Exosome membrane protein multi-target detection method for esophageal cancer diagnosis

Abstract

The invention relates to the field of detection, in particular to an exosome membrane protein multi-target detection method for esophageal cancer diagnosis. Comprising the following steps: separating exosomes from a plasma sample, and marking the exosomes to obtain initial fluorescence intensity; processing the initial fluorescence intensity by using an exosome membrane protein fluorescence intensity topological enhancement algorithm to generate enhanced fluorescence intensity; optimizing the enhanced fluorescence intensity by using a multi-target fluorescence intensity processing algorithm to obtain the optimized fluorescence intensity; the weights of different individuals of the target spot are calculated, and the fluorescence intensity is integrated into a comprehensive fluorescence intensity index in a weighted summation mode; constructing a dynamic threshold value, and combining with a comprehensive fluorescence intensity index to obtain a diagnosis ratio so as to judge whether a patient suffers from esophageal cancer or not. The technical problems that the initial fluorescence intensity of a low-expression target spot is often weak and the low-expression target spot is difficult to be effectively captured by fluorescence detection equipment when magnetic bead immunocapture and fluorescence labeled antibody are used for detecting exosome membrane protein are solved.

Description

Technical Field

[0001] The present invention relates to the field of detection, and in particular to a multi-target detection method for exosome membrane proteins for diagnosing esophageal cancer. Background Art

[0002] Esophageal cancer is a highly malignant and aggressive digestive system malignancy with high morbidity and mortality worldwide. Traditional diagnostic methods for esophageal cancer rely primarily on gastroscopy and tissue biopsy, which have certain limitations, such as high invasiveness, poor patient tolerance, low recognition rate of early lesions, and reliance on subjective judgment by pathologists. Therefore, the search for non-invasive, easy-to-use, and efficient molecular diagnostic methods has become an important research direction for the clinical diagnosis of esophageal cancer. Exosomes carry multiple biomarkers, including proteins, RNA, DNA, and lipids, which can reflect the biological characteristics of tumor cells and have become an important research subject for tumor liquid biopsy. Compared with free DNA and protein markers, the biomolecules in exosomes are protected by the lipid bilayer and can remain stable in body fluids for a long time. Therefore, exosome-based multi-target detection methods have high sensitivity and specificity, providing a new direction for the precise diagnosis of esophageal cancer.

[0003] With the deepening of exosome research and the continuous development of technical means, combined with the development of artificial intelligence, machine learning and other technologies, the multi-target detection method of exosome membrane proteins can further enhance its intelligence level, improve diagnostic accuracy, and provide a basis for personalized treatment, thus contributing to improving patient survival rate and quality of life.

[0004] However, the existing endoscopic examination and tissue biopsy techniques are still highly invasive, requiring patients to endure certain pain, and are complex and expensive to operate, making them unsuitable for widespread screening. Although imaging examinations can provide more comprehensive lesion information, they cannot sensitively detect early lesions and expose patients to high radiation, affecting their physical health. Currently, common blood biomarkers such as CEA, CYFRA21-1, and SCC have low sensitivity in the early diagnosis of esophageal cancer, and the markers have poor specificity and may be affected by other diseases. Summary of the Invention

[0005] The present invention provides a multi-target detection method for exosome membrane proteins for the diagnosis of esophageal cancer. The method aims to address the following technical issues: when using magnetic bead immunocapture and fluorescently labeled antibodies to detect exosome membrane proteins, the initial fluorescence intensity of low-expression targets is often weak, making it difficult for fluorescence detection equipment to effectively capture them, leading to the risk of missed detection; the method fails to consider the non-uniform distribution of exosome membrane proteins on the membrane surface (such as local aggregation), resulting in the loss of information on spatial variations in fluorescence intensity, affecting diagnostic specificity; the possible co-expression relationship between targets makes it easy to ignore the synergistic effect or over-amplify, resulting in distorted results; non-specific binding can cause the fluorescence intensity of the target to be erroneously enhanced; and the existing methods use a fixed threshold to judge the disease status, which fails to adapt to the characteristics of fluorescence intensity decay over time and may lead to diagnostic misjudgment.

[0006] The present invention provides a multi-target detection method for exosome membrane proteins for esophageal cancer diagnosis, which specifically includes the following technical solutions:

[0007] A multi-target detection method for exosome membrane proteins for diagnosing esophageal cancer comprises the following steps:

[0008] S1. Isolate exosomes from patient plasma samples and label them with fluorescently labeled antibodies to obtain the initial fluorescence intensity of each target. Process the initial fluorescence intensity using an exosome membrane protein fluorescence intensity topology enhancement algorithm to generate enhanced fluorescence intensity.

[0009] S2. optimizing the enhanced fluorescence intensity using a multi-target fluorescence intensity processing algorithm to obtain an optimized fluorescence intensity;

[0010] S3. Calculate the weights of different individuals for each type of target based on the optimized fluorescence intensity, and integrate the fluorescence intensities of all categories into a comprehensive fluorescence intensity index through weighted summation. Construct a dynamic threshold, and combine it with the comprehensive fluorescence intensity index to obtain the diagnostic ratio. Determine whether the patient has esophageal cancer based on the diagnostic ratio.

[0011] Preferably, the S1 specifically includes:

[0012] The topological enhancement algorithm for the initial fluorescence intensity of exosome membrane proteins generates enhanced fluorescence intensity by combining topological distribution characteristics and local amplification effects.

[0013] Preferably, the S1 specifically includes:

[0014] In the process of implementing the topological enhancement algorithm of the initial fluorescence intensity of exosome membrane proteins, an enhancement mechanism based on spatial distribution was designed. According to the spatial variation of the initial fluorescence intensity, the fluorescence intensity topological distribution coefficient was introduced to reflect the uniqueness of each target in the spatial distribution.

[0015] Preferably, the S1 specifically includes:

[0016] In the implementation of the topological enhancement algorithm for the initial fluorescence intensity of exosome membrane proteins, the average of the initial fluorescence intensities of all targets is calculated, and the spatial variation of the initial fluorescence intensity is compared with the average of the initial fluorescence intensities of all targets to identify the areas where the initial fluorescence intensity needs to be enhanced.

[0017] Preferably, the S1 specifically includes:

[0018] In the implementation of the topological enhancement algorithm for the initial fluorescence intensity of exosome membrane proteins, a local amplification factor is introduced based on the influence of the density of membrane proteins in the local area on the initial fluorescence intensity. Combined with the exponential decay function, the enhanced fluorescence intensity is obtained. The specific implementation formula is:

[0019]

[0020] Among them, S' i,k (t) represents the enhanced fluorescence intensity, that is, the enhanced fluorescence intensity of individual k at target i at time t; S i,k (t) represents the initial fluorescence intensity, i.e., the initial fluorescence intensity of individual k at target i at time t; η i represents the topological distribution coefficient of the fluorescence intensity of target i; represents the spatial fluorescence intensity gradient of individual k at target point i at time t; represents the average of the initial fluorescence intensities of all targets; represents the local amplification term; a i represents the local amplification factor of target i; represents the exponential decay function; d i,k represents the average distribution distance of individual k of target point i; b represents the distribution characteristic length.

[0021] Preferably, the S2 specifically includes:

[0022] In the process of implementing the multi-target fluorescence intensity processing algorithm, according to the mutual enhancement relationship between different targets, the synergistic factor and the multi-target synergistic saturation function are introduced to simulate the biological saturation effect of synergistic expression; and by combining the interference correction factor, the optimized fluorescence intensity is obtained.

[0023] Preferably, the S2 specifically includes:

[0024] The optimized calculation formula for fluorescence intensity is:

[0025]

[0026] in, represents the fluorescence intensity of target i after individual k optimization at time t; represents the synergistic amplification term; ∑ j≠i represents the summation of all targets j that are different from target i, and the accumulation of the synergistic effects of all other targets on target i; α ij represents the synergy factor between target i and target j; represents the multi-target cooperative saturation function; represents the average enhanced fluorescence intensity of target j; represents the interference correction term; γ ij represents the interference correction factor; represents the normalized interference weight.

[0027] Preferably, the S3 specifically includes:

[0028] In the process of constructing the dynamic threshold, a basic reference value and a time adjustment factor are introduced. The time adjustment factor consists of two parts: one is the adjustment coefficient, and the other is the time attenuation term. The dynamic threshold calculation formula is:

[0029]

[0030] Among them, D adapt (t) represents the dynamic threshold at time t; D base Indicates the basic reference value; represents the time adjustment factor; λ represents the adjustment coefficient; represents the time decay term, which is realized in an exponential form; τ represents the characteristic time constant.

[0031] The beneficial effects of the technical solution of the present invention are:

[0032] 1. By introducing the fluorescence intensity topological distribution coefficient and local amplification factor, combined with spatial gradient analysis and exponential decay function, this method can quantify the spatial variation and local density of fluorescence intensity on the exosome membrane surface, solving the problem of fluorescence signal masking due to uneven distribution of membrane proteins in traditional fluorescence detection, and allowing the fluorescence intensity of low-expression targets (such as CD63, CD9, and EpCAM) to be revealed.

[0033] 2. By introducing synergistic factors and multi-target synergistic saturation functions, the biological mutual enhancement relationship between targets (such as CD63 and CD9) was simulated, and excessive amplification was limited by the half-saturation constant to ensure that the optimized fluorescence intensity is more consistent with the actual pathological state.

[0034] 3. Through the interference correction factor and normalized interference weight, the fluorescence intensity deviation caused by nonspecific binding is quantified and corrected, which improves the specificity of the detection and reduces the false positive rate. In particular, it avoids the erroneous influence of one target on another in multi-target detection.

[0035] 4. By introducing a dynamic threshold, combined with a basic reference value and a time adjustment factor, the characteristic of fluorescence intensity decreasing over time due to dye quenching or sample degradation is adapted, ensuring the consistency of test results at different time points, avoiding misjudgments due to differences in detection timing, and improving the robustness of the multi-target detection method for exosome membrane proteins. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of the multi-target detection method for exosome membrane proteins for esophageal cancer diagnosis described in the present invention. DETAILED DESCRIPTION

[0037] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0038] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0039] The following describes in detail a method for multi-target detection of exosome membrane proteins for diagnosing esophageal cancer provided by the present invention with reference to the accompanying drawings.

[0040] Refer to the attached Figure 1 , which shows a flow chart of a multi-target detection method for exosome membrane proteins for esophageal cancer diagnosis provided by one embodiment of the present invention, the method comprising the following steps:

[0041] S1. Exosomes are isolated from patient plasma samples and labeled with fluorescently labeled antibodies to obtain the initial fluorescence intensity of each target. The initial fluorescence intensity is processed using the exosome membrane protein fluorescence intensity topological enhancement algorithm to generate enhanced fluorescence intensity.

[0042] Exosomes are isolated from patient plasma samples (usually 1-2 mL) using ultracentrifugation or a commercial exosome isolation kit (such as ExoQuick). Exosomes expressing target membrane proteins are enriched using magnetic beads coupled to specific antibodies (such as anti-CD63, anti-CD9, and anti-EpCAM magnetic beads) using existing magnetic bead immunocapture technology. The enriched exosomes are then labeled with fluorescently labeled antibodies (such as FITC-anti-CD63, PE-anti-CD9, and APC-anti-EpCAM). After labeling, the initial fluorescence intensity of different individuals at various target sites is directly measured using a fluorescence detection device (such as a flow cytometer or fluorescence microscope), which is recorded as S. i,k (t), where i represents the target type, such as CD63, CD9, and EpCAM, and k represents the individual type of target i;

[0043] After measuring the initial fluorescence intensity of each target, the exosome membrane protein fluorescence intensity topological enhancement algorithm is used to process the initial fluorescence intensity. The exosome membrane protein initial fluorescence intensity topological enhancement algorithm generates enhanced fluorescence intensity by combining topological distribution characteristics and local amplification effects. It aims to improve the detection sensitivity and accuracy of multiple exosome membrane protein targets. The specific implementation is as follows:

[0044] To capture the non-uniform distribution of membrane proteins, the exosomal membrane protein initial fluorescence intensity topological enhancement algorithm designed an enhancement mechanism based on spatial distribution. This algorithm considers the spatial variation of the initial fluorescence intensity, namely the difference in initial fluorescence intensity in different regions, which can be obtained through microscopic imaging analysis and manifested as the spatial gradient of the initial fluorescence intensity. To quantify the spatial variation of the initial fluorescence intensity, a fluorescence intensity topological distribution coefficient was introduced. This coefficient ranges from 0 to 1 and is determined by experimental data. It reflects the uniqueness of each target in its spatial distribution. If the initial fluorescence intensity of the target varies dramatically in certain regions, the fluorescence intensity topological distribution coefficient will be larger, thus giving it a higher enhancement weight in the calculation.

[0045] Furthermore, a benchmark is needed to normalize the spatial variation of the initial fluorescence intensity. By calculating the average of the initial fluorescence intensities of all targets and comparing the spatial variation of the initial fluorescence intensity with the average of the initial fluorescence intensities of all targets, it is possible to identify which areas need to have their initial fluorescence intensity enhanced. This can be used to adjust the initial fluorescence intensity so that the spatial distribution characteristics of the initial fluorescence intensity are reflected.

[0046] In addition to spatial distribution characteristics, the topological enhancement algorithm for exosomal membrane protein fluorescence intensity also considers the effect of local density of exosomal membrane proteins on the initial fluorescence intensity. On the exosome membrane surface, targets may cluster in specific areas, resulting in weak local initial fluorescence intensity but significant biological significance. To prevent targets with weak local initial fluorescence intensity from being masked by averaging, a local amplification factor ranging from 0 to 1 was introduced. This factor was determined through local density experiments and fluorescence microscopy analysis of the local density distribution of membrane proteins. The average distribution distance of different individuals of each type of target was further measured, which was measured by imaging analysis. To convert the average distribution distance of different individuals of each type of target into a local amplification effect, an exponential decay function was designed. If the average distribution distance of different individuals of each type of target is small, that is, the density is high, the value of the exponential decay function will be large, thereby significantly amplifying the initial fluorescence intensity of the area. Conversely, if the average distribution distance of different individuals of each type of target is large, the amplification effect will be weakened, ensuring that the weak fluorescence intensity of the local dense area can be effectively enhanced.

[0047] The calculation formula for the enhanced fluorescence intensity is:

[0048]

[0049] Among them, S' i,k (t) represents the enhanced fluorescence intensity, that is, the enhanced fluorescence intensity of individual k at target i at time t; S i,k (t) represents the initial fluorescence intensity, i.e., the initial fluorescence intensity of individual k at target i at time t; η i Represents the topological distribution coefficient of the fluorescence intensity of target i, ranging from 0 to η i ≤1, determined by experimental data, reflecting the uniqueness of each target in spatial distribution; The fluorescence intensity gradient of individual k at target point i at time t reflects the rate of change of fluorescence intensity with spatial position and is used to measure the degree of non-uniform distribution of fluorescence intensity on the membrane surface. The larger the fluorescence intensity gradient, the more non-uniform the distribution, and the greater the enhancement weight should be assigned. It is calculated by fluorescence microscopy imaging analysis. The specific method is: obtain the fluorescence intensity distribution image of the exosome surface and calculate the spatial change rate of the fluorescence intensity of each target point. This is a technical means well known to those skilled in the art and will not be described in detail here. represents the average of the initial fluorescence intensities of all targets, which is used as a normalization factor to standardize the fluorescence intensity gradient; represents the local amplification term; a i Represents the local amplification factor of target i, 0≤a i ≤1, used to regulate the size of the local amplification term, reflecting the need for enhanced fluorescence intensity in the local dense area of membrane proteins; represents an exponential decay function. The local amplification effect shows an exponential decay form with the average distribution distance of different targets. The closer the average distribution distance between targets, the stronger the local amplification effect. i,k represents the average distribution distance of individual k of target i, measured by imaging analysis; b represents the characteristic length of the distribution, which is a standardized parameter and can be set according to the specific implementation scenario and is not limited here;

[0050] By introducing topological distribution characteristics and local amplification effects, the non-uniform distribution information of exosome membrane proteins on the membrane surface is captured, improving the detection sensitivity of multiple exosome membrane protein targets, enabling the fluorescence intensity of low-expression targets to be revealed, enhancing data specificity, and highlighting the contribution of spatial distribution differences to diagnosis. This provides optimized input for subsequent diagnostic steps and helps achieve early and highly specific diagnosis of esophageal cancer.

[0051] S2. Optimizing the enhanced fluorescence intensity using a multi-target fluorescence intensity processing algorithm to obtain an optimized fluorescence intensity;

[0052] The enhanced fluorescence intensity is optimized using a multi-target fluorescence intensity processing algorithm to obtain an optimized fluorescence intensity;

[0053] The multi-target fluorescence intensity processing algorithm includes two parts: one is to amplify the synergistic expression effect between targets, and the other is to reduce the influence of non-specific interference;

[0054] When addressing the synergistic expression effect, we considered the possible mutual enhancement relationship between different targets and introduced a synergistic factor to represent the synergistic strength between two targets. To prevent the synergistic expression effect from being over-amplified, we further designed a saturation mechanism. When the enhanced fluorescence intensity of a target increases to a certain level, the synergistic effect on other targets will tend to stabilize rather than grow indefinitely. The saturation mechanism is implemented through a proportional calculation based on the enhanced fluorescence intensity and involves a half-saturation constant, which is used to control the curve shape of the multi-target synergistic saturation function and limit the upper limit of amplification. This is determined through standard curve fitting experiments.

[0055] Nonspecific interference between targets refers to the fact that the enhanced fluorescence intensity of a target can erroneously affect the measured value of another target due to nonspecific binding. To quantify and subtract this interference, an interference correction factor was introduced. This factor was derived by comparing single-target and multi-target experiments. The calculation of the interference correction factor is based on a normalized interference weight, ensuring that the interference correction factor is related to the dynamic changes in actual fluorescence intensity rather than a fixed constant.

[0056] The optimized calculation formula for fluorescence intensity is:

[0057]

[0058] in, represents the fluorescence intensity of target i after individual k optimization at time t; represents the synergistic amplification term, reflecting the amplification contribution of target i to the synergistic expression effect of other targets j, simulating the enhancement effect when multiple targets are biologically synergistically expressed, and limiting excessive amplification through the multi-target synergistic saturation function; ∑ j≠i represents the summation of all targets j that are different from target i, and the accumulation of the synergistic effects of all other targets on target i, ensuring that the interactions between multiple targets are fully considered; α ij The synergy factor between target i and target j is used to quantify the degree of synergy between targets and is determined by a dual-target control experiment. It represents the multi-target cooperative saturation function, and the calculation formula is: Used to simulate the biological saturation effect of synergistic expression to avoid infinite amplification of fluorescence intensity due to synergistic effects, K sat It represents the half-saturation constant in the multi-target cooperative saturation function, which is used to control the curve shape of the multi-target cooperative saturation function and limit the upper limit of amplification. It is determined by standard curve fitting experiments; It represents the average enhanced fluorescence intensity of target j, and the formula is: N j represents the number of individuals at target point j; represents the interference correction term, which is used to describe the correction contribution of target i to the nonspecific interference of other targets j; γ ij It represents the interference correction factor, which reflects the intensity of nonspecific interference and can be set according to the specific implementation scenario and is not limited here; represents the normalized interference weight;

[0059] S3. Calculate the weights of different individuals for each target type based on the optimized fluorescence intensity. Integrate the fluorescence intensities of all categories into a comprehensive fluorescence intensity index through weighted summation. Construct a dynamic threshold and, combined with the comprehensive fluorescence intensity index, obtain a diagnostic ratio. Esophageal cancer is determined based on the diagnostic ratio.

[0060] The weights of different individuals of each target type are calculated based on the optimized fluorescence intensity. The optimized fluorescence intensity is compared with the sum of the fluorescence intensities of all targets. The contribution of a single target type is normalized to ensure that the sum of the weights of different individuals of each target type is 1, thereby avoiding a single type of target from excessively dominating the results. After calculating the weights of different individuals of each target type, the optimized fluorescence intensities of all target types are integrated into a comprehensive fluorescence intensity index through weighted summation. The formula is as follows:

[0061]

[0062] Where D(t) represents the comprehensive fluorescence intensity index at time t; ∑ k Indicates the summation operation of different individuals of each type of target; w i,k (t) represents the weight of individual k at target point i at time t, and the calculation formula is:

[0063] A dynamic threshold that changes with time is constructed for comparison with the comprehensive fluorescence intensity index. The construction of the dynamic threshold is based on two principles: first, a basic reference value is required, and second, it must adapt to the time-varying characteristics of fluorescence intensity. The basic reference value is determined by analyzing plasma sample data from healthy people and esophageal cancer patients, and a statistical method is used to derive an initial standard that can distinguish between healthy and diseased states. Since the fluorescence intensity will weaken over time due to dye quenching or sample degradation during the detection process, a time adjustment factor is introduced. The time adjustment factor consists of two parts: one is a fixed adjustment coefficient, which is used to control the adjustment amplitude of the time decay term on the dynamic threshold. It can be set according to the specific implementation scenario and is not limited here. The second is the time decay term, which is implemented in an exponential form and is based on the ratio of the current time to the characteristic time constant. It reflects the time scale of the decrease in fluorescence intensity reliability, ensuring that the diagnostic results remain consistent at different time points and avoiding misjudgment due to differences in detection timing.

[0064] The dynamic threshold calculation formula is:

[0065]

[0066] Among them, D adapt (t) represents the dynamic threshold at time t; D base Indicates the basic reference value; represents the time adjustment factor, which adjusts the basic reference value through the time decay term; λ represents the adjustment coefficient, which is used to control the adjustment range of the time decay term on the dynamic threshold. It can be set according to the specific implementation scenario and is not limited here; represents the time decay term, which is realized in an exponential form; τ represents the characteristic time constant, which reflects the time scale of the decrease in the reliability of the fluorescence intensity;

[0067] The comprehensive fluorescence intensity index is divided by the dynamic threshold to obtain the diagnostic ratio, which quantifies the relative relationship between the fluorescence intensity of multiple target types and the dynamic threshold. The calculation formula is as follows:

[0068]

[0069] Based on the diagnostic ratio, whether the patient has esophageal cancer is determined according to the following rules: if the diagnostic ratio is greater than 1, it means that the comprehensive fluorescence intensity index exceeds the dynamic threshold, and the plasma sample is judged to be positive, which means that the patient may have esophageal cancer; if the diagnostic ratio is less than or equal to 1, it means that the comprehensive fluorescence intensity index is less than or equal to the dynamic threshold, and the plasma sample is judged to be negative, which means that the patient may not have esophageal cancer; based on statistical analysis, the comprehensive fluorescence intensity index of positive plasma samples is usually significantly higher than that of healthy samples, and the dynamic threshold provides the boundary for distinguishing positive plasma samples from negative plasma samples.

[0070] In summary, a multi-target detection method for exosome membrane proteins for the diagnosis of esophageal cancer was completed.

[0071] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0073] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A multi-target detection method for exosome membrane proteins for diagnosing esophageal cancer, characterized in that: The following steps are involved: S1. Isolate exosomes from patient plasma samples, label them with fluorescently labeled antibodies, and obtain the initial fluorescence intensity of each target site; The initial fluorescence intensity is processed using the exosome membrane protein fluorescence intensity topology enhancement algorithm to generate enhanced fluorescence intensity; S2. optimizing the enhanced fluorescence intensity using a multi-target fluorescence intensity processing algorithm to obtain an optimized fluorescence intensity; S3. Calculate the weights of different individuals for each type of target based on the optimized fluorescence intensity, and integrate the fluorescence intensities of all categories into a comprehensive fluorescence intensity index through weighted summation. Construct a dynamic threshold, and combine it with the comprehensive fluorescence intensity index to obtain the diagnostic ratio. Determine whether the patient has esophageal cancer based on the diagnostic ratio.

2. The multi-target detection method for exosome membrane proteins for esophageal cancer diagnosis according to claim 1, characterized in that: Said S1 specifically includes: The topological enhancement algorithm for the initial fluorescence intensity of exosome membrane proteins generates enhanced fluorescence intensity by combining topological distribution characteristics and local amplification effects.

3. The multi-target detection method for exosome membrane proteins for esophageal cancer diagnosis according to claim 2, characterized in that: Said S1 specifically includes: In the process of implementing the topological enhancement algorithm of the initial fluorescence intensity of exosome membrane proteins, an enhancement mechanism based on spatial distribution was designed. According to the spatial variation of the initial fluorescence intensity, the fluorescence intensity topological distribution coefficient was introduced to reflect the uniqueness of each target in the spatial distribution.

4. The multi-target detection method for exosome membrane proteins for esophageal cancer diagnosis according to claim 3, characterized in that: Said S1 specifically includes: In the implementation of the topological enhancement algorithm for the initial fluorescence intensity of exosome membrane proteins, the average of the initial fluorescence intensities of all targets is calculated, and the spatial variation of the initial fluorescence intensity is compared with the average of the initial fluorescence intensities of all targets to identify the areas where the initial fluorescence intensity needs to be enhanced.

5. The multi-target detection method for exosome membrane proteins for esophageal cancer diagnosis according to claim 4, characterized in that: Said S1 specifically includes: In the implementation of the topological enhancement algorithm for the initial fluorescence intensity of exosome membrane proteins, a local amplification factor is introduced based on the influence of the density of membrane proteins in the local area on the initial fluorescence intensity. Combined with the exponential decay function, the enhanced fluorescence intensity is obtained. The specific implementation formula is: Among them, S' i,k (t) represents the enhanced fluorescence intensity, that is, the enhanced fluorescence intensity of individual k at target i at time t; S i,k (t) represents the initial fluorescence intensity, i.e., the initial fluorescence intensity of individual k at target i at time t; η i represents the topological distribution coefficient of the fluorescence intensity of target i; represents the spatial fluorescence intensity gradient of individual k at target point i at time t; represents the average of the initial fluorescence intensities of all targets; represents the local amplification term; a i represents the local amplification factor of target i; represents the exponential decay function; d i,k represents the average distribution distance of individual k of target point i; b represents the distribution characteristic length.

6. The multi-target detection method for exosome membrane proteins for esophageal cancer diagnosis according to claim 1, characterized in that: Said S2 specifically includes: In the process of implementing the multi-target fluorescence intensity processing algorithm, according to the mutual enhancement relationship between different targets, the synergistic factor and the multi-target synergistic saturation function are introduced to simulate the biological saturation effect of synergistic expression; and by combining the interference correction factor, the optimized fluorescence intensity is obtained.

7. The method for multi-target detection of exosome membrane proteins for diagnosing esophageal cancer according to claim 6, characterized in that: Said S2 specifically includes: The optimized calculation formula for fluorescence intensity is: in, represents the fluorescence intensity of target i after individual k optimization at time t; represents the synergistic amplification term; ∑ j≠i represents the summation of all targets j that are different from target i, and the accumulation of the synergistic effects of all other targets on target i; α ij represents the synergy factor between target i and target j; represents the multi-target cooperative saturation function; represents the average enhanced fluorescence intensity of target j; represents the interference correction term; γ ij represents the interference correction factor; represents the normalized interference weight.

8. The multi-target detection method for exosome membrane proteins for esophageal cancer diagnosis according to claim 1, characterized in that: Said S3 specifically includes: In the process of constructing the dynamic threshold, a basic reference value and a time adjustment factor are introduced. The time adjustment factor consists of two parts: one is the adjustment coefficient, and the other is the time attenuation term. The dynamic threshold calculation formula is: Among them, D adapt (t) represents the dynamic threshold at time t; D base Indicates the basic reference value; represents the time adjustment factor; λ represents the adjustment coefficient; represents the time decay term, which is realized in an exponential form; τ represents the characteristic time constant.

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