System and method for evaluating risk of social activity on breast cancer through Mendel randomization

Abstract

The invention relates to the technical field of artificial intelligence prediction and evaluation, and discloses a Mendel randomization evaluation system and method for risk of social activities to breast cancer, the system comprises a data acquisition module, a data processing module, a double-sample Mendel randomization analysis module and a result output module; the data acquisition module serves as an information source of the whole system, has the capability of accurately collecting data from a diversified and high-quality data source, and can retrieve genetic polymorphic data related to social isolation and interaction from a public or private database. According to the system and method for evaluating the risk of the social activity on the breast cancer through Mendel randomization, by integrating multi-source genetic data and applying a double-sample Mendel randomization analysis technology, the problems of confounding factors and inherent deviation in traditional epidemiological research are solved, and the risk of the social activity on the breast cancer is evaluated. According to the system, the potential causal relationship between the breast cancer and social activities such as social isolation and social interaction can be accurately evaluated, and a reliable analysis result is provided for a user.

Description

Technical Field

[0001] The present invention relates to the technical field of artificial intelligence prediction and evaluation, and specifically to a system and method for evaluating the risk of breast cancer by social activities using Mendelian randomization. Background Art

[0002] Breast Cancer, also known as breast cancer, is a malignant tumor caused by the uncontrolled proliferation of breast epithelial cells. Genetic factors, reproductive factors, sex hormones, diet and obesity are all high-risk factors for breast cancer. It mainly occurs in the breast glandular epithelial tissue, seriously affecting women's physical and mental health, and even endangering their lives.

[0003] Breast cancer is a major threat to women's health. Its pathogenesis is complex and involves multiple factors. Traditional epidemiological techniques face challenges in determining the relationship between social factors such as social isolation and social interaction and breast cancer because they cannot completely eliminate confounding factors and inherent biases. Therefore, a Mendelian randomization system and method for evaluating the risk of breast cancer in social activities is proposed to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a system and method for evaluating the effect of social activities on breast cancer risk using Mendelian randomization, so as to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a system for evaluating the risk of breast cancer by social activities using Mendelian randomization, the system comprising a data acquisition module, a data processing module, a two-sample Mendelian randomization analysis module and a result output module;

[0006] As the information source of the entire system, the data acquisition module has the ability to accurately collect data from diversified and high-quality data sources. It can not only retrieve genetic polymorphism data related to social isolation and interaction from public or private databases, but also obtain comprehensive genetic information of breast cancer patients from relevant databases or research institutions, including but not limited to key data such as gene mutations and single nucleotide polymorphisms. To achieve this goal, the data acquisition module has built-in genetic polymorphism database interfaces and breast cancer genetic database interfaces to ensure the accuracy and completeness of the data.

[0007] The data processing module is responsible for preprocessing the collected data, including data cleaning, data integration and data formatting, wherein data cleaning is used to remove duplicate, erroneous or missing data, data integration is used to merge data from different sources into a unified data set, and data formatting is used to convert data into the format required by the appropriate analysis software or algorithm. The data processing module is also equipped with a data quality control unit and a data standardization unit. The data quality control unit is responsible for detecting and correcting errors and outliers in the data to ensure the accuracy and consistency of the data, and the data standardization unit converts data from different sources into a unified format to facilitate subsequent analysis and comparison;

[0008] The two-sample Mendelian randomization analysis module is responsible for performing a two-sample Mendelian randomization analysis using the preprocessed data to evaluate the potential causal relationship between social isolation, social interaction and breast cancer. The two-sample Mendelian randomization analysis module uses a variety of statistical methods, including but not limited to the inverse variance weighted method, the MR Egger method and the weighted median method, to improve the accuracy and reliability of the analysis;

[0009] The result output module is responsible for presenting the analysis results in a user-friendly manner, which may include generating charts, reports and explanatory texts. These outputs help users understand the degree of association between social activities and breast cancer risk, as well as the significance of these associations.

[0010] Preferably, the data acquisition module also has the following deepening functions: through the genetic polymorphism database interface, the system can automatically retrieve genetic polymorphism data related to social isolation and interaction from multiple public or private databases, and perform intelligent screening and integration. At the same time, the breast cancer genetic database interface can intelligently obtain comprehensive genetic information of breast cancer patients from relevant databases or research institutions, including key data such as gene mutations and single nucleotide polymorphisms, and perform in-depth mining and analysis.

[0011] Preferably, the data processing module also includes the following refined processing functions: the data quality control unit uses advanced algorithms and technologies to perform comprehensive quality detection and correction on the data to ensure the accuracy and consistency of the data; the data standardization unit can convert data from different sources and in different formats into a unified format to facilitate subsequent analysis and comparison.

[0012] Preferably, the two-sample Mendelian randomization analysis module also has the following characteristics: it uses at least one of the inverse variance weighted method, the MR Egger method and the weighted median method for analysis, and the selection and combination of these methods are based on the characteristics of the data and the analysis objectives. At the same time, the module also has the ability to perform sensitivity analysis and robustness testing on the analysis results to further verify and optimize the analysis results.

[0013] Preferably, the result output module also has the following intelligent functions: the visualization unit uses advanced chart and graphic display technology to intuitively present key information of the analysis results, such as correlation degree, confidence interval and p-value, and supports users to customize chart styles and layouts; the explanatory text generation unit uses natural language processing technology to automatically generate explanatory text for the analysis results, including the meaning, importance and possible biological explanations of the results, etc., to provide users with valuable reference information.

[0014] The Mendelian randomization method for evaluating the effect of social activities on breast cancer risk includes the following steps: S1: Data collection and integration: Comprehensively collect genetic polymorphism data related to social isolation and interaction and genetic information of breast cancer patients from multiple high-quality data sources, and perform intelligent screening and integration to form a complete data set;

[0015] S2: Data preprocessing and quality control: The collected data is carefully preprocessed, including data cleaning, integration, formatting and quality control processes. Data cleaning is used to remove duplicate, erroneous or missing data. Data integration is used to merge data from different sources into a unified data set. Data formatting is used to convert data into a format suitable for subsequent analysis. Quality control is used to detect and correct errors and outliers in the data to ensure the accuracy and consistency of the data.

[0016] S3: Feature extraction and dimensionality reduction: Extract features and reduce dimensionality of preprocessed data to simplify data and improve analysis efficiency. This step may include the application of principal component analysis, cluster analysis and other methods;

[0017] S4: Two-sample Mendelian randomization analysis: Using the high-quality data after preprocessing, perform a complex two-sample Mendelian randomization analysis to reveal the potential causal relationship between social isolation, social interaction and breast cancer. This step may include the application of multiple statistical methods such as the inverse variance weighting method, MR Egger method and weighted median method;

[0018] S5: Result verification and optimization: Verify and optimize the analysis results to improve the accuracy and reliability of the analysis. This step may include sensitivity analysis, robustness testing, and the application of machine learning algorithms.

[0019] S6: Result output and interpretation: Present the analysis results to the user in an intuitive and understandable way. This step may include generating output forms such as charts, reports, and explanatory texts. Charts and reports are used to intuitively display the key information of the analysis results, and explanatory texts are used to explain and illustrate the analysis results in detail, helping users to deeply understand the complex relationship between social activities and breast cancer risk.

[0020] Preferably, the preprocessing in step S2 also includes strict control and standardization of data quality, ensuring the accuracy and consistency of the data through comprehensive quality testing and correction. At the same time, data from different sources and in different formats are converted into a unified format to facilitate subsequent analysis and comparison.

[0021] Preferably, the two-sample Mendelian randomization analysis in step S4 is analyzed using at least one of the inverse variance weighted method, the MR Egger method and the weighted median method. The selection and combination of these methods are based on the characteristics of the data and the analysis objectives. At the same time, this step also has the ability to perform sensitivity analysis and robustness testing on the analysis results to further verify and optimize the analysis results.

[0022] Preferably, the output results in step S6 include not only the degree of association between five social activities, such as frequent attendance at bars and social clubs, frequent attendance at sports clubs and gyms, loneliness and solitude (MTAG), and breast cancer risk, the corresponding confidence intervals and p-values ​​and other key information, but may also cover other complex factors related to social activities and their impact on breast cancer risk. At the same time, the explanatory text provides a detailed explanation and description of the analysis results, including the meaning, importance and possible biological explanations of the results, etc., to provide users with valuable reference information.

[0023] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0024] The present invention integrates multi-source genetic data and uses two-sample Mendelian randomization analysis technology to effectively overcome the problems of confounding factors and inherent bias in traditional epidemiological studies. The system can accurately evaluate the potential causal relationship between social activities such as social isolation and social interaction and breast cancer, and provide users with reliable analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of the overall evaluation method of the present invention;

[0026] Figure 2 It is a data processing flow chart of the present invention;

[0027] Figure 3 The figure is a flow chart of the two-sample Mendelian randomization analysis of the present invention;

[0028] Figure 4 Output a flow chart for the results of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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 creative work are within the scope of protection of the present invention.

[0030] The present invention provides the following technical solutions:

[0031] Embodiment 1

[0032] A system for evaluating the effect of social activities on breast cancer risk by Mendelian randomization, the system comprising a data acquisition module, a data processing module, a two-sample Mendelian randomization analysis module, and a result output module;

[0033] As the information source of the entire system, the data acquisition module has the ability to accurately collect data from diversified and high-quality data sources. It can not only retrieve genetic polymorphism data related to social isolation and interaction from public or private databases, but also obtain comprehensive genetic information of breast cancer patients from relevant databases or research institutions, including but not limited to key data such as gene mutations and single nucleotide polymorphisms. To achieve this goal, the data acquisition module has built-in genetic polymorphism database interfaces and breast cancer genetic database interfaces to ensure the accuracy and completeness of the data;

[0034] The data processing module is responsible for preprocessing the collected data, including data cleaning, data integration and data formatting. Data cleaning is used to remove duplicate, erroneous or missing data, data integration is used to merge data from different sources into a unified data set, and data formatting is used to convert data into the format required by the appropriate analysis software or algorithm. The data processing module is also equipped with a data quality control unit and a data standardization unit. The data quality control unit is responsible for detecting and correcting errors and outliers in the data to ensure the accuracy and consistency of the data, and the data standardization unit converts data from different sources into a unified format to facilitate subsequent analysis and comparison;

[0035] The two-sample Mendelian randomization analysis module is responsible for using the preprocessed data to perform a two-sample Mendelian randomization analysis to evaluate the potential causal relationship between social isolation, social interaction and breast cancer. The two-sample Mendelian randomization analysis module uses a variety of statistical methods, including but not limited to the inverse variance weighted method, MR Egger method and weighted median method, to improve the accuracy and reliability of the analysis;

[0036] The result output module is responsible for presenting the analysis results in a user-friendly manner, which may include generating charts, reports, and explanatory texts. These outputs help users understand the extent of the association between social activities and breast cancer risk, as well as the significance of these associations.

[0037] The data acquisition module also has the following in-depth functions: through the genetic polymorphism database interface, the system can automatically retrieve genetic polymorphism data related to social isolation and interaction from multiple public or private databases, and perform intelligent screening and integration. At the same time, the breast cancer genetic database interface can intelligently obtain comprehensive genetic information of breast cancer patients from relevant databases or research institutions, including key data such as gene mutations and single nucleotide polymorphisms, and perform in-depth mining and analysis.

[0038] The data processing module also includes the following refined processing functions: the data quality control unit uses advanced algorithms and technologies to conduct comprehensive quality detection and correction of the data to ensure the accuracy and consistency of the data; the data standardization unit can convert data from different sources and in different formats into a unified format, facilitating subsequent analysis and comparison.

[0039] The two-sample Mendelian randomization analysis module also has the following features: it uses at least one of the inverse variance weighted method, MREgger method and weighted median method for analysis. The selection and combination of these methods are based on the characteristics of the data and the analysis objectives. At the same time, the module also has the ability to perform sensitivity analysis and robustness testing on the analysis results to further verify and optimize the analysis results.

[0040] The result output module also has the following intelligent functions: the visualization unit uses advanced chart and graphic display technology to intuitively present key information of the analysis results, such as correlation degree, confidence interval and p-value, and supports users to customize chart styles and layouts. The explanatory text generation unit uses natural language processing technology to automatically generate explanatory text for the analysis results, including the meaning, importance and possible biological explanations of the results, providing users with valuable reference information.

[0041] Embodiment 2

[0042] The Mendelian randomization method for assessing the effect of social activities on breast cancer risk included the following steps:

[0043] S1: Data collection and integration: Comprehensively collect genetic polymorphism data related to social isolation and interaction and genetic information of breast cancer patients from multiple high-quality data sources, and intelligently screen and integrate them to form a complete data set;

[0044] S2: Data preprocessing and quality control: The collected data is carefully preprocessed, including data cleaning, integration, formatting and quality control processes. Data cleaning is used to remove duplicate, erroneous or missing data. Data integration is used to merge data from different sources into a unified data set. Data formatting is used to convert data into a format suitable for subsequent analysis. Quality control is used to detect and correct errors and outliers in the data to ensure the accuracy and consistency of the data.

[0045] S3: Feature extraction and dimensionality reduction: Extract features and reduce dimensionality of preprocessed data to simplify data and improve analysis efficiency. This step may include the application of principal component analysis, cluster analysis and other methods;

[0046] S4: Two-sample Mendelian randomization analysis: Using the high-quality data after preprocessing, perform a complex two-sample Mendelian randomization analysis to reveal the potential causal relationship between social isolation, social interaction and breast cancer. This step may include the application of multiple statistical methods such as the inverse variance weighting method, MR Egger method and weighted median method;

[0047] S5: Result verification and optimization: Verify and optimize the analysis results to improve the accuracy and reliability of the analysis. This step may include sensitivity analysis, robustness testing, and the application of machine learning algorithms.

[0048] S6: Result output and interpretation: Present the analysis results to the user in an intuitive and understandable way. This step may include generating output forms such as charts, reports, and explanatory texts. Charts and reports are used to intuitively display the key information of the analysis results, and explanatory texts are used to explain and illustrate the analysis results in detail, helping users to deeply understand the complex relationship between social activities and breast cancer risk.

[0049] Specifically, the preprocessing in step S2 also includes strict control and standardization of data quality. Through comprehensive quality testing and correction, the accuracy and consistency of the data are ensured. At the same time, data from different sources and in different formats are converted into a unified format to facilitate subsequent analysis and comparison.

[0050] Through the above technical solutions, strict control of data quality is the key to ensuring the accuracy of the final analysis results. This step not only involves removing duplicate, erroneous or missing data, but more deeply, it also includes the detection and correction of errors and outliers in the data. Erroneous data may come from clerical errors during data entry, equipment failure or distortion in data transmission, while outliers may be caused by sample specificity, measurement errors or extreme events. By applying statistical methods and data cleaning algorithms, such as conditional screening, data smoothing, interpolation or machine learning-based anomaly detection models, we can effectively identify and correct these problem data, thereby maintaining the integrity and reliability of the data set.

[0051] Specifically, the two-sample Mendelian randomization analysis in step S4 uses at least one of the inverse variance weighted method, the MR Egger method and the weighted median method for analysis. The selection and combination of these methods are based on the characteristics of the data and the analysis objectives. At the same time, this step also has the ability to perform sensitivity analysis and robustness testing on the analysis results to further verify and optimize the analysis results.

[0052] Through the above technical scheme, the inverse variance weighting method improves the statistical power of the analysis by assigning a weight to each genetic variant based on the accuracy of its effect estimate and integrating the results of multiple genetic variants. This method is particularly suitable when the strength of association between most genetic variants and conditions such as social activities and breast cancer is similar; the MR Egger method specifically considers the potential horizontal pleiotropy problem, that is, genetic variants may affect multiple phenotypes at the same time, including variables other than exposure variables and outcome variables. By introducing a regression model, the MR Egger method can reduce this bias and provide a more robust causal effect estimate; the weighted median method is a more conservative analysis strategy that focuses on calculating the median of all genetic variant effect estimates, thereby reducing the impact of extreme values ​​on the overall results. This method is particularly useful when there are a few strong-effect genetic variants or when there is large heterogeneity in the data.

[0053] Specifically, the output results in step S6 include not only key information such as the degree of association between five social activities, such as frequent attendance at bars and social clubs, frequent attendance at sports clubs and gyms, loneliness and solitude (MTAG), and breast cancer risk, the corresponding confidence intervals and p-values, but may also cover other complex factors related to social activities and their impact on breast cancer risk. At the same time, the explanatory text provides a detailed explanation and description of the analysis results, including the meaning, importance and possible biological explanations of the results, etc., to provide users with valuable reference information.

[0054] Through the above technical scheme, this study conducted a two-sample MR analysis based on the sample size (genetic diversity sample size 302,567-487,647, BC sample size 212,402), and found that frequent visits to bars and social clubs were the only social participation activities associated with an increased risk of breast cancer (BC). The IVW OR of frequent visits to bars and social clubs was 1.11 per standard deviation change, with a 95% confidence interval of 1.03-1.19 and a P value of 0.006. The output results not only covered the association between frequent visits to bars and social clubs, frequent visits to sports clubs and gyms and the risk of breast cancer, but also considered the impact of the subjective feeling of loneliness (represented by the loneliness genetic risk score MTAG) on the risk of breast cancer. These output results are presented in intuitive graphical forms (such as scatter plots, forest plots or bar graphs) and detailed explanatory texts to help users quickly understand the strength and direction of the association between each social activity and the risk of breast cancer, as well as the statistical significance of this association.

[0055] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit thereof, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A Mendelian randomization system for assessing the effect of social activities on breast cancer risk, characterized by: The system includes a data acquisition module, a data processing module, a two-sample Mendelian randomization analysis module and a result output module; As the information source of the entire system, the data acquisition module has the ability to accurately collect data from diversified and high-quality data sources. It can not only retrieve genetic polymorphism data related to social isolation and interaction from public or private databases, but also obtain comprehensive genetic information of breast cancer patients from relevant databases or research institutions, including but not limited to key data such as gene mutations and single nucleotide polymorphisms. To achieve this goal, the data acquisition module has built-in genetic polymorphism database interfaces and breast cancer genetic database interfaces to ensure the accuracy and completeness of the data. The data processing module is responsible for preprocessing the collected data, including data cleaning, data integration and data formatting, wherein data cleaning is used to remove duplicate, erroneous or missing data, data integration is used to merge data from different sources into a unified data set, and data formatting is used to convert data into the format required by the appropriate analysis software or algorithm. The data processing module is also equipped with a data quality control unit and a data standardization unit. The data quality control unit is responsible for detecting and correcting errors and outliers in the data to ensure the accuracy and consistency of the data, and the data standardization unit converts data from different sources into a unified format to facilitate subsequent analysis and comparison; The two-sample Mendelian randomization analysis module is responsible for performing a two-sample Mendelian randomization analysis using the preprocessed data to evaluate the potential causal relationship between social isolation, social interaction and breast cancer. The two-sample Mendelian randomization analysis module uses a variety of statistical methods, including but not limited to the inverse variance weighted method, the MR Egger method and the weighted median method, to improve the accuracy and reliability of the analysis; The result output module is responsible for presenting the analysis results in a user-friendly manner, which may include generating charts, reports and explanatory texts. These outputs help users understand the degree of association between social activities and breast cancer risk, as well as the significance of these associations.

2. The Mendelian randomization system for assessing the effect of social activities on breast cancer risk according to claim 1, characterized in that: The data acquisition module also has the following deepening functions: through the genetic polymorphism database interface, the system can automatically retrieve genetic polymorphism data related to social isolation and interaction from multiple public or private databases, and perform intelligent screening and integration. At the same time, the breast cancer genetic database interface can intelligently obtain comprehensive genetic information of breast cancer patients from relevant databases or research institutions, including key data such as gene mutations and single nucleotide polymorphisms, and perform in-depth mining and analysis.

3. The Mendelian randomization system for assessing the effect of social activities on breast cancer risk according to claim 1, characterized in that: The data processing module also includes the following refined processing functions: the data quality control unit uses advanced algorithms and technologies to perform comprehensive quality detection and correction on the data to ensure the accuracy and consistency of the data; the data standardization unit can convert data from different sources and in different formats into a unified format to facilitate subsequent analysis and comparison.

4. The Mendelian randomization system for assessing the effect of social activities on breast cancer risk according to claim 1, characterized in that: The two-sample Mendelian randomization analysis module also has the following characteristics: it uses at least one of the inverse variance weighted method, the MR Egger method and the weighted median method for analysis. The selection and combination of these methods are based on the characteristics of the data and the analysis objectives. At the same time, the module also has the ability to perform sensitivity analysis and robustness testing on the analysis results to further verify and optimize the analysis results.

5. The Mendelian randomization system for assessing the effect of social activities on breast cancer risk according to claim 1, characterized in that: The result output module also has the following intelligent functions: the visualization unit uses advanced chart and graphic display technology to intuitively present key information of the analysis results, such as correlation degree, confidence interval and p-value, and supports users to customize chart styles and layouts. The explanatory text generation unit uses natural language processing technology to automatically generate explanatory text for the analysis results, including the meaning, importance and possible biological explanations of the results, providing users with valuable reference information.

6. The method for evaluating the effect of social activities on breast cancer risk by Mendelian randomization according to claim 1, characterized in that: The Method The following steps are involved: S1: Data collection and integration: Comprehensively collect genetic polymorphism data related to social isolation and interaction and genetic information of breast cancer patients from multiple high-quality data sources, and intelligently screen and integrate them to form a complete data set; S2: Data preprocessing and quality control: The collected data is carefully preprocessed, including data cleaning, integration, formatting and quality control processes. Data cleaning is used to remove duplicate, erroneous or missing data. Data integration is used to merge data from different sources into a unified data set. Data formatting is used to convert data into a format suitable for subsequent analysis. Quality control is used to detect and correct errors and outliers in the data to ensure the accuracy and consistency of the data. S3: Feature extraction and dimensionality reduction: Extract features and reduce dimensionality of preprocessed data to simplify data and improve analysis efficiency. This step may include the application of principal component analysis, cluster analysis and other methods; S4: Two-sample Mendelian randomization analysis: Using the high-quality data after preprocessing, perform a complex two-sample Mendelian randomization analysis to reveal the potential causal relationship between social isolation, social interaction and breast cancer. This step may include the application of multiple statistical methods such as the inverse variance weighting method, MR Egger method and weighted median method; S5: Result verification and optimization: Verify and optimize the analysis results to improve the accuracy and reliability of the analysis. This step may include sensitivity analysis, robustness testing, and the application of machine learning algorithms. S6: Result output and interpretation: Present the analysis results to the user in an intuitive and understandable way. This step may include generating output forms such as charts, reports, and explanatory texts. Charts and reports are used to intuitively display the key information of the analysis results, and explanatory texts are used to explain and illustrate the analysis results in detail, helping users to deeply understand the complex relationship between social activities and breast cancer risk.

7. The method for evaluating the effect of social activities on breast cancer risk by Mendelian randomization according to claim 6, characterized in that: The preprocessing in step S2 also includes strict control and standardization of data quality. Through comprehensive quality testing and correction, the accuracy and consistency of the data are ensured. At the same time, data from different sources and in different formats are converted into a unified format to facilitate subsequent analysis and comparison.

8. The method for evaluating the effect of social activities on breast cancer risk by Mendelian randomization according to claim 6, characterized in that: The two-sample Mendelian randomization analysis in step S4 is analyzed using at least one of the inverse variance weighted method, the MR Egger method, and the weighted median method. The selection and combination of these methods are based on the characteristics of the data and the analysis objectives. At the same time, this step also has the ability to perform sensitivity analysis and robustness testing on the analysis results to further verify and optimize the analysis results.

9. The method for evaluating the effect of social activities on breast cancer risk by Mendelian randomization according to claim 6, characterized in that: The output results in step S6 include not only the key information such as the degree of association between five social activities, such as frequent attendance at bars and social clubs, frequent attendance at sports clubs and gyms, loneliness and solitude (MTAG), and breast cancer risk, the corresponding confidence intervals and p-values, but may also cover other complex factors related to social activities and their impact on breast cancer risk. At the same time, the explanatory text provides a detailed explanation and description of the analysis results, including the meaning, importance and possible biological explanations of the results, etc., to provide users with valuable reference information.

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