitting substrate, catalyzes the light-emitting substrate through a light-emitting catalyst to make it reach an excited state, and releases light energy during the process of converting to a ground state to label the surface of extracellular vesicles with light signals.

Further, the weighted summation method for calculating the protein intensity of extracellular vesicles in the signal processing unit includes:

Step a: Set the weighted expression intensity of the protein as the dependent variable Y, set the optical parameter of the extracellular vesicle markers measured by the signal processing unit as the independent variable X, then the optical parameters of different markers on the extracellular vesicles are measured according to The order is set to: X ₁ , X ₂ , ..., X _k ;

Step b: Since the weighted expression intensity Y has a linear relationship with the optical parameter X, the following calculation is performed at this time:

Y=β ₀ +β ₁ X ₁ +β ₂ X ₂ +...+β _k X _k +ε (1)

Among them, β ₀ , β ₁ , β ₂ , ..., β _k are regression parameters, and ε is a random error term;

Step c: make basic assumptions about the formula (1) and the optical parameter X in the step b to ensure the validity of parameter estimation, statistical test and confidence interval estimation when the data is weighted and summed;

Step d: When the formula (1) and the optical parameter X satisfy the assumption, take the expectation on both sides of the formula (1), and obtain:

E(YX ₁ , X ₂ ... X _k )=β ₀ +β ₁ X ₁ +β ₂ X ₂ +...+β _k X _k (2)

Wherein, E(Y|X ₁ , X ₂ , . . . , X _k ) represents the conditional mean of the protein-weighted expression intensity Y under the condition of a given light parameter X _i ;

Step e: After the expectation of the formula (1) is completed, the corresponding estimated values of the regression parameters β ₀ , β ₁ , β ₂ , . . . , β _k are given according to the optical parameter X At this point, the corresponding estimated value of the protein-weighted weighted expression intensity Y is obtained:

The above formula (3) is the point estimated value of E(Y|X ₁ , X ₂ , . . . , X _k );

Step f: When the formula (1) and the optical parameter X satisfy the assumptions in the step c, the parameter estimation is obtained by least squares. At this time, it is assumed that

In the formula (4), respectively Finding the partial derivative, and setting the partial derivative equal to 0, yields:

Solve the equation system in the above formula (5) to obtain the estimated values of the regression parameters β ₀ , β ₁ , β ₂ ,..., β _k and protein-weighted weighted expression intensity Y.

Further, the light parameter in the step a is one or more of light brightness L, light intensity C, absorbance A or light frequency λ.

Further, the basic assumptions made to the formula (1) and the optical parameter X in the step c include:

Assuming c1: the probability distribution of the random error term ε has zero mean, E(ε)=0;

Assumption c2: The probability distribution of the random error term ε has homoscedasticity for different independent variable performance values, the variance of ε does not change with the change of X _ij , D(ε)=σ ² ;

Assuming c3: there is no autocorrelation in the random error term ε, cov(ε _i , ε _j )=0;

Assuming c4: ε _i is not related to any explanatory variable Xi, cov(ε _{i ,} Xi ₎ =0;

Assumption c5: There is no complete collinearity between independent variables X;

Among them, the above assumptions c1-c4 are the same as those of the univariate regression analysis, and the assumption c5 is used for explanatory variables.

Further, the weighted summation method for calculating the total abundance of proteins expressed in extracellular vesicles in the signal processing unit includes:

Step a: Set the total abundance of proteins expressed in extracellular vesicles as the dependent variable M, and set the optical parameter of the extracellular vesicle marker as the independent variable D, then set the measured optical parameter as the order of the detected expressed proteins respectively. : D ₁ , D ₂ ,..., D _k ;

Step b: Since the abundance of different types of expressed proteins is different between different patients, it is necessary to set the corresponding weight coefficients α ₁ , α ₂ ,...α _k according to different types of expressed proteins, then the extracellular vesicles The total abundance M of the expressed protein can be obtained by the following formula:

M=α ₁ D ₁ +α ₂ D ₂ +...+α _k D _k (6)

Step c: Determine the total number N of cancer _types , and determine the number n ₁ , n ₂ , . . . The proportions with high expression are:

Step d: take the average value of the optical parameter D of each expressed protein in the step a And find the variance of the light parameter D:

Step f: Determine the weight coefficient α according to the data pairs obtained in the steps c and d:

Step g: After the weight coefficient α is determined, calculate the total abundance of extracellular vesicles expressed by the formula in step b:

Further, the sample compartment unit is arranged on one side of the heating unit, and a sample liquid is arranged inside the sample compartment unit for loading the extracellular vesicles and aptamers, including:

a first heat-conducting surface arranged on one side of the heating unit and made of a transparent material for absorbing the heat of the heating unit;

a second heat-conducting surface disposed below the first heat-conducting surface and made of a transparent material for absorbing the heat of the heating unit, wherein the heat-conductivity of the second heat-conducting surface is higher than that of the first heat-conducting surface;

The gasket is arranged between the first heat conducting surface and the second heat conducting surface and has a through hole in the center for loading the sample liquid.

Further, the signal amplifying unit is disposed on the side of the sample chamber unit away from the heating unit to amplify the light signal on the surface of the extracellular vesicle, including:

an objective lens arranged on the side of the second heat-conducting surface away from the heating unit for observing optical signals;

a collection mirror that is arranged on the side of the objective lens away from the heating unit and forms a certain angle with the objective lens, and is used to reflect light marks;

a magnifying mirror arranged on the side of the objective lens away from the heating unit and at a certain angle with the objective lens to reflect the light source;

The observation light source is arranged on one side of the magnifying mirror and used to provide the magnifying light source for the light mark.

In another aspect, the present invention provides a method for detecting gastric cancer based on the detection of thermophoretic extracellular vesicles, which is characterized by comprising:

Obtain a patient's blood sample as a sample liquid, and incubate the extracellular vesicles with light-labeled aptamers or antibodies, and use the aptamers or antibodies with the surface of the extracellular vesicles to specifically express proteins that are highly expressed in gastric cancer combined to label the surface of extracellular vesicles with light signals;

The incubated extracellular vesicles are put into the sample compartment unit, and the sample compartment unit is heated to generate thermophoresis effect and convection, and the extracellular vesicles are gathered on the low temperature side in the sample compartment unit to Amplifying the light signal on the extracellular vesicle, and converting the light signal into a corresponding specific single species value by calculating at least one light signal parameter;

After the detection is completed, the above steps are repeated, and different types of aptamers or antibodies are used to label and detect various expressed proteins in the extracellular vesicles that are highly expressed in gastric cancer, to obtain the expression of various expressed proteins in the extracellular vesicles. value group;

Bring the corresponding numerical groups obtained for different optical parameters into the weighted model and/or the unweighted model to calculate the corresponding optical parameters, and obtain the weighted expression intensity and/or unweighted expression intensity of the extracellular vesicle protein and the total expressed protein. Abundance, the SUM expression map of extracellular vesicles is obtained by combining the three values, and whether the test subject has gastric cancer is determined according to the SUM expression map.

In another aspect, the present invention provides a method for detecting gastric cancer using chemiluminescence based on thermophoretic extracellular vesicle detection, comprising:

Obtain the patient's blood sample as the sample liquid, and incubate the extracellular vesicles with aptamers or antibodies with luminescent catalysts, and conduct the expression of gastric cancer through the aptamers or antibodies and the surface of the extracellular vesicles. Specific binding to label the surface of extracellular vesicles with luminescent catalysts;

Put the incubated extracellular vesicles into the sample compartment unit, and add a luminescent substrate to the sample compartment unit, so that it catalyzes with the luminescent catalyst on the surface of the extracellular vesicles to reach the excited state, and in the process of converting to the ground state release light energy to label the surface of extracellular vesicles with light signals;

The sample chamber unit is heated to generate thermophoresis effect and convection, and the extracellular vesicles are gathered on the low temperature side inside the sample chamber unit to amplify the light signal on the extracellular vesicle; after amplification, a signal processing unit is used Collect and analyze the optical signal, and convert the optical signal into a corresponding specific single type value by calculating different optical parameters;

After the detection is completed, the above steps are repeated, and different types of aptamers or antibodies are used to label and detect the various expressed proteins in the extracellular vesicles, respectively, to obtain numerical groups of the various expressed proteins in the extracellular vesicles;

Bring the corresponding numerical groups obtained for different optical parameters into the weighted model and/or the unweighted model to calculate the corresponding optical parameters, and obtain the weighted expression intensity and/or unweighted expression intensity of the extracellular vesicle protein and the total expressed protein. Abundance, the SUM expression map of extracellular vesicles is obtained by combining the three values, and whether the test subject has gastric cancer is determined according to the SUM expression map.

Compared with the prior art, the beneficial effect of the present invention lies in that, by using an aptamer or an antibody, the expression protein with high expression of gastric cancer on the surface of the extracellular vesicles of the patient's blood is optically labeled, and a detection unit is used to detect the light in the optical labeling. The physical parameters are detected and processed. By analyzing the optical parameters, the weighted expression intensity of the expression protein that the extracellular vesicles have high expression on gastric cancer can be obtained quickly and accurately, and the detection accuracy is high. In particular, the present invention adopts the accumulation detection of a variety of physical parameters, rather than directly through the biological reaction detection, the detection process is simpler and easier to operate, and the sample consumption is small, and through the accumulation detection of physical parameters, compared with biological reaction detection, The expression of the detection amount is easier to quantify, and it can clearly determine whether the test subject has gastric cancer. In particular, in the present invention, the detection of the expressed protein that is highly expressed by extracellular vesicles to gastric cancer and the parameterization of the luminosity are obtained based on the calculation method of the weighted model and/or the unweighted summation model, and then based on the standard protein marker concentration. The standard function relationship with a certain parameter of light is used to determine the degree of canceration. For example, the determination is made by detecting physical quantities of light properties such as light intensity C, light luminance L, light frequency λ, and sample concentration at a specific wavelength absorbance A. In addition, in the detection process based on the same antibody or aptamer, the final canceration result is determined by detecting and calculating various physical parameters, comparing with each other, and selecting the expression of the optimal physical parameter.

In particular, the present invention adopts the light accumulation method combining chemiluminescence, thermophoresis effect and convection, and reaches the excited state through the catalysis of the aptamer or the light-emitting catalyst in the antibody combined with the extracellular vesicle, and in the process of converting to the ground state. The light energy is released to label the surface of the extracellular vesicle with a light signal, and when detected, the luminescent catalyst can remain luminescent for a long time.

In particular, the present invention can accurately determine the degree of canceration through the calculation method of the weighted model and/or the unweighted summation model and the quantitative and accurate calculation of the proteins expressed in various extracellular vesicles.

Further, the sample chamber unit of the present invention is provided with a first heat conducting surface and a second heat conducting surface made of transparent materials, and the extracellular vesicles are heated to a thermophoretic effect and move to a low temperature by heating the sample liquid inside them. After rising, the sample liquid will generate thermal convection and accumulate extracellular vesicles at the designated position, thereby amplifying the light signal of extracellular vesicles, so that extracellular vesicles can be observed more accurately when detecting optical parameters. The specific value of the bubble light parameter further improves the detection accuracy of the detection system. In particular, in this way, multiple optical physical parameters can be collected at the same time, such as the detection of parameters such as light intensity C, light brightness L, light frequency λ, and absorbance A at a specific wavelength.

Further, the detection system of the present invention uses thermophoresis effect and thermal convection effect to accumulate extracellular vesicles, therefore, the detection system has no specific limitation on the size of the sample compartment unit. The sample liquid in the sample compartment unit is used to load extracellular vesicles and aptamers or antibodies and enable them to generate thermophoresis effect and convection, so the detection system does not have specific restrictions on the selection of the sample liquid, as long as the The sample liquid can drive the extracellular vesicles to move and accumulate under the effect of thermal convection. Further, the extracellular vesicles and aptamers or antibodies are linked together by specific binding, so that the optical label can be firmly linked to the extracellular vesicles, and when the extracellular vesicles accumulate, it can also be more The optical parameters of extracellular vesicles are accurately observed, which further improves the detection accuracy of the detection system. The force received by extracellular vesicles under the thermophoretic effect is proportional to the square of its diameter, and has nothing to do with the number of extracellular vesicles. Therefore, only a small amount of blood sample is needed to complete the detection, and only 0.1 μm is required for extracellular vesicles. liters of sample volume, and no sample preparation is required.

Further, when the sample compartment unit is heated, as long as there is a temperature difference between the first heat conducting surface and the second heat conducting surface, thermophoresis effect and thermal convection can be generated in the sample compartment unit, and there will be a Light-labeled extracellular vesicles accumulate at designated locations. The detection system only needs to detect the optical parameters of the accumulated extracellular vesicles without using other special instruments, and the cost of the detection device is saved without affecting the detection accuracy of the detection system.

Further, the detection system is provided with a data acquisition unit, which can extract the specified optical parameters from the collected protein expression profiles, and bring them into a weighted model and/or an unweighted summation model to calculate the cells. The weighted expression intensity and/or the unweighted expression intensity of the protein expressed in outer vesicles further improves the detection accuracy of the detection system by converting the intuitive image into specific numbers.

Further, the detection system can select corresponding aptamers or antibodies for highly expressed proteins in different cancers to label them, so as to measure the abundance map of the corresponding expressed proteins and calculate the expression intensity. In this way, the detection system not only It can detect gastric cancer and other cancers quickly and accurately, such as: lung cancer, pancreatic cancer, colorectal cancer, stomach cancer, prostate cancer, head and neck cancer, skin cancer, kidney cancer, testicular cancer, thyroid cancer , bladder cancer, uterine cancer, vaginal cancer, endometrial cancer, ovarian cancer, esophageal cancer, oral cancer, salivary gland cancer, laryngeal cancer, peritoneal cancer, nose cancer, laryngeal cancer, fallopian tube cancer, Wilms cancer, lymphoma, Cholangiocarcinoma, as well as swing sarcoma, synovial sarcoma, medulloblastoma, trophoblastic tumor, glioma, glioblastoma, cholesteatoma, chondrosarcoma, ependymoma, schwannoma, neuroma, Rhabdomyosarcoma.

Description of drawings

1 is a structural diagram of a gastric cancer detection system based on thermophoretic extracellular vesicle detection according to the present invention;

2 is a structural diagram of a gastric cancer detection system based on thermophoretic extracellular vesicle detection using chemiluminescence according to the present invention;

3 is a schematic diagram of the present invention using a monochromator to detect the absorbance of extracellular vesicle samples;

Fig. 4 is the protein abundance map of extracellular vesicles expressed in gastric cancer patients detected by a chemiluminescence gastric cancer detection system based on thermophoretic extracellular vesicle detection;

Detailed ways

In order to make the purpose and advantages of the present invention clearer, the present invention will be further described below with reference to the embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention.

The above and other technical features and advantages of the present invention will be described in more detail below with reference to the accompanying drawings.

Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principle of the present invention, and are not intended to limit the protection scope of the present invention.

It should be noted that, in the description of the present invention, the terms “upper”, “lower”, “left”, “right”, “inner”, “outer” and other terms indicated in the direction or the positional relationship are based on the drawings. The direction or positional relationship shown is only for the convenience of description, rather than indicating or implying that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

In addition, it should also be noted that, in the description of the present invention, unless otherwise expressly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it may be a fixed connection or a It is a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal communication between two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

System Embodiment 1

An embodiment of the present invention is a gastric cancer detection system based on thermophoretic extracellular vesicles. Please refer to FIG. 1 , which is a schematic structural diagram of a gastric cancer detection system based on thermophoretic extracellular vesicle detection according to the present invention. It includes a heating unit 1, a sample compartment unit 2, a signal amplification unit 3 and a signal processing unit 4, wherein the heating unit 1 is arranged above the sample compartment unit 2, and is used for the sample compartment unit 2. The sample inside is heated; the sample chamber unit 2 is filled with sample liquid for loading extracellular vesicles and aptamers with fluorescent labels; the signal amplification unit 3 is arranged below the sample chamber unit 2, In order to amplify the fluorescent signal of the fluorescent label, the signal processing unit 4 is arranged on the side of the signal amplifying unit 3 to collect and record the amplified fluorescent signal, and to measure the brightness and light intensity of the fluorescent signal. One or more of the light wavelength parameters and the light wavelength parameters are performed or obtained, and the degree of cancer lesions is detected by the extracellular vesicles of the sample to be tested by using weighted and unweighted summation.

Specifically, before the gastric cancer detection system based on thermophoretic extracellular vesicle detection works, the extracellular vesicles and the fluorescently labeled aptamer are put into the sample compartment unit 2, and the aptamer and the extracellular vesicle A protein on the surface of vesicles that is highly expressed in gastric cancer is specifically bound to fluorescently label the extracellular vesicles. After the labeling is completed, the heating unit 1 starts to heat the sample compartment unit 2. After the sample compartment unit 2 is heated, the sample liquid inside it begins to produce a thermophoretic effect and convection, and aggregates the labeled extracellular vesicles. At the designated position; after the aggregation is completed, the signal amplification unit 3 will emit a contrast light source to the position where the extracellular vesicles are aggregated, and the signal processing unit 4 will collect the relevant information of the aggregated extracellular vesicles, and analyze them accordingly. One or more of the parameters of light brightness L, light intensity C, and wavelength λ are obtained, and the degree of cancer lesions is judged by means of weighted and unweighted summation. Those skilled in the art can understand that the gastric cancer detection system based on thermophoretic extracellular vesicle detection in this embodiment can not only be used for the aggregation and detection of extracellular vesicles, but also can be used for extracellular vesicles or other types of microscopic Nanoparticles are used for detection, as long as the gastric cancer detection system based on thermophoretic extracellular vesicle detection can reach its designated working state.

Please continue to refer to FIG. 1 , the heating unit 1 of the embodiment of the present invention is a laser heater, which is arranged above the sample chamber unit 2 to heat the sample liquid inside the sample chamber unit 2 , to create a circular heating area inside it. After the extracellular vesicles are marked, the heating unit 1 heats the sample liquid inside the sample chamber unit 2 to aggregate the extracellular vesicles. It can be understood that the heating method of the heating unit 1 is not limited to laser irradiation, and the selection of the direction and power of laser irradiation is not specifically limited in this embodiment, as long as the heating unit 1 can make the sample chamber A temperature difference is generated inside the unit 2 to gather extracellular vesicles.

Please continue to refer to FIG. 1 , the sample chamber unit 2 according to the embodiment of the present invention is disposed below the heating unit 1 for containing the sample liquid containing extracellular vesicles and aptamers, including the first heat conducting surface 21 , The second heat-conducting surface 22 and the gasket 23; wherein the gasket 23 is arranged under and in contact with the first heat-conducting surface 21 to hold the sample liquid, and the second heat-conducting surface 22 is arranged on the gasket 23 is used to seal the sample liquid inside the gasket 23 together with the first heat conducting surface 21 . When the heating unit 1 heats the sample chamber unit 2, the laser will pass through the first heat conduction surface 21 and the second heat conduction surface 22 in turn and heat them. Later, the temperature of the extracellular vesicles will also increase. At this time, the extracellular vesicles will generate a thermophoresis effect and move toward the lower temperature first heat conduction surface 21 and the second heat conduction surface 22. The thermal conductivity of the surface 21 is different from that of the second thermal conductive surface 22. After heating, the temperature at the heating point of the first thermal conductive surface 21 will be higher than the temperature at the heating point of the second thermal conductive surface 22, which will cause a temperature difference in the sample liquid. The liquid thus generates thermal convection in the direction of the low temperature to the high temperature in the sample compartment unit 2 , so that the extracellular vesicles in the sample migrate and accumulate at the second heat conducting surface 22 . It can be understood that the sample compartment unit 2 in this embodiment can be arranged below, above, left or right of the heating unit 1 as long as the heating unit 1 can heat the sample compartment unit 2 It is sufficient to raise the temperature of the sample liquid inside it.

Specifically, the first heat-conducting surface 21 of the present invention is a glass sheet, which is arranged above the gasket 23 to seal the sample liquid inside the gasket 23 and heat the sample liquid together with the heating unit 1 . heating. When the laser of the heating unit 1 passes through the first heat-conducting surface 21, it will heat the center of the first heat-conducting surface 21 and increase the temperature of the heating place. The thermally conductive surface 21 will transfer heat to the sample liquid in the spacer 23 and cause convection of the sample liquid to accumulate extracellular vesicles. It can be understood that, the material of the first thermal conductive surface 21 can be glass, polymethyl methacrylate (PMMA), polydimethylsiloxane (PDMS), sapphire or other kinds of transparent materials, as long as all requirements are met. The first heat conduction surface 21 only needs to be able to be heated up.

Specifically, the second heat-conducting surface 22 of the present invention is a glass sheet with higher thermal conductivity than the first heat-conducting surface 21 , which is disposed under the gasket 23 to seal the samples inside the gasket 23 . liquid and heat the sample liquid together with the heating unit 1 . When the laser of the heating unit 1 passes through the second heat-conducting surface 22, it will heat the center of the second heat-conducting surface 22 and increase the temperature of the heating place. The heat conduction surface 22 will transfer heat to the sample liquid in the gasket 23. Since the thermal conductivity of the second heat conduction surface 22 is higher than that of the first heat conduction surface 21, after the heating unit 1 is completed, the The temperature of the second heat-conducting surface 22 will be lower than the temperature of the first heat-conducting surface 21 , a temperature difference is generated in the gasket 23 , and convection of the sample liquid is generated to accumulate extracellular vesicles. It can be understood that the material of the second thermally conductive surface 22 can be glass, polymethyl methacrylate (PMMA), polydimethylsiloxane (PDMS), sapphire or other kinds of transparent materials, as long as all requirements are met. The second heat conduction surface 22 can be heated and its temperature is lower than the central temperature of the sample liquid.

Specifically, the gasket 23 is a circular sheet with a through hole, which is arranged between the first heat conducting surface 21 and the second heat conducting surface 22, and is used for loading sample liquid and accumulating extracellular vesicles. When the heating unit 1 heats the sample chamber unit 2, the focus of the heating laser will be located at the sample liquid inside the gasket, and the extracellular vesicles inside the sample liquid will generate heat by heating the sample liquid At the same time, due to the temperature difference between the first thermal conduction surface 21 and the second thermal conduction surface 22, the sample liquid begins to generate convection and accumulate extracellular vesicles on the second thermal conduction surface 22. The laser-irradiated place of the heat-conducting surface 22 . It can be understood that the material of the sample liquid in the gasket 23 can be plasma, serum or any form of blood or a processed derivative sample, as long as the sample liquid can be loaded with extracellular vesicles and aptamers and can It can produce thermophoresis effect and convection.

Please continue to refer to FIG. 1 , the signal amplifying unit 3 according to the embodiment of the present invention is disposed below the sample chamber unit 2 to irradiate the extracellular vesicles accumulated in the second heat conducting surface 22 and convert the The fluorescent signal in the extracellular vesicle is amplified, including the objective lens 31 , the collecting mirror 32 , the magnifying mirror 33 and the observation light source 34 . The objective lens 31 is arranged below the second heat-conducting surface 22 to collect the fluorescent signal of the extracellular vesicles with fluorescent labels, and the collection mirror 32 is arranged below the eyepiece 31 to collect the fluorescent signal. The amplified fluorescent signal is reflected to the signal processing unit 4, and the amplifying mirror 33 is arranged below the collecting mirror 32 to reflect the light from the observation light source 34 to the objective lens 31, so The observation light source 34 is arranged on the right side of the magnifying mirror 33 to provide light for amplifying the fluorescent signal. When the signal amplifying unit starts to work, the observation light source 34 emits light, which is reflected by the magnifying mirror 33 to the objective lens 31 , and the objective lens 31 irradiates the light to the cells in the second heat-conducting surface 22 where the extracellular vesicles gather, thereby amplifying the fluorescent signal of the extracellular vesicles. After the amplification is completed, the signal processing unit 4 will use the acquisition mirror 32 to collect the amplified fluorescent signal, thereby Complete the collection and processing of fluorescent signals. It can be understood that the signal amplifying unit 3 can be arranged above, below, left or right of the sample compartment unit 2, as long as it can collect the fluorescence signal in the sample compartment.

Specifically, the objective lens 31 of the present invention is arranged below the second heat-conducting surface 22 where extracellular vesicles gather, so as to collect the fluorescence signals in the extracellular vesicles. When the light from the observation light source 34 When the objective lens 31 is irradiated, the objective lens 31 will irradiate the light on the second heat conducting surface 22, so as to amplify the fluorescent signal on the extracellular vesicles. It can be understood that the type of the objective lens 31 is not specifically limited in this embodiment, as long as the objective lens 31 can reach its designated working state.

Specifically, the collecting reflector 32 in the embodiment of the present invention is a plane mirror, which is arranged below the objective lens 31 and forms an included angle of 45° with the objective lens 31 to reflect the amplified fluorescent signal. After the fluorescent signal of the extracellular vesicle is amplified, the collection mirror 32 reflects the fluorescent marker to the signal processing unit 4 to complete the collection of the fluorescent signal. It can be understood that the size of the collection mirror 32 is not specifically limited in this embodiment, as long as the collection mirror 32 can completely reflect the fluorescent signal to the signal collection unit.

Please continue to refer to FIG. 1 , the signal processing unit 4 according to the embodiment of the present invention includes a CCD camera, which is disposed on the right side of the collecting mirror 32 for collecting the fluorescent signal of the extracellular vesicles. After the fluorescence signal is amplified, the collection mirror 32 will reflect the amplified fluorescence signal to the signal processing unit 4, and the signal processing unit 4 collects and organizes the fluorescence signal to form a single detection It can be understood that the signal processing unit 4 may include a CDD camera, or any instrument capable of detecting fluorescent signals, as long as the signal processing unit 4 can detect the fluorescently labeled cells through the signal amplifying unit 3 The outer vesicles can be photographed to obtain information. Of course, the signal processing unit 4 may be located on the left, right, upper or lower side of the signal amplifying unit 3 as long as the signal processing unit 4 can collect and process the fluorescence signal through the signal amplifying unit 3 . .

When the detection system of this embodiment of the system detects whether the subject has gastric cancer, the fluorescent label is first connected to the aptamer, and then the aptamer is incubated with the extracellular vesicles of the sample to be tested to label the extracellular vesicles with fluorescence. The marking is simple to operate and easy to execute. When using the system to detect multiple subjects to be tested, the patient only needs to provide a small amount of blood samples, and the patient's condition can be quickly diagnosed.

System embodiment two

The embodiment of the present invention is a gastric cancer detection system using chemiluminescence based on thermophoretic extracellular vesicle detection. As a preferred embodiment of the present invention, please refer to FIG. 2 , which is an embodiment of the present invention based on thermophoretic extracellular vesicle detection. A schematic structural diagram of a gastric cancer detection system using chemiluminescence, the system in this embodiment includes a heating unit 1 , a sample chamber unit 2 and a signal processing unit 4 , which are the same as the first embodiment.

Different from the above-mentioned first embodiment, the optical labeling method of the present invention adopts the chemiluminescence labeling method. Before using the gastric cancer detection system based on thermophoretic extracellular vesicle detection, the test sample of extracellular vesicles is firstly labeled with luminescence catalysis. The luminescent catalyst can be an enzyme, and the extracellular vesicles are labeled with the enzyme through the specific binding of the antibody to the protein expressed by the extracellular vesicles. After the labeling is completed, the incubated samples are placed into the sample chamber unit. 2, adding a luminescent substrate to the interior of the sample compartment unit, the enzyme catalyzes the luminescent substrate and makes it reach an excited state, and emits a light signal when it is converted into a ground state.

When the gastric cancer detection system based on thermophoretic extracellular vesicle detection and utilizing chemiluminescence starts to work, the heating unit 1 heats the sample chamber unit 2 to make the extracellular vesicles inside produce thermophoretic effect And start to move to the lower temperature side, and at the same time, the temperature difference between the two sides of the sample compartment unit causes convection inside the sample compartment unit 2, and the extracellular vesicles are accumulated in the designated position. After the accumulation is completed, the The signal collection unit 3 starts to collect the light signal of the extracellular vesicles, and obtains the abundance map of the proteins expressed on the surface of the extracellular vesicles. Please continue to refer to FIG. 2 , the signal processing unit 4 according to the embodiment of the present invention is disposed below the sample chamber unit 2 to observe and collect the aggregated extracellular vesicles.

Specifically, in the extracellular vesicle detection system using chemiluminescence in this embodiment, the labeled enzyme may be horseradish peroxidase (HRP), alkaline phosphatase (ALP) or other kinds of labeled enzymes; The luminescent substrate is luminol (32 amino phthalic hydrazide), isoluminol (42 amino phthalic hydrazide) or other derivatives, as long as the labeled enzyme can interact with the cells. The extracellular vesicles react and stick to the surface of the extracellular vesicles, and the luminescent substrate can react with the labeled enzyme and emit light.

Compared with the first embodiment of the actual detection system in this embodiment, the structures, principles and working functions of the heating unit 1 and the sample chamber unit 2 are the same. After the highly expressed expression protein of gastric cancer is labeled, the surface of the extracellular vesicle will maintain a long-term high-brightness light signal, so this embodiment does not use the magnifying mirror 33 and the observation light source 34 to measure the light signal. Amplification also enables accurate observation and collection of light signals from extracellular vesicles.

In the detection system of this embodiment, the antibody and the extracellular vesicles are first incubated together and connected to each other. After the incubation, the extracellular vesicles and the luminescent substrate are put into the sample chamber unit 2 together. The medium luminescence catalyst catalyzes the luminescence substrate to reach the excited state, and releases light energy when it is converted into the ground state, thereby marking the light signal on the surface of the extracellular vesicle. At the same time, the chemical reaction described in this embodiment is a catalytic reaction, As a catalyst, the luminescent catalyst can always catalyze the reaction of the luminescent substrate and continuously generate light, so that the extracellular vesicle can keep emitting light for a long time during detection.

Further, since the optical signal can be maintained for a long time, there is no need to amplify the optical signal twice. When collecting the optical signal, compared with the first embodiment of the system, this embodiment can clear the optical signal only once. Accurate collection saves the detection time of the system and improves the detection efficiency.

Further, during detection, the labeled extracellular vesicles are transferred from the incubation container to the sample compartment unit 2 containing the sample liquid, so that the luminescent catalyst and luminescent substrate in the excess antibody are excluded. The phenomenon of reacting and emitting light together due to the catalysis of the substance makes the thermophoretic extracellular vesicle-based cancer detection system detect extracellular vesicles with the thermophoretic extracellular vesicle detection system when it detects extracellular vesicles. Based on the advantages of the cancer detection system, it has a high degree of accuracy.

Based on the above two embodiments, the detection and parameterization of the luminosity of extracellular vesicles are obtained based on the calculation method of weighted and unweighted summation, and then based on the standard protein marker concentration and the standard of a certain parameter of light functional relationship to determine the degree of cancer. For example, the determination is made by detecting physical quantities of light properties such as light intensity, light brightness, light frequency, and sample concentration at a specific wavelength absorbance.

The following description will be given by way of examples.

As a preferred embodiment, the light parameter X in this embodiment adopts the light intensity L, and the collector in the detection system chooses a CCD camera. The light emitted after the collection of vesicles is photographed to obtain a single map of the light signal of extracellular vesicles. After the measurement is completed, for multiple measurement results of a single expressed protein, the light in each map can be compared using the brightness comparison table. The brightness L is converted from the image into specific values L ₁ , L ₂ . . . L _k , and is taken as the independent variable X into the weighted sum model to calculate the weighted weighted expression intensity Y of a certain protein type, so as to The weighted weighted expression intensity Y of a certain protein class is obtained.

At this time, the protein-weighted expression intensity Y _L of the extracellular vesicles is:

Y _L =β ₀ +β ₁ L ₁ +β ₂ L ₂ +...+β _k L _k +ε (10)

After assuming the above formula (10), taking the expectation on both sides, we can get:

E(Y _L |L ₁ , L ₂ ...L _k )=β ₀ +β ₁ L ₁ +β ₂ L ₂ +...+β _k L _k (11)

After the expectation of the formula (10) is completed, the corresponding estimated values of the regression parameters β ₀ , β ₁ , β ₂ ,..., β _k are given according to the brightness L At this point, the corresponding estimated value of the protein-weighted expression intensity Y _L can be obtained:

The parameter estimates are then obtained using least squares:

In the formula (13), respectively Finding the partial derivative, and setting the partial derivative equal to 0, yields:

By solving the equation system in the above formula (14), the estimated values of the regression parameters β ₀ , β ₁ , β ₂ ,..., β _k can be obtained and protein-weighted expression intensity Y _L .

find an estimate of the parameter After the protein weighted expression intensity Y _L is calculated, the weighted expression intensity Y _L is determined according to the type of expressed protein and the weighted expression intensity of each type of protein.

After calculation, it can be concluded that when the light intensity L is used as the light parameter X for calculation, the measured light map can more intuitively express the abundance of proteins expressed in extracellular vesicles, and the calculation is also relatively simple and the measurement period is short.

As a preferred embodiment, in this embodiment, the light intensity C is used instead of the light intensity L for measurement, and the collector used in the detection system selects an illuminometer (or lux meter) to measure the extracellular vesicle light signal The light intensity in C. Among them, the illuminometer is a photoelectric element that directly converts light energy into electrical energy. When the light hits the surface of the selenium photocell, the incident light passes through the metal thin film and reaches the interface between the semiconductor selenium layer and the metal thin film, and a photoelectric effect is generated on the interface. The magnitude of the generated photocurrent is proportional to the illuminance on the light-receiving surface of the photocell. At this time, if an external circuit is connected, a current will flow through, and the current value will be indicated on a microammeter with lux (Lx) as the scale.

After measuring the light intensity of a certain type of expressed protein in a number of extracellular vesicles by using an illuminometer, the light intensity C ₁ , C ₂ ... C _k of a certain type of expressed protein will be obtained, and it will be expressed as This is brought into the weighted sum model as an independent variable X to calculate the weighted expression intensity Y _C of a certain protein class, so as to obtain the weighted expression intensity Y _C of a certain protein class.

At this time, the protein-weighted expression intensity Y _C of the extracellular vesicle is:

Y _C =β ₀ +β ₁ C ₁ +β ₂ C ₂ +...+β _k C _k +ε (15)

After assuming the above formula (15), taking the expectation on both sides, we can get:

E(Y _C |C ₁ ,C ₂ ...C _k )=β ₀ +β ₁ C ₁ +β ₂ C ₂ +...+β _k C _k (16)

After the expectation of the formula (15) is completed, the corresponding estimated values of the regression parameters β ₀ , β ₁ , β ₂ ,..., β _k are given according to the light intensity C At this point, the corresponding estimated value of protein-weighted expression intensity Y _C can be obtained:

The parameter estimates are then obtained using least squares:

In the formula (18), respectively Finding the partial derivative, and setting the partial derivative equal to 0, yields:

By solving the equation system in the above formula (19), the estimated values of the regression parameters β ₀ , β ₁ , β ₂ , ..., β _k can be obtained and protein-weighted expression intensity Y _C .

find an estimate of the parameter After the protein weighted expression intensity Y _C is obtained, the weighted expression intensity Y _C is determined according to the type of expressed protein and the weighted expression intensity of each type of protein.

When the light intensity C is used as the light parameter X for calculation, it has high anti-interference when measuring the light map, and the obtained extracellular vesicle weighted expression intensity Y _C is relatively accurate;

As another preferred embodiment, in this embodiment, the light frequency ν is used instead of the light intensity L and light intensity C for measurement. At this time, the collector selected in the detection system is a spectrometer. When measuring the light signal of the vesicle, the wavelength λ ₁ , λ ₂ ... λ _k of the light signal of each extracellular vesicle in a certain type of expressed protein is measured by the spectrometer, and after obtaining the data of the light wavelength, according to the formula λν =c=299792458(m/s) to calculate the optical frequency values ν ₁ , ν ₂ . . . ν _k corresponding to each optical signal, and after the calculation is completed, take it as the independent variable X into the weighted summation model for The weighted expression intensity _Yν of a certain protein class is calculated to obtain the weighted expression intensity _Yν of a certain protein class.

At this time, the protein-weighted expression intensity _Yv of the extracellular vesicles is:

Y _ν =β ₀ +β ₁ ν ₁ +β ₂ ν ₂ +...+β _k ν _k +ε (20)

After assuming the above formula (20), taking the expectation on both sides, we can get:

E(Y _ν |ν ₁ ,ν ₂ ...ν _k )=β ₀ +β ₁ ν ₁ +β ₂ ν ₂ +...+β _k ν _k (21)

After the expectation of the formula (20) is completed, the corresponding estimated values of the regression parameters β ₀ , β ₁ , β ₂ ,..., β _k are given according to the light intensity ν At this point, the corresponding estimate of the protein-weighted expression intensity Y _ν can be obtained:

The parameter estimates are then obtained using least squares:

In the formula (23), respectively Finding the partial derivative, and setting the partial derivative equal to 0, yields:

By solving the equation system in the above formula (24), the estimated values of the regression parameters β ₀ , β ₁ , β ₂ , ..., β _k can be obtained and protein-weighted expression intensity _Yν .

When the optical frequency ν is used as the optical parameter X for calculation, the measured optical spectrum can be accurately digitally converted, and the obtained extracellular vesicle expression intensity Y _ν value has the highest accuracy.

As another preferred embodiment, in this embodiment, the concentration H of the extracellular vesicle sample is used instead of the brightness L, the light intensity C or the light frequency ν for measurement, and the collector selected in the detection system is: A monochromator, wherein the principle of using the monochromator to measure the absorbance is shown in FIG. 3 , the monochromator includes a light source 5, a monochromator 6, an adjustment hole 7, a glass tube 8, a photoresistor 9, an amplifier 10 and Output screen 11; when the light source 5 emits light with a specified light intensity and illuminates the monochromator 6, the monochromator 6 will disperse the light of the optical signal into monochromatic light of different wavelengths; 7. Adjust to allow the monochromatic light of 450nm wavelength to pass through and block monochromatic light of other wavelengths; the glass tube 8 contains the extracellular vesicle sample to be detected, when the monochromatic light of 450nm wavelength passes through In the glass tube 8, the extracellular vesicle sample will absorb part of the light intensity of the monochromatic light with a wavelength of 450 nm; the absorbed monochromatic light will be irradiated on the photoresistor 9, and the light intensity will be converted into resistance; the photoresistor 9 After being amplified by the amplifier 10, it is sent to the output screen 11 to display the light intensity of the monochromatic light after absorption on the screen. The absorbance A of the extracellular vesicle sample can be calculated by obtaining the post-absorption light intensity and combining the pre-absorption light intensity.

Before measuring the light signal of extracellular vesicles, the specific functional relationship between concentration and absorbance was obtained by measuring the absorbance at a wavelength of 450 nm under a series of known concentrations of protein markers as a reference. The monochromator measures the absorbance A ₁ , A ₂ . . . A _k of the optical signal at the wavelength of 450 nm for each case of extracellular vesicles of unknown concentration, and after obtaining the data of the optical signal absorbance, calculates according to the functional relationship Calculate the concentration H ₁ , H ₂ . . . H _k corresponding to the absorbance of each case, and after the calculation is completed, it is taken as the independent variable X and brought into the weighted sum model to calculate the weighted expression intensity Y _H of a certain type of protein, In this way, the weighted expression intensity _YH of a certain type of protein is obtained.

At this time, the protein-weighted expression intensity YH of the extracellular _vesicles is:

Y _H =β ₀ +β ₁ H ₁ +β ₂ H ₂ +...+β _k H _k +ε (25)

After assuming the above formula (25), taking the expectation on both sides, we can get:

E(Y _H |H ₁ ,H ₂ ...H _k )=β ₀ +β ₁ H ₁ +β ₂ H ₂ +...+β _k H _k (26)

After the expectation of the formula (25) is completed, the corresponding estimated values of the regression parameters β ₀ , β ₁ , β ₂ ,..., β _k are given according to the light intensity ν At this point, the corresponding estimate of the protein-weighted expression intensity Y _ν can be obtained:

The parameter estimates are then obtained using least squares:

In the formula (28), respectively Finding the partial derivative, and setting the partial derivative equal to 0, yields:

By solving the equation system in the above equation (29), the estimated values of the regression parameters β ₀ , β ₁ , β ₂ ,..., β _k can be obtained and protein-weighted expression intensity _YH .

When using the extracellular vesicle sample concentration H as the optical parameter X for calculation, the measured optical spectrum can be accurately digitally converted, and the obtained extracellular vesicle weighted expression intensity Y _H has the highest accuracy.

In the following, 10 patients with gastric cancer were selected, and the patients were first detected by conventional detection methods to obtain the actual weighted expression intensity Y ₀ of each expressed protein in each patient, and the above four measurement methods were used to measure the expression of each expressed protein in each patient. The weighted expression intensity is measured to obtain the measured intensity of expressed protein Y _L , Y _C , Y _ν , Y _H , and the measured intensity of each expressed protein Y _L , Y _C , Y _ν , Y _H and the actual intensity of the expressed protein are obtained by the following formula The deviation value з of Y ₀ is used to judge the detection accuracy of the above four methods:

Among them, among the extracellular vesicle markers detected in this example, CD9, CD63, and CD81 generally have highly expressed proteins, so the above three expressed proteins are selected for the detection of the above four methods. The detection accuracy is compared, and the comparison results are shown in Table 1:

Table 1

According to Table 1, it can be concluded that, compared with the other two methods, when the light intensity C extracellular vesicle sample concentration H is used as the independent variable X to detect and calculate the patient's extracellular vesicle protein weighted weighted expression intensity Y, it is obtained. The deviation value of the patient's extracellular vesicle protein is relatively high, and the measurement accuracy is low; and when the light intensity L and the light frequency ν are selected as the independent variables X to detect and calculate the weighted expression intensity Y of the patient's extracellular vesicle protein, the obtained deviation value is obviously low. Because of the light intensity deviation value з _C , in this embodiment, a parameter from light intensity L and light frequency ν is selected as the independent variable X in the weighted sum model relative to light intensity C and extracellular vesicle sample concentration H.

However, when the light frequency v is used as the independent variable X for the calculation, the data value used is very large, so it will consume a lot of time before determining the protein-weighted weighted expression intensity Y, and after the data processing is completed, use the weighted When the summation model operates on the sorted light frequency data, since the sorted light frequency ν value is also huge, a lot of calculations are needed to obtain the extracellular vesicle protein weighted weighted expression intensity Y. The whole process It will consume a lot of time and calculation. Therefore, in the case of similar deviation values, this embodiment selects the lightness L, which is relatively simple in the data processing process, as the independent variable X of the weighted sum model.

Specifically, the method for weighted summation of protein abundances expressed in extracellular vesicles in this detection system includes:

Step a: Set the total abundance of proteins expressed in extracellular vesicles as the dependent variable M, and set the optical parameter of a certain marker as the independent variable D, then set the measured optical parameters as: D ₁ , D ₂ ,...,D _k ;

Step b: Since the abundance of different types of expressed proteins is different between different patients, it is necessary to set the corresponding weight coefficients α ₁ , α ₂ ,...α _k according to different types of expressed proteins, then the extracellular capsule The total abundance M of vesicle expressed proteins can be obtained by the following formula:

M=α ₁ D ₁ +α ₂ D ₂ +...+α _k D _k (6)

Step c: Determine the total number N of cancer _types , and determine the number n ₁ , n ₂ , . . . The proportions with high expression are:

Step d: take the average value of the optical parameter D of each expressed protein in the step a And find the variance of the light parameter D:

Step f: Determine the weight coefficient α according to the data pairs obtained in the steps c and d:

Step g: After the weight coefficient α is determined, the total abundance of proteins expressed in extracellular vesicles can be obtained according to the formula in step b:

Wherein, the light intensity L is selected as the light parameter.

Example 1

Studies have shown that cells of almost all species can secrete extracellular vesicles. According to different sources, extracellular vesicles can be divided into three categories: exosomes, microvesicles and apoptotic bodies. Among them, exosomes contain complex Lipids, RNA and proteins.

Exosomes are rich in cholesterol and sphingomyelin, and the mRNA components they carry can enter the cytoplasm and be translated into proteins, not only mRNAs, but also microRNAs transferred by exosomes. Targeted regulation of mRNA levels in cells.

To sum up, in this embodiment, exosomes are selected as the detection sample to detect whether the person to be tested has gastric cancer, including the following steps:

Step 1: Obtain the patient's blood sample as the sample liquid, and incubate the exosomes in it with the fluorescently labeled aptamer, so that the fluorescently labeled aptamer can specifically bind to the expressed protein on the surface of the exosome, In this way, the surface of exosomes is labeled with light;

Step 2: Put the exosomes incubated in the step 1 into the sample compartment unit 2;

Step 3: After the addition of the exosomes is completed, use the heating unit 1 to heat the sample chamber unit 2, and set the focus of the laser on the sample liquid inside the sample chamber unit 2. After heating, the sample The exosomes in the liquid will have a thermophoretic effect and move to the low temperature area; at the same time, the sample liquid will be heated and expanded to generate buoyancy, thereby generating convection in the sample chamber unit 2, and the direction of the convection will point to the sample chamber from the surroundings. The heating area of unit 2 gathers surrounding exosomes on the low temperature side of sample compartment unit 2;

Step 4: After the accumulation of the exosomes is completed, use the signal acquisition unit 4 to collect the fluorescent signals of the accumulated exosomes, and use the signal amplification unit 3 to perform a measurement on the accumulated exosomes after the collection is completed. Lighting is performed, and after the lighting is completed, the signal collection unit 4 is used to perform secondary collection of the fluorescent signal of the accumulated exosomes;

Step 5: After the collection is completed, the abundance of a single expressed protein in the exosome is obtained by parameterizing and subtracting the fluorescence signals collected before and after irradiation;

Step 6: After the detection is completed, repeat steps 1 to 5, and use different types of aptamers to label and detect the various expressed proteins in the exosomes, and obtain the abundance of the various expressed proteins in the exosomes. degree map;

Step 7: Convert each luminosity in the protein map into data in combination with the comparison table in the exosome-expressed protein map in step 6, and use weighted and unweighted summation to obtain the weighted expression intensity Y of the exosome-expressed protein in the sample to be tested _L , expressed protein abundance M and unweighted expression intensity ∑L, and obtained the exosome SUM expression map according to the three values;

Step 8: Determine the severity of the patient according to the exosome expression protein map obtained in the step 6 and the exosome SUM expression map in the step 7.

Wherein, the aptamers described in this example are selected from oligonucleotide fragments that can specifically bind proteins or other small molecules, which are screened by the in vitro screening technology SELEX (systematic evolution of exponential enrichment ligands).

The basic idea of the SELEX technology is to chemically synthesize a single-stranded oligonucleotide library in vitro, use it to mix with the target substance, there is a complex between the target substance and the nucleic acid in the mixed solution, wash away the nucleic acid that is not bound to the target substance, and separate it. The nucleic acid molecule bound to the target substance is used as a template for PCR amplification, and the next round of screening process is performed. Through repeated screening and amplification, some DNA or RNA molecules that do not bind to the target substance or have low or medium affinity with the target substance are washed away, while the aptamer (Adaptorprotein) is the DNA or RNA molecules with high affinity to the target substance. RNA is isolated from very large random libraries and the purity increases as the SELEX process progresses, from p to n moles, eventually occupying the majority (>90% or so) of the library. It has the characteristics of large library capacity, wide adaptability, high resolution, high affinity, relatively simple, fast, economical, practical and small size of aptamers in the screening process.

Specifically, the fluorescently labeled aptamer in this embodiment is a 40-base single-stranded DNA, the diameter of the coil in the sample liquid is less than 5 nanometers, and the diameter of the exosome is 30-150 nanometers;

Specifically, the exosome samples in this example are cell culture medium supernatants, and the incubation conditions of the samples are: incubation time of 2 hours, aptamer concentration of 0.1 micromol per liter, and incubation temperature of room temperature.

Specifically, the heating unit described in this embodiment uses an infrared laser with a wavelength of 1480 nm for sample heating, the power is 200 mW, and the diameter of the laser spot out of the focus is about 200 microns. In this embodiment, the laser is irradiated from top to bottom, the upper heat-conducting surface of the sample chamber unit is made of clear material, such as glass, PMMA, PDMS, the lower heat-conducting surface is made of sapphire with better heat conductivity, and a low-temperature area is formed on the bottom surface to make the external The exosome thermophoresis converges on the bottom surface. The thickness of the upper heat-conducting surface is 1 mm, the thickness of the lower heat-conducting surface is 1 mm, and the heights of the middle gasket and the sample compartment unit are both 240 mm.

When the aptamer recognizes and binds to the exosome-expressed protein, the fluorescent label on the aptamer follows the exosome to be concentrated in the bottom area of the sample compartment unit under the laser spot, and generates an enhanced fluorescent signal; when the aptamer does not When recognizing exosome-expressed proteins, free aptamers cannot converge due to their small size, and the signal is not enhanced.

In this example, the excitation/emission wavelength of the luminescent group Cy5 is 649/666 nm, and the fluorescence signal is recorded by a CCD connected to a light microscope. The fluorescence signals before and after laser irradiation were recorded by CCD, and the abundance of proteins expressed on the surface of exosomes was obtained by analyzing the fluorescence signals before and after laser irradiation.

The markers detected in this example include exosome markers: CD9, CD63, and CD81 are generally highly expressed proteins in exosomes; and cancer-related protein markers expressed on the surface of exosomes: CEA, EGFR, EpCAM, HER2 and CCR6. For the exosome detection of the above markers, analyze their expression distribution.

Thirty-four gastric cancer patients were selected for detection, and 8 different nucleic acid aptamers were used to detect the abundance of 8 expressed proteins in exosomes in serum samples.

However, due to the high heterogeneity in the expression of exosome surface markers in patients, it is difficult to effectively distinguish gastric cancer patients from non-cancer patients by a single type of marker.

However, good diagnostic efficacy can be obtained by summing the expression levels of the detection markers with weighting and unweighting. The fluorescence brightness of different markers on _exosomes in this example is set as L ₁ , L ₂ ,... In the model, as shown in formula (10), at this time, after assuming the above formula (10), taking the expectation on both sides, the formula (11) can be obtained, and the regression parameters β ₀ , β ₁ are given according to the fluorescence brightness L , β ₂ , ..., the corresponding estimated values of β _k The corresponding estimated value of the protein weighted expression intensity Y _L can be obtained by formula (12); at this time, the least squares are used to estimate the parameters of formula (12), and formula (13) can be obtained in the formula (13), respectively Find the partial derivative, and set the partial derivative equal to 0 to obtain the equation (14). After solving the equation (14), the estimation of the regression parameters β ₀ , β ₁ , β ₂ ,..., β _k can be obtained value and protein-weighted expression intensity Y _L . find an estimate of the parameter After the protein weighted expression intensity Y _L is calculated, the weighted expression intensity Y _L is determined according to the type of expressed protein and the weighted expression intensity of each type of protein.

The fluorescence brightness of different markers on _exosomes in this example is set as L ₁ , L ₂ ,... , as shown in formula (6), at the same time, use formula (7) to calculate the variance σ ² of the fluorescence brightness L ₁ , L ₂ ,...L _k , and detect the total number of cancer N and a certain type of expressed protein in The numbers n ₁ , n ₂ , . . . of the cancer types with high expression are brought into formula (8) together with the variance σ ² to determine the weighting _coefficients α ₁ , α ₂ , .. .α _k , after the determination, use formula (6) to calculate the total abundance M of a certain type of exosome expressed protein.

The fluorescence brightness of different markers on _exosomes in this example is set as L ₁ , L ₂ ,... The unweighted expression intensity ΣL=L ₁ +L ₂ +...+L _k of a certain type of expressed protein in the body.

Combined with the weighted expression intensity Y _L of various proteins, the total abundance M of a certain type of expressed protein in exosomes, and the unweighted expression intensity ΣL, the exosome SUM expression map of the sample to be tested is made.

The subject operating characteristic curve (ROC) was drawn on the SUM expression map, and the area under the curve (Auc) was calculated to evaluate the diagnostic efficacy of the detection system of this embodiment.

After calculation, in the patients of this embodiment, the AUC of using ROC to diagnose gastric cancer is 0.9521, which shows that the detection system described in this embodiment has good diagnostic efficiency.

Example 2

In this embodiment, exosomes are selected as the detection sample to detect whether the person to be tested has gastric cancer, including the following steps:

Step 1: Obtain a patient's blood sample as a sample liquid, and incubate the extracellular vesicles in it with an antibody with a luminescent catalyst to label the antibody on the exosomes;

Step 2: Put the incubated exosome sample into the sample compartment unit 2, and add a luminescent substrate, so that the luminescent substrate and the luminescent catalyst on the antibody are catalyzed to reach an excited state, and in the process of converting to a ground state release light energy in the exosome to label the exosome surface with a light signal;

Step 3: Use the heating unit 1 to heat the sample chamber unit 2, and set the focus of the laser on the sample liquid inside the sample chamber unit 2. After heating, the exosomes in the sample liquid will produce a thermophoretic effect , and move to the low temperature area; at the same time, the sample liquid is heated and expanded to generate buoyancy, thereby generating convection in the sample chamber unit 2, and the direction of the convection points from the surrounding to the heating area of the sample chamber unit 2, and the surrounding outer The exosomes converge on the low temperature side of the sample compartment unit 2;

Step 4: use the signal processing unit 4 to obtain the amplified light signal after exosome accumulation, and obtain the abundance map of a single expressed protein of the exosome by analyzing the light signal;

Step 5: After the detection is completed, repeat steps 1 to 4, and use different types of antibodies to label and detect the various expressed proteins in the exosomes to obtain the abundance of the various expressed proteins in the exosomes atlas;

Step 6: Convert each luminosity in the protein map into data in combination with the comparison table in the exosome-expressed protein map in step 5, and use weighted and unweighted summation to obtain the weighted expression intensity Y of the exosome-expressed protein in the sample to be tested _L , expressed protein abundance M and unweighted expression intensity ∑L, and obtained the exosome SUM expression map according to the three values;

Step 7: Determine the severity of the patient according to the exosome expression protein map obtained in the step 5 and the exosome SUM expression map in the step 6.

The chemiluminescence immunoassay consists of two parts, namely the immunoreaction system and the chemiluminescence analysis system. The chemiluminescence analysis system uses the chemiluminescence catalyst to form an excited state intermediate through the catalysis of the catalyst and the oxidation of the oxidant. When the excited state intermediate returns to the stable ground state, it emits photons (hM) at the same time, using The luminescence signal measuring instrument measures the light quantum yield. The immune response system is to directly label the luminescent catalyst on the antigen or antibody, or the enzyme acts on the luminescent substrate.

For the current thermophoresis system, in this example, the marker to be detected on the exosome is used as the antigen, the role of the antibody is realized by the nucleic acid aptamer, the enzyme is linked to the nucleic acid aptamer, and the luminescent substrate can be added to the sample before detection. . To sum up, luminol is selected as the luminescent catalyst on the antibody described in this example, and hydrogen peroxide is selected as the luminescent substrate.

Specifically, the incubation conditions of the samples in this example, the selection of materials for each component in the system, and the heating parameters and directions of the heating unit 1 are the same as those in the example 1.

The markers detected in this example include exosome markers: CD9, CD63, and CD81 are generally highly expressed proteins in exosomes; and cancer-related protein markers expressed on the surface of exosomes: CEA, EGFR, EpCAM, HER2 and CCR6. For the exosome detection of the above markers, analyze their expression distribution.

Thirty-four gastric cancer patients were selected for detection, and 8 different nucleic acid aptamers were used to detect the abundance of 8 expressed proteins in exosomes in serum samples. The detection results are shown in Figure 4.

According to Figure 4, the serum exosomes of all puncture patients contained CD9, CD63, and CD81 proteins, and the cancer-related markers CEA, EGFR, EpCAM, HER2, and CCR6 were different from the exosome markers CD9, CD63, and CD81 proteins. There are also differences in expression between patients.

However, due to the high degree of heterogeneity in the expression of exosome surface markers in patients, it is difficult to effectively distinguish liver cancer patients from non-cancer patients by a single type of marker.

However, good diagnostic efficacy can be obtained by performing unweighted summation (SUM) and weighted summation of the expression levels of the detection markers.

The brightness of different markers on _exosomes in this example is set as L ₁ , _{L 2} ,... , the total abundance M of a certain type of exosome expressed protein, and the unweighted expression intensity ∑L to make the exosome SUM expression map of the sample to be tested. Wherein, the weighted sum calculation method and the unweighted sum calculation method in this embodiment are the same as the calculation method in the first embodiment.

The operator's operating characteristic curve (ROC) was drawn for SUM and the total abundance M of a single type of exosome expressed protein, and the area under the curve (Auc) was calculated to evaluate the diagnostic efficacy of the detection system of this embodiment.

According to calculation, among the 34 test subjects in this embodiment, the AUC for diagnosing gastric cancer by ROC is 0.9631, which shows that the detection system described in this embodiment has good diagnostic efficiency.

So far, the technical solutions of the present invention have been described with reference to the preferred embodiments shown in the accompanying drawings, however, those skilled in the art can easily understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention; for those skilled in the art, the present invention may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (10)

1. a gastric cancer detection system based on thermophoretic extracellular vesicle detection, is characterized in that, comprises: A heating unit for heating extracellular vesicles in the blood of the subject; A sample compartment unit arranged on one side of the heating unit and used to load extracellular vesicles, and the expressed protein that is highly expressed to gastric cancer in the extracellular vesicles in the sample compartment unit can be specific to aptamers or antibodies After the heating unit heats the sample compartment unit, thermophoresis effect and convection are generated in the sample compartment unit, and extracellular vesicles are gathered in the sample compartment unit the cooler side; a signal amplifying unit arranged on the side of the sample chamber unit away from the heating unit for amplifying and reflecting the light signal, and the signal amplifying unit will reflect the light signal to a designated position; A signal processing unit arranged on one side of the sample chamber unit for collecting and calculating the amplified optical signal, the signal processing unit acquires at least one optical signal parameter, and quantifies the optical parameter and adopts the signal processing unit. Unweighted and/or weighted models determine the corresponding light signal parameters, and obtain the expression intensity of the expressed proteins that are highly expressed in gastric cancer in a single case of extracellular vesicles. 2 . The gastric cancer detection system based on thermophoretic extracellular vesicle detection according to claim 1 , wherein the chemiluminescence process is: firstly combining an aptamer or antibody with a luminescent catalyst with the extracellular vesicles. 3 . The vesicles are incubated together, the luminescent catalyst is labeled on the surface of extracellular vesicles through specific binding, and the luminescent substrate is added to it. Light energy is released in the medium to label the extracellular vesicle surface with a light signal. 3. The gastric cancer detection system based on thermophoretic extracellular vesicle detection according to claim 1 or 2, wherein the weighted summation method for calculating the protein intensity of extracellular vesicles in the signal processing unit comprises: Step a: Set the weighted expression intensity of the protein as the dependent variable Y, set the optical parameter of the extracellular vesicle markers measured by the signal processing unit as the independent variable X, then the optical parameters of different markers on the extracellular vesicles are measured according to The order is set to: X ₁ , X ₂ , ..., X _k ; Step b: Since the weighted expression intensity Y has a linear relationship with the optical parameter X, the following calculation is performed at this time: Y=β ₀ +β ₁ X ₁ +β ₂ X ₂ +...+β _k X _k +ε (1) Among them, β ₀ , β ₁ , β ₂ , ..., β _k are regression parameters, and ε is a random error term; Step c: make basic assumptions about the formula (1) and the optical parameter X in the step b to ensure the validity of parameter estimation, statistical test and confidence interval estimation when the data is weighted and summed; Step d: When the formula (1) and the optical parameter X satisfy the assumption, take the expectation on both sides of the formula (1), and obtain: E(Y|X ₁ , X ₂ ... X _k )=β ₀ +β ₁ X ₁ +β ₂ X ₂ +...+β _k X _k (2) Wherein, E(Y|X ₁ , X ₂ , . . . , X _k ) represents the conditional mean of the protein-weighted expression intensity Y under the condition of a given light parameter X _i ; Step e: After the expectation of the formula (1) is completed, the corresponding estimated values of the regression parameters β ₀ , β ₁ , β ₂ , . . . , β _k are given according to the optical parameter X At this point, the corresponding estimated value of the protein-weighted weighted expression intensity Y is obtained: The above formula (3) is the point estimated value of E(Y|X ₁ , X ₂ , . . . , X _k ); Step f: When the formula (1) and the optical parameter X satisfy the assumptions in the step c, the parameter estimation is obtained by least squares. At this time, it is assumed that In the formula (4), respectively Finding the partial derivative, and setting the partial derivative equal to 0, yields: Solve the equation system in the above formula (5) to obtain the estimated values of the regression parameters β ₀ , β ₁ , β ₂ ,..., β _k and protein-weighted weighted expression intensity Y. 4. The gastric cancer detection system based on thermophoretic extracellular vesicle detection according to claim 3, is characterized in that, the light parameter in described step a is in light brightness L, light intensity C, absorbance A or light frequency λ one or more of. 5. The gastric cancer detection system based on thermophoretic extracellular vesicle detection according to claim 3, wherein the basic assumptions made to the formula (1) and the optical parameter X in the step c include: Assuming c1: the probability distribution of the random error term ε has zero mean, E(ε)=0; Assumption c2: The probability distribution of the random error term ε has homoscedasticity for different independent variable performance values, the variance of ε does not change with the change of X _ij , D(ε)=σ ² ; Assuming c3: there is no autocorrelation in the random error term ε, cov(ε _i , ε _j )=0; Assuming c4: ε _i is not related to any explanatory variable Xi, cov(ε _{i ,} Xi ₎ =0; Assumption c5: There is no complete collinearity between independent variables X; Among them, the above assumptions c1-c4 are the same as those of the univariate regression analysis, and the assumption c5 is used for explanatory variables. 6. The gastric cancer detection system based on thermophoretic extracellular vesicle detection according to claim 1 or 2, wherein the weighted summation method for calculating the total abundance of proteins expressed in extracellular vesicles in the signal processing unit comprises the following steps: : Step a: Set the total abundance of proteins expressed in extracellular vesicles as the dependent variable M, and set the optical parameter of the extracellular vesicle marker as the independent variable D, then set the measured optical parameter as the order of the detected expressed proteins respectively. : D ₁ , D ₂ ,...,D _k ; Step b: Since the abundance of different types of expressed proteins is different between different patients, it is necessary to set the corresponding weight coefficients α ₁ , α ₂ ,...α _k according to different types of expressed proteins, then the extracellular vesicles The total abundance M of the expressed protein can be obtained by the following formula: M=α ₁ D ₁ +α ₂ D ₂ +...+α _k D _k (6) Step c: Determine the total number N of cancer _types , and determine the number n ₁ , n ₂ , . . . The proportions with high expression are: Step d: take the average value of the optical parameter D of each expressed protein in the step a And find the variance of the light parameter D: Step f: Determine the weight coefficient α according to the data pairs obtained in the steps c and d: Step g: After the weight coefficient α is determined, calculate the total abundance of extracellular vesicles expressed by the formula in step b: . 7 . The gastric cancer detection system based on thermophoretic extracellular vesicle detection according to claim 1 , wherein the sample chamber unit is arranged on one side of the heating unit, and the sample chamber unit is installed inside the sample chamber unit. 8 . There are sample fluids for loading the extracellular vesicles and aptamers or antibodies, including: a first heat-conducting surface arranged on one side of the heating unit and made of a transparent material for absorbing the heat of the heating unit; a second heat-conducting surface disposed below the first heat-conducting surface and made of a transparent material for absorbing the heat of the heating unit, wherein the heat-conductivity of the second heat-conducting surface is higher than that of the first heat-conducting surface; The gasket is arranged between the first heat conducting surface and the second heat conducting surface and has a through hole in the center for loading the sample liquid. 8 . The gastric cancer detection system based on thermophoretic extracellular vesicle detection according to claim 1 , wherein the signal amplifying unit is disposed on the side of the sample chamber unit away from the heating unit, for Amplifies light signals on the surface of extracellular vesicles, including: an objective lens arranged on the side of the second heat-conducting surface away from the heating unit for observing optical signals; a collection mirror that is arranged on the side of the objective lens away from the heating unit and forms a certain angle with the objective lens, and is used to reflect light marks; a magnifying mirror arranged on the side of the objective lens away from the heating unit and at a certain angle with the objective lens to reflect the light source; The observation light source is arranged on one side of the magnifying mirror and used to provide the magnifying light source for the light mark. 9. A gastric cancer detection method based on thermophoretic extracellular vesicle detection, characterized in that, comprising: Obtain a patient's blood sample as a sample liquid, and incubate the extracellular vesicles with light-labeled aptamers or antibodies, and use the aptamers or antibodies with the surface of the extracellular vesicles to specifically express proteins that are highly expressed in gastric cancer combined to label the surface of extracellular vesicles with light signals; The incubated extracellular vesicles are put into the sample compartment unit, and the sample compartment unit is heated to generate thermophoresis effect and convection, and the extracellular vesicles are gathered on the low temperature side in the sample compartment unit to Amplifying the light signal on the extracellular vesicle, and converting the light signal into a corresponding specific single species value by calculating at least one light signal parameter; After the detection is completed, the above steps are repeated, and different types of aptamers or antibodies are used to label and detect various expressed proteins in the extracellular vesicles that are highly expressed in gastric cancer, to obtain the expression of various expressed proteins in the extracellular vesicles. value group; Bring the corresponding numerical groups obtained for different optical parameters into the weighted model and/or the unweighted model to calculate the corresponding optical parameters, and obtain the weighted expression intensity and/or unweighted expression intensity of the extracellular vesicle protein and the total expressed protein. Abundance, the SUM expression map of extracellular vesicles is obtained by combining the three values, and whether the test subject has gastric cancer is determined according to the SUM expression map. 10. A method for detecting gastric cancer using chemiluminescence based on thermophoretic extracellular vesicle detection, characterized in that, comprising: Obtain the patient's blood sample as the sample liquid, and incubate the extracellular vesicles with aptamers or antibodies with luminescent catalysts, and conduct the expression of gastric cancer through the aptamers or antibodies and the surface of the extracellular vesicles. Specific binding to label the surface of extracellular vesicles with luminescent catalysts; Put the incubated extracellular vesicles into the sample compartment unit, and add a luminescent substrate to the sample compartment unit, so that it catalyzes with the luminescent catalyst on the surface of the extracellular vesicles to reach the excited state, and in the process of converting to the ground state release light energy to label the surface of extracellular vesicles with light signals; The sample chamber unit is heated to generate thermophoresis effect and convection, and the extracellular vesicles are gathered on the low temperature side inside the sample chamber unit to amplify the light signal on the extracellular vesicle; after amplification, a signal processing unit is used Collect and analyze the optical signal, and convert the optical signal into a corresponding specific single type value by calculating different optical parameters; After the detection is completed, the above steps are repeated, and different types of aptamers or antibodies are used to label and detect the various expressed proteins in the extracellular vesicles, respectively, to obtain numerical groups of the various expressed proteins in the extracellular vesicles; Bring the corresponding numerical groups obtained for different optical parameters into the weighted model and/or the unweighted model to calculate the corresponding optical parameters, and obtain the weighted expression intensity and/or unweighted expression intensity of the extracellular vesicle protein and the total expressed protein. Abundance, the SUM expression map of extracellular vesicles is obtained by combining the three values, and whether the test subject has gastric cancer is determined according to the SUM expression map.