Biomarker combination, kit, method and system for postoperative prognosis analysis of recurrent ovarian cancer

Abstract

The invention discloses a biomarker combination, a kit, a method and a system for postoperative prognosis analysis of recurrent ovarian cancer. The biomarker combination comprises CA199, IL10, a mononuclear cell and a D-dimer. The invention provides a preoperative inflammatory scoring system with higher specificity and sensitivity to predict the postoperative prognosis of the recurrent ovarian cancer patient by mainly utilizing a statistical method and a data analysis technology in combination with inflammatory biomarkers of the patient, overcomes the defects of poor specificity, low sensitivity and single scoring system, and provides a basis for predicting the postoperative prognosis of the recurrent ovarian cancer patient. Therefore, clinical doctors are helped to make individualized treatment decisions, and the treatment accuracy and the individualized medical level are improved.

Description

Technical Field

[0001] The present invention belongs to the field of medical technology evaluation, and particularly relates to a biomarker combination, a kit, a method and a system for postoperative prognosis analysis of recurrent ovarian cancer. Background Art

[0002] The incidence of ovarian cancer ranks seventh among common malignant tumors in women and is also the eighth leading cause of cancer death in women. The five-year survival rate of ovarian cancer is about 47%. This is mainly due to the high recurrence rate and chemotherapy resistance. Although about 80% of patients can achieve complete clinical remission after cytoreductive surgery and first-line systemic chemotherapy, most clinically completely remitted patients will develop recurrent disease. Approximately 70% of advanced ovarian cancer patients experience recurrence within 3 years after first-line treatment, and the average five-year survival rate after recurrence is less than 10%.

[0003] In current medical practice, the treatment of ovarian cancer mainly includes surgery, chemotherapy, targeted therapy, immunotherapy, etc. The prognosis of ovarian cancer is affected by various factors, including the stage of the tumor, the nutritional status of the patient, the inflammatory state, etc. At present, for the treatment of recurrent ovarian cancer, surgery is still one of the main treatment methods. Existing research shows that in the case of recurrence, surgery reduces tumor cells and the prognosis will be improved. However, how to accurately predict the prognosis of surgery at the preoperative stage and select patients eligible for surgery is crucial for clinical decision-making.

[0004] With the gradual in-depth understanding of the tumor microenvironment, the roles of inflammatory response and immune status in tumor progression have been gradually emphasized. In recent years, the role of inflammation in tumorigenesis, development and recurrence has attracted extensive attention. A number of studies have shown that chronic inflammation is closely related to the occurrence and progression of tumors. Inflammatory cells in the tumor microenvironment can promote the proliferation, invasion and metastasis of cancer cells. For ovarian cancer, the roles of inflammatory cytokines, chemokines, etc. in tumor growth and metastasis have also been gradually revealed. Existing research has shown that preoperative inflammatory-related scores, such as Glasgow Prognostic Score (GPS), Prognostic Inflammatory Score (PIS), Naples Prognostic Score (NPS), platelet / lymphocyte ratio (PLR) and neutrophil / lymphocyte ratio (NLR), have certain predictive value for the postoperative prognosis of ovarian cancer patients. However, in the postoperative prognosis prediction of recurrent ovarian cancer, the application of its inflammatory-related scores is still in the exploratory stage.

[0005] Existing evaluation methods or systems have the following problems: (1) Poor specificity: Existing inflammatory scoring systems are mainly based on general inflammatory markers. These markers usually have a certain predictive value for various types of cancer, but their specificity in patients with recurrent ovarian malignancies is relatively low. Since these inflammatory markers also increase in other non-neoplastic inflammatory diseases, relying solely on these indicators to evaluate the prognosis of ovarian cancer easily leads to insufficient specificity and inability to effectively distinguish the special inflammatory responses of tumors. This defect of poor specificity limits the accuracy of these scoring systems in predicting the prognosis after recurrent ovarian cancer surgery. (2) Low sensitivity: Although existing studies have shown that inflammatory scoring systems are somewhat correlated with the prognosis of ovarian cancer, their sensitivity is low. Existing inflammatory scoring systems cannot sensitively predict the postoperative prognosis of patients. Therefore, improving the sensitivity of preoperative inflammatory scoring systems remains one of the difficulties in the existing technology. (3) Single scoring system and failure to comprehensively reflect the inflammatory and immune status: Existing inflammatory scoring methods usually only consider single or a small number of inflammatory markers, without comprehensively considering other factors that may affect the postoperative prognosis, such as cellular immune status, concentrations of specific inflammatory factors, etc. The inflammatory responses of patients with recurrent ovarian cancer are complex, and using only limited inflammatory markers may not be able to comprehensively reflect the inflammatory and immune status of patients. This single scoring method makes it difficult for existing scoring systems to accurately evaluate individualized prognosis, thus limiting their clinical applications. (4) Limited application scenarios: Detection of some inflammatory markers may require specific laboratory conditions and testing equipment, which limits the application scenarios of these scoring systems. For medical institutions with relatively limited resources, it is difficult to achieve comprehensive detection of these inflammatory markers, restricting the popularization and application of these scoring systems in primary healthcare or remote areas.

[0006] In the prediction of postoperative prognosis of recurrent ovarian cancer, traditional prognostic factors and detection methods have certain limitations. As a simple and easily obtainable biomarker, preoperative inflammatory scoring has the potential to be used for prognosis prediction and the formulation of individualized treatment plans. The present invention uses preoperative inflammatory scoring for predicting the postoperative prognosis of recurrent ovarian malignancies, which helps to supplement traditional prognostic methods and provides a new tool for clinical decision-making. Summary of the Invention

[0007] The present invention aims to at least solve one of the technical problems existing in the above-mentioned prior art. For this purpose, the present invention provides a biomarker combination, a kit, a method, and a system for prognostic analysis of recurrent ovarian cancer. By improving the preoperative inflammatory scoring system and combining a wider range of inflammatory biomarkers, the present invention can more accurately predict the postoperative prognosis of patients with recurrent ovarian cancer, overcoming the defects of poor specificity, low sensitivity, and single scoring system.

[0008] The present invention discloses a biomarker combination for postoperative prognosis analysis of recurrent ovarian cancer, and the biomarker combination includes CA199, IL10, monocytes, and D-dimer.

[0009] The present invention discloses a kit for postoperative prognosis analysis of recurrent ovarian cancer, and the kit includes reagents for detecting CA199, IL10, monocytes, and D-dimer.

[0010] The present invention discloses a method for postoperative prognosis analysis of recurrent ovarian cancer, including the following steps: detecting CA199, IL10, monocytes, and D-dimer in a sample, and analyzing the postoperative prognosis effect of recurrent ovarian cancer according to a postoperative prognosis analysis model for recurrent ovarian cancer.

[0011] In some embodiments of the present invention, the analysis of the postoperative prognosis effect of recurrent ovarian cancer according to the postoperative prognosis analysis model for recurrent ovarian cancer includes: determining an inflammatory score according to the detection results of CA199, IL10, monocytes, and D-dimer, and determining the postoperative prognosis effect of recurrent ovarian cancer according to the inflammatory score; wherein, the inflammatory score = β1×CA199 + β2×IL10 + β3×monocytes + β4×D-dimer; preferably, β1 = 0.001985, β2 = 0.101091, β3 = 0.463452, β4 = 0.098389.

[0012] In some embodiments of the present invention, the construction method of the postoperative prognosis analysis model for recurrent ovarian cancer includes the following steps: obtaining the preoperative inflammatory indexes and survival time of patients with recurrent ovarian cancer; performing univariate Cox analysis on the preoperative inflammatory indexes and the survival time, and calculating the variance inflation factor to evaluate multicollinearity to obtain relevant inflammatory indexes significantly related to the postoperative prognosis of patients with recurrent ovarian cancer; performing multivariate Cox analysis on the relevant inflammatory indexes and the survival time to establish a postoperative prognosis analysis model for recurrent ovarian cancer.

[0013] The present invention discloses a construction method of a postoperative prognosis analysis model for recurrent ovarian cancer, which is characterized by including the following steps:

[0014] S1. Obtaining the preoperative inflammatory indexes and survival time of patients with recurrent ovarian cancer;

[0015] S2. Performing univariate Cox analysis on the preoperative inflammatory indexes and the survival time, and calculating the variance inflation factor to evaluate multicollinearity to obtain relevant inflammatory indexes significantly related to the postoperative prognosis of patients with recurrent ovarian cancer;

[0016] S3. Performing multivariate Cox analysis on the relevant inflammatory indexes and the survival time to establish a postoperative prognosis analysis model for recurrent ovarian cancer.

[0017] In some embodiments of the present invention, in step S1, the preoperative inflammatory indexes include CA125, CA199, HE4, premenopausal ROMA, postmenopausal ROMA, CA153, PAB, ALB, CRP, Treg, Ts / Tc (CD3+ / CD8+), B cells, IL10, red blood cells, hemoglobin, neutrophils, lymphocytes, monocytes, NLR, PLR, D-dimer, fibrinogen.

[0018] In some embodiments of the present invention, in step S2, the relevant inflammatory indexes include CA199, IL10, monocytes and D-dimer.

[0019] In the present invention, by optimizing the selection of preoperative inflammatory indexes, specific inflammatory markers suitable for recurrent ovarian malignant tumors are selected, the accuracy of the scoring system is improved, and the problem that the existing scoring system is based on general inflammatory markers and lacks specificity is solved. Through COX univariate analysis and multivariate analysis, inflammatory markers with higher correlation with recurrent ovarian malignant tumors are selected. The present invention can more accurately reflect the inflammatory state in the tumor microenvironment, improve the specificity of the preoperative scoring system, and thus more effectively predict the postoperative recurrence risk.

[0020] In some embodiments of the present invention, in step S2, it further includes: selecting preoperative inflammatory indexes with p < 0.05 and variance inflation factor > 3 in the Cox univariate analysis as the relevant inflammatory indexes.

[0021] In some embodiments of the present invention, in step S3, it further includes: weighting the relevant inflammatory indexes according to the regression β coefficient to obtain an inflammatory score; wherein, the inflammatory score = β1×CA199 + β2×IL10 + β3×monocytes + β4×D-dimer; preferably, β1 = 0.001985, β2 = 0.101091, β3 = 0.463452, β4 = 0.098389. β1, β2, β3, and β4 respectively correspond to the regression β coefficients of CA199, IL10, monocytes, and D-dimer.

[0022] In some preferred embodiments of the present invention, it further includes: when the inflammatory score is less than 0.85586949, the postoperative prognosis is better; when the inflammatory score is greater than 0.85586949, the postoperative prognosis is worse. Among them, a better postoperative prognosis means that the survival time range is 1.1 - 19.5 months; a worse postoperative prognosis means that the survival time range is 0.3 - 18.5 months, and the median survival time is 4.13 months.

[0023] In the present invention, a multi-factor scoring system is constructed by integrating various markers of the inflammatory and immune systems, improving the overall sensitivity and specificity, and solving the problem that the existing methods are single and fail to comprehensively reflect the inflammatory and immune states of patients. By combining data such as inflammatory markers and the proportions of immune cells, a multi-dimensional comprehensive scoring system is formed, enabling the present invention to more acutely reflect the comprehensive immune-inflammatory state of patients and improving the sensitivity and prediction accuracy of the system.

[0024] The present invention also discloses a system for postoperative prognosis analysis of recurrent ovarian cancer, characterized by comprising the following modules:

[0025] A data acquisition module for obtaining the preoperative inflammatory indexes and survival time of patients with recurrent ovarian cancer;

[0026] An index selection module for performing univariate Cox analysis on the preoperative inflammatory indexes and the survival time, and calculating the variance inflation factor to evaluate multicollinearity, so as to obtain inflammatory indexes significantly related to the postoperative prognosis of patients with recurrent ovarian cancer;

[0027] A prognosis model construction module for performing multivariate Cox analysis on the related inflammatory indexes and the survival time to establish a postoperative prognosis analysis model for recurrent ovarian cancer;

[0028] A prognosis analysis module for analyzing the postoperative prognosis effect of recurrent ovarian cancer according to the postoperative prognosis analysis model for recurrent ovarian cancer.

[0029] The present invention mainly uses statistical methods and data analysis techniques, combines the inflammatory biomarkers of patients, and provides a preoperative inflammatory scoring system with higher specificity and sensitivity to predict the postoperative prognosis of patients with recurrent ovarian cancer, thereby helping clinicians make individualized treatment decisions and improving the precision of treatment and the level of individualized medicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The following further describes the present invention with reference to the drawings and embodiments, wherein:

[0031] Figure 1 It is a grouped statistical chart of the prognosis analysis model in Example 1 of the present invention;

[0032] Figure 2 It is an ROC curve graph of the prognosis analysis model in Example 1 of the present invention;

[0033] Figure 3 It is an ROC curve graph of 1000 bootstrap resamplings in Example 1 of the present invention;

[0034] Figure 4 It is an ROC curve graph for comparison with the existing scoring system in Comparative Example 1 of the present invention. Detailed implementation manners

[0035] The following will clearly and completely describe the concept of the present invention and the technical effects generated in combination with the embodiments, so as to fully understand the purpose, features and effects of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative efforts all fall within the scope of protection of the present invention. The test methods used in the embodiments are all conventional methods without special instructions; the materials, reagents, etc. used, without special instructions, are all reagents and materials that can be obtained from commercial channels.

[0036] Example 1: Construction and verification of a prognostic analysis model for recurrent ovarian cancer after surgery

[0037] 1. Sources of data and materials:

[0038] Blood samples: Recurrent ovarian cancer patients (a total of 82 patients, 89 cases of data) from the Department of Gynecology of Shanghai Tenth People's Hospital. Blood samples were collected before surgery. All samples were approved by the ethics committee. Sources of blood sample test results: All from the Clinical Laboratory of Shanghai Tenth People's Hospital. The above recurrent ovarian cancer patients all underwent surgical treatment subsequently, and the postoperative survival status and survival time of the patients were recorded.

[0039] 2. Construction of the prognostic analysis model:

[0040] 2.1 Obtaining biomarkers: Collect the preoperative test indicators and clinical characteristics of the patients to obtain the data required for the inflammatory scores of all patients. The preoperative test indicators of the patients mainly include inflammatory indicators, specifically including: CA125, CA199, HE4, premenopausal ROMA, postmenopausal ROMA, CA153, PAB, ALB, CRP, Treg, Ts / Tc (CD3+ / CD8+), B cells, IL10, red blood cells, hemoglobin, neutrophils, lymphocytes, monocytes, NLR, PLR, D-dimer, fibrinogen; the clinical characteristics of the patients mainly include survival status and survival time, etc.

[0041] 2.2 Calculation of the inflammatory scoring system: Through Cox univariate and multivariate regression analysis, obtain the inflammatory indicators related to the postoperative prognosis of recurrent ovarian cancer patients, and calculate the preoperative inflammatory score according to the weighted ratio of each biomarker. The specific steps are as follows:

[0042] (1) Use a univariate Cox regression model to analyze the relationship between inflammatory indicators and survival time, and include the variables with p<0.05 in the univariate Cox model into the multivariate Cox regression model (the specific results are shown in Table 1);

[0043] (2) Evaluate multicollinearity by calculating the variance inflation factor (VIF), and manually exclude variables with VIF > 3 (the specific results are shown in Table 2);

[0044] (3) Incorporate the remaining variables into a multivariate Cox regression model, and use forward stepwise regression to determine the final model (the specific results are shown in Table 3); After establishing the multivariate Cox regression model, weight the meaningful predictive variables according to their regression β coefficients to obtain the inflammation score: Risk Score = 0.001985 × CA199 + 0.101091 × IL10 + 0.463452 × M + 0.098389 × D-dimer.

[0045] Table 1. Univariate Cox regression analysis

[0046]

[0047]

[0048] Explanation of variables in Table 1 above: HE4: Human Epididymis Protein 4, PAB: Prealbumin, ALB: Albumin, CRP: C-Reactive Protein, Treg: Regulatory T Cell, B cell: B Cell Count, IL10: Interleukin 10, RBC: Red Blood Cell Count, Hb: Hemoglobin, N%: Neutrophil Percentage, L%: LymphocytePercentage, N: Neutrophil Count, L: LymphocyteCount, M: Monocyte Count, NLR: Neutrophil-to-Lymphocyte Ratio, PLR: Platelet-to-Lymphocyte Ratio, DD: D-Dimer, Fib: Fibrinogen. HR: hazard ratio; CI: confidence intervals.

[0049] Table 2. Results of multicollinearity analysis

[0050]

[0051] Table 3. Multivariate Cox proportional hazards regression analysis based on overall survival

[0052]

[0053]

[0054] 2.3 Grouping and data statistics: According to the median of the scores of the scoring system, patients were divided into high-score group and low-score group, and the postoperative survival status and survival time of the patients were recorded. Statistics showed that the median was 0.85586949, with 45 people in the high-score group and 44 people in the low-score group; when the scoring score was less than the median, the postoperative prognosis was better.

[0055] 3. Validation of the prognostic analysis model: By comparing the postoperative survival status and survival time of patients in the high-score group and the low-score group, Kaplan-Meier survival analysis was used for statistical analysis, and the log-rank test was used for comparison between groups to evaluate the reliability of the score in terms of discrimination ability. The Receiver Operating Characteristic (ROC) curve was used to evaluate the discrimination performance of the measurement model. Bootstrap resampling was used 1000 times for the ROC curve and calibration curve for internal validation.

[0056] The grouping situation is as Figure 1 shown. The postoperative survival time of patients in the high-score group was significantly lower than that in the low-score group (p<0.01), verifying the accuracy of the scoring system. The statistical table of the number of people with postoperative survival time in the high-score group and the low-score group is shown in Table 4 below:

[0057] Table 4. Statistical table of the number of people with postoperative survival time in the high-score group and the low-score group

[0058]

[0059] As Figure 1 and the results of Table 4 show, the survival time range of the low-score group was 1.1 - 19.5 months, and the median survival time was not reached; for the high-score group: 0.3 - 18.5 months, and the median survival time was 4.13 months. The ROC curve of the prognostic analysis model is as Figure 2 shown, and the ROC curve of bootstrap resampling 1000 times is as Figure 3As shown, the AUC value of the ROC curve is 0.771, the AUC value of the bootstrap ROC curve is 0.776, and the C-index of the calibration curve: 0.667 (95% CI 0.583 - 0.746) indicates that the prediction specificity and sensitivity of this scoring system for postoperative prognosis are superior to those of the single biomarker scoring system.

[0060] Comparative Example 1: Comparative experiment between the prognostic scoring system of the present application and existing scoring systems

[0061] 1. Experimental subjects: Recurrent ovarian cancer patients in the Department of Gynecology of Shanghai Tenth People's Hospital (a total of 82 patients, 89 cases of data).

[0062] 2. Experimental steps:

[0063] Score calculation: Calculate the preoperative inflammatory score according to the scoring system of Example 1 of the present invention and the traditional scoring system respectively.

[0064] Follow-up record: Record the postoperative survival status and survival time of the patients.

[0065] 3. Experimental results:

[0066] Table 5. AUC values of each inflammatory score

[0067]

[0068] The AUC value predicted by this scoring system is 0.771, the AUC value predicted by NPS is 0.632, the AUC value predicted by GPS is 0.647, the AUC value predicted by PIS is 0.399, the AUC value predicted by NLR is 0.678, and the AUC value predicted by PLR is 0.516, indicating that this scoring system is significantly more accurate in predicting postoperative survival status and survival time. The ROC curve of the experimental results is as Figure 4 shown.

[0069] In the Kaplan-Meier analysis, this scoring system can more clearly distinguish high-risk and low-risk patients, verifying the superiority of the scoring system of the present invention.

[0070] Compared with primary ovarian cancer, the immune-inflammatory microenvironment of recurrent ovarian cancer is accompanied by higher levels of pro-inflammatory factors, which further strengthens the chronic inflammatory environment of tumor tissues, leading to the gradual generation of "tolerance" by the immune system, that is, the ability to recognize and respond to tumor cells is weakened, more immunosuppressive, more likely to induce immune tolerance and escape mechanisms, and also brings greater challenges to the treatment of recurrent ovarian cancer. Existing inflammatory scoring systems are mostly based on primary ovarian cancer patients, and moreover, these scoring systems usually only consider single or a small number of inflammatory markers, which have certain limitations in reflecting the inflammatory and immune status of recurrent ovarian cancer patients.

[0071] The above embodiments and comparative experiments show that the preoperative inflammatory scoring system proposed by the present invention exhibits high specificity and sensitivity in predicting the postoperative recurrence risk of recurrent ovarian malignancies. Through comparative experiments, the superiority of the scoring system of the present invention in terms of prediction accuracy, individualized application, clinical stability, etc. is confirmed, providing a scientific and effective reference tool for the postoperative prognosis management of patients with recurrent ovarian cancer. Specifically, the present invention has the following beneficial effects:

[0072] 1. Improve the accuracy of postoperative prognosis prediction: The comprehensive scoring system based on multiple inflammatory markers significantly improves the accuracy of postoperative prognosis prediction for recurrent ovarian malignancies; research data show that the AUC of the scoring system of the present invention reaches 0.771, which is superior to the traditional single-marker scoring method.

[0073] 2. Improve specificity and sensitivity and reduce the misjudgment rate: The multi-dimensional inflammatory scoring method of the present invention has high specificity and sensitivity, can more accurately identify high-risk patients, reduce the misjudgment rate, and help clinicians better perform individualized postoperative interventions.

[0074] 3. Standardize and unify the operation process for easy clinical promotion: The present invention standardizes the scoring operation process, including data collection, calculation method, score interpretation, etc., ensuring the stability and consistency of the scoring system among different hospitals and laboratories, and facilitating clinical promotion and application.

[0075] 4. Reduce medical costs and expand the scope of application: The present invention reduces the dependence on expensive equipment and specialized technologies, thereby reducing the overall detection cost of the scoring system.

[0076] 5. Reduce side effects and improve the quality of life and compliance of patients: Through the accurate prediction of postoperative prognosis, patients can obtain a clearer prognosis and treatment plan of the condition, reduce unnecessary interventions, improve the safety and compliance of treatment, and promote the postoperative rehabilitation and improvement of the quality of life of patients.

[0077] The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings. However, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge scope of those of ordinary skill in the art. In addition, the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

Claims

1. A biomarker combination for postoperative prognosis analysis of recurrent ovarian cancer, characterized in that: The biomarker panel includes CA199, IL10, monocytes and D-dimer.

2. A kit for postoperative prognosis analysis of recurrent ovarian cancer, characterized in that: The kit includes reagents for detecting CA199, IL10, monocytes and D-dimer.

3. A method for postoperative prognosis analysis of recurrent ovarian cancer, characterized in that: The following steps are involved: CA199, IL10, monocytes and D-dimer in the samples were detected, and the postoperative prognostic effect of recurrent ovarian cancer was analyzed according to the postoperative prognostic analysis model of recurrent ovarian cancer.

4. The method according to claim 3, characterized in that The method of determining the postoperative prognostic effect of recurrent ovarian cancer according to the postoperative prognostic analysis model of recurrent ovarian cancer comprises: Determining an inflammatory score according to the detection results of CA199, IL10, monocytes and D-dimer, and determining the postoperative prognosis of recurrent ovarian cancer according to the inflammatory score; Among them, inflammatory score = β1×CA199+β2×IL10+β3×monocytes+β4×D-dimer; preferably, β1=0.001985, β2=0.101091, β3=0.463452, β4=0.098389.

5. The method according to claim 3, characterized in that: The method for constructing the postoperative prognostic analysis model for recurrent ovarian cancer comprises the following steps: To obtain preoperative inflammatory markers and survival time of patients with recurrent ovarian cancer; The preoperative inflammatory indexes and the survival data were subjected to univariate Cox analysis, and the variance inflation factor was calculated to evaluate multicollinearity, so as to obtain inflammatory indexes significantly related to the postoperative prognosis of patients with recurrent ovarian cancer; Multivariate Cox analysis was performed on the relevant inflammatory indicators and the survival data to establish a prognostic analysis model for recurrent ovarian cancer after surgery.

6. A method for constructing a postoperative prognostic analysis model for recurrent ovarian cancer, characterized in that: The following steps are involved: S1. Obtain preoperative inflammatory markers and survival time of patients with recurrent ovarian cancer; S2. Performing univariate Cox analysis on the preoperative inflammatory indexes and the survival data, and calculating the variance inflation factor to assess multicollinearity, to obtain inflammatory indexes significantly associated with the postoperative prognosis of patients with recurrent ovarian cancer; S3. Perform multifactorial Cox analysis on the relevant inflammatory indicators and the survival data to establish a prognostic analysis model for postoperative recurrent ovarian cancer.

7. The construction method according to claim 6, characterized in that: In step S1, the preoperative inflammatory indicators include CA125, CA199, HE4, premenopausal ROMA, postmenopausal ROMA, CA153, PAB, ALB, CRP, Treg, Ts / Tc (CD3+ / CD8+), B cells, IL10, red blood cells, hemoglobin, neutrophils, lymphocytes, monocytes, NLR, PLR, D-dimer, fibrinogen; and / or In step S2, the relevant inflammatory indicators include CA199, IL10, monocytes and D-dimer.

8. The construction method according to claim 6, characterized in that: Step S2 also includes: selecting preoperative inflammatory indicators with p<0.05 and variance inflation factor>3 in Cox univariate analysis as the relevant inflammatory indicators.

9. The construction method according to claim 6, characterized in that: Step S3 further includes: weighting the relevant inflammatory indexes according to the regression β coefficient to obtain an inflammatory score; Among them, inflammatory score = β1×CA199+β2×IL10+β3×monocytes+β4×D-dimer; preferably, β1=0.001985, β2=0.101091, β3=0.463452, β4=0.098389.

10. A system for postoperative prognosis analysis of recurrent ovarian cancer, characterized in that: Includes the following modules: A data collection module to obtain preoperative inflammatory indicators and survival time of patients with recurrent ovarian cancer; A prognostic model building module is used to perform univariate Cox analysis on the preoperative inflammatory index and the survival time, and calculate the variance inflation factor to evaluate multicollinearity, obtain relevant inflammatory indexes that are significantly related to the postoperative prognosis of patients with recurrent ovarian cancer, perform multivariate Cox analysis on the relevant inflammatory indexes and the survival time, and establish a postoperative prognostic analysis model for recurrent ovarian cancer; The prognostic analysis module is used to analyze the postoperative prognostic effect of recurrent ovarian cancer according to the postoperative prognostic analysis model of recurrent ovarian cancer.

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