Apatinib medication monitoring information analysis system for ovarian cancer patient

Abstract

The invention relates to the technical field of medical data processing, and particularly discloses an apatinib medication monitoring information analysis system for an ovarian cancer patient. The system comprises a module for acquiring pharmacodynamic data of apatinib on an ovarian cancer patient, a module for detecting blood after the ovarian cancer patient uses apatinib, a proteome chip module, an apatinib medication evaluation module and an apatinib medication model verification module, the pharmacodynamic data acquisition module of apatinib for ovarian cancer patients can quickly complete screening of organism physiological change data of apatinib drugs in the first stage, the second stage and the third stage, and the blood detection module and the proteome chip module are combined after the ovarian cancer patients use apatinib. The quality of the apatinib medication evaluation model of the ovarian cancer patient can be visually shown, the established model is good in apatinib medication data applicability of the ovarian cancer first stage, and an analysis method and basis can be provided for apatinib medication data analysis of the ovarian cancer first stage patient.

Description

Technical Field

[0001] This invention relates to the field of pharmaceutical data processing technology, and in particular to an apatinib medication monitoring and information analysis system for ovarian cancer patients. Background Technology

[0002] Ovarian cancer is a malignant tumor on a woman's ovary. The main treatment methods are generally surgical cure, chemotherapy, and targeted drug therapy. Among them, targeted drug therapy uses drugs to target specific locations on the tumor. The specificity of targeted drugs does not damage normal cells, and they are widely used in the treatment of cancer. Apatinib is a frequently chosen targeted drug for ovarian cancer.

[0003] Apatinib is a poly(adenosine diphosphate ribose) polymerase (PDGFR) inhibitor that can inhibit tumor angiogenesis and growth, and can act on the PARP enzyme catalytic gene site to induce apoptosis in cancer cells. However, the efficacy of apatinib in ovarian cancer patients is affected by individual physiological factors, resulting in different pharmacodynamic effects. Furthermore, with prolonged use and drug abuse, drug resistance can develop, severely affecting the efficacy of apatinib. Therefore, monitoring apatinib use in ovarian cancer patients is particularly important.

[0004] Current research on apatinib monitoring in stage I, II, and III ovarian cancer patients only provides bedside care recommendations based on ovarian cancer phenotypic symptoms. It lacks systematic and scientific data supporting the changes in apatinib efficacy across these stages. However, studies have found that proteomic microarrays can screen for cancer metabolic markers, survival function data can show the proportion of surviving individuals at a given time point, and Karnofsky Performance Status (KPS) scores can define the patient's overall functional quality. Therefore, combining proteomic microarrays, survival functions, and KPS scores can observe the drug's impact on the body and provide research data for ovarian cancer patients undergoing apatinib treatment at different stages.

[0005] Based on the above characteristics, this study combines apatinib use data for ovarian cancer with similarity coefficients of survival function data and Karnofsky Performance Status scores with risk coefficients of differentially expressed protein genes related to the efficacy of apatinib in ovarian cancer, providing an apatinib use monitoring information analysis system for ovarian cancer patients. Summary of the Invention

[0006] The purpose of this invention is to provide an apatinib medication monitoring and information analysis system for ovarian cancer patients. The apatinib efficacy data acquisition module for ovarian cancer patients can quickly screen physiological change data of apatinib in phases I, II, and III. The blood testing module and proteomic chip module combined with apatinib administration can establish a high-quality apatinib medication monitoring information model for ovarian cancer patients. The apatinib medication model validation module for ovarian cancer patients validates the obtained information model, providing a clear indication of the model's quality. Furthermore, the apatinib medication data for phase I ovarian cancer patients shows good applicability, significant data similarity, and high sensitivity, providing analytical methods and basis for apatinib medication data analysis in phase I ovarian cancer patients.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: An apatinib medication monitoring and information analysis system for ovarian cancer patients includes an apatinib efficacy data acquisition module for ovarian cancer patients, a blood testing module after apatinib administration in ovarian cancer patients, a proteomic microarray module, an apatinib medication evaluation module for ovarian cancer, and an apatinib medication model validation module for ovarian cancer. The apatinib efficacy data acquisition module for ovarian cancer patients is used to summarize and classify apatinib efficacy data for ovarian cancer. A blood testing module for ovarian cancer patients after using apatinib is used to detect the expression levels of ovarian cancer markers and the content of immune cells in the blood. Proteomics microarray module analysis of the correlation between apatinib and differentially expressed proteins; The apatinib medication assessment module for ovarian cancer patients is used to evaluate the trend of apatinib efficacy changes in ovarian cancer patients and to construct an analysis model for apatinib medication monitoring information in ovarian cancer patients. The apatinib dosing model validation module for ovarian cancer patients is used to optimize the apatinib dosing monitoring information analysis model for ovarian cancer patients.

[0008] Furthermore, the apatinib efficacy data acquisition module for ovarian cancer patients classifies the efficacy of apatinib, collects physiological data changes in ovarian cancer patients using apatinib in phases I, II, and III, and collects clinical and physiological data of ovarian cancer patients from hospitals and published databases. The collected clinical and physiological data of ovarian cancer patients are screened and cleaned using R functions and divided into an internal information analysis validation set and an external information validation set. The internal information analysis validation set accounts for 80% of the total screened data, and the external information validation set accounts for 20% of the total data.

[0009] Furthermore, the apatinib efficacy data acquisition module for ovarian cancer patients used SPSS software to analyze the survival and mortality function data and the Karnofsky Performance Status (KPS) score, determining the correlation between the survival function data and the KPS score. KPS score data processing was performed, normalizing each KPS score by using the ratio of each KPS score to 100. The similarity coefficient between the survival function data and the KPS score was calculated by comparing the intersection of the survival function dataset and the number of elements in the constrained KPS score set with the union of the number of elements in the constrained KPS score set. This similarity coefficient was then imported into the regression analysis model of the survival function data and the KPS score. The formula for the similarity coefficient is: ; in, The ratio of the number of elements in the intersection of the survival function data and the Karnofsky function state after constraint to the sum of the number of elements in the union of the survival function data and the Karnofsky function state after constraint to the ... The value is between 0 and 1. A value of 0 indicates zero-level similarity. A value of 1 indicates a level 1 similarity. The set of elements of the survival function data. The set of elements after being constrained by the Karl von Scheres-Bauer functional state score. The number of elements in the intersection of the elements of the survival function data and the elements of the Karl von Schereschewsky function state score. The number of elements in the union of the element set of the survival function data and the element set after limiting the Karl von Schereschewsky functional state score.

[0010] Furthermore, a blood assay module was used in ovarian cancer patients after apatinib treatment to detect PARP mRNA expression levels and TILs. S Cellular content, of which PARP mRNA expression level was detected using real-time PCR, TIL S Cell count was determined using flow cytometry.

[0011] Furthermore, the method used in the proteomic chip module analysis of the correlation between apatinib and differentially expressed proteins in ovarian cancer patients was to detect the number of differentially expressed protein genes by indirect enzyme-linked immunosorbent assay (ELISA), then detect the expression level of the differentially expressed protein genes, and finally use the Cox proportional hazards model to analyze the risk coefficient between apatinib use and the number of differentially expressed protein genes in ovarian cancer patients.

[0012] Furthermore, the apatinib medication assessment module for ovarian cancer patients used the similarity coefficient between survival function data and Karnofsky Performance Status (KPS) scores, the risk coefficient of apatinib correlation with differentially expressed proteins, and apatinib dosage. A regression model for the apatinib medication assessment module for ovarian cancer patients was constructed using a multivariate nonlinear regression model. The apatinib medication monitoring model for ovarian cancer patients is as follows: ; Wherein, Y represents the analysis results of apatinib medication monitoring information in ovarian cancer patients. The similarity coefficient between the survival function data and the Karnofsky Performance Status (KPS) score. Risk factors for apatinib use and differentially expressed protein gene count in ovarian cancer patients. denoted as , where i is the sequence number of the differentially expressed protein gene, and n is the number of differentially expressed protein genes. The dosage of apatinib at different drug resistance stages is given, where t represents different drug resistance stages in ovarian cancer patients. To further specify the apatinib medication monitoring model for ovarian cancer patients, a Y value of 0-0.3 indicates high apatinib resistance, a Y value of 0.3-0.6 indicates moderate apatinib resistance, and a Y value of 0.6-1 indicates low apatinib resistance.

[0013] Furthermore, the data sources for the validation module of the apatinib dosing model for ovarian cancer patients were: internal validation using a summarized internal dataset of ovarian cancer patients with apatinib, external validation using an external information validation set, and ROC analysis for model quality detection.

[0014] In the apatinib medication monitoring and information analysis system for ovarian cancer patients, the apatinib efficacy data acquisition module is used to summarize and classify the efficacy data of apatinib in ovarian cancer patients. It filters and cleans samples suitable for analysis by the blood testing module and proteomics chip module after apatinib use. The blood testing module detects biomarkers and immune cells in the blood to corroborate the proteomics chip module's results. The proteomics chip module is used to screen for differentially expressed proteins related to apatinib use in ovarian cancer patients. Genetic analysis was conducted, and the results of blood tests following apatinib administration were compared to establish a risk coefficient between apatinib use and the number of differentially expressed protein genes in ovarian cancer patients. Data from the apatinib efficacy data acquisition module, blood tests following apatinib use, and proteomic microarray module were aggregated into the apatinib use assessment module for ovarian cancer patients to establish an apatinib use monitoring model. This model was then validated by the apatinib use model verification module for ovarian cancer patients, ultimately outputting an apatinib use monitoring information analysis model for ovarian cancer patients.

[0015] Therefore, the connection relationship between modules in the apatinib medication monitoring information analysis system for ovarian cancer patients is as follows: the apatinib efficacy data acquisition module for ovarian cancer patients is connected to the blood testing module and proteome chip module after ovarian cancer patients use apatinib; the apatinib efficacy data acquisition module for ovarian cancer patients, the blood testing module and proteome chip module after ovarian cancer patients use apatinib are connected to the apatinib medication evaluation module for ovarian cancer patients; and the apatinib medication evaluation module for ovarian cancer patients is connected to the apatinib medication model validation module for ovarian cancer patients.

[0016] This invention discloses an apatinib medication monitoring and information analysis system for ovarian cancer patients. The apatinib efficacy data acquisition module for ovarian cancer patients is based on phase I, II, and III data classification of apatinib efficacy in ovarian cancer patients. Using survival function data and Karnofsky Performance Status (KPS) scores as boundaries, it can clearly and efficiently complete data screening. Furthermore, the combination analysis of the similarity coefficients of survival function data and KPS scores with the risk coefficients of differentially expressed protein genes related to apatinib efficacy in ovarian cancer patients enables the rapid establishment of an apatinib medication monitoring information model for ovarian cancer patients. Most importantly, the apatinib medication monitoring and information analysis system for ovarian cancer patients established based on similarity and risk coefficients provides a novel and superior research data foundation for exploring the efficacy of apatinib in ovarian cancer patients. The apatinib medication data from phase I ovarian cancer patients shows good applicability, significant data similarity, and high sensitivity, providing analytical methods and basis for the analysis of apatinib medication data in phase I ovarian cancer patients.

[0017] The parts not covered in this method are the same as or can be implemented using existing technologies. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0019] The principles and features of the present invention are described below with reference to examples. The examples are only used to explain the present invention and are not intended to limit the scope of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Example

[0020] The data source for validating the apatinib dosing model in ovarian cancer patients was the aggregated internal dataset of ovarian cancer patients using apatinib for internal validation, followed by external validation using an external information validation set. After ROC analysis to check the model quality, the following steps were performed: (1) Using the apatinib efficacy data acquisition module for ovarian cancer patients, collect physiological data changes of ovarian cancer patients using apatinib in the first phase, and collect clinical manifestations and physiological data of ovarian cancer patients from hospitals and published databases; (2) The survival function and Karnofsky Performance Status score of patients with stage I ovarian cancer after using apatinib were analyzed using the similarity coefficient formula; (3) A blood testing module for ovarian cancer patients after apatinib administration was used to detect PARP mRNA expression levels and TILs. S Cellular content, of which PARP mRNA expression level was detected using real-time PCR, TIL S Cell count was determined using flow cytometry. The proteomic microarray analysis of the correlation between apatinib and differentially expressed proteins in ovarian cancer patients employed indirect enzyme-linked immunosorbent assay (ELISA) to detect the number of differentially expressed protein genes. By searching for genes among the differentially expressed proteins detected by indirect ELISA, it was verified that the PARP gene was detected by indirect ELISA, and TIL... S Increased cell count led to the apatinib treatment evaluation module for ovarian cancer patients. (5) The apatinib medication assessment module for ovarian cancer patients uses the similarity coefficient between survival function data and Karnofsky Performance Status (KPS) scores, the risk coefficient of apatinib correlation with differentially expressed proteins, and the apatinib dosage. A regression model for the apatinib medication assessment module for ovarian cancer patients is constructed using a multivariate nonlinear regression model. The apatinib medication monitoring model for ovarian cancer patients is as follows: ; Wherein, Y represents the analysis results of apatinib medication monitoring information in ovarian cancer patients. The similarity coefficient between the survival function data and the Karnofsky Performance Status (KPS) score. Risk factors for apatinib use and differentially expressed protein gene count in ovarian cancer patients. denoted as , where i is the sequence number of the differentially expressed protein gene, and n is the number of differentially expressed protein genes. The dosage of apatinib at different drug resistance stages is given, where t represents different drug resistance stages in ovarian cancer patients. Based on the Y-value defined in the apatinib medication monitoring model for ovarian cancer patients, the results of apatinib medication monitoring in phase I ovarian cancer patients were analyzed. The medication monitoring model was run on a sample of ovarian cancer patients whose efficacy of apatinib in phase I resulted in a Y-value of 0.83. Example

[0021] The data source for validating the apatinib dosing model in ovarian cancer patients was the aggregated internal dataset of ovarian cancer patients using apatinib for internal validation, followed by external validation using an external information validation set. After ROC analysis to check the model quality, the following steps were performed: (1) Using the apatinib efficacy data acquisition module for ovarian cancer patients, collect physiological data changes of ovarian cancer patients in the second phase of apatinib treatment, and collect clinical manifestations and physiological data of ovarian cancer patients from hospitals and published databases. (2) The survival function and Karnofsky Performance Status score of stage II ovarian cancer patients after using apatinib were analyzed using the similarity coefficient formula; (3) A blood testing module for ovarian cancer patients after apatinib administration was used to detect PARP mRNA expression levels and TILs. S Cellular content, of which PARP mRNA expression level was detected using real-time PCR, TIL S Cell count was determined using flow cytometry. The proteomic microarray analysis of the correlation between apatinib and differentially expressed proteins in ovarian cancer patients employed indirect enzyme-linked immunosorbent assay (ELISA) to detect the number of differentially expressed protein genes. By searching for genes among the differentially expressed proteins detected by indirect ELISA, it was verified that the PARP gene was detected by indirect ELISA, and TIL... S Increased cell count led to the apatinib treatment evaluation module for ovarian cancer patients. (5) The apatinib medication assessment module for ovarian cancer patients uses the similarity coefficient between survival function data and Karnofsky Performance Status (KPS) scores, the risk coefficient of apatinib correlation with differentially expressed proteins, and the apatinib dosage. A regression model for the apatinib medication assessment module for ovarian cancer patients is constructed using a multivariate nonlinear regression model. The apatinib medication monitoring model for ovarian cancer patients is as follows: ; Wherein, Y represents the analysis results of apatinib medication monitoring information in ovarian cancer patients. The similarity coefficient between the survival function data and the Karnofsky Performance Status (KPS) score. Risk factors for apatinib use and differentially expressed protein gene count in ovarian cancer patients. denoted as , where i is the sequence number of the differentially expressed protein gene, and n is the number of differentially expressed protein genes. The dosage of apatinib at different drug resistance stages is given, where t represents different drug resistance stages in ovarian cancer patients. Based on the Y-value defined in the apatinib medication monitoring model for ovarian cancer patients, the results of apatinib medication monitoring in stage II ovarian cancer patients were analyzed. The medication monitoring model was run on the sample of ovarian cancer patients with apatinib efficacy in stage II, and the Y-value was 0.62. At this point, the analysis results of apatinib medication monitoring information for ovarian cancer patients exceeded the limit. ROC was used to test the model quality, and the ROC score was 0.65. Further external information validation of apatinib use in stage II ovarian cancer patients was performed, and the validation results showed large discrepancies. Example

[0022] Following the above steps, physiological information of patients with stage III ovarian cancer using apatinib was collected. The results were analyzed by combining the similarity coefficients of survival function data and Karnofsky Performance Status scores with the risk coefficients of differentially expressed protein genes showing the efficacy of apatinib in ovarian cancer. The Y-value was 0.4. ROC analysis was used to assess model quality, and the ROC score was lower than that of patients with stage II ovarian cancer using apatinib.

[0023] In summary, the model of the apatinib medication monitoring information analysis system for ovarian cancer patients proposed in this invention has good applicability to apatinib medication data in stage I ovarian cancer, with significant data similarity and high sensitivity, and can provide analytical methods and basis for apatinib medication data analysis in stage I ovarian cancer patients.

[0024] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An apatinib medication monitoring and information analysis system for ovarian cancer patients, comprising an apatinib efficacy data acquisition module for ovarian cancer patients, a blood testing module for ovarian cancer patients after using apatinib, a proteomic chip module, an apatinib medication evaluation module, and an apatinib medication model validation module; The efficacy data acquisition module uses SPSS software to analyze the survival and mortality function data and the Karnofsky Performance Status (KPS) scores based on the survival function data and KPS scores. This analysis determines the correlation between the survival function data and the KPS scores. KPS data processing is then performed, normalizing each KPS score by using the ratio of each KPS score to 100. The similarity coefficient between the survival function data and the KPS score is calculated by comparing the intersection of the number of elements in the survival function dataset and the number of elements in the KPS score set with the union of the number of elements in the survival function dataset and the KPS score set. This similarity coefficient is then imported into the regression analysis model of the survival function data and the KPS scores. The formula for the similarity coefficient is: ； in, The ratio of the number of elements in the intersection of the survival function data and the Karnofsky function state after constraint to the sum of the number of elements in the union of the survival function data and the Karnofsky function state after constraint to the ... The value is between 0 and 1. A value of 0 indicates zero-level similarity. A value of 1 indicates a level 1 similarity. The set of elements of the survival function data. The set of elements after being constrained by the Karl von Scheres-Bauer functional state score. The number of elements in the intersection of the elements of the survival function data and the elements of the Karl von Schereschewsky function state score. The number of elements in the union of the element set of the survival function data and the element set after limiting the Karl von Schereschewsky functional state score.

2. The apatinib medication monitoring and information analysis system for ovarian cancer patients according to claim 1, characterized in that, The apatinib efficacy data acquisition module for ovarian cancer patients is used to summarize and classify the efficacy data of apatinib for ovarian cancer patients. A blood testing module for ovarian cancer patients after using apatinib is used to detect the expression levels of ovarian cancer markers and the content of immune cells in the blood. Proteomics microarray module analysis of the correlation between apatinib and differentially expressed proteins; The apatinib medication assessment module for ovarian cancer patients is used to evaluate the trend of apatinib efficacy changes in ovarian cancer patients and to construct an analysis model for apatinib medication monitoring information in ovarian cancer patients. The apatinib dosing model validation module for ovarian cancer patients is used to optimize the apatinib dosing monitoring information analysis model for ovarian cancer patients.

3. The apatinib medication monitoring and information analysis system for ovarian cancer patients according to claim 1, characterized in that, The apatinib efficacy data acquisition module for ovarian cancer patients classifies the efficacy of apatinib and collects physiological data changes in ovarian cancer patients during phase I, II, and III of apatinib treatment. It also collects clinical and physiological data of ovarian cancer patients from hospitals and published databases. The module uses R functions to filter and clean the collected clinical and physiological data, dividing it into an internal information analysis validation set and an external information validation set. The internal information analysis validation set accounts for 80% of the total filtered data, while the external information validation set accounts for 20%.

4. The apatinib medication monitoring and information analysis system for ovarian cancer patients according to claim 1, characterized in that, The blood test module for detecting the mRNA expression of PARP and TIL S cell content, wherein the mRNA expression of PARP is detected using a fluorescent quantitative PCR technique, and the TIL S cell content is detected using a flow cytometry technique.

5. The apatinib medication monitoring and information analysis system for ovarian cancer patients according to claim 1, characterized in that, The method used by the proteomic chip module to analyze the correlation between apatinib and differentially expressed proteins in ovarian cancer patients was as follows: indirect enzyme-linked immunosorbent assay (ELISA) was used to detect the number of differentially expressed protein genes, followed by the detection of the expression level of the differentially expressed protein genes. Then, the Cox proportional hazards model was used to analyze the risk coefficient between apatinib use and the number of differentially expressed protein genes in ovarian cancer patients.

6. The apatinib medication monitoring and information analysis system for ovarian cancer patients according to claim 1, characterized in that, The apatinib medication assessment module for ovarian cancer patients uses the similarity coefficient between survival function data and Karnofsky Performance Status (KPS) scores, the risk coefficient of apatinib correlation with differentially expressed proteins, and apatinib dosage. A regression model for the apatinib medication assessment module for ovarian cancer patients is constructed using a multivariate nonlinear regression model. The apatinib medication monitoring model for ovarian cancer patients is as follows: ； Wherein, Y represents the analysis results of apatinib medication monitoring information in ovarian cancer patients. The similarity coefficient between the survival function data and the Karnofsky Performance Status (KPS) score. Risk factors for apatinib use and differentially expressed protein gene count in ovarian cancer patients. denoted as , where i is the sequence number of the differentially expressed protein gene, and n is the number of differentially expressed protein genes. The values ​​represent the dosage of apatinib at different stages of drug resistance, t represents different stages of drug resistance in ovarian cancer patients, Y values ​​of 0-0.3 indicate high resistance to apatinib, Y values ​​of 0.3-0.6 indicate moderate resistance to apatinib, and Y values ​​of 0.6-1 indicate low resistance to apatinib.

7. The apatinib medication monitoring and information analysis system for ovarian cancer patients according to claim 1, characterized in that, The apatinib efficacy data acquisition module for ovarian cancer patients is connected to the blood testing module and proteomic chip module after ovarian cancer patients use apatinib. The apatinib efficacy data acquisition module for ovarian cancer patients, the blood testing module and proteomic chip module after ovarian cancer patients use apatinib are connected to the apatinib use evaluation module for ovarian cancer patients. The apatinib use evaluation module for ovarian cancer patients is connected to the apatinib use model validation module for ovarian cancer patients.

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