Prostate cancer early diagnosis model and construction method thereof

Abstract

The invention relates to a prostatic cancer early diagnosis model based on multi-group typing and liquid biopsy and a construction method of the prostatic cancer early diagnosis model. Aiming at challenges in early diagnosis and immunotherapy of prostatic cancer, the specific molecular marker for early diagnosis of prostatic cancer is screened by using biomarkers such as free DNA, RNA and exosome in blood and urine and combining high-throughput sequencing and bioinformatics analysis through a liquid biopsy technology. Meanwhile, multiple omics data of genomics, transcriptomics, immunomics and the like are integrated, and a prostatic cancer immunotherapy resistance molecular mechanism and an immune escape path are deeply analyzed. Based on this, a precise treatment strategy is constructed, the immunotherapy response of the patient is predicted by using a machine learning algorithm, and the formulation of an individualized treatment scheme is realized. The invention not only improves the accuracy and sensitivity of early diagnosis of prostatic cancer, but also provides a scientific basis for optimizing an immunotherapy strategy, and has significant clinical application value and scientific significance.

Description

Technical Field

[0001] The present invention relates to the field of medical diagnosis models, and in particular to an early diagnosis model for prostate cancer and a construction method thereof. Background Art

[0002] Prostate cancer, one of the most common malignant tumors of the male reproductive system, has seen a global increase in incidence in recent years, posing a serious threat to men's health. While advances in medical technology have led to significant progress in the diagnosis and treatment of prostate cancer, accurate early diagnosis and effective treatment remain significant clinical challenges.

[0003] Currently, conventional diagnostic methods for prostate cancer mainly include serum prostate-specific antigen (PSA) testing, digital rectal examination (DRE), and tissue biopsy. However, these methods all have certain limitations. Although serum PSA testing has the advantages of simple operation and low cost, its specificity is low and it is easily interfered with by various factors, such as benign prostatic hyperplasia and prostatitis, resulting in a high false positive rate. Digital rectal examination relies on the doctor's experience and technique, is highly subjective, and has limited sensitivity for detecting early prostate cancer. As the "gold standard" for the diagnosis of prostate cancer, tissue biopsy is highly accurate, but it is an invasive examination that will cause certain pain and complications to patients, and it is difficult to conduct comprehensive and repeated tests in the early stages of the disease.

[0004] In the treatment of prostate cancer, traditional treatments such as surgery, radiotherapy, and chemotherapy can control the disease to a certain extent, but the treatment effects are often unsatisfactory for patients in the advanced stage or with prostate cancer with specific biological characteristics. In recent years, immunotherapy, as an emerging treatment method, has shown good application prospects in the treatment of various tumors. However, in the treatment of prostate cancer, there are large individual differences in the effect of immunotherapy. Some patients are insensitive to immunotherapy and even show resistance to immunotherapy. At present, the mechanism of resistance to immunotherapy in prostate cancer has not been fully elucidated, and there is a lack of effective prediction and evaluation methods. This has, to a certain extent, limited the widespread application of immunotherapy in prostate cancer.

[0005] In addition, the development and progression of prostate cancer is a complex, multi-factor, multi-step process involving abnormal changes in multiple genes and signaling pathways. Prostate cancers in different patients exhibit significant heterogeneity at the molecular level, which not only affects the patient's prognosis but also poses a challenge to the development of precision treatment strategies. Currently, treatment plans are mainly formulated clinically based on traditional indicators such as the patient's clinical stage and pathological grade. However, these indicators are difficult to fully reflect the biological characteristics of the tumor and cannot achieve true precision treatment.

[0006] Liquid biopsy technology, as an emerging detection method, can achieve early diagnosis, disease monitoring and prognosis assessment of tumors by detecting biomarkers such as free DNA, RNA and exosomes in body fluids such as blood and urine. Compared with traditional tissue biopsies, liquid biopsies have the advantages of being non-invasive, highly reproducible, and able to reflect tumor heterogeneity. Multi-omics technology can comprehensively analyze the molecular characteristics of tumors from multiple levels such as the genome, transcriptome, and proteome, providing an important basis for a deeper understanding of the pathogenesis of tumors and the search for new therapeutic targets. However, the research on combining liquid biopsy technology with multi-omics technology for the early diagnosis and precision treatment of prostate cancer is still in its infancy, and the relevant technologies and methods are not yet mature.

[0007] In order to solve the above problems, the applicant proposed an early diagnosis model for prostate cancer and a method for constructing the same. Summary of the Invention

[0008] The purpose of the present invention is to provide an early diagnosis model for prostate cancer and a method for constructing the same, so as to solve the problems in the prior art.

[0009] To achieve the above objectives, the present invention provides the following technical solutions: a method for early diagnosis of prostate cancer, comprising:

[0010] Extraction of free DNA, RNA, and exosomes from the blood or urine of prostate cancer patients;

[0011] High-throughput sequencing technology was used to analyze the extracted free DNA and exosomal RNA;

[0012] Combine multi-omics data to screen early characteristic markers of prostate cancer for early diagnosis of prostate cancer.

[0013] Optionally, the multi-omics data includes genomics, transcriptomics, and proteomics data.

[0014] A method for dissecting the mechanisms of resistance to immunotherapy in prostate cancer, comprising:

[0015] Obtain multi-omics data including genomics, transcriptomics, and proteomics of prostate cancer patients;

[0016] Analyze immune checkpoint molecule expression and immune cell infiltration using flow cytometry, RNA-Seq, proteomics, and other methods;

[0017] The molecular mechanisms of immunotherapy resistance were revealed based on the analysis results.

[0018] A method for exploring and optimizing a precision treatment strategy for prostate cancer, comprising:

[0019] Based on the multi-omics classification of prostate cancer, we can obtain data on patients' gene mutations, immune cell infiltration, and other conditions;

[0020] A machine learning algorithm was used to construct an immunotherapy response prediction model to assess the sensitivity of patients of different subtypes to immunotherapy;

[0021] Develop precise immunotherapy plans based on the evaluation results to achieve precise and personalized treatment of prostate cancer.

[0022] Optionally, the machine learning algorithm includes random forest and support vector machine.

[0023] Beneficial effects: 1. Improve the accuracy and sensitivity of early diagnosis of prostate cancer

[0024] By combining liquid biopsy technology with multi-omics data analysis, this method can non-invasively detect biomarkers such as free DNA, RNA, and exosomes in patients' blood and urine. Compared with traditional methods such as serum PSA testing, digital rectal examination, and tissue biopsy, this method can identify characteristic markers of prostate cancer earlier and more accurately, significantly improving the sensitivity and specificity of early diagnosis of prostate cancer, facilitating early detection and intervention of prostate cancer, and thus improving patient prognosis.

[0025] 2. Comprehensively analyze the molecular characteristics of prostate cancer and reveal the mechanism of resistance to immunotherapy

[0026] This study integrates multi-omics data, including genomics, transcriptomics, and proteomics, to comprehensively analyze the molecular characteristics of prostate cancer and delve into the molecular mechanisms of tumor development. By analyzing immune checkpoint molecule expression and immune cell infiltration, the study reveals the molecular mechanisms of prostate cancer resistance to immunotherapy, providing a theoretical basis for overcoming this resistance and contributing to the development of more effective immunotherapy regimens.

[0027] 3. Achieve precision treatment and personalized medicine for prostate cancer

[0028] Based on the multi-omics classification of prostate cancer, the present invention uses a machine learning algorithm to construct an immunotherapy response prediction model, which can accurately assess the sensitivity of patients with different classifications to immunotherapy. Incorporating individual characteristics such as gene mutations and immune cell infiltration, the present invention can formulate precise immunotherapy plans, achieving precise and personalized prostate cancer treatment. This not only improves treatment efficacy, but also reduces unnecessary treatment side effects and enhances patients' quality of life.

[0029] 4. Non-invasive testing reduces patient pain and risks

[0030] Liquid biopsy technology, the core testing method of this invention, offers the advantages of being non-invasive and highly reproducible. Compared to traditional tissue biopsies, liquid biopsies do not require surgical intervention, avoiding the pain and risk of complications associated with surgery, and reducing the physical and mental burden on patients. Furthermore, liquid biopsies allow for multiple sampling, facilitating dynamic monitoring of disease progression and providing more comprehensive information support for clinical decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following describes preferred embodiments of the present invention with reference to the accompanying drawings to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0033] In the drawings, components with identical structures are denoted by the same reference numerals, and components with similar structures or functions are denoted by similar reference numerals. The size and thickness of each component shown in the drawings are arbitrary and are not limited by the present invention. For clarity, the thickness of components in some places in the drawings is appropriately exaggerated.

[0034] Example 1

[0035] Construction of an early diagnosis model for prostate cancer

[0036] Sample collection and processing

[0037] Blood and urine samples were collected from prostate cancer patients and healthy controls. Blood samples were collected using specialized blood collection equipment under strict aseptic conditions. After collection, they were centrifuged as quickly as possible to separate the plasma and store it properly. Urine samples were collected within a specified timeframe to avoid contamination and were processed or stored immediately. The collected samples were pre-processed to remove impurities and substances that could affect subsequent analysis. For example, specific kits were used to remove proteins and cell debris from the blood, and urine was centrifuged and filtered.

[0038] Biomarker extraction

[0039] Free DNA, RNA, and exosomes are extracted from pretreated blood and urine samples. For free DNA and RNA, optimized extraction methods are employed, such as using specific nucleic acid extraction kits. Conditions are adjusted according to sample type to ensure efficient and accurate extraction of target nucleic acids. Exosomes are isolated and purified using methods such as ultracentrifugation, density gradient centrifugation, or immunoaffinity capture. Exosomes are identified and characterized using techniques such as transmission electron microscopy and nanoparticle tracking analysis to ensure their quality and purity.

[0040] High-throughput sequencing analysis

[0041] Perform high-throughput sequencing on the extracted cell-free DNA and RNA. For DNA, construct appropriate sequencing libraries and employ methods such as whole-genome sequencing, whole-exome sequencing, or targeted sequencing, selecting the appropriate sequencing strategy based on the research objectives. For RNA, perform transcriptome sequencing, including analysis of mRNA, lncRNA, and miRNA. Utilizing advanced sequencing platforms, such as the Illumina series of sequencers, and following standard sequencing protocols, obtain high-quality sequencing data.

[0042] Multi-omics data integration and analysis

[0043] Data obtained from high-throughput sequencing are integrated with multi-omics data, including proteomics and metabolomics. Bioinformatics methods, such as data standardization and normalization, are used to eliminate systematic errors between different omics data. Machine learning algorithms, such as random forests and support vector machines, are used to analyze the integrated multi-omics data and screen for characteristic markers associated with early prostate cancer. Simultaneously, a model for early prostate cancer diagnosis is constructed and evaluated and optimized through cross-validation and independent sample validation to improve its accuracy and reliability.

[0044] Methods for analyzing mechanisms of resistance to immunotherapy

[0045] Multi-omics data collection

[0046] Acquire multi-omics data, including genomics, transcriptomics, and proteomics, from prostate cancer patients. Use technologies such as gene chips and whole-genome sequencing to obtain genomic data; RNA-Seq to obtain transcriptomics data; and mass spectrometry to obtain proteomics data. Ensure the quality and accuracy of data acquisition, performing quality control and preprocessing on the collected data.

[0047] Analysis of immune-related indicators

[0048] Flow cytometry is used to analyze the proportions and functions of immune cell subsets, such as CD4+ and CD8+ T cells, and regulatory T cells. RNA-Seq is used to analyze the expression levels of immune checkpoint molecules, such as PD-1, PD-L1, and CTLA-4. Immunohistochemistry and Western blotting are used to detect the expression and localization of immune-related proteins. Comprehensive analysis of immune-related indicators reveals the characteristics of the prostate cancer immune microenvironment and the mechanisms of immunotherapy resistance.

[0049] Molecular mechanism verification

[0050] The identified molecular mechanisms of immunotherapy resistance are validated through cell-based and animal experiments. Prostate cancer cell lines and animal models are constructed to simulate immunotherapy resistance. Using gene editing technologies, such as the CRISPR / Cas9 system, key genes are knocked out or overexpressed. Changes in the responses of cells and animal models to immunotherapy are observed to verify the correctness of the identified molecular mechanisms.

[0051] Exploration and optimization of precision treatment strategies

[0052] Multi-omics typing and data acquisition

[0053] Based on multi-omics data from prostate cancer, cluster analysis and principal component analysis are used to categorize prostate cancer. Gene mutations, immune cell infiltration, and other data are collected from patients with different categorizations to establish a detailed patient information database.

[0054] Construction of a prediction model for immunotherapy response

[0055] Machine learning algorithms, such as random forests, support vector machines, and neural networks, are used to construct models predicting immunotherapy response. The models are trained and optimized using multi-omics patient data as input features and immunotherapy response (e.g., efficacy and survival) as output variables. Model performance is evaluated through cross-validation and independent sample validation to ensure good predictive accuracy.

[0056] Precision treatment plan formulation

[0057] Based on the evaluation results of the immunotherapy response prediction model and the patient's individual characteristics, such as age, physical condition, and comorbidities, a precise immunotherapy plan is developed. For patients sensitive to immunotherapy, appropriate immunotherapy drugs and dosages are selected. For patients resistant to immunotherapy, combination therapy options, such as immunotherapy combined with targeted therapy and chemotherapy, are explored to improve treatment efficacy. Furthermore, throughout the treatment process, timely adjustments to the treatment plan are made based on the patient's condition and response to treatment, achieving precise and personalized prostate cancer treatment.

[0058] (1) Implementation steps for constructing an early diagnosis model for prostate cancer

[0059] Sample collection and pretreatment

[0060] Blood and urine samples were collected from prostate cancer patients and healthy controls according to standard operating procedures. After blood sample collection, the samples were immediately centrifuged at 3000 rpm for 10 minutes at 4°C to separate the plasma, which was then aliquoted and stored at -80°C. Urine samples were centrifuged within 2 hours of collection, then centrifuged at 1500 rpm for 5 minutes to remove the precipitate, and the supernatant was stored at -20°C. The stored samples were pretreated and free DNA and RNA were extracted from plasma and urine using the QIAamp Circulating Nucleic Acid Kit (Qiagen) according to the kit instructions. For exosome extraction, ultracentrifugation was used, and the samples were centrifuged at 100,000 g for 70 minutes at 4°C. The exosome precipitate was collected, resuspended in PBS, and stored.

[0061] High-throughput sequencing and data analysis

[0062] Whole-genome sequencing was performed on the extracted cell-free DNA, and sequencing libraries were constructed and sequenced using the Illumina HiSeq X Ten sequencing platform. RNA transcriptome sequencing was performed, and libraries were constructed using the NEBNext Ultra II Directional RNA Library Prep Kit (New England Biolabs) and sequenced on an Illumina NovaSeq 6000 sequencer. Sequencing data were analyzed using tools such as BWA and GATK for alignment and variant detection. Sequencing data were integrated with proteomics and metabolomics data, and data normalization and processing were performed using the R language. A random forest algorithm was used to identify characteristic markers, and an early diagnosis model was constructed. Model performance was evaluated using 5-fold cross-validation.

[0063] Model validation and optimization

[0064] Independent samples from prostate cancer patients and healthy controls were collected and processed, sequenced, and analyzed according to the aforementioned methods. The results were then incorporated into the early diagnosis model for validation. Based on the validation results, model parameters were adjusted to optimize model performance and improve its accuracy and reliability.

[0065] (II) Implementation steps for analyzing immunotherapy resistance mechanisms

[0066] Multi-omics data collection

[0067] Genomic data were collected using Affymetrix gene chips, and experiments were performed according to the chip operating manual. Transcriptomics data were collected using Illumina RNA-Seq technology, and libraries were constructed and sequenced. Proteomics data were collected using a Thermo Fisher Scientific Orbitrap mass spectrometer, and proteins were identified and quantified.

[0068] Analysis of immune-related indicators

[0069] Immune cell subsets were analyzed using a BD FACSCanto II flow cytometer and stained with specific fluorescent-labeled antibodies. Immune checkpoint molecule expression levels were analyzed using RNA-Seq data and quantified using the FPKM (Fragments Per Kilobase of transcript per Million mapped reads) method. The expression of immune-related proteins was detected by immunohistochemistry, using DAB as the staining medium, and the results were observed and analyzed under a light microscope.

[0070] Molecular mechanism verification

[0071] Prostate cancer cell lines PC-3 and animal models were constructed. Key genes were edited using the CRISPR / Cas9 system to observe changes in the cell and animal models' responses to immunotherapy. Treatment efficacy was assessed through tumor volume measurement and survival analysis, verifying the underlying molecular mechanisms.

[0072] (III) Implementation steps for exploration and optimization of precision treatment strategies

[0073] Multi-omics typing and data acquisition

[0074] Based on multi-omics data, K-means cluster analysis was used to categorize prostate cancer. Gene mutation data from patients with different genotypes were collected and analyzed using whole-exome sequencing. Immune cell infiltration data were obtained using flow cytometry and immunohistochemistry.

[0075] Construction of a prediction model for immunotherapy response

[0076] Using the Python scikit-learn library and the random forest algorithm, we constructed an immunotherapy response prediction model. We used multi-omics patient data as input features and immunotherapy response as the output variable for model training and optimization. We evaluated model performance through 5-fold cross-validation and adjusted model parameters to improve predictive accuracy.

[0077] Precision treatment plan formulation

[0078] Based on the evaluation results of the immunotherapy response prediction model and the individual patient characteristics, a precise immunotherapy plan is developed. For patients predicted to be sensitive to immunotherapy, immunotherapy drugs such as pembrolizumab are selected for treatment. For patients predicted to be resistant to immunotherapy, combination therapies such as pembrolizumab plus abiraterone are explored. During treatment, patients are regularly examined and treatment plans are adjusted promptly based on changes in their condition.

[0079] Improving the accuracy and sensitivity of early diagnosis of prostate cancer

[0080] Liquid biopsy technology and multi-omics analysis can detect characteristic markers of prostate cancer early and accurately, overcoming the limitations of traditional diagnostic methods, helping to achieve early detection and intervention of prostate cancer, and improving patients' survival rate and quality of life.

[0081] In-depth analysis of the mechanism of resistance to immunotherapy

[0082] Integrating multi-omics data and comprehensively analyzing immune-related indicators revealed the molecular mechanism of prostate cancer resistance to immunotherapy, providing a theoretical basis for overcoming immunotherapy resistance and helping to develop more effective immunotherapy plans.

[0083] Achieving precision treatment and personalized medicine for prostate cancer

[0084] Based on multi-omics typing and immunotherapy response prediction models, precise immunotherapy plans are formulated to achieve precise and personalized prostate cancer treatment. This improves treatment efficacy, reduces unnecessary treatment side effects, and enhances patients' quality of life.

[0085] Driving progress in prostate cancer research

[0086] The implementation of the present invention provides new ideas and methods for the study of prostate cancer, promotes the application and development of liquid biopsy technology and multi-omics technology in the field of prostate cancer research, and helps to accelerate technological innovation and achievement transformation in the field of prostate cancer treatment.

[0087] In summary, the present invention provides a comprehensive and effective solution for the diagnosis and treatment of prostate cancer by constructing an early diagnosis model for prostate cancer, analyzing the mechanism of resistance to immunotherapy, and exploring precise treatment strategies. It has important clinical application value and social significance.

[0088] The above patent technology solutions are for reference only. When applying for a patent, further improvement and adjustment are required according to the specific requirements of the patent laws of each country, and professional patent agencies or lawyers may be required to assist.

[0089] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, from all points of view, the embodiments should be regarded as illustrative and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and range of equivalents of the claims are included in the present invention. Any reference signs in the claims should not be construed as limiting the claim to which they relate.

[0090] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A method for early diagnosis of prostate cancer, characterized in that: include: Extraction of free DNA, RNA, and exosomes from the blood or urine of prostate cancer patients; High-throughput sequencing technology was used to analyze the extracted free DNA and exosomal RNA; Combine multi-omics data to screen early characteristic markers of prostate cancer for early diagnosis of prostate cancer.

2. The method for early diagnosis of prostate cancer according to claim 1, wherein The multi-omics data includes genomics, transcriptomics, and proteomics data.

3. A method for analyzing the mechanism of resistance to immunotherapy in prostate cancer, characterized in that: include: Obtain multi-omics data including genomics, transcriptomics, and proteomics of prostate cancer patients; Analyze immune checkpoint molecule expression and immune cell infiltration using flow cytometry, RNA-Seq, proteomics, and other methods; The molecular mechanisms of immunotherapy resistance were revealed based on the analysis results.

4. A method for exploring and optimizing a precise treatment strategy for prostate cancer, characterized in that: include: Based on the multi-omics classification of prostate cancer, we can obtain data on patients' gene mutations, immune cell infiltration, and other conditions; A machine learning algorithm was used to construct an immunotherapy response prediction model to assess the sensitivity of patients of different subtypes to immunotherapy; Develop precise immunotherapy plans based on the evaluation results to achieve precise and personalized treatment of prostate cancer.

5. The method for exploring and optimizing precise treatment strategies for prostate cancer according to claim 4, characterized in that: The machine learning algorithms include random forest and support vector machine.

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