A 5hmC molecular marker for predicting treatment sensitivity of sudden deafness and its application

Abstract

The present invention belongs to the field of biomedicine and specifically relates to a 5hmC molecular marker for predicting treatment sensitivity for sudden deafness. The present invention provides a 5hmC molecular marker for predicting treatment sensitivity for sudden deafness, comprising genes encoding the proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF, and EPHX2. The 5hmC molecular marker provided by the present invention can effectively predict treatment sensitivity for sudden deafness, with an AUC as high as 0.994. Using this molecular marker, treatment outcomes can be predicted before treatment, screening out patients with poor or ineffective treatment outcomes, allowing for preemptive comprehensive treatment and improving patient prognosis.

Description

Technical Field

[0001] The present invention belongs to the field of medical biotechnology, and particularly relates to a 5hmC molecular marker for predicting sensitivity to treatment of sudden deafness and an application thereof. Background Art

[0002] Sudden deafness (SDD) is a major cause of sensorineural hearing loss, with an unclear pathogenesis and an annual incidence of 5 to 20 cases per 100,000 people. Currently, treatment for SDD is primarily based on medication, but the delayed onset of treatment leads to poorer results, making it an acute condition requiring early treatment. The overall efficacy rate for SDD is approximately 80%, but treatment remains ineffective in approximately 20% of patients. The more severe the hearing loss, the worse the prognosis, significantly impacting patients' quality of life. In recent years, research on the prognosis of SDD has steadily increased, and numerous factors potentially influencing prognosis have been reported, including clinical features, inflammatory markers, coagulation function, and genetics. However, these methods have limitations and cannot predict treatment outcomes. Therefore, there is an urgent need for biomarkers that can predict treatment efficacy, allowing for early intervention and improved outcomes in the large number of patients who do not respond to treatment.

[0003] The pathological mechanism of sudden deafness is related to damage to inner ear hair cells. Previous studies have found that epigenetic regulatory mechanisms play a crucial role in inner ear development, primarily through three key processes: histone modification, DNA methylation and demethylation, and chromosomal reorganization. DNA methylation and demethylation can promote the growth and development of cochlear cells. 5-Hydroxymethylcytosine (5hmC) is considered a key epigenetic marker and a relatively stable and representative marker of DNA demethylation. Its enrichment levels vary significantly under different body conditions, meaning the amount of demethylated cytosine varies without altering the DNA sequence. Based on this characteristic, researchers have been able to stably capture changes in 5hmC levels and have applied it extensively to the diagnosis, treatment, and efficacy prediction of various diseases. Studies have also shown that extracellular vesicle DNA (EVsDNA), DNA encapsulated in extracellular vesicles (EVs) in the blood, carries epigenetic information from primary cells. Its copy number and sensitivity are high, making it a promising predictor of disease. Therefore, the 5hmC marker in EVsDNA was used to study sudden deafness, and 5hmC markers with significantly different enrichment levels were found to predict treatment sensitivity and to intervene in patients who were ineffective in treatment in advance. Summary of the Invention

[0004] The purpose of the present invention is to provide a new detection marker and a new detection method for predicting the treatment sensitivity of sudden deafness, clarify the treatment effect of patients through 5hmC high-channel sequencing, provide precise and individualized treatment measures, and help improve the treatment effect of patients with sudden deafness.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] The present invention provides a 5hmC molecular marker for predicting the sensitivity of sudden deafness treatment. The 5hmC molecular marker includes genes encoding proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF and EPHX2.

[0007] Preferably, the coding gene sequences of the proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF and EPHX2 are shown as SEQ ID NO.1, SEQ ID NO.2, SEQ ID NO.3, SEQ ID NO.4, SEQ ID NO.5, SEQ ID NO.6, SEQ ID NO.7, SEQ ID NO.8, SEQ ID NO.9, SEQ ID NO.10 and SEQ ID NO.11, respectively; or, the nucleotide sequences of the coding genes of the proteins are sequences having more than 85% similarity to the sequences shown in SEQ ID NO.1, SEQ ID NO.2, SEQ ID NO.3, SEQ ID NO.4, SEQ ID NO.5, SEQ ID NO.6, SEQ ID NO.7, SEQ ID NO.8, SEQ ID NO.9, SEQ ID NO.10 and SEQ ID NO.11, respectively, through nucleotide substitution, deletion or addition.

[0008] The present invention also provides a kit for predicting the sensitivity of sudden deafness treatment, which includes a reagent for detecting the expression level of 5hmC molecular markers, and the molecular markers include the coding genes of proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF and EPHX2.

[0009] Preferably, the kit is a nucleic acid detection kit, which comprises primer pairs for amplifying the genes encoding proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF and EPHX2, respectively.

[0010] The present invention also provides a process for constructing a model for predicting sensitivity to treatment of sudden deafness, comprising the following steps:

[0011] (1) Detect samples from multiple patients with sudden deafness who underwent chemotherapy, construct a 5hmC library, and obtain 5hmC sequencing fragments by high-throughput sequencing;

[0012] (2) aligning the 5hmC sequencing fragments obtained in step (1) with the human genome, obtaining 5hmC-enriched regions through a first filtration, and then identifying and filtering them through a second filtration to obtain 5hmC molecular markers for predicting sudden deafness;

[0013] (3) Identify effective and ineffective differentially modified 5hmC regions, perform a third filtering, and then use the selected features (parameters used: maxiter = 100, method = "lbfgs") to train a logistic regression CV model to obtain a prediction model; the trained model is used to predict the patient's sudden deafness treatment sensitivity;

[0014] The first filtering includes: retaining unique non-repeated matches to the human genome;

[0015] The identification includes: using MACS software to identify potential 5hmC-enriched regions, using the parameters macs 14-p1e-3-fBAM-g hs;

[0016] The second filtering includes: retaining peak regions that appear in more than 10 samples and are smaller than 1000 bp;

[0017] The identification of effective and ineffective differentially modified 5hmC regions includes: using the EdgeR package for identification, with filtering thresholds of pvalue < 0.05 and log-2FoldChange > 0.5;

[0018] The third filtering includes: using the recursive feature elimination algorithm in Scikit-Learn to filter the DhMRs of the training cohort, with parameters: estimator=Logistic Regression CV(class_weight='balanced', cv=2, maxiter=1000), scoring='accuracy'.

[0019] Preferably, the input variables of the model are high-throughput sequencing fragments of the 5hmC library.

[0020] More preferably, the 5hmC library is constructed from the patient's peripheral blood through terminal modification, PCR amplification, and purification.

[0021] The present invention also provides a reagent for detecting the expression level of the 5hmC molecular marker in the preparation of a product for predicting the sensitivity of sudden deafness treatment, wherein the reagent includes the coding genes of the proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF and EPHX2.

[0022] Beneficial effects of the present invention:

[0023] This invention is the first to apply the efficacy prediction method of the 5hmC molecular marker to sudden deafness. There is no such research at home or abroad. This study is a prospective trial. Sampling the patients before treatment can understand their treatment effects.

[0024] The test method is minimally invasive, requiring only a small blood sample during a routine hospital checkup. It also boasts high accuracy, with an AUC of up to 0.998, high sensitivity and specificity, and relatively reliable results. This method can predict treatment outcomes for a large number of patients experiencing poor outcomes, allowing for targeted, comprehensive treatments to be administered in advance, improving efficacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 A flowchart of the method for predicting sudden deafness screening and prognosis of the present invention;

[0027] Figure 2 The 5hmC enrichment of patients with sudden deafness and normal hearing people in the embodiment of the present invention is shown;

[0028] Figure 3 The heatmap comparison between sudden deafness patients and normal hearing people in the embodiment of the present invention is shown;

[0029] Figure 4 This is a cluster analysis comparison between sudden deafness patients and normal hearing people according to an embodiment of the present invention.

[0030] Figure 5 The 5hmC enrichment of effective and ineffective people in the sudden deafness prognosis prediction method according to the embodiment of the present invention;

[0031] Figure 6 The heatmap comparison of effective and ineffective groups in the sudden deafness prognosis prediction method according to an embodiment of the present invention is shown;

[0032] Figure 7 The cluster analysis comparison of effective and ineffective groups in the sudden deafness prognosis prediction method according to the embodiment of the present invention is shown;

[0033] Figure 8 The prediction efficiency of the sudden deafness prognosis prediction method of the embodiment of the present invention for effective and ineffective populations.

[0034] Figure 9 The heatmap comparison of effective and ineffective populations in the verification test of the embodiment of the present invention is shown;

[0035] Figure 10 The cluster analysis comparison of effective and ineffective populations in the validation test of the embodiment of the present invention is shown;

[0036] Figure 11 It is the predictive efficacy of effective and ineffective populations in the verification test of the embodiment of the present invention. DETAILED DESCRIPTION

[0037] The 5hmC molecular markers described in the present invention include genes encoding proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF and EPHX2, and the nucleotide sequences of the genes encoding the proteins are replaced, deleted or added with nucleotides to form sequences having a similarity of more than 85% with the sequences shown in SEQ ID NO.1, SEQ ID NO.2, SEQ ID NO.3, SEQID NO.4, SEQ ID NO.5, SEQ ID NO.6, SEQ ID NO.7, SEQ ID NO.8, SEQ ID NO.9, SEQ ID NO.10 and SEQ ID NO.11, respectively.

[0038] The present invention constructs a process for constructing a model for predicting the sensitivity of sudden deafness treatment, comprising the following steps:

[0039] (1) Detect samples from multiple patients with sudden deafness who underwent chemotherapy, construct a 5hmC library, and obtain 5hmC sequencing fragments by high-throughput sequencing;

[0040] (2) Aligning the 5hmC sequencing fragments obtained in step (1) with the human genome, retaining unique non-repeated matches with the human genome after the first filtering, obtaining 5hmC-enriched regions, and then using MACS software to identify potential 5hmC-enriched regions. The parameters used were macs 14-p 1e-3-fBAM-g hs;

[0041] , the second filtering obtains the 5hmC molecular marker for predicting sudden deafness;

[0042] (3) Using the EdgeR package, the filtering thresholds were p value < 0.05 and log-2FoldChange > 0.5; effective and ineffective differentially modified 5hmC regions were identified. The third filtering was performed using the recursive feature elimination algorithm in Scikit-Learn to filter the DhMRs of the training cohort. The parameters were: estimator = Logistic Regression CV (class_weight = 'balanced', cv = 2, maxiter = 1000), scoring = 'accuracy'. The selected features (parameters used: maxiter = 100, method = "lbfgs") were then used to train a logistic regression CV model to obtain a prediction model. The trained model was used to predict the sensitivity of patients to sudden deafness treatment.

[0043] The input variables of the model are high-throughput sequencing fragments of the 5hmC library; the 5hmC library is constructed from the patient's peripheral blood through end modification, PCR amplification and purification.

[0044] In the embodiments of the present invention, "patient," "subject," or "individual" may refer to an organism, and in some aspects, the subject may be a human. The subject providing the sample may include a population at risk for a potential disease or a population diagnosed with a disease. The disease described in the present invention refers to sudden deafness.

[0045] The 5hmC molecular marker of the present invention can provide a method for predicting the sensitivity of sudden deafness treatment. Simply drawing a small amount of peripheral blood from the subject, and performing high-throughput 5hmC sequencing, the patient's treatment effect can be determined, providing precise and individualized treatment measures. Furthermore, based on the 5hmC molecular marker, genetic drugs for treating sudden deafness and products for detecting sudden deafness treatment sensitivity, such as nucleic acid detection kits, can also be developed.

[0046] In order to further illustrate the present invention, the technical solution provided by the present invention is described in detail below with reference to the accompanying drawings and embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0047] The concentrated kit of the present invention was purchased from ZYMO, AMPure XP beads were purchased from Beckman, and the pure beads were purchased from Life Technologies. DBCO-peg4-biotin was purchased from Click Chemistry Tools, and the model was 4.5 mM stock in DMSO.

[0048] Unless otherwise specified, the production processes, experimental methods, or detection methods involved in the embodiments of the present invention are all conventional methods in the prior art, and their names and / or abbreviations are all conventional names in the art. Unless otherwise specified, the experimental methods used in the following examples are all conventional methods. Specifically, they can be carried out with reference to the specific methods listed in the book "Molecular Cloning Laboratory Manual (4th Edition)" (authors: MR Green, J. Sambrook), or according to the kits and product instructions; the materials, reagents, etc. used in the following examples, unless otherwise specified, can be obtained from commercial channels.

[0049] Example 1 Differences in 5hmC enrichment between sudden deafness and normal hearing people

[0050] The specific steps are as follows:

[0051] The flowchart of the prediction construction method of this embodiment is as follows Figure 1 shown.

[0052] 1. Acquisition of cfDNA and Construction of 5hmC Library for High-Throughput Sequencing

[0053] (1) According to the guidelines for the diagnosis and treatment of sudden deafness, 57 patients with sudden deafness and 58 healthy hearing subjects were selected. 4-8 ml of peripheral blood was collected from the subjects and centrifuged: the first centrifugation was at 1350 g for 12 minutes at 4°C, and the supernatant was collected; the second centrifugation was at 13500 g for 5 minutes at 4°C, and the supernatant was collected to obtain EVsDNA, which was then transferred to cell cryopreservation tubes and stored at -80°C.

[0054] (2) The EVsDNA samples were end-repaired and 3′-adenylated using the KAPA Hyper Prep Kit (KAPA Biosystems), followed by ligation with an Erie-compatible adapter. The ligated EVsDNA was added to 25 μL of a solution containing 50 mM HEPES buffer (pH 8.0), 25 mM magnesium chloride, 100 μM UDP-6-N3-Glc, and 1 μM β-NEB enzyme (glycosylation) and incubated at 37°C for 2 h.

[0055] (3) Purify EVsDNA using a DNA cleaning and concentration kit. After incubation of the purified DNA with 1 μL of DBCO-peg4-biotin at 37°C for 2 hours, the DNA was purified again using a DNA cleaning and concentration kit. At the same time, 2.5 μL of streptavidin beads were directly added to 1× buffer (5 mM Tris pH 7.5, 0.5 mM EDTA, 1 M sodium chloride, 0.2% Tween20) and reacted at room temperature for 30 minutes. Finally, wash with buffer 8 times, 5 minutes each time. All binding and washing steps were performed at room temperature with gentle rotation.

[0056] (4) Resuspend in RNase-free water and perform 14-16 cycles of PCR amplification. PCR products are purified using AMPure XP beads. Library concentration is measured using a Qubit 3.0 fluorometer. Paired-end 39bp high-throughput sequencing is performed on the NextSeq 500 platform.

[0057] 2. Mapping and Identifying 5hmC-enriched Regions

[0058] FastQC (version 0.11.5) was used to assess sequence quality. Raw reads were aligned to the human genome using Bowtie2 and further filtered using SAMtools (version 1.3.1) (using the following parameters: SAMtools view -f2 -F 1548 -q 30 and SAMtoolsrmdup) to retain unique, non-duplicate matches to the genome. The sequences were expanded and converted to bedgraph format using bedtools, normalized to the total number of aligned reads, and then converted to big format using bedGraphToBigWig from the UCSC Genome Browser for visualization in the integrated genome viewer to obtain 5hmC-enriched regions.

[0059] Potential 5hmC-enriched regions (hMRs) were identified using MACS (version 1.4.2) using the parameters macs14-p1e-3-fBAM-g hs. Peak calls were merged using bedtools, retaining only peak regions smaller than 1000 bp that appeared in more than 10 samples. Genomic regions that tended to show spurious signals were also filtered based on the encoding. hMRs for each patient were generated by intersecting the individual peak call files with the merged peak file.

[0060] 3.5hmC feature selection

[0061] The train_test_ split in SciPythot-Learn (version 0.22.1) was used. Differentially modified 5hmC regions (DhMRs) were identified in all patients and healthy subjects using the EdgeR package (version 3.24.3) with filtering thresholds (p value < 0.05 and log-2FoldChange > 0.5) as shown in Figure 5. Figure 2 , as shown in 3.

[0062] Figure 3 In the heatmap shown, the red bar represents a significant difference in the enrichment content of this marker. The greater the difference, the darker the red. The difference includes up-regulation differences or down-regulation differences.

[0063] The recursive feature elimination algorithm (RFECV) in Scikit-Learn was used to further filter the DhMRs of the training cohort (parameters: estimator = Logistic Regression CV (class_weight = 'balanced', cv = 2, maxiter = 1000), scoring = 'accuracy'). Finally, the selected features were used to train a logistic regression CV model (LR) and the principal component analysis (PCA) was obtained by cluster analysis. Figure 4 As shown in Figure 2, the trained LR model was used to screen subjects for sudden deafness.

[0064] 4. Comparison of enriched regions and markers

[0065] The obtained 5hmC-enriched regions were compared with 11 5hmC molecular markers. Seven of these markers were downregulated compared to those in normal hearing subjects: CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, and MCAT. Four other markers were upregulated compared to those in normal hearing subjects: MFNG, ATP9B, EGF, and EPHX2 (baseline: p value < 0.05 and log-2FoldChange > 0.5). The results of this example demonstrate a significant difference in 5hmC marker enrichment between sudden deafness and normal hearing subjects.

[0066] Example 2 Prediction of treatment sensitivity of sudden deafness using 5hmC molecular markers

[0067] 1. Acquisition of cfDNA and Construction of 5hmC Library for High-Throughput Sequencing

[0068] (1) According to the guidelines for the diagnosis and treatment of sudden deafness, 4-8 ml of peripheral blood was drawn from 57 patients diagnosed with sudden deafness before treatment. The blood samples were centrifuged: the first centrifugation was at 1350 g for 12 minutes at 4°C, and the supernatant was collected; the second centrifugation was at 13500 g for 5 minutes at 4°C, and the supernatant was collected to obtain cfDNA, which was transferred to cell cryopreservation tubes and stored at -80°C.

[0069] The treatment plan for sudden deafness is: give the patient a 10-14 day drug treatment, mainly including hormones (dexamethasone), circulation-improving drugs (ginkgo leaf extract), nerve nourishing drugs (methylcobalamin), and fibrinogen-lowering drugs (batixone). After the treatment is completed, the hearing is rechecked. An improvement of 15dB HL or more is considered effective, and less than 15dB HL is considered ineffective.

[0070] (2) The cfDNA samples were end-repaired, 3'-adenylated using the KAPAHyper Prep Kit (KAPA Biosystems), and then ligated with an Erie-compatible adapter. The ligated cfDNA was added to 25 μL of a solution containing 50 mM HEPES buffer (pH 8.0), 25 mM magnesium chloride, 100 μM UDP-6-N3-Glc, and 1 μM β-NEB enzyme (glycosylation) and incubated at 37°C for 2 hours.

[0071] (3) Purify cfDNA using a DNA cleaning and concentration kit. After incubation of the purified DNA with 1 μL of DBCO-peg4-biotin at 37°C for 2 hours, the DNA was purified again using a DNA cleaning and concentration kit. At the same time, 2.5 μL of streptavidin beads were directly added to 1× buffer (5 mM Tris pH 7.5, 0.5 mM EDTA, 1 M sodium chloride, 0.2% Tween20) and reacted at room temperature for 30 minutes. Finally, wash with buffer 8 times, 5 minutes each time. All binding and washing steps were performed at room temperature with gentle rotation.

[0072] (4) Resuspend in RNase-free water and perform 14-16 cycles of PCR amplification. PCR products are purified using AMPure XP beads. Library concentration is measured using a Qubit 3.0 fluorometer. Paired-end 39bp high-throughput sequencing is performed on the NextSeq 500 platform.

[0073] 2. Mapping and Identifying 5hmC-enriched Regions

[0074] FastQC (version 0.11.5) was used to assess sequence quality. Raw reads were aligned to the human genome using Bowtie2 and further filtered using SAMtools (version 1.3.1) (using the following parameters: SAMtools view -f2 -F 1548 -q 30 and SAMtoolsrmdup) to retain unique, non-duplicate matches to the genome. The sequences were expanded and converted to bedgraph format using bedtools, normalized to the total number of aligned reads, and then converted to big format using bedGraphToBigWig from the UCSC Genome Browser for visualization in the integrated genome viewer to obtain 5hmC-enriched regions.

[0075] Potential 5hmC-enriched regions (hMRs) were identified using MACS (version 1.4.2) using the parameters macs14-p1e-3-fBAM-g hs. Peak calls were merged using bedtools, retaining only peak regions smaller than 1000 bp that appeared in more than 10 samples. Genomic regions that tended to show spurious signals were also filtered based on the encoding. hMRs for each patient were generated by intersecting the individual peak call files with the merged peak file.

[0076] 3.5hmC feature selection

[0077] The train_test_ split in SciPythot-Learn (version 0.22.1) was used. Active and inactive differentially modified 5hmC regions (DhMRs) were identified using the EdgeR package (version 3.24.3) with filtering thresholds (p value < 0.05 and log-2FoldChange > 0.5).

[0078] The filtered raw data were statistically analyzed to show the differences in expression intensities of the 11 5hmC molecular markers and to create a heat map such as Figure 5 , as shown in 6. Figure 6 Medium blue indicates a small difference in enrichment content, red indicates a large difference in enrichment content, and the darker the red, the greater the difference.

[0079] The recursive feature elimination algorithm (RFECV) in Scikit-Learn was used to further filter the DhMRs of the training cohort (parameters: estimator = Logistic Regression CV (class_weight = 'balanced', cv = 2, maxiter = 1000), scoring = 'accuracy'). Finally, the selected features were used to train a logistic regression CV model (LR) and the principal component analysis (PCA) was obtained by cluster analysis. Figure 7 The trained LR model was used to predict the treatment outcomes of patients. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the model. The sklearn.mectrics module was used to calculate the area under the curve (AUC), the optimal cutoff point, and the optimal Figure 8 shown.

[0080] 4. Comparison of enriched regions and markers

[0081] The 5hmC-enriched regions were compared with 11 5hmC markers, and consistency with marker enrichment levels predicted treatment response or ineffectiveness. Downregulated markers in ineffective patients included CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, and MCAT, while upregulated markers in effective patients included MFNG, ATP9B, EGF, and EPHX2 (baseline: p value < 0.05 and log-2FoldChange > 0.5).

[0082] The results of this example demonstrate that the 5hmC molecular marker of the present invention provides a method for predicting the treatment sensitivity of sudden deafness. Simply drawing a small amount of peripheral blood from the subject, high-throughput 5hmC sequencing can determine the patient's treatment response and provide precise, individualized treatment measures. Furthermore, the 5hmC molecular marker can also be used to develop genetic drugs for the treatment of sudden deafness.

[0083] Example 3: Validation of the 5hmC molecular marker for predicting treatment sensitivity of sudden deafness

[0084] 1. Acquisition of cfDNA and Construction of 5hmC Library for High-Throughput Sequencing

[0085] (1) According to the guidelines for the diagnosis and treatment of sudden deafness, 37 patients with sudden deafness different from those in Example 1-2 were selected. Before treatment, 4-8 ml of peripheral blood was drawn from the patients and the blood samples were centrifuged: the first centrifugation was at 1350 g for 12 minutes at 4°C, and the supernatant was collected; the second centrifugation was at 13500 g for 5 minutes at 4°C, and the supernatant was collected to obtain cfDNA, which was transferred to a cell cryopreservation tube for storage at -80°C.

[0086] (2) The cfDNA samples were end-repaired, 3'-adenylated using the KAPAHyper Prep Kit (KAPA Biosystems), and then ligated with an Erie-compatible adapter. The ligated cfDNA was added to 25 μL of a solution containing 50 mM HEPES buffer (pH 8.0), 25 mM magnesium chloride, 100 μM UDP-6-N3-Glc, and 1 μM β-NEB enzyme (glycosylation) and incubated at 37°C for 2 hours.

[0087] (3) Purify cfDNA using a DNA cleaning and concentration kit. After incubation of the purified DNA with 1 μL of DBCO-peg4-biotin at 37°C for 2 hours, the DNA was purified again using a DNA cleaning and concentration kit. At the same time, 2.5 μL of streptavidin beads were directly added to 1× buffer (5 mM Tris pH 7.5, 0.5 mM EDTA, 1 M sodium chloride, 0.2% Tween20) and reacted at room temperature for 30 minutes. Finally, wash with buffer 8 times, 5 minutes each time. All binding and washing steps were performed at room temperature with gentle rotation.

[0088] (4) Resuspend in RNase-free water and perform 14-16 cycles of PCR amplification. PCR products are purified using AMPure XP beads. Library concentration is measured using a Qubit 3.0 fluorometer. Paired-end 39bp high-throughput sequencing is performed on the NextSeq 500 platform.

[0089] 2. Verify 5hmC-enriched regions

[0090] Identify the enrichment of 11 5hmC molecular markers in the library (CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF, EPHX2). Obtain a heat map, such as Figure 9 As shown in , and the principal component analysis (PCA) is obtained through cluster analysis, as shown in Figure 10 As shown in Figure 2, receiver operating characteristic (ROC) analysis was used to evaluate the performance of the model. The area under the curve (AUC) and the optimal cutoff point were calculated using the sklearn.mectrics module, as shown in Figure 2. Figure 11 shown.

[0091] The results of this example show that: among the 37 sample sets, 17 were effective and 20 were ineffective after treatment with the treatment regimen of Example 2. The sample sets were reselected to verify the results of the 5hmC molecular marker in predicting the sensitivity of sudden deafness treatment. It was found that its accuracy was high, with an AUC of 0.994. 5hmC can be used as a molecular marker for predicting the sensitivity of sudden deafness treatment.

[0092] Although the above embodiment provides a detailed description of the present invention, it is only a part of the embodiments of the present invention, not all of the embodiments. People can also obtain other embodiments based on this embodiment without creativity, and these embodiments all fall within the scope of protection of the present invention.

Claims

1. A 5hmC molecular marker for predicting sensitivity to treatment of sudden deafness, characterized in that: The 5hmC molecular marker consists of the proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF and EPHX2.

2. The 5hmC molecular marker according to claim 1, characterized in that The nucleotide molecules encoding the proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF and EPHX2 are shown as SEQ ID NO. 1, SEQ ID NO. 2, SEQ ID NO. 3, SEQ ID NO. 4, SEQ ID NO. 5, SEQ ID NO. 6, SEQ ID NO. 7, SEQ ID NO. 8, SEQ ID NO. 9, SEQ ID NO. 10 and SEQ ID NO. 11, respectively.

3. A kit for predicting sensitivity to treatment of sudden deafness, characterized in that: The kit includes reagents for detecting the expression level of the 5hmC molecular marker, and the molecular marker consists of proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF and EPHX2.

4. The kit according to claim 3, wherein The kit is a nucleic acid detection kit, which includes primer pairs for amplifying nucleotide molecules encoding the proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF and EPHX2, respectively; the nucleotide molecules encoding the proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF and EPHX2 are shown as SEQ ID NO. 1, SEQ ID NO. 2, SEQ ID NO. 3, SEQ ID NO. 4, SEQ ID NO. 5, SEQ ID NO. 6, SEQ ID NO. 7, SEQ ID NO. 8, SEQ ID NO. 9, SEQ ID NO. 10 and SEQ ID NO. 11, respectively.

5. Use of a reagent for detecting the expression level of the 5hmC molecular marker in the preparation of a product for predicting the sensitivity of sudden deafness treatment, characterized in that: The molecular markers consist of proteins CDK5R1, TAF6L, MFSD9, CDH7, PLCH1, MARCHF7, MCAT, MFNG, ATP9B, EGF and EPHX2.

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