Early cervical cancer diagnostic markers and uses thereof

Abstract

The application discloses an early cervical cancer diagnosis marker and application thereof, and relates to the technical field of cervical cancer detection.The methylation marker combination comprises markers 1-12, and the DNA regions of the markers 1-12 have significant differences in the methylation levels in the bodies of early cervical cancer patients and healthy people.Through quantitative detection of multiple methylation marker genes specific to cervical cancer, high-sensitivity, high-specificity and rapid early cervical cancer signal detection is realized.

Description

Technical Field

[0001] This invention relates to the field of cervical cancer detection technology, and more specifically, to diagnostic markers for early cervical cancer and their applications. Background Technology

[0002] Cervical cancer is the most common malignant tumor of the female reproductive system, with a significantly higher incidence rate than the combined incidence of endometrial and ovarian cancer. According to global cancer statistics in 2022, there were 662,000 new cases of cervical cancer and 349,000 deaths, ranking fourth in both incidence and mortality among female malignant tumors. Because early clinical symptoms of cervical cancer are often atypical, most patients are diagnosed at an advanced stage, severely impacting treatment effectiveness and prognosis.

[0003] Cervical cytology (TCT), as one of the most commonly used screening methods, falls under the category of morphological testing. Its detection efficiency is highly dependent on sample quality and operational procedures, and it suffers from limitations such as subjective interpretation, poor repeatability, and insufficient sensitivity, leading to a high rate of missed diagnoses in clinical practice. Traditional serum tumor markers (such as CA125 and SCC) are widely used in the auxiliary diagnosis, treatment monitoring, and prognostic assessment of cervical cancer, but they are easily interfered with by benign gynecological diseases such as pelvic inflammatory disease and endometriosis, resulting in a high false-positive rate. Furthermore, some patients with early-stage malignant lesions do not show significantly elevated serum marker levels, posing a false-negative risk and hindering accurate early screening for cervical cancer. In recent years, liquid biopsy technology based on circulating cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA) has made significant progress in the field of gynecological oncology. Among these, DNA methylation detection technology, due to its ability to accurately identify early epigenetic abnormalities in tumor development, has become a core research direction for early screening and diagnosis of cervical cancer. Multiple clinical studies have confirmed that abnormal methylation modifications of CpG islands in the genome can occur in the very early stages of cervical cancer development. This is mainly manifested as gene silencing and loss of function due to hypermethylation in the promoter regions of tumor suppressor genes, and genomic instability caused by hypomethylation in proto-oncogene-related regions, both driving abnormal proliferation and malignant transformation of cervical epithelial cells. By detecting the methylation characteristics of exfoliated cells in vaginal secretions, it is possible to non-invasively capture specific molecular abnormal signals related to cervical cancer.

[0004] Currently, the main technologies for early cervical cancer methylation detection based on vaginal secretions include methylation-specific PCR (MSP), real-time quantitative methylation PCR (qMSP), digital PCR (ddPCR), targeted methylation next-generation sequencing, and whole-genome methylation sequencing. With the development of high-throughput sequencing technology, detection models based on the joint analysis of multiple gene methylation sites have gradually become the mainstream approach. By integrating data from multiple differentially methylated regions (DMRs) and applying machine learning algorithms to construct classification models, detection performance can be significantly improved. However, existing technologies still have several problems that urgently need to be addressed: the lack of standardization of methylation biomarkers used in different studies; the low ctDNA content in early lesions limiting detection sensitivity; and the high cost and complex data analysis of high-throughput sequencing. Therefore, developing vaginal secretion methylation biomarkers and their detection methods with higher specificity and sensitivity, suitable for large-scale population screening, remains an important direction in the current research field.

[0005] DNA methylation abnormalities, as early molecular events in tumor development and progression, typically require long-term environmental stimulation to change their methylation patterns once established. Therefore, they serve as important molecular markers for early tumor screening, auxiliary diagnosis, and prognostic assessment. Current first-generation methylation detection technologies based on quantitative PCR (Q-PCR) can only provide qualitative detection results for specific sites on individual genes, exhibiting limitations in detection range and sensitivity. In contrast, high-throughput methylation detection technologies based on next-generation sequencing (NGS) can accurately quantify the methylation rate of multiple sites on specific genes and simultaneously perform panoramic analysis of the methylation status of a large number of genes, significantly improving detection accuracy. Furthermore, NGS technology supports large-scale parallel testing, effectively reducing unit testing costs and shortening the testing cycle, making it the mainstream technology for early tumor methylation detection now and in the future.

[0006] In view of this, the present invention is proposed. Summary of the Invention

[0007] The purpose of this invention is to provide early cervical cancer diagnostic markers and their applications. By quantitatively detecting multiple methylation marker genes specific to cervical cancer, a high-sensitivity, high-specificity, and rapid early cervical cancer signal detection can be achieved.

[0008] This invention is implemented as follows: In a first aspect, the present invention provides the application of a reagent for detecting a combination of methylation markers in the preparation of auxiliary diagnostic or diagnostic products for early cervical cancer. The reagent for detecting the combination of methylation markers is used to detect the methylation level of the combination of methylation markers, which includes markers 1-12, with GRch37 as a reference genome. The positional information of each marker is as follows: Marker 1: chr5:115151893-115152043, Marker 2: chr11:101000324-101000474, Marker 3: chr3:68981579-68981724, Marker 4: chr6:123317421-123317570, Marker 5: chr19:52222597-52222745, Marker 6: chr19:58220323-58220466, Mark Marker 7: chr2:95691383-95691528, Marker 8: chr20:57875890-57876033, Marker 9: chr11:101000335-101000484, Marker 10: chr19:30019567-30019706, Marker 11: chr13:112722947-112723090, Marker 12: chr7:19157542-19157685.

[0009] Secondly, the present invention provides a primer combination for the auxiliary diagnosis or diagnosis of early cervical cancer, comprising a primer combination for detecting the above-mentioned markers 1-12, the primer combination comprising: a first primer pair-a twelfth primer pair for detecting markers 1-12 respectively, the nucleotide sequences of the first primer pair-a twelfth primer pair being as shown in SEQ ID NO.1-SEQ ID NO.24 respectively.

[0010] Thirdly, the present invention provides an auxiliary diagnostic or diagnostic product for early cervical cancer, the product being selected from reagent kits, biochips, or detection devices, which includes a primer combination for auxiliary diagnosis or diagnosis of early cervical cancer.

[0011] Fourthly, the present invention provides a method for constructing an auxiliary diagnostic model or diagnostic model for early cervical cancer, comprising the following steps: Obtain the methylation rate of the region where each of the above methylation biomarkers is located in the training samples to establish a dataset; use the dataset to construct an early cervical cancer auxiliary diagnosis or diagnostic model through a machine learning model. The machine learning model is selected from at least one of the following: logistic regression, random forest, K-nearest neighbors, support vector machine, AdaBoost, XGBoost, Naive Bayes, and DT decision tree.

[0012] Fifthly, the present invention provides a device for the auxiliary diagnosis or diagnosis of early cervical cancer. The device includes a processor and a memory. The memory stores a set of executable program instructions. The set of executable program instructions is loaded and executed by the processor to implement the above-mentioned method for constructing an auxiliary diagnosis or diagnostic model for early cervical cancer, and to perform auxiliary diagnosis or diagnosis of early cervical cancer.

[0013] In a sixth aspect, the present invention provides a computer-readable storage medium storing an executable program instruction set, which is loaded and executed by a processor to construct the aforementioned method for assisting in the diagnosis or diagnosis model of early cervical cancer.

[0014] The present invention has the following beneficial effects: This invention screens the performance of candidate biomarkers in vaginal secretion samples through multiple rounds of experiments. Based on indicators such as methylation rate, sequencing depth, contribution rate, and specificity, it identifies diagnostic biomarkers with high sensitivity and specificity. Compared to traditional imaging, endoscopic, or serum biomarkers (such as CEA and SCC), the diagnostic biomarkers provided by this invention are more suitable for early screening, possessing the technical advantages of high sensitivity and high specificity. They enable non-invasive early signal detection and are suitable for large-scale screening.

[0015] The methylation biomarker combination provided by this invention supports multiple sample types and can be extended to prognostic assessment, helping patients to intervene early and improve survival rates. The diagnostic applications and methods based on this biomarker can utilize standard equipment (such as Illumina MiniSeq) and reagent kits, offering advantages such as low testing costs, standardized operation, and suitability for clinical application. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 The ROC curve is the test result of Model 1 of this invention on the training set; Figure 2 The ROC curve of the test results of Model 2 of this invention on the training set; Figure 3 The ROC curve of the test results of Model 3 of this invention on the training set; Figure 4 The ROC curve is the test result of Model 1 of this invention on the validation set; Figure 5 The ROC curve of the test results of Model 2 of this invention on the validation set; Figure 6 This is the ROC curve of the test results of Model 3 of this invention on the validation set. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased commercially.

[0019] The term “subject” as used in this article can be understood as anyone involved in early screening, cervical cancer screening, or cervical cancer prognostic assessment for early cervical cancer.

[0020] The term "threshold" refers to a threshold used in diagnosing the risk of early cervical cancer. It compares the methylation rate of a combination of biomarkers in a subject's sample to a given threshold, and outputs the subject's risk based on the comparison result. Specifically, the threshold includes, but is not limited to, a positive cutoff value—a critical value used to delineate results and define the boundaries of judgment. The threshold must be set based on the performance requirements of the target detection scenario (such as sensitivity, specificity, and accuracy), and determined through clinical data validation, statistical model analysis (such as ROC curve analysis and Youden's index calculation), or industry standard calibration to ensure its validity and reliability in the detection method or diagnostic model.

[0021] Methylation rate at each site = number of methylated reads / (number of methylated reads + number of unmethylated reads).

[0022] The average methylation rate of each marker region is calculated based on its site.

[0023] The term "early cervical cancer" includes early cervical cancer stages I and II, specifically: IA1, IA2, IB1, IB2, IB3, and II.

[0024] In a first aspect, the present invention provides the application of a reagent for detecting a combination of methylation markers in the preparation of auxiliary diagnostic or diagnostic products for early cervical cancer. The reagent for detecting the combination of methylation markers is used to detect the methylation level of the combination of methylation markers, which includes markers 1-12, with GRch37 as a reference genome. The positional information of each marker is as follows: Marker 1: chr5:115151893-115152043, Marker 2: chr11:101000324-101000474, Marker 3: chr3:68981579-68981724, Marker 4: chr6:123317421-123317570, Marker 5: chr19:52222597-52222745, Marker 6: chr19:58220323-58220466, Mark Marker 7: chr2:95691383-95691528, Marker 8: chr20:57875890-57876033, Marker 9: chr11:101000335-101000484, Marker 10: chr19:30019567-30019706, Marker 11: chr13:112722947-112723090, Marker 12: chr7:19157542-19157685.

[0025] Furthermore, marker 1 includes methylation sites: chr5:115151913, chr5:115151915, chr5:115151932, chr5:115151941, chr5:115151956, chr5:115151965, chr5:115151971, chr5:115152016, and chr5:115152019; Marker 2 includes methylation sites: chr11:101000437, chr11:101000432, chr11:101000419, chr11:101000395, chr11:101000382, chr11:101000378, chr11:101000371, chr11:101000354, and chr11:101000328; Marker 3 includes methylation sites: chr3:68981720, chr3:68981718, chr3:68981705, chr3:68981699, chr3:68981685, chr3:68981683, chr3:68981680, chr3:68981672, chr3:68981666, chr3:68981661, chr3:68981655, chr3:68981653, chr3:68981651, chr3:68981649, chr3:68981643, chr3:68981632, chr3:68981611, and chr3:68981580; Marker 4 includes methylation sites: chr6:123317421, chr6:123317440, chr6:123317445, chr6:123317449, chr6:123317473, chr6:123317476, chr6:123317478, chr6:123317487, chr6:123317492, chr6:123317497, chr6:123317509, chr6:123317519, chr6:123317536, chr6:123317543, chr6:123317547 and chr6:123317569; Marker 5 includes methylation sites: chr19:52222597, chr19:52222610, chr19:52222620, chr19:52222623, chr19:52222630, chr19:52222632, chr19:52222636, chr19:52222639, chr19:52222642, chr19:522 22644, chr19:52222671, chr19:52222680, chr19:52222684, chr19:52222689, chr19:522226 98. chr19:52222704, chr19:52222713, chr19:52222715, chr19:52222725 and chr19:52222743; Marker 6 includes methylation sites: chr19:58220461, chr19:58220447, chr19:58220444, chr19:58220441, chr19:58220425, chr19:58220404, chr19:58220395, chr19:58220378, chr19:58220374, chr19:58220371, chr19:58220365, chr19:58220350, and chr19:58220332; Marker 7 includes methylation sites: chr2:95691524, chr2:95691521, chr2:95691510, chr2:95691505, chr2:95691501, chr2:95691498, chr2:95691487, chr2:95691485, chr2:95691474, c hr2:95691465, chr2:95691463, chr2:95691444, chr2:95691438, chr2:95691436, chr2:95691434, chr2:95691432, chr2:95691411, chr2:95691388 and chr2:95691384; Marker 8 includes methylation sites: chr20:57875894, chr20:57875912, chr20:57875915, chr20:57875925, chr20:57875929, chr20:57875934, chr20:57875937, chr20:57875939, chr20:57875937, chr20:578 75951, chr20:57875953, chr20:57875957, chr20:57875978, chr20:57875987, chr20:57875992, chr20:57875994, chr20:57876007, chr20:57876010, chr20:57876012 and chr20:57876014; Marker 9 includes methylation sites: chr11:101000353, chr11:101000370, chr11:101000377, chr11:101000381, chr11:101000394, chr11:101000418, chr11:101000431 and chr11:101000436; Marker 10 includes methylation sites: chr19:30019604, chr19:30019609, chr19:30019612, chr19:30019638, chr19:30019645, chr19:30019647, chr19:30019661, chr19:30019663 and chr19:30019677; Marker 11 includes methylation sites: chr13:112722949, chr13:112722953, chr13:112722961, chr13:112722967, chr13:112722969. chr13:112722973、chr13:112722976、chr13:112722983、chr13:112722993、chr13:112722 995, chr13: 112723019, chr13: 112723022, chr13: 112723025, chr13: 112723034, chr13: 11 2723039, chr13:112723042, chr13:112723045, chr13:112723054, chr13:112723058, chr13:112723060, chr13:112723063, chr13:112723071, chr13:112723081 and chr13:112723087; Marker 12 includes methylation sites: chr7:19157659, chr7:19157651, chr7:19157635, chr7:19157633, chr7:19157614, chr7:19157610, chr7:19157596, chr7:19157586, chr7:19157580, chr7:19157573, chr7:19157569, chr7:19157564, chr7:19157561, chr7:19157548, chr7:19157543.

[0026] The methylation levels of the aforementioned DNA regions differ between patients with early-stage cervical cancer and healthy individuals. This combination of biomarkers can identify the risk of early-stage cervical cancer with high sensitivity, high specificity, and rapidity, contributing to the early prevention of this disease.

[0027] In a preferred embodiment of the present invention, the reagent for detecting the methylation marker combination is selected from at least one reagent used in a method for detecting the methylation level of the methylation marker combination: Pyrosequencing, bisulfite conversion sequencing, methylation array, qPCR, digital PCR, second-generation sequencing, third-generation sequencing, whole-genome methylation sequencing, DNA enrichment detection, simplified bisulfite sequencing, HPLC, MassArray, and methylation-specific PCR; Early cervical cancer auxiliary diagnosis or diagnostic products are selected from reagent kits, biochips or detection devices.

[0028] In a preferred embodiment of the present invention, the reagent for detecting the combination of methylation markers includes primers for detecting the combination of methylation markers, the primers including: a first primer pair to a twelfth primer pair for detecting markers 1 to 12 respectively, the nucleotide sequences of the first primer pair to the twelfth primer pair being as shown in SEQ ID NO.1 to SEQ ID NO.24 respectively; The samples targeted by early cervical cancer auxiliary diagnosis or diagnostic products are vaginal secretions, vaginal lavage fluid, cells, tissues, plasma, urine, whole blood, cervical swabs, uterine cavity lavage fluid, menstrual blood, cervical canal brush samples, or ascites.

[0029] Secondly, the present invention provides a primer combination for the auxiliary diagnosis or diagnosis of early cervical cancer, comprising a primer combination for detecting the aforementioned biomarkers 1-12. The primer combination includes a first primer pair to a twelfth primer pair for detecting biomarkers 1-12, respectively, the nucleotide sequences of which are shown sequentially as SEQ ID NO. 1-SEQ ID NO. 24. The primer sequences are shown in Table 1 below. Table 1. Sequence list of each marker

[0030] Y represents C / T, and R represents A / G.

[0031] Thirdly, the present invention provides an auxiliary diagnostic or diagnostic product for early cervical cancer, the product being selected from reagent kits, biochips, or detection devices, which includes a primer combination for auxiliary diagnosis or diagnosis of early cervical cancer.

[0032] The term "kit" is used to refer to a combination of reagents and other materials that aid in sample analysis. In some embodiments, the kit described herein includes primers and a binding agent containing a detectable portion. Systems for amplifying the signal generated by the detectable portion may or may not be included in the kit. Furthermore, in other embodiments, the kit includes, but is not limited to, components such as devices for sample collection, sample tubes, supports, trays, racks, plates, plates, user instructions for the kit, solutions or other chemical reagents, and sample and / or control samples for standardization and normalization.

[0033] In one embodiment, the kit includes: primers for detecting methylation markers, sulfite conversion reagents, PCR amplification reagents (including enzymes, dNTPs, and buffers), magnetic bead purification reagents, and NGS library construction reagents.

[0034] Biochips include, but are not limited to, microfluidic chips or microarray chips.

[0035] In a preferred embodiment of the present invention, the product further includes primers for detecting the internal reference gene ACTB, the primer sequences of which are shown in SEQ ID NO.25-SEQ ID NO.26.

[0036] The methylation sites of the internal reference gene ACTB include: chr7:5571938, chr7:5571933, chr7:5571932, chr7:5571926, chr7:5571921, chr7:5571917, chr7:5571916, chr7:5571915, chr7:5571912, chr7:5571911, chr7:5571910, chr7:5571909, chr7:5571902, chr7:5571891, chr7:5571887, and chr7:557187. 6. chr7:5571875, chr7:5571872, chr7:5571868, chr7:5571846, chr7:5571844, chr7:5571841, chr7:5571836, chr7:5571833, chr7: 5571828, chr7:5571821, chr7:5571820, chr7:5571814, chr7:5571811, chr7:5571810, chr7:5571808, chr7:5571801, chr7:5571789.

[0037] In one implementation, quality control can be ensured by setting the internal control ACTB conversion rate to >99.5%, providing a panoramic high-resolution view that is superior to qualitative detection by first-generation Q-PCR.

[0038] Specifically, in all samples, the methylation conversion rate of the 33 methylation sites on the internal reference gene marker ACTB was greater than 99.5%, indicating successful methylation conversion in this detection step. Conversion rate = 1 - (number of unconverted cytosines / total number of cytosines at the detection site) × 100%. A conversion rate of ACTB in the internal reference region > 99.5% is required; otherwise, the tested samples are discarded.

[0039] Fourthly, the present invention provides a method for constructing an auxiliary diagnostic model or diagnostic model for early cervical cancer, comprising the following steps: Obtain the methylation rate of the region where each of the above methylation biomarkers is located in the training samples to establish a dataset; use the dataset to construct an early cervical cancer auxiliary diagnosis or diagnostic model through a machine learning model. The machine learning model is selected from at least one of the following: logistic regression, random forest, K-nearest neighbors, support vector machine, AdaBoost, XGBoost, Naive Bayes, and DT decision tree.

[0040] In a preferred embodiment of the present invention, when the machine learning model is logistic regression, the model prediction value of the early cervical cancer auxiliary diagnosis or diagnostic model is calculated based on the average methylation rate of all methylation sites in the region where each marker in the combination of methylation markers in the tested sample is located. Model predicted value = Σ (average methylation rate_i × weight coefficient_i), where i represents the marker number, and its value ranges from 1 to 12; the model predicted value is compared with the preset threshold. If the model predicted value is greater than the preset threshold, it is judged as early cervical cancer positive. The samples to be tested are selected from vaginal secretions, vaginal lavage fluid, cells, tissues, plasma, urine, whole blood, cervical swabs, uterine cavity lavage fluid, menstrual blood, cervical canal brush samples, or ascites.

[0041] In one implementation, the LogisticRegression model (solver='liblinear') from the Python sklearn library is used.

[0042] The method for calculating the weight coefficients (w) in logistic regression is as follows: Let the features be $x = (x_1, x_2, ..., x_n)$, the coefficients be $\boldsymbol{w} = (w_1, w_2, ..., w_n)$, and the bias be $w_0$.

[0043] (1) Linear part: $$z = w_0 + w_1x_1 + w_2x_2 + \dots + w_nx_n = \boldsymbol{w}^T\boldsymbol{x}$$; (2) Solve for the pre-weight coefficients of the above features: Core logic: The Sigmoid function maps linear predicted values ​​to the [0,1] interval, representing the probability that the dependent variable is 1. (i) Sigmoid function: $$\sigma(z) = \frac{1}{1+e^{-z}}$$, with a range of $(0,1)$, is used to map linear combinations to probabilities.

[0044] (ii) Predicted probabilities after mapping: $$\hat y = \sigma(\boldsymbol{w}^T\boldsymbol{x}) = \frac{1}{1+e^{-\boldsymbol{w}^T\boldsymbol{x}}}$$.

[0045] (iii) Binary label $y\in\{0,1\}$.

[0046] The solution for w in the predicted probability is not unique. To find the solution for w in this formula, we use... Iterative Weighted Least Squares (IRLS) It can be written in the following form: $$\boldsymbol{w}_{k+1} = \boldsymbol{w}_k - (X^TWX)^{-1}X^T(\hat y- y)$$; $W$ is a diagonal weight matrix composed of predicted probabilities.

[0047] (iv) Input the binary classification label y of the training set and the methylation rate x of the training set data into the formula, and iterate continuously according to the gradient convergence to finally obtain the weight coefficients.

[0048] Fifthly, the present invention provides a device for the auxiliary diagnosis or diagnosis of early cervical cancer. The device includes a processor and a memory. The memory stores a set of executable program instructions. The set of executable program instructions is loaded and executed by the processor to implement the above-mentioned method for constructing an auxiliary diagnosis or diagnostic model for early cervical cancer, and to perform auxiliary diagnosis or diagnosis of early cervical cancer.

[0049] The term "computer-readable storage medium" includes data storage media or cloud drives based on a single physical entity such as a CD, CD-ROM, hard disk, optical storage medium, or magnetic disk. Furthermore, the term also includes data storage media composed of physically separate entities that are effectively connected to each other, preferably in a query-and-search manner, to provide the aforementioned data set.

[0050] In a sixth aspect, the present invention provides a computer-readable storage medium storing an executable program instruction set, which is loaded and executed by a processor to construct the aforementioned method for assisting in the diagnosis or diagnosis model of early cervical cancer.

[0051] Specifically, the electronic device may include a memory, a processor, a bus, and a communication interface, which are electrically connected directly or indirectly to each other to enable data transmission or interaction. For example, these components may be electrically connected to each other via one or more buses or signal lines. The processor may process information and / or data related to target identification to perform one or more functions described in this application.

[0052] The memory can be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc.

[0053] A processor can be an integrated circuit chip with signal processing capabilities. This processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0054] In a seventh aspect, the present invention also provides an apparatus for the auxiliary diagnosis or diagnosis of early-stage cervical cancer, comprising: an input module, a control module, and an output module; The input module is configured to: obtain the methylation rate of the region where each marker in the combination of methylation markers is located in the subject's biological sample, wherein the combination of methylation markers is the combination of methylation markers mentioned above; The control module includes: a results analysis module, which is configured to: generate a prediction result of the subject's early cervical cancer risk or generate a negative or positive result of the subject's early cervical cancer based on the methylation rate of the combination of methylation markers contained in the subject's sample. The output module is configured to output the subject's early cervical cancer risk prediction results or the subject's negative or positive results for early cervical cancer.

[0055] In one implementation, the results analysis module is configured to: calculate, based on the average methylation rate of all methylation sites in the region of each marker in the combination of methylation markers in the subject sample, the model prediction value of the early cervical cancer diagnostic model according to the following formula: Model predicted value = Σ (average methylation rate_i × weight coefficient_i), where i represents the marker number, and its value ranges from 1 to 16; the model predicted value is compared with the threshold. If the model predicted value is greater than the threshold, it is judged as early cervical cancer positive.

[0056] The features and performance of the present invention will be further described in detail below with reference to embodiments.

[0057] Example 1 This embodiment provides an experimental method for detecting the methylation level of candidate biomarkers in a sample to be tested.

[0058] The specific steps are as follows: (a) Collection of exfoliated cells from vaginal secretion samples 1. Collection of vaginal secretions Vaginal secretions were collected and preserved from 118 patients with early-stage (stage I and II) cervical cancer (mean age 58 years, of which 66 were assigned to the training set and 52 to the validation set) and 231 healthy individuals (mean age 55 years, of which 142 were assigned to the training set and 89 to the validation set) using a disposable vaginal secretion collection kit (Zhejiang Medical Device Registration No. 20260023).

[0059] 2. Preparation of exfoliated cell samples After collection, the tampons containing vaginal secretions were mixed with the preservation solution. The bottom end of the tampons was clamped between the body and cap of the preservation tube using tweezers. The sample preservation tube was centrifuged at 4000 rpm for 10 minutes. After centrifugation, the tampons were removed, avoiding contact with the preservation solution. The preservation solution containing the exfoliated cell sample was the sample required for the experiment.

[0060] (ii) DNA extraction from exfoliated cell samples The methylation detection sample pretreatment reagent (plasma sample extraction and bisulfite conversion kit (magnetic bead method), manufacturer: Jiaxing Yunying Medical Laboratory Co., Ltd.; catalog number: C128) was used to lyse, convert, and purify the sample. The operation steps are as follows: 1. Preparation of reagents in the kit: Prepare the reagents in the above kit according to Table 2 below.

[0061] Table 2. Reagent Preparation

[0062] 2. DNA extraction from exfoliated cells: Take 1.7 ml of preservation solution containing cervical exfoliated cell sample, add 100 μl of 20% SDS, mix by inverting, centrifuge briefly, then add 30 μl of PK enzyme (20 mg / ml), vortex 4-5 times to mix; incubate at 60℃ for 40 min, then at 80℃ for 20 min, place at room temperature for 5 min, and centrifuge briefly.

[0063] The above mixture was further extracted using an Auto-Pure-20B purifier. The program was run according to the settings. After the program was completed, the liquid was taken out from the last well and placed into a new PCR tube. The tube was labeled with the corresponding sample number. This is the exfoliated cell DNA sample, which can be used for the next step of sulfite conversion.

[0064] The specific operating steps for the purification instrument are as follows: (1) Sample addition: Add all the solution in the EP tube to column 1 of the purification kit (add different samples to different wells) and record the sample number; (2) After installing the magnetic rod sleeve, place the purification kit into the fully automated nucleic acid extractor and set it up and operate it according to the prescribed procedure: (3) After the program finishes running, the solution is aspirated into a new 0.5 mL centrifuge tube. The collected solution is the DNA sample of exfoliated cells. The DNA concentration is quantified using a Qubit fluorometer.

[0065] (III) Sulfite Conversion System preparation: Prepare sulfite reaction systems in 0.5 mL PCR tubes according to the extracted DNA samples as shown in Table 3 below.

[0066] Table 3. Conversion Table for Sulfite Reaction System

[0067] Sulfite conversion procedure: After vortexing and mixing, and briefly centrifuging, place the PCR tube in a PCR instrument and incubate at 95℃ for 5 min and 85℃ for 60 min. After incubation, cool to room temperature and briefly centrifuge to obtain the transformation product.

[0068] (iv) Bis-DNA purification treatment The purified product was run using the Auto-Pure-32A purifier according to the set program. After the run was completed, the liquid was removed from the well and placed into a new PCR eight-tube strip, which was then labeled with the corresponding sample number. This is the purified Bis-DNA, which can be used for the next library enrichment reaction.

[0069] (1) Sample addition: Add all the solution from the PCR tube to column 1 of the purification kit (add different samples to different wells) and record the sample number; (2) After installing the magnetic rod sleeve, place the purification kit into the fully automated nucleic acid extractor and set it up and operate it according to the prescribed procedure: (3) After the program finishes running, aspirate the solution into a new 1.5 mL centrifuge tube. The collected solution is the DNA after sulfite conversion and should be used for library construction immediately.

[0070] (v) Library enrichment reaction The Bis DNA obtained after purification in the previous step was subjected to a library enrichment reaction.

[0071] Preparation of enrichment PCR reaction Prepare the enrichment reaction solution as needed according to the formula shown in Table 4. After preparation, aliquot the solution into clean eight-slot containers. The aliquoted enrichment reaction solutions are grouped according to the mixed primers and named MJ enrichment PCR reaction strip-1 to MJ enrichment PCR reaction strip-4.

[0072] The 12 cancer biomarker primers and 1 internal reference biomarker primer in Table 1 were divided into 4 combinations, each containing different biomarker primers. The 4 mixed enrichment reaction solutions were used for the enrichment of different biomarkers and were named MJ enrichment PCR reaction solution-1 to MJ enrichment PCR reaction solution-4.

[0073] Table 4. Enrichment Reaction Table

[0074] The primer sequence list for each marker is shown in Table 1.

[0075] The enrichment reaction shall be carried out according to the following steps: 1. Uncap the tubes from MJ enriched PCR reaction strip-1 to MJ enriched PCR reaction strip-4 prepared in the previous step, and add 1 μL Taq enzyme to each of MJ enriched PCR reaction solution-1 to MJ enriched PCR reaction solution-4, ensuring that the pipette tip is fully inserted into the MJ enriched reaction solution. 2. Add 20 μL of the Bis DNA sample to be tested after sulfite conversion to the four enrichment reaction strips in sequence along the PCR tube wall, and carefully close the tube caps. 3. After vortexing the PCR reaction strip to mix well, centrifuge, taking care to avoid air bubbles; 4. Place the above four enrichment PCR reaction strips into the PCR instrument; 5. Open the PCR instrument settings interface, set the amplification program according to Table 5 below, and perform PCR amplification.

[0076] Table 5. PCR reaction procedure for library enrichment

[0077] (vi) Library preparation reaction: Preparation for the connector connection reaction: Prepare the required number of adapters according to the formula shown in Table 6 (excluding Ace Taq enzyme and the PCR reaction product enriched in the previous step). In other embodiments, the adapters can also be prepared in advance and dispensed into clean 8-strand strips and stored at -20°C for later use.

[0078] After removing the connector reaction solution from the refrigerator and allowing it to melt, centrifuge it briefly in a centrifuge before use.

[0079] Table 6. Reaction Components for Library Preparation

[0080] The connection reaction steps are as follows: 1. Uncover the cap of the connector reaction strip and add 0.5 μL of Taq enzyme into the connector reaction strip; 2. Take 5 μL of the enriched PCR amplification product from the previous step and add it sequentially along the wall of the PCR tube to the adapter connection reaction strip, then cap the tube. 3. Connect the connector to the reaction strip, shake to mix thoroughly, and then centrifuge. 4. Connect the above-mentioned adapter to the reaction strip and place it into the PCR instrument; 5. Open the PCR instrument settings interface, set the amplification program according to Table 7 below, and perform PCR amplification.

[0081] Table 7. PCR Reaction Procedure for Library Preparation

[0082] (vii) Sorting of Library Products 1. Remove the magnetic beads used for purification, shake to mix, and incubate at room temperature for at least 30 minutes; 2. Take 15 μL, 20 μL, 20 μL and 15 μL of MJ library-1, MJ library-2, MJ library-3 and MJ library-4 respectively and add them to a new eight-tube set. The total volume of the libraries in the reaction tubes is 70 μL. 3. Take 126 μL of the incubated magnetic beads (shake well again before use) and add them sequentially to each sample reaction tube. Shake well and then incubate briefly to completely mix and resuspend the DNA and magnetic beads. 4. Incubate at room temperature for 5 minutes; 5. Incubate on a magnetic rack for 5 minutes until the solution is clear. Carefully aspirate and discard the supernatant, being careful not to disturb the magnetic beads. 6. Add 200µL of freshly prepared 80% ethanol solution (the ethanol solution should just cover the magnetic bead sample), place it on a magnetic rack, and incubate it on the magnetic rack for 30 seconds until the solution becomes clear. Discard the supernatant. 7. Repeat step 6 above for a second wash; 8. Ensure that the ethanol solution in the centrifuge tube has been completely discarded, and place the eight-tube or PCR tube on a magnetic rack to air dry at room temperature for 3-5 minutes; 9. Remove the eight-tube or PCR tube from the magnetic rack, add 70 μL of purified water to fully wet the magnetic beads, shake well to mix, and then quickly centrifuge to collect the liquid at the bottom of the tube. Let it stand at room temperature for 5 min. 10. Place the 8-tube or PCR tube on a magnetic rack and let it stand for 5 minutes until the solution is clear. Transfer 60 μL of the supernatant to a new 8-tube or 1.5 mL centrifuge tube to obtain the sequencing library. Use the Equalbit 1×dsDNA HS Assay Kit and its matching Qubit fluorescence instrument to detect the concentration. The library concentration should be ≥1 ng / μL. Otherwise, the library preparation sample does not meet the requirements and should be reconstructed.

[0083] 11. Perform deep sequencing on an Illumina MiniSeq sequencer, with each sample being sequenced at 200M.

[0084] (viii) Methylation data processing workflow: The first step is data processing and quality control.

[0085] 1. The methylation rate of 180 methylation sites for 12 gene markers in vaginal secretion samples from cervical cancer-positive patients and healthy individuals was statistically analyzed. The methylation rate was calculated as: (Number of methylated reads at that site / (Number of methylated reads at that site + Number of unmethylated reads at that site)). Data from methylation sites located on primers were removed, and the methylation rate of methylation sites with excessively low sequencing depth (below 10000X) was excluded. The average methylation rate of all methylation sites within each gene marker was calculated to represent the methylation rate of that gene marker. Gene marker 1 contains 9 methylation sites, gene marker 2 contains 9 methylation sites, gene marker 3 contains 18 methylation sites, gene marker 4 contains 16 methylation sites, gene marker 5 contains 20 methylation sites, gene marker 6 contains 13 methylation sites, gene marker 7 contains 19 methylation sites, gene marker 8 contains 20 methylation sites, gene marker 9 contains 8 methylation sites, gene marker 10 contains 9 methylation sites, gene marker 11 contains 24 methylation sites, and gene marker 12 contains 15 methylation sites.

[0086] Marker 13 (chr1: 103574463-103574609) includes the following methylation sites: chr1: 103574469, chr1: 103574484, chr1: 103574491, chr1: 103574494, chr1: 103574514, chr1: 103574521, chr1: 103574529, chr1: 103574550, chr1: 103574581, chr1: 103574591; Marker 14 (chr18:74170559-74170707) includes the following methylation sites: chr:74170566, chr:74170579, chr:74170582, chr:74170586, chr:74170602, chr:74170605, chr:74170609, chr:74170615, chr:74170625, chr:74170628, chr:74170636, chr:74170646, chr:74170649, chr:74170657, chr:74170667, chr:74170670, chr:74170674.

[0087] Marker 15 (chr17: 48545865-48546011) includes the following methylation sites: chr17: 48545886, chr17: 48545888, chr17: 48545898, chr17: 48545901, chr17: 48545909, chr17: 48545911, chr17: 48545941, chr17: 48545948, chr17: 48545958, chr17: 48545963, chr17: 48545981, chr17: 48545994.

[0088] Marker 16 (chr7: 72742179-72742323) includes the following methylation sites: chr7: 72742188, chr7: 72742200, chr7: 72742210, chr7: 72742212, chr7: 72742236, chr7: 72742238, chr7: 72742251, chr7: 72742257, chr7: 72742260, chr7: 72742269, chr7: 72742274, chr7: 72742289, chr7: 72742309, chr7: 72742312, chr7: 72742315.

[0089] Marker 17 (chrX: 66763884-66764036) includes the following methylation sites: chrX: 66763906, chrX: 66763911, chrX: 66763916, chrX: 66763942, chrX: 66763961, chrX: 66763970, chrX: 66763992, chrX: 66764000, chrX: 66764007, chrX: 66764021, chrX: 66764030.

[0090] Marker 18 (chr7:1544223-1544371) includes the following methylation sites: chr7:1544248, chr7:1544253, chr7:1544261, chr7:1544273, chr7:1544277, chr7:1544280, chr7:1544293, chr7:1544304, chr7:1544306, chr7:1544333, chr7:1544338, chr7:1544345, chr7:1544351, chr7:1544355, chr7:1544360.

[0091] Marker 19 (chr10: 131265425-131265574) includes the following methylation sites: chr10: 131265428, chr10: 131265435, chr10: 131265438, chr10: 131265447, chr10: 131265456, chr10: 131265462, chr10: 131265468, chr10: 131265470, chr10: 131265475, chr 10: 131265494, chr10: 131265496, chr10: 131265507, chr10: 131265514, chr10: 131265519, chr10: 131265522, c hr10: 131265526, chr10: 131265536, chr10: 131265538, chr10: 131265543, chr10: 131265548, chr10: 131265554.

[0092] Marker 20 (chr5: 1295039-1295173) includes the following methylation sites: chr5: 1295047, chr5: 1295049, chr5: 1295061, chr5: 1295073, chr5: 1295077, chr5: 1295082, chr5: 1295088, chr5: 1295095, chr5: 1295097, c hr5: 1295099, chr5: 1295105, chr5: 1295107, chr5: 1295117, chr5: 1295135, chr5: 1295139, chr5: 1295149, chr5: 1295154, chr5: 1295168, chr5: 1295170, chr5: 1295173, chr5: 1295175.

[0093] 2. The methylation conversion rate of all 33 methylation sites on the internal reference gene marker ACTB in all samples was greater than 99.5%, indicating that the methylation conversion in this experiment was successful.

[0094] The second step is model training. The methylation rates of the 12 genes selected from the training set data are substituted into the logistic regression model for training. The coefficients and thresholds corresponding to each gene marker are obtained. The methylation level of each gene marker is multiplied by the corresponding coefficient and then added together. If the sum is greater than the threshold, the sample is judged as positive; otherwise, it is negative.

[0095] The third step is model tuning and evaluation. The weights of the dependent variable in the model are adjusted according to the ratio of positive and negative data to balance the training set data. The regularization parameters are tuned to find the optimal parameters. The threshold is adjusted to adapt to clinical testing. The sensitivity and specificity of different models are calculated. ROC curves are plotted to evaluate the excellence of different models and select the best model.

[0096] The fourth step is to perform model validation analysis on the validation set using the optimal model described above, calculate its sensitivity and specificity, and plot the ROC curve.

[0097] Example 2 Following the method in Example 1, and after multiple rounds of screening and validation, a method for detecting methylation biomarkers in early cervical cancer based on NGS sequencing technology was established. 180 methylation sites in 12 DNA regions were selected from the initial candidate biomarkers and systematically validated in vaginal secretion samples. Finally, a logistic regression detection model was constructed.

[0098] This embodiment provides a screening process for combinations of methylation markers.

[0099] (I) Specific detection of vaginal secretion samples from normal individuals: Twenty candidate biomarkers were initially tested using vaginal secretion samples from normal individuals to confirm the methylation rate and sequencing depth of each gene, and biomarkers with an average methylation rate of less than 5% at methylation sites were screened.

[0100] Experimental methods: 1. Collect vaginal secretion samples from 20 normal individuals; 2. PCR amplification was performed using candidate primer combinations; the primer sequences for detecting markers 13-20 are shown in Table 8 below: Table 8 Primer Sequence Listing

[0101] 3. Perform NGS sequencing to analyze the average methylation rate and average sequencing depth of methylation sites; 4. Screening criteria: average methylation rate <5%, average sequencing depth >1000×.

[0102] Experimental Results and Analysis: 1. All biomarkers showed good sequencing depth, meeting the requirement of sequencing depth >1000×; 2. Among the 20 biomarkers, based on the average methylation rate and average sequencing depth of each biomarker, 5 biomarkers (chr1:103574463-103574609, chrX:66763884-66764036, chr18:74170559-74170707, chr17:48545865-48546011, chr7:72742179-72742323) had an average methylation rate >5%, which did not meet the screening criteria. 3. The 15 selected biomarkers were then used in the validation phase of vaginal secretions from cervical cancer patients.

[0103] Conclusion: Fifteen candidate biomarkers with low methylation rates and good sequencing depth in normal vaginal secretion samples were successfully screened, laying the foundation for subsequent validation in vaginal secretion samples from cervical cancer patients.

[0104] (II) Detection of the contribution rate of vaginal secretion samples: The study aimed to validate biomarkers selected from vaginal secretion samples from healthy individuals in vaginal secretion samples from cervical cancer patients, assess the contribution rate of each biomarker in vaginal secretion samples from cervical cancer patients, and screen biomarker combinations suitable for vaginal secretion sample detection.

[0105] Experimental methods: 1. Collect vaginal secretion samples from 20 patients with cervical cancer; 2. Detection was performed using primer combinations selected from normal vaginal secretion samples; 3. Analyze the contribution rate of each biomarker in vaginal secretion samples from cervical cancer patients; Experimental Results and Analysis: The 15 biomarkers screened in the early stage were tested. The average methylation of each biomarker was greater than 5% as a positive marker and the contribution rate was not less than 70%. Statistical calculations were performed, and the contribution rates of 3 biomarkers (chr7:1544223-1544371, chr10:131265425-131265574, chr5:1295039-1295173) were less than 70%, which did not meet the screening criteria (Table 9).

[0106] Table 9. Contribution rate of various markers in vaginal secretions

[0107] Conclusion: Twelve biomarkers with good contribution rates in vaginal secretion samples of cervical cancer were successfully screened.

[0108] Through multiple rounds of screening, 12 biomarker combinations were finally identified, totaling 180 methylation sites, for subsequent model construction.

[0109] Example 3 This embodiment involves sample collection, data processing, and detection model construction.

[0110] 1. Sample Collection Training set: Vaginal secretion samples were collected from 66 patients with early-stage cervical cancer (pathological examination included cervical squamous cell carcinoma, cervical adenocarcinoma, cervical adenosquamous carcinoma, cervical mucinous adenocarcinoma, and cervical gastric adenocarcinoma, etc.) and 142 healthy individuals. Among the positive cervical cancer samples, cervical squamous cell carcinoma accounted for the highest proportion; negative samples were from healthy individuals.

[0111] Validation set: vaginal secretion samples from 52 patients with positive cervical cancer and vaginal secretion samples from 89 normal individuals.

[0112] 2. DNA extraction and sulfite conversion DNA from exfoliated cells was extracted from vaginal secretion samples and transformed using a sulfite transformation kit. Transformation efficiency was assessed using the transformation rate of the internal control region ACTB, calculated as: Transformation rate = (Number of transformed cytosines / Total number of cytosines at the detection site) × 100%. A transformation rate > 99.5% was required; otherwise, the sample was discarded.

[0113] 3. Targeted amplification and NGS sequencing Targeted amplification was performed using the primer combinations described above. After purification, the amplified products were deep sequenced on an Illumina MiniSeq sequencer (200M reads per sample). Sequencing data processing included removing primer sites and filtering sites with sequencing depths lower than 10000X. The methylation rate for each site was calculated as: number of methylated reads / (number of methylated reads + number of unmethylated reads).

[0114] The average methylation rate of each marker was calculated based on its site: Marker 1: 9 loci; Marker 2: 9 loci; Marker 3: 18 loci; Marker 4: 16 loci; Marker 5: 20 loci; Marker 6: 13 loci; Marker 7: 19 loci; Marker 8: 20 loci; Marker 9: 8 loci; Marker 10: 9 loci; Marker 11: 24 loci; Marker 12: 15 loci.

[0115] 4. Model Training and Optimization Three models were trained using the LogisticRegression model (solver='liblinear') from the Python sklearn library, based on the average methylation rate of 12 markers in the training set. In the `class_weight` parameter, 0 represents negative samples and 1 represents positive samples, with the weight of positive samples being a multiple of that of negative samples.

[0116] Table 10. Model Parameters for Cervical Cancer Methylation Project

[0117] Model predicted value = Σ (average methylation rate_i × weight coefficient_i). If the predicted value > threshold, it is considered positive.

[0118] Table 11. Training set model for cervical cancer methylation project

[0119] Table 11 combined Figure 1 , Figure 2 and Figure 3The training set shown shows that among the three training models, Model 1 identified 63 positive cases and 134 negative cases, with a sensitivity of 95.45% and a specificity of 94.37%, a threshold of 0.65, and an area under the ROC curve (AUC) of 0.982288, demonstrating the best identification ability. Model 2 identified 64 positive cases and 132 negative cases, with a sensitivity of 96.97% and a specificity of 92.96%, a threshold of 0.9, and an AUC of 0.981221. Model 3 identified 60 positive cases and 135 negative cases, with a sensitivity of 90.91% and a specificity of 95.07%, a threshold of 0.53, and an AUC of 0.982074.

[0120] The test results on the training set show that Model 1 is the best, with a sensitivity of 95.45% (63 / 66), a specificity of 94.37% (134 / 142), a threshold of 0.65, and an AUC of 0.982288.

[0121] Table 12. Validation set results of cervical cancer methylation test

[0122] Table 12 combined with Figure 4 , Figure 5 and Figure 6 The validation set results were as follows: In the validation set (52 cervical cancer patients + 89 normal individuals), Model 1 had 50 cases judged as positive (sensitivity 96.15%), 84 cases judged as negative (specificity 94.38%), and the area under the ROC curve (AUC) was 0.982498; Model 2 had 50 cases judged as positive (sensitivity 96.15%), 81 cases judged as negative (specificity 91.01%), and the area under the ROC curve (AUC) was 0.979689; Model 3 had 48 cases judged as positive (sensitivity 92.31%), 85 cases judged as negative (specificity 95.51%), and the area under the ROC curve (AUC) was 0.982282.

[0123] Example 4 This embodiment provides a kit for the diagnosis of early cervical cancer. The kit includes: primer combinations (optimized concentration) as shown in Table 1 above, sulfite conversion reagent, PCR amplification reagent (including enzymes, dNTPs, and buffer), magnetic bead purification reagent, and NGS library construction reagent.

[0124] The kit is used as follows: DNA is extracted from vaginal secretions, subjected to sulfite conversion, amplification, and sequencing. The methylation rate is calculated and input into the model for judgment. It is suitable for clinical screening, with a sensitivity >90% and specificity >92%.

[0125] Example 5 This embodiment provides a device for early cervical cancer diagnosis, which includes: an input module, a control module, and an output module; The input module is configured to: obtain the methylation rate of the region where each marker in the combination of methylation markers is located in the subject's biological sample, wherein the combination of methylation markers is the combination of methylation markers mentioned above; The control module includes: a results analysis module, which is configured to: generate a prediction result of the subject's early cervical cancer risk or generate a negative or positive result of the subject's early cervical cancer based on the methylation rate of the combination of methylation markers contained in the subject's sample. The output module is configured to output the subject's early cervical cancer risk prediction result (risk score) or the subject's early cervical cancer negative or positive result.

[0126] In one implementation, the results analysis module is configured to: calculate, based on the average methylation rate of all methylation sites in the region of each marker in the combination of methylation markers in the subject sample, the model prediction value of the early cervical cancer diagnostic model according to the following formula: Model predicted value = Σ (average methylation rate_i × weight coefficient_i), where i represents the marker number, and its value ranges from 1 to 12; the model predicted value is compared with the threshold. If the model predicted value is greater than the threshold, it is judged as early cervical cancer positive.

[0127] The aforementioned device can be integrated into a cloud platform, supporting batch processing. Clinical application: In screening high-risk populations, positive samples can be further confirmed endoscopically, reducing unnecessary invasive examinations.

[0128] The above embodiments verify the effectiveness of the biomarker combination, reagent kit and system of the present invention in the early detection of cervical cancer, which can achieve non-invasive and highly accurate detection.

[0129] In summary, the diagnostic model provided by this invention can identify the risk of early cervical cancer with a highly sensitive, specific, rapid, and non-invasive method, enabling early medical examinations and prevention.

[0130] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. The application of a reagent for detecting a combination of methylation markers in the preparation of auxiliary diagnostic or diagnostic products for early cervical cancer, characterized in that, The reagent for detecting the methylation biomarker combination is used to detect the methylation level of the methylation biomarker combination, which includes biomarkers 1-12, with GRch37 as the reference genome. The location information of each biomarker is as follows: Marker 1: chr5:115151893-115152043, Marker 2: chr11:101000324-101000474, Marker 3: chr3:68981579-68981724, Marker 4: chr6:123317421-123317570, Marker 5: chr19:52222597-52222745, Marker 6: chr19:58220323-58220466, Mark Marker 7: chr2:95691383-95691528, Marker 8: chr20:57875890-57876033, Marker 9: chr11:101000335-101000484, Marker 10: chr19:30019567-30019706, Marker 11: chr13:112722947-112723090, Marker 12: chr7:19157542-19157685.

2. The application according to claim 1, characterized in that, The reagent used to detect the combination of methylation markers is selected from at least one of the reagents used in methods for detecting the methylation level of the combination of methylation markers: Pyrosequencing, bisulfite conversion sequencing, methylation array, qPCR, digital PCR, second-generation sequencing, third-generation sequencing, whole-genome methylation sequencing, DNA enrichment detection, simplified bisulfite sequencing, HPLC, MassArray, and methylation-specific PCR; The early cervical cancer auxiliary diagnostic or diagnostic products are selected from reagent kits, biochips, or detection devices.

3. The application according to claim 2, characterized in that, The reagent for detecting the combination of methylation markers includes primers for detecting the combination of methylation markers, the primers comprising: a first primer pair to a twelfth primer pair for detecting markers 1 to 12 respectively, the nucleotide sequences of the first primer pair to the twelfth primer pair being as shown in SEQ ID NO.1-SEQ ID NO.24 respectively; The samples targeted by the aforementioned early cervical cancer auxiliary diagnostic or diagnostic products include vaginal secretions, vaginal lavage fluid, cells, tissues, plasma, urine, whole blood, cervical swabs, uterine cavity lavage fluid, menstrual blood, cervical canal brush samples, or ascites.

4. A primer combination for the auxiliary diagnosis or diagnosis of early cervical cancer, characterized in that, It includes a primer combination for detecting marker 1-marker 12 as described in any one of claims 1-3, the primer combination comprising: a first primer pair-a twelfth primer pair for detecting marker 1-marker 12 respectively, the nucleotide sequences of the first primer pair-a twelfth primer pair being as shown in SEQ ID NO.1-SEQ ID NO.24 respectively.

5. A diagnostic or auxiliary product for early cervical cancer, said product being selected from reagent kits, biochips, or detection devices, characterized in that, It includes the primer combination for the auxiliary diagnosis or diagnosis of early cervical cancer as described in claim 4.

6. The early cervical cancer auxiliary diagnostic or diagnostic product according to claim 5, characterized in that, The product also includes primers for detecting the internal reference gene ACTB, the primer sequences of which are shown in SEQ ID NO.25-SEQ ID NO.

26.

7. A method for constructing an auxiliary diagnostic model or diagnostic model for early cervical cancer, characterized in that, It includes the following steps: Obtain the methylation rate of the region where each marker in the combination of methylation markers described in any one of claims 1-3 is located in the representative training sample, and establish a dataset; use the dataset to construct an auxiliary diagnosis or diagnostic model for early cervical cancer through a machine learning model; The machine learning model is selected from at least one of the following: logistic regression, random forest, K-nearest neighbors, support vector machine, AdaBoost, XGBoost, Naive Bayes, and DT decision tree.

8. The method for constructing an auxiliary diagnostic or diagnostic model for early cervical cancer according to claim 7, characterized in that, When the machine learning model is logistic regression, the model prediction value of the early cervical cancer auxiliary diagnosis or diagnostic model is calculated based on the average methylation rate of all methylation sites in the region of each marker in the combination of methylation markers in the measured sample. Model predicted value = Σ (average methylation rate_i × weight coefficient_i), where i represents the marker number, and its value ranges from 1 to 12; the model predicted value is compared with a preset threshold. If the model predicted value is greater than the preset threshold, it is judged as early cervical cancer positive. The samples to be tested are selected from vaginal secretions, vaginal lavage fluid, cells, tissues, plasma, urine, whole blood, cervical swabs, uterine cavity lavage fluid, menstrual blood, cervical canal brush samples, or ascites.

9. A device for the auxiliary diagnosis or diagnosis of early cervical cancer, characterized in that, The device includes a processor and a memory, the memory storing a set of executable program instructions, which are loaded and executed by the processor to implement the method for constructing an auxiliary diagnosis or diagnostic model for early cervical cancer as described in any one of claims 7-8, and to perform auxiliary diagnosis or diagnosis of early cervical cancer.

10. A computer-readable storage medium storing an executable program instruction set, the executable program instruction set being loaded by a processor and executed by a method for constructing an auxiliary diagnosis or diagnostic model for early cervical cancer as described in any one of claims 7-8.

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