Methylation marker combination for detecting cervical cancer and / or precancerous lesions of cervical cancer as well as application, product, detection device, computer readable storage medium, electronic terminal and computer program of methylation marker combination

Abstract

The invention relates to the field of biological medicine, in particular to a methylation marker combination for detecting cervical cancer and / or precancerous lesions of cervical cancer, and the methylation marker combination comprises a methylation ZNF536 gene or a fragment thereof, and any one or more of methylation NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F or WEE1P1 genes or a fragment of the methylation NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F or WEE1P1 genes. According to the application, methylation differences between patients with cervical cancer and / or cervical precancerous lesions and healthy people are researched through cervical exfoliated cell DNA of Chinese population, and five optimal methylation marker combinations are screened for the first time and are used for establishing a method for predicting the risk of cervical cancer and / or cervical precancerous lesions of Chinese population; the method is suitable for risk assessment of early-stage cervical cancer and / or precancerous lesions of cervical cancer in Chinese population and screening and diagnosis of cervical cancer and / or precancerous lesions of cervical cancer.

Description

Technical Field

[0001] The present application relates to the field of biomedicine, and in particular to a methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions, and its application, product, detection device, computer-readable storage medium, electronic terminal and computer program. Background Art

[0002] Only persistent infection with high-risk human papillomavirus (hrHPV) can lead to cervical lesions, which progress from mild cervical precancerous lesions (cervical intraepithelial neoplasia, CIN), moderate CIN and severe CIN, and eventually develop into cervical cancer. Current screening guidelines recommend HPV DNA testing as the preferred method, or combined with thin-layer cytology (Thinprep cytologic test, TCT) testing. hrHPV test results are objective and highly reproducible, but they cannot distinguish between transient and transformed infections, which may lead to an increase in colposcopy referrals and anxiety in women with positive results. Cytological screening methods such as TCT have high specificity, but have limitations such as diagnostic subjectivity and low sensitivity, which can lead to missed diagnoses.

[0003] Studies in recent years have shown that early epigenetic changes are important features of tumor occurrence and development. DNA methylation detection has gradually become an emerging means of detecting cervical cancer and cervical precancerous lesions. Various methylated genes such as FAM19A4, Mir124-2, PAX1, ZNF582, SOX1, EPB41L3, etc. have been considered as biomarkers for cervical cancer screening. However, the detection efficiency of cervical cancer and cervical precancerous lesions in the Chinese population is average. Therefore, it is of great clinical significance to explore and develop more effective and objective biomarkers. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a group of biomarkers closely related to the occurrence and development of cervical cancer and precancerous lesions from the Chinese population, and develops detection primers, probes and detection kits based on methylation markers. The kit has the characteristics of objectivity, high repeatability, low cost, high sensitivity and high specificity, and provides new ideas and detection methods for the early diagnosis and treatment of cervical cancer and precancerous lesions. The main contribution of the present application is the discovery of these methylation marker combinations, and any methylation level analysis method can be used to detect the methylation level of the methylation markers discovered in the present application.

[0005] The present application provides a methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions, wherein the methylation marker combination includes a methylated ZNF536 gene or a fragment thereof, and any one or more of a methylated NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F or WEE1P1 gene or a fragment thereof.

[0006] The present application also provides the use of the above-mentioned methylation marker combination or its detection object in the preparation of a product for detecting cervical cancer and / or cervical precancerous lesions.

[0007] The present application also provides a product for detecting cervical cancer and / or cervical precancerous lesions, wherein the product comprises the above-mentioned methylation marker combination or a detection substance thereof.

[0008] The present application also provides a cervical cancer and / or cervical precancerous lesion detection device, comprising the following modules: a data acquisition module, used to provide disease risk level data of the target marker combination of the sample to be tested, wherein the target marker combination is the above-mentioned methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions; a judgment module, used to evaluate the cervical cancer and / or cervical precancerous lesion status of the individual corresponding to the sample to be tested based on the disease risk level data of the target marker combination of the sample to be tested.

[0009] The present application also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, a method for detecting cervical cancer and / or cervical precancerous lesions is implemented, and the method includes: obtaining disease risk level data of the target marker combination of the sample to be tested, wherein the target marker combination is the above-mentioned methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions; based on the disease risk level data of the target marker combination of the sample to be tested, evaluating the cervical cancer and / or cervical precancerous lesions of the individual corresponding to the sample to be tested.

[0010] The present application also provides an electronic terminal, comprising: a processor, a memory, a network interface and a user interface; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes the method for detecting cervical cancer and / or cervical precancerous lesions described in the above-mentioned computer-readable storage medium.

[0011] The present application also provides a computer program, which, when executed by a processor, implements the method for detecting cervical cancer and / or cervical precancerous lesions described in the above-mentioned computer-readable storage medium.

[0012] The beneficial effects brought by this application include but are not limited to: (1) This application provides new biomarkers for detecting cervical cancer and cervical precancerous lesions from the Chinese population, which have the characteristics of high sensitivity and high specificity, and provide new ideas for the early diagnosis and treatment of cervical cancer and cervical precancerous lesions. (2) This application also provides nucleic acids, nucleic acid groups and / or kits for determining the modification status of DNA regions, which can confirm the existence of cervical cancer and cervical precancerous lesions, evaluate the formation or formation risk of cervical cancer and cervical precancerous lesions, and / or evaluate the progression of cervical cancer and cervical precancerous lesions, and provide useful guidance for the screening, auxiliary diagnosis and prognosis of cervical cancer and cervical precancerous lesions. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present application will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not restrictive, and include: Figure 1 The methylation levels of methylation markers in cervical exfoliated samples from different populations in Example 1 of the present application are shown. ★ p<0.05, ★★ p<0.01, ★★★ p<0.001; Figure 1 A shows the methylation level of ZNF536 gene in cervical exfoliated samples from different populations; Figure 1 B shows the methylation level of NOL4 gene in cervical exfoliated samples from different populations; Figure 1 C shows the methylation level of ZNF671 gene in cervical exfoliated samples from different populations; Figure 1 D shows the methylation level of HTR1F gene in cervical exfoliated samples from different populations; Figure 1 E shows the methylation level of PROX1-AS1 gene in cervical exfoliated samples from different populations; Figure 1 F shows the methylation level of WEE1P1 gene in cervical exfoliated samples from different populations; Figure 1 G shows the methylation level of ARHGEF4 gene in cervical exfoliated samples from different populations; Figure 1 H shows the methylation level of TTC34 gene in cervical exfoliated samples from different populations.

[0014] Figure 2 The methylation levels of methylation markers in CIN2- and CIN3+ cervical exfoliated samples of Example 2 of the present application are shown. ★ p<0.05, ★★ p<0.01, ★★★ p<0.001.

[0015] Figure 3 The receiver operating characteristic curve (ROC) diagram of the combination of five methylation markers in Example 3 of the present application for screening cervical cancer and cervical precancerous lesions is shown.

[0016] Figure 4 The receiver operating characteristic curve (ROC) diagram of the combination of five methylation markers in Example 4 of the present application for screening cervical cancer and cervical precancerous lesions in validation samples is shown.

[0017] Figure 5 This is a module diagram of a device for detecting cervical cancer and / or cervical precancerous lesions according to some embodiments of the present application.

[0018] Figure 6 This is a flow chart of a method for detecting cervical cancer and / or cervical precancerous lesions according to some embodiments of the present application.

[0019] Figure 7 Schematic diagram of the structure of an electronic terminal 700 according to some embodiments of the present application. DETAILED DESCRIPTION

[0020] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0021] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0022] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0023] The present application provides a methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions, wherein the methylation marker combination includes a methylated ZNF536 gene or a fragment thereof, and any one or more of a methylated NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F or WEE1P1 gene or a fragment thereof.

[0024] In some embodiments, the gene fragment may include a gene sub-region. A gene sub-region is a different functional region within a gene, such as a coding region and a non-coding region.

[0025] In some embodiments, the methylation markers may include methylated ZNF536 gene subregions, and any one or more of methylated NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F, or WEE1P1 gene subregions.

[0026] In some embodiments, the ZNF536 gene subregion nucleotide sequence may be as shown in SEQ ID NO.1.

[0027] SEQ ID NO.1: In some embodiments, the NOL4 gene subregion nucleotide sequence may be as shown in SEQ ID NO.5.

[0028] SEQ ID NO.5: In some embodiments, the ZNF671 gene subregion nucleotide sequence may be as shown in SEQ ID NO.9.

[0029] SEQ ID NO.9: In some embodiments, the TTC34 gene subregion nucleotide sequence may be as shown in SEQ ID NO.29.

[0030] SEQ ID NO.29: In some embodiments, the PROX1-AS1 gene subregion nucleotide sequence may be as shown in SEQ ID NO.17.

[0031] SEQ ID NO.17: In some embodiments, the ARHGEF4 gene subregion nucleotide sequence may be as shown in SEQ ID NO.25.

[0032] SEQ ID NO.25: In some embodiments, the HTR1F gene subregion nucleotide sequence may be as shown in SEQ ID NO.13.

[0033] SEQ ID NO.13: In some embodiments, the WEE1P1 gene subregion nucleotide sequence may be as shown in SEQ ID NO.21.

[0034] SEQ ID NO.21: In some embodiments, the cervical precancerous lesions may be severe cervical precancerous lesions.

[0035] The present application also provides the use of the above-mentioned methylation marker combination or its detection object in the preparation of a product for detecting cervical cancer and / or cervical precancerous lesions.

[0036] In some embodiments, the detection substance can be selected from any one or more of antibodies, membrane strips, chips, probes or primers.

[0037] In some embodiments, the test sample of the product may include any one or more of blood, serum, plasma, lymph, urine, cervical scraping cells or tissue, biopsy tissue, surgical tissue, or cervical exfoliated cells. In some embodiments, preferably, the test sample of the product may be cervical exfoliated cells.

[0038] The present application also provides a product for detecting cervical cancer and / or cervical precancerous lesions, wherein the product comprises the above-mentioned methylation marker combination or a detection substance thereof.

[0039] In some embodiments, the detector may be a detector for detecting the methylation level of a methylation marker.

[0040] In some embodiments, the detector may include a probe and a primer.

[0041] The term "primer" as used herein refers to a naturally occurring oligonucleotide (e.g., a restriction fragment) or a synthetically produced oligonucleotide that can be used as a starting point for the synthesis of a primer extension product that is complementary to a nucleic acid strand (a template or target sequence) when placed under appropriate conditions (e.g., buffer, salt, temperature, and pH) and in the presence of nucleotides and reagents for nucleic acid polymerization (e.g., DNA-dependent or RNA-dependent polymerases). Typically, a set of primers will consist of at least two primers, an "upstream primer" and a "downstream primer" that together define the amplicon (the sequence to be amplified using the primers).

[0042] The term "probe" refers to any molecule that can selectively bind to a target biomolecule (e.g., a nucleic acid sequence that hybridizes to the probe). In some embodiments, the probe can be labeled, for example, with a fluorescent group and a quenching group. In some embodiments, the probe can be a Taqman probe with a fluorescent reporter group added to the 5' end and a fluorescent quenching group added to the 3' end.

[0043] In some embodiments, the primers and probes may include specific primers and probes for the transformed sequences of the following genes or fragments thereof: ZNF536, and any one or more of NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F, or WEE1P1.

[0044] In some embodiments, the conversion may be to convert unmethylated cytosine in the gene or fragment thereof into uracil.

[0045] In some embodiments, the nucleotide sequences of the primers for detecting the methylation marker ZNF536 gene or a fragment thereof may be as shown in SEQ ID NO.2 and SEQ ID NO.3.

[0046] In some embodiments, the nucleotide sequences of the primers for detecting the methylation marker NOL4 gene or a fragment thereof may be as shown in SEQ ID NO.6 and SEQ ID NO.7.

[0047] In some embodiments, the nucleotide sequences of the primers for detecting the methylation marker ZNF671 gene or a fragment thereof may be as shown in SEQ ID NO. 10 and SEQ ID NO.11.

[0048] In some embodiments, the nucleotide sequences of the primers for detecting the methylation marker HTR1F gene or a fragment thereof may be as shown in SEQ ID NO. 14 and SEQ ID NO.15.

[0049] In some embodiments, the nucleotide sequences of the primers for detecting the methylation marker PROX1-AS1 gene or a fragment thereof may be as shown in SEQ ID NO.18 and SEQ ID NO.19.

[0050] In some embodiments, the nucleotide sequences of the primers for detecting the methylation marker WEE1P1 gene or a fragment thereof may be as shown in SEQ ID NO. 22 and SEQ ID NO.23.

[0051] In some embodiments, the nucleotide sequences of the primers for detecting the methylation marker ARHGEF4 gene or a fragment thereof may be as shown in SEQ ID NO.26 and SEQ ID NO.27.

[0052] In some embodiments, the nucleotide sequences of the primers for detecting the methylation marker TTC34 gene or a fragment thereof may be as shown in SEQ ID NO. 30 and SEQ ID NO.31.

[0053] In some embodiments, the nucleotide sequence of the probe for detecting the methylation marker ZNF536 gene or a fragment thereof may be as shown in SEQ ID NO. 4.

[0054] In some embodiments, the nucleotide sequence of the probe for detecting the methylation marker NOL4 gene or a fragment thereof may be as shown in SEQ ID NO.8.

[0055] In some embodiments, the nucleotide sequence of the probe for detecting the methylation marker ZNF671 gene or a fragment thereof may be as shown in SEQ ID NO. 12.

[0056] In some embodiments, the nucleotide sequence of the probe for detecting the methylation marker HTR1F gene or a fragment thereof may be as shown in SEQ ID NO. 16.

[0057] In some embodiments, the nucleotide sequence of the probe for detecting the methylation marker PROX1-AS1 gene or a fragment thereof may be as shown in SEQ ID NO.20.

[0058] In some embodiments, the nucleotide sequence of the probe for detecting the methylation marker WEE1P1 gene or a fragment thereof may be as shown in SEQ ID NO. 24.

[0059] In some embodiments, the nucleotide sequence of the probe for detecting the methylation marker ARHGEF4 gene or a fragment thereof may be as shown in SEQ ID NO. 28.

[0060] In some embodiments, the nucleotide sequence of the probe for detecting the methylation marker TTC34 gene or a fragment thereof may be as shown in SEQ ID NO. 32.

[0061] In some embodiments, the product can be any one of a kit, a chip, a membrane strip, a protein array, a composition or a detection system.

[0062] The term "membrane strip" refers to a diagnostic tool that utilizes the principle of specific biomolecule recognition to immobilize biomolecules such as antigens or antibodies on a membrane, specifically bind to the substances to be tested in the sample, and qualitatively or quantitatively analyze the target substances in the sample through visualization or other signal detection methods.

[0063] Protein array, also known as protein microarray, is a high-throughput biotechnology tool that allows the analysis and study of a large number of proteins at the same time. This technology allows rapid protein expression analysis, protein-protein interaction, protein-small molecule binding studies, etc. by orderly arranging thousands of different protein or protein interaction probes on a solid surface.

[0064] The term "chip" generally refers to a microdevice that integrates biosensors and microfluidic technologies, which can perform a variety of operations such as sample preparation, reaction, and detection in biological, chemical, and medical analysis processes at the microscopic level to achieve rapid and accurate detection of disease-related biomarkers.

[0065] The term "kit" refers to a packaged collection of related components, such as one or more polynucleotides or compositions, and one or more related materials, such as a delivery device (e.g., a syringe), solvents, solutions, buffers, instructions, or desiccant.

[0066] In some embodiments, the product may include one or more of DNA polymerase, deoxynucleotide (dNTP) mixture, buffer solution, primers, probes, sodium bisulfite, positive control or negative control.

[0067] The present application also provides a device for detecting cervical cancer and / or cervical precancerous lesions, such as Figure 5 As shown, the following modules are included: a data acquisition module 510 and a judgment module 520.

[0068] The data acquisition module 510 is used to provide disease risk level data of the target marker combination of the sample to be tested, where the target marker combination is the above-mentioned methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions.

[0069] In some embodiments, the disease risk level data may be a risk score value of a combination of methylation markers.

[0070] In some embodiments, the risk score value can be calculated by substituting the BV value of a single methylation marker of each sample to be tested into a Logistic regression model, wherein the regression model is obtained by training the methylation marker detection data of known samples; In data analysis and statistics, binary values ​​(BV) refer to converting raw data into numbers with only two possible values, 0 and 1. This conversion process is called binarization, which is used to represent the presence (1) or absence (0) of a feature or condition.

[0071] In this application, 0 represents negative and 1 represents positive.

[0072] The steps for binarization of ΔCt values ​​are as follows: The ΔCt value of each methylation marker first needs to be compared with the preset cutoff value (Cutoff value); If the ΔCt value is less than or equal to the cutoff value, the binary variable for that marker is 1 (positive); If the ΔCt value is greater than the cutoff value, the binary variable for that marker is 0 (negative).

[0073] In some embodiments, the ΔCt value may be the difference between the Ct value of a single methylation marker and the Ct value of an internal reference gene.

[0074] In some embodiments, the Ct value can be obtained by quantitative methylation-specific PCR detection, and the internal reference gene can be β-actin.

[0075] In some embodiments, the β-actin nucleotide sequence can be as shown in SEQ ID NO.33.

[0076] SEQ ID NO.33: AGCACAATGAAGATCAAGGTGGGTGTCTTTCCTGCCTGAGCTGACCTGGGCAGGTCGGCTGTGGGGTCCTGTGGTGTGTGGGGAGCTG In some embodiments, the ΔCt cutoff value may be the ΔCt value that maximizes the Youden Index.

[0077] Substitute into the Logistic regression model: The binary variable (0 or 1) after binarization was substituted into the Logistic regression model instead of the original ΔCt value.

[0078] The weight (coefficient) of each marker in the logistic regression model is calculated based on these binary variables.

[0079] For example, there is a ΔCt value of the methylation level of a gene, and its cutoff value is 9.73: if the ΔCt value of a sample is 9.5 (less than or equal to 9.73), the binary value of the gene is 1; if the ΔCt value of a sample is 10.0 (greater than 9.73), the binary value of the gene is 0; then, these binary results are substituted into the Logistic regression model to calculate the risk score value.

[0080] In some embodiments, in the judgment module, when the risk score value of the methylation marker combination of the sample to be tested is greater than or equal to the cutoff value, the sample to be tested is judged to be positive; when the risk score value of the methylation marker combination of the sample to be tested is less than the cutoff value, the sample to be tested is judged to be negative.

[0081] In some embodiments, the positive result means that the individual corresponding to the sample to be tested is a patient with severe cervical precancerous lesions or cervical cancer, and the negative result means that the individual corresponding to the sample to be tested is a healthy person, a patient with mild cervical precancerous lesions or a patient with moderate cervical precancerous lesions.

[0082] In some embodiments, the sample to be tested may include any one or more of blood, serum, plasma, lymph, urine, cervical scraping cells or tissue, biopsy tissue, surgical tissue, or cervical exfoliated cells. In some embodiments, preferably, the sample to be tested may be cervical exfoliated cells.

[0083] In some embodiments, the methylation marker combination may include any one or more of the following combinations: Combination 1: ZNF536 and NOL4; Combination 2: ZNF536, ZNF671, and TTC34; Combination 3: ZNF536, PROX1-AS1, and TTC34; Combination 4: ZNF536, ZNF671, ARHGEF4, and TTC34; Combination 5: ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34.

[0084] The judgment module 520 is used to evaluate the cervical cancer and / or cervical precancerous lesions of the individual corresponding to the sample to be tested based on the disease risk level data of the target marker combination of the sample to be tested.

[0085] In some embodiments, the cutoff value of combination 1 can be -1.133, risk score = -4.032 + 3.375 × BV ZNF536 +2.424×BV NOL4 .

[0086] In some embodiments, the cutoff value of combination 2 can be -1.656, risk score = -4.240 + 2.685 × BV ZNF536 +1.025× BV ZNF671 +2.484×BV TTC34 .

[0087] In some embodiments, the cutoff value of combination 3 can be -1.204, risk score = -4.295 + 2.796 × BV ZNF536 +0.865× BV PROX1-AS1 +2.521×BV TTC34 .

[0088] In some embodiments, the cutoff value of combination 4 can be -1.756, risk score = -4.272 + 2.314 × BV ZNF536 +0.755× BV ZNF671 +1.334×BV ARHGEF4 +1.963×BV TTC34 .

[0089] In some embodiments, the cutoff value of combination 5 can be -1.271, risk score = -4.322 + 2.391 × BV ZNF536 +0.614×BV ZNF671 +0.665×BV HTR1F +0.655×BV WEE1P1 +2.145×BV TTC34 .

[0090] The present application also provides a computer-readable storage medium, wherein the storage medium stores computer instructions. When the computer instructions are executed by a processor, a method for detecting cervical cancer and / or cervical precancerous lesions is implemented. The flowchart of the method is as follows: Figure 6 shown.

[0091] In step S610, the disease risk level data of the target marker combination of the sample to be tested is obtained, and the target marker combination is the above-mentioned methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions.

[0092] In some embodiments, the disease risk level data may be a risk score value of a combination of methylation markers.

[0093] In some embodiments, the risk score value can be calculated by substituting the BV value of a single methylation marker of each sample to be tested into a Logistic regression model, wherein the regression model is obtained by training the methylation marker detection data of known samples; In data analysis and statistics, binary values ​​(BV) refer to converting raw data into numbers with only two possible values, 0 and 1. This conversion process is called binarization, which is used to represent the presence (1) or absence (0) of a feature or condition.

[0094] In this application, 0 represents negative and 1 represents positive.

[0095] The steps for binarization of ΔCt values ​​are as follows: The ΔCt value of each methylation marker first needs to be compared with the preset cutoff value (Cutoff value); If the ΔCt value is less than or equal to the cutoff value, the binary variable for that marker is 1 (positive); If the ΔCt value is greater than the cutoff value, the binary variable for that marker is 0 (negative).

[0096] In some embodiments, the ΔCt value may be the difference between the Ct value of a single methylation marker and the Ct value of an internal reference gene.

[0097] In some embodiments, the Ct value can be obtained by quantitative methylation-specific PCR detection, and the internal reference gene can be β-actin.

[0098] In some embodiments, the β-actin nucleotide sequence can be as shown in SEQ ID NO.33.

[0099] SEQ ID NO.33: AGCACAATGAAGATCAAGGTGGGTGTCTTTCCTGCCTGAGCTGACCTGGGCAGGTCGGCTGTGGGGTCCTGTGGTGTGTGGGGAGCTG In some embodiments, the ΔCt cutoff value may be the ΔCt value that maximizes the Youden Index.

[0100] Substitute into the Logistic regression model: The binary variable (0 or 1) after binarization was substituted into the Logistic regression model instead of the original ΔCt value.

[0101] The weight (coefficient) of each marker in the logistic regression model is calculated based on these binary variables.

[0102] For example, there is a ΔCt value of the methylation level of a gene, and its cutoff value is 9.73: if the ΔCt value of a sample is 9.5 (less than or equal to 9.73), the binary value of the gene is 1; if the ΔCt value of a sample is 10.0 (greater than 9.73), the binary value of the gene is 0; then, these binary results are substituted into the Logistic regression model to calculate the risk score value.

[0103] In some embodiments, in the judgment module, when the risk score value of the methylation marker combination of the sample to be tested is greater than or equal to the cutoff value, the sample to be tested is judged to be positive; when the risk score value of the methylation marker combination of the sample to be tested is less than the cutoff value, the sample to be tested is judged to be negative.

[0104] In some embodiments, the positive result means that the individual corresponding to the sample to be tested is a patient with severe cervical precancerous lesions or cervical cancer, and the negative result means that the individual corresponding to the sample to be tested is a healthy person, a patient with mild cervical precancerous lesions or a patient with moderate cervical precancerous lesions.

[0105] In some embodiments, the sample to be tested may include any one or more of blood, serum, plasma, lymph, urine, cervical scraping cells or tissue, biopsy tissue, surgical tissue, or cervical exfoliated cells. In some embodiments, preferably, the sample to be tested may be cervical exfoliated cells.

[0106] In some embodiments, the methylation marker combination may include any one or more of the following combinations: Combination 1: ZNF536 and NOL4; Combination 2: ZNF536, ZNF671, and TTC34; Combination 3: ZNF536, PROX1-AS1, and TTC34; Combination 4: ZNF536, ZNF671, ARHGEF4, and TTC34; Combination 5: ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34.

[0107] In step S620, based on the disease risk level data of the target marker combination of the sample to be tested, the cervical cancer and / or cervical precancerous lesions of the individual corresponding to the sample to be tested are evaluated.

[0108] In some embodiments, the cutoff value of combination 1 can be -1.133, risk score = -4.032 + 3.375 × BV ZNF536 +2.424×BV NOL4 .

[0109] In some embodiments, the cutoff value of combination 2 can be -1.656, risk score = -4.240 + 2.685 × BV ZNF536 +1.025× BV ZNF671 +2.484×BV TTC34 .

[0110] In some embodiments, the cutoff value of combination 3 can be -1.204, risk score = -4.295 + 2.796 × BV ZNF536 +0.865× BV PROX1-AS1 +2.521×BV TTC34 .

[0111] In some embodiments, the cutoff value of combination 4 can be -1.756, risk score = -4.272 + 2.314 × BV ZNF536 +0.755× BV ZNF671 +1.334×BV ARHGEF4 +1.963×BV TTC34 .

[0112] In some embodiments, the cutoff value of combination 5 can be -1.271, risk score = -4.322 + 2.391 × BV ZNF536 +0.614×BV ZNF671 +0.665×BV HTR1F+0.655×BV WEE1P1 +2.145×BV TTC34 .

[0113] The present application also provides an electronic terminal 700, including: a processor 701, a memory 702, a network interface 704 and a user interface 703; the memory 702 is used to store computer programs, and the processor 701 is used to execute the computer programs stored in the memory 702, so that the terminal executes the method for detecting cervical cancer and / or cervical precancerous lesions described in the above-mentioned computer-readable storage medium.

[0114] Specific as Figure 7 As shown, it is an optional hardware structure diagram of an electronic terminal 700 provided in an embodiment of the present application. The terminal 700 can be a mobile phone, a computer device, a tablet device, a personal digital processing device, a factory background processing device, etc. The electronic terminal 700 includes: at least one processor 701, a memory 702, at least one network interface 704 and a user interface 703. The various components in the device are coupled together through a bus system 705. It can be understood that the bus system 705 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 705 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, in Figure 7 In the specification, various buses are labeled as bus systems.

[0115] The user interface 703 may include a display, a keyboard, a mouse, a trackball, a click gun, keys, buttons, a touch pad or a touch screen.

[0116] It is understood that the memory 702 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM). The memory described in the embodiments of the present application is intended to include but is not limited to these and any other suitable categories of memory.

[0117] The memory 702 in the embodiment of the present application is used to store various categories of data to support the operation of the electronic terminal 700. Examples of these data include: any executable program for operating on the electronic terminal 700, such as an operating system 7021 and an application 7022; the operating system 7021 includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks. The application 7022 may include various applications, such as a media player (MediaPlayer), a browser (Browser), etc., for implementing various application services. The method for detecting cervical cancer and / or cervical precancerous lesions provided in the embodiment of the present application may be included in the application 7022.

[0118] The method disclosed in the above embodiment of the present application can be applied to the processor 701, or implemented by the processor 701. The processor 701 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 701 or an instruction in the form of software. The above processor 701 may be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 701 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general processor 701 may be a microprocessor or any conventional processor, etc. In combination with the steps of the method provided in the embodiment of the present application, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0119] In an exemplary embodiment, the electronic terminal 700 may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD) to execute the aforementioned method.

[0120] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other media that can store program codes.

[0121] In the embodiments provided in the present application, the computer readable and writable storage medium may include a read-only memory, a random access memory, an EEPROM, a CD-ROM or other optical disk storage device, a disk storage device or other magnetic storage device, a flash memory, a USB flash drive, a mobile hard disk, or any other medium that can be used to store the desired program code in the form of an instruction or data structure and can be accessed by a computer. In addition, any connection can be appropriately referred to as a computer-readable medium. For example, if the instruction is sent from a website, a server or other remote source using a coaxial cable, an optical fiber cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, optical fiber cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. However, it should be understood that computer readable and writable storage media and data storage media do not include connections, carriers, signals, or other temporary media, but are intended to be non-temporary, tangible storage media. Disk and disc, as used in this application, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers.

[0122] The present application also provides a computer program, which, when executed by a processor, implements the method for detecting cervical cancer and / or cervical precancerous lesions described in the above-mentioned computer-readable storage medium.

[0123] The present application also provides a method for diagnosing cervical cancer and / or cervical precancerous lesions, the method comprising: Obtaining disease risk level data of a target marker combination of a sample to be tested, wherein the target marker combination is the above-mentioned methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions; Based on the disease risk level data of the target marker combination of the sample to be tested, the cervical cancer and / or cervical precancerous lesions of the individual corresponding to the sample to be tested are evaluated.

[0124] In some embodiments, the disease risk level data may be a risk score value of a combination of methylation markers.

[0125] In some embodiments, the risk score value can be calculated by substituting the BV value of a single methylation marker of each sample to be tested into a Logistic regression model, wherein the regression model is obtained by training the methylation marker detection data of known samples; In data analysis and statistics, binary values ​​(BV) refer to converting raw data into numbers with only two possible values, 0 and 1. This conversion process is called binarization, which is used to represent the presence (1) or absence (0) of a feature or condition.

[0126] In this application, 0 represents negative and 1 represents positive.

[0127] The steps for binarization of ΔCt values ​​are as follows: The ΔCt value of each methylation marker first needs to be compared with the preset cutoff value (Cutoff value); If the ΔCt value is less than or equal to the cutoff value, the binary variable for that marker is 1 (positive); If the ΔCt value is greater than the cutoff value, the binary variable for that marker is 0 (negative).

[0128] In some embodiments, the ΔCt value may be the difference between the Ct value of a single methylation marker and the Ct value of an internal reference gene.

[0129] In some embodiments, the Ct value can be obtained by quantitative methylation-specific PCR detection, and the internal reference gene can be β-actin.

[0130] In some embodiments, the β-actin nucleotide sequence can be as shown in SEQ ID NO.33.

[0131] SEQ ID NO.33: AGCACAATGAAGATCAAGGTGGGTGTCTTTCCTGCCTGAGCTGACCTGGGCAGGTCGGCTGTGGGGTCCTGTGGTGTGTGGGGAGCTG In some embodiments, the ΔCt cutoff value may be the ΔCt value that maximizes the Youden Index.

[0132] Substitute into the Logistic regression model: The binary variable (0 or 1) after binarization was substituted into the Logistic regression model instead of the original ΔCt value.

[0133] The weight (coefficient) of each marker in the logistic regression model is calculated based on these binary variables.

[0134] For example, there is a ΔCt value of the methylation level of a gene, and its cutoff value is 9.73: if the ΔCt value of a sample is 9.5 (less than or equal to 9.73), the binary value of the gene is 1; if the ΔCt value of a sample is 10.0 (greater than 9.73), the binary value of the gene is 0; then, these binary results are substituted into the Logistic regression model to calculate the risk score value.

[0135] In some embodiments, in the judgment module, when the risk score value of the methylation marker combination of the sample to be tested is greater than or equal to the cutoff value, the sample to be tested is judged to be positive; when the risk score value of the methylation marker combination of the sample to be tested is less than the cutoff value, the sample to be tested is judged to be negative.

[0136] In some embodiments, the positive result means that the individual corresponding to the sample to be tested is a patient with severe cervical precancerous lesions or cervical cancer, and the negative result means that the individual corresponding to the sample to be tested is a healthy person, a patient with mild cervical precancerous lesions or a patient with moderate cervical precancerous lesions.

[0137] In some embodiments, the sample to be tested may include any one or more of blood, serum, plasma, lymph, urine, cervical scraping cells or tissue, biopsy tissue, surgical tissue, or cervical exfoliated cells. In some embodiments, preferably, the sample to be tested may be cervical exfoliated cells.

[0138] In some embodiments, the methylation marker combination may include any one or more of the following combinations: Combination 1: ZNF536 and NOL4; Combination 2: ZNF536, ZNF671, and TTC34; Combination 3: ZNF536, PROX1-AS1, and TTC34; Combination 4: ZNF536, ZNF671, ARHGEF4, and TTC34; Combination 5: ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34.

[0139] In some embodiments, the cutoff value of combination 1 can be -1.133, risk score = -4.032 + 3.375 × BV ZNF536 +2.424×BV NOL4 .

[0140] In some embodiments, the cutoff value of combination 2 can be -1.656, risk score = -4.240 + 2.685 × BV ZNF536 +1.025× BV ZNF671 +2.484×BV TTC34.

[0141] In some embodiments, the cutoff value of combination 3 can be -1.204, risk score = -4.295 + 2.796 × BV ZNF536 +0.865× BV PROX1-AS1 +2.521×BV TTC34 .

[0142] In some embodiments, the cutoff value of combination 4 can be -1.756, risk score = -4.272 + 2.314 × BV ZNF536 +0.755× BV ZNF671 +1.334×BV ARHGEF4 +1.963×BV TTC34 .

[0143] In some embodiments, the cutoff value of combination 5 can be -1.271, risk score = -4.322 + 2.391 × BV ZNF536 +0.614×BV ZNF671 +0.665×BV HTR1F +0.655×BV WEE1P1 +2.145×BV TTC34 .

[0144] The experimental methods in the following examples are conventional methods unless otherwise specified. The experimental materials used in the following examples are purchased from conventional biochemical reagent companies unless otherwise specified. The quantitative tests in the following examples are repeated three times, and the results are averaged.

[0145] The negative population (Normal), mild cervical precancer lesions (CIN1), moderate cervical precancer lesions (CIN2), severe cervical precancer lesions (CIN3) and cervical cancer patients (Cancer) involved in the embodiment all meet the diagnostic criteria. The diagnostic criteria refer to the fifth edition of the World Health Organization (WHO) Classification of Female Reproductive Organ Tumors.

[0146] Cervical exfoliated cell specimens, reagent materials used and their sources in the embodiments: 1. Specimens The biological samples used in the examples are cervical exfoliated cells collected by the Institute of Clinical Pharmacology, Xiangya Hospital, Central South University from January 2020 to December 2022, all of which are samples with known pathological information.

[0147] 2. Main reagents and materials The genomic DNA extraction kit for cervical exfoliated cells was HiPure Universal DNA Kit (Magen); the conversion kit was EZ DNA Methylation Kit (ZYMO); the primers and probes used were synthesized by Invitrogen (Shanghai) Trading Co., Ltd.; the nuclease-free water, 10×Ex Buffer, Ex Taq HS enzyme, and dNTPs used were purchased from Yubao Biotechnology (Dalian) Co., Ltd.

[0148] Example 1: Comparison of methylation levels of methylation markers in cervical exfoliated cell samples from Normal, CIN1, CIN2, CIN3 and Cancer groups

[0149] (1) Sample collection and genomic DNA extraction Samples were collected from cervical exfoliated cells of patients with negative population (275 cases), CIN1 (83 cases), CIN2 (72 cases), CIN3 (53 cases) and cervical cancer (53 cases). Genomic DNA was extracted from cervical exfoliated cell samples using HiPure Universal DNA Kit (Magen) according to the kit instructions.

[0150] (2) DNA bisulfite conversion The extracted genomic DNA was subjected to bisulfite conversion using the EZ DNA Methylation Kit (ZYMO). During the conversion process, unmethylated cytosine (C) in DNA is converted to uracil (U), while methylated cytosine (C) remains unchanged. After purification, bisulfite-converted DNA (m-DNA) is obtained.

[0151] (3) Fluorescence PCR detection Use fluorescent PCR technology to detect the fluorescent Ct value of methylation markers. The specific steps are as follows: Reaction system configuration: 20μL PCR reaction system includes: 18.5 μL PCR mixture, containing: 2 μL 10×Ex PCR buffer 2 μL dNTPs 0.2 μL Ex Taq HS enzyme 2.5 μL of primer and probe premix (including detection sites and internal reference genes) 0.16 μL ROX 11.64 μL of nuclease-free water 1.5 μL of bisulfite converted DNA (m-DNA) The final concentration of each primer was 400 nM, and the final concentration of each detection probe was 200 nM.

[0152] PCR reaction conditions: Initial denaturation: 95°C for 5 minutes Cycling reaction: 95℃ for 15 seconds, 60℃ for 30 seconds (collecting fluorescence), a total of 50 cycles Detection equipment: Use ABI7500 Real-Time PCR System to detect different fluorescence signals in the corresponding fluorescence channels.

[0153] (4) Data processing and analysis For targets with no detected amplification signal, the Ct value was set to 50.

[0154] Calculate the methylation level (ΔCt) of a single methylation marker using the formula: ΔCt = Ct 甲基化标志物 -Ct β-actin .

[0155] The ΔCt values ​​of cervical exfoliated cell samples of negative people, CIN1, CIN2, CIN3 and cervical cancer patients were compared, and the differences between different groups were analyzed.

[0156] (5) Primer and probe sequences The primer sequences for each methylation marker are shown in Table 1.

[0157] The probe sequences are shown in Table 2.

[0158] Table 1 Primer sequences SEQ ID NO. name sequence 2 ZNF536 forward primer CGAGGTTTGTCGTTCGGTTT 3 ZNF536 reverse primer CCAACGTCACAACCACGAATAT 6 NOL4 forward primer GGAGTTGTTTGCGTTTCGTTC 7 NOL4 reverse primer GCGCGTCTCCGAAAAAATC 10 ZNF671 forward primer GTCGTTTTCGGTAGTTGTTCGC 11 ZNF671 reverse primer AAAAACGCAACACCCACCC 14 HTR1F forward primer GGGGGCGTTATTTTGTTAGTTTC 15 HTR1F reverse primer CCGAACTCAACGCTAATCCG 18 PROX1-AS1 forward primer GGCGTTCGTTTTTTTTGGTC 19 PROX1-AS1 reverse primer GACCCGCCCTTCCTAACCT 22 WEE1P1 forward primer CCGAACTCAACGCTAATCCG 23 WEE1P1 reverse primer CCTCCGAACTCTCAAATATAACCG 26 ARHGEF4 forward primer GCGGTCGCGGAGGTAAC 27 ARHGEF4 reverse primer GACGATAACCGAACGCAAACTC 30 TTC34 forward primer GGAGAGGTGCGCGGTTT 31 TTC34 reverse primer CGCCCGAACCATATACGAA 34 β-actin forward primer AGTATAATGAAGATTAAGGTGGGTGTTTT 35 β-actin reverse primer CAACTCCCCACACACCACAA Table 2 Probe sequences SEQ ID NO. name sequence 4 ZNF536 probe ATCGCGATTAAAAAATACG 8 NOL4 probe TCGGTTTTAGTCGTCGC 12 ZNF671 probe TTTACGGTTTTATGGCGGA 16 HTR1F probe TGTAGGGAGCGTTAGGG 20 PROX1-AS1 Probe TCGCGATATTTAGCGC 24 WEE1P1 probe TTTATTCGTAGTCGTTATCGT 28 ARHGEF4 probe AAACACGACGCCGC 32 TTC34 probe CGTGCGGGTCGTAGTA 36 β-actin probe TTGTTTGAGTTGATTTGGG Figure 1 The results of A-1H showed that the ΔCt values ​​detected in patients with cervical cancer and CIN3 were small, and the methylation levels were high, indicating stronger methylation signals. The ΔCt values ​​detected in negative populations and CIN1 patients were large, and target methylation signals could not be detected in most samples. This indicates that these targets have the potential to detect cervical cancer and severe precancerous lesions. This proves that the selected target markers are feasible for detecting cervical cancer and severe precancerous lesions.

[0159] Example 2: Comparison of methylation levels of methylation markers in cervical exfoliated cell samples from CIN2- and CIN3+ patients

[0160] The negative population, CIN1 and CIN2 patients were defined as CIN2-, and CIN3 and cervical cancer patients were defined as CIN3+ patients. The 430 CIN2- samples and 106 CIN3+ samples in Example 1 were analyzed.

[0161] Table 3 Sensitivity and specificity of methylation markers for detecting CIN3+ Methylation markers Sensitivity% Specificity% ZNF536 90.6 93.3 NOL4 86.8 92.8 ZNF671 85.9 92.3 HTR1F 76.4 82.8 PROX1-AS1 87.7 92.3 WEE1P1 86.8 93.5 ARHGEF4 86.8 93.3 TTC34 90.6 92.3 SPSS statistic 21 software was used to obtain the receiver operating curve of each gene, with sensitivity (true positive rate) as the ordinate and 1-specificity (false positive rate) as the abscissa. Youden index = sensitivity-(1-specificity), and the optimal critical point (cutoff value of ΔCt) was determined according to the maximum value of Youden index. The cutoff values ​​of ΔCt for ZNF536, NOL4, ZNF671, HTR1F, PROX1-AS1, WEE1P1, ARHGEF4 and TTC34 were 9.73, 7.15, 6.13, 8.56, 10.09, 7.81, 7.44 and 8.09, respectively.

[0162] The positive and negative methylation of the corresponding gene in the clinical sample is judged according to the cutoff value of the ΔCt of each gene methylation. When the Ct value of the methylation of a gene in the sample is greater than the cutoff value of the ΔCt of the gene, it indicates that the methylation of the gene in the sample is negative, otherwise it is positive.

[0163] Figure 2 The methylation ΔCt values ​​of each gene in CIN2- and CIN3+, i.e., the methylation levels, are shown. The positive and negative characteristics of CIN2- and CIN3+ samples are judged according to the cutoff values ​​of the ΔCt of each gene methylation. The results in Table 3 show that the selected gene methylation markers have high sensitivity and specificity for cervical exfoliated cell samples of patients with cervical cancer and precancerous lesions (CIN3+).

[0164] Example 3: Combined analysis of methylation markers using logistic regression method

[0165] The negative population, CIN1 and CIN2 patients were defined as CIN2-, and CIN3 and cervical cancer patients were defined as CIN3+ patients. The methylation marker compositions of 430 CIN2- samples and 106 CIN3+ samples in Example 1 were analyzed.

[0166] Use SPSS statistic 21 software to perform logistic regression analysis on the combination of methylation markers of each target. Open SPSS software, find "Analysis" in the menu bar and click it, select "Regression", and click "Binary Logistic Regression". Include "Clinical Grouping" as the dependent variable and include influencing factors as the independent variable (i.e., different gene combinations). Check "95% Confidence Interval", click "Continue", and finally click "OK" to obtain the formula corresponding to the combination. According to the formula, the risk score of each sample in different marker combinations is calculated. Taking CIN3+ as the endpoint event, ROC analysis is performed on the risk score of each marker combination to obtain the risk score cutoff value of different marker combinations. According to the risk score cutoff value, the predicted grouping of the sample, i.e., "group member", is determined. According to the above steps, the "group member" generated by different combinations is used as the independent variable for binary logistic regression, and the combination ranked Top5 by Exp(B) is selected for analysis.

[0167] Combined with ZNF536 and NOL4, risk score = -4.032 + 3.375 × BV ZNF536 +2.424×BV NOL4 The receiver operating curve of the combination was obtained using SPSS statistic 21 software, the maximum value of the Youden index was calculated, and the corresponding cutoff value was obtained. The cutoff value of the combination was -1.133. When the risk score value of the combination of clinical samples was greater than or equal to -1.133, it was judged to be positive, otherwise it was negative. Statistics show that the combination can detect CIN3+ with a sensitivity of 90.6% and a specificity of 93.3%.

[0168] Combined with ZNF536, ZNF671 and TTC34, risk score = -4.240 + 2.685 × BV ZNF536 +1.025×BV ZNF671 +2.484×BV TTC34 The receiver operating curve of the combination was obtained using SPSS statistic 21 software, the maximum value of the Youden index was calculated, and the corresponding cutoff value was obtained. The cutoff value of the combination was -1.656. When the risk score value of the combination of clinical samples was greater than or equal to -1.656, it was judged to be positive, otherwise it was negative. Statistics show that the combination can detect CIN3+ with a sensitivity of 92.5% and a specificity of 92.8%.

[0169] Combined with ZNF536, PROX1-AS1 and TTC34, risk score = -4.295 + 2.796 × BV ZNF536 +0.865×BV PROX1-AS1 +2.521×BV TTC34The receiver operating curve of the combination was obtained using SPSS statistic 21 software, the maximum value of the Youden index was calculated, and the corresponding cutoff value was obtained. The cutoff value of the combination was -1.204. When the risk score value of the combination of clinical samples was greater than or equal to -1.204, it was judged to be positive, otherwise it was negative. Statistics show that the combination can detect CIN3+ with a sensitivity of 92.5% and a specificity of 94.4%.

[0170] Combined with ZNF536, ZNF671, ARHGEF4 and TTC34, risk score = -4.272 + 2.314 × BV ZNF536 +0.755×BV ZNF671 +1.334×BV ARHGEF4 +1.963×BV TTC34 The receiver operating curve of the combination was obtained using SPSS statistic 21 software, the maximum value of the Youden index was calculated, and the corresponding cutoff value was obtained. The cutoff value of the combination was -1.756. When the risk score value of the combination of clinical samples was greater than or equal to -1.756, it was judged to be positive, otherwise it was negative. Statistics show that the combination can detect CIN3+ with a sensitivity of 95.3% and a specificity of 94.9%.

[0171] Combined with ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34, risk score = -4.322 + 2.391 × BV ZNF536 +0.614×BV ZNF671 +0.665×BV HTR1F +0.655×BV WEE1P1 +2.145×BV TTC34 The receiver operating curve of the combination was obtained using SPSS statistic 21 software, the maximum value of the Youden index was calculated, and the corresponding cutoff value was obtained. The cutoff value of the combination was -1.271. When the risk score value of the combination of clinical samples was greater than or equal to -1.271, it was judged to be positive, otherwise it was negative. According to statistics, the combination can detect CIN3+ with a sensitivity of 91.5% and a specificity of 94.7%.

[0172] Note: BV is the abbreviation of Binary Values.

[0173] Figure 3 The receiver operating characteristic curve (ROC) of the combination of 5 methylation markers for screening cervical cancer and cervical precancerous lesions is shown. The results show that the screening combination has high sensitivity and specificity for cervical exfoliated cell samples of patients with cervical cancer and precancerous lesions (CIN3+), which can be further verified.

[0174] Example 4: Verification of the clinical efficacy of the methylation marker composition in screening cervical cancer and precancerous lesions

[0175] Genomic DNA samples were obtained from cervical exfoliated cells from negative population (85 cases), CIN1 (38 cases), CIN2 (43 cases), CIN3 (45 cases) and cervical cancer (12 cases) patients, and the genomic DNA in the cervical exfoliated cell samples was extracted using the HiPureUniversal DNA Kit (Magen) according to the instructions. The DNA bisulfite conversion and fluorescence PCR detection steps were the same as in Example 1.

[0176] Data processing and analysis: Treatment of undetectable amplification signals: For targets with no detected amplification signals, the Ct value was set to 50.

[0177] Methylation level calculation: Calculate the methylation level (ΔCt) of a single methylation marker using the formula: ΔCt = Ct 甲基化标志物 -Ct β-actin The ΔCt cutoff values ​​of the methylation markers included in the five compositions: ZNF536, NOL4, ZNF671, HTR1F, PROX1-AS1, WEE1P1, ARHGEF4, and TTC34 were 9.73, 7.15, 6.13, 8.56, 10.09, 7.81, 7.44, and 8.09, respectively.

[0178] Binarization: Clinical samples are interpreted according to the cutoff value of ΔCt of each marker. When the ΔCt value of the target marker of the sample to be tested is less than or equal to the cutoff value of ΔCt, the sample to be tested is judged to be positive (i.e., 1); otherwise, the sample to be tested is judged to be negative (i.e., 0). Table 4 shows the sensitivity and specificity of a single methylation marker in screening CIN3+ in validation samples.

[0179] Table 4 Sensitivity and specificity of methylation markers for detecting CIN3+ in validation samples Methylation markers Sensitivity% Specificity% ZNF536 87.7 93.4 NOL4 84.2 92.8 ZNF671 82.5 92.8 HTR1F 77.2 83.1 PROX1-AS1 82.5 91.6 WEE1P1 80.7 89.2 ARHGEF4 84.2 94.0 TTC34 91.2 92.8 Marker combination analysis: Substitute the numerical value (0 or 1) of the target marker of the sample to be tested into the formula of each combination, and determine the final positive or negative of the sample to be tested based on the risk score of the combination.

[0180] Table 5 Sensitivity and specificity of methylation marker combination for detecting CIN3+ in validation samples Group Name Combined content Sensitivity% Specificity% Combination 1 ZNF536 and NOL4 87.7 93.4 Combination 2 ZNF536, ZNF671, and TTC34 93.0 93.4 Combination 3 ZNF536, PROX1-AS1, and TTC34 91.2 94.0 Combination 4 ZNF536, ZNF671, ARHGEF4, and TTC34 98.2 95.8 Combination 5 ZNF536, ZNF671, HTR1F, WEE1P1, and TTC34 91.2 94.6 Figure 4The receiver operating characteristic curve (ROC) of the five methylation marker combinations in the validation samples for screening cervical cancer and cervical precancerous lesions is shown. The results show that the selected methylation marker combination has high sensitivity and specificity for cervical exfoliated cell samples of patients with cervical cancer and precancerous lesions (CIN3+) in the validation samples. The above five target combinations have all been proven to have excellent performance in distinguishing CIN2- from CIN3+, with good sensitivity and specificity.

[0181] The results in Table 5 show that the selected methylation marker combination has high sensitivity and specificity for cervical exfoliated cell samples of patients with cervical cancer and precancerous lesions (CIN3+) in the validation samples. For example, the sensitivity of combination 4 (ZNF536, ZNF671, ARHGEF4 and TTC34) is 98.2%, and the specificity is 95.8%; the sensitivity of combination 5 (ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34) is 91.2%, and the specificity is 94.6%. The above five target combinations have all been proven to have excellent performance in distinguishing CIN2- from CIN3+, with good sensitivity and specificity.

[0182] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0183] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0184] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining the digits. Although the numerical domains and parameters used to confirm the breadth of the range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0185] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions, characterized in that: The methylation marker combination includes methylated ZNF536 gene or a fragment thereof, and any one or more of methylated NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F or WEE1P1 gene or a fragment thereof.

2. The methylation marker combination according to claim 1, characterized in that: The gene fragment includes a gene subregion; And / or, the methylation markers include methylated ZNF536 gene subregion, and any one or more of methylated NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F or WEE1P1 gene subregions; and / or, the cervical precancerous lesions are severe cervical precancerous lesions.

3. The methylation marker combination according to claim 2, characterized in that: The nucleotide sequence of the ZNF536 gene subregion is shown in SEQ ID NO.1; And / or, the nucleotide sequence of the NOL4 gene subregion is shown in SEQ ID NO.5; And / or, the nucleotide sequence of the ZNF671 gene subregion is shown in SEQ ID NO.9; And / or, the TTC34 gene subregion nucleotide sequence is shown in SEQ ID NO.29; And / or, the nucleotide sequence of the PROX1-AS1 gene subregion is shown in SEQ ID NO.17; And / or, the nucleotide sequence of the ARHGEF4 gene subregion is shown in SEQ ID NO.25; And / or, the nucleotide sequence of the HTR1F gene subregion is shown in SEQ ID NO.13; And / or, the nucleotide sequence of the WEE1P1 gene subregion is shown as SEQ ID NO.

21.

4. Use of the methylation marker combination or the detection substance thereof according to any one of claims 1 to 3 in the preparation of a product for detecting cervical cancer and / or cervical precancerous lesions.

5. The use according to claim 4, characterized in that The test samples of the product include any one or more of blood, serum, plasma, lymph, urine, cervical scraping cells or tissue, biopsy tissue, surgical tissue or cervical exfoliated cells.

6. A product for detecting cervical cancer and / or cervical precancerous lesions, characterized in that: The product comprises the methylation marker combination or the detection substance thereof according to any one of claims 1 to 3.

7. The product according to claim 6, characterized in that The detection object includes probes and primers, and the primers and probes include specific primers and probes for the transformed sequences of the following genes or their fragments: ZNF536, and any one or more of NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F or WEE1P1.

8. The product according to claim 7, characterized in that The nucleotide sequences of the primers for detecting the methylation marker ZNF536 gene or its fragment are shown in SEQ ID NO.2 and SEQ ID NO.3; And / or, the nucleotide sequences of the primers for detecting the methylation marker NOL4 gene or its fragment are shown in SEQ ID NO.6 and SEQ ID NO.7; And / or, the nucleotide sequences of the primers for detecting the methylation marker ZNF671 gene or its fragment are shown in SEQ ID NO.10 and SEQ ID NO.11; And / or, the nucleotide sequences of the primers for detecting the methylation marker HTR1F gene or its fragment are shown in SEQ ID NO.14 and SEQ ID NO.15; And / or, the nucleotide sequences of the primers for detecting the methylation marker PROX1-AS1 gene or its fragment are shown in SEQ ID NO.18 and SEQ ID NO.19; And / or, the nucleotide sequences of the primers for detecting the methylation marker WEE1P1 gene or its fragment are shown in SEQ ID NO.22 and SEQ ID NO.23; And / or, the nucleotide sequences of the primers for detecting the methylation marker ARHGEF4 gene or its fragment are shown in SEQ ID NO.26 and SEQ ID NO.27; And / or, the nucleotide sequences of the primers for detecting the methylation marker TTC34 gene or its fragment are shown in SEQ ID NO.30 and SEQ ID NO.31; And / or, the nucleotide sequence of the probe for detecting the methylation marker ZNF536 gene or a fragment thereof is shown in SEQ ID NO. 4; And / or, the nucleotide sequence of the probe for detecting the methylation marker NOL4 gene or a fragment thereof is shown in SEQ ID NO.8; And / or, the nucleotide sequence of the probe for detecting the methylation marker ZNF671 gene or a fragment thereof is shown in SEQ ID NO. 12; And / or, the nucleotide sequence of the probe for detecting the methylation marker HTR1F gene or a fragment thereof is shown in SEQ ID NO.16; And / or, the nucleotide sequence of the probe for detecting the methylation marker PROX1-AS1 gene or a fragment thereof is shown in SEQ ID NO.20; And / or, the nucleotide sequence of the probe for detecting the methylation marker WEE1P1 gene or a fragment thereof is shown in SEQ ID NO. 24; And / or, the nucleotide sequence of the probe for detecting the methylation marker ARHGEF4 gene or a fragment thereof is shown in SEQ ID NO. 28; And / or, the nucleotide sequence of the probe for detecting the methylation marker TTC34 gene or its fragment is shown in SEQ ID NO.

32.

9. A device for detecting cervical cancer and / or cervical precancerous lesions, characterized in that: Includes the following modules: A data acquisition module, used for providing disease risk level data of a target marker combination of a sample to be tested, wherein the target marker combination is a methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions according to any one of claims 1 to 3; The judgment module is used to evaluate the cervical cancer and / or cervical precancerous lesions of the individual corresponding to the sample to be tested based on the disease risk level data of the target marker combination of the sample to be tested.

10. The detection device according to claim 9, characterized in that: The disease risk level data is a risk score value of the methylation marker combination, and the risk score value is calculated by substituting the BV value of a single methylation marker of each sample to be tested into a Logistic regression model, and the regression model is obtained by training the methylation marker detection data of known samples; In the judgment module, when the risk score value of the methylation marker combination of the sample to be tested is greater than or equal to the cutoff value, the sample to be tested is judged to be positive; when the risk score value of the methylation marker combination of the sample to be tested is less than the cutoff value, the sample to be tested is judged to be negative.

11. The detection device according to claim 9, characterized in that: The sample to be tested includes any one or more of blood, serum, plasma, lymph, urine, cervical scraping cells or tissue, biopsy tissue, surgical tissue or cervical exfoliated cells; And / or, the methylation marker combination includes any one or more of the following combinations: Combination 1: ZNF536 and NOL4; Combination 2: ZNF536, ZNF671, and TTC34; Combination 3: ZNF536, PROX1-AS1, and TTC34; Combination 4: ZNF536, ZNF671, ARHGEF4, and TTC34; Combination 5: ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34.

12. The detection device according to claim 11, characterized in that: The cutoff value of combination 1 is -1.133, and the risk score = -4.032 + 3.375 × BV ZNF536 +2.424×BV NOL4 ; And / or, the cutoff value of combination 2 is -1.656, risk score = -4.240 + 2.685 × BV ZNF536 +1.025×BV ZNF671 +2.484×BV TTC34 ; And / or, the cutoff value of combination 3 is -1.204, risk score = -4.295 + 2.796 × BV ZNF536 +0.865×BV PROX1-AS1 +2.521×BV TTC34 ; And / or, the cutoff value of combination 4 is -1.756, risk score = -4.272 + 2.314 × BV ZNF536 +0.755×BV ZNF671 +1.334×BV ARHGEF4 +1.963×BV TTC34 ; And / or, the cutoff value of combination 5 is -1.271, risk score = -4.322 + 2.391 × BV ZNF536 +0.614×BV ZNF671 +0.665×BV HTR1F +0.655×BV WEE1P1 +2.145×BV TTC34 .

13. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions. When the computer instructions are executed by the processor, a method for detecting cervical cancer and / or cervical precancerous lesions is implemented. The method includes: Obtaining disease risk level data of a target marker combination of a sample to be tested, wherein the target marker combination is a methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions according to any one of claims 1 to 3; Based on the disease risk level data of the target marker combination of the sample to be tested, the cervical cancer and / or cervical precancerous lesions of the individual corresponding to the sample to be tested are evaluated.

14. The computer-readable storage medium of claim 13, wherein: The disease risk level data is a risk score of the methylation marker combination, and the risk score value is calculated by substituting the BV value of a single methylation marker of each sample to be tested into a Logistic regression model, and the regression model is obtained by training the methylation marker detection data of known samples; the evaluation of the cervical cancer and / or cervical precancerous lesions of the individual corresponding to the sample to be tested is: when the risk score value of the methylation marker combination of the sample to be tested is greater than or equal to the cutoff value, the sample to be tested is judged to be positive; when the risk score value of the methylation marker combination of the sample to be tested is less than the cutoff value, the sample to be tested is judged to be negative.

15. The computer-readable storage medium of claim 13, wherein: The sample to be tested includes any one or more of blood, serum, plasma, lymph, urine, cervical scraping cells or tissue, biopsy tissue, surgical tissue or cervical exfoliated cells; And / or, the methylation marker combination includes any one or more of the following combinations: Combination 1: ZNF536 and NOL4; Combination 2: ZNF536, ZNF671, and TTC34; Combination 3: ZNF536, PROX1-AS1, and TTC34; Combination 4: ZNF536, ZNF671, ARHGEF4, and TTC34; Combination 5: ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34.

16. The computer-readable storage medium of claim 15, wherein: The cutoff value of combination 1 is -1.133, and the risk score = -4.032 + 3.375 × BV ZNF536 +2.424×BV NOL4 ; And / or, the cutoff value of combination 2 is -1.656, risk score = -4.240 + 2.685 × BV ZNF536 +1.025×BV ZNF671 +2.484×BV TTC34 ; And / or, the cutoff value of combination 3 is -1.204, risk score = -4.295 + 2.796 × BV ZNF536 +0.865×BV PROX1-AS1 +2.521×BV TTC34 ; And / or, the cutoff value of combination 4 is -1.756, risk score = -4.272 + 2.314 × BV ZNF536 +0.755×BV ZNF671 +1.334×BV ARHGEF4 +1.963×BV TTC34 ; And / or, the cutoff value of combination 5 is -1.271, risk score = -4.322 + 2.391 × BV ZNF536 +0.614×BV ZNF671 +0.665×BV HTR1F +0.655×BV WEE1P1 +2.145×BV TTC34 .

17. An electronic terminal, characterized in that: include: Processor, memory, network interface and user interface; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes the method for detecting cervical cancer and / or cervical precancerous lesions as described in the computer-readable storage medium as described in any one of claims 13 to 16.

18. A computer program, characterized in that When the computer program is executed by a processor, the method for detecting cervical cancer and / or cervical precancerous lesions described in the computer-readable storage medium according to any one of claims 13 to 16 is implemented.

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