Detection method of exosome marker combination for esophageal cancer postoperative recurrence monitoring

Abstract

The invention relates to the field of detection, in particular to a detection method of an exosome marker combination for monitoring postoperative recurrence of esophageal cancer. The method comprises the following steps: extracting exosomes from a serum sample of a patient, measuring expression levels of biomarkers related to recurrence of esophageal cancer in different exosome subgroups as original signal values, and selecting representative signal values from the original signal values; and performing standardization processing on the representative signal value, calculating a recurrence probability based on the standardized representative signal value of the biomarker, and performing risk classification. The problems that in a traditional exosome marker detection method, an average value or single subgroup data is generally directly taken as a representative signal, and abnormal expression characteristics cannot be highlighted; generally, the recurrence risk is predicted directly based on the expression level of a single time point, and the dynamic change trend of recent signals may be ignored. The technical problems of lack of comprehensive evaluation of the overall deviation trend of all markers and neglecting of the correlation between the distribution disorder degree and the recurrence risk in the prior art are generally solved.

Description

Technical Field

[0001] The present invention relates to the field of detection, and in particular to a method for detecting an exosome marker combination for monitoring postoperative recurrence of esophageal cancer. Background Art

[0002] Exosomes have attracted widespread attention as potential markers for the diagnosis and prognosis of esophageal cancer. Studies have shown that the level of exosomes in the body fluids of esophageal cancer patients is significantly higher than that in healthy people, and their contents (such as miRNA, lncRNA, and proteins) are closely related to the aggressiveness, stage, and recurrence risk of the tumor.

[0003] Although single exosome markers have shown certain potential in monitoring esophageal cancer recurrence, their sensitivity and specificity still need to be improved. Single markers are easily affected by individual differences, tumor heterogeneity, and non-tumor factors. Therefore, researchers have proposed a detection strategy based on a combination of multiple markers. The exosome marker combination can more comprehensively reflect the biological behavior of the tumor and improve the accuracy of diagnosis by integrating multidimensional information such as miRNA, lncRNA, and proteins.

[0004] Detection methods based on exosome marker combinations have many advantages in monitoring recurrence after esophageal cancer surgery. Non-invasive sampling (such as blood draws) reduces the burden on patients and facilitates dynamic follow-up. Marker combinations improve the sensitivity and specificity of detection and can detect recurrent lesions before imaging evidence appears, buying time for early intervention. Dynamic changes in exosome markers may also reflect treatment response and drug resistance, providing a basis for personalized treatment. In the future, with technological advancements and the development of multicenter studies, exosome marker combination detection is expected to become an important tool for monitoring recurrence after esophageal cancer surgery.

[0005] Traditional methods for detecting exosome biomarkers have the following technical problems: when measuring the expression levels of biomarkers in exosome subpopulations, the average value or data from a single subpopulation is usually directly taken as the representative signal, which fails to highlight abnormal expression characteristics. Averaging may mask abnormal signals in certain subpopulations that are highly correlated with esophageal cancer recurrence; the degree of mutation of biomarker expression within a short-term time window is usually not considered, making it difficult to capture abnormal fluctuations related to recurrence. Directly predicting the risk of recurrence based on the expression level at a single time point may ignore the dynamic change trend of recent signals; the expression level of a single marker is usually analyzed separately, lacking a comprehensive assessment of the overall deviation trend of all markers, resulting in insufficient judgment of the group characteristics of recurrence risk; and the focus may be solely on signal intensity, while ignoring the correlation between distribution disorder and recurrence risk. Summary of the Invention

[0006] The present invention provides a detection method for an exosome marker combination for monitoring postoperative recurrence of esophageal cancer. This method addresses the technical issues that traditional exosome marker detection methods, when measuring the expression levels of biomarkers in exosome subpopulations, typically directly take the average value or single subpopulation data as the representative signal, failing to highlight abnormal expression characteristics. The averaging process may mask abnormal signals in certain subpopulations that are highly correlated with esophageal cancer recurrence. The method also typically does not consider the degree of mutation in biomarker expression within a short time window, making it difficult to capture abnormal fluctuations associated with recurrence. Recurrence risk is directly predicted based on expression levels at a single time point, potentially ignoring recent dynamic changes in signals. The method also typically analyzes the expression levels of individual markers independently, lacks a comprehensive assessment of the overall deviation trends of all markers, resulting in insufficient judgment of the population characteristics of recurrence risk. Furthermore, the method may focus solely on signal intensity while ignoring the correlation between distribution disorder and recurrence risk.

[0007] The present invention provides a method for detecting a combination of exosome markers for monitoring postoperative recurrence of esophageal cancer, which specifically includes the following technical solutions:

[0008] A method for detecting an exosome marker combination for monitoring recurrence of esophageal cancer after surgery, comprising the following steps:

[0009] S1. Extract exosomes from patient serum samples, measure the expression levels of biomarkers associated with esophageal cancer recurrence in different exosome subpopulations, use them as raw signal values, and select representative signal values from the raw signal values; standardize the representative signal values to obtain standardized representative signal values of the biomarkers;

[0010] S2. Based on the standardized representative signal values of the biomarkers, the recurrence probability is calculated using the esophageal cancer recurrence probability assessment algorithm, and risk classification is performed based on the recurrence probability.

[0011] Preferably, the S1 specifically includes:

[0012] A representative signal extraction algorithm is introduced to select representative signal values from the original signal values.

[0013] Preferably, the S1 specifically includes:

[0014] The representative signal extraction algorithm calculates the deviation amplitude based on the original signal value, combined with the average signal value of the healthy population and the signal standard deviation of the healthy population, and introduces an exponential function to amplify the deviation amplitude; the original signal value is multiplied by the exponential amplification result, and the representative signal value is selected through the maximum value selection strategy; the calculation formula of the representative signal value is:

[0015]

[0016] Among them, S i(t) represents the representative signal value of the i-th biomarker at time t; S i,k (t) represents the original signal value of the i-th biomarker in the k-th exosome subpopulation at time t; δ is the amplification factor; μ represents the degree to which the original signal value of the i-th biomarker in the k-th exosome subpopulation at time t deviates from the normal value, indicating the deviation amplitude; i,ctrl represents the average signal value of the healthy population for the i-th biomarker; σ i,ctrl represents the standard deviation of the signal of the healthy population for the i-th biomarker.

[0017] Preferably, the S2 specifically includes:

[0018] In the implementation of the esophageal cancer recurrence probability assessment algorithm, a short-term time window was introduced to calculate the amplitude of change between the standardized representative signal value of the biomarker at each time point and the previous time point; combined with the signal standard deviation of the healthy population, an exponential function was introduced to amplify the amplitude of change to obtain the signal mutation factor of the biomarker.

[0019] Preferably, the S2 specifically includes:

[0020] Based on the representative signal values of all standardized biomarkers at the current time point, combined with the signal mutation factors of the biomarkers, the overall abnormal trend of all markers is evaluated to obtain the population consistency deviation; the specific calculation formula is:

[0021]

[0022] Where G(t) represents the population consistency deviation at time t; n represents the total number of biomarkers associated with esophageal cancer recurrence; S' i (t) represents the representative signal value of the i-th biomarker after normalization at time t; η represents the adjustment parameter of the signal mutation factor of the biomarker; M i (t) represents the signal mutation factor of the i-th biomarker at time t.

[0023] Preferably, the S2 specifically includes:

[0024] In the implementation of the esophageal cancer recurrence probability assessment algorithm, the distance between the current time and the historical time point within the short-term time window is calculated, and a weighted value is generated using the base of the natural logarithm and the distance as the exponent to obtain the time weighting factor.

[0025] Preferably, the S2 specifically includes:

[0026] Based on the time weighting factor, the distribution disorder of the biomarker signal in a short time window is quantified and the time-weighted signal entropy is calculated.

[0027] Preferably, the S2 specifically includes:

[0028] Based on group consistency offset and time-weighted signal entropy, a comprehensive risk index is obtained; the inverse tangent function is introduced to transform the comprehensive risk index, and a scaling factor is introduced to calculate the recurrence probability; risk classification is performed based on the recurrence probability.

[0029] The beneficial effects of the technical solution of the present invention are:

[0030] 1. Exosomes were extracted from patient serum samples using established laboratory techniques (such as ultracentrifugation or immunocapture), and the expression levels of biomarkers associated with esophageal cancer recurrence (such as miRNA-21, HOTAIR, CD63, etc.) in different exosome subpopulations were measured. This yielded raw signal values reflecting the patient's cell status, enabling precise capture of biological information in exosomes. Exosomes are rich in biological information about cell status and can sensitively detect potential abnormal signals associated with esophageal cancer recurrence, providing highly specific data support for early monitoring of disease recurrence.

[0031] 2. Using a representative signal extraction algorithm, combined with the average signal value of a healthy population and the standard deviation of the signal of a healthy population, the deviation amplitude is calculated, and an exponential function is used to amplify abnormal signals. The representative signal value of each biomarker is selected, which highlights abnormal expression characteristics and retains key information, avoiding signal loss that may be caused by averaging processing. It significantly improves the detection sensitivity and specificity of abnormal signals related to esophageal cancer recurrence, allowing subsequent analysis to focus on the most representative signal features, and enhancing the accuracy of the detection method.

[0032] 3. In the esophageal cancer recurrence probability assessment algorithm, by introducing a short-term time window, collecting the representative signal values of standardized biomarkers, and calculating the signal mutation factor, dynamic capture of the recent biomarker change trends of patients' exosome subpopulations can be achieved. It can highlight abnormal fluctuations in the short term and obtain the degree of signal mutation reflecting the risk of esophageal cancer recurrence. It improves the sensitivity to early signals of esophageal cancer recurrence and avoids the limitations of relying on long-term static data, thereby enhancing the timeliness and predictive ability of recurrence risk assessment.

[0033] 4. By calculating the population consistency offset of all biomarkers and combining it with signal mutation factors for weighted amplification, we can evaluate the overall abnormal trend of all biomarkers relative to the healthy baseline from a population perspective, obtain a comprehensive risk indicator to measure the risk of recurrence, enhance the ability to detect synergistic abnormalities of multiple markers, improve the comprehensiveness and accuracy of recurrence risk prediction, and avoid the one-sidedness that may be caused by single marker analysis.

[0034] 5. By calculating the time-weighted signal entropy, emphasizing the importance of recent signals and quantifying the disorder of biomarker signal distribution, the diversity and complex changes of abnormalities are captured, and an entropy value indicator reflecting the degree of signal chaos is obtained. This improves the ability to recognize complex signal patterns related to recurrence, enhances the robustness of the detection method and its sensitivity to abnormal distribution.

[0035] 6. Based on group consistency offset and time-weighted signal entropy, the recurrence probability is calculated through inverse tangent function mapping, and risk classification is performed according to the judgment rules, realizing the quantitative conversion from biomarker data to recurrence risk, obtaining intuitive recurrence probability values and risk levels, and converting complex biological signals into clinically actionable risk classification results, providing a scientific basis for early warning and intervention of esophageal cancer recurrence, and improving the practicality and clinical guidance value of the detection method. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of a method for detecting an exosome marker combination for monitoring postoperative recurrence of esophageal cancer according to 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 specific scheme of a detection method for an exosome marker combination for monitoring recurrence of esophageal cancer after surgery 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 method for detecting an exosome marker combination for monitoring recurrence of esophageal cancer after surgery, provided by one embodiment of the present invention. The method comprises the following steps:

[0041] S1. Extract exosomes from patient serum samples, measure the expression levels of biomarkers associated with esophageal cancer recurrence in different exosome subpopulations, use them as raw signal values, and select representative signal values from the raw signal values; standardize the representative signal values to obtain standardized representative signal values of the biomarkers;

[0042] Exosomes are extracted from patient serum samples using established laboratory techniques (such as ultracentrifugation or immunocapture). Exosomes are tiny vesicles secreted by cells that are rich in biological information reflecting the cell state.

[0043] After extracting exosomes, a panel of biomarkers associated with esophageal cancer recurrence needs to be identified, such as specific microRNAs (e.g., miRNA-21), long noncoding RNAs (e.g., HOTAIR), and proteins (e.g., CD63). For each selected biomarker, its expression level in different exosome subpopulations is measured as the raw signal value.

[0044] In order to highlight the abnormal expression features related to esophageal cancer recurrence and avoid losing key information due to averaging, a representative signal extraction algorithm was used to select representative signal values;

[0045] The representative signal extraction algorithm calculates the raw signal values of all exosome subpopulations for each biomarker and calculates the difference between the raw signal value of a specific exosome subpopulation for a specific marker and the average signal value of a healthy population. The absolute value of the difference represents the degree to which the signal deviates from the normal level. To measure the significance of the deviation, the absolute value of the difference is divided by the standard deviation of the signal of the healthy population to obtain a dimensionless deviation amplitude. The average signal value and standard deviation of the signal of the healthy population are used as baseline data and are determined in advance through control experiments.

[0046] The exponential function is used to amplify the deviation amplitude. Specifically, the deviation amplitude is multiplied by a fixed amplification coefficient as the power of the exponent and applied to the base of the natural logarithm, which can significantly increase the weight of the abnormal signal. The original signal value is multiplied by the exponential amplification result, and the representative signal value is selected through the maximum value selection strategy.

[0047] The calculation formula for the representative signal value is:

[0048]

[0049] Among them, S i (t) represents the representative signal value of the i-th biomarker at time t; Indicates that the largest original signal value is selected from all exosome subpopulations of the i-th biomarker as the representative signal value of the i-th biomarker; S i,k (t) represents the raw signal value of the i-th biomarker in the k-th exosome subpopulation at time t; Indicates that the abnormal signal is amplified by an exponential function; δ is the amplification coefficient, which can be set according to the specific implementation scenario and is not limited here; μ represents the degree to which the original signal value of the i-th biomarker in the k-th exosome subpopulation deviates from the normal value at time t, that is, the deviation amplitude; i,ctrl represents the average signal value of the healthy population of the i-th biomarker, measured experimentally; σ i,ctrl represents the signal standard deviation of the healthy population for the i-th biomarker;

[0050] The representative signal value is normalized to obtain a normalized representative signal value of the biomarker. The normalization method is well known to those skilled in the art and will not be described in detail here. The normalization formula is as follows:

[0051]

[0052] Among them, S' i (t) represents the representative signal value of the i-th biomarker after normalization at time t;

[0053] Standardization eliminates differences in measurement units and dimensions among different biomarkers, facilitating the comprehensive analysis of multi-marker data while preserving the independent characteristics of each biomarker.

[0054] S2. Based on the standardized representative signal values of the biomarkers, the recurrence probability is calculated using the esophageal cancer recurrence probability assessment algorithm, and risk classification is performed based on the recurrence probability;

[0055] Based on the standardized representative signal values of the biomarkers, the recurrence probability is calculated using the esophageal cancer recurrence probability assessment algorithm, and risk classification is performed based on the calculated recurrence probability;

[0056] Based on the needs of actual clinical monitoring, in order to ensure that the recent change trend of the patient's exosomal biomarkers can be reflected, the esophageal cancer recurrence probability assessment algorithm introduces a short-term time window. Starting from the current time point (expressed in months after surgery), it traces back a certain period of time and collects the standardized representative signal value of each biomarker at each time point, so that the assessment of the recurrence probability focuses on short-term changes rather than relying on long-term static data; for each biomarker, the degree of mutation within the short-term time window is calculated to capture abnormal fluctuations in the signal; for each biomarker, the standardized representative signal value of the biomarker at each time point within the short-term time window is checked, and the change amplitude between it and the previous time point is calculated, that is, the time point difference of the signal, and the change amplitude is converted into The degree of abnormal fluctuation is compared with the normal fluctuation range of healthy people to obtain an indicator of relative change. In order to highlight abnormal fluctuations, an exponential function is used for amplification. The base of the exponential function is the base of the natural logarithm. The exponential term contains a sensitivity parameter to adjust the degree of amplification. The strength of the signal itself is also considered, that is, the absolute value of the standardized representative signal value of the biomarker at each time point. The absolute value of the standardized representative signal value of the biomarker at each time point is multiplied by the exponential amplification result to generate the mutation contribution value at each time point. The mutation contribution values of all time points in the short-term time window are summed and divided by the length of the short-term time window to obtain the signal mutation factor of the biomarker. A large value of the signal mutation factor indicates that the biomarker has undergone significant abnormal fluctuations in the recent period, which may be associated with the risk of recurrence.

[0057] Based on the analysis of individual biomarker mutations, the overall abnormal trend of all biomarkers, namely the population consistency deviation, is further evaluated. For each biomarker, the square of the standardized representative signal value of the biomarker is calculated to amplify the degree of deviation from the average signal value of the healthy population. The biomarker signal mutation factor is introduced as a weighting term and amplified by an exponential function, so that biomarkers with greater mutation levels occupy a greater proportion in the overall assessment.

[0058] The calculation formula for group consistency deviation is:

[0059]

[0060] Where G(t) represents the population consistency deviation at time t, which is used to measure the overall deviation of all biomarkers relative to the healthy baseline at the current time point and evaluate the recurrence risk from a population perspective; n represents the total number of biomarkers associated with esophageal cancer recurrence; Indicates taking the square root of the summation result and adjusting the magnitude of the square sum to the same order as the signal offset amplitude to facilitate subsequent calculations; (S' i (t))2 It represents the square of the representative signal value of the i-th biomarker after normalization at time t, which is used to amplify the deviation amplitude and highlight significant abnormalities. represents the mutation weighting term, which is an exponential weighting of the biomarker's signal mutation factor, and is used to enhance the contribution of biomarkers with large mutations to the overall offset; η represents the adjustment parameter of the biomarker's signal mutation factor, which is used to control the weighted impact of mutations on the offset. It can be set according to the specific implementation scenario and is not limited here; M i (t) represents the signal mutation factor of the i-th biomarker at time t, which is used to reflect the mutation degree of the i-th biomarker in a short time window. The larger the value, the more abnormal the recent signal fluctuation. The calculation formula of the biomarker signal mutation factor is:

[0061]

[0062] in, It represents the average factor, which is the inverse of the short-term time window length τ. It is used to calculate the average value within the short-term time window so as to standardize the mutation value within the window to the average level and ensure that different time windows are comparable. It represents the sum of the mutation contribution values of all time points in the time window [t-τ+1,t]; Represents the mutation contribution value at time s; |S' i (s)| represents the absolute value of the representative signal value of the i-th biomarker after normalization at time s; represents the exponential amplification term, which amplifies the short-term changes in the representative signal value of the standardized biomarker through an exponential function to highlight the mutation amplitude of adjacent time points and enhance the sensitivity to abnormal fluctuations; λ represents the sensitivity parameter, which is used to control the degree of exponential amplification and can be set according to the specific implementation scenario and is not limited here; σ i,ctrl (s) represents the signal standard deviation of the healthy population of the i-th biomarker at time s; S' i (s-1) represents the representative signal value of the i-th biomarker after normalization at time s-1; |S' i (s)-S' i (s-1)| represents the amplitude of change in the representative signal value of the normalized biomarker, which is used to quantify the dynamic fluctuation of the biomarker signal;

[0063] To quantify the disordered distribution of biomarker signals within a short-term time window and highlight the importance of recent data through time weighting, time-weighted signal entropy is introduced. The time-weighted signal entropy is calculated according to the definition of information entropy. The larger the entropy value, the more disordered the distribution of the representative signal of the standardized biomarker and the higher the abnormal diversity. The abnormal diversity may be related to the complex changes associated with recurrence. To emphasize the importance of recent signals, a time weighting factor is introduced. Specifically, for each time point, the distance between the current time and the historical time point within the time window is calculated, and a weighted value, i.e., the time weighting factor, is generated using the base of the natural logarithm as the base and the exponent as the distance. The time weighting factor increases as the time distance decreases, thereby giving recent signals a greater influence.

[0064] The calculation formula of time-weighted signal entropy is:

[0065]

[0066] Where H(t) represents the time-weighted signal entropy at time t, which is used to measure the disorder of the biomarker signal distribution in a short time window and reflect the abnormal distribution characteristics. The larger the value of the time-weighted signal entropy, the higher the degree of disorder of the biomarker signal distribution. represents entropy calculation, which is based on the Shannon entropy formula to calculate the disorder of biomarker signal distribution and is used to quantify the diversity and disorder of biomarker signals; p i (t) represents the probability distribution of the weighted signal of the i-th biomarker in the total signal at time t, and the calculation formula is:

[0067]

[0068] in, represents the weighted signal sum of the i-th biomarker in the time window; e t-s represents the time weighting factor, giving greater weight to recent signals; |S' i (s)|·e t-s represents the weighted signal value of the i-th biomarker at time s; represents the total weighted signal sum of all biomarkers, which serves as a normalization factor to ensure that p i The sum is 1;

[0069] The recurrence probability is calculated based on the group consistency offset and time-weighted signal entropy. The influence of the time-weighted signal entropy is amplified by an exponential function. The exponential term includes an entropy sensitivity coefficient to enhance the effect of disorder on the recurrence probability. The amplified entropy value is multiplied by the group consistency offset to obtain a comprehensive risk index. The comprehensive risk index is transformed using the inverse tangent function (arctan) and a scaling factor is introduced to calculate the recurrence probability to ensure that the recurrence probability falls between 0 and 1. The formula for calculating the recurrence probability is:

[0070]

[0071] Among them, P represents the probability of recurrence, which is used to comprehensively evaluate the risk of recurrence, and the output value is between 0 and 1; Represents the scaling factor used to scale the output range of the inverse tangent function Mapped to (0,1); arctan represents the inverse tangent function, which is used to nonlinearly map the input value to a limited range to ensure smooth changes in the recurrence probability; G represents the group consistency offset; H represents the time-weighted signal entropy; α represents the entropy sensitivity coefficient, which is used to control the degree to which the time-weighted signal entropy amplifies the recurrence probability. It can be set according to the specific implementation scenario and is not limited here; G·e α·H represents the comprehensive risk indicator;

[0072] Risk classification is performed based on the calculated recurrence probability, and the judgment rules are as follows:

[0073] If P < 0.3: low risk of recurrence;

[0074] If 0.3≤P<0.7: medium risk of recurrence;

[0075] If P ≥ 0.7: high risk of recurrence.

[0076] In summary, a detection method for a combination of exosome markers for monitoring recurrence after esophageal cancer surgery was completed.

[0077] 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.

[0078] 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.

[0079] 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 method for detecting a combination of exosome markers for monitoring postoperative recurrence of esophageal cancer, characterized in that: The following steps are involved: S1. Extract exosomes from patient serum samples, measure the expression levels of biomarkers associated with esophageal cancer recurrence in different exosome subpopulations, use them as raw signal values, and select representative signal values from the raw signal values; standardize the representative signal values to obtain standardized representative signal values of the biomarkers; S2. Based on the standardized representative signal values of the biomarkers, the recurrence probability is calculated using the esophageal cancer recurrence probability assessment algorithm, and risk classification is performed based on the recurrence probability.

2. The method for detecting a combination of exosome markers for monitoring postoperative recurrence of esophageal cancer according to claim 1, characterized in that: Said S1 specifically includes: A representative signal extraction algorithm is introduced to select representative signal values from the original signal values.

3. The method for detecting a combination of exosome markers for monitoring postoperative recurrence of esophageal cancer according to claim 2, characterized in that: Said S1 specifically includes: The representative signal extraction algorithm calculates the deviation amplitude based on the original signal value, combined with the average signal value of the healthy population and the signal standard deviation of the healthy population, and introduces an exponential function to amplify the deviation amplitude; the original signal value is multiplied by the exponential amplification result, and the representative signal value is selected through the maximum value selection strategy; the calculation formula of the representative signal value is: Among them, S i (t) represents the representative signal value of the i-th biomarker at time t; S i,k (t) represents the original signal value of the kth biomarker in the kth exosome subpopulation at time t; δ is the amplification factor; μ represents the degree to which the original signal value of the i-th biomarker in the k-th exosome subpopulation at time t deviates from the normal value, indicating the deviation amplitude; i,ctrl represents the average signal value of the healthy population for the i-th biomarker; σ i,ctrl represents the standard deviation of the signal of the healthy population for the i-th biomarker.

4. The method for detecting a combination of exosome markers for monitoring postoperative recurrence of esophageal cancer according to claim 1, characterized in that: Said S2 specifically includes: In the implementation of the esophageal cancer recurrence probability assessment algorithm, a short-term time window was introduced to calculate the amplitude of change between the standardized representative signal value of the biomarker at each time point and the previous time point; combined with the signal standard deviation of the healthy population, an exponential function was introduced to amplify the amplitude of change to obtain the signal mutation factor of the biomarker.

5. The method for detecting a combination of exosome markers for monitoring postoperative recurrence of esophageal cancer according to claim 4, characterized in that: Said S2 specifically includes: Based on the representative signal values of all standardized biomarkers at the current time point, combined with the signal mutation factors of the biomarkers, the overall abnormal trend of all markers is evaluated to obtain the population consistency deviation; the specific calculation formula is: Where G(t) represents the population consistency deviation at time t; n represents the total number of biomarkers associated with esophageal cancer recurrence; S' i (t) represents the representative signal value of the i-th biomarker after normalization at time t; η represents the adjustment parameter of the signal mutation factor of the biomarker; M i (t) represents the signal mutation factor of the i-th biomarker at time t.

6. The method for detecting a combination of exosome markers for monitoring postoperative recurrence of esophageal cancer according to claim 5, characterized in that: Said S2 specifically includes: In the implementation of the esophageal cancer recurrence probability assessment algorithm, the distance between the current time and the historical time point within the short-term time window is calculated, and a weighted value is generated using the base of the natural logarithm and the distance as the exponent to obtain the time weighting factor.

7. The method for detecting a combination of exosome markers for monitoring postoperative recurrence of esophageal cancer according to claim 6, characterized in that: Said S2 specifically includes: Based on the time weighting factor, the distribution disorder of the biomarker signal in a short time window is quantified and the time-weighted signal entropy is calculated.

8. The method for detecting a combination of exosome markers for monitoring postoperative recurrence of esophageal cancer according to claim 7, characterized in that: Said S2 specifically includes: Based on group consistency offset and time-weighted signal entropy, a comprehensive risk index is obtained; the inverse tangent function is introduced to transform the comprehensive risk index, and a scaling factor is introduced to calculate the recurrence probability; risk classification is performed based on the recurrence probability.

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