Methylation marker panel and its application in prediction of risk of urothelial carcinoma
Patent Information
- Application Number
- CN202510532231.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-25
- Publication Date
- 2026-10-27
AI Technical Summary
然而，相关的检测方法仍存在一些不足，如检测灵敏度、特异性不足等
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Figure CN122979443A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of biotechnology, and in particular to methylation biomarkers and their application in predicting the risk of urothelial carcinoma. Background Technology
[0002] Urothelial carcinoma is a multifocal malignant tumor originating from the urothelial tract and is the most common tumor of the urinary system. Urothelial carcinoma includes renal pelvis cancer, ureteral cancer, bladder cancer, and urethral cancer, with bladder cancer accounting for the highest proportion, exceeding 90%. Hematuria is the most common and earliest symptom of bladder cancer. This hematuria is often painless, intermittent, and can subside or stop on its own, easily creating the illusion that the disease has been cured. Bladder cancer is characterized by long survival and a high recurrence rate (50%-70% of patients relapse within 5 years), therefore, bladder cancer screening and recurrence monitoring are essential.
[0003] Commonly used methods for detecting urothelial carcinoma include urine cytology, urine tumor marker testing, imaging studies, and endoscopy. However, these methods still have some limitations, such as insufficient sensitivity and specificity. Summary of the Invention
[0004] This application is based on the inventor's discovery of the following problems:
[0005] Urine cytology is an important method for the diagnosis and postoperative follow-up of bladder cancer. Its sensitivity is 13%–75%, and its specificity is 85%–100%, but its sensitivity for G1 and low-grade tumors is only 16%. In addition to low sensitivity, the performance of urine cytology is also affected by factors such as low number of exfoliated cells, urinary tract infection, stones, or bladder instillation therapy.
[0006] Urinary tumor marker detection includes nuclear matrix protein 22 (NMP22) and bladder tumor antigens (BTAstat and BTAtrak). Because urinary tumor marker detection has high sensitivity but low specificity, it has not been widely used in clinical practice.
[0007] Imaging examinations include ultrasound, CT and computed tomography urography (CTU), magnetic resonance imaging (MRI), and magnetic resonance urography (MRU). Cystoscopy and biopsy are currently the most reliable methods for diagnosing bladder cancer and are also one of the main means of monitoring postoperative recurrence. However, imaging and endoscopic examinations are expensive, and due to limitations in equipment and medical resources such as physicians, large-scale screening is not possible. Furthermore, some examinations may cause harm to patients. For example, cystoscopy may cause complications such as urogenital infections, urethral and bladder bleeding, urethral injury, and urethral stricture. Patient compliance is poor, and repeated monitoring places a significant burden on patients.
[0008] This application aims to at least partially address one of the aforementioned technical problems or at least provide a useful commercial option. In view of this, this application provides a set of methylation biomarkers for the detection of urothelial carcinoma, which offers advantages such as high sensitivity and high specificity.
[0009] Specifically, the technical solution of this application is as follows:
[0010] In a first aspect of this application, a set of methylation markers is proposed. According to embodiments of this application, the methylation marker set includes: at least one of the chromosomal regions shown as 1)-17), with hg19 as the reference genome; 1) chr1: 119528469-119530711; 2) chr1: 36042374-36043443; 3) chr10: 112838029-112838910; 4) chr10: 105344152-105344747; 5) chr11: 2890715-2891126; 6) chr12: 54348801-54349427; 7) chr17: 40700226-40700922; 8) chr17: 41363502-41364563;
[0011] 9) chr18: 46354015-46354306; 10) chr19: 45655294-45657245; 11) chr2: 63282360-63284369; 12) chr3: 147113840-147115561; 13) chr5: 14080 2400-140802884; 14) chr7: 27225125-27225962; 15) chr8: 57358060-57 359512; 16) chr8: 57359216-57359347; 17) chr8: 144789639-144790191.
[0012] Compared to traditional methods such as urine exfoliative cytology, urine tumor marker detection, and imaging examinations, detecting urothelial carcinoma based on one or more of the aforementioned methylation markers can effectively improve the sensitivity and specificity of the test, increase the early detection rate of urothelial carcinoma, reduce anxiety and overtreatment caused by false positives, save costs, improve patient compliance, and can be used for large-scale medical screening.
[0013] It is understandable that the aforementioned methylation markers include, but are not limited to, selection of a subset of 1)-17), simple substitution, simple addition, etc.
[0014] It is understood that the representation of the above methylation markers is a conventional representation for those skilled in the art. For example, chr1:119528469-119530711 represents positions 119528469-119530711 on chromosome 1. The representation of the remaining methylation markers is the same and will not be described again here.
[0015] It should be noted that this application uses hg19 as the reference genome. Based on the chromosome number and the chromosome position under that number, those skilled in the art can know its specific sequence. The specific sequences of 1)-17) will not be shown here.
[0016] It should be noted that this application only shows methylation markers with hg19 as the reference genome. Those skilled in the art will understand that, based on the methylation markers disclosed in this application with hg19 as the reference genome, methylation markers obtained based on other reference genomes (such as HG38, GRCh36, GRCh37, GRCh38, etc.) are also within the scope of protection of this application.
[0017] In some preferred embodiments of this application, the aforementioned methylation marker set consists of all or part of the chromosomal regions shown in 1)-17). Detection of urothelial carcinoma based on all or part of the methylation marker set can effectively improve detection sensitivity and specificity.
[0018] In some preferred examples of this application, the aforementioned methylation biomarker set consists of all the chromosomal regions shown in 1)-17). Detection of urothelial carcinoma based on the entire methylation biomarker set can significantly improve detection sensitivity and specificity.
[0019] In a second aspect of this application, the application proposes the use of the methylation biomarker group of the first aspect in any of the following: (B1) preparing a product for the diagnosis or auxiliary diagnosis of urothelial carcinoma; (B2) preparing a product for the early warning of urothelial carcinoma before clinical symptoms appear; (B3) preparing a product for the differentiation or auxiliary differentiation of urothelial carcinoma and normal samples.
[0020] The methylation biomarker group of this application is used to prepare products for the auxiliary diagnosis of urothelial carcinoma samples, which have high sensitivity and high specificity.
[0021] In a third aspect of this application, the application discloses the use of reagents for detecting the methylation marker group of the first aspect in the preparation of a detection product for the diagnosis of urothelial carcinoma.
[0022] The detection products prepared based on the reagents for detecting the methylation biomarkers of this application can be used for the detection of urothelial carcinoma with high sensitivity and high specificity.
[0023] In some examples of this application, the aforementioned reagents include primer sets and probe sets.
[0024] In some examples of this application, the aforementioned testing products include: reagent kits, test strips, or chips.
[0025] In a fourth aspect of this application, a method for predicting the risk of urothelial carcinoma is proposed. According to an embodiment of this application, the method includes: obtaining the detection result of the methylation rate of hypermethylated differentially methylated regions in a biological sample; inputting the detection result into a risk prediction model to determine the risk of urothelial carcinoma in the biological sample; wherein the hypermethylated differentially methylated regions (Hyper-DMR) are as shown in the first aspect.
[0026] Predicting the risk of urothelial carcinoma based on the aforementioned methods can effectively improve the efficiency and accuracy of urothelial carcinoma diagnosis, save diagnostic time and labor costs, and is suitable for large-scale screening.
[0027] In some examples of this application, the aforementioned biological sample is selected from tissues or body fluids. The aforementioned body fluids may be selected from blood, saliva, tissues, or urine, etc. In some preferred examples of this application, the aforementioned biological sample is selected from urine samples or nucleic acid samples derived from urine. Based on urine samples or nucleic acid samples derived from urine, invasive endoscopic examinations are avoided, which can effectively reduce the physical and mental burden on the examinee and improve testing compliance.
[0028] In some examples of this application, the methylation rate of the aforementioned hypermethylated differentially methylated region is determined based on methylated cytosine and all cytosine in the methylation sites within the hypermethylated differentially methylated region.
[0029] In some examples of this application, the methylation rate of the aforementioned hypermethylated differentially methylated region is determined based on the ratio of methylated cytosine to all cytosine in the methylation sites within the hypermethylated differentially methylated region. This standardized calculation method ensures the repeatability and stability of the detection results.
[0030] In some examples of this application, the aforementioned risk prediction model is trained as follows: training set data is obtained, including the methylation rate of hypermethylated differentially methylated regions of training samples; a machine learning model is used to model the training set data to construct the risk prediction model. The model trained based on the method of this application can be used to accurately predict the risk of urothelial carcinoma.
[0031] It should be noted that the risk prediction model in this application was obtained by training a machine learning model using conventional methods in the art, such as constructing a random forest model using 10-fold cross-validation with the Python scikit-learn toolkit.
[0032] In some examples of this application, the aforementioned machine learning model is selected from ridge regression, LASSO regression, logistic regression, random forest or support vector machine.
[0033] In some examples of this application, the aforementioned detection results are obtained based on methylation-specific PCR, digital PCR quantification, real-time PCR, DNA methylation arrays, pyrosequencing, high-throughput sequencing, third-generation sequencing, single-molecule sequencing, or targeted DNA methylation sequencing. In some preferred examples of this application, the aforementioned detection results are obtained through high-throughput methylation sequencing.
[0034] In a fifth aspect of this application, a method for assisting in the diagnosis of the risk of urothelial carcinoma is proposed. According to an embodiment of this application, the method includes: acquiring methylation data of hypermethylated differentially methylated regions in a biological sample; analyzing the methylation level or state of the hypermethylated differentially methylated regions based on the methylation data; and determining the risk of urothelial carcinoma in the biological sample based on the methylation level or state, wherein the hypermethylated differentially methylated regions are as shown in the first aspect of this application.
[0035] Using the aforementioned methods for auxiliary diagnosis of urothelial carcinoma risk can effectively improve the efficiency and accuracy of urothelial carcinoma diagnosis, save diagnostic time and labor costs, and is suitable for large-scale screening.
[0036] In some examples of this application, the aforementioned biological sample is selected from tissues or body fluids. The aforementioned body fluids may be selected from blood, saliva, tissues, or urine, etc. In some preferred examples of this application, the aforementioned biological sample is selected from urine samples or nucleic acid samples derived from urine. Based on urine samples or nucleic acid samples derived from urine, invasive endoscopic examinations are avoided, which can effectively reduce the physical and mental burden on the examinee and improve testing compliance.
[0037] In some examples of this application, the aforementioned methylation data were obtained based on methylation-specific PCR, digital PCR quantification, real-time PCR, DNA methylation microarray, pyrosequencing, high-throughput sequencing, third-generation sequencing, single-molecule sequencing, or targeted DNA methylation sequencing.
[0038] In some examples of this application, the methylation status of the hypermethylated differentially methylated regions is analyzed based on the methylation data, including but not limited to comparing the Ct value of the methylation marker, comparing the ΔCt value (the difference between the Ct value of the marker and the internal reference gene), comparing the melting curve, comparing sequence information, calculating the methylation rate, or comparing the results with the standard.
[0039] In some examples of this application, the risk of urothelial carcinoma can also be aided in by setting a positive cutoff value / threshold or by judging the methylation status.
[0040] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description
[0041] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0042] Figure 1 This is a schematic diagram of a DMR heatmap based on a tissue sample (dataset I) according to an embodiment of this application; wherein cancerous tissue is on the left and adjacent tissue is on the right; each row represents a Hyper-DMR; each cell represents the DMR methylation rate of the corresponding sample at that site, which ranges from 0 to 0.5; the closer the methylation rate is to 0, the lighter the color.
[0043] Figure 2 This is a schematic diagram showing the performance validation results of a prediction model based on 17 Hyper-DMRs according to an embodiment of this application on dataset I; the horizontal axis represents the false positive rate (1-specificity), and the vertical axis represents the sensitivity.
[0044] Figure 3 This is a schematic diagram of a Hyper-DMR heatmap based on a tissue sample (dataset III) according to an embodiment of this application; wherein, cancerous tissue is on the left and adjacent tissue is on the right; each row represents a Hyper-DMR; each cell represents the DMR methylation rate of the corresponding sample at that site, which ranges from 0 to 0.5; the closer the methylation rate is to 0, the lighter the color.
[0045] Figure 4 This is a schematic diagram showing the performance verification results of a methylation model based on 17 DMRs according to an embodiment of this application on dataset III; where the horizontal axis represents the false positive rate (1-specificity) and the vertical axis represents the sensitivity.
[0046] Figure 5 This is a schematic diagram showing the performance verification results of a methylation model based on 17 DMRs according to an embodiment of this application on dataset IV; where the horizontal axis represents the false positive rate (1-specificity) and the vertical axis represents the sensitivity. Detailed Implementation
[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0048] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.
[0049] The endpoints and any values of the ranges disclosed herein are not limited to the precise ranges or values, and these ranges or values should be understood to include values close to these ranges or values. For numerical ranges, the endpoint values of the various ranges, the endpoint values of the various ranges and individual point values, and individual point values can be combined with each other to obtain one or more new numerical ranges, which should be considered as specifically disclosed herein.
[0050] In this article, unless otherwise specified, "methylation" is a form of DNA chemical modification that can alter genetic expression without changing the DNA sequence. DNA methylation refers to the covalent binding of a methyl group to the 5th carbon position of cytosine in a CpG dinucleotide of the genome, under the action of DNA methyltransferases. DNA methylation can cause changes in chromatin structure, DNA conformation, DNA stability, and the way DNA interacts with proteins, thereby controlling gene expression.
[0051] In this article, unless otherwise specified, "methylation level" refers to whether cytosine in one or more CpG dinucleotides within a DNA sequence is methylated, or the frequency / proportion / percentage of methylation, representing both a qualitative and quantitative concept. In practical applications, different detection indicators can be used to compare DNA methylation levels depending on the specific circumstances. For example, in some cases, comparisons can be made based on the Ct values of the samples; in others, the proportion of gene methylation in the sample can be calculated as (number of methylated molecules / (number of methylated molecules + number of unmethylated molecules)) × 100, and then compared; in still others, statistical analysis and integration of various indicators are performed to derive the final judgment index.
[0052] In this paper, unless otherwise specified, a "predictive model" is an algorithm that can automatically perform tasks such as prediction, classification, recognition, or decision-making by learning and analyzing input data. The learning process of this model is based on statistical principles and data pattern recognition, using a training dataset to tune model parameters and optimize the model to improve its predictive or inference capabilities. The model can employ various algorithms and techniques, such as neural networks, deep learning, support vector machines, decision trees, and random forests. These models can be trained and optimized through supervised learning, unsupervised learning, or reinforcement learning. In practical applications, the model can be used in various fields, such as natural language processing, image recognition, pattern recognition, data mining, recommender systems, and predictive analytics. It has significant application potential in handling large-scale data, automated decision-making, and intelligent systems.
[0053] In this paper, unless otherwise specified, the "training set" refers to the data used to train the machine learning model. It contains a series of input samples (independent variable: methylation rate of hypermethylated and differentially methylated regions) and corresponding known outputs (dependent variable: risk of urothelial carcinoma). During the training phase, the deep learning model uses samples from the training set to learn and adjust its parameters (weights) to minimize the error between the predicted results and the true labels. By continuously trying different parameters and algorithms, the model gradually learns the patterns and characteristics between the samples, thus achieving more accurate predictive capabilities.
[0054] In this paper, unless otherwise specified, the prediction model trained on the training set is further validated using a test set. The test set is a dataset used to evaluate the performance of the deep learning model. It contains (independent variable: methylation rate of hypermethylated and differentially methylated regions) and the corresponding true output (dependent variable: risk of urothelial carcinoma). However, the samples in the test set were not used during the model training phase, ensuring that the data in the test set is unknown to the model. After training, the model's performance is evaluated using the test set by predicting the samples in the test set and comparing the predictions with the true labels. By comparing the prediction results with the true labels, the model's performance on unknown data is evaluated, and its generalization ability and prediction accuracy are assessed.
[0055] It should be noted that the urothelial carcinomas involved in this application include at least bladder cancer, ureteral cancer, and renal pelvis cancer.
[0056] It should be noted that the methylation markers in this application represent hypermethylated differentially methylated regions. The methylation rate of the hypermethylated differentially methylated regions in this application refers to the methylation rate of the methylation markers.
[0057] The present invention will be explained below with reference to embodiments. Those skilled in the art will understand that the following embodiments are for illustrative purposes only and should not be considered as limiting the scope of the invention. Where specific techniques or conditions are not specified in the embodiments, they are performed according to the techniques or conditions described in the literature in the field or according to the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be obtained commercially.
[0058] Example 1: Screening and Prediction Model Construction for Methylation Markers
[0059] Whole-genome methylation sequencing was performed on DNA extracted from 49 cancerous tissues and 32 adjacent normal tissues from bladder cancer patients. Combined with methylation microarray data from The Cancer Genome Atlas (TCGA-BLCA), data analysis was used to identify DMRs associated with the development and progression of bladder cancer. These included:
[0060] I. Experimental Methods
[0061] 1. DNA preparation
[0062] Tissue sample extraction: DNA was extracted from 49 cancer tissue and 32 adjacent normal tissue samples from bladder cancer patients using DNeasy Blood & Tissue Ki (Qiagen, #69506).
[0063] 2. Library Construction
[0064] Library construction was performed using the MGI Easy whole genome methylation library preparation kit (MGI, #940-001527-00). The following steps were performed according to the instructions: DNA fragmentation and screening, end repair and addition of dA tails, adapter ligation, purification of ligation products, methylation transformation and purification, PCR amplification, PCR product purification, PCR product quality testing, and subsequent circularization and DNB preparation.
[0065] 3. Sequencing
[0066] PE100 sequencing was performed using the MGISEQ-2000 (MGI). Sequencing can be performed by purchasing the corresponding commercial kit (MGISEQ-2000RS High-Throughput Sequencing Kit (FCL PE100), #1000012554) or by entrusting a sequencing service provider to perform the sequencing.
[0067] II. Data Analysis
[0068] Whole-genome methylation sequencing data (dataset I) from 49 bladder cancer tissues and 32 adjacent normal tissues were analyzed to calculate the methylation rate of each CpG site. By comparing the differences in methylation rates of each CpG site between bladder cancer tissues and adjacent normal tissues, a hierarchical Bayesian approach was used to identify 30,643 hypermethylated differentially methylated sites (Hyper-DML) and 808 hypermethylated differentially methylated regions (Hyper-DMR).
[0069] Methylation microarray data (dataset II) of 415 bladder cancer and 21 adjacent normal tissue samples were downloaded from The Cancer Genome Atlas (TCGA-BLCA), and 38,237 Hyper-DMLs were further identified through linear model analysis.
[0070] The data were screened using strict criteria (including AUC, methylation rate differences, etc.). Subsequently, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to select features from the selected Hyper-DMLs. Finally, by integrating the results from the two sets of data, 17 Hyper-DMLs were selected as methylation biomarkers for urothelial carcinoma detection (as shown in Table 1).
[0071] Table 1
[0072] DMR number chromosome Starting position End position DMR length 1 chr1 119528469 119530711 2243 2 chr1 36042374 36043443 1070 3 chr10 112838029 112838910 882 4 chr10 105344152 105344747 596 5 chr11 2890715 2891126 412 6 chr12 54348801 54349427 627 7 chr17 40700226 40700922 697 8 chr17 41363502 41364563 1062 9 chr18 46354015 46354306 292 10 chr19 45655294 45657245 1952 11 chr2 63282360 63284369 2010 12 chr3 147113840 147115561 1722 13 chr5 140802400 140802884 485 14 chr7 27225125 27225962 838 15 chr8 57358060 57359512 1453 16 chr8 57359216 57359347 132 17 chr8 144789639 144790191 553
[0073] Furthermore, the aforementioned methylation markers were used for model construction. First, the methylation rates (e.g., methylation rates of 17 Hyper-DMR markers) were calculated in 49 bladder cancer tissue samples and 32 adjacent normal tissue samples. Figure 1 As shown in the figure, this represents the ratio of methylated cytosine to all cytosine in the CpG sites within the DMR region. Then, various machine learning algorithms (including Ridge Regression, LASSO Regression, Logistic Regression, Random Forest, and Support Vector Machine) were applied to model these 81 sample data, and the model parameters were optimized through 10 repeated 10-fold cross-validation. The best-performing model was selected by comparing the AUC values of each model on the above dataset. Ultimately, the Ridge Regression model performed best, and its Receiver Operating Characteristic (ROC) curve was plotted. Figure 2 Its AUC value is 0.925.
[0074] Understandably, the above analysis was implemented using packages such as glmnet and tidymodels in the R language environment. Specifically, the glmnet package was used for feature selection, while the tidymodels package was used for data preprocessing, model building, and cross-validation to evaluate the performance of each model.
[0075] Example 2: Performance Validation of Predictive Model Based on Public Dataset
[0076] This embodiment uses the RRBS public dataset to verify the performance of the prediction model obtained in Example 1. Specifically, it includes:
[0077] RRBS methylation data (dataset III) containing bladder cancer and adjacent normal tissue were downloaded from the SRA public database. This dataset contains 46 samples, 31 of which are bladder cancer samples and 15 are adjacent normal tissue samples. We downloaded the original fastq files and performed in-depth analysis, calculating the methylation rate of each CpG site and the methylation rate of 17 Hyper-DMR sites. Figure 3 The trained model was used to perform predictive analysis on the dataset, and ROC curves were plotted. Figure 4 The final AUC value for this dataset was 0.951. This result shows that the models built using 17 Hyper-DMRs also exhibit excellent discriminative ability on independent datasets.
[0078] Example 3: Performance Validation of Predictive Model Based on Clinical Samples
[0079] This embodiment uses clinical urine samples to validate the performance of the prediction model obtained in Example 1. Specifically, it includes:
[0080] Whole-genome methylation sequencing was performed on urinary exfoliated cell samples from 25 patients with urothelial carcinoma (22 with bladder cancer, 2 with ureteral cancer, and 1 with renal pelvis cancer) and 25 control subjects. After filtering and alignment, the methylation rate of the relevant Hyper-DMR was calculated. A trained model was used to perform predictive analysis on this dataset, and ROC curves were plotted. Figure 5 The final AUC value for this dataset was 0.947. This result demonstrates that the models constructed using 17 Hyper-DMRs also exhibit excellent discriminative ability on urine exfoliated cell samples.
[0081] In summary, the Hyper-DMR (Table 1) provided in this application can be used for urothelial carcinoma screening. By detecting the methylation rate of these Hyper-DMRs and combining it with a predictive model to predict the likelihood of a subject developing urothelial carcinoma, the purpose of screening for urothelial carcinoma can be achieved in the general population or high-risk groups. This invention provides an accurate, simple, and economical method for early screening of urothelial carcinoma, which can improve the detection rate of urothelial carcinoma, especially early-stage urothelial carcinoma, in high-risk groups and general physical examination populations, thereby improving the survival rate of urothelial carcinoma patients and saving significant medical expenses and reducing the medical burden.
[0082] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0083] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application without departing from the principles and spirit of this application.
Claims
1. A set of methylation markers, characterized in that, include: Using hg19 as a reference genome, it has at least one of the chromosomal regions shown in 1)-17); 1) chr1: 119528469-119530711; 2) chr1: 36042374-36043443; 3)chr10:112838029-112838910; 4)chr10:105344152-105344747; 5) chr11: 2890715-2891126; 6)chr12:54348801-54349427; 7)chr17:40700226-40700922; 8)chr17:41363502-41364563; 9)chr18:46354015-46354306; 10)chr19:45655294-45657245; 11) chr2: 63282360-63284369; 12) chr3: 147113840-147115561; 13) chr5: 140802400-140802884; 14)chr7:27225125-27225962; 15)chr8:57358060-57359512; 16)chr8:57359216-57359347; 17) chr8: 144789639-144790191.
2. The methylation marker set according to claim 1, characterized in that, It consists of all or part of the chromosomal regions shown in 1)-17).
3. The use of the methylation marker group according to claim 1 or 2 in any of the following: (B1) Prepare products for the diagnosis or auxiliary diagnosis of urothelial carcinoma; (B2) Prepare products for early warning of urothelial carcinoma before clinical symptoms appear; (B3) Prepare products for distinguishing or assisting in the distinction between urothelial carcinoma and normal samples.
4. The use of reagents for detecting the methylation marker group of claim 1 or 2 in the preparation of a detection product for the diagnosis of urothelial carcinoma.
5. The application according to claim 4, characterized in that, The reagents include: primer set and probe set.
6. The application according to claim 4 or 5, characterized in that, The testing products include: reagent kits, test strips, or chips.
7. A method for predicting the risk of urothelial carcinoma, characterized in that, include: To obtain the detection results of methylation rate of hypermethylated differentially methylated regions in biological samples; The test results are input into a risk prediction model to determine the risk of urothelial carcinoma in the biological sample. Wherein, the hypermethylated differential methylation region is as described in claim 1 or 2.
8. The method according to claim 7, characterized in that, The biological samples are selected from tissues or body fluids.
9. The method according to claim 8, characterized in that, The methylation rate of the hypermethylated differential methylation region is determined based on the methylated cytosine and all cytosines at the methylation sites within the hypermethylated differential methylation region.
10. The method according to claim 9, characterized in that, The methylation rate of the hypermethylated differential methylation region is determined based on the ratio of methylated cytosine to all cytosine in the methylation sites within the hypermethylated differential methylation region.
11. The method according to claim 7, characterized in that, The risk prediction model was trained in the following manner: Acquire training set data, which includes: methylation rate of hypermethylated differentially methylated regions of training samples; The risk prediction model is constructed by using a machine learning model to model the training set data.
12. The method according to claim 11, characterized in that, The machine learning model is selected from ridge regression, LASSO regression, logistic regression, random forest or support vector machine.
13. The method according to any one of claims 7-12, characterized in that, The detection results are obtained based on methylation-specific PCR, digital PCR quantification, real-time PCR, DNA methylation chip, pyrosequencing, high-throughput sequencing, third-generation sequencing, single-molecule sequencing, or targeted DNA methylation sequencing.