CfDNA methylation marker for early detection of head and neck cancer, screening method and detection kit

Abstract

The invention discloses a cfDNA methylation marker for early detection of head and neck cancer, a screening method and a detection kit, and relates to the technical field of gene diagnosis and early screening and early detection of cancers. On the basis of large-scale methylation sequencing data, the head and neck cell phenotype specificity differential methylation region (DMR) is screened and verified for the first time, and the detectability of the DMR in the plasma cfDNA of a head and neck cancer patient is defined; high-sensitivity and high-specificity detection of peripheral blood cfDNA is achieved by combining low-initial-quantity (< = 10 ng) targeted methylation sequencing with a machine learning model, the defects that in the prior art, sensitivity is insufficient, tissue traceability is difficult, and trauma is large are overcome, the blank of head and neck cancer cfDNA methylation non-invasive early screening products is filled, and the application prospect is wide. And a foundation is laid for developing sensitive and efficient head and neck cancer early-stage noninvasive detection markers and kits.

Description

Technical Field

[0001] This invention relates to the field of gene diagnosis and early cancer screening and detection technology, and in particular to a cfDNA methylation marker, screening method and detection kit for early detection of head and neck cancer. Background Technology

[0002] Head and neck cancer is the seventh most common malignant tumor worldwide, with approximately 890,000 new cases and 500,000 deaths annually, and its incidence rate is increasing year by year. Due to its hidden anatomical location and lack of specific early symptoms, more than 60% of patients are diagnosed at stage III-IV, resulting in poor prognosis and a five-year survival rate of less than 50%. In contrast, patients in the early stages (stage I-II) can achieve a five-year survival rate of over 80% after standardized treatment. Therefore, developing new non-invasive, highly sensitive, and specific methods for early detection of head and neck cancer is urgently needed, and early diagnosis and treatment are key to improving overall survival rates.

[0003] Currently, the main clinical methods for early screening include electronic nasopharyngoscopy, neck ultrasound, and CT / MRI imaging examinations. However, these methods have limited sensitivity for superficial or in-situ lesions and rely heavily on operator experience, with false negative rates reaching 20–30%. Serological markers such as squamous cell carcinoma antigen (SCC-Ag) and carcinoembryonic antigen (CEA), while simple to perform, have a positive rate of less than 30% in the early stages and poor specificity, easily affected by smoking and chronic inflammation, making them unsuitable as independent screening tools. Although needle biopsy is the gold standard, it is an invasive procedure with low patient compliance and risks such as inducing tumor implantation and metastasis, and bleeding, making it unsuitable for dynamic monitoring and large-scale screening of asymptomatic populations.

[0004] Liquid biopsy, which involves collecting peripheral blood, saliva, and other bodily fluids to detect tumor-derived biological information, offers advantages such as being non-invasive or minimally invasive, convenient, low-cost, and repeatable, providing a new approach for early cancer screening. Among these, circulating cell-free DNA (cfDNA), released into the bloodstream early in tumor development and carrying complete gene mutations and methylation molecular characteristics, has become the most promising target for translational analysis. cfDNA methylation features combine high stability with tissue traceability: ① Some methylation patterns change significantly in the pre-carcinogenesis stage; ② cfDNA from different tissue sources possesses unique and stable methylation "fingerprints," allowing for the location of the primary tumor site through algorithmic deconvolution; ③ Methylated fragments are more resistant to nuclease degradation than mutated fragments, making them more suitable for low-abundance detection. DNA methylation is tissue- or cell-specific; cfDNA from different cell types has different DNA methylation characteristics, making it possible to trace the origin of diseased tissue through cfDNA. Damaged tissue releases cfDNA in the early stages of tumor development; therefore, cfDNA-based methylation analysis provides a more reliable and sensitive method for early cancer diagnosis. Develop early screening tools and diagnostic kits based on cfDNA methylation markers to capture malignant signals before they are visible on imaging, thus achieving true "early" diagnosis.

[0005] In recent years, with the development of tumor marker screening and detection technologies, various liquid biopsy analysis methods based on cfDNA methylation have emerged. Methylation detection technologies include whole-genome bisulfite sequencing (WGBS), reduced representative bisulfite sequencing (RRBS), targeted bisulfite sequencing (TBS), and methylation-specific PCR (MSP). These technologies help obtain more accurate cfDNA methylation information and enable tissue origin analysis of plasma cfDNA through machine learning algorithms. However, WGBS has high resolution but is costly and involves huge amounts of data; RRBS has limited coverage; and MSP has low throughput and is prone to false positives. Balancing sensitivity, specificity, and clinical accessibility remains a bottleneck in technology transfer. Furthermore, there is currently no specific combination of cfDNA methylation biomarkers and a corresponding detection system for head and neck cancer, failing to meet the urgent clinical need for non-invasive early screening. cfDNA methylation detection is one of the most promising development directions in the field of liquid biopsy. This technology provides a non-invasive solution for early tumor screening, accurate diagnosis, and dynamic monitoring by analyzing changes in the DNA methylation patterns released by tumor cells in the blood.

[0006] Therefore, this invention aims to develop a method for screening cfDNA methylation markers for early detection of head and neck cancer and a corresponding detection kit. Summary of the Invention

[0007] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is to provide a cfDNA methylation marker, screening method and detection kit for early detection of head and neck cancer.

[0008] To achieve the above objectives, the present invention provides a cfDNA methylation marker, screening method, and detection kit for early detection of head and neck cancer.

[0009] Furthermore, a cfDNA methylation biomarker for early detection of head and neck cancer, wherein the region corresponding to the cfDNA methylation biomarker is located on the hg38 genome in one or more of the following: chr21:36700976-36701074, chr6:117270059-117270171, chr3:43998153-43998241, chr1:117088255-117088365, chr1:1785845-1785925, or chr15:75476375-75476476.

[0010] Furthermore, the cfDNA methylation marker is present in plasma.

[0011] Furthermore, the screening method for cfDNA methylation markers includes the following steps: Step 1: Obtain whole-genome bisulfite sequencing data of different tissues or cells in healthy human tissues, the data including methylation level information of each CpG site; divide the tissue or cell samples into target group and background group, wherein the target group is head and neck related tissue and cell samples, and the background group is other tissue and cell samples other than head and neck related tissues; Step 2: Based on the whole-genome bisulfite sequencing data, screen CpG sites that show specific hypermethylation or specific hypomethylation only in the target group as candidate specific sites; Step 3: From the candidate specific sites, screen out genomic regions with more than 5 consecutive specific methylation sites within 150 bp. These genomic regions are the target cfDNA methylation markers.

[0012] Furthermore, in step one, the head and neck related tissues or cells include thyroid epithelial cells, pharyngeal epithelial cells, laryngeal epithelial cells, tonsil epithelial cells, and tongue epithelial cells.

[0013] Furthermore, in step one, the sequencing data is stored in the form of .pat and .beta files. The .pat file stores the methylation status of CpG sites in the sequencing read fragment, where C indicates that the CpG site is methylated and T indicates that the CpG site is unmethylated. The .pat file can obtain the association information of multiple CpG sites on the same read fragment. The .beta file stores the methylation level of each CpG site, including the total number of observations and the number of times the methylation status of the CpG site was observed.

[0014] Furthermore, in step two, the criteria for determining specific hypermethylation are: the methylation level of the corresponding CpG site in the target group sample is higher than 0.8, and the methylation level of the CpG site in all samples of the background group is lower than 0.5; the criteria for determining specific hypomethylation are: the methylation level of the corresponding CpG site in the target group sample is lower than 0.5, and the methylation level of the CpG site in all samples of the background group is higher than 0.8.

[0015] Furthermore, step two employs a two-stage screening process, as follows: First, the find_markers function in wgbstools is used for preliminary screening to obtain differential information on individual CpG sites. Then, the beta_to_table function in wgbstools and Python code are used to analyze the methylation levels of all CpG sites according to the aforementioned criteria for further screening.

[0016] Furthermore, a detection kit contains reagents for detecting the said cfDNA methylation markers.

[0017] Furthermore, the detection kit contains PCR primers designed for the methylation signature regions of the cfDNA methylation markers.

[0018] Furthermore, the kit also contains one or more of the following: sulfite conversion reagent, DNA polymerase, dNTPs, library construction reagent, and sequencing adapter.

[0019] In a preferred embodiment 1 of the present invention, the screening process for type-specific methylation sites of head and neck cells is described in detail.

[0020] In another preferred embodiment 2 of the present invention, the screening and analysis process of head and neck cell-specific methylation markers is described in detail.

[0021] In another preferred embodiment 3 of the present invention, the collection of plasma from patients with head and neck cancer and the amplification of the marker region of their cfDNA are described in detail.

[0022] In another preferred embodiment 4 of the present invention, the verification process of the application of cell type-specific methylation markers in the diagnosis of head and neck cancer patients is described in detail.

[0023] Technical effects: This invention, based on large-scale methylation sequencing data mining, has for the first time screened and validated a group of phenotype-specific differentially methylated regions (DMRs) of head and neck cells, clarifying their detectability in plasma cfDNA of head and neck cancer patients. By combining low-starting-volume (≤10ng) targeted methylation sequencing with a machine learning model, it achieves highly sensitive and specific detection of peripheral blood cfDNA, overcoming the shortcomings of existing technologies such as insufficient sensitivity, difficulty in tissue tracing, and high invasiveness. This fills the gap in non-invasive early screening products for head and neck cancer cfDNA methylation, laying the foundation for the development of sensitive and efficient non-invasive biomarkers and kits for early detection of head and neck cancer. Specific advantages are as follows: 1. It achieves highly sensitive identification of early-stage head and neck cancers that cannot be detected by traditional technologies, which can greatly reduce the high mortality rate of head and neck cancer patients caused by late detection, and has significant clinical and social benefits.

[0024] 2. It solves the problem that traditional liquid biopsy biopsy markers only provide abnormal information but not location information, and also solves the problem of low concentration and insufficient sensitivity of tumor-derived cfDNA biopsy markers, greatly improving the application value of cfDNA biopsy markers.

[0025] 3. It solves the problems of low sensitivity and low signal-to-noise ratio when using a single methylation site as a marker, and also solves the problems of high sequencing detection costs and a lot of useless information caused by multiple discrete specific sites, providing a more accurate and convenient way for the analysis of cfDNA methylation markers.

[0026] The following will further explain the concept, specific structure, and technical effects of the present invention in conjunction with the accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Attached Figure Description

[0027] Figure 1 This is a flowchart of the analysis of cfDNA methylation markers in head and neck cancer according to a preferred embodiment 1 of the present invention; Figure 2 This is a clustering heatmap of specific sites screened in 205 tissue samples, according to a preferred embodiment 1 of the present invention. Figure 3 This is a statistical chart of the proportion of the pharyngeal epithelium-M-1 marker in a preferred embodiment 2 of the present invention, wherein a is the proportion of the marker in 205 samples, and b is the proportion of the marker in the pharyngeal epithelium sample and the remaining samples.

[0028] Figure 4This is a statistical chart of the proportion of the pharyngeal epithelium-M-2 marker in a preferred embodiment 2 of the present invention, wherein a is the proportion of the marker in 205 samples, and b is the proportion of the marker in the pharyngeal epithelium sample and the remaining samples.

[0029] Figure 5 This is a statistical chart of the proportion of thyroid epithelial-M-1 markers in a preferred embodiment 2 of the present invention, wherein a is the proportion of the marker in 205 samples, and b is the proportion of the marker in thyroid epithelial samples and other samples.

[0030] Figure 6 This is a statistical chart of the proportion of thyroid epithelial-U-1 markers in a preferred embodiment 2 of the present invention, wherein a is the proportion of the marker in 205 samples, and b is the proportion of the marker in thyroid epithelial samples and other samples.

[0031] Figure 7 This is a statistical chart of the proportion of thyroid epithelial-U-2 markers in a preferred embodiment 2 of the present invention, wherein a is the proportion of the marker in 205 samples, and b is the proportion of the marker in the thyroid epithelial sample and the remaining samples.

[0032] Figure 8 This is a statistical chart of the proportion of thyroid epithelial-U-3 markers in a preferred embodiment 2 of the present invention, wherein a is the proportion of the marker in 205 samples, and b is the proportion of the marker in thyroid epithelial samples and other samples.

[0033] Figure 9 This is a comparison chart of the proportion of cfDNA marker fragments in the plasma of head and neck cancer patients and healthy individuals, which is a preferred embodiment 4 of the present invention.

[0034] Figure 10 This is a comparison chart of the presence of biomarker fragments in the plasma of head and neck cancer patients and healthy individuals, representing a preferred embodiment 4 of the present invention. Detailed Implementation

[0035] The following description, with reference to the accompanying drawings, illustrates several preferred embodiments of the present invention to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0036] Example 1: Screening of Cell Type-Specific Methylation Sites in the Head and Neck Region The data used in this invention were obtained from the GEO database (GSE186458), containing bisulfite sequencing (WGBS) data from 205 samples of 39 different tissues or cells isolated from healthy human tissues. This included 14 head and neck related samples: 3 thyroid epithelial samples, 1 pharyngeal epithelial sample, 1 laryngeal epithelial sample, 5 tonsillar epithelial samples, and 4 lingual epithelial samples. Sequencing data were stored in .pat and .beta files. The .pat file stores the methylation status of CpG sites in the sequencing reads, where C indicates a methylated CpG site and T indicates a non-methylated CpG site. The .pat file can obtain association information for multiple CpG sites on the same read. The .beta file stores the methylation level of each CpG site, including the total number of observations and the number of times the site was observed to be methylated.

[0037] Based on the collected data, screen for head and neck cell type-specific methylation sites using the following steps: First, the find_markers function in wgbstools (v0.2.2) is used to perform preliminary screening of head and neck related markers. The beta file, grouping file and block file of CpG sites of 205 samples are used as input. The grouping decomposition divides the samples into background group and target group. Each block in the block file is a single CpG site to obtain the differential information of a single CpG site. The remaining parameters are default parameters.

[0038] Subsequently, in order to obtain highly specific biomarkers, the beta_to_table function in wgbstools (v0.2.2) and Python code were used to analyze the methylation levels of all CpG sites, and CpG sites that were specifically hypermethylated (methylation level of target sample greater than 0.8, methylation level of other samples less than 0.5) or specifically hypomethylated (methylation level of target sample less than 0.5, methylation level of other samples greater than 0.8) were screened out only in all target samples.

[0039] Results: A total of 1157 specific loci were identified (Table 1). Among them, 146 were hypermethylation-specific loci and 1011 were hypomethylation-specific loci. Cluster heatmaps of the methylation levels of these 1157 specific loci in 205 samples were generated using the R language's pheatmap package. Figure 2 It can be seen that thyroid epithelium, laryngeal epithelium, and pharyngeal epithelium are distinguished from other tissues when clustered.

[0040] Table 1. Statistics on the number of specific sites screened.

[0041] Example 2: Screening and Analysis of Cell-Specific Methylation Markers in Head and Neck

[0042] Based on the 1157 head and neck cell type-specific methylation sites screened in Example 1, the construction and specificity validation of head and neck cell type-specific methylation biomarkers were carried out. Addressing the issues of low sensitivity and signal-to-noise ratio of single methylation site biomarkers, and high cost and excessive invalid information in discrete multi-site detection, this invention combines multiple adjacent cell-specific methylation sites into a continuous region biomarker, rather than using a single site or scattered independent sites, thereby improving the accuracy and convenience of cfDNA methylation biomarker detection. Specific implementation methods are as follows: 1. Methods for selecting biomarkers Based on the obtained dataset of tissue-specific high / low methylation sites, Python code was written to screen regions that meet the following criteria as tissue-specific biomarkers: containing more than 5 consecutive specific methylation sites within a 150bp range; this length limit matches the 150bp single-end read length of next-generation sequencing, which can increase the probability that a single sequencing fragment covers all CpG sites in the target region. The screened target tissue-specific biomarkers are summarized in Table 2.

[0043] 2. Specificity verification of biomarkers

[0044] The specificity of the biomarkers in Table 2 was verified in the WGBS data pat file of 205 samples: only sequencing fragments that completely covered all CpG sites of the biomarker were included in the statistical range, and sequencing fragments with fully methylated or fully unmethylated target CpG sites within this range were defined as target biomarker fragments; then the proportion of target biomarker fragments to the total sequencing fragments in the above statistical range in each sample was counted, and the signal-to-noise ratio of the cell type-specific biomarker was obtained by calculating the ratio of the average of this proportion in the target tissue samples to the average of the background tissue samples.

[0045] Result: As Figure 3-8 As shown, compared to the specificity of a single biomarker, biomarkers composed of multiple adjacent specific sites within a certain base range have higher signal-to-noise ratio and specificity, and are found almost exclusively in sequencing samples of the target tissue, with fewer occurrences in sequencing samples of all other tissues.

[0046] Table 2. Cell-specific methylation markers of head and neck

[0047] Example 3: Collection of plasma from head and neck cancer patients and amplification of cfDNA marker regions

[0048] Based on the head and neck cell type-specific methylation markers constructed in Example 2, with "pharyngeal epithelium-M-2" as the target marker, plasma samples from head and neck cancer patients were collected, cfDNA was extracted, and specific amplification experiments of the marker region were carried out.

[0049] 1. Obtaining patient plasma and extracting plasma cfDNA

[0050] The subjects had confirmed head and neck cancer. Their plasma was obtained by a professional doctor at the hospital and stored at -80°C. After being transported to the laboratory, plasma cfDNA was extracted using the QIAamp Circulating Nucleic Acid Kit in accordance with its instructions. The extracted cfDNA was then stored at -80°C.

[0051] 2. Sulfite treatment of cfDNA

[0052] The experiment used the EZ DNA Methylation-Gold™ Kit to treat plasma cfDNA with sulfite, following the instructions for use.

[0053] Reagent preparation: (1) Add 900 μl of water, 300 μl of M-Dilution Buffer, and 50 μl of M-Dissolving Buffer to the conversion reagent tube. (2) Mix at room temperature, vortex or shake for 10 minutes, store the prepared reagent away from light, store at 4°C for one week, or at -20°C for one month. The converted reagent solution after storage needs to be heated to 37°C and shaken before use.

[0054] (3) Add 24 ml of 100% ethanol to 6 ml of M-Wash Buffer and mix well.

[0055] Sample processing: (1) Add 20 µl of DNA sample to the PCR tube. If the DNA sample volume is less than 20 µl, make up the difference with double-distilled water. Then add 130 µl of CT conversion reagent. Mix the sample by shaking the tube or by aspirating from top to bottom, and then centrifuge the liquid to the bottom of the PCR tube.

[0056] (2) Place the sample tube into the PCR instrument and perform the following reaction program: 98℃ for 10 minutes, followed by 64℃ for 2.5 hours. After the program is completed, store at 4℃ for up to 20 hours.

[0057] (3) Add 600µl of M-Binding Buffer to the Zymo-Spin™ IC column and place the column into the provided collection tube.

[0058] (4) Load the sample (1.2.2) into a Zymo-Spin™ IC column containing M-Binding Buffer. Cover the column and mix by inverting it multiple times.

[0059] (5) Centrifuge at full speed (>10000x g) for 30 seconds and discard the filtrate.

[0060] (6) Add 100 μl of M-Wash Buffer to the column and centrifuge at full speed for 30 seconds.

[0061] (7) Add 200 μl of M-Desulphonation Buffer to the column and incubate at room temperature (20-30℃) for 15-20 minutes. After incubation, centrifuge at full speed for 30 seconds.

[0062] (8) Add 200 μl of M-Wash Buffer to the column, centrifuge at full speed for 30 seconds, and repeat once.

[0063] (9) Place the column into a 1.5 ml microcentrifuge tube and add 10 μl of M-Elution Buffer to the column matrix. Centrifuge at full speed for 30 seconds to elute the DNA.

[0064] (10) The concentration of the prepared DNA was determined using Qubit: 198 μl of Qubit buffer and 1 μl of Qubit dye were mixed, then 1 μl of extracted cfDNA was added, mixed, and incubated at room temperature in the dark for 2 min. The concentration was then measured. The prepared DNA was stored at -20℃ or lower, and for long-term storage, it should be kept below -70℃.

[0065] 3. Targeted amplification of biomarker regions

[0066] For the selected specific biomarkers, the sequence of the gene containing the biomarker was first obtained from the UCSC genome database. The biomarker fragment and approximately 200 bp upstream and downstream were used as input to design sulfite-treated PCR primers in the primer design tool MethPrimer. Primer design should avoid CpG sites as much as possible, and the product size should be similar to the length of the cfDNA. However, some biomarker regions have many upstream and downstream CpG sites, making it impossible to find suitable target amplification primers. For these cases, the primer design tools MethPrimer and Primer3Plus were used, referencing methylation-specific PCR (MSP) primer design, to design two types of primers for both methylated and unmethylated bisulfite-treated sequences of the target region.

[0067] For the designed primers, preliminary experiments were first conducted using genomic DNA to observe the specificity of the target fragment amplification and the optimal amplification temperature T. Appropriate primers and temperatures were selected as amplification conditions for the specific marker region of cfDNA in patient plasma. The methylated primer (M primer) and unmethylated primer (U primer) sequences designed for "pharyngeal epithelium-M-2" are shown in Table 3.

[0068] Table 3. Sequences of methylated and unmethylated primers designed for "pharyngeal epithelium-M-2".

[0069] a. PCR amplification of the target biomarker region: (1) Prepare a reaction system with a total volume of 25 ml: Table 4 PCR amplification system for target biomarker regions

[0070] (2) Gently mix the reaction system, centrifuge briefly, place it in a PCR instrument, and set the reaction program: 95℃ for 1 minute; then 98℃ for 10 seconds, T℃ for 30 seconds, and 72℃ for 20 seconds. Repeat the above three steps for 40 cycles; after the program is finished, keep warm at 4℃.

[0071] (3) After the PCR reaction is completed, the concentration is determined using Qubit.

[0072] b. 2% agarose gel electrophoresis: (1) Pour 30ml of 0.5xTBE into an Erlenmeyer flask and add 0.6g of agarose.

[0073] (2) Cover and heat for 1 minute until melted, then let it cool naturally for about 5 minutes until it is no longer hot to the touch. Add dye (2 μl) at a ratio of 1:10000 and shake well.

[0074] (3) Pour it onto the sizing plate and let it solidify for about 30 minutes.

[0075] (4) Take about 50 ng of the target sample according to the concentration, mix it with 1 μl of DNA ladder (6×), add ddH2O to 6 μl and mix well. Spot the sample into the well of the agarose gel and perform agarose gel electrophoresis to check the length of the amplified band.

[0076] c. Sample magnetic bead purification: (1) Take out the Beckman Ampure XP Beads stored at 4℃ in advance and allow them to return to room temperature for at least 30 minutes.

[0077] (2) Measure the volume of each PCR system, add 1.8× Ampure XP Beads, and vortex thoroughly to mix.

[0078] (3) Let stand at room temperature for 5 minutes.

[0079] (4) After instantaneous centrifugation, place the sample on a magnetic rack for enrichment for 5 minutes, and carefully aspirate the supernatant.

[0080] (5) Add 200 μl of freshly prepared 80% ethanol to rinse the magnetic beads. After 30 seconds, remove the ethanol and repeat the operation once.

[0081] (6) After thoroughly absorbing the ethanol, let it air dry for about 2 minutes until there is no water film on the surface of the magnetic beads.

[0082] (7) Add 12 μl of 10 mM Tris-HCl (pH 8.0), vortex to mix, and make sure the magnetic beads are fully dispersed in the solvent.

[0083] (8) Let stand at room temperature for 5 minutes.

[0084] (9) After instantaneous centrifugation, place on a magnetic rack for enrichment for 5 minutes, carefully aspirate 15 μl and transfer to a new tube to obtain 15 μl of eluent.

[0085] (10) The concentration was determined using Qubit, and the purified sample was stored at -20℃.

[0086] 4. Construction of methylation sequencing libraries

[0087] For the screened cfDNA samples, methylation sequencing libraries were constructed using the NEB NEXT Ultra II library. The end repair and A-tailing procedures are as follows: (1) Measure the sample volume from the previous step and bring it up to 50 μl with 10 mM Tris-HCl (pH 8.0); (2) Prepare a 60 μl reaction system on ice: Table 5. End-of-phase remediation reaction system

[0088] Adjust a 100μl pipette to 40μl, mix thoroughly by pipetting at least 10 times, and then centrifuge briefly.

[0089] (1) Place it in a PCR instrument, set the temperature of the hot lid to ≥75℃, and set the reaction conditions to: 20℃ for 30 minutes, 65℃ for 30 minutes, and 4℃ for maintenance.

[0090] (2) After the reaction is complete, remove the system and place it on an ice box. Connect the reaction system with a 93.5 μl connector. Before mixing, repeatedly pipette the NEBNext Ultra II Ligation Master Mix to prevent uneven mixing. Table 6. Joint Connection Reaction System

[0091] After preparation, adjust the 100μl pipette to 80μl, mix by pipetting at least 10 times, and then centrifuge briefly.

[0092] (5) Place it in a PCR instrument and react at 20°C for 15 minutes. Then close the hot lid.

[0093] (6) After the reaction is complete, take out the PCR tube, add 3 μl of USER enzyme in the clean bench, mix by pipetting and then centrifuge briefly.

[0094] (7) Place it in a PCR instrument and react at 37°C for 15 minutes. Set the temperature of the hot lid to ≥47°C. Store the sample at -20°C after the reaction.

[0095] (8) After digestion, the reaction system was purified using 1.0× Ampure XP Beads, following the same steps as the magnetic bead purification steps in 2.2.5, and eluted with 32 μl 10 mM Tris-HCl.

[0096] (9) Take 1 μl and use Qubit to measure and record the concentration.

[0097] Library expansion: (1) Prepare a 50 μl amplification system on ice: Table 7 Library amplification reaction system

[0098] After configuration, mix well and disconnect instantly.

[0099] (2) Place the sample in the PCR instrument and set the reaction conditions as follows: Place the sample in the PCR instrument and perform pre-denaturation at 95℃ for 30 seconds; then perform 15 cycles of amplification, each cycle including 95℃ for 30 seconds, 61℃ for 30 seconds, and 68℃ for 30 seconds; after the cycle, perform final extension at 68℃ for 5 minutes; finally, store at 4℃.

[0100] (3) After the reaction is complete, take 1 μl and use Qubit for quantification.

[0101] (4) The reaction system was purified using 1.0×Ampure XP Beads, following the same procedure as with the magnetic beads.

[0102] For purification, elution was performed using 30 μl of 10 mM Tris-HCl.

[0103] (5) After purification, take 1 μl and use Qubit for quantification.

[0104] (6) Perform gel electrophoresis to observe the fragment size distribution in the amplified system, and reuse the gel electrophoresis as needed.

[0105] Ampure XP Beads are used for magnetic bead purification.

[0106] Before proceeding with next-generation sequencing, the fragment distribution of the library is checked to ensure there is no primer dimer contamination, and then sequencing is performed.

[0107] Example 4: Validation of the application of the cell type-specific methylation marker "pharyngeal epithelial-M-2" in the diagnosis of head and neck cancer patients.

[0108] In this embodiment, the cfDNA biomarker region from the plasma of three head and neck cancer patients was sulfite-treated, and the "pharyngeal epithelium-M-2" biomarker region was amplified and sequenced. The data were compared with WGBS data from 23 healthy individuals in the GEO database. Quality control of the head and neck cancer patient samples was performed using an Agilent 2100, and the sequencing platform was NovaSeq 6000, with paired-end 2×150bp sequencing. The resulting data was in fq.gz format. The plasma cfDNA sequencing data from healthy individuals was in .pat format aligned to the human genome hg38.

[0109] The sequencing data from three head and neck cancer patients were quality assessed using FastQC (v0.11.5) to obtain the quality scores of the sequencing sequences and the information of the sequencing adapters. Trim-Galore (v0.6.10) was then used to remove adapter sequences and low-quality sequences. After the cutting process was completed, FastQC (v0.11.5) was used again for quality control to check the data quality. After adapter removal and quality control, Bismark (v0.24.2) was used to align the sequencing data with the human reference genome hg38 to obtain the bam file.

[0110] To determine whether the sequencing data contained the target fragment, the `sort` function in `samtools` (v1.19) was used to sort the BAM files obtained after alignment. After sorting, the `bam2pat` function in `wgbstools` (v0.2.2) was used to convert the BAM files into PAT files. WGBS sequencing data (GSE186458) of plasma cfDNA from 23 healthy individuals were collected, and the files were PAT files aligned to the human genome hg38.

[0111] Based on the obtained PAT files, codes were written to statistically analyze the proportion of biomarkers in plasma cfDNA samples from healthy individuals and head and neck cancer patients, observing the presence of biomarker fragments in the plasma cfDNA of head and neck cancer patients and healthy individuals. Theoretically, sequencing results from healthy individuals should not contain biomarker fragments; however, in head and neck cancer patients, cell death in the head and neck tissues increases, and dead cells release DNA into the plasma; therefore, sequencing results from head and neck cancer patients should include biomarker fragments.

[0112] Results analysis: Based on the sequencing results pat files of head and neck cancer patients, fragments covering all biomarker sites were screened, and biomarker fragments with complete methylation at the biomarker sites were screened. The results are shown in Table 8. Biomarker fragments were present in all 3 patient samples.

[0113] Based on the sequencing results of plasma cfDNA from 23 healthy individuals, the same screening method was used to obtain the results, as shown in Table 9. No biomarker fragments were found in any of the 23 healthy control samples. A bar chart was plotted to compare the results. Figure 9 The presence of biomarker fragments in plasma can be calculated from the proportion of biomarkers present in the sequencing results, and a scatter plot can be plotted as follows. Figure 10 .

[0114] The above results indicate that the "pharyngeal epithelium-M-2" biomarker fragment was present in all patient samples, while it was absent in the plasma cfDNA of all healthy individuals. Based on a test set consisting of 3 patients and 23 healthy individuals, and using the detection of "pharyngeal epithelium-M-2" as the criterion, this detection method achieved 100% sensitivity and 100% specificity in this study, with no false positives or false negatives. These data suggest that the "pharyngeal epithelium-M-2" biomarker has the potential to distinguish between head and neck cancer patients and healthy individuals, providing a basis for the development of diagnostic kits.

[0115] Table 8. Presence of biomarker fragments in plasma cfDNA of head and neck cancer patients

[0116] Table 9. Presence of biomarker fragments in plasma cfDNA of healthy individuals

[0117] The non-invasive detection method and kit for early head and neck cancer based on cell type-specific DNA methylation markers provided by this invention have significant clinical applicability, technical feasibility, and broad market prospects, specifically reflected in the following three aspects: 1. This invention overcomes the limitations of existing cfDNA methylation markers that rely on single / discrete multi-site sites. It combines multiple adjacent cell-specific methylation sites into a continuous region marker, improving the detection signal-to-noise ratio and reducing sequencing costs while increasing sensitivity and specificity. This invention relies on cell type-specific methylation markers to pinpoint the tumor occurrence area to a smaller range, while avoiding the problem of unstable detection results due to the limited amount of tumor-derived DNA. It can detect markers from diseased tissue in precancerous lesions or early stages of cancer, enabling early non-invasive screening for head and neck cancer. It complements other detection methods and meets essential clinical needs.

[0118] 2. The methylation marker regions obtained by this invention exhibit high stability, with highly consistent methylation status in target tissues and significantly reduced signal in non-target tissues. The signal-to-noise ratio is comparable to single CpG site detection, and the marker detection rate in the plasma of head and neck cancer patients is 100%, demonstrating high sensitivity and no detection in healthy controls, highlighting its specificity. Furthermore, the detection technology of this invention is based on commercially available reagent kits and standardized procedures, resulting in high experimental repeatability and stable results. It requires only 2-4 mL of plasma, is compatible with standard blood collection tubes and automated extraction equipment, and is easy to promote in primary hospitals. Moreover, the bioinformatics analysis process used in this invention can be automated through standardized scripts, making it feasible to further develop into software tools or detection algorithm modules.

[0119] 3. The biomarkers provided by this invention have clear genomic coordinates and simple sequences, and there are no technical barriers to primer design and synthesis. In addition, the core process of this invention is highly standardized, and domestic manufacturers have launched domestically produced reagent kits with equivalent performance. The raw material supply chain is mature and stable, and primers and probes can be outsourced for synthesis without production capacity bottlenecks. Moreover, this invention can achieve early non-invasive screening for head and neck cancer, which can significantly improve the 5-year survival rate of patients and greatly reduce the cost of clinical treatment, making it of great market application value.

Claims

1. A cfDNA methylation biomarker for early detection of head and neck cancer, characterized in that, The regions corresponding to the cfDNA methylation markers on the hg38 genome are located in one or more of the following: chr21:36700976-36701074, chr6:117270059-117270171, chr3:43998153-43998241, chr1:117088255-117088365, chr1:1785845-1785925, or chr15:75476375-75476476.

2. The cfDNA methylation marker as described in claim 1, characterized in that, The cfDNA methylation marker is present in plasma.

3. The screening method for cfDNA methylation markers according to claim 1, characterized in that, Includes the following steps: Step 1: Obtain whole-genome bisulfite sequencing data of different tissues or cells in healthy human tissues, the data including methylation level information of each CpG site; divide the tissue or cell samples into target group and background group, wherein the target group is head and neck related tissue and cell samples, and the background group is other tissue and cell samples other than head and neck related tissues; Step 2: Based on the whole-genome bisulfite sequencing data, screen CpG sites that show specific hypermethylation or specific hypomethylation only in the target group as candidate specific sites; Step 3: From the candidate specific sites, screen out genomic regions with more than 5 consecutive specific methylation sites within 150 bp. These genomic regions are the target cfDNA methylation markers.

4. The screening method for cfDNA methylation markers as described in claim 3, characterized in that, In step one, the head and neck related tissues or cells include thyroid epithelial cells, pharyngeal epithelial cells, laryngeal epithelial cells, tonsil epithelial cells, and tongue epithelial cells.

5. The screening method for cfDNA methylation markers as described in claim 3, characterized in that, In step one, sequencing data is stored in the form of pat and beta files. The pat file stores the methylation status of CpG sites in the sequencing read fragment, where C indicates that the CpG site is methylated and T indicates that the CpG site is unmethylated. The pat file can obtain the association information of multiple CpG sites on the same read fragment. The beta file stores the methylation level of each CpG site, including the total number of observations of the CpG site and the number of times the methylation status was observed.

6. The screening method for cfDNA methylation markers as described in claim 3, characterized in that, In step two, the criteria for determining specific hypermethylation are: the methylation level of the corresponding CpG site in the target group sample is higher than 0.8, and the methylation level of the CpG site in all samples of the background group is lower than 0.5; the criteria for determining specific hypomethylation are: the methylation level of the corresponding CpG site in the target group sample is lower than 0.5, and the methylation level of the CpG site in all samples of the background group is higher than 0.

8.

7. The screening method for cfDNA methylation markers as described in claim 6, characterized in that, Step two employs a two-stage screening process, as follows: First, the find_markers function in wgbstools is used for preliminary screening to obtain differential information on individual CpG sites. Then, the beta_to_table function in wgbstools and Python code are used to analyze the methylation levels of all CpG sites according to the judgment criteria described in claim 6 for further screening.

8. A test kit, characterized in that, It contains reagents for detecting the cfDNA methylation markers of claim 1.

9. The detection kit as described in claim 8, characterized in that, It includes PCR primers designed for the methylation signature regions of the cfDNA methylation markers.

10. The detection kit as described in claim 8, characterized in that, The kit also contains one or more of the following: sulfite conversion reagent, DNA polymerase, dNTPs, library construction reagent, and sequencing adapter.

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