Colorectal cancer genetic data analysis and interpretation system

Abstract

The invention relates to the technical field of data analysis and interpretation, in particular to a colorectal cancer genetic data analysis and interpretation system, which comprises the steps of collecting various types of genetic data from a colorectal cancer patient, performing primary processing, identifying gene variation and biomarkers related to colorectal cancer, and analyzing and interpreting the colorectal cancer genetic data. The method comprises the following steps of: extracting potential genetic characteristics and correlation between the potential genetic characteristics and colorectal cancer onset risks, performing function annotation and interpretation on identified gene variation and biomarkers, and generating personalized treatment suggestions, including targeted treatment schemes, drug selection and prognosis prediction. According to the invention, the specific effect of the gene in colorectal cancer pathogenesis and the influence of the gene on disease progression are deeply analyzed, a targeted treatment scheme, drug selection and prognosis prediction are generated, and precise medical development of colorectal cancer is promoted. The high efficiency and accuracy of the system significantly improve the quality of genetic data analysis, and an important innovative tool is provided for early diagnosis, prognosis evaluation and personalized treatment of colorectal cancer.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis and interpretation, and in particular to a colorectal cancer genetic data analysis and interpretation system. Background Art

[0002] Colorectal cancer is a type of malignant tumor with a high incidence rate worldwide. Its pathogenesis is complex and involves multiple genetic and environmental factors. In recent years, with the rapid development of genomics and bioinformatics technologies, researchers have been able to gain a deeper understanding of the genetic basis of colorectal cancer. The development of technologies such as genome sequencing, single-cell sequencing, and epigenetics has provided powerful tools for studying the pathogenesis of colorectal cancer and finding potential therapeutic targets.

[0003] However, the inventors have discovered that this technical solution still has at least the following defects:

[0004] First: Although the present invention is significantly innovative in genetic data analysis and colorectal cancer research, there are still some potential defects. The application of deep learning and machine learning algorithms requires a large amount of labeled data for training and verification, while high-quality labeled data in actual clinical practice may be relatively limited, resulting in unsatisfactory model training results and affecting the overall performance and application effect of the system.

[0005] Second: Although the system integrates multi-omics data analysis and biological function annotation, in practical applications, the integration and association analysis between different omics data may encounter problems of algorithm adaptation and computational complexity. This invention has great potential in genetic data analysis and interpretation, but it still needs further optimization and improvement in data processing, algorithm training and information updating. Summary of the invention

[0006] Based on the above objectives, the present invention provides a colorectal cancer genetic data analysis and interpretation system.

[0007] The colorectal cancer genetic data analysis and interpretation system includes a data acquisition module, a data preprocessing module, a data analysis module, a pattern recognition module, an interpretation module, and a personalized treatment recommendation module, among which:

[0008] The data acquisition module collects various types of genetic data from colorectal cancer patients, including whole genome sequencing data, single cell sequencing data, and peripheral blood sample data;

[0009] The data preprocessing module performs preliminary processing on the collected genetic data, including data cleaning, standardization and format conversion;

[0010] The data analysis module identifies genetic variations and biomarkers associated with colorectal cancer;

[0011] The pattern recognition module performs pattern recognition on the integrated data to mine potential genetic characteristics and their association with the risk of colorectal cancer;

[0012] The interpretation module combines biomedical literature and databases to functionally annotate and interpret the identified gene variants and biomarkers;

[0013] The personalized treatment recommendation module generates personalized treatment recommendations based on the patient's genetic information and analysis results, including targeted treatment plans, drug selection, and prognosis prediction.

[0014] Optionally, the data acquisition module includes:

[0015] Sample collection: Tissue and blood samples were collected from patients with colorectal cancer;

[0016] Sample marking: uniquely identify and encode the collected samples;

[0017] DNA extraction: Extract genomic DNA from tissue samples and blood samples;

[0018] RNA extraction: Extract total RNA from tissue samples and blood samples;

[0019] Sample storage: Store the processed samples at low temperature;

[0020] Data transfer: Through a secure data transfer protocol, sample information and extracted DNA and RNA data are uploaded to a central database for storage and subsequent analysis.

[0021] Optionally, the data preprocessing module includes:

[0022] Noise filtering: using bioinformatics algorithms to identify and remove noise and outliers in sequencing data;

[0023] Missing value processing: fill in or remove missing values ​​in the data;

[0024] Quantitative standardization: convert data from different sources into a unified dimension; Data normalization: normalize the data so that each data point is in the same numerical range to improve the stability of the algorithm;

[0025] Data format conversion: convert the cleaned and standardized data into a unified standard format;

[0026] Metadata addition: Add necessary metadata tags to the converted data, including sample ID, collection time, and processing steps.

[0027] Optionally, the data analysis module includes:

[0028] Variant calling: Analyze the preprocessed genomic data using variant calling algorithms to identify single nucleotide variants and insertion and deletion variants;

[0029] Differential expression analysis: Statistical methods were used to perform differential expression analysis on RNA sequencing data to identify genes that were significantly up-regulated or down-regulated in colorectal cancer patients;

[0030] Co-expression network construction: Using co-expression network analysis methods, we constructed a gene co-expression network to identify key gene modules and biomarkers related to colorectal cancer;

[0031] Association studies: Genotype-phenotype association analysis combined with case-control data to identify genetic variants that are significantly associated with colorectal cancer risk;

[0032] Functional validation: Verify the biological functions and clinical relevance of identified gene variants and biomarkers through literature search and database comparison;

[0033] Multi-omics data integration: Integrate genomic, transcriptomic and epigenetic data to conduct multi-level comprehensive analysis and identify comprehensive biomarkers;

[0034] Pathway analysis: Pathway enrichment analysis was performed using bioinformatics tools to identify key biological pathways and networks associated with colorectal cancer.

[0035] Optionally, the multi-omics data integration includes:

[0036] Data normalization: Normalize genomic, transcriptomic and epigenetic data;

[0037] Data integration: A data fusion algorithm is used to integrate the standardized genome, transcriptome, and epigenetic group data to construct a unified multi-omics data matrix;

[0038] Feature extraction: Use the principal component analysis method to reduce the dimension of the integrated data matrix and extract the main features;

[0039] Cluster analysis: K-means clustering algorithm was used to perform cluster analysis on the data after dimensionality reduction to identify genes and samples with similar characteristics.

[0040] Optionally, the pattern recognition module includes:

[0041] Feature selection: Use feature selection algorithms to screen out the most representative genetic features from the integrated multi-omics data;

[0042] Feature construction: construct and combine the selected features to generate new feature vectors, and use dimensionality reduction technology to simplify the data structure;

[0043] Pattern training: Use deep learning algorithms to train the constructed feature vectors, identify potential patterns related to colorectal cancer through multi-layer neural networks, and consider the heterogeneity and complexity of samples during model training;

[0044] Model validation: The cross-validation method is used to validate the trained model and evaluate the accuracy and robustness of the model.

[0045] Optionally, the interpretation module includes:

[0046] Literature search: Use automated literature search tools to retrieve relevant literature from biomedical databases and screen research results related to the identified genetic variants and biomarkers;

[0047] Database comparison: compare the identified genetic variants and biomarkers with existing genetic databases and cancer databases to verify the accuracy and clinical relevance of the variants and markers;

[0048] Functional annotation: Based on the results of literature search and database comparison, functional annotation of gene variants and biomarkers is performed to clarify their roles in biological processes, signaling pathways and disease mechanisms;

[0049] Biological interpretation: Combined with the gene function annotation results, in-depth biological interpretation is carried out to explain the specific role of gene variants and biomarkers in the pathogenesis of colorectal cancer and their impact on disease progression.

[0050] Optionally, the biological interpretation includes:

[0051] Gene function association analysis: Combined with gene function annotation results, analyze the role of gene variation and biomarkers in biological processes to determine their specific functions in cell proliferation, differentiation and apoptosis;

[0052] Pathway analysis: Mapping genetic variants and biomarkers to known biological pathways, identifying the effects of variants on key signaling and metabolic pathways, and understanding their specific mechanisms of action in colorectal cancer;

[0053] Interaction network construction: Construct a gene interaction network to analyze the location and role of gene variants and biomarkers in the molecular network and identify the interaction relationship with other key genes and proteins.

[0054] Optionally, the personalized treatment suggestion module includes:

[0055] Genetic information analysis: Based on the patient's genome sequencing and single-cell sequencing data, identify the patient's unique gene variants and biomarkers, and combine the results of the data analysis and pattern recognition modules to understand the patient's genetic background;

[0056] Design of targeted therapy: combining the patient's genetic information to identify potential targeted therapy targets, and referring to the latest clinical trials and drug research results;

[0057] Drug selection: Based on pharmacogenomic data, the patient's response and sensitivity to different drugs are evaluated and the most suitable drug and dosage are recommended for the patient.

[0058] Beneficial effects of the present invention:

[0059] The present invention provides a colorectal cancer genetic data analysis and interpretation system that integrates multiple technologies. It has significant innovation and practicality. The system can identify key gene variants and biomarkers related to colorectal cancer, explore their association with the risk of disease, and provide solid data support for clinical diagnosis and personalized treatment.

[0060] This invention deeply analyzes its specific role in the pathogenesis of colorectal cancer and its impact on disease progression, generates targeted treatment plans, drug selection and prognosis prediction, and promotes the development of precision medicine for colorectal cancer. The efficiency and accuracy of the system significantly improve the quality of genetic data analysis, providing an important innovative tool for early diagnosis, prognosis assessment and personalized treatment of colorectal cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 Schematic diagram of a colorectal cancer genetic data analysis and interpretation system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0064] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0065] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0066] like Figure 1 As shown, the colorectal cancer genetic data analysis and interpretation system includes a data acquisition module, a data preprocessing module, a data analysis module, a pattern recognition module, an interpretation module, and a personalized treatment recommendation module, wherein:

[0067] The data collection module collects multiple types of genetic data from colorectal cancer patients, including whole genome sequencing data, single-cell sequencing data, and peripheral blood sample data;

[0068] The data preprocessing module performs preliminary processing on the collected genetic data, including data cleaning, standardization, and format conversion;

[0069] The data analysis module identifies genetic variants and biomarkers associated with colorectal cancer;

[0070] The pattern recognition module performs pattern recognition on the integrated data to mine potential genetic features and their association with the risk of colorectal cancer;

[0071] The interpretation module combines biomedical literature and databases to provide functional annotation and interpretation of the identified gene variants and biomarkers;

[0072] Personalized treatment recommendation module: Generates personalized treatment recommendations based on the patient's genetic information and analysis results, including targeted treatment plans, drug selection, and prognosis prediction.

[0073] The data acquisition module includes:

[0074] Sample collection: Tissue and blood samples were collected from patients with colorectal cancer;

[0075] Sample marking: uniquely identify and encode the collected samples;

[0076] DNA extraction: Extract genomic DNA from tissue samples and blood samples;

[0077] RNA extraction: Extract total RNA from tissue samples and blood samples;

[0078] Sample storage: Store the processed samples at low temperature;

[0079] Data transfer: Through a secure data transfer protocol, sample information and extracted DNA and RNA data are uploaded to a central database for storage and subsequent analysis.

[0080] The data preprocessing module includes:

[0081] Noise filtering: using bioinformatics algorithms to identify and remove noise and outliers in sequencing data;

[0082] Missing value processing: fill in or remove missing values ​​in the data;

[0083] Quantitative standardization: convert data from different sources into a unified dimension; Data normalization: normalize the data so that each data point is in the same numerical range to improve the stability of the algorithm;

[0084] Data format conversion: convert the cleaned and standardized data into a unified standard format;

[0085] Metadata addition: Add necessary metadata tags to the converted data, including sample ID, collection time, and processing steps.

[0086] Data analysis modules include:

[0087] Variant calling: Analyze the preprocessed genomic data using variant calling algorithms to identify single nucleotide variants and insertion and deletion variants;

[0088] Differential expression analysis: Statistical methods were used to perform differential expression analysis on RNA sequencing data to identify genes that were significantly up-regulated or down-regulated in colorectal cancer patients;

[0089] Co-expression network construction: Using co-expression network analysis methods, we constructed a gene co-expression network to identify key gene modules and biomarkers related to colorectal cancer;

[0090] Association studies: Genotype-phenotype association analysis combined with case-control data to identify genetic variants that are significantly associated with colorectal cancer risk;

[0091] Functional validation: Verify the biological functions and clinical relevance of identified gene variants and biomarkers through literature search and database comparison;

[0092] Multi-omics data integration: Integrate genomic, transcriptomic and epigenetic data to conduct multi-level comprehensive analysis and identify comprehensive biomarkers;

[0093] Pathway analysis: Pathway enrichment analysis was performed using bioinformatics tools to identify key biological pathways and networks associated with colorectal cancer.

[0094] Multi-omics data integration includes:

[0095] Data normalization: Normalize genomic, transcriptomic and epigenetic data;

[0096] Data integration: The standardized genome, transcriptome and epigenetic data were integrated using a data fusion algorithm to construct a unified multi-omics data matrix, which is expressed as:

[0097] D = αG + βT + γE;

[0098] Among them, D is the integrated data matrix, G is the genome data, T is the transcriptome data, E is the epigenetic group data, α, β, γ are weight coefficients, which are adjusted according to the importance and reliability of the data;

[0099] Feature extraction: Use the principal component analysis (PCA) method to reduce the dimension of the integrated data matrix and extract the main features, which are expressed as:

[0100] Z = XW;

[0101] Among them, Z is the data after dimensionality reduction, X is the integrated data matrix, and W is the principal component coefficient matrix;

[0102] Cluster analysis: K-means clustering algorithm is used to perform cluster analysis on the reduced-dimensional data to identify genes and samples with similar characteristics, expressed as:

[0103]

[0104] Among them, J is the objective function, k is the number of clusters, and n is i is the number of samples in the ith cluster, is the jth sample in the i-th cluster, μ i is the center of the ith cluster.

[0105] The pattern recognition module includes:

[0106] Feature selection: Use feature selection algorithms to screen out the most representative genetic features from the integrated multi-omics data;

[0107] Feature construction: construct and combine the selected features to generate new feature vectors, and use dimensionality reduction technology to simplify the data structure;

[0108] Pattern training: Use deep learning algorithms to train the constructed feature vectors, identify potential patterns related to colorectal cancer through multi-layer neural networks, and consider the heterogeneity and complexity of samples during model training;

[0109] Model validation: The cross-validation method is used to validate the trained model and evaluate the accuracy and robustness of the model.

[0110] The interpretation module includes:

[0111] Literature search: Use automated literature search tools to retrieve relevant literature from biomedical databases and screen research results related to the identified genetic variants and biomarkers;

[0112] Database comparison: compare the identified genetic variants and biomarkers with existing genetic databases and cancer databases to verify the accuracy and clinical relevance of the variants and markers;

[0113] Functional annotation: Based on the results of literature search and database comparison, functional annotation of gene variants and biomarkers is performed to clarify their roles in biological processes, signaling pathways and disease mechanisms;

[0114] Biological interpretation: Combined with the gene function annotation results, in-depth biological interpretation is carried out to explain the specific role of gene variants and biomarkers in the pathogenesis of colorectal cancer and their impact on disease progression.

[0115] Biological interpretation includes:

[0116] Gene function association analysis: Combined with gene function annotation results, analyze the role of gene variation and biomarkers in biological processes to determine their specific functions in cell proliferation, differentiation and apoptosis;

[0117] Pathway analysis: Mapping genetic variants and biomarkers to known biological pathways, identifying the effects of variants on key signaling and metabolic pathways, and understanding their specific mechanisms of action in colorectal cancer;

[0118] Interaction network construction: Construct a gene interaction network to analyze the location and role of gene variants and biomarkers in the molecular network and identify the interaction relationship with other key genes and proteins.

[0119] The personalized treatment recommendation module includes:

[0120] Genetic information analysis: Based on the patient's genome sequencing and single-cell sequencing data, identify the patient's unique gene variants and biomarkers, and combine the results of the data analysis and pattern recognition modules to understand the patient's genetic background;

[0121] Design of targeted therapy: combining the patient's genetic information to identify potential targeted therapy targets, and referring to the latest clinical trials and drug research results;

[0122] Drug selection: Based on pharmacogenomic data, the patient's response and sensitivity to different drugs are evaluated and the most suitable drug and dosage are recommended for the patient.

[0123] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0124] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A colorectal cancer genetic data analysis and interpretation system, characterized in that: It includes data acquisition module, data preprocessing module, data analysis module, pattern recognition module, interpretation module and personalized treatment suggestion module, among which: The data acquisition module collects various types of genetic data from colorectal cancer patients, including whole genome sequencing data, single cell sequencing data, and peripheral blood sample data; The data preprocessing module performs preliminary processing on the collected genetic data, including data cleaning, standardization and format conversion; The data analysis module identifies genetic variations and biomarkers associated with colorectal cancer; The pattern recognition module performs pattern recognition on the integrated data to mine potential genetic characteristics and their association with the risk of colorectal cancer; The interpretation module combines biomedical literature and databases to functionally annotate and interpret the identified gene variants and biomarkers; The personalized treatment recommendation module generates personalized treatment recommendations based on the patient's genetic information and analysis results, including targeted treatment plans, drug selection, and prognosis prediction.

2. The colorectal cancer genetic data analysis and interpretation system according to claim 1, characterized in that: The data acquisition module comprises: Sample collection: Tissue and blood samples were collected from patients with colorectal cancer; Sample marking: uniquely identify and encode the collected samples; DNA extraction: Extract genomic DNA from tissue samples and blood samples; RNA extraction: Extract total RNA from tissue samples and blood samples; Sample storage: Store the processed samples at low temperature; Data transfer: Through a secure data transfer protocol, sample information and extracted DNA and RNA data are uploaded to a central database for storage and subsequent analysis.

3. The colorectal cancer genetic data analysis and interpretation system according to claim 1, characterized in that: The data preprocessing module comprises: Noise filtering: using bioinformatics algorithms to identify and remove noise and outliers in sequencing data; Missing value processing: fill in or remove missing values ​​in the data; Quantitative standardization: convert data from different sources into a unified dimension; Data normalization: normalize the data so that each data point is in the same numerical range to improve the stability of the algorithm; Data format conversion: convert the cleaned and standardized data into a unified standard format; Metadata addition: Add necessary metadata tags to the converted data, including sample ID, collection time, and processing steps.

4. The colorectal cancer genetic data analysis and interpretation system according to claim 1, characterized in that: The data analysis module includes: Variant calling: Analyze the preprocessed genomic data using variant calling algorithms to identify single nucleotide variants and insertion and deletion variants; Differential expression analysis: Statistical methods were used to perform differential expression analysis on RNA sequencing data to identify genes that were significantly up-regulated or down-regulated in colorectal cancer patients; Co-expression network construction: Using co-expression network analysis methods, we constructed a gene co-expression network to identify key gene modules and biomarkers related to colorectal cancer; Association studies: Genotype-phenotype association analysis combined with case-control data to identify genetic variants that are significantly associated with colorectal cancer risk; Functional validation: Verify the biological functions and clinical relevance of identified gene variants and biomarkers through literature search and database comparison; Multi-omics data integration: Integrate genomic, transcriptomic and epigenetic data to conduct multi-level comprehensive analysis and identify comprehensive biomarkers; Pathway analysis: Pathway enrichment analysis was performed using bioinformatics tools to identify key biological pathways and networks associated with colorectal cancer.

5. The colorectal cancer genetic data analysis and interpretation system according to claim 4, characterized in that: The multi-omics data integration includes: Data normalization: Normalize genomic, transcriptomic and epigenetic data; Data integration: A data fusion algorithm is used to integrate the standardized genome, transcriptome, and epigenetic group data to construct a unified multi-omics data matrix; Feature extraction: Use the principal component analysis method to reduce the dimension of the integrated data matrix and extract the main features; Cluster analysis: K-means clustering algorithm was used to perform cluster analysis on the data after dimensionality reduction to identify genes and samples with similar characteristics.

6. The colorectal cancer genetic data analysis and interpretation system according to claim 1, characterized in that: The pattern recognition module comprises: Feature selection: Use feature selection algorithms to screen out the most representative genetic features from the integrated multi-omics data; Feature construction: construct and combine the selected features to generate new feature vectors, and use dimensionality reduction technology to simplify the data structure; Pattern training: Use deep learning algorithms to train the constructed feature vectors, identify potential patterns related to colorectal cancer through multi-layer neural networks, and consider the heterogeneity and complexity of samples during model training; Model validation: The cross-validation method is used to validate the trained model and evaluate the accuracy and robustness of the model.

7. The colorectal cancer genetic data analysis and interpretation system according to claim 1, characterized in that: The interpretation module comprises: Literature search: Use automated literature search tools to retrieve relevant literature from biomedical databases and screen research results related to the identified genetic variants and biomarkers; Database comparison: compare the identified genetic variants and biomarkers with existing genetic databases and cancer databases to verify the accuracy and clinical relevance of the variants and markers; Functional annotation: Based on the results of literature search and database comparison, functional annotation of gene variants and biomarkers is performed to clarify their roles in biological processes, signaling pathways and disease mechanisms; Biological interpretation: Combined with the gene function annotation results, in-depth biological interpretation is carried out to explain the specific role of gene variants and biomarkers in the pathogenesis of colorectal cancer and their impact on disease progression.

8. The colorectal cancer genetic data analysis and interpretation system according to claim 7, characterized in that: The biological interpretation includes: Gene function association analysis: Combined with gene function annotation results, analyze the role of gene variation and biomarkers in biological processes to determine their specific functions in cell proliferation, differentiation and apoptosis; Pathway analysis: Mapping genetic variants and biomarkers to known biological pathways, identifying the effects of variants on key signaling and metabolic pathways, and understanding their specific mechanisms of action in colorectal cancer; Interaction network construction: Construct a gene interaction network to analyze the location and role of gene variants and biomarkers in the molecular network and identify the interaction relationship with other key genes and proteins.

9. The colorectal cancer genetic data analysis and interpretation system according to claim 1, characterized in that: The personalized treatment suggestion module comprises: Genetic information analysis: Based on the patient's genome sequencing and single-cell sequencing data, identify the patient's unique gene variants and biomarkers, and combine the results of the data analysis and pattern recognition modules to understand the patient's genetic background; Design of targeted therapy: combining the patient's genetic information to identify potential targeted therapy targets, and referring to the latest clinical trials and drug research results; Drug selection: Based on pharmacogenomic data, the patient's response and sensitivity to different drugs are evaluated and the most suitable drug and dosage are recommended for the patient.

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