A combination of methylation markers for the detection of cervical cancer and / or cervical precancerous lesions, and its applications, products, detection devices, computer-readable storage media, electronic terminals and computer programs

Abstract

This application relates to the field of biomedicine, and particularly to a methylation biomarker combination for the detection of cervical cancer and / or cervical precancerous lesions. The methylation biomarker combination includes the methylated ZNF536 gene or a fragment thereof, and any one or more of the methylated NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F, or WEE1P1 genes or fragments thereof. This application studies the methylation differences between patients with cervical cancer and / or cervical precancerous lesions and healthy people through the DNA of cervical exfoliated cells in the Chinese population, and for the first time screens out 5 optimal methylation biomarker combinations for establishing a risk prediction method for cervical cancer and / or cervical precancerous lesions in the Chinese population, which is applicable to the risk assessment of the occurrence of early cervical cancer and / or cervical precancerous lesions in the Chinese population, as well as the screening and diagnosis of cervical cancer and / or cervical precancerous lesions.

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 applications, products, detection devices, computer-readable storage media, electronic terminals, and computer programs. Background Art

[0002] Only persistent infection with high-risk human papillomavirus (hrHPV) can lead to cervical lesions, progressing through mild cervical precancerous lesions (cervical intraepithelial neoplasia, CIN), moderate CIN, and severe CIN, ultimately leading to cervical cancer. Current screening guidelines recommend HPV DNA testing as the preferred method, or in combination with thin-layer cytology (TCT). While hrHPV testing is objective and highly reproducible, it cannot distinguish between transient and transformed infections, potentially leading to increased colposcopy referrals and anxiety among women who test positive. Cytological screening methods, such as TCT, have high specificity but limitations such as diagnostic subjectivity and low sensitivity, which can lead to missed diagnoses.

[0003] Recent studies have shown that early epigenetic changes are important features of tumor development and progression. DNA methylation testing has become an emerging tool for detecting cervical cancer and cervical precancerous lesions. Several methylated genes, such as FAM19A4, Mir124-2, PAX1, ZNF582, SOX1, and EPB41L3, have been considered biomarkers for cervical cancer screening. However, their efficacy in detecting cervical cancer and cervical precancerous lesions in the Chinese population is modest. Therefore, the exploration and development of more effective and objective biomarkers is of great clinical significance. Summary of the Invention

[0004] To address the above technical issues, this application provides a set of biomarkers closely related to the development and progression of cervical cancer and precancerous lesions from the Chinese population, and based on these, develops detection primers, probes, and detection kits based on methylation markers. The kits are objective, highly repeatable, low-cost, highly sensitive, and highly specific, providing new ideas and detection methods for the early diagnosis and treatment of cervical cancer and precancerous lesions. The main contribution of this application is the discovery of these methylation marker combinations. Any methylation level analysis method can be used to detect the methylation levels of the methylation markers discovered in this 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 cervical cancer and / or cervical precancerous lesion detection; 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. 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 of this application include but are not limited to: (1) This application provides a new biomarker for detecting cervical cancer and cervical precancerous lesions derived from the Chinese population, which has the characteristics of high sensitivity and high specificity, and provides a new idea 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 be used in materials to confirm the presence 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 terms of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are non-limiting, and include:

[0014] 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 exfoliation samples from different populations; Figure 1 B shows the methylation level of NOL4 gene in cervical exfoliation samples from different populations; Figure 1 C shows the methylation level of ZNF671 gene in cervical exfoliation samples from different populations; Figure 1 D shows the methylation level of HTR1F gene in cervical detachment samples from different populations; Figure 1 E shows the methylation level of PROX1-AS1 gene in cervical exfoliation samples from different populations; Figure 1 F shows the methylation level of WEE1P1 gene in cervical exfoliation samples from different populations; Figure 1 G shows the methylation level of ARHGEF4 gene in cervical exfoliation samples from different populations; Figure 1 H shows the methylation level of TTC34 gene in cervical exfoliated samples from different populations.

[0015] Figure 2 The methylation levels of methylation markers in CIN2- and CIN3+ cervical exfoliation samples of Example 2 of the present application are shown. ★ p<0.05, ★★ p<0.01, ★★★ p<0.001.

[0016] Figure 3The figure shows the receiver operating characteristic curve (ROC) of the combination of 5 methylation markers in Example 3 of the present application for screening cervical cancer and cervical precancerous lesions.

[0017] Figure 4 The figure shows the receiver operating characteristic curve (ROC) of the combination of five methylation markers in Example 4 of the present application for screening cervical cancer and cervical precancerous lesions in the validation samples.

[0018] 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.

[0019] 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.

[0020] Figure 7 Schematic diagram of the structure of an electronic terminal 700 according to some embodiments of the present application. DETAILED DESCRIPTION

[0021] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0022] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0023] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0024] 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.

[0025] In some embodiments, the gene fragment may include gene subregions. Gene subregions are different functional regions within a gene, such as coding regions and non-coding regions.

[0026] In some embodiments, the methylation markers may include methylated ZNF536 gene subregions, and any one or more methylated NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F, or WEE1P1 gene subregions.

[0027] In some embodiments, the ZNF536 gene subregion nucleotide sequence may be as shown in SEQ ID NO.1.

[0028] SEQ ID NO.1:

[0029]

[0030] In some embodiments, the NOL4 gene subregion nucleotide sequence may be as shown in SEQ ID NO.5.

[0031] SEQ ID NO.5:

[0032]

[0033] In some embodiments, the ZNF671 gene subregion nucleotide sequence may be as shown in SEQ ID NO.9.

[0034] SEQ ID NO.9:

[0035]

[0036] In some embodiments, the TTC34 gene subregion nucleotide sequence may be as shown in SEQ ID NO.29.

[0037] SEQ ID NO.29:

[0038]

[0039] In some embodiments, the nucleotide sequence of the PROX1-AS1 gene subregion may be as shown in SEQ ID NO.17.

[0040] SEQ ID NO.17:

[0041]

[0042] In some embodiments, the ARHGEF4 gene subregion nucleotide sequence may be as shown in SEQ ID NO.25.

[0043] SEQ ID NO.25:

[0044]

[0045] In some embodiments, the HTR1F gene subregion nucleotide sequence may be as shown in SEQ ID NO.13.

[0046] SEQ ID NO.13:

[0047]

[0048] In some embodiments, the nucleotide sequence of the WEE1P1 gene subregion may be as shown in SEQ ID NO.21.

[0049] SEQ ID NO.21:

[0050]

[0051] In some embodiments, the cervical precancerous lesions may be severe cervical precancerous lesions.

[0052] 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.

[0053] In some embodiments, the detection substance can be selected from any one or more of an antibody, a membrane strip, a chip, a probe, or a primer.

[0054] 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.

[0055] 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.

[0056] In some embodiments, the detector may be a detector for detecting the methylation level of a methylation marker.

[0057] In some embodiments, the detector may include a probe and a primer.

[0058] As used herein, the term "primer" refers to a naturally occurring oligonucleotide (e.g., a restriction fragment) or a synthetically produced oligonucleotide that is capable of serving 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., a DNA-dependent or RNA-dependent polymerase). Typically, a primer set will consist of at least two primers, an "upstream primer" and a "downstream primer," which together define the amplicon (the sequence to be amplified using the primers).

[0059] The term "probe" refers to any molecule capable of selectively binding to a target biomolecule (e.g., a nucleic acid sequence to which the probe hybridizes). In some embodiments, the probe may be labeled, for example, with a fluorescent moiety and a quencher moiety. In some embodiments, the probe may be a Taqman probe with a fluorescent reporter moiety attached to the 5' end and a fluorescent quencher moiety attached to the 3' end.

[0060] In some embodiments, the primers and probes may include primers and probes specific 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.

[0061] In some embodiments, the conversion may be converting unmethylated cytosine in the gene or fragment thereof into uracil.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] The term "membrane strip" refers to a diagnostic tool that utilizes the principle of specific biomolecular recognition to immobilize biomolecules such as antigens or antibodies on the membrane, specifically bind to the test substance in the sample, and qualitatively or quantitatively analyze the target substance in the sample through visualization or other signal detection methods.

[0080] Protein arrays, also known as protein microarrays, are high-throughput biotechnology tools that allow for the simultaneous analysis and study of large numbers of proteins. This technology enables rapid analysis of protein expression, protein-protein interactions, and protein-small molecule binding studies by orderly arranging thousands of different protein or protein interaction probes on a solid surface.

[0081] The term "chip" generally refers to a microdevice that integrates biosensors and microfluidic technology. It can perform various 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.

[0082] 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.

[0083] 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.

[0084] The present application also provides a device for detecting cervical cancer and / or cervical precancerous lesions, such as Figure 5 As shown, the system includes the following modules: a data acquisition module 510 and a judgment module 520 .

[0085] 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.

[0086] In some embodiments, the disease risk level data may be a risk score value of a methylation marker combination.

[0087] 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 trained using methylation marker detection data of known samples;

[0088] 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, and it is used to represent the presence (1) or absence (0) of a feature or condition.

[0089] In this application, 0 represents negative and 1 represents positive.

[0090] The steps for binarization of ΔCt values ​​are as follows:

[0091] The ΔCt value of each methylation marker first needs to be compared with the preset cutoff value (Cutoff value);

[0092] If the ΔCt value is less than or equal to the cutoff value, the binary variable for that marker is 1 (positive);

[0093] If the ΔCt value is greater than the cutoff value, the binary variable for that marker is 0 (negative).

[0094] 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.

[0095] In some embodiments, the Ct value can be obtained by quantitative methylation-specific PCR detection, and the internal reference gene can be β-actin.

[0096] In some embodiments, the β-actin nucleotide sequence can be as shown in SEQ ID NO.33.

[0097] SEQ ID NO.33:

[0098] AGCACAATGAAGATCAAGGTGGGTGTCTTTCCTGCCTGAGCTGACCTGGGCAGGTCGGCTGTGGGGTCCTGTGGTGTGTGGGGAGCTG

[0099] In some embodiments, the ΔCt cutoff value may be the ΔCt value that maximizes the Youden Index.

[0100] Substitute into the Logistic regression model:

[0101] The binary variable (0 or 1) after binarization was substituted into the logistic regression model instead of the original ΔCt value.

[0102] The weight (coefficient) of each marker in the logistic regression model is calculated based on these binary variables.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] In some embodiments, the methylation marker combination may include any one or more of the following combinations:

[0108] Combination 1: ZNF536 and NOL4;

[0109] Combination 2: ZNF536, ZNF671, and TTC34;

[0110] Combination 3: ZNF536, PROX1-AS1, and TTC34;

[0111] Combination 4: ZNF536, ZNF671, ARHGEF4, and TTC34;

[0112] Combination 5: ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34.

[0113] 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.

[0114] In some embodiments, the cutoff value of the combination 1 can be -1.133, and the risk score = -4.032 + 3.375 × BV ZNF536 +2.424×BV NOL4 .

[0115] In some embodiments, the cutoff value of the combination 2 can be -1.656, and the risk score = -4.240 + 2.685 × BV ZNF536 +1.025× BV ZNF671 +2.484×BV TTC34 .

[0116] In some embodiments, the cutoff value of the combination 3 can be -1.204, and the risk score = -4.295 + 2.796 × BV ZNF536 +0.865× BV PROX1-AS1 +2.521×BV TTC34 .

[0117] In some embodiments, the cutoff value of the combination 4 can be -1.756, and the risk score = -4.272 + 2.314 × BV ZNF536 +0.755× BV ZNF671 +1.334×BV ARHGEF4 +1.963×BV TTC34 .

[0118] In some embodiments, the cutoff value of the combination 5 can be -1.271, and the risk score = -4.322 + 2.391 × BV ZNF536 +0.614×BV ZNF671 +0.665×BV HTR1F +0.655×BV WEE1P1 +2.145×BV TTC34 .

[0119] The present application also provides a computer-readable storage medium storing 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 shown in FIG. Figure 6 shown.

[0120] In step S610, disease risk level data of the target marker combination of the sample to be tested is obtained, where the target marker combination is the above-mentioned methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions.

[0121] In some embodiments, the disease risk level data may be a risk score value of a methylation marker combination.

[0122] 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 trained using methylation marker detection data of known samples;

[0123] 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, and it is used to represent the presence (1) or absence (0) of a feature or condition.

[0124] In this application, 0 represents negative and 1 represents positive.

[0125] The steps for binarization of ΔCt values ​​are as follows:

[0126] The ΔCt value of each methylation marker first needs to be compared with the preset cutoff value (Cutoff value);

[0127] If the ΔCt value is less than or equal to the cutoff value, the binary variable for that marker is 1 (positive);

[0128] If the ΔCt value is greater than the cutoff value, the binary variable for that marker is 0 (negative).

[0129] 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.

[0130] In some embodiments, the Ct value can be obtained by quantitative methylation-specific PCR detection, and the internal reference gene can be β-actin.

[0131] In some embodiments, the β-actin nucleotide sequence can be as shown in SEQ ID NO.33.

[0132] SEQ ID NO.33:

[0133] AGCACAATGAAGATCAAGGTGGGTGTCTTTCCTGCCTGAGCTGACCTGGGCAGGTCGGCTGTGGGGTCCTGTGGTGTGTGGGGAGCTG

[0134] In some embodiments, the ΔCt cutoff value may be the ΔCt value that maximizes the Youden Index.

[0135] Substitute into the Logistic regression model:

[0136] The binary variable (0 or 1) after binarization was substituted into the logistic regression model instead of the original ΔCt value.

[0137] The weight (coefficient) of each marker in the logistic regression model is calculated based on these binary variables.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] In some embodiments, the methylation marker combination may include any one or more of the following combinations:

[0143] Combination 1: ZNF536 and NOL4;

[0144] Combination 2: ZNF536, ZNF671, and TTC34;

[0145] Combination 3: ZNF536, PROX1-AS1, and TTC34;

[0146] Combination 4: ZNF536, ZNF671, ARHGEF4, and TTC34;

[0147] Combination 5: ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34.

[0148] 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.

[0149] In some embodiments, the cutoff value of the combination 1 can be -1.133, and the risk score = -4.032 + 3.375 × BV ZNF536 +2.424×BV NOL4 .

[0150] In some embodiments, the cutoff value of the combination 2 can be -1.656, and the risk score = -4.240 + 2.685 × BV ZNF536 +1.025× BV ZNF671 +2.484×BV TTC34 .

[0151] In some embodiments, the cutoff value of the combination 3 can be -1.204, and the risk score = -4.295 + 2.796 × BV ZNF536 +0.865× BV PROX1-AS1+2.521×BV TTC34 .

[0152] In some embodiments, the cutoff value of the combination 4 can be -1.756, and the risk score = -4.272 + 2.314 × BV ZNF536 +0.755× BV ZNF671 +1.334×BV ARHGEF4 +1.963×BV TTC34 .

[0153] In some embodiments, the cutoff value of the combination 5 can be -1.271, and the risk score = -4.322 + 2.391 × BV ZNF536 +0.614×BV ZNF671 +0.665×BV HTR1F +0.655×BV WEE1P1 +2.145×BV TTC34 .

[0154] The present application also provides an electronic terminal 700, comprising: 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.

[0155] Specific as Figure 7 As shown, it is an optional hardware structure diagram of the 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, Figure 7 Various buses are labeled as bus systems.

[0156] 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.

[0157] It will be appreciated that memory 702 can be volatile memory or non-volatile memory, or can include both volatile and non-volatile memory. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), which can be used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memory described in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0158] The memory 702 in the embodiment of the present application is used to store various types of data to support the operation of the electronic terminal 700. Examples of this data include: any executable program used to operate on the electronic terminal 700, such as the operating system 7021 and the application 7022; the operating system 7021 includes various system programs, such as the framework layer, the core library layer, and the driver layer, which are used to implement various basic services and handle hardware-based tasks. The application 7022 can include various application programs, such as a media player (MediaPlayer) and a browser (Browser), which are used to implement various application services. The method for detecting cervical cancer and / or cervical precancerous lesions provided in the embodiment of the present application can be included in the application 7022.

[0159] The methods disclosed in the above embodiments of the present application can be applied to or implemented by processor 701. Processor 701 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in processor 701 or by software instructions. The above processor 701 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc. Processor 701 can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor 701 may be a microprocessor or any conventional processor. The steps of the methods provided in the embodiments of this application can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium located in a memory. The processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0160] In an exemplary embodiment, the electronic terminal 700 may be implemented by one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), and complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0161] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with a computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0162] In the embodiments provided herein, the computer-readable and writable storage medium may include read-only memory, random access memory, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage device, flash memory, USB flash drive, removable hard drive, or any other medium capable of storing desired program code in the form of instructions or data structures and capable of being accessed by a computer. In addition, any connection may be appropriately referred to as a computer-readable medium. For example, if instructions are sent from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, fiber optic 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 medium and data storage medium do not include connections, carrier waves, signals, or other temporary media, but are intended to refer to 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.

[0163] 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.

[0164] The present application also provides a method for diagnosing cervical cancer and / or cervical precancerous lesions, the method comprising:

[0165] Obtaining disease risk level data for a target marker combination of the sample to be tested, wherein the target marker combination is the aforementioned methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions;

[0166] 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.

[0167] In some embodiments, the disease risk level data may be a risk score value of a methylation marker combination.

[0168] 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 trained using methylation marker detection data of known samples;

[0169] 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, and it is used to represent the presence (1) or absence (0) of a feature or condition.

[0170] In this application, 0 represents negative and 1 represents positive.

[0171] The steps for binarization of ΔCt values ​​are as follows:

[0172] The ΔCt value of each methylation marker first needs to be compared with the preset cutoff value (Cutoff value);

[0173] If the ΔCt value is less than or equal to the cutoff value, the binary variable for that marker is 1 (positive);

[0174] If the ΔCt value is greater than the cutoff value, the binary variable for that marker is 0 (negative).

[0175] 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.

[0176] In some embodiments, the Ct value can be obtained by quantitative methylation-specific PCR detection, and the internal reference gene can be β-actin.

[0177] In some embodiments, the β-actin nucleotide sequence can be as shown in SEQ ID NO.33.

[0178] SEQ ID NO.33:

[0179] AGCACAATGAAGATCAAGGTGGGTGTCTTTCCTGCCTGAGCTGACCTGGGCAGGTCGGCTGTGGGGTCCTGTGGTGTGTGGGGAGCTG

[0180] In some embodiments, the ΔCt cutoff value may be the ΔCt value that maximizes the Youden Index.

[0181] Substitute into the Logistic regression model:

[0182] The binary variable (0 or 1) after binarization was substituted into the logistic regression model instead of the original ΔCt value.

[0183] The weight (coefficient) of each marker in the logistic regression model is calculated based on these binary variables.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] In some embodiments, the methylation marker combination may include any one or more of the following combinations:

[0189] Combination 1: ZNF536 and NOL4;

[0190] Combination 2: ZNF536, ZNF671, and TTC34;

[0191] Combination 3: ZNF536, PROX1-AS1, and TTC34;

[0192] Combination 4: ZNF536, ZNF671, ARHGEF4, and TTC34;

[0193] Combination 5: ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34.

[0194] In some embodiments, the cutoff value of the combination 1 can be -1.133, and the risk score = -4.032 + 3.375 × BV ZNF536 +2.424×BV NOL4 .

[0195] In some embodiments, the cutoff value of the combination 2 can be -1.656, and the risk score = -4.240 + 2.685 × BV ZNF536 +1.025× BV ZNF671 +2.484×BV TTC34 .

[0196] In some embodiments, the cutoff value of the combination 3 can be -1.204, and the risk score = -4.295 + 2.796 × BV ZNF536 +0.865× BV PROX1-AS1 +2.521×BV TTC34 .

[0197] In some embodiments, the cutoff value of the combination 4 can be -1.756, and the risk score = -4.272 + 2.314 × BV ZNF536 +0.755× BV ZNF671 +1.334×BV ARHGEF4 +1.963×BV TTC34 .

[0198] In some embodiments, the cutoff value of the combination 5 can be -1.271, and the risk score = -4.322 + 2.391 × BV ZNF536 +0.614×BV ZNF671 +0.665×BV HTR1F +0.655×BV WEE1P1 +2.145×BV TTC34 .

[0199] The experimental methods in the following examples, unless otherwise specified, are all conventional methods. The experimental materials used in the following examples, unless otherwise specified, were purchased from conventional biochemical reagent companies. The quantitative experiments in the following examples were all repeated three times, and the results were averaged.

[0200] The negative population (Normal), mild cervical precancerous lesions (CIN1), moderate cervical precancerous lesions (CIN2), severe cervical precancerous lesions (CIN3), and cervical cancer patients (Cancer) mentioned in the examples all meet the diagnostic criteria. The diagnostic criteria refer to the fifth edition of the World Health Organization (WHO) Classification of Tumors of the Female Genital Organs.

[0201] Cervical exfoliated cell specimens, reagent materials used and their sources in the examples:

[0202] 1. Specimen

[0203] 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.

[0204] 2. Main reagents and materials

[0205] The genomic DNA extraction kit for cervical exfoliated cells was the HiPure Universal DNA Kit (Magen); the conversion kit was the EZ DNA Methylation Kit (ZYMO); the primers and probes used were synthesized by Invitrogen (Shanghai) Trading Co., Ltd.; and nuclease-free water, 10× Ex Buffer, Ex Taq HS enzyme, and dNTPs were purchased from Yubao Biological (Dalian) Co., Ltd.

[0206] Example 1: Comparison of methylation markers in cervical exfoliated cell samples from Normal, CIN1, CIN2, CIN3, and Cancer groups

[0207] (1) Sample collection and genomic DNA extraction

[0208] Cervical exfoliated cell samples were collected from patients with negative cervical neoplasia (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 the HiPure Universal DNA Kit (Magen) according to the kit's instructions.

[0209] (2) DNA bisulfite conversion

[0210] Extracted genomic DNA was bisulfite-converted using the EZ DNA Methylation Kit (ZYMO). During this conversion, unmethylated cytosine (C) in the DNA is converted to uracil (U), while methylated cytosine (C) remains unchanged. After purification, bisulfite-converted DNA (m-DNA) was obtained.

[0211] (3) Fluorescence PCR detection

[0212] Fluorescence PCR technology is used to detect the fluorescence Ct value of methylation markers. The specific steps are as follows:

[0213] Reaction system configuration: 20μL PCR reaction system includes:

[0214] 18.5 μL PCR mixture, containing:

[0215] 2 μL of 10× Ex PCR buffer

[0216] 2 μL dNTPs

[0217] 0.2 μL Ex Taq HS enzyme

[0218] 2.5 μL of primer and probe premix (including detection sites and internal reference genes)

[0219] 0.16 μL ROX

[0220] 11.64 μL of nuclease-free water

[0221] 1.5 μL of bisulfite-converted DNA (m-DNA)

[0222] The final concentration of each primer was 400 nM, and the final concentration of each detection probe was 200 nM.

[0223] PCR reaction conditions:

[0224] Initial denaturation: 95°C for 5 minutes

[0225] Cycling reaction: 95℃ for 15 seconds, 60℃ for 30 seconds (fluorescence collection), a total of 50 cycles

[0226] Detection equipment: Use ABI7500 Real-Time PCR System to detect different fluorescent signals in the corresponding fluorescence channels.

[0227] (4) Data processing and analysis

[0228] For targets where no amplification signal was detected, the Ct value was set to 50.

[0229] Calculate the methylation level (ΔCt) of a single methylation marker using the formula: ΔCt = Ct 甲基化标志物 -Ct β-actin .

[0230] The ΔCt values ​​of cervical exfoliated cell samples of negative population, CIN1, CIN2, CIN3 and cervical cancer patients were compared to analyze the differences between different groups.

[0231] (5) Primer and probe sequences

[0232] The primer sequences for each methylation marker are shown in Table 1 .

[0233] The probe sequences are shown in Table 2.

[0234] Table 1 Primer sequences

[0235] 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

[0236] Table 2 Probe sequences

[0237] 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

[0238] Figure 1 The A-1H results showed that patients with cervical cancer and CIN3 had lower ΔCt values ​​and higher methylation levels, indicating stronger methylation signals. In negative samples and CIN1 patients, higher ΔCt values ​​were detected, indicating that no target methylation signals were detected in most samples. This indicates that these targets have the potential to detect cervical cancer and severe precancerous lesions. This demonstrates the feasibility of the selected target markers for detecting cervical cancer and severe precancerous lesions.

[0239] Example 2: Comparison of methylation levels of methylation markers in cervical exfoliated cell samples from CIN2- and CIN3+ patients

[0240] The negative population, CIN1 and CIN2 patients were defined as CIN2-, and CIN3 and cervical cancer patients were defined as CIN3+. 430 CIN2- samples and 106 CIN3+ samples in Example 1 were analyzed.

[0241] Table 3 Sensitivity and specificity of methylation markers for detecting CIN3+

[0242] 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

[0243] SPSS statistic 21 software was used to generate receiver operating characteristic curves for each gene, with sensitivity (true positive rate) as the ordinate and 1-specificity (false positive rate) as the abscissa. The Youden index (Youden index) = sensitivity - (1-specificity). The optimal cutoff point (ΔCt cutoff) was determined based on the maximum Youden index. The ΔCt cutoff values ​​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.

[0244] The methylation status of a gene in a clinical sample is determined based on the ΔCt cutoff value for each gene. A Ct value greater than the ΔCt cutoff value for that gene indicates negative methylation of that gene in the sample; otherwise, it indicates positive methylation.

[0245] Figure 2 The methylation ΔCt values ​​(i.e., methylation levels) for each gene in CIN2- and CIN3+ are displayed. Based on the ΔCt cutoff values ​​for each gene methylation, the positive and negative characteristics of CIN2- and CIN3+ samples were determined. The results in Table 3 demonstrate that the selected gene methylation markers have high sensitivity and specificity for cervical exfoliated cell samples from patients with cervical cancer and precancerous lesions (CIN3+).

[0246] Example 3: Combined analysis of methylation markers using logistic regression

[0247] The negative population, CIN1 and CIN2 patients were defined as CIN2-, and CIN3 and cervical cancer patients were defined as CIN3+. The methylation marker compositions of 430 CIN2- samples and 106 CIN3+ samples in Example 1 were analyzed.

[0248] Use SPSS statistic 21 software to perform logistic regression analysis on each target methylation marker combination. Open SPSS, find "Analyze" in the menu bar, click "Regression," and then click "Binary Logistic Regression." Include "Clinical Group" as the dependent variable and influencing factors as the independent variables (i.e., each different gene combination). Select "95% Confidence Interval," click "Continue," and finally click "OK" to obtain the formula corresponding to that combination. Calculate the risk score for each sample based on the formula for each marker combination. Using CIN3+ as the endpoint, perform receiver operating characteristic (ROC) analysis on the risk scores for each marker combination to obtain risk score cutoffs for different marker combinations. Based on the risk score cutoffs, determine the predicted group ("group membership") for the sample. Follow the above steps to perform binary logistic regression using the "group membership" generated by each combination as the independent variable. Select the top five combinations ranked by Exp(B) for analysis.

[0249] Combined ZNF536 and NOL4, risk score = -4.032 + 3.375 × BV ZNF536 +2.424×BV NOL4 SPSS statistic 21 software was used to obtain the receiver operating curve of the combination, calculate the maximum value of the Youden index, and obtain the corresponding cutoff value. 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 as 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%.

[0250] Combined with ZNF536, ZNF671 and TTC34, risk score = -4.240 + 2.685 × BV ZNF536 +1.025×BV ZNF671 +2.484×BV TTC34 SPSS statistic 21 software was used to obtain the receiver operating curve of the combination, calculate the maximum value of the Youden index, and obtain the corresponding cutoff value. 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%.

[0251] Combined with ZNF536, PROX1-AS1 and TTC34, risk score = -4.295 + 2.796 × BV ZNF536 +0.865×BV PROX1-AS1 +2.521×BV TTC34SPSS statistic 21 software was used to obtain the receiver operating curve of the combination, calculate the maximum value of the Youden index, and obtain the corresponding cutoff value. 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%.

[0252] 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 SPSS statistic 21 software was used to obtain the receiver operating curve of the combination, calculate the maximum value of the Youden index, and obtain the corresponding cutoff value. 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%.

[0253] 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 SPSS statistic 21 software was used to obtain the receiver operating curve of the combination, calculate the maximum value of the Youden index, and obtain the corresponding cutoff value. 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. Statistics show that the combination can detect CIN3+ with a sensitivity of 91.5% and a specificity of 94.7%.

[0254] Note: BV is the abbreviation of Binary Values.

[0255] Figure 3 The receiver operating characteristic (ROC) curve for a combination of five methylation markers for screening cervical cancer and cervical precancerous lesions is shown. The results indicate that the screening combination has high sensitivity and specificity for cervical exfoliated cell samples from patients with cervical cancer and precancerous lesions (CIN3+), and warrants further validation.

[0256] Example 4: Verification of the clinical efficacy of the methylation marker composition in screening cervical cancer and precancerous lesions

[0257] Genomic DNA samples were obtained from cervical exfoliated cells from patients with negative cervical necrosis factor (CIN) (85 cases), CIN1 (38 cases), CIN2 (43 cases), CIN3 (45 cases), and cervical cancer (12 cases). Genomic DNA was extracted from cervical exfoliated cell samples using the HiPureUniversal DNA Kit (Magen) according to the manufacturer's instructions. DNA bisulfite conversion and fluorescent PCR detection procedures were the same as in Example 1.

[0258] Data processing and analysis:

[0259] Treatment of undetectable amplification signals: For targets with no detected amplification signals, the Ct value was set to 50.

[0260] 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.

[0261] Binarization: Clinical samples were interpreted based on the ΔCt cutoff value for each marker. If the ΔCt value of the target marker in the sample was less than or equal to the ΔCt cutoff value, the sample was considered positive (i.e., 1); otherwise, the sample was considered negative (i.e., 0). Table 4 shows the sensitivity and specificity of individual methylation markers for screening CIN3+ in validation samples.

[0262] Table 4 Sensitivity and specificity of methylation markers for detecting CIN3+ in validation samples

[0263] 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

[0264] Marker combination analysis: Substitute the target marker value (0 or 1) 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.

[0265] Table 5 Sensitivity and specificity of methylation marker combinations for detecting CIN3+ in validation samples

[0266] 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

[0267] Figure 4 The receiver operating characteristic (ROC) curves for five methylation marker combinations in screening for cervical cancer and cervical precancerous lesions in validation samples are shown. The results demonstrate that the selected methylation marker combination exhibits high sensitivity and specificity for cervical exfoliated cells from patients with cervical cancer and precancerous lesions (CIN3+) in validation samples. All five target combinations demonstrated superior performance in distinguishing CIN2- from CIN3+, with good sensitivity and specificity.

[0268] The results in Table 5 show that the selected methylation marker combinations demonstrated high sensitivity and specificity in cervical exfoliated cytology samples from patients with cervical cancer and precancerous lesions (CIN3+) in the validation sample. For example, combination 4 (ZNF536, ZNF671, ARHGEF4, and TTC34) achieved a sensitivity of 98.2% and a specificity of 95.8%, while combination 5 (ZNF536, ZNF671, HTR1F, WEE1P1, and TTC34) achieved a sensitivity of 91.2% and a specificity of 94.6%. All five target combinations demonstrated excellent performance in distinguishing CIN2- from CIN3+, with good sensitivity and specificity.

[0269] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0270] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0271] In some embodiments, numbers describing the quantity 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 stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may vary according to the required features of the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0272] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not 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 described and illustrated 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 is: Combination 3: ZNF536, PROX1-AS1, and TTC34; or Combination 5: ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34.

2. Use of the methylation marker combination or its detection object according to claim 1 in the preparation of a kit for detecting cervical cancer and / or cervical precancerous lesions, wherein the detection object comprises a probe and a primer.

3. The use according to claim 2, characterized in that The test samples of the kit include any one or more of cervical scraping cells or tissues, biopsy tissues, surgical tissues or cervical exfoliated cells.

4. A kit for detecting cervical cancer and / or cervical precancerous lesions, characterized in that: The kit comprises the methylation marker combination or a detection object thereof according to claim 1, wherein the detection object comprises a probe and a primer.

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

32.

6. 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 the methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions according to claim 1; 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.

7. The detection device according to claim 6, 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.

8. The detection device according to claim 6, characterized in that: The sample to be tested includes any one or more of cervical scraping cells or tissues, biopsy tissues, surgical tissues or cervical exfoliated cells.

9. The detection device according to claim 8, characterized in that: The cutoff value of combination 3 is -1.204, and the risk score = -4.295 + 2.796 × BV ZNF536 +0.865×BV PROX1-AS1 +2.521×BV TTC34 ;or The cutoff value of combination 5 is -1.271, and the risk score = -4.322 + 2.391 × BV ZNF536 +0.614×BV ZNF671 +0.665×BV HTR1F +0.655×BV WEE1P1 +2.145×BV TTC34 .

10. 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 the sample to be tested, wherein the target marker combination is the methylation marker combination for detecting cervical cancer and / or cervical precancerous lesions according to claim 1; 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.

11. The computer-readable storage medium of claim 10, 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.

12. The computer-readable storage medium of claim 10, wherein: The sample to be tested includes any one or more of cervical scraping cells or tissues, biopsy tissues, surgical tissues or cervical exfoliated cells.

13. The computer-readable storage medium of claim 12, wherein: The cutoff value of combination 3 is -1.204, and the risk score = -4.295 + 2.796 × BV ZNF536 +0.865×BV PROX1-AS1 +2.521×BV TTC34 ;or The cutoff value of combination 5 is -1.271, and the risk score = -4.322 + 2.391 × BV ZNF536 +0.614×BV ZNF671 +0.665×BV HTR1F +0.655×BV WEE1P1 +2.145×BV TTC34 .

14. 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 10 to 13.

15. 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 10 to 13 is implemented.

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