A methylation marker combination for detection of cervical cancer and / or cervical precancerous lesions

Abstract

The present application relates to the field of biological medicine, in particular to a methylation marker combination for detection of cervical cancer and / or cervical precancerous lesion, which comprises the methylation ZNF536 gene or a fragment thereof, and any one or more of the methylation ZNF671, TTC34 or ARHGEF4 gene or a fragment thereof. The present application studies the methylation difference between the patients with cervical cancer and / or cervical precancerous lesion and healthy people through the DNA of cervical exfoliated cells of Chinese population, and for the first time screens out five best methylation marker combinations, which are used to establish the risk prediction method of cervical cancer and / or cervical precancerous lesion of Chinese population, and are suitable for the risk assessment of early cervical cancer and / or cervical precancerous lesion of Chinese population, and the screening and diagnosis of cervical cancer and / or cervical precancerous lesion.

Description

[0001] This case is a divisional application of the following patent application:

[0002] Application number: 202510323811.3;

[0003] Application date: March 19, 2025;

[0004] Invention Title: A combination of methylation biomarkers for the detection of cervical cancer and / or precancerous cervical lesions, and its applications, products, detection devices, computer-readable storage media, electronic terminals, and computer programs. Technical Field

[0005] This application relates to the field of biomedicine, and in particular to a combination of methylation markers for the detection of cervical cancer and / or precancerous lesions of the cervix, and its applications, products, detection devices, computer-readable storage media, electronic terminals and computer programs. Background Technology

[0006] Only persistent infection with high-risk human papillomavirus (hrHPV) can lead to cervical lesions, progressing from mild cervical intraepithelial neoplasia (CIN), moderate CIN, and severe CIN, eventually developing into cervical cancer. Current screening guidelines recommend HPV DNA testing as the first-line method, or in combination with thin-layer cytologic test (TCT). hrHPV testing is objective and highly reproducible, but it cannot distinguish between transient and transformative infections, potentially leading to increased colposcopy referrals and causing anxiety in women with positive results. Cytological screening methods such as TCT have high specificity, but limitations such as diagnostic subjectivity and low sensitivity can lead to missed diagnoses.

[0007] Recent studies have shown that early epigenetic alterations are an important characteristic of tumor development and progression. DNA methylation detection is gradually becoming an emerging method for detecting cervical cancer and precancerous lesions. Various methylation genes, such as FAM19A4, Mir124-2, PAX1, ZNF582, SOX1, and EPB41L3, have been considered biomarkers for cervical cancer screening. However, their detection efficacy for cervical cancer and precancerous lesions in the Chinese population is generally limited. Therefore, exploring and developing more effective and objective biomarkers has significant clinical implications. Summary of the Invention

[0008] To address the aforementioned technical challenges, this application provides a set of biomarkers derived from the Chinese population that are closely related to the development and progression of cervical cancer and precancerous lesions. Based on these biomarkers, detection primers, probes, and a detection kit have been developed. This kit is characterized by objectivity, high reproducibility, low cost, high sensitivity, and high specificity, providing new ideas and detection methods for the early diagnosis and treatment of cervical cancer and precancerous lesions. The main contribution of this application lies in the discovery of these combinations of methylation biomarkers, and any methylation level analysis method can be used to detect the methylation levels of the methylation biomarkers discovered in this application.

[0009] This application provides a combination of methylation markers for the detection of cervical cancer and / or precancerous cervical lesions, the combination of methylation markers including methylated ZNF536 gene or fragment thereof, and any one or more of methylated ZNF671, TTC34 or ARHGEF4 genes or fragment thereof.

[0010] This application also provides the use of the above-mentioned combination of methylation markers or their detection agents in the preparation of products for detecting cervical cancer and / or precancerous lesions of the cervix.

[0011] This application also provides a product for detecting cervical cancer and / or precancerous lesions of the cervix, the product comprising the above-described combination of methylation markers or their detection agents.

[0012] This application also provides a device for detecting cervical cancer and / or precancerous lesions of the cervix, comprising the following modules: a data acquisition module, used to provide disease risk level data of a combination of target biomarkers for a test sample, wherein the combination of target biomarkers is the aforementioned combination of methylation biomarkers for detecting cervical cancer and / or precancerous lesions of the cervix; and a judgment module, used to assess the cervical cancer and / or precancerous lesion status of the individual corresponding to the test sample based on the disease risk level data of the combination of target biomarkers for the test sample.

[0013] This application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement a method for detecting cervical cancer and / or precancerous lesions of the cervix. The method includes: acquiring disease risk level data of a combination of target biomarkers for a test sample, wherein the combination of target biomarkers is the aforementioned combination of methylation biomarkers for detecting cervical cancer and / or precancerous lesions of the cervix; and assessing the status of cervical cancer and / or precancerous lesions of the individual corresponding to the test sample based on the disease risk level data of the combination of target biomarkers for the test sample.

[0014] This application also provides an electronic terminal, including: 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 performs the method for detecting cervical cancer and / or precancerous lesions of the cervix described in the above-described computer-readable storage medium.

[0015] This application also provides a computer program that, when executed by a processor, implements the method for detecting cervical cancer and / or precancerous lesions of the above-described computer-readable storage medium.

[0016] The beneficial effects of this application include, but are not limited to: (1) This application provides new biomarkers for detecting cervical cancer and precancerous lesions from the Chinese population, which have high sensitivity and high specificity, providing new ideas for the early diagnosis and treatment of cervical cancer and precancerous lesions. (2) This application also provides nucleic acids, nucleic acid genomes and / or kits for determining the modification status of DNA regions, which can be used to confirm the presence of cervical cancer and precancerous lesions, assess the formation or risk of cervical cancer and precancerous lesions and / or assess the progression of cervical cancer and precancerous lesions, providing useful guidance for the screening, auxiliary diagnosis and prognosis of cervical cancer and precancerous lesions. Attached Figure Description

[0017] This application will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, wherein:

[0018] Figure 1 The methylation levels of methylation markers in cervical exfoliation samples from different populations in Example 1 of this application are shown. ★ p<0.05, ★★ p<0.01, ★★★ p<0.001; Figure 1 A shows the methylation level of the ZNF536 gene in cervical exfoliation samples from different populations; Figure 1 B shows the methylation level of the NOL4 gene in cervical exfoliation samples from different populations; Figure 1 C shows the methylation level of the ZNF671 gene in cervical exfoliation samples from different populations; Figure 1 D shows the methylation level of the HTR1F gene in cervical exfoliation samples from different populations; Figure 1 E shows the methylation level of the PROX1-AS1 gene in cervical exfoliation samples from different populations; Figure 1 F shows the methylation level of the WEE1P1 gene in cervical exfoliation samples from different populations; Figure 1 G indicates the methylation level of the ARHGEF4 gene in cervical exfoliation samples from different populations; Figure 1H indicates the methylation level of the TTC34 gene in cervical exfoliation samples from different populations.

[0019] Figure 2 The methylation levels of methylation markers in CIN2- and CIN3+ cervical exfoliation samples from Example 2 of this application are shown. ★ p<0.05, ★★ p<0.01, ★★★ p<0.001.

[0020] Figure 3 The present invention illustrates the receiver operating characteristic (ROC) curves for screening cervical cancer and precancerous cervical lesions using a combination of five methylation markers in Example 3 of this application.

[0021] Figure 4 The present invention illustrates the receiver operating characteristic (ROC) curves of the five methylation marker combinations in Example 4 of this application for screening cervical cancer and precancerous cervical lesions in validation samples.

[0022] Figure 5 This is a block diagram of a cervical cancer and / or precancerous lesion detection device according to some embodiments of this application.

[0023] Figure 6 This is a flowchart illustrating a method for detecting cervical cancer and / or precancerous lesions of the cervix according to some embodiments of this application.

[0024] Figure 7 This is a schematic diagram of the structure of an electronic terminal 700 according to some embodiments of this application. Detailed Implementation

[0025] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0026] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0027] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0028] This application provides a combination of methylation markers for the detection of cervical cancer and / or precancerous cervical lesions, the combination of methylation markers including methylated ZNF536 gene or fragment thereof, and any one or more of methylated NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F or WEE1P1 gene or fragment thereof.

[0029] In some embodiments, the gene fragment may include gene subregions. Gene subregions are different functional regions within a gene, such as coding and non-coding regions.

[0030] In some embodiments, the methylation marker may include methylated ZNF536 gene subregion, and any one or more of methylated NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F, or WEE1P1 gene subregion.

[0031] In some embodiments, the nucleotide sequence of the ZNF536 gene subregion may be as shown in SEQ ID NO.1.

[0032] In some embodiments, the nucleotide sequence of the NOL4 gene subregion may be as shown in SEQ ID NO.5.

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

[0034] In some embodiments, the nucleotide sequence of the TTC34 gene subregion may be as shown in SEQ ID NO.29.

[0035] In some embodiments, the nucleotide sequence of the PROX1-AS1 gene subregion may be as shown in SEQ ID NO.17.

[0036] In some embodiments, the nucleotide sequence of the ARHGEF4 gene subregion may be as shown in SEQ ID NO.25.

[0037] In some embodiments, the nucleotide sequence of the HTR1F gene subregion may be as shown in SEQ ID NO.13.

[0038] In some embodiments, the nucleotide sequence of the WEE1P1 gene subregion may be as shown in SEQ ID NO.21.

[0039] In some embodiments, the cervical precancerous lesion may be a severe cervical precancerous lesion.

[0040] This application also provides the use of the above-mentioned combination of methylation markers or their detection agents in the preparation of products for detecting cervical cancer and / or precancerous lesions of the cervix.

[0041] In some embodiments, the detection substance may be selected from any one or more of antibodies, membrane strips, chips, probes, or primers.

[0042] In some embodiments, the test sample for the product may include any one or more of the following: 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 for the product may be cervical exfoliated cells.

[0043] This application also provides a product for detecting cervical cancer and / or precancerous lesions of the cervix, the product comprising the above-described combination of methylation markers or their detection agents.

[0044] In some embodiments, the detector may be a detector for detecting the methylation level of methylation markers.

[0045] In some embodiments, the detection substance may include probes and primers.

[0046] As used in this application, the term "primer" 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, which, 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), is complementary to the nucleic acid strand (template or target sequence). 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).

[0047] The term "probe" refers to any molecule capable of selectively binding to a target biomolecule (e.g., a nucleic acid sequence that hybridizes with the probe). In some embodiments, the probe may be labeled, for example, with a fluorescent group and a quencher group. In some embodiments, the probe may be a Taqman probe with a fluorescent reporter group added to the 5' end and a fluorescent quencher group added to the 3' end.

[0048] In some embodiments, the primers and probes may include specific primers and probes for the transformed sequences of the following genes or fragments: ZNF536, and any one or more of NOL4, ZNF671, TTC34, PROX1-AS1, ARHGEF4, HTR1F, or WEE1P1.

[0049] In some embodiments, the conversion may be the conversion of unmethylated cytosine in the gene or a fragment thereof into uracil.

[0050] In some embodiments, the primer nucleotide sequences for detecting the methylation marker ZNF536 gene or fragments thereof may be as shown in SEQ ID NO.2 and SEQ ID NO.3.

[0051] In some embodiments, the primer nucleotide sequences for detecting the methylation marker NOL4 gene or fragments thereof may be as shown in SEQ ID NO. 6 and SEQ ID NO. 7.

[0052] In some embodiments, the primer nucleotide sequences for detecting the methylation marker ZNF671 gene or fragments thereof may be as shown in SEQ ID NO.10 and SEQ ID NO.11.

[0053] In some embodiments, the primer nucleotide sequences for detecting the methylation marker HTR1F gene or fragments thereof may be as shown in SEQ ID NO.14 and SEQ ID NO.15.

[0054] In some embodiments, the primer nucleotide sequences for detecting the methylation marker PROX1-AS1 gene or fragments thereof may be as shown in SEQ ID NO.18 and SEQ ID NO.19.

[0055] In some embodiments, the primer nucleotide sequences for detecting the methylation marker WEE1P1 gene or fragments thereof may be as shown in SEQ ID NO.22 and SEQ ID NO.23.

[0056] In some embodiments, the primer nucleotide sequences for detecting the methylation marker ARHGEF4 gene or fragments thereof may be as shown in SEQ ID NO.26 and SEQ ID NO.27.

[0057] In some embodiments, the primer nucleotide sequences for detecting the methylation marker TTC34 gene or fragments thereof may be as shown in SEQ ID NO.30 and SEQ ID NO.31.

[0058] In some embodiments, the nucleotide sequence of the probe for detecting the methylation marker ZNF536 gene or fragments thereof may be as shown in SEQ ID NO.4.

[0059] 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.

[0060] In some embodiments, the nucleotide sequence of the probe for detecting the methylation marker ZNF671 gene or fragments thereof may be as shown in SEQ ID NO.12.

[0061] 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.

[0062] 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.

[0063] In some embodiments, the nucleotide sequence of the probe for detecting the WEE1P1 gene or a fragment thereof, a methylation marker, may be as shown in SEQ ID NO.24.

[0064] 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.

[0065] 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.

[0066] In some embodiments, the product may be any of a reagent kit, chip, membrane strip, protein array, composition, or detection system.

[0067] The term "membrane strip" refers to a diagnostic tool that utilizes the principle of specific biomolecular recognition. It involves immobilizing biomolecules such as antigens or antibodies on a membrane, allowing them to specifically bind to the analyte in a sample, and then using visualization or other signal detection methods to qualitatively or quantitatively analyze the target substance in the sample.

[0068] 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 by arranging thousands of different proteins or protein-protein interaction probes in an orderly manner on a solid surface.

[0069] The term "chip" typically refers to a miniature device that integrates biosensors and microfluidics 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.

[0070] 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 delivery devices (e.g., syringes), solvents, solutions, buffers, instructions, or desiccants.

[0071] In some embodiments, the product may include one or more of the following: DNA polymerase, a mixture of deoxynucleotides (dNTPs), a buffer solution, primers, probes, sodium bisulfite, a positive control, or a negative control.

[0072] This application also provides a device for detecting cervical cancer and / or precancerous cervical lesions, such as Figure 5 As shown, it includes the following modules: data acquisition module 510 and judgment module 520.

[0073] The data acquisition module 510 is used to provide disease risk level data of the target biomarker combination of the sample to be tested, wherein the target biomarker combination is the methylation biomarker combination mentioned above for the detection of cervical cancer and / or precancerous lesions of the cervix.

[0074] In some embodiments, the disease risk level data may be a risk score value for a combination of methylation biomarkers.

[0075] In some embodiments, the risk score can be calculated by substituting the BV value of a single methylation biomarker for each sample into a Logistic regression model, wherein the regression model is trained using methylation biomarker detection data of known samples.

[0076] In data analysis and statistics, binary values ​​(BV) refer to converting raw data into numerical values ​​with only two possible values, namely 0 and 1. This transformation process is called binarization, and it is used to represent the presence (1) or absence (0) of a feature or condition.

[0077] In this application, 0 represents negative and 1 represents positive.

[0078] The binarization steps for the ΔCt value are as follows:

[0079] The ΔCt value of each methylation marker must first be compared with a preset cutoff value;

[0080] If the ΔCt value is less than or equal to the cutoff value, then the binary variable of the marker is 1 (positive);

[0081] If the ΔCt value is greater than the cutoff value, then the binary variable of the marker is 0 (negative).

[0082] In some embodiments, the ΔCt value can be the difference between the Ct value of a single methylation marker and the Ct value of an internal reference gene.

[0083] In some embodiments, the Ct value can be obtained by quantitative methylation-specific PCR detection, and the internal reference gene can be β-actin.

[0084] In some embodiments, the β-actin nucleotide sequence may be as shown in SEQ ID NO.33.

[0085] In some embodiments, the cutoff value of ΔCt can be the ΔCt value that maximizes the Yoden exponent.

[0086] Substitute into the Logistic regression model:

[0087] The binary variable (0 or 1) that is binarized is substituted into the Logistic regression model, instead of the original ΔCt value.

[0088] The weights (coefficients) of each marker in the logistic regression model are calculated based on these binary variables.

[0089] For example, consider a gene methylation level ΔCt value with a cutoff of 9.73: if a sample has a ΔCt value of 9.5 (less than or equal to 9.73), the binarized value of the gene is 1; if a sample has a ΔCt value of 10.0 (greater than 9.73), the binarized value of the gene is 0. These binarization results are then substituted into a Logistic regression model to calculate the risk score.

[0090] In some embodiments, the determination module determines the sample to be positive when the risk score of the methylation marker combination of the sample to be tested is greater than or equal to the cutoff value; and determines the sample to be negative when the risk score of the methylation marker combination of the sample to be tested is less than the cutoff value.

[0091] In some embodiments, a positive result means that the individual corresponding to the test sample is a patient with severe cervical precancerous lesions or cervical cancer, and a negative result means that the individual corresponding to the test sample is a healthy person, a patient with mild cervical precancerous lesions, or a patient with moderate cervical precancerous lesions.

[0092] 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.

[0093] In some embodiments, the combination of methylation markers may include any one or more of the following combinations:

[0094] Combination 1: ZNF536 and NOL4;

[0095] Combination 2: ZNF536, ZNF671 and TTC34;

[0096] Combination 3: ZNF536, PROX1-AS1, and TTC34;

[0097] Combination 4: ZNF536, ZNF671, ARHGEF4 and TTC34;

[0098] Combination 5: ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34.

[0099] The judgment module 520 is used to assess the cervical cancer and / or cervical precancerous lesions of the individual corresponding to the test sample based on the disease risk level data of the combination of target biomarkers in the test sample.

[0100] In some embodiments, the cutoff value for combination 1 can be -1.133, and the risk score = -4.032 + 3.375 × BV. ZNF536 +2.424×BV NOL4 .

[0101] In some embodiments, the cutoff value for combination 2 can be -1.656, and the risk score = -4.240 + 2.685 × BV ZNF536 +1.025×BV ZNF671 +2.484×BV TTC34 .

[0102] In some embodiments, the cutoff value for 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 .

[0103] In some embodiments, the cutoff value for combination 4 can be -1.756, and the risk score = -4.272 + 2.314 × BV ZNF536 +0.755×BV ZNF671 +1.334×BVARHGEF4 +1.963×BV TTC34 .

[0104] In some embodiments, the cutoff value for 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 .

[0105] This application also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement a method for detecting cervical cancer and / or precancerous cervical lesions. A flowchart of the method is shown below. Figure 6 As shown.

[0106] In step S610, disease risk level data of the target biomarker combination of the sample to be tested is obtained, wherein the target biomarker combination is the methylation biomarker combination for the detection of cervical cancer and / or precancerous lesions of the cervix described above.

[0107] In some embodiments, the disease risk level data may be a risk score value for a combination of methylation biomarkers.

[0108] In some embodiments, the risk score can be calculated by substituting the BV value of a single methylation biomarker for each sample into a Logistic regression model, wherein the regression model is trained using methylation biomarker detection data of known samples.

[0109] In data analysis and statistics, binary values ​​(BV) refer to converting raw data into numerical values ​​with only two possible values, namely 0 and 1. This transformation process is called binarization, and it is used to represent the presence (1) or absence (0) of a feature or condition.

[0110] In this application, 0 represents negative and 1 represents positive.

[0111] The binarization steps for the ΔCt value are as follows:

[0112] The ΔCt value of each methylation marker must first be compared with a preset cutoff value;

[0113] If the ΔCt value is less than or equal to the cutoff value, then the binary variable of the marker is 1 (positive);

[0114] If the ΔCt value is greater than the cutoff value, then the binary variable of the marker is 0 (negative).

[0115] In some embodiments, the ΔCt value can be the difference between the Ct value of a single methylation marker and the Ct value of an internal reference gene.

[0116] In some embodiments, the Ct value can be obtained by quantitative methylation-specific PCR detection, and the internal reference gene can be β-actin.

[0117] In some embodiments, the β-actin nucleotide sequence may be as shown in SEQ ID NO.33.

[0118] In some embodiments, the cutoff value of ΔCt can be the ΔCt value that maximizes the Yoden exponent.

[0119] Substitute into the Logistic regression model:

[0120] The binary variable (0 or 1) that is binarized is substituted into the Logistic regression model, instead of the original ΔCt value.

[0121] The weights (coefficients) of each marker in the logistic regression model are calculated based on these binary variables.

[0122] For example, consider a gene methylation level ΔCt value with a cutoff of 9.73: if a sample has a ΔCt value of 9.5 (less than or equal to 9.73), the binarized value of the gene is 1; if a sample has a ΔCt value of 10.0 (greater than 9.73), the binarized value of the gene is 0. These binarization results are then substituted into a Logistic regression model to calculate the risk score.

[0123] In some embodiments, the determination module determines the sample to be positive when the risk score of the methylation marker combination of the sample to be tested is greater than or equal to the cutoff value; and determines the sample to be negative when the risk score of the methylation marker combination of the sample to be tested is less than the cutoff value.

[0124] In some embodiments, a positive result means that the individual corresponding to the test sample is a patient with severe cervical precancerous lesions or cervical cancer, and a negative result means that the individual corresponding to the test sample is a healthy person, a patient with mild cervical precancerous lesions, or a patient with moderate cervical precancerous lesions.

[0125] 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.

[0126] In some embodiments, the combination of methylation markers may include any one or more of the following combinations:

[0127] Combination 1: ZNF536 and NOL4;

[0128] Combination 2: ZNF536, ZNF671 and TTC34;

[0129] Combination 3: ZNF536, PROX1-AS1, and TTC34;

[0130] Combination 4: ZNF536, ZNF671, ARHGEF4 and TTC34;

[0131] Combination 5: ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34.

[0132] In step S620, the cervical cancer and / or precancerous lesions of the individual corresponding to the test sample are assessed based on the disease risk level data of the combination of target biomarkers in the test sample.

[0133] In some embodiments, the cutoff value for combination 1 can be -1.133, and the risk score = -4.032 + 3.375 × BV. ZNF536 +2.424×BV NOL4 .

[0134] In some embodiments, the cutoff value for combination 2 can be -1.656, and the risk score = -4.240 + 2.685 × BV ZNF536 +1.025×BV ZNF671 +2.484×BV TTC34 .

[0135] In some embodiments, the cutoff value for 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 .

[0136] In some embodiments, the cutoff value for 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 .

[0137] In some embodiments, the cutoff value for combination 5 can be -1.271, and the risk score = -4.322 + 2.391 × BV ZNF536 +0.614×BVZNF671 +0.665×BV HTR1F +0.655×BV WEE1P1 +2.145×BV TTC34 .

[0138] This 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 a computer program, and the processor 701 is used to execute the computer program stored in the memory 702, so that the terminal performs the method for detecting cervical cancer and / or precancerous lesions of the cervix as described in the above-mentioned computer-readable storage medium.

[0139] Specifically, such as Figure 7 The diagram shown illustrates an optional hardware structure of an electronic terminal 700 provided in an embodiment of this application. The terminal 700 can be a mobile phone, computer device, tablet device, personal digital processing device, factory back-end 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 via a bus system 705. It is understood that the bus system 705 is used to implement communication between these components. In addition to a data bus, the bus system 705 also includes a power bus, a control bus, and a status signal bus. However, for clarity, ... Figure 7 The general will label all buses as bus systems.

[0140] The user interface 703 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0141] It is understood that memory 702 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable categories of memory.

[0142] In this embodiment, the memory 702 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 that operates on the electronic terminal 700, such as the operating system 7021 and application programs 7022. The operating system 7021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., used to implement various basic services and handle hardware-based tasks. The application program 7022 may contain various applications, such as a media player, browser, etc., used to implement various application services. The method for detecting cervical cancer and / or precancerous lesions provided in this embodiment can be included in the application program 7022.

[0143] The methods disclosed in the embodiments of this application can be applied to processor 701, or implemented by processor 701. 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 the integrated logic circuit of the hardware in processor 701 or by instructions in the form of software. The processor 701 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 701 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. General-purpose processor 701 may be a microprocessor or any conventional processor, etc. The steps of the methods provided in the embodiments of this application can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0144] In an exemplary embodiment, the electronic terminal 700 may be used by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs) to execute the aforementioned method.

[0145] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented using computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0146] In the embodiments provided in this application, the computer-readable and writable storage medium may include read-only memory, random access memory, EEPROM, CD-ROM or other optical disc storage devices, disk storage devices or other magnetic storage devices, flash memory, USB flash drive, portable hard drive, or any other medium capable of storing desired program code in the form of instructions or data structures and accessible by a computer. Additionally, any connection may be appropriately referred to as a computer-readable medium. For example, if instructions are transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave 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, carrier waves, signals, or other transient media, but are intended for non-transient, tangible storage media. The disks and optical discs used in the application include compact discs (CDs), laser discs, optical discs, digital multifunction discs (DVDs), floppy disks, and Blu-ray discs, where disks typically copy data magnetically, while optical discs use lasers to copy data optically.

[0147] This application also provides a computer program that, when executed by a processor, implements the method for detecting cervical cancer and / or precancerous lesions of the above-described computer-readable storage medium.

[0148] This application also provides a method for diagnosing cervical cancer and / or precancerous cervical lesions, the method comprising:

[0149] Obtain disease risk level data for the target biomarker combination of the sample to be tested, wherein the target biomarker combination is the methylation biomarker combination mentioned above for the detection of cervical cancer and / or precancerous lesions of the cervix;

[0150] Based on the disease risk level data of the combination of target biomarkers in the test sample, assess the cervical cancer and / or cervical precancerous lesions of the corresponding individual.

[0151] In some embodiments, the disease risk level data may be a risk score value for a combination of methylation biomarkers.

[0152] In some embodiments, the risk score can be calculated by substituting the BV value of a single methylation biomarker for each sample into a Logistic regression model, wherein the regression model is trained using methylation biomarker detection data of known samples.

[0153] In data analysis and statistics, binary values ​​(BV) refer to converting raw data into numerical values ​​with only two possible values, namely 0 and 1. This transformation process is called binarization, and it is used to represent the presence (1) or absence (0) of a feature or condition.

[0154] In this application, 0 represents negative and 1 represents positive.

[0155] The binarization steps for the ΔCt value are as follows:

[0156] The ΔCt value of each methylation marker must first be compared with a preset cutoff value;

[0157] If the ΔCt value is less than or equal to the cutoff value, then the binary variable of the marker is 1 (positive);

[0158] If the ΔCt value is greater than the cutoff value, then the binary variable of the marker is 0 (negative).

[0159] In some embodiments, the ΔCt value can be the difference between the Ct value of a single methylation marker and the Ct value of an internal reference gene.

[0160] In some embodiments, the Ct value can be obtained by quantitative methylation-specific PCR detection, and the internal reference gene can be β-actin.

[0161] In some embodiments, the β-actin nucleotide sequence may be as shown in SEQ ID NO.33.

[0162] In some embodiments, the cutoff value of ΔCt can be the ΔCt value that maximizes the Yoden exponent.

[0163] Substitute into the Logistic regression model:

[0164] The binary variable (0 or 1) that is binarized is substituted into the Logistic regression model, instead of the original ΔCt value.

[0165] The weights (coefficients) of each marker in the logistic regression model are calculated based on these binary variables.

[0166] For example, consider a gene methylation level ΔCt value with a cutoff of 9.73: if a sample has a ΔCt value of 9.5 (less than or equal to 9.73), the binarized value of the gene is 1; if a sample has a ΔCt value of 10.0 (greater than 9.73), the binarized value of the gene is 0. These binarization results are then substituted into a Logistic regression model to calculate the risk score.

[0167] In some embodiments, the determination module determines the sample to be positive when the risk score of the methylation marker combination of the sample to be tested is greater than or equal to the cutoff value; and determines the sample to be negative when the risk score of the methylation marker combination of the sample to be tested is less than the cutoff value.

[0168] In some embodiments, a positive result means that the individual corresponding to the test sample is a patient with severe cervical precancerous lesions or cervical cancer, and a negative result means that the individual corresponding to the test sample is a healthy person, a patient with mild cervical precancerous lesions, or a patient with moderate cervical precancerous lesions.

[0169] 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.

[0170] In some embodiments, the combination of methylation markers may include any one or more of the following combinations:

[0171] Combination 1: ZNF536 and NOL4;

[0172] Combination 2: ZNF536, ZNF671 and TTC34;

[0173] Combination 3: ZNF536, PROX1-AS1, and TTC34;

[0174] Combination 4: ZNF536, ZNF671, ARHGEF4 and TTC34;

[0175] Combination 5: ZNF536, ZNF671, HTR1F, WEE1P1 and TTC34.

[0176] In some embodiments, the cutoff value for combination 1 can be -1.133, and the risk score = -4.032 + 3.375 × BV. ZNF536 +2.424×BV NOL4 .

[0177] In some embodiments, the cutoff value for combination 2 can be -1.656, and the risk score = -4.240 + 2.685 × BVZNF536 +1.025×BV ZNF671 +2.484×BV TTC34 .

[0178] In some embodiments, the cutoff value for 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 .

[0179] In some embodiments, the cutoff value for 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 .

[0180] In some embodiments, the cutoff value for 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 .

[0181] Unless otherwise specified, the experimental methods used in the following examples are conventional methods. Unless otherwise specified, the experimental materials used in the following examples were all purchased from conventional biochemical reagent companies. All quantitative experiments in the following examples were performed in triplicate, and the results were averaged.

[0182] The individuals included in this example (Normal, CIN1, CIN2, CIN3, and cervical cancer patients) all met the diagnostic criteria. The diagnostic criteria were based on the fifth edition of the World Health Organization (WHO) classification of tumors of the female reproductive organs.

[0183] The cervical exfoliated cell specimens, reagents and materials used, and their sources in the examples are as follows:

[0184] 1. Specimen

[0185] The biological samples used in the examples were cervical exfoliated cells collected by the Institute of Clinical Pharmacology, Xiangya Hospital, Central South University, between January 2020 and December 2022, and all of them were samples with known pathological information.

[0186] 2. Main Reagents and Materials

[0187] The cervical exfoliated cell genomic DNA extraction kit was the HiPure Universal DNA Kit (Magen); the transformation kit was the 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 Takara Bio (Dalian) Co., Ltd.

[0188] Example 1: Comparison of methylation levels of methylation markers in cervical exfoliated cell samples from the Normal, CIN1, CIN2, CIN3, and Cancer groups.

[0189] (1) Sample collection and genomic DNA extraction

[0190] Samples were collected from cervical exfoliated cells in patients with negative cervical cancer (275 cases), CIN1 (83 cases), CIN2 (72 cases), CIN3 (53 cases), and cervical cancer (53 cases). Genomic DNA was extracted from the cervical exfoliated cell samples using the HiPure Universal DNA Kit (Magen) extraction kit, following the kit instructions.

[0191] (2) DNA bisulfite conversion

[0192] Extracted genomic DNA was subjected to bisulfite conversion using the EZ DNA Methylation Kit (ZYMO). During the conversion, unmethylated cytosine (C) in the DNA was converted to uracil (U), while methylated cytosine (C) remained unchanged. After purification, bisulfite-converted DNA (m-DNA) was obtained.

[0193] (3) Fluorescent PCR detection

[0194] The fluorescence Ct values ​​of methylation markers were detected using fluorescent PCR technology. The specific steps are as follows:

[0195] Reaction system preparation: A 20 μL PCR reaction system includes:

[0196] 18.5 μL PCR mixture, containing:

[0197] 2 μL of 10×Ex PCR buffer

[0198] 2 μL of dNTP

[0199] 0.2 μL of Ex Taq HS enzyme

[0200] 2.5 μL of primer and probe premix (containing detection sites and internal reference genes)

[0201] 0.16 μL of ROX

[0202] 11.64 μL of nuclease-free water

[0203] DNA (m-DNA) was transformed with 1.5 μL of bisulfite.

[0204] The final concentration of each primer is 400 nM, and the final concentration of each detection probe is 200 nM.

[0205] PCR reaction conditions:

[0206] Initial denaturation: 95℃ for 5 minutes

[0207] Cyclic reaction: 95℃ for 15 seconds, 60℃ for 30 seconds (fluorescence collection), for a total of 50 cycles.

[0208] Detection equipment: An ABI 7500 Real-Time PCR System was used to detect different fluorescence signals in the corresponding fluorescence channels.

[0209] (4) Data processing and analysis

[0210] For target sites where no amplification signal was detected, the Ct value was set to 50.

[0211] The methylation level (ΔCt) of a single methylation marker is calculated using the formula: ΔCt = Ct 甲基化标志物 -Ct β-actin .

[0212] The ΔCt values ​​of cervical exfoliated cell samples from negative individuals, CIN1, CIN2, CIN3, and cervical cancer patients were compared to analyze the differences between different groups.

[0213] (5) Primer and probe sequences

[0214] The primer sequences for each methylation marker are shown in Table 1.

[0215] The probe sequences are shown in Table 2.

[0216] Table 1 Primer sequences

[0217]

[0218] Table 2 Probe Sequences

[0219]

[0220] Figure 1The A-1H results showed that patients with cervical cancer and CIN3 had small ΔCt values ​​and high methylation levels, indicating stronger methylation signals. In the negative population and CIN1 patients, the ΔCt values ​​were large, and most samples did not show detectable target methylation signals. This suggests that these targets have the potential to detect cervical cancer and severe precancerous lesions. This demonstrates the feasibility of using the selected target biomarkers to detect cervical cancer and severe precancerous lesions.

[0221] Example 2: Comparison of methylation levels of methylation markers in cervical exfoliated cell samples from CIN2- and CIN3+ patients

[0222] Patients with negative cervical cancer, CIN1, and CIN2 were defined as CIN2-, and patients with CIN3 and cervical cancer were defined as CIN3+. 430 CIN2- samples and 106 CIN3+ samples from Example 1 were analyzed.

[0223] Table 3 Sensitivity and specificity of methylation markers for detecting CIN3+

[0224]

[0225]

[0226] Receiver operating curves for each gene were obtained using SPSS Statistical 21 software. Sensitivity (true positive rate) was plotted on the ordinate, and 1-specificity (false positive rate) on the x-axis. The Youden index was calculated as sensitivity minus (1-specificity). The optimal cutoff point (ΔCt) was determined based on the maximum value of the Youden index. The cutoff values ​​for 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.

[0227] The methylation status of corresponding genes in clinical samples is determined based on the cutoff value of ΔCt for each gene. If the Ct value of a gene methylation in a sample is greater than the cutoff value of ΔCt for that gene, it indicates that the methylation of that gene in the sample is negative; otherwise, it is positive.

[0228] Figure 2 The ΔCt values, or methylation levels, of each gene in CIN2- and CIN3+ are presented. The positive and negative results of CIN2- and CIN3+ samples were determined based on the ΔCt cutoff values ​​of each gene's methylation. Table 3 shows 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+).

[0229] Example 3: Combinatorial analysis of methylation biomarkers using Logistic regression.

[0230] Patients with negative cervical cancer, CIN1, and CIN2 were defined as CIN2-, and patients with CIN3 and cervical cancer were defined as CIN3+. The methylation marker compositions from 430 CIN2- samples and 106 CIN3+ samples in Example 1 were analyzed.

[0231] Logistic regression analysis was performed on combinations of methylation biomarkers for each target using SPSS Statistic 21 software. Open SPSS, find and click "Analyze" in the menu bar, select "Regression," and then click "Binary Logistic Regression." List "Clinical Grouping" as the dependent variable and the influencing factors as independent variables (i.e., different gene combinations). Check "95% Confidence Interval," click "Continue," and finally click "OK" to obtain the formula for the combination. Calculate the risk score for each sample under different biomarker combinations using the formula. Using CIN3+ as the endpoint, perform ROC analysis on the risk scores of each biomarker combination to obtain the risk score cutoff values ​​for different combinations. Based on the risk score cutoff values, determine the predicted group ("group member") of the sample. Following the above steps, use the "group members" generated by different combinations as independent variables for binary logistic regression, selecting the top 5 combinations in Exp(B) ranking for analysis.

[0232] Combined with ZNF536 and NOL4, the risk score is -4.032 + 3.375 × BV. ZNF536 +2.424×BV NOL4 The receiver operating curve for the composition 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 for this composition was -1.133. When the risk score of the composition in a clinical sample was greater than or equal to -1.133, it was considered positive; otherwise, it was considered negative. Statistically, this composition can achieve a sensitivity of 90.6% and a specificity of 93.3% for detecting CIN3+.

[0233] Combining ZNF536, ZNF671, and TTC34, the risk score is -4.240 + 2.685 × BV. ZNF536 +1.025×BV ZNF671 +2.484×BV TTC34SPSS Statistic 21 software was used to obtain the receiver operating curve for the composition, calculate the maximum value of the Youden index, and obtain the corresponding cutoff value. The cutoff value for this composition was -1.656. When the risk score of the composition in a clinical sample was greater than or equal to -1.656, it was considered positive; otherwise, it was considered negative. Statistically, this composition can achieve a sensitivity of 92.5% and a specificity of 92.8% for detecting CIN3+.

[0234] Combined with ZNF536, PROX1-AS1, and TTC34, the risk score is -4.295 + 2.796 × BV. ZNF536 +0.865×BV PROX1-AS1 +2.521×BV TTC34 SPSS Statistic 21 software was used to obtain the receiver operating curve of the composition, calculate the maximum value of the Youden index, and obtain the corresponding cutoff value. The cutoff value of this composition is -1.204. When the risk score of the composition in the clinical sample is greater than or equal to -1.204, it is judged as positive, otherwise it is negative. Statistically, this composition can make the sensitivity of CIN3+ detection 92.5% and the specificity 94.4%.

[0235] Combined with ZNF536, ZNF671, ARHGEF4, and TTC34, the risk score is -4.272 + 2.314 × BV. ZNF536 +0.755×BV ZNF671 +1.334×BV ARHGEF4 +1.963×BV TTC34 The receiver operating curve for the composition 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 for this composition was -1.756. When the risk score of the composition in a clinical sample was greater than or equal to -1.756, it was considered positive; otherwise, it was considered negative. Statistically, this composition can achieve a sensitivity of 95.3% and a specificity of 94.9% for detecting CIN3+.

[0236] Combined with ZNF536, ZNF671, HTR1F, WEE1P1, and TTC34, the risk score is -4.322 + 2.391 × BV. ZNF536 +0.614×BV ZNF671 +0.665×BV HTR1F +0.655×BV WEE1P1 +2.145×BV TTC34The receiver operating curve for the composition 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 for this composition was -1.271. When the risk score of the composition in a clinical sample was greater than or equal to -1.271, it was considered positive; otherwise, it was considered negative. Statistically, this composition can achieve a sensitivity of 91.5% and a specificity of 94.7% for detecting CIN3+.

[0237] Note: BV is an abbreviation for Binary Values.

[0238] Figure 3 The receiver operating characteristic (ROC) curves for screening cervical cancer and precancerous cervical lesions using a combination of five methylation markers are shown. The results indicate that the selected combination has high sensitivity and specificity for cervical exfoliated cell samples from patients with cervical cancer and precancerous lesions (CIN3+), and further validation is warranted.

[0239] Example 4: Validating the clinical efficacy of methylation marker compositions in screening for cervical cancer and precancerous lesions.

[0240] Genomic DNA samples were obtained from cervical exfoliated cells derived from individuals with negative cervical cancer (85 cases), CIN1 (38 cases), CIN2 (43 cases), CIN3 (45 cases), and cervical cancer (12 cases). Genomic DNA was extracted from the cervical exfoliated cell samples using the HiPure Universal DNA Kit (Magen) according to the manufacturer's instructions. DNA bisulfite conversion and fluorescent PCR detection procedures were the same as in Example 1.

[0241] Data processing and analysis:

[0242] Handling of no amplification signal detected: For target sites where no amplification signal is detected, the Ct value is set to 50.

[0243] Methylation level calculation: The methylation level (ΔCt) of a single methylation marker is calculated using the formula: ΔCt = Ct 甲基化标志物 -Ct β-actin The cutoff values ​​of ΔCt for the methylation markers ZNF536, NOL4, ZNF671, HTR1F, PROX1-AS1, WEE1P1, ARHGEF4 and TTC34 contained in the five compositions were 9.73, 7.15, 6.13, 8.56, 10.09, 7.81, 7.44 and 8.09, respectively.

[0244] Binarization: Clinical samples are interpreted based on the cutoff values ​​of ΔCt for each biomarker. If the ΔCt value of the target biomarker in the sample is less than or equal to the cutoff value, the sample is considered positive (i.e., 1); otherwise, it is considered negative (i.e., 0). Table 4 shows the sensitivity and specificity of a single methylation biomarker in screening CIN3+ in validation samples.

[0245] Table 4. Sensitivity and specificity of methylation markers for detecting CIN3+ in validation samples.

[0246] 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

[0247] Biomarker combination analysis: Substitute the value (0 or 1) of the target biomarker of the sample to be tested into the formula of each combination, and determine the final positive or negative result of the sample to be tested based on the risk score of the combination.

[0248] Table 5. Sensitivity and specificity of methylation biomarker combinations for detecting CIN3+ in validation samples.

[0249]

[0250] Figure 4 The receiver operating characteristic (ROC) curves of five methylation biomarker combinations for screening cervical cancer and precancerous cervical lesions in validation samples are shown. The results demonstrate that the selected methylation biomarker combinations exhibit high sensitivity and specificity for cervical exfoliated cell samples from patients with cervical cancer and precancerous lesions (CIN3+) in validation samples. All five target combinations have demonstrated superior performance in distinguishing between CIN2- and CIN3+, exhibiting good sensitivity and specificity.

[0251] Table 5 shows that the selected methylation marker compositions demonstrated high sensitivity and specificity for cervical exfoliated cell samples from patients with cervical cancer and precancerous lesions (CIN3+) in the validation samples. For example, combination 4 (ZNF536, ZNF671, ARHGEF4, and TTC34) had a sensitivity of 98.2% and a specificity of 95.8%; combination 5 (ZNF536, ZNF671, HTR1F, WEE1P1, and TTC34) had a sensitivity of 91.2% and a specificity of 94.6%, etc. All five target combinations demonstrated superior performance in distinguishing between CIN2- and CIN3+, exhibiting good sensitivity and specificity.

[0252] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, 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 and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0253] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0254] 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 embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates 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 be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0255] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A combination of methylation biomarkers for the detection of cervical cancer and / or precancerous cervical lesions, characterized in that, The methylation marker combination is as follows Combination 2: ZNF536, ZNF671, and TTC34; or Combination 4: ZNF536, ZNF671, ARHGEF4 and TTC34; The cervical precancerous lesion was CIN3+; The ZNF536, ZNF671, ARHGEF4, and TTC34 genes include gene subregions; The nucleotide sequence of the ZNF536 gene subregion is shown in SEQ ID NO.1; The nucleotide sequence of the ZNF671 gene subregion is shown in SEQ ID NO.9; The nucleotide sequence of the TTC34 gene subregion is shown in SEQ ID NO.29; The nucleotide sequence of the ARHGEF4 gene subregion is shown in SEQ ID NO.

25.

2. The application of the methylation marker combination of claim 1 in the preparation of a product for detecting cervical cancer and / or cervical precancerous lesions, wherein the test sample of the product is exfoliated cervical cells and the cervical precancerous lesion is CIN3+.

3. The application as described in claim 2, characterized in that, The detector comprises a probe and primers, the probe and primers being used to detect the combination of methylation markers as described in claim 1.

4. The application as described in claim 3, characterized in that, The primer nucleotide sequences for detecting the methylation marker ZNF536 gene are shown in SEQ ID NO.2 and SEQ ID NO.3; The primer nucleotide sequences for detecting the methylation marker ZNF671 gene are shown in SEQ ID NO. 10 and SEQ ID NO. 11; The primer nucleotide sequences for detecting the methylation marker ARHGEF4 gene are shown in SEQ ID NO.26 and SEQ ID NO.27; The primer nucleotide sequences for detecting the methylation marker TTC34 gene are shown in SEQ ID NO. 30 and SEQ ID NO. 31; The nucleotide sequence of the probe for detecting the methylation marker ZNF536 gene is shown in SEQ ID NO. 4; The nucleotide sequence of the probe for detecting the methylation marker ZNF671 gene is shown in SEQ ID NO. 12; The nucleotide sequence of the probe for detecting the methylation marker ARHGEF4 gene is shown in SEQ ID NO. 28; The nucleotide sequence of the probe for detecting the methylation marker TTC34 gene is shown in SEQ ID NO.

32.

5. A device for detecting cervical cancer and / or precancerous lesions of the cervix, characterized in that, Includes the following modules: The data acquisition module is used to provide disease risk level data of the target biomarker combination of the sample to be tested, wherein the target biomarker combination is the methylation biomarker combination for the detection of cervical cancer and / or cervical precancerous lesions as described in claim 1; The judgment module is used to assess the cervical cancer and / or precancerous lesions of the individual corresponding to the test sample based on the disease risk level data of the combination of target biomarkers in the test sample. The disease risk level data is the risk score value of the combination of methylation biomarkers. The risk score value is calculated by substituting the BV value of a single methylation biomarker of each sample into a Logistic regression model. The regression model is trained from the methylation biomarker detection data of known samples. In the judgment module, when the risk score of the combination of methylation markers in the test sample is greater than or equal to the cutoff value, the test sample is judged to be positive; when the risk score of the combination of methylation markers in the test sample is less than the cutoff value, the test sample is judged to be negative. The BV value is a binary value, and the binary processing steps of the ΔCt value are as follows: the ΔCt value of each methylation marker first needs to be compared with the preset cutoff value. If the ΔCt value is less than or equal to the cutoff value, then the BV value of the marker is 1. If the ΔCt value is greater than the cutoff value, then the BV value of the marker is 0; the ΔCt value is the difference between the Ct value of a single methylation marker and the Ct value of the internal reference gene. The sample to be tested was exfoliated cervical cells, and the precancerous cervical lesion was CIN3+.

6. The detection device as described in claim 5, characterized in that, The cutoff value for combination 2 is -1.656, and the risk score is -4.240 + 2.685 × BV. ZNF536 +1.025× BV ZNF671 +2.484×BV TTC34 ; And / or, the cutoff value for combination 4 is -1.756, and the risk score is -4.272 + 2.314 × BV. ZNF536 +0.755×BV ZNF671 +1.334×BV ARHGEF4 +1.963×BV TTC34 .

7. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions, which, when executed by a processor, implement a method for detecting cervical cancer and / or precancerous cervical lesions, the method comprising: Obtain disease risk level data of the target biomarker combination of the sample to be tested, wherein the target biomarker combination is the methylation biomarker combination for the detection of cervical cancer and / or precancerous lesions of the cervix as described in claim 1; Based on the disease risk level data of the combination of target biomarkers in the test sample, assess the cervical cancer and / or cervical precancerous lesions of the corresponding individual in the test sample. The disease risk level data is a risk score of a combination of methylation biomarkers. The risk score is calculated by substituting the BV value of each individual methylation biomarker in the test sample into a Logistic regression model, which is trained using methylation biomarker detection data from known samples. The assessment of the individual's cervical cancer and / or precancerous lesions corresponding to the test sample is as follows: when the risk score of the combination of methylation biomarkers in the test sample is greater than or equal to the cutoff value, the test sample is determined to be positive; when the risk score of the combination of methylation biomarkers in the test sample is less than the cutoff value, the test sample is determined to be negative. The BV value is a binary value, and the binarization process of the ΔCt value is as follows: the ΔCt value of each methylation biomarker is first compared with a preset cutoff value. If the ΔCt value is less than or equal to the cutoff value, the BV value of the biomarker is 1; if the ΔCt value is greater than the cutoff value, the BV value of the biomarker is 0. The ΔCt value is the difference between the Ct value of a single methylation biomarker and the Ct value of the internal reference gene. The sample to be tested was exfoliated cervical cells, and the precancerous cervical lesion was CIN3+.

8. The computer-readable storage medium as claimed in claim 7, characterized in that, The cutoff value for combination 2 is -1.656, and the risk score is -4.240 + 2.685 × BV. ZNF536 +1.025× BV ZNF671 +2.484×BV TTC34 ; And / or, the cutoff value for combination 4 is -1.756, and the risk score is -4.272 + 2.314 × BV. ZNF536 +0.755×BV ZNF671 +1.334×BV ARHGEF4 +1.963×BV TTC34 .

9. 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 to cause the terminal to perform the method for detecting cervical cancer and / or precancerous lesions of the cervix as described in claim 7 or 8 in a computer-readable storage medium.

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