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WO2024222786A1 - Marqueur de site de méthylation du cancer de la vessie et son utilisation - Google Patents

Marqueur de site de méthylation du cancer de la vessie et son utilisation Download PDF

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WO2024222786A1
WO2024222786A1 PCT/CN2024/089761 CN2024089761W WO2024222786A1 WO 2024222786 A1 WO2024222786 A1 WO 2024222786A1 CN 2024089761 W CN2024089761 W CN 2024089761W WO 2024222786 A1 WO2024222786 A1 WO 2024222786A1
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methylation
bladder cancer
site
auxiliary diagnosis
model
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薛蔚
曹明
杨国良
曹炀
陈海戈
金迪
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Renji Hospital
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30—Detection of binding sites or motifs
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/154—Methylation markers
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/166—Oligonucleotides used as internal standards, controls or normalisation probes

Definitions

  • the present invention relates to bladder cancer methylation site markers and applications thereof, and in particular to a bladder cancer diagnostic kit using urine methylated DNA as a biomarker, a bladder cancer auxiliary diagnosis model, a method for constructing the model, and an auxiliary diagnosis system, and belongs to the technical field of biomedical diagnosis.
  • Bladder cancer is the most common malignant tumor of the urinary system that occurs on the bladder mucosa. According to the clinical statistics of cancer in 2020, the incidence of bladder cancer ranks 12th in the general population, 6th in the male population, and 9th in mortality. In clinical diagnosis, bladder cancer is divided into two pathological types: 70% to 75% of new patients are diagnosed with non-muscle invasive bladder cancer (NMIBC), and about 25% of new patients are diagnosed with muscle invasive bladder cancer (MIBC), of which 5% to 10% of MIBC patients will have tumor metastasis.
  • NMIBC non-muscle invasive bladder cancer
  • MIBC muscle invasive bladder cancer
  • NMIBC includes Ta stage that occurs in the bladder epithelial mucosa, T1 stage that occurs in the submucosal infiltration, and Tis stage tumors that occur in situ, which account for about 70%, 20% and 10% of NMIBC, respectively.
  • Patients with early stage Ta of bladder cancer account for the majority, so it is very necessary to perform non-invasive and accurate detection of bladder cancer stage Ta.
  • the current first-line treatment for NMIBC patients is transurethral resection of bladder tumor (TURBt). About 70% of patients relapse after surgery, and 15% of patients progress to advanced pathology. Therefore, bladder cancer patients require long-term diagnosis and monitoring, and the average lifetime treatment cost is the highest.
  • cystoscopy is the gold standard with high sensitivity, but it is an invasive test that causes discomfort to patients and is expensive.
  • urine cytology has high specificity, but its sensitivity is very low, only 25% to 35%, and even only 4% to 15% for the detection of low-grade bladder cancer in stage Ta.
  • the sensitivity of FISH detection is slightly higher, at 60% to 80%, but it is also very low for the detection of low-grade bladder cancer. Therefore, there is an urgent need to develop non-invasive and effective molecular diagnostic technology for bladder cancer to reduce patient pain and improve the diagnosis and treatment monitoring effect.
  • urine samples are in direct contact with bladder cancer lesions. It has natural advantages in early tumor detection.
  • Urine biopsy is currently the main non-invasive diagnosis method for tumors.
  • This non-invasive diagnostic method analyzes tumor DNA molecules in urine to obtain effective diagnostic information, and methylation is the mainstream molecular marker for non-invasive diagnosis of liquid biopsy.
  • DNA methylation is a form of DNA chemical modification that can change molecular genetic expression without changing the DNA sequence.
  • tumor suppressor gene methylation is enhanced, so DNA methylation is an excellent biomarker for early detection of tumors.
  • Previous studies have found that certain methylated genes can be used as candidate biomarkers for bladder cancer. At present, some research teams have developed bladder cancer biomarker detection methods, many of which are urine DNA methylation detection.
  • utMeMA developed by Guangzhou Benchmark Medical detects multiple DNA methylation sites in urine tumors through a time-of-flight mass spectrometry platform and establishes a diagnostic model based on two methylation markers.
  • the sensitivity of this model in early detection of bladder cancer is only 64.5%.
  • Another example is Mi'anjian, developed by Guangzhou Dajian Biotechnology, which uses qMSP (quantitative methylation-specific polymerase chain reaction, methylation-specific fluorescent quantitative PCR) technology to detect multiple DNA methylation sites in urine for bladder cancer. It only makes positive and negative judgments for bladder cancer based on the PCR delta CT numerical threshold of the detection site.
  • the purpose of the present invention is: to address the deficiencies of bladder cancer diagnostic methods and diagnostic markers in the prior art, the present invention provides a bladder cancer methylation site marker and its application, and to prepare a diagnostic kit and construct a diagnostic model based on the bladder cancer methylation site marker of the present invention, which is helpful to achieve early diagnosis and large-scale screening of bladder cancer in clinical practice through non-invasive detection.
  • the first aspect of the present invention provides a DNA methylation site marker for detecting bladder cancer, wherein the methylation site marker is selected from at least one of chr20:45160389, chr3:5137773, chr1:47910843 and chr10:118899291;
  • the site number of chr20:45160389 is cg01362243;
  • the locus number of chr3:5137773 is cg07351192;
  • locus number of chr1:47910843 is cg06829686;
  • the site number of chr10:118899291 is cg26595643.
  • the second aspect of the present invention provides the use of the above-mentioned DNA methylation site marker for detecting bladder cancer or the reagent for detecting its methylation level in the preparation of a bladder cancer detection product.
  • the detection product comprises a detection kit.
  • the reagent for detecting the methylation level is a reagent used to detect the methylation level of the DNA methylation site marker by a methylation-specific fluorescent quantitative PCR method, wherein the test sample is urine of the subject.
  • the reagent comprises a methylation detection primer pair and a probe for detecting the DNA methylation site marker.
  • the methylation detection primer pair comprises at least one of the following primer pairs:
  • the primer pair used to detect the site cg01362243 has the sequence shown in SEQ ID NO: 1-2;
  • the primer pair used to detect the site cg07351192 has a sequence as shown in SEQ ID NO: 3-4;
  • the primer pair used to detect the site cg06829686 has the sequence shown in SEQ ID NO: 5-6;
  • the primer pair used to detect the site cg26595643 has a sequence as shown in SEQ ID NO: 7-8;
  • the methylation detection probe includes at least one of the following probes:
  • a probe for detecting site cg07351192 the sequence of which is shown in SEQ ID NO: 12;
  • a probe for detecting site cg06829686 the sequence of which is shown in SEQ ID NO: 13;
  • the probe used to detect site cg26595643 has a sequence as shown in SEQ ID NO: 14.
  • it also includes a primer pair for detecting the internal reference gene GAPDH, and the sequence of the primer pair is shown in SEQ ID NO: 9-10;
  • the third aspect of the present invention provides a method for constructing a diagnostic model based on the above-mentioned DNA methylation site marker for detecting bladder cancer, wherein the diagnostic model includes a bladder cancer auxiliary diagnostic model, an upper urinary tract urothelial carcinoma auxiliary diagnostic model or a bladder cancer specific auxiliary diagnostic model, and the method specifically comprises the following steps:
  • Step 1) Collect the auxiliary diagnosis models for bladder cancer and upper urinary tract urothelial carcinoma respectively. Urine samples from the training cohort of the model or bladder cancer-specific auxiliary diagnosis model;
  • Step 2) performing qMSP detection on the urine samples collected in step 1) to obtain the methylation value of the methylation site marker of each sample;
  • Step 3) Using the methylation value as the molecular feature, the random forest model is trained using the random forest machine learning classification algorithm to obtain a bladder cancer auxiliary diagnosis model, an upper urinary tract urothelial carcinoma auxiliary diagnosis model or a bladder cancer specific auxiliary diagnosis model.
  • the training data of the bladder cancer auxiliary diagnosis model in step 3) is the methylation value of the sites numbered cg01362243 and cg07351192 to each sample;
  • the training data of the upper urinary tract urothelial carcinoma auxiliary diagnosis model is the methylation value of the site numbered cg01362243 to each sample;
  • the training data of the bladder cancer specific auxiliary diagnosis model is the methylation value of the sites numbered cg06829686 and cg26595643 to each sample.
  • the fourth aspect of the present invention provides the application of the diagnostic model constructed by the above method in a bladder cancer auxiliary diagnosis system.
  • a fifth aspect of the present invention provides a bladder cancer auxiliary diagnosis system, including a bladder cancer auxiliary diagnosis system, and/or an upper urinary tract urothelial carcinoma auxiliary diagnosis system, and/or a bladder cancer specific auxiliary diagnosis system, wherein the diagnosis system comprises:
  • a data module used to obtain methylation level data of a methylation marker of a sample to be tested, wherein the methylation marker is the DNA methylation site marker for diagnosing bladder cancer according to claim 1;
  • a scoring module used to obtain a probability value through a diagnostic model based on the methylation level data of the methylation marker of the sample to be tested, wherein the diagnostic model is a diagnostic model constructed by the method according to claim 8;
  • the judgment module is used to judge whether the subject suffers from the corresponding cancer type based on the probability value.
  • the bladder cancer auxiliary diagnosis system is used to determine whether the subject has bladder cancer
  • the upper urinary tract urothelial carcinoma auxiliary diagnosis system is used to determine whether the subject has upper urinary tract urothelial carcinoma
  • the bladder cancer-specific auxiliary diagnosis system is used to determine whether the subject has bladder cancer or non-urothelial cancer.
  • the sixth aspect of the present invention provides the above-mentioned screening method for detecting DNA methylation site markers for bladder cancer, which mainly comprises the following steps:
  • Step 1) Obtain the methylation chip datasets of the discovery cohort and the validation cohort;
  • Step 2) standardizing the methylation level of the methylation probes in each set of methylation chip data sets obtained in step 1);
  • Step 3) Screening methylation site marker combinations from the methylation chip dataset of the discovery cohort.
  • the discovery cohort includes two sets of methylation chip data sets, one set of methylation chip data sets is a methylation chip data set of bladder cancer and normal tissue samples obtained from the BLCA project of the TCGA database, and the other set of methylation chip data sets is a methylation chip data set of bladder cancer and normal blood samples obtained from the BLCA project of the TCGA database and the E-GEOD-40279 project of the GEO database;
  • the validation cohort is a methylation chip data set of bladder cancer and normal tissue samples obtained from the GSE52955 project and the GSE111933 project of the GEO database.
  • the method for standardization in step 2 comprises the following steps:
  • Step 21): quality filtering of methylation probes; the probes filtered out may be: probes with detection p-values > 0.01, or probes with magnetic beads ⁇ 3 in more than 5% of the samples, or non-CpG probes, or SNP-related probes, or probes mapped to multiple genomic locations, or X and Y chromosome probes.
  • Step 22 filling in missing values of the methylation probes filtered in step 21);
  • the step 3) comprises the following steps:
  • Step 31 performing differential methylation site analysis based on the methylation levels of the standardized methylation probes and calculating the differential methylation sites;
  • Step 32 selecting differentially methylated sites that are reproducible in different methylation chip datasets of the discovery cohort;
  • Step 33 Select differential methylation sites with a difference value greater than 0.3 and a standard deviation within the group less than 0.12 in each set of methylation chip data sets in the discovery cohort as candidate methylation site markers;
  • Step 34 using a random forest model to evaluate the model importance of candidate methylation site markers, and selecting the methylation sites with the highest importance as the first optimized methylation site marker set;
  • Step 35 screening methylation sites with a methylation level greater than 0.5 in bladder cancer samples and a methylation level less than 0.2 in normal samples, constructing a random forest model for each screened methylation site and performing model validation, and selecting methylation sites with a sensitivity, specificity and AUC of more than 93% as the optimized methylation site marker set 2;
  • the DNA was used as a template for sulfite conversion, and then the primers of the detection site were relatively quantified and the melting curve was detected.
  • the site with only a single melting peak in the melting curve and a large difference in the ⁇ Ct value between the detection site and the internal reference gene was selected as the final DNA methylation site marker to distinguish bladder cancer from normal tissues.
  • the seventh aspect of the present invention provides a primer set for detecting the methylation level of the above-mentioned bladder cancer DNA methylation site marker, comprising at least one of the following primer pairs:
  • the primer pair used to detect the site cg01362243 has the sequence shown in SEQ ID NO: 1-2;
  • the primer pair used to detect the site cg07351192 has a sequence as shown in SEQ ID NO: 3-4;
  • the primer pair used to detect the site cg06829686 has the sequence shown in SEQ ID NO: 5-6;
  • the primer pair used to detect site cg26595643 has a sequence as shown in SEQ ID NO: 7-8.
  • it also includes a primer pair for detecting the internal reference gene GAPDH, and the sequence of the primer pair is shown in SEQ ID NO: 9-10.
  • the eighth aspect of the present invention provides a probe for detecting the methylation level of the above-mentioned bladder cancer DNA methylation site marker, comprising at least one of the following probes:
  • a probe for detecting site cg07351192 the sequence of which is shown in SEQ ID NO: 12;
  • a probe for detecting site cg06829686 the sequence of which is shown in SEQ ID NO: 13;
  • the probe used to detect site cg26595643 has a sequence as shown in SEQ ID NO: 14.
  • it also includes a probe for detecting the internal reference gene GAPDH, the sequence of the probe is shown in SEQ ID NO: 15.
  • the present invention screens DNA methylation site markers that distinguish bladder cancer from normal tissues, and uses the qMSP method to perform bladder cancer-specific methylation site detection on the four screened methylation site markers, and then constructs a bladder cancer auxiliary diagnosis system based on the methylation levels of the four methylation site markers obtained by the detection; compared with the existing bladder cancer diagnosis technology, the present invention has the following beneficial effects:
  • qMSP detection technology different PCR primers are designed based on the differences in methylated and non-methylated single-stranded DNA sequences produced after DNA is treated with sodium bisulfite.
  • the methylation detection has high sensitivity and can detect methylation as low as 0.1% (that is, 50pg methylated DNA in 50ng total DNA) or 1% methylation (that is, 0.1ng methylated DNA in 10ng total DNA).
  • the detection cost is low, the detection results are not easily affected by the differences in detection instruments and equipment, and the detection is more robust. More suitable for auxiliary diagnosis and screening of large populations of bladder cancer;
  • the present invention constructs a complete auxiliary diagnosis system for bladder cancer, which can effectively improve the sensitivity and specificity of auxiliary diagnosis for bladder cancer;
  • the system includes three auxiliary diagnosis models: an auxiliary diagnosis model for bladder cancer, an auxiliary diagnosis model for upper urinary tract urothelial carcinoma with similar pathology to bladder cancer, and a bladder cancer-specific auxiliary diagnosis model; among them, the combined use of the auxiliary diagnosis model for bladder cancer and the auxiliary diagnosis model for upper urinary tract urothelial carcinoma can effectively prevent the missed diagnosis of patients with upper urinary tract urothelial carcinoma with similar pathology to bladder cancer, and improve the detection sensitivity of bladder cancer and upper urinary tract urothelial carcinoma.
  • the bladder cancer-specific auxiliary diagnosis model can effectively reduce the probability of misdiagnosis of bladder cancer as other non-urothelial cancers (such as kidney cancer and prostate cancer).
  • FIG1 is an independent validation performance diagram of the auxiliary diagnosis model for bladder cancer
  • FIG2 is a U-Me score diagram of the auxiliary diagnosis model for bladder cancer at different stages of bladder cancer
  • FIG3 is an independent validation performance graph of the auxiliary diagnosis model for upper urinary tract urothelial carcinoma
  • FIG4 is a graph showing the independent validation performance of the bladder cancer-specific auxiliary diagnosis model.
  • the methylation chip dataset of bladder cancer and normal tissue samples of the discovery cohort 1 was obtained from the BLCA (bladder cancer-specific nuclear matrix protein) project of the TCGA (Tumor Genome Atlas) database (dataset 1), and the methylation chip dataset of bladder cancer and normal blood samples of the BLCA project of the TCGA database and the E-GEOD-40279 project of the GEO (Gene Expression Omnibus data base) database (dataset 2) were obtained; at the same time, the methylation chip dataset of bladder cancer and normal tissue samples of the validation cohort-GEO database GSE52955 project and GSE111933 project were obtained (dataset 3).
  • type I probes and type II probes are usually mixed.
  • type I probes two types of probes are designed for each methylation site: M-type magnetic beads and U-type magnetic beads.
  • the tail of the M-type magnetic beads is G, which is used to detect methylation sites; the tail of the U-type magnetic beads is A, which is used to detect unmethylated sites. If a site on the genome is methylated, then under the treatment of bisulfite, GC remains GC, and paired with M-type magnetic beads, the fluorescent signal can be detected after the fluorescent-labeled nucleotides are incorporated, and the M-type magnetic beads emit light.
  • GC becomes GT
  • U-type magnetic beads paired with U-type magnetic beads
  • the U-type magnetic beads emit light after extension.
  • type II probes only one type of magnetic bead is used, the probe end is C, and only a single base is incorporated after pairing. The type of base incorporated is judged according to the type of fluorescence, and whether it is methylated can be determined.
  • the specific method for standardizing the methylation level is: using the Beta Mixture Quantiledilation (BMIQ) method in the champ.norm function provided by the ChAMP methylation chip process package, taking type I probes as reference, based on the principle of eBayes, the methylation level of type II probes is stretched to the level of type I probes, thereby standardizing the methylation value of type II probes.
  • BMIQ Beta Mixture Quantiledilation
  • DMPs with a DMP difference value greater than 0.3 between bladder cancer and normal tissue samples or blood samples, and a standard deviation within the bladder cancer group and normal tissue sample group or bladder cancer group and blood sample group in each data set less than 0.12 were selected as candidate methylation site markers.
  • the model importance of the candidate methylation site markers is evaluated based on the random forest model, and the top five methylation sites in importance are further screened as the optimized methylation site marker set 1.
  • methylation sites with methylation levels > 0.5 in bladder cancer samples and ⁇ 0.2 in normal tissue and normal blood samples were screened.
  • a random forest model was constructed using the methylation profiles of bladder cancer and normal tissue samples in the discovery cohort obtained in the above methylation level standardization step.
  • the model was validated using the methylation profiles of bladder cancer and normal tissue samples in the validation cohort obtained in the above methylation level standardization step to obtain sensitivity, specificity, and receiver operating characteristic curve area (Area under the Curve of Receiver operating characteristic, AUC). 22 methylation sites with sensitivity, specificity, and AUC > 93% were selected as the optimized methylation site marker set II.
  • methylation site marker sets 1 and 2 a total of 27 methylation site markers were screened out, and MethyLight methylation primers and probes were designed for these 27 sites.
  • Each pair of primers and probes contained multiple CpG sequences.
  • CpG unmethylated and CpG fully methylated DNA were used as templates, respectively.
  • the primers of the detection sites were relatively quantified (GAPDH internal reference gene) and the melting curve was detected using the SYBR Green dye method.
  • the sites with only a single product (i.e., a single melting peak) in the melting curve and a large difference in the ⁇ Ct value between the detection site and the GAPDH internal reference gene were selected as the final DNA methylation site markers for distinguishing bladder cancer from normal tissues.
  • a total of 4 methylation sites were obtained, including cg01362243 (lncRNA RP11-89), cg07351192 (the intergenic region between the BHLHE40 gene and the ARL8B gene), cg06829686 (the intergenic region between the FOXD2 gene and the TRAD2B gene), and cg26595643 (the TSS1500 region of the VAX1 gene).
  • the specific information is shown in Table 1.
  • Urine separation Collect 60 mL of morning urine sample and send it to Ningbo Ajie Kangning Biotechnology Co., Ltd. The collected urine samples were centrifuged at 1600 g for 20 min, and the supernatant was discarded to obtain the urine sediment sample. The sediment cells were resuspended in 200 ⁇ L PBS for extraction.
  • the system for Bisulfite conversion includes: 130 ⁇ L Bisulfite conversion solution, 20-80 ng DNA, and water to 150 ⁇ L. Put the prepared system on the PCR instrument and perform the conversion according to the procedure in Table 2.
  • cg01362243-P and cg07351192-P are labeled with FAM probes, and the quenching group is BHQ1; cg01362243-P and cg07351192-P are labeled with CY5 probes, and the quenching group is BHQ2; GAPDH-P is labeled with HEX probe, and the quenching group is BHQ1.
  • the probes were synthesized by Suzhou Jinweizhi Biotechnology Co., Ltd.
  • the 10X primer-probe reaction solution system includes: 2 ⁇ L of forward primer, 2 ⁇ L of reverse primer, and 1 ⁇ L of probe for methylation detection of methylation site markers, 2 ⁇ L of forward primer, 2 ⁇ L of reverse primer, and 1 ⁇ L of probe for internal reference genes, and 90 ⁇ L of water.
  • the 10X primer probe reaction liquid system for detecting the methylation level of the cg01362243 site is: 100 ⁇ M cg01362243-F 2 ⁇ L, 100 ⁇ M cg01362243-R 2 ⁇ L, 100 ⁇ M cg01362243-P 1 ⁇ L, 100 ⁇ M GAPDH-F 2 ⁇ L, 100 ⁇ M GAPDH-R 2 ⁇ L, 100 ⁇ M GAPDH-P 1 ⁇ L, and 90 ⁇ L water.
  • the methylation fluorescence quantitative PCR reaction system (see Table 5) so that the final concentration of the primers in the system is The concentration is 200 nM, the final concentration of the probe is 100 nM; the amount of DNA template added in each reaction is 10-40 ng; the PCR reaction enzyme is a common fluorescent quantitative PCR enzyme.
  • PCR reaction systems were prepared for four loci, cg01362243, cg07351192, cg06829686 and cg26595643, and methylation fluorescence quantitative PCR detection and analysis were performed using Roche LightCycler 480II.
  • each reaction also required a reaction of a methylation-positive DNA (CpG methylated human genomic DNA, Thermo Fisher) sample converted by Bisulfite as a control.
  • the PCR reaction program selected FAM (465-510), CY5 (618-660) and VIC/HEX/Yellow555 (533-580) as fluorescence detection channels.
  • the PCR reaction program is shown in Table 6.
  • the methylation fluorescence quantitative PCR results are analyzed by the "Abs Quant/Fit Point" method, and the Ct value of the sample test results is output.
  • the Ct value of the cg01362243 site of sample 1 is X1
  • the Ct value of the methylation-positive DNA is Xp1
  • the Ct value of the reference gene GAPDH of sample 1 is Y1
  • the Ct value of the methylation-positive DNA is Yp1
  • Urine samples from 47 bladder cancer patients and 39 healthy volunteers were collected as a training cohort, and methylation site marker qMSP detection was performed according to the method of Example 2.
  • the methylation values of sites cg01362243 and cg07351192 in the bladder cancer group vs the healthy normal group were used as molecular features.
  • the random forest machine learning classification algorithm that is, the randomForest function of the randomForest tool, was used to construct a random forest model to obtain a bladder cancer auxiliary diagnosis model.
  • the input training data are the label (label, i.e., positive or negative sample) to which each sample belongs, and the methylation values of the cg01362243 and cg07351192 sites.
  • the importance of each methylation feature and the importance of each sample to the model construction are evaluated using the random forest algorithm.
  • the classification voting results of the N decision trees in the random forest are displayed in percentage form, i.e., the result of each sample belonging to each label is expressed in percentage probability.
  • the predict function of R language is used to identify the test samples.
  • the predict parameter type is set to prob, which can identify the probability of the test samples being classified as bladder cancer and healthy.
  • the specific probability is determined by the classification votes of the N decision trees in the random forest. The calculation formula is as follows:
  • N1 represents the number of decision trees that predict the sample as bladder cancer
  • N is the number of decision trees in the random forest.
  • the test sample is judged to be a positive sample (ie, bladder cancer); when the probability P1 of the test sample being classified as bladder cancer is ⁇ 0.4, the test sample is judged to be a negative sample (ie, healthy and normal).
  • Verification of the auxiliary diagnosis model for bladder cancer Urine samples from 23 bladder cancer patients and 27 healthy volunteers were collected as an independent verification cohort, and methylation site marker qMSP detection was performed according to the method of Example 2 to obtain the methylation values of sites cg01362243 and cg07351192. Based on the above auxiliary diagnosis model for bladder cancer, Use the predict function of R language to make predictions, set the predict parameter type to prob, and obtain the probability P1 that the validation sample is classified as bladder cancer. The calculation formula is the same as above.
  • the validation sample is judged to be a positive sample (i.e., bladder cancer); when the probability of the validation sample being classified as bladder cancer is ⁇ 0.4, the validation sample is judged to be a negative sample (i.e., healthy and normal).
  • the model prediction results of the validation cohort samples were compared with the actual clinical grouping labels (bladder cancer and healthy and normal), and the sensitivity, specificity, and AUC of the bladder cancer diagnosis model were calculated. The results are shown in Figure 1.
  • the sensitivity of the bladder cancer auxiliary diagnosis model is 84%, the specificity is 93.33%, and the AUC is 87.53%.
  • the auxiliary diagnosis model U-Me score has significant differences (see Figure 2). Even in the earliest stage Ta patients with bladder cancer, the auxiliary diagnosis model U-Me score can be significantly distinguished from the healthy and normal population.
  • Urine samples from 19 UTUC (upper tract urothelial cancer) patients and 45 healthy volunteers were collected as a training cohort.
  • the methylation site marker qMSP detection was performed according to the method of Example 2.
  • the methylation value of the site cg01362243 in the UTUC group vs the healthy normal group was used as the molecular feature.
  • the random forest machine learning classification algorithm that is, the randomForest function of the randomForest tool, was used to construct a random forest model to obtain an auxiliary diagnosis model for upper tract urothelial carcinoma.
  • the random forest algorithm is used to evaluate the importance of each methylation feature factor and the importance of each sample to the model construction.
  • the classification voting results of the N decision trees in the random forest are displayed in the form of percentages, that is, the result of each sample belonging to each label is expressed in percentage probability.
  • the predict function of the R language is used to discriminate the test samples.
  • the predict parameter type is set to prob, which can determine the probability of the test sample being classified as UTUC and healthy.
  • the specific probability is determined by the classification votes of the N decision trees in the random forest.
  • the calculation formula is as follows:
  • N2 represents the number of decision trees whose predicted samples are UTUC
  • N is the number of decision trees in the random forest.
  • the test sample is judged to be a positive sample (ie, UTUC); when the probability P2 of the test sample being classified as UTUC is ⁇ 0.5, the test sample is judged to be a negative sample (ie, healthy and normal).
  • auxiliary diagnosis model for upper tract urothelial carcinoma Urine samples from 7 UTUC patients and 30 healthy volunteers were collected as an independent validation cohort, and the methylation site marker qMSP detection was performed according to the method of Example 2 to obtain the methylation value of the site cg01362243.
  • the predict function of the R language was used to perform prediction based on the auxiliary diagnosis model for upper tract urothelial carcinoma, and the predict parameter type was set to prob to obtain the probability P2 that the validation sample was classified as UTUC, and the calculation formula was the same as above.
  • the validation sample When the probability of the validation sample being classified as UTUC P2 ⁇ 0.5, the validation sample is judged as a positive sample (ie, UTUC); when the probability of the validation sample being classified as UTUC P2 ⁇ 0.5, the validation sample is judged as a negative sample (ie, healthy and normal).
  • the model prediction results of the validation cohort samples were compared with the actual clinical grouping labels (UTUC and healthy and normal), and the sensitivity, specificity, and AUC of the auxiliary diagnosis model for upper urinary tract urothelial carcinoma were calculated. The results are shown in Figure 3.
  • the sensitivity of the auxiliary diagnosis model for upper urinary tract urothelial carcinoma is 85.71%, specificity is 93.33%, and AUC is 88.33%.
  • Urine samples from 23 bladder cancer patients and 18 non-urothelial cancer patients were collected as a training cohort, and methylation site marker qMSP detection was performed according to the method of Example 2.
  • the methylation values of sites cg06829686 and cg26595643 in the bladder cancer group vs the non-urothelial cancer group were used as molecular features.
  • the random forest machine learning classification algorithm i.e., the randomForest function of the randomForest tool, was used to construct a random forest model to obtain a bladder cancer-specific auxiliary diagnosis model.
  • the input training data is the label to which each sample belongs (1abel, that is, positive or negative sample), and the methylation values of the cg06829686 and cg26595643 sites.
  • the random forest algorithm is used to evaluate the importance of each methylation feature factor and the importance of each sample to the model construction.
  • the classification voting results of the N decision trees in the random forest are displayed in the form of percentages, that is, the results of each sample belonging to each label. Expressed as a percentage probability.
  • the predict function of R language is used to distinguish the test samples.
  • the predict parameter type is set to prob, which can determine the probability of the test samples being classified as bladder cancer and non-urothelial cancer.
  • the specific probability is determined by the classification votes of N decision trees in the random forest.
  • the calculation formula is as follows:
  • N3 represents the number of decision trees that predict the sample as bladder cancer
  • N is the number of decision trees in the random forest.
  • the test sample is judged to be a positive sample (ie, bladder cancer); when the probability P3 of the test sample being classified as bladder cancer is ⁇ 0.5, the test sample is judged to be a negative sample (non-urothelial cancer).
  • Verification of the bladder cancer-specific auxiliary diagnosis model Urine samples from 24 bladder cancer patients and 8 non-urothelial cancer patients were collected as an independent verification cohort, and the methylation site marker qMSP detection was performed according to the method of Example 2 to obtain the methylation values of sites cg06829686 and cg26595643. Based on the bladder cancer-specific auxiliary diagnosis model, the predict function of the R language was used for prediction, and the predict parameter type was set to prob to obtain the probability P3 of the verification sample being classified as bladder cancer, and the calculation formula was the same as above.
  • the validation sample When the probability of the validation sample being classified as bladder cancer P3 ⁇ 0.5, the validation sample is judged to be a positive sample (i.e., bladder cancer); when the probability of the validation sample being classified as bladder cancer P3 ⁇ 0.5, the validation sample is judged to be a negative sample (i.e., non-urothelial cancer).
  • the model prediction results of the validation cohort samples were compared with the actual clinical grouping labels (bladder cancer and non-urothelial cancer), and the sensitivity, specificity, and AUC of the bladder cancer-specific auxiliary diagnosis model were calculated. The results are shown in Figure 4.
  • the sensitivity of the bladder cancer-specific auxiliary diagnosis model is 83.33%, the specificity is 75%, and the AUC is 69.01%.
  • Urine samples were collected from people at high risk of bladder cancer or people undergoing normal physical examinations, and qMSP detection was performed on the four bladder cancer-specific methylation sites to obtain the methylation values of the four bladder cancer-specific methylation sites.
  • the bladder cancer auxiliary diagnosis model and the upper urinary tract urothelial carcinoma auxiliary diagnosis model were used for analysis. If the judgment results of the bladder cancer auxiliary diagnosis model and the upper urinary tract urothelial carcinoma auxiliary diagnosis model are both negative, the subject is judged to be healthy and normal, and only needs to maintain follow-up screening. If the judgment result of the bladder cancer auxiliary diagnosis model or the upper urinary tract urothelial carcinoma auxiliary diagnosis model is positive, the bladder cancer-specific auxiliary diagnosis model is needed to further analyze the tested sample.
  • the bladder cancer-specific auxiliary diagnosis model If the judgment result of the bladder cancer-specific auxiliary diagnosis model is positive, the subject needs to undergo further cystoscopy to determine whether he has bladder cancer or UTUC; if .... If the result of the sex-assisted diagnosis model is negative, the person being tested needs to undergo further cystoscopy and other urological cancer examinations to determine whether he or she has bladder cancer and other urological cancers.
  • Example 2 urine samples from 25 bladder cancer patients, 7 UTUC patients, 8 non-urothelial carcinoma patients and 30 healthy normal subjects were subjected to methylation qMSP detection to obtain the methylation values of 4 bladder cancer-specific methylation sites, which were analyzed using the bladder cancer auxiliary diagnosis system constructed in Example 3.
  • the results are shown in Table 7 (the slash "/" in the table indicates a model training set sample).
  • bladder cancer patients 20 patients were diagnosed as "bladder cancer", 2 patients were diagnosed as “bladder cancer or non-urothelial cancer", 2 patients were diagnosed as "non-urothelial cancer", and 1 patient was diagnosed as "healthy and normal”; among 7 UTUC patients, 6 patients were diagnosed as "UTUC” and 1 patient was diagnosed as "healthy and normal”; among 8 non-urothelial cancer patients, 6 patients were diagnosed as "non-urothelial cancer”; 2 patients were diagnosed as "bladder cancer”; among 30 healthy normal people, 26 healthy normal people were diagnosed as "healthy and normal", 2 healthy normal people were diagnosed as "bladder cancer", and 2 healthy normal people were diagnosed as "UTUC”.
  • the above results prove that the bladder cancer auxiliary diagnosis system constructed by the present invention has clinical application value and high diagnostic performance.

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Abstract

L'invention concerne un marqueur de site de méthylation du cancer de la vessie et son utilisation. Un groupe de marqueurs de sites de méthylation de l'ADN du cancer de la vessie est criblé, contenant quatre sites de méthylation : cg01362243, cg07351192, cg06829686 et cg26595643. La présente invention concerne également un procédé de criblage des marqueurs de sites, un kit de détection et son utilisation, ainsi qu'un modèle de diagnostic auxiliaire du cancer de la vessie construit à partir des marqueurs de sites, un procédé de construction du modèle et un système de diagnostic auxiliaire du cancer de la vessie. Grâce au criblage du marqueur de sites de méthylation de l'ADN spécifique du cancer de la vessie et à la construction du système de diagnostic auxiliaire du cancer de la vessie en fonction du niveau de méthylation du marqueur criblé, un moyen technique pratique et efficace est présenté pour le diagnostic précoce et le dépistage à grande échelle du cancer de la vessie.
PCT/CN2024/089761 2023-04-26 2024-04-25 Marqueur de site de méthylation du cancer de la vessie et son utilisation Pending WO2024222786A1 (fr)

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CN117187385B (zh) * 2023-08-18 2024-05-14 上海爱谱蒂康生物科技有限公司 生物标志物在制备预测和/或诊断utuc的试剂盒中的应用
CN117363733B (zh) * 2023-12-07 2024-02-23 湖南宏雅基因技术有限公司 Per1和lox双基因甲基化联合诊断的检测引物探针组在制备膀胱癌诊断试剂中的应用
CN117831633B (zh) * 2023-12-15 2024-11-19 江苏和福生物科技有限公司 一种基于诊断模型的膀胱癌生物标志物提取方法

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102311953A (zh) * 2011-09-23 2012-01-11 上海市肿瘤研究所 尿液诊断膀胱癌的方法和试剂盒
WO2014168346A1 (fr) * 2013-04-10 2014-10-16 (주)지노믹트리 Méthode de dépistage du cancer de la vessie au moyen d'un gène marqueur épigénétique spécifique du cancer de la vessie
CN109844139A (zh) * 2016-09-29 2019-06-04 哈鲁曼有限公司 散发性大肠癌发病可能性的判定方法
CN116479132A (zh) * 2023-04-26 2023-07-25 上海交通大学医学院附属仁济医院 膀胱癌甲基化位点标志物及其应用

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102424840B (zh) * 2011-12-20 2013-12-18 上海市肿瘤研究所 尿液诊断膀胱癌患者复发危险性的方法和试剂盒
US20230115828A1 (en) * 2020-04-03 2023-04-13 Decipher Biosciences, Inc. Genomic classifiers for prognosing and treating clinically aggressive luminal bladder cancer

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102311953A (zh) * 2011-09-23 2012-01-11 上海市肿瘤研究所 尿液诊断膀胱癌的方法和试剂盒
WO2014168346A1 (fr) * 2013-04-10 2014-10-16 (주)지노믹트리 Méthode de dépistage du cancer de la vessie au moyen d'un gène marqueur épigénétique spécifique du cancer de la vessie
CN109844139A (zh) * 2016-09-29 2019-06-04 哈鲁曼有限公司 散发性大肠癌发病可能性的判定方法
CN116479132A (zh) * 2023-04-26 2023-07-25 上海交通大学医学院附属仁济医院 膀胱癌甲基化位点标志物及其应用

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
HE CONGCONG, ZHANG YUJING, JIANG HANGHANG, NIU XUELI, QI RUIQUN, GAO XINGHUA: "Identification of differentially expressed methylated genes in melanoma versus nevi using bioinformatics methods", PEERJ, vol. 8, pages e9273, XP093228951, ISSN: 2167-8359, DOI: 10.7717/peerj.9273 *
WANG SHUYU, XU DALI, GAO BO, YAN SHUHAN, SUN YIWEI, TANG XINXING, JIAO YANJIA, HUANG SHAN, ZHANG SHUMEI: "Heterogeneity Analysis of Bladder Cancer Based on DNA Methylation Molecular Profiling", FRONTIERS IN ONCOLOGY, FRONTIERS MEDIA S.A., vol. 12, XP093228949, ISSN: 2234-943X, DOI: 10.3389/fonc.2022.915542 *
WU HAO, ZHANG ZI-YI, ZHANG ZE, XIAO XIN-YING, GAO SHENG-LIN, LU CHAO, ZUO LI, ZHANG LI-FENG: "Prediction of bladder cancer outcome by identifying and validating a mutation-derived genomic instability-associated long noncoding RNA (lncRNA) signature", BIOENGINEERED, LANDES BIOSCIENCE, US, vol. 12, no. 1, 1 January 2021 (2021-01-01), US , pages 1725 - 1738, XP093228950, ISSN: 2165-5979, DOI: 10.1080/21655979.2021.1924555 *

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